From 8c1e05a21ff414a4c35047766bbad3eeb08a64ab Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 7 Sep 2023 19:36:51 +0200 Subject: [PATCH 01/45] update gitlab --- environment.yml | 5 ++--- example/train.json | 6 +++--- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/environment.yml b/environment.yml index 3c2e7a6c..6209f1a9 100644 --- a/environment.yml +++ b/environment.yml @@ -16,8 +16,7 @@ dependencies: - rdkit=2022.03.1 - scikit-learn=1.0.2 - seaborn=0.12.2 - - tensorflow-gpu=2.6.0 + - tensorflow - wandb=0.12 - - umap=0.1.1 - pip: - - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 \ No newline at end of file + - git+https://github.com/soulios/chemprop/tree/dfpl_chemprop diff --git a/example/train.json b/example/train.json index 62f2abb4..2ffda6be 100755 --- a/example/train.json +++ b/example/train.json @@ -6,8 +6,8 @@ "ecWeightsFile": "random_autoencoder.hdf5", "verbose": 2, - "trainAC": true, - "compressFeatures": true, + "trainAC": false, + "compressFeatures": false, "encFPSize": 256, "aeSplitType": "random", @@ -35,7 +35,7 @@ "fnnType": "FNN", "optimizer": "Adam", "lossFunction": "bce", - "epochs": 11, + "epochs": 100, "batchSize": 128, "activationFunction": "selu", "dropout": 0.0107, From 05f505593a1fddadb6344e6194637662f76f47b2 Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 7 Sep 2023 20:01:11 +0200 Subject: [PATCH 02/45] added build --- build/lib/dfpl/__init__.py | 0 build/lib/dfpl/__main__.py | 321 ++++ build/lib/dfpl/autoencoder.py | 415 +++++ build/lib/dfpl/callbacks.py | 89 ++ .../dfpl/deepFPlearn-HyperParameterTuning.py | 128 ++ ...deepFPlearn-HyperParameterTuning_single.py | 426 +++++ build/lib/dfpl/dfplmodule.py | 1413 +++++++++++++++++ build/lib/dfpl/feedforwardNN.py | 464 ++++++ build/lib/dfpl/fingerprint.py | 164 ++ .../lib/dfpl/harmonize_mol_representations.py | 171 ++ build/lib/dfpl/history.py | 41 + build/lib/dfpl/options.py | 1297 +++++++++++++++ build/lib/dfpl/plot.py | 228 +++ build/lib/dfpl/predictions.py | 39 + build/lib/dfpl/rbm.py | 259 +++ build/lib/dfpl/settings.py | 52 + build/lib/dfpl/single_label_model.py | 1008 ++++++++++++ build/lib/dfpl/utils.py | 329 ++++ build/lib/dfpl/vae.py | 337 ++++ 19 files changed, 7181 insertions(+) create mode 100644 build/lib/dfpl/__init__.py create mode 100644 build/lib/dfpl/__main__.py create mode 100644 build/lib/dfpl/autoencoder.py create mode 100644 build/lib/dfpl/callbacks.py create mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning.py create mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py create mode 100644 build/lib/dfpl/dfplmodule.py create mode 100644 build/lib/dfpl/feedforwardNN.py create mode 100644 build/lib/dfpl/fingerprint.py create mode 100644 build/lib/dfpl/harmonize_mol_representations.py create mode 100644 build/lib/dfpl/history.py create mode 100644 build/lib/dfpl/options.py create mode 100644 build/lib/dfpl/plot.py create mode 100644 build/lib/dfpl/predictions.py create mode 100644 build/lib/dfpl/rbm.py create mode 100644 build/lib/dfpl/settings.py create mode 100644 build/lib/dfpl/single_label_model.py create mode 100644 build/lib/dfpl/utils.py create mode 100644 build/lib/dfpl/vae.py diff --git a/build/lib/dfpl/__init__.py b/build/lib/dfpl/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/build/lib/dfpl/__main__.py b/build/lib/dfpl/__main__.py new file mode 100644 index 00000000..5c12705b --- /dev/null +++ b/build/lib/dfpl/__main__.py @@ -0,0 +1,321 @@ +import os.path +import dataclasses +import logging +import pathlib +from argparse import Namespace +from os import path + +import pandas as pd +from keras.models import load_model + +from dfpl import autoencoder as ac +from dfpl import feedforwardNN as fNN +from dfpl import fingerprint as fp +from dfpl import options, predictions + +# from dfpl import rbm as rbm +from dfpl import single_label_model as sl +from dfpl import vae as vae +from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute +import chemprop as cp + +project_directory = pathlib.Path(".").parent.parent.absolute() +test_train_opts = options.Options( + inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", + outputDir=f"{project_directory}/output_data/console_test", + ecWeightsFile=f"{project_directory}/output_data/case_00/AE_S/ae_S.encoder.hdf5", + ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", + type="smiles", + fpType="topological", + epochs=100, + batchSize=1024, + fpSize=2048, + encFPSize=256, + enableMultiLabel=False, + testSize=0.2, + kFolds=2, + verbose=2, + trainAC=False, + trainFNN=True, + compressFeatures=True, + activationFunction="selu", + lossFunction="bce", + optimizer="Adam", + fnnType="FNN", +) + +test_pred_opts = options.Options( + inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", + outputDir=f"{project_directory}/output_data/console_test", + outputFile=f"{project_directory}/output_data/console_test/S_dataset.predictions_ER.csv", + ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", + fnnModelDir=f"{project_directory}/output_data/console_test/ER_saved_model", + type="smiles", + fpType="topological", +) + + +def traindmpnn(opts: options.GnnOptions): + """ + Train a D-MPNN model using the given options. + Args: + - opts: options.GnnOptions instance containing the details of the training + Returns: + - None + """ + os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" + ignore_elements = ["py/object"] + # Load options from a JSON file and replace the relevant attributes in `opts` + arguments = createArgsFromJson( + opts.configFile, ignore_elements, return_json_object=False + ) + opts = cp.args.TrainArgs().parse_args(arguments) + logging.info("Training DMPNN...") + # Train the model and get the mean and standard deviation of AUC score from cross-validation + mean_score, std_score = cp.train.cross_validate( + args=opts, train_func=cp.train.run_training + ) + logging.info(f"Results: {mean_score:.5f} +/- {std_score:.5f}") + + +def predictdmpnn(opts: options.GnnOptions, json_arg_path: str) -> None: + """ + Predict the values using a trained D-MPNN model with the given options. + Args: + - opts: options.GnnOptions instance containing the details of the prediction + - JSON_ARG_PATH: path to a JSON file containing additional arguments for prediction + Returns: + - None + """ + ignore_elements = [ + "py/object", + "gnn_type", + "checkpoint_paths", + "save_dir", + "saving_name", + ] + # Load options and additional arguments from a JSON file + arguments, data = createArgsFromJson( + json_arg_path, ignore_elements, return_json_object=True + ) + arguments.append("--preds_path") + arguments.append("") + save_dir = data.get("save_dir") + name = data.get("saving_name") + # Replace relevant attributes in `opts` with loaded options + opts = cp.args.PredictArgs().parse_args(arguments) + opts.preds_path = save_dir + "/" + name + df = pd.read_csv(opts.test_path) + smiles = [] + for index, rows in df.iterrows(): + my_list = [rows.smiles] + smiles.append(my_list) + # Make predictions and return the result + cp.train.make_predictions(args=opts, smiles=smiles) + + +def train(opts: options.Options): + """ + Run the main training procedure + :param opts: Options defining the details of the training + """ + + os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" + + # import data from file and create DataFrame + if "tsv" in opts.inputFile: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize + ) + else: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize + ) + # initialize encoders to None + encoder = None + autoencoder = None + if opts.trainAC: + if opts.aeType == "deterministic": + encoder, train_indices, test_indices = ac.train_full_ac(df, opts) + elif opts.aeType == "variational": + encoder, train_indices, test_indices = vae.train_full_vae(df, opts) + else: + raise ValueError(f"Unknown autoencoder type: {opts.aeType}") + + # if feature compression is enabled + if opts.compressFeatures: + if not opts.trainAC: + if opts.aeType == "deterministic": + (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) + elif opts.aeType == "variational": + (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) + elif opts.ecWeightsFile == "": + encoder = load_model(opts.ecModelDir) + else: + autoencoder.load_weights( + os.path.join(opts.ecModelDir, opts.ecWeightsFile) + ) + # compress the fingerprints using the autoencoder + df = ac.compress_fingerprints(df, encoder) + # ac.visualize_fingerprints( + # df, + # before_col="fp", + # after_col="fpcompressed", + # train_indices=train_indices, + # test_indices=test_indices, + # save_as=f"UMAP_{opts.aeSplitType}.png", + # ) + # train single label models if requested + if opts.trainFNN and not opts.enableMultiLabel: + sl.train_single_label_models(df=df, opts=opts) + + # train multi-label models if requested + if opts.trainFNN and opts.enableMultiLabel: + fNN.train_nn_models_multi(df=df, opts=opts) + + +def predict(opts: options.Options) -> None: + """ + Run prediction given specific options + :param opts: Options defining the details of the prediction + """ + # import data from file and create DataFrame + if "tsv" in opts.inputFile: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize + ) + print(df) + else: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize + ) + + if opts.compressFeatures: + # load trained model for autoencoder + if opts.aeType == "deterministic": + (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) + if opts.aeType == "variational": + (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) + # Load trained model for autoencoder + if opts.ecWeightsFile == "": + encoder = load_model(opts.ecModelDir) + else: + encoder.load_weights(os.path.join(opts.ecModelDir, opts.ecWeightsFile)) + df = ac.compress_fingerprints(df, encoder) + + # Run predictions on the compressed fingerprints and store the results in a dataframe + df2 = predictions.predict_values(df=df, opts=opts) + + # Extract the column names from the dataframe, excluding the 'fp' and 'fpcompressed' columns + names_columns = [c for c in df2.columns if c not in ["fp", "fpcompressed"]] + + # Save the predicted values to a CSV file in the output directory + df2[names_columns].to_csv(path_or_buf=path.join(opts.outputDir, opts.outputFile)) + + # Log successful completion of prediction and the file path where the results were saved + logging.info( + f"Prediction successful. Results written to '{path.join(opts.outputDir, opts.outputFile)}'" + ) + + +def createLogger(filename: str) -> None: + """ + Set up a logger for the main function that also saves to a log file + """ + # get root logger and set its level + logger = logging.getLogger() + logger.setLevel(logging.INFO) + # create file handler which logs info messages + fh = logging.FileHandler(filename, mode="w") + fh.setLevel(logging.INFO) + # create console handler + ch = logging.StreamHandler() + ch.setLevel(logging.INFO) + # create formatter and add it to the handlers + formatterFile = logging.Formatter( + "{asctime} - {name} - {levelname} - {message}", style="{" + ) + formatterConsole = logging.Formatter("{levelname} {message}", style="{") + fh.setFormatter(formatterFile) + ch.setFormatter(formatterConsole) + # add the handlers to the logger + logger.addHandler(fh) + logger.addHandler(ch) + + +def main(): + """ + Main function that runs training/prediction defined by command line arguments + """ + + parser = options.createCommandlineParser() + prog_args: Namespace = parser.parse_args() + try: + if prog_args.method == "convert": + directory = makePathAbsolute(prog_args.f) + if path.isdir(directory): + createLogger(path.join(directory, "convert.log")) + logging.info(f"Convert all data files in {directory}") + fp.convert_all(directory) + else: + raise ValueError("Input directory is not a directory") + elif prog_args.method == "traingnn": + traingnn_opts = options.GnnOptions.fromCmdArgs(prog_args) + + traindmpnn(traingnn_opts) + + elif prog_args.method == "predictgnn": + predictgnn_opts = options.GnnOptions.fromCmdArgs(prog_args) + fixed_opts = dataclasses.replace( + predictgnn_opts, + test_path=makePathAbsolute(predictgnn_opts.test_path), + preds_path=makePathAbsolute(predictgnn_opts.preds_path), + ) + + logging.info( + f"The following arguments are received or filled with default values:\n{prog_args}" + ) + + if predictgnn_opts.gnn_type == "dmpnn": + predictdmpnn(fixed_opts, prog_args.configFile) + + elif prog_args.method == "train": + train_opts = options.Options.fromCmdArgs(prog_args) + fixed_opts = dataclasses.replace( + train_opts, + inputFile=makePathAbsolute(train_opts.inputFile), + outputDir=makePathAbsolute(train_opts.outputDir), + ) + createDirectory(fixed_opts.outputDir) + createLogger(path.join(fixed_opts.outputDir, "train.log")) + logging.info( + f"The following arguments are received or filled with default values:\n{fixed_opts}" + ) + train(fixed_opts) + elif prog_args.method == "predict": + predict_opts = options.Options.fromCmdArgs(prog_args) + fixed_opts = dataclasses.replace( + predict_opts, + inputFile=makePathAbsolute(predict_opts.inputFile), + outputDir=makePathAbsolute(predict_opts.outputDir), + outputFile=makePathAbsolute( + path.join(predict_opts.outputDir, predict_opts.outputFile) + ), + ecModelDir=makePathAbsolute(predict_opts.ecModelDir), + fnnModelDir=makePathAbsolute(predict_opts.fnnModelDir), + trainAC=False, + trainFNN=False, + ) + createDirectory(fixed_opts.outputDir) + createLogger(path.join(fixed_opts.outputDir, "predict.log")) + logging.info( + f"The following arguments are received or filled with default values:\n{prog_args}" + ) + predict(fixed_opts) + except AttributeError as e: + print(e) + parser.print_usage() + + +if __name__ == "__main__": + main() diff --git a/build/lib/dfpl/autoencoder.py b/build/lib/dfpl/autoencoder.py new file mode 100644 index 00000000..1f638122 --- /dev/null +++ b/build/lib/dfpl/autoencoder.py @@ -0,0 +1,415 @@ +import logging +import math +import os.path +from os.path import basename + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import umap +import wandb +from sklearn.model_selection import train_test_split +from tensorflow.keras import initializers, losses, optimizers +from tensorflow.keras.layers import Dense, Input +from tensorflow.keras.models import Model +from typing import Tuple + +from dfpl import callbacks +from dfpl import history as ht +from dfpl import options, settings +from dfpl.utils import ae_scaffold_split, weight_split + + +def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: + """ + This function provides an autoencoder model to reduce a certain input to a compressed version. + + :param opts: Training options that provide values for adjusting the neural net + :param output_bias: Bias used to initialize the last layer. It gives the net a head start in training on + imbalanced data (which the fingerprints are, because they have many more 0's than 1's in them). + :return: a tuple of autoencoder and encoder models + """ + input_size = opts.fpSize + encoding_dim = opts.encFPSize + ac_optimizer = optimizers.Adam( + learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay + ) + + if output_bias is not None: + output_bias = initializers.Constant(output_bias) + + # get the number of meaningful hidden layers (latent space included) + hidden_layer_count = round(math.log2(input_size / encoding_dim)) + + # the input placeholder + input_vec = Input(shape=(input_size,)) + + # 1st hidden layer, that receives weights from input layer + # equals bottleneck layer, if hidden_layer_count==1! + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / 2), activation=opts.aeActivationFunction + )(input_vec) + else: + encoded = Dense( + units=int(input_size / 2), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(input_vec) + + if hidden_layer_count > 1: + # encoding layers, incl. bottle-neck + for i in range(1, hidden_layer_count): + factor_units = 2 ** (i + 1) + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # 1st decoding layer + factor_units = 2 ** (hidden_layer_count - 1) + if opts.aeActivationFunction != "selu": + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # decoding layers + for i in range(hidden_layer_count - 2, 0, -1): + factor_units = 2**i + if opts.aeActivationFunction != "selu": + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(decoded) + else: + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(decoded) + + # output layer + # to either 0 or 1 and hence we use sigmoid activation function. + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(decoded) + + else: + # output layer + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(encoded) + + autoencoder = Model(input_vec, decoded) + encoder = Model(input_vec, encoded) + autoencoder.summary(print_fn=logging.info) + + autoencoder.compile( + optimizer=ac_optimizer, + loss=losses.BinaryCrossentropy(), + # metrics=[ + # metrics.AUC(), + # metrics.Precision(), + # metrics.Recall() + # ] + ) + return autoencoder, encoder + + +def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: + """ + Trains an autoencoder on the given feature matrix X. The response matrix is only used to + split the data into meaningful test and train sets. + + :param opts: Command line arguments as defined in options.py + :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder + :return: The encoder model of the trained autoencoder + """ + + # If wandb tracking is enabled for autoencoder weights but not for the main program, initialize a new wandb run + if opts.aeWabTracking and not opts.wabTracking: + wandb.init(project=f"AE_{opts.aeSplitType}") + + # Define output files for autoencoder and encoder weights + if opts.ecWeightsFile == "": + # If no encoder weights file is specified, use the input file name to generate a default file name + logging.info("No AE encoder weights file specified") + base_file_name = ( + os.path.splitext(basename(opts.inputFile))[0] + opts.aeSplitType + ) + logging.info( + f"(auto)encoder weights will be saved in {base_file_name}.autoencoder.hdf5" + ) + ac_weights_file = os.path.join( + opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" + ) + # ec_weights_file = os.path.join( + # opts.outputDir, base_file_name + ".encoder.weights.hdf5" + # ) + else: + # If an encoder weights file is specified, use it as the encoder weights file name + logging.info(f"AE encoder will be saved in {opts.ecWeightsFile}") + base_file_name = ( + os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType + ) + ac_weights_file = os.path.join( + opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" + ) + # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) + + # Collect the callbacks for training + callback_list = callbacks.autoencoder_callback( + checkpoint_path=ac_weights_file, opts=opts + ) + + # Select all fingerprints that are valid and turn them into a numpy array + fp_matrix = np.array( + df[df["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info( + f"Training AC on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" + ) + + # When training the final AE, we don't want any test data. We want to train it on all available fingerprints. + assert 0.0 <= opts.testSize <= 0.5 + if opts.aeSplitType == "random": + logging.info("Training autoencoder using random split") + initial_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # Split data into test and training data + if opts.aeWabTracking: + x_train, x_test, train_indices, test_indices = train_test_split( + fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train, x_test, train_indices, test_indices = train_test_split( + fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "scaffold_balanced": + logging.info("Training autoencoder using scaffold split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + train_data, val_data, test_data = ae_scaffold_split( + df, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=True, + seed=42, + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + train_indices = df[ + df.index.isin(train_data[train_data["fp"].notnull()].index) + ].index.to_numpy() + test_indices = df[ + df.index.isin(test_data[test_data["fp"].notnull()].index) + ].index.to_numpy() + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "molecular_weight": + logging.info("Training autoencoder using molecular weight split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + train_data, val_data, test_data = weight_split( + df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + df_sorted = df.sort_values(by="mol_weight", ascending=True) + # Get the sorted indices from the sorted DataFrame + sorted_indices = df_sorted.index.to_numpy() + + # Find the corresponding indices for train_data, val_data, and test_data in the sorted DataFrame + train_indices = sorted_indices[df.index.isin(train_data.index)] + # val_indices = sorted_indices[df.index.isin(val_data.index)] + test_indices = sorted_indices[df.index.isin(test_data.index)] + else: + x_train = fp_matrix + x_test = None + else: + raise ValueError(f"Invalid split type: {opts.split_type}") + + # Calculate the initial bias aka the log ratio between 1's and 0'1 in all fingerprints + ids, counts = np.unique(x_train.flatten(), return_counts=True) + count_dict = dict(zip(ids, counts)) + if count_dict[0] == 0: + initial_bias = None + logging.info("No zeroes in training labels. Setting initial_bias to None.") + else: + initial_bias = np.log([count_dict[1] / count_dict[0]]) + logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") + + # Check if we're doing training/testing mode or full training mode + if opts.testSize > 0.0: + logging.info(f"AE training/testing mode with train- and test-samples") + logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") + logging.info(f"AC test data shape {x_test.shape} with type {x_test.dtype}") + else: + logging.info(f"AE full train mode without test-samples") + logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") + + # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) + (autoencoder, encoder) = define_ac_model(opts, output_bias=initial_bias) + + # Train the autoencoder on the training data + auto_hist = autoencoder.fit( + x_train, + x_train, + callbacks=callback_list, + epochs=opts.aeEpochs, + batch_size=opts.aeBatchSize, + verbose=opts.verbose, + validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, + ) + logging.info(f"Autoencoder weights stored in file: {ac_weights_file}") + + # Store the autoencoder training history and plot the metrics + ht.store_and_plot_history( + base_file_name=os.path.join(opts.outputDir, base_file_name + ".AC"), + hist=auto_hist, + ) + + # Save the autoencoder callback model to disk + save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_autoencoder") + if opts.testSize > 0.0: + (callback_autoencoder, callback_encoder) = define_ac_model(opts) + callback_encoder.save(filepath=save_path) + else: + encoder.save(filepath=save_path) + # Return the encoder model of the trained autoencoder + return encoder, train_indices, test_indices + + +def compress_fingerprints(dataframe: pd.DataFrame, encoder: Model) -> pd.DataFrame: + """ + Adds a column of the compressed version of the fingerprints to the original dataframe. + + :param dataframe: Dataframe containing a column named 'fp' with the fingerprints + :param encoder: The trained autoencoder that is used for compressing the fingerprints + :return: The input dataframe extended by a column containing the compressed version of the fingerprints + """ + logging.info("Adding compressed fingerprints") + idx = dataframe[dataframe["fp"].notnull()].index + fp_matrix = np.array( + dataframe[dataframe["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info( + f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" + ) + logging.info("Compressed fingerprints are added to input dataframe.") + dataframe["fpcompressed"] = pd.DataFrame( + {"fpcompressed": [s for s in encoder.predict(fp_matrix)]}, idx + ) + return dataframe + + +def visualize_fingerprints( + df: pd.DataFrame, + before_col: str, + after_col: str, + train_indices: np.ndarray, + test_indices: np.ndarray, + save_as: str, +): + # Calculate the number of samples to be taken from each set + num_samples = 1000 + train_samples = int(num_samples * len(train_indices) / len(df)) + test_samples = num_samples - train_samples + + # Assign train and test data points separately + train_data = df.loc[train_indices] + test_data = df.loc[test_indices] + + # Sample train and test data points + train_data_sampled = train_data.sample(n=train_samples, random_state=42) + test_data_sampled = test_data.sample(n=test_samples, random_state=42) + + # Concatenate the sampled train and test data + df_sampled = pd.concat([train_data_sampled, test_data_sampled]) + + # Convert the boolean values in the after_col column to floats + df_sampled[after_col] = df_sampled[after_col].apply( + lambda x: np.array(x, dtype=float) + ) + + df_sampled.loc[train_data_sampled.index, "set"] = "train" + df_sampled.loc[test_data_sampled.index, "set"] = "test" + # Apply UMAP + umap_model = umap.UMAP( + n_neighbors=15, min_dist=0.1, metric="euclidean", random_state=42 + ) + # Filter out the rows with invalid arrays + umap_results = umap_model.fit_transform(df_sampled[after_col].tolist()) + # Add UMAP results to the DataFrame + df_sampled["umap_x"] = umap_results[:, 0] + df_sampled["umap_y"] = umap_results[:, 1] + + # Define custom color palette + palette = {"train": "blue", "test": "red"} + + # Create the scatter plot + sns.set(style="white") + fig, ax = plt.subplots(figsize=(10, 8)) + split = save_as.split("_", 1) + part_after_underscore = split[1] + split_type = part_after_underscore.split(".")[0] + # Plot the UMAP results + for label, grp in df_sampled.groupby("set"): + set_label = label + color = palette[set_label] + alpha = ( + 0.09 if set_label == "train" else 0.9 + ) # Set different opacities for train and test + ax.scatter( + grp["umap_x"], grp["umap_y"], label=f"{set_label}", c=color, alpha=alpha + ) + + # Customize the plot + ax.set_title( + f"UMAP visualization of molecular fingerprints using {split_type} split", + fontsize=14, + ) + ax.set_xlabel("UMAP 1") + ax.set_ylabel("UMAP 2") + ax.legend(title="", loc="upper right") + sns.despine(ax=ax, offset=10) + save_path = os.path.join(os.getcwd(), save_as) + plt.savefig(save_path) diff --git a/build/lib/dfpl/callbacks.py b/build/lib/dfpl/callbacks.py new file mode 100644 index 00000000..6eae7965 --- /dev/null +++ b/build/lib/dfpl/callbacks.py @@ -0,0 +1,89 @@ +# for NN model functions +from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint + +# for testing in Weights & Biases +from wandb.keras import WandbCallback + +from dfpl import options, settings + + +def autoencoder_callback(checkpoint_path: str, opts: options.Options) -> list: + """ + Callbacks for fitting the autoencoder + + :param checkpoint_path: The output directory to store the checkpoint weight files + :param opts: Training options provided to the run + :return: List of ModelCheckpoint and EarlyStopping class. + """ + callbacks = [] + + if opts.testSize > 0.0: + target = "val_loss" + else: + target = "loss" + # enable this checkpoint to restore the weights of the best performing model + checkpoint = ModelCheckpoint( + checkpoint_path, + monitor=target, + mode="min", + verbose=1, + period=settings.ac_train_check_period, + save_best_only=True, + save_weights_only=True, + ) + callbacks.append(checkpoint) + + # enable early stopping if val_loss is not improving anymore + early_stop = EarlyStopping( + monitor=target, + mode="min", + patience=settings.ac_train_patience, + min_delta=settings.ac_train_min_delta, + verbose=1, + restore_best_weights=True, + ) + callbacks.append(early_stop) + + if opts.aeWabTracking and not opts.wabTracking: + callbacks.append(WandbCallback(save_model=False)) + return callbacks + + +def nn_callback(checkpoint_path: str, opts: options.Options) -> list: + """ + Callbacks for fitting the feed forward network (FNN) + + :param checkpoint_path: The output directory to store the checkpoint weight files + :param opts: Training options provided to the run + :return: List of ModelCheckpoint and EarlyStopping class. + """ + + callbacks = [] + + if opts.testSize > 0.0: + # enable this checkpoint to restore the weights of the best performing model + checkpoint = ModelCheckpoint( + checkpoint_path, + verbose=1, + period=settings.nn_train_check_period, + save_best_only=True, + monitor="val_loss", + mode="min", + save_weights_only=True, + ) + callbacks.append(checkpoint) + + # enable early stopping if val_loss is not improving anymore + early_stop = EarlyStopping( + patience=settings.nn_train_patience, + monitor="val_loss", + mode="min", + min_delta=settings.nn_train_min_delta, + verbose=1, + restore_best_weights=True, + ) + callbacks.append(early_stop) + + if opts.wabTracking: + callbacks.append(WandbCallback(save_model=False)) + return callbacks diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py new file mode 100644 index 00000000..ab29cdea --- /dev/null +++ b/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py @@ -0,0 +1,128 @@ +from time import time + +import numpy as np +import pandas as pd +from sklearn.model_selection import GridSearchCV, train_test_split +from sklearn.preprocessing import LabelEncoder +from tensorflow.keras.layers import Dense, Dropout +from tensorflow.keras.models import Sequential +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.wrappers.scikit_learn import KerasClassifier + + +def tuning_model(optimizer, activation, init, dropout=0.2): + """ + model for tuning optimizer, activation functions and initialization of hidden layers + + :param optimizer: + :param activation: + :param init: + :param dropout: + :return: + """ + model = Sequential() + model.add(Dense(1024, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(341, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(85, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(17, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(2, activation="softmax")) + model.compile( + loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] + ) + return model + + +# --------------------------------------------------------------------------- # + +# this stores the best performing parameters for each model for each target +results = pd.DataFrame() + +np.random.seed(0) + +modelfilepathprefix = ( + "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" +) + +filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" +outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.HPtuning.results.txt" +dataset = pd.read_csv(filepath) + +le = LabelEncoder() + +# which target column to use? put this in a loop later for all targets +# here we need a for loop +for target in dataset.columns[1:7]: + # target = dataset.columns[1] # 'AR' + + modelfilepathW = str(modelfilepathprefix) + "/model." + target + ".weights.h5" + modelfilepathM = str(modelfilepathprefix) + "/model." + target + ".json" + + tmp = dataset[target].astype("category") + Y = np.asarray(tmp) + naRows = np.isnan(Y) + + d = dataset[~naRows] + + # generate X from fingerprints + # split all fingerprints into vectors + Nrows = d.shape[0] + Ncols = d["fp"][0].__len__() + # Store all fingerprints in numpy array + X = np.empty((Nrows, Ncols), int) + + # keep old indexes of this subset of arrays + d["oldIdx"] = d.index.values + d["newIdx"] = range(Nrows) + d = d.set_index("newIdx") + del d.index.name + + # generate X matrix + for i in range(Nrows): + fp = d["fp"][i] + X[i] = np.array(list(fp), dtype="int") + + # generate y vector(s) + y = to_categorical(np.array(d[target]), num_classes=2) + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=0 + ) + + start = time() + + model = KerasClassifier(build_fn=tuning_model, batch_size=20) + + batch_sizes = [20] + epochs = [30, 100] + inits = ["glorot_uniform", "normal", "uniform"] + optimizers = ["SGD", "RMSprop", "Adagrad", "Adadelta", "Adam", "Adamax", "Nadam"] + activation_functions = ["sigmoid", "tanh", "relu"] + parameters = { + "batch_size": batch_sizes, + "epochs": epochs, + "optimizer": optimizers, + "activation": activation_functions, + "init": inits, + } + + # Tuning ALL parameters, Note, this takes hours of computation!!! + clf = GridSearchCV(model, parameters, verbose=0) + clf_results = clf.fit(X_train, y_train) + + # save best estimator per target + clf_results.best_estimator_.model.save(filepath=modelfilepathM) + clf_results.best_estimator_.model.save_weights(filepath=modelfilepathW) + + # find best performing parameters + file = open(outfilepath, "a") + file.write( + "# --------------------------------------------------------------------------- #\n" + ) + file.write(f"### Results for {target} target ###\n") + file.write(f"Best: {clf_results.best_score_} using {clf_results.best_params_}\n") + file.write(f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n") + file.close() diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py new file mode 100644 index 00000000..9251cbdc --- /dev/null +++ b/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py @@ -0,0 +1,426 @@ +import argparse +import os +import re +import sys +from time import time + +import numpy as np +import pandas as pd +from sklearn.model_selection import GridSearchCV, train_test_split +from tensorflow.keras.layers import Dense, Dropout +from tensorflow.keras.models import Sequential +from tensorflow.keras.wrappers.scikit_learn import KerasClassifier + +from dfpl import dfplmodule as dfpl + +# ------------------------------------------------------------------------------------- # + + +def parseInput(): + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + parser = argparse.ArgumentParser( + description="Perform Hyperparameter tuning for a " "given set of parameters." + ) + # input + parser.add_argument( + "-i", + metavar="FILE", + type=str, + nargs=1, + required=True, + help="The file containin the data for training in (unquoted) " + "comma separated CSV format. The first column(s) contain " + "the target associations, while the last column, named 'fp' " + "contains the fingerprint of the compound", + ) + # output + parser.add_argument( + "-p", + metavar="PATH", + type=str, + nargs=1, + required=True, + help="The path to a directory where the results of the hp tuning " + "shall be stored", + ) + # select target(s) or use all + parser.add_argument( + "-t", + metavar="STR", + type=str, + nargs="+", + required=False, + help="A space separated list of target names (according to column " + " names in the input file (-i)) for which the huperparameter " + "tuning should be performed. If not provided, it is performed " + "for each target seperately.", + ) + + # parameters to be tested for HP tuning + parser.add_argument( + "--batchSizes", + metavar="INT", + type=int, + nargs="+", + required=False, + default=[20], + help="Space separated list of integers for batch sizes that should " + "be tested. Example: --batchSizes 32 64", + ) + parser.add_argument( + "--epochs", + metavar="INT", + type=int, + nargs="+", + required=False, + default=[30], + help="Space separated list of integers for number of training epochs " + "that should be tested. Example: --epochs 30 50 100", + ) + parser.add_argument( + "--inits", + metavar="STR", + type=str, + nargs="+", + required=False, + default=["normal"], + help="Space separated list of methods for initializing the weights " + "at inner layers that should be tested. Available methods are: " + "'glorot_uniform', 'normal', 'uniform'. " + "Example: --inits 'glorot_uniform' 'normal'", + ) + parser.add_argument( + "--optimizers", + metavar="STR", + type=str, + nargs="+", + required=False, + default=["Adam"], + help="Space separated list of optimizers for the DNN model " + "that should be tested." + "Available optimizers are listed here: https://keras.io/optimizers/ ." + "Example: --optimizers 'Adadelta' 'Adam' 'Adamax'", + ) + parser.add_argument( + "--activations", + metavar="STR", + type=str, + nargs="+", + required=False, + default=["relu"], + help="Space separated list of activation fucntions for the DNN model " + "that should be tested." + "Available functions are listed here: https://keras.io/activations/ ." + "Example: --activations 'sigmoid' 'relu'", + ) + + return parser.parse_args() + + +# ----------------------------------------------------------------------------- # + + +# model for tuning epochs and batchsizes only +def c_model(dropout=0.2): + model = Sequential() + model.add(Dense(1024, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(341, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(85, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(17, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(2, activation="softmax")) + model.compile( + loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"] + ) + return model + + +# ----------------------------------------------------------------------------- # + + +# model for tuning optmizer, activation functions and initialization of hidden layers +def tuning_model(optimizer, activation, init, dropout=0.2): + model = Sequential() + model.add(Dense(1024, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(341, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(85, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(17, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(2, activation="softmax")) + model.compile( + loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] + ) + return model + + +# --------------------------------------------------------------------------- # + +# ===================================================================================== # + +if __name__ == "__main__": + # get all arguments + args = parseInput() + + # print(args) + # exit(1) + + # this stores the best performing parameters for each model for each target + results = pd.DataFrame() + + np.random.seed(0) + + # filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" + # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub(".csv", + # ".hpTuningResults.txt", os.path.basename(filepath)) dataset = pd.read_csv(filepath) + + dataset = pd.read_csv(args.i[0]) + + # le = LabelEncoder() + + # which target column to use? put this in a loop later for all targets + # here we need a for loop + + for target in args.t: + target = "ER" + print(target) + # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub( + # ".csv", '.hpTuningResults.' + target + '.txt', os.path.basename(filepath)) + outfilepath = args.p[0] + re.sub( + ".csv", ".hpTuningResults." + target + ".txt", os.path.basename(args.i[0]) + ) + + if target in dataset.columns: + # modelfilepathW = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + + # '/model.' + target + '.weights.h5' modelfilepathM = + # "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + '/model.' + target + + # '.json' + modelfilepathW = args.p[0] + "/model." + target + ".weights.h5" + modelfilepathM = args.p[0] + "/model." + target + ".json" + + tmp = dataset[target].astype("category") + Y = np.asarray(tmp) + naRows = np.isnan(Y) + + d = dataset[~naRows] # d is a copy! + + # generate X from fingerprints + # split all fingerprints into vectors + Nrows = d.shape[0] + Ncols = d["fp"][0].__len__() + # Store all fingerprints in numpy array + X = np.empty((Nrows, Ncols), int) + + # keep old indexes of this subset of arrays + d["oldIdx"] = d.index.values + d["newIdx"] = range(Nrows) + d = d.set_index("newIdx") + del d.index.name + + # generate X matrix + for i in range(Nrows): + fp = d["fp"][i] + X[i] = np.array(list(fp), dtype="int") + + # generate y vector(s) + # y = to_categorical(np.array(d[target]), num_classes=2) + y = d[target] + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=0 + ) + + start = time() + + # find best performing parameters + sys.stdout.write( + "# --------------------------------------------------------------------------- #\n" + ) + sys.stdout.write(f"#target = {target}\n") + + # Start optimizing epochs and batchsizes (if more than one provided) + + # batchSizes = args.batchSizes + batchSizes = [64, 128, 256] + epochs = args.epochs # epochs = [5, 10] + + if batchSizes.__len__() > 1: + # model = KerasClassifier(build_fn=dfpl.defineNNmodel2) + model = KerasClassifier(build_fn=dfpl.defineNNmodel) + + parameters = {"batch_size": batchSizes} + + clf = GridSearchCV(model, parameters, verbose=0) + clf.fit(X_train, y_train) + + # save best estimator for epoochs/batchSize tuning per target + modelfilepathW = ( + args.p[0] + "/model.tuning-BatchSize." + target + ".weights.h5" + ) + modelfilepathM = ( + args.p[0] + "/model.tuning-BatchSize." + target + ".json" + ) + outfilepath = args.p[0] + re.sub( + ".csv", + ".hpTuningResults.01-BatchSize" + target + ".csv", + os.path.basename(args.i[0]), + ) + # outfilepath = '/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning + # /Sun_etal_dataset.fingerprints.hpTuningResults.ER.txt' + clf.best_estimator_.model.save(filepath=modelfilepathM) + clf.best_estimator_.model.save_weights(filepath=modelfilepathW) + + df = pd.concat( + [ + pd.DataFrame(clf.cv_results_["param_batch_size"]), + pd.DataFrame(clf.cv_results_["mean_test_score"]), + pd.DataFrame(clf.cv_results_["std_test_score"]), + pd.DataFrame(clf.cv_results_["rank_test_score"]), + ], + axis=1, + ) + df.columns = [ + "epoch", + "batch_size", + "mean_test_score", + "std_test_score", + "rank_test_score", + ] + df.to_csv(outfilepath, header=True) + + selected_bs = clf.best_params_["batch_size"] + selected_epochs = clf.best_params_["epochs"] + + else: + selected_bs = batchSizes[0] + selected_epochs = epochs[0] + + # Hypertune optimizers + optimizers = ["SGD", "Adam"] + # optimizers = args.optimizers + + if optimizers.__len__() > 1: + model = KerasClassifier( + build_fn=dfpl.defineNNmodel, + epochs=selected_epochs, + batch_size=selected_bs, + ) + parameters = { + "optimizer": optimizers + } # ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']} + clf = GridSearchCV(model, parameters, verbose=0) + clf.fit(X_train, y_train) + + # save best estimator for optimizer tuning per target + modelfilepathW = ( + args.p[0] + "/model.tuning-optimizer." + target + ".weights.h5" + ) + modelfilepathM = ( + args.p[0] + "/model.tuning-optimizer." + target + ".json" + ) + outfilepath = args.p[0] + re.sub( + ".csv", + ".hpTuningResults.02-Optimizer" + target + ".csv", + os.path.basename(args.i[0]), + ) + + clf.best_estimator_.model.save(filepath=modelfilepathM) + clf.best_estimator_.model.save_weights(filepath=modelfilepathW) + + df = pd.concat( + [ + pd.DataFrame(clf.cv_results_["param_optimizer"]), + pd.DataFrame(clf.cv_results_["mean_test_score"]), + pd.DataFrame(clf.cv_results_["std_test_score"]), + pd.DataFrame(clf.cv_results_["rank_test_score"]), + ], + axis=1, + ) + df.columns = [ + "optimizer", + "mean_test_score", + "std_test_score", + "rank_test_score", + ] + df.to_csv(outfilepath, header=True) + + selected_optimizer = clf.best_params_["optimizer"] + + else: + selected_optimizer = optimizers[0] + + # Hypertune activation functions + activations = args.activations # 'sigmoid', 'tanh', 'relu' + + if activations.__len__() > 1: + model = KerasClassifier( + build_fn=dfpl.defineNNmodel, + epochs=selected_epochs, + batch_size=selected_bs, + ) + parameters = { + "optimizer": [selected_optimizer], + "activation": args.activations, + } + clf = GridSearchCV(model, parameters, verbose=0) + clf.fit(X_train, y_train) + + # save best estimator for optimizer tuning per target + modelfilepathW = ( + args.p[0] + "/model.tuning-activation." + target + ".weights.h5" + ) + modelfilepathM = ( + args.p[0] + "/model.tuning-activation." + target + ".json" + ) + outfilepath = args.p[0] + re.sub( + ".csv", + ".hpTuningResults.03-Activation" + target + ".csv", + os.path.basename(args.i[0]), + ) + + clf.best_estimator_.model.save(filepath=modelfilepathM) + clf.best_estimator_.model.save_weights(filepath=modelfilepathW) + + df = pd.concat( + [ + pd.DataFrame(clf.cv_results_["param_optimizer"]), + pd.DataFrame(clf.cv_results_["param_activation"]), + pd.DataFrame(clf.cv_results_["mean_test_score"]), + pd.DataFrame(clf.cv_results_["std_test_score"]), + pd.DataFrame(clf.cv_results_["rank_test_score"]), + ], + axis=1, + ) + df.columns = [ + "optimizer", + "activation", + "mean_test_score", + "std_test_score", + "rank_test_score", + ] + df.to_csv(outfilepath, header=True) + + selected_activation = clf.best_params_["activation"] + + else: + selected_activation = activations[0] + + # Maybe also optimize weight initializations?? + # inits = ['glorot_uniform', 'normal', 'uniform'] + + # find best performing parameters + sys.stdout.write( + f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n" + ) + else: + sys.stderr.write( + f"ERROR: the target that you provide ({target}) is not contained in your data file ({args.i[0]})" + ) diff --git a/build/lib/dfpl/dfplmodule.py b/build/lib/dfpl/dfplmodule.py new file mode 100644 index 00000000..d0838ebf --- /dev/null +++ b/build/lib/dfpl/dfplmodule.py @@ -0,0 +1,1413 @@ +""" +Python module for deepFPlearn tools +""" + +import argparse +import csv +import math + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns # for drawing the heatmaps +from rdkit import Chem, DataStructs # for fingerprint generation +from rdkit.Chem import MACCSkeys +from rdkit.Chem.AtomPairs import Pairs, Torsions +from sklearn.model_selection import train_test_split +from tensorflow.keras import optimizers, regularizers +from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau +from tensorflow.keras.layers import Dense, Dropout, Input + +# for NN model functions +from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.optimizers import SGD + +# GENERAL FUNCTIONS --------------------------------------------------------- # + + +def gather(df, key, value, cols): + """ + Simple emulation of R's gather function using pandas melt() function in python + + :param df: The data frame + :param key: The key column name + :param value: The value column name + :param cols: list of column names to gather + :return: + """ + id_vars = [col for col in df.columns if col not in cols] + id_values = cols + var_name = key + value_name = value + return pd.melt(df, id_vars, id_values, var_name, value_name) + + +def shuffleDataPriorToTraining(x, y): + """ + Returns a gathered variant of the input x and y matrices + :param x: + :param y: + :return: + """ + + # merge x and y + df0 = pd.concat([x.reset_index(drop=True), y.reset_index(drop=True)], axis=1) + # shuffle rows, drop NAs, reset index + df1 = df0.sample(frac=1).dropna(axis=0).reset_index() + + return (df1.iloc[:, 0 : x.shape[1]], df1.iloc[:, x.shape[1] :]) + + # return gather(df0, key="target", value="association", + # cols=y.columns) + + +def str2bool(v): + if isinstance(v, bool): + return v + if v.lower() in ("yes", "true", "t", "y", "1"): + return True + elif v.lower() in ("no", "false", "f", "n", "0"): + return False + else: + raise argparse.ArgumentTypeError("Boolean value expected.") + + +def smi2fp(smile, fptype, size=2048): + """ + Convert a SMILES string to a fingerprint object of a certain type using functions + from the RDKIT python library. + + :param smile: A single SMILES string + :param fptype: The type of fingerprint to which the SMILES should be converted. Valid + values are: 'topological' (default), 'MACCS' + :return: A fingerprint object or None, if fingerprint object could not be created + (Respective error message is provided in STDERR). + """ + # generate a mol object from smiles string + + # print(smile) + cs = None + # first transform to canoncial smiles + try: + cs = Chem.CanonSmiles(smile) + except Exception: + print( + f"[WARNING]: Not able to transform your smile to a canonical version of it: {smile}" + ) + if not cs: + return None + + mol = None + try: + mol = Chem.MolFromSmiles(cs) + except Exception: + print( + f"[WARNING]: Not able to extract molecule from (canonically transformed) SMILES: {cs}\n Original SMILE: {smile}" + ) + if not mol: + return None + + # init fp, any better idea? e.g. calling a constructor? + # fp = Chem.Mol # FingerprintMols.FingerprintMol(mol) + + if fptype == "topological": # 2048 bits + # Topological Fingerprints: + # The fingerprinting algorithm used is similar to that used in the Daylight + # fingerprinter: it identifies and hashes topological paths (e.g. along bonds) + # in the molecule and then uses them to set bits in a fingerprint of user-specified + # lengths. After all paths have been identified, the fingerprint is typically + # folded down until a particular density of set bits is obtained. + try: + # fp = Chem.RDKFingerprint(mol, fpSize=size) + return Chem.RDKFingerprint(mol, fpSize=size) + except Exception: + print("SMILES not convertable to topological fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + elif fptype == "MACCS": + # MACCS Keys: + # There is a SMARTS-based implementation of the 166 public MACCS keys. + # The MACCS keys were critically evaluated and compared to other MACCS + # implementations in Q3 2008. In cases where the public keys are fully defined, + # things looked pretty good. + + try: + # fp = MACCSkeys.GenMACCSKeys(mol) + return MACCSkeys.GenMACCSKeys(mol) + except Exception: + print("SMILES not convertable to MACSS fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + elif fptype == "atompairs": + # Atom Pairs: + # Atom-pair descriptors [3] are available in several different forms. + # The standard form is as fingerprint including counts for each bit instead + # of just zeros and ones. Nevertheless we use the BitVect variant here. + + try: + # fp = Pairs.GetAtomPairFingerprintAsBitVect(mol) + return Pairs.GetAtomPairFingerprintAsBitVect(mol) + # counts if features also possible here! needs to be parsed differently + # fps.update({i:Pairs.GetAtomPairFingerprintAsIntVect(mols[i])}) + except Exception: + print("SMILES not convertable to atompairs fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + else: + # Topological Torsions: + # At the time of this writing, topological torsion fingerprints have too + # many bits to be encodeable using the BitVector machinery, so there is no + # GetTopologicalTorsionFingerprintAsBitVect function. + + try: + # fp = Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) + return Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) + except Exception: + print("SMILES not convertable to torsions fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + +def XandYfromInput( + csvfilename: str, + rtype: str, + fptype: str, + printfp: bool = False, + size: int = 2048, + verbose: int = 2, + returnY: bool = True, +) -> tuple: + """ + Return the matrix of features for training and testing NN models (X) as numpy array. + Provided SMILES are transformed to fingerprints, fingerprint strings are then split + into vectors and added as row to the array which is returned. + :param csvfilename: Filename of CSV files containing the training data. The + SMILES/Fingerprints are stored 1st column + :param rtype: Type of structure representation. Valid values are: 'fp' and 'smile' + :param fptype: Type of fingerprint to be generated out + :param printfp: Print generated fingerprints to file, namely the input file with the + file ending '.fingerprints.csv'. Default:False + :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If + no outcome data is provided, the Y matrix is a None object. + """ + + # TODOs: implement other types of fingerprint! + + df = pd.read_csv(csvfilename) + cnames = df.columns + + if rtype not in cnames: + print( + f"[ERROR:] There is no column named {rtype} in your input training set file" + ) + exit(0) + + dfX = None + if rtype == "smiles": # transform to canonical smiles, and then to fp + dfX = pd.DataFrame( + df["smiles"] + .transform( + lambda x: np.array( + Chem.RDKFingerprint( + Chem.MolFromSmiles(Chem.CanonSmiles(x)), fpSize=size + ) + ) + ) + .to_list() + ) + else: # split fingerprint into matrix + dfX = pd.DataFrame(df["fp"].transform(lambda x: list(x)).to_list()) + + # add 'id' as rownames of dataframe + if "id" in cnames: + dfX.index = df["id"] + + dfY = None + if returnY: + # names in Y contain 'id' if present, and all other columns (=target columns) + namesY = [c for c in cnames if c not in ["id", "smiles", "fp"]] + dfY = df[namesY] + # add 'id' as rownames of dataframe + if "id" in cnames: + dfY.index = df["id"] + + return (dfX, dfY) + + +def TrainingDataHeatmap(x, y): + x["ID"] = x.index + y["ID"] = y.index + + # xy = pd.merge(x,y,on="ID") + + # clustermap dies because of too many iterations.. + # sns.clustermap(x, metric="correlation", method="single", cmap="Blues", standard_scale=1) #, row_colors=row_colors) + + # try to cluster prior to viz + # check this out + # https://stackoverflow.com/questions/2455761/reordering-matrix-elements-to-reflect-column-and-row-clustering-in-naiive-python + + # viz using matplotlib heatmap https://matplotlib.org/3.1.1/gallery/images_contours_and_fields/image_annotated_heatmap.html + + return 1 + + +def removeDuplicates(x, y): + """ + Remove duplicated feature - outcome pairs from feature matrix and outcome vector combination. + + :param x: Feature matrix + :param y: Outcome vector + + :return: Tuple of unique Feature matrix and outcome vector combinations + """ + + # add y as additional column to x + joined = np.append(x, [[x] for x in y], axis=1) + # merge all columns into one string per row + + fpstrings = [] + for i in range(0, len(y)): + fpstrings.append(["".join([str(int(c)) for c in joined[i]])]) + + fpstrings_unique = np.unique(fpstrings, return_index=True) + + return (x[fpstrings_unique[1]], y[fpstrings_unique[1]]) + + +def defineCallbacks( + checkpointpath: str, + patience: int, + rlrop: bool = False, + rlropfactor: float = 0.1, + rlroppatience: int = 50, +) -> list: + """ + + :param checkpointpath: + :param patience: + :param rlrop: + :param rlropfactor: + :param rlroppatience: + :return: + """ + + # enable this checkpoint to restore the weights of the best performing model + checkpoint = ModelCheckpoint( + checkpointpath, + monitor="val_loss", + verbose=1, + period=10, + save_best_only=True, + mode="min", + save_weights_only=True, + ) + + # enable early stopping if val_loss is not improving anymore + earlystop = EarlyStopping( + monitor="val_loss", + min_delta=0, + patience=patience, + verbose=1, + restore_best_weights=True, + ) + + callbacks = [] + if rlrop: + rlrop = ReduceLROnPlateau( + monitor="val_loss", factor=rlropfactor, patience=rlroppatience + ) + callbacks = [checkpoint, earlystop, rlrop] + else: + callbacks = [checkpoint, earlystop] + + # Return list of callbacks - collect the callbacks for training + return callbacks + + +def defineNNmodelMulti( + inputSize=2048, + outputSize=None, + l2reg=0.001, + dropout=0.2, + activation="relu", + optimizer="Adam", + lr=0.001, + decay=0.01, +): + if optimizer == "Adam": + myoptimizer = optimizers.Adam( + learning_rate=lr, decay=decay + ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) + elif optimizer == "SGD": + myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) + else: + myoptimizer = optimizer + + nhl = int(math.log2(inputSize) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(inputSize / 2), + input_dim=inputSize, + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout)) + # next hidden layers + for i in range(1, nhl): + factorunits = 2 ** (i + 1) + factordropout = 2 * i + model.add( + Dense( + units=int(inputSize / factorunits), + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout / factordropout)) + # multi-class output layer + # use sigmoid to get independent probabilities for each output node + # (need not add up to one, as they would using softmax) + # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ + model.add(Dense(units=outputSize, activation="sigmoid")) + + model.summary() + + # compile model + model.compile( + loss="binary_crossentropy", optimizer=myoptimizer, metrics=["accuracy"] + ) + + return model + + +def defineNNmodel( + inputSize: int = 2048, + l2reg: float = 0.001, + dropout: float = 0.2, + activation: str = "relu", + optimizer: str = "Adam", + lr: float = 0.001, + decay: float = 0.01, +) -> Model: + """ + + :param inputSize: + :param l2reg: + :param dropout: + :param activation: + :param optimizer: + :param lr: + :param decay: + :return: + """ + + if optimizer == "Adam": + myoptimizer = optimizers.Adam( + learning_rate=lr, decay=decay + ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) + elif optimizer == "SGD": + myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) + else: + myoptimizer = optimizer + + myhiddenlayers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} + + if not str(inputSize) in myhiddenlayers.keys(): + print("Wrong inputsize. Must be in {2048, 1024, 999, 512, 256}.") + return None + + nhl = int(math.log2(inputSize) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(inputSize / 2), + input_dim=inputSize, + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout)) + # next hidden layers + for i in range(1, nhl): + factorunits = 2 ** (i + 1) + factordropout = 2 * i + model.add( + Dense( + units=int(inputSize / factorunits), + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout / factordropout)) + # output layer + model.add(Dense(units=1, activation="sigmoid")) + + model.summary() + + # compile model + model.compile(loss="mse", optimizer=myoptimizer, metrics=["accuracy"]) + + return model + + +def autoencoderModel( + input_size: int = 2048, + encoding_dim: int = 256, + myloss: object = "binary_crossentropy", + mylr: float = 0.001, + mydecay: float = 0.01, +) -> tuple: + """ + This function provides an autoencoder model to reduce a certain input to a compressed version. + + :param encoding_dim: Size of the compressed representation. Default: 85 + :param input_size: Size of the input. Default: 2048 + :param myactivation: Activation function, see Keras activation functions for potential values. Default: relu + :param myoptimizer: Optimizer, see Keras optmizers for potential values. Default: adadelta + :param myloss: Loss function, see Keras Loss functions for potential values. Default: binary_crossentropy + :return: a tuple of autoencoder and encoder models + """ + + myoptimizer = optimizers.Adam( + learning_rate=mylr, decay=mydecay + ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) + + # get the number of meaningful hidden layers (latent space included) + nhl = round(math.log2(input_size / encoding_dim)) + + # the input placeholder + input_vec = Input(shape=(input_size,)) + + # 1st hidden layer, that receives weights from input layer + # equals bottle neck layer, if nhl==1! + encoded = Dense(units=int(input_size / 2), activation="relu")(input_vec) + + if nhl > 1: + # encoding layers, incl. bottle neck + for i in range(1, nhl): + factorunits = 2 ** (i + 1) + # print(f'{factorunits}: {int(input_size / factorunits)}') + encoded = Dense(units=int(input_size / factorunits), activation="relu")( + encoded + ) + + # encoding_dim = int(input_size/factorunits) + + # 1st decoding layer + factorunits = 2 ** (nhl - 1) + decoded = Dense(units=int(input_size / factorunits), activation="relu")(encoded) + + # decoding layers + for i in range(nhl - 2, 0, -1): + factorunits = 2**i + # print(f'{factorunits}: {int(input_size/factorunits)}') + decoded = Dense(units=int(input_size / factorunits), activation="relu")( + decoded + ) + + # output layer + # The output layer needs to predict the probability of an output which needs to either 0 or 1 and hence we use sigmoid activation function. + decoded = Dense(units=input_size, activation="sigmoid")(decoded) + + else: + # output layer + decoded = Dense(units=input_size, activation="sigmoid")(encoded) + + autoencoder = Model(input_vec, decoded) + encoder = Model(input_vec, encoded) + + autoencoder.summary() + encoder.summary() + + # We compile the autoencoder model with adam optimizer. + # As fingerprint positions have a value of 0 or 1 we use binary_crossentropy as the loss function + autoencoder.compile(optimizer=myoptimizer, loss=myloss) + + return (autoencoder, encoder) + + +# def predict_values(ac_model_file_path, model_file_path, pdx): +# """ +# Predict a set of chemicals using a selected model. +# +# :param model_file_path: Path to the model weights of the prediction DNN +# :param pdx: A matrix containing the fingerprints of the chemicals, generated via XfromInput function +# :return: A dataframe of 2 columns: random - predicted values using random model, trained - predicted values +# using trained model. Rownames are consecutive numbers of the input rows, or if provided in the input file +# the values of the id column +# """ +# +# # ac_model_file_path="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.AC-model.hdf5" +# # model_file_path ="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.model.hdf5" +# +# print(f"[INFO:] Loaded model weights for autoencoder: '{model_file_path}'") +# print(f"[INFO:] Loaded model weights for prediction DNN: '{model_file_path}'") +# +# start = time() +# # create autoencoder +# (autoencoder, encoder) = autoencoderModel(input_size=pdx.shape[1], encoding_dim=256) +# # load AC weights +# autoencoder.load_weights(ac_model_file_path) +# # encode input +# encodedX = encoder.predict(pdx) +# +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# print(f"[INFO:] Computation time used for encoding: {trainTime} min") +# +# start = time() +# # create DNN +# predictor = defineNNmodel(inputSize=encodedX.shape[1]) +# # predict with random weights +# predictions_random = predictor.predict(encodedX) +# # load DNN weights +# predictor.load_weights(model_file_path) +# # predict encoded input +# predictions = predictor.predict(encodedX) +# +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# print(f"[INFO:] Computation time used for the predictions: {trainTime} min") +# +# df = pd.DataFrame(data={'random': predictions_random.flatten(), +# 'trained': predictions.flatten()}, +# columns=['random', 'trained'], +# index=pdx.index) +# print(df) +# return (df) + + +def trainfullac( + X: pd.DataFrame, + y: pd.DataFrame, + useweights: str = None, + epochs: int = 0, + encdim: int = 256, + checkpointpath: str = None, + verbose: int = 0, +) -> Model: + """ + Train an autoencoder on the given feature matrix X. Response matrix is only used to + split meaningfully in test and train data set. + + :param X: Feature matrix. + :param y: Matrix containing the response variables. + :param epochs: Number of epochs to train the autoencoder + :param encdim: Dimension of latent space. This and the column dimension of X control + the number and size of the hidden layers. + :param checkpointpath: path to prefix the model files during callbacks + :param verbose: Verbosity level. Is forwarded to the Keras fit function that prints + training states in different verbosity levels + :return: The encoder model of the trained autoencoder + """ + + # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) + (autoencoder, encoder) = autoencoderModel( + input_size=X.shape[1], encoding_dim=encdim + ) + + if useweights: # don't train, use existing weights file and load it into AC model + # autoencoder.load_weights(useweights) + encoder.load_weights(useweights) + else: + # collect the callbacks for training + callback_list = defineCallbacks( + checkpointpath=checkpointpath, patience=20, rlrop=False + ) + + # split data into test and training data + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + # Fit the AC + autohist = autoencoder.fit( + X_train, + X_train, + callbacks=callback_list, + epochs=epochs, + batch_size=256, + shuffle=True, + verbose=verbose, + validation_data=(X_test, X_test), + ) + # history + ac_loss = autohist.history["loss"] + ac_val_loss = autohist.history["val_loss"] + ac_epochs = range(ac_loss.__len__()) + pd.DataFrame( + data={"loss": ac_loss, "val_loss": ac_val_loss, "epoch": ac_epochs} + ).to_csv(checkpointpath.replace(".hdf5", "_trainValLoss_AC.csv"), index=False) + # generate a figure of the losses for this fold + plt.figure() + plt.plot(ac_epochs, ac_loss, "bo", label="Training loss") + plt.plot(ac_epochs, ac_val_loss, "b", label="Validation loss") + plt.title("Training and validation loss of AC") + plt.legend() + plt.savefig( + fname=checkpointpath.replace(".hdf5", "_trainValLoss_AC.svg"), format="svg" + ) + plt.close() + # write the losses to .csv file for later data visualization + + # model needs to be saved and restored when predicting new input! + # use encode() of train data as input for DL model to associate to chemical + return encoder + + +def plotTrainHistory(hist, target, fileAccuracy, fileLoss): + """ + Plot the training performance in terms of accuracy and loss values for each epoch. + :param hist: The history returned by model.fit function + :param target: The name of the target of the model + :param fileAccuracy: The filename for plotting accuracy values + :param fileLoss: The filename for plotting loss values + :return: none + """ + + # plot accuracy + plt.figure() + plt.plot(hist.history["accuracy"]) + if "val_accuracy" in hist.history.keys(): + plt.plot(hist.history["val_accuracy"]) + plt.title("Model accuracy - " + target) + plt.ylabel("Accuracy") + plt.xlabel("Epoch") + if "val_accuracy" in hist.history.keys(): + plt.legend(["Train", "Test"], loc="upper left") + else: + plt.legend(["Train"], loc="upper_left") + plt.savefig(fname=fileAccuracy, format="svg") + + # Plot training & validation loss values + plt.figure() + plt.plot(hist.history["loss"]) + plt.plot(hist.history["val_loss"]) + plt.title("Model loss - " + target) + plt.ylabel("Loss") + plt.xlabel("Epoch") + plt.legend(["Train", "Test"], loc="upper left") + # plt.show() + plt.savefig(fname=fileLoss, format="svg") + plt.close() + + +def plotAUC(fpr, tpr, auc, target, filename, title=""): + plt.figure() + plt.plot([0, 1], [0, 1], "k--") + plt.plot(fpr, tpr, label=f"Keras (area = {auc:.3f})") + plt.xlabel("False positive rate") + plt.ylabel("True positive rate") + plt.title(f"ROC curve {target}") + plt.legend(loc="best") + plt.savefig(fname=filename, format="svg") + plt.close() + + +def plotHeatmap(matrix, filename, title=""): + plt.figure() + plt.imshow(matrix, cmap="Greys", interpolation="nearest") + plt.title(title) + plt.savefig(fname=filename, format="svg") + plt.close() + + +# ------------------------------------------------------------------------------------- # + +# plot model history more easily, see whyboris@github: https://gist.github.com/whyboris/91ee793ddc92cf1e824978cf31bb790c +# when plotting, smooth out the points by some factor (0.5 = rough, 0.99 = smooth) +# method taken from `Deep Learning with Python` by François Chollet + +# 01 --------------------------------------------------------------------------------- # + +# def smooth_curve(points, factor=0.75): +# smoothed_points = [] +# for point in points: +# if smoothed_points: +# previous = smoothed_points[-1] +# smoothed_points.append(previous * factor + point * (1 - factor)) +# else: +# smoothed_points.append(point) +# return smoothed_points +# +# +# # 02 ---------------------------------------------------------------------------------- # +# +# def set_plot_history_data(ax, history, which_graph): +# if which_graph == 'acc': +# train = smooth_curve(history.history['accuracy']) +# valid = smooth_curve(history.history['val_accuracy']) +# +# if which_graph == 'loss': +# train = smooth_curve(history.history['loss']) +# valid = smooth_curve(history.history['val_loss']) +# +# # plt.xkcd() # make plots look like xkcd +# +# epochs = range(1, len(train) + 1) +# +# trim = 0 # remove first 5 epochs +# # when graphing loss the first few epochs may skew the (loss) graph +# +# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', linewidth=15, alpha=0.1) +# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', label=('Training')) +# +# ax.plot(epochs[trim:], valid[trim:], 'g', linewidth=15, alpha=0.1) +# ax.plot(epochs[trim:], valid[trim:], 'g', label=('Validation')) +# + +# 03 ---------------------------------------------------------------------------------- # +# +# def get_max_validation_accuracy(history): +# validation = smooth_curve(history.history['val_accuracy']) +# ymax = max(validation) +# return 'Max validation accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' +# +# +# def get_max_training_accuracy(history): +# training = smooth_curve(history.history['accuracy']) +# ymax = max(training) +# return 'Max training accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' +# + +# 04---------------------------------------------------------------------------------- # + +# def plot_history(history, file): +# fig, (ax1, ax2) = plt.subplots(nrows=2, +# ncols=1, +# figsize=(10, 6), +# sharex=True, +# gridspec_kw={'height_ratios': [5, 2]}) +# +# set_plot_history_data(ax1, history, 'acc') +# +# set_plot_history_data(ax2, history, 'loss') +# +# # Accuracy graph +# ax1.set_ylabel('Accuracy') +# ax1.set_ylim(bottom=0.5, top=1) +# ax1.legend(loc="lower right") +# ax1.spines['top'].set_visible(False) +# ax1.spines['right'].set_visible(False) +# ax1.xaxis.set_ticks_position('none') +# ax1.spines['bottom'].set_visible(False) +# +# # max accuracy text +# plt.text(0.5, +# 0.6, +# get_max_validation_accuracy(history), +# horizontalalignment='right', +# verticalalignment='top', +# transform=ax1.transAxes, +# fontsize=12) +# plt.text(0.5, +# 0.8, +# get_max_training_accuracy(history), +# horizontalalignment='right', +# verticalalignment='top', +# transform=ax1.transAxes, +# fontsize=12) +# +# # Loss graph +# ax2.set_ylabel('Loss') +# ax2.set_yticks([]) +# ax2.plot(legend=False) +# ax2.set_xlabel('Epochs') +# ax2.spines['top'].set_visible(False) +# ax2.spines['right'].set_visible(False) +# +# plt.tight_layout() +# plt.savefig(fname=file, format='svg') +# plt.close() +# + + +def drawHeatmap(data, anno): + # (data=pd.DataFrame(Xt), anno = pd.DataFrame(Yt.astype(int))) + + # annotation bar colors + my_ann_colors = dict(zip(anno[0].unique(), ["blue", "red"])) + row_colors = anno[0].map(my_ann_colors) + + cl = sns.clustermap( + data, + metric="euclidean", + method="single", + z_score=None, + standard_scale=None, + col_cluster=False, + cmap="Greys", + row_colors=row_colors, + yticklabels=False, + ) + cl.fig.suptitle("Distributions of [1,0] in fingerprints of target: AR") + + url = "https://python-graph-gallery.com/wp-content/uploads/mtcars.csv" + df = pd.read_csv(url) + # set df index using existing columns + df = df.set_index("model") + # remove name of index column, numbered rows have been the index before, they are gone now + del df.index.name + df + my_palette = dict(zip(df.cyl.unique(), ["orange", "yellow", "brown"])) + row_colors = df.cyl.map(my_palette) + + # Default plot + # sns.clustermap(df) + + # Clustermethods + my_palette = dict() + sns.clustermap( + df, + metric="correlation", + standard_scale=1, + method="single", + cmap="Blues", + row_colors=row_colors, + ) + + +############################################################################### +# TRAIN FUNCTIONS --------------------------------------------------------- # + + +def defineOutfileNames(pathprefix: str, target: str, fold: int) -> tuple: + """ + This function returns the required paths for output files or directories. + + :param pathprefix: A file path prefix for all files. + :param mtype: The model type. Its set by the trainNNmodels function with information on autoencoder or not, + and if AC is used, then with its parameters. + :param target: The name of the target. + + :return: A tuple of 14 output file names. + """ + + modelname = target + ".Fold-" + str(fold) + + modelfilepathW = str(pathprefix) + "." + modelname + ".weights.h5" + modelfilepathM = str(pathprefix) + "." + modelname + ".json" + modelhistplotpathL = str(pathprefix) + "." + modelname + ".loss.svg" + modelhistplotpathA = str(pathprefix) + "." + modelname + ".acc.svg" + modelhistplotpath = str(pathprefix) + "." + modelname + ".history.svg" + modelhistcsvpath = str(pathprefix) + "." + modelname + ".history.csv" + modelvalidation = str(pathprefix) + "." + modelname + ".validation.csv" + modelAUCfile = str(pathprefix) + "." + modelname + ".auc_value.svg" + modelAUCfiledata = str(pathprefix) + "." + modelname + ".auc_value.data.csv" + outfilepath = str(pathprefix) + "." + modelname + ".trainingResults.txt" + checkpointpath = str(pathprefix) + "." + modelname + ".checkpoint.model.hdf5" + modelheatmapX = str(pathprefix) + "." + modelname + ".heatmap.X.svg" + modelheatmapZ = str(pathprefix) + "." + modelname + ".AC.heatmap.Z.svg" + + return ( + modelfilepathW, + modelfilepathM, + modelhistplotpathL, + modelhistplotpathA, + modelhistplotpath, + modelhistcsvpath, + modelvalidation, + modelAUCfile, + modelAUCfiledata, + outfilepath, + checkpointpath, + modelheatmapX, + modelheatmapZ, + ) + + +# ------------------------------------------------------------------------------------- # + + +def eval01Distributions(Xt, Yt, y_train, y_test, verbosity=0): + """ + Evaluate the percentage of 0 values in the outcome variable of the whole dataset and the splitted (train,test) + dataset, and the percentage of 0 values in the feature matrix. + + :param Xt: The whole feature matrix + :param Yt: The whole outcome vector + :param y_train: The outcome vector of the training set + :param y_test: The outcome vector of the test set + :param verbosity: The verbosity level. Info is only printed if verbosity is not 0. + + :return: Nothing is returned. + """ + + if verbosity == 0: + return + else: + unique, counts = np.unique(Yt, return_counts=True) + perc = round(100 / len(Yt) * counts[1]) + print( + f"[INFO:] Percentage of '1' values in outcome variable (whole dataset): {perc}\n" + ) + + uniqueRtr, countsRtr = np.unique(y_train, return_counts=True) + uniqueRte, countsRte = np.unique(y_test, return_counts=True) + perc = round(100 / len(y_train) * countsRtr[1]) + print(f"[INFO:] Percentage of '1' values in training outcomes: {perc}\n") + perc = round(100 / len(y_test) * countsRte[1]) + print(f"[INFO:] Percentage of '1' values in test outcomes: {perc}\n") + + print( + f"[INFO:] Average percentage of '0' positions in fingerprints: {round(np.sum(Xt == 0) / (np.sum(Xt == 0) + np.sum(Xt == 1)), ndigits=4)}" + ) + return + + +# ------------------------------------------------------------------------------------- # + + +def prepareDataSet(y: pd.DataFrame, x: pd.DataFrame, t: str) -> tuple: + """ + A function to remove NA values from the output column, duplicates from input and output + and to transform the data into numpy arrays for keras functions. + + :param y: The target vector that may contain NAs. + :param x: The input matrix that may contain duplicates (including resp. target value!) + :param t: The target string + :return: Tuple of x and y ready for splitting into test and train sets for ML with keras. + """ + + # which rows contain 'NA' in target column + tmp = y[t].astype("category") + Y = np.asarray(tmp) + naRows = np.isnan(Y) + + # transform pd dataframe to numpy array for keras + X = x.to_numpy() + + # subset data according to target non-NA values + Ytall = Y[~naRows] + Xtall = X[~naRows] + + # remove all duplicated feature values - outcome pairs, and + # shuffle data, such that validation_split in fit function selects a random subset! + # Remember, that it takes the fraction for validation from the end of our data! + + (XtU, YtU) = removeDuplicates(x=Xtall, y=Ytall) + + # return(shuffleDataPriorToTraining(x=XtU, y=YtU)) + return (XtU, YtU) + + +# ------------------------------------------------------------------------------------- # + +# def plot_history_vis(hist, model_hist_plot_path, model_hist_csv_path, +# model_hist_plot_path_a, model_hist_plot_path_l, target): +# plot_history(history=hist, file=model_hist_plot_path) +# histDF = pd.DataFrame(hist.history) +# histDF.to_csv(model_hist_csv_path) +# +# # plot accuracy and loss for the training and validation during training +# plotTrainHistory(hist=hist, target=target, +# file_accuracy=model_hist_plot_path_a, +# file_loss=model_hist_plot_path_l) +# + +# ------------------------------------------------------------------------------------- # +# +# def validate_multi_model_on_test_data(x_test, checkpoint_path, y_test, col_names, result_file): +# # load checkpoint model with min(val_loss) +# trainedmodel = define_nn_model_multi(input_size=x_test.shape[1], output_size=y_test.shape[1]) +# +# # predict values with random model +# predictions_random = pd.DataFrame(trainedmodel.predict(x_test), columns=col_names + '-predRandom') +# +# # load weights into random model +# trainedmodel.load_weights(checkpoint_path) +# +# # predict with trained model +# predictions = pd.DataFrame(trainedmodel.predict(x_test), +# columns=col_names + '-pred') +# scores = pd.DataFrame((predictions.round() == y_test).sum() / y_test.shape[0], columns=['correctPredictions']) +# +# results = pd.concat([predictions_random, predictions, pd.DataFrame(y_test, columns=col_names + '-true')], axis=1) +# results.to_csv(result_file) +# +# return scores + + +# ------------------------------------------------------------------------------------- # + +# def validate_model_on_test_data(x_test, checkpoint_path, y_test, model_type, model_validation, target, +# model_auc_file_data, model_auc_file): +# """ +# Function that validates trained model with test data set. History and AUC plots are generated. +# Accuracy and Loss of model on test data, as well as MCC and confusion matrix is calculated and returned. +# +# :param x_test: +# :param checkpoint_path: +# :param y_test: +# :param model_type: +# :param model_validation: +# :param target: +# :param model_auc_file_data: +# :param model_auc_file: +# :return: Tupel containing Loss, Accuracy, MCC, tn, fp, fn, tp of trained model on test data. +# """ +# +# # load checkpoint model with min(val_loss) +# trainedmodel = define_nn_model(input_size=x_test.shape[1]) +# +# # predict values with random model +# predictions_random = pd.DataFrame(trainedmodel.predict(x_test)) +# +# # load weights into random model +# trainedmodel.load_weights(checkpoint_path) +# # predict with trained model +# predictions = pd.DataFrame(trainedmodel.predict(x_test)) +# +# # save validation data to .csv file +# validation = pd.DataFrame({'predicted': predictions[0].ravel(), +# 'true': list(y_test), +# 'predicted_random': predictions_random[0].ravel(), +# 'model_type': model_type}) +# validation.to_csv(model_validation) +# +# # compute MCC +# predictionsInt = [int(round(x)) for x in predictions[0].ravel()] +# ytrueInt = [int(y) for y in y_test] +# MCC = matthews_corrcoef(ytrueInt, predictionsInt) +# +# # generate the AUC-ROC curve data from the validation data +# fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, predictions, drop_intermediate=False) +# +# auc_keras = auc_value(fpr_keras, tpr_keras) +# +# aucdata = pd.DataFrame(list(zip(fpr_keras, +# tpr_keras, +# [auc_keras for x in range(1, len(fpr_keras))], +# [target for x in range(1, len(fpr_keras))])), +# columns=['fpr', 'tpr', 'auc_value', 'target']) +# aucdata.to_csv(model_auc_file_data) +# +# plotAUC(fpr=fpr_keras, tpr=tpr_keras, target=target, auc_value=auc_keras, filename=model_auc_file) +# +# # [[tn, fp] +# # [fn, tp]] +# cfm = confusion_matrix(y_true=ytrueInt, y_pred=predictionsInt) +# +# scores = trainedmodel.evaluate(x_test, y_test, verbose=0) +# +# print(f'TARGET: {target} Loss: {scores[0].__round__(2)} Acc: {scores[1].__round__(2)}') +# print(f'MCC: {MCC.__round__(2)}') +# print(f'CFM: \n\tTN={cfm[0][0]}\tFP={cfm[0][1]}\n\tFN={cfm[1][0]}\tTP={cfm[1][1]}') +# +# return (scores[0], scores[1], MCC, cfm[0][0], cfm[0][1], cfm[1][0], cfm[1][1]) + + +# ------------------------------------------------------------------------------------- # +# def train_nn_models_multi(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, +# split: float = 0.2, epochs: int = 500, +# verbose: int= 2, kfold: int = 5) -> None: +# # remove 'id' column if present +# if 'id' in x.columns: +# x = x.drop('id', axis=1) +# if 'id' in y.columns: +# y = y.drop('id', axis=1) +# +# # drop compounds that are not measured for all target columns, transform to numpy +# (xmulti, ymulti) = shuffleDataPriorToTraining(x, y) +# +# # do a kfold cross validation for the autoencoder training +# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) +# +# # store acc and loss for each fold +# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"]) # FNN test data +# +# fold_no = 1 +# +# # split the data +# for train, test in kfoldCValidator.split(xmulti, ymulti): +# +# # define all the output file/path names +# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, +# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, +# model_auc_file_data, outfilepath, checkpoint_path, +# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, +# target="multi", fold=fold_no) +# +# # use a dnn for multi-class prediction +# model = define_nn_model_multi(input_size=xmulti[train].shape[1], output_size=ymulti.shape[1]) +# +# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # measure the training time +# start = time() +# +# # train and validate +# hist = model.fit(xmulti[train], ymulti[train], +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the multi-label FNN: {trainTime} min") +# +# # validate model on test data set (x_test, y_test) +# scores = validate_multi_model_on_test_data(x_test=xmulti[test], +# checkpoint_path=checkpoint_path, +# y_test=ymulti[test], +# col_names=y.columns, +# result_file=outfilepath.replace("trainingResults.txt", +# "predictionResults.csv")) +# +# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) +# row_df = pd.DataFrame([[fold_no, +# hist.history['loss'][idx], hist.history['val_loss'][idx], +# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], +# scores[0], scores[1], scores[2]]], +# columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"] +# ) +# print(row_df) +# allscores = allscores.append(row_df, ignore_index=True) +# +# fold_no = fold_no + 1 +# del model +# +# print(allscores) +# +# # finalize model +# # 1. provide best performing fold variant +# # select best model based on MCC +# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] +# fold_no = allscores._get_value(idx2, 'fold_no') +# +# modelname = 'multi.Fold-' + str(fold_no) +# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' +# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") +# +# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) +# allscores.to_csv(file) +# +# # copy best DNN model +# shutil.copyfile(checkpoint_path, bestModelfile) +# print(f'[INFO]: Best models for FNN is saved:\n - {bestModelfile}') +# +# # AND retrain with full data set +# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") +# # measure the training time +# start = time() +# +# model = define_nn_model(input_size=xmulti[train].shape[1]) +# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # train and validate +# hist = model.fit(xmulti, ymulti, +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# # validation_data=(x_test, y_test)) # this overwrites val_split! +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") +# plot_history_vis(hist, +# model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), +# model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) +# print(f'[INFO]: Full models for DNN is saved:\n - {fullModelfile}') +# +# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) +# # stats.append([target, [x.__round__(2) for x in scores]]) + + +# ------------------------------------------------------------------------------------- # + +# def trainNNmodels(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, +# split: float = 0.2, epochs: int = 50, params: str = None, +# verbose: int = 2, kfold: int = 5) -> None: +# """ +# Train individual models for all targets (columns) present in the provided target data (y) and a multi-label +# model that classifies all targets at once. For each individual target the data is first subsetted to exclude NA +# values (for target associations). A random sample of the remaining data (size is the split fraction) is used for +# training and the remaining data for validation. +# +# :param modelfilepathprefix: A path prefix for all output files +# :param x: The feature matrix. +# :param y: The outcome matrix. +# :param split: The percentage of data used for validation. +# :param epochs: The number of epochs for training the autoencoder and the DNN for classification. +# Note: Early stopping and fallback is enabled. +# :param params: A .csv files containing paramters that should be evaluated. See file tunedParams.csv. +# :param verbose: Verbosity level. +# +# :return: A list with loss and accuracy values for each individual model. +# """ +# +# # remove 'id' column if present +# if 'id' in x.columns: +# x = x.drop('id', axis=1) +# if 'id' in y.columns: +# y = y.drop('id', axis=1) +# +# if params: +# parameters = pd.read_csv(params) +# +# # add a target 'summarized' for association to ANY of the target genes +# # maybe it improves the detection of '1's due to increased data set +# mysum = y.sum(axis=1) +# y['summarized'] = [0 if s == 0 else 1 for s in mysum] +# +# ### For each individual target (+ summarized target) +# for target in y.columns: # [:1]: +# # target=y.columns[0] # --> only for testing the code +# +# # rm NAs and duplicates, shuffle, and transform to numpy arrays +# (Xt, Yt) = prepareDataSet(y, x, target) +# +# # do a kfold cross validation for the FNN training +# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) +# +# # store acc and loss for each fold +# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"]) # FNN test data +# +# fold_no = 1 +# +# # split the data +# for train, test in kfoldCValidator.split(Xt, Yt): +# +# if verbose > 0: +# print(f'[INFO]: Training of fold number: {fold_no} ------------------------------------\n') +# +# # define all the output file/path names +# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, +# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, +# model_auc_file_data, outfilepath, checkpoint_path, +# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, +# target=target, fold=fold_no) +# +# model = define_nn_model(input_size=Xt[train].shape[1]) +# +# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # measure the training time +# start = time() +# # train and validate +# hist = model.fit(Xt[train], Yt[train], +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# # validation_data=(x_test, y_test)) # this overwrites val_split! +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the single-label FNN: {trainTime} min") +# +# # validate model on test data set (x_test, y_test) +# scores = validate_model_on_test_data(Xt[test], checkpoint_path, Yt[test], +# "FNN", model_validation, target, +# model_auc_file_data, model_auc_file) +# +# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) +# +# row_df = pd.DataFrame([[fold_no, +# hist.history['loss'][idx], hist.history['val_loss'][idx], +# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], +# scores[0], scores[1], scores[2]]], +# columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"] +# ) +# print(row_df) +# allscores = allscores.append(row_df, ignore_index=True) +# fold_no = fold_no + 1 +# del model +# # now next fold +# +# print(allscores) +# +# # finalize model +# # 1. provide best performing fold variant +# # select best model based on MCC +# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] +# fold_no = allscores._get_value(idx2, 'fold_no') +# +# modelname = target + '.Fold-' + str(fold_no) +# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' +# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") +# +# # store all scores +# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) +# allscores.to_csv(file) +# +# # copy best DNN model +# shutil.copyfile(checkpoint_path, bestModelfile) +# print(f'[INFO]: Best model for FNN is saved:\n - {bestModelfile}') +# +# # AND retrain with full data set +# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") +# # measure the training time +# start = time() +# +# model = define_nn_model(input_size=Xt.shape[1]) # X_train.shape[1]) +# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # train and validate +# hist = model.fit(Xt, Yt, +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# # validation_data=(x_test, y_test)) # this overwrites val_split! +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") +# # plot_history_vis(hist, +# # model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# # model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# # model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), +# # model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) +# # print(f'[INFO]: Full model for DNN is saved:\n - {fullModelfile}') +# +# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) +# +# del model +# # now next target +# + +# ------------------------------------------------------------------------------------- # + + +def smilesSet2fpSet(csvfilename, outfilename, fptype): + """ + + :param csvfilename: csv file containing a column named 'smiles' + :param fptype: + :return: void + """ + # csvfilename="/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.csv" + # outfilename = "/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.fp.csv" + + # read csv and generate/add fingerprints to dict + with open(csvfilename, "r") as f: + reader = csv.DictReader(f, delimiter=";") + + feature = "smiles" + + with open(outfilename, "w", newline="") as out: + writer = csv.DictWriter(out, fieldnames=["fp"]) + writer.writeheader() + for row in reader: + # smiles, need to be converted to fp first + fp = smi2fp(smile=row[feature], fptype=fptype) + writer.writerow({"fp": DataStructs.BitVectToText(fp)}) + + return diff --git a/build/lib/dfpl/feedforwardNN.py b/build/lib/dfpl/feedforwardNN.py new file mode 100644 index 00000000..702fff27 --- /dev/null +++ b/build/lib/dfpl/feedforwardNN.py @@ -0,0 +1,464 @@ +import array +import logging +import math +import re +import shutil +import sys +from time import time +from typing import List, Union +import numpy as np +import pandas as pd +from sklearn.metrics import f1_score +from sklearn.model_selection import KFold, train_test_split +from tensorflow.keras import metrics, optimizers, regularizers +from tensorflow.keras.layers import Dense, Dropout + +# for NN model functions +from tensorflow.keras.models import Model, Sequential + +from dfpl import callbacks as cb +from dfpl import history as ht +from dfpl import options, settings + + +def define_out_file_names(path_prefix: str, target: str, fold: int = -1) -> tuple: + """ + This function returns the required paths for output files or directories. + + :param path_prefix: A file path prefix for all files. + :param target: The name of the target. + :param fold: + + :return: A tuple of 14 output file names. + """ + + if fold == -1: + model_name = "/" + target + ".Full" + else: + model_name = "/" + target + ".Fold-" + str(fold) + + model_file_path_weights = str(path_prefix) + model_name + ".weights.h5" + model_file_path_json = str(path_prefix) + model_name + ".json" + model_hist_path = str(path_prefix) + model_name + model_hist_csv_path = str(path_prefix) + model_name + ".history.csv" + model_predict_valset_csv_path = str(path_prefix) + model_name + ".predictValSet.csv" + model_validation = str(path_prefix) + model_name + ".validation.csv" + model_auc_file = str(path_prefix) + model_name + ".auc_value.svg" + model_auc_file_data = str(path_prefix) + model_name + ".auc_value.data.csv" + out_file_path = str(path_prefix) + model_name + ".trainingResults.txt" + checkpoint_path = str(path_prefix) + model_name + ".checkpoint.model.hdf5" + model_heatmap_x = str(path_prefix) + model_name + ".heatmap.X.svg" + model_heatmap_z = str(path_prefix) + model_name + ".AC.heatmap.Z.svg" + + return ( + model_file_path_weights, + model_file_path_json, + model_hist_path, + model_hist_csv_path, + model_predict_valset_csv_path, + model_validation, + model_auc_file, + model_auc_file_data, + out_file_path, + checkpoint_path, + model_heatmap_x, + model_heatmap_z, + ) + + +def define_nn_multi_label_model( + input_size: int, output_size: int, opts: options.Options +) -> Model: + if opts.optimizer == "Adam": + my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + elif opts.optimizer == "SGD": + my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) + else: + logging.error(f"Your selected optimizer is not supported:{opts.optimizer}.") + sys.exit("Unsupported optimizer.") + + nhl = int(math.log2(input_size) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation=opts.activationFunction, + kernel_regularizer=regularizers.l2(opts.l2reg), + ) + ) + model.add(Dropout(opts.dropout)) + # next hidden layers + for i in range(1, nhl): + factor_units = 2 ** (i + 1) + factor_dropout = 2 * i + model.add( + Dense( + units=int(input_size / factor_units), + activation=opts.activationFunction, + kernel_regularizer=regularizers.l2(opts.l2reg), + ) + ) + model.add(Dropout(opts.dropout / factor_dropout)) + # multi-class output layer + # use sigmoid to get independent probabilities for each output node + # we use sigmoid since one chemical can be active for multiple targets + # (need not add up to one, as they would using softmax) + # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ + model.add(Dense(units=output_size, activation="sigmoid")) + + model.summary(print_fn=logging.info) + + # compile model + model.compile( + loss="binary_crossentropy", + optimizer=my_optimizer, + metrics=["accuracy", metrics.Recall(), metrics.Precision()], + ) + + return model + + +def define_nn_model_multi( + input_size: int = 2048, + output_size: int = None, + l2reg: float = 0.001, + dropout: float = 0.2, + activation: str = "relu", + optimizer: str = "Adam", + lr: float = 0.001, + decay: float = 0.01, +) -> Model: + if optimizer == "Adam": + my_optimizer = optimizers.Adam(learning_rate=lr, decay=decay) + elif optimizer == "SGD": + my_optimizer = optimizers.SGD(lr=lr, momentum=0.9, decay=decay) + else: + my_optimizer = optimizer + + nhl = int(math.log2(input_size) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout)) + # next hidden layers + for i in range(1, nhl): + factor_units = 2 ** (i + 1) + factor_dropout = 2 * i + model.add( + Dense( + units=int(input_size / factor_units), + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout / factor_dropout)) + # multi-class output layer + # use sigmoid to get independent probabilities for each output node + # (need not add up to one, as they would using softmax) + # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ + model.add(Dense(units=output_size, activation="sigmoid")) + + model.summary(print_fn=logging.info) + + # compile model + model.compile( + loss="binary_crossentropy", optimizer=my_optimizer, metrics=["accuracy"] + ) + + return model + + +def validate_multi_model_on_test_data( + x_test: np.ndarray, + checkpoint_path: str, + y_test: np.ndarray, + col_names: List[str], + result_file: str, +) -> List[Union[int, float, str]]: + """ + Validate the multi label model on a test data set. + + :param x_test: The feature matrix of the test data set + :param checkpoint_path: The path containing the weights of the FNN model + :param y_test: The outcome matrix of the test data set + :param col_names: The names of the columns that are targets + :param result_file: The filename of the output file + :return: A pandas Dataframe containing the percentage of correct predictions for each target + """ + # load checkpoint model with min(val_loss) + trained_model = define_nn_model_multi( + input_size=x_test.shape[1], output_size=y_test.shape[1] + ) + + # predict values with random model + predictions_random = pd.DataFrame( + trained_model.predict(x_test), columns=[n for n in col_names] + ) + + # load weights into random model + trained_model.load_weights(checkpoint_path) + + # predict with trained model + predictions = pd.DataFrame( + trained_model.predict(x_test), columns=[n for n in col_names] + ) + + y_true = pd.DataFrame(y_test, columns=col_names) + + f1_random = f1_score( + y_true=y_true, y_pred=predictions_random.round(), average="weighted" + ) + + f1_trained = f1_score(y_true=y_true, y_pred=predictions.round(), average="weighted") + + predictions_random.columns = [n + "-random" for n in col_names] + predictions.columns = [n + "-trained" for n in col_names] + + results = pd.concat([predictions_random, predictions, y_true], axis=1) + results.to_csv(result_file) + + return [f1_random, f1_trained] + + +def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: + # find target columns + names_y = [ + c + for c in df.columns + if c not in ["cid", "id", "smiles", "fp", "inchi", "fpcompressed"] + ] + selector = df[names_y].notna().apply(np.logical_and.reduce, axis=1) + + if opts.compressFeatures: + # get compressed fingerprints as numpy array + fpMatrix = np.array( + df[df["fpcompressed"].notnull() & selector]["fpcompressed"].to_list(), + dtype=settings.nn_multi_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + y = np.array( + df[df["fpcompressed"].notnull() & selector][names_y], + dtype=settings.nn_multi_target_numpy_type, + copy=settings.numpy_copy_values, + ) + else: + # get fingerprints as numpy array + fpMatrix = np.array( + df[df["fp"].notnull() & selector]["fp"].to_list(), + dtype=settings.nn_multi_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + + y = np.array( + df[df["fp"].notnull() & selector][names_y], + dtype=settings.nn_multi_target_numpy_type, + copy=settings.numpy_copy_values, + ) + + if opts.kFolds > 0: + # do a kfold cross validation for the autoencoder training + kfold_c_validator = KFold(n_splits=opts.kFolds, shuffle=True, random_state=42) + + # store acc and loss for each fold + all_scores = pd.DataFrame( + columns=[ + "fold_no", # fold number of k-fold CV + "loss", + "val_loss", + "acc", + "val_acc", # FNN training + "f1_random", + "f1_trained", + ] + ) # F1 scores of predictions + + fold_no = 1 + + # split the data + for train, test in kfold_c_validator.split(fpMatrix, y): + # kf = kfold_c_validator.split(fpMatrix, y) + # train, test = next(kf) + + ( + model_file_path_weights, + model_file_path_json, + model_hist_path, + model_validation, + model_auc_file, + model_auc_file_data, + out_file_path, + checkpoint_path, + model_heatmap_x, + model_heatmap_z, + ) = define_out_file_names( + path_prefix=opts.outputDir, + target="multi" + "_compressed-" + str(opts.compressFeatures), + fold=fold_no, + ) + + # use a dnn for multi-class prediction + model = define_nn_model_multi( + input_size=fpMatrix[train].shape[1], output_size=y.shape[1] + ) + + callback_list = cb.nn_callback(checkpoint_path=checkpoint_path, opts=opts) + # measure the training time + start = time() + + # train and validate + hist = model.fit( + fpMatrix[train], + y[train], + callbacks=callback_list, + epochs=opts.epochs, + batch_size=256, + verbose=opts.verbose, + validation_split=opts.testSize, + ) + + trainTime = str(round((time() - start) / 60, ndigits=2)) + + if opts.verbose > 0: + logging.info( + "Computation time for training the multi-label FNN: " + + trainTime + + " min" + ) + + ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) + # pd.DataFrame(hist.history).to_csv(model_hist_csv_path) + + # validate model on test data set (fpMatrix_test, y_test) + scores = validate_multi_model_on_test_data( + x_test=fpMatrix[test], + checkpoint_path=checkpoint_path, + y_test=y[test], + col_names=names_y, + result_file=out_file_path.replace( + "trainingResults.txt", "predictionResults.csv" + ), + ) + + idx = hist.history["val_loss"].index(min(hist.history["val_loss"])) + row_df = pd.DataFrame( + [ + [ + fold_no, + hist.history["loss"][idx], + hist.history["val_loss"][idx], + hist.history["accuracy"][idx], + hist.history["val_accuracy"][idx], + scores[0], + scores[1], + ] + ], + columns=[ + "fold_no", # fold number of k-fold CV + "loss", + "val_loss", + "acc", + "val_acc", + "f1_random", + "f1_trained", + ], + ) + + logging.info(row_df) + all_scores = all_scores.append(row_df, ignore_index=True) + + fold_no += 1 + del model + + logging.info(all_scores) + + # finalize model + # 1. provide best performing fold variant + # select best model based on MCC + idx2 = all_scores[["f1_trained"]].idxmax().ravel()[0] + fold_no = all_scores.iloc[idx2]["fold_no"] + + model_name = ( + "multi_compressed-" + str(opts.compressFeatures) + ".Fold-" + str(fold_no) + ) + checkpoint_path = opts.outputDir + "/" + model_name + ".checkpoint.model.hdf5" + best_model_file = checkpoint_path.replace( + "Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-" + ) + + file = re.sub( + ".hdf5", + "scores.csv", + re.sub("Fold-..checkpoint", "Fold-All", checkpoint_path), + ) + all_scores.to_csv(file) + + # copy best DNN model + shutil.copyfile(checkpoint_path, best_model_file) + logging.info("Best models for FNN is saved:\n" + best_model_file) + + # AND retrain with full data set + # full_model_file = f"{opts.outputDir}/multi_full.FNN.checkpoint.model.hdf5" + + ( + model_file_path_weights, + model_file_path_json, + model_hist_path, + model_hist_csv_path, + model_predict_valset_csv_path, + model_validation, + model_auc_file, + model_auc_file_data, + out_file_path, + checkpoint_path, + model_heatmap_x, + model_heatmap_z, + ) = define_out_file_names( + path_prefix=opts.outputDir, + target="multi" + "_compressed-" + str(opts.compressFeatures), + ) + + # test training data split + X_train, X_test, y_train, y_test = train_test_split( + fpMatrix, y, test_size=opts.testSize + ) + + # measure the training time + model = define_nn_multi_label_model( + input_size=fpMatrix.shape[1], output_size=y.shape[1], opts=opts + ) + model.evaluate(X_test, y_test) + + # model = define_nn_model_multi(input_size=fpMatrix.shape[1], + # output_size=y.shape[1]) + # callback_list = nn_callback(checkpoint_path=full_model_file, opts=opts) + # train and validate + start = time() + hist = model.fit( + X_train, + y_train, + # callbacks=callback_list, + epochs=opts.epochs, + batch_size=opts.batchSize, + verbose=opts.verbose, + validation_data=(X_test, y_test), + ) + trainTime = str(round((time() - start) / 60, ndigits=2)) + + # yhat = model.predict(X_test) + model.evaluate(X_test, y_test) + + logging.info( + "Computation time for training the full multi-label FNN: " + trainTime + " min" + ) + ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) diff --git a/build/lib/dfpl/fingerprint.py b/build/lib/dfpl/fingerprint.py new file mode 100644 index 00000000..291689c2 --- /dev/null +++ b/build/lib/dfpl/fingerprint.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +"""Calculate fingerprints""" +import logging +import multiprocessing +import os +from functools import partial +from os.path import isfile, join +from typing import Any, Callable, List + +import numpy as np +import pandas as pd +from rdkit import Chem, DataStructs, RDLogger +from rdkit.Chem import AllChem + +# import settings +from dfpl import settings + +default_fp_size = 2048 + + +def addFPColumn(data_frame: pd.DataFrame, fp_size: int) -> pd.DataFrame: + """ + Adds a fingerprint to each row in the dataframe. This function works on + parallel chunks of the original dataframe. + :param data_frame: Input dataframe that needs to have a "smiles" or an "inchi" column + :param fp_size: Number of bits in the fingerprint + :return: The dataframe with an additional "fp" column + """ + + def smile2fp(smile: str) -> Any: + """ + Calculates one fingerprint from a SMILE + :param smile: Input SMILE + :return: List of bits if conversion is successfull, + None otherwise + """ + + # generate morgan fp (circular, ecfp) + # smile = df['smiles'][1] + # mol = Chem.MolFromSmiles(smile) + # from rdkit.Chem import AllChem + # morgan = AllChem.GetMorganFingerprintAsBitVect(mol, 2) + # npa = np.zeros((0,), dtype=np.bool) + # from rdkit import DataStructs + # DataStructs.ConvertToNumpyArray(morgan, npa) + + npa = np.zeros((0,), dtype=np.bool_) + try: + DataStructs.ConvertToNumpyArray( + AllChem.GetMorganFingerprintAsBitVect( + Chem.MolFromSmiles(smile), 2, nBits=fp_size + ), + npa, + ) + return npa + except Exception: + return None + + # try: + # return np.array( + # Chem.RDKFingerprint(Chem.MolFromSmiles(smile), fpSize=fp_size), + # dtype=settings.df_fp_numpy_type, copy=settings.numpy_copy_values) + # except: + # # Note: We don't need to log here since rdkit already logs + # return None + + def inchi2fp(inchi: str) -> Any: + """ + Calculates one fingerprint from InChI + :param inchi: Input InChI + :return: List of bits if conversion is successfull, + None otherwise + """ + try: + return np.array( + Chem.RDKFingerprint(Chem.MolFromInchi(inchi), fpSize=fp_size), + dtype=settings.df_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + except Exception: + # Note: We don't need to log here since rdkit already logs + return None + + if "smiles" in data_frame: + func = smile2fp + accessor = "smiles" + else: + if "inchi" in data_frame: + func = inchi2fp + accessor = "inchi" + else: + raise ValueError("Neither smiles nor inchi column in data-frame") + + data_frame["fp"] = data_frame[accessor].apply(func) + return data_frame + + +def importDataFile( + file_name: str, + import_function: Callable[[str], pd.DataFrame] = pd.read_csv, + fp_size: int = default_fp_size, +) -> pd.DataFrame: + """ + Reads data as CSV or TSV and calculates fingerprints from the SMILES in the data. + :param import_function: + :param file_name: Filename of CSV files containing the training data. The + SMILES/Fingerprints are stored 1st column + :param fp_size: Number of bits in the fingerprint + :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If + no outcome data is provided, the Y matrix is a None object. + """ + # Read the data as Pandas pickle which already contains the calculated fingerprints + name, ext = os.path.splitext(file_name) + if ext == ".pkl": + return pd.read_pickle(file_name) + + df = import_function(file_name) + + # disable the rdkit logger. We know that some inchis will fail and we took care of it. No use to spam the console + RDLogger.DisableLog("rdApp.*") + n_cores = multiprocessing.cpu_count() + df_split = np.array_split(df, n_cores) + with multiprocessing.Pool(n_cores) as pool: + df = pd.concat(pool.map(partial(addFPColumn, fp_size=fp_size), df_split)) + pool.close() + pool.join() + return df + + +def importSmilesCSV(csvfilename: str) -> pd.DataFrame: + return pd.read_csv(csvfilename) + + +def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: + return pd.read_table(tsvfilename, names=["toxid", "inchi", "key"]) + + +conversion_rules = { + # "S_dataset.csv": importSmilesCSV, + # "S_dataset_extended.csv": importSmilesCSV, + # "D_dataset.tsv": importDstoxTSV, + # "train_data.csv": importSmilesCSV, + # "predict_data.csv": importDstoxTSV, + "B_data_ER.csv": importDstoxTSV +} + + +def convert_all(directory: str) -> List[str]: + files = [ + f + for f in os.listdir(directory) + for key, value in conversion_rules.items() + if isfile(join(directory, f)) and f in key + ] + logging.info(f"Found {len(files)} files to convert") + for f in files: + path = join(directory, f) + logging.info(f"Importing file {f}") + df = importDataFile(path, import_function=conversion_rules[f]) + name, ext = os.path.splitext(f) + output_file = join(directory, name + ".pkl") + logging.info(f"Saving pickle of file {f}") + df.to_pickle(output_file) + return files diff --git a/build/lib/dfpl/harmonize_mol_representations.py b/build/lib/dfpl/harmonize_mol_representations.py new file mode 100644 index 00000000..7c17e4e6 --- /dev/null +++ b/build/lib/dfpl/harmonize_mol_representations.py @@ -0,0 +1,171 @@ +from __future__ import annotations + +import logging +import sys +import urllib.error +import urllib.parse +import urllib.request +from dataclasses import dataclass +from functools import lru_cache + +import pandas as pd +from rdkit import Chem, DataStructs +from simple_parsing import ArgumentParser, choice +from simple_parsing.helpers import Serializable + +# Use this conda env: mamba create - n tidyChem - c conda - forge r - base r - tidyverse r - stringdist python rdkit +# numpy pandas simple - parsing + + +@dataclass +class ProgramConfig(Serializable): + """ + Configuration options for running the program + """ + + # file containing SMILES or InChikeys 2B harmonized + input_file: str = ( + "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_fragment.csv.tsv" + ) + # file containing the input for the harmonized molecular representations,tanimoto similarity and levenstein distance + output_file: str = ( + "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_dataset.harmonized.csv" + ) + input_column: str = choice("smiles", "inchikey", default="inchikey") + # column name of the column that contains the molecular representation to harmonize + input_type: str = choice( + "smiles", "inchikey", default="inchikey" + ) # the type of molecular representation of the input + output_type: str = choice( + "smiles", "inchikey", default="smiles" + ) # the type of molecular representation of the output + + +def createCommandlineParser(): + """ + Build the cmd_parser for arguments + Parse the input arguments. + :return: Tuple of commandlineparser, arguments + """ + cmd_parser = ArgumentParser(prog="harmonize_mol_representations") + cmd_parser.add_arguments(ProgramConfig, dest="config") + cmd_args, unknown = cmd_parser.parse_known_args() + return cmd_parser, cmd_args + + +def getLevensteinDistance(a, b): + """ + This function will calculate the levenshtein distance between two input + strings a and b + + params: + a (String) : The first string you want to compare + b (String) : The second string you want to compare + + returns: + This function will return the distnace between string a and b. + + example: + a = 'stamp' + b = 'stomp' + getLevensteinDistance(a,b) + >> 1.0 + """ + + if a is None and b is not None: + return len(b) + if a is not None and b is None: + return len(a) + + @lru_cache(None) # for memorization + def min_dist(s1, s2): + if s1 == len(a) or s2 == len(b): + return len(a) - s1 + len(b) - s2 + + # no change required + if a[s1] == b[s2]: + return min_dist(s1 + 1, s2 + 1) + + return 1 + min( + min_dist(s1, s2 + 1), # insert character + min_dist(s1 + 1, s2), # delete character + min_dist(s1 + 1, s2 + 1), # replace character + ) + + return min_dist(0, 0) + + +def getTanimotoSimilarityFromTwoSMILES( + smile1: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O", + smile2: str = "COC1=C(C=CC(=C1)C=O)O", +) -> float: + if smile1 is None or smile2 is None: + return 0 + + mol1 = Chem.MolFromSmiles(smile1) + mol2 = Chem.MolFromSmiles(smile2) + if mol1 is None or mol2 is None: + return 0 + + fp1 = Chem.RDKFingerprint(mol1, fpSize=2048) + fp2 = Chem.RDKFingerprint(mol2, fpSize=2048) + if fp1 is None or fp2 is None: + return 0 + + return DataStructs.TanimotoSimilarity(fp1, fp2) + + +def getHarmonizedSmiles(smiles: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O") -> str: + link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(smiles)}/smiles" + try: + f = urllib.request.urlopen(link) + myfile = f.read() + sh = myfile.decode("UTF-8") + except urllib.error.HTTPError: + sh = None + + return sh + + +def getHarmonizedSmilesfrominchis(inchikey: str = "CUWVNOSSZYUJAE-UHFFFAOYSA-N") -> str: + link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(inchikey)}/smiles" + try: + f = urllib.request.urlopen(link) + myfile = f.read() + sh = myfile.decode("UTF-8") + except urllib.error.HTTPError: + sh = None + + return sh + + +if __name__ == "__main__": + parser, args = createCommandlineParser() + opts = args.config + + # read data of smiles and harmonized smiles + df = pd.read_csv(filepath_or_buffer=opts.input_file) + if opts.input_type == "smiles": + df["smiles_harmonized"] = df[opts.input_column].apply(getHarmonizedSmiles) + df["tanimoto_similiarity"] = df.apply( + lambda x: getTanimotoSimilarityFromTwoSMILES( + smile1=x[opts.input_column], smile2=x["smiles_harmonized"] + ), + axis=1, + ) + df["levenstein_distance"] = df.apply( + lambda x: getLevensteinDistance( + a=x[opts.input_column], b=x["smiles_harmonized"] + ), + axis=1, + ) + elif opts.input_type == "inchikey": + df["smiles_harmonized"] = df[opts.input_column].apply( + getHarmonizedSmilesfrominchis + ) + + else: + logging.error(f"Your selected input is not supported: {opts.input_file}.") + sys.exit("Unsupported input type. Must be either smiles or inchikey") + + df.to_csv(path_or_buf=opts.output_file) diff --git a/build/lib/dfpl/history.py b/build/lib/dfpl/history.py new file mode 100644 index 00000000..1f16c2a9 --- /dev/null +++ b/build/lib/dfpl/history.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- +"""Store and visualise training histories""" +import logging + +import matplotlib as mpl +import matplotlib.pyplot as plt +import pandas as pd +from tensorflow.keras.callbacks import History + +mpl.use("Agg") + + +def store_and_plot_history(base_file_name: str, hist: History) -> None: + """ + + :param base_file_name: + :param hist: + :return: + """ + + (ac_history_csv, ac_history_svg) = ( + base_file_name + ".history.csv", + base_file_name + ".history.svg", + ) + + # store history + pd.DataFrame(hist.history).to_csv(ac_history_csv) + logging.info(f"Neural network training history saved in file: {ac_history_csv}") + + # plot history + ac_epochs = hist.epoch + + # generate a figure of the losses for this fold + plt.figure() + for k in hist.history.keys(): + plt.plot(ac_epochs, hist.history[k], label=k) + plt.title("Training and validation metrics of neural network") + plt.legend() + plt.savefig(fname=ac_history_svg, format="svg") + plt.close() + logging.info(f"Neural network training history plotted in file: {ac_history_svg}") diff --git a/build/lib/dfpl/options.py b/build/lib/dfpl/options.py new file mode 100644 index 00000000..bb753f05 --- /dev/null +++ b/build/lib/dfpl/options.py @@ -0,0 +1,1297 @@ +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import jsonpickle +import torch +from chemprop.args import TrainArgs + +from dfpl.utils import makePathAbsolute + + +@dataclass +class Options: + """ + Dataclass for all options necessary for training the neural nets + """ + + configFile: str = "./example/train.json" + inputFile: str = "/deepFPlearn/CMPNN/data/tox21.csv" + outputDir: str = "." + outputFile: str = "" + ecWeightsFile: str = "AE.encoder.weights.hdf5" + ecModelDir: str = "AE_encoder" + fnnModelDir: str = "modeltraining" + type: str = "smiles" + fpType: str = "topological" # also "MACCS", "atompairs" + epochs: int = 512 + fpSize: int = 2048 + encFPSize: int = 256 + kFolds: int = 0 + testSize: float = 0.2 + enableMultiLabel: bool = False + verbose: int = 0 + trainAC: bool = True # if set to False, an AC weight file must be provided! + trainFNN: bool = True + compressFeatures: bool = True + sampleFractionOnes: float = 0.5 # Only used when value is in [0,1] + sampleDown: bool = False + split_type: str = "random" + aeSplitType: str = "random" + aeType: str = "deterministic" + aeEpochs: int = 3000 + aeBatchSize: int = 512 + aeLearningRate: float = 0.001 + aeLearningRateDecay: float = 0.01 + aeActivationFunction: str = "relu" + aeOptimizer: str = "Adam" + fnnType: str = "FNN" + batchSize: int = 128 + optimizer: str = "Adam" + learningRate: float = 0.001 + lossFunction: str = "bce" + activationFunction: str = "relu" + l2reg: float = 0.001 + dropout: float = 0.2 + threshold: float = 0.5 + gpu: str = "" + snnDepth = 8 + snnWidth = 50 + aeWabTracking: str = "" # Wand & Biases autoencoder tracking + wabTracking: str = "" # Wand & Biases FNN tracking + wabTarget: str = "ER" # Wand & Biases target used for showing training progress + + def saveToFile(self, file: str) -> None: + """ + Saves an instance to a JSON file + """ + jsonFile = Path(file) + with jsonFile.open("w") as f: + f.write(jsonpickle.encode(self)) + + @classmethod + def fromJson(cls, file: str) -> Options: + """ + Create an instance from a JSON file + """ + jsonFile = Path(file) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + return jsonpickle.decode(content) + raise ValueError("JSON file does not exist or is not readable") + + @classmethod + def fromCmdArgs(cls, args: argparse.Namespace) -> Options: + """ + Creates Options instance from cmdline arguments. + + If a training file (JSON) is provided, the values from that file are used. + However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the + JSON file and on the commandline, then the value of the commandline argument will be used. + """ + result = Options() + if "configFile" in vars(args).keys(): + jsonFile = Path(makePathAbsolute(args.configFile)) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + result = jsonpickle.decode(content) + else: + raise ValueError("Could not find JSON input file") + + for key, value in vars(args).items(): + # The args dict will contain a "method" key from the subparser. + # We don't use this. + if key != "method": + result.__setattr__(key, value) + return result + + +@dataclass +class GnnOptions(TrainArgs): + """ + Dataclass to hold all options used for training the graph models + """ + + total_epochs: int = 30 + save: bool = True + configFile: str = "./example/traingnn.json" + data_path: str = "./example/data/tox21.csv" + use_compound_names: bool = False + save_dir: str = "" + no_cache: bool = False + features_scaling: bool = True + use_input_features: str = "" + cuda: bool = False + num_lrs: int = 2 + minimize_score: bool = False + num_tasks: int = 12 + preds_path: str = "./tox21dmpnn.csv" + test_path: str = "" + save_preds: bool = True + + @classmethod + def fromCmdArgs(cls, args: argparse.Namespace) -> GnnOptions: + """ + Creates Options instance from cmdline arguments. + + If a training file (JSON) is provided, the values from that file are used. + However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the + JSON file and on the commandline, then the value of the commandline argument will be used. + """ + result = GnnOptions() + if "configFile" in vars(args).keys(): + jsonFile = Path(makePathAbsolute(args.configFile)) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + result = jsonpickle.decode(content) + else: + raise ValueError("Could not find JSON input file") + + return result + + @classmethod + def fromJson(cls, file: str) -> GnnOptions: + """ + Create an instance from a JSON file + """ + jsonFile = Path(file) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + return jsonpickle.decode(content) + raise ValueError("JSON file does not exist or is not readable") + + +def createCommandlineParser() -> argparse.ArgumentParser: + """ + Build the parser for arguments with its two subparsers + """ + parser = argparse.ArgumentParser(prog="deepFPlearn") + subparsers = parser.add_subparsers(help="Sub programs of deepFPlearn") + + parser_train_gnn = subparsers.add_parser( + "traingnn", help="Train new GNN models with your data" + ) + parser_train_gnn.set_defaults(method="traingnn") + parseTrainGnn(parser_train_gnn) + + parser_predict_gnn = subparsers.add_parser( + "predictgnn", help="Predict with your GNN models" + ) + parser_predict_gnn.set_defaults(method="predictgnn") + parsePredictGnn(parser_predict_gnn) + + parser_train = subparsers.add_parser( + "train", help="Train new models with your data" + ) + parser_train.set_defaults(method="train") + parseInputTrain(parser_train) + + parser_predict = subparsers.add_parser( + "predict", help="Predict your data with existing models" + ) + parser_predict.set_defaults(method="predict") + parseInputPredict(parser_predict) + + parser_convert = subparsers.add_parser( + "convert", help="Convert known data files to pickle serialization files" + ) + parser_convert.set_defaults(method="convert") + parseInputConvert(parser_convert) + return parser + + +def parseInputTrain(parser: argparse.ArgumentParser) -> None: + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + # Create argument groups + general_args = parser.add_argument_group("Model Configuration") + autoencoder_args = parser.add_argument_group("Autoencoder Configuration") + training_args = parser.add_argument_group("Training Configuration") + tracking_args = parser.add_argument_group("Tracking Configuration") + + # Model Configuration + general_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-i", + "--inputFile", + metavar="FILE", + type=str, + help="The file containing the data for training in " + "comma separated CSV format.The first column should be smiles.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-o", + "--outputDir", + metavar="DIR", + type=str, + help="Prefix of output file name. Trained model and " + "respective stats will be returned in this directory.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-t", + "--type", + metavar="STRING", + type=str, + choices=["fp", "smiles"], + help="Type of the chemical representation. Choices: 'fp', 'smiles'.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-thr", + "--threshold", + type=float, + metavar="FLOAT", + help="Threshold for binary classification.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-gpu", + "--gpu", + metavar="INT", + type=int, + help="Select which gpu to use. If not available, leave empty.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-k", + "--fpType", + metavar="STR", + type=str, + choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], + help="The type of fingerprint to be generated/used in input file.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-s", + "--fpSize", + type=int, + help="Size of fingerprint that should be generated.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-c", + "--compressFeatures", + metavar="BOOL", + type=bool, + help="Should the fingerprints be compressed or not. Activates the autoencoder. ", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-m", + "--enableMultiLabel", + metavar="BOOL", + type=bool, + help="Train multi-label classification model in addition to the individual models.", + default=argparse.SUPPRESS, + ) + # Autoencoder Configuration + autoencoder_args.add_argument( + "-a", + "--ecWeightsFile", + type=str, + metavar="FILE", + help="The .hdf5 file of a trained encoder", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--ecModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the encoder will be saved", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeType", + metavar="STRING", + type=str, + choices=["variational", "deterministic"], + help="Autoencoder type, variational or deterministic.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeEpochs", + metavar="INT", + type=int, + help="Number of epochs for autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeBatchSize", + metavar="INT", + type=int, + help="Batch size in autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeActivationFunction", + metavar="STRING", + type=str, + choices=["relu", "selu"], + help="The activation function for the hidden layers in the autoencoder.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeLearningRate", + metavar="FLOAT", + type=float, + help="Learning rate for autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeLearningRateDecay", + metavar="FLOAT", + type=float, + help="Learning rate decay for autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeSplitType", + metavar="STRING", + type=str, + choices=["scaffold_balanced", "random", "molecular_weight"], + help="Set how the data is going to be split for the autoencoder", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "-d", + "--encFPSize", + metavar="INT", + type=int, + help="Size of encoded fingerprint (z-layer of autoencoder).", + default=argparse.SUPPRESS, + ) + # Training Configuration + training_args.add_argument( + "--split_type", + metavar="STRING", + type=str, + choices=["scaffold_balanced", "random", "molecular_weight"], + help="Set how the data is going to be split for the feedforward neural network", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-l", + "--testSize", + metavar="FLOAT", + type=float, + help="Fraction of the dataset that should be used for testing. Value in [0,1].", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-K", + "--kFolds", + metavar="INT", + type=int, + help="K that is used for K-fold cross-validation in the training procedure.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-v", + "--verbose", + metavar="INT", + type=int, + choices=[0, 1, 2], + help="Verbosity level. O: No additional output, " + + "1: Some additional output, 2: full additional output", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--trainAC", + metavar="BOOL", + type=bool, + help="Choose to train or not, the autoencoder based on the input file", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--trainFNN", + metavar="BOOL", + type=bool, + help="Train the feedforward network either with provided weights.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--sampleFractionOnes", + metavar="FLOAT", + type=float, + help="This is the fraction of positive target associations (1s) in comparison to the majority class(0s)." + "only works if --sampleDown is enabled", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--sampleDown", + metavar="BOOL", + type=bool, + help="Enable automatic down sampling of the 0 valued samples.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-e", + "--epochs", + metavar="INT", + type=int, + help="Number of epochs that should be used for the FNN training", + default=argparse.SUPPRESS, + ) + + training_args.add_argument( + "--lossFunction", + metavar="STRING", + type=str, + choices=["mse", "bce", "focal"], + help="Loss function to use during training. mse - mean squared error, bce - binary cross entropy.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--optimizer", + metavar="STRING", + type=str, + choices=["Adam", "SGD"], + help='Optimizer to use for backpropagation in the FNN. Possible values: "Adam", "SGD"', + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--batchSize", + metavar="INT", + type=int, + help="Batch size in FNN training.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--l2reg", + metavar="FLOAT", + type=float, + help="Value for l2 kernel regularizer.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--dropout", + metavar="FLOAT", + type=float, + help="The fraction of data that is dropped out in each dropout layer.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--learningRate", + metavar="FLOAT", + type=float, + help="Learning rate size in FNN training.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--activationFunction", + metavar="STRING", + type=str, + choices=["relu", "selu"], + help="The activation function for hidden layers in the FNN.", + default=argparse.SUPPRESS, + ) + # Tracking Configuration + tracking_args.add_argument( + "--aeWabTracking", + metavar="BOOL", + type=bool, + help="Track autoencoder performance via Weights & Biases, see https://wandb.ai.", + default=argparse.SUPPRESS, + ) + tracking_args.add_argument( + "--wabTracking", + metavar="BOOL", + type=bool, + help="Track FNN performance via Weights & Biases, see https://wandb.ai.", + default=argparse.SUPPRESS, + ) + tracking_args.add_argument( + "--wabTarget", + metavar="STRING", + type=str, + choices=["AR", "ER", "ED", "GR", "TR", "PPARg", "Aromatase"], + help="Which target to use for tracking performance via Weights & Biases, see https://wandb.ai.", + default=argparse.SUPPRESS, + ) + + +def parseTrainGnn(parser: argparse.ArgumentParser) -> None: + general_args = parser.add_argument_group("General Configuration") + data_args = parser.add_argument_group("Data Configuration") + files_args = parser.add_argument_group("Files") + model_args = parser.add_argument_group("Model arguments") + training_args = parser.add_argument_group("Training Configuration") + + # General arguments + general_args.add_argument("--split_key_molecule", type=int) + general_args.add_argument("--pytorch_seed", type=int) + general_args.add_argument("--cache_cutoff", type=float) + general_args.add_argument("--save_preds", type=bool) + general_args.add_argument( + "--cuda", action="store_true", default=False, help="Turn on cuda" + ) + general_args.add_argument( + "--save_smiles_splits", + action="store_true", + default=False, + help="Save smiles for each train/val/test splits for prediction convenience later", + ) + general_args.add_argument( + "--test", + action="store_true", + default=False, + help="Whether to skip training and only test the model", + ) + general_args.add_argument( + "--gpu", + type=int, + choices=list(range(torch.cuda.device_count())), + help="Which GPU to use", + ) + general_args.add_argument("--save", type=bool) + general_args.add_argument( + "--quiet", + action="store_true", + default=False, + help="Skip non-essential print statements", + ) + general_args.add_argument( + "--log_frequency", + type=int, + metavar="INT", + default=10, + help="The number of batches between each logging of the training loss", + ) + general_args.add_argument( + "--no_cuda", action="store_true", default=True, help="Turn off cuda" + ) + general_args.add_argument( + "--no_cache", + action="store_true", + default=False, + help="Turn off caching mol2graph computation", + ) + + # FILES ARGUMENTS + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + ) + files_args.add_argument( + "--config_path", + type=str, + metavar="FILE", + help="Path to a .json file containing arguments. Any arguments present in the config" + "file will override arguments specified via the command line or by the defaults.", + ) + files_args.add_argument( + "--save_dir", + type=str, + metavar="DIR", + default="./ckpt/", + help="Directory where model checkpoints will be saved", + ) + files_args.add_argument( + "--checkpoint_dir", + type=str, + metavar="DIR", + default=None, + help="Directory from which to load model checkpoints" + "(walks directory and ensembles all models that are found)", + ) + files_args.add_argument( + "--checkpoint_path", + type=str, + metavar="FILE", + default=None, + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--checkpoint_paths", + type=str, + metavar="FILE", + nargs="*", + default=None, + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--separate_val_path", + type=str, + metavar="FILE", + help="Path to separate val set, optional", + ) + files_args.add_argument( + "--separate_val_features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to file with features for separate val set", + ) + files_args.add_argument( + "--separate_test_path", + type=str, + metavar="FILE", + help="Path to separate test set, optional", + ) + files_args.add_argument( + "--separate_test_features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to file with features for separate test set", + ) + files_args.add_argument( + "--folds_file", + type=str, + metavar="FILE", + default=None, + help="Optional file of fold labels", + ) + files_args.add_argument( + "--val_fold_index", + type=int, + metavar="INT", + default=None, + help="Which fold to use as val for cross val", + ) + files_args.add_argument( + "--test_fold_index", + type=int, + metavar="INT", + default=None, + help="Which fold to use as test for cross val", + ) + files_args.add_argument( + "--crossval_index_dir", + type=str, + metavar="DIR", + help="Directory in which to find cross validation index files", + ) + files_args.add_argument( + "--crossval_index_file", + type=str, + metavar="FILE", + help="Indices of files to use as train/val/test" + "Overrides --num_folds and --seed.", + ) + files_args.add_argument( + "--data_weights_path", + type=str, + metavar="FILE", + help="Path where the data weight are saved", + ) + files_args.add_argument( + "--features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to features to use in FNN (instead of features_generator)", + ) + + files_args.add_argument( + "--separate_val_phase_features_path", type=str, metavar="FILE" + ) + files_args.add_argument( + "--separate_test_phase_features_path", type=str, metavar="FILE" + ) + + files_args.add_argument( + "--separate_val_atom_descriptors_path", type=str, metavar="FILE" + ) + files_args.add_argument( + "--separate_test_atom_descriptors_path", type=str, metavar="FILE" + ) + # Data related arguments + data_args.add_argument( + "--data_path", + type=str, + metavar="FILE", + help="Path to data CSV file", + default="", + ) + data_args.add_argument( + "--use_compound_names", + action="store_true", + default=False, + help="Use when test data file contains compound names in addition to SMILES strings", + ) + data_args.add_argument( + "--max_data_size", + type=int, + metavar="INT", + help="Maximum number of data points to load", + ) + + data_args.add_argument( + "--features_only", + action="store_true", + default=False, + help="Use only the additional features in an FFN, no graph network", + ) + data_args.add_argument( + "--dataset_type", + type=str, + metavar="STRING", + choices=["classification", "regression", "multiclass"], + help="Type of dataset, e.g. classification or regression." + "This determines the loss function used during training.", + default="regression", + ) # classification + data_args.add_argument( + "--multiclass_num_classes", + type=int, + metavar="INT", + default=3, + help="Number of classes when running multiclass classification", + ) + data_args.add_argument( + "--split_type", + type=str, + metavar="STRING", + default="random", + choices=[ + "random", + "scaffold_balanced", + "predetermined", + "crossval", + "index_predetermined", + ], + help="Method of splitting the data into train/val/test", + ) + data_args.add_argument( + "--split_sizes", + type=float, + metavar="FLOAT", + nargs=3, + default=[0.8, 0.2, 0.0], + help="Split proportions for train/validation/test sets", + ) + + data_args.add_argument( + "--seed", + type=int, + default=0, + help="Random seed to use when splitting data into train/val/test sets." + "When `num_folds` > 1, the first fold uses this seed and all" + "subsequent folds add 1 to the seed.", + ) + data_args.add_argument( + "--smiles_columns", + type=str, + metavar="STRING", + help="Name of the smiles columns", + ) + + data_args.add_argument( + "--target_columns", + type=str, + metavar="STRING", + help="Name of the target columns", + ) + + data_args.add_argument( + "--ignore_columns", + type=str, + metavar="STRING", + help="Names of the columns to ignore", + ) + data_args.add_argument( + "--num_tasks", type=int, metavar="INT", help="NUmber of tasks" + ) + data_args.add_argument( + "--no_features_scaling", + action="store_true", + default=False, + help="Turn off scaling of features", + ) + data_args.add_argument( + "--features_scaling", + action="store_true", + default=False, + help="Turn on scaling of features", + ) + data_args.add_argument( + "--use_input_features", + type=str, + metavar="STRING", + help="Turn on scaling of features", + ) + + # Model arguments + model_args.add_argument( + "--ensemble_size", + type=int, + metavar="INT", + default=1, + help="Number of models in ensemble", + ) + model_args.add_argument( + "--hidden_size", + type=int, + metavar="INT", + default=300, + help="Dimensionality of hidden layers in MPN", + ) + model_args.add_argument( + "--bias", + action="store_true", + default=False, + help="Whether to add bias to linear layers", + ) + model_args.add_argument( + "--depth", + type=int, + metavar="INT", + default=3, + help="Number of message passing steps", + ) + model_args.add_argument( + "--dropout", + type=float, + metavar="FLOAT", + default=0.0, + help="Dropout probability", + ) + model_args.add_argument( + "--activation", + type=str, + metavar="STRING", + default="ReLU", + choices=["ReLU", "LeakyReLU", "PReLU", "tanh", "SELU", "ELU"], + help="Activation function", + ) + model_args.add_argument( + "--undirected", + action="store_true", + default=False, + help="Undirected edges (always sum the two relevant bond vectors)", + ) + model_args.add_argument( + "--ffn_hidden_size", + type=int, + metavar="INT", + default=2, + help="Hidden dim for higher-capacity FFN (defaults to hidden_size)", + ) + model_args.add_argument( + "--ffn_num_layers", + type=int, + metavar="INT", + default=2, + help="Number of layers in FFN after MPN encoding", + ) + model_args.add_argument( + "--atom_messages", + action="store_true", + default=False, + help="Use messages on atoms instead of messages on bonds", + ) + + model_args.add_argument( + "--num_lrs", + type=int, + metavar="INT", + default=2, + help="Number of layers in FFN after MPN encoding", + ) + model_args.add_argument("--checkpoint_frzn", type=str, metavar="STRING") + + # Model arguments + model_args.add_argument("--mpn_shared", type=bool, metavar="BOOL") + model_args.add_argument( + "--show_individual_scores", + action="store_true", + default=True, + help="Show all scores for individual targets, not just average, at the end", + ) + model_args.add_argument("--aggregation", choices=["mean", "sum", "norm"]) + model_args.add_argument("--aggregation_norm", type=int) + model_args.add_argument("--explicit_h", type=bool, metavar="BOOL") + model_args.add_argument("--adding_h", type=bool, metavar="BOOL") + # Training arguments + model_args.add_argument("--class_balance", type=bool, metavar="BOOL") + model_args.add_argument("--evidential_regularization", type=float, metavar="FLOAT") + model_args.add_argument( + "--overwrite_default_atom_features", type=bool, metavar="BOOL" + ) + model_args.add_argument("--no_atom_descriptor_scaling", type=bool, metavar="BOOL") + model_args.add_argument( + "--overwrite_default_bond_features", type=bool, metavar="BOOL" + ) + model_args.add_argument("--frzn_ffn_layers", type=int, metavar="INT") + model_args.add_argument("--freeze_first_only", type=bool, metavar="BOOL") + # Training arguments + training_args.add_argument( + "--epochs", type=int, metavar="INT", default=30, help="Number of epochs to run" + ) + training_args.add_argument( + "--total_epochs", + type=int, + metavar="INT", + default=30, + help="Number of total epochs to run", + ) + training_args.add_argument( + "--batch_size", type=int, metavar="INT", default=50, help="Batch size" + ) + training_args.add_argument( + "--warmup_epochs", + type=int, + metavar="INT", + default=2, + help="Number of epochs during which learning rate increases linearly from" + "init_lr to max_lr. Afterwards, learning rate decreases exponentially" + "from max_lr to final_lr.", + ) + training_args.add_argument( + "--init_lr", + type=float, + metavar="FLOAT", + default=1e-4, + help="Initial learning rate", + ) + training_args.add_argument( + "--max_lr", + type=float, + metavar="FLOAT", + default=1e-3, + help="Maximum learning rate", + ) + training_args.add_argument( + "--final_lr", + type=float, + metavar="FLOAT", + default=1e-4, + help="Final learning rate", + ) + training_args.add_argument( + "--extra_metrics", + type=str, + metavar="STRING", + nargs="*", + help="Extra metrics to use", + ) + training_args.add_argument( + "--loss_function", + type=str, + metavar="STRING", + choices=[ + "mse", + "bounded_mse", + "binary_cross_entropy", + "cross_entropy", + "mcc", + "sid", + "wasserstein", + "mve", + "evidential", + "dirichlet", + ], + ) + training_args.add_argument("--grad_clip", type=float) + training_args.add_argument( + "--metric", + type=str, + metavar="STRING", + default=None, + choices=[ + "auc", + "prc-auc", + "rmse", + "mae", + "mse", + "r2", + "accuracy", + "cross_entropy", + ], + help="Metric to use during evaluation." + "Note: Does NOT affect loss function used during training" + "(loss is determined by the `dataset_type` argument)." + 'Note: Defaults to "auc" for classification and "rmse" for regression.', + ) + training_args.add_argument( + "--num_folds", + type=int, + metavar="INT", + default=1, + help="Number of folds when performing cross validation", + ) + + +def parseInputPredict(parser: argparse.ArgumentParser) -> None: + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + parser = argparse.ArgumentParser( + description="Use models that have been generated by " + "to make predictions on chemicals (provide SMILES or " + "topological fingerprints)." + ) + general_args = parser.add_argument_group("General Configuration") + files_args = parser.add_argument_group("Files") + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "-i", + "--inputFile", + metavar="FILE", + type=str, + help="The file containing the data for the prediction in (unquoted) " + "comma separated CSV format. The column named 'smiles' or 'fp'" + "contains the field to be predicted. Please adjust the type " + "that should be predicted (fp or smile) with -t option appropriately." + "An optional column 'id' is used to assign the outcomes to the" + "original identifiers. If this column is missing, the results are" + "numbered in the order of their appearance in the input file." + "A header is expected and respective column names are used.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "-o", + "--outputDir", + metavar="DIR", + type=str, + help="Prefix of output directory. It will contain a log file and the file specified" + "with --outputFile.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "--outputFile", + metavar="FILE", + type=str, + help="Output .CSV file name which will contain one prediction per input line. " + "Default: prefix of input file name.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-t", + "--type", + metavar="STR", + type=str, + choices=["fp", "smiles"], + help="Type of the chemical representation. Choices: 'fp', 'smiles'.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-k", + "--fpType", + metavar="STR", + type=str, + choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], + help="The type of fingerprint to be generated/used in input file.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "--ecModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the encoder will be saved (if trainAE=True) or " + "loaded from (if trainAE=False). Provide a full path here.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "--fnnModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the fnn is loaded from. " + "Provide a full path here.", + default=argparse.SUPPRESS, + ) + + +def parsePredictGnn(parser: argparse.ArgumentParser) -> None: + general_args = parser.add_argument_group("General Configuration") + data_args = parser.add_argument_group("Data Configuration") + files_args = parser.add_argument_group("Files") + training_args = parser.add_argument_group("Training Configuration") + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "--gpu", + type=int, + metavar="INT", + choices=list(range(torch.cuda.device_count())), + help="Which GPU to use", + ) + general_args.add_argument( + "--no_cuda", action="store_true", default=False, help="Turn off cuda" + ) + general_args.add_argument( + "--num_workers", + type=int, + metavar="INT", + help="Number of workers for the parallel data loading 0 means sequential", + ) + general_args.add_argument( + "--no_cache", + type=bool, + metavar="BOOL", + default=False, + help="Turn off caching mol2graph computation", + ) + general_args.add_argument( + "--no_cache_mol", + type=bool, + metavar="BOOL", + default=False, + help="Whether to not cache the RDKit molecule for each SMILES string to reduce memory\ + usage cached by default", + ) + general_args.add_argument( + "--empty_cache", + type=bool, + metavar="BOOL", + help="Whether to empty all caches before training or predicting. This is necessary if\ + multiple jobs are run within a single script and the atom or bond features change", + ) + files_args.add_argument( + "--preds_path", + type=str, + metavar="FILE", + help="Path to CSV file where predictions will be saved", + default="", + ) + files_args.add_argument( + "--checkpoint_dir", + type=str, + metavar="DIR", + help="Directory from which to load model checkpoints" + "(walks directory and ensembles all models that are found)", + default="./ckpt", + ) + files_args.add_argument( + "--checkpoint_path", + type=str, + metavar="DIR", + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--checkpoint_paths", + type=str, + metavar="FILE", + nargs="*", + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--data_path", + type=str, + metavar="FILE", + help="Path to CSV file containing testing data for which predictions will be made", + default="", + ) + files_args.add_argument( + "--test_path", + type=str, + metavar="FILE", + help="Path to CSV file containing testing data for which predictions will be made", + default="", + ) + files_args.add_argument( + "--features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to features to use in FNN (instead of features_generator)", + ) + files_args.add_argument( + "--atom_descriptors_path", + type=str, + metavar="FILE", + help="Path to the extra atom descriptors.", + ) + data_args.add_argument( + "--use_compound_names", + action="store_true", + default=False, + help="Use when test data file contains compound names in addition to SMILES strings", + ) + data_args.add_argument( + "--no_features_scaling", + action="store_true", + default=False, + help="Turn off scaling of features", + ) + data_args.add_argument( + "--max_data_size", + type=int, + metavar="INT", + help="Maximum number of data points to load", + ) + data_args.add_argument( + "--smiles_columns", + type=str, + metavar="STRING", + help="List of names of the columns containing SMILES strings.By default, uses the first\ + number_of_molecules columns.", + ) + data_args.add_argument( + "--number_of_molecules", + type=int, + metavar="INT", + help="Number of molecules in each input to the model.This must equal the length of\ + smiles_columns if not None", + ) + + data_args.add_argument( + "--atom_descriptors", + type=bool, + metavar="Bool", + help="Use or not atom descriptors", + ) + + data_args.add_argument( + "--bond_features_size", + type=int, + metavar="INT", + help="Size of the extra bond descriptors that will be used as bond features to featurize a\ + given molecule", + ) + training_args.add_argument( + "--batch_size", type=int, metavar="INT", default=50, help="Batch size" + ) + + +def parseInputConvert(parser: argparse.ArgumentParser) -> None: + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + parser.add_argument( + "-f", + metavar="FILE", + type=str, + help="Input directory where your CSV/TSV files are stored.", + required=True, + default="", + ) diff --git a/build/lib/dfpl/plot.py b/build/lib/dfpl/plot.py new file mode 100644 index 00000000..7e8f6ac1 --- /dev/null +++ b/build/lib/dfpl/plot.py @@ -0,0 +1,228 @@ +import array + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import wandb +from matplotlib.axes import Axes +from typing import List +# for NN model functions +from tensorflow.keras.callbacks import History + + +def get_max_validation_accuracy(history: History) -> str: + validation = smooth_curve(history.history["val_accuracy"]) + y_max: float = max(validation) + return "Max validation accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_validation_balanced_accuracy(history: History) -> str: + validation_bal_acc = smooth_curve(history.history["val_balanced_accuracy"]) + y_max: float = max(validation_bal_acc) + return "Max validation balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_training_balanced_accuracy(history: History) -> str: + training_bal_acc = smooth_curve(history.history["balanced_accuracy"]) + y_max: float = max(training_bal_acc) + return "Training balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_training_auc(history: History) -> str: + training_auc = smooth_curve(history.history["auc"]) + y_max: float = max(training_auc) + return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_validation_auc(history: History) -> str: + validation_auc = smooth_curve(history.history["val_auc"]) + y_max: float = max(validation_auc) + return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_training_accuracy(history: History) -> str: + training = smooth_curve(history.history["accuracy"]) + y_max: float = max(training) + return "Max training accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def smooth_curve(points: np.ndarray, factor: float = 0.75) -> np.ndarray: + smoothed_points: List[float] = [] + for point in points: + if smoothed_points: + previous = smoothed_points[-1] + smoothed_points.append(previous * factor + point * (1 - factor)) + else: + smoothed_points.append(point) + return smoothed_points + + +def set_plot_history_data(ax: Axes, history: History, which_graph: str) -> None: + (train, valid) = (None, None) + + if which_graph == "acc": + train = smooth_curve(history.history["accuracy"]) + valid = smooth_curve(history.history["val_accuracy"]) + + if which_graph == "loss": + train = smooth_curve(history.history["loss"]) + valid = smooth_curve(history.history["val_loss"]) + + # plt.xkcd() # make plots look like xkcd + + epochs = range(1, len(train) + 1) + + trim = 0 # remove first 5 epochs + # when graphing loss the first few epochs may skew the (loss) graph + + ax.plot(epochs[trim:], train[trim:], "dodgerblue", linewidth=15, alpha=0.1) + ax.plot(epochs[trim:], train[trim:], "dodgerblue", label="Training") + + ax.plot(epochs[trim:], valid[trim:], "g", linewidth=15, alpha=0.1) + ax.plot(epochs[trim:], valid[trim:], "g", label="Validation") + + +def plot_history(history: History, file: str) -> None: + fig, (ax1, ax2) = plt.subplots( + nrows=2, + ncols=1, + figsize=(10, 6), + sharex="all", + gridspec_kw={"height_ratios": [5, 2]}, + ) + + set_plot_history_data(ax1, history, "acc") + + set_plot_history_data(ax2, history, "loss") + + # Accuracy graph + ax1.set_ylabel("Accuracy") + ax1.set_ylim(bottom=0.5, top=1) + ax1.legend(loc="lower right") + ax1.spines["top"].set_visible(False) + ax1.spines["right"].set_visible(False) + ax1.xaxis.set_ticks_position("none") + ax1.spines["bottom"].set_visible(False) + + # max accuracy text + plt.text( + 0.5, + 0.6, + get_max_validation_balanced_accuracy(history), + horizontalalignment="right", + verticalalignment="top", + transform=ax1.transAxes, + fontsize=12, + ) + plt.text( + 0.5, + 0.8, + get_max_training_balanced_accuracy(history), + horizontalalignment="right", + verticalalignment="top", + transform=ax1.transAxes, + fontsize=12, + ) + + # Loss graph + ax2.set_ylabel("Loss") + ax2.set_yticks([]) + ax2.plot(legend=False) + ax2.set_xlabel("Epochs") + ax2.spines["top"].set_visible(False) + ax2.spines["right"].set_visible(False) + + plt.tight_layout() + plt.savefig(fname=file, format="svg") + plt.close() + + +def plot_train_history(hist, target, file_accuracy, file_loss): + """ + Plot the training performance in terms of accuracy and loss values for each epoch. + :param hist: The history returned by model.fit function + :param target: The name of the target of the model + :param file_accuracy: The filename for plotting accuracy values + :param file_loss: The filename for plotting loss values + :return: none + """ + + # plot accuracy + plt.figure() + plt.plot(hist.history["accuracy"]) + if "val_accuracy" in hist.history.keys(): + plt.plot(hist.history["val_accuracy"]) + plt.title("Model accuracy - " + target) + plt.ylabel("Accuracy") + plt.xlabel("Epoch") + if "val_accuracy" in hist.history.keys(): + plt.legend(["Train", "Test"], loc="upper left") + else: + plt.legend(["Train"], loc="upper_left") + plt.savefig(fname=file_accuracy, format="svg") + + # Plot training & validation loss values + plt.figure() + plt.plot(hist.history["loss"]) + plt.plot(hist.history["val_loss"]) + plt.title("Model loss - " + target) + plt.ylabel("Loss") + plt.xlabel("Epoch") + plt.legend(["Train", "Test"], loc="upper left") + # plt.show() + plt.savefig(fname=file_loss, format="svg") + plt.close() + + +def plot_history_vis( + hist: History, + model_hist_plot_path: str, + model_hist_csv_path: str, + model_hist_plot_path_a: str, + model_hist_plot_path_l: str, + target: str, +) -> None: + plot_history(history=hist, file=model_hist_plot_path) + histDF = pd.DataFrame(hist.history) + histDF.to_csv(model_hist_csv_path) + + # plot accuracy and loss for the training and validation during training + plot_train_history( + hist=hist, + target=target, + file_accuracy=model_hist_plot_path_a, + file_loss=model_hist_plot_path_l, + ) + + +def plot_auc( + fpr: np.ndarray, + tpr: np.ndarray, + auc_value: float, + target: str, + filename: str, + wandb_logging: bool = False, +) -> None: + """ + Plot the area under the curve to the provided file + + :param fpr: An array containing the false positives + :param tpr: An array containing the true positives + :param auc_value: The value of the area under the curve + :param target: The name of the training target + :param filename: The filename to which the plot should be stored + :param wandb_logging: Whether to log the plot to wandb + :rtype: None + """ + # Create a boolean mask to filter out zero values + plt.figure() + plt.plot([0, 1], [0, 1], "k--") + plt.plot(fpr, tpr, label=f"Keras (area = {auc_value:.3f})") + plt.xlabel("False positive rate") + plt.ylabel("True positive rate") + plt.title("ROC curve " + target) + plt.legend(loc="best") + plt.savefig(fname=filename, format="png") + if wandb_logging: + wandb.log({"roc_plot": plt}) + plt.close() diff --git a/build/lib/dfpl/predictions.py b/build/lib/dfpl/predictions.py new file mode 100644 index 00000000..29e73142 --- /dev/null +++ b/build/lib/dfpl/predictions.py @@ -0,0 +1,39 @@ +import logging + +import numpy as np +import pandas as pd +import tensorflow.keras.models + +from dfpl import options, settings + + +def predict_values(df: pd.DataFrame, opts: options.Options) -> pd.DataFrame: + """ + Predict a set of chemicals using a selected model. + + :param df: + :param opts: + :return: + """ + model = tensorflow.keras.models.load_model(opts.fnnModelDir, compile=False) + model.compile(loss=opts.lossFunction, optimizer=opts.optimizer) + if opts.compressFeatures: + sub_df = df[df["fpcompressed"].notnull()] + x = np.array( + sub_df["fpcompressed"].to_list(), + dtype=settings.nn_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info(f"Compressed FP matrix with shape {x.shape} and type {x.dtype}") + sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) + return sub_df + else: + sub_df = df[df["fp"].notnull()] + x = np.array( + sub_df["fp"].to_list(), + dtype=settings.nn_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info(f"Uncompressed FP matrix with shape {x.shape} and type {x.dtype}") + sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) + return sub_df diff --git a/build/lib/dfpl/rbm.py b/build/lib/dfpl/rbm.py new file mode 100644 index 00000000..fe5b1ac5 --- /dev/null +++ b/build/lib/dfpl/rbm.py @@ -0,0 +1,259 @@ +import os.path +from os.path import basename +import pandas as pd +import logging +import tensorflow as tf +import numpy as np +from tensorflow.keras import Model +from sklearn.model_selection import train_test_split +import math + +from dfpl import callbacks +from dfpl import options +from dfpl import history as ht +from dfpl import settings + + +class RBMLayer(tf.keras.layers.Layer): + def __init__(self, input_size, output_size, name="RBMLayer", **kwargs): + super(RBMLayer, self).__init__(name=name, **kwargs) + self._input_size = input_size + self._output_size = output_size + self._name = name + # self._activation_function = activation_function + + def build(self, input_shape): + self.w = self.add_weight( + shape=(input_shape[-1], self._output_size), + initializer="random_normal", + trainable=True, name=f"w_{self._name}" + ) + self.hb = self.add_weight( + shape=(self._output_size,), initializer="random_normal", trainable=True, name=f"hb_{self._name}" + ) + self.vb = self.add_weight( + shape=(input_shape[-1],), initializer="random_normal", trainable=True, name=f"vb_{self._name}" + ) + + def __prob_h_given_v(self, visible, w, hb): + return tf.nn.sigmoid(tf.matmul(visible, w) + hb) + + def __prob_v_given_h(self, hidden, w, vb): + return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb) + + def __sample_prob(self, probs): + return tf.nn.relu(tf.sign(probs - tf.random.uniform(tf.shape(probs)))) + + def call(self, X): + out = tf.nn.sigmoid(tf.matmul(X, self.w) + self.hb) + hiddenGen = self.__sample_prob(self.__prob_h_given_v(X, self.w, self.hb)) + visibleGen = self.__sample_prob(self.__prob_v_given_h(hiddenGen, self.w, self.vb)) + h1 = self.__prob_h_given_v(visibleGen, self.w, self.hb) + return out, visibleGen, hiddenGen, h1 + + def get_config(self): + return {"input_size": self._input_size, + "output_size": self._output_size} + + +class RBM(tf.keras.Model): + + def __init__(self, input_size, output_size, name="RBM", **kwargs): + super(RBM, self).__init__(name=name, **kwargs) + # Define hyperparameters + self._input_size = input_size + self._output_size = output_size + self.n_layer = round(math.log2(input_size / output_size)) + + self.last_out = None + + # layers defination + # encoding layers + self.rbm_encoder_layers = [] + self.rbm_decoder_layers = [] + self.rbm_encoder1 = RBMLayer(self._input_size, int(self._input_size // 2), name="encoder_0") + + if self.n_layer > 1: + for i in range(1, self.n_layer): + factor_units = 2 ** (i + 1) + self.rbm_encoder_layers.append( + RBMLayer(int(self._input_size // factor_units) * 2, int(self._input_size // factor_units), + name=f"encoder_{i}")) + + # decoding layers + factor_units = 2 ** (self.n_layer - 1) + self.rbm_decoder1 = RBMLayer(int(input_size // factor_units) // 2, int(input_size // factor_units), + name="decoder_0") + + for layer_num, i in enumerate(range(self.n_layer - 2, 0, -1)): + factor_units = 2 ** i + self.rbm_decoder_layers.append( + RBMLayer(int(input_size // (factor_units * 2)), int(input_size // factor_units), + name=f"decoder_{layer_num + 1}")) + + self.rbm_decoder_layers.append( + RBMLayer(int(input_size // 2), int(input_size), name=f"decoder_{self.n_layer - 1}")) + + def call(self, X, training=None): + outs = {} + out, visibleGen, hiddenGen, h1 = self.rbm_encoder1(X) + # out, visibleGen, hiddenGen, h1 = self.rbm_encoder(X) + outs[f'encoder_{0}'] = (out, visibleGen, hiddenGen, h1) + for i, layer in enumerate(self.rbm_encoder_layers): + out, visibleGen, hiddenGen, h1 = layer(hiddenGen) + outs[f'encoder_{i + 1}'] = (out, visibleGen, hiddenGen, h1) + + out, visibleGen, hiddenGen, h1 = self.rbm_decoder1(hiddenGen) + outs[f'decoder_{0}'] = (out, visibleGen, hiddenGen, h1) + for j, layer in enumerate(self.rbm_decoder_layers): + out, visibleGen, hiddenGen, h1 = layer(hiddenGen) + outs[f'decoder_{j + 1}'] = (out, visibleGen, hiddenGen, h1) + return out, visibleGen, hiddenGen, h1, outs + + def train_step(self, data): + X, y = data + + learning_rate = self.optimizer._decayed_lr('float32') + out, visibleGen, hiddenGen, h1, out_dict = self(X, training=True) + weights_dict = {} + for lay in self.layers: + weights_dict[lay.name] = lay + + last_out = X + for idx, (key, value) in enumerate(out_dict.items()): + out, visibleGen, hiddenGen, h1 = value + positive_grad = tf.matmul(tf.transpose(last_out), hiddenGen) + negative_grad = tf.matmul(tf.transpose(visibleGen), h1) + # loss = tf.reduce_mean(tf.square(y, visibleGen)) + # loss = self.compiled_loss(y, visibleGen) + + # update the weights of the model based on the loss and LR. + curr_layer = weights_dict[key] + curr_layer.weights[0].assign(curr_layer.weights[0] + learning_rate * + (positive_grad - negative_grad) / tf.cast(tf.shape(last_out)[0], + dtype=tf.float32)) + curr_layer.weights[1].assign(curr_layer.weights[1] + learning_rate * tf.math.reduce_mean(hiddenGen - h1, 0)) + curr_layer.weights[2].assign( + curr_layer.weights[2] + learning_rate * tf.math.reduce_mean(last_out - visibleGen, 0)) + + last_out = hiddenGen + return {m.name: m.result() for m in self.metrics} + + def test_step(self, data): + # Unpack the data + x, y = data + # Compute predictions + out, visibleGen, hiddenGen, h1, out_dict = self(x, training=False) + # Updates the metrics tracking the loss + self.compiled_loss(y, hiddenGen, regularization_losses=self.losses) + # Update the metrics. + self.compiled_metrics.update_state(y, hiddenGen) + # Return a dict mapping metric names to current value. + # Note that it will include the loss (tracked in self.metrics). + return {m.name: m.result() for m in self.metrics} + + +def define_rbm_model(opts: options.Options, + input_size: int = 2048, + encoding_dim: int = 256) -> Model: + """ + This function provides an autoencoder model to reduce a certain input to a compressed version. + + :param encoding_dim: Size of the compressed representation. Default: 85 + :param input_size: Size of the input. Default: 2048 + :return: a tuple of autoencoder and encoder models + """ + if opts.aeOptimizer == "Adam": + ac_optimizer = tf.keras.optimizers.Adam(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) + elif opts.aeOptimizer == "SGD": + ac_optimizer = tf.keras.optimizers.SGD(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) + + rbm_model = RBM(input_size=input_size, + output_size=encoding_dim) + rbm_model.compile(optimizer=ac_optimizer, loss=opts.lossFunction) + + return rbm_model + + +def train_full_rbm(df: pd.DataFrame, opts: options.Options) -> Model: + """ + Train an autoencoder on the given feature matrix X. Response matrix is only used to + split meaningfully in test and train data set. + + :param opts: Command line arguments as defined in options.py + :param df: Pandas dataframe that contains the smiles/inchi data for training the autoencoder + :return: The encoder model of the trained autoencoder + """ + + # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) + rbm = define_rbm_model(opts, input_size=opts.fpSize, encoding_dim=opts.encFPSize) + # define output file for autoencoder and encoder weights + if opts.ecWeightsFile == "": + logging.info("No RBM encoder weights file specified") + base_file_name = os.path.splitext(basename(opts.inputFile))[0] + logging.info(f"RBM weights will be saved in {base_file_name}.rbm{opts.encFPSize}.hdf5") + rbm_weights_file = os.path.join(opts.outputDir, base_file_name + f".rbm{opts.encFPSize}.hdf5") + # ec_weights_file = os.path.join(opts.outputDir, base_file_name + ".rbm_encoder.weights.hdf5") + else: + logging.info(f"RBM encoder will be saved in {opts.ecWeightsFile}") + base_file_name = os.path.splitext(basename(opts.ecWeightsFile))[0] + rbm_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) + + # collect the callbacks for training + callback_list = callbacks.autoencoder_callback(checkpoint_path=rbm_weights_file, opts=opts) + + # Select all fps that are valid and turn them into a numpy array + # This step is crucial for speed!!! + fp_matrix = np.array(df[df["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values) + logging.info(f"Training RBM on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") + + # split data into test and training data + x_train, x_test = train_test_split(fp_matrix, + test_size=0.2, + random_state=42) + x_train = tf.cast(tf.where(x_train, 1, 0), tf.float32) + x_test = tf.cast(tf.where(x_test, 1, 0), tf.float32) + logging.info(f"RBM train data shape {x_train.shape} with type {x_train.dtype}") + logging.info(f"RBM test data shape {x_test.shape} with type {x_test.dtype}") + auto_hist = rbm.fit(x=x_train, y=x_train, + callbacks=callback_list, + epochs=opts.aeEpochs, + batch_size=opts.aeBatchSize, + verbose=opts.verbose, + steps_per_epoch=25, + validation_data=(x_test, x_test)) + rbm.summary(print_fn=logging.info) + ht.store_and_plot_history(base_file_name=os.path.join(opts.outputDir, base_file_name + ".RBM"), + hist=auto_hist) + rbm.save_weights(rbm_weights_file) + save_path = os.path.join(opts.ecModelDir, "rbm_model") + rbm.save(save_path) + logging.info(f"RBM weights stored in file: {rbm_weights_file}") + + return rbm + + +def compress_fingerprints(dataframe: pd.DataFrame, + encoder: Model, layer_num: int) -> pd.DataFrame: + """ + Adds a column of the compressed version of the fingerprints to the original dataframe. + + :param dataframe: Dataframe containing a column named 'fp' with the fingerprints + :param encoder: The trained autoencoder that is used for compressing the fingerprints + :return: The input dataframe extended by a column containing the compressed version of the fingerprints + """ + logging.info("Adding rbm compressed fingerprints") + idx = dataframe[dataframe["fp"].notnull()].index + fp_matrix = np.array(dataframe[dataframe["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values) + logging.info(f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") + logging.info("Compressed fingerprints from rbm are added to input dataframe.") + fp_matrix = tf.cast(tf.where(fp_matrix, 1, 0), tf.float32) + out, visibleGen, hiddenGen, h1, out_dict = encoder.predict(fp_matrix) + out, visibleGen, hiddenGen, h1 = out_dict[f'encoder_{layer_num-1}'] + dataframe['fpcompressed'] = pd.DataFrame({'fpcompressed': [s for s in hiddenGen]}, idx) + + return dataframe diff --git a/build/lib/dfpl/settings.py b/build/lib/dfpl/settings.py new file mode 100644 index 00000000..2d900cda --- /dev/null +++ b/build/lib/dfpl/settings.py @@ -0,0 +1,52 @@ +import numpy as np + +# Datatype that is used for fingerprint vectors stored inside +# dataframe used during the program +df_fp_numpy_type = np.bool8 + +# Do we need copies when creating numpy matrices from the pandas dataframes +# for training? Everything seems to work fine with False and it saves memory. +numpy_copy_values = False + +# Autoencoder data type settings + +# Type that is given as input numpy array to the network +# We need to carefully test the performance of the training here +# The big dsstox dataset runs out of mem on Patrick's machine when +# we use floats here. +# Also, we possibly should give the FNN a float type fp +ac_fp_numpy_type = np.bool8 + +# Type used for the compressed fp which is an inner layer of the ac +# an MUST be of float type. On Patrick's machine, (GPU) tensorflow layers +# are float32 per default. Not sure if float64 can be used with GPU +# training and if it enhances results. Needs to be evaluated. +ac_fp_compressed_numpy_type = np.bool8 + +# Feedforward Neuronal Network data type settings +# +# The sections below define the types of the numpy matrices used for training +# the FNNs. In short: +# XX_fp_numpy_type: used for uncompressed fingerprint tensors +# XX_fp_compressed_numpy_type: used for compressed fingerprint tensors +# XX_target_numpy_type: used for the target tensors + +nn_fp_numpy_type = np.float32 +nn_fp_compressed_numpy_type = np.float32 +nn_target_numpy_type = np.short + +nn_multi_fp_numpy_type = np.float32 +nn_multi_fp_compressed_numpy_type = np.float32 +nn_multi_target_numpy_type = np.float32 + +# Training settings + +# Training settings of the AC that were magic numbers in the code before. +ac_train_min_delta = 0.0001 +ac_train_check_period = 1 +ac_train_patience = 1 + +# Training settings of the FNN that were magic numbers in the code before. +nn_train_min_delta = 0.0001 +nn_train_check_period = 5 +nn_train_patience = 5 diff --git a/build/lib/dfpl/single_label_model.py b/build/lib/dfpl/single_label_model.py new file mode 100644 index 00000000..de437d1e --- /dev/null +++ b/build/lib/dfpl/single_label_model.py @@ -0,0 +1,1008 @@ +import logging +import math +import os +import shutil +import sys +from os import path +from time import time + +import numpy as np +import pandas as pd +import tensorflow as tf +import tensorflow.keras.backend as K +import wandb +from typing import Union, Tuple + +from sklearn.metrics import ( + auc, + classification_report, + confusion_matrix, + matthews_corrcoef, + roc_curve, +) +from sklearn.model_selection import StratifiedKFold, train_test_split +from tensorflow.keras import metrics, optimizers, regularizers +from tensorflow.keras.layers import AlphaDropout, Dense, Dropout +from tensorflow.keras.losses import ( + BinaryCrossentropy, + MeanSquaredError, + BinaryFocalCrossentropy, +) +from tensorflow.keras.models import Model, Sequential + +from dfpl import callbacks as cb +from dfpl import options +from dfpl import plot as pl +from dfpl import settings +from dfpl.utils import ae_scaffold_split, weight_split + + +def prepare_nn_training_data( + df: pd.DataFrame, target: str, opts: options.Options, return_dataframe: bool = False +) -> Union[Tuple[np.ndarray, np.ndarray], pd.DataFrame]: + # Check the value counts and abort if too imbalanced + allowed_imbalance = 0.1 + # If feature compression is enabled, use compressed fingerprints + if opts.compressFeatures: + vc = df[df[target].notna() & df["fpcompressed"].notnull()][ + target + ].value_counts() + # Otherwise, use uncompressed fingerprints + else: + vc = df[df[target].notna() & df["fp"].notnull()][target].value_counts() + + # If the dataset is extremely unbalanced, adjust sampling options + if min(vc) < max(vc) * allowed_imbalance: + logging.info( + f" Your training data is extremely unbalanced ({target}): 0 - {vc[0]}, and 1 - {vc[1]} values." + ) + if opts.sampleDown: + logging.info(f" I will down-sample your data") + opts.sampleFractionOnes = allowed_imbalance + else: + logging.info(f" I will not down-sample your data automatically.") + logging.info( + f" Consider to enable down sampling of the 0 values with --sampleDown option." + ) + + logging.info("Preparing training data matrices") + + # If feature compression is enabled, use compressed fingerprints + if opts.compressFeatures: + logging.info("Using compressed fingerprints") + df_fpc = df[df[target].notna() & df["fpcompressed"].notnull()] + logging.info( + f"DataSet has {df_fpc.shape[0]} valid entries in fpcompressed and {target}" + ) + + # If sampling down is enabled, adjust sampling options and downsample the data + if opts.sampleDown: + assert 0.0 < opts.sampleFractionOnes < 1.0 + logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") + # How many ones and zeros are in the dataset + counts = df_fpc[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + + # Add a sample of 0s to the df of 1s + dfX = df_fpc[df_fpc[target] == 1].append( + df_fpc[df_fpc[target] == 0].sample( + int(min(counts[0], counts[1] / opts.sampleFractionOnes)) + ) + ) + # Convert fingerprints and targets to numpy arrays + x = np.array( + dfX["fpcompressed"].to_list(), + dtype=settings.nn_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + y = np.array( + dfX[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + # Return the dataframe if specified + if return_dataframe: + return dfX + else: + logging.info("Fraction sampling is OFF") + # How many ones and zeros are in the dataset + counts = df_fpc[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + + # Convert fingerprints and targets to numpy arrays + x = np.array( + df_fpc["fpcompressed"].to_list(), + dtype=settings.nn_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + y = np.array( + df_fpc[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + if return_dataframe: + return df_fpc + + # If feature compression is not enabled, use uncompressed fingerprints + else: + logging.info("Using uncompressed fingerprints") + # select rows where the target is not null and the fingerprint is not null + df_fp = df[df[target].notna() & df["fp"].notnull()] + logging.info(f"DataSet has {df_fp.shape[0]} valid entries in fp and {target}") + + if opts.sampleDown: + logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") + # count number of each class value + counts = df_fp[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + # downsample the majority class by taking a fraction of its samples and combining with all of the + # minority class + dfX = df_fp[df_fp[target] == 1.0].append( + df_fp[df_fp[target] == 0.0].sample( + int(min(counts[0], counts[1] / opts.sampleFractionOnes)) + ) + ) + # convert fingerprints to numpy array + x = np.array( + dfX["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + # convert target values to numpy array + y = np.array( + dfX[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + if return_dataframe: + return dfX + else: + logging.info("Fraction sampling is OFF") + # count number of each class value + counts = df_fp[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + # convert fingerprints to numpy array + x = np.array( + df_fp["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + # convert target values to numpy array + y = np.array( + df_fp[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + if return_dataframe: + return df_fp + # return the numpy arrays of fingerprints and targets + return x, y + + +# This function defines a feedforward neural network (FNN) with the given input size, options, and output bias +def build_fnn_network( + input_size: int, opts: options.Options, output_bias=None +) -> Model: + # Set the output bias if it is provided + if output_bias is not None: + output_bias = tf.keras.initializers.Constant(output_bias) + + # Define the number of hidden layers based on the input size + my_hidden_layers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} + if not str(input_size) in my_hidden_layers.keys(): + raise ValueError("Wrong input-size. Must be in {2048, 1024, 999, 512, 256}.") + nhl = int(math.log2(input_size) / 2 - 1) + + # Create a sequential model + model = Sequential() + + # Add the first hidden layer + if opts.activationFunction == "relu": + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation="relu", + kernel_regularizer=regularizers.l2(opts.l2reg), + kernel_initializer="he_uniform", + ) + ) + model.add(Dropout(opts.dropout)) + elif opts.activationFunction == "selu": + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation="selu", + kernel_initializer="lecun_normal", + ) + ) + model.add(AlphaDropout(opts.dropout)) + else: + logging.error("Only 'relu' and 'selu' activation is supported") + sys.exit(-1) + + # Add additional hidden layers + for i in range(1, nhl): + factor_units = 2 ** (i + 1) + factor_dropout = 2 * i + if opts.activationFunction == "relu": + model.add( + Dense( + units=int(input_size / factor_units), + activation="relu", + kernel_regularizer=regularizers.l2(opts.l2reg), + kernel_initializer="he_uniform", + ) + ) + model.add(Dropout(opts.dropout / factor_dropout)) + elif opts.activationFunction == "selu": + model.add( + Dense( + units=int(input_size / factor_units), + activation="selu", + kernel_initializer="lecun_normal", + ) + ) + model.add(AlphaDropout(opts.dropout / factor_dropout)) + else: + logging.error("Only 'relu' and 'selu' activation is supported") + sys.exit(-1) + + # Add the output layer with a sigmoid activation function and the output bias if provided + model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) + model.summary() + return model + + +# This function defines a shallow neural network (SNN) with the given input size, options, and output bias +def build_snn_network( + input_size: int, opts: options.Options, output_bias=None +) -> Model: + # Set the output bias if it is provided + if output_bias is not None: + output_bias = tf.keras.initializers.Constant(output_bias) + + # Create a sequential model + model = Sequential() + + # Add the first hidden layer + model.add( + Dense( + input_dim=input_size, + units=50, + activation="selu", + kernel_initializer="lecun_normal", + ) + ) + model.add(AlphaDropout(opts.dropout)) + + # Add 7 hidden layers with 50 units each, a SELU activation function, and a LeCun normal kernel initializer + for i in range(7): + model.add(Dense(units=50, activation="selu", kernel_initializer="lecun_normal")) + # Add an alpha dropout layer with the given dropout rate + model.add(AlphaDropout(opts.dropout)) + + # Add the output layer with 1 unit and a sigmoid activation function + # If output_bias is not None, set the bias initializer to the specified value + model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) + + return model + + +def balanced_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> np.float64: + """ + Computes the balanced accuracy metric. + + Args: + y_true (np.ndarray): The true values of the labels. + y_pred (np.ndarray): The predicted values of the labels. + + Returns: + np.float64: The balanced accuracy score. + """ + y_pred = K.round(y_pred) # Convert continuous predictions to binary class labels + tn = K.sum((1 - y_true) * (1 - y_pred)) + fp = K.sum((1 - y_true) * y_pred) + fn = K.sum(y_true * (1 - y_pred)) + tp = K.sum(y_true * y_pred) + balanced_acc = (tp / (tp + fn + 1e-10) + tn / (tn + fp + 1e-10)) / 2 + return balanced_acc + + +def define_single_label_model( + input_size: int, opts: options.Options, output_bias=None +) -> Model: + """ + Defines and compiles the single-label neural network model. + + Args: + input_size (int): The size of the input layer. + opts (options.Options): The options used in the model. + output_bias (float): The initial bias for the last sigmoid layer of the model. + + Returns: + tensorflow.keras.Model: The compiled model. + """ + # Set the loss function according to the option selected + if opts.lossFunction == "bce": + loss_function = BinaryCrossentropy() + elif opts.lossFunction == "mse": + loss_function = MeanSquaredError() + elif opts.lossFunction == "focal": + loss_function = BinaryFocalCrossentropy() + else: + logging.error(f"Your selected loss is not supported: {opts.lossFunction}.") + sys.exit("Unsupported loss function") + + # Set the optimizer according to the option selected + if opts.optimizer == "Adam": + my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + elif opts.optimizer == "SGD": + my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) + else: + logging.error(f"Your selected optimizer is not supported: {opts.optimizer}.") + sys.exit("Unsupported optimizer") + + # Set the type of neural network according to the option selected + if opts.fnnType == "FNN": + model = build_fnn_network(input_size, opts, output_bias) + elif opts.fnnType == "SNN": + model = build_snn_network(input_size, opts, output_bias) + else: + raise ValueError(f'Option FNN Type is not "FNN" or "SNN", but {opts.fnnType}.') + + logging.info(f"Network type: {opts.fnnType}") + model.summary(print_fn=logging.info) + + # Compile the model with the defined options + model.compile( + loss=loss_function, + optimizer=my_optimizer, + metrics=[ + metrics.BinaryAccuracy(name="accuracy"), + metrics.AUC(), + metrics.Precision(), + metrics.Recall(), + balanced_accuracy, + ], + ) + + return model + + +def evaluate_model( + x_test: np.ndarray, + y_test: np.ndarray, + file_prefix: str, + model: Model, + target: str, + fold: int, +) -> pd.DataFrame: + # Log that we're evaluating the model on the test data + name = path.basename(file_prefix).replace("_", " ") + logging.info(f"Evaluating trained model '{name}' on test data") + + # Predict the test set to compute MCC, AUC, ROC curve, etc. + threshold = 0.5 # TODO: Introduce thresholds different from 0.5! + y_predict = model.predict(x_test).flatten() + y_predict_int = (y_predict >= threshold).astype(np.short) + y_test_int = y_test.astype(np.short) + + # Save the predictions to a CSV file + ( + pd.DataFrame( + { + "y_true": y_test_int, + "y_predicted": y_predict, + "y_predicted_int": y_predict_int, + "target": target, + "fold": fold, + } + ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.csv") + ) + + # Compute the confusion matrix + confusion_matrix(y_true=y_test_int, y_pred=y_predict_int) + + # Compute the metrics that depend on the class label + precision_recall = classification_report( + y_test_int, y_predict_int, output_dict=True + ) + prf = pd.DataFrame.from_dict(precision_recall)[["0", "1"]] + + # Add balanced accuracy to the computed metrics + prf.to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.prec_rec_f1.csv") + + # Evaluate the model on the validation set and log the results + loss, acc, auc_value, precision, recall, balanced_acc = tuple( + model.evaluate(x=x_test, y=y_test) + ) + + logging.info(f"Loss: {round(loss, 4)}") + logging.info(f"Accuracy: {round(acc, 4)}") + logging.info(f"AUC: {round(auc_value, 4)}") + logging.info(f"Precision: {round(precision, 4)}") + logging.info(f"Recall: {round(recall, 4)}") + logging.info(f"Balanced Accuracy: {round(balanced_acc, 4)}") + + # Compute the Matthews Correlation Coefficient (MCC) + MCC = matthews_corrcoef(y_test_int, y_predict_int) + logging.info(f"MCC: {round(MCC, 4)}") + + # Generate the AUC-ROC curve data from the validation data + FPR, TPR, thresholds_keras = roc_curve( + y_true=y_test_int, y_score=y_predict, drop_intermediate=False + ) + AUC = auc(FPR, TPR) + nonzero_mask = (FPR != 0) | (TPR != 0) + FPR_filtered = FPR[nonzero_mask] + TPR_filtered = TPR[nonzero_mask] + + # Save AUC data to CSV file + pd.DataFrame( + list( + zip( + FPR_filtered, + TPR_filtered, + [AUC] * len(FPR_filtered), + [target] * len(FPR_filtered), + [fold] * len(FPR_filtered), + ) + ), + columns=["fpr", "tpr", "auc_value", "target", "fold"], + ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.aucdata.csv") + # Generate and save AUC-ROC curve plot + pl.plot_auc( + fpr=FPR, + tpr=TPR, + target=target, + auc_value=AUC, + filename=f"{file_prefix}_auc_data.png", + wandb_logging=False, + ) + + # Return a DataFrame containing the computed metrics + return pd.DataFrame.from_dict( + { + "p_0": prf["0"]["precision"], + "r_0": prf["0"]["recall"], + "f1_0": prf["0"]["f1-score"], + "p_1": prf["1"]["precision"], + "r_1": prf["1"]["recall"], + "f1_1": prf["1"]["f1-score"], + "loss": loss, + "accuracy": acc, + "balanced_accuracy": balanced_acc, + "MCC": MCC, + "AUC": AUC, + "target": target, + "fold": fold, + }, + orient="index", + ).T + + +def fit_and_evaluate_model( + x_train: np.ndarray, + x_test: np.ndarray, + y_train: np.ndarray, + y_test: np.ndarray, + fold: int, + target: str, + opts: options.Options, +) -> pd.DataFrame: + # Print info about training + logging.info(f"Training of fold number: {fold}") + + # Define file name prefix for saving models + model_file_prefix = path.join( + opts.outputDir, f"{target}_{opts.split_type}_single-labeled_Fold-{fold}" + ) + + # Compute class imbalance + ids, counts = np.unique(y_train, return_counts=True) + count_dict = dict(zip(ids, counts)) + + # Set initial bias for last sigmoid layer to counter class imbalance + if count_dict[0] == 0: + initial_bias = None + logging.info("No zeroes in training labels. Setting initial_bias to None.") + else: + initial_bias = np.log([count_dict[1] / count_dict[0]]) + logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") + + # Define model + model = define_single_label_model( + input_size=x_train.shape[1], opts=opts, output_bias=initial_bias + ) + + # Define checkpoint to save model weights during training + checkpoint_model_weights_path = f"{model_file_prefix}.model.weights.hdf5" + callback_list = cb.nn_callback( + checkpoint_path=checkpoint_model_weights_path, opts=opts + ) + + # Train model + start = time() + hist = model.fit( + x_train, + y_train, + callbacks=callback_list, + epochs=opts.epochs, + batch_size=opts.batchSize, + verbose=opts.verbose, + validation_data=(x_test, y_test), + ) + trainTime = str(round((time() - start) / 60, ndigits=2)) + logging.info( + f"Computation time for training the single-label model for {target}: {trainTime} min" + ) + + # Save and plot model history + pd.DataFrame(hist.history).to_csv(path_or_buf=f"{model_file_prefix}.history.csv") + pl.plot_history(history=hist, file=f"{model_file_prefix}.history.svg") + # Evaluate model + callback_model = define_single_label_model(input_size=x_train.shape[1], opts=opts) + callback_model.load_weights(filepath=checkpoint_model_weights_path) + performance = evaluate_model( + x_test=x_test, + y_test=y_test, + file_prefix=model_file_prefix, + model=callback_model, + target=target, + fold=fold, + ) + + return performance + + +def get_x_y( + df: pd.DataFrame, + target: str, + train_set: pd.DataFrame, + test_set: pd.DataFrame, + opts: options.Options, +): + train_indices = train_set.index + test_indices = test_set.index + if opts.compressFeatures: + accessor = "fpcompressed" + else: + accessor = "fp" + new_df = pd.DataFrame( + [{x: y for x, y in enumerate(item)} for item in df[accessor].values.tolist()], + index=df.index, + ) + x_train = new_df.iloc[train_indices, :].values + y_train = df.iloc[train_indices][target].values + x_test = new_df.iloc[test_indices, :].values + y_test = df.iloc[test_indices][target].values + x_train = x_train.astype("float32") + y_train = y_train.astype("float32") + x_test = x_test.astype("float32") + y_test = y_test.astype("float32") + return x_train, y_train, x_test, y_test + + +def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: + """ + Train individual models for all targets (columns) present in the provided target data (y) and a multi-label + model that classifies all targets at once. For each individual target the data is first subset to exclude NA + values (for target associations). A random sample of the remaining data (size is the split fraction) is used for + training and the remaining data for validation. + + :param opts: The command line arguments in the options class + :param df: The dataframe containing x matrix and at least one column for a y target. + """ + + # find target columns + targets = [ + c + for c in df.columns + if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] + ] + if opts.wabTracking and opts.wabTarget != "": + # For W&B tracking, we only train one target that's specified as wabTarget "ER". + # In case it's not there, we use the first one available + if opts.wabTarget in targets: + targets = [opts.wabTarget] + elif opts.wabTarget == "": + targets = targets + else: + logging.error( + f"The specified wabTarget for Weights & Biases tracking does not exist: {opts.wabTarget}" + ) + targets = [targets[0]] + + # Collect metrics for each fold and target + performance_list = [] + if opts.split_type == "random": + for target in targets: # [:1]: + # target=targets[1] # --> only for testing the code + x, y = prepare_nn_training_data(df, target, opts, return_dataframe=False) + if x is None: + continue + + logging.info(f"X training matrix of shape {x.shape} and type {x.dtype}") + logging.info(f"Y training matrix of shape {y.shape} and type {y.dtype}") + + if opts.kFolds == 1: + # for single 'folds' and when sweeping on W&B, we fix the random state + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}", + group=f"{target}", + name=f"{target}_single_fold", + ) + if opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", + name=f"{target}_single_fold", + group=f"{target}", + reinit=True, + ) + x_train, x_test, y_train, y_test = train_test_split( + x, y, stratify=y, test_size=opts.testSize, random_state=1 + ) + logging.info( + f"Splitting train/test data with fixed random initializer" + ) + else: + x_train, x_test, y_train, y_test = train_test_split( + x, y, stratify=y, test_size=opts.testSize + ) + + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=0, + target=target, + opts=opts, + ) + performance_list.append(performance) + # save complete model + trained_model = define_single_label_model( + input_size=len(x[0]), opts=opts + ) + # trained_model.load_weights + # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_single-labeled_Fold-0.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join(opts.outputDir, f"{target}_saved_model") + ) + + elif 1 < opts.kFolds < 10: # int(x.shape[0] / 100): + # do a kfold cross-validation + kfold_c_validator = StratifiedKFold( + n_splits=opts.kFolds, shuffle=True, random_state=42 + ) + fold_no = 1 + # split the data + for train, test in kfold_c_validator.split(x, y): + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FNN_{opts.threshold}_{opts.split_type}", + group=f"{target}", + name=f"{target}-{fold_no}", + reinit=True, + ) + if opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}", + name=f"AE_{target}-{fold_no}", + group=f"{target}", + reinit=True, + ) + + # Initialize wandb for the feed forward model + wandb.init( + project=f"FNN_{opts.threshold}_{opts.split_type}", + group=f"{target}", + name=f"FNN_{target}-{fold_no}", + reinit=True, + ) + + performance = fit_and_evaluate_model( + x_train=x[train], + x_test=x[test], + y_train=y[train], + y_test=y[test], + fold=fold_no, + target=target, + opts=opts, + ) + performance_list.append(performance) + + # save complete model + trained_model = define_single_label_model( + input_size=len(x[0]), opts=opts + ) + # trained_model.load_weights + # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_single-labeled_Fold-{fold_no}.model.weights.hdf5", + ) + ) + # create output directory and store complete model + trained_model.save( + filepath=path.join( + opts.outputDir, f"{target}-{fold_no}_saved_model" + ) + ) + if opts.wabTracking: + wandb.finish() + fold_no += 1 + + # select and copy best model - how to define the best model? + best_fold = pd.concat(performance_list, ignore_index=True).sort_values( + by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True + )["fold"][0] + # rename the fold to best fold + src = os.path.join( + opts.outputDir, + f"{target}_single-labeled_Fold-{best_fold}.model.weights.hdf5", + ) + dst = os.path.join( + opts.outputDir, + f"{target}_single-labeled_Best_Fold-{best_fold}.model.weights.hdf5", + ) + os.rename(src, dst) + + src_dir = os.path.join( + opts.outputDir, f"{target}-{best_fold}_saved_model" + ) + dst_dir = os.path.join( + opts.outputDir, f"{target}-{best_fold}_best_saved_model" + ) + + if path.isdir(dst_dir): + shutil.rmtree(dst_dir) + + # Rename source directory to destination directory + os.rename(src_dir, dst_dir) + + # save complete model + else: + logging.info( + "Your selected number of folds for Cross validation is out of range. " + "It must be 1 or smaller than 1 hundredth of the number of samples." + ) + sys.exit("Number of folds out of range") + ( + pd.concat(performance_list, ignore_index=True).to_csv( + path_or_buf=path.join( + opts.outputDir, "single_label_random_model.evaluation.csv" + ) + ) + ) + # For each individual target train a model + elif opts.split_type == "scaffold_balanced": + # df, irrelevant_columns = preprocess_dataframe(df, opts) + for idx, target in enumerate(targets): + df = prepare_nn_training_data(df, target, opts, return_dataframe=True) + relevant_cols = ["smiles"] + ["fp"] + [target] # list(irrelevant_columns) + if opts.compressFeatures: + relevant_cols = relevant_cols + ["fpcompressed"] + df_task = df.loc[:, relevant_cols] + # Drop rows with missing values in the target column + df_task.dropna(subset=[target], inplace=True) + df_task.reset_index(drop=True, inplace=True) + if opts.kFolds == 1: + train_set, val_set, test_set = ae_scaffold_split( + df_task, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=False, + seed=42, + ) + x_train, y_train, x_test, y_test = get_x_y( + df_task, target, train_set, test_set, opts + ) + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}", + group=f"{target}", + name=f"{target}_single_fold", + ) + elif opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", + name=f"{target}-single-fold", + group=f"{target}", + ) + + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=0, + target=target, + opts=opts, + ) + performance_list.append(performance) + trained_model = define_single_label_model( + input_size=len(x_train[0]), opts=opts + ) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_Fold-0.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join( + opts.outputDir, f"{target}_scaffold_saved_model_0" + ) + ) + elif opts.kFolds > 1: + for fold_no in range(1, opts.kFolds + 1): + print(f"Splitting data with seed {fold_no}") + train_set, val_set, test_set = ae_scaffold_split( + df_task, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=True, + seed=fold_no, + ) + x_train, y_train, x_test, y_test = get_x_y( + df_task, target, train_set, test_set, opts + ) + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}", + group=f"{target}", + name=f"{target}-{fold_no}", + reinit=True, + ) + elif opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", + name=f"{target}-{fold_no}", + group=f"{target}", + reinit=True, + ) + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=fold_no, + target=target, + opts=opts, + ) + performance_list.append(performance) + + trained_model = define_single_label_model( + input_size=len(x_train[0]), opts=opts + ) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_Fold-{fold_no}.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join( + opts.outputDir, f"{target}_scaffold_saved_model_{fold_no}" + ) + ) + if opts.wabTracking: + wandb.finish() + fold_no += 1 + # find best fold + best_fold = pd.concat(performance_list, ignore_index=True).sort_values( + by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True + )["fold"][0] + # rename the fold to best fold + src = os.path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_Fold-{best_fold}.model.weights.hdf5", + ) + dst = os.path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_BEST_Fold-{best_fold}.model.weights.hdf5", + ) + os.rename(src, dst) + + src_dir = os.path.join( + opts.outputDir, f"{target}_scaffold_saved_model_{best_fold}" + ) + dst_dir = os.path.join( + opts.outputDir, + f"{target}_scaffold_saved_model_BEST_FOLD_{best_fold}", + ) + + if path.isdir(dst_dir): + shutil.rmtree(dst_dir) + + # Rename source directory to destination directory + os.rename(src_dir, dst_dir) + + else: + logging.info( + "Your selected number of folds for Cross validation is out of range. " + "It must be 1 or smaller than 1 hundredth of the number of samples." + ) + sys.exit("Number of folds out of range") + ( + pd.concat(performance_list, ignore_index=True).to_csv( + path_or_buf=path.join( + opts.outputDir, "single_label_scaffold_model.evaluation.csv" + ) + ) + ) + elif opts.split_type == "molecular_weight": + logging.info("You can use molecular_weight split once.") + for idx, target in enumerate(targets): + df = prepare_nn_training_data(df, target, opts, return_dataframe=True) + relevant_cols = ["smiles"] + ["fp"] + [target] + if opts.compressFeatures: + relevant_cols = relevant_cols + ["fpcompressed"] + df_task = df.loc[:, relevant_cols] + df_task.dropna(subset=[target], inplace=True) + df_task.reset_index(drop=True, inplace=True) + if opts.kFolds == 1: + train_set, val_set, test_set = weight_split( + df_task, bias="small", sizes=(1 - opts.testSize, 0.0, opts.testSize) + ) + x_train, y_train, x_test, y_test = get_x_y( + df_task, target, train_set, test_set, opts + ) + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}AE_{opts.aeSplitType}", + group=f"{target}", + name=f"{target}_single_fold", + ) + elif opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_weight_split_{opts.split_type}", + name=f"{target}-single-fold", + group=f"{target}", + ) + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=0, + target=target, + opts=opts, + ) + performance_list.append(performance) + trained_model = define_single_label_model( + input_size=len(x_train[0]), opts=opts + ) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_weight_single-labeled_Fold-0.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join(opts.outputDir, f"{target}_weight_saved_model_0") + ) + elif opts.kFolds > 1: + raise Exception( + f"Unsupported number of folds: {opts.kFolds} for {opts.split_type} split.\ + You CANNOT do multiple folds based on molecular_weight." + ) + + ( + pd.concat(performance_list, ignore_index=True).to_csv( + path_or_buf=path.join( + opts.outputDir, "single_label_molecular_weight_model.evaluation.csv" + ) + ) + ) + else: + raise Exception(f"Unsupported split type: {opts.split_type}") diff --git a/build/lib/dfpl/utils.py b/build/lib/dfpl/utils.py new file mode 100644 index 00000000..b45bde62 --- /dev/null +++ b/build/lib/dfpl/utils.py @@ -0,0 +1,329 @@ +import json +import logging +import os +import pathlib +import warnings +from collections import defaultdict +from random import Random +from typing import Dict, List, Set, Tuple, Union + +import numpy as np +import pandas as pd +from rdkit import Chem, RDLogger +from rdkit.Chem import rdMolDescriptors +from rdkit.Chem.Scaffolds import MurckoScaffold +from tqdm import tqdm + +RDLogger.DisableLog("rdApp.*") + + +def makePathAbsolute(p: str) -> str: + path = pathlib.Path(p) + if path.is_absolute(): + return p + else: + return str(path.absolute()) + + +def createDirectory(directory: str): + path = makePathAbsolute(directory) + if not os.path.exists(path): + os.makedirs(path) + + +def createArgsFromJson(in_json: str, ignore_elements: list, return_json_object: bool): + arguments = [] + with open(in_json, "r") as f: + data = json.load(f) + for key, value in data.items(): + if key not in ignore_elements: + if key == "extra_metrics" and isinstance(value, list): + arguments.append("--extra_metrics") + arguments.extend(value) + else: + arguments.append("--" + str(key)) + arguments.append(str(value)) + if return_json_object: + return arguments, data + return arguments + + +def make_mol(s: str, keep_h: bool, add_h: bool, keep_atom_map: bool): + """ + Builds an RDKit molecule from a SMILES string. + + :param s: SMILES string. + :param keep_h: Boolean whether to keep hydrogens in the input smiles. This does not add hydrogens, it only keeps the + m if they are specified. + :param add_h: Boolean whether to add hydrogens to the input smiles. + :param keep_atom_map: Boolean whether to keep the original atom mapping. + :return: RDKit molecule. + """ + params = Chem.SmilesParserParams() + params.removeHs = not keep_h if not keep_atom_map else False + mol = Chem.MolFromSmiles(s, params) + + if add_h: + mol = Chem.AddHs(mol) + + if keep_atom_map: + atom_map_numbers = tuple(atom.GetAtomMapNum() for atom in mol.GetAtoms()) + for idx, map_num in enumerate(atom_map_numbers): + if idx + 1 != map_num: + new_order = np.argsort(atom_map_numbers).tolist() + return Chem.rdmolops.RenumberAtoms(mol, new_order) + + return mol + + +def generate_scaffold( + mol: Union[str, Chem.Mol, Tuple[Chem.Mol, Chem.Mol]], include_chirality: bool = True +) -> str: + """ + Computes the Bemis-Murcko scaffold for a SMILES string, an RDKit molecule, or an InChI string or InChIKey. + + :param mol: A SMILES, RDKit molecule, InChI string, or InChIKey string. + :param include_chirality: Whether to include chirality in the computed scaffold. + :return: The Bemis-Murcko scaffold for the molecule. + """ + if isinstance(mol, str): + if mol.startswith("InChI="): + mol = inchi_to_mol(mol) + else: + mol = make_mol(mol, keep_h=False, add_h=False, keep_atom_map=False) + elif isinstance(mol, tuple): + mol = mol[0] + scaffold = MurckoScaffold.MurckoScaffoldSmiles( + mol=mol, includeChirality=include_chirality + ) + + return scaffold + + +def scaffold_to_smiles( + mols: List[str], use_indices: bool = False +) -> Dict[str, Union[Set[str], Set[int]]]: + """ + Computes the scaffold for each SMILES and returns a mapping from scaffolds to sets of smiles (or indices). + :param mols: A list of SMILES. + :param use_indices: Whether to map to the SMILES's index in :code:`mols` rather than + mapping to the smiles string itself. This is necessary if there are duplicate smiles. + :return: A dictionary mapping each unique scaffold to all SMILES (or indices) which have that scaffold. + """ + scaffolds = defaultdict(set) + for i, mol in tqdm(enumerate(mols), total=len(mols)): + scaffold = generate_scaffold(mol) + if use_indices: + scaffolds[scaffold].add(i) + else: + scaffolds[scaffold].add(mol) + + return scaffolds + + +# def inchi_to_mol(inchi: str) -> Chem.Mol: +# return Chem.inchi.MolFromInchi(inchi) +def smiles_to_mol(smiles: str) -> Chem.Mol: + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return None + # raise ValueError(f"Could not convert SMILES to Mol: {smiles}") + return mol + + +def inchi_to_mol(inchi: str) -> Chem.Mol: + mol = Chem.MolFromInchi(inchi) + if mol is None: + return None + # raise ValueError(f"Could not convert InChI to Mol: {inchi}") + return mol + + +def weight_split( + data: pd.DataFrame, bias: str, sizes: Tuple[float, float, float] = (0.8, 0, 0.2) +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): + raise ValueError(f"Invalid train/val/test splits! got: {sizes}") + # initial_indices = data.index.to_numpy() + train_size, val_size, _ = ( + sizes[0] * len(data), + sizes[1] * len(data), + sizes[2] * len(data), + ) + if "inchi" in [x.lower() for x in data.columns]: + data["mol"] = data["inchi"].apply(inchi_to_mol) + elif "smiles" in [x.lower() for x in data.columns]: + data["mol"] = data["smiles"].apply(smiles_to_mol) + else: + logging.info("Dataframe does not have a SMILES or InChi column") + none_mols = data["mol"].isnull().sum() + logging.info(f"There are {none_mols} chemicals with no mol objects ") + data.dropna(subset=["mol"], inplace=True) + data["mol_weight"] = data.apply( + lambda row: rdMolDescriptors.CalcExactMolWt(row["mol"]) + if row["mol"] is not None + else None, + axis=1, + ) + # data = data.drop(columns=['mol','fp','inchi','toxid','key'], axis=1) + sorted_data = data.copy() + if bias == "big": + sorted_data = data.sort_values(by="mol_weight", ascending=False) + elif bias == "small": + sorted_data = data.sort_values(by="mol_weight", ascending=True) + else: + print("Wrong bias, choose small or big") + indices = np.arange(len(sorted_data)) + train_end_idx = int(train_size) + val_end_idx = int(train_size + val_size) + train_indices = indices[:train_end_idx] + val_indices = indices[train_end_idx:val_end_idx] + test_indices = indices[val_end_idx:] + train_df = sorted_data.iloc[train_indices].reset_index(drop=True) + val_df = sorted_data.iloc[val_indices].reset_index(drop=True) + test_df = sorted_data.iloc[test_indices].reset_index(drop=True) + + return train_df, val_df, test_df + + +def ae_scaffold_split( + data: pd.DataFrame, + sizes: Tuple[float, float, float] = (0.8, 0, 0.2), + balanced: bool = False, + key_molecule_index: int = 0, + seed: int = 0, +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Splits a pandas DataFrame by scaffold so that no molecules sharing a scaffold are in different splits. + :param data: A pandas DataFrame containing SMILES strings and molecule properties. + :param sizes: A length-3 tuple with the proportions of data in the train, validation, and test sets. + :param balanced: Whether to balance the sizes of scaffolds in each set rather than putting the smallest in test set. + :param key_molecule_index: For data with multiple molecules, this sets which molecule will be considered during splitting. + :param seed: Random seed for shuffling when doing balanced splitting. + :return: A tuple of pandas DataFrames containing the train, validation, and test splits of the data. + """ + if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): + raise ValueError(f"Invalid train/val/test splits! got: {sizes}") + + # Split + train_size, val_size, test_size = ( + sizes[0] * len(data), + sizes[1] * len(data), + sizes[2] * len(data), + ) + train: List[int] = [] + val: List[int] = [] + test: List[int] = [] + train_scaffold_count, val_scaffold_count, test_scaffold_count = 0, 0, 0 + + # Map from scaffold to index in the data + key_colnames = data.columns + if "inchi" in key_colnames: + key_molecule_index = next( + (i for i, colname in enumerate(data.columns) if "inchi" in colname.lower()), + None, + ) + if key_molecule_index is None: + warnings.warn("No column with 'inchi' found in the DataFrame. Proceeding with caution.") + key_mols = data.iloc[:, key_molecule_index].apply(inchi_to_mol).dropna() + else: + key_mols = data.iloc[:, key_molecule_index] + scaffold_to_indices = scaffold_to_smiles(key_mols.tolist(), use_indices=True) + # Seed randomness + random = Random(seed) + + if ( + balanced + ): # Put stuff that's bigger than half the val/test size into train, rest just order randomly + index_sets = list(scaffold_to_indices.values()) + big_index_sets = [] + small_index_sets = [] + for index_set in index_sets: + if len(index_set) > val_size / 2 or len(index_set) > test_size / 2: + big_index_sets.append(index_set) + else: + small_index_sets.append(index_set) + random.seed(seed) + random.shuffle(big_index_sets) + random.shuffle(small_index_sets) + index_sets = big_index_sets + small_index_sets + else: # Sort from largest to smallest scaffold sets + index_sets = sorted( + list(scaffold_to_indices.values()), + key=lambda index_set: len(index_set), + reverse=True, + ) + for index_set in index_sets: + if len(test) + len(index_set) <= test_size: + test += index_set + test_scaffold_count += 1 + elif len(train) + len(index_set) <= train_size: + train += index_set + train_scaffold_count += 1 + else: + val += index_set + val_scaffold_count += 1 + logging.info( + f"Total scaffolds = {len(scaffold_to_indices):,} | " + f"train scaffolds = {train_scaffold_count:,} | " + f"val scaffolds = {val_scaffold_count:,} | " + f"test scaffolds = {test_scaffold_count:,}" + ) + + log_scaffold_stats(data, index_sets) + # Map from indices to data + + train_df = data.iloc[train] + val_df = data.iloc[val] + test_df = data.iloc[test] + return train_df, val_df, test_df + + +def log_scaffold_stats( + data: pd.DataFrame, + index_sets: List[Set[int]], + num_scaffolds: int = 10, + num_labels: int = 20, +) -> List[Tuple[List[float], List[int]]]: + + """ + Logs and returns statistics about counts and average target values in molecular scaffolds. + :param data: A pandas DataFrame containing SMILES strings and molecule properties. + :param index_sets: A list of sets of indices representing splits of the data. + :param num_scaffolds: The number of scaffolds about which to display statistics. + :param num_labels: The number of labels about which to display statistics. + :return: A list of tuples where each tuple contains a list of average target values + across the first :code:num_labels labels and a list of the number of non-zero values for + the first :code:num_scaffolds scaffolds, sorted in decreasing order of scaffold frequency. + """ + logging.info( + "Label averages per scaffold, in decreasing order of scaffold frequency, " + f"capped at {num_scaffolds} scaffolds and {num_labels} labels:" + ) + + stats = [] + index_sets = sorted(index_sets, key=lambda idx_set: len(idx_set), reverse=True) + for scaffold_num, index_set in enumerate(index_sets[:num_scaffolds]): + data_set = data.iloc[list(index_set)] + targets = [ + c + for c in data.columns + if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] + ] + # targets = data_set.iloc[:, 2:].values + targets = data_set.loc[:, targets].values + + with warnings.catch_warnings(): # Likely warning of empty slice of target has no values besides NaN + warnings.simplefilter("ignore", category=RuntimeWarning) + target_avgs = np.nanmean(targets, axis=0)[:num_labels] + + counts = np.count_nonzero(~np.isnan(targets), axis=0)[:num_labels] + stats.append((target_avgs, counts)) + + logging.info(f"Scaffold {scaffold_num}") + for task_num, (target_avg, count) in enumerate(zip(target_avgs, counts)): + logging.info( + f"Task {task_num}: count = {count:,} | target average = {target_avg:.6f}" + ) + logging.info("\n") + return stats diff --git a/build/lib/dfpl/vae.py b/build/lib/dfpl/vae.py new file mode 100644 index 00000000..17b7dc6f --- /dev/null +++ b/build/lib/dfpl/vae.py @@ -0,0 +1,337 @@ +import logging +import math +import os.path +from os.path import basename + +import numpy as np +import pandas as pd +import wandb +from keras import backend as K +from sklearn.model_selection import train_test_split +import tensorflow.keras.metrics as metrics +from tensorflow.keras import initializers, optimizers +from tensorflow.keras.layers import Dense, Input, Lambda +from tensorflow.keras.models import Model +from tensorflow.python.framework.ops import disable_eager_execution +from typing import Tuple +import csv + +from dfpl import history as ht +from dfpl import callbacks +from dfpl import options, settings +from dfpl.utils import ae_scaffold_split, weight_split + +disable_eager_execution() + + +def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: + input_size = opts.fpSize + encoding_dim = opts.encFPSize + ac_optimizer = optimizers.Adam( + learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay + ) + + if output_bias is not None: + output_bias = initializers.Constant(output_bias) + + # get the number of meaningful hidden layers (latent space included) + hidden_layer_count = round(math.log2(input_size / encoding_dim)) + + # the input placeholder + input_vec = Input(shape=(input_size,)) + + # 1st hidden layer, that receives weights from input layer + # equals bottleneck layer, if hidden_layer_count==1! + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / 2), activation=opts.aeActivationFunction + )(input_vec) + else: + encoded = Dense( + units=int(input_size / 2), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(input_vec) + + if hidden_layer_count > 1: + # encoding layers, incl. bottle-neck + for i in range(1, hidden_layer_count): + factor_units = 2 ** (i + 1) + # print(f'{factor_units}: {int(input_size / factor_units)}') + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # latent space layers + factor_units = 2 ** (hidden_layer_count - 1) + if opts.aeActivationFunction != "selu": + z_mean = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + z_log_var = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + z_mean = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + z_log_var = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # sampling layer + def sampling(args): + z_mean, z_log_var = args + batch = K.shape(z_mean)[0] + dim = K.int_shape(z_mean)[1] + epsilon = K.random_normal(shape=(batch, dim)) + return z_mean + K.exp(0.5 * z_log_var) * epsilon + + # sample from latent space + z = Lambda(sampling, output_shape=(int(input_size / factor_units),))( + [z_mean, z_log_var] + ) + decoded = z + # decoding layers + for i in range(hidden_layer_count - 2, 0, -1): + factor_units = 2**i + # print(f'{factor_units}: {int(input_size/factor_units)}') + if opts.aeActivationFunction != "selu": + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(decoded) + else: + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(decoded) + + # output layer + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(decoded) + + else: + # output layer + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(encoded) + + autoencoder = Model(input_vec, decoded) + + # KL divergence loss + def kl_loss(z_mean, z_log_var): + return -0.5 * K.sum( + 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1 + ) + + # binary cross-entropy loss + def bce_loss(y_true, y_pred): + return metrics.binary_crossentropy(y_true, y_pred) + + # combined loss + def vae_loss(y_true, y_pred): + bce = bce_loss(y_true, y_pred) + kl = kl_loss(z_mean, z_log_var) + return bce + 0.5 * kl + + autoencoder.compile( + optimizer=ac_optimizer, loss=vae_loss, metrics=[bce_loss, kl_loss] + ) + + # build encoder model + encoder = Model(input_vec, z_mean) + + return autoencoder, encoder + + +def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: + """ + Trains an autoencoder on the given feature matrix X. The response matrix is only used to + split the data into meaningful test and train sets. + + :param opts: Command line arguments as defined in options.py + :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder + :return: The encoder model of the trained autoencoder + """ + + # If wandb tracking is enabled for VAE weights but not for the main program, initialize a new wandb run + if opts.aeWabTracking and not opts.wabTracking: + wandb.init(project=f"VAE_{opts.aeSplitType}") + + # Define output files for VAE and encoder weights + if opts.ecWeightsFile == "": + # If no encoder weights file is specified, use the input file name to generate a default file name + logging.info("No VAE encoder weights file specified") + base_file_name = ( + os.path.splitext(basename(opts.inputFile))[0] + + opts.aeType + + opts.aeSplitType + ) + logging.info( + f"(variational) encoder weights will be saved in {base_file_name}.autoencoder.hdf5" + ) + vae_weights_file = os.path.join( + opts.outputDir, base_file_name + ".vae.weights.hdf5" + ) + # ec_weights_file = os.path.join( + # opts.outputDir, base_file_name + ".encoder.weights.hdf5" + # ) + else: + # If an encoder weights file is specified, use it as the encoder weights file name + logging.info(f"VAE encoder will be saved in {opts.ecWeightsFile}") + base_file_name = ( + os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType + ) + vae_weights_file = os.path.join( + opts.outputDir, base_file_name + ".vae.weights.hdf5" + ) + # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) + + # Collect the callbacks for training + callback_list = callbacks.autoencoder_callback( + checkpoint_path=vae_weights_file, opts=opts + ) + # Select all fingerprints that are valid and turn them into a numpy array + fp_matrix = np.array( + df[df["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info( + f"Training VAE on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" + ) + assert 0.0 <= opts.testSize <= 0.5 + if opts.aeSplitType == "random": + logging.info("Training VAE using random split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # Split data into test and training data + if opts.aeWabTracking: + x_train, x_test, _, _ = train_test_split( + fp_matrix, train_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train, x_test, _, _ = train_test_split( + fp_matrix, train_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "scaffold_balanced": + logging.info("Training autoencoder using scaffold split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # if opts.aeWabTracking: + train_data, val_data, test_data = ae_scaffold_split( + df, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=True, + seed=42, + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "molecular_weight": + logging.info("Training autoencoder using molecular weight split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # if opts.aeWabTracking: + train_data, val_data, test_data = weight_split( + df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + else: + x_train = fp_matrix + x_test = None + else: + raise ValueError(f"Invalid split type: {opts.split_type}") + if opts.testSize > 0.0: + train_indices = train_indices[train_indices < x_train.shape[0]] + test_indices = np.arange(x_train.shape[0], x_train.shape[0] + x_test.shape[0]) + else: + test_indices = None + ids, counts = np.unique(x_train.flatten(), return_counts=True) + count_dict = dict(zip(ids, counts)) + if count_dict[0] == 0: + initial_bias = None + logging.info("No zeroes in training labels. Setting initial_bias to None.") + else: + initial_bias = np.log([count_dict[1] / count_dict[0]]) + logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") + if opts.testSize > 0.0: + logging.info(f"VAE training/testing mode with train- and test-samples") + logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") + logging.info(f"VAE test data shape {x_test.shape} with type {x_test.dtype}") + else: + logging.info(f"VAE full train mode without test-samples") + logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") + + (vae, encoder) = define_vae_model(opts, output_bias=initial_bias) + # Train the VAE on the training data + vae_hist = vae.fit( + x_train, + x_train, + epochs=opts.aeEpochs, + batch_size=opts.aeBatchSize, + verbose=opts.verbose, + callbacks=callback_list, + validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, + ) + + # Save the VAE weights + logging.info(f"VAE weights stored in file: {vae_weights_file}") + ht.store_and_plot_history( + base_file_name=os.path.join(opts.outputDir, base_file_name + ".VAE"), + hist=vae_hist, + ) + save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_VAE.h5") + if opts.testSize > 0.0: + (callback_vae, callback_encoder) = define_vae_model(opts) + callback_vae.load_weights(filepath=vae_weights_file) + callback_encoder.save(filepath=save_path) + else: + encoder.save(filepath=save_path) + latent_space = encoder.predict(fp_matrix) + latent_space_file = os.path.join( + opts.outputDir, base_file_name + ".latent_space.csv" + ) + with open(latent_space_file, "w", newline="") as file: + writer = csv.writer(file) + writer.writerows(latent_space) + return encoder, train_indices, test_indices From 79354fcc2976004387d978c2007c534ed417e5e2 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 8 Sep 2023 15:33:35 +0200 Subject: [PATCH 03/45] updated environment, adam from legacy --- build/lib/dfpl/__init__.py | 0 build/lib/dfpl/__main__.py | 321 ---- build/lib/dfpl/autoencoder.py | 415 ----- build/lib/dfpl/callbacks.py | 89 -- .../dfpl/deepFPlearn-HyperParameterTuning.py | 128 -- ...deepFPlearn-HyperParameterTuning_single.py | 426 ----- build/lib/dfpl/dfplmodule.py | 1413 ----------------- build/lib/dfpl/feedforwardNN.py | 464 ------ build/lib/dfpl/fingerprint.py | 164 -- .../lib/dfpl/harmonize_mol_representations.py | 171 -- build/lib/dfpl/history.py | 41 - build/lib/dfpl/options.py | 1297 --------------- build/lib/dfpl/plot.py | 228 --- build/lib/dfpl/predictions.py | 39 - build/lib/dfpl/rbm.py | 259 --- build/lib/dfpl/settings.py | 52 - build/lib/dfpl/single_label_model.py | 1008 ------------ build/lib/dfpl/utils.py | 329 ---- build/lib/dfpl/vae.py | 337 ---- dfpl/autoencoder.py | 5 +- dfpl/single_label_model.py | 3 +- dfpl/vae.py | 5 +- environment.yml | 30 +- example/train.json | 4 +- 24 files changed, 26 insertions(+), 7202 deletions(-) delete mode 100644 build/lib/dfpl/__init__.py delete mode 100644 build/lib/dfpl/__main__.py delete mode 100644 build/lib/dfpl/autoencoder.py delete mode 100644 build/lib/dfpl/callbacks.py delete mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning.py delete mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py delete mode 100644 build/lib/dfpl/dfplmodule.py delete mode 100644 build/lib/dfpl/feedforwardNN.py delete mode 100644 build/lib/dfpl/fingerprint.py delete mode 100644 build/lib/dfpl/harmonize_mol_representations.py delete mode 100644 build/lib/dfpl/history.py delete mode 100644 build/lib/dfpl/options.py delete mode 100644 build/lib/dfpl/plot.py delete mode 100644 build/lib/dfpl/predictions.py delete mode 100644 build/lib/dfpl/rbm.py delete mode 100644 build/lib/dfpl/settings.py delete mode 100644 build/lib/dfpl/single_label_model.py delete mode 100644 build/lib/dfpl/utils.py delete mode 100644 build/lib/dfpl/vae.py diff --git a/build/lib/dfpl/__init__.py b/build/lib/dfpl/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/build/lib/dfpl/__main__.py b/build/lib/dfpl/__main__.py deleted file mode 100644 index 5c12705b..00000000 --- a/build/lib/dfpl/__main__.py +++ /dev/null @@ -1,321 +0,0 @@ -import os.path -import dataclasses -import logging -import pathlib -from argparse import Namespace -from os import path - -import pandas as pd -from keras.models import load_model - -from dfpl import autoencoder as ac -from dfpl import feedforwardNN as fNN -from dfpl import fingerprint as fp -from dfpl import options, predictions - -# from dfpl import rbm as rbm -from dfpl import single_label_model as sl -from dfpl import vae as vae -from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute -import chemprop as cp - -project_directory = pathlib.Path(".").parent.parent.absolute() -test_train_opts = options.Options( - inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", - outputDir=f"{project_directory}/output_data/console_test", - ecWeightsFile=f"{project_directory}/output_data/case_00/AE_S/ae_S.encoder.hdf5", - ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", - type="smiles", - fpType="topological", - epochs=100, - batchSize=1024, - fpSize=2048, - encFPSize=256, - enableMultiLabel=False, - testSize=0.2, - kFolds=2, - verbose=2, - trainAC=False, - trainFNN=True, - compressFeatures=True, - activationFunction="selu", - lossFunction="bce", - optimizer="Adam", - fnnType="FNN", -) - -test_pred_opts = options.Options( - inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", - outputDir=f"{project_directory}/output_data/console_test", - outputFile=f"{project_directory}/output_data/console_test/S_dataset.predictions_ER.csv", - ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", - fnnModelDir=f"{project_directory}/output_data/console_test/ER_saved_model", - type="smiles", - fpType="topological", -) - - -def traindmpnn(opts: options.GnnOptions): - """ - Train a D-MPNN model using the given options. - Args: - - opts: options.GnnOptions instance containing the details of the training - Returns: - - None - """ - os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" - ignore_elements = ["py/object"] - # Load options from a JSON file and replace the relevant attributes in `opts` - arguments = createArgsFromJson( - opts.configFile, ignore_elements, return_json_object=False - ) - opts = cp.args.TrainArgs().parse_args(arguments) - logging.info("Training DMPNN...") - # Train the model and get the mean and standard deviation of AUC score from cross-validation - mean_score, std_score = cp.train.cross_validate( - args=opts, train_func=cp.train.run_training - ) - logging.info(f"Results: {mean_score:.5f} +/- {std_score:.5f}") - - -def predictdmpnn(opts: options.GnnOptions, json_arg_path: str) -> None: - """ - Predict the values using a trained D-MPNN model with the given options. - Args: - - opts: options.GnnOptions instance containing the details of the prediction - - JSON_ARG_PATH: path to a JSON file containing additional arguments for prediction - Returns: - - None - """ - ignore_elements = [ - "py/object", - "gnn_type", - "checkpoint_paths", - "save_dir", - "saving_name", - ] - # Load options and additional arguments from a JSON file - arguments, data = createArgsFromJson( - json_arg_path, ignore_elements, return_json_object=True - ) - arguments.append("--preds_path") - arguments.append("") - save_dir = data.get("save_dir") - name = data.get("saving_name") - # Replace relevant attributes in `opts` with loaded options - opts = cp.args.PredictArgs().parse_args(arguments) - opts.preds_path = save_dir + "/" + name - df = pd.read_csv(opts.test_path) - smiles = [] - for index, rows in df.iterrows(): - my_list = [rows.smiles] - smiles.append(my_list) - # Make predictions and return the result - cp.train.make_predictions(args=opts, smiles=smiles) - - -def train(opts: options.Options): - """ - Run the main training procedure - :param opts: Options defining the details of the training - """ - - os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" - - # import data from file and create DataFrame - if "tsv" in opts.inputFile: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize - ) - else: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize - ) - # initialize encoders to None - encoder = None - autoencoder = None - if opts.trainAC: - if opts.aeType == "deterministic": - encoder, train_indices, test_indices = ac.train_full_ac(df, opts) - elif opts.aeType == "variational": - encoder, train_indices, test_indices = vae.train_full_vae(df, opts) - else: - raise ValueError(f"Unknown autoencoder type: {opts.aeType}") - - # if feature compression is enabled - if opts.compressFeatures: - if not opts.trainAC: - if opts.aeType == "deterministic": - (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) - elif opts.aeType == "variational": - (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) - elif opts.ecWeightsFile == "": - encoder = load_model(opts.ecModelDir) - else: - autoencoder.load_weights( - os.path.join(opts.ecModelDir, opts.ecWeightsFile) - ) - # compress the fingerprints using the autoencoder - df = ac.compress_fingerprints(df, encoder) - # ac.visualize_fingerprints( - # df, - # before_col="fp", - # after_col="fpcompressed", - # train_indices=train_indices, - # test_indices=test_indices, - # save_as=f"UMAP_{opts.aeSplitType}.png", - # ) - # train single label models if requested - if opts.trainFNN and not opts.enableMultiLabel: - sl.train_single_label_models(df=df, opts=opts) - - # train multi-label models if requested - if opts.trainFNN and opts.enableMultiLabel: - fNN.train_nn_models_multi(df=df, opts=opts) - - -def predict(opts: options.Options) -> None: - """ - Run prediction given specific options - :param opts: Options defining the details of the prediction - """ - # import data from file and create DataFrame - if "tsv" in opts.inputFile: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize - ) - print(df) - else: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize - ) - - if opts.compressFeatures: - # load trained model for autoencoder - if opts.aeType == "deterministic": - (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) - if opts.aeType == "variational": - (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) - # Load trained model for autoencoder - if opts.ecWeightsFile == "": - encoder = load_model(opts.ecModelDir) - else: - encoder.load_weights(os.path.join(opts.ecModelDir, opts.ecWeightsFile)) - df = ac.compress_fingerprints(df, encoder) - - # Run predictions on the compressed fingerprints and store the results in a dataframe - df2 = predictions.predict_values(df=df, opts=opts) - - # Extract the column names from the dataframe, excluding the 'fp' and 'fpcompressed' columns - names_columns = [c for c in df2.columns if c not in ["fp", "fpcompressed"]] - - # Save the predicted values to a CSV file in the output directory - df2[names_columns].to_csv(path_or_buf=path.join(opts.outputDir, opts.outputFile)) - - # Log successful completion of prediction and the file path where the results were saved - logging.info( - f"Prediction successful. Results written to '{path.join(opts.outputDir, opts.outputFile)}'" - ) - - -def createLogger(filename: str) -> None: - """ - Set up a logger for the main function that also saves to a log file - """ - # get root logger and set its level - logger = logging.getLogger() - logger.setLevel(logging.INFO) - # create file handler which logs info messages - fh = logging.FileHandler(filename, mode="w") - fh.setLevel(logging.INFO) - # create console handler - ch = logging.StreamHandler() - ch.setLevel(logging.INFO) - # create formatter and add it to the handlers - formatterFile = logging.Formatter( - "{asctime} - {name} - {levelname} - {message}", style="{" - ) - formatterConsole = logging.Formatter("{levelname} {message}", style="{") - fh.setFormatter(formatterFile) - ch.setFormatter(formatterConsole) - # add the handlers to the logger - logger.addHandler(fh) - logger.addHandler(ch) - - -def main(): - """ - Main function that runs training/prediction defined by command line arguments - """ - - parser = options.createCommandlineParser() - prog_args: Namespace = parser.parse_args() - try: - if prog_args.method == "convert": - directory = makePathAbsolute(prog_args.f) - if path.isdir(directory): - createLogger(path.join(directory, "convert.log")) - logging.info(f"Convert all data files in {directory}") - fp.convert_all(directory) - else: - raise ValueError("Input directory is not a directory") - elif prog_args.method == "traingnn": - traingnn_opts = options.GnnOptions.fromCmdArgs(prog_args) - - traindmpnn(traingnn_opts) - - elif prog_args.method == "predictgnn": - predictgnn_opts = options.GnnOptions.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - predictgnn_opts, - test_path=makePathAbsolute(predictgnn_opts.test_path), - preds_path=makePathAbsolute(predictgnn_opts.preds_path), - ) - - logging.info( - f"The following arguments are received or filled with default values:\n{prog_args}" - ) - - if predictgnn_opts.gnn_type == "dmpnn": - predictdmpnn(fixed_opts, prog_args.configFile) - - elif prog_args.method == "train": - train_opts = options.Options.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - train_opts, - inputFile=makePathAbsolute(train_opts.inputFile), - outputDir=makePathAbsolute(train_opts.outputDir), - ) - createDirectory(fixed_opts.outputDir) - createLogger(path.join(fixed_opts.outputDir, "train.log")) - logging.info( - f"The following arguments are received or filled with default values:\n{fixed_opts}" - ) - train(fixed_opts) - elif prog_args.method == "predict": - predict_opts = options.Options.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - predict_opts, - inputFile=makePathAbsolute(predict_opts.inputFile), - outputDir=makePathAbsolute(predict_opts.outputDir), - outputFile=makePathAbsolute( - path.join(predict_opts.outputDir, predict_opts.outputFile) - ), - ecModelDir=makePathAbsolute(predict_opts.ecModelDir), - fnnModelDir=makePathAbsolute(predict_opts.fnnModelDir), - trainAC=False, - trainFNN=False, - ) - createDirectory(fixed_opts.outputDir) - createLogger(path.join(fixed_opts.outputDir, "predict.log")) - logging.info( - f"The following arguments are received or filled with default values:\n{prog_args}" - ) - predict(fixed_opts) - except AttributeError as e: - print(e) - parser.print_usage() - - -if __name__ == "__main__": - main() diff --git a/build/lib/dfpl/autoencoder.py b/build/lib/dfpl/autoencoder.py deleted file mode 100644 index 1f638122..00000000 --- a/build/lib/dfpl/autoencoder.py +++ /dev/null @@ -1,415 +0,0 @@ -import logging -import math -import os.path -from os.path import basename - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns -import umap -import wandb -from sklearn.model_selection import train_test_split -from tensorflow.keras import initializers, losses, optimizers -from tensorflow.keras.layers import Dense, Input -from tensorflow.keras.models import Model -from typing import Tuple - -from dfpl import callbacks -from dfpl import history as ht -from dfpl import options, settings -from dfpl.utils import ae_scaffold_split, weight_split - - -def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: - """ - This function provides an autoencoder model to reduce a certain input to a compressed version. - - :param opts: Training options that provide values for adjusting the neural net - :param output_bias: Bias used to initialize the last layer. It gives the net a head start in training on - imbalanced data (which the fingerprints are, because they have many more 0's than 1's in them). - :return: a tuple of autoencoder and encoder models - """ - input_size = opts.fpSize - encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( - learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay - ) - - if output_bias is not None: - output_bias = initializers.Constant(output_bias) - - # get the number of meaningful hidden layers (latent space included) - hidden_layer_count = round(math.log2(input_size / encoding_dim)) - - # the input placeholder - input_vec = Input(shape=(input_size,)) - - # 1st hidden layer, that receives weights from input layer - # equals bottleneck layer, if hidden_layer_count==1! - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / 2), activation=opts.aeActivationFunction - )(input_vec) - else: - encoded = Dense( - units=int(input_size / 2), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(input_vec) - - if hidden_layer_count > 1: - # encoding layers, incl. bottle-neck - for i in range(1, hidden_layer_count): - factor_units = 2 ** (i + 1) - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # 1st decoding layer - factor_units = 2 ** (hidden_layer_count - 1) - if opts.aeActivationFunction != "selu": - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # decoding layers - for i in range(hidden_layer_count - 2, 0, -1): - factor_units = 2**i - if opts.aeActivationFunction != "selu": - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(decoded) - else: - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(decoded) - - # output layer - # to either 0 or 1 and hence we use sigmoid activation function. - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(decoded) - - else: - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(encoded) - - autoencoder = Model(input_vec, decoded) - encoder = Model(input_vec, encoded) - autoencoder.summary(print_fn=logging.info) - - autoencoder.compile( - optimizer=ac_optimizer, - loss=losses.BinaryCrossentropy(), - # metrics=[ - # metrics.AUC(), - # metrics.Precision(), - # metrics.Recall() - # ] - ) - return autoencoder, encoder - - -def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: - """ - Trains an autoencoder on the given feature matrix X. The response matrix is only used to - split the data into meaningful test and train sets. - - :param opts: Command line arguments as defined in options.py - :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder - :return: The encoder model of the trained autoencoder - """ - - # If wandb tracking is enabled for autoencoder weights but not for the main program, initialize a new wandb run - if opts.aeWabTracking and not opts.wabTracking: - wandb.init(project=f"AE_{opts.aeSplitType}") - - # Define output files for autoencoder and encoder weights - if opts.ecWeightsFile == "": - # If no encoder weights file is specified, use the input file name to generate a default file name - logging.info("No AE encoder weights file specified") - base_file_name = ( - os.path.splitext(basename(opts.inputFile))[0] + opts.aeSplitType - ) - logging.info( - f"(auto)encoder weights will be saved in {base_file_name}.autoencoder.hdf5" - ) - ac_weights_file = os.path.join( - opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" - ) - # ec_weights_file = os.path.join( - # opts.outputDir, base_file_name + ".encoder.weights.hdf5" - # ) - else: - # If an encoder weights file is specified, use it as the encoder weights file name - logging.info(f"AE encoder will be saved in {opts.ecWeightsFile}") - base_file_name = ( - os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType - ) - ac_weights_file = os.path.join( - opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" - ) - # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - - # Collect the callbacks for training - callback_list = callbacks.autoencoder_callback( - checkpoint_path=ac_weights_file, opts=opts - ) - - # Select all fingerprints that are valid and turn them into a numpy array - fp_matrix = np.array( - df[df["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info( - f"Training AC on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" - ) - - # When training the final AE, we don't want any test data. We want to train it on all available fingerprints. - assert 0.0 <= opts.testSize <= 0.5 - if opts.aeSplitType == "random": - logging.info("Training autoencoder using random split") - initial_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # Split data into test and training data - if opts.aeWabTracking: - x_train, x_test, train_indices, test_indices = train_test_split( - fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train, x_test, train_indices, test_indices = train_test_split( - fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "scaffold_balanced": - logging.info("Training autoencoder using scaffold split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - train_data, val_data, test_data = ae_scaffold_split( - df, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=True, - seed=42, - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - train_indices = df[ - df.index.isin(train_data[train_data["fp"].notnull()].index) - ].index.to_numpy() - test_indices = df[ - df.index.isin(test_data[test_data["fp"].notnull()].index) - ].index.to_numpy() - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "molecular_weight": - logging.info("Training autoencoder using molecular weight split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - train_data, val_data, test_data = weight_split( - df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - df_sorted = df.sort_values(by="mol_weight", ascending=True) - # Get the sorted indices from the sorted DataFrame - sorted_indices = df_sorted.index.to_numpy() - - # Find the corresponding indices for train_data, val_data, and test_data in the sorted DataFrame - train_indices = sorted_indices[df.index.isin(train_data.index)] - # val_indices = sorted_indices[df.index.isin(val_data.index)] - test_indices = sorted_indices[df.index.isin(test_data.index)] - else: - x_train = fp_matrix - x_test = None - else: - raise ValueError(f"Invalid split type: {opts.split_type}") - - # Calculate the initial bias aka the log ratio between 1's and 0'1 in all fingerprints - ids, counts = np.unique(x_train.flatten(), return_counts=True) - count_dict = dict(zip(ids, counts)) - if count_dict[0] == 0: - initial_bias = None - logging.info("No zeroes in training labels. Setting initial_bias to None.") - else: - initial_bias = np.log([count_dict[1] / count_dict[0]]) - logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") - - # Check if we're doing training/testing mode or full training mode - if opts.testSize > 0.0: - logging.info(f"AE training/testing mode with train- and test-samples") - logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") - logging.info(f"AC test data shape {x_test.shape} with type {x_test.dtype}") - else: - logging.info(f"AE full train mode without test-samples") - logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") - - # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) - (autoencoder, encoder) = define_ac_model(opts, output_bias=initial_bias) - - # Train the autoencoder on the training data - auto_hist = autoencoder.fit( - x_train, - x_train, - callbacks=callback_list, - epochs=opts.aeEpochs, - batch_size=opts.aeBatchSize, - verbose=opts.verbose, - validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, - ) - logging.info(f"Autoencoder weights stored in file: {ac_weights_file}") - - # Store the autoencoder training history and plot the metrics - ht.store_and_plot_history( - base_file_name=os.path.join(opts.outputDir, base_file_name + ".AC"), - hist=auto_hist, - ) - - # Save the autoencoder callback model to disk - save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_autoencoder") - if opts.testSize > 0.0: - (callback_autoencoder, callback_encoder) = define_ac_model(opts) - callback_encoder.save(filepath=save_path) - else: - encoder.save(filepath=save_path) - # Return the encoder model of the trained autoencoder - return encoder, train_indices, test_indices - - -def compress_fingerprints(dataframe: pd.DataFrame, encoder: Model) -> pd.DataFrame: - """ - Adds a column of the compressed version of the fingerprints to the original dataframe. - - :param dataframe: Dataframe containing a column named 'fp' with the fingerprints - :param encoder: The trained autoencoder that is used for compressing the fingerprints - :return: The input dataframe extended by a column containing the compressed version of the fingerprints - """ - logging.info("Adding compressed fingerprints") - idx = dataframe[dataframe["fp"].notnull()].index - fp_matrix = np.array( - dataframe[dataframe["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info( - f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" - ) - logging.info("Compressed fingerprints are added to input dataframe.") - dataframe["fpcompressed"] = pd.DataFrame( - {"fpcompressed": [s for s in encoder.predict(fp_matrix)]}, idx - ) - return dataframe - - -def visualize_fingerprints( - df: pd.DataFrame, - before_col: str, - after_col: str, - train_indices: np.ndarray, - test_indices: np.ndarray, - save_as: str, -): - # Calculate the number of samples to be taken from each set - num_samples = 1000 - train_samples = int(num_samples * len(train_indices) / len(df)) - test_samples = num_samples - train_samples - - # Assign train and test data points separately - train_data = df.loc[train_indices] - test_data = df.loc[test_indices] - - # Sample train and test data points - train_data_sampled = train_data.sample(n=train_samples, random_state=42) - test_data_sampled = test_data.sample(n=test_samples, random_state=42) - - # Concatenate the sampled train and test data - df_sampled = pd.concat([train_data_sampled, test_data_sampled]) - - # Convert the boolean values in the after_col column to floats - df_sampled[after_col] = df_sampled[after_col].apply( - lambda x: np.array(x, dtype=float) - ) - - df_sampled.loc[train_data_sampled.index, "set"] = "train" - df_sampled.loc[test_data_sampled.index, "set"] = "test" - # Apply UMAP - umap_model = umap.UMAP( - n_neighbors=15, min_dist=0.1, metric="euclidean", random_state=42 - ) - # Filter out the rows with invalid arrays - umap_results = umap_model.fit_transform(df_sampled[after_col].tolist()) - # Add UMAP results to the DataFrame - df_sampled["umap_x"] = umap_results[:, 0] - df_sampled["umap_y"] = umap_results[:, 1] - - # Define custom color palette - palette = {"train": "blue", "test": "red"} - - # Create the scatter plot - sns.set(style="white") - fig, ax = plt.subplots(figsize=(10, 8)) - split = save_as.split("_", 1) - part_after_underscore = split[1] - split_type = part_after_underscore.split(".")[0] - # Plot the UMAP results - for label, grp in df_sampled.groupby("set"): - set_label = label - color = palette[set_label] - alpha = ( - 0.09 if set_label == "train" else 0.9 - ) # Set different opacities for train and test - ax.scatter( - grp["umap_x"], grp["umap_y"], label=f"{set_label}", c=color, alpha=alpha - ) - - # Customize the plot - ax.set_title( - f"UMAP visualization of molecular fingerprints using {split_type} split", - fontsize=14, - ) - ax.set_xlabel("UMAP 1") - ax.set_ylabel("UMAP 2") - ax.legend(title="", loc="upper right") - sns.despine(ax=ax, offset=10) - save_path = os.path.join(os.getcwd(), save_as) - plt.savefig(save_path) diff --git a/build/lib/dfpl/callbacks.py b/build/lib/dfpl/callbacks.py deleted file mode 100644 index 6eae7965..00000000 --- a/build/lib/dfpl/callbacks.py +++ /dev/null @@ -1,89 +0,0 @@ -# for NN model functions -from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint - -# for testing in Weights & Biases -from wandb.keras import WandbCallback - -from dfpl import options, settings - - -def autoencoder_callback(checkpoint_path: str, opts: options.Options) -> list: - """ - Callbacks for fitting the autoencoder - - :param checkpoint_path: The output directory to store the checkpoint weight files - :param opts: Training options provided to the run - :return: List of ModelCheckpoint and EarlyStopping class. - """ - callbacks = [] - - if opts.testSize > 0.0: - target = "val_loss" - else: - target = "loss" - # enable this checkpoint to restore the weights of the best performing model - checkpoint = ModelCheckpoint( - checkpoint_path, - monitor=target, - mode="min", - verbose=1, - period=settings.ac_train_check_period, - save_best_only=True, - save_weights_only=True, - ) - callbacks.append(checkpoint) - - # enable early stopping if val_loss is not improving anymore - early_stop = EarlyStopping( - monitor=target, - mode="min", - patience=settings.ac_train_patience, - min_delta=settings.ac_train_min_delta, - verbose=1, - restore_best_weights=True, - ) - callbacks.append(early_stop) - - if opts.aeWabTracking and not opts.wabTracking: - callbacks.append(WandbCallback(save_model=False)) - return callbacks - - -def nn_callback(checkpoint_path: str, opts: options.Options) -> list: - """ - Callbacks for fitting the feed forward network (FNN) - - :param checkpoint_path: The output directory to store the checkpoint weight files - :param opts: Training options provided to the run - :return: List of ModelCheckpoint and EarlyStopping class. - """ - - callbacks = [] - - if opts.testSize > 0.0: - # enable this checkpoint to restore the weights of the best performing model - checkpoint = ModelCheckpoint( - checkpoint_path, - verbose=1, - period=settings.nn_train_check_period, - save_best_only=True, - monitor="val_loss", - mode="min", - save_weights_only=True, - ) - callbacks.append(checkpoint) - - # enable early stopping if val_loss is not improving anymore - early_stop = EarlyStopping( - patience=settings.nn_train_patience, - monitor="val_loss", - mode="min", - min_delta=settings.nn_train_min_delta, - verbose=1, - restore_best_weights=True, - ) - callbacks.append(early_stop) - - if opts.wabTracking: - callbacks.append(WandbCallback(save_model=False)) - return callbacks diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py deleted file mode 100644 index ab29cdea..00000000 --- a/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py +++ /dev/null @@ -1,128 +0,0 @@ -from time import time - -import numpy as np -import pandas as pd -from sklearn.model_selection import GridSearchCV, train_test_split -from sklearn.preprocessing import LabelEncoder -from tensorflow.keras.layers import Dense, Dropout -from tensorflow.keras.models import Sequential -from tensorflow.keras.utils import to_categorical -from tensorflow.keras.wrappers.scikit_learn import KerasClassifier - - -def tuning_model(optimizer, activation, init, dropout=0.2): - """ - model for tuning optimizer, activation functions and initialization of hidden layers - - :param optimizer: - :param activation: - :param init: - :param dropout: - :return: - """ - model = Sequential() - model.add(Dense(1024, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(341, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(85, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(17, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(2, activation="softmax")) - model.compile( - loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] - ) - return model - - -# --------------------------------------------------------------------------- # - -# this stores the best performing parameters for each model for each target -results = pd.DataFrame() - -np.random.seed(0) - -modelfilepathprefix = ( - "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" -) - -filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" -outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.HPtuning.results.txt" -dataset = pd.read_csv(filepath) - -le = LabelEncoder() - -# which target column to use? put this in a loop later for all targets -# here we need a for loop -for target in dataset.columns[1:7]: - # target = dataset.columns[1] # 'AR' - - modelfilepathW = str(modelfilepathprefix) + "/model." + target + ".weights.h5" - modelfilepathM = str(modelfilepathprefix) + "/model." + target + ".json" - - tmp = dataset[target].astype("category") - Y = np.asarray(tmp) - naRows = np.isnan(Y) - - d = dataset[~naRows] - - # generate X from fingerprints - # split all fingerprints into vectors - Nrows = d.shape[0] - Ncols = d["fp"][0].__len__() - # Store all fingerprints in numpy array - X = np.empty((Nrows, Ncols), int) - - # keep old indexes of this subset of arrays - d["oldIdx"] = d.index.values - d["newIdx"] = range(Nrows) - d = d.set_index("newIdx") - del d.index.name - - # generate X matrix - for i in range(Nrows): - fp = d["fp"][i] - X[i] = np.array(list(fp), dtype="int") - - # generate y vector(s) - y = to_categorical(np.array(d[target]), num_classes=2) - - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=0 - ) - - start = time() - - model = KerasClassifier(build_fn=tuning_model, batch_size=20) - - batch_sizes = [20] - epochs = [30, 100] - inits = ["glorot_uniform", "normal", "uniform"] - optimizers = ["SGD", "RMSprop", "Adagrad", "Adadelta", "Adam", "Adamax", "Nadam"] - activation_functions = ["sigmoid", "tanh", "relu"] - parameters = { - "batch_size": batch_sizes, - "epochs": epochs, - "optimizer": optimizers, - "activation": activation_functions, - "init": inits, - } - - # Tuning ALL parameters, Note, this takes hours of computation!!! - clf = GridSearchCV(model, parameters, verbose=0) - clf_results = clf.fit(X_train, y_train) - - # save best estimator per target - clf_results.best_estimator_.model.save(filepath=modelfilepathM) - clf_results.best_estimator_.model.save_weights(filepath=modelfilepathW) - - # find best performing parameters - file = open(outfilepath, "a") - file.write( - "# --------------------------------------------------------------------------- #\n" - ) - file.write(f"### Results for {target} target ###\n") - file.write(f"Best: {clf_results.best_score_} using {clf_results.best_params_}\n") - file.write(f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n") - file.close() diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py deleted file mode 100644 index 9251cbdc..00000000 --- a/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py +++ /dev/null @@ -1,426 +0,0 @@ -import argparse -import os -import re -import sys -from time import time - -import numpy as np -import pandas as pd -from sklearn.model_selection import GridSearchCV, train_test_split -from tensorflow.keras.layers import Dense, Dropout -from tensorflow.keras.models import Sequential -from tensorflow.keras.wrappers.scikit_learn import KerasClassifier - -from dfpl import dfplmodule as dfpl - -# ------------------------------------------------------------------------------------- # - - -def parseInput(): - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - parser = argparse.ArgumentParser( - description="Perform Hyperparameter tuning for a " "given set of parameters." - ) - # input - parser.add_argument( - "-i", - metavar="FILE", - type=str, - nargs=1, - required=True, - help="The file containin the data for training in (unquoted) " - "comma separated CSV format. The first column(s) contain " - "the target associations, while the last column, named 'fp' " - "contains the fingerprint of the compound", - ) - # output - parser.add_argument( - "-p", - metavar="PATH", - type=str, - nargs=1, - required=True, - help="The path to a directory where the results of the hp tuning " - "shall be stored", - ) - # select target(s) or use all - parser.add_argument( - "-t", - metavar="STR", - type=str, - nargs="+", - required=False, - help="A space separated list of target names (according to column " - " names in the input file (-i)) for which the huperparameter " - "tuning should be performed. If not provided, it is performed " - "for each target seperately.", - ) - - # parameters to be tested for HP tuning - parser.add_argument( - "--batchSizes", - metavar="INT", - type=int, - nargs="+", - required=False, - default=[20], - help="Space separated list of integers for batch sizes that should " - "be tested. Example: --batchSizes 32 64", - ) - parser.add_argument( - "--epochs", - metavar="INT", - type=int, - nargs="+", - required=False, - default=[30], - help="Space separated list of integers for number of training epochs " - "that should be tested. Example: --epochs 30 50 100", - ) - parser.add_argument( - "--inits", - metavar="STR", - type=str, - nargs="+", - required=False, - default=["normal"], - help="Space separated list of methods for initializing the weights " - "at inner layers that should be tested. Available methods are: " - "'glorot_uniform', 'normal', 'uniform'. " - "Example: --inits 'glorot_uniform' 'normal'", - ) - parser.add_argument( - "--optimizers", - metavar="STR", - type=str, - nargs="+", - required=False, - default=["Adam"], - help="Space separated list of optimizers for the DNN model " - "that should be tested." - "Available optimizers are listed here: https://keras.io/optimizers/ ." - "Example: --optimizers 'Adadelta' 'Adam' 'Adamax'", - ) - parser.add_argument( - "--activations", - metavar="STR", - type=str, - nargs="+", - required=False, - default=["relu"], - help="Space separated list of activation fucntions for the DNN model " - "that should be tested." - "Available functions are listed here: https://keras.io/activations/ ." - "Example: --activations 'sigmoid' 'relu'", - ) - - return parser.parse_args() - - -# ----------------------------------------------------------------------------- # - - -# model for tuning epochs and batchsizes only -def c_model(dropout=0.2): - model = Sequential() - model.add(Dense(1024, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(341, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(85, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(17, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(2, activation="softmax")) - model.compile( - loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"] - ) - return model - - -# ----------------------------------------------------------------------------- # - - -# model for tuning optmizer, activation functions and initialization of hidden layers -def tuning_model(optimizer, activation, init, dropout=0.2): - model = Sequential() - model.add(Dense(1024, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(341, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(85, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(17, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(2, activation="softmax")) - model.compile( - loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] - ) - return model - - -# --------------------------------------------------------------------------- # - -# ===================================================================================== # - -if __name__ == "__main__": - # get all arguments - args = parseInput() - - # print(args) - # exit(1) - - # this stores the best performing parameters for each model for each target - results = pd.DataFrame() - - np.random.seed(0) - - # filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" - # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub(".csv", - # ".hpTuningResults.txt", os.path.basename(filepath)) dataset = pd.read_csv(filepath) - - dataset = pd.read_csv(args.i[0]) - - # le = LabelEncoder() - - # which target column to use? put this in a loop later for all targets - # here we need a for loop - - for target in args.t: - target = "ER" - print(target) - # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub( - # ".csv", '.hpTuningResults.' + target + '.txt', os.path.basename(filepath)) - outfilepath = args.p[0] + re.sub( - ".csv", ".hpTuningResults." + target + ".txt", os.path.basename(args.i[0]) - ) - - if target in dataset.columns: - # modelfilepathW = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + - # '/model.' + target + '.weights.h5' modelfilepathM = - # "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + '/model.' + target + - # '.json' - modelfilepathW = args.p[0] + "/model." + target + ".weights.h5" - modelfilepathM = args.p[0] + "/model." + target + ".json" - - tmp = dataset[target].astype("category") - Y = np.asarray(tmp) - naRows = np.isnan(Y) - - d = dataset[~naRows] # d is a copy! - - # generate X from fingerprints - # split all fingerprints into vectors - Nrows = d.shape[0] - Ncols = d["fp"][0].__len__() - # Store all fingerprints in numpy array - X = np.empty((Nrows, Ncols), int) - - # keep old indexes of this subset of arrays - d["oldIdx"] = d.index.values - d["newIdx"] = range(Nrows) - d = d.set_index("newIdx") - del d.index.name - - # generate X matrix - for i in range(Nrows): - fp = d["fp"][i] - X[i] = np.array(list(fp), dtype="int") - - # generate y vector(s) - # y = to_categorical(np.array(d[target]), num_classes=2) - y = d[target] - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=0 - ) - - start = time() - - # find best performing parameters - sys.stdout.write( - "# --------------------------------------------------------------------------- #\n" - ) - sys.stdout.write(f"#target = {target}\n") - - # Start optimizing epochs and batchsizes (if more than one provided) - - # batchSizes = args.batchSizes - batchSizes = [64, 128, 256] - epochs = args.epochs # epochs = [5, 10] - - if batchSizes.__len__() > 1: - # model = KerasClassifier(build_fn=dfpl.defineNNmodel2) - model = KerasClassifier(build_fn=dfpl.defineNNmodel) - - parameters = {"batch_size": batchSizes} - - clf = GridSearchCV(model, parameters, verbose=0) - clf.fit(X_train, y_train) - - # save best estimator for epoochs/batchSize tuning per target - modelfilepathW = ( - args.p[0] + "/model.tuning-BatchSize." + target + ".weights.h5" - ) - modelfilepathM = ( - args.p[0] + "/model.tuning-BatchSize." + target + ".json" - ) - outfilepath = args.p[0] + re.sub( - ".csv", - ".hpTuningResults.01-BatchSize" + target + ".csv", - os.path.basename(args.i[0]), - ) - # outfilepath = '/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning - # /Sun_etal_dataset.fingerprints.hpTuningResults.ER.txt' - clf.best_estimator_.model.save(filepath=modelfilepathM) - clf.best_estimator_.model.save_weights(filepath=modelfilepathW) - - df = pd.concat( - [ - pd.DataFrame(clf.cv_results_["param_batch_size"]), - pd.DataFrame(clf.cv_results_["mean_test_score"]), - pd.DataFrame(clf.cv_results_["std_test_score"]), - pd.DataFrame(clf.cv_results_["rank_test_score"]), - ], - axis=1, - ) - df.columns = [ - "epoch", - "batch_size", - "mean_test_score", - "std_test_score", - "rank_test_score", - ] - df.to_csv(outfilepath, header=True) - - selected_bs = clf.best_params_["batch_size"] - selected_epochs = clf.best_params_["epochs"] - - else: - selected_bs = batchSizes[0] - selected_epochs = epochs[0] - - # Hypertune optimizers - optimizers = ["SGD", "Adam"] - # optimizers = args.optimizers - - if optimizers.__len__() > 1: - model = KerasClassifier( - build_fn=dfpl.defineNNmodel, - epochs=selected_epochs, - batch_size=selected_bs, - ) - parameters = { - "optimizer": optimizers - } # ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']} - clf = GridSearchCV(model, parameters, verbose=0) - clf.fit(X_train, y_train) - - # save best estimator for optimizer tuning per target - modelfilepathW = ( - args.p[0] + "/model.tuning-optimizer." + target + ".weights.h5" - ) - modelfilepathM = ( - args.p[0] + "/model.tuning-optimizer." + target + ".json" - ) - outfilepath = args.p[0] + re.sub( - ".csv", - ".hpTuningResults.02-Optimizer" + target + ".csv", - os.path.basename(args.i[0]), - ) - - clf.best_estimator_.model.save(filepath=modelfilepathM) - clf.best_estimator_.model.save_weights(filepath=modelfilepathW) - - df = pd.concat( - [ - pd.DataFrame(clf.cv_results_["param_optimizer"]), - pd.DataFrame(clf.cv_results_["mean_test_score"]), - pd.DataFrame(clf.cv_results_["std_test_score"]), - pd.DataFrame(clf.cv_results_["rank_test_score"]), - ], - axis=1, - ) - df.columns = [ - "optimizer", - "mean_test_score", - "std_test_score", - "rank_test_score", - ] - df.to_csv(outfilepath, header=True) - - selected_optimizer = clf.best_params_["optimizer"] - - else: - selected_optimizer = optimizers[0] - - # Hypertune activation functions - activations = args.activations # 'sigmoid', 'tanh', 'relu' - - if activations.__len__() > 1: - model = KerasClassifier( - build_fn=dfpl.defineNNmodel, - epochs=selected_epochs, - batch_size=selected_bs, - ) - parameters = { - "optimizer": [selected_optimizer], - "activation": args.activations, - } - clf = GridSearchCV(model, parameters, verbose=0) - clf.fit(X_train, y_train) - - # save best estimator for optimizer tuning per target - modelfilepathW = ( - args.p[0] + "/model.tuning-activation." + target + ".weights.h5" - ) - modelfilepathM = ( - args.p[0] + "/model.tuning-activation." + target + ".json" - ) - outfilepath = args.p[0] + re.sub( - ".csv", - ".hpTuningResults.03-Activation" + target + ".csv", - os.path.basename(args.i[0]), - ) - - clf.best_estimator_.model.save(filepath=modelfilepathM) - clf.best_estimator_.model.save_weights(filepath=modelfilepathW) - - df = pd.concat( - [ - pd.DataFrame(clf.cv_results_["param_optimizer"]), - pd.DataFrame(clf.cv_results_["param_activation"]), - pd.DataFrame(clf.cv_results_["mean_test_score"]), - pd.DataFrame(clf.cv_results_["std_test_score"]), - pd.DataFrame(clf.cv_results_["rank_test_score"]), - ], - axis=1, - ) - df.columns = [ - "optimizer", - "activation", - "mean_test_score", - "std_test_score", - "rank_test_score", - ] - df.to_csv(outfilepath, header=True) - - selected_activation = clf.best_params_["activation"] - - else: - selected_activation = activations[0] - - # Maybe also optimize weight initializations?? - # inits = ['glorot_uniform', 'normal', 'uniform'] - - # find best performing parameters - sys.stdout.write( - f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n" - ) - else: - sys.stderr.write( - f"ERROR: the target that you provide ({target}) is not contained in your data file ({args.i[0]})" - ) diff --git a/build/lib/dfpl/dfplmodule.py b/build/lib/dfpl/dfplmodule.py deleted file mode 100644 index d0838ebf..00000000 --- a/build/lib/dfpl/dfplmodule.py +++ /dev/null @@ -1,1413 +0,0 @@ -""" -Python module for deepFPlearn tools -""" - -import argparse -import csv -import math - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns # for drawing the heatmaps -from rdkit import Chem, DataStructs # for fingerprint generation -from rdkit.Chem import MACCSkeys -from rdkit.Chem.AtomPairs import Pairs, Torsions -from sklearn.model_selection import train_test_split -from tensorflow.keras import optimizers, regularizers -from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau -from tensorflow.keras.layers import Dense, Dropout, Input - -# for NN model functions -from tensorflow.keras.models import Model, Sequential -from tensorflow.keras.optimizers import SGD - -# GENERAL FUNCTIONS --------------------------------------------------------- # - - -def gather(df, key, value, cols): - """ - Simple emulation of R's gather function using pandas melt() function in python - - :param df: The data frame - :param key: The key column name - :param value: The value column name - :param cols: list of column names to gather - :return: - """ - id_vars = [col for col in df.columns if col not in cols] - id_values = cols - var_name = key - value_name = value - return pd.melt(df, id_vars, id_values, var_name, value_name) - - -def shuffleDataPriorToTraining(x, y): - """ - Returns a gathered variant of the input x and y matrices - :param x: - :param y: - :return: - """ - - # merge x and y - df0 = pd.concat([x.reset_index(drop=True), y.reset_index(drop=True)], axis=1) - # shuffle rows, drop NAs, reset index - df1 = df0.sample(frac=1).dropna(axis=0).reset_index() - - return (df1.iloc[:, 0 : x.shape[1]], df1.iloc[:, x.shape[1] :]) - - # return gather(df0, key="target", value="association", - # cols=y.columns) - - -def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -def smi2fp(smile, fptype, size=2048): - """ - Convert a SMILES string to a fingerprint object of a certain type using functions - from the RDKIT python library. - - :param smile: A single SMILES string - :param fptype: The type of fingerprint to which the SMILES should be converted. Valid - values are: 'topological' (default), 'MACCS' - :return: A fingerprint object or None, if fingerprint object could not be created - (Respective error message is provided in STDERR). - """ - # generate a mol object from smiles string - - # print(smile) - cs = None - # first transform to canoncial smiles - try: - cs = Chem.CanonSmiles(smile) - except Exception: - print( - f"[WARNING]: Not able to transform your smile to a canonical version of it: {smile}" - ) - if not cs: - return None - - mol = None - try: - mol = Chem.MolFromSmiles(cs) - except Exception: - print( - f"[WARNING]: Not able to extract molecule from (canonically transformed) SMILES: {cs}\n Original SMILE: {smile}" - ) - if not mol: - return None - - # init fp, any better idea? e.g. calling a constructor? - # fp = Chem.Mol # FingerprintMols.FingerprintMol(mol) - - if fptype == "topological": # 2048 bits - # Topological Fingerprints: - # The fingerprinting algorithm used is similar to that used in the Daylight - # fingerprinter: it identifies and hashes topological paths (e.g. along bonds) - # in the molecule and then uses them to set bits in a fingerprint of user-specified - # lengths. After all paths have been identified, the fingerprint is typically - # folded down until a particular density of set bits is obtained. - try: - # fp = Chem.RDKFingerprint(mol, fpSize=size) - return Chem.RDKFingerprint(mol, fpSize=size) - except Exception: - print("SMILES not convertable to topological fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - elif fptype == "MACCS": - # MACCS Keys: - # There is a SMARTS-based implementation of the 166 public MACCS keys. - # The MACCS keys were critically evaluated and compared to other MACCS - # implementations in Q3 2008. In cases where the public keys are fully defined, - # things looked pretty good. - - try: - # fp = MACCSkeys.GenMACCSKeys(mol) - return MACCSkeys.GenMACCSKeys(mol) - except Exception: - print("SMILES not convertable to MACSS fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - elif fptype == "atompairs": - # Atom Pairs: - # Atom-pair descriptors [3] are available in several different forms. - # The standard form is as fingerprint including counts for each bit instead - # of just zeros and ones. Nevertheless we use the BitVect variant here. - - try: - # fp = Pairs.GetAtomPairFingerprintAsBitVect(mol) - return Pairs.GetAtomPairFingerprintAsBitVect(mol) - # counts if features also possible here! needs to be parsed differently - # fps.update({i:Pairs.GetAtomPairFingerprintAsIntVect(mols[i])}) - except Exception: - print("SMILES not convertable to atompairs fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - else: - # Topological Torsions: - # At the time of this writing, topological torsion fingerprints have too - # many bits to be encodeable using the BitVector machinery, so there is no - # GetTopologicalTorsionFingerprintAsBitVect function. - - try: - # fp = Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) - return Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) - except Exception: - print("SMILES not convertable to torsions fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - -def XandYfromInput( - csvfilename: str, - rtype: str, - fptype: str, - printfp: bool = False, - size: int = 2048, - verbose: int = 2, - returnY: bool = True, -) -> tuple: - """ - Return the matrix of features for training and testing NN models (X) as numpy array. - Provided SMILES are transformed to fingerprints, fingerprint strings are then split - into vectors and added as row to the array which is returned. - :param csvfilename: Filename of CSV files containing the training data. The - SMILES/Fingerprints are stored 1st column - :param rtype: Type of structure representation. Valid values are: 'fp' and 'smile' - :param fptype: Type of fingerprint to be generated out - :param printfp: Print generated fingerprints to file, namely the input file with the - file ending '.fingerprints.csv'. Default:False - :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If - no outcome data is provided, the Y matrix is a None object. - """ - - # TODOs: implement other types of fingerprint! - - df = pd.read_csv(csvfilename) - cnames = df.columns - - if rtype not in cnames: - print( - f"[ERROR:] There is no column named {rtype} in your input training set file" - ) - exit(0) - - dfX = None - if rtype == "smiles": # transform to canonical smiles, and then to fp - dfX = pd.DataFrame( - df["smiles"] - .transform( - lambda x: np.array( - Chem.RDKFingerprint( - Chem.MolFromSmiles(Chem.CanonSmiles(x)), fpSize=size - ) - ) - ) - .to_list() - ) - else: # split fingerprint into matrix - dfX = pd.DataFrame(df["fp"].transform(lambda x: list(x)).to_list()) - - # add 'id' as rownames of dataframe - if "id" in cnames: - dfX.index = df["id"] - - dfY = None - if returnY: - # names in Y contain 'id' if present, and all other columns (=target columns) - namesY = [c for c in cnames if c not in ["id", "smiles", "fp"]] - dfY = df[namesY] - # add 'id' as rownames of dataframe - if "id" in cnames: - dfY.index = df["id"] - - return (dfX, dfY) - - -def TrainingDataHeatmap(x, y): - x["ID"] = x.index - y["ID"] = y.index - - # xy = pd.merge(x,y,on="ID") - - # clustermap dies because of too many iterations.. - # sns.clustermap(x, metric="correlation", method="single", cmap="Blues", standard_scale=1) #, row_colors=row_colors) - - # try to cluster prior to viz - # check this out - # https://stackoverflow.com/questions/2455761/reordering-matrix-elements-to-reflect-column-and-row-clustering-in-naiive-python - - # viz using matplotlib heatmap https://matplotlib.org/3.1.1/gallery/images_contours_and_fields/image_annotated_heatmap.html - - return 1 - - -def removeDuplicates(x, y): - """ - Remove duplicated feature - outcome pairs from feature matrix and outcome vector combination. - - :param x: Feature matrix - :param y: Outcome vector - - :return: Tuple of unique Feature matrix and outcome vector combinations - """ - - # add y as additional column to x - joined = np.append(x, [[x] for x in y], axis=1) - # merge all columns into one string per row - - fpstrings = [] - for i in range(0, len(y)): - fpstrings.append(["".join([str(int(c)) for c in joined[i]])]) - - fpstrings_unique = np.unique(fpstrings, return_index=True) - - return (x[fpstrings_unique[1]], y[fpstrings_unique[1]]) - - -def defineCallbacks( - checkpointpath: str, - patience: int, - rlrop: bool = False, - rlropfactor: float = 0.1, - rlroppatience: int = 50, -) -> list: - """ - - :param checkpointpath: - :param patience: - :param rlrop: - :param rlropfactor: - :param rlroppatience: - :return: - """ - - # enable this checkpoint to restore the weights of the best performing model - checkpoint = ModelCheckpoint( - checkpointpath, - monitor="val_loss", - verbose=1, - period=10, - save_best_only=True, - mode="min", - save_weights_only=True, - ) - - # enable early stopping if val_loss is not improving anymore - earlystop = EarlyStopping( - monitor="val_loss", - min_delta=0, - patience=patience, - verbose=1, - restore_best_weights=True, - ) - - callbacks = [] - if rlrop: - rlrop = ReduceLROnPlateau( - monitor="val_loss", factor=rlropfactor, patience=rlroppatience - ) - callbacks = [checkpoint, earlystop, rlrop] - else: - callbacks = [checkpoint, earlystop] - - # Return list of callbacks - collect the callbacks for training - return callbacks - - -def defineNNmodelMulti( - inputSize=2048, - outputSize=None, - l2reg=0.001, - dropout=0.2, - activation="relu", - optimizer="Adam", - lr=0.001, - decay=0.01, -): - if optimizer == "Adam": - myoptimizer = optimizers.Adam( - learning_rate=lr, decay=decay - ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) - elif optimizer == "SGD": - myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) - else: - myoptimizer = optimizer - - nhl = int(math.log2(inputSize) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(inputSize / 2), - input_dim=inputSize, - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout)) - # next hidden layers - for i in range(1, nhl): - factorunits = 2 ** (i + 1) - factordropout = 2 * i - model.add( - Dense( - units=int(inputSize / factorunits), - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout / factordropout)) - # multi-class output layer - # use sigmoid to get independent probabilities for each output node - # (need not add up to one, as they would using softmax) - # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ - model.add(Dense(units=outputSize, activation="sigmoid")) - - model.summary() - - # compile model - model.compile( - loss="binary_crossentropy", optimizer=myoptimizer, metrics=["accuracy"] - ) - - return model - - -def defineNNmodel( - inputSize: int = 2048, - l2reg: float = 0.001, - dropout: float = 0.2, - activation: str = "relu", - optimizer: str = "Adam", - lr: float = 0.001, - decay: float = 0.01, -) -> Model: - """ - - :param inputSize: - :param l2reg: - :param dropout: - :param activation: - :param optimizer: - :param lr: - :param decay: - :return: - """ - - if optimizer == "Adam": - myoptimizer = optimizers.Adam( - learning_rate=lr, decay=decay - ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) - elif optimizer == "SGD": - myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) - else: - myoptimizer = optimizer - - myhiddenlayers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} - - if not str(inputSize) in myhiddenlayers.keys(): - print("Wrong inputsize. Must be in {2048, 1024, 999, 512, 256}.") - return None - - nhl = int(math.log2(inputSize) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(inputSize / 2), - input_dim=inputSize, - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout)) - # next hidden layers - for i in range(1, nhl): - factorunits = 2 ** (i + 1) - factordropout = 2 * i - model.add( - Dense( - units=int(inputSize / factorunits), - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout / factordropout)) - # output layer - model.add(Dense(units=1, activation="sigmoid")) - - model.summary() - - # compile model - model.compile(loss="mse", optimizer=myoptimizer, metrics=["accuracy"]) - - return model - - -def autoencoderModel( - input_size: int = 2048, - encoding_dim: int = 256, - myloss: object = "binary_crossentropy", - mylr: float = 0.001, - mydecay: float = 0.01, -) -> tuple: - """ - This function provides an autoencoder model to reduce a certain input to a compressed version. - - :param encoding_dim: Size of the compressed representation. Default: 85 - :param input_size: Size of the input. Default: 2048 - :param myactivation: Activation function, see Keras activation functions for potential values. Default: relu - :param myoptimizer: Optimizer, see Keras optmizers for potential values. Default: adadelta - :param myloss: Loss function, see Keras Loss functions for potential values. Default: binary_crossentropy - :return: a tuple of autoencoder and encoder models - """ - - myoptimizer = optimizers.Adam( - learning_rate=mylr, decay=mydecay - ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) - - # get the number of meaningful hidden layers (latent space included) - nhl = round(math.log2(input_size / encoding_dim)) - - # the input placeholder - input_vec = Input(shape=(input_size,)) - - # 1st hidden layer, that receives weights from input layer - # equals bottle neck layer, if nhl==1! - encoded = Dense(units=int(input_size / 2), activation="relu")(input_vec) - - if nhl > 1: - # encoding layers, incl. bottle neck - for i in range(1, nhl): - factorunits = 2 ** (i + 1) - # print(f'{factorunits}: {int(input_size / factorunits)}') - encoded = Dense(units=int(input_size / factorunits), activation="relu")( - encoded - ) - - # encoding_dim = int(input_size/factorunits) - - # 1st decoding layer - factorunits = 2 ** (nhl - 1) - decoded = Dense(units=int(input_size / factorunits), activation="relu")(encoded) - - # decoding layers - for i in range(nhl - 2, 0, -1): - factorunits = 2**i - # print(f'{factorunits}: {int(input_size/factorunits)}') - decoded = Dense(units=int(input_size / factorunits), activation="relu")( - decoded - ) - - # output layer - # The output layer needs to predict the probability of an output which needs to either 0 or 1 and hence we use sigmoid activation function. - decoded = Dense(units=input_size, activation="sigmoid")(decoded) - - else: - # output layer - decoded = Dense(units=input_size, activation="sigmoid")(encoded) - - autoencoder = Model(input_vec, decoded) - encoder = Model(input_vec, encoded) - - autoencoder.summary() - encoder.summary() - - # We compile the autoencoder model with adam optimizer. - # As fingerprint positions have a value of 0 or 1 we use binary_crossentropy as the loss function - autoencoder.compile(optimizer=myoptimizer, loss=myloss) - - return (autoencoder, encoder) - - -# def predict_values(ac_model_file_path, model_file_path, pdx): -# """ -# Predict a set of chemicals using a selected model. -# -# :param model_file_path: Path to the model weights of the prediction DNN -# :param pdx: A matrix containing the fingerprints of the chemicals, generated via XfromInput function -# :return: A dataframe of 2 columns: random - predicted values using random model, trained - predicted values -# using trained model. Rownames are consecutive numbers of the input rows, or if provided in the input file -# the values of the id column -# """ -# -# # ac_model_file_path="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.AC-model.hdf5" -# # model_file_path ="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.model.hdf5" -# -# print(f"[INFO:] Loaded model weights for autoencoder: '{model_file_path}'") -# print(f"[INFO:] Loaded model weights for prediction DNN: '{model_file_path}'") -# -# start = time() -# # create autoencoder -# (autoencoder, encoder) = autoencoderModel(input_size=pdx.shape[1], encoding_dim=256) -# # load AC weights -# autoencoder.load_weights(ac_model_file_path) -# # encode input -# encodedX = encoder.predict(pdx) -# -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# print(f"[INFO:] Computation time used for encoding: {trainTime} min") -# -# start = time() -# # create DNN -# predictor = defineNNmodel(inputSize=encodedX.shape[1]) -# # predict with random weights -# predictions_random = predictor.predict(encodedX) -# # load DNN weights -# predictor.load_weights(model_file_path) -# # predict encoded input -# predictions = predictor.predict(encodedX) -# -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# print(f"[INFO:] Computation time used for the predictions: {trainTime} min") -# -# df = pd.DataFrame(data={'random': predictions_random.flatten(), -# 'trained': predictions.flatten()}, -# columns=['random', 'trained'], -# index=pdx.index) -# print(df) -# return (df) - - -def trainfullac( - X: pd.DataFrame, - y: pd.DataFrame, - useweights: str = None, - epochs: int = 0, - encdim: int = 256, - checkpointpath: str = None, - verbose: int = 0, -) -> Model: - """ - Train an autoencoder on the given feature matrix X. Response matrix is only used to - split meaningfully in test and train data set. - - :param X: Feature matrix. - :param y: Matrix containing the response variables. - :param epochs: Number of epochs to train the autoencoder - :param encdim: Dimension of latent space. This and the column dimension of X control - the number and size of the hidden layers. - :param checkpointpath: path to prefix the model files during callbacks - :param verbose: Verbosity level. Is forwarded to the Keras fit function that prints - training states in different verbosity levels - :return: The encoder model of the trained autoencoder - """ - - # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) - (autoencoder, encoder) = autoencoderModel( - input_size=X.shape[1], encoding_dim=encdim - ) - - if useweights: # don't train, use existing weights file and load it into AC model - # autoencoder.load_weights(useweights) - encoder.load_weights(useweights) - else: - # collect the callbacks for training - callback_list = defineCallbacks( - checkpointpath=checkpointpath, patience=20, rlrop=False - ) - - # split data into test and training data - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - # Fit the AC - autohist = autoencoder.fit( - X_train, - X_train, - callbacks=callback_list, - epochs=epochs, - batch_size=256, - shuffle=True, - verbose=verbose, - validation_data=(X_test, X_test), - ) - # history - ac_loss = autohist.history["loss"] - ac_val_loss = autohist.history["val_loss"] - ac_epochs = range(ac_loss.__len__()) - pd.DataFrame( - data={"loss": ac_loss, "val_loss": ac_val_loss, "epoch": ac_epochs} - ).to_csv(checkpointpath.replace(".hdf5", "_trainValLoss_AC.csv"), index=False) - # generate a figure of the losses for this fold - plt.figure() - plt.plot(ac_epochs, ac_loss, "bo", label="Training loss") - plt.plot(ac_epochs, ac_val_loss, "b", label="Validation loss") - plt.title("Training and validation loss of AC") - plt.legend() - plt.savefig( - fname=checkpointpath.replace(".hdf5", "_trainValLoss_AC.svg"), format="svg" - ) - plt.close() - # write the losses to .csv file for later data visualization - - # model needs to be saved and restored when predicting new input! - # use encode() of train data as input for DL model to associate to chemical - return encoder - - -def plotTrainHistory(hist, target, fileAccuracy, fileLoss): - """ - Plot the training performance in terms of accuracy and loss values for each epoch. - :param hist: The history returned by model.fit function - :param target: The name of the target of the model - :param fileAccuracy: The filename for plotting accuracy values - :param fileLoss: The filename for plotting loss values - :return: none - """ - - # plot accuracy - plt.figure() - plt.plot(hist.history["accuracy"]) - if "val_accuracy" in hist.history.keys(): - plt.plot(hist.history["val_accuracy"]) - plt.title("Model accuracy - " + target) - plt.ylabel("Accuracy") - plt.xlabel("Epoch") - if "val_accuracy" in hist.history.keys(): - plt.legend(["Train", "Test"], loc="upper left") - else: - plt.legend(["Train"], loc="upper_left") - plt.savefig(fname=fileAccuracy, format="svg") - - # Plot training & validation loss values - plt.figure() - plt.plot(hist.history["loss"]) - plt.plot(hist.history["val_loss"]) - plt.title("Model loss - " + target) - plt.ylabel("Loss") - plt.xlabel("Epoch") - plt.legend(["Train", "Test"], loc="upper left") - # plt.show() - plt.savefig(fname=fileLoss, format="svg") - plt.close() - - -def plotAUC(fpr, tpr, auc, target, filename, title=""): - plt.figure() - plt.plot([0, 1], [0, 1], "k--") - plt.plot(fpr, tpr, label=f"Keras (area = {auc:.3f})") - plt.xlabel("False positive rate") - plt.ylabel("True positive rate") - plt.title(f"ROC curve {target}") - plt.legend(loc="best") - plt.savefig(fname=filename, format="svg") - plt.close() - - -def plotHeatmap(matrix, filename, title=""): - plt.figure() - plt.imshow(matrix, cmap="Greys", interpolation="nearest") - plt.title(title) - plt.savefig(fname=filename, format="svg") - plt.close() - - -# ------------------------------------------------------------------------------------- # - -# plot model history more easily, see whyboris@github: https://gist.github.com/whyboris/91ee793ddc92cf1e824978cf31bb790c -# when plotting, smooth out the points by some factor (0.5 = rough, 0.99 = smooth) -# method taken from `Deep Learning with Python` by François Chollet - -# 01 --------------------------------------------------------------------------------- # - -# def smooth_curve(points, factor=0.75): -# smoothed_points = [] -# for point in points: -# if smoothed_points: -# previous = smoothed_points[-1] -# smoothed_points.append(previous * factor + point * (1 - factor)) -# else: -# smoothed_points.append(point) -# return smoothed_points -# -# -# # 02 ---------------------------------------------------------------------------------- # -# -# def set_plot_history_data(ax, history, which_graph): -# if which_graph == 'acc': -# train = smooth_curve(history.history['accuracy']) -# valid = smooth_curve(history.history['val_accuracy']) -# -# if which_graph == 'loss': -# train = smooth_curve(history.history['loss']) -# valid = smooth_curve(history.history['val_loss']) -# -# # plt.xkcd() # make plots look like xkcd -# -# epochs = range(1, len(train) + 1) -# -# trim = 0 # remove first 5 epochs -# # when graphing loss the first few epochs may skew the (loss) graph -# -# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', linewidth=15, alpha=0.1) -# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', label=('Training')) -# -# ax.plot(epochs[trim:], valid[trim:], 'g', linewidth=15, alpha=0.1) -# ax.plot(epochs[trim:], valid[trim:], 'g', label=('Validation')) -# - -# 03 ---------------------------------------------------------------------------------- # -# -# def get_max_validation_accuracy(history): -# validation = smooth_curve(history.history['val_accuracy']) -# ymax = max(validation) -# return 'Max validation accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' -# -# -# def get_max_training_accuracy(history): -# training = smooth_curve(history.history['accuracy']) -# ymax = max(training) -# return 'Max training accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' -# - -# 04---------------------------------------------------------------------------------- # - -# def plot_history(history, file): -# fig, (ax1, ax2) = plt.subplots(nrows=2, -# ncols=1, -# figsize=(10, 6), -# sharex=True, -# gridspec_kw={'height_ratios': [5, 2]}) -# -# set_plot_history_data(ax1, history, 'acc') -# -# set_plot_history_data(ax2, history, 'loss') -# -# # Accuracy graph -# ax1.set_ylabel('Accuracy') -# ax1.set_ylim(bottom=0.5, top=1) -# ax1.legend(loc="lower right") -# ax1.spines['top'].set_visible(False) -# ax1.spines['right'].set_visible(False) -# ax1.xaxis.set_ticks_position('none') -# ax1.spines['bottom'].set_visible(False) -# -# # max accuracy text -# plt.text(0.5, -# 0.6, -# get_max_validation_accuracy(history), -# horizontalalignment='right', -# verticalalignment='top', -# transform=ax1.transAxes, -# fontsize=12) -# plt.text(0.5, -# 0.8, -# get_max_training_accuracy(history), -# horizontalalignment='right', -# verticalalignment='top', -# transform=ax1.transAxes, -# fontsize=12) -# -# # Loss graph -# ax2.set_ylabel('Loss') -# ax2.set_yticks([]) -# ax2.plot(legend=False) -# ax2.set_xlabel('Epochs') -# ax2.spines['top'].set_visible(False) -# ax2.spines['right'].set_visible(False) -# -# plt.tight_layout() -# plt.savefig(fname=file, format='svg') -# plt.close() -# - - -def drawHeatmap(data, anno): - # (data=pd.DataFrame(Xt), anno = pd.DataFrame(Yt.astype(int))) - - # annotation bar colors - my_ann_colors = dict(zip(anno[0].unique(), ["blue", "red"])) - row_colors = anno[0].map(my_ann_colors) - - cl = sns.clustermap( - data, - metric="euclidean", - method="single", - z_score=None, - standard_scale=None, - col_cluster=False, - cmap="Greys", - row_colors=row_colors, - yticklabels=False, - ) - cl.fig.suptitle("Distributions of [1,0] in fingerprints of target: AR") - - url = "https://python-graph-gallery.com/wp-content/uploads/mtcars.csv" - df = pd.read_csv(url) - # set df index using existing columns - df = df.set_index("model") - # remove name of index column, numbered rows have been the index before, they are gone now - del df.index.name - df - my_palette = dict(zip(df.cyl.unique(), ["orange", "yellow", "brown"])) - row_colors = df.cyl.map(my_palette) - - # Default plot - # sns.clustermap(df) - - # Clustermethods - my_palette = dict() - sns.clustermap( - df, - metric="correlation", - standard_scale=1, - method="single", - cmap="Blues", - row_colors=row_colors, - ) - - -############################################################################### -# TRAIN FUNCTIONS --------------------------------------------------------- # - - -def defineOutfileNames(pathprefix: str, target: str, fold: int) -> tuple: - """ - This function returns the required paths for output files or directories. - - :param pathprefix: A file path prefix for all files. - :param mtype: The model type. Its set by the trainNNmodels function with information on autoencoder or not, - and if AC is used, then with its parameters. - :param target: The name of the target. - - :return: A tuple of 14 output file names. - """ - - modelname = target + ".Fold-" + str(fold) - - modelfilepathW = str(pathprefix) + "." + modelname + ".weights.h5" - modelfilepathM = str(pathprefix) + "." + modelname + ".json" - modelhistplotpathL = str(pathprefix) + "." + modelname + ".loss.svg" - modelhistplotpathA = str(pathprefix) + "." + modelname + ".acc.svg" - modelhistplotpath = str(pathprefix) + "." + modelname + ".history.svg" - modelhistcsvpath = str(pathprefix) + "." + modelname + ".history.csv" - modelvalidation = str(pathprefix) + "." + modelname + ".validation.csv" - modelAUCfile = str(pathprefix) + "." + modelname + ".auc_value.svg" - modelAUCfiledata = str(pathprefix) + "." + modelname + ".auc_value.data.csv" - outfilepath = str(pathprefix) + "." + modelname + ".trainingResults.txt" - checkpointpath = str(pathprefix) + "." + modelname + ".checkpoint.model.hdf5" - modelheatmapX = str(pathprefix) + "." + modelname + ".heatmap.X.svg" - modelheatmapZ = str(pathprefix) + "." + modelname + ".AC.heatmap.Z.svg" - - return ( - modelfilepathW, - modelfilepathM, - modelhistplotpathL, - modelhistplotpathA, - modelhistplotpath, - modelhistcsvpath, - modelvalidation, - modelAUCfile, - modelAUCfiledata, - outfilepath, - checkpointpath, - modelheatmapX, - modelheatmapZ, - ) - - -# ------------------------------------------------------------------------------------- # - - -def eval01Distributions(Xt, Yt, y_train, y_test, verbosity=0): - """ - Evaluate the percentage of 0 values in the outcome variable of the whole dataset and the splitted (train,test) - dataset, and the percentage of 0 values in the feature matrix. - - :param Xt: The whole feature matrix - :param Yt: The whole outcome vector - :param y_train: The outcome vector of the training set - :param y_test: The outcome vector of the test set - :param verbosity: The verbosity level. Info is only printed if verbosity is not 0. - - :return: Nothing is returned. - """ - - if verbosity == 0: - return - else: - unique, counts = np.unique(Yt, return_counts=True) - perc = round(100 / len(Yt) * counts[1]) - print( - f"[INFO:] Percentage of '1' values in outcome variable (whole dataset): {perc}\n" - ) - - uniqueRtr, countsRtr = np.unique(y_train, return_counts=True) - uniqueRte, countsRte = np.unique(y_test, return_counts=True) - perc = round(100 / len(y_train) * countsRtr[1]) - print(f"[INFO:] Percentage of '1' values in training outcomes: {perc}\n") - perc = round(100 / len(y_test) * countsRte[1]) - print(f"[INFO:] Percentage of '1' values in test outcomes: {perc}\n") - - print( - f"[INFO:] Average percentage of '0' positions in fingerprints: {round(np.sum(Xt == 0) / (np.sum(Xt == 0) + np.sum(Xt == 1)), ndigits=4)}" - ) - return - - -# ------------------------------------------------------------------------------------- # - - -def prepareDataSet(y: pd.DataFrame, x: pd.DataFrame, t: str) -> tuple: - """ - A function to remove NA values from the output column, duplicates from input and output - and to transform the data into numpy arrays for keras functions. - - :param y: The target vector that may contain NAs. - :param x: The input matrix that may contain duplicates (including resp. target value!) - :param t: The target string - :return: Tuple of x and y ready for splitting into test and train sets for ML with keras. - """ - - # which rows contain 'NA' in target column - tmp = y[t].astype("category") - Y = np.asarray(tmp) - naRows = np.isnan(Y) - - # transform pd dataframe to numpy array for keras - X = x.to_numpy() - - # subset data according to target non-NA values - Ytall = Y[~naRows] - Xtall = X[~naRows] - - # remove all duplicated feature values - outcome pairs, and - # shuffle data, such that validation_split in fit function selects a random subset! - # Remember, that it takes the fraction for validation from the end of our data! - - (XtU, YtU) = removeDuplicates(x=Xtall, y=Ytall) - - # return(shuffleDataPriorToTraining(x=XtU, y=YtU)) - return (XtU, YtU) - - -# ------------------------------------------------------------------------------------- # - -# def plot_history_vis(hist, model_hist_plot_path, model_hist_csv_path, -# model_hist_plot_path_a, model_hist_plot_path_l, target): -# plot_history(history=hist, file=model_hist_plot_path) -# histDF = pd.DataFrame(hist.history) -# histDF.to_csv(model_hist_csv_path) -# -# # plot accuracy and loss for the training and validation during training -# plotTrainHistory(hist=hist, target=target, -# file_accuracy=model_hist_plot_path_a, -# file_loss=model_hist_plot_path_l) -# - -# ------------------------------------------------------------------------------------- # -# -# def validate_multi_model_on_test_data(x_test, checkpoint_path, y_test, col_names, result_file): -# # load checkpoint model with min(val_loss) -# trainedmodel = define_nn_model_multi(input_size=x_test.shape[1], output_size=y_test.shape[1]) -# -# # predict values with random model -# predictions_random = pd.DataFrame(trainedmodel.predict(x_test), columns=col_names + '-predRandom') -# -# # load weights into random model -# trainedmodel.load_weights(checkpoint_path) -# -# # predict with trained model -# predictions = pd.DataFrame(trainedmodel.predict(x_test), -# columns=col_names + '-pred') -# scores = pd.DataFrame((predictions.round() == y_test).sum() / y_test.shape[0], columns=['correctPredictions']) -# -# results = pd.concat([predictions_random, predictions, pd.DataFrame(y_test, columns=col_names + '-true')], axis=1) -# results.to_csv(result_file) -# -# return scores - - -# ------------------------------------------------------------------------------------- # - -# def validate_model_on_test_data(x_test, checkpoint_path, y_test, model_type, model_validation, target, -# model_auc_file_data, model_auc_file): -# """ -# Function that validates trained model with test data set. History and AUC plots are generated. -# Accuracy and Loss of model on test data, as well as MCC and confusion matrix is calculated and returned. -# -# :param x_test: -# :param checkpoint_path: -# :param y_test: -# :param model_type: -# :param model_validation: -# :param target: -# :param model_auc_file_data: -# :param model_auc_file: -# :return: Tupel containing Loss, Accuracy, MCC, tn, fp, fn, tp of trained model on test data. -# """ -# -# # load checkpoint model with min(val_loss) -# trainedmodel = define_nn_model(input_size=x_test.shape[1]) -# -# # predict values with random model -# predictions_random = pd.DataFrame(trainedmodel.predict(x_test)) -# -# # load weights into random model -# trainedmodel.load_weights(checkpoint_path) -# # predict with trained model -# predictions = pd.DataFrame(trainedmodel.predict(x_test)) -# -# # save validation data to .csv file -# validation = pd.DataFrame({'predicted': predictions[0].ravel(), -# 'true': list(y_test), -# 'predicted_random': predictions_random[0].ravel(), -# 'model_type': model_type}) -# validation.to_csv(model_validation) -# -# # compute MCC -# predictionsInt = [int(round(x)) for x in predictions[0].ravel()] -# ytrueInt = [int(y) for y in y_test] -# MCC = matthews_corrcoef(ytrueInt, predictionsInt) -# -# # generate the AUC-ROC curve data from the validation data -# fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, predictions, drop_intermediate=False) -# -# auc_keras = auc_value(fpr_keras, tpr_keras) -# -# aucdata = pd.DataFrame(list(zip(fpr_keras, -# tpr_keras, -# [auc_keras for x in range(1, len(fpr_keras))], -# [target for x in range(1, len(fpr_keras))])), -# columns=['fpr', 'tpr', 'auc_value', 'target']) -# aucdata.to_csv(model_auc_file_data) -# -# plotAUC(fpr=fpr_keras, tpr=tpr_keras, target=target, auc_value=auc_keras, filename=model_auc_file) -# -# # [[tn, fp] -# # [fn, tp]] -# cfm = confusion_matrix(y_true=ytrueInt, y_pred=predictionsInt) -# -# scores = trainedmodel.evaluate(x_test, y_test, verbose=0) -# -# print(f'TARGET: {target} Loss: {scores[0].__round__(2)} Acc: {scores[1].__round__(2)}') -# print(f'MCC: {MCC.__round__(2)}') -# print(f'CFM: \n\tTN={cfm[0][0]}\tFP={cfm[0][1]}\n\tFN={cfm[1][0]}\tTP={cfm[1][1]}') -# -# return (scores[0], scores[1], MCC, cfm[0][0], cfm[0][1], cfm[1][0], cfm[1][1]) - - -# ------------------------------------------------------------------------------------- # -# def train_nn_models_multi(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, -# split: float = 0.2, epochs: int = 500, -# verbose: int= 2, kfold: int = 5) -> None: -# # remove 'id' column if present -# if 'id' in x.columns: -# x = x.drop('id', axis=1) -# if 'id' in y.columns: -# y = y.drop('id', axis=1) -# -# # drop compounds that are not measured for all target columns, transform to numpy -# (xmulti, ymulti) = shuffleDataPriorToTraining(x, y) -# -# # do a kfold cross validation for the autoencoder training -# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) -# -# # store acc and loss for each fold -# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"]) # FNN test data -# -# fold_no = 1 -# -# # split the data -# for train, test in kfoldCValidator.split(xmulti, ymulti): -# -# # define all the output file/path names -# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, -# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, -# model_auc_file_data, outfilepath, checkpoint_path, -# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, -# target="multi", fold=fold_no) -# -# # use a dnn for multi-class prediction -# model = define_nn_model_multi(input_size=xmulti[train].shape[1], output_size=ymulti.shape[1]) -# -# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # measure the training time -# start = time() -# -# # train and validate -# hist = model.fit(xmulti[train], ymulti[train], -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the multi-label FNN: {trainTime} min") -# -# # validate model on test data set (x_test, y_test) -# scores = validate_multi_model_on_test_data(x_test=xmulti[test], -# checkpoint_path=checkpoint_path, -# y_test=ymulti[test], -# col_names=y.columns, -# result_file=outfilepath.replace("trainingResults.txt", -# "predictionResults.csv")) -# -# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) -# row_df = pd.DataFrame([[fold_no, -# hist.history['loss'][idx], hist.history['val_loss'][idx], -# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], -# scores[0], scores[1], scores[2]]], -# columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"] -# ) -# print(row_df) -# allscores = allscores.append(row_df, ignore_index=True) -# -# fold_no = fold_no + 1 -# del model -# -# print(allscores) -# -# # finalize model -# # 1. provide best performing fold variant -# # select best model based on MCC -# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] -# fold_no = allscores._get_value(idx2, 'fold_no') -# -# modelname = 'multi.Fold-' + str(fold_no) -# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' -# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") -# -# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) -# allscores.to_csv(file) -# -# # copy best DNN model -# shutil.copyfile(checkpoint_path, bestModelfile) -# print(f'[INFO]: Best models for FNN is saved:\n - {bestModelfile}') -# -# # AND retrain with full data set -# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") -# # measure the training time -# start = time() -# -# model = define_nn_model(input_size=xmulti[train].shape[1]) -# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # train and validate -# hist = model.fit(xmulti, ymulti, -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# # validation_data=(x_test, y_test)) # this overwrites val_split! -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") -# plot_history_vis(hist, -# model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), -# model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) -# print(f'[INFO]: Full models for DNN is saved:\n - {fullModelfile}') -# -# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) -# # stats.append([target, [x.__round__(2) for x in scores]]) - - -# ------------------------------------------------------------------------------------- # - -# def trainNNmodels(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, -# split: float = 0.2, epochs: int = 50, params: str = None, -# verbose: int = 2, kfold: int = 5) -> None: -# """ -# Train individual models for all targets (columns) present in the provided target data (y) and a multi-label -# model that classifies all targets at once. For each individual target the data is first subsetted to exclude NA -# values (for target associations). A random sample of the remaining data (size is the split fraction) is used for -# training and the remaining data for validation. -# -# :param modelfilepathprefix: A path prefix for all output files -# :param x: The feature matrix. -# :param y: The outcome matrix. -# :param split: The percentage of data used for validation. -# :param epochs: The number of epochs for training the autoencoder and the DNN for classification. -# Note: Early stopping and fallback is enabled. -# :param params: A .csv files containing paramters that should be evaluated. See file tunedParams.csv. -# :param verbose: Verbosity level. -# -# :return: A list with loss and accuracy values for each individual model. -# """ -# -# # remove 'id' column if present -# if 'id' in x.columns: -# x = x.drop('id', axis=1) -# if 'id' in y.columns: -# y = y.drop('id', axis=1) -# -# if params: -# parameters = pd.read_csv(params) -# -# # add a target 'summarized' for association to ANY of the target genes -# # maybe it improves the detection of '1's due to increased data set -# mysum = y.sum(axis=1) -# y['summarized'] = [0 if s == 0 else 1 for s in mysum] -# -# ### For each individual target (+ summarized target) -# for target in y.columns: # [:1]: -# # target=y.columns[0] # --> only for testing the code -# -# # rm NAs and duplicates, shuffle, and transform to numpy arrays -# (Xt, Yt) = prepareDataSet(y, x, target) -# -# # do a kfold cross validation for the FNN training -# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) -# -# # store acc and loss for each fold -# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"]) # FNN test data -# -# fold_no = 1 -# -# # split the data -# for train, test in kfoldCValidator.split(Xt, Yt): -# -# if verbose > 0: -# print(f'[INFO]: Training of fold number: {fold_no} ------------------------------------\n') -# -# # define all the output file/path names -# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, -# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, -# model_auc_file_data, outfilepath, checkpoint_path, -# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, -# target=target, fold=fold_no) -# -# model = define_nn_model(input_size=Xt[train].shape[1]) -# -# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # measure the training time -# start = time() -# # train and validate -# hist = model.fit(Xt[train], Yt[train], -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# # validation_data=(x_test, y_test)) # this overwrites val_split! -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the single-label FNN: {trainTime} min") -# -# # validate model on test data set (x_test, y_test) -# scores = validate_model_on_test_data(Xt[test], checkpoint_path, Yt[test], -# "FNN", model_validation, target, -# model_auc_file_data, model_auc_file) -# -# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) -# -# row_df = pd.DataFrame([[fold_no, -# hist.history['loss'][idx], hist.history['val_loss'][idx], -# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], -# scores[0], scores[1], scores[2]]], -# columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"] -# ) -# print(row_df) -# allscores = allscores.append(row_df, ignore_index=True) -# fold_no = fold_no + 1 -# del model -# # now next fold -# -# print(allscores) -# -# # finalize model -# # 1. provide best performing fold variant -# # select best model based on MCC -# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] -# fold_no = allscores._get_value(idx2, 'fold_no') -# -# modelname = target + '.Fold-' + str(fold_no) -# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' -# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") -# -# # store all scores -# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) -# allscores.to_csv(file) -# -# # copy best DNN model -# shutil.copyfile(checkpoint_path, bestModelfile) -# print(f'[INFO]: Best model for FNN is saved:\n - {bestModelfile}') -# -# # AND retrain with full data set -# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") -# # measure the training time -# start = time() -# -# model = define_nn_model(input_size=Xt.shape[1]) # X_train.shape[1]) -# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # train and validate -# hist = model.fit(Xt, Yt, -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# # validation_data=(x_test, y_test)) # this overwrites val_split! -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") -# # plot_history_vis(hist, -# # model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# # model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# # model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), -# # model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) -# # print(f'[INFO]: Full model for DNN is saved:\n - {fullModelfile}') -# -# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) -# -# del model -# # now next target -# - -# ------------------------------------------------------------------------------------- # - - -def smilesSet2fpSet(csvfilename, outfilename, fptype): - """ - - :param csvfilename: csv file containing a column named 'smiles' - :param fptype: - :return: void - """ - # csvfilename="/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.csv" - # outfilename = "/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.fp.csv" - - # read csv and generate/add fingerprints to dict - with open(csvfilename, "r") as f: - reader = csv.DictReader(f, delimiter=";") - - feature = "smiles" - - with open(outfilename, "w", newline="") as out: - writer = csv.DictWriter(out, fieldnames=["fp"]) - writer.writeheader() - for row in reader: - # smiles, need to be converted to fp first - fp = smi2fp(smile=row[feature], fptype=fptype) - writer.writerow({"fp": DataStructs.BitVectToText(fp)}) - - return diff --git a/build/lib/dfpl/feedforwardNN.py b/build/lib/dfpl/feedforwardNN.py deleted file mode 100644 index 702fff27..00000000 --- a/build/lib/dfpl/feedforwardNN.py +++ /dev/null @@ -1,464 +0,0 @@ -import array -import logging -import math -import re -import shutil -import sys -from time import time -from typing import List, Union -import numpy as np -import pandas as pd -from sklearn.metrics import f1_score -from sklearn.model_selection import KFold, train_test_split -from tensorflow.keras import metrics, optimizers, regularizers -from tensorflow.keras.layers import Dense, Dropout - -# for NN model functions -from tensorflow.keras.models import Model, Sequential - -from dfpl import callbacks as cb -from dfpl import history as ht -from dfpl import options, settings - - -def define_out_file_names(path_prefix: str, target: str, fold: int = -1) -> tuple: - """ - This function returns the required paths for output files or directories. - - :param path_prefix: A file path prefix for all files. - :param target: The name of the target. - :param fold: - - :return: A tuple of 14 output file names. - """ - - if fold == -1: - model_name = "/" + target + ".Full" - else: - model_name = "/" + target + ".Fold-" + str(fold) - - model_file_path_weights = str(path_prefix) + model_name + ".weights.h5" - model_file_path_json = str(path_prefix) + model_name + ".json" - model_hist_path = str(path_prefix) + model_name - model_hist_csv_path = str(path_prefix) + model_name + ".history.csv" - model_predict_valset_csv_path = str(path_prefix) + model_name + ".predictValSet.csv" - model_validation = str(path_prefix) + model_name + ".validation.csv" - model_auc_file = str(path_prefix) + model_name + ".auc_value.svg" - model_auc_file_data = str(path_prefix) + model_name + ".auc_value.data.csv" - out_file_path = str(path_prefix) + model_name + ".trainingResults.txt" - checkpoint_path = str(path_prefix) + model_name + ".checkpoint.model.hdf5" - model_heatmap_x = str(path_prefix) + model_name + ".heatmap.X.svg" - model_heatmap_z = str(path_prefix) + model_name + ".AC.heatmap.Z.svg" - - return ( - model_file_path_weights, - model_file_path_json, - model_hist_path, - model_hist_csv_path, - model_predict_valset_csv_path, - model_validation, - model_auc_file, - model_auc_file_data, - out_file_path, - checkpoint_path, - model_heatmap_x, - model_heatmap_z, - ) - - -def define_nn_multi_label_model( - input_size: int, output_size: int, opts: options.Options -) -> Model: - if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) - elif opts.optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) - else: - logging.error(f"Your selected optimizer is not supported:{opts.optimizer}.") - sys.exit("Unsupported optimizer.") - - nhl = int(math.log2(input_size) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation=opts.activationFunction, - kernel_regularizer=regularizers.l2(opts.l2reg), - ) - ) - model.add(Dropout(opts.dropout)) - # next hidden layers - for i in range(1, nhl): - factor_units = 2 ** (i + 1) - factor_dropout = 2 * i - model.add( - Dense( - units=int(input_size / factor_units), - activation=opts.activationFunction, - kernel_regularizer=regularizers.l2(opts.l2reg), - ) - ) - model.add(Dropout(opts.dropout / factor_dropout)) - # multi-class output layer - # use sigmoid to get independent probabilities for each output node - # we use sigmoid since one chemical can be active for multiple targets - # (need not add up to one, as they would using softmax) - # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ - model.add(Dense(units=output_size, activation="sigmoid")) - - model.summary(print_fn=logging.info) - - # compile model - model.compile( - loss="binary_crossentropy", - optimizer=my_optimizer, - metrics=["accuracy", metrics.Recall(), metrics.Precision()], - ) - - return model - - -def define_nn_model_multi( - input_size: int = 2048, - output_size: int = None, - l2reg: float = 0.001, - dropout: float = 0.2, - activation: str = "relu", - optimizer: str = "Adam", - lr: float = 0.001, - decay: float = 0.01, -) -> Model: - if optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=lr, decay=decay) - elif optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=lr, momentum=0.9, decay=decay) - else: - my_optimizer = optimizer - - nhl = int(math.log2(input_size) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout)) - # next hidden layers - for i in range(1, nhl): - factor_units = 2 ** (i + 1) - factor_dropout = 2 * i - model.add( - Dense( - units=int(input_size / factor_units), - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout / factor_dropout)) - # multi-class output layer - # use sigmoid to get independent probabilities for each output node - # (need not add up to one, as they would using softmax) - # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ - model.add(Dense(units=output_size, activation="sigmoid")) - - model.summary(print_fn=logging.info) - - # compile model - model.compile( - loss="binary_crossentropy", optimizer=my_optimizer, metrics=["accuracy"] - ) - - return model - - -def validate_multi_model_on_test_data( - x_test: np.ndarray, - checkpoint_path: str, - y_test: np.ndarray, - col_names: List[str], - result_file: str, -) -> List[Union[int, float, str]]: - """ - Validate the multi label model on a test data set. - - :param x_test: The feature matrix of the test data set - :param checkpoint_path: The path containing the weights of the FNN model - :param y_test: The outcome matrix of the test data set - :param col_names: The names of the columns that are targets - :param result_file: The filename of the output file - :return: A pandas Dataframe containing the percentage of correct predictions for each target - """ - # load checkpoint model with min(val_loss) - trained_model = define_nn_model_multi( - input_size=x_test.shape[1], output_size=y_test.shape[1] - ) - - # predict values with random model - predictions_random = pd.DataFrame( - trained_model.predict(x_test), columns=[n for n in col_names] - ) - - # load weights into random model - trained_model.load_weights(checkpoint_path) - - # predict with trained model - predictions = pd.DataFrame( - trained_model.predict(x_test), columns=[n for n in col_names] - ) - - y_true = pd.DataFrame(y_test, columns=col_names) - - f1_random = f1_score( - y_true=y_true, y_pred=predictions_random.round(), average="weighted" - ) - - f1_trained = f1_score(y_true=y_true, y_pred=predictions.round(), average="weighted") - - predictions_random.columns = [n + "-random" for n in col_names] - predictions.columns = [n + "-trained" for n in col_names] - - results = pd.concat([predictions_random, predictions, y_true], axis=1) - results.to_csv(result_file) - - return [f1_random, f1_trained] - - -def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: - # find target columns - names_y = [ - c - for c in df.columns - if c not in ["cid", "id", "smiles", "fp", "inchi", "fpcompressed"] - ] - selector = df[names_y].notna().apply(np.logical_and.reduce, axis=1) - - if opts.compressFeatures: - # get compressed fingerprints as numpy array - fpMatrix = np.array( - df[df["fpcompressed"].notnull() & selector]["fpcompressed"].to_list(), - dtype=settings.nn_multi_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - y = np.array( - df[df["fpcompressed"].notnull() & selector][names_y], - dtype=settings.nn_multi_target_numpy_type, - copy=settings.numpy_copy_values, - ) - else: - # get fingerprints as numpy array - fpMatrix = np.array( - df[df["fp"].notnull() & selector]["fp"].to_list(), - dtype=settings.nn_multi_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - - y = np.array( - df[df["fp"].notnull() & selector][names_y], - dtype=settings.nn_multi_target_numpy_type, - copy=settings.numpy_copy_values, - ) - - if opts.kFolds > 0: - # do a kfold cross validation for the autoencoder training - kfold_c_validator = KFold(n_splits=opts.kFolds, shuffle=True, random_state=42) - - # store acc and loss for each fold - all_scores = pd.DataFrame( - columns=[ - "fold_no", # fold number of k-fold CV - "loss", - "val_loss", - "acc", - "val_acc", # FNN training - "f1_random", - "f1_trained", - ] - ) # F1 scores of predictions - - fold_no = 1 - - # split the data - for train, test in kfold_c_validator.split(fpMatrix, y): - # kf = kfold_c_validator.split(fpMatrix, y) - # train, test = next(kf) - - ( - model_file_path_weights, - model_file_path_json, - model_hist_path, - model_validation, - model_auc_file, - model_auc_file_data, - out_file_path, - checkpoint_path, - model_heatmap_x, - model_heatmap_z, - ) = define_out_file_names( - path_prefix=opts.outputDir, - target="multi" + "_compressed-" + str(opts.compressFeatures), - fold=fold_no, - ) - - # use a dnn for multi-class prediction - model = define_nn_model_multi( - input_size=fpMatrix[train].shape[1], output_size=y.shape[1] - ) - - callback_list = cb.nn_callback(checkpoint_path=checkpoint_path, opts=opts) - # measure the training time - start = time() - - # train and validate - hist = model.fit( - fpMatrix[train], - y[train], - callbacks=callback_list, - epochs=opts.epochs, - batch_size=256, - verbose=opts.verbose, - validation_split=opts.testSize, - ) - - trainTime = str(round((time() - start) / 60, ndigits=2)) - - if opts.verbose > 0: - logging.info( - "Computation time for training the multi-label FNN: " - + trainTime - + " min" - ) - - ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) - # pd.DataFrame(hist.history).to_csv(model_hist_csv_path) - - # validate model on test data set (fpMatrix_test, y_test) - scores = validate_multi_model_on_test_data( - x_test=fpMatrix[test], - checkpoint_path=checkpoint_path, - y_test=y[test], - col_names=names_y, - result_file=out_file_path.replace( - "trainingResults.txt", "predictionResults.csv" - ), - ) - - idx = hist.history["val_loss"].index(min(hist.history["val_loss"])) - row_df = pd.DataFrame( - [ - [ - fold_no, - hist.history["loss"][idx], - hist.history["val_loss"][idx], - hist.history["accuracy"][idx], - hist.history["val_accuracy"][idx], - scores[0], - scores[1], - ] - ], - columns=[ - "fold_no", # fold number of k-fold CV - "loss", - "val_loss", - "acc", - "val_acc", - "f1_random", - "f1_trained", - ], - ) - - logging.info(row_df) - all_scores = all_scores.append(row_df, ignore_index=True) - - fold_no += 1 - del model - - logging.info(all_scores) - - # finalize model - # 1. provide best performing fold variant - # select best model based on MCC - idx2 = all_scores[["f1_trained"]].idxmax().ravel()[0] - fold_no = all_scores.iloc[idx2]["fold_no"] - - model_name = ( - "multi_compressed-" + str(opts.compressFeatures) + ".Fold-" + str(fold_no) - ) - checkpoint_path = opts.outputDir + "/" + model_name + ".checkpoint.model.hdf5" - best_model_file = checkpoint_path.replace( - "Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-" - ) - - file = re.sub( - ".hdf5", - "scores.csv", - re.sub("Fold-..checkpoint", "Fold-All", checkpoint_path), - ) - all_scores.to_csv(file) - - # copy best DNN model - shutil.copyfile(checkpoint_path, best_model_file) - logging.info("Best models for FNN is saved:\n" + best_model_file) - - # AND retrain with full data set - # full_model_file = f"{opts.outputDir}/multi_full.FNN.checkpoint.model.hdf5" - - ( - model_file_path_weights, - model_file_path_json, - model_hist_path, - model_hist_csv_path, - model_predict_valset_csv_path, - model_validation, - model_auc_file, - model_auc_file_data, - out_file_path, - checkpoint_path, - model_heatmap_x, - model_heatmap_z, - ) = define_out_file_names( - path_prefix=opts.outputDir, - target="multi" + "_compressed-" + str(opts.compressFeatures), - ) - - # test training data split - X_train, X_test, y_train, y_test = train_test_split( - fpMatrix, y, test_size=opts.testSize - ) - - # measure the training time - model = define_nn_multi_label_model( - input_size=fpMatrix.shape[1], output_size=y.shape[1], opts=opts - ) - model.evaluate(X_test, y_test) - - # model = define_nn_model_multi(input_size=fpMatrix.shape[1], - # output_size=y.shape[1]) - # callback_list = nn_callback(checkpoint_path=full_model_file, opts=opts) - # train and validate - start = time() - hist = model.fit( - X_train, - y_train, - # callbacks=callback_list, - epochs=opts.epochs, - batch_size=opts.batchSize, - verbose=opts.verbose, - validation_data=(X_test, y_test), - ) - trainTime = str(round((time() - start) / 60, ndigits=2)) - - # yhat = model.predict(X_test) - model.evaluate(X_test, y_test) - - logging.info( - "Computation time for training the full multi-label FNN: " + trainTime + " min" - ) - ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) diff --git a/build/lib/dfpl/fingerprint.py b/build/lib/dfpl/fingerprint.py deleted file mode 100644 index 291689c2..00000000 --- a/build/lib/dfpl/fingerprint.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -"""Calculate fingerprints""" -import logging -import multiprocessing -import os -from functools import partial -from os.path import isfile, join -from typing import Any, Callable, List - -import numpy as np -import pandas as pd -from rdkit import Chem, DataStructs, RDLogger -from rdkit.Chem import AllChem - -# import settings -from dfpl import settings - -default_fp_size = 2048 - - -def addFPColumn(data_frame: pd.DataFrame, fp_size: int) -> pd.DataFrame: - """ - Adds a fingerprint to each row in the dataframe. This function works on - parallel chunks of the original dataframe. - :param data_frame: Input dataframe that needs to have a "smiles" or an "inchi" column - :param fp_size: Number of bits in the fingerprint - :return: The dataframe with an additional "fp" column - """ - - def smile2fp(smile: str) -> Any: - """ - Calculates one fingerprint from a SMILE - :param smile: Input SMILE - :return: List of bits if conversion is successfull, - None otherwise - """ - - # generate morgan fp (circular, ecfp) - # smile = df['smiles'][1] - # mol = Chem.MolFromSmiles(smile) - # from rdkit.Chem import AllChem - # morgan = AllChem.GetMorganFingerprintAsBitVect(mol, 2) - # npa = np.zeros((0,), dtype=np.bool) - # from rdkit import DataStructs - # DataStructs.ConvertToNumpyArray(morgan, npa) - - npa = np.zeros((0,), dtype=np.bool_) - try: - DataStructs.ConvertToNumpyArray( - AllChem.GetMorganFingerprintAsBitVect( - Chem.MolFromSmiles(smile), 2, nBits=fp_size - ), - npa, - ) - return npa - except Exception: - return None - - # try: - # return np.array( - # Chem.RDKFingerprint(Chem.MolFromSmiles(smile), fpSize=fp_size), - # dtype=settings.df_fp_numpy_type, copy=settings.numpy_copy_values) - # except: - # # Note: We don't need to log here since rdkit already logs - # return None - - def inchi2fp(inchi: str) -> Any: - """ - Calculates one fingerprint from InChI - :param inchi: Input InChI - :return: List of bits if conversion is successfull, - None otherwise - """ - try: - return np.array( - Chem.RDKFingerprint(Chem.MolFromInchi(inchi), fpSize=fp_size), - dtype=settings.df_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - except Exception: - # Note: We don't need to log here since rdkit already logs - return None - - if "smiles" in data_frame: - func = smile2fp - accessor = "smiles" - else: - if "inchi" in data_frame: - func = inchi2fp - accessor = "inchi" - else: - raise ValueError("Neither smiles nor inchi column in data-frame") - - data_frame["fp"] = data_frame[accessor].apply(func) - return data_frame - - -def importDataFile( - file_name: str, - import_function: Callable[[str], pd.DataFrame] = pd.read_csv, - fp_size: int = default_fp_size, -) -> pd.DataFrame: - """ - Reads data as CSV or TSV and calculates fingerprints from the SMILES in the data. - :param import_function: - :param file_name: Filename of CSV files containing the training data. The - SMILES/Fingerprints are stored 1st column - :param fp_size: Number of bits in the fingerprint - :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If - no outcome data is provided, the Y matrix is a None object. - """ - # Read the data as Pandas pickle which already contains the calculated fingerprints - name, ext = os.path.splitext(file_name) - if ext == ".pkl": - return pd.read_pickle(file_name) - - df = import_function(file_name) - - # disable the rdkit logger. We know that some inchis will fail and we took care of it. No use to spam the console - RDLogger.DisableLog("rdApp.*") - n_cores = multiprocessing.cpu_count() - df_split = np.array_split(df, n_cores) - with multiprocessing.Pool(n_cores) as pool: - df = pd.concat(pool.map(partial(addFPColumn, fp_size=fp_size), df_split)) - pool.close() - pool.join() - return df - - -def importSmilesCSV(csvfilename: str) -> pd.DataFrame: - return pd.read_csv(csvfilename) - - -def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: - return pd.read_table(tsvfilename, names=["toxid", "inchi", "key"]) - - -conversion_rules = { - # "S_dataset.csv": importSmilesCSV, - # "S_dataset_extended.csv": importSmilesCSV, - # "D_dataset.tsv": importDstoxTSV, - # "train_data.csv": importSmilesCSV, - # "predict_data.csv": importDstoxTSV, - "B_data_ER.csv": importDstoxTSV -} - - -def convert_all(directory: str) -> List[str]: - files = [ - f - for f in os.listdir(directory) - for key, value in conversion_rules.items() - if isfile(join(directory, f)) and f in key - ] - logging.info(f"Found {len(files)} files to convert") - for f in files: - path = join(directory, f) - logging.info(f"Importing file {f}") - df = importDataFile(path, import_function=conversion_rules[f]) - name, ext = os.path.splitext(f) - output_file = join(directory, name + ".pkl") - logging.info(f"Saving pickle of file {f}") - df.to_pickle(output_file) - return files diff --git a/build/lib/dfpl/harmonize_mol_representations.py b/build/lib/dfpl/harmonize_mol_representations.py deleted file mode 100644 index 7c17e4e6..00000000 --- a/build/lib/dfpl/harmonize_mol_representations.py +++ /dev/null @@ -1,171 +0,0 @@ -from __future__ import annotations - -import logging -import sys -import urllib.error -import urllib.parse -import urllib.request -from dataclasses import dataclass -from functools import lru_cache - -import pandas as pd -from rdkit import Chem, DataStructs -from simple_parsing import ArgumentParser, choice -from simple_parsing.helpers import Serializable - -# Use this conda env: mamba create - n tidyChem - c conda - forge r - base r - tidyverse r - stringdist python rdkit -# numpy pandas simple - parsing - - -@dataclass -class ProgramConfig(Serializable): - """ - Configuration options for running the program - """ - - # file containing SMILES or InChikeys 2B harmonized - input_file: str = ( - "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_fragment.csv.tsv" - ) - # file containing the input for the harmonized molecular representations,tanimoto similarity and levenstein distance - output_file: str = ( - "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_dataset.harmonized.csv" - ) - input_column: str = choice("smiles", "inchikey", default="inchikey") - # column name of the column that contains the molecular representation to harmonize - input_type: str = choice( - "smiles", "inchikey", default="inchikey" - ) # the type of molecular representation of the input - output_type: str = choice( - "smiles", "inchikey", default="smiles" - ) # the type of molecular representation of the output - - -def createCommandlineParser(): - """ - Build the cmd_parser for arguments - Parse the input arguments. - :return: Tuple of commandlineparser, arguments - """ - cmd_parser = ArgumentParser(prog="harmonize_mol_representations") - cmd_parser.add_arguments(ProgramConfig, dest="config") - cmd_args, unknown = cmd_parser.parse_known_args() - return cmd_parser, cmd_args - - -def getLevensteinDistance(a, b): - """ - This function will calculate the levenshtein distance between two input - strings a and b - - params: - a (String) : The first string you want to compare - b (String) : The second string you want to compare - - returns: - This function will return the distnace between string a and b. - - example: - a = 'stamp' - b = 'stomp' - getLevensteinDistance(a,b) - >> 1.0 - """ - - if a is None and b is not None: - return len(b) - if a is not None and b is None: - return len(a) - - @lru_cache(None) # for memorization - def min_dist(s1, s2): - if s1 == len(a) or s2 == len(b): - return len(a) - s1 + len(b) - s2 - - # no change required - if a[s1] == b[s2]: - return min_dist(s1 + 1, s2 + 1) - - return 1 + min( - min_dist(s1, s2 + 1), # insert character - min_dist(s1 + 1, s2), # delete character - min_dist(s1 + 1, s2 + 1), # replace character - ) - - return min_dist(0, 0) - - -def getTanimotoSimilarityFromTwoSMILES( - smile1: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O", - smile2: str = "COC1=C(C=CC(=C1)C=O)O", -) -> float: - if smile1 is None or smile2 is None: - return 0 - - mol1 = Chem.MolFromSmiles(smile1) - mol2 = Chem.MolFromSmiles(smile2) - if mol1 is None or mol2 is None: - return 0 - - fp1 = Chem.RDKFingerprint(mol1, fpSize=2048) - fp2 = Chem.RDKFingerprint(mol2, fpSize=2048) - if fp1 is None or fp2 is None: - return 0 - - return DataStructs.TanimotoSimilarity(fp1, fp2) - - -def getHarmonizedSmiles(smiles: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O") -> str: - link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(smiles)}/smiles" - try: - f = urllib.request.urlopen(link) - myfile = f.read() - sh = myfile.decode("UTF-8") - except urllib.error.HTTPError: - sh = None - - return sh - - -def getHarmonizedSmilesfrominchis(inchikey: str = "CUWVNOSSZYUJAE-UHFFFAOYSA-N") -> str: - link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(inchikey)}/smiles" - try: - f = urllib.request.urlopen(link) - myfile = f.read() - sh = myfile.decode("UTF-8") - except urllib.error.HTTPError: - sh = None - - return sh - - -if __name__ == "__main__": - parser, args = createCommandlineParser() - opts = args.config - - # read data of smiles and harmonized smiles - df = pd.read_csv(filepath_or_buffer=opts.input_file) - if opts.input_type == "smiles": - df["smiles_harmonized"] = df[opts.input_column].apply(getHarmonizedSmiles) - df["tanimoto_similiarity"] = df.apply( - lambda x: getTanimotoSimilarityFromTwoSMILES( - smile1=x[opts.input_column], smile2=x["smiles_harmonized"] - ), - axis=1, - ) - df["levenstein_distance"] = df.apply( - lambda x: getLevensteinDistance( - a=x[opts.input_column], b=x["smiles_harmonized"] - ), - axis=1, - ) - elif opts.input_type == "inchikey": - df["smiles_harmonized"] = df[opts.input_column].apply( - getHarmonizedSmilesfrominchis - ) - - else: - logging.error(f"Your selected input is not supported: {opts.input_file}.") - sys.exit("Unsupported input type. Must be either smiles or inchikey") - - df.to_csv(path_or_buf=opts.output_file) diff --git a/build/lib/dfpl/history.py b/build/lib/dfpl/history.py deleted file mode 100644 index 1f16c2a9..00000000 --- a/build/lib/dfpl/history.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -"""Store and visualise training histories""" -import logging - -import matplotlib as mpl -import matplotlib.pyplot as plt -import pandas as pd -from tensorflow.keras.callbacks import History - -mpl.use("Agg") - - -def store_and_plot_history(base_file_name: str, hist: History) -> None: - """ - - :param base_file_name: - :param hist: - :return: - """ - - (ac_history_csv, ac_history_svg) = ( - base_file_name + ".history.csv", - base_file_name + ".history.svg", - ) - - # store history - pd.DataFrame(hist.history).to_csv(ac_history_csv) - logging.info(f"Neural network training history saved in file: {ac_history_csv}") - - # plot history - ac_epochs = hist.epoch - - # generate a figure of the losses for this fold - plt.figure() - for k in hist.history.keys(): - plt.plot(ac_epochs, hist.history[k], label=k) - plt.title("Training and validation metrics of neural network") - plt.legend() - plt.savefig(fname=ac_history_svg, format="svg") - plt.close() - logging.info(f"Neural network training history plotted in file: {ac_history_svg}") diff --git a/build/lib/dfpl/options.py b/build/lib/dfpl/options.py deleted file mode 100644 index bb753f05..00000000 --- a/build/lib/dfpl/options.py +++ /dev/null @@ -1,1297 +0,0 @@ -from __future__ import annotations - -import argparse -from dataclasses import dataclass -from pathlib import Path - -import jsonpickle -import torch -from chemprop.args import TrainArgs - -from dfpl.utils import makePathAbsolute - - -@dataclass -class Options: - """ - Dataclass for all options necessary for training the neural nets - """ - - configFile: str = "./example/train.json" - inputFile: str = "/deepFPlearn/CMPNN/data/tox21.csv" - outputDir: str = "." - outputFile: str = "" - ecWeightsFile: str = "AE.encoder.weights.hdf5" - ecModelDir: str = "AE_encoder" - fnnModelDir: str = "modeltraining" - type: str = "smiles" - fpType: str = "topological" # also "MACCS", "atompairs" - epochs: int = 512 - fpSize: int = 2048 - encFPSize: int = 256 - kFolds: int = 0 - testSize: float = 0.2 - enableMultiLabel: bool = False - verbose: int = 0 - trainAC: bool = True # if set to False, an AC weight file must be provided! - trainFNN: bool = True - compressFeatures: bool = True - sampleFractionOnes: float = 0.5 # Only used when value is in [0,1] - sampleDown: bool = False - split_type: str = "random" - aeSplitType: str = "random" - aeType: str = "deterministic" - aeEpochs: int = 3000 - aeBatchSize: int = 512 - aeLearningRate: float = 0.001 - aeLearningRateDecay: float = 0.01 - aeActivationFunction: str = "relu" - aeOptimizer: str = "Adam" - fnnType: str = "FNN" - batchSize: int = 128 - optimizer: str = "Adam" - learningRate: float = 0.001 - lossFunction: str = "bce" - activationFunction: str = "relu" - l2reg: float = 0.001 - dropout: float = 0.2 - threshold: float = 0.5 - gpu: str = "" - snnDepth = 8 - snnWidth = 50 - aeWabTracking: str = "" # Wand & Biases autoencoder tracking - wabTracking: str = "" # Wand & Biases FNN tracking - wabTarget: str = "ER" # Wand & Biases target used for showing training progress - - def saveToFile(self, file: str) -> None: - """ - Saves an instance to a JSON file - """ - jsonFile = Path(file) - with jsonFile.open("w") as f: - f.write(jsonpickle.encode(self)) - - @classmethod - def fromJson(cls, file: str) -> Options: - """ - Create an instance from a JSON file - """ - jsonFile = Path(file) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - return jsonpickle.decode(content) - raise ValueError("JSON file does not exist or is not readable") - - @classmethod - def fromCmdArgs(cls, args: argparse.Namespace) -> Options: - """ - Creates Options instance from cmdline arguments. - - If a training file (JSON) is provided, the values from that file are used. - However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the - JSON file and on the commandline, then the value of the commandline argument will be used. - """ - result = Options() - if "configFile" in vars(args).keys(): - jsonFile = Path(makePathAbsolute(args.configFile)) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - result = jsonpickle.decode(content) - else: - raise ValueError("Could not find JSON input file") - - for key, value in vars(args).items(): - # The args dict will contain a "method" key from the subparser. - # We don't use this. - if key != "method": - result.__setattr__(key, value) - return result - - -@dataclass -class GnnOptions(TrainArgs): - """ - Dataclass to hold all options used for training the graph models - """ - - total_epochs: int = 30 - save: bool = True - configFile: str = "./example/traingnn.json" - data_path: str = "./example/data/tox21.csv" - use_compound_names: bool = False - save_dir: str = "" - no_cache: bool = False - features_scaling: bool = True - use_input_features: str = "" - cuda: bool = False - num_lrs: int = 2 - minimize_score: bool = False - num_tasks: int = 12 - preds_path: str = "./tox21dmpnn.csv" - test_path: str = "" - save_preds: bool = True - - @classmethod - def fromCmdArgs(cls, args: argparse.Namespace) -> GnnOptions: - """ - Creates Options instance from cmdline arguments. - - If a training file (JSON) is provided, the values from that file are used. - However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the - JSON file and on the commandline, then the value of the commandline argument will be used. - """ - result = GnnOptions() - if "configFile" in vars(args).keys(): - jsonFile = Path(makePathAbsolute(args.configFile)) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - result = jsonpickle.decode(content) - else: - raise ValueError("Could not find JSON input file") - - return result - - @classmethod - def fromJson(cls, file: str) -> GnnOptions: - """ - Create an instance from a JSON file - """ - jsonFile = Path(file) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - return jsonpickle.decode(content) - raise ValueError("JSON file does not exist or is not readable") - - -def createCommandlineParser() -> argparse.ArgumentParser: - """ - Build the parser for arguments with its two subparsers - """ - parser = argparse.ArgumentParser(prog="deepFPlearn") - subparsers = parser.add_subparsers(help="Sub programs of deepFPlearn") - - parser_train_gnn = subparsers.add_parser( - "traingnn", help="Train new GNN models with your data" - ) - parser_train_gnn.set_defaults(method="traingnn") - parseTrainGnn(parser_train_gnn) - - parser_predict_gnn = subparsers.add_parser( - "predictgnn", help="Predict with your GNN models" - ) - parser_predict_gnn.set_defaults(method="predictgnn") - parsePredictGnn(parser_predict_gnn) - - parser_train = subparsers.add_parser( - "train", help="Train new models with your data" - ) - parser_train.set_defaults(method="train") - parseInputTrain(parser_train) - - parser_predict = subparsers.add_parser( - "predict", help="Predict your data with existing models" - ) - parser_predict.set_defaults(method="predict") - parseInputPredict(parser_predict) - - parser_convert = subparsers.add_parser( - "convert", help="Convert known data files to pickle serialization files" - ) - parser_convert.set_defaults(method="convert") - parseInputConvert(parser_convert) - return parser - - -def parseInputTrain(parser: argparse.ArgumentParser) -> None: - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - # Create argument groups - general_args = parser.add_argument_group("Model Configuration") - autoencoder_args = parser.add_argument_group("Autoencoder Configuration") - training_args = parser.add_argument_group("Training Configuration") - tracking_args = parser.add_argument_group("Tracking Configuration") - - # Model Configuration - general_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-i", - "--inputFile", - metavar="FILE", - type=str, - help="The file containing the data for training in " - "comma separated CSV format.The first column should be smiles.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-o", - "--outputDir", - metavar="DIR", - type=str, - help="Prefix of output file name. Trained model and " - "respective stats will be returned in this directory.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-t", - "--type", - metavar="STRING", - type=str, - choices=["fp", "smiles"], - help="Type of the chemical representation. Choices: 'fp', 'smiles'.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-thr", - "--threshold", - type=float, - metavar="FLOAT", - help="Threshold for binary classification.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-gpu", - "--gpu", - metavar="INT", - type=int, - help="Select which gpu to use. If not available, leave empty.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-k", - "--fpType", - metavar="STR", - type=str, - choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], - help="The type of fingerprint to be generated/used in input file.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-s", - "--fpSize", - type=int, - help="Size of fingerprint that should be generated.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-c", - "--compressFeatures", - metavar="BOOL", - type=bool, - help="Should the fingerprints be compressed or not. Activates the autoencoder. ", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-m", - "--enableMultiLabel", - metavar="BOOL", - type=bool, - help="Train multi-label classification model in addition to the individual models.", - default=argparse.SUPPRESS, - ) - # Autoencoder Configuration - autoencoder_args.add_argument( - "-a", - "--ecWeightsFile", - type=str, - metavar="FILE", - help="The .hdf5 file of a trained encoder", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--ecModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the encoder will be saved", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeType", - metavar="STRING", - type=str, - choices=["variational", "deterministic"], - help="Autoencoder type, variational or deterministic.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeEpochs", - metavar="INT", - type=int, - help="Number of epochs for autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeBatchSize", - metavar="INT", - type=int, - help="Batch size in autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeActivationFunction", - metavar="STRING", - type=str, - choices=["relu", "selu"], - help="The activation function for the hidden layers in the autoencoder.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeLearningRate", - metavar="FLOAT", - type=float, - help="Learning rate for autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeLearningRateDecay", - metavar="FLOAT", - type=float, - help="Learning rate decay for autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeSplitType", - metavar="STRING", - type=str, - choices=["scaffold_balanced", "random", "molecular_weight"], - help="Set how the data is going to be split for the autoencoder", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "-d", - "--encFPSize", - metavar="INT", - type=int, - help="Size of encoded fingerprint (z-layer of autoencoder).", - default=argparse.SUPPRESS, - ) - # Training Configuration - training_args.add_argument( - "--split_type", - metavar="STRING", - type=str, - choices=["scaffold_balanced", "random", "molecular_weight"], - help="Set how the data is going to be split for the feedforward neural network", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-l", - "--testSize", - metavar="FLOAT", - type=float, - help="Fraction of the dataset that should be used for testing. Value in [0,1].", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-K", - "--kFolds", - metavar="INT", - type=int, - help="K that is used for K-fold cross-validation in the training procedure.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-v", - "--verbose", - metavar="INT", - type=int, - choices=[0, 1, 2], - help="Verbosity level. O: No additional output, " - + "1: Some additional output, 2: full additional output", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--trainAC", - metavar="BOOL", - type=bool, - help="Choose to train or not, the autoencoder based on the input file", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--trainFNN", - metavar="BOOL", - type=bool, - help="Train the feedforward network either with provided weights.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--sampleFractionOnes", - metavar="FLOAT", - type=float, - help="This is the fraction of positive target associations (1s) in comparison to the majority class(0s)." - "only works if --sampleDown is enabled", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--sampleDown", - metavar="BOOL", - type=bool, - help="Enable automatic down sampling of the 0 valued samples.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-e", - "--epochs", - metavar="INT", - type=int, - help="Number of epochs that should be used for the FNN training", - default=argparse.SUPPRESS, - ) - - training_args.add_argument( - "--lossFunction", - metavar="STRING", - type=str, - choices=["mse", "bce", "focal"], - help="Loss function to use during training. mse - mean squared error, bce - binary cross entropy.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--optimizer", - metavar="STRING", - type=str, - choices=["Adam", "SGD"], - help='Optimizer to use for backpropagation in the FNN. Possible values: "Adam", "SGD"', - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--batchSize", - metavar="INT", - type=int, - help="Batch size in FNN training.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--l2reg", - metavar="FLOAT", - type=float, - help="Value for l2 kernel regularizer.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--dropout", - metavar="FLOAT", - type=float, - help="The fraction of data that is dropped out in each dropout layer.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--learningRate", - metavar="FLOAT", - type=float, - help="Learning rate size in FNN training.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--activationFunction", - metavar="STRING", - type=str, - choices=["relu", "selu"], - help="The activation function for hidden layers in the FNN.", - default=argparse.SUPPRESS, - ) - # Tracking Configuration - tracking_args.add_argument( - "--aeWabTracking", - metavar="BOOL", - type=bool, - help="Track autoencoder performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, - ) - tracking_args.add_argument( - "--wabTracking", - metavar="BOOL", - type=bool, - help="Track FNN performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, - ) - tracking_args.add_argument( - "--wabTarget", - metavar="STRING", - type=str, - choices=["AR", "ER", "ED", "GR", "TR", "PPARg", "Aromatase"], - help="Which target to use for tracking performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, - ) - - -def parseTrainGnn(parser: argparse.ArgumentParser) -> None: - general_args = parser.add_argument_group("General Configuration") - data_args = parser.add_argument_group("Data Configuration") - files_args = parser.add_argument_group("Files") - model_args = parser.add_argument_group("Model arguments") - training_args = parser.add_argument_group("Training Configuration") - - # General arguments - general_args.add_argument("--split_key_molecule", type=int) - general_args.add_argument("--pytorch_seed", type=int) - general_args.add_argument("--cache_cutoff", type=float) - general_args.add_argument("--save_preds", type=bool) - general_args.add_argument( - "--cuda", action="store_true", default=False, help="Turn on cuda" - ) - general_args.add_argument( - "--save_smiles_splits", - action="store_true", - default=False, - help="Save smiles for each train/val/test splits for prediction convenience later", - ) - general_args.add_argument( - "--test", - action="store_true", - default=False, - help="Whether to skip training and only test the model", - ) - general_args.add_argument( - "--gpu", - type=int, - choices=list(range(torch.cuda.device_count())), - help="Which GPU to use", - ) - general_args.add_argument("--save", type=bool) - general_args.add_argument( - "--quiet", - action="store_true", - default=False, - help="Skip non-essential print statements", - ) - general_args.add_argument( - "--log_frequency", - type=int, - metavar="INT", - default=10, - help="The number of batches between each logging of the training loss", - ) - general_args.add_argument( - "--no_cuda", action="store_true", default=True, help="Turn off cuda" - ) - general_args.add_argument( - "--no_cache", - action="store_true", - default=False, - help="Turn off caching mol2graph computation", - ) - - # FILES ARGUMENTS - files_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - ) - files_args.add_argument( - "--config_path", - type=str, - metavar="FILE", - help="Path to a .json file containing arguments. Any arguments present in the config" - "file will override arguments specified via the command line or by the defaults.", - ) - files_args.add_argument( - "--save_dir", - type=str, - metavar="DIR", - default="./ckpt/", - help="Directory where model checkpoints will be saved", - ) - files_args.add_argument( - "--checkpoint_dir", - type=str, - metavar="DIR", - default=None, - help="Directory from which to load model checkpoints" - "(walks directory and ensembles all models that are found)", - ) - files_args.add_argument( - "--checkpoint_path", - type=str, - metavar="FILE", - default=None, - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--checkpoint_paths", - type=str, - metavar="FILE", - nargs="*", - default=None, - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--separate_val_path", - type=str, - metavar="FILE", - help="Path to separate val set, optional", - ) - files_args.add_argument( - "--separate_val_features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to file with features for separate val set", - ) - files_args.add_argument( - "--separate_test_path", - type=str, - metavar="FILE", - help="Path to separate test set, optional", - ) - files_args.add_argument( - "--separate_test_features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to file with features for separate test set", - ) - files_args.add_argument( - "--folds_file", - type=str, - metavar="FILE", - default=None, - help="Optional file of fold labels", - ) - files_args.add_argument( - "--val_fold_index", - type=int, - metavar="INT", - default=None, - help="Which fold to use as val for cross val", - ) - files_args.add_argument( - "--test_fold_index", - type=int, - metavar="INT", - default=None, - help="Which fold to use as test for cross val", - ) - files_args.add_argument( - "--crossval_index_dir", - type=str, - metavar="DIR", - help="Directory in which to find cross validation index files", - ) - files_args.add_argument( - "--crossval_index_file", - type=str, - metavar="FILE", - help="Indices of files to use as train/val/test" - "Overrides --num_folds and --seed.", - ) - files_args.add_argument( - "--data_weights_path", - type=str, - metavar="FILE", - help="Path where the data weight are saved", - ) - files_args.add_argument( - "--features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to features to use in FNN (instead of features_generator)", - ) - - files_args.add_argument( - "--separate_val_phase_features_path", type=str, metavar="FILE" - ) - files_args.add_argument( - "--separate_test_phase_features_path", type=str, metavar="FILE" - ) - - files_args.add_argument( - "--separate_val_atom_descriptors_path", type=str, metavar="FILE" - ) - files_args.add_argument( - "--separate_test_atom_descriptors_path", type=str, metavar="FILE" - ) - # Data related arguments - data_args.add_argument( - "--data_path", - type=str, - metavar="FILE", - help="Path to data CSV file", - default="", - ) - data_args.add_argument( - "--use_compound_names", - action="store_true", - default=False, - help="Use when test data file contains compound names in addition to SMILES strings", - ) - data_args.add_argument( - "--max_data_size", - type=int, - metavar="INT", - help="Maximum number of data points to load", - ) - - data_args.add_argument( - "--features_only", - action="store_true", - default=False, - help="Use only the additional features in an FFN, no graph network", - ) - data_args.add_argument( - "--dataset_type", - type=str, - metavar="STRING", - choices=["classification", "regression", "multiclass"], - help="Type of dataset, e.g. classification or regression." - "This determines the loss function used during training.", - default="regression", - ) # classification - data_args.add_argument( - "--multiclass_num_classes", - type=int, - metavar="INT", - default=3, - help="Number of classes when running multiclass classification", - ) - data_args.add_argument( - "--split_type", - type=str, - metavar="STRING", - default="random", - choices=[ - "random", - "scaffold_balanced", - "predetermined", - "crossval", - "index_predetermined", - ], - help="Method of splitting the data into train/val/test", - ) - data_args.add_argument( - "--split_sizes", - type=float, - metavar="FLOAT", - nargs=3, - default=[0.8, 0.2, 0.0], - help="Split proportions for train/validation/test sets", - ) - - data_args.add_argument( - "--seed", - type=int, - default=0, - help="Random seed to use when splitting data into train/val/test sets." - "When `num_folds` > 1, the first fold uses this seed and all" - "subsequent folds add 1 to the seed.", - ) - data_args.add_argument( - "--smiles_columns", - type=str, - metavar="STRING", - help="Name of the smiles columns", - ) - - data_args.add_argument( - "--target_columns", - type=str, - metavar="STRING", - help="Name of the target columns", - ) - - data_args.add_argument( - "--ignore_columns", - type=str, - metavar="STRING", - help="Names of the columns to ignore", - ) - data_args.add_argument( - "--num_tasks", type=int, metavar="INT", help="NUmber of tasks" - ) - data_args.add_argument( - "--no_features_scaling", - action="store_true", - default=False, - help="Turn off scaling of features", - ) - data_args.add_argument( - "--features_scaling", - action="store_true", - default=False, - help="Turn on scaling of features", - ) - data_args.add_argument( - "--use_input_features", - type=str, - metavar="STRING", - help="Turn on scaling of features", - ) - - # Model arguments - model_args.add_argument( - "--ensemble_size", - type=int, - metavar="INT", - default=1, - help="Number of models in ensemble", - ) - model_args.add_argument( - "--hidden_size", - type=int, - metavar="INT", - default=300, - help="Dimensionality of hidden layers in MPN", - ) - model_args.add_argument( - "--bias", - action="store_true", - default=False, - help="Whether to add bias to linear layers", - ) - model_args.add_argument( - "--depth", - type=int, - metavar="INT", - default=3, - help="Number of message passing steps", - ) - model_args.add_argument( - "--dropout", - type=float, - metavar="FLOAT", - default=0.0, - help="Dropout probability", - ) - model_args.add_argument( - "--activation", - type=str, - metavar="STRING", - default="ReLU", - choices=["ReLU", "LeakyReLU", "PReLU", "tanh", "SELU", "ELU"], - help="Activation function", - ) - model_args.add_argument( - "--undirected", - action="store_true", - default=False, - help="Undirected edges (always sum the two relevant bond vectors)", - ) - model_args.add_argument( - "--ffn_hidden_size", - type=int, - metavar="INT", - default=2, - help="Hidden dim for higher-capacity FFN (defaults to hidden_size)", - ) - model_args.add_argument( - "--ffn_num_layers", - type=int, - metavar="INT", - default=2, - help="Number of layers in FFN after MPN encoding", - ) - model_args.add_argument( - "--atom_messages", - action="store_true", - default=False, - help="Use messages on atoms instead of messages on bonds", - ) - - model_args.add_argument( - "--num_lrs", - type=int, - metavar="INT", - default=2, - help="Number of layers in FFN after MPN encoding", - ) - model_args.add_argument("--checkpoint_frzn", type=str, metavar="STRING") - - # Model arguments - model_args.add_argument("--mpn_shared", type=bool, metavar="BOOL") - model_args.add_argument( - "--show_individual_scores", - action="store_true", - default=True, - help="Show all scores for individual targets, not just average, at the end", - ) - model_args.add_argument("--aggregation", choices=["mean", "sum", "norm"]) - model_args.add_argument("--aggregation_norm", type=int) - model_args.add_argument("--explicit_h", type=bool, metavar="BOOL") - model_args.add_argument("--adding_h", type=bool, metavar="BOOL") - # Training arguments - model_args.add_argument("--class_balance", type=bool, metavar="BOOL") - model_args.add_argument("--evidential_regularization", type=float, metavar="FLOAT") - model_args.add_argument( - "--overwrite_default_atom_features", type=bool, metavar="BOOL" - ) - model_args.add_argument("--no_atom_descriptor_scaling", type=bool, metavar="BOOL") - model_args.add_argument( - "--overwrite_default_bond_features", type=bool, metavar="BOOL" - ) - model_args.add_argument("--frzn_ffn_layers", type=int, metavar="INT") - model_args.add_argument("--freeze_first_only", type=bool, metavar="BOOL") - # Training arguments - training_args.add_argument( - "--epochs", type=int, metavar="INT", default=30, help="Number of epochs to run" - ) - training_args.add_argument( - "--total_epochs", - type=int, - metavar="INT", - default=30, - help="Number of total epochs to run", - ) - training_args.add_argument( - "--batch_size", type=int, metavar="INT", default=50, help="Batch size" - ) - training_args.add_argument( - "--warmup_epochs", - type=int, - metavar="INT", - default=2, - help="Number of epochs during which learning rate increases linearly from" - "init_lr to max_lr. Afterwards, learning rate decreases exponentially" - "from max_lr to final_lr.", - ) - training_args.add_argument( - "--init_lr", - type=float, - metavar="FLOAT", - default=1e-4, - help="Initial learning rate", - ) - training_args.add_argument( - "--max_lr", - type=float, - metavar="FLOAT", - default=1e-3, - help="Maximum learning rate", - ) - training_args.add_argument( - "--final_lr", - type=float, - metavar="FLOAT", - default=1e-4, - help="Final learning rate", - ) - training_args.add_argument( - "--extra_metrics", - type=str, - metavar="STRING", - nargs="*", - help="Extra metrics to use", - ) - training_args.add_argument( - "--loss_function", - type=str, - metavar="STRING", - choices=[ - "mse", - "bounded_mse", - "binary_cross_entropy", - "cross_entropy", - "mcc", - "sid", - "wasserstein", - "mve", - "evidential", - "dirichlet", - ], - ) - training_args.add_argument("--grad_clip", type=float) - training_args.add_argument( - "--metric", - type=str, - metavar="STRING", - default=None, - choices=[ - "auc", - "prc-auc", - "rmse", - "mae", - "mse", - "r2", - "accuracy", - "cross_entropy", - ], - help="Metric to use during evaluation." - "Note: Does NOT affect loss function used during training" - "(loss is determined by the `dataset_type` argument)." - 'Note: Defaults to "auc" for classification and "rmse" for regression.', - ) - training_args.add_argument( - "--num_folds", - type=int, - metavar="INT", - default=1, - help="Number of folds when performing cross validation", - ) - - -def parseInputPredict(parser: argparse.ArgumentParser) -> None: - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - parser = argparse.ArgumentParser( - description="Use models that have been generated by " - "to make predictions on chemicals (provide SMILES or " - "topological fingerprints)." - ) - general_args = parser.add_argument_group("General Configuration") - files_args = parser.add_argument_group("Files") - files_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "-i", - "--inputFile", - metavar="FILE", - type=str, - help="The file containing the data for the prediction in (unquoted) " - "comma separated CSV format. The column named 'smiles' or 'fp'" - "contains the field to be predicted. Please adjust the type " - "that should be predicted (fp or smile) with -t option appropriately." - "An optional column 'id' is used to assign the outcomes to the" - "original identifiers. If this column is missing, the results are" - "numbered in the order of their appearance in the input file." - "A header is expected and respective column names are used.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "-o", - "--outputDir", - metavar="DIR", - type=str, - help="Prefix of output directory. It will contain a log file and the file specified" - "with --outputFile.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--outputFile", - metavar="FILE", - type=str, - help="Output .CSV file name which will contain one prediction per input line. " - "Default: prefix of input file name.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-t", - "--type", - metavar="STR", - type=str, - choices=["fp", "smiles"], - help="Type of the chemical representation. Choices: 'fp', 'smiles'.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-k", - "--fpType", - metavar="STR", - type=str, - choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], - help="The type of fingerprint to be generated/used in input file.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--ecModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the encoder will be saved (if trainAE=True) or " - "loaded from (if trainAE=False). Provide a full path here.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--fnnModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the fnn is loaded from. " - "Provide a full path here.", - default=argparse.SUPPRESS, - ) - - -def parsePredictGnn(parser: argparse.ArgumentParser) -> None: - general_args = parser.add_argument_group("General Configuration") - data_args = parser.add_argument_group("Data Configuration") - files_args = parser.add_argument_group("Files") - training_args = parser.add_argument_group("Training Configuration") - files_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "--gpu", - type=int, - metavar="INT", - choices=list(range(torch.cuda.device_count())), - help="Which GPU to use", - ) - general_args.add_argument( - "--no_cuda", action="store_true", default=False, help="Turn off cuda" - ) - general_args.add_argument( - "--num_workers", - type=int, - metavar="INT", - help="Number of workers for the parallel data loading 0 means sequential", - ) - general_args.add_argument( - "--no_cache", - type=bool, - metavar="BOOL", - default=False, - help="Turn off caching mol2graph computation", - ) - general_args.add_argument( - "--no_cache_mol", - type=bool, - metavar="BOOL", - default=False, - help="Whether to not cache the RDKit molecule for each SMILES string to reduce memory\ - usage cached by default", - ) - general_args.add_argument( - "--empty_cache", - type=bool, - metavar="BOOL", - help="Whether to empty all caches before training or predicting. This is necessary if\ - multiple jobs are run within a single script and the atom or bond features change", - ) - files_args.add_argument( - "--preds_path", - type=str, - metavar="FILE", - help="Path to CSV file where predictions will be saved", - default="", - ) - files_args.add_argument( - "--checkpoint_dir", - type=str, - metavar="DIR", - help="Directory from which to load model checkpoints" - "(walks directory and ensembles all models that are found)", - default="./ckpt", - ) - files_args.add_argument( - "--checkpoint_path", - type=str, - metavar="DIR", - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--checkpoint_paths", - type=str, - metavar="FILE", - nargs="*", - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--data_path", - type=str, - metavar="FILE", - help="Path to CSV file containing testing data for which predictions will be made", - default="", - ) - files_args.add_argument( - "--test_path", - type=str, - metavar="FILE", - help="Path to CSV file containing testing data for which predictions will be made", - default="", - ) - files_args.add_argument( - "--features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to features to use in FNN (instead of features_generator)", - ) - files_args.add_argument( - "--atom_descriptors_path", - type=str, - metavar="FILE", - help="Path to the extra atom descriptors.", - ) - data_args.add_argument( - "--use_compound_names", - action="store_true", - default=False, - help="Use when test data file contains compound names in addition to SMILES strings", - ) - data_args.add_argument( - "--no_features_scaling", - action="store_true", - default=False, - help="Turn off scaling of features", - ) - data_args.add_argument( - "--max_data_size", - type=int, - metavar="INT", - help="Maximum number of data points to load", - ) - data_args.add_argument( - "--smiles_columns", - type=str, - metavar="STRING", - help="List of names of the columns containing SMILES strings.By default, uses the first\ - number_of_molecules columns.", - ) - data_args.add_argument( - "--number_of_molecules", - type=int, - metavar="INT", - help="Number of molecules in each input to the model.This must equal the length of\ - smiles_columns if not None", - ) - - data_args.add_argument( - "--atom_descriptors", - type=bool, - metavar="Bool", - help="Use or not atom descriptors", - ) - - data_args.add_argument( - "--bond_features_size", - type=int, - metavar="INT", - help="Size of the extra bond descriptors that will be used as bond features to featurize a\ - given molecule", - ) - training_args.add_argument( - "--batch_size", type=int, metavar="INT", default=50, help="Batch size" - ) - - -def parseInputConvert(parser: argparse.ArgumentParser) -> None: - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - parser.add_argument( - "-f", - metavar="FILE", - type=str, - help="Input directory where your CSV/TSV files are stored.", - required=True, - default="", - ) diff --git a/build/lib/dfpl/plot.py b/build/lib/dfpl/plot.py deleted file mode 100644 index 7e8f6ac1..00000000 --- a/build/lib/dfpl/plot.py +++ /dev/null @@ -1,228 +0,0 @@ -import array - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import wandb -from matplotlib.axes import Axes -from typing import List -# for NN model functions -from tensorflow.keras.callbacks import History - - -def get_max_validation_accuracy(history: History) -> str: - validation = smooth_curve(history.history["val_accuracy"]) - y_max: float = max(validation) - return "Max validation accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_validation_balanced_accuracy(history: History) -> str: - validation_bal_acc = smooth_curve(history.history["val_balanced_accuracy"]) - y_max: float = max(validation_bal_acc) - return "Max validation balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_training_balanced_accuracy(history: History) -> str: - training_bal_acc = smooth_curve(history.history["balanced_accuracy"]) - y_max: float = max(training_bal_acc) - return "Training balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_training_auc(history: History) -> str: - training_auc = smooth_curve(history.history["auc"]) - y_max: float = max(training_auc) - return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_validation_auc(history: History) -> str: - validation_auc = smooth_curve(history.history["val_auc"]) - y_max: float = max(validation_auc) - return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_training_accuracy(history: History) -> str: - training = smooth_curve(history.history["accuracy"]) - y_max: float = max(training) - return "Max training accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def smooth_curve(points: np.ndarray, factor: float = 0.75) -> np.ndarray: - smoothed_points: List[float] = [] - for point in points: - if smoothed_points: - previous = smoothed_points[-1] - smoothed_points.append(previous * factor + point * (1 - factor)) - else: - smoothed_points.append(point) - return smoothed_points - - -def set_plot_history_data(ax: Axes, history: History, which_graph: str) -> None: - (train, valid) = (None, None) - - if which_graph == "acc": - train = smooth_curve(history.history["accuracy"]) - valid = smooth_curve(history.history["val_accuracy"]) - - if which_graph == "loss": - train = smooth_curve(history.history["loss"]) - valid = smooth_curve(history.history["val_loss"]) - - # plt.xkcd() # make plots look like xkcd - - epochs = range(1, len(train) + 1) - - trim = 0 # remove first 5 epochs - # when graphing loss the first few epochs may skew the (loss) graph - - ax.plot(epochs[trim:], train[trim:], "dodgerblue", linewidth=15, alpha=0.1) - ax.plot(epochs[trim:], train[trim:], "dodgerblue", label="Training") - - ax.plot(epochs[trim:], valid[trim:], "g", linewidth=15, alpha=0.1) - ax.plot(epochs[trim:], valid[trim:], "g", label="Validation") - - -def plot_history(history: History, file: str) -> None: - fig, (ax1, ax2) = plt.subplots( - nrows=2, - ncols=1, - figsize=(10, 6), - sharex="all", - gridspec_kw={"height_ratios": [5, 2]}, - ) - - set_plot_history_data(ax1, history, "acc") - - set_plot_history_data(ax2, history, "loss") - - # Accuracy graph - ax1.set_ylabel("Accuracy") - ax1.set_ylim(bottom=0.5, top=1) - ax1.legend(loc="lower right") - ax1.spines["top"].set_visible(False) - ax1.spines["right"].set_visible(False) - ax1.xaxis.set_ticks_position("none") - ax1.spines["bottom"].set_visible(False) - - # max accuracy text - plt.text( - 0.5, - 0.6, - get_max_validation_balanced_accuracy(history), - horizontalalignment="right", - verticalalignment="top", - transform=ax1.transAxes, - fontsize=12, - ) - plt.text( - 0.5, - 0.8, - get_max_training_balanced_accuracy(history), - horizontalalignment="right", - verticalalignment="top", - transform=ax1.transAxes, - fontsize=12, - ) - - # Loss graph - ax2.set_ylabel("Loss") - ax2.set_yticks([]) - ax2.plot(legend=False) - ax2.set_xlabel("Epochs") - ax2.spines["top"].set_visible(False) - ax2.spines["right"].set_visible(False) - - plt.tight_layout() - plt.savefig(fname=file, format="svg") - plt.close() - - -def plot_train_history(hist, target, file_accuracy, file_loss): - """ - Plot the training performance in terms of accuracy and loss values for each epoch. - :param hist: The history returned by model.fit function - :param target: The name of the target of the model - :param file_accuracy: The filename for plotting accuracy values - :param file_loss: The filename for plotting loss values - :return: none - """ - - # plot accuracy - plt.figure() - plt.plot(hist.history["accuracy"]) - if "val_accuracy" in hist.history.keys(): - plt.plot(hist.history["val_accuracy"]) - plt.title("Model accuracy - " + target) - plt.ylabel("Accuracy") - plt.xlabel("Epoch") - if "val_accuracy" in hist.history.keys(): - plt.legend(["Train", "Test"], loc="upper left") - else: - plt.legend(["Train"], loc="upper_left") - plt.savefig(fname=file_accuracy, format="svg") - - # Plot training & validation loss values - plt.figure() - plt.plot(hist.history["loss"]) - plt.plot(hist.history["val_loss"]) - plt.title("Model loss - " + target) - plt.ylabel("Loss") - plt.xlabel("Epoch") - plt.legend(["Train", "Test"], loc="upper left") - # plt.show() - plt.savefig(fname=file_loss, format="svg") - plt.close() - - -def plot_history_vis( - hist: History, - model_hist_plot_path: str, - model_hist_csv_path: str, - model_hist_plot_path_a: str, - model_hist_plot_path_l: str, - target: str, -) -> None: - plot_history(history=hist, file=model_hist_plot_path) - histDF = pd.DataFrame(hist.history) - histDF.to_csv(model_hist_csv_path) - - # plot accuracy and loss for the training and validation during training - plot_train_history( - hist=hist, - target=target, - file_accuracy=model_hist_plot_path_a, - file_loss=model_hist_plot_path_l, - ) - - -def plot_auc( - fpr: np.ndarray, - tpr: np.ndarray, - auc_value: float, - target: str, - filename: str, - wandb_logging: bool = False, -) -> None: - """ - Plot the area under the curve to the provided file - - :param fpr: An array containing the false positives - :param tpr: An array containing the true positives - :param auc_value: The value of the area under the curve - :param target: The name of the training target - :param filename: The filename to which the plot should be stored - :param wandb_logging: Whether to log the plot to wandb - :rtype: None - """ - # Create a boolean mask to filter out zero values - plt.figure() - plt.plot([0, 1], [0, 1], "k--") - plt.plot(fpr, tpr, label=f"Keras (area = {auc_value:.3f})") - plt.xlabel("False positive rate") - plt.ylabel("True positive rate") - plt.title("ROC curve " + target) - plt.legend(loc="best") - plt.savefig(fname=filename, format="png") - if wandb_logging: - wandb.log({"roc_plot": plt}) - plt.close() diff --git a/build/lib/dfpl/predictions.py b/build/lib/dfpl/predictions.py deleted file mode 100644 index 29e73142..00000000 --- a/build/lib/dfpl/predictions.py +++ /dev/null @@ -1,39 +0,0 @@ -import logging - -import numpy as np -import pandas as pd -import tensorflow.keras.models - -from dfpl import options, settings - - -def predict_values(df: pd.DataFrame, opts: options.Options) -> pd.DataFrame: - """ - Predict a set of chemicals using a selected model. - - :param df: - :param opts: - :return: - """ - model = tensorflow.keras.models.load_model(opts.fnnModelDir, compile=False) - model.compile(loss=opts.lossFunction, optimizer=opts.optimizer) - if opts.compressFeatures: - sub_df = df[df["fpcompressed"].notnull()] - x = np.array( - sub_df["fpcompressed"].to_list(), - dtype=settings.nn_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info(f"Compressed FP matrix with shape {x.shape} and type {x.dtype}") - sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) - return sub_df - else: - sub_df = df[df["fp"].notnull()] - x = np.array( - sub_df["fp"].to_list(), - dtype=settings.nn_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info(f"Uncompressed FP matrix with shape {x.shape} and type {x.dtype}") - sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) - return sub_df diff --git a/build/lib/dfpl/rbm.py b/build/lib/dfpl/rbm.py deleted file mode 100644 index fe5b1ac5..00000000 --- a/build/lib/dfpl/rbm.py +++ /dev/null @@ -1,259 +0,0 @@ -import os.path -from os.path import basename -import pandas as pd -import logging -import tensorflow as tf -import numpy as np -from tensorflow.keras import Model -from sklearn.model_selection import train_test_split -import math - -from dfpl import callbacks -from dfpl import options -from dfpl import history as ht -from dfpl import settings - - -class RBMLayer(tf.keras.layers.Layer): - def __init__(self, input_size, output_size, name="RBMLayer", **kwargs): - super(RBMLayer, self).__init__(name=name, **kwargs) - self._input_size = input_size - self._output_size = output_size - self._name = name - # self._activation_function = activation_function - - def build(self, input_shape): - self.w = self.add_weight( - shape=(input_shape[-1], self._output_size), - initializer="random_normal", - trainable=True, name=f"w_{self._name}" - ) - self.hb = self.add_weight( - shape=(self._output_size,), initializer="random_normal", trainable=True, name=f"hb_{self._name}" - ) - self.vb = self.add_weight( - shape=(input_shape[-1],), initializer="random_normal", trainable=True, name=f"vb_{self._name}" - ) - - def __prob_h_given_v(self, visible, w, hb): - return tf.nn.sigmoid(tf.matmul(visible, w) + hb) - - def __prob_v_given_h(self, hidden, w, vb): - return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb) - - def __sample_prob(self, probs): - return tf.nn.relu(tf.sign(probs - tf.random.uniform(tf.shape(probs)))) - - def call(self, X): - out = tf.nn.sigmoid(tf.matmul(X, self.w) + self.hb) - hiddenGen = self.__sample_prob(self.__prob_h_given_v(X, self.w, self.hb)) - visibleGen = self.__sample_prob(self.__prob_v_given_h(hiddenGen, self.w, self.vb)) - h1 = self.__prob_h_given_v(visibleGen, self.w, self.hb) - return out, visibleGen, hiddenGen, h1 - - def get_config(self): - return {"input_size": self._input_size, - "output_size": self._output_size} - - -class RBM(tf.keras.Model): - - def __init__(self, input_size, output_size, name="RBM", **kwargs): - super(RBM, self).__init__(name=name, **kwargs) - # Define hyperparameters - self._input_size = input_size - self._output_size = output_size - self.n_layer = round(math.log2(input_size / output_size)) - - self.last_out = None - - # layers defination - # encoding layers - self.rbm_encoder_layers = [] - self.rbm_decoder_layers = [] - self.rbm_encoder1 = RBMLayer(self._input_size, int(self._input_size // 2), name="encoder_0") - - if self.n_layer > 1: - for i in range(1, self.n_layer): - factor_units = 2 ** (i + 1) - self.rbm_encoder_layers.append( - RBMLayer(int(self._input_size // factor_units) * 2, int(self._input_size // factor_units), - name=f"encoder_{i}")) - - # decoding layers - factor_units = 2 ** (self.n_layer - 1) - self.rbm_decoder1 = RBMLayer(int(input_size // factor_units) // 2, int(input_size // factor_units), - name="decoder_0") - - for layer_num, i in enumerate(range(self.n_layer - 2, 0, -1)): - factor_units = 2 ** i - self.rbm_decoder_layers.append( - RBMLayer(int(input_size // (factor_units * 2)), int(input_size // factor_units), - name=f"decoder_{layer_num + 1}")) - - self.rbm_decoder_layers.append( - RBMLayer(int(input_size // 2), int(input_size), name=f"decoder_{self.n_layer - 1}")) - - def call(self, X, training=None): - outs = {} - out, visibleGen, hiddenGen, h1 = self.rbm_encoder1(X) - # out, visibleGen, hiddenGen, h1 = self.rbm_encoder(X) - outs[f'encoder_{0}'] = (out, visibleGen, hiddenGen, h1) - for i, layer in enumerate(self.rbm_encoder_layers): - out, visibleGen, hiddenGen, h1 = layer(hiddenGen) - outs[f'encoder_{i + 1}'] = (out, visibleGen, hiddenGen, h1) - - out, visibleGen, hiddenGen, h1 = self.rbm_decoder1(hiddenGen) - outs[f'decoder_{0}'] = (out, visibleGen, hiddenGen, h1) - for j, layer in enumerate(self.rbm_decoder_layers): - out, visibleGen, hiddenGen, h1 = layer(hiddenGen) - outs[f'decoder_{j + 1}'] = (out, visibleGen, hiddenGen, h1) - return out, visibleGen, hiddenGen, h1, outs - - def train_step(self, data): - X, y = data - - learning_rate = self.optimizer._decayed_lr('float32') - out, visibleGen, hiddenGen, h1, out_dict = self(X, training=True) - weights_dict = {} - for lay in self.layers: - weights_dict[lay.name] = lay - - last_out = X - for idx, (key, value) in enumerate(out_dict.items()): - out, visibleGen, hiddenGen, h1 = value - positive_grad = tf.matmul(tf.transpose(last_out), hiddenGen) - negative_grad = tf.matmul(tf.transpose(visibleGen), h1) - # loss = tf.reduce_mean(tf.square(y, visibleGen)) - # loss = self.compiled_loss(y, visibleGen) - - # update the weights of the model based on the loss and LR. - curr_layer = weights_dict[key] - curr_layer.weights[0].assign(curr_layer.weights[0] + learning_rate * - (positive_grad - negative_grad) / tf.cast(tf.shape(last_out)[0], - dtype=tf.float32)) - curr_layer.weights[1].assign(curr_layer.weights[1] + learning_rate * tf.math.reduce_mean(hiddenGen - h1, 0)) - curr_layer.weights[2].assign( - curr_layer.weights[2] + learning_rate * tf.math.reduce_mean(last_out - visibleGen, 0)) - - last_out = hiddenGen - return {m.name: m.result() for m in self.metrics} - - def test_step(self, data): - # Unpack the data - x, y = data - # Compute predictions - out, visibleGen, hiddenGen, h1, out_dict = self(x, training=False) - # Updates the metrics tracking the loss - self.compiled_loss(y, hiddenGen, regularization_losses=self.losses) - # Update the metrics. - self.compiled_metrics.update_state(y, hiddenGen) - # Return a dict mapping metric names to current value. - # Note that it will include the loss (tracked in self.metrics). - return {m.name: m.result() for m in self.metrics} - - -def define_rbm_model(opts: options.Options, - input_size: int = 2048, - encoding_dim: int = 256) -> Model: - """ - This function provides an autoencoder model to reduce a certain input to a compressed version. - - :param encoding_dim: Size of the compressed representation. Default: 85 - :param input_size: Size of the input. Default: 2048 - :return: a tuple of autoencoder and encoder models - """ - if opts.aeOptimizer == "Adam": - ac_optimizer = tf.keras.optimizers.Adam(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) - elif opts.aeOptimizer == "SGD": - ac_optimizer = tf.keras.optimizers.SGD(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) - - rbm_model = RBM(input_size=input_size, - output_size=encoding_dim) - rbm_model.compile(optimizer=ac_optimizer, loss=opts.lossFunction) - - return rbm_model - - -def train_full_rbm(df: pd.DataFrame, opts: options.Options) -> Model: - """ - Train an autoencoder on the given feature matrix X. Response matrix is only used to - split meaningfully in test and train data set. - - :param opts: Command line arguments as defined in options.py - :param df: Pandas dataframe that contains the smiles/inchi data for training the autoencoder - :return: The encoder model of the trained autoencoder - """ - - # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) - rbm = define_rbm_model(opts, input_size=opts.fpSize, encoding_dim=opts.encFPSize) - # define output file for autoencoder and encoder weights - if opts.ecWeightsFile == "": - logging.info("No RBM encoder weights file specified") - base_file_name = os.path.splitext(basename(opts.inputFile))[0] - logging.info(f"RBM weights will be saved in {base_file_name}.rbm{opts.encFPSize}.hdf5") - rbm_weights_file = os.path.join(opts.outputDir, base_file_name + f".rbm{opts.encFPSize}.hdf5") - # ec_weights_file = os.path.join(opts.outputDir, base_file_name + ".rbm_encoder.weights.hdf5") - else: - logging.info(f"RBM encoder will be saved in {opts.ecWeightsFile}") - base_file_name = os.path.splitext(basename(opts.ecWeightsFile))[0] - rbm_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - - # collect the callbacks for training - callback_list = callbacks.autoencoder_callback(checkpoint_path=rbm_weights_file, opts=opts) - - # Select all fps that are valid and turn them into a numpy array - # This step is crucial for speed!!! - fp_matrix = np.array(df[df["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values) - logging.info(f"Training RBM on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") - - # split data into test and training data - x_train, x_test = train_test_split(fp_matrix, - test_size=0.2, - random_state=42) - x_train = tf.cast(tf.where(x_train, 1, 0), tf.float32) - x_test = tf.cast(tf.where(x_test, 1, 0), tf.float32) - logging.info(f"RBM train data shape {x_train.shape} with type {x_train.dtype}") - logging.info(f"RBM test data shape {x_test.shape} with type {x_test.dtype}") - auto_hist = rbm.fit(x=x_train, y=x_train, - callbacks=callback_list, - epochs=opts.aeEpochs, - batch_size=opts.aeBatchSize, - verbose=opts.verbose, - steps_per_epoch=25, - validation_data=(x_test, x_test)) - rbm.summary(print_fn=logging.info) - ht.store_and_plot_history(base_file_name=os.path.join(opts.outputDir, base_file_name + ".RBM"), - hist=auto_hist) - rbm.save_weights(rbm_weights_file) - save_path = os.path.join(opts.ecModelDir, "rbm_model") - rbm.save(save_path) - logging.info(f"RBM weights stored in file: {rbm_weights_file}") - - return rbm - - -def compress_fingerprints(dataframe: pd.DataFrame, - encoder: Model, layer_num: int) -> pd.DataFrame: - """ - Adds a column of the compressed version of the fingerprints to the original dataframe. - - :param dataframe: Dataframe containing a column named 'fp' with the fingerprints - :param encoder: The trained autoencoder that is used for compressing the fingerprints - :return: The input dataframe extended by a column containing the compressed version of the fingerprints - """ - logging.info("Adding rbm compressed fingerprints") - idx = dataframe[dataframe["fp"].notnull()].index - fp_matrix = np.array(dataframe[dataframe["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values) - logging.info(f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") - logging.info("Compressed fingerprints from rbm are added to input dataframe.") - fp_matrix = tf.cast(tf.where(fp_matrix, 1, 0), tf.float32) - out, visibleGen, hiddenGen, h1, out_dict = encoder.predict(fp_matrix) - out, visibleGen, hiddenGen, h1 = out_dict[f'encoder_{layer_num-1}'] - dataframe['fpcompressed'] = pd.DataFrame({'fpcompressed': [s for s in hiddenGen]}, idx) - - return dataframe diff --git a/build/lib/dfpl/settings.py b/build/lib/dfpl/settings.py deleted file mode 100644 index 2d900cda..00000000 --- a/build/lib/dfpl/settings.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np - -# Datatype that is used for fingerprint vectors stored inside -# dataframe used during the program -df_fp_numpy_type = np.bool8 - -# Do we need copies when creating numpy matrices from the pandas dataframes -# for training? Everything seems to work fine with False and it saves memory. -numpy_copy_values = False - -# Autoencoder data type settings - -# Type that is given as input numpy array to the network -# We need to carefully test the performance of the training here -# The big dsstox dataset runs out of mem on Patrick's machine when -# we use floats here. -# Also, we possibly should give the FNN a float type fp -ac_fp_numpy_type = np.bool8 - -# Type used for the compressed fp which is an inner layer of the ac -# an MUST be of float type. On Patrick's machine, (GPU) tensorflow layers -# are float32 per default. Not sure if float64 can be used with GPU -# training and if it enhances results. Needs to be evaluated. -ac_fp_compressed_numpy_type = np.bool8 - -# Feedforward Neuronal Network data type settings -# -# The sections below define the types of the numpy matrices used for training -# the FNNs. In short: -# XX_fp_numpy_type: used for uncompressed fingerprint tensors -# XX_fp_compressed_numpy_type: used for compressed fingerprint tensors -# XX_target_numpy_type: used for the target tensors - -nn_fp_numpy_type = np.float32 -nn_fp_compressed_numpy_type = np.float32 -nn_target_numpy_type = np.short - -nn_multi_fp_numpy_type = np.float32 -nn_multi_fp_compressed_numpy_type = np.float32 -nn_multi_target_numpy_type = np.float32 - -# Training settings - -# Training settings of the AC that were magic numbers in the code before. -ac_train_min_delta = 0.0001 -ac_train_check_period = 1 -ac_train_patience = 1 - -# Training settings of the FNN that were magic numbers in the code before. -nn_train_min_delta = 0.0001 -nn_train_check_period = 5 -nn_train_patience = 5 diff --git a/build/lib/dfpl/single_label_model.py b/build/lib/dfpl/single_label_model.py deleted file mode 100644 index de437d1e..00000000 --- a/build/lib/dfpl/single_label_model.py +++ /dev/null @@ -1,1008 +0,0 @@ -import logging -import math -import os -import shutil -import sys -from os import path -from time import time - -import numpy as np -import pandas as pd -import tensorflow as tf -import tensorflow.keras.backend as K -import wandb -from typing import Union, Tuple - -from sklearn.metrics import ( - auc, - classification_report, - confusion_matrix, - matthews_corrcoef, - roc_curve, -) -from sklearn.model_selection import StratifiedKFold, train_test_split -from tensorflow.keras import metrics, optimizers, regularizers -from tensorflow.keras.layers import AlphaDropout, Dense, Dropout -from tensorflow.keras.losses import ( - BinaryCrossentropy, - MeanSquaredError, - BinaryFocalCrossentropy, -) -from tensorflow.keras.models import Model, Sequential - -from dfpl import callbacks as cb -from dfpl import options -from dfpl import plot as pl -from dfpl import settings -from dfpl.utils import ae_scaffold_split, weight_split - - -def prepare_nn_training_data( - df: pd.DataFrame, target: str, opts: options.Options, return_dataframe: bool = False -) -> Union[Tuple[np.ndarray, np.ndarray], pd.DataFrame]: - # Check the value counts and abort if too imbalanced - allowed_imbalance = 0.1 - # If feature compression is enabled, use compressed fingerprints - if opts.compressFeatures: - vc = df[df[target].notna() & df["fpcompressed"].notnull()][ - target - ].value_counts() - # Otherwise, use uncompressed fingerprints - else: - vc = df[df[target].notna() & df["fp"].notnull()][target].value_counts() - - # If the dataset is extremely unbalanced, adjust sampling options - if min(vc) < max(vc) * allowed_imbalance: - logging.info( - f" Your training data is extremely unbalanced ({target}): 0 - {vc[0]}, and 1 - {vc[1]} values." - ) - if opts.sampleDown: - logging.info(f" I will down-sample your data") - opts.sampleFractionOnes = allowed_imbalance - else: - logging.info(f" I will not down-sample your data automatically.") - logging.info( - f" Consider to enable down sampling of the 0 values with --sampleDown option." - ) - - logging.info("Preparing training data matrices") - - # If feature compression is enabled, use compressed fingerprints - if opts.compressFeatures: - logging.info("Using compressed fingerprints") - df_fpc = df[df[target].notna() & df["fpcompressed"].notnull()] - logging.info( - f"DataSet has {df_fpc.shape[0]} valid entries in fpcompressed and {target}" - ) - - # If sampling down is enabled, adjust sampling options and downsample the data - if opts.sampleDown: - assert 0.0 < opts.sampleFractionOnes < 1.0 - logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") - # How many ones and zeros are in the dataset - counts = df_fpc[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - - # Add a sample of 0s to the df of 1s - dfX = df_fpc[df_fpc[target] == 1].append( - df_fpc[df_fpc[target] == 0].sample( - int(min(counts[0], counts[1] / opts.sampleFractionOnes)) - ) - ) - # Convert fingerprints and targets to numpy arrays - x = np.array( - dfX["fpcompressed"].to_list(), - dtype=settings.nn_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - y = np.array( - dfX[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - # Return the dataframe if specified - if return_dataframe: - return dfX - else: - logging.info("Fraction sampling is OFF") - # How many ones and zeros are in the dataset - counts = df_fpc[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - - # Convert fingerprints and targets to numpy arrays - x = np.array( - df_fpc["fpcompressed"].to_list(), - dtype=settings.nn_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - y = np.array( - df_fpc[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - if return_dataframe: - return df_fpc - - # If feature compression is not enabled, use uncompressed fingerprints - else: - logging.info("Using uncompressed fingerprints") - # select rows where the target is not null and the fingerprint is not null - df_fp = df[df[target].notna() & df["fp"].notnull()] - logging.info(f"DataSet has {df_fp.shape[0]} valid entries in fp and {target}") - - if opts.sampleDown: - logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") - # count number of each class value - counts = df_fp[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - # downsample the majority class by taking a fraction of its samples and combining with all of the - # minority class - dfX = df_fp[df_fp[target] == 1.0].append( - df_fp[df_fp[target] == 0.0].sample( - int(min(counts[0], counts[1] / opts.sampleFractionOnes)) - ) - ) - # convert fingerprints to numpy array - x = np.array( - dfX["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - # convert target values to numpy array - y = np.array( - dfX[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - if return_dataframe: - return dfX - else: - logging.info("Fraction sampling is OFF") - # count number of each class value - counts = df_fp[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - # convert fingerprints to numpy array - x = np.array( - df_fp["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - # convert target values to numpy array - y = np.array( - df_fp[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - if return_dataframe: - return df_fp - # return the numpy arrays of fingerprints and targets - return x, y - - -# This function defines a feedforward neural network (FNN) with the given input size, options, and output bias -def build_fnn_network( - input_size: int, opts: options.Options, output_bias=None -) -> Model: - # Set the output bias if it is provided - if output_bias is not None: - output_bias = tf.keras.initializers.Constant(output_bias) - - # Define the number of hidden layers based on the input size - my_hidden_layers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} - if not str(input_size) in my_hidden_layers.keys(): - raise ValueError("Wrong input-size. Must be in {2048, 1024, 999, 512, 256}.") - nhl = int(math.log2(input_size) / 2 - 1) - - # Create a sequential model - model = Sequential() - - # Add the first hidden layer - if opts.activationFunction == "relu": - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation="relu", - kernel_regularizer=regularizers.l2(opts.l2reg), - kernel_initializer="he_uniform", - ) - ) - model.add(Dropout(opts.dropout)) - elif opts.activationFunction == "selu": - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout)) - else: - logging.error("Only 'relu' and 'selu' activation is supported") - sys.exit(-1) - - # Add additional hidden layers - for i in range(1, nhl): - factor_units = 2 ** (i + 1) - factor_dropout = 2 * i - if opts.activationFunction == "relu": - model.add( - Dense( - units=int(input_size / factor_units), - activation="relu", - kernel_regularizer=regularizers.l2(opts.l2reg), - kernel_initializer="he_uniform", - ) - ) - model.add(Dropout(opts.dropout / factor_dropout)) - elif opts.activationFunction == "selu": - model.add( - Dense( - units=int(input_size / factor_units), - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout / factor_dropout)) - else: - logging.error("Only 'relu' and 'selu' activation is supported") - sys.exit(-1) - - # Add the output layer with a sigmoid activation function and the output bias if provided - model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) - model.summary() - return model - - -# This function defines a shallow neural network (SNN) with the given input size, options, and output bias -def build_snn_network( - input_size: int, opts: options.Options, output_bias=None -) -> Model: - # Set the output bias if it is provided - if output_bias is not None: - output_bias = tf.keras.initializers.Constant(output_bias) - - # Create a sequential model - model = Sequential() - - # Add the first hidden layer - model.add( - Dense( - input_dim=input_size, - units=50, - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout)) - - # Add 7 hidden layers with 50 units each, a SELU activation function, and a LeCun normal kernel initializer - for i in range(7): - model.add(Dense(units=50, activation="selu", kernel_initializer="lecun_normal")) - # Add an alpha dropout layer with the given dropout rate - model.add(AlphaDropout(opts.dropout)) - - # Add the output layer with 1 unit and a sigmoid activation function - # If output_bias is not None, set the bias initializer to the specified value - model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) - - return model - - -def balanced_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> np.float64: - """ - Computes the balanced accuracy metric. - - Args: - y_true (np.ndarray): The true values of the labels. - y_pred (np.ndarray): The predicted values of the labels. - - Returns: - np.float64: The balanced accuracy score. - """ - y_pred = K.round(y_pred) # Convert continuous predictions to binary class labels - tn = K.sum((1 - y_true) * (1 - y_pred)) - fp = K.sum((1 - y_true) * y_pred) - fn = K.sum(y_true * (1 - y_pred)) - tp = K.sum(y_true * y_pred) - balanced_acc = (tp / (tp + fn + 1e-10) + tn / (tn + fp + 1e-10)) / 2 - return balanced_acc - - -def define_single_label_model( - input_size: int, opts: options.Options, output_bias=None -) -> Model: - """ - Defines and compiles the single-label neural network model. - - Args: - input_size (int): The size of the input layer. - opts (options.Options): The options used in the model. - output_bias (float): The initial bias for the last sigmoid layer of the model. - - Returns: - tensorflow.keras.Model: The compiled model. - """ - # Set the loss function according to the option selected - if opts.lossFunction == "bce": - loss_function = BinaryCrossentropy() - elif opts.lossFunction == "mse": - loss_function = MeanSquaredError() - elif opts.lossFunction == "focal": - loss_function = BinaryFocalCrossentropy() - else: - logging.error(f"Your selected loss is not supported: {opts.lossFunction}.") - sys.exit("Unsupported loss function") - - # Set the optimizer according to the option selected - if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) - elif opts.optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) - else: - logging.error(f"Your selected optimizer is not supported: {opts.optimizer}.") - sys.exit("Unsupported optimizer") - - # Set the type of neural network according to the option selected - if opts.fnnType == "FNN": - model = build_fnn_network(input_size, opts, output_bias) - elif opts.fnnType == "SNN": - model = build_snn_network(input_size, opts, output_bias) - else: - raise ValueError(f'Option FNN Type is not "FNN" or "SNN", but {opts.fnnType}.') - - logging.info(f"Network type: {opts.fnnType}") - model.summary(print_fn=logging.info) - - # Compile the model with the defined options - model.compile( - loss=loss_function, - optimizer=my_optimizer, - metrics=[ - metrics.BinaryAccuracy(name="accuracy"), - metrics.AUC(), - metrics.Precision(), - metrics.Recall(), - balanced_accuracy, - ], - ) - - return model - - -def evaluate_model( - x_test: np.ndarray, - y_test: np.ndarray, - file_prefix: str, - model: Model, - target: str, - fold: int, -) -> pd.DataFrame: - # Log that we're evaluating the model on the test data - name = path.basename(file_prefix).replace("_", " ") - logging.info(f"Evaluating trained model '{name}' on test data") - - # Predict the test set to compute MCC, AUC, ROC curve, etc. - threshold = 0.5 # TODO: Introduce thresholds different from 0.5! - y_predict = model.predict(x_test).flatten() - y_predict_int = (y_predict >= threshold).astype(np.short) - y_test_int = y_test.astype(np.short) - - # Save the predictions to a CSV file - ( - pd.DataFrame( - { - "y_true": y_test_int, - "y_predicted": y_predict, - "y_predicted_int": y_predict_int, - "target": target, - "fold": fold, - } - ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.csv") - ) - - # Compute the confusion matrix - confusion_matrix(y_true=y_test_int, y_pred=y_predict_int) - - # Compute the metrics that depend on the class label - precision_recall = classification_report( - y_test_int, y_predict_int, output_dict=True - ) - prf = pd.DataFrame.from_dict(precision_recall)[["0", "1"]] - - # Add balanced accuracy to the computed metrics - prf.to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.prec_rec_f1.csv") - - # Evaluate the model on the validation set and log the results - loss, acc, auc_value, precision, recall, balanced_acc = tuple( - model.evaluate(x=x_test, y=y_test) - ) - - logging.info(f"Loss: {round(loss, 4)}") - logging.info(f"Accuracy: {round(acc, 4)}") - logging.info(f"AUC: {round(auc_value, 4)}") - logging.info(f"Precision: {round(precision, 4)}") - logging.info(f"Recall: {round(recall, 4)}") - logging.info(f"Balanced Accuracy: {round(balanced_acc, 4)}") - - # Compute the Matthews Correlation Coefficient (MCC) - MCC = matthews_corrcoef(y_test_int, y_predict_int) - logging.info(f"MCC: {round(MCC, 4)}") - - # Generate the AUC-ROC curve data from the validation data - FPR, TPR, thresholds_keras = roc_curve( - y_true=y_test_int, y_score=y_predict, drop_intermediate=False - ) - AUC = auc(FPR, TPR) - nonzero_mask = (FPR != 0) | (TPR != 0) - FPR_filtered = FPR[nonzero_mask] - TPR_filtered = TPR[nonzero_mask] - - # Save AUC data to CSV file - pd.DataFrame( - list( - zip( - FPR_filtered, - TPR_filtered, - [AUC] * len(FPR_filtered), - [target] * len(FPR_filtered), - [fold] * len(FPR_filtered), - ) - ), - columns=["fpr", "tpr", "auc_value", "target", "fold"], - ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.aucdata.csv") - # Generate and save AUC-ROC curve plot - pl.plot_auc( - fpr=FPR, - tpr=TPR, - target=target, - auc_value=AUC, - filename=f"{file_prefix}_auc_data.png", - wandb_logging=False, - ) - - # Return a DataFrame containing the computed metrics - return pd.DataFrame.from_dict( - { - "p_0": prf["0"]["precision"], - "r_0": prf["0"]["recall"], - "f1_0": prf["0"]["f1-score"], - "p_1": prf["1"]["precision"], - "r_1": prf["1"]["recall"], - "f1_1": prf["1"]["f1-score"], - "loss": loss, - "accuracy": acc, - "balanced_accuracy": balanced_acc, - "MCC": MCC, - "AUC": AUC, - "target": target, - "fold": fold, - }, - orient="index", - ).T - - -def fit_and_evaluate_model( - x_train: np.ndarray, - x_test: np.ndarray, - y_train: np.ndarray, - y_test: np.ndarray, - fold: int, - target: str, - opts: options.Options, -) -> pd.DataFrame: - # Print info about training - logging.info(f"Training of fold number: {fold}") - - # Define file name prefix for saving models - model_file_prefix = path.join( - opts.outputDir, f"{target}_{opts.split_type}_single-labeled_Fold-{fold}" - ) - - # Compute class imbalance - ids, counts = np.unique(y_train, return_counts=True) - count_dict = dict(zip(ids, counts)) - - # Set initial bias for last sigmoid layer to counter class imbalance - if count_dict[0] == 0: - initial_bias = None - logging.info("No zeroes in training labels. Setting initial_bias to None.") - else: - initial_bias = np.log([count_dict[1] / count_dict[0]]) - logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") - - # Define model - model = define_single_label_model( - input_size=x_train.shape[1], opts=opts, output_bias=initial_bias - ) - - # Define checkpoint to save model weights during training - checkpoint_model_weights_path = f"{model_file_prefix}.model.weights.hdf5" - callback_list = cb.nn_callback( - checkpoint_path=checkpoint_model_weights_path, opts=opts - ) - - # Train model - start = time() - hist = model.fit( - x_train, - y_train, - callbacks=callback_list, - epochs=opts.epochs, - batch_size=opts.batchSize, - verbose=opts.verbose, - validation_data=(x_test, y_test), - ) - trainTime = str(round((time() - start) / 60, ndigits=2)) - logging.info( - f"Computation time for training the single-label model for {target}: {trainTime} min" - ) - - # Save and plot model history - pd.DataFrame(hist.history).to_csv(path_or_buf=f"{model_file_prefix}.history.csv") - pl.plot_history(history=hist, file=f"{model_file_prefix}.history.svg") - # Evaluate model - callback_model = define_single_label_model(input_size=x_train.shape[1], opts=opts) - callback_model.load_weights(filepath=checkpoint_model_weights_path) - performance = evaluate_model( - x_test=x_test, - y_test=y_test, - file_prefix=model_file_prefix, - model=callback_model, - target=target, - fold=fold, - ) - - return performance - - -def get_x_y( - df: pd.DataFrame, - target: str, - train_set: pd.DataFrame, - test_set: pd.DataFrame, - opts: options.Options, -): - train_indices = train_set.index - test_indices = test_set.index - if opts.compressFeatures: - accessor = "fpcompressed" - else: - accessor = "fp" - new_df = pd.DataFrame( - [{x: y for x, y in enumerate(item)} for item in df[accessor].values.tolist()], - index=df.index, - ) - x_train = new_df.iloc[train_indices, :].values - y_train = df.iloc[train_indices][target].values - x_test = new_df.iloc[test_indices, :].values - y_test = df.iloc[test_indices][target].values - x_train = x_train.astype("float32") - y_train = y_train.astype("float32") - x_test = x_test.astype("float32") - y_test = y_test.astype("float32") - return x_train, y_train, x_test, y_test - - -def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: - """ - Train individual models for all targets (columns) present in the provided target data (y) and a multi-label - model that classifies all targets at once. For each individual target the data is first subset to exclude NA - values (for target associations). A random sample of the remaining data (size is the split fraction) is used for - training and the remaining data for validation. - - :param opts: The command line arguments in the options class - :param df: The dataframe containing x matrix and at least one column for a y target. - """ - - # find target columns - targets = [ - c - for c in df.columns - if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] - ] - if opts.wabTracking and opts.wabTarget != "": - # For W&B tracking, we only train one target that's specified as wabTarget "ER". - # In case it's not there, we use the first one available - if opts.wabTarget in targets: - targets = [opts.wabTarget] - elif opts.wabTarget == "": - targets = targets - else: - logging.error( - f"The specified wabTarget for Weights & Biases tracking does not exist: {opts.wabTarget}" - ) - targets = [targets[0]] - - # Collect metrics for each fold and target - performance_list = [] - if opts.split_type == "random": - for target in targets: # [:1]: - # target=targets[1] # --> only for testing the code - x, y = prepare_nn_training_data(df, target, opts, return_dataframe=False) - if x is None: - continue - - logging.info(f"X training matrix of shape {x.shape} and type {x.dtype}") - logging.info(f"Y training matrix of shape {y.shape} and type {y.dtype}") - - if opts.kFolds == 1: - # for single 'folds' and when sweeping on W&B, we fix the random state - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}", - group=f"{target}", - name=f"{target}_single_fold", - ) - if opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", - name=f"{target}_single_fold", - group=f"{target}", - reinit=True, - ) - x_train, x_test, y_train, y_test = train_test_split( - x, y, stratify=y, test_size=opts.testSize, random_state=1 - ) - logging.info( - f"Splitting train/test data with fixed random initializer" - ) - else: - x_train, x_test, y_train, y_test = train_test_split( - x, y, stratify=y, test_size=opts.testSize - ) - - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=0, - target=target, - opts=opts, - ) - performance_list.append(performance) - # save complete model - trained_model = define_single_label_model( - input_size=len(x[0]), opts=opts - ) - # trained_model.load_weights - # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join(opts.outputDir, f"{target}_saved_model") - ) - - elif 1 < opts.kFolds < 10: # int(x.shape[0] / 100): - # do a kfold cross-validation - kfold_c_validator = StratifiedKFold( - n_splits=opts.kFolds, shuffle=True, random_state=42 - ) - fold_no = 1 - # split the data - for train, test in kfold_c_validator.split(x, y): - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FNN_{opts.threshold}_{opts.split_type}", - group=f"{target}", - name=f"{target}-{fold_no}", - reinit=True, - ) - if opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}", - name=f"AE_{target}-{fold_no}", - group=f"{target}", - reinit=True, - ) - - # Initialize wandb for the feed forward model - wandb.init( - project=f"FNN_{opts.threshold}_{opts.split_type}", - group=f"{target}", - name=f"FNN_{target}-{fold_no}", - reinit=True, - ) - - performance = fit_and_evaluate_model( - x_train=x[train], - x_test=x[test], - y_train=y[train], - y_test=y[test], - fold=fold_no, - target=target, - opts=opts, - ) - performance_list.append(performance) - - # save complete model - trained_model = define_single_label_model( - input_size=len(x[0]), opts=opts - ) - # trained_model.load_weights - # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-{fold_no}.model.weights.hdf5", - ) - ) - # create output directory and store complete model - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}-{fold_no}_saved_model" - ) - ) - if opts.wabTracking: - wandb.finish() - fold_no += 1 - - # select and copy best model - how to define the best model? - best_fold = pd.concat(performance_list, ignore_index=True).sort_values( - by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True - )["fold"][0] - # rename the fold to best fold - src = os.path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-{best_fold}.model.weights.hdf5", - ) - dst = os.path.join( - opts.outputDir, - f"{target}_single-labeled_Best_Fold-{best_fold}.model.weights.hdf5", - ) - os.rename(src, dst) - - src_dir = os.path.join( - opts.outputDir, f"{target}-{best_fold}_saved_model" - ) - dst_dir = os.path.join( - opts.outputDir, f"{target}-{best_fold}_best_saved_model" - ) - - if path.isdir(dst_dir): - shutil.rmtree(dst_dir) - - # Rename source directory to destination directory - os.rename(src_dir, dst_dir) - - # save complete model - else: - logging.info( - "Your selected number of folds for Cross validation is out of range. " - "It must be 1 or smaller than 1 hundredth of the number of samples." - ) - sys.exit("Number of folds out of range") - ( - pd.concat(performance_list, ignore_index=True).to_csv( - path_or_buf=path.join( - opts.outputDir, "single_label_random_model.evaluation.csv" - ) - ) - ) - # For each individual target train a model - elif opts.split_type == "scaffold_balanced": - # df, irrelevant_columns = preprocess_dataframe(df, opts) - for idx, target in enumerate(targets): - df = prepare_nn_training_data(df, target, opts, return_dataframe=True) - relevant_cols = ["smiles"] + ["fp"] + [target] # list(irrelevant_columns) - if opts.compressFeatures: - relevant_cols = relevant_cols + ["fpcompressed"] - df_task = df.loc[:, relevant_cols] - # Drop rows with missing values in the target column - df_task.dropna(subset=[target], inplace=True) - df_task.reset_index(drop=True, inplace=True) - if opts.kFolds == 1: - train_set, val_set, test_set = ae_scaffold_split( - df_task, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=False, - seed=42, - ) - x_train, y_train, x_test, y_test = get_x_y( - df_task, target, train_set, test_set, opts - ) - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}", - group=f"{target}", - name=f"{target}_single_fold", - ) - elif opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", - name=f"{target}-single-fold", - group=f"{target}", - ) - - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=0, - target=target, - opts=opts, - ) - performance_list.append(performance) - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}_scaffold_saved_model_0" - ) - ) - elif opts.kFolds > 1: - for fold_no in range(1, opts.kFolds + 1): - print(f"Splitting data with seed {fold_no}") - train_set, val_set, test_set = ae_scaffold_split( - df_task, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=True, - seed=fold_no, - ) - x_train, y_train, x_test, y_test = get_x_y( - df_task, target, train_set, test_set, opts - ) - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}", - group=f"{target}", - name=f"{target}-{fold_no}", - reinit=True, - ) - elif opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", - name=f"{target}-{fold_no}", - group=f"{target}", - reinit=True, - ) - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=fold_no, - target=target, - opts=opts, - ) - performance_list.append(performance) - - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-{fold_no}.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}_scaffold_saved_model_{fold_no}" - ) - ) - if opts.wabTracking: - wandb.finish() - fold_no += 1 - # find best fold - best_fold = pd.concat(performance_list, ignore_index=True).sort_values( - by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True - )["fold"][0] - # rename the fold to best fold - src = os.path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-{best_fold}.model.weights.hdf5", - ) - dst = os.path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_BEST_Fold-{best_fold}.model.weights.hdf5", - ) - os.rename(src, dst) - - src_dir = os.path.join( - opts.outputDir, f"{target}_scaffold_saved_model_{best_fold}" - ) - dst_dir = os.path.join( - opts.outputDir, - f"{target}_scaffold_saved_model_BEST_FOLD_{best_fold}", - ) - - if path.isdir(dst_dir): - shutil.rmtree(dst_dir) - - # Rename source directory to destination directory - os.rename(src_dir, dst_dir) - - else: - logging.info( - "Your selected number of folds for Cross validation is out of range. " - "It must be 1 or smaller than 1 hundredth of the number of samples." - ) - sys.exit("Number of folds out of range") - ( - pd.concat(performance_list, ignore_index=True).to_csv( - path_or_buf=path.join( - opts.outputDir, "single_label_scaffold_model.evaluation.csv" - ) - ) - ) - elif opts.split_type == "molecular_weight": - logging.info("You can use molecular_weight split once.") - for idx, target in enumerate(targets): - df = prepare_nn_training_data(df, target, opts, return_dataframe=True) - relevant_cols = ["smiles"] + ["fp"] + [target] - if opts.compressFeatures: - relevant_cols = relevant_cols + ["fpcompressed"] - df_task = df.loc[:, relevant_cols] - df_task.dropna(subset=[target], inplace=True) - df_task.reset_index(drop=True, inplace=True) - if opts.kFolds == 1: - train_set, val_set, test_set = weight_split( - df_task, bias="small", sizes=(1 - opts.testSize, 0.0, opts.testSize) - ) - x_train, y_train, x_test, y_test = get_x_y( - df_task, target, train_set, test_set, opts - ) - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}AE_{opts.aeSplitType}", - group=f"{target}", - name=f"{target}_single_fold", - ) - elif opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_weight_split_{opts.split_type}", - name=f"{target}-single-fold", - group=f"{target}", - ) - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=0, - target=target, - opts=opts, - ) - performance_list.append(performance) - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_weight_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join(opts.outputDir, f"{target}_weight_saved_model_0") - ) - elif opts.kFolds > 1: - raise Exception( - f"Unsupported number of folds: {opts.kFolds} for {opts.split_type} split.\ - You CANNOT do multiple folds based on molecular_weight." - ) - - ( - pd.concat(performance_list, ignore_index=True).to_csv( - path_or_buf=path.join( - opts.outputDir, "single_label_molecular_weight_model.evaluation.csv" - ) - ) - ) - else: - raise Exception(f"Unsupported split type: {opts.split_type}") diff --git a/build/lib/dfpl/utils.py b/build/lib/dfpl/utils.py deleted file mode 100644 index b45bde62..00000000 --- a/build/lib/dfpl/utils.py +++ /dev/null @@ -1,329 +0,0 @@ -import json -import logging -import os -import pathlib -import warnings -from collections import defaultdict -from random import Random -from typing import Dict, List, Set, Tuple, Union - -import numpy as np -import pandas as pd -from rdkit import Chem, RDLogger -from rdkit.Chem import rdMolDescriptors -from rdkit.Chem.Scaffolds import MurckoScaffold -from tqdm import tqdm - -RDLogger.DisableLog("rdApp.*") - - -def makePathAbsolute(p: str) -> str: - path = pathlib.Path(p) - if path.is_absolute(): - return p - else: - return str(path.absolute()) - - -def createDirectory(directory: str): - path = makePathAbsolute(directory) - if not os.path.exists(path): - os.makedirs(path) - - -def createArgsFromJson(in_json: str, ignore_elements: list, return_json_object: bool): - arguments = [] - with open(in_json, "r") as f: - data = json.load(f) - for key, value in data.items(): - if key not in ignore_elements: - if key == "extra_metrics" and isinstance(value, list): - arguments.append("--extra_metrics") - arguments.extend(value) - else: - arguments.append("--" + str(key)) - arguments.append(str(value)) - if return_json_object: - return arguments, data - return arguments - - -def make_mol(s: str, keep_h: bool, add_h: bool, keep_atom_map: bool): - """ - Builds an RDKit molecule from a SMILES string. - - :param s: SMILES string. - :param keep_h: Boolean whether to keep hydrogens in the input smiles. This does not add hydrogens, it only keeps the - m if they are specified. - :param add_h: Boolean whether to add hydrogens to the input smiles. - :param keep_atom_map: Boolean whether to keep the original atom mapping. - :return: RDKit molecule. - """ - params = Chem.SmilesParserParams() - params.removeHs = not keep_h if not keep_atom_map else False - mol = Chem.MolFromSmiles(s, params) - - if add_h: - mol = Chem.AddHs(mol) - - if keep_atom_map: - atom_map_numbers = tuple(atom.GetAtomMapNum() for atom in mol.GetAtoms()) - for idx, map_num in enumerate(atom_map_numbers): - if idx + 1 != map_num: - new_order = np.argsort(atom_map_numbers).tolist() - return Chem.rdmolops.RenumberAtoms(mol, new_order) - - return mol - - -def generate_scaffold( - mol: Union[str, Chem.Mol, Tuple[Chem.Mol, Chem.Mol]], include_chirality: bool = True -) -> str: - """ - Computes the Bemis-Murcko scaffold for a SMILES string, an RDKit molecule, or an InChI string or InChIKey. - - :param mol: A SMILES, RDKit molecule, InChI string, or InChIKey string. - :param include_chirality: Whether to include chirality in the computed scaffold. - :return: The Bemis-Murcko scaffold for the molecule. - """ - if isinstance(mol, str): - if mol.startswith("InChI="): - mol = inchi_to_mol(mol) - else: - mol = make_mol(mol, keep_h=False, add_h=False, keep_atom_map=False) - elif isinstance(mol, tuple): - mol = mol[0] - scaffold = MurckoScaffold.MurckoScaffoldSmiles( - mol=mol, includeChirality=include_chirality - ) - - return scaffold - - -def scaffold_to_smiles( - mols: List[str], use_indices: bool = False -) -> Dict[str, Union[Set[str], Set[int]]]: - """ - Computes the scaffold for each SMILES and returns a mapping from scaffolds to sets of smiles (or indices). - :param mols: A list of SMILES. - :param use_indices: Whether to map to the SMILES's index in :code:`mols` rather than - mapping to the smiles string itself. This is necessary if there are duplicate smiles. - :return: A dictionary mapping each unique scaffold to all SMILES (or indices) which have that scaffold. - """ - scaffolds = defaultdict(set) - for i, mol in tqdm(enumerate(mols), total=len(mols)): - scaffold = generate_scaffold(mol) - if use_indices: - scaffolds[scaffold].add(i) - else: - scaffolds[scaffold].add(mol) - - return scaffolds - - -# def inchi_to_mol(inchi: str) -> Chem.Mol: -# return Chem.inchi.MolFromInchi(inchi) -def smiles_to_mol(smiles: str) -> Chem.Mol: - mol = Chem.MolFromSmiles(smiles) - if mol is None: - return None - # raise ValueError(f"Could not convert SMILES to Mol: {smiles}") - return mol - - -def inchi_to_mol(inchi: str) -> Chem.Mol: - mol = Chem.MolFromInchi(inchi) - if mol is None: - return None - # raise ValueError(f"Could not convert InChI to Mol: {inchi}") - return mol - - -def weight_split( - data: pd.DataFrame, bias: str, sizes: Tuple[float, float, float] = (0.8, 0, 0.2) -) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: - if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): - raise ValueError(f"Invalid train/val/test splits! got: {sizes}") - # initial_indices = data.index.to_numpy() - train_size, val_size, _ = ( - sizes[0] * len(data), - sizes[1] * len(data), - sizes[2] * len(data), - ) - if "inchi" in [x.lower() for x in data.columns]: - data["mol"] = data["inchi"].apply(inchi_to_mol) - elif "smiles" in [x.lower() for x in data.columns]: - data["mol"] = data["smiles"].apply(smiles_to_mol) - else: - logging.info("Dataframe does not have a SMILES or InChi column") - none_mols = data["mol"].isnull().sum() - logging.info(f"There are {none_mols} chemicals with no mol objects ") - data.dropna(subset=["mol"], inplace=True) - data["mol_weight"] = data.apply( - lambda row: rdMolDescriptors.CalcExactMolWt(row["mol"]) - if row["mol"] is not None - else None, - axis=1, - ) - # data = data.drop(columns=['mol','fp','inchi','toxid','key'], axis=1) - sorted_data = data.copy() - if bias == "big": - sorted_data = data.sort_values(by="mol_weight", ascending=False) - elif bias == "small": - sorted_data = data.sort_values(by="mol_weight", ascending=True) - else: - print("Wrong bias, choose small or big") - indices = np.arange(len(sorted_data)) - train_end_idx = int(train_size) - val_end_idx = int(train_size + val_size) - train_indices = indices[:train_end_idx] - val_indices = indices[train_end_idx:val_end_idx] - test_indices = indices[val_end_idx:] - train_df = sorted_data.iloc[train_indices].reset_index(drop=True) - val_df = sorted_data.iloc[val_indices].reset_index(drop=True) - test_df = sorted_data.iloc[test_indices].reset_index(drop=True) - - return train_df, val_df, test_df - - -def ae_scaffold_split( - data: pd.DataFrame, - sizes: Tuple[float, float, float] = (0.8, 0, 0.2), - balanced: bool = False, - key_molecule_index: int = 0, - seed: int = 0, -) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: - """ - Splits a pandas DataFrame by scaffold so that no molecules sharing a scaffold are in different splits. - :param data: A pandas DataFrame containing SMILES strings and molecule properties. - :param sizes: A length-3 tuple with the proportions of data in the train, validation, and test sets. - :param balanced: Whether to balance the sizes of scaffolds in each set rather than putting the smallest in test set. - :param key_molecule_index: For data with multiple molecules, this sets which molecule will be considered during splitting. - :param seed: Random seed for shuffling when doing balanced splitting. - :return: A tuple of pandas DataFrames containing the train, validation, and test splits of the data. - """ - if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): - raise ValueError(f"Invalid train/val/test splits! got: {sizes}") - - # Split - train_size, val_size, test_size = ( - sizes[0] * len(data), - sizes[1] * len(data), - sizes[2] * len(data), - ) - train: List[int] = [] - val: List[int] = [] - test: List[int] = [] - train_scaffold_count, val_scaffold_count, test_scaffold_count = 0, 0, 0 - - # Map from scaffold to index in the data - key_colnames = data.columns - if "inchi" in key_colnames: - key_molecule_index = next( - (i for i, colname in enumerate(data.columns) if "inchi" in colname.lower()), - None, - ) - if key_molecule_index is None: - warnings.warn("No column with 'inchi' found in the DataFrame. Proceeding with caution.") - key_mols = data.iloc[:, key_molecule_index].apply(inchi_to_mol).dropna() - else: - key_mols = data.iloc[:, key_molecule_index] - scaffold_to_indices = scaffold_to_smiles(key_mols.tolist(), use_indices=True) - # Seed randomness - random = Random(seed) - - if ( - balanced - ): # Put stuff that's bigger than half the val/test size into train, rest just order randomly - index_sets = list(scaffold_to_indices.values()) - big_index_sets = [] - small_index_sets = [] - for index_set in index_sets: - if len(index_set) > val_size / 2 or len(index_set) > test_size / 2: - big_index_sets.append(index_set) - else: - small_index_sets.append(index_set) - random.seed(seed) - random.shuffle(big_index_sets) - random.shuffle(small_index_sets) - index_sets = big_index_sets + small_index_sets - else: # Sort from largest to smallest scaffold sets - index_sets = sorted( - list(scaffold_to_indices.values()), - key=lambda index_set: len(index_set), - reverse=True, - ) - for index_set in index_sets: - if len(test) + len(index_set) <= test_size: - test += index_set - test_scaffold_count += 1 - elif len(train) + len(index_set) <= train_size: - train += index_set - train_scaffold_count += 1 - else: - val += index_set - val_scaffold_count += 1 - logging.info( - f"Total scaffolds = {len(scaffold_to_indices):,} | " - f"train scaffolds = {train_scaffold_count:,} | " - f"val scaffolds = {val_scaffold_count:,} | " - f"test scaffolds = {test_scaffold_count:,}" - ) - - log_scaffold_stats(data, index_sets) - # Map from indices to data - - train_df = data.iloc[train] - val_df = data.iloc[val] - test_df = data.iloc[test] - return train_df, val_df, test_df - - -def log_scaffold_stats( - data: pd.DataFrame, - index_sets: List[Set[int]], - num_scaffolds: int = 10, - num_labels: int = 20, -) -> List[Tuple[List[float], List[int]]]: - - """ - Logs and returns statistics about counts and average target values in molecular scaffolds. - :param data: A pandas DataFrame containing SMILES strings and molecule properties. - :param index_sets: A list of sets of indices representing splits of the data. - :param num_scaffolds: The number of scaffolds about which to display statistics. - :param num_labels: The number of labels about which to display statistics. - :return: A list of tuples where each tuple contains a list of average target values - across the first :code:num_labels labels and a list of the number of non-zero values for - the first :code:num_scaffolds scaffolds, sorted in decreasing order of scaffold frequency. - """ - logging.info( - "Label averages per scaffold, in decreasing order of scaffold frequency, " - f"capped at {num_scaffolds} scaffolds and {num_labels} labels:" - ) - - stats = [] - index_sets = sorted(index_sets, key=lambda idx_set: len(idx_set), reverse=True) - for scaffold_num, index_set in enumerate(index_sets[:num_scaffolds]): - data_set = data.iloc[list(index_set)] - targets = [ - c - for c in data.columns - if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] - ] - # targets = data_set.iloc[:, 2:].values - targets = data_set.loc[:, targets].values - - with warnings.catch_warnings(): # Likely warning of empty slice of target has no values besides NaN - warnings.simplefilter("ignore", category=RuntimeWarning) - target_avgs = np.nanmean(targets, axis=0)[:num_labels] - - counts = np.count_nonzero(~np.isnan(targets), axis=0)[:num_labels] - stats.append((target_avgs, counts)) - - logging.info(f"Scaffold {scaffold_num}") - for task_num, (target_avg, count) in enumerate(zip(target_avgs, counts)): - logging.info( - f"Task {task_num}: count = {count:,} | target average = {target_avg:.6f}" - ) - logging.info("\n") - return stats diff --git a/build/lib/dfpl/vae.py b/build/lib/dfpl/vae.py deleted file mode 100644 index 17b7dc6f..00000000 --- a/build/lib/dfpl/vae.py +++ /dev/null @@ -1,337 +0,0 @@ -import logging -import math -import os.path -from os.path import basename - -import numpy as np -import pandas as pd -import wandb -from keras import backend as K -from sklearn.model_selection import train_test_split -import tensorflow.keras.metrics as metrics -from tensorflow.keras import initializers, optimizers -from tensorflow.keras.layers import Dense, Input, Lambda -from tensorflow.keras.models import Model -from tensorflow.python.framework.ops import disable_eager_execution -from typing import Tuple -import csv - -from dfpl import history as ht -from dfpl import callbacks -from dfpl import options, settings -from dfpl.utils import ae_scaffold_split, weight_split - -disable_eager_execution() - - -def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: - input_size = opts.fpSize - encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( - learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay - ) - - if output_bias is not None: - output_bias = initializers.Constant(output_bias) - - # get the number of meaningful hidden layers (latent space included) - hidden_layer_count = round(math.log2(input_size / encoding_dim)) - - # the input placeholder - input_vec = Input(shape=(input_size,)) - - # 1st hidden layer, that receives weights from input layer - # equals bottleneck layer, if hidden_layer_count==1! - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / 2), activation=opts.aeActivationFunction - )(input_vec) - else: - encoded = Dense( - units=int(input_size / 2), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(input_vec) - - if hidden_layer_count > 1: - # encoding layers, incl. bottle-neck - for i in range(1, hidden_layer_count): - factor_units = 2 ** (i + 1) - # print(f'{factor_units}: {int(input_size / factor_units)}') - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # latent space layers - factor_units = 2 ** (hidden_layer_count - 1) - if opts.aeActivationFunction != "selu": - z_mean = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - z_log_var = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - z_mean = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - z_log_var = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # sampling layer - def sampling(args): - z_mean, z_log_var = args - batch = K.shape(z_mean)[0] - dim = K.int_shape(z_mean)[1] - epsilon = K.random_normal(shape=(batch, dim)) - return z_mean + K.exp(0.5 * z_log_var) * epsilon - - # sample from latent space - z = Lambda(sampling, output_shape=(int(input_size / factor_units),))( - [z_mean, z_log_var] - ) - decoded = z - # decoding layers - for i in range(hidden_layer_count - 2, 0, -1): - factor_units = 2**i - # print(f'{factor_units}: {int(input_size/factor_units)}') - if opts.aeActivationFunction != "selu": - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(decoded) - else: - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(decoded) - - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(decoded) - - else: - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(encoded) - - autoencoder = Model(input_vec, decoded) - - # KL divergence loss - def kl_loss(z_mean, z_log_var): - return -0.5 * K.sum( - 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1 - ) - - # binary cross-entropy loss - def bce_loss(y_true, y_pred): - return metrics.binary_crossentropy(y_true, y_pred) - - # combined loss - def vae_loss(y_true, y_pred): - bce = bce_loss(y_true, y_pred) - kl = kl_loss(z_mean, z_log_var) - return bce + 0.5 * kl - - autoencoder.compile( - optimizer=ac_optimizer, loss=vae_loss, metrics=[bce_loss, kl_loss] - ) - - # build encoder model - encoder = Model(input_vec, z_mean) - - return autoencoder, encoder - - -def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: - """ - Trains an autoencoder on the given feature matrix X. The response matrix is only used to - split the data into meaningful test and train sets. - - :param opts: Command line arguments as defined in options.py - :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder - :return: The encoder model of the trained autoencoder - """ - - # If wandb tracking is enabled for VAE weights but not for the main program, initialize a new wandb run - if opts.aeWabTracking and not opts.wabTracking: - wandb.init(project=f"VAE_{opts.aeSplitType}") - - # Define output files for VAE and encoder weights - if opts.ecWeightsFile == "": - # If no encoder weights file is specified, use the input file name to generate a default file name - logging.info("No VAE encoder weights file specified") - base_file_name = ( - os.path.splitext(basename(opts.inputFile))[0] - + opts.aeType - + opts.aeSplitType - ) - logging.info( - f"(variational) encoder weights will be saved in {base_file_name}.autoencoder.hdf5" - ) - vae_weights_file = os.path.join( - opts.outputDir, base_file_name + ".vae.weights.hdf5" - ) - # ec_weights_file = os.path.join( - # opts.outputDir, base_file_name + ".encoder.weights.hdf5" - # ) - else: - # If an encoder weights file is specified, use it as the encoder weights file name - logging.info(f"VAE encoder will be saved in {opts.ecWeightsFile}") - base_file_name = ( - os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType - ) - vae_weights_file = os.path.join( - opts.outputDir, base_file_name + ".vae.weights.hdf5" - ) - # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - - # Collect the callbacks for training - callback_list = callbacks.autoencoder_callback( - checkpoint_path=vae_weights_file, opts=opts - ) - # Select all fingerprints that are valid and turn them into a numpy array - fp_matrix = np.array( - df[df["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info( - f"Training VAE on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" - ) - assert 0.0 <= opts.testSize <= 0.5 - if opts.aeSplitType == "random": - logging.info("Training VAE using random split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # Split data into test and training data - if opts.aeWabTracking: - x_train, x_test, _, _ = train_test_split( - fp_matrix, train_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train, x_test, _, _ = train_test_split( - fp_matrix, train_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "scaffold_balanced": - logging.info("Training autoencoder using scaffold split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # if opts.aeWabTracking: - train_data, val_data, test_data = ae_scaffold_split( - df, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=True, - seed=42, - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "molecular_weight": - logging.info("Training autoencoder using molecular weight split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # if opts.aeWabTracking: - train_data, val_data, test_data = weight_split( - df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - else: - x_train = fp_matrix - x_test = None - else: - raise ValueError(f"Invalid split type: {opts.split_type}") - if opts.testSize > 0.0: - train_indices = train_indices[train_indices < x_train.shape[0]] - test_indices = np.arange(x_train.shape[0], x_train.shape[0] + x_test.shape[0]) - else: - test_indices = None - ids, counts = np.unique(x_train.flatten(), return_counts=True) - count_dict = dict(zip(ids, counts)) - if count_dict[0] == 0: - initial_bias = None - logging.info("No zeroes in training labels. Setting initial_bias to None.") - else: - initial_bias = np.log([count_dict[1] / count_dict[0]]) - logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") - if opts.testSize > 0.0: - logging.info(f"VAE training/testing mode with train- and test-samples") - logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") - logging.info(f"VAE test data shape {x_test.shape} with type {x_test.dtype}") - else: - logging.info(f"VAE full train mode without test-samples") - logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") - - (vae, encoder) = define_vae_model(opts, output_bias=initial_bias) - # Train the VAE on the training data - vae_hist = vae.fit( - x_train, - x_train, - epochs=opts.aeEpochs, - batch_size=opts.aeBatchSize, - verbose=opts.verbose, - callbacks=callback_list, - validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, - ) - - # Save the VAE weights - logging.info(f"VAE weights stored in file: {vae_weights_file}") - ht.store_and_plot_history( - base_file_name=os.path.join(opts.outputDir, base_file_name + ".VAE"), - hist=vae_hist, - ) - save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_VAE.h5") - if opts.testSize > 0.0: - (callback_vae, callback_encoder) = define_vae_model(opts) - callback_vae.load_weights(filepath=vae_weights_file) - callback_encoder.save(filepath=save_path) - else: - encoder.save(filepath=save_path) - latent_space = encoder.predict(fp_matrix) - latent_space_file = os.path.join( - opts.outputDir, base_file_name + ".latent_space.csv" - ) - with open(latent_space_file, "w", newline="") as file: - writer = csv.writer(file) - writer.writerows(latent_space) - return encoder, train_indices, test_indices diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index 99bf4578..eec143c7 100644 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -11,9 +11,10 @@ import umap import wandb from sklearn.model_selection import train_test_split -from tensorflow.keras import initializers, losses, optimizers +from tensorflow.keras import initializers, losses from tensorflow.keras.layers import Dense, Input from tensorflow.keras.models import Model +from tensorflow.keras.optimizers.legacy import Adam from dfpl import callbacks from dfpl import history as ht @@ -32,7 +33,7 @@ def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Mod """ input_size = opts.fpSize encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( + ac_optimizer = Adam( learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay ) diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py index 18402f09..6216edc4 100644 --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -28,6 +28,7 @@ MeanSquaredError, ) from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.optimizers.legacy import Adam from dfpl import callbacks as cb from dfpl import options @@ -336,7 +337,7 @@ def define_single_label_model( # Set the optimizer according to the option selected if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + my_optimizer = Adam(learning_rate=opts.learningRate) elif opts.optimizer == "SGD": my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) else: diff --git a/dfpl/vae.py b/dfpl/vae.py index d0a89dbe..2c5ff8fa 100644 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -11,9 +11,10 @@ import wandb from keras import backend as K from sklearn.model_selection import train_test_split -from tensorflow.keras import initializers, optimizers +from tensorflow.keras import initializers from tensorflow.keras.layers import Dense, Input, Lambda from tensorflow.keras.models import Model +from tensorflow.keras.optimizers.legacy import Adam from tensorflow.python.framework.ops import disable_eager_execution from dfpl import callbacks @@ -27,7 +28,7 @@ def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: input_size = opts.fpSize encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( + ac_optimizer = Adam( learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay ) diff --git a/environment.yml b/environment.yml index 6209f1a9..ac482012 100644 --- a/environment.yml +++ b/environment.yml @@ -4,19 +4,21 @@ channels: - defaults dependencies: # dev requirements - - conda-build=3.21.8 - - conda=4.12.0 - - pip=22.0.4 - - pytest=7.1.1 + - conda-build>=3.21.8 + - conda>=4.12.0 + - pip>=22.0.4 + - pytest>=7.1.1 # application requirements - - jsonpickle=2.1 - - matplotlib=3.5.1 - - numpy=1.19.5 - - pandas=1.4.2 - - rdkit=2022.03.1 - - scikit-learn=1.0.2 - - seaborn=0.12.2 - - tensorflow - - wandb=0.12 + - jsonpickle>=2.1 + - matplotlib>=3.5.1 + - numpy>=1.19.5 + - pandas>=1.4.2 + - rdkit>=2022.03.1 + - scikit-learn>=1.0.2 + - seaborn>=0.12.2 + - tensorflow>=2.10 + - wandb>=0.12 + - umap>=0.1.1 - pip: - - git+https://github.com/soulios/chemprop/tree/dfpl_chemprop + - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 + diff --git a/example/train.json b/example/train.json index 2ffda6be..a40d5ab2 100755 --- a/example/train.json +++ b/example/train.json @@ -6,8 +6,8 @@ "ecWeightsFile": "random_autoencoder.hdf5", "verbose": 2, - "trainAC": false, - "compressFeatures": false, + "trainAC": true, + "compressFeatures": true, "encFPSize": 256, "aeSplitType": "random", From b72ee696e5507480b46d6f0373ce5db261aabc0f Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 8 Sep 2023 15:49:03 +0200 Subject: [PATCH 04/45] updated readme with mamba a link to the published article at BiB --- README.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 69a86a4e..2c99997b 100644 --- a/README.md +++ b/README.md @@ -101,22 +101,23 @@ In order to use the environment it needs to be activated with `. ENV_PATH/bin/ac To use this tool in a conda environment: -1. Create the conda env from scratch +1. Install mamba. For details follow the installation guide here. https://mamba.readthedocs.io/en/latest/mamba-installation.html#mamba-install} +2. Create the mamba env from scratch From within the `deepFPlearn` directory, you can create the conda environment with the provided yaml file that contains all information and necessary packages ```shell - conda env create -f environment.yml + mamba env create -f environment.yml ``` -2. Activate the `dfpl_env` environment with +3. Activate the `dfpl_env` environment with ```shell - conda activate dfpl_env + mamba activate dfpl_env ``` -3. Install the local `dfpl` package by calling +4. Install the local `dfpl` package by calling ```shell pip install --no-deps ./ @@ -287,6 +288,6 @@ The `convert` mode is used to convert `.csv` or `.tsv` files into `.pkl` files f # Please note that: This work has been submitted in a research paper and is currently under review. -You may read the preprint @bioRxiv: https://doi.org/10.1101/2021.06.24.449697 +You may read the published article @Briefings in Bioinformatics: https://doi.org/10.1093/bib/bbac257 For questions, comments please contact me: jana.schor@ufz.de From fec4b59f4204c4da5fe4b8cd70618ac519361e5e Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 31 Aug 2023 10:16:02 +0200 Subject: [PATCH 05/45] removed gnn_type and edited readme --- README.md | 77 +++++++++++++++++++++++++++++++++-------- example/predictgnn.json | 1 - example/traingnn.json | 1 - 3 files changed, 62 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 0e70b9fe..0c2684c6 100644 --- a/README.md +++ b/README.md @@ -183,16 +183,27 @@ The train mode is used to train models to predict the association of molecular s ```shell python -m dfpl train -f example/train.json ``` -Training with the configurations from the `example/train.json` file will take approximately 4 minutes on a single CPU. The trained models, training histories, respective plots, as well as the predictions on the test data are stored in the `example/results_train` folder as defined in the `example/train.json` file (you may change this). - -- `train.json` options file provides various options for training a model on the input data. -- One option is `trainAC`, which when set to true, trains an autoencoder model. -- Another option is `trainRBM`, which when set to true, trains a deep belief network (DBN) using Restricted Boltzmann Machines (RBM). -- The `useRBM` option specifies whether the RBM weights should be used to initialize the model parameters for training the final neural network. -- The `split_type` and `aeSplitType` option specifies the type of data splitting used for training the models. It can be set to either `scaffold_balanced`, `random` or `molecular weight`. -- In addition, `train.json` also includes options for setting the type of fingerprint (`fpType`), the size of the fingerprint (`fpSize`), the type of neural network (`fnnType`), the optimizer (`optimizer`), the loss function (`lossFunction`), the number of epochs (`epochs`), the batch size (`batchSize`), the learning rate (`learningRate`), the L2 regularization parameter (`l2reg`), and more. -- The `wabTracking` or `aeWabTracking` option specifies whether Weights and Biases (WANDB) tracking should be used to monitor model performance during training. If either is set to true, the `wabTarget` option can be used to specify the target value for WAB tracking. +Training with the configurations from the `example/train.json` file will take approximately 4 minutes on a single CPU. +The trained models, training histories, respective plots, as well as the predictions on the test data are stored in the +`example/results_train` folder as defined in the `example/train.json` file (this can be changed). + +- `configFile or -f`: The path to the `train.json` options file that provides various configurations for training the model. +- `outputDir`: The directory where the trained models and other output files will be saved. +- `trainAC`: Whether to train an autoencoder model. +- `aeType`: Whether to train a deterministic or variational autoencoder +- `split_type` and `aeSplitType`: The type of data splitting used for training. Options are `scaffold_balanced`, `random`, or `molecular weight`. +- `fpType`: The type of fingerprint used. +- `fpSize`: The size of the fingerprint. +- `fnnType`: The type of Feedforward Neural Network used. +- `optimizer`: The optimization algorithm used. +- `lossFunction`: The loss function used. +- `epochs`: The number of training epochs. +- `batchSize`: The size of the training batch. +- `learningRate`: The learning rate for the optimizer. +- `l2reg`: The L2 regularization parameter. +- `wabTracking` or `aeWabTracking`: Whether to use Weights and Biases (WANDB) for tracking. +- `wabTarget`: The target metric for WANDB tracking. ## Predict The `predict` mode is used to predict the from molecular structures. Use this command to predict the provided data for prediction: @@ -208,7 +219,6 @@ The compounds are predicted with the provided model and results are returned as - `ecWeightsFile`: The name of the file containing the weights of the autoencoder. This is not needed for predicting with an AR model. - `fnnModelDir`: The directory where the FNN model is saved. - `compressFeatures`: Whether to compress the features using the autoencoder or not. -- `useRBM`: Whether to use a trained deep belief network or not. This is not needed for predicting with an AR model. - `trainAC`: Whether to train a new autoencoder or use a pre-trained one. - `trainFNN`: Whether to train a new FNN model or use a pre-trained one. @@ -216,21 +226,58 @@ The compounds are predicted with the provided model and results are returned as ```shell python -m dfpl traingnn -f example/traingnn.json ``` -The `traingnn` mode is used to train models using a graph neural network to predict binary fingerprints from molecular structures. The training data contains three targets and you may train models for each using the following command: +The `traingnn` mode is used to train models using a graph neural network to predict the chemical-biological associations +from molecular structures. If the training data contains multiple targets the GNN will perform nultilabel classification. + +The trained models, training histories, and respective plots, as well as the predictions on the test data, are stored +in the `example/results_traingnn` folder as defined in the `example/traingnn.json` file (you may change this). Similar +and even more options are offered via the GNN model. Go to `chemprop/args.py` to take a peek and set your options. + +Some basic arguments include: +- `data_path`: Specifies the path to the dataset in CSV format. This is where the model will read the training data from. + +- `save_dir`: The directory where the trained model and any generated output files will be saved. Make sure the directory exists or can be created. + +- `epochs`: The number of training epochs. An epoch is one complete forward and backward pass of all the training examples. + +- `num_folds`: The number of folds to use for cross-validation. Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. + +- `metric`: The primary metric used for model evaluation. In this case, it's "accuracy". + +- `loss_function`: Specifies the loss function to be minimized during training. Here, it's set to "binary_cross_entropy". + +- `split_type`: Defines the strategy used for splitting the data into training and test sets. The "random" option will randomly split the data. + +- `dataset_type`: Specifies the type of problem you are solving; in this case, it's a "classification" problem. -The trained models, training histories, and respective plots, as well as the predictions on the test data, are stored in the `example/results_traingnn` folder as defined in the `example/traingnn.json` file (you may change this). Similar and even more options are offered via the GNN model. Go to `chemprop/args.py` to take a peek and set your options. +- `smiles_columns`: The name of the column in the CSV file that contains the SMILES strings of the molecules. + +- `extra_metrics`: A list of additional metrics that you want to compute for model evaluation. These could include metrics like "balanced_accuracy", "Area Under Curve (AUC)", "F1 score", "Matthews Correlation Coefficient (MCC)", "recall", "specificity", and "precision". + +- `hidden_size`: Specifies the size of the hidden layers in the neural network. A larger size may capture more complex representations but could risk overfitting. ## Predictgnn ```shell python -m dfpl predictgnn -f example/predictgnn.json ``` -The `predictgnn` mode is used to predict binary fingerprints from molecular structures using a graph neural network. Use this command to predict the provided data for prediction: +The `predictgnn` mode is using trained GNN model to predict these chemical-biological target associations from molecular +structures using a graph neural network. -## Convert +Again some basic arguments: -The `convert` mode is used to convert `.csv` or `.tsv` files into `.pkl` files for easy access in Python and to reduce memory on disk. +- `test_path`: The path to the dataset containing smiles in CSV format. +- `checkpoint_path`: Specifies the path to the trained model checkpoint file, usually with a `.pt` extension. +This is the model that will be used for making predictions. +- `save_dir`: The directory where the prediction results will be saved. +Make sure the directory exists or can be created. + +- `saving_name`: The name of the CSV file where the predictions will be saved. + This file will be created in the `save_dir`. +## Convert + +The `convert` mode is used to convert `.csv` or `.tsv` files into `.pkl` files for easy access in Python and to reduce memory on disk. # Please note that: diff --git a/example/predictgnn.json b/example/predictgnn.json index 0cd5a575..d1326404 100644 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,6 +1,5 @@ { "py/object": "dfpl.options.GnnOptions", - "gnn_type": "dmpnn", "test_path": "tests/data/D_smile_15.csv", "checkpoint_path": "example/model.pt", "save_dir": "preds_dmpnn_D", diff --git a/example/traingnn.json b/example/traingnn.json index b54c9c5c..8e868bc3 100644 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -1,7 +1,6 @@ { "py/object": "dfpl.options.GnnOptions", "data_path": "tests/data/S_dataset.csv", - "gnn_type": "dmpnn", "save_dir": "dmpnn-random/", "epochs": 20, "num_folds": 5, From 226ee9da6b04895b5103e036b6b8654b91739d6e Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 31 Aug 2023 10:22:19 +0200 Subject: [PATCH 06/45] edited readme --- README.md | 12 ++++++++---- example/traingnn.json | 2 +- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 0c2684c6..69a86a4e 100644 --- a/README.md +++ b/README.md @@ -238,7 +238,7 @@ Some basic arguments include: - `save_dir`: The directory where the trained model and any generated output files will be saved. Make sure the directory exists or can be created. -- `epochs`: The number of training epochs. An epoch is one complete forward and backward pass of all the training examples. +- `epochs`: The number of training epochs. - `num_folds`: The number of folds to use for cross-validation. Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. @@ -246,13 +246,17 @@ Some basic arguments include: - `loss_function`: Specifies the loss function to be minimized during training. Here, it's set to "binary_cross_entropy". -- `split_type`: Defines the strategy used for splitting the data into training and test sets. The "random" option will randomly split the data. +- `split_type`: Defines the strategy used for splitting the data into training and test sets. + The **"random"** option will randomly split the data. + The **"scaffold_balanced"** based on the chemical scaffold (It also logs stats of the split). + The **molecular_weight** puts the small molecules in training and large in the test set based on their MW. -- `dataset_type`: Specifies the type of problem you are solving; in this case, it's a "classification" problem. +- `dataset_type`: Specifies the type of problem you are solving; i.e classification. - `smiles_columns`: The name of the column in the CSV file that contains the SMILES strings of the molecules. -- `extra_metrics`: A list of additional metrics that you want to compute for model evaluation. These could include metrics like "balanced_accuracy", "Area Under Curve (AUC)", "F1 score", "Matthews Correlation Coefficient (MCC)", "recall", "specificity", and "precision". +- `extra_metrics`: A list of additional metrics that you want to compute for model evaluation. + These could include metrics like "balanced_accuracy", "(AUC)", "F1 score", "Matthews Correlation Coefficient (MCC)", "recall", "specificity", and "precision". - `hidden_size`: Specifies the size of the hidden layers in the neural network. A larger size may capture more complex representations but could risk overfitting. diff --git a/example/traingnn.json b/example/traingnn.json index 8e868bc3..b43bdc14 100644 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -6,7 +6,7 @@ "num_folds": 5, "metric": "accuracy", "loss_function": "binary_cross_entropy", - "split_type": "random", + "split_type": "molecular_weight", "dataset_type": "classification", "smiles_columns": "smiles", "extra_metrics": ["balanced_accuracy","auc","f1","mcc","recall","specificity","precision"], From 59dce16a99f4655d964bf5eb2870f1f18164d615 Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 31 Aug 2023 23:06:19 +0200 Subject: [PATCH 07/45] edited mypy errors and added tests for GNN --- dfpl/__main__.py | 10 +++----- dfpl/autoencoder.py | 5 ++-- dfpl/feedforwardNN.py | 11 ++++---- dfpl/fingerprint.py | 2 +- dfpl/plot.py | 19 +++++++------- dfpl/utils.py | 8 +++++- dfpl/vae.py | 28 ++++++++++----------- example/predictgnn.json | 6 ++--- example/train.json | 10 ++++---- example/traingnn.json | 8 +++--- tests/run_autoencoder.py | 4 +-- tests/run_dbn.py | 54 ---------------------------------------- tests/run_fnntraining.py | 10 ++++---- tests/run_predictgnn.py | 54 ++++++++++++++++++++++++++++++++++++++++ tests/run_prediction.py | 7 ++++-- tests/run_traingnn.py | 26 +++++++++++-------- tests/run_vae.py | 4 +-- 17 files changed, 139 insertions(+), 127 deletions(-) delete mode 100644 tests/run_dbn.py create mode 100644 tests/run_predictgnn.py diff --git a/dfpl/__main__.py b/dfpl/__main__.py index 2a295167..6d5f6eba 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -13,7 +13,6 @@ from dfpl import fingerprint as fp from dfpl import options, predictions -# from dfpl import rbm as rbm from dfpl import single_label_model as sl from dfpl import vae as vae from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute @@ -89,7 +88,6 @@ def predictdmpnn(opts: options.GnnOptions, json_arg_path: str) -> None: """ ignore_elements = [ "py/object", - "gnn_type", "checkpoint_paths", "save_dir", "saving_name", @@ -146,9 +144,9 @@ def train(opts: options.Options): if opts.compressFeatures: if not opts.trainAC: if opts.aeType == "deterministic": - (autoencoder, encoder) = ac.define_ac_model(opts=options.Options) + (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) elif opts.aeType == "variational": - (autoencoder, encoder) = vae.define_vae_model(opts=options.Options) + (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) elif opts.ecWeightsFile == "": encoder = load_model(opts.ecModelDir) else: @@ -193,9 +191,9 @@ def predict(opts: options.Options) -> None: if opts.compressFeatures: # load trained model for autoencoder if opts.aeType == "deterministic": - (autoencoder, encoder) = ac.define_ac_model(opts=options.Options) + (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) if opts.aeType == "variational": - (autoencoder, encoder) = vae.define_vae_model(opts=options.Options) + (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) # Load trained model for autoencoder if opts.ecWeightsFile == "": encoder = load_model(opts.ecModelDir) diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index fb453b85..1f638122 100644 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -13,6 +13,7 @@ from tensorflow.keras import initializers, losses, optimizers from tensorflow.keras.layers import Dense, Input from tensorflow.keras.models import Model +from typing import Tuple from dfpl import callbacks from dfpl import history as ht @@ -20,7 +21,7 @@ from dfpl.utils import ae_scaffold_split, weight_split -def define_ac_model(opts: options.Options, output_bias=None) -> (Model, Model): +def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: """ This function provides an autoencoder model to reduce a certain input to a compressed version. @@ -142,7 +143,7 @@ def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: # If wandb tracking is enabled for autoencoder weights but not for the main program, initialize a new wandb run if opts.aeWabTracking and not opts.wabTracking: - wandb.init(project=f"AE_{opts.aeSplitType}", config=opts) + wandb.init(project=f"AE_{opts.aeSplitType}") # Define output files for autoencoder and encoder weights if opts.ecWeightsFile == "": diff --git a/dfpl/feedforwardNN.py b/dfpl/feedforwardNN.py index 7aaa43c9..c2ceb03a 100644 --- a/dfpl/feedforwardNN.py +++ b/dfpl/feedforwardNN.py @@ -1,11 +1,10 @@ -import array import logging import math import re import shutil import sys from time import time - +from typing import List, Union import numpy as np import pandas as pd from sklearn.metrics import f1_score @@ -180,12 +179,12 @@ def define_nn_model_multi( def validate_multi_model_on_test_data( - x_test: array, + x_test: np.ndarray, checkpoint_path: str, - y_test: array, - col_names: list, + y_test: np.ndarray, + col_names: List[str], result_file: str, -) -> list: +) -> List[Union[int, float, str]]: """ Validate the multi label model on a test data set. diff --git a/dfpl/fingerprint.py b/dfpl/fingerprint.py index af6f7c41..291689c2 100644 --- a/dfpl/fingerprint.py +++ b/dfpl/fingerprint.py @@ -44,7 +44,7 @@ def smile2fp(smile: str) -> Any: # from rdkit import DataStructs # DataStructs.ConvertToNumpyArray(morgan, npa) - npa = np.zeros((0,), dtype=np.bool) + npa = np.zeros((0,), dtype=np.bool_) try: DataStructs.ConvertToNumpyArray( AllChem.GetMorganFingerprintAsBitVect( diff --git a/dfpl/plot.py b/dfpl/plot.py index e18b4ff2..655fc08f 100644 --- a/dfpl/plot.py +++ b/dfpl/plot.py @@ -1,10 +1,9 @@ -import array - import matplotlib.pyplot as plt import numpy as np import pandas as pd import wandb from matplotlib.axes import Axes +from typing import List # for NN model functions from tensorflow.keras.callbacks import History @@ -12,42 +11,42 @@ def get_max_validation_accuracy(history: History) -> str: validation = smooth_curve(history.history["val_accuracy"]) - y_max = max(validation) + y_max: float = max(validation) return "Max validation accuracy ≈ " + str(round(y_max, 3) * 100) + "%" def get_max_validation_balanced_accuracy(history: History) -> str: validation_bal_acc = smooth_curve(history.history["val_balanced_accuracy"]) - y_max = max(validation_bal_acc) + y_max: float = max(validation_bal_acc) return "Max validation balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" def get_max_training_balanced_accuracy(history: History) -> str: training_bal_acc = smooth_curve(history.history["balanced_accuracy"]) - y_max = max(training_bal_acc) + y_max: float = max(training_bal_acc) return "Training balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" def get_max_training_auc(history: History) -> str: training_auc = smooth_curve(history.history["auc"]) - y_max = max(training_auc) + y_max: float = max(training_auc) return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" def get_max_validation_auc(history: History) -> str: validation_auc = smooth_curve(history.history["val_auc"]) - y_max = max(validation_auc) + y_max: float = max(validation_auc) return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" def get_max_training_accuracy(history: History) -> str: training = smooth_curve(history.history["accuracy"]) - y_max = max(training) + y_max: float = max(training) return "Max training accuracy ≈ " + str(round(y_max, 3) * 100) + "%" -def smooth_curve(points: array, factor: float = 0.75) -> array: - smoothed_points = [] +def smooth_curve(points: np.ndarray, factor: float = 0.75) -> np.ndarray: + smoothed_points: List[float] = [] for point in points: if smoothed_points: previous = smoothed_points[-1] diff --git a/dfpl/utils.py b/dfpl/utils.py index f84c3e7f..db3d6ec1 100644 --- a/dfpl/utils.py +++ b/dfpl/utils.py @@ -211,7 +211,9 @@ def ae_scaffold_split( sizes[1] * len(data), sizes[2] * len(data), ) - train, val, test = [], [], [] + train: List[int] = [] + val: List[int] = [] + test: List[int] = [] train_scaffold_count, val_scaffold_count, test_scaffold_count = 0, 0, 0 # Map from scaffold to index in the data @@ -221,6 +223,10 @@ def ae_scaffold_split( (i for i, colname in enumerate(data.columns) if "inchi" in colname.lower()), None, ) + if key_molecule_index is None: + warnings.warn( + "No column with 'inchi' found in the DataFrame. Proceeding with caution." + ) key_mols = data.iloc[:, key_molecule_index].apply(inchi_to_mol).dropna() else: key_mols = data.iloc[:, key_molecule_index] diff --git a/dfpl/vae.py b/dfpl/vae.py index b9506938..17b7dc6f 100644 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -8,11 +8,14 @@ import wandb from keras import backend as K from sklearn.model_selection import train_test_split +import tensorflow.keras.metrics as metrics from tensorflow.keras import initializers, optimizers from tensorflow.keras.layers import Dense, Input, Lambda from tensorflow.keras.models import Model from tensorflow.python.framework.ops import disable_eager_execution +from typing import Tuple import csv + from dfpl import history as ht from dfpl import callbacks from dfpl import options, settings @@ -21,7 +24,7 @@ disable_eager_execution() -def define_vae_model(opts: options.Options, output_bias=None) -> (Model, Model): +def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: input_size = opts.fpSize encoding_dim = opts.encFPSize ac_optimizer = optimizers.Adam( @@ -133,20 +136,17 @@ def sampling(args): autoencoder = Model(input_vec, decoded) # KL divergence loss - def kl_loss(z_mean: np.ndarray, z_log_var: np.ndarray) -> np.ndarray: - return -0.5 * np.sum( - 1 + z_log_var - np.square(z_mean) - np.exp(z_log_var), axis=-1 + def kl_loss(z_mean, z_log_var): + return -0.5 * K.sum( + 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1 ) - def bce_loss(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray: - return -np.sum(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)) + # binary cross-entropy loss + def bce_loss(y_true, y_pred): + return metrics.binary_crossentropy(y_true, y_pred) - def vae_loss( - y_true: np.ndarray, - y_pred: np.ndarray, - z_mean: np.ndarray, - z_log_var: np.ndarray, - ) -> np.ndarray: + # combined loss + def vae_loss(y_true, y_pred): bce = bce_loss(y_true, y_pred) kl = kl_loss(z_mean, z_log_var) return bce + 0.5 * kl @@ -173,7 +173,7 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: # If wandb tracking is enabled for VAE weights but not for the main program, initialize a new wandb run if opts.aeWabTracking and not opts.wabTracking: - wandb.init(project=f"VAE_{opts.aeSplitType}", config=opts) + wandb.init(project=f"VAE_{opts.aeSplitType}") # Define output files for VAE and encoder weights if opts.ecWeightsFile == "": @@ -264,7 +264,7 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: if opts.testSize > 0.0: # if opts.aeWabTracking: train_data, val_data, test_data = weight_split( - df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small", seed=42 + df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" ) x_train = np.array( train_data[train_data["fp"].notnull()]["fp"].to_list(), diff --git a/example/predictgnn.json b/example/predictgnn.json index d1326404..db8d2afc 100644 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,7 +1,7 @@ { "py/object": "dfpl.options.GnnOptions", - "test_path": "tests/data/D_smile_15.csv", - "checkpoint_path": "example/model.pt", + "test_path": "data/smiles.csv", + "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt", "save_dir": "preds_dmpnn_D", - "saving_name": "D_preds_ER_random_dmpnn_15.csv" + "saving_name": "DMONN_preds.csv" } \ No newline at end of file diff --git a/example/train.json b/example/train.json index 773598ab..f8fce8dc 100755 --- a/example/train.json +++ b/example/train.json @@ -6,11 +6,11 @@ "ecWeightsFile": "", "verbose": 2, - "trainAC": false, - "compressFeatures": false, + "trainAC": true, + "compressFeatures": true, "encFPSize": 256, - "aeSplitType": "random", + "aeSplitType": "scaffold_balanced", "aeEpochs": 2, "aeBatchSize": 351, "aeOptimizer": "Adam", @@ -29,13 +29,13 @@ "gpu": "", "trainFNN": true, - "kFolds": 3, + "kFolds": 1, "threshold": 0.5, "testSize": 0.2, "fnnType": "FNN", "optimizer": "Adam", "lossFunction": "bce", - "epochs": 5, + "epochs": 11, "batchSize": 128, "activationFunction": "selu", "dropout": 0.0107, diff --git a/example/traingnn.json b/example/traingnn.json index b43bdc14..4473da6a 100644 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -1,12 +1,12 @@ { "py/object": "dfpl.options.GnnOptions", - "data_path": "tests/data/S_dataset.csv", + "data_path": "data/S_dataset.csv", "save_dir": "dmpnn-random/", - "epochs": 20, - "num_folds": 5, + "epochs": 2, + "num_folds": 1, "metric": "accuracy", "loss_function": "binary_cross_entropy", - "split_type": "molecular_weight", + "split_type": "random", "dataset_type": "classification", "smiles_columns": "smiles", "extra_metrics": ["balanced_accuracy","auc","f1","mcc","recall","specificity","precision"], diff --git a/tests/run_autoencoder.py b/tests/run_autoencoder.py index 2bb84e1e..d61fcfae 100644 --- a/tests/run_autoencoder.py +++ b/tests/run_autoencoder.py @@ -10,7 +10,7 @@ test_train_args = opt.Options( inputFile=utils.makePathAbsolute(f"{project_directory}/data/S_dataset.csv"), ecModelDir=utils.makePathAbsolute(f"{project_directory}/data"), - outputDir=utils.makePathAbsolute(f"{project_directory}/output_data"), + outputDir=utils.makePathAbsolute(f"{project_directory}/output"), ecWeightsFile="D_datasetdeterministicrandom.autoencoder.weights.hdf5", type="smiles", fpType="topological", @@ -19,7 +19,7 @@ encFPSize=256, enableMultiLabel=False, testSize=0.2, - kFolds=5, + kFolds=2, verbose=2, trainFNN=False, trainAC=True, diff --git a/tests/run_dbn.py b/tests/run_dbn.py deleted file mode 100644 index 63d13b88..00000000 --- a/tests/run_dbn.py +++ /dev/null @@ -1,54 +0,0 @@ -import logging -import pathlib - -import dfpl.fingerprint as fp -import dfpl.options as opt -import dfpl.rbm as rbm -import dfpl.utils as utils - -project_directory = pathlib.Path(__file__).parent.absolute() -test_train_args = opt.Options( - inputFile=utils.makePathAbsolute(f"{project_directory}/data/S_dataset.csv"), - ecModelDir=utils.makePathAbsolute(f"{project_directory}/output_data/modeltraining"), - outputDir=utils.makePathAbsolute(f"{project_directory}/output_data"), - ecWeightsFile="", - type="smiles", - fpType="topological", - aeEpochs=2, - fpSize=2048, - encFPSize=256, - enableMultiLabel=False, - testSize=0.2, - kFolds=5, - verbose=2, - compressFeatures=True, - trainFNN=False, - trainAC=True, - trainRBM=True, - useRBM=True, - gpu="", -) - - -def runDBN(opts: opt.Options) -> None: - """ - Run and test auto-encoder - """ - logging.basicConfig( - format="DFPL-{levelname}: {message}", style="{", level=logging.INFO - ) - logging.info("Adding fingerprint to dataset") - df = fp.importDataFile( - opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize - ) - logging.info("Training DBN") - rbm.train_full_rbm(df, opts) - logging.info("Done") - - -if __name__ == "__main__": - logging.basicConfig( - format="DFPL-{levelname}: {message}", style="{", level=logging.INFO - ) - utils.createDirectory(test_train_args.outputDir) - runDBN(test_train_args) diff --git a/tests/run_fnntraining.py b/tests/run_fnntraining.py index 694753d2..6668b2ee 100644 --- a/tests/run_fnntraining.py +++ b/tests/run_fnntraining.py @@ -10,13 +10,13 @@ test_train_args = opt.Options( inputFile=utils.makePathAbsolute(f"{project_directory}/data/S_dataset.csv"), ecModelDir=utils.makePathAbsolute( - f"{project_directory}/output_data/fnnTrainingCompressed/modeltraining" + f"{project_directory}/output/fnnTrainingCompressed/" ), - outputDir=utils.makePathAbsolute(f"{project_directory}/output_data/fnnTraining"), + outputDir=utils.makePathAbsolute(f"{project_directory}/output/fnnTraining"), ecWeightsFile="", type="smiles", fpType="topological", - epochs=10, + epochs=11, aeEpochs=3, fpSize=2048, encFPSize=256, @@ -35,7 +35,7 @@ def run_single_label_training(opts: opt.Options) -> None: logging.info("Adding fingerprint to dataset") opts.outputDir = utils.makePathAbsolute( - f"{project_directory}/output_data/fnnTrainingCompressed" + f"{project_directory}/output/fnnTrainingCompressed" ) utils.createDirectory(opts.outputDir) @@ -64,7 +64,7 @@ def run_single_label_training(opts: opt.Options) -> None: # train FNNs with uncompressed features opts.outputDir = utils.makePathAbsolute( - f"{project_directory}/output_data/fnnTrainingUncompressed" + f"{project_directory}/output/fnnTrainingUncompressed" ) utils.createDirectory(opts.outputDir) logging.info("Training the FNN using un-compressed input data.") diff --git a/tests/run_predictgnn.py b/tests/run_predictgnn.py new file mode 100644 index 00000000..6618875e --- /dev/null +++ b/tests/run_predictgnn.py @@ -0,0 +1,54 @@ +import logging +import os +import pathlib +import dfpl.options as options +import dfpl.utils as utils +import dfpl.__main__ as main +import pandas as pd +from chemprop import train, args + + +project_directory = pathlib.Path(__file__).parent.absolute() +example_directory = ( + project_directory.parent / "example" +) # Path to the example directory + +test_predict_args = options.GnnOptions( + configFile=utils.makePathAbsolute(f"{example_directory}/predictgnn.json"), + save_dir=utils.makePathAbsolute(f"{project_directory}/output"), +) + + +def test_predictdmpnn(opts: options.GnnOptions) -> None: + print("Running predictdmpnn test...") + logging.basicConfig( + format="DFPL-{levelname}: {message}", style="{", level=logging.INFO + ) + + json_arg_path = utils.makePathAbsolute(f"{example_directory}/predictgnn.json") + ignore_elements = [ + "py/object", + "checkpoint_paths", + "save_dir", + "saving_name", + ] + arguments, data = utils.createArgsFromJson( + json_arg_path, ignore_elements, return_json_object=True + ) + arguments.append("--preds_path") + arguments.append("") + save_dir = data.get("save_dir") + name = data.get("saving_name") + + opts = args.PredictArgs().parse_args(arguments) + opts.preds_path = os.path.join(save_dir, name) + df = pd.read_csv(opts.test_path) + smiles = [[row.smiles] for _, row in df.iterrows()] + + train.make_predictions(args=opts, smiles=smiles) + + print("predictdmpnn test complete.") + + +if __name__ == "__main__": + test_predictdmpnn(test_predict_args) diff --git a/tests/run_prediction.py b/tests/run_prediction.py index 83fc607f..7e675230 100644 --- a/tests/run_prediction.py +++ b/tests/run_prediction.py @@ -12,8 +12,11 @@ test_predict_args = opt.Options( inputFile=f"{project_directory}/data/smiles.csv", outputDir=f"{project_directory}/preds/", - ecWeightsFile=f"{project_directory}/data/D_datasetdeterministicrandom.autoencoder.weights.hdf5", - fnnModelDir=f"{project_directory}/output_data/fnnTrainingCompressed/AR_saved_model", + ecModelDir=utils.makePathAbsolute(f"{project_directory}/output/"), + ecWeightsFile=utils.makePathAbsolute( + f"{project_directory}/output/D_datasetdeterministicrandom.autoencoder.weightsrandom.autoencoder.weights.hdf5" + ), + fnnModelDir=f"{project_directory}/fnnTrainingCompressed/AR_saved_model", fpSize=2048, type="smiles", fpType="topological", diff --git a/tests/run_traingnn.py b/tests/run_traingnn.py index ecd10859..74f317bb 100644 --- a/tests/run_traingnn.py +++ b/tests/run_traingnn.py @@ -1,24 +1,30 @@ +import logging import pathlib -import chemprop as cp -from chemprop import args - +import dfpl.options as opt import dfpl.utils as utils +import dfpl.__main__ as main + project_directory = pathlib.Path(__file__).parent.absolute() -test_train_args = args.TrainArgs( - inputFile=utils.makePathAbsolute(f"{project_directory}/data/S_dataset.csv"), +example_directory = project_directory.parent / "example" +test_train_args = opt.GnnOptions( + configFile=utils.makePathAbsolute(f"{example_directory}/traingnn.json"), + save_dir=utils.makePathAbsolute(f"{project_directory}/output"), + total_epochs=1, ) -def test_traindmpnn(opts: args.TrainArgs) -> None: +def test_traindmpnn(opts: opt.GnnOptions) -> None: print("Running traindmpnn test...") + logging.basicConfig( + format="DFPL-{levelname}: {message}", style="{", level=logging.INFO + ) + logging.info("Adding fingerprint to dataset") + + main.traindmpnn(opts) print("Training DMPNN...") - mean_score, std_score = cp.train.cross_validate( - args=opts, train_func=cp.train.run_training - ) - print(f"Results: {mean_score:.5f} +/- {std_score:.5f}") print("traindmpnn test complete.") diff --git a/tests/run_vae.py b/tests/run_vae.py index 311b8e38..d6095d96 100644 --- a/tests/run_vae.py +++ b/tests/run_vae.py @@ -9,8 +9,8 @@ project_directory = pathlib.Path(__file__).parent.absolute() test_train_args = opt.Options( inputFile=utils.makePathAbsolute(f"{project_directory}/data/S_dataset.csv"), - ecModelDir=utils.makePathAbsolute(f"{project_directory}/output_data/modeltraining"), - outputDir=utils.makePathAbsolute(f"{project_directory}/output_data"), + ecModelDir=utils.makePathAbsolute(f"{project_directory}/data"), + outputDir=utils.makePathAbsolute(f"{project_directory}/output"), ecWeightsFile="", type="smiles", fpType="topological", From bdbc3545cd05654ca5d232dd02e0acb342653c30 Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 31 Aug 2023 23:42:43 +0200 Subject: [PATCH 08/45] adding umap as requirement --- environment.yml | 1 + setup.py | 1 + tests/run_predictgnn.py | 7 +++---- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/environment.yml b/environment.yml index 80023288..3c2e7a6c 100644 --- a/environment.yml +++ b/environment.yml @@ -18,5 +18,6 @@ dependencies: - seaborn=0.12.2 - tensorflow-gpu=2.6.0 - wandb=0.12 + - umap=0.1.1 - pip: - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 \ No newline at end of file diff --git a/setup.py b/setup.py index a20215ac..2e76e617 100644 --- a/setup.py +++ b/setup.py @@ -29,6 +29,7 @@ "keras==2.9.0", "tensorflow-gpu==2.9.3", "wandb~=0.12.0", + "umap~=0.1.1", "seaborn~=0.12.2", "chemprop @ git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338", ], diff --git a/tests/run_predictgnn.py b/tests/run_predictgnn.py index 6618875e..99a6185c 100644 --- a/tests/run_predictgnn.py +++ b/tests/run_predictgnn.py @@ -1,9 +1,8 @@ import logging import os import pathlib -import dfpl.options as options +import dfpl.options as opt import dfpl.utils as utils -import dfpl.__main__ as main import pandas as pd from chemprop import train, args @@ -13,13 +12,13 @@ project_directory.parent / "example" ) # Path to the example directory -test_predict_args = options.GnnOptions( +test_predict_args = opt.GnnOptions( configFile=utils.makePathAbsolute(f"{example_directory}/predictgnn.json"), save_dir=utils.makePathAbsolute(f"{project_directory}/output"), ) -def test_predictdmpnn(opts: options.GnnOptions) -> None: +def test_predictdmpnn(opts: opt.GnnOptions) -> None: print("Running predictdmpnn test...") logging.basicConfig( format="DFPL-{levelname}: {message}", style="{", level=logging.INFO From 0c715d944bb22168e5ca2f09097cab17e25c91fa Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 31 Aug 2023 23:58:15 +0200 Subject: [PATCH 09/45] trying to isort --- dfpl/__main__.py | 5 ++--- dfpl/autoencoder.py | 2 +- dfpl/feedforwardNN.py | 1 + dfpl/plot.py | 3 ++- dfpl/single_label_model.py | 5 ++--- dfpl/vae.py | 8 ++++---- example/train.json | 2 +- tests/run_fnntraining.py | 1 + tests/run_predictgnn.py | 7 ++++--- tests/run_traingnn.py | 4 ++-- tests/test_fingerprint.py | 1 - tests/test_fractional_sampling.py | 1 + 12 files changed, 21 insertions(+), 19 deletions(-) diff --git a/dfpl/__main__.py b/dfpl/__main__.py index 6d5f6eba..cfef84d8 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -1,10 +1,11 @@ -import os.path import dataclasses import logging +import os.path import pathlib from argparse import Namespace from os import path +import chemprop as cp import pandas as pd from keras.models import load_model @@ -12,11 +13,9 @@ from dfpl import feedforwardNN as fNN from dfpl import fingerprint as fp from dfpl import options, predictions - from dfpl import single_label_model as sl from dfpl import vae as vae from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute -import chemprop as cp project_directory = pathlib.Path(".").parent.parent.absolute() test_train_opts = options.Options( diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index 1f638122..99bf4578 100644 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -2,6 +2,7 @@ import math import os.path from os.path import basename +from typing import Tuple import matplotlib.pyplot as plt import numpy as np @@ -13,7 +14,6 @@ from tensorflow.keras import initializers, losses, optimizers from tensorflow.keras.layers import Dense, Input from tensorflow.keras.models import Model -from typing import Tuple from dfpl import callbacks from dfpl import history as ht diff --git a/dfpl/feedforwardNN.py b/dfpl/feedforwardNN.py index c2ceb03a..e9c88776 100644 --- a/dfpl/feedforwardNN.py +++ b/dfpl/feedforwardNN.py @@ -5,6 +5,7 @@ import sys from time import time from typing import List, Union + import numpy as np import pandas as pd from sklearn.metrics import f1_score diff --git a/dfpl/plot.py b/dfpl/plot.py index 655fc08f..30d9503f 100644 --- a/dfpl/plot.py +++ b/dfpl/plot.py @@ -1,9 +1,10 @@ +from typing import List + import matplotlib.pyplot as plt import numpy as np import pandas as pd import wandb from matplotlib.axes import Axes -from typing import List # for NN model functions from tensorflow.keras.callbacks import History diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py index de437d1e..18402f09 100644 --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -5,14 +5,13 @@ import sys from os import path from time import time +from typing import Tuple, Union import numpy as np import pandas as pd import tensorflow as tf import tensorflow.keras.backend as K import wandb -from typing import Union, Tuple - from sklearn.metrics import ( auc, classification_report, @@ -25,8 +24,8 @@ from tensorflow.keras.layers import AlphaDropout, Dense, Dropout from tensorflow.keras.losses import ( BinaryCrossentropy, - MeanSquaredError, BinaryFocalCrossentropy, + MeanSquaredError, ) from tensorflow.keras.models import Model, Sequential diff --git a/dfpl/vae.py b/dfpl/vae.py index 17b7dc6f..d0a89dbe 100644 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -1,23 +1,23 @@ +import csv import logging import math import os.path from os.path import basename +from typing import Tuple import numpy as np import pandas as pd +import tensorflow.keras.metrics as metrics import wandb from keras import backend as K from sklearn.model_selection import train_test_split -import tensorflow.keras.metrics as metrics from tensorflow.keras import initializers, optimizers from tensorflow.keras.layers import Dense, Input, Lambda from tensorflow.keras.models import Model from tensorflow.python.framework.ops import disable_eager_execution -from typing import Tuple -import csv -from dfpl import history as ht from dfpl import callbacks +from dfpl import history as ht from dfpl import options, settings from dfpl.utils import ae_scaffold_split, weight_split diff --git a/example/train.json b/example/train.json index f8fce8dc..4649e349 100755 --- a/example/train.json +++ b/example/train.json @@ -1,6 +1,6 @@ { "py/object": "dfpl.options.Options", - "inputFile": "/home/soulios/data/S_dataset-AR.csv", + "inputFile": "/home/soulios/tests/data/S_dataset.csv", "outputDir": "example/grokking", "ecModelDir": "", "ecWeightsFile": "", diff --git a/tests/run_fnntraining.py b/tests/run_fnntraining.py index 6668b2ee..4146ad4a 100644 --- a/tests/run_fnntraining.py +++ b/tests/run_fnntraining.py @@ -1,5 +1,6 @@ import logging import pathlib + import dfpl.autoencoder as ac import dfpl.fingerprint as fp import dfpl.options as opt diff --git a/tests/run_predictgnn.py b/tests/run_predictgnn.py index 99a6185c..979c2868 100644 --- a/tests/run_predictgnn.py +++ b/tests/run_predictgnn.py @@ -1,11 +1,12 @@ import logging import os import pathlib -import dfpl.options as opt -import dfpl.utils as utils + import pandas as pd -from chemprop import train, args +from chemprop import args, train +import dfpl.options as opt +import dfpl.utils as utils project_directory = pathlib.Path(__file__).parent.absolute() example_directory = ( diff --git a/tests/run_traingnn.py b/tests/run_traingnn.py index 74f317bb..582d4627 100644 --- a/tests/run_traingnn.py +++ b/tests/run_traingnn.py @@ -1,9 +1,9 @@ import logging import pathlib + +import dfpl.__main__ as main import dfpl.options as opt import dfpl.utils as utils -import dfpl.__main__ as main - project_directory = pathlib.Path(__file__).parent.absolute() example_directory = project_directory.parent / "example" diff --git a/tests/test_fingerprint.py b/tests/test_fingerprint.py index 14746143..75364553 100644 --- a/tests/test_fingerprint.py +++ b/tests/test_fingerprint.py @@ -1,7 +1,6 @@ import pandas as pd from rdkit import RDLogger - from dfpl import fingerprint as fp correct_smiles = [ diff --git a/tests/test_fractional_sampling.py b/tests/test_fractional_sampling.py index a0e416a4..bf27f2e6 100644 --- a/tests/test_fractional_sampling.py +++ b/tests/test_fractional_sampling.py @@ -1,5 +1,6 @@ import os.path import pathlib + import numpy as np from dfpl import fingerprint as fp From c6c9295d8810e67f8a1b24c5186baa87d8fbe656 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 08:39:23 +0200 Subject: [PATCH 10/45] where is the path defined? --- dfpl/fingerprint.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dfpl/fingerprint.py b/dfpl/fingerprint.py index 291689c2..f72e7e24 100644 --- a/dfpl/fingerprint.py +++ b/dfpl/fingerprint.py @@ -136,12 +136,12 @@ def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: conversion_rules = { - # "S_dataset.csv": importSmilesCSV, + "/deepFPlearn/tests/data/S_dataset.csv": importSmilesCSV, # "S_dataset_extended.csv": importSmilesCSV, # "D_dataset.tsv": importDstoxTSV, # "train_data.csv": importSmilesCSV, # "predict_data.csv": importDstoxTSV, - "B_data_ER.csv": importDstoxTSV + # "B_data_ER.csv": importDstoxTSV } From 7546f2d39e8a4a982a9834fc749023a67f6e9d56 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 08:51:12 +0200 Subject: [PATCH 11/45] where is the path defined? --- dfpl/fingerprint.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/dfpl/fingerprint.py b/dfpl/fingerprint.py index f72e7e24..4c8f5cdb 100644 --- a/dfpl/fingerprint.py +++ b/dfpl/fingerprint.py @@ -135,8 +135,12 @@ def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: return pd.read_table(tsvfilename, names=["toxid", "inchi", "key"]) +# Update the directory path to point to the correct location +directory = "/deepFPlearn/tests/data/" + conversion_rules = { - "/deepFPlearn/tests/data/S_dataset.csv": importSmilesCSV, + os.path.join(directory, "S_dataset.csv"): importSmilesCSV, + # "/tests/data/S_dataset.csv": importSmilesCSV, # "S_dataset_extended.csv": importSmilesCSV, # "D_dataset.tsv": importDstoxTSV, # "train_data.csv": importSmilesCSV, From 598c10e7d738e120bc7adcc494643d762f4a8ac5 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 09:17:42 +0200 Subject: [PATCH 12/45] test_fractional_sampling path --- dfpl/fingerprint.py | 6 +----- tests/test_fractional_sampling.py | 9 +++++++-- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/dfpl/fingerprint.py b/dfpl/fingerprint.py index 4c8f5cdb..85ad8b69 100644 --- a/dfpl/fingerprint.py +++ b/dfpl/fingerprint.py @@ -135,12 +135,8 @@ def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: return pd.read_table(tsvfilename, names=["toxid", "inchi", "key"]) -# Update the directory path to point to the correct location -directory = "/deepFPlearn/tests/data/" - conversion_rules = { - os.path.join(directory, "S_dataset.csv"): importSmilesCSV, - # "/tests/data/S_dataset.csv": importSmilesCSV, + "/S_dataset.csv": importSmilesCSV, # "S_dataset_extended.csv": importSmilesCSV, # "D_dataset.tsv": importDstoxTSV, # "train_data.csv": importSmilesCSV, diff --git a/tests/test_fractional_sampling.py b/tests/test_fractional_sampling.py index bf27f2e6..65757648 100644 --- a/tests/test_fractional_sampling.py +++ b/tests/test_fractional_sampling.py @@ -9,8 +9,13 @@ def test_fractional_sampling(): - test_directory = pathlib.Path(__file__).parent.absolute() - df = fp.importDataFile(os.path.join(test_directory, "data", "S_dataset.csv")) + script_directory = os.path.dirname(os.path.abspath(__file__)) + # Construct the absolute path to the data directory + data_directory = os.path.join(script_directory, "data") + df = fp.importDataFile(os.path.join(data_directory, "S_dataset.csv")) + + # test_directory = pathlib.Path(__file__).parent.absolute() + # df = fp.importDataFile(os.path.join(test_directory, "data", "S_dataset.csv")) targets = ["AR", "ER", "GR"] fractions = [0.5, 1.0, 2.0, 3.0] From d678ab76d1d0f1e4aa25905b87e1136f361920c5 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 09:27:15 +0200 Subject: [PATCH 13/45] clueless --- tests/test_fractional_sampling.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/tests/test_fractional_sampling.py b/tests/test_fractional_sampling.py index 65757648..4c50d1df 100644 --- a/tests/test_fractional_sampling.py +++ b/tests/test_fractional_sampling.py @@ -9,13 +9,10 @@ def test_fractional_sampling(): - script_directory = os.path.dirname(os.path.abspath(__file__)) - # Construct the absolute path to the data directory - data_directory = os.path.join(script_directory, "data") - df = fp.importDataFile(os.path.join(data_directory, "S_dataset.csv")) - # test_directory = pathlib.Path(__file__).parent.absolute() # df = fp.importDataFile(os.path.join(test_directory, "data", "S_dataset.csv")) + csv_file_path = "/deepFPlearn/tests/data/S_dataset.csv" + df = fp.importDataFile(csv_file_path) targets = ["AR", "ER", "GR"] fractions = [0.5, 1.0, 2.0, 3.0] From c6369de21e35e7d09eb0bdb7b2935767f3de39bb Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 10:01:21 +0200 Subject: [PATCH 14/45] test_fractional_sampling path --- tests/test_fractional_sampling.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tests/test_fractional_sampling.py b/tests/test_fractional_sampling.py index 4c50d1df..bf27f2e6 100644 --- a/tests/test_fractional_sampling.py +++ b/tests/test_fractional_sampling.py @@ -9,10 +9,8 @@ def test_fractional_sampling(): - # test_directory = pathlib.Path(__file__).parent.absolute() - # df = fp.importDataFile(os.path.join(test_directory, "data", "S_dataset.csv")) - csv_file_path = "/deepFPlearn/tests/data/S_dataset.csv" - df = fp.importDataFile(csv_file_path) + test_directory = pathlib.Path(__file__).parent.absolute() + df = fp.importDataFile(os.path.join(test_directory, "data", "S_dataset.csv")) targets = ["AR", "ER", "GR"] fractions = [0.5, 1.0, 2.0, 3.0] From b4cb81d2cb7dd94afac3f7a4149ea2967c42b2a1 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 10:20:02 +0200 Subject: [PATCH 15/45] json path --- example/train.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/train.json b/example/train.json index 4649e349..3f320c06 100755 --- a/example/train.json +++ b/example/train.json @@ -1,6 +1,6 @@ { "py/object": "dfpl.options.Options", - "inputFile": "/home/soulios/tests/data/S_dataset.csv", + "inputFile": "deepFPlearn/tests/data/S_dataset.csv", "outputDir": "example/grokking", "ecModelDir": "", "ecWeightsFile": "", From e420304a322e1c12e284b48744c35a02d575f306 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 10:46:00 +0200 Subject: [PATCH 16/45] json path --- example/train.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/train.json b/example/train.json index 3f320c06..7794dc4e 100755 --- a/example/train.json +++ b/example/train.json @@ -1,6 +1,6 @@ { "py/object": "dfpl.options.Options", - "inputFile": "deepFPlearn/tests/data/S_dataset.csv", + "inputFile": "tests/data/S_dataset.csv", "outputDir": "example/grokking", "ecModelDir": "", "ecWeightsFile": "", From cdfff083a291c04bd0392379300b8086aea04355 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 10:55:03 +0200 Subject: [PATCH 17/45] json path --- example/train.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/example/train.json b/example/train.json index 7794dc4e..9eea3a02 100755 --- a/example/train.json +++ b/example/train.json @@ -1,7 +1,7 @@ { "py/object": "dfpl.options.Options", "inputFile": "tests/data/S_dataset.csv", - "outputDir": "example/grokking", + "outputDir": "tests/output/", "ecModelDir": "", "ecWeightsFile": "", "verbose": 2, @@ -10,14 +10,14 @@ "compressFeatures": true, "encFPSize": 256, - "aeSplitType": "scaffold_balanced", + "aeSplitType": "random", "aeEpochs": 2, "aeBatchSize": 351, "aeOptimizer": "Adam", "aeActivationFunction": "relu", "aeLearningRate": 0.001, "aeLearningRateDecay": 0.0001, - "aeType": "variational", + "aeType": "deterministic", "type": "smiles", "fpType": "topological", From d78019755da395054cd69adc80d8873df3522cef Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 11:12:42 +0200 Subject: [PATCH 18/45] fixing paths? --- tests/run_fnntraining.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/run_fnntraining.py b/tests/run_fnntraining.py index 4146ad4a..10ddcbbd 100644 --- a/tests/run_fnntraining.py +++ b/tests/run_fnntraining.py @@ -11,9 +11,9 @@ test_train_args = opt.Options( inputFile=utils.makePathAbsolute(f"{project_directory}/data/S_dataset.csv"), ecModelDir=utils.makePathAbsolute( - f"{project_directory}/output/fnnTrainingCompressed/" + f"{project_directory}/output/" ), - outputDir=utils.makePathAbsolute(f"{project_directory}/output/fnnTraining"), + outputDir=utils.makePathAbsolute(f"{project_directory}/output/"), ecWeightsFile="", type="smiles", fpType="topological", @@ -36,7 +36,7 @@ def run_single_label_training(opts: opt.Options) -> None: logging.info("Adding fingerprint to dataset") opts.outputDir = utils.makePathAbsolute( - f"{project_directory}/output/fnnTrainingCompressed" + f"{project_directory}/output/" ) utils.createDirectory(opts.outputDir) @@ -65,7 +65,7 @@ def run_single_label_training(opts: opt.Options) -> None: # train FNNs with uncompressed features opts.outputDir = utils.makePathAbsolute( - f"{project_directory}/output/fnnTrainingUncompressed" + f"{project_directory}/output/" ) utils.createDirectory(opts.outputDir) logging.info("Training the FNN using un-compressed input data.") From 1aea913ad257536ac532cd712bc04c5ebdfe19c4 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 12:01:21 +0200 Subject: [PATCH 19/45] tests locally run --- example/traingnn.json | 2 +- tests/run_fnntraining.py | 8 ++++---- tests/run_prediction.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/example/traingnn.json b/example/traingnn.json index 4473da6a..dca10049 100644 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -3,7 +3,7 @@ "data_path": "data/S_dataset.csv", "save_dir": "dmpnn-random/", "epochs": 2, - "num_folds": 1, + "num_folds": 2, "metric": "accuracy", "loss_function": "binary_cross_entropy", "split_type": "random", diff --git a/tests/run_fnntraining.py b/tests/run_fnntraining.py index 10ddcbbd..4146ad4a 100644 --- a/tests/run_fnntraining.py +++ b/tests/run_fnntraining.py @@ -11,9 +11,9 @@ test_train_args = opt.Options( inputFile=utils.makePathAbsolute(f"{project_directory}/data/S_dataset.csv"), ecModelDir=utils.makePathAbsolute( - f"{project_directory}/output/" + f"{project_directory}/output/fnnTrainingCompressed/" ), - outputDir=utils.makePathAbsolute(f"{project_directory}/output/"), + outputDir=utils.makePathAbsolute(f"{project_directory}/output/fnnTraining"), ecWeightsFile="", type="smiles", fpType="topological", @@ -36,7 +36,7 @@ def run_single_label_training(opts: opt.Options) -> None: logging.info("Adding fingerprint to dataset") opts.outputDir = utils.makePathAbsolute( - f"{project_directory}/output/" + f"{project_directory}/output/fnnTrainingCompressed" ) utils.createDirectory(opts.outputDir) @@ -65,7 +65,7 @@ def run_single_label_training(opts: opt.Options) -> None: # train FNNs with uncompressed features opts.outputDir = utils.makePathAbsolute( - f"{project_directory}/output/" + f"{project_directory}/output/fnnTrainingUncompressed" ) utils.createDirectory(opts.outputDir) logging.info("Training the FNN using un-compressed input data.") diff --git a/tests/run_prediction.py b/tests/run_prediction.py index 7e675230..cb7d1fea 100644 --- a/tests/run_prediction.py +++ b/tests/run_prediction.py @@ -16,7 +16,7 @@ ecWeightsFile=utils.makePathAbsolute( f"{project_directory}/output/D_datasetdeterministicrandom.autoencoder.weightsrandom.autoencoder.weights.hdf5" ), - fnnModelDir=f"{project_directory}/fnnTrainingCompressed/AR_saved_model", + fnnModelDir=f"{project_directory}/output/fnnTrainingCompressed/AR_saved_model", fpSize=2048, type="smiles", fpType="topological", From ad50a2afe45e664d81e019cec78cb023b44ccf06 Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 15:05:07 +0200 Subject: [PATCH 20/45] changing output dir to match the pr.yml --- example/predict.json | 4 ++-- example/train.json | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/example/predict.json b/example/predict.json index 6559a854..b663d701 100755 --- a/example/predict.json +++ b/example/predict.json @@ -1,11 +1,11 @@ { "py/object": "dfpl.options.Options", "inputFile": "tests/data/smiles.csv", - "outputDir": "example/results_predict_fnn", + "outputDir": "example/results_predict/", "outputFile": "smiles.csv", "ecModelDir": "", "ecWeightsFile": "", - "fnnModelDir": "example/grokking/AR-2_best_saved_model", + "fnnModelDir": "example/AR-2_saved_model", "compressFeatures": false, "useRBM": false, "trainAC": false, diff --git a/example/train.json b/example/train.json index 9eea3a02..5cd19d9a 100755 --- a/example/train.json +++ b/example/train.json @@ -1,7 +1,7 @@ { "py/object": "dfpl.options.Options", "inputFile": "tests/data/S_dataset.csv", - "outputDir": "tests/output/", + "outputDir": "example/results_train/", "ecModelDir": "", "ecWeightsFile": "", "verbose": 2, From 337ea17a612acc29441f17cc58556e20e40d746f Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 1 Sep 2023 15:13:47 +0200 Subject: [PATCH 21/45] changing output dir to match the pr.yml --- example/predict.json | 1 - 1 file changed, 1 deletion(-) diff --git a/example/predict.json b/example/predict.json index b663d701..904e30ca 100755 --- a/example/predict.json +++ b/example/predict.json @@ -7,7 +7,6 @@ "ecWeightsFile": "", "fnnModelDir": "example/AR-2_saved_model", "compressFeatures": false, - "useRBM": false, "trainAC": false, "trainFNN": false } From 2b02476a8fe13f6945c618f861c08676e64eca6f Mon Sep 17 00:00:00 2001 From: soulios Date: Mon, 4 Sep 2023 10:49:27 +0200 Subject: [PATCH 22/45] finishing pr.yml --- .github/workflows/pr.yml | 22 ++++++++++++++++++---- dfpl/__main__.py | 4 +--- dfpl/options.py | 6 +----- dfpl/settings.py | 4 ++-- example/predict.json | 6 +++--- example/predictgnn.json | 4 ++-- example/train.json | 4 ++-- example/traingnn.json | 2 +- 8 files changed, 30 insertions(+), 22 deletions(-) diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index 0b130577..829d8ab9 100644 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -75,11 +75,25 @@ jobs: echo "predict result directory missing" >&2 exit 1 fi - echo "result lines "$(wc -l example/results_predict/predict_data_AR.csv) - if [ "$(cat example/results_predict/predict_data_AR.csv | wc -l)" -lt "2000" ]; then - echo "predict result should have at least 2000 lines. But had only $(cat example/results_predict/predict_data_AR.csv | wc -l)" >&2 + echo "result lines "$(wc -l example/results_predict/smiles.csv) + if [ "$(cat example/results_predict/smiles.csv | wc -l)" -lt "5" ]; then + echo "predict result should have at least 5 lines. But had only $(cat example/results_predict/predict_data_AR.csv | wc -l)" >&2 + exit 1 + fi + tree example + + dfpl traingnn -f example/traingnn.json + if [ ! -d dmpnn-random/ ]; then + echo "predict result directory missing" >&2 exit 1 fi tree example - dfpl convert -f example/data \ No newline at end of file + dfpl predictgnn -f example/predictgnn.json + if [ ! -d preds_dmpnn/ ]; then + echo "predict result directory missing" >&2 + exit 1 + fi + + + dfpl convert -f tests/data \ No newline at end of file diff --git a/dfpl/__main__.py b/dfpl/__main__.py index cfef84d8..7896d451 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -181,7 +181,6 @@ def predict(opts: options.Options) -> None: df = fp.importDataFile( opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize ) - print(df) else: df = fp.importDataFile( opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize @@ -273,8 +272,7 @@ def main(): f"The following arguments are received or filled with default values:\n{prog_args}" ) - if predictgnn_opts.gnn_type == "dmpnn": - predictdmpnn(fixed_opts, prog_args.configFile) + predictdmpnn(fixed_opts, prog_args.configFile) elif prog_args.method == "train": train_opts = options.Options.fromCmdArgs(prog_args) diff --git a/dfpl/options.py b/dfpl/options.py index bb753f05..6d84dbc4 100644 --- a/dfpl/options.py +++ b/dfpl/options.py @@ -1040,11 +1040,7 @@ def parseInputPredict(parser: argparse.ArgumentParser) -> None: :return: A namespace object built up from attributes parsed out of the cmd line. """ - parser = argparse.ArgumentParser( - description="Use models that have been generated by " - "to make predictions on chemicals (provide SMILES or " - "topological fingerprints)." - ) + general_args = parser.add_argument_group("General Configuration") files_args = parser.add_argument_group("Files") files_args.add_argument( diff --git a/dfpl/settings.py b/dfpl/settings.py index 64eac190..8d3d2792 100644 --- a/dfpl/settings.py +++ b/dfpl/settings.py @@ -48,5 +48,5 @@ # Training settings of the FNN that were magic numbers in the code before. nn_train_min_delta = 0.0001 -nn_train_check_period = 10 -nn_train_patience = 20 +nn_train_check_period = 1 +nn_train_patience = 1 diff --git a/example/predict.json b/example/predict.json index 904e30ca..252965e3 100755 --- a/example/predict.json +++ b/example/predict.json @@ -3,10 +3,10 @@ "inputFile": "tests/data/smiles.csv", "outputDir": "example/results_predict/", "outputFile": "smiles.csv", - "ecModelDir": "", + "ecModelDir": "example/results_train/random_autoencoder/", "ecWeightsFile": "", - "fnnModelDir": "example/AR-2_saved_model", - "compressFeatures": false, + "fnnModelDir": "example/results_train/AR_saved_model", + "compressFeatures": true, "trainAC": false, "trainFNN": false } diff --git a/example/predictgnn.json b/example/predictgnn.json index db8d2afc..75836c26 100644 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,7 +1,7 @@ { "py/object": "dfpl.options.GnnOptions", - "test_path": "data/smiles.csv", + "test_path": "tests/data/smiles.csv", "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt", - "save_dir": "preds_dmpnn_D", + "save_dir": "preds_dmpnn", "saving_name": "DMONN_preds.csv" } \ No newline at end of file diff --git a/example/train.json b/example/train.json index 5cd19d9a..62f2abb4 100755 --- a/example/train.json +++ b/example/train.json @@ -2,8 +2,8 @@ "py/object": "dfpl.options.Options", "inputFile": "tests/data/S_dataset.csv", "outputDir": "example/results_train/", - "ecModelDir": "", - "ecWeightsFile": "", + "ecModelDir": "example/results_train/", + "ecWeightsFile": "random_autoencoder.hdf5", "verbose": 2, "trainAC": true, diff --git a/example/traingnn.json b/example/traingnn.json index dca10049..7a5a0712 100644 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -1,6 +1,6 @@ { "py/object": "dfpl.options.GnnOptions", - "data_path": "data/S_dataset.csv", + "data_path": "tests/data/S_dataset.csv", "save_dir": "dmpnn-random/", "epochs": 2, "num_folds": 2, From 48fed75ea74019603f2b5e6cdbd2d0304c6613fd Mon Sep 17 00:00:00 2001 From: soulios Date: Mon, 4 Sep 2023 11:10:23 +0200 Subject: [PATCH 23/45] finishing pr.yml --- dfpl/fingerprint.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/dfpl/fingerprint.py b/dfpl/fingerprint.py index 85ad8b69..16c0c87b 100644 --- a/dfpl/fingerprint.py +++ b/dfpl/fingerprint.py @@ -136,7 +136,9 @@ def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: conversion_rules = { - "/S_dataset.csv": importSmilesCSV, + "S_dataset.csv": importSmilesCSV, + "smiles.csv": importSmilesCSV, + "inchi.tsv": importDstoxTSV # "S_dataset_extended.csv": importSmilesCSV, # "D_dataset.tsv": importDstoxTSV, # "train_data.csv": importSmilesCSV, From bacbae8ddc3783cc0c0748560d2e1f66f4eff46a Mon Sep 17 00:00:00 2001 From: soulios Date: Mon, 4 Sep 2023 14:05:38 +0200 Subject: [PATCH 24/45] adding test for predictgnn.json --- .github/workflows/pr.yml | 10 ++++++++-- example/predictgnn.json | 2 +- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index 829d8ab9..27f43c34 100644 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -76,8 +76,8 @@ jobs: exit 1 fi echo "result lines "$(wc -l example/results_predict/smiles.csv) - if [ "$(cat example/results_predict/smiles.csv | wc -l)" -lt "5" ]; then - echo "predict result should have at least 5 lines. But had only $(cat example/results_predict/predict_data_AR.csv | wc -l)" >&2 + if [ "$(cat example/results_predict/smiles.csv | wc -l)" -lt "6" ]; then + echo "predict result should have at least 6 lines. But had only $(cat example/results_predict/smiles.csv | wc -l)" >&2 exit 1 fi tree example @@ -94,6 +94,12 @@ jobs: echo "predict result directory missing" >&2 exit 1 fi + + echo "result lines "$(wc -l preds_dmpnn/DMPNN_preds.csv) + if [ "$(cat preds_dmpnn/DMPNN_preds.csv | wc -l)" -lt "6" ]; then + echo "predict result should have at least 5 lines. But had only $(cat preds_dmpnn/DMPNN_preds.csv | wc -l)" >&2 + exit 1 + fi dfpl convert -f tests/data \ No newline at end of file diff --git a/example/predictgnn.json b/example/predictgnn.json index 75836c26..157b5e05 100644 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -3,5 +3,5 @@ "test_path": "tests/data/smiles.csv", "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt", "save_dir": "preds_dmpnn", - "saving_name": "DMONN_preds.csv" + "saving_name": "DMPNN_preds.csv" } \ No newline at end of file From 76435df61091b0e23d11f436c879871373d6726f Mon Sep 17 00:00:00 2001 From: soulios Date: Mon, 4 Sep 2023 14:48:28 +0200 Subject: [PATCH 25/45] changed nn_check period again --- dfpl/settings.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dfpl/settings.py b/dfpl/settings.py index 8d3d2792..64eac190 100644 --- a/dfpl/settings.py +++ b/dfpl/settings.py @@ -48,5 +48,5 @@ # Training settings of the FNN that were magic numbers in the code before. nn_train_min_delta = 0.0001 -nn_train_check_period = 1 -nn_train_patience = 1 +nn_train_check_period = 10 +nn_train_patience = 20 From 9c0e1e63a86e23beb405426a585bf2760a82c74b Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 7 Sep 2023 19:36:51 +0200 Subject: [PATCH 26/45] update gitlab --- environment.yml | 5 ++--- example/train.json | 6 +++--- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/environment.yml b/environment.yml index 3c2e7a6c..6209f1a9 100644 --- a/environment.yml +++ b/environment.yml @@ -16,8 +16,7 @@ dependencies: - rdkit=2022.03.1 - scikit-learn=1.0.2 - seaborn=0.12.2 - - tensorflow-gpu=2.6.0 + - tensorflow - wandb=0.12 - - umap=0.1.1 - pip: - - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 \ No newline at end of file + - git+https://github.com/soulios/chemprop/tree/dfpl_chemprop diff --git a/example/train.json b/example/train.json index 62f2abb4..2ffda6be 100755 --- a/example/train.json +++ b/example/train.json @@ -6,8 +6,8 @@ "ecWeightsFile": "random_autoencoder.hdf5", "verbose": 2, - "trainAC": true, - "compressFeatures": true, + "trainAC": false, + "compressFeatures": false, "encFPSize": 256, "aeSplitType": "random", @@ -35,7 +35,7 @@ "fnnType": "FNN", "optimizer": "Adam", "lossFunction": "bce", - "epochs": 11, + "epochs": 100, "batchSize": 128, "activationFunction": "selu", "dropout": 0.0107, From 8770c6c555aa1b15ed9e4fbc339eefef8fa983cd Mon Sep 17 00:00:00 2001 From: soulios Date: Thu, 7 Sep 2023 20:01:11 +0200 Subject: [PATCH 27/45] added build --- build/lib/dfpl/__init__.py | 0 build/lib/dfpl/__main__.py | 321 ++++ build/lib/dfpl/autoencoder.py | 415 +++++ build/lib/dfpl/callbacks.py | 89 ++ .../dfpl/deepFPlearn-HyperParameterTuning.py | 128 ++ ...deepFPlearn-HyperParameterTuning_single.py | 426 +++++ build/lib/dfpl/dfplmodule.py | 1413 +++++++++++++++++ build/lib/dfpl/feedforwardNN.py | 464 ++++++ build/lib/dfpl/fingerprint.py | 164 ++ .../lib/dfpl/harmonize_mol_representations.py | 171 ++ build/lib/dfpl/history.py | 41 + build/lib/dfpl/options.py | 1297 +++++++++++++++ build/lib/dfpl/plot.py | 228 +++ build/lib/dfpl/predictions.py | 39 + build/lib/dfpl/rbm.py | 259 +++ build/lib/dfpl/settings.py | 52 + build/lib/dfpl/single_label_model.py | 1008 ++++++++++++ build/lib/dfpl/utils.py | 329 ++++ build/lib/dfpl/vae.py | 337 ++++ 19 files changed, 7181 insertions(+) create mode 100644 build/lib/dfpl/__init__.py create mode 100644 build/lib/dfpl/__main__.py create mode 100644 build/lib/dfpl/autoencoder.py create mode 100644 build/lib/dfpl/callbacks.py create mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning.py create mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py create mode 100644 build/lib/dfpl/dfplmodule.py create mode 100644 build/lib/dfpl/feedforwardNN.py create mode 100644 build/lib/dfpl/fingerprint.py create mode 100644 build/lib/dfpl/harmonize_mol_representations.py create mode 100644 build/lib/dfpl/history.py create mode 100644 build/lib/dfpl/options.py create mode 100644 build/lib/dfpl/plot.py create mode 100644 build/lib/dfpl/predictions.py create mode 100644 build/lib/dfpl/rbm.py create mode 100644 build/lib/dfpl/settings.py create mode 100644 build/lib/dfpl/single_label_model.py create mode 100644 build/lib/dfpl/utils.py create mode 100644 build/lib/dfpl/vae.py diff --git a/build/lib/dfpl/__init__.py b/build/lib/dfpl/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/build/lib/dfpl/__main__.py b/build/lib/dfpl/__main__.py new file mode 100644 index 00000000..5c12705b --- /dev/null +++ b/build/lib/dfpl/__main__.py @@ -0,0 +1,321 @@ +import os.path +import dataclasses +import logging +import pathlib +from argparse import Namespace +from os import path + +import pandas as pd +from keras.models import load_model + +from dfpl import autoencoder as ac +from dfpl import feedforwardNN as fNN +from dfpl import fingerprint as fp +from dfpl import options, predictions + +# from dfpl import rbm as rbm +from dfpl import single_label_model as sl +from dfpl import vae as vae +from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute +import chemprop as cp + +project_directory = pathlib.Path(".").parent.parent.absolute() +test_train_opts = options.Options( + inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", + outputDir=f"{project_directory}/output_data/console_test", + ecWeightsFile=f"{project_directory}/output_data/case_00/AE_S/ae_S.encoder.hdf5", + ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", + type="smiles", + fpType="topological", + epochs=100, + batchSize=1024, + fpSize=2048, + encFPSize=256, + enableMultiLabel=False, + testSize=0.2, + kFolds=2, + verbose=2, + trainAC=False, + trainFNN=True, + compressFeatures=True, + activationFunction="selu", + lossFunction="bce", + optimizer="Adam", + fnnType="FNN", +) + +test_pred_opts = options.Options( + inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", + outputDir=f"{project_directory}/output_data/console_test", + outputFile=f"{project_directory}/output_data/console_test/S_dataset.predictions_ER.csv", + ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", + fnnModelDir=f"{project_directory}/output_data/console_test/ER_saved_model", + type="smiles", + fpType="topological", +) + + +def traindmpnn(opts: options.GnnOptions): + """ + Train a D-MPNN model using the given options. + Args: + - opts: options.GnnOptions instance containing the details of the training + Returns: + - None + """ + os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" + ignore_elements = ["py/object"] + # Load options from a JSON file and replace the relevant attributes in `opts` + arguments = createArgsFromJson( + opts.configFile, ignore_elements, return_json_object=False + ) + opts = cp.args.TrainArgs().parse_args(arguments) + logging.info("Training DMPNN...") + # Train the model and get the mean and standard deviation of AUC score from cross-validation + mean_score, std_score = cp.train.cross_validate( + args=opts, train_func=cp.train.run_training + ) + logging.info(f"Results: {mean_score:.5f} +/- {std_score:.5f}") + + +def predictdmpnn(opts: options.GnnOptions, json_arg_path: str) -> None: + """ + Predict the values using a trained D-MPNN model with the given options. + Args: + - opts: options.GnnOptions instance containing the details of the prediction + - JSON_ARG_PATH: path to a JSON file containing additional arguments for prediction + Returns: + - None + """ + ignore_elements = [ + "py/object", + "gnn_type", + "checkpoint_paths", + "save_dir", + "saving_name", + ] + # Load options and additional arguments from a JSON file + arguments, data = createArgsFromJson( + json_arg_path, ignore_elements, return_json_object=True + ) + arguments.append("--preds_path") + arguments.append("") + save_dir = data.get("save_dir") + name = data.get("saving_name") + # Replace relevant attributes in `opts` with loaded options + opts = cp.args.PredictArgs().parse_args(arguments) + opts.preds_path = save_dir + "/" + name + df = pd.read_csv(opts.test_path) + smiles = [] + for index, rows in df.iterrows(): + my_list = [rows.smiles] + smiles.append(my_list) + # Make predictions and return the result + cp.train.make_predictions(args=opts, smiles=smiles) + + +def train(opts: options.Options): + """ + Run the main training procedure + :param opts: Options defining the details of the training + """ + + os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" + + # import data from file and create DataFrame + if "tsv" in opts.inputFile: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize + ) + else: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize + ) + # initialize encoders to None + encoder = None + autoencoder = None + if opts.trainAC: + if opts.aeType == "deterministic": + encoder, train_indices, test_indices = ac.train_full_ac(df, opts) + elif opts.aeType == "variational": + encoder, train_indices, test_indices = vae.train_full_vae(df, opts) + else: + raise ValueError(f"Unknown autoencoder type: {opts.aeType}") + + # if feature compression is enabled + if opts.compressFeatures: + if not opts.trainAC: + if opts.aeType == "deterministic": + (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) + elif opts.aeType == "variational": + (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) + elif opts.ecWeightsFile == "": + encoder = load_model(opts.ecModelDir) + else: + autoencoder.load_weights( + os.path.join(opts.ecModelDir, opts.ecWeightsFile) + ) + # compress the fingerprints using the autoencoder + df = ac.compress_fingerprints(df, encoder) + # ac.visualize_fingerprints( + # df, + # before_col="fp", + # after_col="fpcompressed", + # train_indices=train_indices, + # test_indices=test_indices, + # save_as=f"UMAP_{opts.aeSplitType}.png", + # ) + # train single label models if requested + if opts.trainFNN and not opts.enableMultiLabel: + sl.train_single_label_models(df=df, opts=opts) + + # train multi-label models if requested + if opts.trainFNN and opts.enableMultiLabel: + fNN.train_nn_models_multi(df=df, opts=opts) + + +def predict(opts: options.Options) -> None: + """ + Run prediction given specific options + :param opts: Options defining the details of the prediction + """ + # import data from file and create DataFrame + if "tsv" in opts.inputFile: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize + ) + print(df) + else: + df = fp.importDataFile( + opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize + ) + + if opts.compressFeatures: + # load trained model for autoencoder + if opts.aeType == "deterministic": + (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) + if opts.aeType == "variational": + (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) + # Load trained model for autoencoder + if opts.ecWeightsFile == "": + encoder = load_model(opts.ecModelDir) + else: + encoder.load_weights(os.path.join(opts.ecModelDir, opts.ecWeightsFile)) + df = ac.compress_fingerprints(df, encoder) + + # Run predictions on the compressed fingerprints and store the results in a dataframe + df2 = predictions.predict_values(df=df, opts=opts) + + # Extract the column names from the dataframe, excluding the 'fp' and 'fpcompressed' columns + names_columns = [c for c in df2.columns if c not in ["fp", "fpcompressed"]] + + # Save the predicted values to a CSV file in the output directory + df2[names_columns].to_csv(path_or_buf=path.join(opts.outputDir, opts.outputFile)) + + # Log successful completion of prediction and the file path where the results were saved + logging.info( + f"Prediction successful. Results written to '{path.join(opts.outputDir, opts.outputFile)}'" + ) + + +def createLogger(filename: str) -> None: + """ + Set up a logger for the main function that also saves to a log file + """ + # get root logger and set its level + logger = logging.getLogger() + logger.setLevel(logging.INFO) + # create file handler which logs info messages + fh = logging.FileHandler(filename, mode="w") + fh.setLevel(logging.INFO) + # create console handler + ch = logging.StreamHandler() + ch.setLevel(logging.INFO) + # create formatter and add it to the handlers + formatterFile = logging.Formatter( + "{asctime} - {name} - {levelname} - {message}", style="{" + ) + formatterConsole = logging.Formatter("{levelname} {message}", style="{") + fh.setFormatter(formatterFile) + ch.setFormatter(formatterConsole) + # add the handlers to the logger + logger.addHandler(fh) + logger.addHandler(ch) + + +def main(): + """ + Main function that runs training/prediction defined by command line arguments + """ + + parser = options.createCommandlineParser() + prog_args: Namespace = parser.parse_args() + try: + if prog_args.method == "convert": + directory = makePathAbsolute(prog_args.f) + if path.isdir(directory): + createLogger(path.join(directory, "convert.log")) + logging.info(f"Convert all data files in {directory}") + fp.convert_all(directory) + else: + raise ValueError("Input directory is not a directory") + elif prog_args.method == "traingnn": + traingnn_opts = options.GnnOptions.fromCmdArgs(prog_args) + + traindmpnn(traingnn_opts) + + elif prog_args.method == "predictgnn": + predictgnn_opts = options.GnnOptions.fromCmdArgs(prog_args) + fixed_opts = dataclasses.replace( + predictgnn_opts, + test_path=makePathAbsolute(predictgnn_opts.test_path), + preds_path=makePathAbsolute(predictgnn_opts.preds_path), + ) + + logging.info( + f"The following arguments are received or filled with default values:\n{prog_args}" + ) + + if predictgnn_opts.gnn_type == "dmpnn": + predictdmpnn(fixed_opts, prog_args.configFile) + + elif prog_args.method == "train": + train_opts = options.Options.fromCmdArgs(prog_args) + fixed_opts = dataclasses.replace( + train_opts, + inputFile=makePathAbsolute(train_opts.inputFile), + outputDir=makePathAbsolute(train_opts.outputDir), + ) + createDirectory(fixed_opts.outputDir) + createLogger(path.join(fixed_opts.outputDir, "train.log")) + logging.info( + f"The following arguments are received or filled with default values:\n{fixed_opts}" + ) + train(fixed_opts) + elif prog_args.method == "predict": + predict_opts = options.Options.fromCmdArgs(prog_args) + fixed_opts = dataclasses.replace( + predict_opts, + inputFile=makePathAbsolute(predict_opts.inputFile), + outputDir=makePathAbsolute(predict_opts.outputDir), + outputFile=makePathAbsolute( + path.join(predict_opts.outputDir, predict_opts.outputFile) + ), + ecModelDir=makePathAbsolute(predict_opts.ecModelDir), + fnnModelDir=makePathAbsolute(predict_opts.fnnModelDir), + trainAC=False, + trainFNN=False, + ) + createDirectory(fixed_opts.outputDir) + createLogger(path.join(fixed_opts.outputDir, "predict.log")) + logging.info( + f"The following arguments are received or filled with default values:\n{prog_args}" + ) + predict(fixed_opts) + except AttributeError as e: + print(e) + parser.print_usage() + + +if __name__ == "__main__": + main() diff --git a/build/lib/dfpl/autoencoder.py b/build/lib/dfpl/autoencoder.py new file mode 100644 index 00000000..1f638122 --- /dev/null +++ b/build/lib/dfpl/autoencoder.py @@ -0,0 +1,415 @@ +import logging +import math +import os.path +from os.path import basename + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import umap +import wandb +from sklearn.model_selection import train_test_split +from tensorflow.keras import initializers, losses, optimizers +from tensorflow.keras.layers import Dense, Input +from tensorflow.keras.models import Model +from typing import Tuple + +from dfpl import callbacks +from dfpl import history as ht +from dfpl import options, settings +from dfpl.utils import ae_scaffold_split, weight_split + + +def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: + """ + This function provides an autoencoder model to reduce a certain input to a compressed version. + + :param opts: Training options that provide values for adjusting the neural net + :param output_bias: Bias used to initialize the last layer. It gives the net a head start in training on + imbalanced data (which the fingerprints are, because they have many more 0's than 1's in them). + :return: a tuple of autoencoder and encoder models + """ + input_size = opts.fpSize + encoding_dim = opts.encFPSize + ac_optimizer = optimizers.Adam( + learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay + ) + + if output_bias is not None: + output_bias = initializers.Constant(output_bias) + + # get the number of meaningful hidden layers (latent space included) + hidden_layer_count = round(math.log2(input_size / encoding_dim)) + + # the input placeholder + input_vec = Input(shape=(input_size,)) + + # 1st hidden layer, that receives weights from input layer + # equals bottleneck layer, if hidden_layer_count==1! + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / 2), activation=opts.aeActivationFunction + )(input_vec) + else: + encoded = Dense( + units=int(input_size / 2), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(input_vec) + + if hidden_layer_count > 1: + # encoding layers, incl. bottle-neck + for i in range(1, hidden_layer_count): + factor_units = 2 ** (i + 1) + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # 1st decoding layer + factor_units = 2 ** (hidden_layer_count - 1) + if opts.aeActivationFunction != "selu": + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # decoding layers + for i in range(hidden_layer_count - 2, 0, -1): + factor_units = 2**i + if opts.aeActivationFunction != "selu": + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(decoded) + else: + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(decoded) + + # output layer + # to either 0 or 1 and hence we use sigmoid activation function. + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(decoded) + + else: + # output layer + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(encoded) + + autoencoder = Model(input_vec, decoded) + encoder = Model(input_vec, encoded) + autoencoder.summary(print_fn=logging.info) + + autoencoder.compile( + optimizer=ac_optimizer, + loss=losses.BinaryCrossentropy(), + # metrics=[ + # metrics.AUC(), + # metrics.Precision(), + # metrics.Recall() + # ] + ) + return autoencoder, encoder + + +def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: + """ + Trains an autoencoder on the given feature matrix X. The response matrix is only used to + split the data into meaningful test and train sets. + + :param opts: Command line arguments as defined in options.py + :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder + :return: The encoder model of the trained autoencoder + """ + + # If wandb tracking is enabled for autoencoder weights but not for the main program, initialize a new wandb run + if opts.aeWabTracking and not opts.wabTracking: + wandb.init(project=f"AE_{opts.aeSplitType}") + + # Define output files for autoencoder and encoder weights + if opts.ecWeightsFile == "": + # If no encoder weights file is specified, use the input file name to generate a default file name + logging.info("No AE encoder weights file specified") + base_file_name = ( + os.path.splitext(basename(opts.inputFile))[0] + opts.aeSplitType + ) + logging.info( + f"(auto)encoder weights will be saved in {base_file_name}.autoencoder.hdf5" + ) + ac_weights_file = os.path.join( + opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" + ) + # ec_weights_file = os.path.join( + # opts.outputDir, base_file_name + ".encoder.weights.hdf5" + # ) + else: + # If an encoder weights file is specified, use it as the encoder weights file name + logging.info(f"AE encoder will be saved in {opts.ecWeightsFile}") + base_file_name = ( + os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType + ) + ac_weights_file = os.path.join( + opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" + ) + # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) + + # Collect the callbacks for training + callback_list = callbacks.autoencoder_callback( + checkpoint_path=ac_weights_file, opts=opts + ) + + # Select all fingerprints that are valid and turn them into a numpy array + fp_matrix = np.array( + df[df["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info( + f"Training AC on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" + ) + + # When training the final AE, we don't want any test data. We want to train it on all available fingerprints. + assert 0.0 <= opts.testSize <= 0.5 + if opts.aeSplitType == "random": + logging.info("Training autoencoder using random split") + initial_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # Split data into test and training data + if opts.aeWabTracking: + x_train, x_test, train_indices, test_indices = train_test_split( + fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train, x_test, train_indices, test_indices = train_test_split( + fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "scaffold_balanced": + logging.info("Training autoencoder using scaffold split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + train_data, val_data, test_data = ae_scaffold_split( + df, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=True, + seed=42, + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + train_indices = df[ + df.index.isin(train_data[train_data["fp"].notnull()].index) + ].index.to_numpy() + test_indices = df[ + df.index.isin(test_data[test_data["fp"].notnull()].index) + ].index.to_numpy() + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "molecular_weight": + logging.info("Training autoencoder using molecular weight split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + train_data, val_data, test_data = weight_split( + df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + df_sorted = df.sort_values(by="mol_weight", ascending=True) + # Get the sorted indices from the sorted DataFrame + sorted_indices = df_sorted.index.to_numpy() + + # Find the corresponding indices for train_data, val_data, and test_data in the sorted DataFrame + train_indices = sorted_indices[df.index.isin(train_data.index)] + # val_indices = sorted_indices[df.index.isin(val_data.index)] + test_indices = sorted_indices[df.index.isin(test_data.index)] + else: + x_train = fp_matrix + x_test = None + else: + raise ValueError(f"Invalid split type: {opts.split_type}") + + # Calculate the initial bias aka the log ratio between 1's and 0'1 in all fingerprints + ids, counts = np.unique(x_train.flatten(), return_counts=True) + count_dict = dict(zip(ids, counts)) + if count_dict[0] == 0: + initial_bias = None + logging.info("No zeroes in training labels. Setting initial_bias to None.") + else: + initial_bias = np.log([count_dict[1] / count_dict[0]]) + logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") + + # Check if we're doing training/testing mode or full training mode + if opts.testSize > 0.0: + logging.info(f"AE training/testing mode with train- and test-samples") + logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") + logging.info(f"AC test data shape {x_test.shape} with type {x_test.dtype}") + else: + logging.info(f"AE full train mode without test-samples") + logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") + + # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) + (autoencoder, encoder) = define_ac_model(opts, output_bias=initial_bias) + + # Train the autoencoder on the training data + auto_hist = autoencoder.fit( + x_train, + x_train, + callbacks=callback_list, + epochs=opts.aeEpochs, + batch_size=opts.aeBatchSize, + verbose=opts.verbose, + validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, + ) + logging.info(f"Autoencoder weights stored in file: {ac_weights_file}") + + # Store the autoencoder training history and plot the metrics + ht.store_and_plot_history( + base_file_name=os.path.join(opts.outputDir, base_file_name + ".AC"), + hist=auto_hist, + ) + + # Save the autoencoder callback model to disk + save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_autoencoder") + if opts.testSize > 0.0: + (callback_autoencoder, callback_encoder) = define_ac_model(opts) + callback_encoder.save(filepath=save_path) + else: + encoder.save(filepath=save_path) + # Return the encoder model of the trained autoencoder + return encoder, train_indices, test_indices + + +def compress_fingerprints(dataframe: pd.DataFrame, encoder: Model) -> pd.DataFrame: + """ + Adds a column of the compressed version of the fingerprints to the original dataframe. + + :param dataframe: Dataframe containing a column named 'fp' with the fingerprints + :param encoder: The trained autoencoder that is used for compressing the fingerprints + :return: The input dataframe extended by a column containing the compressed version of the fingerprints + """ + logging.info("Adding compressed fingerprints") + idx = dataframe[dataframe["fp"].notnull()].index + fp_matrix = np.array( + dataframe[dataframe["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info( + f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" + ) + logging.info("Compressed fingerprints are added to input dataframe.") + dataframe["fpcompressed"] = pd.DataFrame( + {"fpcompressed": [s for s in encoder.predict(fp_matrix)]}, idx + ) + return dataframe + + +def visualize_fingerprints( + df: pd.DataFrame, + before_col: str, + after_col: str, + train_indices: np.ndarray, + test_indices: np.ndarray, + save_as: str, +): + # Calculate the number of samples to be taken from each set + num_samples = 1000 + train_samples = int(num_samples * len(train_indices) / len(df)) + test_samples = num_samples - train_samples + + # Assign train and test data points separately + train_data = df.loc[train_indices] + test_data = df.loc[test_indices] + + # Sample train and test data points + train_data_sampled = train_data.sample(n=train_samples, random_state=42) + test_data_sampled = test_data.sample(n=test_samples, random_state=42) + + # Concatenate the sampled train and test data + df_sampled = pd.concat([train_data_sampled, test_data_sampled]) + + # Convert the boolean values in the after_col column to floats + df_sampled[after_col] = df_sampled[after_col].apply( + lambda x: np.array(x, dtype=float) + ) + + df_sampled.loc[train_data_sampled.index, "set"] = "train" + df_sampled.loc[test_data_sampled.index, "set"] = "test" + # Apply UMAP + umap_model = umap.UMAP( + n_neighbors=15, min_dist=0.1, metric="euclidean", random_state=42 + ) + # Filter out the rows with invalid arrays + umap_results = umap_model.fit_transform(df_sampled[after_col].tolist()) + # Add UMAP results to the DataFrame + df_sampled["umap_x"] = umap_results[:, 0] + df_sampled["umap_y"] = umap_results[:, 1] + + # Define custom color palette + palette = {"train": "blue", "test": "red"} + + # Create the scatter plot + sns.set(style="white") + fig, ax = plt.subplots(figsize=(10, 8)) + split = save_as.split("_", 1) + part_after_underscore = split[1] + split_type = part_after_underscore.split(".")[0] + # Plot the UMAP results + for label, grp in df_sampled.groupby("set"): + set_label = label + color = palette[set_label] + alpha = ( + 0.09 if set_label == "train" else 0.9 + ) # Set different opacities for train and test + ax.scatter( + grp["umap_x"], grp["umap_y"], label=f"{set_label}", c=color, alpha=alpha + ) + + # Customize the plot + ax.set_title( + f"UMAP visualization of molecular fingerprints using {split_type} split", + fontsize=14, + ) + ax.set_xlabel("UMAP 1") + ax.set_ylabel("UMAP 2") + ax.legend(title="", loc="upper right") + sns.despine(ax=ax, offset=10) + save_path = os.path.join(os.getcwd(), save_as) + plt.savefig(save_path) diff --git a/build/lib/dfpl/callbacks.py b/build/lib/dfpl/callbacks.py new file mode 100644 index 00000000..6eae7965 --- /dev/null +++ b/build/lib/dfpl/callbacks.py @@ -0,0 +1,89 @@ +# for NN model functions +from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint + +# for testing in Weights & Biases +from wandb.keras import WandbCallback + +from dfpl import options, settings + + +def autoencoder_callback(checkpoint_path: str, opts: options.Options) -> list: + """ + Callbacks for fitting the autoencoder + + :param checkpoint_path: The output directory to store the checkpoint weight files + :param opts: Training options provided to the run + :return: List of ModelCheckpoint and EarlyStopping class. + """ + callbacks = [] + + if opts.testSize > 0.0: + target = "val_loss" + else: + target = "loss" + # enable this checkpoint to restore the weights of the best performing model + checkpoint = ModelCheckpoint( + checkpoint_path, + monitor=target, + mode="min", + verbose=1, + period=settings.ac_train_check_period, + save_best_only=True, + save_weights_only=True, + ) + callbacks.append(checkpoint) + + # enable early stopping if val_loss is not improving anymore + early_stop = EarlyStopping( + monitor=target, + mode="min", + patience=settings.ac_train_patience, + min_delta=settings.ac_train_min_delta, + verbose=1, + restore_best_weights=True, + ) + callbacks.append(early_stop) + + if opts.aeWabTracking and not opts.wabTracking: + callbacks.append(WandbCallback(save_model=False)) + return callbacks + + +def nn_callback(checkpoint_path: str, opts: options.Options) -> list: + """ + Callbacks for fitting the feed forward network (FNN) + + :param checkpoint_path: The output directory to store the checkpoint weight files + :param opts: Training options provided to the run + :return: List of ModelCheckpoint and EarlyStopping class. + """ + + callbacks = [] + + if opts.testSize > 0.0: + # enable this checkpoint to restore the weights of the best performing model + checkpoint = ModelCheckpoint( + checkpoint_path, + verbose=1, + period=settings.nn_train_check_period, + save_best_only=True, + monitor="val_loss", + mode="min", + save_weights_only=True, + ) + callbacks.append(checkpoint) + + # enable early stopping if val_loss is not improving anymore + early_stop = EarlyStopping( + patience=settings.nn_train_patience, + monitor="val_loss", + mode="min", + min_delta=settings.nn_train_min_delta, + verbose=1, + restore_best_weights=True, + ) + callbacks.append(early_stop) + + if opts.wabTracking: + callbacks.append(WandbCallback(save_model=False)) + return callbacks diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py new file mode 100644 index 00000000..ab29cdea --- /dev/null +++ b/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py @@ -0,0 +1,128 @@ +from time import time + +import numpy as np +import pandas as pd +from sklearn.model_selection import GridSearchCV, train_test_split +from sklearn.preprocessing import LabelEncoder +from tensorflow.keras.layers import Dense, Dropout +from tensorflow.keras.models import Sequential +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.wrappers.scikit_learn import KerasClassifier + + +def tuning_model(optimizer, activation, init, dropout=0.2): + """ + model for tuning optimizer, activation functions and initialization of hidden layers + + :param optimizer: + :param activation: + :param init: + :param dropout: + :return: + """ + model = Sequential() + model.add(Dense(1024, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(341, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(85, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(17, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(2, activation="softmax")) + model.compile( + loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] + ) + return model + + +# --------------------------------------------------------------------------- # + +# this stores the best performing parameters for each model for each target +results = pd.DataFrame() + +np.random.seed(0) + +modelfilepathprefix = ( + "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" +) + +filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" +outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.HPtuning.results.txt" +dataset = pd.read_csv(filepath) + +le = LabelEncoder() + +# which target column to use? put this in a loop later for all targets +# here we need a for loop +for target in dataset.columns[1:7]: + # target = dataset.columns[1] # 'AR' + + modelfilepathW = str(modelfilepathprefix) + "/model." + target + ".weights.h5" + modelfilepathM = str(modelfilepathprefix) + "/model." + target + ".json" + + tmp = dataset[target].astype("category") + Y = np.asarray(tmp) + naRows = np.isnan(Y) + + d = dataset[~naRows] + + # generate X from fingerprints + # split all fingerprints into vectors + Nrows = d.shape[0] + Ncols = d["fp"][0].__len__() + # Store all fingerprints in numpy array + X = np.empty((Nrows, Ncols), int) + + # keep old indexes of this subset of arrays + d["oldIdx"] = d.index.values + d["newIdx"] = range(Nrows) + d = d.set_index("newIdx") + del d.index.name + + # generate X matrix + for i in range(Nrows): + fp = d["fp"][i] + X[i] = np.array(list(fp), dtype="int") + + # generate y vector(s) + y = to_categorical(np.array(d[target]), num_classes=2) + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=0 + ) + + start = time() + + model = KerasClassifier(build_fn=tuning_model, batch_size=20) + + batch_sizes = [20] + epochs = [30, 100] + inits = ["glorot_uniform", "normal", "uniform"] + optimizers = ["SGD", "RMSprop", "Adagrad", "Adadelta", "Adam", "Adamax", "Nadam"] + activation_functions = ["sigmoid", "tanh", "relu"] + parameters = { + "batch_size": batch_sizes, + "epochs": epochs, + "optimizer": optimizers, + "activation": activation_functions, + "init": inits, + } + + # Tuning ALL parameters, Note, this takes hours of computation!!! + clf = GridSearchCV(model, parameters, verbose=0) + clf_results = clf.fit(X_train, y_train) + + # save best estimator per target + clf_results.best_estimator_.model.save(filepath=modelfilepathM) + clf_results.best_estimator_.model.save_weights(filepath=modelfilepathW) + + # find best performing parameters + file = open(outfilepath, "a") + file.write( + "# --------------------------------------------------------------------------- #\n" + ) + file.write(f"### Results for {target} target ###\n") + file.write(f"Best: {clf_results.best_score_} using {clf_results.best_params_}\n") + file.write(f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n") + file.close() diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py new file mode 100644 index 00000000..9251cbdc --- /dev/null +++ b/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py @@ -0,0 +1,426 @@ +import argparse +import os +import re +import sys +from time import time + +import numpy as np +import pandas as pd +from sklearn.model_selection import GridSearchCV, train_test_split +from tensorflow.keras.layers import Dense, Dropout +from tensorflow.keras.models import Sequential +from tensorflow.keras.wrappers.scikit_learn import KerasClassifier + +from dfpl import dfplmodule as dfpl + +# ------------------------------------------------------------------------------------- # + + +def parseInput(): + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + parser = argparse.ArgumentParser( + description="Perform Hyperparameter tuning for a " "given set of parameters." + ) + # input + parser.add_argument( + "-i", + metavar="FILE", + type=str, + nargs=1, + required=True, + help="The file containin the data for training in (unquoted) " + "comma separated CSV format. The first column(s) contain " + "the target associations, while the last column, named 'fp' " + "contains the fingerprint of the compound", + ) + # output + parser.add_argument( + "-p", + metavar="PATH", + type=str, + nargs=1, + required=True, + help="The path to a directory where the results of the hp tuning " + "shall be stored", + ) + # select target(s) or use all + parser.add_argument( + "-t", + metavar="STR", + type=str, + nargs="+", + required=False, + help="A space separated list of target names (according to column " + " names in the input file (-i)) for which the huperparameter " + "tuning should be performed. If not provided, it is performed " + "for each target seperately.", + ) + + # parameters to be tested for HP tuning + parser.add_argument( + "--batchSizes", + metavar="INT", + type=int, + nargs="+", + required=False, + default=[20], + help="Space separated list of integers for batch sizes that should " + "be tested. Example: --batchSizes 32 64", + ) + parser.add_argument( + "--epochs", + metavar="INT", + type=int, + nargs="+", + required=False, + default=[30], + help="Space separated list of integers for number of training epochs " + "that should be tested. Example: --epochs 30 50 100", + ) + parser.add_argument( + "--inits", + metavar="STR", + type=str, + nargs="+", + required=False, + default=["normal"], + help="Space separated list of methods for initializing the weights " + "at inner layers that should be tested. Available methods are: " + "'glorot_uniform', 'normal', 'uniform'. " + "Example: --inits 'glorot_uniform' 'normal'", + ) + parser.add_argument( + "--optimizers", + metavar="STR", + type=str, + nargs="+", + required=False, + default=["Adam"], + help="Space separated list of optimizers for the DNN model " + "that should be tested." + "Available optimizers are listed here: https://keras.io/optimizers/ ." + "Example: --optimizers 'Adadelta' 'Adam' 'Adamax'", + ) + parser.add_argument( + "--activations", + metavar="STR", + type=str, + nargs="+", + required=False, + default=["relu"], + help="Space separated list of activation fucntions for the DNN model " + "that should be tested." + "Available functions are listed here: https://keras.io/activations/ ." + "Example: --activations 'sigmoid' 'relu'", + ) + + return parser.parse_args() + + +# ----------------------------------------------------------------------------- # + + +# model for tuning epochs and batchsizes only +def c_model(dropout=0.2): + model = Sequential() + model.add(Dense(1024, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(341, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(85, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(17, activation="relu")) + model.add(Dropout(dropout)) + model.add(Dense(2, activation="softmax")) + model.compile( + loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"] + ) + return model + + +# ----------------------------------------------------------------------------- # + + +# model for tuning optmizer, activation functions and initialization of hidden layers +def tuning_model(optimizer, activation, init, dropout=0.2): + model = Sequential() + model.add(Dense(1024, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(341, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(85, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(17, activation=activation, init=init)) + model.add(Dropout(dropout)) + model.add(Dense(2, activation="softmax")) + model.compile( + loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] + ) + return model + + +# --------------------------------------------------------------------------- # + +# ===================================================================================== # + +if __name__ == "__main__": + # get all arguments + args = parseInput() + + # print(args) + # exit(1) + + # this stores the best performing parameters for each model for each target + results = pd.DataFrame() + + np.random.seed(0) + + # filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" + # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub(".csv", + # ".hpTuningResults.txt", os.path.basename(filepath)) dataset = pd.read_csv(filepath) + + dataset = pd.read_csv(args.i[0]) + + # le = LabelEncoder() + + # which target column to use? put this in a loop later for all targets + # here we need a for loop + + for target in args.t: + target = "ER" + print(target) + # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub( + # ".csv", '.hpTuningResults.' + target + '.txt', os.path.basename(filepath)) + outfilepath = args.p[0] + re.sub( + ".csv", ".hpTuningResults." + target + ".txt", os.path.basename(args.i[0]) + ) + + if target in dataset.columns: + # modelfilepathW = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + + # '/model.' + target + '.weights.h5' modelfilepathM = + # "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + '/model.' + target + + # '.json' + modelfilepathW = args.p[0] + "/model." + target + ".weights.h5" + modelfilepathM = args.p[0] + "/model." + target + ".json" + + tmp = dataset[target].astype("category") + Y = np.asarray(tmp) + naRows = np.isnan(Y) + + d = dataset[~naRows] # d is a copy! + + # generate X from fingerprints + # split all fingerprints into vectors + Nrows = d.shape[0] + Ncols = d["fp"][0].__len__() + # Store all fingerprints in numpy array + X = np.empty((Nrows, Ncols), int) + + # keep old indexes of this subset of arrays + d["oldIdx"] = d.index.values + d["newIdx"] = range(Nrows) + d = d.set_index("newIdx") + del d.index.name + + # generate X matrix + for i in range(Nrows): + fp = d["fp"][i] + X[i] = np.array(list(fp), dtype="int") + + # generate y vector(s) + # y = to_categorical(np.array(d[target]), num_classes=2) + y = d[target] + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=0 + ) + + start = time() + + # find best performing parameters + sys.stdout.write( + "# --------------------------------------------------------------------------- #\n" + ) + sys.stdout.write(f"#target = {target}\n") + + # Start optimizing epochs and batchsizes (if more than one provided) + + # batchSizes = args.batchSizes + batchSizes = [64, 128, 256] + epochs = args.epochs # epochs = [5, 10] + + if batchSizes.__len__() > 1: + # model = KerasClassifier(build_fn=dfpl.defineNNmodel2) + model = KerasClassifier(build_fn=dfpl.defineNNmodel) + + parameters = {"batch_size": batchSizes} + + clf = GridSearchCV(model, parameters, verbose=0) + clf.fit(X_train, y_train) + + # save best estimator for epoochs/batchSize tuning per target + modelfilepathW = ( + args.p[0] + "/model.tuning-BatchSize." + target + ".weights.h5" + ) + modelfilepathM = ( + args.p[0] + "/model.tuning-BatchSize." + target + ".json" + ) + outfilepath = args.p[0] + re.sub( + ".csv", + ".hpTuningResults.01-BatchSize" + target + ".csv", + os.path.basename(args.i[0]), + ) + # outfilepath = '/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning + # /Sun_etal_dataset.fingerprints.hpTuningResults.ER.txt' + clf.best_estimator_.model.save(filepath=modelfilepathM) + clf.best_estimator_.model.save_weights(filepath=modelfilepathW) + + df = pd.concat( + [ + pd.DataFrame(clf.cv_results_["param_batch_size"]), + pd.DataFrame(clf.cv_results_["mean_test_score"]), + pd.DataFrame(clf.cv_results_["std_test_score"]), + pd.DataFrame(clf.cv_results_["rank_test_score"]), + ], + axis=1, + ) + df.columns = [ + "epoch", + "batch_size", + "mean_test_score", + "std_test_score", + "rank_test_score", + ] + df.to_csv(outfilepath, header=True) + + selected_bs = clf.best_params_["batch_size"] + selected_epochs = clf.best_params_["epochs"] + + else: + selected_bs = batchSizes[0] + selected_epochs = epochs[0] + + # Hypertune optimizers + optimizers = ["SGD", "Adam"] + # optimizers = args.optimizers + + if optimizers.__len__() > 1: + model = KerasClassifier( + build_fn=dfpl.defineNNmodel, + epochs=selected_epochs, + batch_size=selected_bs, + ) + parameters = { + "optimizer": optimizers + } # ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']} + clf = GridSearchCV(model, parameters, verbose=0) + clf.fit(X_train, y_train) + + # save best estimator for optimizer tuning per target + modelfilepathW = ( + args.p[0] + "/model.tuning-optimizer." + target + ".weights.h5" + ) + modelfilepathM = ( + args.p[0] + "/model.tuning-optimizer." + target + ".json" + ) + outfilepath = args.p[0] + re.sub( + ".csv", + ".hpTuningResults.02-Optimizer" + target + ".csv", + os.path.basename(args.i[0]), + ) + + clf.best_estimator_.model.save(filepath=modelfilepathM) + clf.best_estimator_.model.save_weights(filepath=modelfilepathW) + + df = pd.concat( + [ + pd.DataFrame(clf.cv_results_["param_optimizer"]), + pd.DataFrame(clf.cv_results_["mean_test_score"]), + pd.DataFrame(clf.cv_results_["std_test_score"]), + pd.DataFrame(clf.cv_results_["rank_test_score"]), + ], + axis=1, + ) + df.columns = [ + "optimizer", + "mean_test_score", + "std_test_score", + "rank_test_score", + ] + df.to_csv(outfilepath, header=True) + + selected_optimizer = clf.best_params_["optimizer"] + + else: + selected_optimizer = optimizers[0] + + # Hypertune activation functions + activations = args.activations # 'sigmoid', 'tanh', 'relu' + + if activations.__len__() > 1: + model = KerasClassifier( + build_fn=dfpl.defineNNmodel, + epochs=selected_epochs, + batch_size=selected_bs, + ) + parameters = { + "optimizer": [selected_optimizer], + "activation": args.activations, + } + clf = GridSearchCV(model, parameters, verbose=0) + clf.fit(X_train, y_train) + + # save best estimator for optimizer tuning per target + modelfilepathW = ( + args.p[0] + "/model.tuning-activation." + target + ".weights.h5" + ) + modelfilepathM = ( + args.p[0] + "/model.tuning-activation." + target + ".json" + ) + outfilepath = args.p[0] + re.sub( + ".csv", + ".hpTuningResults.03-Activation" + target + ".csv", + os.path.basename(args.i[0]), + ) + + clf.best_estimator_.model.save(filepath=modelfilepathM) + clf.best_estimator_.model.save_weights(filepath=modelfilepathW) + + df = pd.concat( + [ + pd.DataFrame(clf.cv_results_["param_optimizer"]), + pd.DataFrame(clf.cv_results_["param_activation"]), + pd.DataFrame(clf.cv_results_["mean_test_score"]), + pd.DataFrame(clf.cv_results_["std_test_score"]), + pd.DataFrame(clf.cv_results_["rank_test_score"]), + ], + axis=1, + ) + df.columns = [ + "optimizer", + "activation", + "mean_test_score", + "std_test_score", + "rank_test_score", + ] + df.to_csv(outfilepath, header=True) + + selected_activation = clf.best_params_["activation"] + + else: + selected_activation = activations[0] + + # Maybe also optimize weight initializations?? + # inits = ['glorot_uniform', 'normal', 'uniform'] + + # find best performing parameters + sys.stdout.write( + f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n" + ) + else: + sys.stderr.write( + f"ERROR: the target that you provide ({target}) is not contained in your data file ({args.i[0]})" + ) diff --git a/build/lib/dfpl/dfplmodule.py b/build/lib/dfpl/dfplmodule.py new file mode 100644 index 00000000..d0838ebf --- /dev/null +++ b/build/lib/dfpl/dfplmodule.py @@ -0,0 +1,1413 @@ +""" +Python module for deepFPlearn tools +""" + +import argparse +import csv +import math + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns # for drawing the heatmaps +from rdkit import Chem, DataStructs # for fingerprint generation +from rdkit.Chem import MACCSkeys +from rdkit.Chem.AtomPairs import Pairs, Torsions +from sklearn.model_selection import train_test_split +from tensorflow.keras import optimizers, regularizers +from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau +from tensorflow.keras.layers import Dense, Dropout, Input + +# for NN model functions +from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.optimizers import SGD + +# GENERAL FUNCTIONS --------------------------------------------------------- # + + +def gather(df, key, value, cols): + """ + Simple emulation of R's gather function using pandas melt() function in python + + :param df: The data frame + :param key: The key column name + :param value: The value column name + :param cols: list of column names to gather + :return: + """ + id_vars = [col for col in df.columns if col not in cols] + id_values = cols + var_name = key + value_name = value + return pd.melt(df, id_vars, id_values, var_name, value_name) + + +def shuffleDataPriorToTraining(x, y): + """ + Returns a gathered variant of the input x and y matrices + :param x: + :param y: + :return: + """ + + # merge x and y + df0 = pd.concat([x.reset_index(drop=True), y.reset_index(drop=True)], axis=1) + # shuffle rows, drop NAs, reset index + df1 = df0.sample(frac=1).dropna(axis=0).reset_index() + + return (df1.iloc[:, 0 : x.shape[1]], df1.iloc[:, x.shape[1] :]) + + # return gather(df0, key="target", value="association", + # cols=y.columns) + + +def str2bool(v): + if isinstance(v, bool): + return v + if v.lower() in ("yes", "true", "t", "y", "1"): + return True + elif v.lower() in ("no", "false", "f", "n", "0"): + return False + else: + raise argparse.ArgumentTypeError("Boolean value expected.") + + +def smi2fp(smile, fptype, size=2048): + """ + Convert a SMILES string to a fingerprint object of a certain type using functions + from the RDKIT python library. + + :param smile: A single SMILES string + :param fptype: The type of fingerprint to which the SMILES should be converted. Valid + values are: 'topological' (default), 'MACCS' + :return: A fingerprint object or None, if fingerprint object could not be created + (Respective error message is provided in STDERR). + """ + # generate a mol object from smiles string + + # print(smile) + cs = None + # first transform to canoncial smiles + try: + cs = Chem.CanonSmiles(smile) + except Exception: + print( + f"[WARNING]: Not able to transform your smile to a canonical version of it: {smile}" + ) + if not cs: + return None + + mol = None + try: + mol = Chem.MolFromSmiles(cs) + except Exception: + print( + f"[WARNING]: Not able to extract molecule from (canonically transformed) SMILES: {cs}\n Original SMILE: {smile}" + ) + if not mol: + return None + + # init fp, any better idea? e.g. calling a constructor? + # fp = Chem.Mol # FingerprintMols.FingerprintMol(mol) + + if fptype == "topological": # 2048 bits + # Topological Fingerprints: + # The fingerprinting algorithm used is similar to that used in the Daylight + # fingerprinter: it identifies and hashes topological paths (e.g. along bonds) + # in the molecule and then uses them to set bits in a fingerprint of user-specified + # lengths. After all paths have been identified, the fingerprint is typically + # folded down until a particular density of set bits is obtained. + try: + # fp = Chem.RDKFingerprint(mol, fpSize=size) + return Chem.RDKFingerprint(mol, fpSize=size) + except Exception: + print("SMILES not convertable to topological fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + elif fptype == "MACCS": + # MACCS Keys: + # There is a SMARTS-based implementation of the 166 public MACCS keys. + # The MACCS keys were critically evaluated and compared to other MACCS + # implementations in Q3 2008. In cases where the public keys are fully defined, + # things looked pretty good. + + try: + # fp = MACCSkeys.GenMACCSKeys(mol) + return MACCSkeys.GenMACCSKeys(mol) + except Exception: + print("SMILES not convertable to MACSS fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + elif fptype == "atompairs": + # Atom Pairs: + # Atom-pair descriptors [3] are available in several different forms. + # The standard form is as fingerprint including counts for each bit instead + # of just zeros and ones. Nevertheless we use the BitVect variant here. + + try: + # fp = Pairs.GetAtomPairFingerprintAsBitVect(mol) + return Pairs.GetAtomPairFingerprintAsBitVect(mol) + # counts if features also possible here! needs to be parsed differently + # fps.update({i:Pairs.GetAtomPairFingerprintAsIntVect(mols[i])}) + except Exception: + print("SMILES not convertable to atompairs fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + else: + # Topological Torsions: + # At the time of this writing, topological torsion fingerprints have too + # many bits to be encodeable using the BitVector machinery, so there is no + # GetTopologicalTorsionFingerprintAsBitVect function. + + try: + # fp = Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) + return Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) + except Exception: + print("SMILES not convertable to torsions fingerprint:") + assert isinstance(smile, object) + print("SMILES: " + smile) + return None + + +def XandYfromInput( + csvfilename: str, + rtype: str, + fptype: str, + printfp: bool = False, + size: int = 2048, + verbose: int = 2, + returnY: bool = True, +) -> tuple: + """ + Return the matrix of features for training and testing NN models (X) as numpy array. + Provided SMILES are transformed to fingerprints, fingerprint strings are then split + into vectors and added as row to the array which is returned. + :param csvfilename: Filename of CSV files containing the training data. The + SMILES/Fingerprints are stored 1st column + :param rtype: Type of structure representation. Valid values are: 'fp' and 'smile' + :param fptype: Type of fingerprint to be generated out + :param printfp: Print generated fingerprints to file, namely the input file with the + file ending '.fingerprints.csv'. Default:False + :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If + no outcome data is provided, the Y matrix is a None object. + """ + + # TODOs: implement other types of fingerprint! + + df = pd.read_csv(csvfilename) + cnames = df.columns + + if rtype not in cnames: + print( + f"[ERROR:] There is no column named {rtype} in your input training set file" + ) + exit(0) + + dfX = None + if rtype == "smiles": # transform to canonical smiles, and then to fp + dfX = pd.DataFrame( + df["smiles"] + .transform( + lambda x: np.array( + Chem.RDKFingerprint( + Chem.MolFromSmiles(Chem.CanonSmiles(x)), fpSize=size + ) + ) + ) + .to_list() + ) + else: # split fingerprint into matrix + dfX = pd.DataFrame(df["fp"].transform(lambda x: list(x)).to_list()) + + # add 'id' as rownames of dataframe + if "id" in cnames: + dfX.index = df["id"] + + dfY = None + if returnY: + # names in Y contain 'id' if present, and all other columns (=target columns) + namesY = [c for c in cnames if c not in ["id", "smiles", "fp"]] + dfY = df[namesY] + # add 'id' as rownames of dataframe + if "id" in cnames: + dfY.index = df["id"] + + return (dfX, dfY) + + +def TrainingDataHeatmap(x, y): + x["ID"] = x.index + y["ID"] = y.index + + # xy = pd.merge(x,y,on="ID") + + # clustermap dies because of too many iterations.. + # sns.clustermap(x, metric="correlation", method="single", cmap="Blues", standard_scale=1) #, row_colors=row_colors) + + # try to cluster prior to viz + # check this out + # https://stackoverflow.com/questions/2455761/reordering-matrix-elements-to-reflect-column-and-row-clustering-in-naiive-python + + # viz using matplotlib heatmap https://matplotlib.org/3.1.1/gallery/images_contours_and_fields/image_annotated_heatmap.html + + return 1 + + +def removeDuplicates(x, y): + """ + Remove duplicated feature - outcome pairs from feature matrix and outcome vector combination. + + :param x: Feature matrix + :param y: Outcome vector + + :return: Tuple of unique Feature matrix and outcome vector combinations + """ + + # add y as additional column to x + joined = np.append(x, [[x] for x in y], axis=1) + # merge all columns into one string per row + + fpstrings = [] + for i in range(0, len(y)): + fpstrings.append(["".join([str(int(c)) for c in joined[i]])]) + + fpstrings_unique = np.unique(fpstrings, return_index=True) + + return (x[fpstrings_unique[1]], y[fpstrings_unique[1]]) + + +def defineCallbacks( + checkpointpath: str, + patience: int, + rlrop: bool = False, + rlropfactor: float = 0.1, + rlroppatience: int = 50, +) -> list: + """ + + :param checkpointpath: + :param patience: + :param rlrop: + :param rlropfactor: + :param rlroppatience: + :return: + """ + + # enable this checkpoint to restore the weights of the best performing model + checkpoint = ModelCheckpoint( + checkpointpath, + monitor="val_loss", + verbose=1, + period=10, + save_best_only=True, + mode="min", + save_weights_only=True, + ) + + # enable early stopping if val_loss is not improving anymore + earlystop = EarlyStopping( + monitor="val_loss", + min_delta=0, + patience=patience, + verbose=1, + restore_best_weights=True, + ) + + callbacks = [] + if rlrop: + rlrop = ReduceLROnPlateau( + monitor="val_loss", factor=rlropfactor, patience=rlroppatience + ) + callbacks = [checkpoint, earlystop, rlrop] + else: + callbacks = [checkpoint, earlystop] + + # Return list of callbacks - collect the callbacks for training + return callbacks + + +def defineNNmodelMulti( + inputSize=2048, + outputSize=None, + l2reg=0.001, + dropout=0.2, + activation="relu", + optimizer="Adam", + lr=0.001, + decay=0.01, +): + if optimizer == "Adam": + myoptimizer = optimizers.Adam( + learning_rate=lr, decay=decay + ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) + elif optimizer == "SGD": + myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) + else: + myoptimizer = optimizer + + nhl = int(math.log2(inputSize) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(inputSize / 2), + input_dim=inputSize, + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout)) + # next hidden layers + for i in range(1, nhl): + factorunits = 2 ** (i + 1) + factordropout = 2 * i + model.add( + Dense( + units=int(inputSize / factorunits), + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout / factordropout)) + # multi-class output layer + # use sigmoid to get independent probabilities for each output node + # (need not add up to one, as they would using softmax) + # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ + model.add(Dense(units=outputSize, activation="sigmoid")) + + model.summary() + + # compile model + model.compile( + loss="binary_crossentropy", optimizer=myoptimizer, metrics=["accuracy"] + ) + + return model + + +def defineNNmodel( + inputSize: int = 2048, + l2reg: float = 0.001, + dropout: float = 0.2, + activation: str = "relu", + optimizer: str = "Adam", + lr: float = 0.001, + decay: float = 0.01, +) -> Model: + """ + + :param inputSize: + :param l2reg: + :param dropout: + :param activation: + :param optimizer: + :param lr: + :param decay: + :return: + """ + + if optimizer == "Adam": + myoptimizer = optimizers.Adam( + learning_rate=lr, decay=decay + ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) + elif optimizer == "SGD": + myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) + else: + myoptimizer = optimizer + + myhiddenlayers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} + + if not str(inputSize) in myhiddenlayers.keys(): + print("Wrong inputsize. Must be in {2048, 1024, 999, 512, 256}.") + return None + + nhl = int(math.log2(inputSize) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(inputSize / 2), + input_dim=inputSize, + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout)) + # next hidden layers + for i in range(1, nhl): + factorunits = 2 ** (i + 1) + factordropout = 2 * i + model.add( + Dense( + units=int(inputSize / factorunits), + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout / factordropout)) + # output layer + model.add(Dense(units=1, activation="sigmoid")) + + model.summary() + + # compile model + model.compile(loss="mse", optimizer=myoptimizer, metrics=["accuracy"]) + + return model + + +def autoencoderModel( + input_size: int = 2048, + encoding_dim: int = 256, + myloss: object = "binary_crossentropy", + mylr: float = 0.001, + mydecay: float = 0.01, +) -> tuple: + """ + This function provides an autoencoder model to reduce a certain input to a compressed version. + + :param encoding_dim: Size of the compressed representation. Default: 85 + :param input_size: Size of the input. Default: 2048 + :param myactivation: Activation function, see Keras activation functions for potential values. Default: relu + :param myoptimizer: Optimizer, see Keras optmizers for potential values. Default: adadelta + :param myloss: Loss function, see Keras Loss functions for potential values. Default: binary_crossentropy + :return: a tuple of autoencoder and encoder models + """ + + myoptimizer = optimizers.Adam( + learning_rate=mylr, decay=mydecay + ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) + + # get the number of meaningful hidden layers (latent space included) + nhl = round(math.log2(input_size / encoding_dim)) + + # the input placeholder + input_vec = Input(shape=(input_size,)) + + # 1st hidden layer, that receives weights from input layer + # equals bottle neck layer, if nhl==1! + encoded = Dense(units=int(input_size / 2), activation="relu")(input_vec) + + if nhl > 1: + # encoding layers, incl. bottle neck + for i in range(1, nhl): + factorunits = 2 ** (i + 1) + # print(f'{factorunits}: {int(input_size / factorunits)}') + encoded = Dense(units=int(input_size / factorunits), activation="relu")( + encoded + ) + + # encoding_dim = int(input_size/factorunits) + + # 1st decoding layer + factorunits = 2 ** (nhl - 1) + decoded = Dense(units=int(input_size / factorunits), activation="relu")(encoded) + + # decoding layers + for i in range(nhl - 2, 0, -1): + factorunits = 2**i + # print(f'{factorunits}: {int(input_size/factorunits)}') + decoded = Dense(units=int(input_size / factorunits), activation="relu")( + decoded + ) + + # output layer + # The output layer needs to predict the probability of an output which needs to either 0 or 1 and hence we use sigmoid activation function. + decoded = Dense(units=input_size, activation="sigmoid")(decoded) + + else: + # output layer + decoded = Dense(units=input_size, activation="sigmoid")(encoded) + + autoencoder = Model(input_vec, decoded) + encoder = Model(input_vec, encoded) + + autoencoder.summary() + encoder.summary() + + # We compile the autoencoder model with adam optimizer. + # As fingerprint positions have a value of 0 or 1 we use binary_crossentropy as the loss function + autoencoder.compile(optimizer=myoptimizer, loss=myloss) + + return (autoencoder, encoder) + + +# def predict_values(ac_model_file_path, model_file_path, pdx): +# """ +# Predict a set of chemicals using a selected model. +# +# :param model_file_path: Path to the model weights of the prediction DNN +# :param pdx: A matrix containing the fingerprints of the chemicals, generated via XfromInput function +# :return: A dataframe of 2 columns: random - predicted values using random model, trained - predicted values +# using trained model. Rownames are consecutive numbers of the input rows, or if provided in the input file +# the values of the id column +# """ +# +# # ac_model_file_path="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.AC-model.hdf5" +# # model_file_path ="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.model.hdf5" +# +# print(f"[INFO:] Loaded model weights for autoencoder: '{model_file_path}'") +# print(f"[INFO:] Loaded model weights for prediction DNN: '{model_file_path}'") +# +# start = time() +# # create autoencoder +# (autoencoder, encoder) = autoencoderModel(input_size=pdx.shape[1], encoding_dim=256) +# # load AC weights +# autoencoder.load_weights(ac_model_file_path) +# # encode input +# encodedX = encoder.predict(pdx) +# +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# print(f"[INFO:] Computation time used for encoding: {trainTime} min") +# +# start = time() +# # create DNN +# predictor = defineNNmodel(inputSize=encodedX.shape[1]) +# # predict with random weights +# predictions_random = predictor.predict(encodedX) +# # load DNN weights +# predictor.load_weights(model_file_path) +# # predict encoded input +# predictions = predictor.predict(encodedX) +# +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# print(f"[INFO:] Computation time used for the predictions: {trainTime} min") +# +# df = pd.DataFrame(data={'random': predictions_random.flatten(), +# 'trained': predictions.flatten()}, +# columns=['random', 'trained'], +# index=pdx.index) +# print(df) +# return (df) + + +def trainfullac( + X: pd.DataFrame, + y: pd.DataFrame, + useweights: str = None, + epochs: int = 0, + encdim: int = 256, + checkpointpath: str = None, + verbose: int = 0, +) -> Model: + """ + Train an autoencoder on the given feature matrix X. Response matrix is only used to + split meaningfully in test and train data set. + + :param X: Feature matrix. + :param y: Matrix containing the response variables. + :param epochs: Number of epochs to train the autoencoder + :param encdim: Dimension of latent space. This and the column dimension of X control + the number and size of the hidden layers. + :param checkpointpath: path to prefix the model files during callbacks + :param verbose: Verbosity level. Is forwarded to the Keras fit function that prints + training states in different verbosity levels + :return: The encoder model of the trained autoencoder + """ + + # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) + (autoencoder, encoder) = autoencoderModel( + input_size=X.shape[1], encoding_dim=encdim + ) + + if useweights: # don't train, use existing weights file and load it into AC model + # autoencoder.load_weights(useweights) + encoder.load_weights(useweights) + else: + # collect the callbacks for training + callback_list = defineCallbacks( + checkpointpath=checkpointpath, patience=20, rlrop=False + ) + + # split data into test and training data + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42 + ) + # Fit the AC + autohist = autoencoder.fit( + X_train, + X_train, + callbacks=callback_list, + epochs=epochs, + batch_size=256, + shuffle=True, + verbose=verbose, + validation_data=(X_test, X_test), + ) + # history + ac_loss = autohist.history["loss"] + ac_val_loss = autohist.history["val_loss"] + ac_epochs = range(ac_loss.__len__()) + pd.DataFrame( + data={"loss": ac_loss, "val_loss": ac_val_loss, "epoch": ac_epochs} + ).to_csv(checkpointpath.replace(".hdf5", "_trainValLoss_AC.csv"), index=False) + # generate a figure of the losses for this fold + plt.figure() + plt.plot(ac_epochs, ac_loss, "bo", label="Training loss") + plt.plot(ac_epochs, ac_val_loss, "b", label="Validation loss") + plt.title("Training and validation loss of AC") + plt.legend() + plt.savefig( + fname=checkpointpath.replace(".hdf5", "_trainValLoss_AC.svg"), format="svg" + ) + plt.close() + # write the losses to .csv file for later data visualization + + # model needs to be saved and restored when predicting new input! + # use encode() of train data as input for DL model to associate to chemical + return encoder + + +def plotTrainHistory(hist, target, fileAccuracy, fileLoss): + """ + Plot the training performance in terms of accuracy and loss values for each epoch. + :param hist: The history returned by model.fit function + :param target: The name of the target of the model + :param fileAccuracy: The filename for plotting accuracy values + :param fileLoss: The filename for plotting loss values + :return: none + """ + + # plot accuracy + plt.figure() + plt.plot(hist.history["accuracy"]) + if "val_accuracy" in hist.history.keys(): + plt.plot(hist.history["val_accuracy"]) + plt.title("Model accuracy - " + target) + plt.ylabel("Accuracy") + plt.xlabel("Epoch") + if "val_accuracy" in hist.history.keys(): + plt.legend(["Train", "Test"], loc="upper left") + else: + plt.legend(["Train"], loc="upper_left") + plt.savefig(fname=fileAccuracy, format="svg") + + # Plot training & validation loss values + plt.figure() + plt.plot(hist.history["loss"]) + plt.plot(hist.history["val_loss"]) + plt.title("Model loss - " + target) + plt.ylabel("Loss") + plt.xlabel("Epoch") + plt.legend(["Train", "Test"], loc="upper left") + # plt.show() + plt.savefig(fname=fileLoss, format="svg") + plt.close() + + +def plotAUC(fpr, tpr, auc, target, filename, title=""): + plt.figure() + plt.plot([0, 1], [0, 1], "k--") + plt.plot(fpr, tpr, label=f"Keras (area = {auc:.3f})") + plt.xlabel("False positive rate") + plt.ylabel("True positive rate") + plt.title(f"ROC curve {target}") + plt.legend(loc="best") + plt.savefig(fname=filename, format="svg") + plt.close() + + +def plotHeatmap(matrix, filename, title=""): + plt.figure() + plt.imshow(matrix, cmap="Greys", interpolation="nearest") + plt.title(title) + plt.savefig(fname=filename, format="svg") + plt.close() + + +# ------------------------------------------------------------------------------------- # + +# plot model history more easily, see whyboris@github: https://gist.github.com/whyboris/91ee793ddc92cf1e824978cf31bb790c +# when plotting, smooth out the points by some factor (0.5 = rough, 0.99 = smooth) +# method taken from `Deep Learning with Python` by François Chollet + +# 01 --------------------------------------------------------------------------------- # + +# def smooth_curve(points, factor=0.75): +# smoothed_points = [] +# for point in points: +# if smoothed_points: +# previous = smoothed_points[-1] +# smoothed_points.append(previous * factor + point * (1 - factor)) +# else: +# smoothed_points.append(point) +# return smoothed_points +# +# +# # 02 ---------------------------------------------------------------------------------- # +# +# def set_plot_history_data(ax, history, which_graph): +# if which_graph == 'acc': +# train = smooth_curve(history.history['accuracy']) +# valid = smooth_curve(history.history['val_accuracy']) +# +# if which_graph == 'loss': +# train = smooth_curve(history.history['loss']) +# valid = smooth_curve(history.history['val_loss']) +# +# # plt.xkcd() # make plots look like xkcd +# +# epochs = range(1, len(train) + 1) +# +# trim = 0 # remove first 5 epochs +# # when graphing loss the first few epochs may skew the (loss) graph +# +# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', linewidth=15, alpha=0.1) +# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', label=('Training')) +# +# ax.plot(epochs[trim:], valid[trim:], 'g', linewidth=15, alpha=0.1) +# ax.plot(epochs[trim:], valid[trim:], 'g', label=('Validation')) +# + +# 03 ---------------------------------------------------------------------------------- # +# +# def get_max_validation_accuracy(history): +# validation = smooth_curve(history.history['val_accuracy']) +# ymax = max(validation) +# return 'Max validation accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' +# +# +# def get_max_training_accuracy(history): +# training = smooth_curve(history.history['accuracy']) +# ymax = max(training) +# return 'Max training accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' +# + +# 04---------------------------------------------------------------------------------- # + +# def plot_history(history, file): +# fig, (ax1, ax2) = plt.subplots(nrows=2, +# ncols=1, +# figsize=(10, 6), +# sharex=True, +# gridspec_kw={'height_ratios': [5, 2]}) +# +# set_plot_history_data(ax1, history, 'acc') +# +# set_plot_history_data(ax2, history, 'loss') +# +# # Accuracy graph +# ax1.set_ylabel('Accuracy') +# ax1.set_ylim(bottom=0.5, top=1) +# ax1.legend(loc="lower right") +# ax1.spines['top'].set_visible(False) +# ax1.spines['right'].set_visible(False) +# ax1.xaxis.set_ticks_position('none') +# ax1.spines['bottom'].set_visible(False) +# +# # max accuracy text +# plt.text(0.5, +# 0.6, +# get_max_validation_accuracy(history), +# horizontalalignment='right', +# verticalalignment='top', +# transform=ax1.transAxes, +# fontsize=12) +# plt.text(0.5, +# 0.8, +# get_max_training_accuracy(history), +# horizontalalignment='right', +# verticalalignment='top', +# transform=ax1.transAxes, +# fontsize=12) +# +# # Loss graph +# ax2.set_ylabel('Loss') +# ax2.set_yticks([]) +# ax2.plot(legend=False) +# ax2.set_xlabel('Epochs') +# ax2.spines['top'].set_visible(False) +# ax2.spines['right'].set_visible(False) +# +# plt.tight_layout() +# plt.savefig(fname=file, format='svg') +# plt.close() +# + + +def drawHeatmap(data, anno): + # (data=pd.DataFrame(Xt), anno = pd.DataFrame(Yt.astype(int))) + + # annotation bar colors + my_ann_colors = dict(zip(anno[0].unique(), ["blue", "red"])) + row_colors = anno[0].map(my_ann_colors) + + cl = sns.clustermap( + data, + metric="euclidean", + method="single", + z_score=None, + standard_scale=None, + col_cluster=False, + cmap="Greys", + row_colors=row_colors, + yticklabels=False, + ) + cl.fig.suptitle("Distributions of [1,0] in fingerprints of target: AR") + + url = "https://python-graph-gallery.com/wp-content/uploads/mtcars.csv" + df = pd.read_csv(url) + # set df index using existing columns + df = df.set_index("model") + # remove name of index column, numbered rows have been the index before, they are gone now + del df.index.name + df + my_palette = dict(zip(df.cyl.unique(), ["orange", "yellow", "brown"])) + row_colors = df.cyl.map(my_palette) + + # Default plot + # sns.clustermap(df) + + # Clustermethods + my_palette = dict() + sns.clustermap( + df, + metric="correlation", + standard_scale=1, + method="single", + cmap="Blues", + row_colors=row_colors, + ) + + +############################################################################### +# TRAIN FUNCTIONS --------------------------------------------------------- # + + +def defineOutfileNames(pathprefix: str, target: str, fold: int) -> tuple: + """ + This function returns the required paths for output files or directories. + + :param pathprefix: A file path prefix for all files. + :param mtype: The model type. Its set by the trainNNmodels function with information on autoencoder or not, + and if AC is used, then with its parameters. + :param target: The name of the target. + + :return: A tuple of 14 output file names. + """ + + modelname = target + ".Fold-" + str(fold) + + modelfilepathW = str(pathprefix) + "." + modelname + ".weights.h5" + modelfilepathM = str(pathprefix) + "." + modelname + ".json" + modelhistplotpathL = str(pathprefix) + "." + modelname + ".loss.svg" + modelhistplotpathA = str(pathprefix) + "." + modelname + ".acc.svg" + modelhistplotpath = str(pathprefix) + "." + modelname + ".history.svg" + modelhistcsvpath = str(pathprefix) + "." + modelname + ".history.csv" + modelvalidation = str(pathprefix) + "." + modelname + ".validation.csv" + modelAUCfile = str(pathprefix) + "." + modelname + ".auc_value.svg" + modelAUCfiledata = str(pathprefix) + "." + modelname + ".auc_value.data.csv" + outfilepath = str(pathprefix) + "." + modelname + ".trainingResults.txt" + checkpointpath = str(pathprefix) + "." + modelname + ".checkpoint.model.hdf5" + modelheatmapX = str(pathprefix) + "." + modelname + ".heatmap.X.svg" + modelheatmapZ = str(pathprefix) + "." + modelname + ".AC.heatmap.Z.svg" + + return ( + modelfilepathW, + modelfilepathM, + modelhistplotpathL, + modelhistplotpathA, + modelhistplotpath, + modelhistcsvpath, + modelvalidation, + modelAUCfile, + modelAUCfiledata, + outfilepath, + checkpointpath, + modelheatmapX, + modelheatmapZ, + ) + + +# ------------------------------------------------------------------------------------- # + + +def eval01Distributions(Xt, Yt, y_train, y_test, verbosity=0): + """ + Evaluate the percentage of 0 values in the outcome variable of the whole dataset and the splitted (train,test) + dataset, and the percentage of 0 values in the feature matrix. + + :param Xt: The whole feature matrix + :param Yt: The whole outcome vector + :param y_train: The outcome vector of the training set + :param y_test: The outcome vector of the test set + :param verbosity: The verbosity level. Info is only printed if verbosity is not 0. + + :return: Nothing is returned. + """ + + if verbosity == 0: + return + else: + unique, counts = np.unique(Yt, return_counts=True) + perc = round(100 / len(Yt) * counts[1]) + print( + f"[INFO:] Percentage of '1' values in outcome variable (whole dataset): {perc}\n" + ) + + uniqueRtr, countsRtr = np.unique(y_train, return_counts=True) + uniqueRte, countsRte = np.unique(y_test, return_counts=True) + perc = round(100 / len(y_train) * countsRtr[1]) + print(f"[INFO:] Percentage of '1' values in training outcomes: {perc}\n") + perc = round(100 / len(y_test) * countsRte[1]) + print(f"[INFO:] Percentage of '1' values in test outcomes: {perc}\n") + + print( + f"[INFO:] Average percentage of '0' positions in fingerprints: {round(np.sum(Xt == 0) / (np.sum(Xt == 0) + np.sum(Xt == 1)), ndigits=4)}" + ) + return + + +# ------------------------------------------------------------------------------------- # + + +def prepareDataSet(y: pd.DataFrame, x: pd.DataFrame, t: str) -> tuple: + """ + A function to remove NA values from the output column, duplicates from input and output + and to transform the data into numpy arrays for keras functions. + + :param y: The target vector that may contain NAs. + :param x: The input matrix that may contain duplicates (including resp. target value!) + :param t: The target string + :return: Tuple of x and y ready for splitting into test and train sets for ML with keras. + """ + + # which rows contain 'NA' in target column + tmp = y[t].astype("category") + Y = np.asarray(tmp) + naRows = np.isnan(Y) + + # transform pd dataframe to numpy array for keras + X = x.to_numpy() + + # subset data according to target non-NA values + Ytall = Y[~naRows] + Xtall = X[~naRows] + + # remove all duplicated feature values - outcome pairs, and + # shuffle data, such that validation_split in fit function selects a random subset! + # Remember, that it takes the fraction for validation from the end of our data! + + (XtU, YtU) = removeDuplicates(x=Xtall, y=Ytall) + + # return(shuffleDataPriorToTraining(x=XtU, y=YtU)) + return (XtU, YtU) + + +# ------------------------------------------------------------------------------------- # + +# def plot_history_vis(hist, model_hist_plot_path, model_hist_csv_path, +# model_hist_plot_path_a, model_hist_plot_path_l, target): +# plot_history(history=hist, file=model_hist_plot_path) +# histDF = pd.DataFrame(hist.history) +# histDF.to_csv(model_hist_csv_path) +# +# # plot accuracy and loss for the training and validation during training +# plotTrainHistory(hist=hist, target=target, +# file_accuracy=model_hist_plot_path_a, +# file_loss=model_hist_plot_path_l) +# + +# ------------------------------------------------------------------------------------- # +# +# def validate_multi_model_on_test_data(x_test, checkpoint_path, y_test, col_names, result_file): +# # load checkpoint model with min(val_loss) +# trainedmodel = define_nn_model_multi(input_size=x_test.shape[1], output_size=y_test.shape[1]) +# +# # predict values with random model +# predictions_random = pd.DataFrame(trainedmodel.predict(x_test), columns=col_names + '-predRandom') +# +# # load weights into random model +# trainedmodel.load_weights(checkpoint_path) +# +# # predict with trained model +# predictions = pd.DataFrame(trainedmodel.predict(x_test), +# columns=col_names + '-pred') +# scores = pd.DataFrame((predictions.round() == y_test).sum() / y_test.shape[0], columns=['correctPredictions']) +# +# results = pd.concat([predictions_random, predictions, pd.DataFrame(y_test, columns=col_names + '-true')], axis=1) +# results.to_csv(result_file) +# +# return scores + + +# ------------------------------------------------------------------------------------- # + +# def validate_model_on_test_data(x_test, checkpoint_path, y_test, model_type, model_validation, target, +# model_auc_file_data, model_auc_file): +# """ +# Function that validates trained model with test data set. History and AUC plots are generated. +# Accuracy and Loss of model on test data, as well as MCC and confusion matrix is calculated and returned. +# +# :param x_test: +# :param checkpoint_path: +# :param y_test: +# :param model_type: +# :param model_validation: +# :param target: +# :param model_auc_file_data: +# :param model_auc_file: +# :return: Tupel containing Loss, Accuracy, MCC, tn, fp, fn, tp of trained model on test data. +# """ +# +# # load checkpoint model with min(val_loss) +# trainedmodel = define_nn_model(input_size=x_test.shape[1]) +# +# # predict values with random model +# predictions_random = pd.DataFrame(trainedmodel.predict(x_test)) +# +# # load weights into random model +# trainedmodel.load_weights(checkpoint_path) +# # predict with trained model +# predictions = pd.DataFrame(trainedmodel.predict(x_test)) +# +# # save validation data to .csv file +# validation = pd.DataFrame({'predicted': predictions[0].ravel(), +# 'true': list(y_test), +# 'predicted_random': predictions_random[0].ravel(), +# 'model_type': model_type}) +# validation.to_csv(model_validation) +# +# # compute MCC +# predictionsInt = [int(round(x)) for x in predictions[0].ravel()] +# ytrueInt = [int(y) for y in y_test] +# MCC = matthews_corrcoef(ytrueInt, predictionsInt) +# +# # generate the AUC-ROC curve data from the validation data +# fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, predictions, drop_intermediate=False) +# +# auc_keras = auc_value(fpr_keras, tpr_keras) +# +# aucdata = pd.DataFrame(list(zip(fpr_keras, +# tpr_keras, +# [auc_keras for x in range(1, len(fpr_keras))], +# [target for x in range(1, len(fpr_keras))])), +# columns=['fpr', 'tpr', 'auc_value', 'target']) +# aucdata.to_csv(model_auc_file_data) +# +# plotAUC(fpr=fpr_keras, tpr=tpr_keras, target=target, auc_value=auc_keras, filename=model_auc_file) +# +# # [[tn, fp] +# # [fn, tp]] +# cfm = confusion_matrix(y_true=ytrueInt, y_pred=predictionsInt) +# +# scores = trainedmodel.evaluate(x_test, y_test, verbose=0) +# +# print(f'TARGET: {target} Loss: {scores[0].__round__(2)} Acc: {scores[1].__round__(2)}') +# print(f'MCC: {MCC.__round__(2)}') +# print(f'CFM: \n\tTN={cfm[0][0]}\tFP={cfm[0][1]}\n\tFN={cfm[1][0]}\tTP={cfm[1][1]}') +# +# return (scores[0], scores[1], MCC, cfm[0][0], cfm[0][1], cfm[1][0], cfm[1][1]) + + +# ------------------------------------------------------------------------------------- # +# def train_nn_models_multi(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, +# split: float = 0.2, epochs: int = 500, +# verbose: int= 2, kfold: int = 5) -> None: +# # remove 'id' column if present +# if 'id' in x.columns: +# x = x.drop('id', axis=1) +# if 'id' in y.columns: +# y = y.drop('id', axis=1) +# +# # drop compounds that are not measured for all target columns, transform to numpy +# (xmulti, ymulti) = shuffleDataPriorToTraining(x, y) +# +# # do a kfold cross validation for the autoencoder training +# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) +# +# # store acc and loss for each fold +# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"]) # FNN test data +# +# fold_no = 1 +# +# # split the data +# for train, test in kfoldCValidator.split(xmulti, ymulti): +# +# # define all the output file/path names +# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, +# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, +# model_auc_file_data, outfilepath, checkpoint_path, +# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, +# target="multi", fold=fold_no) +# +# # use a dnn for multi-class prediction +# model = define_nn_model_multi(input_size=xmulti[train].shape[1], output_size=ymulti.shape[1]) +# +# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # measure the training time +# start = time() +# +# # train and validate +# hist = model.fit(xmulti[train], ymulti[train], +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the multi-label FNN: {trainTime} min") +# +# # validate model on test data set (x_test, y_test) +# scores = validate_multi_model_on_test_data(x_test=xmulti[test], +# checkpoint_path=checkpoint_path, +# y_test=ymulti[test], +# col_names=y.columns, +# result_file=outfilepath.replace("trainingResults.txt", +# "predictionResults.csv")) +# +# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) +# row_df = pd.DataFrame([[fold_no, +# hist.history['loss'][idx], hist.history['val_loss'][idx], +# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], +# scores[0], scores[1], scores[2]]], +# columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"] +# ) +# print(row_df) +# allscores = allscores.append(row_df, ignore_index=True) +# +# fold_no = fold_no + 1 +# del model +# +# print(allscores) +# +# # finalize model +# # 1. provide best performing fold variant +# # select best model based on MCC +# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] +# fold_no = allscores._get_value(idx2, 'fold_no') +# +# modelname = 'multi.Fold-' + str(fold_no) +# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' +# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") +# +# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) +# allscores.to_csv(file) +# +# # copy best DNN model +# shutil.copyfile(checkpoint_path, bestModelfile) +# print(f'[INFO]: Best models for FNN is saved:\n - {bestModelfile}') +# +# # AND retrain with full data set +# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") +# # measure the training time +# start = time() +# +# model = define_nn_model(input_size=xmulti[train].shape[1]) +# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # train and validate +# hist = model.fit(xmulti, ymulti, +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# # validation_data=(x_test, y_test)) # this overwrites val_split! +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") +# plot_history_vis(hist, +# model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), +# model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) +# print(f'[INFO]: Full models for DNN is saved:\n - {fullModelfile}') +# +# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) +# # stats.append([target, [x.__round__(2) for x in scores]]) + + +# ------------------------------------------------------------------------------------- # + +# def trainNNmodels(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, +# split: float = 0.2, epochs: int = 50, params: str = None, +# verbose: int = 2, kfold: int = 5) -> None: +# """ +# Train individual models for all targets (columns) present in the provided target data (y) and a multi-label +# model that classifies all targets at once. For each individual target the data is first subsetted to exclude NA +# values (for target associations). A random sample of the remaining data (size is the split fraction) is used for +# training and the remaining data for validation. +# +# :param modelfilepathprefix: A path prefix for all output files +# :param x: The feature matrix. +# :param y: The outcome matrix. +# :param split: The percentage of data used for validation. +# :param epochs: The number of epochs for training the autoencoder and the DNN for classification. +# Note: Early stopping and fallback is enabled. +# :param params: A .csv files containing paramters that should be evaluated. See file tunedParams.csv. +# :param verbose: Verbosity level. +# +# :return: A list with loss and accuracy values for each individual model. +# """ +# +# # remove 'id' column if present +# if 'id' in x.columns: +# x = x.drop('id', axis=1) +# if 'id' in y.columns: +# y = y.drop('id', axis=1) +# +# if params: +# parameters = pd.read_csv(params) +# +# # add a target 'summarized' for association to ANY of the target genes +# # maybe it improves the detection of '1's due to increased data set +# mysum = y.sum(axis=1) +# y['summarized'] = [0 if s == 0 else 1 for s in mysum] +# +# ### For each individual target (+ summarized target) +# for target in y.columns: # [:1]: +# # target=y.columns[0] # --> only for testing the code +# +# # rm NAs and duplicates, shuffle, and transform to numpy arrays +# (Xt, Yt) = prepareDataSet(y, x, target) +# +# # do a kfold cross validation for the FNN training +# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) +# +# # store acc and loss for each fold +# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"]) # FNN test data +# +# fold_no = 1 +# +# # split the data +# for train, test in kfoldCValidator.split(Xt, Yt): +# +# if verbose > 0: +# print(f'[INFO]: Training of fold number: {fold_no} ------------------------------------\n') +# +# # define all the output file/path names +# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, +# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, +# model_auc_file_data, outfilepath, checkpoint_path, +# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, +# target=target, fold=fold_no) +# +# model = define_nn_model(input_size=Xt[train].shape[1]) +# +# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # measure the training time +# start = time() +# # train and validate +# hist = model.fit(Xt[train], Yt[train], +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# # validation_data=(x_test, y_test)) # this overwrites val_split! +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the single-label FNN: {trainTime} min") +# +# # validate model on test data set (x_test, y_test) +# scores = validate_model_on_test_data(Xt[test], checkpoint_path, Yt[test], +# "FNN", model_validation, target, +# model_auc_file_data, model_auc_file) +# +# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) +# +# row_df = pd.DataFrame([[fold_no, +# hist.history['loss'][idx], hist.history['val_loss'][idx], +# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], +# scores[0], scores[1], scores[2]]], +# columns=["fold_no", # fold number of k-fold CV +# "loss", "val_loss", "acc", "val_acc", # FNN training +# "loss_test", "acc_test", "mcc_test"] +# ) +# print(row_df) +# allscores = allscores.append(row_df, ignore_index=True) +# fold_no = fold_no + 1 +# del model +# # now next fold +# +# print(allscores) +# +# # finalize model +# # 1. provide best performing fold variant +# # select best model based on MCC +# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] +# fold_no = allscores._get_value(idx2, 'fold_no') +# +# modelname = target + '.Fold-' + str(fold_no) +# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' +# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") +# +# # store all scores +# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) +# allscores.to_csv(file) +# +# # copy best DNN model +# shutil.copyfile(checkpoint_path, bestModelfile) +# print(f'[INFO]: Best model for FNN is saved:\n - {bestModelfile}') +# +# # AND retrain with full data set +# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") +# # measure the training time +# start = time() +# +# model = define_nn_model(input_size=Xt.shape[1]) # X_train.shape[1]) +# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, +# rlrop=True, rlropfactor=0.1, rlroppatience=100) +# # train and validate +# hist = model.fit(Xt, Yt, +# callbacks=callback_list, +# epochs=epochs, batch_size=256, verbose=2, validation_split=split) +# # validation_data=(x_test, y_test)) # this overwrites val_split! +# trainTime = str(round((time() - start) / 60, ndigits=2)) +# +# if verbose > 0: +# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") +# # plot_history_vis(hist, +# # model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# # model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), +# # model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), +# # model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) +# # print(f'[INFO]: Full model for DNN is saved:\n - {fullModelfile}') +# +# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) +# +# del model +# # now next target +# + +# ------------------------------------------------------------------------------------- # + + +def smilesSet2fpSet(csvfilename, outfilename, fptype): + """ + + :param csvfilename: csv file containing a column named 'smiles' + :param fptype: + :return: void + """ + # csvfilename="/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.csv" + # outfilename = "/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.fp.csv" + + # read csv and generate/add fingerprints to dict + with open(csvfilename, "r") as f: + reader = csv.DictReader(f, delimiter=";") + + feature = "smiles" + + with open(outfilename, "w", newline="") as out: + writer = csv.DictWriter(out, fieldnames=["fp"]) + writer.writeheader() + for row in reader: + # smiles, need to be converted to fp first + fp = smi2fp(smile=row[feature], fptype=fptype) + writer.writerow({"fp": DataStructs.BitVectToText(fp)}) + + return diff --git a/build/lib/dfpl/feedforwardNN.py b/build/lib/dfpl/feedforwardNN.py new file mode 100644 index 00000000..702fff27 --- /dev/null +++ b/build/lib/dfpl/feedforwardNN.py @@ -0,0 +1,464 @@ +import array +import logging +import math +import re +import shutil +import sys +from time import time +from typing import List, Union +import numpy as np +import pandas as pd +from sklearn.metrics import f1_score +from sklearn.model_selection import KFold, train_test_split +from tensorflow.keras import metrics, optimizers, regularizers +from tensorflow.keras.layers import Dense, Dropout + +# for NN model functions +from tensorflow.keras.models import Model, Sequential + +from dfpl import callbacks as cb +from dfpl import history as ht +from dfpl import options, settings + + +def define_out_file_names(path_prefix: str, target: str, fold: int = -1) -> tuple: + """ + This function returns the required paths for output files or directories. + + :param path_prefix: A file path prefix for all files. + :param target: The name of the target. + :param fold: + + :return: A tuple of 14 output file names. + """ + + if fold == -1: + model_name = "/" + target + ".Full" + else: + model_name = "/" + target + ".Fold-" + str(fold) + + model_file_path_weights = str(path_prefix) + model_name + ".weights.h5" + model_file_path_json = str(path_prefix) + model_name + ".json" + model_hist_path = str(path_prefix) + model_name + model_hist_csv_path = str(path_prefix) + model_name + ".history.csv" + model_predict_valset_csv_path = str(path_prefix) + model_name + ".predictValSet.csv" + model_validation = str(path_prefix) + model_name + ".validation.csv" + model_auc_file = str(path_prefix) + model_name + ".auc_value.svg" + model_auc_file_data = str(path_prefix) + model_name + ".auc_value.data.csv" + out_file_path = str(path_prefix) + model_name + ".trainingResults.txt" + checkpoint_path = str(path_prefix) + model_name + ".checkpoint.model.hdf5" + model_heatmap_x = str(path_prefix) + model_name + ".heatmap.X.svg" + model_heatmap_z = str(path_prefix) + model_name + ".AC.heatmap.Z.svg" + + return ( + model_file_path_weights, + model_file_path_json, + model_hist_path, + model_hist_csv_path, + model_predict_valset_csv_path, + model_validation, + model_auc_file, + model_auc_file_data, + out_file_path, + checkpoint_path, + model_heatmap_x, + model_heatmap_z, + ) + + +def define_nn_multi_label_model( + input_size: int, output_size: int, opts: options.Options +) -> Model: + if opts.optimizer == "Adam": + my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + elif opts.optimizer == "SGD": + my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) + else: + logging.error(f"Your selected optimizer is not supported:{opts.optimizer}.") + sys.exit("Unsupported optimizer.") + + nhl = int(math.log2(input_size) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation=opts.activationFunction, + kernel_regularizer=regularizers.l2(opts.l2reg), + ) + ) + model.add(Dropout(opts.dropout)) + # next hidden layers + for i in range(1, nhl): + factor_units = 2 ** (i + 1) + factor_dropout = 2 * i + model.add( + Dense( + units=int(input_size / factor_units), + activation=opts.activationFunction, + kernel_regularizer=regularizers.l2(opts.l2reg), + ) + ) + model.add(Dropout(opts.dropout / factor_dropout)) + # multi-class output layer + # use sigmoid to get independent probabilities for each output node + # we use sigmoid since one chemical can be active for multiple targets + # (need not add up to one, as they would using softmax) + # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ + model.add(Dense(units=output_size, activation="sigmoid")) + + model.summary(print_fn=logging.info) + + # compile model + model.compile( + loss="binary_crossentropy", + optimizer=my_optimizer, + metrics=["accuracy", metrics.Recall(), metrics.Precision()], + ) + + return model + + +def define_nn_model_multi( + input_size: int = 2048, + output_size: int = None, + l2reg: float = 0.001, + dropout: float = 0.2, + activation: str = "relu", + optimizer: str = "Adam", + lr: float = 0.001, + decay: float = 0.01, +) -> Model: + if optimizer == "Adam": + my_optimizer = optimizers.Adam(learning_rate=lr, decay=decay) + elif optimizer == "SGD": + my_optimizer = optimizers.SGD(lr=lr, momentum=0.9, decay=decay) + else: + my_optimizer = optimizer + + nhl = int(math.log2(input_size) / 2 - 1) + + model = Sequential() + # From input to 1st hidden layer + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout)) + # next hidden layers + for i in range(1, nhl): + factor_units = 2 ** (i + 1) + factor_dropout = 2 * i + model.add( + Dense( + units=int(input_size / factor_units), + activation=activation, + kernel_regularizer=regularizers.l2(l2reg), + ) + ) + model.add(Dropout(dropout / factor_dropout)) + # multi-class output layer + # use sigmoid to get independent probabilities for each output node + # (need not add up to one, as they would using softmax) + # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ + model.add(Dense(units=output_size, activation="sigmoid")) + + model.summary(print_fn=logging.info) + + # compile model + model.compile( + loss="binary_crossentropy", optimizer=my_optimizer, metrics=["accuracy"] + ) + + return model + + +def validate_multi_model_on_test_data( + x_test: np.ndarray, + checkpoint_path: str, + y_test: np.ndarray, + col_names: List[str], + result_file: str, +) -> List[Union[int, float, str]]: + """ + Validate the multi label model on a test data set. + + :param x_test: The feature matrix of the test data set + :param checkpoint_path: The path containing the weights of the FNN model + :param y_test: The outcome matrix of the test data set + :param col_names: The names of the columns that are targets + :param result_file: The filename of the output file + :return: A pandas Dataframe containing the percentage of correct predictions for each target + """ + # load checkpoint model with min(val_loss) + trained_model = define_nn_model_multi( + input_size=x_test.shape[1], output_size=y_test.shape[1] + ) + + # predict values with random model + predictions_random = pd.DataFrame( + trained_model.predict(x_test), columns=[n for n in col_names] + ) + + # load weights into random model + trained_model.load_weights(checkpoint_path) + + # predict with trained model + predictions = pd.DataFrame( + trained_model.predict(x_test), columns=[n for n in col_names] + ) + + y_true = pd.DataFrame(y_test, columns=col_names) + + f1_random = f1_score( + y_true=y_true, y_pred=predictions_random.round(), average="weighted" + ) + + f1_trained = f1_score(y_true=y_true, y_pred=predictions.round(), average="weighted") + + predictions_random.columns = [n + "-random" for n in col_names] + predictions.columns = [n + "-trained" for n in col_names] + + results = pd.concat([predictions_random, predictions, y_true], axis=1) + results.to_csv(result_file) + + return [f1_random, f1_trained] + + +def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: + # find target columns + names_y = [ + c + for c in df.columns + if c not in ["cid", "id", "smiles", "fp", "inchi", "fpcompressed"] + ] + selector = df[names_y].notna().apply(np.logical_and.reduce, axis=1) + + if opts.compressFeatures: + # get compressed fingerprints as numpy array + fpMatrix = np.array( + df[df["fpcompressed"].notnull() & selector]["fpcompressed"].to_list(), + dtype=settings.nn_multi_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + y = np.array( + df[df["fpcompressed"].notnull() & selector][names_y], + dtype=settings.nn_multi_target_numpy_type, + copy=settings.numpy_copy_values, + ) + else: + # get fingerprints as numpy array + fpMatrix = np.array( + df[df["fp"].notnull() & selector]["fp"].to_list(), + dtype=settings.nn_multi_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + + y = np.array( + df[df["fp"].notnull() & selector][names_y], + dtype=settings.nn_multi_target_numpy_type, + copy=settings.numpy_copy_values, + ) + + if opts.kFolds > 0: + # do a kfold cross validation for the autoencoder training + kfold_c_validator = KFold(n_splits=opts.kFolds, shuffle=True, random_state=42) + + # store acc and loss for each fold + all_scores = pd.DataFrame( + columns=[ + "fold_no", # fold number of k-fold CV + "loss", + "val_loss", + "acc", + "val_acc", # FNN training + "f1_random", + "f1_trained", + ] + ) # F1 scores of predictions + + fold_no = 1 + + # split the data + for train, test in kfold_c_validator.split(fpMatrix, y): + # kf = kfold_c_validator.split(fpMatrix, y) + # train, test = next(kf) + + ( + model_file_path_weights, + model_file_path_json, + model_hist_path, + model_validation, + model_auc_file, + model_auc_file_data, + out_file_path, + checkpoint_path, + model_heatmap_x, + model_heatmap_z, + ) = define_out_file_names( + path_prefix=opts.outputDir, + target="multi" + "_compressed-" + str(opts.compressFeatures), + fold=fold_no, + ) + + # use a dnn for multi-class prediction + model = define_nn_model_multi( + input_size=fpMatrix[train].shape[1], output_size=y.shape[1] + ) + + callback_list = cb.nn_callback(checkpoint_path=checkpoint_path, opts=opts) + # measure the training time + start = time() + + # train and validate + hist = model.fit( + fpMatrix[train], + y[train], + callbacks=callback_list, + epochs=opts.epochs, + batch_size=256, + verbose=opts.verbose, + validation_split=opts.testSize, + ) + + trainTime = str(round((time() - start) / 60, ndigits=2)) + + if opts.verbose > 0: + logging.info( + "Computation time for training the multi-label FNN: " + + trainTime + + " min" + ) + + ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) + # pd.DataFrame(hist.history).to_csv(model_hist_csv_path) + + # validate model on test data set (fpMatrix_test, y_test) + scores = validate_multi_model_on_test_data( + x_test=fpMatrix[test], + checkpoint_path=checkpoint_path, + y_test=y[test], + col_names=names_y, + result_file=out_file_path.replace( + "trainingResults.txt", "predictionResults.csv" + ), + ) + + idx = hist.history["val_loss"].index(min(hist.history["val_loss"])) + row_df = pd.DataFrame( + [ + [ + fold_no, + hist.history["loss"][idx], + hist.history["val_loss"][idx], + hist.history["accuracy"][idx], + hist.history["val_accuracy"][idx], + scores[0], + scores[1], + ] + ], + columns=[ + "fold_no", # fold number of k-fold CV + "loss", + "val_loss", + "acc", + "val_acc", + "f1_random", + "f1_trained", + ], + ) + + logging.info(row_df) + all_scores = all_scores.append(row_df, ignore_index=True) + + fold_no += 1 + del model + + logging.info(all_scores) + + # finalize model + # 1. provide best performing fold variant + # select best model based on MCC + idx2 = all_scores[["f1_trained"]].idxmax().ravel()[0] + fold_no = all_scores.iloc[idx2]["fold_no"] + + model_name = ( + "multi_compressed-" + str(opts.compressFeatures) + ".Fold-" + str(fold_no) + ) + checkpoint_path = opts.outputDir + "/" + model_name + ".checkpoint.model.hdf5" + best_model_file = checkpoint_path.replace( + "Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-" + ) + + file = re.sub( + ".hdf5", + "scores.csv", + re.sub("Fold-..checkpoint", "Fold-All", checkpoint_path), + ) + all_scores.to_csv(file) + + # copy best DNN model + shutil.copyfile(checkpoint_path, best_model_file) + logging.info("Best models for FNN is saved:\n" + best_model_file) + + # AND retrain with full data set + # full_model_file = f"{opts.outputDir}/multi_full.FNN.checkpoint.model.hdf5" + + ( + model_file_path_weights, + model_file_path_json, + model_hist_path, + model_hist_csv_path, + model_predict_valset_csv_path, + model_validation, + model_auc_file, + model_auc_file_data, + out_file_path, + checkpoint_path, + model_heatmap_x, + model_heatmap_z, + ) = define_out_file_names( + path_prefix=opts.outputDir, + target="multi" + "_compressed-" + str(opts.compressFeatures), + ) + + # test training data split + X_train, X_test, y_train, y_test = train_test_split( + fpMatrix, y, test_size=opts.testSize + ) + + # measure the training time + model = define_nn_multi_label_model( + input_size=fpMatrix.shape[1], output_size=y.shape[1], opts=opts + ) + model.evaluate(X_test, y_test) + + # model = define_nn_model_multi(input_size=fpMatrix.shape[1], + # output_size=y.shape[1]) + # callback_list = nn_callback(checkpoint_path=full_model_file, opts=opts) + # train and validate + start = time() + hist = model.fit( + X_train, + y_train, + # callbacks=callback_list, + epochs=opts.epochs, + batch_size=opts.batchSize, + verbose=opts.verbose, + validation_data=(X_test, y_test), + ) + trainTime = str(round((time() - start) / 60, ndigits=2)) + + # yhat = model.predict(X_test) + model.evaluate(X_test, y_test) + + logging.info( + "Computation time for training the full multi-label FNN: " + trainTime + " min" + ) + ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) diff --git a/build/lib/dfpl/fingerprint.py b/build/lib/dfpl/fingerprint.py new file mode 100644 index 00000000..291689c2 --- /dev/null +++ b/build/lib/dfpl/fingerprint.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +"""Calculate fingerprints""" +import logging +import multiprocessing +import os +from functools import partial +from os.path import isfile, join +from typing import Any, Callable, List + +import numpy as np +import pandas as pd +from rdkit import Chem, DataStructs, RDLogger +from rdkit.Chem import AllChem + +# import settings +from dfpl import settings + +default_fp_size = 2048 + + +def addFPColumn(data_frame: pd.DataFrame, fp_size: int) -> pd.DataFrame: + """ + Adds a fingerprint to each row in the dataframe. This function works on + parallel chunks of the original dataframe. + :param data_frame: Input dataframe that needs to have a "smiles" or an "inchi" column + :param fp_size: Number of bits in the fingerprint + :return: The dataframe with an additional "fp" column + """ + + def smile2fp(smile: str) -> Any: + """ + Calculates one fingerprint from a SMILE + :param smile: Input SMILE + :return: List of bits if conversion is successfull, + None otherwise + """ + + # generate morgan fp (circular, ecfp) + # smile = df['smiles'][1] + # mol = Chem.MolFromSmiles(smile) + # from rdkit.Chem import AllChem + # morgan = AllChem.GetMorganFingerprintAsBitVect(mol, 2) + # npa = np.zeros((0,), dtype=np.bool) + # from rdkit import DataStructs + # DataStructs.ConvertToNumpyArray(morgan, npa) + + npa = np.zeros((0,), dtype=np.bool_) + try: + DataStructs.ConvertToNumpyArray( + AllChem.GetMorganFingerprintAsBitVect( + Chem.MolFromSmiles(smile), 2, nBits=fp_size + ), + npa, + ) + return npa + except Exception: + return None + + # try: + # return np.array( + # Chem.RDKFingerprint(Chem.MolFromSmiles(smile), fpSize=fp_size), + # dtype=settings.df_fp_numpy_type, copy=settings.numpy_copy_values) + # except: + # # Note: We don't need to log here since rdkit already logs + # return None + + def inchi2fp(inchi: str) -> Any: + """ + Calculates one fingerprint from InChI + :param inchi: Input InChI + :return: List of bits if conversion is successfull, + None otherwise + """ + try: + return np.array( + Chem.RDKFingerprint(Chem.MolFromInchi(inchi), fpSize=fp_size), + dtype=settings.df_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + except Exception: + # Note: We don't need to log here since rdkit already logs + return None + + if "smiles" in data_frame: + func = smile2fp + accessor = "smiles" + else: + if "inchi" in data_frame: + func = inchi2fp + accessor = "inchi" + else: + raise ValueError("Neither smiles nor inchi column in data-frame") + + data_frame["fp"] = data_frame[accessor].apply(func) + return data_frame + + +def importDataFile( + file_name: str, + import_function: Callable[[str], pd.DataFrame] = pd.read_csv, + fp_size: int = default_fp_size, +) -> pd.DataFrame: + """ + Reads data as CSV or TSV and calculates fingerprints from the SMILES in the data. + :param import_function: + :param file_name: Filename of CSV files containing the training data. The + SMILES/Fingerprints are stored 1st column + :param fp_size: Number of bits in the fingerprint + :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If + no outcome data is provided, the Y matrix is a None object. + """ + # Read the data as Pandas pickle which already contains the calculated fingerprints + name, ext = os.path.splitext(file_name) + if ext == ".pkl": + return pd.read_pickle(file_name) + + df = import_function(file_name) + + # disable the rdkit logger. We know that some inchis will fail and we took care of it. No use to spam the console + RDLogger.DisableLog("rdApp.*") + n_cores = multiprocessing.cpu_count() + df_split = np.array_split(df, n_cores) + with multiprocessing.Pool(n_cores) as pool: + df = pd.concat(pool.map(partial(addFPColumn, fp_size=fp_size), df_split)) + pool.close() + pool.join() + return df + + +def importSmilesCSV(csvfilename: str) -> pd.DataFrame: + return pd.read_csv(csvfilename) + + +def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: + return pd.read_table(tsvfilename, names=["toxid", "inchi", "key"]) + + +conversion_rules = { + # "S_dataset.csv": importSmilesCSV, + # "S_dataset_extended.csv": importSmilesCSV, + # "D_dataset.tsv": importDstoxTSV, + # "train_data.csv": importSmilesCSV, + # "predict_data.csv": importDstoxTSV, + "B_data_ER.csv": importDstoxTSV +} + + +def convert_all(directory: str) -> List[str]: + files = [ + f + for f in os.listdir(directory) + for key, value in conversion_rules.items() + if isfile(join(directory, f)) and f in key + ] + logging.info(f"Found {len(files)} files to convert") + for f in files: + path = join(directory, f) + logging.info(f"Importing file {f}") + df = importDataFile(path, import_function=conversion_rules[f]) + name, ext = os.path.splitext(f) + output_file = join(directory, name + ".pkl") + logging.info(f"Saving pickle of file {f}") + df.to_pickle(output_file) + return files diff --git a/build/lib/dfpl/harmonize_mol_representations.py b/build/lib/dfpl/harmonize_mol_representations.py new file mode 100644 index 00000000..7c17e4e6 --- /dev/null +++ b/build/lib/dfpl/harmonize_mol_representations.py @@ -0,0 +1,171 @@ +from __future__ import annotations + +import logging +import sys +import urllib.error +import urllib.parse +import urllib.request +from dataclasses import dataclass +from functools import lru_cache + +import pandas as pd +from rdkit import Chem, DataStructs +from simple_parsing import ArgumentParser, choice +from simple_parsing.helpers import Serializable + +# Use this conda env: mamba create - n tidyChem - c conda - forge r - base r - tidyverse r - stringdist python rdkit +# numpy pandas simple - parsing + + +@dataclass +class ProgramConfig(Serializable): + """ + Configuration options for running the program + """ + + # file containing SMILES or InChikeys 2B harmonized + input_file: str = ( + "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_fragment.csv.tsv" + ) + # file containing the input for the harmonized molecular representations,tanimoto similarity and levenstein distance + output_file: str = ( + "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_dataset.harmonized.csv" + ) + input_column: str = choice("smiles", "inchikey", default="inchikey") + # column name of the column that contains the molecular representation to harmonize + input_type: str = choice( + "smiles", "inchikey", default="inchikey" + ) # the type of molecular representation of the input + output_type: str = choice( + "smiles", "inchikey", default="smiles" + ) # the type of molecular representation of the output + + +def createCommandlineParser(): + """ + Build the cmd_parser for arguments + Parse the input arguments. + :return: Tuple of commandlineparser, arguments + """ + cmd_parser = ArgumentParser(prog="harmonize_mol_representations") + cmd_parser.add_arguments(ProgramConfig, dest="config") + cmd_args, unknown = cmd_parser.parse_known_args() + return cmd_parser, cmd_args + + +def getLevensteinDistance(a, b): + """ + This function will calculate the levenshtein distance between two input + strings a and b + + params: + a (String) : The first string you want to compare + b (String) : The second string you want to compare + + returns: + This function will return the distnace between string a and b. + + example: + a = 'stamp' + b = 'stomp' + getLevensteinDistance(a,b) + >> 1.0 + """ + + if a is None and b is not None: + return len(b) + if a is not None and b is None: + return len(a) + + @lru_cache(None) # for memorization + def min_dist(s1, s2): + if s1 == len(a) or s2 == len(b): + return len(a) - s1 + len(b) - s2 + + # no change required + if a[s1] == b[s2]: + return min_dist(s1 + 1, s2 + 1) + + return 1 + min( + min_dist(s1, s2 + 1), # insert character + min_dist(s1 + 1, s2), # delete character + min_dist(s1 + 1, s2 + 1), # replace character + ) + + return min_dist(0, 0) + + +def getTanimotoSimilarityFromTwoSMILES( + smile1: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O", + smile2: str = "COC1=C(C=CC(=C1)C=O)O", +) -> float: + if smile1 is None or smile2 is None: + return 0 + + mol1 = Chem.MolFromSmiles(smile1) + mol2 = Chem.MolFromSmiles(smile2) + if mol1 is None or mol2 is None: + return 0 + + fp1 = Chem.RDKFingerprint(mol1, fpSize=2048) + fp2 = Chem.RDKFingerprint(mol2, fpSize=2048) + if fp1 is None or fp2 is None: + return 0 + + return DataStructs.TanimotoSimilarity(fp1, fp2) + + +def getHarmonizedSmiles(smiles: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O") -> str: + link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(smiles)}/smiles" + try: + f = urllib.request.urlopen(link) + myfile = f.read() + sh = myfile.decode("UTF-8") + except urllib.error.HTTPError: + sh = None + + return sh + + +def getHarmonizedSmilesfrominchis(inchikey: str = "CUWVNOSSZYUJAE-UHFFFAOYSA-N") -> str: + link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(inchikey)}/smiles" + try: + f = urllib.request.urlopen(link) + myfile = f.read() + sh = myfile.decode("UTF-8") + except urllib.error.HTTPError: + sh = None + + return sh + + +if __name__ == "__main__": + parser, args = createCommandlineParser() + opts = args.config + + # read data of smiles and harmonized smiles + df = pd.read_csv(filepath_or_buffer=opts.input_file) + if opts.input_type == "smiles": + df["smiles_harmonized"] = df[opts.input_column].apply(getHarmonizedSmiles) + df["tanimoto_similiarity"] = df.apply( + lambda x: getTanimotoSimilarityFromTwoSMILES( + smile1=x[opts.input_column], smile2=x["smiles_harmonized"] + ), + axis=1, + ) + df["levenstein_distance"] = df.apply( + lambda x: getLevensteinDistance( + a=x[opts.input_column], b=x["smiles_harmonized"] + ), + axis=1, + ) + elif opts.input_type == "inchikey": + df["smiles_harmonized"] = df[opts.input_column].apply( + getHarmonizedSmilesfrominchis + ) + + else: + logging.error(f"Your selected input is not supported: {opts.input_file}.") + sys.exit("Unsupported input type. Must be either smiles or inchikey") + + df.to_csv(path_or_buf=opts.output_file) diff --git a/build/lib/dfpl/history.py b/build/lib/dfpl/history.py new file mode 100644 index 00000000..1f16c2a9 --- /dev/null +++ b/build/lib/dfpl/history.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- +"""Store and visualise training histories""" +import logging + +import matplotlib as mpl +import matplotlib.pyplot as plt +import pandas as pd +from tensorflow.keras.callbacks import History + +mpl.use("Agg") + + +def store_and_plot_history(base_file_name: str, hist: History) -> None: + """ + + :param base_file_name: + :param hist: + :return: + """ + + (ac_history_csv, ac_history_svg) = ( + base_file_name + ".history.csv", + base_file_name + ".history.svg", + ) + + # store history + pd.DataFrame(hist.history).to_csv(ac_history_csv) + logging.info(f"Neural network training history saved in file: {ac_history_csv}") + + # plot history + ac_epochs = hist.epoch + + # generate a figure of the losses for this fold + plt.figure() + for k in hist.history.keys(): + plt.plot(ac_epochs, hist.history[k], label=k) + plt.title("Training and validation metrics of neural network") + plt.legend() + plt.savefig(fname=ac_history_svg, format="svg") + plt.close() + logging.info(f"Neural network training history plotted in file: {ac_history_svg}") diff --git a/build/lib/dfpl/options.py b/build/lib/dfpl/options.py new file mode 100644 index 00000000..bb753f05 --- /dev/null +++ b/build/lib/dfpl/options.py @@ -0,0 +1,1297 @@ +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import jsonpickle +import torch +from chemprop.args import TrainArgs + +from dfpl.utils import makePathAbsolute + + +@dataclass +class Options: + """ + Dataclass for all options necessary for training the neural nets + """ + + configFile: str = "./example/train.json" + inputFile: str = "/deepFPlearn/CMPNN/data/tox21.csv" + outputDir: str = "." + outputFile: str = "" + ecWeightsFile: str = "AE.encoder.weights.hdf5" + ecModelDir: str = "AE_encoder" + fnnModelDir: str = "modeltraining" + type: str = "smiles" + fpType: str = "topological" # also "MACCS", "atompairs" + epochs: int = 512 + fpSize: int = 2048 + encFPSize: int = 256 + kFolds: int = 0 + testSize: float = 0.2 + enableMultiLabel: bool = False + verbose: int = 0 + trainAC: bool = True # if set to False, an AC weight file must be provided! + trainFNN: bool = True + compressFeatures: bool = True + sampleFractionOnes: float = 0.5 # Only used when value is in [0,1] + sampleDown: bool = False + split_type: str = "random" + aeSplitType: str = "random" + aeType: str = "deterministic" + aeEpochs: int = 3000 + aeBatchSize: int = 512 + aeLearningRate: float = 0.001 + aeLearningRateDecay: float = 0.01 + aeActivationFunction: str = "relu" + aeOptimizer: str = "Adam" + fnnType: str = "FNN" + batchSize: int = 128 + optimizer: str = "Adam" + learningRate: float = 0.001 + lossFunction: str = "bce" + activationFunction: str = "relu" + l2reg: float = 0.001 + dropout: float = 0.2 + threshold: float = 0.5 + gpu: str = "" + snnDepth = 8 + snnWidth = 50 + aeWabTracking: str = "" # Wand & Biases autoencoder tracking + wabTracking: str = "" # Wand & Biases FNN tracking + wabTarget: str = "ER" # Wand & Biases target used for showing training progress + + def saveToFile(self, file: str) -> None: + """ + Saves an instance to a JSON file + """ + jsonFile = Path(file) + with jsonFile.open("w") as f: + f.write(jsonpickle.encode(self)) + + @classmethod + def fromJson(cls, file: str) -> Options: + """ + Create an instance from a JSON file + """ + jsonFile = Path(file) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + return jsonpickle.decode(content) + raise ValueError("JSON file does not exist or is not readable") + + @classmethod + def fromCmdArgs(cls, args: argparse.Namespace) -> Options: + """ + Creates Options instance from cmdline arguments. + + If a training file (JSON) is provided, the values from that file are used. + However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the + JSON file and on the commandline, then the value of the commandline argument will be used. + """ + result = Options() + if "configFile" in vars(args).keys(): + jsonFile = Path(makePathAbsolute(args.configFile)) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + result = jsonpickle.decode(content) + else: + raise ValueError("Could not find JSON input file") + + for key, value in vars(args).items(): + # The args dict will contain a "method" key from the subparser. + # We don't use this. + if key != "method": + result.__setattr__(key, value) + return result + + +@dataclass +class GnnOptions(TrainArgs): + """ + Dataclass to hold all options used for training the graph models + """ + + total_epochs: int = 30 + save: bool = True + configFile: str = "./example/traingnn.json" + data_path: str = "./example/data/tox21.csv" + use_compound_names: bool = False + save_dir: str = "" + no_cache: bool = False + features_scaling: bool = True + use_input_features: str = "" + cuda: bool = False + num_lrs: int = 2 + minimize_score: bool = False + num_tasks: int = 12 + preds_path: str = "./tox21dmpnn.csv" + test_path: str = "" + save_preds: bool = True + + @classmethod + def fromCmdArgs(cls, args: argparse.Namespace) -> GnnOptions: + """ + Creates Options instance from cmdline arguments. + + If a training file (JSON) is provided, the values from that file are used. + However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the + JSON file and on the commandline, then the value of the commandline argument will be used. + """ + result = GnnOptions() + if "configFile" in vars(args).keys(): + jsonFile = Path(makePathAbsolute(args.configFile)) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + result = jsonpickle.decode(content) + else: + raise ValueError("Could not find JSON input file") + + return result + + @classmethod + def fromJson(cls, file: str) -> GnnOptions: + """ + Create an instance from a JSON file + """ + jsonFile = Path(file) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = f.read() + return jsonpickle.decode(content) + raise ValueError("JSON file does not exist or is not readable") + + +def createCommandlineParser() -> argparse.ArgumentParser: + """ + Build the parser for arguments with its two subparsers + """ + parser = argparse.ArgumentParser(prog="deepFPlearn") + subparsers = parser.add_subparsers(help="Sub programs of deepFPlearn") + + parser_train_gnn = subparsers.add_parser( + "traingnn", help="Train new GNN models with your data" + ) + parser_train_gnn.set_defaults(method="traingnn") + parseTrainGnn(parser_train_gnn) + + parser_predict_gnn = subparsers.add_parser( + "predictgnn", help="Predict with your GNN models" + ) + parser_predict_gnn.set_defaults(method="predictgnn") + parsePredictGnn(parser_predict_gnn) + + parser_train = subparsers.add_parser( + "train", help="Train new models with your data" + ) + parser_train.set_defaults(method="train") + parseInputTrain(parser_train) + + parser_predict = subparsers.add_parser( + "predict", help="Predict your data with existing models" + ) + parser_predict.set_defaults(method="predict") + parseInputPredict(parser_predict) + + parser_convert = subparsers.add_parser( + "convert", help="Convert known data files to pickle serialization files" + ) + parser_convert.set_defaults(method="convert") + parseInputConvert(parser_convert) + return parser + + +def parseInputTrain(parser: argparse.ArgumentParser) -> None: + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + # Create argument groups + general_args = parser.add_argument_group("Model Configuration") + autoencoder_args = parser.add_argument_group("Autoencoder Configuration") + training_args = parser.add_argument_group("Training Configuration") + tracking_args = parser.add_argument_group("Tracking Configuration") + + # Model Configuration + general_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-i", + "--inputFile", + metavar="FILE", + type=str, + help="The file containing the data for training in " + "comma separated CSV format.The first column should be smiles.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-o", + "--outputDir", + metavar="DIR", + type=str, + help="Prefix of output file name. Trained model and " + "respective stats will be returned in this directory.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-t", + "--type", + metavar="STRING", + type=str, + choices=["fp", "smiles"], + help="Type of the chemical representation. Choices: 'fp', 'smiles'.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-thr", + "--threshold", + type=float, + metavar="FLOAT", + help="Threshold for binary classification.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-gpu", + "--gpu", + metavar="INT", + type=int, + help="Select which gpu to use. If not available, leave empty.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-k", + "--fpType", + metavar="STR", + type=str, + choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], + help="The type of fingerprint to be generated/used in input file.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-s", + "--fpSize", + type=int, + help="Size of fingerprint that should be generated.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-c", + "--compressFeatures", + metavar="BOOL", + type=bool, + help="Should the fingerprints be compressed or not. Activates the autoencoder. ", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-m", + "--enableMultiLabel", + metavar="BOOL", + type=bool, + help="Train multi-label classification model in addition to the individual models.", + default=argparse.SUPPRESS, + ) + # Autoencoder Configuration + autoencoder_args.add_argument( + "-a", + "--ecWeightsFile", + type=str, + metavar="FILE", + help="The .hdf5 file of a trained encoder", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--ecModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the encoder will be saved", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeType", + metavar="STRING", + type=str, + choices=["variational", "deterministic"], + help="Autoencoder type, variational or deterministic.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeEpochs", + metavar="INT", + type=int, + help="Number of epochs for autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeBatchSize", + metavar="INT", + type=int, + help="Batch size in autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeActivationFunction", + metavar="STRING", + type=str, + choices=["relu", "selu"], + help="The activation function for the hidden layers in the autoencoder.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeLearningRate", + metavar="FLOAT", + type=float, + help="Learning rate for autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeLearningRateDecay", + metavar="FLOAT", + type=float, + help="Learning rate decay for autoencoder training.", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "--aeSplitType", + metavar="STRING", + type=str, + choices=["scaffold_balanced", "random", "molecular_weight"], + help="Set how the data is going to be split for the autoencoder", + default=argparse.SUPPRESS, + ) + autoencoder_args.add_argument( + "-d", + "--encFPSize", + metavar="INT", + type=int, + help="Size of encoded fingerprint (z-layer of autoencoder).", + default=argparse.SUPPRESS, + ) + # Training Configuration + training_args.add_argument( + "--split_type", + metavar="STRING", + type=str, + choices=["scaffold_balanced", "random", "molecular_weight"], + help="Set how the data is going to be split for the feedforward neural network", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-l", + "--testSize", + metavar="FLOAT", + type=float, + help="Fraction of the dataset that should be used for testing. Value in [0,1].", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-K", + "--kFolds", + metavar="INT", + type=int, + help="K that is used for K-fold cross-validation in the training procedure.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-v", + "--verbose", + metavar="INT", + type=int, + choices=[0, 1, 2], + help="Verbosity level. O: No additional output, " + + "1: Some additional output, 2: full additional output", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--trainAC", + metavar="BOOL", + type=bool, + help="Choose to train or not, the autoencoder based on the input file", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--trainFNN", + metavar="BOOL", + type=bool, + help="Train the feedforward network either with provided weights.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--sampleFractionOnes", + metavar="FLOAT", + type=float, + help="This is the fraction of positive target associations (1s) in comparison to the majority class(0s)." + "only works if --sampleDown is enabled", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--sampleDown", + metavar="BOOL", + type=bool, + help="Enable automatic down sampling of the 0 valued samples.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "-e", + "--epochs", + metavar="INT", + type=int, + help="Number of epochs that should be used for the FNN training", + default=argparse.SUPPRESS, + ) + + training_args.add_argument( + "--lossFunction", + metavar="STRING", + type=str, + choices=["mse", "bce", "focal"], + help="Loss function to use during training. mse - mean squared error, bce - binary cross entropy.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--optimizer", + metavar="STRING", + type=str, + choices=["Adam", "SGD"], + help='Optimizer to use for backpropagation in the FNN. Possible values: "Adam", "SGD"', + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--batchSize", + metavar="INT", + type=int, + help="Batch size in FNN training.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--l2reg", + metavar="FLOAT", + type=float, + help="Value for l2 kernel regularizer.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--dropout", + metavar="FLOAT", + type=float, + help="The fraction of data that is dropped out in each dropout layer.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--learningRate", + metavar="FLOAT", + type=float, + help="Learning rate size in FNN training.", + default=argparse.SUPPRESS, + ) + training_args.add_argument( + "--activationFunction", + metavar="STRING", + type=str, + choices=["relu", "selu"], + help="The activation function for hidden layers in the FNN.", + default=argparse.SUPPRESS, + ) + # Tracking Configuration + tracking_args.add_argument( + "--aeWabTracking", + metavar="BOOL", + type=bool, + help="Track autoencoder performance via Weights & Biases, see https://wandb.ai.", + default=argparse.SUPPRESS, + ) + tracking_args.add_argument( + "--wabTracking", + metavar="BOOL", + type=bool, + help="Track FNN performance via Weights & Biases, see https://wandb.ai.", + default=argparse.SUPPRESS, + ) + tracking_args.add_argument( + "--wabTarget", + metavar="STRING", + type=str, + choices=["AR", "ER", "ED", "GR", "TR", "PPARg", "Aromatase"], + help="Which target to use for tracking performance via Weights & Biases, see https://wandb.ai.", + default=argparse.SUPPRESS, + ) + + +def parseTrainGnn(parser: argparse.ArgumentParser) -> None: + general_args = parser.add_argument_group("General Configuration") + data_args = parser.add_argument_group("Data Configuration") + files_args = parser.add_argument_group("Files") + model_args = parser.add_argument_group("Model arguments") + training_args = parser.add_argument_group("Training Configuration") + + # General arguments + general_args.add_argument("--split_key_molecule", type=int) + general_args.add_argument("--pytorch_seed", type=int) + general_args.add_argument("--cache_cutoff", type=float) + general_args.add_argument("--save_preds", type=bool) + general_args.add_argument( + "--cuda", action="store_true", default=False, help="Turn on cuda" + ) + general_args.add_argument( + "--save_smiles_splits", + action="store_true", + default=False, + help="Save smiles for each train/val/test splits for prediction convenience later", + ) + general_args.add_argument( + "--test", + action="store_true", + default=False, + help="Whether to skip training and only test the model", + ) + general_args.add_argument( + "--gpu", + type=int, + choices=list(range(torch.cuda.device_count())), + help="Which GPU to use", + ) + general_args.add_argument("--save", type=bool) + general_args.add_argument( + "--quiet", + action="store_true", + default=False, + help="Skip non-essential print statements", + ) + general_args.add_argument( + "--log_frequency", + type=int, + metavar="INT", + default=10, + help="The number of batches between each logging of the training loss", + ) + general_args.add_argument( + "--no_cuda", action="store_true", default=True, help="Turn off cuda" + ) + general_args.add_argument( + "--no_cache", + action="store_true", + default=False, + help="Turn off caching mol2graph computation", + ) + + # FILES ARGUMENTS + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + ) + files_args.add_argument( + "--config_path", + type=str, + metavar="FILE", + help="Path to a .json file containing arguments. Any arguments present in the config" + "file will override arguments specified via the command line or by the defaults.", + ) + files_args.add_argument( + "--save_dir", + type=str, + metavar="DIR", + default="./ckpt/", + help="Directory where model checkpoints will be saved", + ) + files_args.add_argument( + "--checkpoint_dir", + type=str, + metavar="DIR", + default=None, + help="Directory from which to load model checkpoints" + "(walks directory and ensembles all models that are found)", + ) + files_args.add_argument( + "--checkpoint_path", + type=str, + metavar="FILE", + default=None, + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--checkpoint_paths", + type=str, + metavar="FILE", + nargs="*", + default=None, + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--separate_val_path", + type=str, + metavar="FILE", + help="Path to separate val set, optional", + ) + files_args.add_argument( + "--separate_val_features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to file with features for separate val set", + ) + files_args.add_argument( + "--separate_test_path", + type=str, + metavar="FILE", + help="Path to separate test set, optional", + ) + files_args.add_argument( + "--separate_test_features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to file with features for separate test set", + ) + files_args.add_argument( + "--folds_file", + type=str, + metavar="FILE", + default=None, + help="Optional file of fold labels", + ) + files_args.add_argument( + "--val_fold_index", + type=int, + metavar="INT", + default=None, + help="Which fold to use as val for cross val", + ) + files_args.add_argument( + "--test_fold_index", + type=int, + metavar="INT", + default=None, + help="Which fold to use as test for cross val", + ) + files_args.add_argument( + "--crossval_index_dir", + type=str, + metavar="DIR", + help="Directory in which to find cross validation index files", + ) + files_args.add_argument( + "--crossval_index_file", + type=str, + metavar="FILE", + help="Indices of files to use as train/val/test" + "Overrides --num_folds and --seed.", + ) + files_args.add_argument( + "--data_weights_path", + type=str, + metavar="FILE", + help="Path where the data weight are saved", + ) + files_args.add_argument( + "--features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to features to use in FNN (instead of features_generator)", + ) + + files_args.add_argument( + "--separate_val_phase_features_path", type=str, metavar="FILE" + ) + files_args.add_argument( + "--separate_test_phase_features_path", type=str, metavar="FILE" + ) + + files_args.add_argument( + "--separate_val_atom_descriptors_path", type=str, metavar="FILE" + ) + files_args.add_argument( + "--separate_test_atom_descriptors_path", type=str, metavar="FILE" + ) + # Data related arguments + data_args.add_argument( + "--data_path", + type=str, + metavar="FILE", + help="Path to data CSV file", + default="", + ) + data_args.add_argument( + "--use_compound_names", + action="store_true", + default=False, + help="Use when test data file contains compound names in addition to SMILES strings", + ) + data_args.add_argument( + "--max_data_size", + type=int, + metavar="INT", + help="Maximum number of data points to load", + ) + + data_args.add_argument( + "--features_only", + action="store_true", + default=False, + help="Use only the additional features in an FFN, no graph network", + ) + data_args.add_argument( + "--dataset_type", + type=str, + metavar="STRING", + choices=["classification", "regression", "multiclass"], + help="Type of dataset, e.g. classification or regression." + "This determines the loss function used during training.", + default="regression", + ) # classification + data_args.add_argument( + "--multiclass_num_classes", + type=int, + metavar="INT", + default=3, + help="Number of classes when running multiclass classification", + ) + data_args.add_argument( + "--split_type", + type=str, + metavar="STRING", + default="random", + choices=[ + "random", + "scaffold_balanced", + "predetermined", + "crossval", + "index_predetermined", + ], + help="Method of splitting the data into train/val/test", + ) + data_args.add_argument( + "--split_sizes", + type=float, + metavar="FLOAT", + nargs=3, + default=[0.8, 0.2, 0.0], + help="Split proportions for train/validation/test sets", + ) + + data_args.add_argument( + "--seed", + type=int, + default=0, + help="Random seed to use when splitting data into train/val/test sets." + "When `num_folds` > 1, the first fold uses this seed and all" + "subsequent folds add 1 to the seed.", + ) + data_args.add_argument( + "--smiles_columns", + type=str, + metavar="STRING", + help="Name of the smiles columns", + ) + + data_args.add_argument( + "--target_columns", + type=str, + metavar="STRING", + help="Name of the target columns", + ) + + data_args.add_argument( + "--ignore_columns", + type=str, + metavar="STRING", + help="Names of the columns to ignore", + ) + data_args.add_argument( + "--num_tasks", type=int, metavar="INT", help="NUmber of tasks" + ) + data_args.add_argument( + "--no_features_scaling", + action="store_true", + default=False, + help="Turn off scaling of features", + ) + data_args.add_argument( + "--features_scaling", + action="store_true", + default=False, + help="Turn on scaling of features", + ) + data_args.add_argument( + "--use_input_features", + type=str, + metavar="STRING", + help="Turn on scaling of features", + ) + + # Model arguments + model_args.add_argument( + "--ensemble_size", + type=int, + metavar="INT", + default=1, + help="Number of models in ensemble", + ) + model_args.add_argument( + "--hidden_size", + type=int, + metavar="INT", + default=300, + help="Dimensionality of hidden layers in MPN", + ) + model_args.add_argument( + "--bias", + action="store_true", + default=False, + help="Whether to add bias to linear layers", + ) + model_args.add_argument( + "--depth", + type=int, + metavar="INT", + default=3, + help="Number of message passing steps", + ) + model_args.add_argument( + "--dropout", + type=float, + metavar="FLOAT", + default=0.0, + help="Dropout probability", + ) + model_args.add_argument( + "--activation", + type=str, + metavar="STRING", + default="ReLU", + choices=["ReLU", "LeakyReLU", "PReLU", "tanh", "SELU", "ELU"], + help="Activation function", + ) + model_args.add_argument( + "--undirected", + action="store_true", + default=False, + help="Undirected edges (always sum the two relevant bond vectors)", + ) + model_args.add_argument( + "--ffn_hidden_size", + type=int, + metavar="INT", + default=2, + help="Hidden dim for higher-capacity FFN (defaults to hidden_size)", + ) + model_args.add_argument( + "--ffn_num_layers", + type=int, + metavar="INT", + default=2, + help="Number of layers in FFN after MPN encoding", + ) + model_args.add_argument( + "--atom_messages", + action="store_true", + default=False, + help="Use messages on atoms instead of messages on bonds", + ) + + model_args.add_argument( + "--num_lrs", + type=int, + metavar="INT", + default=2, + help="Number of layers in FFN after MPN encoding", + ) + model_args.add_argument("--checkpoint_frzn", type=str, metavar="STRING") + + # Model arguments + model_args.add_argument("--mpn_shared", type=bool, metavar="BOOL") + model_args.add_argument( + "--show_individual_scores", + action="store_true", + default=True, + help="Show all scores for individual targets, not just average, at the end", + ) + model_args.add_argument("--aggregation", choices=["mean", "sum", "norm"]) + model_args.add_argument("--aggregation_norm", type=int) + model_args.add_argument("--explicit_h", type=bool, metavar="BOOL") + model_args.add_argument("--adding_h", type=bool, metavar="BOOL") + # Training arguments + model_args.add_argument("--class_balance", type=bool, metavar="BOOL") + model_args.add_argument("--evidential_regularization", type=float, metavar="FLOAT") + model_args.add_argument( + "--overwrite_default_atom_features", type=bool, metavar="BOOL" + ) + model_args.add_argument("--no_atom_descriptor_scaling", type=bool, metavar="BOOL") + model_args.add_argument( + "--overwrite_default_bond_features", type=bool, metavar="BOOL" + ) + model_args.add_argument("--frzn_ffn_layers", type=int, metavar="INT") + model_args.add_argument("--freeze_first_only", type=bool, metavar="BOOL") + # Training arguments + training_args.add_argument( + "--epochs", type=int, metavar="INT", default=30, help="Number of epochs to run" + ) + training_args.add_argument( + "--total_epochs", + type=int, + metavar="INT", + default=30, + help="Number of total epochs to run", + ) + training_args.add_argument( + "--batch_size", type=int, metavar="INT", default=50, help="Batch size" + ) + training_args.add_argument( + "--warmup_epochs", + type=int, + metavar="INT", + default=2, + help="Number of epochs during which learning rate increases linearly from" + "init_lr to max_lr. Afterwards, learning rate decreases exponentially" + "from max_lr to final_lr.", + ) + training_args.add_argument( + "--init_lr", + type=float, + metavar="FLOAT", + default=1e-4, + help="Initial learning rate", + ) + training_args.add_argument( + "--max_lr", + type=float, + metavar="FLOAT", + default=1e-3, + help="Maximum learning rate", + ) + training_args.add_argument( + "--final_lr", + type=float, + metavar="FLOAT", + default=1e-4, + help="Final learning rate", + ) + training_args.add_argument( + "--extra_metrics", + type=str, + metavar="STRING", + nargs="*", + help="Extra metrics to use", + ) + training_args.add_argument( + "--loss_function", + type=str, + metavar="STRING", + choices=[ + "mse", + "bounded_mse", + "binary_cross_entropy", + "cross_entropy", + "mcc", + "sid", + "wasserstein", + "mve", + "evidential", + "dirichlet", + ], + ) + training_args.add_argument("--grad_clip", type=float) + training_args.add_argument( + "--metric", + type=str, + metavar="STRING", + default=None, + choices=[ + "auc", + "prc-auc", + "rmse", + "mae", + "mse", + "r2", + "accuracy", + "cross_entropy", + ], + help="Metric to use during evaluation." + "Note: Does NOT affect loss function used during training" + "(loss is determined by the `dataset_type` argument)." + 'Note: Defaults to "auc" for classification and "rmse" for regression.', + ) + training_args.add_argument( + "--num_folds", + type=int, + metavar="INT", + default=1, + help="Number of folds when performing cross validation", + ) + + +def parseInputPredict(parser: argparse.ArgumentParser) -> None: + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + parser = argparse.ArgumentParser( + description="Use models that have been generated by " + "to make predictions on chemicals (provide SMILES or " + "topological fingerprints)." + ) + general_args = parser.add_argument_group("General Configuration") + files_args = parser.add_argument_group("Files") + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "-i", + "--inputFile", + metavar="FILE", + type=str, + help="The file containing the data for the prediction in (unquoted) " + "comma separated CSV format. The column named 'smiles' or 'fp'" + "contains the field to be predicted. Please adjust the type " + "that should be predicted (fp or smile) with -t option appropriately." + "An optional column 'id' is used to assign the outcomes to the" + "original identifiers. If this column is missing, the results are" + "numbered in the order of their appearance in the input file." + "A header is expected and respective column names are used.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "-o", + "--outputDir", + metavar="DIR", + type=str, + help="Prefix of output directory. It will contain a log file and the file specified" + "with --outputFile.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "--outputFile", + metavar="FILE", + type=str, + help="Output .CSV file name which will contain one prediction per input line. " + "Default: prefix of input file name.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-t", + "--type", + metavar="STR", + type=str, + choices=["fp", "smiles"], + help="Type of the chemical representation. Choices: 'fp', 'smiles'.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "-k", + "--fpType", + metavar="STR", + type=str, + choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], + help="The type of fingerprint to be generated/used in input file.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "--ecModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the encoder will be saved (if trainAE=True) or " + "loaded from (if trainAE=False). Provide a full path here.", + default=argparse.SUPPRESS, + ) + files_args.add_argument( + "--fnnModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the fnn is loaded from. " + "Provide a full path here.", + default=argparse.SUPPRESS, + ) + + +def parsePredictGnn(parser: argparse.ArgumentParser) -> None: + general_args = parser.add_argument_group("General Configuration") + data_args = parser.add_argument_group("Data Configuration") + files_args = parser.add_argument_group("Files") + training_args = parser.add_argument_group("Training Configuration") + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting.", + default=argparse.SUPPRESS, + ) + general_args.add_argument( + "--gpu", + type=int, + metavar="INT", + choices=list(range(torch.cuda.device_count())), + help="Which GPU to use", + ) + general_args.add_argument( + "--no_cuda", action="store_true", default=False, help="Turn off cuda" + ) + general_args.add_argument( + "--num_workers", + type=int, + metavar="INT", + help="Number of workers for the parallel data loading 0 means sequential", + ) + general_args.add_argument( + "--no_cache", + type=bool, + metavar="BOOL", + default=False, + help="Turn off caching mol2graph computation", + ) + general_args.add_argument( + "--no_cache_mol", + type=bool, + metavar="BOOL", + default=False, + help="Whether to not cache the RDKit molecule for each SMILES string to reduce memory\ + usage cached by default", + ) + general_args.add_argument( + "--empty_cache", + type=bool, + metavar="BOOL", + help="Whether to empty all caches before training or predicting. This is necessary if\ + multiple jobs are run within a single script and the atom or bond features change", + ) + files_args.add_argument( + "--preds_path", + type=str, + metavar="FILE", + help="Path to CSV file where predictions will be saved", + default="", + ) + files_args.add_argument( + "--checkpoint_dir", + type=str, + metavar="DIR", + help="Directory from which to load model checkpoints" + "(walks directory and ensembles all models that are found)", + default="./ckpt", + ) + files_args.add_argument( + "--checkpoint_path", + type=str, + metavar="DIR", + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--checkpoint_paths", + type=str, + metavar="FILE", + nargs="*", + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--data_path", + type=str, + metavar="FILE", + help="Path to CSV file containing testing data for which predictions will be made", + default="", + ) + files_args.add_argument( + "--test_path", + type=str, + metavar="FILE", + help="Path to CSV file containing testing data for which predictions will be made", + default="", + ) + files_args.add_argument( + "--features_path", + type=str, + metavar="FILE", + nargs="*", + help="Path to features to use in FNN (instead of features_generator)", + ) + files_args.add_argument( + "--atom_descriptors_path", + type=str, + metavar="FILE", + help="Path to the extra atom descriptors.", + ) + data_args.add_argument( + "--use_compound_names", + action="store_true", + default=False, + help="Use when test data file contains compound names in addition to SMILES strings", + ) + data_args.add_argument( + "--no_features_scaling", + action="store_true", + default=False, + help="Turn off scaling of features", + ) + data_args.add_argument( + "--max_data_size", + type=int, + metavar="INT", + help="Maximum number of data points to load", + ) + data_args.add_argument( + "--smiles_columns", + type=str, + metavar="STRING", + help="List of names of the columns containing SMILES strings.By default, uses the first\ + number_of_molecules columns.", + ) + data_args.add_argument( + "--number_of_molecules", + type=int, + metavar="INT", + help="Number of molecules in each input to the model.This must equal the length of\ + smiles_columns if not None", + ) + + data_args.add_argument( + "--atom_descriptors", + type=bool, + metavar="Bool", + help="Use or not atom descriptors", + ) + + data_args.add_argument( + "--bond_features_size", + type=int, + metavar="INT", + help="Size of the extra bond descriptors that will be used as bond features to featurize a\ + given molecule", + ) + training_args.add_argument( + "--batch_size", type=int, metavar="INT", default=50, help="Batch size" + ) + + +def parseInputConvert(parser: argparse.ArgumentParser) -> None: + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + parser.add_argument( + "-f", + metavar="FILE", + type=str, + help="Input directory where your CSV/TSV files are stored.", + required=True, + default="", + ) diff --git a/build/lib/dfpl/plot.py b/build/lib/dfpl/plot.py new file mode 100644 index 00000000..7e8f6ac1 --- /dev/null +++ b/build/lib/dfpl/plot.py @@ -0,0 +1,228 @@ +import array + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import wandb +from matplotlib.axes import Axes +from typing import List +# for NN model functions +from tensorflow.keras.callbacks import History + + +def get_max_validation_accuracy(history: History) -> str: + validation = smooth_curve(history.history["val_accuracy"]) + y_max: float = max(validation) + return "Max validation accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_validation_balanced_accuracy(history: History) -> str: + validation_bal_acc = smooth_curve(history.history["val_balanced_accuracy"]) + y_max: float = max(validation_bal_acc) + return "Max validation balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_training_balanced_accuracy(history: History) -> str: + training_bal_acc = smooth_curve(history.history["balanced_accuracy"]) + y_max: float = max(training_bal_acc) + return "Training balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_training_auc(history: History) -> str: + training_auc = smooth_curve(history.history["auc"]) + y_max: float = max(training_auc) + return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_validation_auc(history: History) -> str: + validation_auc = smooth_curve(history.history["val_auc"]) + y_max: float = max(validation_auc) + return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" + + +def get_max_training_accuracy(history: History) -> str: + training = smooth_curve(history.history["accuracy"]) + y_max: float = max(training) + return "Max training accuracy ≈ " + str(round(y_max, 3) * 100) + "%" + + +def smooth_curve(points: np.ndarray, factor: float = 0.75) -> np.ndarray: + smoothed_points: List[float] = [] + for point in points: + if smoothed_points: + previous = smoothed_points[-1] + smoothed_points.append(previous * factor + point * (1 - factor)) + else: + smoothed_points.append(point) + return smoothed_points + + +def set_plot_history_data(ax: Axes, history: History, which_graph: str) -> None: + (train, valid) = (None, None) + + if which_graph == "acc": + train = smooth_curve(history.history["accuracy"]) + valid = smooth_curve(history.history["val_accuracy"]) + + if which_graph == "loss": + train = smooth_curve(history.history["loss"]) + valid = smooth_curve(history.history["val_loss"]) + + # plt.xkcd() # make plots look like xkcd + + epochs = range(1, len(train) + 1) + + trim = 0 # remove first 5 epochs + # when graphing loss the first few epochs may skew the (loss) graph + + ax.plot(epochs[trim:], train[trim:], "dodgerblue", linewidth=15, alpha=0.1) + ax.plot(epochs[trim:], train[trim:], "dodgerblue", label="Training") + + ax.plot(epochs[trim:], valid[trim:], "g", linewidth=15, alpha=0.1) + ax.plot(epochs[trim:], valid[trim:], "g", label="Validation") + + +def plot_history(history: History, file: str) -> None: + fig, (ax1, ax2) = plt.subplots( + nrows=2, + ncols=1, + figsize=(10, 6), + sharex="all", + gridspec_kw={"height_ratios": [5, 2]}, + ) + + set_plot_history_data(ax1, history, "acc") + + set_plot_history_data(ax2, history, "loss") + + # Accuracy graph + ax1.set_ylabel("Accuracy") + ax1.set_ylim(bottom=0.5, top=1) + ax1.legend(loc="lower right") + ax1.spines["top"].set_visible(False) + ax1.spines["right"].set_visible(False) + ax1.xaxis.set_ticks_position("none") + ax1.spines["bottom"].set_visible(False) + + # max accuracy text + plt.text( + 0.5, + 0.6, + get_max_validation_balanced_accuracy(history), + horizontalalignment="right", + verticalalignment="top", + transform=ax1.transAxes, + fontsize=12, + ) + plt.text( + 0.5, + 0.8, + get_max_training_balanced_accuracy(history), + horizontalalignment="right", + verticalalignment="top", + transform=ax1.transAxes, + fontsize=12, + ) + + # Loss graph + ax2.set_ylabel("Loss") + ax2.set_yticks([]) + ax2.plot(legend=False) + ax2.set_xlabel("Epochs") + ax2.spines["top"].set_visible(False) + ax2.spines["right"].set_visible(False) + + plt.tight_layout() + plt.savefig(fname=file, format="svg") + plt.close() + + +def plot_train_history(hist, target, file_accuracy, file_loss): + """ + Plot the training performance in terms of accuracy and loss values for each epoch. + :param hist: The history returned by model.fit function + :param target: The name of the target of the model + :param file_accuracy: The filename for plotting accuracy values + :param file_loss: The filename for plotting loss values + :return: none + """ + + # plot accuracy + plt.figure() + plt.plot(hist.history["accuracy"]) + if "val_accuracy" in hist.history.keys(): + plt.plot(hist.history["val_accuracy"]) + plt.title("Model accuracy - " + target) + plt.ylabel("Accuracy") + plt.xlabel("Epoch") + if "val_accuracy" in hist.history.keys(): + plt.legend(["Train", "Test"], loc="upper left") + else: + plt.legend(["Train"], loc="upper_left") + plt.savefig(fname=file_accuracy, format="svg") + + # Plot training & validation loss values + plt.figure() + plt.plot(hist.history["loss"]) + plt.plot(hist.history["val_loss"]) + plt.title("Model loss - " + target) + plt.ylabel("Loss") + plt.xlabel("Epoch") + plt.legend(["Train", "Test"], loc="upper left") + # plt.show() + plt.savefig(fname=file_loss, format="svg") + plt.close() + + +def plot_history_vis( + hist: History, + model_hist_plot_path: str, + model_hist_csv_path: str, + model_hist_plot_path_a: str, + model_hist_plot_path_l: str, + target: str, +) -> None: + plot_history(history=hist, file=model_hist_plot_path) + histDF = pd.DataFrame(hist.history) + histDF.to_csv(model_hist_csv_path) + + # plot accuracy and loss for the training and validation during training + plot_train_history( + hist=hist, + target=target, + file_accuracy=model_hist_plot_path_a, + file_loss=model_hist_plot_path_l, + ) + + +def plot_auc( + fpr: np.ndarray, + tpr: np.ndarray, + auc_value: float, + target: str, + filename: str, + wandb_logging: bool = False, +) -> None: + """ + Plot the area under the curve to the provided file + + :param fpr: An array containing the false positives + :param tpr: An array containing the true positives + :param auc_value: The value of the area under the curve + :param target: The name of the training target + :param filename: The filename to which the plot should be stored + :param wandb_logging: Whether to log the plot to wandb + :rtype: None + """ + # Create a boolean mask to filter out zero values + plt.figure() + plt.plot([0, 1], [0, 1], "k--") + plt.plot(fpr, tpr, label=f"Keras (area = {auc_value:.3f})") + plt.xlabel("False positive rate") + plt.ylabel("True positive rate") + plt.title("ROC curve " + target) + plt.legend(loc="best") + plt.savefig(fname=filename, format="png") + if wandb_logging: + wandb.log({"roc_plot": plt}) + plt.close() diff --git a/build/lib/dfpl/predictions.py b/build/lib/dfpl/predictions.py new file mode 100644 index 00000000..29e73142 --- /dev/null +++ b/build/lib/dfpl/predictions.py @@ -0,0 +1,39 @@ +import logging + +import numpy as np +import pandas as pd +import tensorflow.keras.models + +from dfpl import options, settings + + +def predict_values(df: pd.DataFrame, opts: options.Options) -> pd.DataFrame: + """ + Predict a set of chemicals using a selected model. + + :param df: + :param opts: + :return: + """ + model = tensorflow.keras.models.load_model(opts.fnnModelDir, compile=False) + model.compile(loss=opts.lossFunction, optimizer=opts.optimizer) + if opts.compressFeatures: + sub_df = df[df["fpcompressed"].notnull()] + x = np.array( + sub_df["fpcompressed"].to_list(), + dtype=settings.nn_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info(f"Compressed FP matrix with shape {x.shape} and type {x.dtype}") + sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) + return sub_df + else: + sub_df = df[df["fp"].notnull()] + x = np.array( + sub_df["fp"].to_list(), + dtype=settings.nn_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info(f"Uncompressed FP matrix with shape {x.shape} and type {x.dtype}") + sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) + return sub_df diff --git a/build/lib/dfpl/rbm.py b/build/lib/dfpl/rbm.py new file mode 100644 index 00000000..fe5b1ac5 --- /dev/null +++ b/build/lib/dfpl/rbm.py @@ -0,0 +1,259 @@ +import os.path +from os.path import basename +import pandas as pd +import logging +import tensorflow as tf +import numpy as np +from tensorflow.keras import Model +from sklearn.model_selection import train_test_split +import math + +from dfpl import callbacks +from dfpl import options +from dfpl import history as ht +from dfpl import settings + + +class RBMLayer(tf.keras.layers.Layer): + def __init__(self, input_size, output_size, name="RBMLayer", **kwargs): + super(RBMLayer, self).__init__(name=name, **kwargs) + self._input_size = input_size + self._output_size = output_size + self._name = name + # self._activation_function = activation_function + + def build(self, input_shape): + self.w = self.add_weight( + shape=(input_shape[-1], self._output_size), + initializer="random_normal", + trainable=True, name=f"w_{self._name}" + ) + self.hb = self.add_weight( + shape=(self._output_size,), initializer="random_normal", trainable=True, name=f"hb_{self._name}" + ) + self.vb = self.add_weight( + shape=(input_shape[-1],), initializer="random_normal", trainable=True, name=f"vb_{self._name}" + ) + + def __prob_h_given_v(self, visible, w, hb): + return tf.nn.sigmoid(tf.matmul(visible, w) + hb) + + def __prob_v_given_h(self, hidden, w, vb): + return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb) + + def __sample_prob(self, probs): + return tf.nn.relu(tf.sign(probs - tf.random.uniform(tf.shape(probs)))) + + def call(self, X): + out = tf.nn.sigmoid(tf.matmul(X, self.w) + self.hb) + hiddenGen = self.__sample_prob(self.__prob_h_given_v(X, self.w, self.hb)) + visibleGen = self.__sample_prob(self.__prob_v_given_h(hiddenGen, self.w, self.vb)) + h1 = self.__prob_h_given_v(visibleGen, self.w, self.hb) + return out, visibleGen, hiddenGen, h1 + + def get_config(self): + return {"input_size": self._input_size, + "output_size": self._output_size} + + +class RBM(tf.keras.Model): + + def __init__(self, input_size, output_size, name="RBM", **kwargs): + super(RBM, self).__init__(name=name, **kwargs) + # Define hyperparameters + self._input_size = input_size + self._output_size = output_size + self.n_layer = round(math.log2(input_size / output_size)) + + self.last_out = None + + # layers defination + # encoding layers + self.rbm_encoder_layers = [] + self.rbm_decoder_layers = [] + self.rbm_encoder1 = RBMLayer(self._input_size, int(self._input_size // 2), name="encoder_0") + + if self.n_layer > 1: + for i in range(1, self.n_layer): + factor_units = 2 ** (i + 1) + self.rbm_encoder_layers.append( + RBMLayer(int(self._input_size // factor_units) * 2, int(self._input_size // factor_units), + name=f"encoder_{i}")) + + # decoding layers + factor_units = 2 ** (self.n_layer - 1) + self.rbm_decoder1 = RBMLayer(int(input_size // factor_units) // 2, int(input_size // factor_units), + name="decoder_0") + + for layer_num, i in enumerate(range(self.n_layer - 2, 0, -1)): + factor_units = 2 ** i + self.rbm_decoder_layers.append( + RBMLayer(int(input_size // (factor_units * 2)), int(input_size // factor_units), + name=f"decoder_{layer_num + 1}")) + + self.rbm_decoder_layers.append( + RBMLayer(int(input_size // 2), int(input_size), name=f"decoder_{self.n_layer - 1}")) + + def call(self, X, training=None): + outs = {} + out, visibleGen, hiddenGen, h1 = self.rbm_encoder1(X) + # out, visibleGen, hiddenGen, h1 = self.rbm_encoder(X) + outs[f'encoder_{0}'] = (out, visibleGen, hiddenGen, h1) + for i, layer in enumerate(self.rbm_encoder_layers): + out, visibleGen, hiddenGen, h1 = layer(hiddenGen) + outs[f'encoder_{i + 1}'] = (out, visibleGen, hiddenGen, h1) + + out, visibleGen, hiddenGen, h1 = self.rbm_decoder1(hiddenGen) + outs[f'decoder_{0}'] = (out, visibleGen, hiddenGen, h1) + for j, layer in enumerate(self.rbm_decoder_layers): + out, visibleGen, hiddenGen, h1 = layer(hiddenGen) + outs[f'decoder_{j + 1}'] = (out, visibleGen, hiddenGen, h1) + return out, visibleGen, hiddenGen, h1, outs + + def train_step(self, data): + X, y = data + + learning_rate = self.optimizer._decayed_lr('float32') + out, visibleGen, hiddenGen, h1, out_dict = self(X, training=True) + weights_dict = {} + for lay in self.layers: + weights_dict[lay.name] = lay + + last_out = X + for idx, (key, value) in enumerate(out_dict.items()): + out, visibleGen, hiddenGen, h1 = value + positive_grad = tf.matmul(tf.transpose(last_out), hiddenGen) + negative_grad = tf.matmul(tf.transpose(visibleGen), h1) + # loss = tf.reduce_mean(tf.square(y, visibleGen)) + # loss = self.compiled_loss(y, visibleGen) + + # update the weights of the model based on the loss and LR. + curr_layer = weights_dict[key] + curr_layer.weights[0].assign(curr_layer.weights[0] + learning_rate * + (positive_grad - negative_grad) / tf.cast(tf.shape(last_out)[0], + dtype=tf.float32)) + curr_layer.weights[1].assign(curr_layer.weights[1] + learning_rate * tf.math.reduce_mean(hiddenGen - h1, 0)) + curr_layer.weights[2].assign( + curr_layer.weights[2] + learning_rate * tf.math.reduce_mean(last_out - visibleGen, 0)) + + last_out = hiddenGen + return {m.name: m.result() for m in self.metrics} + + def test_step(self, data): + # Unpack the data + x, y = data + # Compute predictions + out, visibleGen, hiddenGen, h1, out_dict = self(x, training=False) + # Updates the metrics tracking the loss + self.compiled_loss(y, hiddenGen, regularization_losses=self.losses) + # Update the metrics. + self.compiled_metrics.update_state(y, hiddenGen) + # Return a dict mapping metric names to current value. + # Note that it will include the loss (tracked in self.metrics). + return {m.name: m.result() for m in self.metrics} + + +def define_rbm_model(opts: options.Options, + input_size: int = 2048, + encoding_dim: int = 256) -> Model: + """ + This function provides an autoencoder model to reduce a certain input to a compressed version. + + :param encoding_dim: Size of the compressed representation. Default: 85 + :param input_size: Size of the input. Default: 2048 + :return: a tuple of autoencoder and encoder models + """ + if opts.aeOptimizer == "Adam": + ac_optimizer = tf.keras.optimizers.Adam(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) + elif opts.aeOptimizer == "SGD": + ac_optimizer = tf.keras.optimizers.SGD(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) + + rbm_model = RBM(input_size=input_size, + output_size=encoding_dim) + rbm_model.compile(optimizer=ac_optimizer, loss=opts.lossFunction) + + return rbm_model + + +def train_full_rbm(df: pd.DataFrame, opts: options.Options) -> Model: + """ + Train an autoencoder on the given feature matrix X. Response matrix is only used to + split meaningfully in test and train data set. + + :param opts: Command line arguments as defined in options.py + :param df: Pandas dataframe that contains the smiles/inchi data for training the autoencoder + :return: The encoder model of the trained autoencoder + """ + + # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) + rbm = define_rbm_model(opts, input_size=opts.fpSize, encoding_dim=opts.encFPSize) + # define output file for autoencoder and encoder weights + if opts.ecWeightsFile == "": + logging.info("No RBM encoder weights file specified") + base_file_name = os.path.splitext(basename(opts.inputFile))[0] + logging.info(f"RBM weights will be saved in {base_file_name}.rbm{opts.encFPSize}.hdf5") + rbm_weights_file = os.path.join(opts.outputDir, base_file_name + f".rbm{opts.encFPSize}.hdf5") + # ec_weights_file = os.path.join(opts.outputDir, base_file_name + ".rbm_encoder.weights.hdf5") + else: + logging.info(f"RBM encoder will be saved in {opts.ecWeightsFile}") + base_file_name = os.path.splitext(basename(opts.ecWeightsFile))[0] + rbm_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) + + # collect the callbacks for training + callback_list = callbacks.autoencoder_callback(checkpoint_path=rbm_weights_file, opts=opts) + + # Select all fps that are valid and turn them into a numpy array + # This step is crucial for speed!!! + fp_matrix = np.array(df[df["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values) + logging.info(f"Training RBM on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") + + # split data into test and training data + x_train, x_test = train_test_split(fp_matrix, + test_size=0.2, + random_state=42) + x_train = tf.cast(tf.where(x_train, 1, 0), tf.float32) + x_test = tf.cast(tf.where(x_test, 1, 0), tf.float32) + logging.info(f"RBM train data shape {x_train.shape} with type {x_train.dtype}") + logging.info(f"RBM test data shape {x_test.shape} with type {x_test.dtype}") + auto_hist = rbm.fit(x=x_train, y=x_train, + callbacks=callback_list, + epochs=opts.aeEpochs, + batch_size=opts.aeBatchSize, + verbose=opts.verbose, + steps_per_epoch=25, + validation_data=(x_test, x_test)) + rbm.summary(print_fn=logging.info) + ht.store_and_plot_history(base_file_name=os.path.join(opts.outputDir, base_file_name + ".RBM"), + hist=auto_hist) + rbm.save_weights(rbm_weights_file) + save_path = os.path.join(opts.ecModelDir, "rbm_model") + rbm.save(save_path) + logging.info(f"RBM weights stored in file: {rbm_weights_file}") + + return rbm + + +def compress_fingerprints(dataframe: pd.DataFrame, + encoder: Model, layer_num: int) -> pd.DataFrame: + """ + Adds a column of the compressed version of the fingerprints to the original dataframe. + + :param dataframe: Dataframe containing a column named 'fp' with the fingerprints + :param encoder: The trained autoencoder that is used for compressing the fingerprints + :return: The input dataframe extended by a column containing the compressed version of the fingerprints + """ + logging.info("Adding rbm compressed fingerprints") + idx = dataframe[dataframe["fp"].notnull()].index + fp_matrix = np.array(dataframe[dataframe["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values) + logging.info(f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") + logging.info("Compressed fingerprints from rbm are added to input dataframe.") + fp_matrix = tf.cast(tf.where(fp_matrix, 1, 0), tf.float32) + out, visibleGen, hiddenGen, h1, out_dict = encoder.predict(fp_matrix) + out, visibleGen, hiddenGen, h1 = out_dict[f'encoder_{layer_num-1}'] + dataframe['fpcompressed'] = pd.DataFrame({'fpcompressed': [s for s in hiddenGen]}, idx) + + return dataframe diff --git a/build/lib/dfpl/settings.py b/build/lib/dfpl/settings.py new file mode 100644 index 00000000..2d900cda --- /dev/null +++ b/build/lib/dfpl/settings.py @@ -0,0 +1,52 @@ +import numpy as np + +# Datatype that is used for fingerprint vectors stored inside +# dataframe used during the program +df_fp_numpy_type = np.bool8 + +# Do we need copies when creating numpy matrices from the pandas dataframes +# for training? Everything seems to work fine with False and it saves memory. +numpy_copy_values = False + +# Autoencoder data type settings + +# Type that is given as input numpy array to the network +# We need to carefully test the performance of the training here +# The big dsstox dataset runs out of mem on Patrick's machine when +# we use floats here. +# Also, we possibly should give the FNN a float type fp +ac_fp_numpy_type = np.bool8 + +# Type used for the compressed fp which is an inner layer of the ac +# an MUST be of float type. On Patrick's machine, (GPU) tensorflow layers +# are float32 per default. Not sure if float64 can be used with GPU +# training and if it enhances results. Needs to be evaluated. +ac_fp_compressed_numpy_type = np.bool8 + +# Feedforward Neuronal Network data type settings +# +# The sections below define the types of the numpy matrices used for training +# the FNNs. In short: +# XX_fp_numpy_type: used for uncompressed fingerprint tensors +# XX_fp_compressed_numpy_type: used for compressed fingerprint tensors +# XX_target_numpy_type: used for the target tensors + +nn_fp_numpy_type = np.float32 +nn_fp_compressed_numpy_type = np.float32 +nn_target_numpy_type = np.short + +nn_multi_fp_numpy_type = np.float32 +nn_multi_fp_compressed_numpy_type = np.float32 +nn_multi_target_numpy_type = np.float32 + +# Training settings + +# Training settings of the AC that were magic numbers in the code before. +ac_train_min_delta = 0.0001 +ac_train_check_period = 1 +ac_train_patience = 1 + +# Training settings of the FNN that were magic numbers in the code before. +nn_train_min_delta = 0.0001 +nn_train_check_period = 5 +nn_train_patience = 5 diff --git a/build/lib/dfpl/single_label_model.py b/build/lib/dfpl/single_label_model.py new file mode 100644 index 00000000..de437d1e --- /dev/null +++ b/build/lib/dfpl/single_label_model.py @@ -0,0 +1,1008 @@ +import logging +import math +import os +import shutil +import sys +from os import path +from time import time + +import numpy as np +import pandas as pd +import tensorflow as tf +import tensorflow.keras.backend as K +import wandb +from typing import Union, Tuple + +from sklearn.metrics import ( + auc, + classification_report, + confusion_matrix, + matthews_corrcoef, + roc_curve, +) +from sklearn.model_selection import StratifiedKFold, train_test_split +from tensorflow.keras import metrics, optimizers, regularizers +from tensorflow.keras.layers import AlphaDropout, Dense, Dropout +from tensorflow.keras.losses import ( + BinaryCrossentropy, + MeanSquaredError, + BinaryFocalCrossentropy, +) +from tensorflow.keras.models import Model, Sequential + +from dfpl import callbacks as cb +from dfpl import options +from dfpl import plot as pl +from dfpl import settings +from dfpl.utils import ae_scaffold_split, weight_split + + +def prepare_nn_training_data( + df: pd.DataFrame, target: str, opts: options.Options, return_dataframe: bool = False +) -> Union[Tuple[np.ndarray, np.ndarray], pd.DataFrame]: + # Check the value counts and abort if too imbalanced + allowed_imbalance = 0.1 + # If feature compression is enabled, use compressed fingerprints + if opts.compressFeatures: + vc = df[df[target].notna() & df["fpcompressed"].notnull()][ + target + ].value_counts() + # Otherwise, use uncompressed fingerprints + else: + vc = df[df[target].notna() & df["fp"].notnull()][target].value_counts() + + # If the dataset is extremely unbalanced, adjust sampling options + if min(vc) < max(vc) * allowed_imbalance: + logging.info( + f" Your training data is extremely unbalanced ({target}): 0 - {vc[0]}, and 1 - {vc[1]} values." + ) + if opts.sampleDown: + logging.info(f" I will down-sample your data") + opts.sampleFractionOnes = allowed_imbalance + else: + logging.info(f" I will not down-sample your data automatically.") + logging.info( + f" Consider to enable down sampling of the 0 values with --sampleDown option." + ) + + logging.info("Preparing training data matrices") + + # If feature compression is enabled, use compressed fingerprints + if opts.compressFeatures: + logging.info("Using compressed fingerprints") + df_fpc = df[df[target].notna() & df["fpcompressed"].notnull()] + logging.info( + f"DataSet has {df_fpc.shape[0]} valid entries in fpcompressed and {target}" + ) + + # If sampling down is enabled, adjust sampling options and downsample the data + if opts.sampleDown: + assert 0.0 < opts.sampleFractionOnes < 1.0 + logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") + # How many ones and zeros are in the dataset + counts = df_fpc[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + + # Add a sample of 0s to the df of 1s + dfX = df_fpc[df_fpc[target] == 1].append( + df_fpc[df_fpc[target] == 0].sample( + int(min(counts[0], counts[1] / opts.sampleFractionOnes)) + ) + ) + # Convert fingerprints and targets to numpy arrays + x = np.array( + dfX["fpcompressed"].to_list(), + dtype=settings.nn_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + y = np.array( + dfX[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + # Return the dataframe if specified + if return_dataframe: + return dfX + else: + logging.info("Fraction sampling is OFF") + # How many ones and zeros are in the dataset + counts = df_fpc[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + + # Convert fingerprints and targets to numpy arrays + x = np.array( + df_fpc["fpcompressed"].to_list(), + dtype=settings.nn_fp_compressed_numpy_type, + copy=settings.numpy_copy_values, + ) + y = np.array( + df_fpc[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + if return_dataframe: + return df_fpc + + # If feature compression is not enabled, use uncompressed fingerprints + else: + logging.info("Using uncompressed fingerprints") + # select rows where the target is not null and the fingerprint is not null + df_fp = df[df[target].notna() & df["fp"].notnull()] + logging.info(f"DataSet has {df_fp.shape[0]} valid entries in fp and {target}") + + if opts.sampleDown: + logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") + # count number of each class value + counts = df_fp[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + # downsample the majority class by taking a fraction of its samples and combining with all of the + # minority class + dfX = df_fp[df_fp[target] == 1.0].append( + df_fp[df_fp[target] == 0.0].sample( + int(min(counts[0], counts[1] / opts.sampleFractionOnes)) + ) + ) + # convert fingerprints to numpy array + x = np.array( + dfX["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + # convert target values to numpy array + y = np.array( + dfX[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + if return_dataframe: + return dfX + else: + logging.info("Fraction sampling is OFF") + # count number of each class value + counts = df_fp[target].value_counts() + logging.info(f"Number of sampling values: {counts.to_dict()}") + # convert fingerprints to numpy array + x = np.array( + df_fp["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + # convert target values to numpy array + y = np.array( + df_fp[target].to_list(), + dtype=settings.nn_target_numpy_type, + copy=settings.numpy_copy_values, + ) + if return_dataframe: + return df_fp + # return the numpy arrays of fingerprints and targets + return x, y + + +# This function defines a feedforward neural network (FNN) with the given input size, options, and output bias +def build_fnn_network( + input_size: int, opts: options.Options, output_bias=None +) -> Model: + # Set the output bias if it is provided + if output_bias is not None: + output_bias = tf.keras.initializers.Constant(output_bias) + + # Define the number of hidden layers based on the input size + my_hidden_layers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} + if not str(input_size) in my_hidden_layers.keys(): + raise ValueError("Wrong input-size. Must be in {2048, 1024, 999, 512, 256}.") + nhl = int(math.log2(input_size) / 2 - 1) + + # Create a sequential model + model = Sequential() + + # Add the first hidden layer + if opts.activationFunction == "relu": + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation="relu", + kernel_regularizer=regularizers.l2(opts.l2reg), + kernel_initializer="he_uniform", + ) + ) + model.add(Dropout(opts.dropout)) + elif opts.activationFunction == "selu": + model.add( + Dense( + units=int(input_size / 2), + input_dim=input_size, + activation="selu", + kernel_initializer="lecun_normal", + ) + ) + model.add(AlphaDropout(opts.dropout)) + else: + logging.error("Only 'relu' and 'selu' activation is supported") + sys.exit(-1) + + # Add additional hidden layers + for i in range(1, nhl): + factor_units = 2 ** (i + 1) + factor_dropout = 2 * i + if opts.activationFunction == "relu": + model.add( + Dense( + units=int(input_size / factor_units), + activation="relu", + kernel_regularizer=regularizers.l2(opts.l2reg), + kernel_initializer="he_uniform", + ) + ) + model.add(Dropout(opts.dropout / factor_dropout)) + elif opts.activationFunction == "selu": + model.add( + Dense( + units=int(input_size / factor_units), + activation="selu", + kernel_initializer="lecun_normal", + ) + ) + model.add(AlphaDropout(opts.dropout / factor_dropout)) + else: + logging.error("Only 'relu' and 'selu' activation is supported") + sys.exit(-1) + + # Add the output layer with a sigmoid activation function and the output bias if provided + model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) + model.summary() + return model + + +# This function defines a shallow neural network (SNN) with the given input size, options, and output bias +def build_snn_network( + input_size: int, opts: options.Options, output_bias=None +) -> Model: + # Set the output bias if it is provided + if output_bias is not None: + output_bias = tf.keras.initializers.Constant(output_bias) + + # Create a sequential model + model = Sequential() + + # Add the first hidden layer + model.add( + Dense( + input_dim=input_size, + units=50, + activation="selu", + kernel_initializer="lecun_normal", + ) + ) + model.add(AlphaDropout(opts.dropout)) + + # Add 7 hidden layers with 50 units each, a SELU activation function, and a LeCun normal kernel initializer + for i in range(7): + model.add(Dense(units=50, activation="selu", kernel_initializer="lecun_normal")) + # Add an alpha dropout layer with the given dropout rate + model.add(AlphaDropout(opts.dropout)) + + # Add the output layer with 1 unit and a sigmoid activation function + # If output_bias is not None, set the bias initializer to the specified value + model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) + + return model + + +def balanced_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> np.float64: + """ + Computes the balanced accuracy metric. + + Args: + y_true (np.ndarray): The true values of the labels. + y_pred (np.ndarray): The predicted values of the labels. + + Returns: + np.float64: The balanced accuracy score. + """ + y_pred = K.round(y_pred) # Convert continuous predictions to binary class labels + tn = K.sum((1 - y_true) * (1 - y_pred)) + fp = K.sum((1 - y_true) * y_pred) + fn = K.sum(y_true * (1 - y_pred)) + tp = K.sum(y_true * y_pred) + balanced_acc = (tp / (tp + fn + 1e-10) + tn / (tn + fp + 1e-10)) / 2 + return balanced_acc + + +def define_single_label_model( + input_size: int, opts: options.Options, output_bias=None +) -> Model: + """ + Defines and compiles the single-label neural network model. + + Args: + input_size (int): The size of the input layer. + opts (options.Options): The options used in the model. + output_bias (float): The initial bias for the last sigmoid layer of the model. + + Returns: + tensorflow.keras.Model: The compiled model. + """ + # Set the loss function according to the option selected + if opts.lossFunction == "bce": + loss_function = BinaryCrossentropy() + elif opts.lossFunction == "mse": + loss_function = MeanSquaredError() + elif opts.lossFunction == "focal": + loss_function = BinaryFocalCrossentropy() + else: + logging.error(f"Your selected loss is not supported: {opts.lossFunction}.") + sys.exit("Unsupported loss function") + + # Set the optimizer according to the option selected + if opts.optimizer == "Adam": + my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + elif opts.optimizer == "SGD": + my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) + else: + logging.error(f"Your selected optimizer is not supported: {opts.optimizer}.") + sys.exit("Unsupported optimizer") + + # Set the type of neural network according to the option selected + if opts.fnnType == "FNN": + model = build_fnn_network(input_size, opts, output_bias) + elif opts.fnnType == "SNN": + model = build_snn_network(input_size, opts, output_bias) + else: + raise ValueError(f'Option FNN Type is not "FNN" or "SNN", but {opts.fnnType}.') + + logging.info(f"Network type: {opts.fnnType}") + model.summary(print_fn=logging.info) + + # Compile the model with the defined options + model.compile( + loss=loss_function, + optimizer=my_optimizer, + metrics=[ + metrics.BinaryAccuracy(name="accuracy"), + metrics.AUC(), + metrics.Precision(), + metrics.Recall(), + balanced_accuracy, + ], + ) + + return model + + +def evaluate_model( + x_test: np.ndarray, + y_test: np.ndarray, + file_prefix: str, + model: Model, + target: str, + fold: int, +) -> pd.DataFrame: + # Log that we're evaluating the model on the test data + name = path.basename(file_prefix).replace("_", " ") + logging.info(f"Evaluating trained model '{name}' on test data") + + # Predict the test set to compute MCC, AUC, ROC curve, etc. + threshold = 0.5 # TODO: Introduce thresholds different from 0.5! + y_predict = model.predict(x_test).flatten() + y_predict_int = (y_predict >= threshold).astype(np.short) + y_test_int = y_test.astype(np.short) + + # Save the predictions to a CSV file + ( + pd.DataFrame( + { + "y_true": y_test_int, + "y_predicted": y_predict, + "y_predicted_int": y_predict_int, + "target": target, + "fold": fold, + } + ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.csv") + ) + + # Compute the confusion matrix + confusion_matrix(y_true=y_test_int, y_pred=y_predict_int) + + # Compute the metrics that depend on the class label + precision_recall = classification_report( + y_test_int, y_predict_int, output_dict=True + ) + prf = pd.DataFrame.from_dict(precision_recall)[["0", "1"]] + + # Add balanced accuracy to the computed metrics + prf.to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.prec_rec_f1.csv") + + # Evaluate the model on the validation set and log the results + loss, acc, auc_value, precision, recall, balanced_acc = tuple( + model.evaluate(x=x_test, y=y_test) + ) + + logging.info(f"Loss: {round(loss, 4)}") + logging.info(f"Accuracy: {round(acc, 4)}") + logging.info(f"AUC: {round(auc_value, 4)}") + logging.info(f"Precision: {round(precision, 4)}") + logging.info(f"Recall: {round(recall, 4)}") + logging.info(f"Balanced Accuracy: {round(balanced_acc, 4)}") + + # Compute the Matthews Correlation Coefficient (MCC) + MCC = matthews_corrcoef(y_test_int, y_predict_int) + logging.info(f"MCC: {round(MCC, 4)}") + + # Generate the AUC-ROC curve data from the validation data + FPR, TPR, thresholds_keras = roc_curve( + y_true=y_test_int, y_score=y_predict, drop_intermediate=False + ) + AUC = auc(FPR, TPR) + nonzero_mask = (FPR != 0) | (TPR != 0) + FPR_filtered = FPR[nonzero_mask] + TPR_filtered = TPR[nonzero_mask] + + # Save AUC data to CSV file + pd.DataFrame( + list( + zip( + FPR_filtered, + TPR_filtered, + [AUC] * len(FPR_filtered), + [target] * len(FPR_filtered), + [fold] * len(FPR_filtered), + ) + ), + columns=["fpr", "tpr", "auc_value", "target", "fold"], + ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.aucdata.csv") + # Generate and save AUC-ROC curve plot + pl.plot_auc( + fpr=FPR, + tpr=TPR, + target=target, + auc_value=AUC, + filename=f"{file_prefix}_auc_data.png", + wandb_logging=False, + ) + + # Return a DataFrame containing the computed metrics + return pd.DataFrame.from_dict( + { + "p_0": prf["0"]["precision"], + "r_0": prf["0"]["recall"], + "f1_0": prf["0"]["f1-score"], + "p_1": prf["1"]["precision"], + "r_1": prf["1"]["recall"], + "f1_1": prf["1"]["f1-score"], + "loss": loss, + "accuracy": acc, + "balanced_accuracy": balanced_acc, + "MCC": MCC, + "AUC": AUC, + "target": target, + "fold": fold, + }, + orient="index", + ).T + + +def fit_and_evaluate_model( + x_train: np.ndarray, + x_test: np.ndarray, + y_train: np.ndarray, + y_test: np.ndarray, + fold: int, + target: str, + opts: options.Options, +) -> pd.DataFrame: + # Print info about training + logging.info(f"Training of fold number: {fold}") + + # Define file name prefix for saving models + model_file_prefix = path.join( + opts.outputDir, f"{target}_{opts.split_type}_single-labeled_Fold-{fold}" + ) + + # Compute class imbalance + ids, counts = np.unique(y_train, return_counts=True) + count_dict = dict(zip(ids, counts)) + + # Set initial bias for last sigmoid layer to counter class imbalance + if count_dict[0] == 0: + initial_bias = None + logging.info("No zeroes in training labels. Setting initial_bias to None.") + else: + initial_bias = np.log([count_dict[1] / count_dict[0]]) + logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") + + # Define model + model = define_single_label_model( + input_size=x_train.shape[1], opts=opts, output_bias=initial_bias + ) + + # Define checkpoint to save model weights during training + checkpoint_model_weights_path = f"{model_file_prefix}.model.weights.hdf5" + callback_list = cb.nn_callback( + checkpoint_path=checkpoint_model_weights_path, opts=opts + ) + + # Train model + start = time() + hist = model.fit( + x_train, + y_train, + callbacks=callback_list, + epochs=opts.epochs, + batch_size=opts.batchSize, + verbose=opts.verbose, + validation_data=(x_test, y_test), + ) + trainTime = str(round((time() - start) / 60, ndigits=2)) + logging.info( + f"Computation time for training the single-label model for {target}: {trainTime} min" + ) + + # Save and plot model history + pd.DataFrame(hist.history).to_csv(path_or_buf=f"{model_file_prefix}.history.csv") + pl.plot_history(history=hist, file=f"{model_file_prefix}.history.svg") + # Evaluate model + callback_model = define_single_label_model(input_size=x_train.shape[1], opts=opts) + callback_model.load_weights(filepath=checkpoint_model_weights_path) + performance = evaluate_model( + x_test=x_test, + y_test=y_test, + file_prefix=model_file_prefix, + model=callback_model, + target=target, + fold=fold, + ) + + return performance + + +def get_x_y( + df: pd.DataFrame, + target: str, + train_set: pd.DataFrame, + test_set: pd.DataFrame, + opts: options.Options, +): + train_indices = train_set.index + test_indices = test_set.index + if opts.compressFeatures: + accessor = "fpcompressed" + else: + accessor = "fp" + new_df = pd.DataFrame( + [{x: y for x, y in enumerate(item)} for item in df[accessor].values.tolist()], + index=df.index, + ) + x_train = new_df.iloc[train_indices, :].values + y_train = df.iloc[train_indices][target].values + x_test = new_df.iloc[test_indices, :].values + y_test = df.iloc[test_indices][target].values + x_train = x_train.astype("float32") + y_train = y_train.astype("float32") + x_test = x_test.astype("float32") + y_test = y_test.astype("float32") + return x_train, y_train, x_test, y_test + + +def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: + """ + Train individual models for all targets (columns) present in the provided target data (y) and a multi-label + model that classifies all targets at once. For each individual target the data is first subset to exclude NA + values (for target associations). A random sample of the remaining data (size is the split fraction) is used for + training and the remaining data for validation. + + :param opts: The command line arguments in the options class + :param df: The dataframe containing x matrix and at least one column for a y target. + """ + + # find target columns + targets = [ + c + for c in df.columns + if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] + ] + if opts.wabTracking and opts.wabTarget != "": + # For W&B tracking, we only train one target that's specified as wabTarget "ER". + # In case it's not there, we use the first one available + if opts.wabTarget in targets: + targets = [opts.wabTarget] + elif opts.wabTarget == "": + targets = targets + else: + logging.error( + f"The specified wabTarget for Weights & Biases tracking does not exist: {opts.wabTarget}" + ) + targets = [targets[0]] + + # Collect metrics for each fold and target + performance_list = [] + if opts.split_type == "random": + for target in targets: # [:1]: + # target=targets[1] # --> only for testing the code + x, y = prepare_nn_training_data(df, target, opts, return_dataframe=False) + if x is None: + continue + + logging.info(f"X training matrix of shape {x.shape} and type {x.dtype}") + logging.info(f"Y training matrix of shape {y.shape} and type {y.dtype}") + + if opts.kFolds == 1: + # for single 'folds' and when sweeping on W&B, we fix the random state + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}", + group=f"{target}", + name=f"{target}_single_fold", + ) + if opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", + name=f"{target}_single_fold", + group=f"{target}", + reinit=True, + ) + x_train, x_test, y_train, y_test = train_test_split( + x, y, stratify=y, test_size=opts.testSize, random_state=1 + ) + logging.info( + f"Splitting train/test data with fixed random initializer" + ) + else: + x_train, x_test, y_train, y_test = train_test_split( + x, y, stratify=y, test_size=opts.testSize + ) + + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=0, + target=target, + opts=opts, + ) + performance_list.append(performance) + # save complete model + trained_model = define_single_label_model( + input_size=len(x[0]), opts=opts + ) + # trained_model.load_weights + # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_single-labeled_Fold-0.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join(opts.outputDir, f"{target}_saved_model") + ) + + elif 1 < opts.kFolds < 10: # int(x.shape[0] / 100): + # do a kfold cross-validation + kfold_c_validator = StratifiedKFold( + n_splits=opts.kFolds, shuffle=True, random_state=42 + ) + fold_no = 1 + # split the data + for train, test in kfold_c_validator.split(x, y): + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FNN_{opts.threshold}_{opts.split_type}", + group=f"{target}", + name=f"{target}-{fold_no}", + reinit=True, + ) + if opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}", + name=f"AE_{target}-{fold_no}", + group=f"{target}", + reinit=True, + ) + + # Initialize wandb for the feed forward model + wandb.init( + project=f"FNN_{opts.threshold}_{opts.split_type}", + group=f"{target}", + name=f"FNN_{target}-{fold_no}", + reinit=True, + ) + + performance = fit_and_evaluate_model( + x_train=x[train], + x_test=x[test], + y_train=y[train], + y_test=y[test], + fold=fold_no, + target=target, + opts=opts, + ) + performance_list.append(performance) + + # save complete model + trained_model = define_single_label_model( + input_size=len(x[0]), opts=opts + ) + # trained_model.load_weights + # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_single-labeled_Fold-{fold_no}.model.weights.hdf5", + ) + ) + # create output directory and store complete model + trained_model.save( + filepath=path.join( + opts.outputDir, f"{target}-{fold_no}_saved_model" + ) + ) + if opts.wabTracking: + wandb.finish() + fold_no += 1 + + # select and copy best model - how to define the best model? + best_fold = pd.concat(performance_list, ignore_index=True).sort_values( + by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True + )["fold"][0] + # rename the fold to best fold + src = os.path.join( + opts.outputDir, + f"{target}_single-labeled_Fold-{best_fold}.model.weights.hdf5", + ) + dst = os.path.join( + opts.outputDir, + f"{target}_single-labeled_Best_Fold-{best_fold}.model.weights.hdf5", + ) + os.rename(src, dst) + + src_dir = os.path.join( + opts.outputDir, f"{target}-{best_fold}_saved_model" + ) + dst_dir = os.path.join( + opts.outputDir, f"{target}-{best_fold}_best_saved_model" + ) + + if path.isdir(dst_dir): + shutil.rmtree(dst_dir) + + # Rename source directory to destination directory + os.rename(src_dir, dst_dir) + + # save complete model + else: + logging.info( + "Your selected number of folds for Cross validation is out of range. " + "It must be 1 or smaller than 1 hundredth of the number of samples." + ) + sys.exit("Number of folds out of range") + ( + pd.concat(performance_list, ignore_index=True).to_csv( + path_or_buf=path.join( + opts.outputDir, "single_label_random_model.evaluation.csv" + ) + ) + ) + # For each individual target train a model + elif opts.split_type == "scaffold_balanced": + # df, irrelevant_columns = preprocess_dataframe(df, opts) + for idx, target in enumerate(targets): + df = prepare_nn_training_data(df, target, opts, return_dataframe=True) + relevant_cols = ["smiles"] + ["fp"] + [target] # list(irrelevant_columns) + if opts.compressFeatures: + relevant_cols = relevant_cols + ["fpcompressed"] + df_task = df.loc[:, relevant_cols] + # Drop rows with missing values in the target column + df_task.dropna(subset=[target], inplace=True) + df_task.reset_index(drop=True, inplace=True) + if opts.kFolds == 1: + train_set, val_set, test_set = ae_scaffold_split( + df_task, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=False, + seed=42, + ) + x_train, y_train, x_test, y_test = get_x_y( + df_task, target, train_set, test_set, opts + ) + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}", + group=f"{target}", + name=f"{target}_single_fold", + ) + elif opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", + name=f"{target}-single-fold", + group=f"{target}", + ) + + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=0, + target=target, + opts=opts, + ) + performance_list.append(performance) + trained_model = define_single_label_model( + input_size=len(x_train[0]), opts=opts + ) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_Fold-0.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join( + opts.outputDir, f"{target}_scaffold_saved_model_0" + ) + ) + elif opts.kFolds > 1: + for fold_no in range(1, opts.kFolds + 1): + print(f"Splitting data with seed {fold_no}") + train_set, val_set, test_set = ae_scaffold_split( + df_task, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=True, + seed=fold_no, + ) + x_train, y_train, x_test, y_test = get_x_y( + df_task, target, train_set, test_set, opts + ) + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}", + group=f"{target}", + name=f"{target}-{fold_no}", + reinit=True, + ) + elif opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", + name=f"{target}-{fold_no}", + group=f"{target}", + reinit=True, + ) + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=fold_no, + target=target, + opts=opts, + ) + performance_list.append(performance) + + trained_model = define_single_label_model( + input_size=len(x_train[0]), opts=opts + ) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_Fold-{fold_no}.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join( + opts.outputDir, f"{target}_scaffold_saved_model_{fold_no}" + ) + ) + if opts.wabTracking: + wandb.finish() + fold_no += 1 + # find best fold + best_fold = pd.concat(performance_list, ignore_index=True).sort_values( + by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True + )["fold"][0] + # rename the fold to best fold + src = os.path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_Fold-{best_fold}.model.weights.hdf5", + ) + dst = os.path.join( + opts.outputDir, + f"{target}_scaffold_single-labeled_BEST_Fold-{best_fold}.model.weights.hdf5", + ) + os.rename(src, dst) + + src_dir = os.path.join( + opts.outputDir, f"{target}_scaffold_saved_model_{best_fold}" + ) + dst_dir = os.path.join( + opts.outputDir, + f"{target}_scaffold_saved_model_BEST_FOLD_{best_fold}", + ) + + if path.isdir(dst_dir): + shutil.rmtree(dst_dir) + + # Rename source directory to destination directory + os.rename(src_dir, dst_dir) + + else: + logging.info( + "Your selected number of folds for Cross validation is out of range. " + "It must be 1 or smaller than 1 hundredth of the number of samples." + ) + sys.exit("Number of folds out of range") + ( + pd.concat(performance_list, ignore_index=True).to_csv( + path_or_buf=path.join( + opts.outputDir, "single_label_scaffold_model.evaluation.csv" + ) + ) + ) + elif opts.split_type == "molecular_weight": + logging.info("You can use molecular_weight split once.") + for idx, target in enumerate(targets): + df = prepare_nn_training_data(df, target, opts, return_dataframe=True) + relevant_cols = ["smiles"] + ["fp"] + [target] + if opts.compressFeatures: + relevant_cols = relevant_cols + ["fpcompressed"] + df_task = df.loc[:, relevant_cols] + df_task.dropna(subset=[target], inplace=True) + df_task.reset_index(drop=True, inplace=True) + if opts.kFolds == 1: + train_set, val_set, test_set = weight_split( + df_task, bias="small", sizes=(1 - opts.testSize, 0.0, opts.testSize) + ) + x_train, y_train, x_test, y_test = get_x_y( + df_task, target, train_set, test_set, opts + ) + if opts.wabTracking and not opts.aeWabTracking: + wandb.init( + project=f"FFN_{opts.split_type}AE_{opts.aeSplitType}", + group=f"{target}", + name=f"{target}_single_fold", + ) + elif opts.wabTracking and opts.aeWabTracking: + wandb.init( + project=f"AE_{opts.aeSplitType}_FNN_weight_split_{opts.split_type}", + name=f"{target}-single-fold", + group=f"{target}", + ) + performance = fit_and_evaluate_model( + x_train=x_train, + x_test=x_test, + y_train=y_train, + y_test=y_test, + fold=0, + target=target, + opts=opts, + ) + performance_list.append(performance) + trained_model = define_single_label_model( + input_size=len(x_train[0]), opts=opts + ) + trained_model.save_weights( + path.join( + opts.outputDir, + f"{target}_weight_single-labeled_Fold-0.model.weights.hdf5", + ) + ) + trained_model.save( + filepath=path.join(opts.outputDir, f"{target}_weight_saved_model_0") + ) + elif opts.kFolds > 1: + raise Exception( + f"Unsupported number of folds: {opts.kFolds} for {opts.split_type} split.\ + You CANNOT do multiple folds based on molecular_weight." + ) + + ( + pd.concat(performance_list, ignore_index=True).to_csv( + path_or_buf=path.join( + opts.outputDir, "single_label_molecular_weight_model.evaluation.csv" + ) + ) + ) + else: + raise Exception(f"Unsupported split type: {opts.split_type}") diff --git a/build/lib/dfpl/utils.py b/build/lib/dfpl/utils.py new file mode 100644 index 00000000..b45bde62 --- /dev/null +++ b/build/lib/dfpl/utils.py @@ -0,0 +1,329 @@ +import json +import logging +import os +import pathlib +import warnings +from collections import defaultdict +from random import Random +from typing import Dict, List, Set, Tuple, Union + +import numpy as np +import pandas as pd +from rdkit import Chem, RDLogger +from rdkit.Chem import rdMolDescriptors +from rdkit.Chem.Scaffolds import MurckoScaffold +from tqdm import tqdm + +RDLogger.DisableLog("rdApp.*") + + +def makePathAbsolute(p: str) -> str: + path = pathlib.Path(p) + if path.is_absolute(): + return p + else: + return str(path.absolute()) + + +def createDirectory(directory: str): + path = makePathAbsolute(directory) + if not os.path.exists(path): + os.makedirs(path) + + +def createArgsFromJson(in_json: str, ignore_elements: list, return_json_object: bool): + arguments = [] + with open(in_json, "r") as f: + data = json.load(f) + for key, value in data.items(): + if key not in ignore_elements: + if key == "extra_metrics" and isinstance(value, list): + arguments.append("--extra_metrics") + arguments.extend(value) + else: + arguments.append("--" + str(key)) + arguments.append(str(value)) + if return_json_object: + return arguments, data + return arguments + + +def make_mol(s: str, keep_h: bool, add_h: bool, keep_atom_map: bool): + """ + Builds an RDKit molecule from a SMILES string. + + :param s: SMILES string. + :param keep_h: Boolean whether to keep hydrogens in the input smiles. This does not add hydrogens, it only keeps the + m if they are specified. + :param add_h: Boolean whether to add hydrogens to the input smiles. + :param keep_atom_map: Boolean whether to keep the original atom mapping. + :return: RDKit molecule. + """ + params = Chem.SmilesParserParams() + params.removeHs = not keep_h if not keep_atom_map else False + mol = Chem.MolFromSmiles(s, params) + + if add_h: + mol = Chem.AddHs(mol) + + if keep_atom_map: + atom_map_numbers = tuple(atom.GetAtomMapNum() for atom in mol.GetAtoms()) + for idx, map_num in enumerate(atom_map_numbers): + if idx + 1 != map_num: + new_order = np.argsort(atom_map_numbers).tolist() + return Chem.rdmolops.RenumberAtoms(mol, new_order) + + return mol + + +def generate_scaffold( + mol: Union[str, Chem.Mol, Tuple[Chem.Mol, Chem.Mol]], include_chirality: bool = True +) -> str: + """ + Computes the Bemis-Murcko scaffold for a SMILES string, an RDKit molecule, or an InChI string or InChIKey. + + :param mol: A SMILES, RDKit molecule, InChI string, or InChIKey string. + :param include_chirality: Whether to include chirality in the computed scaffold. + :return: The Bemis-Murcko scaffold for the molecule. + """ + if isinstance(mol, str): + if mol.startswith("InChI="): + mol = inchi_to_mol(mol) + else: + mol = make_mol(mol, keep_h=False, add_h=False, keep_atom_map=False) + elif isinstance(mol, tuple): + mol = mol[0] + scaffold = MurckoScaffold.MurckoScaffoldSmiles( + mol=mol, includeChirality=include_chirality + ) + + return scaffold + + +def scaffold_to_smiles( + mols: List[str], use_indices: bool = False +) -> Dict[str, Union[Set[str], Set[int]]]: + """ + Computes the scaffold for each SMILES and returns a mapping from scaffolds to sets of smiles (or indices). + :param mols: A list of SMILES. + :param use_indices: Whether to map to the SMILES's index in :code:`mols` rather than + mapping to the smiles string itself. This is necessary if there are duplicate smiles. + :return: A dictionary mapping each unique scaffold to all SMILES (or indices) which have that scaffold. + """ + scaffolds = defaultdict(set) + for i, mol in tqdm(enumerate(mols), total=len(mols)): + scaffold = generate_scaffold(mol) + if use_indices: + scaffolds[scaffold].add(i) + else: + scaffolds[scaffold].add(mol) + + return scaffolds + + +# def inchi_to_mol(inchi: str) -> Chem.Mol: +# return Chem.inchi.MolFromInchi(inchi) +def smiles_to_mol(smiles: str) -> Chem.Mol: + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return None + # raise ValueError(f"Could not convert SMILES to Mol: {smiles}") + return mol + + +def inchi_to_mol(inchi: str) -> Chem.Mol: + mol = Chem.MolFromInchi(inchi) + if mol is None: + return None + # raise ValueError(f"Could not convert InChI to Mol: {inchi}") + return mol + + +def weight_split( + data: pd.DataFrame, bias: str, sizes: Tuple[float, float, float] = (0.8, 0, 0.2) +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): + raise ValueError(f"Invalid train/val/test splits! got: {sizes}") + # initial_indices = data.index.to_numpy() + train_size, val_size, _ = ( + sizes[0] * len(data), + sizes[1] * len(data), + sizes[2] * len(data), + ) + if "inchi" in [x.lower() for x in data.columns]: + data["mol"] = data["inchi"].apply(inchi_to_mol) + elif "smiles" in [x.lower() for x in data.columns]: + data["mol"] = data["smiles"].apply(smiles_to_mol) + else: + logging.info("Dataframe does not have a SMILES or InChi column") + none_mols = data["mol"].isnull().sum() + logging.info(f"There are {none_mols} chemicals with no mol objects ") + data.dropna(subset=["mol"], inplace=True) + data["mol_weight"] = data.apply( + lambda row: rdMolDescriptors.CalcExactMolWt(row["mol"]) + if row["mol"] is not None + else None, + axis=1, + ) + # data = data.drop(columns=['mol','fp','inchi','toxid','key'], axis=1) + sorted_data = data.copy() + if bias == "big": + sorted_data = data.sort_values(by="mol_weight", ascending=False) + elif bias == "small": + sorted_data = data.sort_values(by="mol_weight", ascending=True) + else: + print("Wrong bias, choose small or big") + indices = np.arange(len(sorted_data)) + train_end_idx = int(train_size) + val_end_idx = int(train_size + val_size) + train_indices = indices[:train_end_idx] + val_indices = indices[train_end_idx:val_end_idx] + test_indices = indices[val_end_idx:] + train_df = sorted_data.iloc[train_indices].reset_index(drop=True) + val_df = sorted_data.iloc[val_indices].reset_index(drop=True) + test_df = sorted_data.iloc[test_indices].reset_index(drop=True) + + return train_df, val_df, test_df + + +def ae_scaffold_split( + data: pd.DataFrame, + sizes: Tuple[float, float, float] = (0.8, 0, 0.2), + balanced: bool = False, + key_molecule_index: int = 0, + seed: int = 0, +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Splits a pandas DataFrame by scaffold so that no molecules sharing a scaffold are in different splits. + :param data: A pandas DataFrame containing SMILES strings and molecule properties. + :param sizes: A length-3 tuple with the proportions of data in the train, validation, and test sets. + :param balanced: Whether to balance the sizes of scaffolds in each set rather than putting the smallest in test set. + :param key_molecule_index: For data with multiple molecules, this sets which molecule will be considered during splitting. + :param seed: Random seed for shuffling when doing balanced splitting. + :return: A tuple of pandas DataFrames containing the train, validation, and test splits of the data. + """ + if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): + raise ValueError(f"Invalid train/val/test splits! got: {sizes}") + + # Split + train_size, val_size, test_size = ( + sizes[0] * len(data), + sizes[1] * len(data), + sizes[2] * len(data), + ) + train: List[int] = [] + val: List[int] = [] + test: List[int] = [] + train_scaffold_count, val_scaffold_count, test_scaffold_count = 0, 0, 0 + + # Map from scaffold to index in the data + key_colnames = data.columns + if "inchi" in key_colnames: + key_molecule_index = next( + (i for i, colname in enumerate(data.columns) if "inchi" in colname.lower()), + None, + ) + if key_molecule_index is None: + warnings.warn("No column with 'inchi' found in the DataFrame. Proceeding with caution.") + key_mols = data.iloc[:, key_molecule_index].apply(inchi_to_mol).dropna() + else: + key_mols = data.iloc[:, key_molecule_index] + scaffold_to_indices = scaffold_to_smiles(key_mols.tolist(), use_indices=True) + # Seed randomness + random = Random(seed) + + if ( + balanced + ): # Put stuff that's bigger than half the val/test size into train, rest just order randomly + index_sets = list(scaffold_to_indices.values()) + big_index_sets = [] + small_index_sets = [] + for index_set in index_sets: + if len(index_set) > val_size / 2 or len(index_set) > test_size / 2: + big_index_sets.append(index_set) + else: + small_index_sets.append(index_set) + random.seed(seed) + random.shuffle(big_index_sets) + random.shuffle(small_index_sets) + index_sets = big_index_sets + small_index_sets + else: # Sort from largest to smallest scaffold sets + index_sets = sorted( + list(scaffold_to_indices.values()), + key=lambda index_set: len(index_set), + reverse=True, + ) + for index_set in index_sets: + if len(test) + len(index_set) <= test_size: + test += index_set + test_scaffold_count += 1 + elif len(train) + len(index_set) <= train_size: + train += index_set + train_scaffold_count += 1 + else: + val += index_set + val_scaffold_count += 1 + logging.info( + f"Total scaffolds = {len(scaffold_to_indices):,} | " + f"train scaffolds = {train_scaffold_count:,} | " + f"val scaffolds = {val_scaffold_count:,} | " + f"test scaffolds = {test_scaffold_count:,}" + ) + + log_scaffold_stats(data, index_sets) + # Map from indices to data + + train_df = data.iloc[train] + val_df = data.iloc[val] + test_df = data.iloc[test] + return train_df, val_df, test_df + + +def log_scaffold_stats( + data: pd.DataFrame, + index_sets: List[Set[int]], + num_scaffolds: int = 10, + num_labels: int = 20, +) -> List[Tuple[List[float], List[int]]]: + + """ + Logs and returns statistics about counts and average target values in molecular scaffolds. + :param data: A pandas DataFrame containing SMILES strings and molecule properties. + :param index_sets: A list of sets of indices representing splits of the data. + :param num_scaffolds: The number of scaffolds about which to display statistics. + :param num_labels: The number of labels about which to display statistics. + :return: A list of tuples where each tuple contains a list of average target values + across the first :code:num_labels labels and a list of the number of non-zero values for + the first :code:num_scaffolds scaffolds, sorted in decreasing order of scaffold frequency. + """ + logging.info( + "Label averages per scaffold, in decreasing order of scaffold frequency, " + f"capped at {num_scaffolds} scaffolds and {num_labels} labels:" + ) + + stats = [] + index_sets = sorted(index_sets, key=lambda idx_set: len(idx_set), reverse=True) + for scaffold_num, index_set in enumerate(index_sets[:num_scaffolds]): + data_set = data.iloc[list(index_set)] + targets = [ + c + for c in data.columns + if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] + ] + # targets = data_set.iloc[:, 2:].values + targets = data_set.loc[:, targets].values + + with warnings.catch_warnings(): # Likely warning of empty slice of target has no values besides NaN + warnings.simplefilter("ignore", category=RuntimeWarning) + target_avgs = np.nanmean(targets, axis=0)[:num_labels] + + counts = np.count_nonzero(~np.isnan(targets), axis=0)[:num_labels] + stats.append((target_avgs, counts)) + + logging.info(f"Scaffold {scaffold_num}") + for task_num, (target_avg, count) in enumerate(zip(target_avgs, counts)): + logging.info( + f"Task {task_num}: count = {count:,} | target average = {target_avg:.6f}" + ) + logging.info("\n") + return stats diff --git a/build/lib/dfpl/vae.py b/build/lib/dfpl/vae.py new file mode 100644 index 00000000..17b7dc6f --- /dev/null +++ b/build/lib/dfpl/vae.py @@ -0,0 +1,337 @@ +import logging +import math +import os.path +from os.path import basename + +import numpy as np +import pandas as pd +import wandb +from keras import backend as K +from sklearn.model_selection import train_test_split +import tensorflow.keras.metrics as metrics +from tensorflow.keras import initializers, optimizers +from tensorflow.keras.layers import Dense, Input, Lambda +from tensorflow.keras.models import Model +from tensorflow.python.framework.ops import disable_eager_execution +from typing import Tuple +import csv + +from dfpl import history as ht +from dfpl import callbacks +from dfpl import options, settings +from dfpl.utils import ae_scaffold_split, weight_split + +disable_eager_execution() + + +def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: + input_size = opts.fpSize + encoding_dim = opts.encFPSize + ac_optimizer = optimizers.Adam( + learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay + ) + + if output_bias is not None: + output_bias = initializers.Constant(output_bias) + + # get the number of meaningful hidden layers (latent space included) + hidden_layer_count = round(math.log2(input_size / encoding_dim)) + + # the input placeholder + input_vec = Input(shape=(input_size,)) + + # 1st hidden layer, that receives weights from input layer + # equals bottleneck layer, if hidden_layer_count==1! + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / 2), activation=opts.aeActivationFunction + )(input_vec) + else: + encoded = Dense( + units=int(input_size / 2), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(input_vec) + + if hidden_layer_count > 1: + # encoding layers, incl. bottle-neck + for i in range(1, hidden_layer_count): + factor_units = 2 ** (i + 1) + # print(f'{factor_units}: {int(input_size / factor_units)}') + if opts.aeActivationFunction != "selu": + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # latent space layers + factor_units = 2 ** (hidden_layer_count - 1) + if opts.aeActivationFunction != "selu": + z_mean = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + z_log_var = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) + else: + z_mean = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + z_log_var = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) + + # sampling layer + def sampling(args): + z_mean, z_log_var = args + batch = K.shape(z_mean)[0] + dim = K.int_shape(z_mean)[1] + epsilon = K.random_normal(shape=(batch, dim)) + return z_mean + K.exp(0.5 * z_log_var) * epsilon + + # sample from latent space + z = Lambda(sampling, output_shape=(int(input_size / factor_units),))( + [z_mean, z_log_var] + ) + decoded = z + # decoding layers + for i in range(hidden_layer_count - 2, 0, -1): + factor_units = 2**i + # print(f'{factor_units}: {int(input_size/factor_units)}') + if opts.aeActivationFunction != "selu": + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(decoded) + else: + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(decoded) + + # output layer + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(decoded) + + else: + # output layer + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(encoded) + + autoencoder = Model(input_vec, decoded) + + # KL divergence loss + def kl_loss(z_mean, z_log_var): + return -0.5 * K.sum( + 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1 + ) + + # binary cross-entropy loss + def bce_loss(y_true, y_pred): + return metrics.binary_crossentropy(y_true, y_pred) + + # combined loss + def vae_loss(y_true, y_pred): + bce = bce_loss(y_true, y_pred) + kl = kl_loss(z_mean, z_log_var) + return bce + 0.5 * kl + + autoencoder.compile( + optimizer=ac_optimizer, loss=vae_loss, metrics=[bce_loss, kl_loss] + ) + + # build encoder model + encoder = Model(input_vec, z_mean) + + return autoencoder, encoder + + +def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: + """ + Trains an autoencoder on the given feature matrix X. The response matrix is only used to + split the data into meaningful test and train sets. + + :param opts: Command line arguments as defined in options.py + :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder + :return: The encoder model of the trained autoencoder + """ + + # If wandb tracking is enabled for VAE weights but not for the main program, initialize a new wandb run + if opts.aeWabTracking and not opts.wabTracking: + wandb.init(project=f"VAE_{opts.aeSplitType}") + + # Define output files for VAE and encoder weights + if opts.ecWeightsFile == "": + # If no encoder weights file is specified, use the input file name to generate a default file name + logging.info("No VAE encoder weights file specified") + base_file_name = ( + os.path.splitext(basename(opts.inputFile))[0] + + opts.aeType + + opts.aeSplitType + ) + logging.info( + f"(variational) encoder weights will be saved in {base_file_name}.autoencoder.hdf5" + ) + vae_weights_file = os.path.join( + opts.outputDir, base_file_name + ".vae.weights.hdf5" + ) + # ec_weights_file = os.path.join( + # opts.outputDir, base_file_name + ".encoder.weights.hdf5" + # ) + else: + # If an encoder weights file is specified, use it as the encoder weights file name + logging.info(f"VAE encoder will be saved in {opts.ecWeightsFile}") + base_file_name = ( + os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType + ) + vae_weights_file = os.path.join( + opts.outputDir, base_file_name + ".vae.weights.hdf5" + ) + # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) + + # Collect the callbacks for training + callback_list = callbacks.autoencoder_callback( + checkpoint_path=vae_weights_file, opts=opts + ) + # Select all fingerprints that are valid and turn them into a numpy array + fp_matrix = np.array( + df[df["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + logging.info( + f"Training VAE on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" + ) + assert 0.0 <= opts.testSize <= 0.5 + if opts.aeSplitType == "random": + logging.info("Training VAE using random split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # Split data into test and training data + if opts.aeWabTracking: + x_train, x_test, _, _ = train_test_split( + fp_matrix, train_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train, x_test, _, _ = train_test_split( + fp_matrix, train_indices, test_size=opts.testSize, random_state=42 + ) + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "scaffold_balanced": + logging.info("Training autoencoder using scaffold split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # if opts.aeWabTracking: + train_data, val_data, test_data = ae_scaffold_split( + df, + sizes=(1 - opts.testSize, 0.0, opts.testSize), + balanced=True, + seed=42, + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + else: + x_train = fp_matrix + x_test = None + elif opts.aeSplitType == "molecular_weight": + logging.info("Training autoencoder using molecular weight split") + train_indices = np.arange(fp_matrix.shape[0]) + if opts.testSize > 0.0: + # if opts.aeWabTracking: + train_data, val_data, test_data = weight_split( + df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" + ) + x_train = np.array( + train_data[train_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + x_test = np.array( + test_data[test_data["fp"].notnull()]["fp"].to_list(), + dtype=settings.ac_fp_numpy_type, + copy=settings.numpy_copy_values, + ) + else: + x_train = fp_matrix + x_test = None + else: + raise ValueError(f"Invalid split type: {opts.split_type}") + if opts.testSize > 0.0: + train_indices = train_indices[train_indices < x_train.shape[0]] + test_indices = np.arange(x_train.shape[0], x_train.shape[0] + x_test.shape[0]) + else: + test_indices = None + ids, counts = np.unique(x_train.flatten(), return_counts=True) + count_dict = dict(zip(ids, counts)) + if count_dict[0] == 0: + initial_bias = None + logging.info("No zeroes in training labels. Setting initial_bias to None.") + else: + initial_bias = np.log([count_dict[1] / count_dict[0]]) + logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") + if opts.testSize > 0.0: + logging.info(f"VAE training/testing mode with train- and test-samples") + logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") + logging.info(f"VAE test data shape {x_test.shape} with type {x_test.dtype}") + else: + logging.info(f"VAE full train mode without test-samples") + logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") + + (vae, encoder) = define_vae_model(opts, output_bias=initial_bias) + # Train the VAE on the training data + vae_hist = vae.fit( + x_train, + x_train, + epochs=opts.aeEpochs, + batch_size=opts.aeBatchSize, + verbose=opts.verbose, + callbacks=callback_list, + validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, + ) + + # Save the VAE weights + logging.info(f"VAE weights stored in file: {vae_weights_file}") + ht.store_and_plot_history( + base_file_name=os.path.join(opts.outputDir, base_file_name + ".VAE"), + hist=vae_hist, + ) + save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_VAE.h5") + if opts.testSize > 0.0: + (callback_vae, callback_encoder) = define_vae_model(opts) + callback_vae.load_weights(filepath=vae_weights_file) + callback_encoder.save(filepath=save_path) + else: + encoder.save(filepath=save_path) + latent_space = encoder.predict(fp_matrix) + latent_space_file = os.path.join( + opts.outputDir, base_file_name + ".latent_space.csv" + ) + with open(latent_space_file, "w", newline="") as file: + writer = csv.writer(file) + writer.writerows(latent_space) + return encoder, train_indices, test_indices From d2ab3bc8282d3d7616ad1ce708b079b868b13c0b Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 8 Sep 2023 15:33:35 +0200 Subject: [PATCH 28/45] updated environment, adam from legacy --- build/lib/dfpl/__init__.py | 0 build/lib/dfpl/__main__.py | 321 ---- build/lib/dfpl/autoencoder.py | 415 ----- build/lib/dfpl/callbacks.py | 89 -- .../dfpl/deepFPlearn-HyperParameterTuning.py | 128 -- ...deepFPlearn-HyperParameterTuning_single.py | 426 ----- build/lib/dfpl/dfplmodule.py | 1413 ----------------- build/lib/dfpl/feedforwardNN.py | 464 ------ build/lib/dfpl/fingerprint.py | 164 -- .../lib/dfpl/harmonize_mol_representations.py | 171 -- build/lib/dfpl/history.py | 41 - build/lib/dfpl/options.py | 1297 --------------- build/lib/dfpl/plot.py | 228 --- build/lib/dfpl/predictions.py | 39 - build/lib/dfpl/rbm.py | 259 --- build/lib/dfpl/settings.py | 52 - build/lib/dfpl/single_label_model.py | 1008 ------------ build/lib/dfpl/utils.py | 329 ---- build/lib/dfpl/vae.py | 337 ---- dfpl/autoencoder.py | 5 +- dfpl/single_label_model.py | 3 +- dfpl/vae.py | 5 +- environment.yml | 30 +- example/train.json | 4 +- 24 files changed, 26 insertions(+), 7202 deletions(-) delete mode 100644 build/lib/dfpl/__init__.py delete mode 100644 build/lib/dfpl/__main__.py delete mode 100644 build/lib/dfpl/autoencoder.py delete mode 100644 build/lib/dfpl/callbacks.py delete mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning.py delete mode 100644 build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py delete mode 100644 build/lib/dfpl/dfplmodule.py delete mode 100644 build/lib/dfpl/feedforwardNN.py delete mode 100644 build/lib/dfpl/fingerprint.py delete mode 100644 build/lib/dfpl/harmonize_mol_representations.py delete mode 100644 build/lib/dfpl/history.py delete mode 100644 build/lib/dfpl/options.py delete mode 100644 build/lib/dfpl/plot.py delete mode 100644 build/lib/dfpl/predictions.py delete mode 100644 build/lib/dfpl/rbm.py delete mode 100644 build/lib/dfpl/settings.py delete mode 100644 build/lib/dfpl/single_label_model.py delete mode 100644 build/lib/dfpl/utils.py delete mode 100644 build/lib/dfpl/vae.py diff --git a/build/lib/dfpl/__init__.py b/build/lib/dfpl/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/build/lib/dfpl/__main__.py b/build/lib/dfpl/__main__.py deleted file mode 100644 index 5c12705b..00000000 --- a/build/lib/dfpl/__main__.py +++ /dev/null @@ -1,321 +0,0 @@ -import os.path -import dataclasses -import logging -import pathlib -from argparse import Namespace -from os import path - -import pandas as pd -from keras.models import load_model - -from dfpl import autoencoder as ac -from dfpl import feedforwardNN as fNN -from dfpl import fingerprint as fp -from dfpl import options, predictions - -# from dfpl import rbm as rbm -from dfpl import single_label_model as sl -from dfpl import vae as vae -from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute -import chemprop as cp - -project_directory = pathlib.Path(".").parent.parent.absolute() -test_train_opts = options.Options( - inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", - outputDir=f"{project_directory}/output_data/console_test", - ecWeightsFile=f"{project_directory}/output_data/case_00/AE_S/ae_S.encoder.hdf5", - ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", - type="smiles", - fpType="topological", - epochs=100, - batchSize=1024, - fpSize=2048, - encFPSize=256, - enableMultiLabel=False, - testSize=0.2, - kFolds=2, - verbose=2, - trainAC=False, - trainFNN=True, - compressFeatures=True, - activationFunction="selu", - lossFunction="bce", - optimizer="Adam", - fnnType="FNN", -) - -test_pred_opts = options.Options( - inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", - outputDir=f"{project_directory}/output_data/console_test", - outputFile=f"{project_directory}/output_data/console_test/S_dataset.predictions_ER.csv", - ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", - fnnModelDir=f"{project_directory}/output_data/console_test/ER_saved_model", - type="smiles", - fpType="topological", -) - - -def traindmpnn(opts: options.GnnOptions): - """ - Train a D-MPNN model using the given options. - Args: - - opts: options.GnnOptions instance containing the details of the training - Returns: - - None - """ - os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" - ignore_elements = ["py/object"] - # Load options from a JSON file and replace the relevant attributes in `opts` - arguments = createArgsFromJson( - opts.configFile, ignore_elements, return_json_object=False - ) - opts = cp.args.TrainArgs().parse_args(arguments) - logging.info("Training DMPNN...") - # Train the model and get the mean and standard deviation of AUC score from cross-validation - mean_score, std_score = cp.train.cross_validate( - args=opts, train_func=cp.train.run_training - ) - logging.info(f"Results: {mean_score:.5f} +/- {std_score:.5f}") - - -def predictdmpnn(opts: options.GnnOptions, json_arg_path: str) -> None: - """ - Predict the values using a trained D-MPNN model with the given options. - Args: - - opts: options.GnnOptions instance containing the details of the prediction - - JSON_ARG_PATH: path to a JSON file containing additional arguments for prediction - Returns: - - None - """ - ignore_elements = [ - "py/object", - "gnn_type", - "checkpoint_paths", - "save_dir", - "saving_name", - ] - # Load options and additional arguments from a JSON file - arguments, data = createArgsFromJson( - json_arg_path, ignore_elements, return_json_object=True - ) - arguments.append("--preds_path") - arguments.append("") - save_dir = data.get("save_dir") - name = data.get("saving_name") - # Replace relevant attributes in `opts` with loaded options - opts = cp.args.PredictArgs().parse_args(arguments) - opts.preds_path = save_dir + "/" + name - df = pd.read_csv(opts.test_path) - smiles = [] - for index, rows in df.iterrows(): - my_list = [rows.smiles] - smiles.append(my_list) - # Make predictions and return the result - cp.train.make_predictions(args=opts, smiles=smiles) - - -def train(opts: options.Options): - """ - Run the main training procedure - :param opts: Options defining the details of the training - """ - - os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" - - # import data from file and create DataFrame - if "tsv" in opts.inputFile: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize - ) - else: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize - ) - # initialize encoders to None - encoder = None - autoencoder = None - if opts.trainAC: - if opts.aeType == "deterministic": - encoder, train_indices, test_indices = ac.train_full_ac(df, opts) - elif opts.aeType == "variational": - encoder, train_indices, test_indices = vae.train_full_vae(df, opts) - else: - raise ValueError(f"Unknown autoencoder type: {opts.aeType}") - - # if feature compression is enabled - if opts.compressFeatures: - if not opts.trainAC: - if opts.aeType == "deterministic": - (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) - elif opts.aeType == "variational": - (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) - elif opts.ecWeightsFile == "": - encoder = load_model(opts.ecModelDir) - else: - autoencoder.load_weights( - os.path.join(opts.ecModelDir, opts.ecWeightsFile) - ) - # compress the fingerprints using the autoencoder - df = ac.compress_fingerprints(df, encoder) - # ac.visualize_fingerprints( - # df, - # before_col="fp", - # after_col="fpcompressed", - # train_indices=train_indices, - # test_indices=test_indices, - # save_as=f"UMAP_{opts.aeSplitType}.png", - # ) - # train single label models if requested - if opts.trainFNN and not opts.enableMultiLabel: - sl.train_single_label_models(df=df, opts=opts) - - # train multi-label models if requested - if opts.trainFNN and opts.enableMultiLabel: - fNN.train_nn_models_multi(df=df, opts=opts) - - -def predict(opts: options.Options) -> None: - """ - Run prediction given specific options - :param opts: Options defining the details of the prediction - """ - # import data from file and create DataFrame - if "tsv" in opts.inputFile: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importDstoxTSV, fp_size=opts.fpSize - ) - print(df) - else: - df = fp.importDataFile( - opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize - ) - - if opts.compressFeatures: - # load trained model for autoencoder - if opts.aeType == "deterministic": - (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) - if opts.aeType == "variational": - (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) - # Load trained model for autoencoder - if opts.ecWeightsFile == "": - encoder = load_model(opts.ecModelDir) - else: - encoder.load_weights(os.path.join(opts.ecModelDir, opts.ecWeightsFile)) - df = ac.compress_fingerprints(df, encoder) - - # Run predictions on the compressed fingerprints and store the results in a dataframe - df2 = predictions.predict_values(df=df, opts=opts) - - # Extract the column names from the dataframe, excluding the 'fp' and 'fpcompressed' columns - names_columns = [c for c in df2.columns if c not in ["fp", "fpcompressed"]] - - # Save the predicted values to a CSV file in the output directory - df2[names_columns].to_csv(path_or_buf=path.join(opts.outputDir, opts.outputFile)) - - # Log successful completion of prediction and the file path where the results were saved - logging.info( - f"Prediction successful. Results written to '{path.join(opts.outputDir, opts.outputFile)}'" - ) - - -def createLogger(filename: str) -> None: - """ - Set up a logger for the main function that also saves to a log file - """ - # get root logger and set its level - logger = logging.getLogger() - logger.setLevel(logging.INFO) - # create file handler which logs info messages - fh = logging.FileHandler(filename, mode="w") - fh.setLevel(logging.INFO) - # create console handler - ch = logging.StreamHandler() - ch.setLevel(logging.INFO) - # create formatter and add it to the handlers - formatterFile = logging.Formatter( - "{asctime} - {name} - {levelname} - {message}", style="{" - ) - formatterConsole = logging.Formatter("{levelname} {message}", style="{") - fh.setFormatter(formatterFile) - ch.setFormatter(formatterConsole) - # add the handlers to the logger - logger.addHandler(fh) - logger.addHandler(ch) - - -def main(): - """ - Main function that runs training/prediction defined by command line arguments - """ - - parser = options.createCommandlineParser() - prog_args: Namespace = parser.parse_args() - try: - if prog_args.method == "convert": - directory = makePathAbsolute(prog_args.f) - if path.isdir(directory): - createLogger(path.join(directory, "convert.log")) - logging.info(f"Convert all data files in {directory}") - fp.convert_all(directory) - else: - raise ValueError("Input directory is not a directory") - elif prog_args.method == "traingnn": - traingnn_opts = options.GnnOptions.fromCmdArgs(prog_args) - - traindmpnn(traingnn_opts) - - elif prog_args.method == "predictgnn": - predictgnn_opts = options.GnnOptions.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - predictgnn_opts, - test_path=makePathAbsolute(predictgnn_opts.test_path), - preds_path=makePathAbsolute(predictgnn_opts.preds_path), - ) - - logging.info( - f"The following arguments are received or filled with default values:\n{prog_args}" - ) - - if predictgnn_opts.gnn_type == "dmpnn": - predictdmpnn(fixed_opts, prog_args.configFile) - - elif prog_args.method == "train": - train_opts = options.Options.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - train_opts, - inputFile=makePathAbsolute(train_opts.inputFile), - outputDir=makePathAbsolute(train_opts.outputDir), - ) - createDirectory(fixed_opts.outputDir) - createLogger(path.join(fixed_opts.outputDir, "train.log")) - logging.info( - f"The following arguments are received or filled with default values:\n{fixed_opts}" - ) - train(fixed_opts) - elif prog_args.method == "predict": - predict_opts = options.Options.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - predict_opts, - inputFile=makePathAbsolute(predict_opts.inputFile), - outputDir=makePathAbsolute(predict_opts.outputDir), - outputFile=makePathAbsolute( - path.join(predict_opts.outputDir, predict_opts.outputFile) - ), - ecModelDir=makePathAbsolute(predict_opts.ecModelDir), - fnnModelDir=makePathAbsolute(predict_opts.fnnModelDir), - trainAC=False, - trainFNN=False, - ) - createDirectory(fixed_opts.outputDir) - createLogger(path.join(fixed_opts.outputDir, "predict.log")) - logging.info( - f"The following arguments are received or filled with default values:\n{prog_args}" - ) - predict(fixed_opts) - except AttributeError as e: - print(e) - parser.print_usage() - - -if __name__ == "__main__": - main() diff --git a/build/lib/dfpl/autoencoder.py b/build/lib/dfpl/autoencoder.py deleted file mode 100644 index 1f638122..00000000 --- a/build/lib/dfpl/autoencoder.py +++ /dev/null @@ -1,415 +0,0 @@ -import logging -import math -import os.path -from os.path import basename - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns -import umap -import wandb -from sklearn.model_selection import train_test_split -from tensorflow.keras import initializers, losses, optimizers -from tensorflow.keras.layers import Dense, Input -from tensorflow.keras.models import Model -from typing import Tuple - -from dfpl import callbacks -from dfpl import history as ht -from dfpl import options, settings -from dfpl.utils import ae_scaffold_split, weight_split - - -def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: - """ - This function provides an autoencoder model to reduce a certain input to a compressed version. - - :param opts: Training options that provide values for adjusting the neural net - :param output_bias: Bias used to initialize the last layer. It gives the net a head start in training on - imbalanced data (which the fingerprints are, because they have many more 0's than 1's in them). - :return: a tuple of autoencoder and encoder models - """ - input_size = opts.fpSize - encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( - learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay - ) - - if output_bias is not None: - output_bias = initializers.Constant(output_bias) - - # get the number of meaningful hidden layers (latent space included) - hidden_layer_count = round(math.log2(input_size / encoding_dim)) - - # the input placeholder - input_vec = Input(shape=(input_size,)) - - # 1st hidden layer, that receives weights from input layer - # equals bottleneck layer, if hidden_layer_count==1! - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / 2), activation=opts.aeActivationFunction - )(input_vec) - else: - encoded = Dense( - units=int(input_size / 2), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(input_vec) - - if hidden_layer_count > 1: - # encoding layers, incl. bottle-neck - for i in range(1, hidden_layer_count): - factor_units = 2 ** (i + 1) - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # 1st decoding layer - factor_units = 2 ** (hidden_layer_count - 1) - if opts.aeActivationFunction != "selu": - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # decoding layers - for i in range(hidden_layer_count - 2, 0, -1): - factor_units = 2**i - if opts.aeActivationFunction != "selu": - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(decoded) - else: - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(decoded) - - # output layer - # to either 0 or 1 and hence we use sigmoid activation function. - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(decoded) - - else: - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(encoded) - - autoencoder = Model(input_vec, decoded) - encoder = Model(input_vec, encoded) - autoencoder.summary(print_fn=logging.info) - - autoencoder.compile( - optimizer=ac_optimizer, - loss=losses.BinaryCrossentropy(), - # metrics=[ - # metrics.AUC(), - # metrics.Precision(), - # metrics.Recall() - # ] - ) - return autoencoder, encoder - - -def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: - """ - Trains an autoencoder on the given feature matrix X. The response matrix is only used to - split the data into meaningful test and train sets. - - :param opts: Command line arguments as defined in options.py - :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder - :return: The encoder model of the trained autoencoder - """ - - # If wandb tracking is enabled for autoencoder weights but not for the main program, initialize a new wandb run - if opts.aeWabTracking and not opts.wabTracking: - wandb.init(project=f"AE_{opts.aeSplitType}") - - # Define output files for autoencoder and encoder weights - if opts.ecWeightsFile == "": - # If no encoder weights file is specified, use the input file name to generate a default file name - logging.info("No AE encoder weights file specified") - base_file_name = ( - os.path.splitext(basename(opts.inputFile))[0] + opts.aeSplitType - ) - logging.info( - f"(auto)encoder weights will be saved in {base_file_name}.autoencoder.hdf5" - ) - ac_weights_file = os.path.join( - opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" - ) - # ec_weights_file = os.path.join( - # opts.outputDir, base_file_name + ".encoder.weights.hdf5" - # ) - else: - # If an encoder weights file is specified, use it as the encoder weights file name - logging.info(f"AE encoder will be saved in {opts.ecWeightsFile}") - base_file_name = ( - os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType - ) - ac_weights_file = os.path.join( - opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" - ) - # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - - # Collect the callbacks for training - callback_list = callbacks.autoencoder_callback( - checkpoint_path=ac_weights_file, opts=opts - ) - - # Select all fingerprints that are valid and turn them into a numpy array - fp_matrix = np.array( - df[df["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info( - f"Training AC on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" - ) - - # When training the final AE, we don't want any test data. We want to train it on all available fingerprints. - assert 0.0 <= opts.testSize <= 0.5 - if opts.aeSplitType == "random": - logging.info("Training autoencoder using random split") - initial_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # Split data into test and training data - if opts.aeWabTracking: - x_train, x_test, train_indices, test_indices = train_test_split( - fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train, x_test, train_indices, test_indices = train_test_split( - fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "scaffold_balanced": - logging.info("Training autoencoder using scaffold split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - train_data, val_data, test_data = ae_scaffold_split( - df, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=True, - seed=42, - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - train_indices = df[ - df.index.isin(train_data[train_data["fp"].notnull()].index) - ].index.to_numpy() - test_indices = df[ - df.index.isin(test_data[test_data["fp"].notnull()].index) - ].index.to_numpy() - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "molecular_weight": - logging.info("Training autoencoder using molecular weight split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - train_data, val_data, test_data = weight_split( - df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - df_sorted = df.sort_values(by="mol_weight", ascending=True) - # Get the sorted indices from the sorted DataFrame - sorted_indices = df_sorted.index.to_numpy() - - # Find the corresponding indices for train_data, val_data, and test_data in the sorted DataFrame - train_indices = sorted_indices[df.index.isin(train_data.index)] - # val_indices = sorted_indices[df.index.isin(val_data.index)] - test_indices = sorted_indices[df.index.isin(test_data.index)] - else: - x_train = fp_matrix - x_test = None - else: - raise ValueError(f"Invalid split type: {opts.split_type}") - - # Calculate the initial bias aka the log ratio between 1's and 0'1 in all fingerprints - ids, counts = np.unique(x_train.flatten(), return_counts=True) - count_dict = dict(zip(ids, counts)) - if count_dict[0] == 0: - initial_bias = None - logging.info("No zeroes in training labels. Setting initial_bias to None.") - else: - initial_bias = np.log([count_dict[1] / count_dict[0]]) - logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") - - # Check if we're doing training/testing mode or full training mode - if opts.testSize > 0.0: - logging.info(f"AE training/testing mode with train- and test-samples") - logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") - logging.info(f"AC test data shape {x_test.shape} with type {x_test.dtype}") - else: - logging.info(f"AE full train mode without test-samples") - logging.info(f"AC train data shape {x_train.shape} with type {x_train.dtype}") - - # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) - (autoencoder, encoder) = define_ac_model(opts, output_bias=initial_bias) - - # Train the autoencoder on the training data - auto_hist = autoencoder.fit( - x_train, - x_train, - callbacks=callback_list, - epochs=opts.aeEpochs, - batch_size=opts.aeBatchSize, - verbose=opts.verbose, - validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, - ) - logging.info(f"Autoencoder weights stored in file: {ac_weights_file}") - - # Store the autoencoder training history and plot the metrics - ht.store_and_plot_history( - base_file_name=os.path.join(opts.outputDir, base_file_name + ".AC"), - hist=auto_hist, - ) - - # Save the autoencoder callback model to disk - save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_autoencoder") - if opts.testSize > 0.0: - (callback_autoencoder, callback_encoder) = define_ac_model(opts) - callback_encoder.save(filepath=save_path) - else: - encoder.save(filepath=save_path) - # Return the encoder model of the trained autoencoder - return encoder, train_indices, test_indices - - -def compress_fingerprints(dataframe: pd.DataFrame, encoder: Model) -> pd.DataFrame: - """ - Adds a column of the compressed version of the fingerprints to the original dataframe. - - :param dataframe: Dataframe containing a column named 'fp' with the fingerprints - :param encoder: The trained autoencoder that is used for compressing the fingerprints - :return: The input dataframe extended by a column containing the compressed version of the fingerprints - """ - logging.info("Adding compressed fingerprints") - idx = dataframe[dataframe["fp"].notnull()].index - fp_matrix = np.array( - dataframe[dataframe["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info( - f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" - ) - logging.info("Compressed fingerprints are added to input dataframe.") - dataframe["fpcompressed"] = pd.DataFrame( - {"fpcompressed": [s for s in encoder.predict(fp_matrix)]}, idx - ) - return dataframe - - -def visualize_fingerprints( - df: pd.DataFrame, - before_col: str, - after_col: str, - train_indices: np.ndarray, - test_indices: np.ndarray, - save_as: str, -): - # Calculate the number of samples to be taken from each set - num_samples = 1000 - train_samples = int(num_samples * len(train_indices) / len(df)) - test_samples = num_samples - train_samples - - # Assign train and test data points separately - train_data = df.loc[train_indices] - test_data = df.loc[test_indices] - - # Sample train and test data points - train_data_sampled = train_data.sample(n=train_samples, random_state=42) - test_data_sampled = test_data.sample(n=test_samples, random_state=42) - - # Concatenate the sampled train and test data - df_sampled = pd.concat([train_data_sampled, test_data_sampled]) - - # Convert the boolean values in the after_col column to floats - df_sampled[after_col] = df_sampled[after_col].apply( - lambda x: np.array(x, dtype=float) - ) - - df_sampled.loc[train_data_sampled.index, "set"] = "train" - df_sampled.loc[test_data_sampled.index, "set"] = "test" - # Apply UMAP - umap_model = umap.UMAP( - n_neighbors=15, min_dist=0.1, metric="euclidean", random_state=42 - ) - # Filter out the rows with invalid arrays - umap_results = umap_model.fit_transform(df_sampled[after_col].tolist()) - # Add UMAP results to the DataFrame - df_sampled["umap_x"] = umap_results[:, 0] - df_sampled["umap_y"] = umap_results[:, 1] - - # Define custom color palette - palette = {"train": "blue", "test": "red"} - - # Create the scatter plot - sns.set(style="white") - fig, ax = plt.subplots(figsize=(10, 8)) - split = save_as.split("_", 1) - part_after_underscore = split[1] - split_type = part_after_underscore.split(".")[0] - # Plot the UMAP results - for label, grp in df_sampled.groupby("set"): - set_label = label - color = palette[set_label] - alpha = ( - 0.09 if set_label == "train" else 0.9 - ) # Set different opacities for train and test - ax.scatter( - grp["umap_x"], grp["umap_y"], label=f"{set_label}", c=color, alpha=alpha - ) - - # Customize the plot - ax.set_title( - f"UMAP visualization of molecular fingerprints using {split_type} split", - fontsize=14, - ) - ax.set_xlabel("UMAP 1") - ax.set_ylabel("UMAP 2") - ax.legend(title="", loc="upper right") - sns.despine(ax=ax, offset=10) - save_path = os.path.join(os.getcwd(), save_as) - plt.savefig(save_path) diff --git a/build/lib/dfpl/callbacks.py b/build/lib/dfpl/callbacks.py deleted file mode 100644 index 6eae7965..00000000 --- a/build/lib/dfpl/callbacks.py +++ /dev/null @@ -1,89 +0,0 @@ -# for NN model functions -from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint - -# for testing in Weights & Biases -from wandb.keras import WandbCallback - -from dfpl import options, settings - - -def autoencoder_callback(checkpoint_path: str, opts: options.Options) -> list: - """ - Callbacks for fitting the autoencoder - - :param checkpoint_path: The output directory to store the checkpoint weight files - :param opts: Training options provided to the run - :return: List of ModelCheckpoint and EarlyStopping class. - """ - callbacks = [] - - if opts.testSize > 0.0: - target = "val_loss" - else: - target = "loss" - # enable this checkpoint to restore the weights of the best performing model - checkpoint = ModelCheckpoint( - checkpoint_path, - monitor=target, - mode="min", - verbose=1, - period=settings.ac_train_check_period, - save_best_only=True, - save_weights_only=True, - ) - callbacks.append(checkpoint) - - # enable early stopping if val_loss is not improving anymore - early_stop = EarlyStopping( - monitor=target, - mode="min", - patience=settings.ac_train_patience, - min_delta=settings.ac_train_min_delta, - verbose=1, - restore_best_weights=True, - ) - callbacks.append(early_stop) - - if opts.aeWabTracking and not opts.wabTracking: - callbacks.append(WandbCallback(save_model=False)) - return callbacks - - -def nn_callback(checkpoint_path: str, opts: options.Options) -> list: - """ - Callbacks for fitting the feed forward network (FNN) - - :param checkpoint_path: The output directory to store the checkpoint weight files - :param opts: Training options provided to the run - :return: List of ModelCheckpoint and EarlyStopping class. - """ - - callbacks = [] - - if opts.testSize > 0.0: - # enable this checkpoint to restore the weights of the best performing model - checkpoint = ModelCheckpoint( - checkpoint_path, - verbose=1, - period=settings.nn_train_check_period, - save_best_only=True, - monitor="val_loss", - mode="min", - save_weights_only=True, - ) - callbacks.append(checkpoint) - - # enable early stopping if val_loss is not improving anymore - early_stop = EarlyStopping( - patience=settings.nn_train_patience, - monitor="val_loss", - mode="min", - min_delta=settings.nn_train_min_delta, - verbose=1, - restore_best_weights=True, - ) - callbacks.append(early_stop) - - if opts.wabTracking: - callbacks.append(WandbCallback(save_model=False)) - return callbacks diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py deleted file mode 100644 index ab29cdea..00000000 --- a/build/lib/dfpl/deepFPlearn-HyperParameterTuning.py +++ /dev/null @@ -1,128 +0,0 @@ -from time import time - -import numpy as np -import pandas as pd -from sklearn.model_selection import GridSearchCV, train_test_split -from sklearn.preprocessing import LabelEncoder -from tensorflow.keras.layers import Dense, Dropout -from tensorflow.keras.models import Sequential -from tensorflow.keras.utils import to_categorical -from tensorflow.keras.wrappers.scikit_learn import KerasClassifier - - -def tuning_model(optimizer, activation, init, dropout=0.2): - """ - model for tuning optimizer, activation functions and initialization of hidden layers - - :param optimizer: - :param activation: - :param init: - :param dropout: - :return: - """ - model = Sequential() - model.add(Dense(1024, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(341, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(85, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(17, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(2, activation="softmax")) - model.compile( - loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] - ) - return model - - -# --------------------------------------------------------------------------- # - -# this stores the best performing parameters for each model for each target -results = pd.DataFrame() - -np.random.seed(0) - -modelfilepathprefix = ( - "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" -) - -filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" -outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.HPtuning.results.txt" -dataset = pd.read_csv(filepath) - -le = LabelEncoder() - -# which target column to use? put this in a loop later for all targets -# here we need a for loop -for target in dataset.columns[1:7]: - # target = dataset.columns[1] # 'AR' - - modelfilepathW = str(modelfilepathprefix) + "/model." + target + ".weights.h5" - modelfilepathM = str(modelfilepathprefix) + "/model." + target + ".json" - - tmp = dataset[target].astype("category") - Y = np.asarray(tmp) - naRows = np.isnan(Y) - - d = dataset[~naRows] - - # generate X from fingerprints - # split all fingerprints into vectors - Nrows = d.shape[0] - Ncols = d["fp"][0].__len__() - # Store all fingerprints in numpy array - X = np.empty((Nrows, Ncols), int) - - # keep old indexes of this subset of arrays - d["oldIdx"] = d.index.values - d["newIdx"] = range(Nrows) - d = d.set_index("newIdx") - del d.index.name - - # generate X matrix - for i in range(Nrows): - fp = d["fp"][i] - X[i] = np.array(list(fp), dtype="int") - - # generate y vector(s) - y = to_categorical(np.array(d[target]), num_classes=2) - - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=0 - ) - - start = time() - - model = KerasClassifier(build_fn=tuning_model, batch_size=20) - - batch_sizes = [20] - epochs = [30, 100] - inits = ["glorot_uniform", "normal", "uniform"] - optimizers = ["SGD", "RMSprop", "Adagrad", "Adadelta", "Adam", "Adamax", "Nadam"] - activation_functions = ["sigmoid", "tanh", "relu"] - parameters = { - "batch_size": batch_sizes, - "epochs": epochs, - "optimizer": optimizers, - "activation": activation_functions, - "init": inits, - } - - # Tuning ALL parameters, Note, this takes hours of computation!!! - clf = GridSearchCV(model, parameters, verbose=0) - clf_results = clf.fit(X_train, y_train) - - # save best estimator per target - clf_results.best_estimator_.model.save(filepath=modelfilepathM) - clf_results.best_estimator_.model.save_weights(filepath=modelfilepathW) - - # find best performing parameters - file = open(outfilepath, "a") - file.write( - "# --------------------------------------------------------------------------- #\n" - ) - file.write(f"### Results for {target} target ###\n") - file.write(f"Best: {clf_results.best_score_} using {clf_results.best_params_}\n") - file.write(f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n") - file.close() diff --git a/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py b/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py deleted file mode 100644 index 9251cbdc..00000000 --- a/build/lib/dfpl/deepFPlearn-HyperParameterTuning_single.py +++ /dev/null @@ -1,426 +0,0 @@ -import argparse -import os -import re -import sys -from time import time - -import numpy as np -import pandas as pd -from sklearn.model_selection import GridSearchCV, train_test_split -from tensorflow.keras.layers import Dense, Dropout -from tensorflow.keras.models import Sequential -from tensorflow.keras.wrappers.scikit_learn import KerasClassifier - -from dfpl import dfplmodule as dfpl - -# ------------------------------------------------------------------------------------- # - - -def parseInput(): - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - parser = argparse.ArgumentParser( - description="Perform Hyperparameter tuning for a " "given set of parameters." - ) - # input - parser.add_argument( - "-i", - metavar="FILE", - type=str, - nargs=1, - required=True, - help="The file containin the data for training in (unquoted) " - "comma separated CSV format. The first column(s) contain " - "the target associations, while the last column, named 'fp' " - "contains the fingerprint of the compound", - ) - # output - parser.add_argument( - "-p", - metavar="PATH", - type=str, - nargs=1, - required=True, - help="The path to a directory where the results of the hp tuning " - "shall be stored", - ) - # select target(s) or use all - parser.add_argument( - "-t", - metavar="STR", - type=str, - nargs="+", - required=False, - help="A space separated list of target names (according to column " - " names in the input file (-i)) for which the huperparameter " - "tuning should be performed. If not provided, it is performed " - "for each target seperately.", - ) - - # parameters to be tested for HP tuning - parser.add_argument( - "--batchSizes", - metavar="INT", - type=int, - nargs="+", - required=False, - default=[20], - help="Space separated list of integers for batch sizes that should " - "be tested. Example: --batchSizes 32 64", - ) - parser.add_argument( - "--epochs", - metavar="INT", - type=int, - nargs="+", - required=False, - default=[30], - help="Space separated list of integers for number of training epochs " - "that should be tested. Example: --epochs 30 50 100", - ) - parser.add_argument( - "--inits", - metavar="STR", - type=str, - nargs="+", - required=False, - default=["normal"], - help="Space separated list of methods for initializing the weights " - "at inner layers that should be tested. Available methods are: " - "'glorot_uniform', 'normal', 'uniform'. " - "Example: --inits 'glorot_uniform' 'normal'", - ) - parser.add_argument( - "--optimizers", - metavar="STR", - type=str, - nargs="+", - required=False, - default=["Adam"], - help="Space separated list of optimizers for the DNN model " - "that should be tested." - "Available optimizers are listed here: https://keras.io/optimizers/ ." - "Example: --optimizers 'Adadelta' 'Adam' 'Adamax'", - ) - parser.add_argument( - "--activations", - metavar="STR", - type=str, - nargs="+", - required=False, - default=["relu"], - help="Space separated list of activation fucntions for the DNN model " - "that should be tested." - "Available functions are listed here: https://keras.io/activations/ ." - "Example: --activations 'sigmoid' 'relu'", - ) - - return parser.parse_args() - - -# ----------------------------------------------------------------------------- # - - -# model for tuning epochs and batchsizes only -def c_model(dropout=0.2): - model = Sequential() - model.add(Dense(1024, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(341, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(85, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(17, activation="relu")) - model.add(Dropout(dropout)) - model.add(Dense(2, activation="softmax")) - model.compile( - loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"] - ) - return model - - -# ----------------------------------------------------------------------------- # - - -# model for tuning optmizer, activation functions and initialization of hidden layers -def tuning_model(optimizer, activation, init, dropout=0.2): - model = Sequential() - model.add(Dense(1024, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(341, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(85, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(17, activation=activation, init=init)) - model.add(Dropout(dropout)) - model.add(Dense(2, activation="softmax")) - model.compile( - loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"] - ) - return model - - -# --------------------------------------------------------------------------- # - -# ===================================================================================== # - -if __name__ == "__main__": - # get all arguments - args = parseInput() - - # print(args) - # exit(1) - - # this stores the best performing parameters for each model for each target - results = pd.DataFrame() - - np.random.seed(0) - - # filepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/input/Sun_etal_dataset.fingerprints.csv" - # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub(".csv", - # ".hpTuningResults.txt", os.path.basename(filepath)) dataset = pd.read_csv(filepath) - - dataset = pd.read_csv(args.i[0]) - - # le = LabelEncoder() - - # which target column to use? put this in a loop later for all targets - # here we need a for loop - - for target in args.t: - target = "ER" - print(target) - # outfilepath = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + re.sub( - # ".csv", '.hpTuningResults.' + target + '.txt', os.path.basename(filepath)) - outfilepath = args.p[0] + re.sub( - ".csv", ".hpTuningResults." + target + ".txt", os.path.basename(args.i[0]) - ) - - if target in dataset.columns: - # modelfilepathW = "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + - # '/model.' + target + '.weights.h5' modelfilepathM = - # "/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning/" + '/model.' + target + - # '.json' - modelfilepathW = args.p[0] + "/model." + target + ".weights.h5" - modelfilepathM = args.p[0] + "/model." + target + ".json" - - tmp = dataset[target].astype("category") - Y = np.asarray(tmp) - naRows = np.isnan(Y) - - d = dataset[~naRows] # d is a copy! - - # generate X from fingerprints - # split all fingerprints into vectors - Nrows = d.shape[0] - Ncols = d["fp"][0].__len__() - # Store all fingerprints in numpy array - X = np.empty((Nrows, Ncols), int) - - # keep old indexes of this subset of arrays - d["oldIdx"] = d.index.values - d["newIdx"] = range(Nrows) - d = d.set_index("newIdx") - del d.index.name - - # generate X matrix - for i in range(Nrows): - fp = d["fp"][i] - X[i] = np.array(list(fp), dtype="int") - - # generate y vector(s) - # y = to_categorical(np.array(d[target]), num_classes=2) - y = d[target] - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=0 - ) - - start = time() - - # find best performing parameters - sys.stdout.write( - "# --------------------------------------------------------------------------- #\n" - ) - sys.stdout.write(f"#target = {target}\n") - - # Start optimizing epochs and batchsizes (if more than one provided) - - # batchSizes = args.batchSizes - batchSizes = [64, 128, 256] - epochs = args.epochs # epochs = [5, 10] - - if batchSizes.__len__() > 1: - # model = KerasClassifier(build_fn=dfpl.defineNNmodel2) - model = KerasClassifier(build_fn=dfpl.defineNNmodel) - - parameters = {"batch_size": batchSizes} - - clf = GridSearchCV(model, parameters, verbose=0) - clf.fit(X_train, y_train) - - # save best estimator for epoochs/batchSize tuning per target - modelfilepathW = ( - args.p[0] + "/model.tuning-BatchSize." + target + ".weights.h5" - ) - modelfilepathM = ( - args.p[0] + "/model.tuning-BatchSize." + target + ".json" - ) - outfilepath = args.p[0] + re.sub( - ".csv", - ".hpTuningResults.01-BatchSize" + target + ".csv", - os.path.basename(args.i[0]), - ) - # outfilepath = '/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/HPtuning - # /Sun_etal_dataset.fingerprints.hpTuningResults.ER.txt' - clf.best_estimator_.model.save(filepath=modelfilepathM) - clf.best_estimator_.model.save_weights(filepath=modelfilepathW) - - df = pd.concat( - [ - pd.DataFrame(clf.cv_results_["param_batch_size"]), - pd.DataFrame(clf.cv_results_["mean_test_score"]), - pd.DataFrame(clf.cv_results_["std_test_score"]), - pd.DataFrame(clf.cv_results_["rank_test_score"]), - ], - axis=1, - ) - df.columns = [ - "epoch", - "batch_size", - "mean_test_score", - "std_test_score", - "rank_test_score", - ] - df.to_csv(outfilepath, header=True) - - selected_bs = clf.best_params_["batch_size"] - selected_epochs = clf.best_params_["epochs"] - - else: - selected_bs = batchSizes[0] - selected_epochs = epochs[0] - - # Hypertune optimizers - optimizers = ["SGD", "Adam"] - # optimizers = args.optimizers - - if optimizers.__len__() > 1: - model = KerasClassifier( - build_fn=dfpl.defineNNmodel, - epochs=selected_epochs, - batch_size=selected_bs, - ) - parameters = { - "optimizer": optimizers - } # ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']} - clf = GridSearchCV(model, parameters, verbose=0) - clf.fit(X_train, y_train) - - # save best estimator for optimizer tuning per target - modelfilepathW = ( - args.p[0] + "/model.tuning-optimizer." + target + ".weights.h5" - ) - modelfilepathM = ( - args.p[0] + "/model.tuning-optimizer." + target + ".json" - ) - outfilepath = args.p[0] + re.sub( - ".csv", - ".hpTuningResults.02-Optimizer" + target + ".csv", - os.path.basename(args.i[0]), - ) - - clf.best_estimator_.model.save(filepath=modelfilepathM) - clf.best_estimator_.model.save_weights(filepath=modelfilepathW) - - df = pd.concat( - [ - pd.DataFrame(clf.cv_results_["param_optimizer"]), - pd.DataFrame(clf.cv_results_["mean_test_score"]), - pd.DataFrame(clf.cv_results_["std_test_score"]), - pd.DataFrame(clf.cv_results_["rank_test_score"]), - ], - axis=1, - ) - df.columns = [ - "optimizer", - "mean_test_score", - "std_test_score", - "rank_test_score", - ] - df.to_csv(outfilepath, header=True) - - selected_optimizer = clf.best_params_["optimizer"] - - else: - selected_optimizer = optimizers[0] - - # Hypertune activation functions - activations = args.activations # 'sigmoid', 'tanh', 'relu' - - if activations.__len__() > 1: - model = KerasClassifier( - build_fn=dfpl.defineNNmodel, - epochs=selected_epochs, - batch_size=selected_bs, - ) - parameters = { - "optimizer": [selected_optimizer], - "activation": args.activations, - } - clf = GridSearchCV(model, parameters, verbose=0) - clf.fit(X_train, y_train) - - # save best estimator for optimizer tuning per target - modelfilepathW = ( - args.p[0] + "/model.tuning-activation." + target + ".weights.h5" - ) - modelfilepathM = ( - args.p[0] + "/model.tuning-activation." + target + ".json" - ) - outfilepath = args.p[0] + re.sub( - ".csv", - ".hpTuningResults.03-Activation" + target + ".csv", - os.path.basename(args.i[0]), - ) - - clf.best_estimator_.model.save(filepath=modelfilepathM) - clf.best_estimator_.model.save_weights(filepath=modelfilepathW) - - df = pd.concat( - [ - pd.DataFrame(clf.cv_results_["param_optimizer"]), - pd.DataFrame(clf.cv_results_["param_activation"]), - pd.DataFrame(clf.cv_results_["mean_test_score"]), - pd.DataFrame(clf.cv_results_["std_test_score"]), - pd.DataFrame(clf.cv_results_["rank_test_score"]), - ], - axis=1, - ) - df.columns = [ - "optimizer", - "activation", - "mean_test_score", - "std_test_score", - "rank_test_score", - ] - df.to_csv(outfilepath, header=True) - - selected_activation = clf.best_params_["activation"] - - else: - selected_activation = activations[0] - - # Maybe also optimize weight initializations?? - # inits = ['glorot_uniform', 'normal', 'uniform'] - - # find best performing parameters - sys.stdout.write( - f"Calculation time: {round((time() - start) / 60, ndigits=2)} min\n\n" - ) - else: - sys.stderr.write( - f"ERROR: the target that you provide ({target}) is not contained in your data file ({args.i[0]})" - ) diff --git a/build/lib/dfpl/dfplmodule.py b/build/lib/dfpl/dfplmodule.py deleted file mode 100644 index d0838ebf..00000000 --- a/build/lib/dfpl/dfplmodule.py +++ /dev/null @@ -1,1413 +0,0 @@ -""" -Python module for deepFPlearn tools -""" - -import argparse -import csv -import math - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns # for drawing the heatmaps -from rdkit import Chem, DataStructs # for fingerprint generation -from rdkit.Chem import MACCSkeys -from rdkit.Chem.AtomPairs import Pairs, Torsions -from sklearn.model_selection import train_test_split -from tensorflow.keras import optimizers, regularizers -from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau -from tensorflow.keras.layers import Dense, Dropout, Input - -# for NN model functions -from tensorflow.keras.models import Model, Sequential -from tensorflow.keras.optimizers import SGD - -# GENERAL FUNCTIONS --------------------------------------------------------- # - - -def gather(df, key, value, cols): - """ - Simple emulation of R's gather function using pandas melt() function in python - - :param df: The data frame - :param key: The key column name - :param value: The value column name - :param cols: list of column names to gather - :return: - """ - id_vars = [col for col in df.columns if col not in cols] - id_values = cols - var_name = key - value_name = value - return pd.melt(df, id_vars, id_values, var_name, value_name) - - -def shuffleDataPriorToTraining(x, y): - """ - Returns a gathered variant of the input x and y matrices - :param x: - :param y: - :return: - """ - - # merge x and y - df0 = pd.concat([x.reset_index(drop=True), y.reset_index(drop=True)], axis=1) - # shuffle rows, drop NAs, reset index - df1 = df0.sample(frac=1).dropna(axis=0).reset_index() - - return (df1.iloc[:, 0 : x.shape[1]], df1.iloc[:, x.shape[1] :]) - - # return gather(df0, key="target", value="association", - # cols=y.columns) - - -def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -def smi2fp(smile, fptype, size=2048): - """ - Convert a SMILES string to a fingerprint object of a certain type using functions - from the RDKIT python library. - - :param smile: A single SMILES string - :param fptype: The type of fingerprint to which the SMILES should be converted. Valid - values are: 'topological' (default), 'MACCS' - :return: A fingerprint object or None, if fingerprint object could not be created - (Respective error message is provided in STDERR). - """ - # generate a mol object from smiles string - - # print(smile) - cs = None - # first transform to canoncial smiles - try: - cs = Chem.CanonSmiles(smile) - except Exception: - print( - f"[WARNING]: Not able to transform your smile to a canonical version of it: {smile}" - ) - if not cs: - return None - - mol = None - try: - mol = Chem.MolFromSmiles(cs) - except Exception: - print( - f"[WARNING]: Not able to extract molecule from (canonically transformed) SMILES: {cs}\n Original SMILE: {smile}" - ) - if not mol: - return None - - # init fp, any better idea? e.g. calling a constructor? - # fp = Chem.Mol # FingerprintMols.FingerprintMol(mol) - - if fptype == "topological": # 2048 bits - # Topological Fingerprints: - # The fingerprinting algorithm used is similar to that used in the Daylight - # fingerprinter: it identifies and hashes topological paths (e.g. along bonds) - # in the molecule and then uses them to set bits in a fingerprint of user-specified - # lengths. After all paths have been identified, the fingerprint is typically - # folded down until a particular density of set bits is obtained. - try: - # fp = Chem.RDKFingerprint(mol, fpSize=size) - return Chem.RDKFingerprint(mol, fpSize=size) - except Exception: - print("SMILES not convertable to topological fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - elif fptype == "MACCS": - # MACCS Keys: - # There is a SMARTS-based implementation of the 166 public MACCS keys. - # The MACCS keys were critically evaluated and compared to other MACCS - # implementations in Q3 2008. In cases where the public keys are fully defined, - # things looked pretty good. - - try: - # fp = MACCSkeys.GenMACCSKeys(mol) - return MACCSkeys.GenMACCSKeys(mol) - except Exception: - print("SMILES not convertable to MACSS fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - elif fptype == "atompairs": - # Atom Pairs: - # Atom-pair descriptors [3] are available in several different forms. - # The standard form is as fingerprint including counts for each bit instead - # of just zeros and ones. Nevertheless we use the BitVect variant here. - - try: - # fp = Pairs.GetAtomPairFingerprintAsBitVect(mol) - return Pairs.GetAtomPairFingerprintAsBitVect(mol) - # counts if features also possible here! needs to be parsed differently - # fps.update({i:Pairs.GetAtomPairFingerprintAsIntVect(mols[i])}) - except Exception: - print("SMILES not convertable to atompairs fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - else: - # Topological Torsions: - # At the time of this writing, topological torsion fingerprints have too - # many bits to be encodeable using the BitVector machinery, so there is no - # GetTopologicalTorsionFingerprintAsBitVect function. - - try: - # fp = Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) - return Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol) - except Exception: - print("SMILES not convertable to torsions fingerprint:") - assert isinstance(smile, object) - print("SMILES: " + smile) - return None - - -def XandYfromInput( - csvfilename: str, - rtype: str, - fptype: str, - printfp: bool = False, - size: int = 2048, - verbose: int = 2, - returnY: bool = True, -) -> tuple: - """ - Return the matrix of features for training and testing NN models (X) as numpy array. - Provided SMILES are transformed to fingerprints, fingerprint strings are then split - into vectors and added as row to the array which is returned. - :param csvfilename: Filename of CSV files containing the training data. The - SMILES/Fingerprints are stored 1st column - :param rtype: Type of structure representation. Valid values are: 'fp' and 'smile' - :param fptype: Type of fingerprint to be generated out - :param printfp: Print generated fingerprints to file, namely the input file with the - file ending '.fingerprints.csv'. Default:False - :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If - no outcome data is provided, the Y matrix is a None object. - """ - - # TODOs: implement other types of fingerprint! - - df = pd.read_csv(csvfilename) - cnames = df.columns - - if rtype not in cnames: - print( - f"[ERROR:] There is no column named {rtype} in your input training set file" - ) - exit(0) - - dfX = None - if rtype == "smiles": # transform to canonical smiles, and then to fp - dfX = pd.DataFrame( - df["smiles"] - .transform( - lambda x: np.array( - Chem.RDKFingerprint( - Chem.MolFromSmiles(Chem.CanonSmiles(x)), fpSize=size - ) - ) - ) - .to_list() - ) - else: # split fingerprint into matrix - dfX = pd.DataFrame(df["fp"].transform(lambda x: list(x)).to_list()) - - # add 'id' as rownames of dataframe - if "id" in cnames: - dfX.index = df["id"] - - dfY = None - if returnY: - # names in Y contain 'id' if present, and all other columns (=target columns) - namesY = [c for c in cnames if c not in ["id", "smiles", "fp"]] - dfY = df[namesY] - # add 'id' as rownames of dataframe - if "id" in cnames: - dfY.index = df["id"] - - return (dfX, dfY) - - -def TrainingDataHeatmap(x, y): - x["ID"] = x.index - y["ID"] = y.index - - # xy = pd.merge(x,y,on="ID") - - # clustermap dies because of too many iterations.. - # sns.clustermap(x, metric="correlation", method="single", cmap="Blues", standard_scale=1) #, row_colors=row_colors) - - # try to cluster prior to viz - # check this out - # https://stackoverflow.com/questions/2455761/reordering-matrix-elements-to-reflect-column-and-row-clustering-in-naiive-python - - # viz using matplotlib heatmap https://matplotlib.org/3.1.1/gallery/images_contours_and_fields/image_annotated_heatmap.html - - return 1 - - -def removeDuplicates(x, y): - """ - Remove duplicated feature - outcome pairs from feature matrix and outcome vector combination. - - :param x: Feature matrix - :param y: Outcome vector - - :return: Tuple of unique Feature matrix and outcome vector combinations - """ - - # add y as additional column to x - joined = np.append(x, [[x] for x in y], axis=1) - # merge all columns into one string per row - - fpstrings = [] - for i in range(0, len(y)): - fpstrings.append(["".join([str(int(c)) for c in joined[i]])]) - - fpstrings_unique = np.unique(fpstrings, return_index=True) - - return (x[fpstrings_unique[1]], y[fpstrings_unique[1]]) - - -def defineCallbacks( - checkpointpath: str, - patience: int, - rlrop: bool = False, - rlropfactor: float = 0.1, - rlroppatience: int = 50, -) -> list: - """ - - :param checkpointpath: - :param patience: - :param rlrop: - :param rlropfactor: - :param rlroppatience: - :return: - """ - - # enable this checkpoint to restore the weights of the best performing model - checkpoint = ModelCheckpoint( - checkpointpath, - monitor="val_loss", - verbose=1, - period=10, - save_best_only=True, - mode="min", - save_weights_only=True, - ) - - # enable early stopping if val_loss is not improving anymore - earlystop = EarlyStopping( - monitor="val_loss", - min_delta=0, - patience=patience, - verbose=1, - restore_best_weights=True, - ) - - callbacks = [] - if rlrop: - rlrop = ReduceLROnPlateau( - monitor="val_loss", factor=rlropfactor, patience=rlroppatience - ) - callbacks = [checkpoint, earlystop, rlrop] - else: - callbacks = [checkpoint, earlystop] - - # Return list of callbacks - collect the callbacks for training - return callbacks - - -def defineNNmodelMulti( - inputSize=2048, - outputSize=None, - l2reg=0.001, - dropout=0.2, - activation="relu", - optimizer="Adam", - lr=0.001, - decay=0.01, -): - if optimizer == "Adam": - myoptimizer = optimizers.Adam( - learning_rate=lr, decay=decay - ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) - elif optimizer == "SGD": - myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) - else: - myoptimizer = optimizer - - nhl = int(math.log2(inputSize) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(inputSize / 2), - input_dim=inputSize, - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout)) - # next hidden layers - for i in range(1, nhl): - factorunits = 2 ** (i + 1) - factordropout = 2 * i - model.add( - Dense( - units=int(inputSize / factorunits), - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout / factordropout)) - # multi-class output layer - # use sigmoid to get independent probabilities for each output node - # (need not add up to one, as they would using softmax) - # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ - model.add(Dense(units=outputSize, activation="sigmoid")) - - model.summary() - - # compile model - model.compile( - loss="binary_crossentropy", optimizer=myoptimizer, metrics=["accuracy"] - ) - - return model - - -def defineNNmodel( - inputSize: int = 2048, - l2reg: float = 0.001, - dropout: float = 0.2, - activation: str = "relu", - optimizer: str = "Adam", - lr: float = 0.001, - decay: float = 0.01, -) -> Model: - """ - - :param inputSize: - :param l2reg: - :param dropout: - :param activation: - :param optimizer: - :param lr: - :param decay: - :return: - """ - - if optimizer == "Adam": - myoptimizer = optimizers.Adam( - learning_rate=lr, decay=decay - ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) - elif optimizer == "SGD": - myoptimizer = SGD(lr=lr, momentum=0.9, decay=decay) - else: - myoptimizer = optimizer - - myhiddenlayers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} - - if not str(inputSize) in myhiddenlayers.keys(): - print("Wrong inputsize. Must be in {2048, 1024, 999, 512, 256}.") - return None - - nhl = int(math.log2(inputSize) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(inputSize / 2), - input_dim=inputSize, - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout)) - # next hidden layers - for i in range(1, nhl): - factorunits = 2 ** (i + 1) - factordropout = 2 * i - model.add( - Dense( - units=int(inputSize / factorunits), - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout / factordropout)) - # output layer - model.add(Dense(units=1, activation="sigmoid")) - - model.summary() - - # compile model - model.compile(loss="mse", optimizer=myoptimizer, metrics=["accuracy"]) - - return model - - -def autoencoderModel( - input_size: int = 2048, - encoding_dim: int = 256, - myloss: object = "binary_crossentropy", - mylr: float = 0.001, - mydecay: float = 0.01, -) -> tuple: - """ - This function provides an autoencoder model to reduce a certain input to a compressed version. - - :param encoding_dim: Size of the compressed representation. Default: 85 - :param input_size: Size of the input. Default: 2048 - :param myactivation: Activation function, see Keras activation functions for potential values. Default: relu - :param myoptimizer: Optimizer, see Keras optmizers for potential values. Default: adadelta - :param myloss: Loss function, see Keras Loss functions for potential values. Default: binary_crossentropy - :return: a tuple of autoencoder and encoder models - """ - - myoptimizer = optimizers.Adam( - learning_rate=mylr, decay=mydecay - ) # , beta_1=0.9, beta_2=0.999, amsgrad=False) - - # get the number of meaningful hidden layers (latent space included) - nhl = round(math.log2(input_size / encoding_dim)) - - # the input placeholder - input_vec = Input(shape=(input_size,)) - - # 1st hidden layer, that receives weights from input layer - # equals bottle neck layer, if nhl==1! - encoded = Dense(units=int(input_size / 2), activation="relu")(input_vec) - - if nhl > 1: - # encoding layers, incl. bottle neck - for i in range(1, nhl): - factorunits = 2 ** (i + 1) - # print(f'{factorunits}: {int(input_size / factorunits)}') - encoded = Dense(units=int(input_size / factorunits), activation="relu")( - encoded - ) - - # encoding_dim = int(input_size/factorunits) - - # 1st decoding layer - factorunits = 2 ** (nhl - 1) - decoded = Dense(units=int(input_size / factorunits), activation="relu")(encoded) - - # decoding layers - for i in range(nhl - 2, 0, -1): - factorunits = 2**i - # print(f'{factorunits}: {int(input_size/factorunits)}') - decoded = Dense(units=int(input_size / factorunits), activation="relu")( - decoded - ) - - # output layer - # The output layer needs to predict the probability of an output which needs to either 0 or 1 and hence we use sigmoid activation function. - decoded = Dense(units=input_size, activation="sigmoid")(decoded) - - else: - # output layer - decoded = Dense(units=input_size, activation="sigmoid")(encoded) - - autoencoder = Model(input_vec, decoded) - encoder = Model(input_vec, encoded) - - autoencoder.summary() - encoder.summary() - - # We compile the autoencoder model with adam optimizer. - # As fingerprint positions have a value of 0 or 1 we use binary_crossentropy as the loss function - autoencoder.compile(optimizer=myoptimizer, loss=myloss) - - return (autoencoder, encoder) - - -# def predict_values(ac_model_file_path, model_file_path, pdx): -# """ -# Predict a set of chemicals using a selected model. -# -# :param model_file_path: Path to the model weights of the prediction DNN -# :param pdx: A matrix containing the fingerprints of the chemicals, generated via XfromInput function -# :return: A dataframe of 2 columns: random - predicted values using random model, trained - predicted values -# using trained model. Rownames are consecutive numbers of the input rows, or if provided in the input file -# the values of the id column -# """ -# -# # ac_model_file_path="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.AC-model.hdf5" -# # model_file_path ="/data/bioinf/projects/data/2019_IDA-chem/deepFPlearn/modeltraining/model.2048.256.ER.checkpoint.model.hdf5" -# -# print(f"[INFO:] Loaded model weights for autoencoder: '{model_file_path}'") -# print(f"[INFO:] Loaded model weights for prediction DNN: '{model_file_path}'") -# -# start = time() -# # create autoencoder -# (autoencoder, encoder) = autoencoderModel(input_size=pdx.shape[1], encoding_dim=256) -# # load AC weights -# autoencoder.load_weights(ac_model_file_path) -# # encode input -# encodedX = encoder.predict(pdx) -# -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# print(f"[INFO:] Computation time used for encoding: {trainTime} min") -# -# start = time() -# # create DNN -# predictor = defineNNmodel(inputSize=encodedX.shape[1]) -# # predict with random weights -# predictions_random = predictor.predict(encodedX) -# # load DNN weights -# predictor.load_weights(model_file_path) -# # predict encoded input -# predictions = predictor.predict(encodedX) -# -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# print(f"[INFO:] Computation time used for the predictions: {trainTime} min") -# -# df = pd.DataFrame(data={'random': predictions_random.flatten(), -# 'trained': predictions.flatten()}, -# columns=['random', 'trained'], -# index=pdx.index) -# print(df) -# return (df) - - -def trainfullac( - X: pd.DataFrame, - y: pd.DataFrame, - useweights: str = None, - epochs: int = 0, - encdim: int = 256, - checkpointpath: str = None, - verbose: int = 0, -) -> Model: - """ - Train an autoencoder on the given feature matrix X. Response matrix is only used to - split meaningfully in test and train data set. - - :param X: Feature matrix. - :param y: Matrix containing the response variables. - :param epochs: Number of epochs to train the autoencoder - :param encdim: Dimension of latent space. This and the column dimension of X control - the number and size of the hidden layers. - :param checkpointpath: path to prefix the model files during callbacks - :param verbose: Verbosity level. Is forwarded to the Keras fit function that prints - training states in different verbosity levels - :return: The encoder model of the trained autoencoder - """ - - # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) - (autoencoder, encoder) = autoencoderModel( - input_size=X.shape[1], encoding_dim=encdim - ) - - if useweights: # don't train, use existing weights file and load it into AC model - # autoencoder.load_weights(useweights) - encoder.load_weights(useweights) - else: - # collect the callbacks for training - callback_list = defineCallbacks( - checkpointpath=checkpointpath, patience=20, rlrop=False - ) - - # split data into test and training data - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - # Fit the AC - autohist = autoencoder.fit( - X_train, - X_train, - callbacks=callback_list, - epochs=epochs, - batch_size=256, - shuffle=True, - verbose=verbose, - validation_data=(X_test, X_test), - ) - # history - ac_loss = autohist.history["loss"] - ac_val_loss = autohist.history["val_loss"] - ac_epochs = range(ac_loss.__len__()) - pd.DataFrame( - data={"loss": ac_loss, "val_loss": ac_val_loss, "epoch": ac_epochs} - ).to_csv(checkpointpath.replace(".hdf5", "_trainValLoss_AC.csv"), index=False) - # generate a figure of the losses for this fold - plt.figure() - plt.plot(ac_epochs, ac_loss, "bo", label="Training loss") - plt.plot(ac_epochs, ac_val_loss, "b", label="Validation loss") - plt.title("Training and validation loss of AC") - plt.legend() - plt.savefig( - fname=checkpointpath.replace(".hdf5", "_trainValLoss_AC.svg"), format="svg" - ) - plt.close() - # write the losses to .csv file for later data visualization - - # model needs to be saved and restored when predicting new input! - # use encode() of train data as input for DL model to associate to chemical - return encoder - - -def plotTrainHistory(hist, target, fileAccuracy, fileLoss): - """ - Plot the training performance in terms of accuracy and loss values for each epoch. - :param hist: The history returned by model.fit function - :param target: The name of the target of the model - :param fileAccuracy: The filename for plotting accuracy values - :param fileLoss: The filename for plotting loss values - :return: none - """ - - # plot accuracy - plt.figure() - plt.plot(hist.history["accuracy"]) - if "val_accuracy" in hist.history.keys(): - plt.plot(hist.history["val_accuracy"]) - plt.title("Model accuracy - " + target) - plt.ylabel("Accuracy") - plt.xlabel("Epoch") - if "val_accuracy" in hist.history.keys(): - plt.legend(["Train", "Test"], loc="upper left") - else: - plt.legend(["Train"], loc="upper_left") - plt.savefig(fname=fileAccuracy, format="svg") - - # Plot training & validation loss values - plt.figure() - plt.plot(hist.history["loss"]) - plt.plot(hist.history["val_loss"]) - plt.title("Model loss - " + target) - plt.ylabel("Loss") - plt.xlabel("Epoch") - plt.legend(["Train", "Test"], loc="upper left") - # plt.show() - plt.savefig(fname=fileLoss, format="svg") - plt.close() - - -def plotAUC(fpr, tpr, auc, target, filename, title=""): - plt.figure() - plt.plot([0, 1], [0, 1], "k--") - plt.plot(fpr, tpr, label=f"Keras (area = {auc:.3f})") - plt.xlabel("False positive rate") - plt.ylabel("True positive rate") - plt.title(f"ROC curve {target}") - plt.legend(loc="best") - plt.savefig(fname=filename, format="svg") - plt.close() - - -def plotHeatmap(matrix, filename, title=""): - plt.figure() - plt.imshow(matrix, cmap="Greys", interpolation="nearest") - plt.title(title) - plt.savefig(fname=filename, format="svg") - plt.close() - - -# ------------------------------------------------------------------------------------- # - -# plot model history more easily, see whyboris@github: https://gist.github.com/whyboris/91ee793ddc92cf1e824978cf31bb790c -# when plotting, smooth out the points by some factor (0.5 = rough, 0.99 = smooth) -# method taken from `Deep Learning with Python` by François Chollet - -# 01 --------------------------------------------------------------------------------- # - -# def smooth_curve(points, factor=0.75): -# smoothed_points = [] -# for point in points: -# if smoothed_points: -# previous = smoothed_points[-1] -# smoothed_points.append(previous * factor + point * (1 - factor)) -# else: -# smoothed_points.append(point) -# return smoothed_points -# -# -# # 02 ---------------------------------------------------------------------------------- # -# -# def set_plot_history_data(ax, history, which_graph): -# if which_graph == 'acc': -# train = smooth_curve(history.history['accuracy']) -# valid = smooth_curve(history.history['val_accuracy']) -# -# if which_graph == 'loss': -# train = smooth_curve(history.history['loss']) -# valid = smooth_curve(history.history['val_loss']) -# -# # plt.xkcd() # make plots look like xkcd -# -# epochs = range(1, len(train) + 1) -# -# trim = 0 # remove first 5 epochs -# # when graphing loss the first few epochs may skew the (loss) graph -# -# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', linewidth=15, alpha=0.1) -# ax.plot(epochs[trim:], train[trim:], 'dodgerblue', label=('Training')) -# -# ax.plot(epochs[trim:], valid[trim:], 'g', linewidth=15, alpha=0.1) -# ax.plot(epochs[trim:], valid[trim:], 'g', label=('Validation')) -# - -# 03 ---------------------------------------------------------------------------------- # -# -# def get_max_validation_accuracy(history): -# validation = smooth_curve(history.history['val_accuracy']) -# ymax = max(validation) -# return 'Max validation accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' -# -# -# def get_max_training_accuracy(history): -# training = smooth_curve(history.history['accuracy']) -# ymax = max(training) -# return 'Max training accuracy ≈ ' + str(round(ymax, 3) * 100) + '%' -# - -# 04---------------------------------------------------------------------------------- # - -# def plot_history(history, file): -# fig, (ax1, ax2) = plt.subplots(nrows=2, -# ncols=1, -# figsize=(10, 6), -# sharex=True, -# gridspec_kw={'height_ratios': [5, 2]}) -# -# set_plot_history_data(ax1, history, 'acc') -# -# set_plot_history_data(ax2, history, 'loss') -# -# # Accuracy graph -# ax1.set_ylabel('Accuracy') -# ax1.set_ylim(bottom=0.5, top=1) -# ax1.legend(loc="lower right") -# ax1.spines['top'].set_visible(False) -# ax1.spines['right'].set_visible(False) -# ax1.xaxis.set_ticks_position('none') -# ax1.spines['bottom'].set_visible(False) -# -# # max accuracy text -# plt.text(0.5, -# 0.6, -# get_max_validation_accuracy(history), -# horizontalalignment='right', -# verticalalignment='top', -# transform=ax1.transAxes, -# fontsize=12) -# plt.text(0.5, -# 0.8, -# get_max_training_accuracy(history), -# horizontalalignment='right', -# verticalalignment='top', -# transform=ax1.transAxes, -# fontsize=12) -# -# # Loss graph -# ax2.set_ylabel('Loss') -# ax2.set_yticks([]) -# ax2.plot(legend=False) -# ax2.set_xlabel('Epochs') -# ax2.spines['top'].set_visible(False) -# ax2.spines['right'].set_visible(False) -# -# plt.tight_layout() -# plt.savefig(fname=file, format='svg') -# plt.close() -# - - -def drawHeatmap(data, anno): - # (data=pd.DataFrame(Xt), anno = pd.DataFrame(Yt.astype(int))) - - # annotation bar colors - my_ann_colors = dict(zip(anno[0].unique(), ["blue", "red"])) - row_colors = anno[0].map(my_ann_colors) - - cl = sns.clustermap( - data, - metric="euclidean", - method="single", - z_score=None, - standard_scale=None, - col_cluster=False, - cmap="Greys", - row_colors=row_colors, - yticklabels=False, - ) - cl.fig.suptitle("Distributions of [1,0] in fingerprints of target: AR") - - url = "https://python-graph-gallery.com/wp-content/uploads/mtcars.csv" - df = pd.read_csv(url) - # set df index using existing columns - df = df.set_index("model") - # remove name of index column, numbered rows have been the index before, they are gone now - del df.index.name - df - my_palette = dict(zip(df.cyl.unique(), ["orange", "yellow", "brown"])) - row_colors = df.cyl.map(my_palette) - - # Default plot - # sns.clustermap(df) - - # Clustermethods - my_palette = dict() - sns.clustermap( - df, - metric="correlation", - standard_scale=1, - method="single", - cmap="Blues", - row_colors=row_colors, - ) - - -############################################################################### -# TRAIN FUNCTIONS --------------------------------------------------------- # - - -def defineOutfileNames(pathprefix: str, target: str, fold: int) -> tuple: - """ - This function returns the required paths for output files or directories. - - :param pathprefix: A file path prefix for all files. - :param mtype: The model type. Its set by the trainNNmodels function with information on autoencoder or not, - and if AC is used, then with its parameters. - :param target: The name of the target. - - :return: A tuple of 14 output file names. - """ - - modelname = target + ".Fold-" + str(fold) - - modelfilepathW = str(pathprefix) + "." + modelname + ".weights.h5" - modelfilepathM = str(pathprefix) + "." + modelname + ".json" - modelhistplotpathL = str(pathprefix) + "." + modelname + ".loss.svg" - modelhistplotpathA = str(pathprefix) + "." + modelname + ".acc.svg" - modelhistplotpath = str(pathprefix) + "." + modelname + ".history.svg" - modelhistcsvpath = str(pathprefix) + "." + modelname + ".history.csv" - modelvalidation = str(pathprefix) + "." + modelname + ".validation.csv" - modelAUCfile = str(pathprefix) + "." + modelname + ".auc_value.svg" - modelAUCfiledata = str(pathprefix) + "." + modelname + ".auc_value.data.csv" - outfilepath = str(pathprefix) + "." + modelname + ".trainingResults.txt" - checkpointpath = str(pathprefix) + "." + modelname + ".checkpoint.model.hdf5" - modelheatmapX = str(pathprefix) + "." + modelname + ".heatmap.X.svg" - modelheatmapZ = str(pathprefix) + "." + modelname + ".AC.heatmap.Z.svg" - - return ( - modelfilepathW, - modelfilepathM, - modelhistplotpathL, - modelhistplotpathA, - modelhistplotpath, - modelhistcsvpath, - modelvalidation, - modelAUCfile, - modelAUCfiledata, - outfilepath, - checkpointpath, - modelheatmapX, - modelheatmapZ, - ) - - -# ------------------------------------------------------------------------------------- # - - -def eval01Distributions(Xt, Yt, y_train, y_test, verbosity=0): - """ - Evaluate the percentage of 0 values in the outcome variable of the whole dataset and the splitted (train,test) - dataset, and the percentage of 0 values in the feature matrix. - - :param Xt: The whole feature matrix - :param Yt: The whole outcome vector - :param y_train: The outcome vector of the training set - :param y_test: The outcome vector of the test set - :param verbosity: The verbosity level. Info is only printed if verbosity is not 0. - - :return: Nothing is returned. - """ - - if verbosity == 0: - return - else: - unique, counts = np.unique(Yt, return_counts=True) - perc = round(100 / len(Yt) * counts[1]) - print( - f"[INFO:] Percentage of '1' values in outcome variable (whole dataset): {perc}\n" - ) - - uniqueRtr, countsRtr = np.unique(y_train, return_counts=True) - uniqueRte, countsRte = np.unique(y_test, return_counts=True) - perc = round(100 / len(y_train) * countsRtr[1]) - print(f"[INFO:] Percentage of '1' values in training outcomes: {perc}\n") - perc = round(100 / len(y_test) * countsRte[1]) - print(f"[INFO:] Percentage of '1' values in test outcomes: {perc}\n") - - print( - f"[INFO:] Average percentage of '0' positions in fingerprints: {round(np.sum(Xt == 0) / (np.sum(Xt == 0) + np.sum(Xt == 1)), ndigits=4)}" - ) - return - - -# ------------------------------------------------------------------------------------- # - - -def prepareDataSet(y: pd.DataFrame, x: pd.DataFrame, t: str) -> tuple: - """ - A function to remove NA values from the output column, duplicates from input and output - and to transform the data into numpy arrays for keras functions. - - :param y: The target vector that may contain NAs. - :param x: The input matrix that may contain duplicates (including resp. target value!) - :param t: The target string - :return: Tuple of x and y ready for splitting into test and train sets for ML with keras. - """ - - # which rows contain 'NA' in target column - tmp = y[t].astype("category") - Y = np.asarray(tmp) - naRows = np.isnan(Y) - - # transform pd dataframe to numpy array for keras - X = x.to_numpy() - - # subset data according to target non-NA values - Ytall = Y[~naRows] - Xtall = X[~naRows] - - # remove all duplicated feature values - outcome pairs, and - # shuffle data, such that validation_split in fit function selects a random subset! - # Remember, that it takes the fraction for validation from the end of our data! - - (XtU, YtU) = removeDuplicates(x=Xtall, y=Ytall) - - # return(shuffleDataPriorToTraining(x=XtU, y=YtU)) - return (XtU, YtU) - - -# ------------------------------------------------------------------------------------- # - -# def plot_history_vis(hist, model_hist_plot_path, model_hist_csv_path, -# model_hist_plot_path_a, model_hist_plot_path_l, target): -# plot_history(history=hist, file=model_hist_plot_path) -# histDF = pd.DataFrame(hist.history) -# histDF.to_csv(model_hist_csv_path) -# -# # plot accuracy and loss for the training and validation during training -# plotTrainHistory(hist=hist, target=target, -# file_accuracy=model_hist_plot_path_a, -# file_loss=model_hist_plot_path_l) -# - -# ------------------------------------------------------------------------------------- # -# -# def validate_multi_model_on_test_data(x_test, checkpoint_path, y_test, col_names, result_file): -# # load checkpoint model with min(val_loss) -# trainedmodel = define_nn_model_multi(input_size=x_test.shape[1], output_size=y_test.shape[1]) -# -# # predict values with random model -# predictions_random = pd.DataFrame(trainedmodel.predict(x_test), columns=col_names + '-predRandom') -# -# # load weights into random model -# trainedmodel.load_weights(checkpoint_path) -# -# # predict with trained model -# predictions = pd.DataFrame(trainedmodel.predict(x_test), -# columns=col_names + '-pred') -# scores = pd.DataFrame((predictions.round() == y_test).sum() / y_test.shape[0], columns=['correctPredictions']) -# -# results = pd.concat([predictions_random, predictions, pd.DataFrame(y_test, columns=col_names + '-true')], axis=1) -# results.to_csv(result_file) -# -# return scores - - -# ------------------------------------------------------------------------------------- # - -# def validate_model_on_test_data(x_test, checkpoint_path, y_test, model_type, model_validation, target, -# model_auc_file_data, model_auc_file): -# """ -# Function that validates trained model with test data set. History and AUC plots are generated. -# Accuracy and Loss of model on test data, as well as MCC and confusion matrix is calculated and returned. -# -# :param x_test: -# :param checkpoint_path: -# :param y_test: -# :param model_type: -# :param model_validation: -# :param target: -# :param model_auc_file_data: -# :param model_auc_file: -# :return: Tupel containing Loss, Accuracy, MCC, tn, fp, fn, tp of trained model on test data. -# """ -# -# # load checkpoint model with min(val_loss) -# trainedmodel = define_nn_model(input_size=x_test.shape[1]) -# -# # predict values with random model -# predictions_random = pd.DataFrame(trainedmodel.predict(x_test)) -# -# # load weights into random model -# trainedmodel.load_weights(checkpoint_path) -# # predict with trained model -# predictions = pd.DataFrame(trainedmodel.predict(x_test)) -# -# # save validation data to .csv file -# validation = pd.DataFrame({'predicted': predictions[0].ravel(), -# 'true': list(y_test), -# 'predicted_random': predictions_random[0].ravel(), -# 'model_type': model_type}) -# validation.to_csv(model_validation) -# -# # compute MCC -# predictionsInt = [int(round(x)) for x in predictions[0].ravel()] -# ytrueInt = [int(y) for y in y_test] -# MCC = matthews_corrcoef(ytrueInt, predictionsInt) -# -# # generate the AUC-ROC curve data from the validation data -# fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, predictions, drop_intermediate=False) -# -# auc_keras = auc_value(fpr_keras, tpr_keras) -# -# aucdata = pd.DataFrame(list(zip(fpr_keras, -# tpr_keras, -# [auc_keras for x in range(1, len(fpr_keras))], -# [target for x in range(1, len(fpr_keras))])), -# columns=['fpr', 'tpr', 'auc_value', 'target']) -# aucdata.to_csv(model_auc_file_data) -# -# plotAUC(fpr=fpr_keras, tpr=tpr_keras, target=target, auc_value=auc_keras, filename=model_auc_file) -# -# # [[tn, fp] -# # [fn, tp]] -# cfm = confusion_matrix(y_true=ytrueInt, y_pred=predictionsInt) -# -# scores = trainedmodel.evaluate(x_test, y_test, verbose=0) -# -# print(f'TARGET: {target} Loss: {scores[0].__round__(2)} Acc: {scores[1].__round__(2)}') -# print(f'MCC: {MCC.__round__(2)}') -# print(f'CFM: \n\tTN={cfm[0][0]}\tFP={cfm[0][1]}\n\tFN={cfm[1][0]}\tTP={cfm[1][1]}') -# -# return (scores[0], scores[1], MCC, cfm[0][0], cfm[0][1], cfm[1][0], cfm[1][1]) - - -# ------------------------------------------------------------------------------------- # -# def train_nn_models_multi(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, -# split: float = 0.2, epochs: int = 500, -# verbose: int= 2, kfold: int = 5) -> None: -# # remove 'id' column if present -# if 'id' in x.columns: -# x = x.drop('id', axis=1) -# if 'id' in y.columns: -# y = y.drop('id', axis=1) -# -# # drop compounds that are not measured for all target columns, transform to numpy -# (xmulti, ymulti) = shuffleDataPriorToTraining(x, y) -# -# # do a kfold cross validation for the autoencoder training -# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) -# -# # store acc and loss for each fold -# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"]) # FNN test data -# -# fold_no = 1 -# -# # split the data -# for train, test in kfoldCValidator.split(xmulti, ymulti): -# -# # define all the output file/path names -# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, -# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, -# model_auc_file_data, outfilepath, checkpoint_path, -# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, -# target="multi", fold=fold_no) -# -# # use a dnn for multi-class prediction -# model = define_nn_model_multi(input_size=xmulti[train].shape[1], output_size=ymulti.shape[1]) -# -# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # measure the training time -# start = time() -# -# # train and validate -# hist = model.fit(xmulti[train], ymulti[train], -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the multi-label FNN: {trainTime} min") -# -# # validate model on test data set (x_test, y_test) -# scores = validate_multi_model_on_test_data(x_test=xmulti[test], -# checkpoint_path=checkpoint_path, -# y_test=ymulti[test], -# col_names=y.columns, -# result_file=outfilepath.replace("trainingResults.txt", -# "predictionResults.csv")) -# -# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) -# row_df = pd.DataFrame([[fold_no, -# hist.history['loss'][idx], hist.history['val_loss'][idx], -# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], -# scores[0], scores[1], scores[2]]], -# columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"] -# ) -# print(row_df) -# allscores = allscores.append(row_df, ignore_index=True) -# -# fold_no = fold_no + 1 -# del model -# -# print(allscores) -# -# # finalize model -# # 1. provide best performing fold variant -# # select best model based on MCC -# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] -# fold_no = allscores._get_value(idx2, 'fold_no') -# -# modelname = 'multi.Fold-' + str(fold_no) -# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' -# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") -# -# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) -# allscores.to_csv(file) -# -# # copy best DNN model -# shutil.copyfile(checkpoint_path, bestModelfile) -# print(f'[INFO]: Best models for FNN is saved:\n - {bestModelfile}') -# -# # AND retrain with full data set -# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") -# # measure the training time -# start = time() -# -# model = define_nn_model(input_size=xmulti[train].shape[1]) -# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # train and validate -# hist = model.fit(xmulti, ymulti, -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# # validation_data=(x_test, y_test)) # this overwrites val_split! -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") -# plot_history_vis(hist, -# model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), -# model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) -# print(f'[INFO]: Full models for DNN is saved:\n - {fullModelfile}') -# -# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) -# # stats.append([target, [x.__round__(2) for x in scores]]) - - -# ------------------------------------------------------------------------------------- # - -# def trainNNmodels(modelfilepathprefix: str, x: pd.DataFrame, y: pd.DataFrame, -# split: float = 0.2, epochs: int = 50, params: str = None, -# verbose: int = 2, kfold: int = 5) -> None: -# """ -# Train individual models for all targets (columns) present in the provided target data (y) and a multi-label -# model that classifies all targets at once. For each individual target the data is first subsetted to exclude NA -# values (for target associations). A random sample of the remaining data (size is the split fraction) is used for -# training and the remaining data for validation. -# -# :param modelfilepathprefix: A path prefix for all output files -# :param x: The feature matrix. -# :param y: The outcome matrix. -# :param split: The percentage of data used for validation. -# :param epochs: The number of epochs for training the autoencoder and the DNN for classification. -# Note: Early stopping and fallback is enabled. -# :param params: A .csv files containing paramters that should be evaluated. See file tunedParams.csv. -# :param verbose: Verbosity level. -# -# :return: A list with loss and accuracy values for each individual model. -# """ -# -# # remove 'id' column if present -# if 'id' in x.columns: -# x = x.drop('id', axis=1) -# if 'id' in y.columns: -# y = y.drop('id', axis=1) -# -# if params: -# parameters = pd.read_csv(params) -# -# # add a target 'summarized' for association to ANY of the target genes -# # maybe it improves the detection of '1's due to increased data set -# mysum = y.sum(axis=1) -# y['summarized'] = [0 if s == 0 else 1 for s in mysum] -# -# ### For each individual target (+ summarized target) -# for target in y.columns: # [:1]: -# # target=y.columns[0] # --> only for testing the code -# -# # rm NAs and duplicates, shuffle, and transform to numpy arrays -# (Xt, Yt) = prepareDataSet(y, x, target) -# -# # do a kfold cross validation for the FNN training -# kfoldCValidator = KFold(n_splits=kfold, shuffle=True, random_state=42) -# -# # store acc and loss for each fold -# allscores = pd.DataFrame(columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"]) # FNN test data -# -# fold_no = 1 -# -# # split the data -# for train, test in kfoldCValidator.split(Xt, Yt): -# -# if verbose > 0: -# print(f'[INFO]: Training of fold number: {fold_no} ------------------------------------\n') -# -# # define all the output file/path names -# (modelfilepathW, modelfilepathM, model_hist_plot_path_l, model_hist_plot_path_a, -# model_hist_plot_path, model_hist_csv_path, model_validation, model_auc_file, -# model_auc_file_data, outfilepath, checkpoint_path, -# modelheatmapX, modelheatmapZ) = define_out_file_names(path_prefix=modelfilepathprefix, -# target=target, fold=fold_no) -# -# model = define_nn_model(input_size=Xt[train].shape[1]) -# -# callback_list = defineCallbacks(checkpoint_path=checkpoint_path, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # measure the training time -# start = time() -# # train and validate -# hist = model.fit(Xt[train], Yt[train], -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# # validation_data=(x_test, y_test)) # this overwrites val_split! -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the single-label FNN: {trainTime} min") -# -# # validate model on test data set (x_test, y_test) -# scores = validate_model_on_test_data(Xt[test], checkpoint_path, Yt[test], -# "FNN", model_validation, target, -# model_auc_file_data, model_auc_file) -# -# idx = hist.history['val_loss'].index(min(hist.history['val_loss'])) -# -# row_df = pd.DataFrame([[fold_no, -# hist.history['loss'][idx], hist.history['val_loss'][idx], -# hist.history['accuracy'][idx], hist.history['val_accuracy'][idx], -# scores[0], scores[1], scores[2]]], -# columns=["fold_no", # fold number of k-fold CV -# "loss", "val_loss", "acc", "val_acc", # FNN training -# "loss_test", "acc_test", "mcc_test"] -# ) -# print(row_df) -# allscores = allscores.append(row_df, ignore_index=True) -# fold_no = fold_no + 1 -# del model -# # now next fold -# -# print(allscores) -# -# # finalize model -# # 1. provide best performing fold variant -# # select best model based on MCC -# idx2 = allscores[['mcc_test']].idxmax().ravel()[0] -# fold_no = allscores._get_value(idx2, 'fold_no') -# -# modelname = target + '.Fold-' + str(fold_no) -# checkpoint_path = str(modelfilepathprefix) + '.' + modelname + '.checkpoint.model.hdf5' -# bestModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-") -# -# # store all scores -# file = re.sub("\.hdf5", "scores.csv", re.sub("Fold-.\.checkpoint", "Fold-All", checkpoint_path)) -# allscores.to_csv(file) -# -# # copy best DNN model -# shutil.copyfile(checkpoint_path, bestModelfile) -# print(f'[INFO]: Best model for FNN is saved:\n - {bestModelfile}') -# -# # AND retrain with full data set -# fullModelfile = checkpoint_path.replace("Fold-" + str(fold_no) + ".checkpoint", "full.FNN-") -# # measure the training time -# start = time() -# -# model = define_nn_model(input_size=Xt.shape[1]) # X_train.shape[1]) -# callback_list = defineCallbacks(checkpoint_path=fullModelfile, patience=20, -# rlrop=True, rlropfactor=0.1, rlroppatience=100) -# # train and validate -# hist = model.fit(Xt, Yt, -# callbacks=callback_list, -# epochs=epochs, batch_size=256, verbose=2, validation_split=split) -# # validation_data=(x_test, y_test)) # this overwrites val_split! -# trainTime = str(round((time() - start) / 60, ndigits=2)) -# -# if verbose > 0: -# print(f"[INFO:] Computation time for training the full classification FNN: {trainTime} min") -# # plot_history_vis(hist, -# # model_hist_plot_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# # model_hist_csv_path.replace("Fold-" + str(fold_no), "full.DNN-model"), -# # model_hist_plot_path_a.replace("Fold-" + str(fold_no), "full.DNN-model"), -# # model_hist_plot_path_l.replace("Fold-" + str(fold_no), "full.DNN-model"), target) -# # print(f'[INFO]: Full model for DNN is saved:\n - {fullModelfile}') -# -# pd.DataFrame(hist.history).to_csv(fullModelfile.replace(".hdf5", ".history.csv")) -# -# del model -# # now next target -# - -# ------------------------------------------------------------------------------------- # - - -def smilesSet2fpSet(csvfilename, outfilename, fptype): - """ - - :param csvfilename: csv file containing a column named 'smiles' - :param fptype: - :return: void - """ - # csvfilename="/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.csv" - # outfilename = "/data/bioinf/projects/data/2019_Sun-etal_Supplement/results/05_04_dataKS.fp.csv" - - # read csv and generate/add fingerprints to dict - with open(csvfilename, "r") as f: - reader = csv.DictReader(f, delimiter=";") - - feature = "smiles" - - with open(outfilename, "w", newline="") as out: - writer = csv.DictWriter(out, fieldnames=["fp"]) - writer.writeheader() - for row in reader: - # smiles, need to be converted to fp first - fp = smi2fp(smile=row[feature], fptype=fptype) - writer.writerow({"fp": DataStructs.BitVectToText(fp)}) - - return diff --git a/build/lib/dfpl/feedforwardNN.py b/build/lib/dfpl/feedforwardNN.py deleted file mode 100644 index 702fff27..00000000 --- a/build/lib/dfpl/feedforwardNN.py +++ /dev/null @@ -1,464 +0,0 @@ -import array -import logging -import math -import re -import shutil -import sys -from time import time -from typing import List, Union -import numpy as np -import pandas as pd -from sklearn.metrics import f1_score -from sklearn.model_selection import KFold, train_test_split -from tensorflow.keras import metrics, optimizers, regularizers -from tensorflow.keras.layers import Dense, Dropout - -# for NN model functions -from tensorflow.keras.models import Model, Sequential - -from dfpl import callbacks as cb -from dfpl import history as ht -from dfpl import options, settings - - -def define_out_file_names(path_prefix: str, target: str, fold: int = -1) -> tuple: - """ - This function returns the required paths for output files or directories. - - :param path_prefix: A file path prefix for all files. - :param target: The name of the target. - :param fold: - - :return: A tuple of 14 output file names. - """ - - if fold == -1: - model_name = "/" + target + ".Full" - else: - model_name = "/" + target + ".Fold-" + str(fold) - - model_file_path_weights = str(path_prefix) + model_name + ".weights.h5" - model_file_path_json = str(path_prefix) + model_name + ".json" - model_hist_path = str(path_prefix) + model_name - model_hist_csv_path = str(path_prefix) + model_name + ".history.csv" - model_predict_valset_csv_path = str(path_prefix) + model_name + ".predictValSet.csv" - model_validation = str(path_prefix) + model_name + ".validation.csv" - model_auc_file = str(path_prefix) + model_name + ".auc_value.svg" - model_auc_file_data = str(path_prefix) + model_name + ".auc_value.data.csv" - out_file_path = str(path_prefix) + model_name + ".trainingResults.txt" - checkpoint_path = str(path_prefix) + model_name + ".checkpoint.model.hdf5" - model_heatmap_x = str(path_prefix) + model_name + ".heatmap.X.svg" - model_heatmap_z = str(path_prefix) + model_name + ".AC.heatmap.Z.svg" - - return ( - model_file_path_weights, - model_file_path_json, - model_hist_path, - model_hist_csv_path, - model_predict_valset_csv_path, - model_validation, - model_auc_file, - model_auc_file_data, - out_file_path, - checkpoint_path, - model_heatmap_x, - model_heatmap_z, - ) - - -def define_nn_multi_label_model( - input_size: int, output_size: int, opts: options.Options -) -> Model: - if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) - elif opts.optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) - else: - logging.error(f"Your selected optimizer is not supported:{opts.optimizer}.") - sys.exit("Unsupported optimizer.") - - nhl = int(math.log2(input_size) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation=opts.activationFunction, - kernel_regularizer=regularizers.l2(opts.l2reg), - ) - ) - model.add(Dropout(opts.dropout)) - # next hidden layers - for i in range(1, nhl): - factor_units = 2 ** (i + 1) - factor_dropout = 2 * i - model.add( - Dense( - units=int(input_size / factor_units), - activation=opts.activationFunction, - kernel_regularizer=regularizers.l2(opts.l2reg), - ) - ) - model.add(Dropout(opts.dropout / factor_dropout)) - # multi-class output layer - # use sigmoid to get independent probabilities for each output node - # we use sigmoid since one chemical can be active for multiple targets - # (need not add up to one, as they would using softmax) - # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ - model.add(Dense(units=output_size, activation="sigmoid")) - - model.summary(print_fn=logging.info) - - # compile model - model.compile( - loss="binary_crossentropy", - optimizer=my_optimizer, - metrics=["accuracy", metrics.Recall(), metrics.Precision()], - ) - - return model - - -def define_nn_model_multi( - input_size: int = 2048, - output_size: int = None, - l2reg: float = 0.001, - dropout: float = 0.2, - activation: str = "relu", - optimizer: str = "Adam", - lr: float = 0.001, - decay: float = 0.01, -) -> Model: - if optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=lr, decay=decay) - elif optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=lr, momentum=0.9, decay=decay) - else: - my_optimizer = optimizer - - nhl = int(math.log2(input_size) / 2 - 1) - - model = Sequential() - # From input to 1st hidden layer - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout)) - # next hidden layers - for i in range(1, nhl): - factor_units = 2 ** (i + 1) - factor_dropout = 2 * i - model.add( - Dense( - units=int(input_size / factor_units), - activation=activation, - kernel_regularizer=regularizers.l2(l2reg), - ) - ) - model.add(Dropout(dropout / factor_dropout)) - # multi-class output layer - # use sigmoid to get independent probabilities for each output node - # (need not add up to one, as they would using softmax) - # https://www.depends-on-the-definition.com/guide-to-multi-label-classification-with-neural-networks/ - model.add(Dense(units=output_size, activation="sigmoid")) - - model.summary(print_fn=logging.info) - - # compile model - model.compile( - loss="binary_crossentropy", optimizer=my_optimizer, metrics=["accuracy"] - ) - - return model - - -def validate_multi_model_on_test_data( - x_test: np.ndarray, - checkpoint_path: str, - y_test: np.ndarray, - col_names: List[str], - result_file: str, -) -> List[Union[int, float, str]]: - """ - Validate the multi label model on a test data set. - - :param x_test: The feature matrix of the test data set - :param checkpoint_path: The path containing the weights of the FNN model - :param y_test: The outcome matrix of the test data set - :param col_names: The names of the columns that are targets - :param result_file: The filename of the output file - :return: A pandas Dataframe containing the percentage of correct predictions for each target - """ - # load checkpoint model with min(val_loss) - trained_model = define_nn_model_multi( - input_size=x_test.shape[1], output_size=y_test.shape[1] - ) - - # predict values with random model - predictions_random = pd.DataFrame( - trained_model.predict(x_test), columns=[n for n in col_names] - ) - - # load weights into random model - trained_model.load_weights(checkpoint_path) - - # predict with trained model - predictions = pd.DataFrame( - trained_model.predict(x_test), columns=[n for n in col_names] - ) - - y_true = pd.DataFrame(y_test, columns=col_names) - - f1_random = f1_score( - y_true=y_true, y_pred=predictions_random.round(), average="weighted" - ) - - f1_trained = f1_score(y_true=y_true, y_pred=predictions.round(), average="weighted") - - predictions_random.columns = [n + "-random" for n in col_names] - predictions.columns = [n + "-trained" for n in col_names] - - results = pd.concat([predictions_random, predictions, y_true], axis=1) - results.to_csv(result_file) - - return [f1_random, f1_trained] - - -def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: - # find target columns - names_y = [ - c - for c in df.columns - if c not in ["cid", "id", "smiles", "fp", "inchi", "fpcompressed"] - ] - selector = df[names_y].notna().apply(np.logical_and.reduce, axis=1) - - if opts.compressFeatures: - # get compressed fingerprints as numpy array - fpMatrix = np.array( - df[df["fpcompressed"].notnull() & selector]["fpcompressed"].to_list(), - dtype=settings.nn_multi_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - y = np.array( - df[df["fpcompressed"].notnull() & selector][names_y], - dtype=settings.nn_multi_target_numpy_type, - copy=settings.numpy_copy_values, - ) - else: - # get fingerprints as numpy array - fpMatrix = np.array( - df[df["fp"].notnull() & selector]["fp"].to_list(), - dtype=settings.nn_multi_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - - y = np.array( - df[df["fp"].notnull() & selector][names_y], - dtype=settings.nn_multi_target_numpy_type, - copy=settings.numpy_copy_values, - ) - - if opts.kFolds > 0: - # do a kfold cross validation for the autoencoder training - kfold_c_validator = KFold(n_splits=opts.kFolds, shuffle=True, random_state=42) - - # store acc and loss for each fold - all_scores = pd.DataFrame( - columns=[ - "fold_no", # fold number of k-fold CV - "loss", - "val_loss", - "acc", - "val_acc", # FNN training - "f1_random", - "f1_trained", - ] - ) # F1 scores of predictions - - fold_no = 1 - - # split the data - for train, test in kfold_c_validator.split(fpMatrix, y): - # kf = kfold_c_validator.split(fpMatrix, y) - # train, test = next(kf) - - ( - model_file_path_weights, - model_file_path_json, - model_hist_path, - model_validation, - model_auc_file, - model_auc_file_data, - out_file_path, - checkpoint_path, - model_heatmap_x, - model_heatmap_z, - ) = define_out_file_names( - path_prefix=opts.outputDir, - target="multi" + "_compressed-" + str(opts.compressFeatures), - fold=fold_no, - ) - - # use a dnn for multi-class prediction - model = define_nn_model_multi( - input_size=fpMatrix[train].shape[1], output_size=y.shape[1] - ) - - callback_list = cb.nn_callback(checkpoint_path=checkpoint_path, opts=opts) - # measure the training time - start = time() - - # train and validate - hist = model.fit( - fpMatrix[train], - y[train], - callbacks=callback_list, - epochs=opts.epochs, - batch_size=256, - verbose=opts.verbose, - validation_split=opts.testSize, - ) - - trainTime = str(round((time() - start) / 60, ndigits=2)) - - if opts.verbose > 0: - logging.info( - "Computation time for training the multi-label FNN: " - + trainTime - + " min" - ) - - ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) - # pd.DataFrame(hist.history).to_csv(model_hist_csv_path) - - # validate model on test data set (fpMatrix_test, y_test) - scores = validate_multi_model_on_test_data( - x_test=fpMatrix[test], - checkpoint_path=checkpoint_path, - y_test=y[test], - col_names=names_y, - result_file=out_file_path.replace( - "trainingResults.txt", "predictionResults.csv" - ), - ) - - idx = hist.history["val_loss"].index(min(hist.history["val_loss"])) - row_df = pd.DataFrame( - [ - [ - fold_no, - hist.history["loss"][idx], - hist.history["val_loss"][idx], - hist.history["accuracy"][idx], - hist.history["val_accuracy"][idx], - scores[0], - scores[1], - ] - ], - columns=[ - "fold_no", # fold number of k-fold CV - "loss", - "val_loss", - "acc", - "val_acc", - "f1_random", - "f1_trained", - ], - ) - - logging.info(row_df) - all_scores = all_scores.append(row_df, ignore_index=True) - - fold_no += 1 - del model - - logging.info(all_scores) - - # finalize model - # 1. provide best performing fold variant - # select best model based on MCC - idx2 = all_scores[["f1_trained"]].idxmax().ravel()[0] - fold_no = all_scores.iloc[idx2]["fold_no"] - - model_name = ( - "multi_compressed-" + str(opts.compressFeatures) + ".Fold-" + str(fold_no) - ) - checkpoint_path = opts.outputDir + "/" + model_name + ".checkpoint.model.hdf5" - best_model_file = checkpoint_path.replace( - "Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-" - ) - - file = re.sub( - ".hdf5", - "scores.csv", - re.sub("Fold-..checkpoint", "Fold-All", checkpoint_path), - ) - all_scores.to_csv(file) - - # copy best DNN model - shutil.copyfile(checkpoint_path, best_model_file) - logging.info("Best models for FNN is saved:\n" + best_model_file) - - # AND retrain with full data set - # full_model_file = f"{opts.outputDir}/multi_full.FNN.checkpoint.model.hdf5" - - ( - model_file_path_weights, - model_file_path_json, - model_hist_path, - model_hist_csv_path, - model_predict_valset_csv_path, - model_validation, - model_auc_file, - model_auc_file_data, - out_file_path, - checkpoint_path, - model_heatmap_x, - model_heatmap_z, - ) = define_out_file_names( - path_prefix=opts.outputDir, - target="multi" + "_compressed-" + str(opts.compressFeatures), - ) - - # test training data split - X_train, X_test, y_train, y_test = train_test_split( - fpMatrix, y, test_size=opts.testSize - ) - - # measure the training time - model = define_nn_multi_label_model( - input_size=fpMatrix.shape[1], output_size=y.shape[1], opts=opts - ) - model.evaluate(X_test, y_test) - - # model = define_nn_model_multi(input_size=fpMatrix.shape[1], - # output_size=y.shape[1]) - # callback_list = nn_callback(checkpoint_path=full_model_file, opts=opts) - # train and validate - start = time() - hist = model.fit( - X_train, - y_train, - # callbacks=callback_list, - epochs=opts.epochs, - batch_size=opts.batchSize, - verbose=opts.verbose, - validation_data=(X_test, y_test), - ) - trainTime = str(round((time() - start) / 60, ndigits=2)) - - # yhat = model.predict(X_test) - model.evaluate(X_test, y_test) - - logging.info( - "Computation time for training the full multi-label FNN: " + trainTime + " min" - ) - ht.store_and_plot_history(base_file_name=model_hist_path, hist=hist) diff --git a/build/lib/dfpl/fingerprint.py b/build/lib/dfpl/fingerprint.py deleted file mode 100644 index 291689c2..00000000 --- a/build/lib/dfpl/fingerprint.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -"""Calculate fingerprints""" -import logging -import multiprocessing -import os -from functools import partial -from os.path import isfile, join -from typing import Any, Callable, List - -import numpy as np -import pandas as pd -from rdkit import Chem, DataStructs, RDLogger -from rdkit.Chem import AllChem - -# import settings -from dfpl import settings - -default_fp_size = 2048 - - -def addFPColumn(data_frame: pd.DataFrame, fp_size: int) -> pd.DataFrame: - """ - Adds a fingerprint to each row in the dataframe. This function works on - parallel chunks of the original dataframe. - :param data_frame: Input dataframe that needs to have a "smiles" or an "inchi" column - :param fp_size: Number of bits in the fingerprint - :return: The dataframe with an additional "fp" column - """ - - def smile2fp(smile: str) -> Any: - """ - Calculates one fingerprint from a SMILE - :param smile: Input SMILE - :return: List of bits if conversion is successfull, - None otherwise - """ - - # generate morgan fp (circular, ecfp) - # smile = df['smiles'][1] - # mol = Chem.MolFromSmiles(smile) - # from rdkit.Chem import AllChem - # morgan = AllChem.GetMorganFingerprintAsBitVect(mol, 2) - # npa = np.zeros((0,), dtype=np.bool) - # from rdkit import DataStructs - # DataStructs.ConvertToNumpyArray(morgan, npa) - - npa = np.zeros((0,), dtype=np.bool_) - try: - DataStructs.ConvertToNumpyArray( - AllChem.GetMorganFingerprintAsBitVect( - Chem.MolFromSmiles(smile), 2, nBits=fp_size - ), - npa, - ) - return npa - except Exception: - return None - - # try: - # return np.array( - # Chem.RDKFingerprint(Chem.MolFromSmiles(smile), fpSize=fp_size), - # dtype=settings.df_fp_numpy_type, copy=settings.numpy_copy_values) - # except: - # # Note: We don't need to log here since rdkit already logs - # return None - - def inchi2fp(inchi: str) -> Any: - """ - Calculates one fingerprint from InChI - :param inchi: Input InChI - :return: List of bits if conversion is successfull, - None otherwise - """ - try: - return np.array( - Chem.RDKFingerprint(Chem.MolFromInchi(inchi), fpSize=fp_size), - dtype=settings.df_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - except Exception: - # Note: We don't need to log here since rdkit already logs - return None - - if "smiles" in data_frame: - func = smile2fp - accessor = "smiles" - else: - if "inchi" in data_frame: - func = inchi2fp - accessor = "inchi" - else: - raise ValueError("Neither smiles nor inchi column in data-frame") - - data_frame["fp"] = data_frame[accessor].apply(func) - return data_frame - - -def importDataFile( - file_name: str, - import_function: Callable[[str], pd.DataFrame] = pd.read_csv, - fp_size: int = default_fp_size, -) -> pd.DataFrame: - """ - Reads data as CSV or TSV and calculates fingerprints from the SMILES in the data. - :param import_function: - :param file_name: Filename of CSV files containing the training data. The - SMILES/Fingerprints are stored 1st column - :param fp_size: Number of bits in the fingerprint - :return: Two pandas dataframe containing the X and Y matrix for training and/or prediction. If - no outcome data is provided, the Y matrix is a None object. - """ - # Read the data as Pandas pickle which already contains the calculated fingerprints - name, ext = os.path.splitext(file_name) - if ext == ".pkl": - return pd.read_pickle(file_name) - - df = import_function(file_name) - - # disable the rdkit logger. We know that some inchis will fail and we took care of it. No use to spam the console - RDLogger.DisableLog("rdApp.*") - n_cores = multiprocessing.cpu_count() - df_split = np.array_split(df, n_cores) - with multiprocessing.Pool(n_cores) as pool: - df = pd.concat(pool.map(partial(addFPColumn, fp_size=fp_size), df_split)) - pool.close() - pool.join() - return df - - -def importSmilesCSV(csvfilename: str) -> pd.DataFrame: - return pd.read_csv(csvfilename) - - -def importDstoxTSV(tsvfilename: str) -> pd.DataFrame: - return pd.read_table(tsvfilename, names=["toxid", "inchi", "key"]) - - -conversion_rules = { - # "S_dataset.csv": importSmilesCSV, - # "S_dataset_extended.csv": importSmilesCSV, - # "D_dataset.tsv": importDstoxTSV, - # "train_data.csv": importSmilesCSV, - # "predict_data.csv": importDstoxTSV, - "B_data_ER.csv": importDstoxTSV -} - - -def convert_all(directory: str) -> List[str]: - files = [ - f - for f in os.listdir(directory) - for key, value in conversion_rules.items() - if isfile(join(directory, f)) and f in key - ] - logging.info(f"Found {len(files)} files to convert") - for f in files: - path = join(directory, f) - logging.info(f"Importing file {f}") - df = importDataFile(path, import_function=conversion_rules[f]) - name, ext = os.path.splitext(f) - output_file = join(directory, name + ".pkl") - logging.info(f"Saving pickle of file {f}") - df.to_pickle(output_file) - return files diff --git a/build/lib/dfpl/harmonize_mol_representations.py b/build/lib/dfpl/harmonize_mol_representations.py deleted file mode 100644 index 7c17e4e6..00000000 --- a/build/lib/dfpl/harmonize_mol_representations.py +++ /dev/null @@ -1,171 +0,0 @@ -from __future__ import annotations - -import logging -import sys -import urllib.error -import urllib.parse -import urllib.request -from dataclasses import dataclass -from functools import lru_cache - -import pandas as pd -from rdkit import Chem, DataStructs -from simple_parsing import ArgumentParser, choice -from simple_parsing.helpers import Serializable - -# Use this conda env: mamba create - n tidyChem - c conda - forge r - base r - tidyverse r - stringdist python rdkit -# numpy pandas simple - parsing - - -@dataclass -class ProgramConfig(Serializable): - """ - Configuration options for running the program - """ - - # file containing SMILES or InChikeys 2B harmonized - input_file: str = ( - "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_fragment.csv.tsv" - ) - # file containing the input for the harmonized molecular representations,tanimoto similarity and levenstein distance - output_file: str = ( - "/home/soulios/git-soulios/DEEPFPLEARN/dfpl_DBN/data/D_dataset.harmonized.csv" - ) - input_column: str = choice("smiles", "inchikey", default="inchikey") - # column name of the column that contains the molecular representation to harmonize - input_type: str = choice( - "smiles", "inchikey", default="inchikey" - ) # the type of molecular representation of the input - output_type: str = choice( - "smiles", "inchikey", default="smiles" - ) # the type of molecular representation of the output - - -def createCommandlineParser(): - """ - Build the cmd_parser for arguments - Parse the input arguments. - :return: Tuple of commandlineparser, arguments - """ - cmd_parser = ArgumentParser(prog="harmonize_mol_representations") - cmd_parser.add_arguments(ProgramConfig, dest="config") - cmd_args, unknown = cmd_parser.parse_known_args() - return cmd_parser, cmd_args - - -def getLevensteinDistance(a, b): - """ - This function will calculate the levenshtein distance between two input - strings a and b - - params: - a (String) : The first string you want to compare - b (String) : The second string you want to compare - - returns: - This function will return the distnace between string a and b. - - example: - a = 'stamp' - b = 'stomp' - getLevensteinDistance(a,b) - >> 1.0 - """ - - if a is None and b is not None: - return len(b) - if a is not None and b is None: - return len(a) - - @lru_cache(None) # for memorization - def min_dist(s1, s2): - if s1 == len(a) or s2 == len(b): - return len(a) - s1 + len(b) - s2 - - # no change required - if a[s1] == b[s2]: - return min_dist(s1 + 1, s2 + 1) - - return 1 + min( - min_dist(s1, s2 + 1), # insert character - min_dist(s1 + 1, s2), # delete character - min_dist(s1 + 1, s2 + 1), # replace character - ) - - return min_dist(0, 0) - - -def getTanimotoSimilarityFromTwoSMILES( - smile1: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O", - smile2: str = "COC1=C(C=CC(=C1)C=O)O", -) -> float: - if smile1 is None or smile2 is None: - return 0 - - mol1 = Chem.MolFromSmiles(smile1) - mol2 = Chem.MolFromSmiles(smile2) - if mol1 is None or mol2 is None: - return 0 - - fp1 = Chem.RDKFingerprint(mol1, fpSize=2048) - fp2 = Chem.RDKFingerprint(mol2, fpSize=2048) - if fp1 is None or fp2 is None: - return 0 - - return DataStructs.TanimotoSimilarity(fp1, fp2) - - -def getHarmonizedSmiles(smiles: str = "CC(C)C=CCCCCC(=O)NCc1ccc(c(c1)OC)O") -> str: - link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(smiles)}/smiles" - try: - f = urllib.request.urlopen(link) - myfile = f.read() - sh = myfile.decode("UTF-8") - except urllib.error.HTTPError: - sh = None - - return sh - - -def getHarmonizedSmilesfrominchis(inchikey: str = "CUWVNOSSZYUJAE-UHFFFAOYSA-N") -> str: - link = f"https://cactus.nci.nih.gov/chemical/structure/{urllib.parse.quote(inchikey)}/smiles" - try: - f = urllib.request.urlopen(link) - myfile = f.read() - sh = myfile.decode("UTF-8") - except urllib.error.HTTPError: - sh = None - - return sh - - -if __name__ == "__main__": - parser, args = createCommandlineParser() - opts = args.config - - # read data of smiles and harmonized smiles - df = pd.read_csv(filepath_or_buffer=opts.input_file) - if opts.input_type == "smiles": - df["smiles_harmonized"] = df[opts.input_column].apply(getHarmonizedSmiles) - df["tanimoto_similiarity"] = df.apply( - lambda x: getTanimotoSimilarityFromTwoSMILES( - smile1=x[opts.input_column], smile2=x["smiles_harmonized"] - ), - axis=1, - ) - df["levenstein_distance"] = df.apply( - lambda x: getLevensteinDistance( - a=x[opts.input_column], b=x["smiles_harmonized"] - ), - axis=1, - ) - elif opts.input_type == "inchikey": - df["smiles_harmonized"] = df[opts.input_column].apply( - getHarmonizedSmilesfrominchis - ) - - else: - logging.error(f"Your selected input is not supported: {opts.input_file}.") - sys.exit("Unsupported input type. Must be either smiles or inchikey") - - df.to_csv(path_or_buf=opts.output_file) diff --git a/build/lib/dfpl/history.py b/build/lib/dfpl/history.py deleted file mode 100644 index 1f16c2a9..00000000 --- a/build/lib/dfpl/history.py +++ /dev/null @@ -1,41 +0,0 @@ -# -*- coding: utf-8 -*- -"""Store and visualise training histories""" -import logging - -import matplotlib as mpl -import matplotlib.pyplot as plt -import pandas as pd -from tensorflow.keras.callbacks import History - -mpl.use("Agg") - - -def store_and_plot_history(base_file_name: str, hist: History) -> None: - """ - - :param base_file_name: - :param hist: - :return: - """ - - (ac_history_csv, ac_history_svg) = ( - base_file_name + ".history.csv", - base_file_name + ".history.svg", - ) - - # store history - pd.DataFrame(hist.history).to_csv(ac_history_csv) - logging.info(f"Neural network training history saved in file: {ac_history_csv}") - - # plot history - ac_epochs = hist.epoch - - # generate a figure of the losses for this fold - plt.figure() - for k in hist.history.keys(): - plt.plot(ac_epochs, hist.history[k], label=k) - plt.title("Training and validation metrics of neural network") - plt.legend() - plt.savefig(fname=ac_history_svg, format="svg") - plt.close() - logging.info(f"Neural network training history plotted in file: {ac_history_svg}") diff --git a/build/lib/dfpl/options.py b/build/lib/dfpl/options.py deleted file mode 100644 index bb753f05..00000000 --- a/build/lib/dfpl/options.py +++ /dev/null @@ -1,1297 +0,0 @@ -from __future__ import annotations - -import argparse -from dataclasses import dataclass -from pathlib import Path - -import jsonpickle -import torch -from chemprop.args import TrainArgs - -from dfpl.utils import makePathAbsolute - - -@dataclass -class Options: - """ - Dataclass for all options necessary for training the neural nets - """ - - configFile: str = "./example/train.json" - inputFile: str = "/deepFPlearn/CMPNN/data/tox21.csv" - outputDir: str = "." - outputFile: str = "" - ecWeightsFile: str = "AE.encoder.weights.hdf5" - ecModelDir: str = "AE_encoder" - fnnModelDir: str = "modeltraining" - type: str = "smiles" - fpType: str = "topological" # also "MACCS", "atompairs" - epochs: int = 512 - fpSize: int = 2048 - encFPSize: int = 256 - kFolds: int = 0 - testSize: float = 0.2 - enableMultiLabel: bool = False - verbose: int = 0 - trainAC: bool = True # if set to False, an AC weight file must be provided! - trainFNN: bool = True - compressFeatures: bool = True - sampleFractionOnes: float = 0.5 # Only used when value is in [0,1] - sampleDown: bool = False - split_type: str = "random" - aeSplitType: str = "random" - aeType: str = "deterministic" - aeEpochs: int = 3000 - aeBatchSize: int = 512 - aeLearningRate: float = 0.001 - aeLearningRateDecay: float = 0.01 - aeActivationFunction: str = "relu" - aeOptimizer: str = "Adam" - fnnType: str = "FNN" - batchSize: int = 128 - optimizer: str = "Adam" - learningRate: float = 0.001 - lossFunction: str = "bce" - activationFunction: str = "relu" - l2reg: float = 0.001 - dropout: float = 0.2 - threshold: float = 0.5 - gpu: str = "" - snnDepth = 8 - snnWidth = 50 - aeWabTracking: str = "" # Wand & Biases autoencoder tracking - wabTracking: str = "" # Wand & Biases FNN tracking - wabTarget: str = "ER" # Wand & Biases target used for showing training progress - - def saveToFile(self, file: str) -> None: - """ - Saves an instance to a JSON file - """ - jsonFile = Path(file) - with jsonFile.open("w") as f: - f.write(jsonpickle.encode(self)) - - @classmethod - def fromJson(cls, file: str) -> Options: - """ - Create an instance from a JSON file - """ - jsonFile = Path(file) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - return jsonpickle.decode(content) - raise ValueError("JSON file does not exist or is not readable") - - @classmethod - def fromCmdArgs(cls, args: argparse.Namespace) -> Options: - """ - Creates Options instance from cmdline arguments. - - If a training file (JSON) is provided, the values from that file are used. - However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the - JSON file and on the commandline, then the value of the commandline argument will be used. - """ - result = Options() - if "configFile" in vars(args).keys(): - jsonFile = Path(makePathAbsolute(args.configFile)) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - result = jsonpickle.decode(content) - else: - raise ValueError("Could not find JSON input file") - - for key, value in vars(args).items(): - # The args dict will contain a "method" key from the subparser. - # We don't use this. - if key != "method": - result.__setattr__(key, value) - return result - - -@dataclass -class GnnOptions(TrainArgs): - """ - Dataclass to hold all options used for training the graph models - """ - - total_epochs: int = 30 - save: bool = True - configFile: str = "./example/traingnn.json" - data_path: str = "./example/data/tox21.csv" - use_compound_names: bool = False - save_dir: str = "" - no_cache: bool = False - features_scaling: bool = True - use_input_features: str = "" - cuda: bool = False - num_lrs: int = 2 - minimize_score: bool = False - num_tasks: int = 12 - preds_path: str = "./tox21dmpnn.csv" - test_path: str = "" - save_preds: bool = True - - @classmethod - def fromCmdArgs(cls, args: argparse.Namespace) -> GnnOptions: - """ - Creates Options instance from cmdline arguments. - - If a training file (JSON) is provided, the values from that file are used. - However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the - JSON file and on the commandline, then the value of the commandline argument will be used. - """ - result = GnnOptions() - if "configFile" in vars(args).keys(): - jsonFile = Path(makePathAbsolute(args.configFile)) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - result = jsonpickle.decode(content) - else: - raise ValueError("Could not find JSON input file") - - return result - - @classmethod - def fromJson(cls, file: str) -> GnnOptions: - """ - Create an instance from a JSON file - """ - jsonFile = Path(file) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - return jsonpickle.decode(content) - raise ValueError("JSON file does not exist or is not readable") - - -def createCommandlineParser() -> argparse.ArgumentParser: - """ - Build the parser for arguments with its two subparsers - """ - parser = argparse.ArgumentParser(prog="deepFPlearn") - subparsers = parser.add_subparsers(help="Sub programs of deepFPlearn") - - parser_train_gnn = subparsers.add_parser( - "traingnn", help="Train new GNN models with your data" - ) - parser_train_gnn.set_defaults(method="traingnn") - parseTrainGnn(parser_train_gnn) - - parser_predict_gnn = subparsers.add_parser( - "predictgnn", help="Predict with your GNN models" - ) - parser_predict_gnn.set_defaults(method="predictgnn") - parsePredictGnn(parser_predict_gnn) - - parser_train = subparsers.add_parser( - "train", help="Train new models with your data" - ) - parser_train.set_defaults(method="train") - parseInputTrain(parser_train) - - parser_predict = subparsers.add_parser( - "predict", help="Predict your data with existing models" - ) - parser_predict.set_defaults(method="predict") - parseInputPredict(parser_predict) - - parser_convert = subparsers.add_parser( - "convert", help="Convert known data files to pickle serialization files" - ) - parser_convert.set_defaults(method="convert") - parseInputConvert(parser_convert) - return parser - - -def parseInputTrain(parser: argparse.ArgumentParser) -> None: - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - # Create argument groups - general_args = parser.add_argument_group("Model Configuration") - autoencoder_args = parser.add_argument_group("Autoencoder Configuration") - training_args = parser.add_argument_group("Training Configuration") - tracking_args = parser.add_argument_group("Tracking Configuration") - - # Model Configuration - general_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-i", - "--inputFile", - metavar="FILE", - type=str, - help="The file containing the data for training in " - "comma separated CSV format.The first column should be smiles.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-o", - "--outputDir", - metavar="DIR", - type=str, - help="Prefix of output file name. Trained model and " - "respective stats will be returned in this directory.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-t", - "--type", - metavar="STRING", - type=str, - choices=["fp", "smiles"], - help="Type of the chemical representation. Choices: 'fp', 'smiles'.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-thr", - "--threshold", - type=float, - metavar="FLOAT", - help="Threshold for binary classification.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-gpu", - "--gpu", - metavar="INT", - type=int, - help="Select which gpu to use. If not available, leave empty.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-k", - "--fpType", - metavar="STR", - type=str, - choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], - help="The type of fingerprint to be generated/used in input file.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-s", - "--fpSize", - type=int, - help="Size of fingerprint that should be generated.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-c", - "--compressFeatures", - metavar="BOOL", - type=bool, - help="Should the fingerprints be compressed or not. Activates the autoencoder. ", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-m", - "--enableMultiLabel", - metavar="BOOL", - type=bool, - help="Train multi-label classification model in addition to the individual models.", - default=argparse.SUPPRESS, - ) - # Autoencoder Configuration - autoencoder_args.add_argument( - "-a", - "--ecWeightsFile", - type=str, - metavar="FILE", - help="The .hdf5 file of a trained encoder", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--ecModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the encoder will be saved", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeType", - metavar="STRING", - type=str, - choices=["variational", "deterministic"], - help="Autoencoder type, variational or deterministic.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeEpochs", - metavar="INT", - type=int, - help="Number of epochs for autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeBatchSize", - metavar="INT", - type=int, - help="Batch size in autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeActivationFunction", - metavar="STRING", - type=str, - choices=["relu", "selu"], - help="The activation function for the hidden layers in the autoencoder.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeLearningRate", - metavar="FLOAT", - type=float, - help="Learning rate for autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeLearningRateDecay", - metavar="FLOAT", - type=float, - help="Learning rate decay for autoencoder training.", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "--aeSplitType", - metavar="STRING", - type=str, - choices=["scaffold_balanced", "random", "molecular_weight"], - help="Set how the data is going to be split for the autoencoder", - default=argparse.SUPPRESS, - ) - autoencoder_args.add_argument( - "-d", - "--encFPSize", - metavar="INT", - type=int, - help="Size of encoded fingerprint (z-layer of autoencoder).", - default=argparse.SUPPRESS, - ) - # Training Configuration - training_args.add_argument( - "--split_type", - metavar="STRING", - type=str, - choices=["scaffold_balanced", "random", "molecular_weight"], - help="Set how the data is going to be split for the feedforward neural network", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-l", - "--testSize", - metavar="FLOAT", - type=float, - help="Fraction of the dataset that should be used for testing. Value in [0,1].", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-K", - "--kFolds", - metavar="INT", - type=int, - help="K that is used for K-fold cross-validation in the training procedure.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-v", - "--verbose", - metavar="INT", - type=int, - choices=[0, 1, 2], - help="Verbosity level. O: No additional output, " - + "1: Some additional output, 2: full additional output", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--trainAC", - metavar="BOOL", - type=bool, - help="Choose to train or not, the autoencoder based on the input file", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--trainFNN", - metavar="BOOL", - type=bool, - help="Train the feedforward network either with provided weights.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--sampleFractionOnes", - metavar="FLOAT", - type=float, - help="This is the fraction of positive target associations (1s) in comparison to the majority class(0s)." - "only works if --sampleDown is enabled", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--sampleDown", - metavar="BOOL", - type=bool, - help="Enable automatic down sampling of the 0 valued samples.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "-e", - "--epochs", - metavar="INT", - type=int, - help="Number of epochs that should be used for the FNN training", - default=argparse.SUPPRESS, - ) - - training_args.add_argument( - "--lossFunction", - metavar="STRING", - type=str, - choices=["mse", "bce", "focal"], - help="Loss function to use during training. mse - mean squared error, bce - binary cross entropy.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--optimizer", - metavar="STRING", - type=str, - choices=["Adam", "SGD"], - help='Optimizer to use for backpropagation in the FNN. Possible values: "Adam", "SGD"', - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--batchSize", - metavar="INT", - type=int, - help="Batch size in FNN training.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--l2reg", - metavar="FLOAT", - type=float, - help="Value for l2 kernel regularizer.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--dropout", - metavar="FLOAT", - type=float, - help="The fraction of data that is dropped out in each dropout layer.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--learningRate", - metavar="FLOAT", - type=float, - help="Learning rate size in FNN training.", - default=argparse.SUPPRESS, - ) - training_args.add_argument( - "--activationFunction", - metavar="STRING", - type=str, - choices=["relu", "selu"], - help="The activation function for hidden layers in the FNN.", - default=argparse.SUPPRESS, - ) - # Tracking Configuration - tracking_args.add_argument( - "--aeWabTracking", - metavar="BOOL", - type=bool, - help="Track autoencoder performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, - ) - tracking_args.add_argument( - "--wabTracking", - metavar="BOOL", - type=bool, - help="Track FNN performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, - ) - tracking_args.add_argument( - "--wabTarget", - metavar="STRING", - type=str, - choices=["AR", "ER", "ED", "GR", "TR", "PPARg", "Aromatase"], - help="Which target to use for tracking performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, - ) - - -def parseTrainGnn(parser: argparse.ArgumentParser) -> None: - general_args = parser.add_argument_group("General Configuration") - data_args = parser.add_argument_group("Data Configuration") - files_args = parser.add_argument_group("Files") - model_args = parser.add_argument_group("Model arguments") - training_args = parser.add_argument_group("Training Configuration") - - # General arguments - general_args.add_argument("--split_key_molecule", type=int) - general_args.add_argument("--pytorch_seed", type=int) - general_args.add_argument("--cache_cutoff", type=float) - general_args.add_argument("--save_preds", type=bool) - general_args.add_argument( - "--cuda", action="store_true", default=False, help="Turn on cuda" - ) - general_args.add_argument( - "--save_smiles_splits", - action="store_true", - default=False, - help="Save smiles for each train/val/test splits for prediction convenience later", - ) - general_args.add_argument( - "--test", - action="store_true", - default=False, - help="Whether to skip training and only test the model", - ) - general_args.add_argument( - "--gpu", - type=int, - choices=list(range(torch.cuda.device_count())), - help="Which GPU to use", - ) - general_args.add_argument("--save", type=bool) - general_args.add_argument( - "--quiet", - action="store_true", - default=False, - help="Skip non-essential print statements", - ) - general_args.add_argument( - "--log_frequency", - type=int, - metavar="INT", - default=10, - help="The number of batches between each logging of the training loss", - ) - general_args.add_argument( - "--no_cuda", action="store_true", default=True, help="Turn off cuda" - ) - general_args.add_argument( - "--no_cache", - action="store_true", - default=False, - help="Turn off caching mol2graph computation", - ) - - # FILES ARGUMENTS - files_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - ) - files_args.add_argument( - "--config_path", - type=str, - metavar="FILE", - help="Path to a .json file containing arguments. Any arguments present in the config" - "file will override arguments specified via the command line or by the defaults.", - ) - files_args.add_argument( - "--save_dir", - type=str, - metavar="DIR", - default="./ckpt/", - help="Directory where model checkpoints will be saved", - ) - files_args.add_argument( - "--checkpoint_dir", - type=str, - metavar="DIR", - default=None, - help="Directory from which to load model checkpoints" - "(walks directory and ensembles all models that are found)", - ) - files_args.add_argument( - "--checkpoint_path", - type=str, - metavar="FILE", - default=None, - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--checkpoint_paths", - type=str, - metavar="FILE", - nargs="*", - default=None, - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--separate_val_path", - type=str, - metavar="FILE", - help="Path to separate val set, optional", - ) - files_args.add_argument( - "--separate_val_features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to file with features for separate val set", - ) - files_args.add_argument( - "--separate_test_path", - type=str, - metavar="FILE", - help="Path to separate test set, optional", - ) - files_args.add_argument( - "--separate_test_features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to file with features for separate test set", - ) - files_args.add_argument( - "--folds_file", - type=str, - metavar="FILE", - default=None, - help="Optional file of fold labels", - ) - files_args.add_argument( - "--val_fold_index", - type=int, - metavar="INT", - default=None, - help="Which fold to use as val for cross val", - ) - files_args.add_argument( - "--test_fold_index", - type=int, - metavar="INT", - default=None, - help="Which fold to use as test for cross val", - ) - files_args.add_argument( - "--crossval_index_dir", - type=str, - metavar="DIR", - help="Directory in which to find cross validation index files", - ) - files_args.add_argument( - "--crossval_index_file", - type=str, - metavar="FILE", - help="Indices of files to use as train/val/test" - "Overrides --num_folds and --seed.", - ) - files_args.add_argument( - "--data_weights_path", - type=str, - metavar="FILE", - help="Path where the data weight are saved", - ) - files_args.add_argument( - "--features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to features to use in FNN (instead of features_generator)", - ) - - files_args.add_argument( - "--separate_val_phase_features_path", type=str, metavar="FILE" - ) - files_args.add_argument( - "--separate_test_phase_features_path", type=str, metavar="FILE" - ) - - files_args.add_argument( - "--separate_val_atom_descriptors_path", type=str, metavar="FILE" - ) - files_args.add_argument( - "--separate_test_atom_descriptors_path", type=str, metavar="FILE" - ) - # Data related arguments - data_args.add_argument( - "--data_path", - type=str, - metavar="FILE", - help="Path to data CSV file", - default="", - ) - data_args.add_argument( - "--use_compound_names", - action="store_true", - default=False, - help="Use when test data file contains compound names in addition to SMILES strings", - ) - data_args.add_argument( - "--max_data_size", - type=int, - metavar="INT", - help="Maximum number of data points to load", - ) - - data_args.add_argument( - "--features_only", - action="store_true", - default=False, - help="Use only the additional features in an FFN, no graph network", - ) - data_args.add_argument( - "--dataset_type", - type=str, - metavar="STRING", - choices=["classification", "regression", "multiclass"], - help="Type of dataset, e.g. classification or regression." - "This determines the loss function used during training.", - default="regression", - ) # classification - data_args.add_argument( - "--multiclass_num_classes", - type=int, - metavar="INT", - default=3, - help="Number of classes when running multiclass classification", - ) - data_args.add_argument( - "--split_type", - type=str, - metavar="STRING", - default="random", - choices=[ - "random", - "scaffold_balanced", - "predetermined", - "crossval", - "index_predetermined", - ], - help="Method of splitting the data into train/val/test", - ) - data_args.add_argument( - "--split_sizes", - type=float, - metavar="FLOAT", - nargs=3, - default=[0.8, 0.2, 0.0], - help="Split proportions for train/validation/test sets", - ) - - data_args.add_argument( - "--seed", - type=int, - default=0, - help="Random seed to use when splitting data into train/val/test sets." - "When `num_folds` > 1, the first fold uses this seed and all" - "subsequent folds add 1 to the seed.", - ) - data_args.add_argument( - "--smiles_columns", - type=str, - metavar="STRING", - help="Name of the smiles columns", - ) - - data_args.add_argument( - "--target_columns", - type=str, - metavar="STRING", - help="Name of the target columns", - ) - - data_args.add_argument( - "--ignore_columns", - type=str, - metavar="STRING", - help="Names of the columns to ignore", - ) - data_args.add_argument( - "--num_tasks", type=int, metavar="INT", help="NUmber of tasks" - ) - data_args.add_argument( - "--no_features_scaling", - action="store_true", - default=False, - help="Turn off scaling of features", - ) - data_args.add_argument( - "--features_scaling", - action="store_true", - default=False, - help="Turn on scaling of features", - ) - data_args.add_argument( - "--use_input_features", - type=str, - metavar="STRING", - help="Turn on scaling of features", - ) - - # Model arguments - model_args.add_argument( - "--ensemble_size", - type=int, - metavar="INT", - default=1, - help="Number of models in ensemble", - ) - model_args.add_argument( - "--hidden_size", - type=int, - metavar="INT", - default=300, - help="Dimensionality of hidden layers in MPN", - ) - model_args.add_argument( - "--bias", - action="store_true", - default=False, - help="Whether to add bias to linear layers", - ) - model_args.add_argument( - "--depth", - type=int, - metavar="INT", - default=3, - help="Number of message passing steps", - ) - model_args.add_argument( - "--dropout", - type=float, - metavar="FLOAT", - default=0.0, - help="Dropout probability", - ) - model_args.add_argument( - "--activation", - type=str, - metavar="STRING", - default="ReLU", - choices=["ReLU", "LeakyReLU", "PReLU", "tanh", "SELU", "ELU"], - help="Activation function", - ) - model_args.add_argument( - "--undirected", - action="store_true", - default=False, - help="Undirected edges (always sum the two relevant bond vectors)", - ) - model_args.add_argument( - "--ffn_hidden_size", - type=int, - metavar="INT", - default=2, - help="Hidden dim for higher-capacity FFN (defaults to hidden_size)", - ) - model_args.add_argument( - "--ffn_num_layers", - type=int, - metavar="INT", - default=2, - help="Number of layers in FFN after MPN encoding", - ) - model_args.add_argument( - "--atom_messages", - action="store_true", - default=False, - help="Use messages on atoms instead of messages on bonds", - ) - - model_args.add_argument( - "--num_lrs", - type=int, - metavar="INT", - default=2, - help="Number of layers in FFN after MPN encoding", - ) - model_args.add_argument("--checkpoint_frzn", type=str, metavar="STRING") - - # Model arguments - model_args.add_argument("--mpn_shared", type=bool, metavar="BOOL") - model_args.add_argument( - "--show_individual_scores", - action="store_true", - default=True, - help="Show all scores for individual targets, not just average, at the end", - ) - model_args.add_argument("--aggregation", choices=["mean", "sum", "norm"]) - model_args.add_argument("--aggregation_norm", type=int) - model_args.add_argument("--explicit_h", type=bool, metavar="BOOL") - model_args.add_argument("--adding_h", type=bool, metavar="BOOL") - # Training arguments - model_args.add_argument("--class_balance", type=bool, metavar="BOOL") - model_args.add_argument("--evidential_regularization", type=float, metavar="FLOAT") - model_args.add_argument( - "--overwrite_default_atom_features", type=bool, metavar="BOOL" - ) - model_args.add_argument("--no_atom_descriptor_scaling", type=bool, metavar="BOOL") - model_args.add_argument( - "--overwrite_default_bond_features", type=bool, metavar="BOOL" - ) - model_args.add_argument("--frzn_ffn_layers", type=int, metavar="INT") - model_args.add_argument("--freeze_first_only", type=bool, metavar="BOOL") - # Training arguments - training_args.add_argument( - "--epochs", type=int, metavar="INT", default=30, help="Number of epochs to run" - ) - training_args.add_argument( - "--total_epochs", - type=int, - metavar="INT", - default=30, - help="Number of total epochs to run", - ) - training_args.add_argument( - "--batch_size", type=int, metavar="INT", default=50, help="Batch size" - ) - training_args.add_argument( - "--warmup_epochs", - type=int, - metavar="INT", - default=2, - help="Number of epochs during which learning rate increases linearly from" - "init_lr to max_lr. Afterwards, learning rate decreases exponentially" - "from max_lr to final_lr.", - ) - training_args.add_argument( - "--init_lr", - type=float, - metavar="FLOAT", - default=1e-4, - help="Initial learning rate", - ) - training_args.add_argument( - "--max_lr", - type=float, - metavar="FLOAT", - default=1e-3, - help="Maximum learning rate", - ) - training_args.add_argument( - "--final_lr", - type=float, - metavar="FLOAT", - default=1e-4, - help="Final learning rate", - ) - training_args.add_argument( - "--extra_metrics", - type=str, - metavar="STRING", - nargs="*", - help="Extra metrics to use", - ) - training_args.add_argument( - "--loss_function", - type=str, - metavar="STRING", - choices=[ - "mse", - "bounded_mse", - "binary_cross_entropy", - "cross_entropy", - "mcc", - "sid", - "wasserstein", - "mve", - "evidential", - "dirichlet", - ], - ) - training_args.add_argument("--grad_clip", type=float) - training_args.add_argument( - "--metric", - type=str, - metavar="STRING", - default=None, - choices=[ - "auc", - "prc-auc", - "rmse", - "mae", - "mse", - "r2", - "accuracy", - "cross_entropy", - ], - help="Metric to use during evaluation." - "Note: Does NOT affect loss function used during training" - "(loss is determined by the `dataset_type` argument)." - 'Note: Defaults to "auc" for classification and "rmse" for regression.', - ) - training_args.add_argument( - "--num_folds", - type=int, - metavar="INT", - default=1, - help="Number of folds when performing cross validation", - ) - - -def parseInputPredict(parser: argparse.ArgumentParser) -> None: - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - parser = argparse.ArgumentParser( - description="Use models that have been generated by " - "to make predictions on chemicals (provide SMILES or " - "topological fingerprints)." - ) - general_args = parser.add_argument_group("General Configuration") - files_args = parser.add_argument_group("Files") - files_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "-i", - "--inputFile", - metavar="FILE", - type=str, - help="The file containing the data for the prediction in (unquoted) " - "comma separated CSV format. The column named 'smiles' or 'fp'" - "contains the field to be predicted. Please adjust the type " - "that should be predicted (fp or smile) with -t option appropriately." - "An optional column 'id' is used to assign the outcomes to the" - "original identifiers. If this column is missing, the results are" - "numbered in the order of their appearance in the input file." - "A header is expected and respective column names are used.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "-o", - "--outputDir", - metavar="DIR", - type=str, - help="Prefix of output directory. It will contain a log file and the file specified" - "with --outputFile.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--outputFile", - metavar="FILE", - type=str, - help="Output .CSV file name which will contain one prediction per input line. " - "Default: prefix of input file name.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-t", - "--type", - metavar="STR", - type=str, - choices=["fp", "smiles"], - help="Type of the chemical representation. Choices: 'fp', 'smiles'.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-k", - "--fpType", - metavar="STR", - type=str, - choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], - help="The type of fingerprint to be generated/used in input file.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--ecModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the encoder will be saved (if trainAE=True) or " - "loaded from (if trainAE=False). Provide a full path here.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--fnnModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the fnn is loaded from. " - "Provide a full path here.", - default=argparse.SUPPRESS, - ) - - -def parsePredictGnn(parser: argparse.ArgumentParser) -> None: - general_args = parser.add_argument_group("General Configuration") - data_args = parser.add_argument_group("Data Configuration") - files_args = parser.add_argument_group("Files") - training_args = parser.add_argument_group("Training Configuration") - files_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "--gpu", - type=int, - metavar="INT", - choices=list(range(torch.cuda.device_count())), - help="Which GPU to use", - ) - general_args.add_argument( - "--no_cuda", action="store_true", default=False, help="Turn off cuda" - ) - general_args.add_argument( - "--num_workers", - type=int, - metavar="INT", - help="Number of workers for the parallel data loading 0 means sequential", - ) - general_args.add_argument( - "--no_cache", - type=bool, - metavar="BOOL", - default=False, - help="Turn off caching mol2graph computation", - ) - general_args.add_argument( - "--no_cache_mol", - type=bool, - metavar="BOOL", - default=False, - help="Whether to not cache the RDKit molecule for each SMILES string to reduce memory\ - usage cached by default", - ) - general_args.add_argument( - "--empty_cache", - type=bool, - metavar="BOOL", - help="Whether to empty all caches before training or predicting. This is necessary if\ - multiple jobs are run within a single script and the atom or bond features change", - ) - files_args.add_argument( - "--preds_path", - type=str, - metavar="FILE", - help="Path to CSV file where predictions will be saved", - default="", - ) - files_args.add_argument( - "--checkpoint_dir", - type=str, - metavar="DIR", - help="Directory from which to load model checkpoints" - "(walks directory and ensembles all models that are found)", - default="./ckpt", - ) - files_args.add_argument( - "--checkpoint_path", - type=str, - metavar="DIR", - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--checkpoint_paths", - type=str, - metavar="FILE", - nargs="*", - help="Path to model checkpoint (.pt file)", - ) - files_args.add_argument( - "--data_path", - type=str, - metavar="FILE", - help="Path to CSV file containing testing data for which predictions will be made", - default="", - ) - files_args.add_argument( - "--test_path", - type=str, - metavar="FILE", - help="Path to CSV file containing testing data for which predictions will be made", - default="", - ) - files_args.add_argument( - "--features_path", - type=str, - metavar="FILE", - nargs="*", - help="Path to features to use in FNN (instead of features_generator)", - ) - files_args.add_argument( - "--atom_descriptors_path", - type=str, - metavar="FILE", - help="Path to the extra atom descriptors.", - ) - data_args.add_argument( - "--use_compound_names", - action="store_true", - default=False, - help="Use when test data file contains compound names in addition to SMILES strings", - ) - data_args.add_argument( - "--no_features_scaling", - action="store_true", - default=False, - help="Turn off scaling of features", - ) - data_args.add_argument( - "--max_data_size", - type=int, - metavar="INT", - help="Maximum number of data points to load", - ) - data_args.add_argument( - "--smiles_columns", - type=str, - metavar="STRING", - help="List of names of the columns containing SMILES strings.By default, uses the first\ - number_of_molecules columns.", - ) - data_args.add_argument( - "--number_of_molecules", - type=int, - metavar="INT", - help="Number of molecules in each input to the model.This must equal the length of\ - smiles_columns if not None", - ) - - data_args.add_argument( - "--atom_descriptors", - type=bool, - metavar="Bool", - help="Use or not atom descriptors", - ) - - data_args.add_argument( - "--bond_features_size", - type=int, - metavar="INT", - help="Size of the extra bond descriptors that will be used as bond features to featurize a\ - given molecule", - ) - training_args.add_argument( - "--batch_size", type=int, metavar="INT", default=50, help="Batch size" - ) - - -def parseInputConvert(parser: argparse.ArgumentParser) -> None: - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - parser.add_argument( - "-f", - metavar="FILE", - type=str, - help="Input directory where your CSV/TSV files are stored.", - required=True, - default="", - ) diff --git a/build/lib/dfpl/plot.py b/build/lib/dfpl/plot.py deleted file mode 100644 index 7e8f6ac1..00000000 --- a/build/lib/dfpl/plot.py +++ /dev/null @@ -1,228 +0,0 @@ -import array - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import wandb -from matplotlib.axes import Axes -from typing import List -# for NN model functions -from tensorflow.keras.callbacks import History - - -def get_max_validation_accuracy(history: History) -> str: - validation = smooth_curve(history.history["val_accuracy"]) - y_max: float = max(validation) - return "Max validation accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_validation_balanced_accuracy(history: History) -> str: - validation_bal_acc = smooth_curve(history.history["val_balanced_accuracy"]) - y_max: float = max(validation_bal_acc) - return "Max validation balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_training_balanced_accuracy(history: History) -> str: - training_bal_acc = smooth_curve(history.history["balanced_accuracy"]) - y_max: float = max(training_bal_acc) - return "Training balanced accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_training_auc(history: History) -> str: - training_auc = smooth_curve(history.history["auc"]) - y_max: float = max(training_auc) - return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_validation_auc(history: History) -> str: - validation_auc = smooth_curve(history.history["val_auc"]) - y_max: float = max(validation_auc) - return "Validation AUC ≈ " + str(round(y_max, 3) * 100) + "%" - - -def get_max_training_accuracy(history: History) -> str: - training = smooth_curve(history.history["accuracy"]) - y_max: float = max(training) - return "Max training accuracy ≈ " + str(round(y_max, 3) * 100) + "%" - - -def smooth_curve(points: np.ndarray, factor: float = 0.75) -> np.ndarray: - smoothed_points: List[float] = [] - for point in points: - if smoothed_points: - previous = smoothed_points[-1] - smoothed_points.append(previous * factor + point * (1 - factor)) - else: - smoothed_points.append(point) - return smoothed_points - - -def set_plot_history_data(ax: Axes, history: History, which_graph: str) -> None: - (train, valid) = (None, None) - - if which_graph == "acc": - train = smooth_curve(history.history["accuracy"]) - valid = smooth_curve(history.history["val_accuracy"]) - - if which_graph == "loss": - train = smooth_curve(history.history["loss"]) - valid = smooth_curve(history.history["val_loss"]) - - # plt.xkcd() # make plots look like xkcd - - epochs = range(1, len(train) + 1) - - trim = 0 # remove first 5 epochs - # when graphing loss the first few epochs may skew the (loss) graph - - ax.plot(epochs[trim:], train[trim:], "dodgerblue", linewidth=15, alpha=0.1) - ax.plot(epochs[trim:], train[trim:], "dodgerblue", label="Training") - - ax.plot(epochs[trim:], valid[trim:], "g", linewidth=15, alpha=0.1) - ax.plot(epochs[trim:], valid[trim:], "g", label="Validation") - - -def plot_history(history: History, file: str) -> None: - fig, (ax1, ax2) = plt.subplots( - nrows=2, - ncols=1, - figsize=(10, 6), - sharex="all", - gridspec_kw={"height_ratios": [5, 2]}, - ) - - set_plot_history_data(ax1, history, "acc") - - set_plot_history_data(ax2, history, "loss") - - # Accuracy graph - ax1.set_ylabel("Accuracy") - ax1.set_ylim(bottom=0.5, top=1) - ax1.legend(loc="lower right") - ax1.spines["top"].set_visible(False) - ax1.spines["right"].set_visible(False) - ax1.xaxis.set_ticks_position("none") - ax1.spines["bottom"].set_visible(False) - - # max accuracy text - plt.text( - 0.5, - 0.6, - get_max_validation_balanced_accuracy(history), - horizontalalignment="right", - verticalalignment="top", - transform=ax1.transAxes, - fontsize=12, - ) - plt.text( - 0.5, - 0.8, - get_max_training_balanced_accuracy(history), - horizontalalignment="right", - verticalalignment="top", - transform=ax1.transAxes, - fontsize=12, - ) - - # Loss graph - ax2.set_ylabel("Loss") - ax2.set_yticks([]) - ax2.plot(legend=False) - ax2.set_xlabel("Epochs") - ax2.spines["top"].set_visible(False) - ax2.spines["right"].set_visible(False) - - plt.tight_layout() - plt.savefig(fname=file, format="svg") - plt.close() - - -def plot_train_history(hist, target, file_accuracy, file_loss): - """ - Plot the training performance in terms of accuracy and loss values for each epoch. - :param hist: The history returned by model.fit function - :param target: The name of the target of the model - :param file_accuracy: The filename for plotting accuracy values - :param file_loss: The filename for plotting loss values - :return: none - """ - - # plot accuracy - plt.figure() - plt.plot(hist.history["accuracy"]) - if "val_accuracy" in hist.history.keys(): - plt.plot(hist.history["val_accuracy"]) - plt.title("Model accuracy - " + target) - plt.ylabel("Accuracy") - plt.xlabel("Epoch") - if "val_accuracy" in hist.history.keys(): - plt.legend(["Train", "Test"], loc="upper left") - else: - plt.legend(["Train"], loc="upper_left") - plt.savefig(fname=file_accuracy, format="svg") - - # Plot training & validation loss values - plt.figure() - plt.plot(hist.history["loss"]) - plt.plot(hist.history["val_loss"]) - plt.title("Model loss - " + target) - plt.ylabel("Loss") - plt.xlabel("Epoch") - plt.legend(["Train", "Test"], loc="upper left") - # plt.show() - plt.savefig(fname=file_loss, format="svg") - plt.close() - - -def plot_history_vis( - hist: History, - model_hist_plot_path: str, - model_hist_csv_path: str, - model_hist_plot_path_a: str, - model_hist_plot_path_l: str, - target: str, -) -> None: - plot_history(history=hist, file=model_hist_plot_path) - histDF = pd.DataFrame(hist.history) - histDF.to_csv(model_hist_csv_path) - - # plot accuracy and loss for the training and validation during training - plot_train_history( - hist=hist, - target=target, - file_accuracy=model_hist_plot_path_a, - file_loss=model_hist_plot_path_l, - ) - - -def plot_auc( - fpr: np.ndarray, - tpr: np.ndarray, - auc_value: float, - target: str, - filename: str, - wandb_logging: bool = False, -) -> None: - """ - Plot the area under the curve to the provided file - - :param fpr: An array containing the false positives - :param tpr: An array containing the true positives - :param auc_value: The value of the area under the curve - :param target: The name of the training target - :param filename: The filename to which the plot should be stored - :param wandb_logging: Whether to log the plot to wandb - :rtype: None - """ - # Create a boolean mask to filter out zero values - plt.figure() - plt.plot([0, 1], [0, 1], "k--") - plt.plot(fpr, tpr, label=f"Keras (area = {auc_value:.3f})") - plt.xlabel("False positive rate") - plt.ylabel("True positive rate") - plt.title("ROC curve " + target) - plt.legend(loc="best") - plt.savefig(fname=filename, format="png") - if wandb_logging: - wandb.log({"roc_plot": plt}) - plt.close() diff --git a/build/lib/dfpl/predictions.py b/build/lib/dfpl/predictions.py deleted file mode 100644 index 29e73142..00000000 --- a/build/lib/dfpl/predictions.py +++ /dev/null @@ -1,39 +0,0 @@ -import logging - -import numpy as np -import pandas as pd -import tensorflow.keras.models - -from dfpl import options, settings - - -def predict_values(df: pd.DataFrame, opts: options.Options) -> pd.DataFrame: - """ - Predict a set of chemicals using a selected model. - - :param df: - :param opts: - :return: - """ - model = tensorflow.keras.models.load_model(opts.fnnModelDir, compile=False) - model.compile(loss=opts.lossFunction, optimizer=opts.optimizer) - if opts.compressFeatures: - sub_df = df[df["fpcompressed"].notnull()] - x = np.array( - sub_df["fpcompressed"].to_list(), - dtype=settings.nn_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info(f"Compressed FP matrix with shape {x.shape} and type {x.dtype}") - sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) - return sub_df - else: - sub_df = df[df["fp"].notnull()] - x = np.array( - sub_df["fp"].to_list(), - dtype=settings.nn_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info(f"Uncompressed FP matrix with shape {x.shape} and type {x.dtype}") - sub_df["predicted"] = pd.DataFrame(model.predict(x), columns=["predicted"]) - return sub_df diff --git a/build/lib/dfpl/rbm.py b/build/lib/dfpl/rbm.py deleted file mode 100644 index fe5b1ac5..00000000 --- a/build/lib/dfpl/rbm.py +++ /dev/null @@ -1,259 +0,0 @@ -import os.path -from os.path import basename -import pandas as pd -import logging -import tensorflow as tf -import numpy as np -from tensorflow.keras import Model -from sklearn.model_selection import train_test_split -import math - -from dfpl import callbacks -from dfpl import options -from dfpl import history as ht -from dfpl import settings - - -class RBMLayer(tf.keras.layers.Layer): - def __init__(self, input_size, output_size, name="RBMLayer", **kwargs): - super(RBMLayer, self).__init__(name=name, **kwargs) - self._input_size = input_size - self._output_size = output_size - self._name = name - # self._activation_function = activation_function - - def build(self, input_shape): - self.w = self.add_weight( - shape=(input_shape[-1], self._output_size), - initializer="random_normal", - trainable=True, name=f"w_{self._name}" - ) - self.hb = self.add_weight( - shape=(self._output_size,), initializer="random_normal", trainable=True, name=f"hb_{self._name}" - ) - self.vb = self.add_weight( - shape=(input_shape[-1],), initializer="random_normal", trainable=True, name=f"vb_{self._name}" - ) - - def __prob_h_given_v(self, visible, w, hb): - return tf.nn.sigmoid(tf.matmul(visible, w) + hb) - - def __prob_v_given_h(self, hidden, w, vb): - return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb) - - def __sample_prob(self, probs): - return tf.nn.relu(tf.sign(probs - tf.random.uniform(tf.shape(probs)))) - - def call(self, X): - out = tf.nn.sigmoid(tf.matmul(X, self.w) + self.hb) - hiddenGen = self.__sample_prob(self.__prob_h_given_v(X, self.w, self.hb)) - visibleGen = self.__sample_prob(self.__prob_v_given_h(hiddenGen, self.w, self.vb)) - h1 = self.__prob_h_given_v(visibleGen, self.w, self.hb) - return out, visibleGen, hiddenGen, h1 - - def get_config(self): - return {"input_size": self._input_size, - "output_size": self._output_size} - - -class RBM(tf.keras.Model): - - def __init__(self, input_size, output_size, name="RBM", **kwargs): - super(RBM, self).__init__(name=name, **kwargs) - # Define hyperparameters - self._input_size = input_size - self._output_size = output_size - self.n_layer = round(math.log2(input_size / output_size)) - - self.last_out = None - - # layers defination - # encoding layers - self.rbm_encoder_layers = [] - self.rbm_decoder_layers = [] - self.rbm_encoder1 = RBMLayer(self._input_size, int(self._input_size // 2), name="encoder_0") - - if self.n_layer > 1: - for i in range(1, self.n_layer): - factor_units = 2 ** (i + 1) - self.rbm_encoder_layers.append( - RBMLayer(int(self._input_size // factor_units) * 2, int(self._input_size // factor_units), - name=f"encoder_{i}")) - - # decoding layers - factor_units = 2 ** (self.n_layer - 1) - self.rbm_decoder1 = RBMLayer(int(input_size // factor_units) // 2, int(input_size // factor_units), - name="decoder_0") - - for layer_num, i in enumerate(range(self.n_layer - 2, 0, -1)): - factor_units = 2 ** i - self.rbm_decoder_layers.append( - RBMLayer(int(input_size // (factor_units * 2)), int(input_size // factor_units), - name=f"decoder_{layer_num + 1}")) - - self.rbm_decoder_layers.append( - RBMLayer(int(input_size // 2), int(input_size), name=f"decoder_{self.n_layer - 1}")) - - def call(self, X, training=None): - outs = {} - out, visibleGen, hiddenGen, h1 = self.rbm_encoder1(X) - # out, visibleGen, hiddenGen, h1 = self.rbm_encoder(X) - outs[f'encoder_{0}'] = (out, visibleGen, hiddenGen, h1) - for i, layer in enumerate(self.rbm_encoder_layers): - out, visibleGen, hiddenGen, h1 = layer(hiddenGen) - outs[f'encoder_{i + 1}'] = (out, visibleGen, hiddenGen, h1) - - out, visibleGen, hiddenGen, h1 = self.rbm_decoder1(hiddenGen) - outs[f'decoder_{0}'] = (out, visibleGen, hiddenGen, h1) - for j, layer in enumerate(self.rbm_decoder_layers): - out, visibleGen, hiddenGen, h1 = layer(hiddenGen) - outs[f'decoder_{j + 1}'] = (out, visibleGen, hiddenGen, h1) - return out, visibleGen, hiddenGen, h1, outs - - def train_step(self, data): - X, y = data - - learning_rate = self.optimizer._decayed_lr('float32') - out, visibleGen, hiddenGen, h1, out_dict = self(X, training=True) - weights_dict = {} - for lay in self.layers: - weights_dict[lay.name] = lay - - last_out = X - for idx, (key, value) in enumerate(out_dict.items()): - out, visibleGen, hiddenGen, h1 = value - positive_grad = tf.matmul(tf.transpose(last_out), hiddenGen) - negative_grad = tf.matmul(tf.transpose(visibleGen), h1) - # loss = tf.reduce_mean(tf.square(y, visibleGen)) - # loss = self.compiled_loss(y, visibleGen) - - # update the weights of the model based on the loss and LR. - curr_layer = weights_dict[key] - curr_layer.weights[0].assign(curr_layer.weights[0] + learning_rate * - (positive_grad - negative_grad) / tf.cast(tf.shape(last_out)[0], - dtype=tf.float32)) - curr_layer.weights[1].assign(curr_layer.weights[1] + learning_rate * tf.math.reduce_mean(hiddenGen - h1, 0)) - curr_layer.weights[2].assign( - curr_layer.weights[2] + learning_rate * tf.math.reduce_mean(last_out - visibleGen, 0)) - - last_out = hiddenGen - return {m.name: m.result() for m in self.metrics} - - def test_step(self, data): - # Unpack the data - x, y = data - # Compute predictions - out, visibleGen, hiddenGen, h1, out_dict = self(x, training=False) - # Updates the metrics tracking the loss - self.compiled_loss(y, hiddenGen, regularization_losses=self.losses) - # Update the metrics. - self.compiled_metrics.update_state(y, hiddenGen) - # Return a dict mapping metric names to current value. - # Note that it will include the loss (tracked in self.metrics). - return {m.name: m.result() for m in self.metrics} - - -def define_rbm_model(opts: options.Options, - input_size: int = 2048, - encoding_dim: int = 256) -> Model: - """ - This function provides an autoencoder model to reduce a certain input to a compressed version. - - :param encoding_dim: Size of the compressed representation. Default: 85 - :param input_size: Size of the input. Default: 2048 - :return: a tuple of autoencoder and encoder models - """ - if opts.aeOptimizer == "Adam": - ac_optimizer = tf.keras.optimizers.Adam(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) - elif opts.aeOptimizer == "SGD": - ac_optimizer = tf.keras.optimizers.SGD(learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay) - - rbm_model = RBM(input_size=input_size, - output_size=encoding_dim) - rbm_model.compile(optimizer=ac_optimizer, loss=opts.lossFunction) - - return rbm_model - - -def train_full_rbm(df: pd.DataFrame, opts: options.Options) -> Model: - """ - Train an autoencoder on the given feature matrix X. Response matrix is only used to - split meaningfully in test and train data set. - - :param opts: Command line arguments as defined in options.py - :param df: Pandas dataframe that contains the smiles/inchi data for training the autoencoder - :return: The encoder model of the trained autoencoder - """ - - # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) - rbm = define_rbm_model(opts, input_size=opts.fpSize, encoding_dim=opts.encFPSize) - # define output file for autoencoder and encoder weights - if opts.ecWeightsFile == "": - logging.info("No RBM encoder weights file specified") - base_file_name = os.path.splitext(basename(opts.inputFile))[0] - logging.info(f"RBM weights will be saved in {base_file_name}.rbm{opts.encFPSize}.hdf5") - rbm_weights_file = os.path.join(opts.outputDir, base_file_name + f".rbm{opts.encFPSize}.hdf5") - # ec_weights_file = os.path.join(opts.outputDir, base_file_name + ".rbm_encoder.weights.hdf5") - else: - logging.info(f"RBM encoder will be saved in {opts.ecWeightsFile}") - base_file_name = os.path.splitext(basename(opts.ecWeightsFile))[0] - rbm_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - - # collect the callbacks for training - callback_list = callbacks.autoencoder_callback(checkpoint_path=rbm_weights_file, opts=opts) - - # Select all fps that are valid and turn them into a numpy array - # This step is crucial for speed!!! - fp_matrix = np.array(df[df["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values) - logging.info(f"Training RBM on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") - - # split data into test and training data - x_train, x_test = train_test_split(fp_matrix, - test_size=0.2, - random_state=42) - x_train = tf.cast(tf.where(x_train, 1, 0), tf.float32) - x_test = tf.cast(tf.where(x_test, 1, 0), tf.float32) - logging.info(f"RBM train data shape {x_train.shape} with type {x_train.dtype}") - logging.info(f"RBM test data shape {x_test.shape} with type {x_test.dtype}") - auto_hist = rbm.fit(x=x_train, y=x_train, - callbacks=callback_list, - epochs=opts.aeEpochs, - batch_size=opts.aeBatchSize, - verbose=opts.verbose, - steps_per_epoch=25, - validation_data=(x_test, x_test)) - rbm.summary(print_fn=logging.info) - ht.store_and_plot_history(base_file_name=os.path.join(opts.outputDir, base_file_name + ".RBM"), - hist=auto_hist) - rbm.save_weights(rbm_weights_file) - save_path = os.path.join(opts.ecModelDir, "rbm_model") - rbm.save(save_path) - logging.info(f"RBM weights stored in file: {rbm_weights_file}") - - return rbm - - -def compress_fingerprints(dataframe: pd.DataFrame, - encoder: Model, layer_num: int) -> pd.DataFrame: - """ - Adds a column of the compressed version of the fingerprints to the original dataframe. - - :param dataframe: Dataframe containing a column named 'fp' with the fingerprints - :param encoder: The trained autoencoder that is used for compressing the fingerprints - :return: The input dataframe extended by a column containing the compressed version of the fingerprints - """ - logging.info("Adding rbm compressed fingerprints") - idx = dataframe[dataframe["fp"].notnull()].index - fp_matrix = np.array(dataframe[dataframe["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values) - logging.info(f"Using input matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}") - logging.info("Compressed fingerprints from rbm are added to input dataframe.") - fp_matrix = tf.cast(tf.where(fp_matrix, 1, 0), tf.float32) - out, visibleGen, hiddenGen, h1, out_dict = encoder.predict(fp_matrix) - out, visibleGen, hiddenGen, h1 = out_dict[f'encoder_{layer_num-1}'] - dataframe['fpcompressed'] = pd.DataFrame({'fpcompressed': [s for s in hiddenGen]}, idx) - - return dataframe diff --git a/build/lib/dfpl/settings.py b/build/lib/dfpl/settings.py deleted file mode 100644 index 2d900cda..00000000 --- a/build/lib/dfpl/settings.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np - -# Datatype that is used for fingerprint vectors stored inside -# dataframe used during the program -df_fp_numpy_type = np.bool8 - -# Do we need copies when creating numpy matrices from the pandas dataframes -# for training? Everything seems to work fine with False and it saves memory. -numpy_copy_values = False - -# Autoencoder data type settings - -# Type that is given as input numpy array to the network -# We need to carefully test the performance of the training here -# The big dsstox dataset runs out of mem on Patrick's machine when -# we use floats here. -# Also, we possibly should give the FNN a float type fp -ac_fp_numpy_type = np.bool8 - -# Type used for the compressed fp which is an inner layer of the ac -# an MUST be of float type. On Patrick's machine, (GPU) tensorflow layers -# are float32 per default. Not sure if float64 can be used with GPU -# training and if it enhances results. Needs to be evaluated. -ac_fp_compressed_numpy_type = np.bool8 - -# Feedforward Neuronal Network data type settings -# -# The sections below define the types of the numpy matrices used for training -# the FNNs. In short: -# XX_fp_numpy_type: used for uncompressed fingerprint tensors -# XX_fp_compressed_numpy_type: used for compressed fingerprint tensors -# XX_target_numpy_type: used for the target tensors - -nn_fp_numpy_type = np.float32 -nn_fp_compressed_numpy_type = np.float32 -nn_target_numpy_type = np.short - -nn_multi_fp_numpy_type = np.float32 -nn_multi_fp_compressed_numpy_type = np.float32 -nn_multi_target_numpy_type = np.float32 - -# Training settings - -# Training settings of the AC that were magic numbers in the code before. -ac_train_min_delta = 0.0001 -ac_train_check_period = 1 -ac_train_patience = 1 - -# Training settings of the FNN that were magic numbers in the code before. -nn_train_min_delta = 0.0001 -nn_train_check_period = 5 -nn_train_patience = 5 diff --git a/build/lib/dfpl/single_label_model.py b/build/lib/dfpl/single_label_model.py deleted file mode 100644 index de437d1e..00000000 --- a/build/lib/dfpl/single_label_model.py +++ /dev/null @@ -1,1008 +0,0 @@ -import logging -import math -import os -import shutil -import sys -from os import path -from time import time - -import numpy as np -import pandas as pd -import tensorflow as tf -import tensorflow.keras.backend as K -import wandb -from typing import Union, Tuple - -from sklearn.metrics import ( - auc, - classification_report, - confusion_matrix, - matthews_corrcoef, - roc_curve, -) -from sklearn.model_selection import StratifiedKFold, train_test_split -from tensorflow.keras import metrics, optimizers, regularizers -from tensorflow.keras.layers import AlphaDropout, Dense, Dropout -from tensorflow.keras.losses import ( - BinaryCrossentropy, - MeanSquaredError, - BinaryFocalCrossentropy, -) -from tensorflow.keras.models import Model, Sequential - -from dfpl import callbacks as cb -from dfpl import options -from dfpl import plot as pl -from dfpl import settings -from dfpl.utils import ae_scaffold_split, weight_split - - -def prepare_nn_training_data( - df: pd.DataFrame, target: str, opts: options.Options, return_dataframe: bool = False -) -> Union[Tuple[np.ndarray, np.ndarray], pd.DataFrame]: - # Check the value counts and abort if too imbalanced - allowed_imbalance = 0.1 - # If feature compression is enabled, use compressed fingerprints - if opts.compressFeatures: - vc = df[df[target].notna() & df["fpcompressed"].notnull()][ - target - ].value_counts() - # Otherwise, use uncompressed fingerprints - else: - vc = df[df[target].notna() & df["fp"].notnull()][target].value_counts() - - # If the dataset is extremely unbalanced, adjust sampling options - if min(vc) < max(vc) * allowed_imbalance: - logging.info( - f" Your training data is extremely unbalanced ({target}): 0 - {vc[0]}, and 1 - {vc[1]} values." - ) - if opts.sampleDown: - logging.info(f" I will down-sample your data") - opts.sampleFractionOnes = allowed_imbalance - else: - logging.info(f" I will not down-sample your data automatically.") - logging.info( - f" Consider to enable down sampling of the 0 values with --sampleDown option." - ) - - logging.info("Preparing training data matrices") - - # If feature compression is enabled, use compressed fingerprints - if opts.compressFeatures: - logging.info("Using compressed fingerprints") - df_fpc = df[df[target].notna() & df["fpcompressed"].notnull()] - logging.info( - f"DataSet has {df_fpc.shape[0]} valid entries in fpcompressed and {target}" - ) - - # If sampling down is enabled, adjust sampling options and downsample the data - if opts.sampleDown: - assert 0.0 < opts.sampleFractionOnes < 1.0 - logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") - # How many ones and zeros are in the dataset - counts = df_fpc[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - - # Add a sample of 0s to the df of 1s - dfX = df_fpc[df_fpc[target] == 1].append( - df_fpc[df_fpc[target] == 0].sample( - int(min(counts[0], counts[1] / opts.sampleFractionOnes)) - ) - ) - # Convert fingerprints and targets to numpy arrays - x = np.array( - dfX["fpcompressed"].to_list(), - dtype=settings.nn_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - y = np.array( - dfX[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - # Return the dataframe if specified - if return_dataframe: - return dfX - else: - logging.info("Fraction sampling is OFF") - # How many ones and zeros are in the dataset - counts = df_fpc[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - - # Convert fingerprints and targets to numpy arrays - x = np.array( - df_fpc["fpcompressed"].to_list(), - dtype=settings.nn_fp_compressed_numpy_type, - copy=settings.numpy_copy_values, - ) - y = np.array( - df_fpc[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - if return_dataframe: - return df_fpc - - # If feature compression is not enabled, use uncompressed fingerprints - else: - logging.info("Using uncompressed fingerprints") - # select rows where the target is not null and the fingerprint is not null - df_fp = df[df[target].notna() & df["fp"].notnull()] - logging.info(f"DataSet has {df_fp.shape[0]} valid entries in fp and {target}") - - if opts.sampleDown: - logging.info(f"Using fractional sampling {opts.sampleFractionOnes}") - # count number of each class value - counts = df_fp[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - # downsample the majority class by taking a fraction of its samples and combining with all of the - # minority class - dfX = df_fp[df_fp[target] == 1.0].append( - df_fp[df_fp[target] == 0.0].sample( - int(min(counts[0], counts[1] / opts.sampleFractionOnes)) - ) - ) - # convert fingerprints to numpy array - x = np.array( - dfX["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - # convert target values to numpy array - y = np.array( - dfX[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - if return_dataframe: - return dfX - else: - logging.info("Fraction sampling is OFF") - # count number of each class value - counts = df_fp[target].value_counts() - logging.info(f"Number of sampling values: {counts.to_dict()}") - # convert fingerprints to numpy array - x = np.array( - df_fp["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - # convert target values to numpy array - y = np.array( - df_fp[target].to_list(), - dtype=settings.nn_target_numpy_type, - copy=settings.numpy_copy_values, - ) - if return_dataframe: - return df_fp - # return the numpy arrays of fingerprints and targets - return x, y - - -# This function defines a feedforward neural network (FNN) with the given input size, options, and output bias -def build_fnn_network( - input_size: int, opts: options.Options, output_bias=None -) -> Model: - # Set the output bias if it is provided - if output_bias is not None: - output_bias = tf.keras.initializers.Constant(output_bias) - - # Define the number of hidden layers based on the input size - my_hidden_layers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} - if not str(input_size) in my_hidden_layers.keys(): - raise ValueError("Wrong input-size. Must be in {2048, 1024, 999, 512, 256}.") - nhl = int(math.log2(input_size) / 2 - 1) - - # Create a sequential model - model = Sequential() - - # Add the first hidden layer - if opts.activationFunction == "relu": - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation="relu", - kernel_regularizer=regularizers.l2(opts.l2reg), - kernel_initializer="he_uniform", - ) - ) - model.add(Dropout(opts.dropout)) - elif opts.activationFunction == "selu": - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout)) - else: - logging.error("Only 'relu' and 'selu' activation is supported") - sys.exit(-1) - - # Add additional hidden layers - for i in range(1, nhl): - factor_units = 2 ** (i + 1) - factor_dropout = 2 * i - if opts.activationFunction == "relu": - model.add( - Dense( - units=int(input_size / factor_units), - activation="relu", - kernel_regularizer=regularizers.l2(opts.l2reg), - kernel_initializer="he_uniform", - ) - ) - model.add(Dropout(opts.dropout / factor_dropout)) - elif opts.activationFunction == "selu": - model.add( - Dense( - units=int(input_size / factor_units), - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout / factor_dropout)) - else: - logging.error("Only 'relu' and 'selu' activation is supported") - sys.exit(-1) - - # Add the output layer with a sigmoid activation function and the output bias if provided - model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) - model.summary() - return model - - -# This function defines a shallow neural network (SNN) with the given input size, options, and output bias -def build_snn_network( - input_size: int, opts: options.Options, output_bias=None -) -> Model: - # Set the output bias if it is provided - if output_bias is not None: - output_bias = tf.keras.initializers.Constant(output_bias) - - # Create a sequential model - model = Sequential() - - # Add the first hidden layer - model.add( - Dense( - input_dim=input_size, - units=50, - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout)) - - # Add 7 hidden layers with 50 units each, a SELU activation function, and a LeCun normal kernel initializer - for i in range(7): - model.add(Dense(units=50, activation="selu", kernel_initializer="lecun_normal")) - # Add an alpha dropout layer with the given dropout rate - model.add(AlphaDropout(opts.dropout)) - - # Add the output layer with 1 unit and a sigmoid activation function - # If output_bias is not None, set the bias initializer to the specified value - model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) - - return model - - -def balanced_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> np.float64: - """ - Computes the balanced accuracy metric. - - Args: - y_true (np.ndarray): The true values of the labels. - y_pred (np.ndarray): The predicted values of the labels. - - Returns: - np.float64: The balanced accuracy score. - """ - y_pred = K.round(y_pred) # Convert continuous predictions to binary class labels - tn = K.sum((1 - y_true) * (1 - y_pred)) - fp = K.sum((1 - y_true) * y_pred) - fn = K.sum(y_true * (1 - y_pred)) - tp = K.sum(y_true * y_pred) - balanced_acc = (tp / (tp + fn + 1e-10) + tn / (tn + fp + 1e-10)) / 2 - return balanced_acc - - -def define_single_label_model( - input_size: int, opts: options.Options, output_bias=None -) -> Model: - """ - Defines and compiles the single-label neural network model. - - Args: - input_size (int): The size of the input layer. - opts (options.Options): The options used in the model. - output_bias (float): The initial bias for the last sigmoid layer of the model. - - Returns: - tensorflow.keras.Model: The compiled model. - """ - # Set the loss function according to the option selected - if opts.lossFunction == "bce": - loss_function = BinaryCrossentropy() - elif opts.lossFunction == "mse": - loss_function = MeanSquaredError() - elif opts.lossFunction == "focal": - loss_function = BinaryFocalCrossentropy() - else: - logging.error(f"Your selected loss is not supported: {opts.lossFunction}.") - sys.exit("Unsupported loss function") - - # Set the optimizer according to the option selected - if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) - elif opts.optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) - else: - logging.error(f"Your selected optimizer is not supported: {opts.optimizer}.") - sys.exit("Unsupported optimizer") - - # Set the type of neural network according to the option selected - if opts.fnnType == "FNN": - model = build_fnn_network(input_size, opts, output_bias) - elif opts.fnnType == "SNN": - model = build_snn_network(input_size, opts, output_bias) - else: - raise ValueError(f'Option FNN Type is not "FNN" or "SNN", but {opts.fnnType}.') - - logging.info(f"Network type: {opts.fnnType}") - model.summary(print_fn=logging.info) - - # Compile the model with the defined options - model.compile( - loss=loss_function, - optimizer=my_optimizer, - metrics=[ - metrics.BinaryAccuracy(name="accuracy"), - metrics.AUC(), - metrics.Precision(), - metrics.Recall(), - balanced_accuracy, - ], - ) - - return model - - -def evaluate_model( - x_test: np.ndarray, - y_test: np.ndarray, - file_prefix: str, - model: Model, - target: str, - fold: int, -) -> pd.DataFrame: - # Log that we're evaluating the model on the test data - name = path.basename(file_prefix).replace("_", " ") - logging.info(f"Evaluating trained model '{name}' on test data") - - # Predict the test set to compute MCC, AUC, ROC curve, etc. - threshold = 0.5 # TODO: Introduce thresholds different from 0.5! - y_predict = model.predict(x_test).flatten() - y_predict_int = (y_predict >= threshold).astype(np.short) - y_test_int = y_test.astype(np.short) - - # Save the predictions to a CSV file - ( - pd.DataFrame( - { - "y_true": y_test_int, - "y_predicted": y_predict, - "y_predicted_int": y_predict_int, - "target": target, - "fold": fold, - } - ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.csv") - ) - - # Compute the confusion matrix - confusion_matrix(y_true=y_test_int, y_pred=y_predict_int) - - # Compute the metrics that depend on the class label - precision_recall = classification_report( - y_test_int, y_predict_int, output_dict=True - ) - prf = pd.DataFrame.from_dict(precision_recall)[["0", "1"]] - - # Add balanced accuracy to the computed metrics - prf.to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.prec_rec_f1.csv") - - # Evaluate the model on the validation set and log the results - loss, acc, auc_value, precision, recall, balanced_acc = tuple( - model.evaluate(x=x_test, y=y_test) - ) - - logging.info(f"Loss: {round(loss, 4)}") - logging.info(f"Accuracy: {round(acc, 4)}") - logging.info(f"AUC: {round(auc_value, 4)}") - logging.info(f"Precision: {round(precision, 4)}") - logging.info(f"Recall: {round(recall, 4)}") - logging.info(f"Balanced Accuracy: {round(balanced_acc, 4)}") - - # Compute the Matthews Correlation Coefficient (MCC) - MCC = matthews_corrcoef(y_test_int, y_predict_int) - logging.info(f"MCC: {round(MCC, 4)}") - - # Generate the AUC-ROC curve data from the validation data - FPR, TPR, thresholds_keras = roc_curve( - y_true=y_test_int, y_score=y_predict, drop_intermediate=False - ) - AUC = auc(FPR, TPR) - nonzero_mask = (FPR != 0) | (TPR != 0) - FPR_filtered = FPR[nonzero_mask] - TPR_filtered = TPR[nonzero_mask] - - # Save AUC data to CSV file - pd.DataFrame( - list( - zip( - FPR_filtered, - TPR_filtered, - [AUC] * len(FPR_filtered), - [target] * len(FPR_filtered), - [fold] * len(FPR_filtered), - ) - ), - columns=["fpr", "tpr", "auc_value", "target", "fold"], - ).to_csv(path_or_buf=f"{file_prefix}.predicted.testdata.aucdata.csv") - # Generate and save AUC-ROC curve plot - pl.plot_auc( - fpr=FPR, - tpr=TPR, - target=target, - auc_value=AUC, - filename=f"{file_prefix}_auc_data.png", - wandb_logging=False, - ) - - # Return a DataFrame containing the computed metrics - return pd.DataFrame.from_dict( - { - "p_0": prf["0"]["precision"], - "r_0": prf["0"]["recall"], - "f1_0": prf["0"]["f1-score"], - "p_1": prf["1"]["precision"], - "r_1": prf["1"]["recall"], - "f1_1": prf["1"]["f1-score"], - "loss": loss, - "accuracy": acc, - "balanced_accuracy": balanced_acc, - "MCC": MCC, - "AUC": AUC, - "target": target, - "fold": fold, - }, - orient="index", - ).T - - -def fit_and_evaluate_model( - x_train: np.ndarray, - x_test: np.ndarray, - y_train: np.ndarray, - y_test: np.ndarray, - fold: int, - target: str, - opts: options.Options, -) -> pd.DataFrame: - # Print info about training - logging.info(f"Training of fold number: {fold}") - - # Define file name prefix for saving models - model_file_prefix = path.join( - opts.outputDir, f"{target}_{opts.split_type}_single-labeled_Fold-{fold}" - ) - - # Compute class imbalance - ids, counts = np.unique(y_train, return_counts=True) - count_dict = dict(zip(ids, counts)) - - # Set initial bias for last sigmoid layer to counter class imbalance - if count_dict[0] == 0: - initial_bias = None - logging.info("No zeroes in training labels. Setting initial_bias to None.") - else: - initial_bias = np.log([count_dict[1] / count_dict[0]]) - logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") - - # Define model - model = define_single_label_model( - input_size=x_train.shape[1], opts=opts, output_bias=initial_bias - ) - - # Define checkpoint to save model weights during training - checkpoint_model_weights_path = f"{model_file_prefix}.model.weights.hdf5" - callback_list = cb.nn_callback( - checkpoint_path=checkpoint_model_weights_path, opts=opts - ) - - # Train model - start = time() - hist = model.fit( - x_train, - y_train, - callbacks=callback_list, - epochs=opts.epochs, - batch_size=opts.batchSize, - verbose=opts.verbose, - validation_data=(x_test, y_test), - ) - trainTime = str(round((time() - start) / 60, ndigits=2)) - logging.info( - f"Computation time for training the single-label model for {target}: {trainTime} min" - ) - - # Save and plot model history - pd.DataFrame(hist.history).to_csv(path_or_buf=f"{model_file_prefix}.history.csv") - pl.plot_history(history=hist, file=f"{model_file_prefix}.history.svg") - # Evaluate model - callback_model = define_single_label_model(input_size=x_train.shape[1], opts=opts) - callback_model.load_weights(filepath=checkpoint_model_weights_path) - performance = evaluate_model( - x_test=x_test, - y_test=y_test, - file_prefix=model_file_prefix, - model=callback_model, - target=target, - fold=fold, - ) - - return performance - - -def get_x_y( - df: pd.DataFrame, - target: str, - train_set: pd.DataFrame, - test_set: pd.DataFrame, - opts: options.Options, -): - train_indices = train_set.index - test_indices = test_set.index - if opts.compressFeatures: - accessor = "fpcompressed" - else: - accessor = "fp" - new_df = pd.DataFrame( - [{x: y for x, y in enumerate(item)} for item in df[accessor].values.tolist()], - index=df.index, - ) - x_train = new_df.iloc[train_indices, :].values - y_train = df.iloc[train_indices][target].values - x_test = new_df.iloc[test_indices, :].values - y_test = df.iloc[test_indices][target].values - x_train = x_train.astype("float32") - y_train = y_train.astype("float32") - x_test = x_test.astype("float32") - y_test = y_test.astype("float32") - return x_train, y_train, x_test, y_test - - -def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: - """ - Train individual models for all targets (columns) present in the provided target data (y) and a multi-label - model that classifies all targets at once. For each individual target the data is first subset to exclude NA - values (for target associations). A random sample of the remaining data (size is the split fraction) is used for - training and the remaining data for validation. - - :param opts: The command line arguments in the options class - :param df: The dataframe containing x matrix and at least one column for a y target. - """ - - # find target columns - targets = [ - c - for c in df.columns - if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] - ] - if opts.wabTracking and opts.wabTarget != "": - # For W&B tracking, we only train one target that's specified as wabTarget "ER". - # In case it's not there, we use the first one available - if opts.wabTarget in targets: - targets = [opts.wabTarget] - elif opts.wabTarget == "": - targets = targets - else: - logging.error( - f"The specified wabTarget for Weights & Biases tracking does not exist: {opts.wabTarget}" - ) - targets = [targets[0]] - - # Collect metrics for each fold and target - performance_list = [] - if opts.split_type == "random": - for target in targets: # [:1]: - # target=targets[1] # --> only for testing the code - x, y = prepare_nn_training_data(df, target, opts, return_dataframe=False) - if x is None: - continue - - logging.info(f"X training matrix of shape {x.shape} and type {x.dtype}") - logging.info(f"Y training matrix of shape {y.shape} and type {y.dtype}") - - if opts.kFolds == 1: - # for single 'folds' and when sweeping on W&B, we fix the random state - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}", - group=f"{target}", - name=f"{target}_single_fold", - ) - if opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", - name=f"{target}_single_fold", - group=f"{target}", - reinit=True, - ) - x_train, x_test, y_train, y_test = train_test_split( - x, y, stratify=y, test_size=opts.testSize, random_state=1 - ) - logging.info( - f"Splitting train/test data with fixed random initializer" - ) - else: - x_train, x_test, y_train, y_test = train_test_split( - x, y, stratify=y, test_size=opts.testSize - ) - - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=0, - target=target, - opts=opts, - ) - performance_list.append(performance) - # save complete model - trained_model = define_single_label_model( - input_size=len(x[0]), opts=opts - ) - # trained_model.load_weights - # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join(opts.outputDir, f"{target}_saved_model") - ) - - elif 1 < opts.kFolds < 10: # int(x.shape[0] / 100): - # do a kfold cross-validation - kfold_c_validator = StratifiedKFold( - n_splits=opts.kFolds, shuffle=True, random_state=42 - ) - fold_no = 1 - # split the data - for train, test in kfold_c_validator.split(x, y): - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FNN_{opts.threshold}_{opts.split_type}", - group=f"{target}", - name=f"{target}-{fold_no}", - reinit=True, - ) - if opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}", - name=f"AE_{target}-{fold_no}", - group=f"{target}", - reinit=True, - ) - - # Initialize wandb for the feed forward model - wandb.init( - project=f"FNN_{opts.threshold}_{opts.split_type}", - group=f"{target}", - name=f"FNN_{target}-{fold_no}", - reinit=True, - ) - - performance = fit_and_evaluate_model( - x_train=x[train], - x_test=x[test], - y_train=y[train], - y_test=y[test], - fold=fold_no, - target=target, - opts=opts, - ) - performance_list.append(performance) - - # save complete model - trained_model = define_single_label_model( - input_size=len(x[0]), opts=opts - ) - # trained_model.load_weights - # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-{fold_no}.model.weights.hdf5", - ) - ) - # create output directory and store complete model - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}-{fold_no}_saved_model" - ) - ) - if opts.wabTracking: - wandb.finish() - fold_no += 1 - - # select and copy best model - how to define the best model? - best_fold = pd.concat(performance_list, ignore_index=True).sort_values( - by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True - )["fold"][0] - # rename the fold to best fold - src = os.path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-{best_fold}.model.weights.hdf5", - ) - dst = os.path.join( - opts.outputDir, - f"{target}_single-labeled_Best_Fold-{best_fold}.model.weights.hdf5", - ) - os.rename(src, dst) - - src_dir = os.path.join( - opts.outputDir, f"{target}-{best_fold}_saved_model" - ) - dst_dir = os.path.join( - opts.outputDir, f"{target}-{best_fold}_best_saved_model" - ) - - if path.isdir(dst_dir): - shutil.rmtree(dst_dir) - - # Rename source directory to destination directory - os.rename(src_dir, dst_dir) - - # save complete model - else: - logging.info( - "Your selected number of folds for Cross validation is out of range. " - "It must be 1 or smaller than 1 hundredth of the number of samples." - ) - sys.exit("Number of folds out of range") - ( - pd.concat(performance_list, ignore_index=True).to_csv( - path_or_buf=path.join( - opts.outputDir, "single_label_random_model.evaluation.csv" - ) - ) - ) - # For each individual target train a model - elif opts.split_type == "scaffold_balanced": - # df, irrelevant_columns = preprocess_dataframe(df, opts) - for idx, target in enumerate(targets): - df = prepare_nn_training_data(df, target, opts, return_dataframe=True) - relevant_cols = ["smiles"] + ["fp"] + [target] # list(irrelevant_columns) - if opts.compressFeatures: - relevant_cols = relevant_cols + ["fpcompressed"] - df_task = df.loc[:, relevant_cols] - # Drop rows with missing values in the target column - df_task.dropna(subset=[target], inplace=True) - df_task.reset_index(drop=True, inplace=True) - if opts.kFolds == 1: - train_set, val_set, test_set = ae_scaffold_split( - df_task, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=False, - seed=42, - ) - x_train, y_train, x_test, y_test = get_x_y( - df_task, target, train_set, test_set, opts - ) - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}", - group=f"{target}", - name=f"{target}_single_fold", - ) - elif opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", - name=f"{target}-single-fold", - group=f"{target}", - ) - - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=0, - target=target, - opts=opts, - ) - performance_list.append(performance) - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}_scaffold_saved_model_0" - ) - ) - elif opts.kFolds > 1: - for fold_no in range(1, opts.kFolds + 1): - print(f"Splitting data with seed {fold_no}") - train_set, val_set, test_set = ae_scaffold_split( - df_task, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=True, - seed=fold_no, - ) - x_train, y_train, x_test, y_test = get_x_y( - df_task, target, train_set, test_set, opts - ) - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}", - group=f"{target}", - name=f"{target}-{fold_no}", - reinit=True, - ) - elif opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_{opts.split_type}", - name=f"{target}-{fold_no}", - group=f"{target}", - reinit=True, - ) - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=fold_no, - target=target, - opts=opts, - ) - performance_list.append(performance) - - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-{fold_no}.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}_scaffold_saved_model_{fold_no}" - ) - ) - if opts.wabTracking: - wandb.finish() - fold_no += 1 - # find best fold - best_fold = pd.concat(performance_list, ignore_index=True).sort_values( - by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True - )["fold"][0] - # rename the fold to best fold - src = os.path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-{best_fold}.model.weights.hdf5", - ) - dst = os.path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_BEST_Fold-{best_fold}.model.weights.hdf5", - ) - os.rename(src, dst) - - src_dir = os.path.join( - opts.outputDir, f"{target}_scaffold_saved_model_{best_fold}" - ) - dst_dir = os.path.join( - opts.outputDir, - f"{target}_scaffold_saved_model_BEST_FOLD_{best_fold}", - ) - - if path.isdir(dst_dir): - shutil.rmtree(dst_dir) - - # Rename source directory to destination directory - os.rename(src_dir, dst_dir) - - else: - logging.info( - "Your selected number of folds for Cross validation is out of range. " - "It must be 1 or smaller than 1 hundredth of the number of samples." - ) - sys.exit("Number of folds out of range") - ( - pd.concat(performance_list, ignore_index=True).to_csv( - path_or_buf=path.join( - opts.outputDir, "single_label_scaffold_model.evaluation.csv" - ) - ) - ) - elif opts.split_type == "molecular_weight": - logging.info("You can use molecular_weight split once.") - for idx, target in enumerate(targets): - df = prepare_nn_training_data(df, target, opts, return_dataframe=True) - relevant_cols = ["smiles"] + ["fp"] + [target] - if opts.compressFeatures: - relevant_cols = relevant_cols + ["fpcompressed"] - df_task = df.loc[:, relevant_cols] - df_task.dropna(subset=[target], inplace=True) - df_task.reset_index(drop=True, inplace=True) - if opts.kFolds == 1: - train_set, val_set, test_set = weight_split( - df_task, bias="small", sizes=(1 - opts.testSize, 0.0, opts.testSize) - ) - x_train, y_train, x_test, y_test = get_x_y( - df_task, target, train_set, test_set, opts - ) - if opts.wabTracking and not opts.aeWabTracking: - wandb.init( - project=f"FFN_{opts.split_type}AE_{opts.aeSplitType}", - group=f"{target}", - name=f"{target}_single_fold", - ) - elif opts.wabTracking and opts.aeWabTracking: - wandb.init( - project=f"AE_{opts.aeSplitType}_FNN_weight_split_{opts.split_type}", - name=f"{target}-single-fold", - group=f"{target}", - ) - performance = fit_and_evaluate_model( - x_train=x_train, - x_test=x_test, - y_train=y_train, - y_test=y_test, - fold=0, - target=target, - opts=opts, - ) - performance_list.append(performance) - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_weight_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join(opts.outputDir, f"{target}_weight_saved_model_0") - ) - elif opts.kFolds > 1: - raise Exception( - f"Unsupported number of folds: {opts.kFolds} for {opts.split_type} split.\ - You CANNOT do multiple folds based on molecular_weight." - ) - - ( - pd.concat(performance_list, ignore_index=True).to_csv( - path_or_buf=path.join( - opts.outputDir, "single_label_molecular_weight_model.evaluation.csv" - ) - ) - ) - else: - raise Exception(f"Unsupported split type: {opts.split_type}") diff --git a/build/lib/dfpl/utils.py b/build/lib/dfpl/utils.py deleted file mode 100644 index b45bde62..00000000 --- a/build/lib/dfpl/utils.py +++ /dev/null @@ -1,329 +0,0 @@ -import json -import logging -import os -import pathlib -import warnings -from collections import defaultdict -from random import Random -from typing import Dict, List, Set, Tuple, Union - -import numpy as np -import pandas as pd -from rdkit import Chem, RDLogger -from rdkit.Chem import rdMolDescriptors -from rdkit.Chem.Scaffolds import MurckoScaffold -from tqdm import tqdm - -RDLogger.DisableLog("rdApp.*") - - -def makePathAbsolute(p: str) -> str: - path = pathlib.Path(p) - if path.is_absolute(): - return p - else: - return str(path.absolute()) - - -def createDirectory(directory: str): - path = makePathAbsolute(directory) - if not os.path.exists(path): - os.makedirs(path) - - -def createArgsFromJson(in_json: str, ignore_elements: list, return_json_object: bool): - arguments = [] - with open(in_json, "r") as f: - data = json.load(f) - for key, value in data.items(): - if key not in ignore_elements: - if key == "extra_metrics" and isinstance(value, list): - arguments.append("--extra_metrics") - arguments.extend(value) - else: - arguments.append("--" + str(key)) - arguments.append(str(value)) - if return_json_object: - return arguments, data - return arguments - - -def make_mol(s: str, keep_h: bool, add_h: bool, keep_atom_map: bool): - """ - Builds an RDKit molecule from a SMILES string. - - :param s: SMILES string. - :param keep_h: Boolean whether to keep hydrogens in the input smiles. This does not add hydrogens, it only keeps the - m if they are specified. - :param add_h: Boolean whether to add hydrogens to the input smiles. - :param keep_atom_map: Boolean whether to keep the original atom mapping. - :return: RDKit molecule. - """ - params = Chem.SmilesParserParams() - params.removeHs = not keep_h if not keep_atom_map else False - mol = Chem.MolFromSmiles(s, params) - - if add_h: - mol = Chem.AddHs(mol) - - if keep_atom_map: - atom_map_numbers = tuple(atom.GetAtomMapNum() for atom in mol.GetAtoms()) - for idx, map_num in enumerate(atom_map_numbers): - if idx + 1 != map_num: - new_order = np.argsort(atom_map_numbers).tolist() - return Chem.rdmolops.RenumberAtoms(mol, new_order) - - return mol - - -def generate_scaffold( - mol: Union[str, Chem.Mol, Tuple[Chem.Mol, Chem.Mol]], include_chirality: bool = True -) -> str: - """ - Computes the Bemis-Murcko scaffold for a SMILES string, an RDKit molecule, or an InChI string or InChIKey. - - :param mol: A SMILES, RDKit molecule, InChI string, or InChIKey string. - :param include_chirality: Whether to include chirality in the computed scaffold. - :return: The Bemis-Murcko scaffold for the molecule. - """ - if isinstance(mol, str): - if mol.startswith("InChI="): - mol = inchi_to_mol(mol) - else: - mol = make_mol(mol, keep_h=False, add_h=False, keep_atom_map=False) - elif isinstance(mol, tuple): - mol = mol[0] - scaffold = MurckoScaffold.MurckoScaffoldSmiles( - mol=mol, includeChirality=include_chirality - ) - - return scaffold - - -def scaffold_to_smiles( - mols: List[str], use_indices: bool = False -) -> Dict[str, Union[Set[str], Set[int]]]: - """ - Computes the scaffold for each SMILES and returns a mapping from scaffolds to sets of smiles (or indices). - :param mols: A list of SMILES. - :param use_indices: Whether to map to the SMILES's index in :code:`mols` rather than - mapping to the smiles string itself. This is necessary if there are duplicate smiles. - :return: A dictionary mapping each unique scaffold to all SMILES (or indices) which have that scaffold. - """ - scaffolds = defaultdict(set) - for i, mol in tqdm(enumerate(mols), total=len(mols)): - scaffold = generate_scaffold(mol) - if use_indices: - scaffolds[scaffold].add(i) - else: - scaffolds[scaffold].add(mol) - - return scaffolds - - -# def inchi_to_mol(inchi: str) -> Chem.Mol: -# return Chem.inchi.MolFromInchi(inchi) -def smiles_to_mol(smiles: str) -> Chem.Mol: - mol = Chem.MolFromSmiles(smiles) - if mol is None: - return None - # raise ValueError(f"Could not convert SMILES to Mol: {smiles}") - return mol - - -def inchi_to_mol(inchi: str) -> Chem.Mol: - mol = Chem.MolFromInchi(inchi) - if mol is None: - return None - # raise ValueError(f"Could not convert InChI to Mol: {inchi}") - return mol - - -def weight_split( - data: pd.DataFrame, bias: str, sizes: Tuple[float, float, float] = (0.8, 0, 0.2) -) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: - if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): - raise ValueError(f"Invalid train/val/test splits! got: {sizes}") - # initial_indices = data.index.to_numpy() - train_size, val_size, _ = ( - sizes[0] * len(data), - sizes[1] * len(data), - sizes[2] * len(data), - ) - if "inchi" in [x.lower() for x in data.columns]: - data["mol"] = data["inchi"].apply(inchi_to_mol) - elif "smiles" in [x.lower() for x in data.columns]: - data["mol"] = data["smiles"].apply(smiles_to_mol) - else: - logging.info("Dataframe does not have a SMILES or InChi column") - none_mols = data["mol"].isnull().sum() - logging.info(f"There are {none_mols} chemicals with no mol objects ") - data.dropna(subset=["mol"], inplace=True) - data["mol_weight"] = data.apply( - lambda row: rdMolDescriptors.CalcExactMolWt(row["mol"]) - if row["mol"] is not None - else None, - axis=1, - ) - # data = data.drop(columns=['mol','fp','inchi','toxid','key'], axis=1) - sorted_data = data.copy() - if bias == "big": - sorted_data = data.sort_values(by="mol_weight", ascending=False) - elif bias == "small": - sorted_data = data.sort_values(by="mol_weight", ascending=True) - else: - print("Wrong bias, choose small or big") - indices = np.arange(len(sorted_data)) - train_end_idx = int(train_size) - val_end_idx = int(train_size + val_size) - train_indices = indices[:train_end_idx] - val_indices = indices[train_end_idx:val_end_idx] - test_indices = indices[val_end_idx:] - train_df = sorted_data.iloc[train_indices].reset_index(drop=True) - val_df = sorted_data.iloc[val_indices].reset_index(drop=True) - test_df = sorted_data.iloc[test_indices].reset_index(drop=True) - - return train_df, val_df, test_df - - -def ae_scaffold_split( - data: pd.DataFrame, - sizes: Tuple[float, float, float] = (0.8, 0, 0.2), - balanced: bool = False, - key_molecule_index: int = 0, - seed: int = 0, -) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: - """ - Splits a pandas DataFrame by scaffold so that no molecules sharing a scaffold are in different splits. - :param data: A pandas DataFrame containing SMILES strings and molecule properties. - :param sizes: A length-3 tuple with the proportions of data in the train, validation, and test sets. - :param balanced: Whether to balance the sizes of scaffolds in each set rather than putting the smallest in test set. - :param key_molecule_index: For data with multiple molecules, this sets which molecule will be considered during splitting. - :param seed: Random seed for shuffling when doing balanced splitting. - :return: A tuple of pandas DataFrames containing the train, validation, and test splits of the data. - """ - if not (len(sizes) == 3 and np.isclose(sum(sizes), 1)): - raise ValueError(f"Invalid train/val/test splits! got: {sizes}") - - # Split - train_size, val_size, test_size = ( - sizes[0] * len(data), - sizes[1] * len(data), - sizes[2] * len(data), - ) - train: List[int] = [] - val: List[int] = [] - test: List[int] = [] - train_scaffold_count, val_scaffold_count, test_scaffold_count = 0, 0, 0 - - # Map from scaffold to index in the data - key_colnames = data.columns - if "inchi" in key_colnames: - key_molecule_index = next( - (i for i, colname in enumerate(data.columns) if "inchi" in colname.lower()), - None, - ) - if key_molecule_index is None: - warnings.warn("No column with 'inchi' found in the DataFrame. Proceeding with caution.") - key_mols = data.iloc[:, key_molecule_index].apply(inchi_to_mol).dropna() - else: - key_mols = data.iloc[:, key_molecule_index] - scaffold_to_indices = scaffold_to_smiles(key_mols.tolist(), use_indices=True) - # Seed randomness - random = Random(seed) - - if ( - balanced - ): # Put stuff that's bigger than half the val/test size into train, rest just order randomly - index_sets = list(scaffold_to_indices.values()) - big_index_sets = [] - small_index_sets = [] - for index_set in index_sets: - if len(index_set) > val_size / 2 or len(index_set) > test_size / 2: - big_index_sets.append(index_set) - else: - small_index_sets.append(index_set) - random.seed(seed) - random.shuffle(big_index_sets) - random.shuffle(small_index_sets) - index_sets = big_index_sets + small_index_sets - else: # Sort from largest to smallest scaffold sets - index_sets = sorted( - list(scaffold_to_indices.values()), - key=lambda index_set: len(index_set), - reverse=True, - ) - for index_set in index_sets: - if len(test) + len(index_set) <= test_size: - test += index_set - test_scaffold_count += 1 - elif len(train) + len(index_set) <= train_size: - train += index_set - train_scaffold_count += 1 - else: - val += index_set - val_scaffold_count += 1 - logging.info( - f"Total scaffolds = {len(scaffold_to_indices):,} | " - f"train scaffolds = {train_scaffold_count:,} | " - f"val scaffolds = {val_scaffold_count:,} | " - f"test scaffolds = {test_scaffold_count:,}" - ) - - log_scaffold_stats(data, index_sets) - # Map from indices to data - - train_df = data.iloc[train] - val_df = data.iloc[val] - test_df = data.iloc[test] - return train_df, val_df, test_df - - -def log_scaffold_stats( - data: pd.DataFrame, - index_sets: List[Set[int]], - num_scaffolds: int = 10, - num_labels: int = 20, -) -> List[Tuple[List[float], List[int]]]: - - """ - Logs and returns statistics about counts and average target values in molecular scaffolds. - :param data: A pandas DataFrame containing SMILES strings and molecule properties. - :param index_sets: A list of sets of indices representing splits of the data. - :param num_scaffolds: The number of scaffolds about which to display statistics. - :param num_labels: The number of labels about which to display statistics. - :return: A list of tuples where each tuple contains a list of average target values - across the first :code:num_labels labels and a list of the number of non-zero values for - the first :code:num_scaffolds scaffolds, sorted in decreasing order of scaffold frequency. - """ - logging.info( - "Label averages per scaffold, in decreasing order of scaffold frequency, " - f"capped at {num_scaffolds} scaffolds and {num_labels} labels:" - ) - - stats = [] - index_sets = sorted(index_sets, key=lambda idx_set: len(idx_set), reverse=True) - for scaffold_num, index_set in enumerate(index_sets[:num_scaffolds]): - data_set = data.iloc[list(index_set)] - targets = [ - c - for c in data.columns - if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] - ] - # targets = data_set.iloc[:, 2:].values - targets = data_set.loc[:, targets].values - - with warnings.catch_warnings(): # Likely warning of empty slice of target has no values besides NaN - warnings.simplefilter("ignore", category=RuntimeWarning) - target_avgs = np.nanmean(targets, axis=0)[:num_labels] - - counts = np.count_nonzero(~np.isnan(targets), axis=0)[:num_labels] - stats.append((target_avgs, counts)) - - logging.info(f"Scaffold {scaffold_num}") - for task_num, (target_avg, count) in enumerate(zip(target_avgs, counts)): - logging.info( - f"Task {task_num}: count = {count:,} | target average = {target_avg:.6f}" - ) - logging.info("\n") - return stats diff --git a/build/lib/dfpl/vae.py b/build/lib/dfpl/vae.py deleted file mode 100644 index 17b7dc6f..00000000 --- a/build/lib/dfpl/vae.py +++ /dev/null @@ -1,337 +0,0 @@ -import logging -import math -import os.path -from os.path import basename - -import numpy as np -import pandas as pd -import wandb -from keras import backend as K -from sklearn.model_selection import train_test_split -import tensorflow.keras.metrics as metrics -from tensorflow.keras import initializers, optimizers -from tensorflow.keras.layers import Dense, Input, Lambda -from tensorflow.keras.models import Model -from tensorflow.python.framework.ops import disable_eager_execution -from typing import Tuple -import csv - -from dfpl import history as ht -from dfpl import callbacks -from dfpl import options, settings -from dfpl.utils import ae_scaffold_split, weight_split - -disable_eager_execution() - - -def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: - input_size = opts.fpSize - encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( - learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay - ) - - if output_bias is not None: - output_bias = initializers.Constant(output_bias) - - # get the number of meaningful hidden layers (latent space included) - hidden_layer_count = round(math.log2(input_size / encoding_dim)) - - # the input placeholder - input_vec = Input(shape=(input_size,)) - - # 1st hidden layer, that receives weights from input layer - # equals bottleneck layer, if hidden_layer_count==1! - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / 2), activation=opts.aeActivationFunction - )(input_vec) - else: - encoded = Dense( - units=int(input_size / 2), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(input_vec) - - if hidden_layer_count > 1: - # encoding layers, incl. bottle-neck - for i in range(1, hidden_layer_count): - factor_units = 2 ** (i + 1) - # print(f'{factor_units}: {int(input_size / factor_units)}') - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # latent space layers - factor_units = 2 ** (hidden_layer_count - 1) - if opts.aeActivationFunction != "selu": - z_mean = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - z_log_var = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - z_mean = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - z_log_var = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # sampling layer - def sampling(args): - z_mean, z_log_var = args - batch = K.shape(z_mean)[0] - dim = K.int_shape(z_mean)[1] - epsilon = K.random_normal(shape=(batch, dim)) - return z_mean + K.exp(0.5 * z_log_var) * epsilon - - # sample from latent space - z = Lambda(sampling, output_shape=(int(input_size / factor_units),))( - [z_mean, z_log_var] - ) - decoded = z - # decoding layers - for i in range(hidden_layer_count - 2, 0, -1): - factor_units = 2**i - # print(f'{factor_units}: {int(input_size/factor_units)}') - if opts.aeActivationFunction != "selu": - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(decoded) - else: - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(decoded) - - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(decoded) - - else: - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(encoded) - - autoencoder = Model(input_vec, decoded) - - # KL divergence loss - def kl_loss(z_mean, z_log_var): - return -0.5 * K.sum( - 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1 - ) - - # binary cross-entropy loss - def bce_loss(y_true, y_pred): - return metrics.binary_crossentropy(y_true, y_pred) - - # combined loss - def vae_loss(y_true, y_pred): - bce = bce_loss(y_true, y_pred) - kl = kl_loss(z_mean, z_log_var) - return bce + 0.5 * kl - - autoencoder.compile( - optimizer=ac_optimizer, loss=vae_loss, metrics=[bce_loss, kl_loss] - ) - - # build encoder model - encoder = Model(input_vec, z_mean) - - return autoencoder, encoder - - -def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: - """ - Trains an autoencoder on the given feature matrix X. The response matrix is only used to - split the data into meaningful test and train sets. - - :param opts: Command line arguments as defined in options.py - :param df: Pandas dataframe that contains the SMILES/InChI data for training the autoencoder - :return: The encoder model of the trained autoencoder - """ - - # If wandb tracking is enabled for VAE weights but not for the main program, initialize a new wandb run - if opts.aeWabTracking and not opts.wabTracking: - wandb.init(project=f"VAE_{opts.aeSplitType}") - - # Define output files for VAE and encoder weights - if opts.ecWeightsFile == "": - # If no encoder weights file is specified, use the input file name to generate a default file name - logging.info("No VAE encoder weights file specified") - base_file_name = ( - os.path.splitext(basename(opts.inputFile))[0] - + opts.aeType - + opts.aeSplitType - ) - logging.info( - f"(variational) encoder weights will be saved in {base_file_name}.autoencoder.hdf5" - ) - vae_weights_file = os.path.join( - opts.outputDir, base_file_name + ".vae.weights.hdf5" - ) - # ec_weights_file = os.path.join( - # opts.outputDir, base_file_name + ".encoder.weights.hdf5" - # ) - else: - # If an encoder weights file is specified, use it as the encoder weights file name - logging.info(f"VAE encoder will be saved in {opts.ecWeightsFile}") - base_file_name = ( - os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType - ) - vae_weights_file = os.path.join( - opts.outputDir, base_file_name + ".vae.weights.hdf5" - ) - # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - - # Collect the callbacks for training - callback_list = callbacks.autoencoder_callback( - checkpoint_path=vae_weights_file, opts=opts - ) - # Select all fingerprints that are valid and turn them into a numpy array - fp_matrix = np.array( - df[df["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - logging.info( - f"Training VAE on a matrix of shape {fp_matrix.shape} with type {fp_matrix.dtype}" - ) - assert 0.0 <= opts.testSize <= 0.5 - if opts.aeSplitType == "random": - logging.info("Training VAE using random split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # Split data into test and training data - if opts.aeWabTracking: - x_train, x_test, _, _ = train_test_split( - fp_matrix, train_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train, x_test, _, _ = train_test_split( - fp_matrix, train_indices, test_size=opts.testSize, random_state=42 - ) - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "scaffold_balanced": - logging.info("Training autoencoder using scaffold split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # if opts.aeWabTracking: - train_data, val_data, test_data = ae_scaffold_split( - df, - sizes=(1 - opts.testSize, 0.0, opts.testSize), - balanced=True, - seed=42, - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - else: - x_train = fp_matrix - x_test = None - elif opts.aeSplitType == "molecular_weight": - logging.info("Training autoencoder using molecular weight split") - train_indices = np.arange(fp_matrix.shape[0]) - if opts.testSize > 0.0: - # if opts.aeWabTracking: - train_data, val_data, test_data = weight_split( - df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" - ) - x_train = np.array( - train_data[train_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - x_test = np.array( - test_data[test_data["fp"].notnull()]["fp"].to_list(), - dtype=settings.ac_fp_numpy_type, - copy=settings.numpy_copy_values, - ) - else: - x_train = fp_matrix - x_test = None - else: - raise ValueError(f"Invalid split type: {opts.split_type}") - if opts.testSize > 0.0: - train_indices = train_indices[train_indices < x_train.shape[0]] - test_indices = np.arange(x_train.shape[0], x_train.shape[0] + x_test.shape[0]) - else: - test_indices = None - ids, counts = np.unique(x_train.flatten(), return_counts=True) - count_dict = dict(zip(ids, counts)) - if count_dict[0] == 0: - initial_bias = None - logging.info("No zeroes in training labels. Setting initial_bias to None.") - else: - initial_bias = np.log([count_dict[1] / count_dict[0]]) - logging.info(f"Initial bias for last sigmoid layer: {initial_bias[0]}") - if opts.testSize > 0.0: - logging.info(f"VAE training/testing mode with train- and test-samples") - logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") - logging.info(f"VAE test data shape {x_test.shape} with type {x_test.dtype}") - else: - logging.info(f"VAE full train mode without test-samples") - logging.info(f"VAE train data shape {x_train.shape} with type {x_train.dtype}") - - (vae, encoder) = define_vae_model(opts, output_bias=initial_bias) - # Train the VAE on the training data - vae_hist = vae.fit( - x_train, - x_train, - epochs=opts.aeEpochs, - batch_size=opts.aeBatchSize, - verbose=opts.verbose, - callbacks=callback_list, - validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, - ) - - # Save the VAE weights - logging.info(f"VAE weights stored in file: {vae_weights_file}") - ht.store_and_plot_history( - base_file_name=os.path.join(opts.outputDir, base_file_name + ".VAE"), - hist=vae_hist, - ) - save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_VAE.h5") - if opts.testSize > 0.0: - (callback_vae, callback_encoder) = define_vae_model(opts) - callback_vae.load_weights(filepath=vae_weights_file) - callback_encoder.save(filepath=save_path) - else: - encoder.save(filepath=save_path) - latent_space = encoder.predict(fp_matrix) - latent_space_file = os.path.join( - opts.outputDir, base_file_name + ".latent_space.csv" - ) - with open(latent_space_file, "w", newline="") as file: - writer = csv.writer(file) - writer.writerows(latent_space) - return encoder, train_indices, test_indices diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index 99bf4578..eec143c7 100644 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -11,9 +11,10 @@ import umap import wandb from sklearn.model_selection import train_test_split -from tensorflow.keras import initializers, losses, optimizers +from tensorflow.keras import initializers, losses from tensorflow.keras.layers import Dense, Input from tensorflow.keras.models import Model +from tensorflow.keras.optimizers.legacy import Adam from dfpl import callbacks from dfpl import history as ht @@ -32,7 +33,7 @@ def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Mod """ input_size = opts.fpSize encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( + ac_optimizer = Adam( learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay ) diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py index 18402f09..6216edc4 100644 --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -28,6 +28,7 @@ MeanSquaredError, ) from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.optimizers.legacy import Adam from dfpl import callbacks as cb from dfpl import options @@ -336,7 +337,7 @@ def define_single_label_model( # Set the optimizer according to the option selected if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + my_optimizer = Adam(learning_rate=opts.learningRate) elif opts.optimizer == "SGD": my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) else: diff --git a/dfpl/vae.py b/dfpl/vae.py index d0a89dbe..2c5ff8fa 100644 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -11,9 +11,10 @@ import wandb from keras import backend as K from sklearn.model_selection import train_test_split -from tensorflow.keras import initializers, optimizers +from tensorflow.keras import initializers from tensorflow.keras.layers import Dense, Input, Lambda from tensorflow.keras.models import Model +from tensorflow.keras.optimizers.legacy import Adam from tensorflow.python.framework.ops import disable_eager_execution from dfpl import callbacks @@ -27,7 +28,7 @@ def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: input_size = opts.fpSize encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( + ac_optimizer = Adam( learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay ) diff --git a/environment.yml b/environment.yml index 6209f1a9..ac482012 100644 --- a/environment.yml +++ b/environment.yml @@ -4,19 +4,21 @@ channels: - defaults dependencies: # dev requirements - - conda-build=3.21.8 - - conda=4.12.0 - - pip=22.0.4 - - pytest=7.1.1 + - conda-build>=3.21.8 + - conda>=4.12.0 + - pip>=22.0.4 + - pytest>=7.1.1 # application requirements - - jsonpickle=2.1 - - matplotlib=3.5.1 - - numpy=1.19.5 - - pandas=1.4.2 - - rdkit=2022.03.1 - - scikit-learn=1.0.2 - - seaborn=0.12.2 - - tensorflow - - wandb=0.12 + - jsonpickle>=2.1 + - matplotlib>=3.5.1 + - numpy>=1.19.5 + - pandas>=1.4.2 + - rdkit>=2022.03.1 + - scikit-learn>=1.0.2 + - seaborn>=0.12.2 + - tensorflow>=2.10 + - wandb>=0.12 + - umap>=0.1.1 - pip: - - git+https://github.com/soulios/chemprop/tree/dfpl_chemprop + - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 + diff --git a/example/train.json b/example/train.json index 2ffda6be..a40d5ab2 100755 --- a/example/train.json +++ b/example/train.json @@ -6,8 +6,8 @@ "ecWeightsFile": "random_autoencoder.hdf5", "verbose": 2, - "trainAC": false, - "compressFeatures": false, + "trainAC": true, + "compressFeatures": true, "encFPSize": 256, "aeSplitType": "random", From 5338ab99e126baf4c3d793af7ced2a645b16478a Mon Sep 17 00:00:00 2001 From: soulios Date: Fri, 8 Sep 2023 15:49:03 +0200 Subject: [PATCH 29/45] updated readme with mamba a link to the published article at BiB --- README.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 69a86a4e..2c99997b 100644 --- a/README.md +++ b/README.md @@ -101,22 +101,23 @@ In order to use the environment it needs to be activated with `. ENV_PATH/bin/ac To use this tool in a conda environment: -1. Create the conda env from scratch +1. Install mamba. For details follow the installation guide here. https://mamba.readthedocs.io/en/latest/mamba-installation.html#mamba-install} +2. Create the mamba env from scratch From within the `deepFPlearn` directory, you can create the conda environment with the provided yaml file that contains all information and necessary packages ```shell - conda env create -f environment.yml + mamba env create -f environment.yml ``` -2. Activate the `dfpl_env` environment with +3. Activate the `dfpl_env` environment with ```shell - conda activate dfpl_env + mamba activate dfpl_env ``` -3. Install the local `dfpl` package by calling +4. Install the local `dfpl` package by calling ```shell pip install --no-deps ./ @@ -287,6 +288,6 @@ The `convert` mode is used to convert `.csv` or `.tsv` files into `.pkl` files f # Please note that: This work has been submitted in a research paper and is currently under review. -You may read the preprint @bioRxiv: https://doi.org/10.1101/2021.06.24.449697 +You may read the published article @Briefings in Bioinformatics: https://doi.org/10.1093/bib/bbac257 For questions, comments please contact me: jana.schor@ufz.de From b390f81bea326e560dcb737fa569611feadde6dc Mon Sep 17 00:00:00 2001 From: Kyriakos Soulios Date: Sun, 10 Sep 2023 15:26:31 +0200 Subject: [PATCH 30/45] Update environment.yml --- environment.yml | 33 ++++++++++++++++++--------------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/environment.yml b/environment.yml index 6209f1a9..f6f04892 100644 --- a/environment.yml +++ b/environment.yml @@ -1,22 +1,25 @@ -name: dfplenv +name: dfpl_env channels: - conda-forge - defaults dependencies: # dev requirements - - conda-build=3.21.8 - - conda=4.12.0 - - pip=22.0.4 - - pytest=7.1.1 + - conda-build>=3.21.8 + - conda>=4.12.0 + - pip>=22.0.4 + - pytest>=7.1.1 # application requirements - - jsonpickle=2.1 - - matplotlib=3.5.1 - - numpy=1.19.5 - - pandas=1.4.2 - - rdkit=2022.03.1 - - scikit-learn=1.0.2 - - seaborn=0.12.2 - - tensorflow - - wandb=0.12 + - jsonpickle>=2.1 + - matplotlib>=3.5.1 + - numpy>=1.19.5 + - pandas>=1.4.2 + - rdkit>=2022.03.1 + - scikit-learn>=1.0.2 + - seaborn>=0.12.2 + - tensorflow>=2.10 + - wandb>=0.12 - pip: - - git+https://github.com/soulios/chemprop/tree/dfpl_chemprop + - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 + - umap>=0.1.1 + + From 6a7d4164df15997d730aa69e98b33ff152f4d63e Mon Sep 17 00:00:00 2001 From: soulios <90351285+soulios@users.noreply.github.com> Date: Sun, 10 Sep 2023 15:28:57 +0200 Subject: [PATCH 31/45] Update environment.yml --- environment.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index ac482012..f6f04892 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: dfplenv +name: dfpl_env channels: - conda-forge - defaults @@ -18,7 +18,8 @@ dependencies: - seaborn>=0.12.2 - tensorflow>=2.10 - wandb>=0.12 - - umap>=0.1.1 - pip: - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 + - umap>=0.1.1 + From 16ea7031b891e92808c19bfa9d7a7854078d8a3a Mon Sep 17 00:00:00 2001 From: soulios Date: Tue, 12 Sep 2023 09:14:21 +0200 Subject: [PATCH 32/45] updated env --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index f6f04892..391fbe6e 100644 --- a/environment.yml +++ b/environment.yml @@ -18,8 +18,8 @@ dependencies: - seaborn>=0.12.2 - tensorflow>=2.10 - wandb>=0.12 + - umap-learn>=0.5.3 - pip: - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 - - umap>=0.1.1 From e35d1508d11da36861a0fd022f560b6be1815d63 Mon Sep 17 00:00:00 2001 From: soulios Date: Wed, 20 Sep 2023 00:56:59 +0200 Subject: [PATCH 33/45] umap as an argument --- dfpl/__main__.py | 15 +++++++-------- dfpl/autoencoder.py | 16 ++++++++++++---- dfpl/options.py | 9 +++++++++ example/train.json | 7 ++++--- example/traingnn.json | 2 +- 5 files changed, 33 insertions(+), 16 deletions(-) diff --git a/dfpl/__main__.py b/dfpl/__main__.py index 7896d451..38eaf9f4 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -154,14 +154,13 @@ def train(opts: options.Options): ) # compress the fingerprints using the autoencoder df = ac.compress_fingerprints(df, encoder) - # ac.visualize_fingerprints( - # df, - # before_col="fp", - # after_col="fpcompressed", - # train_indices=train_indices, - # test_indices=test_indices, - # save_as=f"UMAP_{opts.aeSplitType}.png", - # ) + if opts.visualize_fingerprints: + ac.visualize_fingerprints( + df, + train_indices=train_indices, + test_indices=test_indices, + save_as=f"UMAP_{opts.aeSplitType}.png", + ) # train single label models if requested if opts.trainFNN and not opts.enableMultiLabel: sl.train_single_label_models(df=df, opts=opts) diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index eec143c7..8536c221 100644 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -8,7 +8,7 @@ import numpy as np import pandas as pd import seaborn as sns -import umap +import umap.umap_ as umap import wandb from sklearn.model_selection import train_test_split from tensorflow.keras import initializers, losses @@ -344,14 +344,22 @@ def compress_fingerprints(dataframe: pd.DataFrame, encoder: Model) -> pd.DataFra def visualize_fingerprints( df: pd.DataFrame, - before_col: str, - after_col: str, train_indices: np.ndarray, test_indices: np.ndarray, save_as: str, ): + if len(df) <= 10000: + num_samples = len(df) + elif len(df) > 50000: + logging.info("Cannot return the UMAP due to the large dataset size. Skipping the function.") + return + else: + additional_length = len(df) - 10000 + additional_samples = (additional_length // 10000) * 2000 + num_samples = 10000 + additional_samples + + after_col = "fpcompressed" # Calculate the number of samples to be taken from each set - num_samples = 1000 train_samples = int(num_samples * len(train_indices) / len(df)) test_samples = num_samples - train_samples diff --git a/dfpl/options.py b/dfpl/options.py index 6d84dbc4..3e0a2d07 100644 --- a/dfpl/options.py +++ b/dfpl/options.py @@ -47,6 +47,7 @@ class Options: aeLearningRateDecay: float = 0.01 aeActivationFunction: str = "relu" aeOptimizer: str = "Adam" + visualize_fingerprints: bool = False fnnType: str = "FNN" batchSize: int = 128 optimizer: str = "Adam" @@ -378,6 +379,14 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: help="Size of encoded fingerprint (z-layer of autoencoder).", default=argparse.SUPPRESS, ) + autoencoder_args.add_argument( + "-vis", + "--visualize_fingerprints", + metavar="BOOL", + type=bool, + help="UMAP visualization of compressed fingerprints (z-layer of autoencoder).Works only if compressFeatures is True", + default=argparse.SUPPRESS, + ) # Training Configuration training_args.add_argument( "--split_type", diff --git a/example/train.json b/example/train.json index a40d5ab2..03c7b670 100755 --- a/example/train.json +++ b/example/train.json @@ -10,7 +10,7 @@ "compressFeatures": true, "encFPSize": 256, - "aeSplitType": "random", + "aeSplitType": "molecular_weight", "aeEpochs": 2, "aeBatchSize": 351, "aeOptimizer": "Adam", @@ -18,17 +18,18 @@ "aeLearningRate": 0.001, "aeLearningRateDecay": 0.0001, "aeType": "deterministic", + "visualize_fingerprints": true, "type": "smiles", "fpType": "topological", "fpSize": 2048, - "split_type": "random", + "split_type": "molecular_weight", "enableMultiLabel": false, "sampleFractionOnes": 0, "sampleDown": false, "gpu": "", - "trainFNN": true, + "trainFNN": false, "kFolds": 1, "threshold": 0.5, "testSize": 0.2, diff --git a/example/traingnn.json b/example/traingnn.json index 7a5a0712..5c942d02 100644 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -6,7 +6,7 @@ "num_folds": 2, "metric": "accuracy", "loss_function": "binary_cross_entropy", - "split_type": "random", + "split_type": "molecular_weight", "dataset_type": "classification", "smiles_columns": "smiles", "extra_metrics": ["balanced_accuracy","auc","f1","mcc","recall","specificity","precision"], From 3aed03a05e117b67ffa130d3ca1be8b822fa40ad Mon Sep 17 00:00:00 2001 From: soulios Date: Wed, 6 Dec 2023 16:51:19 +0100 Subject: [PATCH 34/45] paths --- .dockerignore | 0 .github/workflows/pr.yml | 0 .github/workflows/push-to-gitlab.yml | 0 .gitignore | 0 .gitlab-ci.yml | 0 LICENSE.pdf | Bin README.md | 0 container/Dockerfile | 0 container/README.md | 0 dfpl/__init__.py | 0 dfpl/__main__.py | 3 ++- dfpl/autoencoder.py | 0 dfpl/callbacks.py | 0 dfpl/deepFPlearn-HyperParameterTuning.py | 0 dfpl/feedforwardNN.py | 0 dfpl/fingerprint.py | 0 dfpl/history.py | 0 dfpl/options.py | 0 dfpl/plot.py | 0 dfpl/predictions.py | 0 dfpl/settings.py | 0 dfpl/single_label_model.py | 1 - dfpl/utils.py | 22 ++++++++++++++++++++++ dfpl/vae.py | 0 environment.yml | 0 example/predict.json | 8 ++++---- example/predictgnn.json | 2 +- example/train.json | 20 ++++++++++---------- example/traingnn.json | 6 +++--- requirements.txt | 0 scripts/run-feasible-MoleculeNet-cases.sh | 0 setup.cfg | 0 setup.py | 0 singularity_container/environment.yaml | 0 tests/data/S_dataset.csv | 0 tests/data/inchi.tsv | 0 tests/data/smiles.csv | 0 tests/run_autoencoder.py | 0 tests/run_fnntraining.py | 0 tests/run_predictgnn.py | 0 tests/run_prediction.py | 0 tests/run_traingnn.py | 0 tests/run_vae.py | 0 tests/test_fingerprint.py | 0 tests/test_fractional_sampling.py | 0 tests/try_fpcomparison.py | 0 46 files changed, 42 insertions(+), 20 deletions(-) mode change 100644 => 100755 .dockerignore mode change 100644 => 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100755 tests/test_fractional_sampling.py mode change 100644 => 100755 tests/try_fpcomparison.py diff --git a/.dockerignore b/.dockerignore old mode 100644 new mode 100755 diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml old mode 100644 new mode 100755 diff --git a/.github/workflows/push-to-gitlab.yml b/.github/workflows/push-to-gitlab.yml old mode 100644 new mode 100755 diff --git a/.gitignore b/.gitignore old mode 100644 new mode 100755 diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml old mode 100644 new mode 100755 diff --git a/LICENSE.pdf b/LICENSE.pdf old mode 100644 new mode 100755 diff --git a/README.md b/README.md old mode 100644 new mode 100755 diff --git a/container/Dockerfile b/container/Dockerfile old mode 100644 new mode 100755 diff --git a/container/README.md b/container/README.md old mode 100644 new mode 100755 diff --git a/dfpl/__init__.py b/dfpl/__init__.py old mode 100644 new mode 100755 diff --git a/dfpl/__main__.py b/dfpl/__main__.py index 38eaf9f4..9a586f1b 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -15,7 +15,7 @@ from dfpl import options, predictions from dfpl import single_label_model as sl from dfpl import vae as vae -from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute +from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute, mergeConfigFromJson project_directory = pathlib.Path(".").parent.parent.absolute() test_train_opts = options.Options( @@ -67,6 +67,7 @@ def traindmpnn(opts: options.GnnOptions): arguments = createArgsFromJson( opts.configFile, ignore_elements, return_json_object=False ) + mergeConfigFromJson(arguments, opts.configFile) opts = cp.args.TrainArgs().parse_args(arguments) logging.info("Training DMPNN...") # Train the model and get the mean and standard deviation of AUC score from cross-validation diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py old mode 100644 new mode 100755 diff --git a/dfpl/callbacks.py b/dfpl/callbacks.py old mode 100644 new mode 100755 diff --git a/dfpl/deepFPlearn-HyperParameterTuning.py b/dfpl/deepFPlearn-HyperParameterTuning.py old mode 100644 new mode 100755 diff --git a/dfpl/feedforwardNN.py b/dfpl/feedforwardNN.py old mode 100644 new mode 100755 diff --git a/dfpl/fingerprint.py b/dfpl/fingerprint.py old mode 100644 new mode 100755 diff --git a/dfpl/history.py b/dfpl/history.py old mode 100644 new mode 100755 diff --git a/dfpl/options.py b/dfpl/options.py old mode 100644 new mode 100755 diff --git a/dfpl/plot.py b/dfpl/plot.py old mode 100644 new mode 100755 diff --git a/dfpl/predictions.py b/dfpl/predictions.py old mode 100644 new mode 100755 diff --git a/dfpl/settings.py b/dfpl/settings.py old mode 100644 new mode 100755 diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py old mode 100644 new mode 100755 index 6216edc4..6e55b04a --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -787,7 +787,6 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ) # For each individual target train a model elif opts.split_type == "scaffold_balanced": - # df, irrelevant_columns = preprocess_dataframe(df, opts) for idx, target in enumerate(targets): df = prepare_nn_training_data(df, target, opts, return_dataframe=True) relevant_cols = ["smiles"] + ["fp"] + [target] # list(irrelevant_columns) diff --git a/dfpl/utils.py b/dfpl/utils.py old mode 100644 new mode 100755 index db3d6ec1..6040454a --- a/dfpl/utils.py +++ b/dfpl/utils.py @@ -31,6 +31,28 @@ def createDirectory(directory: str): os.makedirs(path) +def mergeConfigFromJson(args, in_json: str): + try: + with open(in_json, "r") as f: + json_config = json.load(f) + + # Find the index of configFile and data_path in args and replace their values + if "configFile" in json_config: + if "--configFile" in args: + index = args.index("--configFile") + 1 + if index < len(args): + args[index] = json_config["configFile"] + + if "data_path" in json_config: + if "--data_path" in args: + index = args.index("--data_path") + 1 + if index < len(args): + args[index] = json_config["data_path"] + + except (FileNotFoundError, json.JSONDecodeError) as e: + print(f"Error loading JSON file: {e}") + + def createArgsFromJson(in_json: str, ignore_elements: list, return_json_object: bool): arguments = [] with open(in_json, "r") as f: diff --git a/dfpl/vae.py b/dfpl/vae.py old mode 100644 new mode 100755 diff --git a/environment.yml b/environment.yml old mode 100644 new mode 100755 diff --git a/example/predict.json b/example/predict.json index 252965e3..4d187a32 100755 --- a/example/predict.json +++ b/example/predict.json @@ -1,11 +1,11 @@ { "py/object": "dfpl.options.Options", - "inputFile": "tests/data/smiles.csv", - "outputDir": "example/results_predict/", + "inputFile": "home/tests/data/smiles.csv", + "outputDir": "home/example/results_predict/", "outputFile": "smiles.csv", - "ecModelDir": "example/results_train/random_autoencoder/", + "ecModelDir": "home/example/results_train/random_autoencoder", "ecWeightsFile": "", - "fnnModelDir": "example/results_train/AR_saved_model", + "fnnModelDir": "home/example/results_train/AR-2_saved_model", "compressFeatures": true, "trainAC": false, "trainFNN": false diff --git a/example/predictgnn.json b/example/predictgnn.json old mode 100644 new mode 100755 index 157b5e05..6893ca67 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,6 +1,6 @@ { "py/object": "dfpl.options.GnnOptions", - "test_path": "tests/data/smiles.csv", + "test_path": "home/tests/data/smiles.csv", "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt", "save_dir": "preds_dmpnn", "saving_name": "DMPNN_preds.csv" diff --git a/example/train.json b/example/train.json index 03c7b670..08c5c972 100755 --- a/example/train.json +++ b/example/train.json @@ -1,16 +1,16 @@ { "py/object": "dfpl.options.Options", - "inputFile": "tests/data/S_dataset.csv", - "outputDir": "example/results_train/", - "ecModelDir": "example/results_train/", - "ecWeightsFile": "random_autoencoder.hdf5", + "inputFile": "home/tests/data/S_dataset.csv", + "outputDir": "home/example/results_train/", + "ecModelDir": "home/example/results_train/", + "ecWeightsFile": "", "verbose": 2, "trainAC": true, "compressFeatures": true, "encFPSize": 256, - "aeSplitType": "molecular_weight", + "aeSplitType": "random", "aeEpochs": 2, "aeBatchSize": 351, "aeOptimizer": "Adam", @@ -18,19 +18,19 @@ "aeLearningRate": 0.001, "aeLearningRateDecay": 0.0001, "aeType": "deterministic", - "visualize_fingerprints": true, + "visualize_fingerprints": false, "type": "smiles", "fpType": "topological", "fpSize": 2048, - "split_type": "molecular_weight", + "split_type": "random", "enableMultiLabel": false, "sampleFractionOnes": 0, "sampleDown": false, - "gpu": "", + "gpu": "0", "trainFNN": false, - "kFolds": 1, + "kFolds": 2, "threshold": 0.5, "testSize": 0.2, "fnnType": "FNN", @@ -40,7 +40,7 @@ "batchSize": 128, "activationFunction": "selu", "dropout": 0.0107, - "learningRate": 0.0000022, + "learningRate": 0.0000078, "l2reg": 0.001, "aeWabTracking": false, diff --git a/example/traingnn.json b/example/traingnn.json old mode 100644 new mode 100755 index 5c942d02..5ca59600 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -1,14 +1,14 @@ { "py/object": "dfpl.options.GnnOptions", - "data_path": "tests/data/S_dataset.csv", + "data_path": "home/tests/data/S_dataset.csv", "save_dir": "dmpnn-random/", "epochs": 2, "num_folds": 2, "metric": "accuracy", "loss_function": "binary_cross_entropy", - "split_type": "molecular_weight", + "split_type": "random", "dataset_type": "classification", "smiles_columns": "smiles", - "extra_metrics": ["balanced_accuracy","auc","f1","mcc","recall","specificity","precision"], + "extra_metrics": "auc", "hidden_size": 256 } \ No newline at end of file diff --git a/requirements.txt b/requirements.txt old mode 100644 new mode 100755 diff --git a/scripts/run-feasible-MoleculeNet-cases.sh b/scripts/run-feasible-MoleculeNet-cases.sh old mode 100644 new mode 100755 diff --git a/setup.cfg b/setup.cfg old mode 100644 new mode 100755 diff --git a/setup.py b/setup.py old mode 100644 new mode 100755 diff --git a/singularity_container/environment.yaml b/singularity_container/environment.yaml old mode 100644 new mode 100755 diff --git a/tests/data/S_dataset.csv b/tests/data/S_dataset.csv old mode 100644 new mode 100755 diff --git a/tests/data/inchi.tsv b/tests/data/inchi.tsv old mode 100644 new mode 100755 diff --git a/tests/data/smiles.csv b/tests/data/smiles.csv old mode 100644 new mode 100755 diff --git a/tests/run_autoencoder.py b/tests/run_autoencoder.py old mode 100644 new mode 100755 diff --git a/tests/run_fnntraining.py b/tests/run_fnntraining.py old mode 100644 new mode 100755 diff --git a/tests/run_predictgnn.py b/tests/run_predictgnn.py old mode 100644 new mode 100755 diff --git a/tests/run_prediction.py b/tests/run_prediction.py old mode 100644 new mode 100755 diff --git a/tests/run_traingnn.py b/tests/run_traingnn.py old mode 100644 new mode 100755 diff --git a/tests/run_vae.py b/tests/run_vae.py old mode 100644 new mode 100755 diff --git a/tests/test_fingerprint.py b/tests/test_fingerprint.py old mode 100644 new mode 100755 diff --git a/tests/test_fractional_sampling.py b/tests/test_fractional_sampling.py old mode 100644 new mode 100755 diff --git a/tests/try_fpcomparison.py b/tests/try_fpcomparison.py old mode 100644 new mode 100755 From e376b7eac6912de3773cf4589e5c7c1fa45c42d5 Mon Sep 17 00:00:00 2001 From: soulios Date: Mon, 1 Jan 2024 20:51:55 +0100 Subject: [PATCH 35/45] update options and saving for encoders --- dfpl/__main__.py | 115 ++-------- dfpl/autoencoder.py | 63 ++---- dfpl/callbacks.py | 33 ++- dfpl/feedforwardNN.py | 16 +- dfpl/options.py | 442 +++++++++++++++++-------------------- dfpl/single_label_model.py | 17 +- dfpl/utils.py | 73 +++++- dfpl/vae.py | 244 ++++++++------------ example/predict.json | 10 +- example/predictgnn.json | 5 +- example/train.json | 18 +- example/traingnn.json | 6 +- 12 files changed, 463 insertions(+), 579 deletions(-) diff --git a/dfpl/__main__.py b/dfpl/__main__.py index 7896d451..aada91a3 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -17,43 +17,8 @@ from dfpl import vae as vae from dfpl.utils import createArgsFromJson, createDirectory, makePathAbsolute -project_directory = pathlib.Path(".").parent.parent.absolute() -test_train_opts = options.Options( - inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", - outputDir=f"{project_directory}/output_data/console_test", - ecWeightsFile=f"{project_directory}/output_data/case_00/AE_S/ae_S.encoder.hdf5", - ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", - type="smiles", - fpType="topological", - epochs=100, - batchSize=1024, - fpSize=2048, - encFPSize=256, - enableMultiLabel=False, - testSize=0.2, - kFolds=2, - verbose=2, - trainAC=False, - trainFNN=True, - compressFeatures=True, - activationFunction="selu", - lossFunction="bce", - optimizer="Adam", - fnnType="FNN", -) -test_pred_opts = options.Options( - inputFile=f"{project_directory}/input_datasets/S_dataset.pkl", - outputDir=f"{project_directory}/output_data/console_test", - outputFile=f"{project_directory}/output_data/console_test/S_dataset.predictions_ER.csv", - ecModelDir=f"{project_directory}/output_data/case_00/AE_S/saved_model", - fnnModelDir=f"{project_directory}/output_data/console_test/ER_saved_model", - type="smiles", - fpType="topological", -) - - -def traindmpnn(opts: options.GnnOptions): +def traindmpnn(opts: options.GnnOptions) -> None: """ Train a D-MPNN model using the given options. Args: @@ -61,54 +26,29 @@ def traindmpnn(opts: options.GnnOptions): Returns: - None """ - os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" - ignore_elements = ["py/object"] # Load options from a JSON file and replace the relevant attributes in `opts` - arguments = createArgsFromJson( - opts.configFile, ignore_elements, return_json_object=False - ) + arguments = createArgsFromJson(jsonFile = opts.configFile) opts = cp.args.TrainArgs().parse_args(arguments) logging.info("Training DMPNN...") - # Train the model and get the mean and standard deviation of AUC score from cross-validation mean_score, std_score = cp.train.cross_validate( args=opts, train_func=cp.train.run_training ) logging.info(f"Results: {mean_score:.5f} +/- {std_score:.5f}") -def predictdmpnn(opts: options.GnnOptions, json_arg_path: str) -> None: +def predictdmpnn(opts: options.GnnOptions) -> None: """ Predict the values using a trained D-MPNN model with the given options. Args: - opts: options.GnnOptions instance containing the details of the prediction - - JSON_ARG_PATH: path to a JSON file containing additional arguments for prediction Returns: - None """ - ignore_elements = [ - "py/object", - "checkpoint_paths", - "save_dir", - "saving_name", - ] # Load options and additional arguments from a JSON file - arguments, data = createArgsFromJson( - json_arg_path, ignore_elements, return_json_object=True - ) - arguments.append("--preds_path") - arguments.append("") - save_dir = data.get("save_dir") - name = data.get("saving_name") - # Replace relevant attributes in `opts` with loaded options + arguments = createArgsFromJson(jsonFile = opts.configFile) opts = cp.args.PredictArgs().parse_args(arguments) - opts.preds_path = save_dir + "/" + name - df = pd.read_csv(opts.test_path) - smiles = [] - for index, rows in df.iterrows(): - my_list = [rows.smiles] - smiles.append(my_list) - # Make predictions and return the result - cp.train.make_predictions(args=opts, smiles=smiles) + + cp.train.make_predictions(args=opts) def train(opts: options.Options): @@ -116,9 +56,6 @@ def train(opts: options.Options): Run the main training procedure :param opts: Options defining the details of the training """ - - os.environ["CUDA_VISIBLE_DEVICES"] = f"{opts.gpu}" - # import data from file and create DataFrame if "tsv" in opts.inputFile: df = fp.importDataFile( @@ -128,7 +65,7 @@ def train(opts: options.Options): df = fp.importDataFile( opts.inputFile, import_function=fp.importSmilesCSV, fp_size=opts.fpSize ) - # initialize encoders to None + # initialize (auto)encoders to None encoder = None autoencoder = None if opts.trainAC: @@ -142,11 +79,12 @@ def train(opts: options.Options): # if feature compression is enabled if opts.compressFeatures: if not opts.trainAC: - if opts.aeType == "deterministic": - (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) - elif opts.aeType == "variational": + if opts.aeType == "variational": (autoencoder, encoder) = vae.define_vae_model(opts=options.Options()) - elif opts.ecWeightsFile == "": + else: + (autoencoder, encoder) = ac.define_ac_model(opts=options.Options()) + + if opts.ecWeightsFile == "": encoder = load_model(opts.ecModelDir) else: autoencoder.load_weights( @@ -154,14 +92,15 @@ def train(opts: options.Options): ) # compress the fingerprints using the autoencoder df = ac.compress_fingerprints(df, encoder) - # ac.visualize_fingerprints( - # df, - # before_col="fp", - # after_col="fpcompressed", - # train_indices=train_indices, - # test_indices=test_indices, - # save_as=f"UMAP_{opts.aeSplitType}.png", - # ) + if opts.visualizeLatent: + ac.visualize_fingerprints( + df, + before_col="fp", + after_col="fpcompressed", + train_indices=train_indices, + test_indices=test_indices, + save_as=f"UMAP_{opts.aeSplitType}.png", + ) # train single label models if requested if opts.trainFNN and not opts.enableMultiLabel: sl.train_single_label_models(df=df, opts=opts) @@ -257,7 +196,7 @@ def main(): raise ValueError("Input directory is not a directory") elif prog_args.method == "traingnn": traingnn_opts = options.GnnOptions.fromCmdArgs(prog_args) - + createLogger("traingnn.log") traindmpnn(traingnn_opts) elif prog_args.method == "predictgnn": @@ -267,12 +206,8 @@ def main(): test_path=makePathAbsolute(predictgnn_opts.test_path), preds_path=makePathAbsolute(predictgnn_opts.preds_path), ) - - logging.info( - f"The following arguments are received or filled with default values:\n{prog_args}" - ) - - predictdmpnn(fixed_opts, prog_args.configFile) + createLogger("predictgnn.log") + predictdmpnn(fixed_opts) elif prog_args.method == "train": train_opts = options.Options.fromCmdArgs(prog_args) @@ -298,8 +233,6 @@ def main(): ), ecModelDir=makePathAbsolute(predict_opts.ecModelDir), fnnModelDir=makePathAbsolute(predict_opts.fnnModelDir), - trainAC=False, - trainFNN=False, ) createDirectory(fixed_opts.outputDir) createLogger(path.join(fixed_opts.outputDir, "predict.log")) diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index 99bf4578..6909b156 100644 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -8,12 +8,12 @@ import numpy as np import pandas as pd import seaborn as sns -import umap +import umap.umap_ as umap import wandb from sklearn.model_selection import train_test_split from tensorflow.keras import initializers, losses, optimizers from tensorflow.keras.layers import Dense, Input -from tensorflow.keras.models import Model +from tensorflow.keras.models import Model, load_model from dfpl import callbacks from dfpl import history as ht @@ -32,9 +32,13 @@ def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Mod """ input_size = opts.fpSize encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( - learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay + lr_schedule = optimizers.schedules.ExponentialDecay( + opts.aeLearningRate, + decay_steps=1000, + decay_rate=opts.aeLearningRateDecay, + staircase=True, ) + ac_optimizer = optimizers.legacy.Adam(learning_rate=lr_schedule) if output_bias is not None: output_bias = initializers.Constant(output_bias) @@ -104,7 +108,6 @@ def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Mod )(decoded) # output layer - # to either 0 or 1 and hence we use sigmoid activation function. decoded = Dense( units=input_size, activation="sigmoid", bias_initializer=output_bias )(decoded) @@ -145,37 +148,8 @@ def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: if opts.aeWabTracking and not opts.wabTracking: wandb.init(project=f"AE_{opts.aeSplitType}") - # Define output files for autoencoder and encoder weights - if opts.ecWeightsFile == "": - # If no encoder weights file is specified, use the input file name to generate a default file name - logging.info("No AE encoder weights file specified") - base_file_name = ( - os.path.splitext(basename(opts.inputFile))[0] + opts.aeSplitType - ) - logging.info( - f"(auto)encoder weights will be saved in {base_file_name}.autoencoder.hdf5" - ) - ac_weights_file = os.path.join( - opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" - ) - # ec_weights_file = os.path.join( - # opts.outputDir, base_file_name + ".encoder.weights.hdf5" - # ) - else: - # If an encoder weights file is specified, use it as the encoder weights file name - logging.info(f"AE encoder will be saved in {opts.ecWeightsFile}") - base_file_name = ( - os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType - ) - ac_weights_file = os.path.join( - opts.outputDir, base_file_name + ".autoencoder.weights.hdf5" - ) - # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - + save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_split_autoencoder") # Collect the callbacks for training - callback_list = callbacks.autoencoder_callback( - checkpoint_path=ac_weights_file, opts=opts - ) # Select all fingerprints that are valid and turn them into a numpy array fp_matrix = np.array( @@ -286,30 +260,35 @@ def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: # Set up the model of the AC w.r.t. the input size and the dimension of the bottle neck (z!) (autoencoder, encoder) = define_ac_model(opts, output_bias=initial_bias) - + callback_list = callbacks.autoencoder_callback(checkpoint_path=save_path, opts=opts) # Train the autoencoder on the training data auto_hist = autoencoder.fit( x_train, x_train, - callbacks=callback_list, + callbacks=[callback_list], epochs=opts.aeEpochs, batch_size=opts.aeBatchSize, verbose=opts.verbose, validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, ) - logging.info(f"Autoencoder weights stored in file: {ac_weights_file}") # Store the autoencoder training history and plot the metrics ht.store_and_plot_history( - base_file_name=os.path.join(opts.outputDir, base_file_name + ".AC"), + base_file_name=save_path, hist=auto_hist, ) # Save the autoencoder callback model to disk - save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_autoencoder") if opts.testSize > 0.0: - (callback_autoencoder, callback_encoder) = define_ac_model(opts) - callback_encoder.save(filepath=save_path) + # Re-define autoencoder and encoder using your function + callback_autoencoder = load_model(filepath=save_path) + _, callback_encoder = define_ac_model(opts) + for i, layer in enumerate(callback_encoder.layers): + layer.set_weights(callback_autoencoder.layers[i].get_weights()) + + # Save the encoder model + encoder_save_path = os.path.join(save_path, "encoder_model") + callback_encoder.save(filepath=encoder_save_path) else: encoder.save(filepath=save_path) # Return the encoder model of the trained autoencoder diff --git a/dfpl/callbacks.py b/dfpl/callbacks.py index 6eae7965..fc1f817c 100644 --- a/dfpl/callbacks.py +++ b/dfpl/callbacks.py @@ -22,28 +22,37 @@ def autoencoder_callback(checkpoint_path: str, opts: options.Options) -> list: else: target = "loss" # enable this checkpoint to restore the weights of the best performing model - checkpoint = ModelCheckpoint( + if opts.aeType == "deterministic": + checkpoint = ModelCheckpoint( checkpoint_path, monitor=target, mode="min", verbose=1, - period=settings.ac_train_check_period, + save_freq="epoch", save_best_only=True, - save_weights_only=True, - ) + ) + else: + checkpoint = ModelCheckpoint( + checkpoint_path, + monitor=target, + mode="min", + verbose=1, + save_freq="epoch", + save_best_only=True, + save_weights_only=True + ) callbacks.append(checkpoint) # enable early stopping if val_loss is not improving anymore early_stop = EarlyStopping( - monitor=target, - mode="min", - patience=settings.ac_train_patience, - min_delta=settings.ac_train_min_delta, - verbose=1, - restore_best_weights=True, + monitor=target, + mode="min", + patience=settings.ac_train_patience, + min_delta=settings.ac_train_min_delta, + verbose=1, + restore_best_weights=True, ) callbacks.append(early_stop) - if opts.aeWabTracking and not opts.wabTracking: callbacks.append(WandbCallback(save_model=False)) return callbacks @@ -65,7 +74,7 @@ def nn_callback(checkpoint_path: str, opts: options.Options) -> list: checkpoint = ModelCheckpoint( checkpoint_path, verbose=1, - period=settings.nn_train_check_period, + save_freq="epoch", save_best_only=True, monitor="val_loss", mode="min", diff --git a/dfpl/feedforwardNN.py b/dfpl/feedforwardNN.py index e9c88776..bf4241aa 100644 --- a/dfpl/feedforwardNN.py +++ b/dfpl/feedforwardNN.py @@ -69,10 +69,16 @@ def define_out_file_names(path_prefix: str, target: str, fold: int = -1) -> tupl def define_nn_multi_label_model( input_size: int, output_size: int, opts: options.Options ) -> Model: + lr_schedule = optimizers.schedules.ExponentialDecay( + opts.aeLearningRate, + decay_steps=1000, + decay_rate=opts.aeLearningRateDecay, + staircase=True, + ) if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + my_optimizer = optimizers.legacy.Adam(learning_rate=lr_schedule) elif opts.optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) + my_optimizer = optimizers.legacy.SGD(lr=lr_schedule, momentum=0.9) else: logging.error(f"Your selected optimizer is not supported:{opts.optimizer}.") sys.exit("Unsupported optimizer.") @@ -132,9 +138,9 @@ def define_nn_model_multi( decay: float = 0.01, ) -> Model: if optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=lr, decay=decay) + my_optimizer = optimizers.legacy.Adam(learning_rate=lr, decay=decay) elif optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=lr, momentum=0.9, decay=decay) + my_optimizer = optimizers.legacy.SGD(lr=lr, momentum=0.9, decay=decay) else: my_optimizer = optimizer @@ -294,6 +300,8 @@ def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: model_file_path_weights, model_file_path_json, model_hist_path, + model_hist_csv_path, + model_predict_valset_csv_path, model_validation, model_auc_file, model_auc_file_data, diff --git a/dfpl/options.py b/dfpl/options.py index 6d84dbc4..d1d657aa 100644 --- a/dfpl/options.py +++ b/dfpl/options.py @@ -3,12 +3,12 @@ import argparse from dataclasses import dataclass from pathlib import Path - +from typing import Optional import jsonpickle import torch from chemprop.args import TrainArgs -from dfpl.utils import makePathAbsolute +from dfpl.utils import makePathAbsolute, parseCmdArgs @dataclass @@ -17,51 +17,51 @@ class Options: Dataclass for all options necessary for training the neural nets """ - configFile: str = "./example/train.json" - inputFile: str = "/deepFPlearn/CMPNN/data/tox21.csv" - outputDir: str = "." - outputFile: str = "" - ecWeightsFile: str = "AE.encoder.weights.hdf5" - ecModelDir: str = "AE_encoder" - fnnModelDir: str = "modeltraining" + configFile: str = None + inputFile: str = "tests/data/smiles.csv" + outputDir: str = "example/results_train/" # changes according to mode + outputFile: str = "results.csv" + ecWeightsFile: str = "" + ecModelDir: str = "example/results_train/AE_encoder/" + fnnModelDir: str = "example/results_train/AR_saved_model/" type: str = "smiles" fpType: str = "topological" # also "MACCS", "atompairs" - epochs: int = 512 + epochs: int = 100 fpSize: int = 2048 encFPSize: int = 256 - kFolds: int = 0 + kFolds: int = 1 testSize: float = 0.2 enableMultiLabel: bool = False - verbose: int = 0 - trainAC: bool = True # if set to False, an AC weight file must be provided! + verbose: int = 2 + trainAC: bool = False trainFNN: bool = True - compressFeatures: bool = True - sampleFractionOnes: float = 0.5 # Only used when value is in [0,1] + compressFeatures: bool = False + sampleFractionOnes: float = 0.5 sampleDown: bool = False split_type: str = "random" aeSplitType: str = "random" aeType: str = "deterministic" - aeEpochs: int = 3000 + aeEpochs: int = 100 aeBatchSize: int = 512 aeLearningRate: float = 0.001 - aeLearningRateDecay: float = 0.01 - aeActivationFunction: str = "relu" + aeLearningRateDecay: float = 0.96 + aeActivationFunction: str = "selu" aeOptimizer: str = "Adam" fnnType: str = "FNN" batchSize: int = 128 optimizer: str = "Adam" learningRate: float = 0.001 + learningRateDecay: float = 0.96 lossFunction: str = "bce" activationFunction: str = "relu" l2reg: float = 0.001 dropout: float = 0.2 threshold: float = 0.5 - gpu: str = "" - snnDepth = 8 - snnWidth = 50 - aeWabTracking: str = "" # Wand & Biases autoencoder tracking - wabTracking: str = "" # Wand & Biases FNN tracking - wabTarget: str = "ER" # Wand & Biases target used for showing training progress + visualizeLatent: bool = False #only if autoencoder is trained or loaded + gpu: int = None + aeWabTracking: bool = False # Wand & Biases autoencoder tracking + wabTracking: bool = False # Wand & Biases FNN tracking + wabTarget: str = "AR" # Wand & Biases target used for showing training progress def saveToFile(self, file: str) -> None: """ @@ -72,42 +72,8 @@ def saveToFile(self, file: str) -> None: f.write(jsonpickle.encode(self)) @classmethod - def fromJson(cls, file: str) -> Options: - """ - Create an instance from a JSON file - """ - jsonFile = Path(file) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - return jsonpickle.decode(content) - raise ValueError("JSON file does not exist or is not readable") - - @classmethod - def fromCmdArgs(cls, args: argparse.Namespace) -> Options: - """ - Creates Options instance from cmdline arguments. - - If a training file (JSON) is provided, the values from that file are used. - However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the - JSON file and on the commandline, then the value of the commandline argument will be used. - """ - result = Options() - if "configFile" in vars(args).keys(): - jsonFile = Path(makePathAbsolute(args.configFile)) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - result = jsonpickle.decode(content) - else: - raise ValueError("Could not find JSON input file") - - for key, value in vars(args).items(): - # The args dict will contain a "method" key from the subparser. - # We don't use this. - if key != "method": - result.__setattr__(key, value) - return result + def fromCmdArgs(cls, args: argparse.Namespace) -> "Options": + return parseCmdArgs(cls, args) @dataclass @@ -134,37 +100,19 @@ class GnnOptions(TrainArgs): save_preds: bool = True @classmethod - def fromCmdArgs(cls, args: argparse.Namespace) -> GnnOptions: - """ - Creates Options instance from cmdline arguments. + def fromCmdArgs(cls, args: argparse.Namespace, json_config: Optional[dict] = None): + # Initialize with JSON config if provided + if json_config: + opts = cls(**json_config) + else: + opts = cls() - If a training file (JSON) is provided, the values from that file are used. - However, additional commandline arguments will be preferred. If, e.g., "fpSize" is specified both in the - JSON file and on the commandline, then the value of the commandline argument will be used. - """ - result = GnnOptions() - if "configFile" in vars(args).keys(): - jsonFile = Path(makePathAbsolute(args.configFile)) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - result = jsonpickle.decode(content) - else: - raise ValueError("Could not find JSON input file") - - return result + # Update with command-line arguments + for key, value in vars(args).items(): + if value is not None: + setattr(opts, key, value) - @classmethod - def fromJson(cls, file: str) -> GnnOptions: - """ - Create an instance from a JSON file - """ - jsonFile = Path(file) - if jsonFile.exists() and jsonFile.is_file(): - with jsonFile.open() as f: - content = f.read() - return jsonpickle.decode(content) - raise ValueError("JSON file does not exist or is not readable") + return opts def createCommandlineParser() -> argparse.ArgumentParser: @@ -225,7 +173,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="FILE", type=str, help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, + default="example/train.json", ) general_args.add_argument( "-i", @@ -234,7 +182,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, help="The file containing the data for training in " "comma separated CSV format.The first column should be smiles.", - default=argparse.SUPPRESS, + default="tests/data/smiles.csv" ) general_args.add_argument( "-o", @@ -243,8 +191,10 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, help="Prefix of output file name. Trained model and " "respective stats will be returned in this directory.", - default=argparse.SUPPRESS, + default="example/results_train/" ) + + # TODO CHECK WHAT IS TYPE DOING? general_args.add_argument( "-t", "--type", @@ -252,7 +202,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["fp", "smiles"], help="Type of the chemical representation. Choices: 'fp', 'smiles'.", - default=argparse.SUPPRESS, + default="fp" ) general_args.add_argument( "-thr", @@ -260,47 +210,41 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=float, metavar="FLOAT", help="Threshold for binary classification.", - default=argparse.SUPPRESS, + default=0.5 ) general_args.add_argument( "-gpu", "--gpu", metavar="INT", type=int, - help="Select which gpu to use. If not available, leave empty.", - default=argparse.SUPPRESS, + help="Select which gpu to use by index. If not available, leave empty", + default=None ) general_args.add_argument( - "-k", "--fpType", metavar="STR", type=str, - choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], - help="The type of fingerprint to be generated/used in input file.", - default=argparse.SUPPRESS, + choices=["topological", "MACCS"], + help="The type of fingerprint to be generated/used in input file. MACCS or topological are available.", + default="topological" ) general_args.add_argument( - "-s", "--fpSize", type=int, - help="Size of fingerprint that should be generated.", - default=argparse.SUPPRESS, + help="Length of the fingerprint that should be generated.", + default=2048 ) general_args.add_argument( - "-c", "--compressFeatures", - metavar="BOOL", - type=bool, - help="Should the fingerprints be compressed or not. Activates the autoencoder. ", - default=argparse.SUPPRESS, + action="store_true", + help="Should the fingerprints be compressed or not. Needs a path of a trained autoencoder or needs the trainAC also set to True.", + default=False ) general_args.add_argument( - "-m", "--enableMultiLabel", - metavar="BOOL", - type=bool, + action="store_true", help="Train multi-label classification model in addition to the individual models.", - default=argparse.SUPPRESS, + default=False ) # Autoencoder Configuration autoencoder_args.add_argument( @@ -309,14 +253,14 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, metavar="FILE", help="The .hdf5 file of a trained encoder", - default=argparse.SUPPRESS, + default="" ) autoencoder_args.add_argument( "--ecModelDir", type=str, metavar="DIR", help="The directory where the full model of the encoder will be saved", - default=argparse.SUPPRESS, + default="example/results_train/AE_encoder/" ) autoencoder_args.add_argument( "--aeType", @@ -324,21 +268,21 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["variational", "deterministic"], help="Autoencoder type, variational or deterministic.", - default=argparse.SUPPRESS, + default="deterministic" ) autoencoder_args.add_argument( "--aeEpochs", metavar="INT", type=int, help="Number of epochs for autoencoder training.", - default=argparse.SUPPRESS, + default=100 ) autoencoder_args.add_argument( "--aeBatchSize", metavar="INT", type=int, help="Batch size in autoencoder training.", - default=argparse.SUPPRESS, + default=512 ) autoencoder_args.add_argument( "--aeActivationFunction", @@ -346,21 +290,21 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["relu", "selu"], help="The activation function for the hidden layers in the autoencoder.", - default=argparse.SUPPRESS, + default="relu" ) autoencoder_args.add_argument( "--aeLearningRate", metavar="FLOAT", type=float, help="Learning rate for autoencoder training.", - default=argparse.SUPPRESS, + default=0.001 ) autoencoder_args.add_argument( "--aeLearningRateDecay", metavar="FLOAT", type=float, help="Learning rate decay for autoencoder training.", - default=argparse.SUPPRESS, + default=0.96 ) autoencoder_args.add_argument( "--aeSplitType", @@ -368,7 +312,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["scaffold_balanced", "random", "molecular_weight"], help="Set how the data is going to be split for the autoencoder", - default=argparse.SUPPRESS, + default="random" ) autoencoder_args.add_argument( "-d", @@ -376,7 +320,13 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="INT", type=int, help="Size of encoded fingerprint (z-layer of autoencoder).", - default=argparse.SUPPRESS, + default=256 + ) + autoencoder_args.add_argument( + "--visualizeLatent", + action="store_true", + help="UMAP the latent space for exploration", + default=False ) # Training Configuration training_args.add_argument( @@ -385,15 +335,14 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["scaffold_balanced", "random", "molecular_weight"], help="Set how the data is going to be split for the feedforward neural network", - default=argparse.SUPPRESS, + default="random" ) training_args.add_argument( - "-l", "--testSize", metavar="FLOAT", type=float, help="Fraction of the dataset that should be used for testing. Value in [0,1].", - default=argparse.SUPPRESS, + default=0.2 ) training_args.add_argument( "-K", @@ -401,7 +350,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="INT", type=int, help="K that is used for K-fold cross-validation in the training procedure.", - default=argparse.SUPPRESS, + default=1 ) training_args.add_argument( "-v", @@ -411,21 +360,19 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: choices=[0, 1, 2], help="Verbosity level. O: No additional output, " + "1: Some additional output, 2: full additional output", - default=argparse.SUPPRESS, + default=2, ) training_args.add_argument( "--trainAC", - metavar="BOOL", - type=bool, + action="store_true", help="Choose to train or not, the autoencoder based on the input file", - default=argparse.SUPPRESS, + default=False, ) training_args.add_argument( "--trainFNN", - metavar="BOOL", - type=bool, - help="Train the feedforward network either with provided weights.", - default=argparse.SUPPRESS, + action="store_false", + help="When called it deactivates the training.", + default=True, ) training_args.add_argument( "--sampleFractionOnes", @@ -433,14 +380,14 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=float, help="This is the fraction of positive target associations (1s) in comparison to the majority class(0s)." "only works if --sampleDown is enabled", - default=argparse.SUPPRESS, + default=0.5, ) training_args.add_argument( "--sampleDown", metavar="BOOL", type=bool, help="Enable automatic down sampling of the 0 valued samples.", - default=argparse.SUPPRESS, + default=False, ) training_args.add_argument( "-e", @@ -448,52 +395,60 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="INT", type=int, help="Number of epochs that should be used for the FNN training", - default=argparse.SUPPRESS, + default=100, ) - + # TODO CHECK IF ALL LOSSES MAKE SENSE HERE training_args.add_argument( "--lossFunction", metavar="STRING", type=str, choices=["mse", "bce", "focal"], help="Loss function to use during training. mse - mean squared error, bce - binary cross entropy.", - default=argparse.SUPPRESS, + default="bce", ) + # TODO DO I NEED ALL ARGUMENTS TO BE USER SPECIFIED? WHAT DOES THE USER KNOW ABOUT OPTIMIZERS? training_args.add_argument( "--optimizer", metavar="STRING", type=str, choices=["Adam", "SGD"], help='Optimizer to use for backpropagation in the FNN. Possible values: "Adam", "SGD"', - default=argparse.SUPPRESS, + default="Adam", ) training_args.add_argument( "--batchSize", metavar="INT", type=int, help="Batch size in FNN training.", - default=argparse.SUPPRESS, + default=128, ) training_args.add_argument( "--l2reg", metavar="FLOAT", type=float, help="Value for l2 kernel regularizer.", - default=argparse.SUPPRESS, + default=0.001, ) training_args.add_argument( "--dropout", metavar="FLOAT", type=float, help="The fraction of data that is dropped out in each dropout layer.", - default=argparse.SUPPRESS, + default=0.2, ) training_args.add_argument( "--learningRate", metavar="FLOAT", type=float, help="Learning rate size in FNN training.", - default=argparse.SUPPRESS, + default=0.000022, + ) + training_args.add_argument( + "--learningRateDecay", + metavar="FLOAT", + type=float, + help="Learning rate size in FNN training.", + default=0.96, ) training_args.add_argument( "--activationFunction", @@ -501,7 +456,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["relu", "selu"], help="The activation function for hidden layers in the FNN.", - default=argparse.SUPPRESS, + default="relu", ) # Tracking Configuration tracking_args.add_argument( @@ -509,14 +464,14 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="BOOL", type=bool, help="Track autoencoder performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, + default=False, ) tracking_args.add_argument( "--wabTracking", metavar="BOOL", type=bool, help="Track FNN performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, + default=False, ) tracking_args.add_argument( "--wabTarget", @@ -524,8 +479,108 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["AR", "ER", "ED", "GR", "TR", "PPARg", "Aromatase"], help="Which target to use for tracking performance via Weights & Biases, see https://wandb.ai.", - default=argparse.SUPPRESS, + default="AR", + ) + + +def parseInputPredict(parser: argparse.ArgumentParser) -> None: + """ + Parse the input arguments. + + :return: A namespace object built up from attributes parsed out of the cmd line. + """ + + general_args = parser.add_argument_group("General Configuration") + files_args = parser.add_argument_group("Files") + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for training/predicting." + ) + files_args.add_argument( + "-i", + "--inputFile", + metavar="FILE", + type=str, + help="The file containing the data for the prediction in (unquoted) " + "comma separated CSV format. The column named 'smiles' or 'fp'" + "contains the field to be predicted. Please adjust the type " + "that should be predicted (fp or smile) with -t option appropriately." + "An optional column 'id' is used to assign the outcomes to the" + "original identifiers. If this column is missing, the results are" + "numbered in the order of their appearance in the input file." + "A header is expected and respective column names are used.", + default="tests/data/smiles.csv", + ) + files_args.add_argument( + "-o", + "--outputDir", + metavar="DIR", + type=str, + help="Prefix of output directory. It will contain a log file and the file specified" + "with --outputFile.", + default="example/results_predict/", + ) + files_args.add_argument( + "--outputFile", + metavar="FILE", + type=str, + help="Output .CSV file name which will contain one prediction per input line. " + "Default: prefix of input file name.", + default="results.csv", + ) + # TODO AGAIN THIS TRASH HERE? CAN WE EVEN PROCESS SMILES? + general_args.add_argument( + "-t", + "--type", + metavar="STR", + type=str, + choices=["fp", "smiles"], + help="Type of the chemical representation. Choices: 'fp', 'smiles'.", + default="fp", + ) + general_args.add_argument( + "-k", + "--fpType", + metavar="STR", + type=str, + choices=["topological", "MACCS"], + help="The type of fingerprint to be generated/used in input file. Should be the same as the type of the fps that the model was trained upon.", + default="topological", + ) + files_args.add_argument( + "--ecModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the encoder will be saved (if trainAE=True) or " + "loaded from (if trainAE=False). Provide a full path here.", + default="", + ) + files_args.add_argument( + "--ecWeightsFile", + type=str, + metavar="STR", + help="The file where the full model of the encoder will be loaded from, to compress the fingerprints. Provide a full path here.", + default="", ) + files_args.add_argument( + "--fnnModelDir", + type=str, + metavar="DIR", + help="The directory where the full model of the fnn is loaded from. " + "Provide a full path here.", + default="example/results_train/AR_saved_model", + ) + general_args.add_argument( + "-c", "--compressFeatures", action="store_true", default=False + ) + (general_args.add_argument( + "--aeType", metavar="STRING", type=str, + choices=["variational", "deterministic"], + help="Autoencoder type, variational or deterministic.", + default="deterministic")) def parseTrainGnn(parser: argparse.ArgumentParser) -> None: @@ -575,9 +630,6 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: default=10, help="The number of batches between each logging of the training loss", ) - general_args.add_argument( - "--no_cuda", action="store_true", default=True, help="Turn off cuda" - ) general_args.add_argument( "--no_cache", action="store_true", @@ -1034,91 +1086,6 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: ) -def parseInputPredict(parser: argparse.ArgumentParser) -> None: - """ - Parse the input arguments. - - :return: A namespace object built up from attributes parsed out of the cmd line. - """ - - general_args = parser.add_argument_group("General Configuration") - files_args = parser.add_argument_group("Files") - files_args.add_argument( - "-f", - "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "-i", - "--inputFile", - metavar="FILE", - type=str, - help="The file containing the data for the prediction in (unquoted) " - "comma separated CSV format. The column named 'smiles' or 'fp'" - "contains the field to be predicted. Please adjust the type " - "that should be predicted (fp or smile) with -t option appropriately." - "An optional column 'id' is used to assign the outcomes to the" - "original identifiers. If this column is missing, the results are" - "numbered in the order of their appearance in the input file." - "A header is expected and respective column names are used.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "-o", - "--outputDir", - metavar="DIR", - type=str, - help="Prefix of output directory. It will contain a log file and the file specified" - "with --outputFile.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--outputFile", - metavar="FILE", - type=str, - help="Output .CSV file name which will contain one prediction per input line. " - "Default: prefix of input file name.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-t", - "--type", - metavar="STR", - type=str, - choices=["fp", "smiles"], - help="Type of the chemical representation. Choices: 'fp', 'smiles'.", - default=argparse.SUPPRESS, - ) - general_args.add_argument( - "-k", - "--fpType", - metavar="STR", - type=str, - choices=["topological", "MACCS"], # , 'atompairs', 'torsions'], - help="The type of fingerprint to be generated/used in input file.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--ecModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the encoder will be saved (if trainAE=True) or " - "loaded from (if trainAE=False). Provide a full path here.", - default=argparse.SUPPRESS, - ) - files_args.add_argument( - "--fnnModelDir", - type=str, - metavar="DIR", - help="The directory where the full model of the fnn is loaded from. " - "Provide a full path here.", - default=argparse.SUPPRESS, - ) - - def parsePredictGnn(parser: argparse.ArgumentParser) -> None: general_args = parser.add_argument_group("General Configuration") data_args = parser.add_argument_group("Data Configuration") @@ -1139,9 +1106,6 @@ def parsePredictGnn(parser: argparse.ArgumentParser) -> None: choices=list(range(torch.cuda.device_count())), help="Which GPU to use", ) - general_args.add_argument( - "--no_cuda", action="store_true", default=False, help="Turn off cuda" - ) general_args.add_argument( "--num_workers", type=int, diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py index 18402f09..191690ba 100644 --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -333,12 +333,17 @@ def define_single_label_model( else: logging.error(f"Your selected loss is not supported: {opts.lossFunction}.") sys.exit("Unsupported loss function") - + lr_schedule = optimizers.schedules.ExponentialDecay( + opts.learningRate, + decay_steps=1000, + decay_rate=opts.learningRateDecay, + staircase=True, + ) # Set the optimizer according to the option selected if opts.optimizer == "Adam": - my_optimizer = optimizers.Adam(learning_rate=opts.learningRate) + my_optimizer = optimizers.legacy.Adam(learning_rate=lr_schedule) elif opts.optimizer == "SGD": - my_optimizer = optimizers.SGD(lr=opts.learningRate, momentum=0.9) + my_optimizer = optimizers.legacy.SGD(lr=lr_schedule, momentum=0.9) else: logging.error(f"Your selected optimizer is not supported: {opts.optimizer}.") sys.exit("Unsupported optimizer") @@ -596,11 +601,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: """ # find target columns - targets = [ - c - for c in df.columns - if c in ["AR", "ER", "ED", "TR", "GR", "PPARg", "Aromatase"] - ] + targets = [c for c in df.columns if c not in ["smiles", "fp", "fpcompressed"]] if opts.wabTracking and opts.wabTarget != "": # For W&B tracking, we only train one target that's specified as wabTarget "ER". # In case it's not there, we use the first one available diff --git a/dfpl/utils.py b/dfpl/utils.py index db3d6ec1..15fd018b 100644 --- a/dfpl/utils.py +++ b/dfpl/utils.py @@ -5,8 +5,14 @@ import warnings from collections import defaultdict from random import Random -from typing import Dict, List, Set, Tuple, Union +from typing import Dict, List, Set, Tuple, Union, Type, TypeVar, Any +# Define a type variable + +from pathlib import Path +import argparse +import jsonpickle +import sys import numpy as np import pandas as pd from rdkit import Chem, RDLogger @@ -15,7 +21,44 @@ from tqdm import tqdm RDLogger.DisableLog("rdApp.*") +T = TypeVar("T") + + +def parseCmdArgs(cls: Type[T], args: argparse.Namespace) -> T: + """ + Parses command-line arguments to create an instance of the given class. + + Args: + cls: The class to create an instance of. + args: argparse.Namespace containing the command-line arguments. + + Returns: + An instance of cls populated with values from the command-line arguments. + """ + # Extract argument flags from sys.argv + arg_flags = {arg.lstrip('-') for arg in sys.argv if arg.startswith('-')} + + # Create the result instance, which will be modified and returned + result = cls() + + # Load JSON file if specified + if hasattr(args, "configFile") and args.configFile: + jsonFile = Path(args.configFile) + if jsonFile.exists() and jsonFile.is_file(): + with jsonFile.open() as f: + content = jsonpickle.decode(f.read()) + for key, value in vars(content).items(): + setattr(result, key, value) + else: + raise ValueError("Could not find JSON input file") + + # Override with user-provided command-line arguments + for key in arg_flags: + if hasattr(args, key): + user_value = getattr(args, key, None) + setattr(result, key, user_value) + return result def makePathAbsolute(p: str) -> str: path = pathlib.Path(p) @@ -31,20 +74,34 @@ def createDirectory(directory: str): os.makedirs(path) -def createArgsFromJson(in_json: str, ignore_elements: list, return_json_object: bool): +def createArgsFromJson(jsonFile: str): arguments = [] - with open(in_json, "r") as f: + ignore_elements = ["py/object"] + + with open(jsonFile, "r") as f: data = json.load(f) + + # Check each key in the JSON file against command-line arguments for key, value in data.items(): if key not in ignore_elements: + # Prepare the command-line argument format + cli_arg_key = f"--{key}" + + # Check if this argument is provided in the command line + if cli_arg_key in sys.argv: + # Find the index of the argument in sys.argv and get its value + arg_index = sys.argv.index(cli_arg_key) + 1 + if arg_index < len(sys.argv): + cli_value = sys.argv[arg_index] + value = cli_value # Override JSON value with command-line value + + # Append the argument and its value to the list if key == "extra_metrics" and isinstance(value, list): - arguments.append("--extra_metrics") + arguments.append(cli_arg_key) arguments.extend(value) else: - arguments.append("--" + str(key)) - arguments.append(str(value)) - if return_json_object: - return arguments, data + arguments.extend([cli_arg_key, str(value)]) + return arguments diff --git a/dfpl/vae.py b/dfpl/vae.py index d0a89dbe..cc61b17d 100644 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -13,7 +13,7 @@ from sklearn.model_selection import train_test_split from tensorflow.keras import initializers, optimizers from tensorflow.keras.layers import Dense, Input, Lambda -from tensorflow.keras.models import Model +from tensorflow.keras.models import Model,load_model from tensorflow.python.framework.ops import disable_eager_execution from dfpl import callbacks @@ -26,114 +26,71 @@ def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: input_size = opts.fpSize - encoding_dim = opts.encFPSize - ac_optimizer = optimizers.Adam( - learning_rate=opts.aeLearningRate, decay=opts.aeLearningRateDecay + encoding_dim = opts.encFPSize # This should be the intended size of your latent space, e.g., 256 + + lr_schedule = optimizers.schedules.ExponentialDecay( + opts.aeLearningRate, + decay_steps=1000, + decay_rate=opts.aeLearningRateDecay, + staircase=True, ) + ac_optimizer = optimizers.legacy.Adam(learning_rate=lr_schedule) if output_bias is not None: output_bias = initializers.Constant(output_bias) - # get the number of meaningful hidden layers (latent space included) hidden_layer_count = round(math.log2(input_size / encoding_dim)) - # the input placeholder input_vec = Input(shape=(input_size,)) - # 1st hidden layer, that receives weights from input layer - # equals bottleneck layer, if hidden_layer_count==1! + # 1st hidden layer if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / 2), activation=opts.aeActivationFunction - )(input_vec) + encoded = Dense(units=int(input_size / 2), activation=opts.aeActivationFunction)(input_vec) else: - encoded = Dense( - units=int(input_size / 2), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(input_vec) - - if hidden_layer_count > 1: - # encoding layers, incl. bottle-neck - for i in range(1, hidden_layer_count): - factor_units = 2 ** (i + 1) - # print(f'{factor_units}: {int(input_size / factor_units)}') - if opts.aeActivationFunction != "selu": - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - else: - encoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # latent space layers - factor_units = 2 ** (hidden_layer_count - 1) + encoded = Dense(units=int(input_size / 2), activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(input_vec) + + # encoding layers + for i in range(1, hidden_layer_count - 1): # Adjust the range to stop before the latent space layers + factor_units = 2 ** (i + 1) if opts.aeActivationFunction != "selu": - z_mean = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) - z_log_var = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(encoded) + encoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction)(encoded) else: - z_mean = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - z_log_var = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(encoded) - - # sampling layer - def sampling(args): - z_mean, z_log_var = args - batch = K.shape(z_mean)[0] - dim = K.int_shape(z_mean)[1] - epsilon = K.random_normal(shape=(batch, dim)) - return z_mean + K.exp(0.5 * z_log_var) * epsilon - - # sample from latent space - z = Lambda(sampling, output_shape=(int(input_size / factor_units),))( - [z_mean, z_log_var] - ) - decoded = z - # decoding layers - for i in range(hidden_layer_count - 2, 0, -1): - factor_units = 2**i - # print(f'{factor_units}: {int(input_size/factor_units)}') - if opts.aeActivationFunction != "selu": - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - )(decoded) - else: - decoded = Dense( - units=int(input_size / factor_units), - activation=opts.aeActivationFunction, - kernel_initializer="lecun_normal", - )(decoded) - - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(decoded) + encoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(encoded) + # latent space layers + if opts.aeActivationFunction != "selu": + z_mean = Dense(units=encoding_dim, activation=opts.aeActivationFunction)(encoded) # Adjusted size to encoding_dim + z_log_var = Dense(units=encoding_dim, activation=opts.aeActivationFunction)(encoded) # Adjusted size to encoding_dim else: - # output layer - decoded = Dense( - units=input_size, activation="sigmoid", bias_initializer=output_bias - )(encoded) + z_mean = Dense(units=encoding_dim, activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(encoded) # Adjusted size to encoding_dim + z_log_var = Dense(units=encoding_dim, activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(encoded) # Adjusted size to encoding_dim + + # sampling layer + def sampling(args): + z_mean, z_log_var = args + batch = K.shape(z_mean)[0] + dim = K.int_shape(z_mean)[1] + epsilon = K.random_normal(shape=(batch, dim)) + return z_mean + K.exp(0.5 * z_log_var) * epsilon + + z = Lambda(sampling, output_shape=(encoding_dim,))([z_mean, z_log_var]) + decoded = z + + # decoding layers + for i in range(hidden_layer_count - 2, 0, -1): + factor_units = 2 ** i + if opts.aeActivationFunction != "selu": + decoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction)(decoded) + else: + decoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(decoded) + + # output layer + decoded = Dense(units=input_size, activation="sigmoid", bias_initializer=output_bias)(decoded) autoencoder = Model(input_vec, decoded) + encoder = Model(input_vec, z) + autoencoder.summary(print_fn=logging.info) + # KL divergence loss def kl_loss(z_mean, z_log_var): @@ -155,9 +112,6 @@ def vae_loss(y_true, y_pred): optimizer=ac_optimizer, loss=vae_loss, metrics=[bce_loss, kl_loss] ) - # build encoder model - encoder = Model(input_vec, z_mean) - return autoencoder, encoder @@ -175,39 +129,9 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: if opts.aeWabTracking and not opts.wabTracking: wandb.init(project=f"VAE_{opts.aeSplitType}") - # Define output files for VAE and encoder weights - if opts.ecWeightsFile == "": - # If no encoder weights file is specified, use the input file name to generate a default file name - logging.info("No VAE encoder weights file specified") - base_file_name = ( - os.path.splitext(basename(opts.inputFile))[0] - + opts.aeType - + opts.aeSplitType - ) - logging.info( - f"(variational) encoder weights will be saved in {base_file_name}.autoencoder.hdf5" - ) - vae_weights_file = os.path.join( - opts.outputDir, base_file_name + ".vae.weights.hdf5" - ) - # ec_weights_file = os.path.join( - # opts.outputDir, base_file_name + ".encoder.weights.hdf5" - # ) - else: - # If an encoder weights file is specified, use it as the encoder weights file name - logging.info(f"VAE encoder will be saved in {opts.ecWeightsFile}") - base_file_name = ( - os.path.splitext(basename(opts.ecWeightsFile))[0] + opts.aeSplitType - ) - vae_weights_file = os.path.join( - opts.outputDir, base_file_name + ".vae.weights.hdf5" - ) - # ec_weights_file = os.path.join(opts.outputDir, opts.ecWeightsFile) - + save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_split_autoencoder") # Collect the callbacks for training - callback_list = callbacks.autoencoder_callback( - checkpoint_path=vae_weights_file, opts=opts - ) + # Select all fingerprints that are valid and turn them into a numpy array fp_matrix = np.array( df[df["fp"].notnull()]["fp"].to_list(), @@ -219,17 +143,17 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: ) assert 0.0 <= opts.testSize <= 0.5 if opts.aeSplitType == "random": - logging.info("Training VAE using random split") - train_indices = np.arange(fp_matrix.shape[0]) + logging.info("Training autoencoder using random split") + initial_indices = np.arange(fp_matrix.shape[0]) if opts.testSize > 0.0: # Split data into test and training data if opts.aeWabTracking: - x_train, x_test, _, _ = train_test_split( - fp_matrix, train_indices, test_size=opts.testSize, random_state=42 + x_train, x_test, train_indices, test_indices = train_test_split( + fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 ) else: - x_train, x_test, _, _ = train_test_split( - fp_matrix, train_indices, test_size=opts.testSize, random_state=42 + x_train, x_test, train_indices, test_indices = train_test_split( + fp_matrix, initial_indices, test_size=opts.testSize, random_state=42 ) else: x_train = fp_matrix @@ -255,6 +179,12 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: dtype=settings.ac_fp_numpy_type, copy=settings.numpy_copy_values, ) + train_indices = df[ + df.index.isin(train_data[train_data["fp"].notnull()].index) + ].index.to_numpy() + test_indices = df[ + df.index.isin(test_data[test_data["fp"].notnull()].index) + ].index.to_numpy() else: x_train = fp_matrix x_test = None @@ -262,7 +192,6 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: logging.info("Training autoencoder using molecular weight split") train_indices = np.arange(fp_matrix.shape[0]) if opts.testSize > 0.0: - # if opts.aeWabTracking: train_data, val_data, test_data = weight_split( df, sizes=(1 - opts.testSize, 0.0, opts.testSize), bias="small" ) @@ -276,16 +205,21 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: dtype=settings.ac_fp_numpy_type, copy=settings.numpy_copy_values, ) + df_sorted = df.sort_values(by="mol_weight", ascending=True) + # Get the sorted indices from the sorted DataFrame + sorted_indices = df_sorted.index.to_numpy() + + # Find the corresponding indices for train_data, val_data, and test_data in the sorted DataFrame + train_indices = sorted_indices[df.index.isin(train_data.index)] + # val_indices = sorted_indices[df.index.isin(val_data.index)] + test_indices = sorted_indices[df.index.isin(test_data.index)] else: x_train = fp_matrix x_test = None else: raise ValueError(f"Invalid split type: {opts.split_type}") - if opts.testSize > 0.0: - train_indices = train_indices[train_indices < x_train.shape[0]] - test_indices = np.arange(x_train.shape[0], x_train.shape[0] + x_test.shape[0]) - else: - test_indices = None + + # Calculate the initial bias aka the log ratio between 1's and 0'1 in all fingerprints ids, counts = np.unique(x_train.flatten(), return_counts=True) count_dict = dict(zip(ids, counts)) if count_dict[0] == 0: @@ -304,34 +238,32 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: (vae, encoder) = define_vae_model(opts, output_bias=initial_bias) # Train the VAE on the training data + callback_list = callbacks.autoencoder_callback(checkpoint_path=f"{save_path}.h5", opts=opts) + vae_hist = vae.fit( x_train, x_train, epochs=opts.aeEpochs, batch_size=opts.aeBatchSize, verbose=opts.verbose, - callbacks=callback_list, + callbacks=[callback_list], validation_data=(x_test, x_test) if opts.testSize > 0.0 else None, ) # Save the VAE weights - logging.info(f"VAE weights stored in file: {vae_weights_file}") ht.store_and_plot_history( - base_file_name=os.path.join(opts.outputDir, base_file_name + ".VAE"), + base_file_name=save_path, hist=vae_hist, ) - save_path = os.path.join(opts.ecModelDir, f"{opts.aeSplitType}_VAE.h5") - if opts.testSize > 0.0: - (callback_vae, callback_encoder) = define_vae_model(opts) - callback_vae.load_weights(filepath=vae_weights_file) - callback_encoder.save(filepath=save_path) - else: - encoder.save(filepath=save_path) - latent_space = encoder.predict(fp_matrix) - latent_space_file = os.path.join( - opts.outputDir, base_file_name + ".latent_space.csv" - ) - with open(latent_space_file, "w", newline="") as file: - writer = csv.writer(file) - writer.writerows(latent_space) + # Re-define autoencoder and encoder using your function + callback_autoencoder, callback_encoder = define_vae_model(opts) + callback_autoencoder.load_weights(filepath=f"{save_path}.h5") + + for i, layer in enumerate(callback_encoder.layers): + layer.set_weights(callback_autoencoder.layers[i].get_weights()) + + # Save the encoder model + encoder_save_path = f"{save_path}_encoder.h5" + callback_encoder.save_weights(filepath=encoder_save_path) + return encoder, train_indices, test_indices diff --git a/example/predict.json b/example/predict.json index 252965e3..e3305c7c 100755 --- a/example/predict.json +++ b/example/predict.json @@ -1,12 +1,12 @@ { "py/object": "dfpl.options.Options", - "inputFile": "tests/data/smiles.csv", + "inputFile": "tests/data/tox21.csv", "outputDir": "example/results_predict/", "outputFile": "smiles.csv", - "ecModelDir": "example/results_train/random_autoencoder/", - "ecWeightsFile": "", - "fnnModelDir": "example/results_train/AR_saved_model", + "ecModelDir": "example/results_train/random_split_autoencoder", + "ecWeightsFile": "random_split_autoencoder_encoder.h5", + "fnnModelDir": "example/results_train/NR-AR-1_best_saved_model", + "aeType": "variational", "compressFeatures": true, - "trainAC": false, "trainFNN": false } diff --git a/example/predictgnn.json b/example/predictgnn.json index 157b5e05..813cf0c5 100644 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,7 +1,6 @@ { "py/object": "dfpl.options.GnnOptions", "test_path": "tests/data/smiles.csv", - "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt", - "save_dir": "preds_dmpnn", - "saving_name": "DMPNN_preds.csv" + "preds_path": "example/results_gnn.csv", + "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt" } \ No newline at end of file diff --git a/example/train.json b/example/train.json index 62f2abb4..53575adc 100755 --- a/example/train.json +++ b/example/train.json @@ -1,22 +1,23 @@ { "py/object": "dfpl.options.Options", - "inputFile": "tests/data/S_dataset.csv", + "inputFile": "tests/data/tox21.csv", "outputDir": "example/results_train/", - "ecModelDir": "example/results_train/", - "ecWeightsFile": "random_autoencoder.hdf5", + "ecModelDir": "example/results_train/random_split_autoencoder/", + "ecWeightsFile": "", "verbose": 2, - "trainAC": true, - "compressFeatures": true, + "trainAC": false, + "compressFeatures": false, + "visualizeLatent": false, "encFPSize": 256, "aeSplitType": "random", - "aeEpochs": 2, + "aeEpochs": 4, "aeBatchSize": 351, "aeOptimizer": "Adam", "aeActivationFunction": "relu", "aeLearningRate": 0.001, - "aeLearningRateDecay": 0.0001, + "aeLearningRateDecay": 0.96, "aeType": "deterministic", "type": "smiles", @@ -29,7 +30,7 @@ "gpu": "", "trainFNN": true, - "kFolds": 1, + "kFolds": 2, "threshold": 0.5, "testSize": 0.2, "fnnType": "FNN", @@ -40,6 +41,7 @@ "activationFunction": "selu", "dropout": 0.0107, "learningRate": 0.0000022, + "learningRateDecay": 0.96, "l2reg": 0.001, "aeWabTracking": false, diff --git a/example/traingnn.json b/example/traingnn.json index 7a5a0712..1ca58a30 100644 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -2,13 +2,13 @@ "py/object": "dfpl.options.GnnOptions", "data_path": "tests/data/S_dataset.csv", "save_dir": "dmpnn-random/", - "epochs": 2, - "num_folds": 2, + "epochs": 4, + "num_folds": 1, "metric": "accuracy", "loss_function": "binary_cross_entropy", "split_type": "random", "dataset_type": "classification", "smiles_columns": "smiles", - "extra_metrics": ["balanced_accuracy","auc","f1","mcc","recall","specificity","precision"], + "extra_metrics": ["balanced_accuracy","auc","f1","mcc","recall","precision"], "hidden_size": 256 } \ No newline at end of file From 998f31283e4b85130c0a9ae6f25bfb8fc5be7b0c Mon Sep 17 00:00:00 2001 From: soulios Date: Mon, 5 Feb 2024 16:57:55 +0100 Subject: [PATCH 36/45] updated env and added interpret functions --- container/Dockerfile | 3 +- dfpl/__main__.py | 56 ++++++++--- dfpl/autoencoder.py | 40 ++++---- dfpl/callbacks.py | 38 ++++---- dfpl/options.py | 195 ++++++++++++++++++++++++++++++------- dfpl/single_label_model.py | 3 +- dfpl/utils.py | 5 +- dfpl/vae.py | 83 ++++++++++++---- environment.yml | 40 ++++++-- example/predict.json | 10 +- example/predictgnn.json | 4 +- example/train.json | 6 +- example/traingnn.json | 8 +- setup.py | 8 +- tests/data/smiles.csv | 14 +-- tests/run_fnntraining.py | 8 +- tests/run_predictgnn.py | 28 +----- tests/run_prediction.py | 15 ++- 18 files changed, 383 insertions(+), 181 deletions(-) diff --git a/container/Dockerfile b/container/Dockerfile index 566685ad..d0165580 100755 --- a/container/Dockerfile +++ b/container/Dockerfile @@ -50,6 +50,7 @@ RUN sh -c 'echo "APT { Get { AllowUnauthenticated \"1\"; }; };" > /etc/apt/apt.c RUN apt -o Acquire::AllowInsecureRepositories=true -o Acquire::AllowDowngradeToInsecureRepositories=true update RUN apt-get install -y curl wget +RUN apt-get install -y git RUN apt-key del 7fa2af80 RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb @@ -118,7 +119,7 @@ RUN ln -s $(which python3) /usr/local/bin/python # does not work sind it just copies the files in dfpl COPY ./ /deepFPlearn/ -# install dfpl +# install dfpl RUN python -m pip install --no-cache-dir /deepFPlearn && pip install --no-cache-dir pytest # The code to run when container is started. diff --git a/dfpl/__main__.py b/dfpl/__main__.py index aada91a3..d37ed30b 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -1,12 +1,10 @@ import dataclasses import logging -import os.path -import pathlib +import os from argparse import Namespace from os import path -import chemprop as cp -import pandas as pd +import chemprop from keras.models import load_model from dfpl import autoencoder as ac @@ -27,11 +25,11 @@ def traindmpnn(opts: options.GnnOptions) -> None: - None """ # Load options from a JSON file and replace the relevant attributes in `opts` - arguments = createArgsFromJson(jsonFile = opts.configFile) - opts = cp.args.TrainArgs().parse_args(arguments) + arguments = createArgsFromJson(jsonFile=opts.configFile) + opts = chemprop.args.TrainArgs().parse_args(arguments) logging.info("Training DMPNN...") - mean_score, std_score = cp.train.cross_validate( - args=opts, train_func=cp.train.run_training + mean_score, std_score = chemprop.train.cross_validate( + args=opts, train_func=chemprop.train.run_training ) logging.info(f"Results: {mean_score:.5f} +/- {std_score:.5f}") @@ -45,11 +43,24 @@ def predictdmpnn(opts: options.GnnOptions) -> None: - None """ # Load options and additional arguments from a JSON file - arguments = createArgsFromJson(jsonFile = opts.configFile) - opts = cp.args.PredictArgs().parse_args(arguments) + arguments = createArgsFromJson(jsonFile=opts.configFile) + opts = chemprop.args.PredictArgs().parse_args(arguments) - cp.train.make_predictions(args=opts) + chemprop.train.make_predictions(args=opts) +def interpretdmpnn(opts: options.GnnOptions) -> None: + """ + Interpret the predictions of a trained D-MPNN model with the given options. + Args: + - opts: options.GnnOptions instance containing the details of the prediction + Returns: + - None + """ + # Load options and additional arguments from a JSON file + arguments = createArgsFromJson(jsonFile=opts.configFile) + opts = chemprop.args.InterpretArgs().parse_args(arguments) + + chemprop.interpret.interpret(args=opts,save_to_csv=True)#,additional_columns=["ID"]) def train(opts: options.Options): """ @@ -92,15 +103,16 @@ def train(opts: options.Options): ) # compress the fingerprints using the autoencoder df = ac.compress_fingerprints(df, encoder) - if opts.visualizeLatent: + if opts.visualizeLatent and opts.trainAC: ac.visualize_fingerprints( df, - before_col="fp", - after_col="fpcompressed", train_indices=train_indices, test_indices=test_indices, - save_as=f"UMAP_{opts.aeSplitType}.png", + save_as=f"{opts.ecModelDir}/UMAP_{opts.aeSplitType}.png", ) + elif opts.visualizeLatent: + logging.info("Visualizing latent space is only available if you train the autoencoder. Skipping visualization.") + # train single label models if requested if opts.trainFNN and not opts.enableMultiLabel: sl.train_single_label_models(df=df, opts=opts) @@ -208,8 +220,20 @@ def main(): ) createLogger("predictgnn.log") predictdmpnn(fixed_opts) + elif prog_args.method == "interpretgnn": + interpretgnn_opts = options.GnnOptions.fromCmdArgs(prog_args) + fixed_opts = dataclasses.replace( + interpretgnn_opts, + test_path=makePathAbsolute(interpretgnn_opts.test_path), + preds_path=makePathAbsolute(interpretgnn_opts.preds_path), + ) + createLogger("interpretgnn.log") + interpretdmpnn(fixed_opts) elif prog_args.method == "train": + if prog_args.configFile is None and prog_args.inputFile is None: + parser.error("Either --configFile or --inputFile must be provided.") + train_opts = options.Options.fromCmdArgs(prog_args) fixed_opts = dataclasses.replace( train_opts, @@ -223,6 +247,8 @@ def main(): ) train(fixed_opts) elif prog_args.method == "predict": + if prog_args.configFile is None and prog_args.inputFile is None: + parser.error("Either --configFile or --inputFile must be provided.") predict_opts = options.Options.fromCmdArgs(prog_args) fixed_opts = dataclasses.replace( predict_opts, diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index 3e95692f..bf7f9fff 100755 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -1,7 +1,6 @@ import logging import math import os.path -from os.path import basename from typing import Tuple import matplotlib.pyplot as plt @@ -11,7 +10,7 @@ import umap.umap_ as umap import wandb from sklearn.model_selection import train_test_split -from tensorflow.keras import initializers, losses +from tensorflow.keras import initializers, losses, optimizers from tensorflow.keras.layers import Dense, Input from tensorflow.keras.models import Model, load_model @@ -122,19 +121,11 @@ def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Mod encoder = Model(input_vec, encoded) autoencoder.summary(print_fn=logging.info) - autoencoder.compile( - optimizer=ac_optimizer, - loss=losses.BinaryCrossentropy(), - # metrics=[ - # metrics.AUC(), - # metrics.Precision(), - # metrics.Recall() - # ] - ) + autoencoder.compile(optimizer=ac_optimizer, loss=losses.BinaryCrossentropy()) return autoencoder, encoder -def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: +def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Tuple[Model, np.ndarray, np.ndarray]: """ Trains an autoencoder on the given feature matrix X. The response matrix is only used to split the data into meaningful test and train sets. @@ -231,7 +222,6 @@ def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Model: # Find the corresponding indices for train_data, val_data, and test_data in the sorted DataFrame train_indices = sorted_indices[df.index.isin(train_data.index)] - # val_indices = sorted_indices[df.index.isin(val_data.index)] test_indices = sorted_indices[df.index.isin(test_data.index)] else: x_train = fp_matrix @@ -326,15 +316,31 @@ def visualize_fingerprints( test_indices: np.ndarray, save_as: str, ): + """ + Visualize fingerprints using UMAP and save the visualization to a file. + + This function takes a Pandas DataFrame containing fingerprint data, calculates + the appropriate number of samples based on the size of the dataset, applies UMAP + for dimensionality reduction, and saves the resulting visualization. + + Parameters: + - df (pd.DataFrame): A Pandas DataFrame containing fingerprint data. + - train_indices (np.ndarray): An array containing indices of training samples. + - test_indices (np.ndarray): An array containing indices of test samples. + - save_as (str): The filename or path where the UMAP visualization will be saved. + Note: + - If the DataFrame size exceeds 50,000 rows, the function logs a message and skips + the UMAP visualization step. + """ if len(df) <= 10000: num_samples = len(df) elif len(df) > 50000: - logging.info("Cannot return the UMAP due to the large dataset size. Skipping the function.") + logging.info( + "Cannot return the UMAP due to the large dataset size. Skipping the function." + ) return else: - additional_length = len(df) - 10000 - additional_samples = (additional_length // 10000) * 2000 - num_samples = 10000 + additional_samples + num_samples = len(df) // 2 after_col = "fpcompressed" # Calculate the number of samples to be taken from each set diff --git a/dfpl/callbacks.py b/dfpl/callbacks.py index fc1f817c..8bf157fd 100755 --- a/dfpl/callbacks.py +++ b/dfpl/callbacks.py @@ -24,33 +24,33 @@ def autoencoder_callback(checkpoint_path: str, opts: options.Options) -> list: # enable this checkpoint to restore the weights of the best performing model if opts.aeType == "deterministic": checkpoint = ModelCheckpoint( - checkpoint_path, - monitor=target, - mode="min", - verbose=1, - save_freq="epoch", - save_best_only=True, + checkpoint_path, + monitor=target, + mode="min", + verbose=1, + save_freq="epoch", + save_best_only=True, ) else: checkpoint = ModelCheckpoint( - checkpoint_path, - monitor=target, - mode="min", - verbose=1, - save_freq="epoch", - save_best_only=True, - save_weights_only=True + checkpoint_path, + monitor=target, + mode="min", + verbose=1, + save_freq="epoch", + save_best_only=True, + save_weights_only=True, ) callbacks.append(checkpoint) # enable early stopping if val_loss is not improving anymore early_stop = EarlyStopping( - monitor=target, - mode="min", - patience=settings.ac_train_patience, - min_delta=settings.ac_train_min_delta, - verbose=1, - restore_best_weights=True, + monitor=target, + mode="min", + patience=settings.ac_train_patience, + min_delta=settings.ac_train_min_delta, + verbose=1, + restore_best_weights=True, ) callbacks.append(early_stop) if opts.aeWabTracking and not opts.wabTracking: diff --git a/dfpl/options.py b/dfpl/options.py index b89de220..ea105c79 100755 --- a/dfpl/options.py +++ b/dfpl/options.py @@ -8,7 +8,7 @@ import torch from chemprop.args import TrainArgs -from dfpl.utils import makePathAbsolute, parseCmdArgs +from dfpl.utils import parseCmdArgs @dataclass @@ -47,7 +47,6 @@ class Options: aeLearningRateDecay: float = 0.96 aeActivationFunction: str = "selu" aeOptimizer: str = "Adam" - visualize_fingerprints: bool = False fnnType: str = "FNN" batchSize: int = 128 optimizer: str = "Adam" @@ -58,7 +57,7 @@ class Options: l2reg: float = 0.001 dropout: float = 0.2 threshold: float = 0.5 - visualizeLatent: bool = False #only if autoencoder is trained or loaded + visualizeLatent: bool = False # only if autoencoder is trained or loaded gpu: int = None aeWabTracking: bool = False # Wand & Biases autoencoder tracking wabTracking: bool = False # Wand & Biases FNN tracking @@ -99,6 +98,11 @@ class GnnOptions(TrainArgs): preds_path: str = "./tox21dmpnn.csv" test_path: str = "" save_preds: bool = True + calibration_method: str = "none" + uncertainty_method: str = "none" + calibration_path: str = "" + evaluation_methods: str = "none" + evaluation_scores_path: str = "" @classmethod def fromCmdArgs(cls, args: argparse.Namespace, json_config: Optional[dict] = None): @@ -132,6 +136,12 @@ def createCommandlineParser() -> argparse.ArgumentParser: parser_predict_gnn = subparsers.add_parser( "predictgnn", help="Predict with your GNN models" ) + parser_interpret_gnn = subparsers.add_parser( + "interpretgnn", help="Interpret your GNN models" + ) + parser_interpret_gnn.set_defaults(method="interpretgnn") + parseInterpretGnn(parser_interpret_gnn) + parser_predict_gnn.set_defaults(method="predictgnn") parsePredictGnn(parser_predict_gnn) @@ -183,7 +193,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, help="The file containing the data for training in " "comma separated CSV format.The first column should be smiles.", - default="tests/data/smiles.csv" + default="tests/data/smiles.csv", ) general_args.add_argument( "-o", @@ -192,7 +202,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, help="Prefix of output file name. Trained model and " "respective stats will be returned in this directory.", - default="example/results_train/" + default="example/results_train/", ) # TODO CHECK WHAT IS TYPE DOING? @@ -203,7 +213,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["fp", "smiles"], help="Type of the chemical representation. Choices: 'fp', 'smiles'.", - default="fp" + default="fp", ) general_args.add_argument( "-thr", @@ -211,7 +221,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=float, metavar="FLOAT", help="Threshold for binary classification.", - default=0.5 + default=0.5, ) general_args.add_argument( "-gpu", @@ -219,7 +229,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="INT", type=int, help="Select which gpu to use by index. If not available, leave empty", - default=None + default=None, ) general_args.add_argument( "--fpType", @@ -227,25 +237,25 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["topological", "MACCS"], help="The type of fingerprint to be generated/used in input file. MACCS or topological are available.", - default="topological" + default="topological", ) general_args.add_argument( "--fpSize", type=int, help="Length of the fingerprint that should be generated.", - default=2048 + default=2048, ) general_args.add_argument( "--compressFeatures", action="store_true", help="Should the fingerprints be compressed or not. Needs a path of a trained autoencoder or needs the trainAC also set to True.", - default=False + default=False, ) general_args.add_argument( "--enableMultiLabel", action="store_true", help="Train multi-label classification model in addition to the individual models.", - default=False + default=False, ) # Autoencoder Configuration autoencoder_args.add_argument( @@ -254,14 +264,14 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, metavar="FILE", help="The .hdf5 file of a trained encoder", - default="" + default="", ) autoencoder_args.add_argument( "--ecModelDir", type=str, metavar="DIR", help="The directory where the full model of the encoder will be saved", - default="example/results_train/AE_encoder/" + default="example/results_train/AE_encoder/", ) autoencoder_args.add_argument( "--aeType", @@ -269,21 +279,21 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["variational", "deterministic"], help="Autoencoder type, variational or deterministic.", - default="deterministic" + default="deterministic", ) autoencoder_args.add_argument( "--aeEpochs", metavar="INT", type=int, help="Number of epochs for autoencoder training.", - default=100 + default=100, ) autoencoder_args.add_argument( "--aeBatchSize", metavar="INT", type=int, help="Batch size in autoencoder training.", - default=512 + default=512, ) autoencoder_args.add_argument( "--aeActivationFunction", @@ -291,21 +301,21 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["relu", "selu"], help="The activation function for the hidden layers in the autoencoder.", - default="relu" + default="relu", ) autoencoder_args.add_argument( "--aeLearningRate", metavar="FLOAT", type=float, help="Learning rate for autoencoder training.", - default=0.001 + default=0.001, ) autoencoder_args.add_argument( "--aeLearningRateDecay", metavar="FLOAT", type=float, help="Learning rate decay for autoencoder training.", - default=0.96 + default=0.96, ) autoencoder_args.add_argument( "--aeSplitType", @@ -313,7 +323,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["scaffold_balanced", "random", "molecular_weight"], help="Set how the data is going to be split for the autoencoder", - default="random" + default="random", ) autoencoder_args.add_argument( "-d", @@ -321,13 +331,13 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="INT", type=int, help="Size of encoded fingerprint (z-layer of autoencoder).", - default=256 + default=256, ) autoencoder_args.add_argument( "--visualizeLatent", action="store_true", help="UMAP the latent space for exploration", - default=False + default=False, ) # Training Configuration training_args.add_argument( @@ -336,14 +346,14 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: type=str, choices=["scaffold_balanced", "random", "molecular_weight"], help="Set how the data is going to be split for the feedforward neural network", - default="random" + default="random", ) training_args.add_argument( "--testSize", metavar="FLOAT", type=float, help="Fraction of the dataset that should be used for testing. Value in [0,1].", - default=0.2 + default=0.2, ) training_args.add_argument( "-K", @@ -351,7 +361,7 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: metavar="INT", type=int, help="K that is used for K-fold cross-validation in the training procedure.", - default=1 + default=1, ) training_args.add_argument( "-v", @@ -498,7 +508,7 @@ def parseInputPredict(parser: argparse.ArgumentParser) -> None: "--configFile", metavar="FILE", type=str, - help="Input JSON file that contains all information for training/predicting." + help="Input JSON file that contains all information for training/predicting.", ) files_args.add_argument( "-i", @@ -577,11 +587,16 @@ def parseInputPredict(parser: argparse.ArgumentParser) -> None: general_args.add_argument( "-c", "--compressFeatures", action="store_true", default=False ) - (general_args.add_argument( - "--aeType", metavar="STRING", type=str, - choices=["variational", "deterministic"], - help="Autoencoder type, variational or deterministic.", - default="deterministic")) + ( + general_args.add_argument( + "--aeType", + metavar="STRING", + type=str, + choices=["variational", "deterministic"], + help="Autoencoder type, variational or deterministic.", + default="deterministic", + ) + ) def parseTrainGnn(parser: argparse.ArgumentParser) -> None: @@ -590,6 +605,39 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: files_args = parser.add_argument_group("Files") model_args = parser.add_argument_group("Model arguments") training_args = parser.add_argument_group("Training Configuration") + uncertainty_args = parser.add_argument_group("Uncertainty Configuration") + uncertainty_args.add_argument( + "--uncertainty_method", + type=str, + metavar="STRING", + choices=[ + 'mve', + 'ensemble', + 'evidential_epistemic', + 'evidential_aleatoric', + 'evidential_total', + 'classification', + 'dropout', + 'spectra_roundrobin', + 'dirichlet'], + help="Method to use for uncertainty estimation", + default="none", + ) + # Uncertainty arguments + uncertainty_args.add_argument( + "--calibration_method", + type=str, + metavar="STRING", + choices=["zscaling", "tscaling", "zelikman_interval", "mve_weighting","platt","isotonic"], + help="Method to use for calibration", + default="none", + ) + uncertainty_args.add_argument( + "--calibration_path", + type=str, + metavar="FILE", + help="Path to file with calibration data", + ) # General arguments general_args.add_argument("--split_key_molecule", type=int) @@ -1092,13 +1140,28 @@ def parsePredictGnn(parser: argparse.ArgumentParser) -> None: data_args = parser.add_argument_group("Data Configuration") files_args = parser.add_argument_group("Files") training_args = parser.add_argument_group("Training Configuration") + uncertainty_args = parser.add_argument_group("Uncertainty Configuration") + + uncertainty_args.add_argument( + "--evaluation_methods", + type=str, + metavar="STRING", + choices=["nll", "spearman", "ence","miscalibration_area"], + help="Method to use for evaluation", + default="none", + ) + uncertainty_args.add_argument( + "--evaluation_scores_path", + type=str, + metavar="FILE", + help="Path to file with evaluation scores", + ) files_args.add_argument( "-f", "--configFile", metavar="FILE", type=str, help="Input JSON file that contains all information for training/predicting.", - default=argparse.SUPPRESS, ) general_args.add_argument( "--gpu", @@ -1240,6 +1303,72 @@ def parsePredictGnn(parser: argparse.ArgumentParser) -> None: training_args.add_argument( "--batch_size", type=int, metavar="INT", default=50, help="Batch size" ) +def parseInterpretGnn(parser: argparse.ArgumentParser) -> None: + files_args = parser.add_argument_group("Files") + interpret_args = parser.add_argument_group("Interpretation Configuration") + files_args.add_argument( + "-f", + "--configFile", + metavar="FILE", + type=str, + help="Input JSON file that contains all information for interpretation.", + ) + files_args.add_argument( + "--preds_path", + type=str, + metavar="FILE", + help="Path to CSV file where predictions will be saved", + default="", + ) + files_args.add_argument( + "--checkpoint_dir", + type=str, + metavar="DIR", + help="Directory from which to load model checkpoints" + "(walks directory and ensembles all models that are found)", + default="./ckpt", + ) + files_args.add_argument( + "--checkpoint_path", + type=str, + metavar="DIR", + help="Path to model checkpoint (.pt file)", + ) + files_args.add_argument( + "--data_path", + type=str, + metavar="FILE", + help="Path to CSV file containing testing data for which predictions will be made" + ) + interpret_args.add_argument( + "--max_atoms", + type=int, + metavar="INT", + help="Maximum number of atoms to use for interpretation") + + interpret_args.add_argument( + "--min_atoms", + type=int, + metavar="INT", + help="Minimum number of atoms to use for interpretation") + + interpret_args.add_argument( + "--prop_delta", + type=float, + metavar="FLOAT", + help="The minimum change in the property of interest that is considered significant") + interpret_args.add_argument( + "--property_id", + type=int, + metavar="INT", + help="The index of the property of interest") + # write the argument for rollouts + interpret_args.add_argument( + "--rollout", + type=int, + metavar="INT", + help="The number of rollouts to use for interpretation") + def parseInputConvert(parser: argparse.ArgumentParser) -> None: diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py index bc73d80e..f8aeaa4c 100755 --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -28,7 +28,6 @@ MeanSquaredError, ) from tensorflow.keras.models import Model, Sequential -from tensorflow.keras.optimizers.legacy import Adam from dfpl import callbacks as cb from dfpl import options @@ -619,7 +618,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: # Collect metrics for each fold and target performance_list = [] if opts.split_type == "random": - for target in targets: # [:1]: + for target in targets[:1]: # target=targets[1] # --> only for testing the code x, y = prepare_nn_training_data(df, target, opts, return_dataframe=False) if x is None: diff --git a/dfpl/utils.py b/dfpl/utils.py index 15fd018b..bea7006f 100755 --- a/dfpl/utils.py +++ b/dfpl/utils.py @@ -5,7 +5,7 @@ import warnings from collections import defaultdict from random import Random -from typing import Dict, List, Set, Tuple, Union, Type, TypeVar, Any +from typing import Dict, List, Set, Tuple, Union, Type, TypeVar # Define a type variable @@ -36,7 +36,7 @@ def parseCmdArgs(cls: Type[T], args: argparse.Namespace) -> T: An instance of cls populated with values from the command-line arguments. """ # Extract argument flags from sys.argv - arg_flags = {arg.lstrip('-') for arg in sys.argv if arg.startswith('-')} + arg_flags = {arg.lstrip("-") for arg in sys.argv if arg.startswith("-")} # Create the result instance, which will be modified and returned result = cls() @@ -60,6 +60,7 @@ def parseCmdArgs(cls: Type[T], args: argparse.Namespace) -> T: return result + def makePathAbsolute(p: str) -> str: path = pathlib.Path(p) if path.is_absolute(): diff --git a/dfpl/vae.py b/dfpl/vae.py index cc61b17d..d357d755 100755 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -1,19 +1,16 @@ -import csv import logging import math import os.path -from os.path import basename from typing import Tuple import numpy as np import pandas as pd import tensorflow.keras.metrics as metrics -import wandb from keras import backend as K from sklearn.model_selection import train_test_split from tensorflow.keras import initializers, optimizers from tensorflow.keras.layers import Dense, Input, Lambda -from tensorflow.keras.models import Model,load_model +from tensorflow.keras.models import Model from tensorflow.python.framework.ops import disable_eager_execution from dfpl import callbacks @@ -21,12 +18,15 @@ from dfpl import options, settings from dfpl.utils import ae_scaffold_split, weight_split -disable_eager_execution() def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: + disable_eager_execution() + input_size = opts.fpSize - encoding_dim = opts.encFPSize # This should be the intended size of your latent space, e.g., 256 + encoding_dim = ( + opts.encFPSize + ) # This should be the intended size of your latent space, e.g., 256 lr_schedule = optimizers.schedules.ExponentialDecay( opts.aeLearningRate, @@ -45,25 +45,56 @@ def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Mo # 1st hidden layer if opts.aeActivationFunction != "selu": - encoded = Dense(units=int(input_size / 2), activation=opts.aeActivationFunction)(input_vec) + encoded = Dense( + units=int(input_size / 2), activation=opts.aeActivationFunction + )(input_vec) else: - encoded = Dense(units=int(input_size / 2), activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(input_vec) + encoded = Dense( + units=int(input_size / 2), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(input_vec) # encoding layers - for i in range(1, hidden_layer_count - 1): # Adjust the range to stop before the latent space layers + for i in range( + 1, hidden_layer_count - 1 + ): # Adjust the range to stop before the latent space layers factor_units = 2 ** (i + 1) if opts.aeActivationFunction != "selu": - encoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction)(encoded) + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(encoded) else: - encoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(encoded) + encoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(encoded) # latent space layers if opts.aeActivationFunction != "selu": - z_mean = Dense(units=encoding_dim, activation=opts.aeActivationFunction)(encoded) # Adjusted size to encoding_dim - z_log_var = Dense(units=encoding_dim, activation=opts.aeActivationFunction)(encoded) # Adjusted size to encoding_dim + z_mean = Dense(units=encoding_dim, activation=opts.aeActivationFunction)( + encoded + ) # Adjusted size to encoding_dim + z_log_var = Dense(units=encoding_dim, activation=opts.aeActivationFunction)( + encoded + ) # Adjusted size to encoding_dim else: - z_mean = Dense(units=encoding_dim, activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(encoded) # Adjusted size to encoding_dim - z_log_var = Dense(units=encoding_dim, activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(encoded) # Adjusted size to encoding_dim + z_mean = Dense( + units=encoding_dim, + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )( + encoded + ) # Adjusted size to encoding_dim + z_log_var = Dense( + units=encoding_dim, + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )( + encoded + ) # Adjusted size to encoding_dim # sampling layer def sampling(args): @@ -78,20 +109,28 @@ def sampling(args): # decoding layers for i in range(hidden_layer_count - 2, 0, -1): - factor_units = 2 ** i + factor_units = 2**i if opts.aeActivationFunction != "selu": - decoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction)(decoded) + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + )(decoded) else: - decoded = Dense(units=int(input_size / factor_units), activation=opts.aeActivationFunction, kernel_initializer="lecun_normal")(decoded) + decoded = Dense( + units=int(input_size / factor_units), + activation=opts.aeActivationFunction, + kernel_initializer="lecun_normal", + )(decoded) # output layer - decoded = Dense(units=input_size, activation="sigmoid", bias_initializer=output_bias)(decoded) + decoded = Dense( + units=input_size, activation="sigmoid", bias_initializer=output_bias + )(decoded) autoencoder = Model(input_vec, decoded) encoder = Model(input_vec, z) autoencoder.summary(print_fn=logging.info) - # KL divergence loss def kl_loss(z_mean, z_log_var): return -0.5 * K.sum( @@ -238,7 +277,9 @@ def train_full_vae(df: pd.DataFrame, opts: options.Options) -> Model: (vae, encoder) = define_vae_model(opts, output_bias=initial_bias) # Train the VAE on the training data - callback_list = callbacks.autoencoder_callback(checkpoint_path=f"{save_path}.h5", opts=opts) + callback_list = callbacks.autoencoder_callback( + checkpoint_path=f"{save_path}.h5", opts=opts + ) vae_hist = vae.fit( x_train, diff --git a/environment.yml b/environment.yml index 391fbe6e..0bd80fbf 100755 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,29 @@ -name: dfpl_env +#name: dfpl_env_cd +#channels: +# - conda-forge +# - defaults +#dependencies: +# # dev requirements +# - conda-build>=3.21.8 +# - conda>=4.12.0 +# - pip>=22.0.4 +# - pytest>=7.1.1 +# # application requirements +# - protobuf==3.20.3 +# - jsonpickle>=2.1 +# - matplotlib>=3.5.1 +# - numpy>=1.22.2 +# - pandas>=1.4.2 +# - rdkit>=2022.03.1 +# - scikit-learn>=1.0.2 +# - seaborn>=0.12.2 +# - tensorflow>=2.13 +# - wandb>=0.16.2 +# - umap-learn>=0.5.3 +# - pip: +# - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 +# +name: dfpl_env_cd channels: - conda-forge - defaults @@ -9,17 +34,16 @@ dependencies: - pip>=22.0.4 - pytest>=7.1.1 # application requirements + - protobuf>=3.21.12 - jsonpickle>=2.1 - matplotlib>=3.5.1 - - numpy>=1.19.5 - - pandas>=1.4.2 - - rdkit>=2022.03.1 + - numpy>=1.22.2 + - rdkit>=2023.03.3 - scikit-learn>=1.0.2 - seaborn>=0.12.2 - - tensorflow>=2.10 - - wandb>=0.12 + - tensorflow>=2.13 + - wandb>=0.16.2 - umap-learn>=0.5.3 - pip: - - git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338 - + - git+https://github.com/soulios/chemprop.git@cfcb67a diff --git a/example/predict.json b/example/predict.json index e3305c7c..a4b17995 100755 --- a/example/predict.json +++ b/example/predict.json @@ -1,12 +1,12 @@ { "py/object": "dfpl.options.Options", - "inputFile": "tests/data/tox21.csv", + "inputFile": "tests/data/S_dataset.csv", "outputDir": "example/results_predict/", "outputFile": "smiles.csv", - "ecModelDir": "example/results_train/random_split_autoencoder", - "ecWeightsFile": "random_split_autoencoder_encoder.h5", - "fnnModelDir": "example/results_train/NR-AR-1_best_saved_model", - "aeType": "variational", + "ecModelDir": "example/results_train/random_split_autoencoder/encoder_model/random_split_autoencoder/encoder_model", + "ecWeightsFile": "", + "fnnModelDir": "example/results_train/AR-1_best_saved_model", + "aeType": "deterministic", "compressFeatures": true, "trainFNN": false } diff --git a/example/predictgnn.json b/example/predictgnn.json index 813cf0c5..7d117dc6 100755 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,6 +1,8 @@ { "py/object": "dfpl.options.GnnOptions", - "test_path": "tests/data/smiles.csv", + "test_path": "tests/data/calibra.csv", "preds_path": "example/results_gnn.csv", + "evaluation_methods": "spearman", + "evaluation_scores_path": "example/eval_gnn.csv", "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt" } \ No newline at end of file diff --git a/example/train.json b/example/train.json index 29457b15..49a0a6c4 100755 --- a/example/train.json +++ b/example/train.json @@ -2,17 +2,15 @@ "py/object": "dfpl.options.Options", "inputFile": "tests/data/S_dataset.csv", "outputDir": "example/results_train/", - "ecModelDir": "example/results_train/random_split_autoencoder/", + "ecModelDir": "example/results_train/random_split_autoencoder/encoder_model/", "ecWeightsFile": "", "verbose": 2, "trainAC": false, "compressFeatures": false, - "visualizeLatent": false, "encFPSize": 256, "aeSplitType": "random", - "aeEpochs": 4, "aeBatchSize": 351, "aeOptimizer": "Adam", "aeActivationFunction": "relu", @@ -37,7 +35,7 @@ "fnnType": "FNN", "optimizer": "Adam", "lossFunction": "bce", - "epochs": 100, + "epochs": 4, "batchSize": 128, "activationFunction": "selu", "dropout": 0.0107, diff --git a/example/traingnn.json b/example/traingnn.json index 1d40e946..36884647 100755 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -1,14 +1,14 @@ { "py/object": "dfpl.options.GnnOptions", - "data_path": "home/tests/data/S_dataset.csv", + "data_path": "tests/data/S_dataset.csv", "save_dir": "dmpnn-random/", - "epochs": 4, - "num_folds": 1, + "epochs": 15, + "num_folds": 5, "metric": "accuracy", "loss_function": "binary_cross_entropy", "split_type": "random", "dataset_type": "classification", "smiles_columns": "smiles", "extra_metrics": ["balanced_accuracy","auc","f1","mcc","recall","precision"], - "hidden_size": 256 + "hidden_size": 300 } \ No newline at end of file diff --git a/setup.py b/setup.py index 2e76e617..a996f7a3 100755 --- a/setup.py +++ b/setup.py @@ -20,16 +20,16 @@ # all packages need for the final usage # for additional packages during development, use requirements.txt install_requires=[ + "protobuf == 3.20.3", "jsonpickle~=2.1.0", "matplotlib==3.5.1", "numpy==1.22.0", "pandas==1.4.2", "rdkit-pypi==2022.03.1", "scikit-learn==1.0.2", - "keras==2.9.0", - "tensorflow-gpu==2.9.3", - "wandb~=0.12.0", - "umap~=0.1.1", + "tensorflow==2.13", + "wandb~=0.16.2", + "umap-learn~=0.5.3", "seaborn~=0.12.2", "chemprop @ git+https://github.com/soulios/chemprop.git@1d73523e49aa28a90b74edc04aaf45d7e124e338", ], diff --git a/tests/data/smiles.csv b/tests/data/smiles.csv index 9383afdd..b965e11e 100755 --- a/tests/data/smiles.csv +++ b/tests/data/smiles.csv @@ -1,7 +1,7 @@ -smiles -CN(C)c1ccc(cc1)C(=O)c2ccc(cc2)N(C)C -CC12CCC3C(CCC4=CC(=O)CCC34C)C1CCC2=O -CC12CCC3C(CCc4cc(O)ccc34)C1CCC2O -Oc1c(Br)cc(cc1Br)C#N -Oc1ccc(C=Cc2cc(O)cc(O)c2)cc1 -Oc1ccc(cc1)c2ccccc2 \ No newline at end of file +smiles,ID +CN(C)c1ccc(cc1)C(=O)c2ccc(cc2)N(C)C,2982 +CC12CCC3C(CCC4=CC(=O)CCC34C)C1CCC2=O,4345435 +CC12CCC3C(CCc4cc(O)ccc34)C1CCC2O,42121 +Oc1c(Br)cc(cc1Br)C#N,421421 +Oc1ccc(C=Cc2cc(O)cc(O)c2)cc1,2142143 +Oc1ccc(cc1)c2ccccc2,2413213 \ No newline at end of file diff --git a/tests/run_fnntraining.py b/tests/run_fnntraining.py index 4146ad4a..40f9287a 100755 --- a/tests/run_fnntraining.py +++ b/tests/run_fnntraining.py @@ -24,7 +24,7 @@ testSize=0.2, kFolds=1, verbose=2, - trainAC=False, + trainAC=True, trainFNN=True, ) @@ -49,11 +49,7 @@ def run_single_label_training(opts: opt.Options) -> None: if opts.trainAC: logging.info("Training autoencoder") - encoder = ac.train_full_ac(df, opts) - # encoder.save_weights(opts.acFile) - else: - logging.info("Using trained autoencoder") - (_, encoder) = ac.define_ac_model(opts) + encoder, train_indices, test_indices = ac.train_full_ac(df, opts) df = ac.compress_fingerprints(df, encoder) diff --git a/tests/run_predictgnn.py b/tests/run_predictgnn.py index 979c2868..493a834e 100755 --- a/tests/run_predictgnn.py +++ b/tests/run_predictgnn.py @@ -1,10 +1,8 @@ import logging -import os import pathlib -import pandas as pd -from chemprop import args, train - +from chemprop import train +import chemprop as cp import dfpl.options as opt import dfpl.utils as utils @@ -26,26 +24,10 @@ def test_predictdmpnn(opts: opt.GnnOptions) -> None: ) json_arg_path = utils.makePathAbsolute(f"{example_directory}/predictgnn.json") - ignore_elements = [ - "py/object", - "checkpoint_paths", - "save_dir", - "saving_name", - ] - arguments, data = utils.createArgsFromJson( - json_arg_path, ignore_elements, return_json_object=True - ) - arguments.append("--preds_path") - arguments.append("") - save_dir = data.get("save_dir") - name = data.get("saving_name") - - opts = args.PredictArgs().parse_args(arguments) - opts.preds_path = os.path.join(save_dir, name) - df = pd.read_csv(opts.test_path) - smiles = [[row.smiles] for _, row in df.iterrows()] + arguments = utils.createArgsFromJson(json_arg_path) + opts = cp.args.PredictArgs().parse_args(arguments) - train.make_predictions(args=opts, smiles=smiles) + train.make_predictions(args=opts) print("predictdmpnn test complete.") diff --git a/tests/run_prediction.py b/tests/run_prediction.py index cb7d1fea..cd0254af 100755 --- a/tests/run_prediction.py +++ b/tests/run_prediction.py @@ -1,7 +1,7 @@ import logging import pathlib from os import path - +from tensorflow.keras.models import load_model import dfpl.autoencoder as ac import dfpl.fingerprint as fp import dfpl.options as opt @@ -12,14 +12,14 @@ test_predict_args = opt.Options( inputFile=f"{project_directory}/data/smiles.csv", outputDir=f"{project_directory}/preds/", - ecModelDir=utils.makePathAbsolute(f"{project_directory}/output/"), - ecWeightsFile=utils.makePathAbsolute( - f"{project_directory}/output/D_datasetdeterministicrandom.autoencoder.weightsrandom.autoencoder.weights.hdf5" + ecModelDir=utils.makePathAbsolute( + f"{project_directory}/data/random_split_autoencoder/encoder_model/" ), fnnModelDir=f"{project_directory}/output/fnnTrainingCompressed/AR_saved_model", fpSize=2048, type="smiles", fpType="topological", + compressFeatures=True, ) @@ -36,16 +36,13 @@ def test_predictions(opts: opt.Options): ) # use_compressed = False - if opts.ecWeightsFile: + if opts.ecModelDir: # use_compressed = True # load trained model for autoencoder (autoencoder, encoder) = ac.define_ac_model(opts, output_bias=None) - autoencoder.load_weights(opts.ecWeightsFile) + encoder = load_model(opts.ecModelDir) # compress the fingerprints using the autoencoder df = ac.compress_fingerprints(df, encoder) - # model = tensorflow.keras.models.load_model(opts.fnnModelDir, compile=False) - # model.compile(loss=opts.lossFunction, optimizer=opts.optimizer) - # predict df2 = p.predict_values(df=df, opts=opts) names_columns = [c for c in df2.columns if c not in ["fp", "fpcompressed"]] From 824cc1d5918b13a71a939588f9d7251a3420527d Mon Sep 17 00:00:00 2001 From: soulios Date: Tue, 6 Feb 2024 10:23:14 +0100 Subject: [PATCH 37/45] add interpret json --- dfpl/__main__.py | 10 +++++-- dfpl/autoencoder.py | 4 ++- dfpl/options.py | 62 ++++++++++++++++++++++++++---------------- dfpl/vae.py | 2 +- example/interpret.json | 10 +++++++ 5 files changed, 60 insertions(+), 28 deletions(-) create mode 100644 example/interpret.json diff --git a/dfpl/__main__.py b/dfpl/__main__.py index d37ed30b..bc28ae9a 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -48,6 +48,7 @@ def predictdmpnn(opts: options.GnnOptions) -> None: chemprop.train.make_predictions(args=opts) + def interpretdmpnn(opts: options.GnnOptions) -> None: """ Interpret the predictions of a trained D-MPNN model with the given options. @@ -60,7 +61,10 @@ def interpretdmpnn(opts: options.GnnOptions) -> None: arguments = createArgsFromJson(jsonFile=opts.configFile) opts = chemprop.args.InterpretArgs().parse_args(arguments) - chemprop.interpret.interpret(args=opts,save_to_csv=True)#,additional_columns=["ID"]) + chemprop.interpret.interpret( + args=opts, save_to_csv=True + ) # ,additional_columns=["ID"]) + def train(opts: options.Options): """ @@ -111,7 +115,9 @@ def train(opts: options.Options): save_as=f"{opts.ecModelDir}/UMAP_{opts.aeSplitType}.png", ) elif opts.visualizeLatent: - logging.info("Visualizing latent space is only available if you train the autoencoder. Skipping visualization.") + logging.info( + "Visualizing latent space is only available if you train the autoencoder. Skipping visualization." + ) # train single label models if requested if opts.trainFNN and not opts.enableMultiLabel: diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index bf7f9fff..e4790e25 100755 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -125,7 +125,9 @@ def define_ac_model(opts: options.Options, output_bias=None) -> Tuple[Model, Mod return autoencoder, encoder -def train_full_ac(df: pd.DataFrame, opts: options.Options) -> Tuple[Model, np.ndarray, np.ndarray]: +def train_full_ac( + df: pd.DataFrame, opts: options.Options +) -> Tuple[Model, np.ndarray, np.ndarray]: """ Trains an autoencoder on the given feature matrix X. The response matrix is only used to split the data into meaningful test and train sets. diff --git a/dfpl/options.py b/dfpl/options.py index ea105c79..546c2397 100755 --- a/dfpl/options.py +++ b/dfpl/options.py @@ -607,28 +607,36 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: training_args = parser.add_argument_group("Training Configuration") uncertainty_args = parser.add_argument_group("Uncertainty Configuration") uncertainty_args.add_argument( - "--uncertainty_method", - type=str, - metavar="STRING", - choices=[ - 'mve', - 'ensemble', - 'evidential_epistemic', - 'evidential_aleatoric', - 'evidential_total', - 'classification', - 'dropout', - 'spectra_roundrobin', - 'dirichlet'], - help="Method to use for uncertainty estimation", - default="none", + "--uncertainty_method", + type=str, + metavar="STRING", + choices=[ + "mve", + "ensemble", + "evidential_epistemic", + "evidential_aleatoric", + "evidential_total", + "classification", + "dropout", + "spectra_roundrobin", + "dirichlet", + ], + help="Method to use for uncertainty estimation", + default="none", ) # Uncertainty arguments uncertainty_args.add_argument( "--calibration_method", type=str, metavar="STRING", - choices=["zscaling", "tscaling", "zelikman_interval", "mve_weighting","platt","isotonic"], + choices=[ + "zscaling", + "tscaling", + "zelikman_interval", + "mve_weighting", + "platt", + "isotonic", + ], help="Method to use for calibration", default="none", ) @@ -1146,7 +1154,7 @@ def parsePredictGnn(parser: argparse.ArgumentParser) -> None: "--evaluation_methods", type=str, metavar="STRING", - choices=["nll", "spearman", "ence","miscalibration_area"], + choices=["nll", "spearman", "ence", "miscalibration_area"], help="Method to use for evaluation", default="none", ) @@ -1303,6 +1311,8 @@ def parsePredictGnn(parser: argparse.ArgumentParser) -> None: training_args.add_argument( "--batch_size", type=int, metavar="INT", default=50, help="Batch size" ) + + def parseInterpretGnn(parser: argparse.ArgumentParser) -> None: files_args = parser.add_argument_group("Files") interpret_args = parser.add_argument_group("Interpretation Configuration") @@ -1338,37 +1348,41 @@ def parseInterpretGnn(parser: argparse.ArgumentParser) -> None: "--data_path", type=str, metavar="FILE", - help="Path to CSV file containing testing data for which predictions will be made" + help="Path to CSV file containing testing data for which predictions will be made", ) interpret_args.add_argument( "--max_atoms", type=int, metavar="INT", - help="Maximum number of atoms to use for interpretation") + help="Maximum number of atoms to use for interpretation", + ) interpret_args.add_argument( "--min_atoms", type=int, metavar="INT", - help="Minimum number of atoms to use for interpretation") + help="Minimum number of atoms to use for interpretation", + ) interpret_args.add_argument( "--prop_delta", type=float, metavar="FLOAT", - help="The minimum change in the property of interest that is considered significant") + help="The minimum change in the property of interest that is considered significant", + ) interpret_args.add_argument( "--property_id", type=int, metavar="INT", - help="The index of the property of interest") + help="The index of the property of interest", + ) # write the argument for rollouts interpret_args.add_argument( "--rollout", type=int, metavar="INT", - help="The number of rollouts to use for interpretation") - + help="The number of rollouts to use for interpretation", + ) def parseInputConvert(parser: argparse.ArgumentParser) -> None: diff --git a/dfpl/vae.py b/dfpl/vae.py index d357d755..e4aa6d51 100755 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -5,6 +5,7 @@ import numpy as np import pandas as pd +import wandb import tensorflow.keras.metrics as metrics from keras import backend as K from sklearn.model_selection import train_test_split @@ -19,7 +20,6 @@ from dfpl.utils import ae_scaffold_split, weight_split - def define_vae_model(opts: options.Options, output_bias=None) -> Tuple[Model, Model]: disable_eager_execution() diff --git a/example/interpret.json b/example/interpret.json new file mode 100644 index 00000000..f5d3a218 --- /dev/null +++ b/example/interpret.json @@ -0,0 +1,10 @@ +{ + "data_path": "tests/data/smiles.csv", + "checkpoint_path": "dmpnn-random-S/fold_0/model_0/model.pt", + "preds_path": "interpretations.csv", + "max_atoms": 20, + "min_atoms": 8, + "prop_delta": 0.5, + "property_id": 5, + "rollout": 20 +} \ No newline at end of file From 1c94c91a13b9a90bc82321c62d579c7f56bb9333 Mon Sep 17 00:00:00 2001 From: soulios Date: Tue, 6 Feb 2024 11:14:24 +0100 Subject: [PATCH 38/45] isorted blacked and flaked --- dfpl/options.py | 1 + dfpl/utils.py | 13 +++++++------ dfpl/vae.py | 2 +- tests/run_predictgnn.py | 3 ++- tests/run_prediction.py | 2 ++ 5 files changed, 13 insertions(+), 8 deletions(-) diff --git a/dfpl/options.py b/dfpl/options.py index 546c2397..1aba859c 100755 --- a/dfpl/options.py +++ b/dfpl/options.py @@ -4,6 +4,7 @@ from dataclasses import dataclass from pathlib import Path from typing import Optional + import jsonpickle import torch from chemprop.args import TrainArgs diff --git a/dfpl/utils.py b/dfpl/utils.py index bea7006f..56068532 100755 --- a/dfpl/utils.py +++ b/dfpl/utils.py @@ -1,18 +1,16 @@ +import argparse import json import logging import os import pathlib +import sys import warnings from collections import defaultdict +from pathlib import Path from random import Random -from typing import Dict, List, Set, Tuple, Union, Type, TypeVar +from typing import Dict, List, Set, Tuple, Type, TypeVar, Union -# Define a type variable - -from pathlib import Path -import argparse import jsonpickle -import sys import numpy as np import pandas as pd from rdkit import Chem, RDLogger @@ -20,6 +18,9 @@ from rdkit.Chem.Scaffolds import MurckoScaffold from tqdm import tqdm +# Define a type variable + + RDLogger.DisableLog("rdApp.*") T = TypeVar("T") diff --git a/dfpl/vae.py b/dfpl/vae.py index e4aa6d51..4c568dc7 100755 --- a/dfpl/vae.py +++ b/dfpl/vae.py @@ -5,8 +5,8 @@ import numpy as np import pandas as pd -import wandb import tensorflow.keras.metrics as metrics +import wandb from keras import backend as K from sklearn.model_selection import train_test_split from tensorflow.keras import initializers, optimizers diff --git a/tests/run_predictgnn.py b/tests/run_predictgnn.py index 493a834e..addebf52 100755 --- a/tests/run_predictgnn.py +++ b/tests/run_predictgnn.py @@ -1,8 +1,9 @@ import logging import pathlib -from chemprop import train import chemprop as cp +from chemprop import train + import dfpl.options as opt import dfpl.utils as utils diff --git a/tests/run_prediction.py b/tests/run_prediction.py index cd0254af..29a974d1 100755 --- a/tests/run_prediction.py +++ b/tests/run_prediction.py @@ -1,7 +1,9 @@ import logging import pathlib from os import path + from tensorflow.keras.models import load_model + import dfpl.autoencoder as ac import dfpl.fingerprint as fp import dfpl.options as opt From caf37eba37ea664ac71c21f9cbe9c4f780f22f3a Mon Sep 17 00:00:00 2001 From: soulios Date: Tue, 6 Feb 2024 11:33:36 +0100 Subject: [PATCH 39/45] changejson to fix cli --- example/predict.json | 4 ++-- example/train.json | 2 +- tests/run_prediction.py | 8 ++++---- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/example/predict.json b/example/predict.json index a4b17995..404c9b78 100755 --- a/example/predict.json +++ b/example/predict.json @@ -3,10 +3,10 @@ "inputFile": "tests/data/S_dataset.csv", "outputDir": "example/results_predict/", "outputFile": "smiles.csv", - "ecModelDir": "example/results_train/random_split_autoencoder/encoder_model/random_split_autoencoder/encoder_model", + "ecModelDir": "example/results_train/random_split_autoencoder/encoder_model", "ecWeightsFile": "", "fnnModelDir": "example/results_train/AR-1_best_saved_model", "aeType": "deterministic", - "compressFeatures": true, + "compressFeatures": false, "trainFNN": false } diff --git a/example/train.json b/example/train.json index 49a0a6c4..f6a620a6 100755 --- a/example/train.json +++ b/example/train.json @@ -2,7 +2,7 @@ "py/object": "dfpl.options.Options", "inputFile": "tests/data/S_dataset.csv", "outputDir": "example/results_train/", - "ecModelDir": "example/results_train/random_split_autoencoder/encoder_model/", + "ecModelDir": "", "ecWeightsFile": "", "verbose": 2, diff --git a/tests/run_prediction.py b/tests/run_prediction.py index 29a974d1..db111aa0 100755 --- a/tests/run_prediction.py +++ b/tests/run_prediction.py @@ -14,14 +14,14 @@ test_predict_args = opt.Options( inputFile=f"{project_directory}/data/smiles.csv", outputDir=f"{project_directory}/preds/", - ecModelDir=utils.makePathAbsolute( - f"{project_directory}/data/random_split_autoencoder/encoder_model/" - ), + # ecModelDir=utils.makePathAbsolute( + # f"{project_directory}/data/random_split_autoencoder/encoder_model/" + # ), fnnModelDir=f"{project_directory}/output/fnnTrainingCompressed/AR_saved_model", fpSize=2048, type="smiles", fpType="topological", - compressFeatures=True, + compressFeatures=False, ) From 8ee47979012e96a4a5b33755c02d351179fa8661 Mon Sep 17 00:00:00 2001 From: soulios Date: Tue, 6 Feb 2024 11:53:57 +0100 Subject: [PATCH 40/45] changejson to fix cli --- example/predict.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/predict.json b/example/predict.json index 404c9b78..14233a36 100755 --- a/example/predict.json +++ b/example/predict.json @@ -3,7 +3,7 @@ "inputFile": "tests/data/S_dataset.csv", "outputDir": "example/results_predict/", "outputFile": "smiles.csv", - "ecModelDir": "example/results_train/random_split_autoencoder/encoder_model", + "ecModelDir": "", "ecWeightsFile": "", "fnnModelDir": "example/results_train/AR-1_best_saved_model", "aeType": "deterministic", From fd27032a7fc4813d6c8e83a1945a47810c39a7e0 Mon Sep 17 00:00:00 2001 From: soulios Date: Tue, 6 Feb 2024 13:40:22 +0100 Subject: [PATCH 41/45] changejson to fix cli --- example/predict.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/predict.json b/example/predict.json index 14233a36..acd4b447 100755 --- a/example/predict.json +++ b/example/predict.json @@ -5,7 +5,7 @@ "outputFile": "smiles.csv", "ecModelDir": "", "ecWeightsFile": "", - "fnnModelDir": "example/results_train/AR-1_best_saved_model", + "fnnModelDir": "./example/results_train/AR-1_best_saved_model", "aeType": "deterministic", "compressFeatures": false, "trainFNN": false From 539e5d1e9eb61e737419a4f5107049ddfe15c6b7 Mon Sep 17 00:00:00 2001 From: soulios Date: Tue, 6 Feb 2024 18:53:47 +0100 Subject: [PATCH 42/45] changejson to fix cli --- dfpl/single_label_model.py | 2 +- example/predict.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py index f8aeaa4c..75ff8df9 100755 --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -618,7 +618,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: # Collect metrics for each fold and target performance_list = [] if opts.split_type == "random": - for target in targets[:1]: + for target in targets: # target=targets[1] # --> only for testing the code x, y = prepare_nn_training_data(df, target, opts, return_dataframe=False) if x is None: diff --git a/example/predict.json b/example/predict.json index acd4b447..57ee750f 100755 --- a/example/predict.json +++ b/example/predict.json @@ -5,7 +5,7 @@ "outputFile": "smiles.csv", "ecModelDir": "", "ecWeightsFile": "", - "fnnModelDir": "./example/results_train/AR-1_best_saved_model", + "fnnModelDir": "./example/results_train/AR-1_saved_model", "aeType": "deterministic", "compressFeatures": false, "trainFNN": false From 941250d310068942363b7ef4fb462d483fbeaa93 Mon Sep 17 00:00:00 2001 From: soulios Date: Wed, 7 Feb 2024 13:40:42 +0100 Subject: [PATCH 43/45] change interpretjson to fix cli --- .github/workflows/pr.yml | 14 +++++++++++++- example/interpret.json | 2 +- example/predictgnn.json | 4 ++-- example/train.json | 4 ++-- example/traingnn.json | 4 ++-- 5 files changed, 20 insertions(+), 8 deletions(-) diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index 27f43c34..5bbea609 100755 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -101,5 +101,17 @@ jobs: exit 1 fi + dfpl interpretgnn -f example/interpret.json + if [ "$(cat interpretations.csv | wc -l)" -lt "6" ]; then + echo "predict result should have at least 5 lines. But had only $(cat interpretations.csv | wc -l)" >&2 + exit 1 + fi + + dfpl convert -f tests/data + if [ "$(find tests/data -name '*.csv' | wc -l)" -ne "$(find tests/data -name '*.pkl' | wc -l)" ]; then + echo "not all csv files are converted to pickle ones" >&2 + exit 1 + fi + - dfpl convert -f tests/data \ No newline at end of file + echo "All tests passed" \ No newline at end of file diff --git a/example/interpret.json b/example/interpret.json index f5d3a218..297a7dc3 100644 --- a/example/interpret.json +++ b/example/interpret.json @@ -1,6 +1,6 @@ { "data_path": "tests/data/smiles.csv", - "checkpoint_path": "dmpnn-random-S/fold_0/model_0/model.pt", + "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt", "preds_path": "interpretations.csv", "max_atoms": 20, "min_atoms": 8, diff --git a/example/predictgnn.json b/example/predictgnn.json index 7d117dc6..3588fdf4 100755 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,8 +1,8 @@ { "py/object": "dfpl.options.GnnOptions", - "test_path": "tests/data/calibra.csv", + "test_path": "tests/data/smiles.csv", "preds_path": "example/results_gnn.csv", - "evaluation_methods": "spearman", + "evaluation_methods": "", "evaluation_scores_path": "example/eval_gnn.csv", "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt" } \ No newline at end of file diff --git a/example/train.json b/example/train.json index f6a620a6..317d5ec2 100755 --- a/example/train.json +++ b/example/train.json @@ -29,13 +29,13 @@ "gpu": "0", "trainFNN": true, - "kFolds": 2, + "kFolds": 1, "threshold": 0.5, "testSize": 0.2, "fnnType": "FNN", "optimizer": "Adam", "lossFunction": "bce", - "epochs": 4, + "epochs": 2, "batchSize": 128, "activationFunction": "selu", "dropout": 0.0107, diff --git a/example/traingnn.json b/example/traingnn.json index 36884647..00dc3c4b 100755 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -2,8 +2,8 @@ "py/object": "dfpl.options.GnnOptions", "data_path": "tests/data/S_dataset.csv", "save_dir": "dmpnn-random/", - "epochs": 15, - "num_folds": 5, + "epochs": 2, + "num_folds": 1, "metric": "accuracy", "loss_function": "binary_cross_entropy", "split_type": "random", From e9deafa74a43d01488f376d465fb6a1b12e63c5e Mon Sep 17 00:00:00 2001 From: soulios Date: Wed, 7 Feb 2024 13:57:36 +0100 Subject: [PATCH 44/45] edit predict json to load the correct model --- example/predict.json | 2 +- example/predictgnn.json | 33 +++++++++++++++++++++++++++++---- tests/data/S_dataset.csv | 0 tests/data/calibra.csv | 26 ++++++++++++++++++++++++++ tests/data/inchi.tsv | 5 ----- tests/data/smiles.csv | 0 6 files changed, 56 insertions(+), 10 deletions(-) mode change 100755 => 100644 tests/data/S_dataset.csv create mode 100644 tests/data/calibra.csv delete mode 100755 tests/data/inchi.tsv mode change 100755 => 100644 tests/data/smiles.csv diff --git a/example/predict.json b/example/predict.json index 57ee750f..7e2a6e8e 100755 --- a/example/predict.json +++ b/example/predict.json @@ -5,7 +5,7 @@ "outputFile": "smiles.csv", "ecModelDir": "", "ecWeightsFile": "", - "fnnModelDir": "./example/results_train/AR-1_saved_model", + "fnnModelDir": "example/results_train/AR_saved_model", "aeType": "deterministic", "compressFeatures": false, "trainFNN": false diff --git a/example/predictgnn.json b/example/predictgnn.json index 3588fdf4..7b84d3ee 100755 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,8 +1,33 @@ { "py/object": "dfpl.options.GnnOptions", - "test_path": "tests/data/smiles.csv", + "test_path": "tests/data/calibra.csv", "preds_path": "example/results_gnn.csv", - "evaluation_methods": "", + "evaluation_methods": "nll", "evaluation_scores_path": "example/eval_gnn.csv", - "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt" -} \ No newline at end of file + "calibration_method": "zscaling", + "calibration_path": "tests/data/calibra.csv", + "calibration_interval_percentile": 95, + "calibration_atom_descriptors_path": "tests/data/atom_descriptors.csv", + "calibration_bond_features_path": "tests/data/bond_features.csv", + "calibration_features_path": "tests/data/features.csv", + "calibration_phase_features_path": "tests/data/phase_features.csv", + "calibration_phase_features_path": "tests/data/phase_features.csv", + "ensemble_variance": true, + "individual_ensemble_predictions": true, + "propertyensemble_size": 5, + "uncertainty_method": "mve", + "uncertainty_dropout_p": 0.1, + "dropout_sampling_size": 10, + "regression_calibrator_metric": "stdev", + "spectra_roundrobin": true +} + + + + + + + + + + diff --git a/tests/data/S_dataset.csv b/tests/data/S_dataset.csv old mode 100755 new mode 100644 diff --git a/tests/data/calibra.csv b/tests/data/calibra.csv new file mode 100644 index 00000000..7e2963d5 --- /dev/null +++ b/tests/data/calibra.csv @@ -0,0 +1,26 @@ +smiles,AR,ER,GR,Aromatase,TR,PPARg,ED +CN(C)c1ccc(cc1)C(=O)c2ccc(cc2)N(C)C,1,1,1,1,1,1,1 +CC12CCC3C(CCC4=CC(=O)CCC34C)C1CCC2=O,1,1,1,1,1,0,1 +CC12CCC3C(CCc4cc(O)ccc34)C1CCC2O,1,1,1,0,1,1,1 +Oc1c(Br)cc(cc1Br)C#N,1,1,1,0,1,1,1 +Oc1ccc(C=Cc2cc(O)cc(O)c2)cc1,1,1,1,1,0,1,1 +Oc1ccc(cc1)c2ccccc2,1,1,0,1,1,1,1 +CC(=CC1C(C(=O)OCN2C(=O)C3=C(CCCC3)C2=O)C1(C)C)C,1,1,0,1,0,1,1 +CC(=O)OCC(=O)C1(O)C(CC2C3CCC4=CC(=O)C=CC4(C)C3(F)C(O)CC12C)OC(=O)C,1,1,1,1,0,0,1 +CC(=O)OCC(=O)C12N=C(C)OC1CC3C4CCC5=CC(=O)C=CC5(C)C4C(O)CC23C,1,1,1,1,0,0,1 +CC(C)(C)C(=O)OCOC(=O)C1N2C(CC2=O)S(=O)(=O)C1(C)C,1,1,1,0,0,1,1 +CC(C)(c1cc(Cl)c(O)c(Cl)c1)c2cc(Cl)c(O)c(Cl)c2,0,1,1,0,1,1,1 +CC1(C)OC2CC3C4CC(F)C5=CC(=O)CCC5(C)C4C(O)CC3(C)C2(O1)C(=O)CO,1,1,1,1,0,0,1 +CC1(C)OC2CC3C4CCC5=CC(=O)C=CC5(C)C4(F)C(O)CC3(C)C2(O1)C(=O)CO,1,1,1,1,0,0,1 +CC12CCC(=O)C=C1CCC3C2CCC4(C)C3CCC4(O)C#C,1,1,1,0,1,0,1 +CC1CC2C3CC(F)C4=CC(=O)C=CC4(C)C3(F)C(O)CC2(C)C1(O)C(=O)CO,1,1,1,1,0,0,1 +CC1CC2C3CC(F)C4=CC(=O)C=CC4(C)C3(F)C(O)CC2(C)C1(OC(=O)C)C(=O)COC(=O)C,1,1,1,1,0,0,1 +CC1CC2C3CCC(O)(C(=O)CO)C3(C)CC(O)C2C4(C)C=CC(=O)C=C14,1,1,1,1,0,0,1 +CC1CC2C3CCC4=CC(=O)C=CC4(C)C3(F)C(O)CC2(C)C1(O)C(=O)CO,1,1,1,1,0,0,1 +CCC(=O)OC1(C(C)CC2C3CCC4=CC(=O)C=CC4(C)C3(F)C(O)CC12C)C(=O)CCl,1,1,1,1,0,0,1 +CCCC(=O)OC1(CCC2C3CCC4=CC(=O)CCC4(C)C3C(O)CC12C)C(=O)CO,1,1,1,1,0,0,1 +CCN(CC)c1ccc(C(=O)c2ccccc2C(=O)O)c(O)c1,1,1,0,1,0,1,1 +CC[N+](CC)(CC)CCOc1ccc(C=Cc2ccccc2)cc1,1,1,0,1,1,0,1 +O=C(Oc1ccccc1)c2cccc(c2)C(=O)Oc3ccccc3,1,1,0,1,1,0,1 +SCC(=O)OCCOC(=O)CS,1,0,1,1,1,0,1 +C(CSc1ccccc1)Sc2ccccc2,1,1,0,1,0,0,1 \ No newline at end of file diff --git a/tests/data/inchi.tsv b/tests/data/inchi.tsv deleted file mode 100755 index bfaccf13..00000000 --- a/tests/data/inchi.tsv +++ /dev/null @@ -1,5 +0,0 @@ -DTXSID7020001 InChI=1S/C11H9N3/c12-10-6-5-8-7-3-1-2-4-9(7)13-11(8)14-10/h1-6H,(H3,12,13,14) FJTNLJLPLJDTRM-UHFFFAOYSA-N -DTXSID5039224 InChI=1S/C2H4O/c1-2-3/h2H,1H3 IKHGUXGNUITLKF-UHFFFAOYSA-N -DTXSID50872971 InChI=1S/C4H8N2O/c1-3-5-6(2)4-7/h3-4H,1-2H3/b5-3+ IMAGWKUTFZRWSB-HWKANZROSA-N -DTXSID2020004 InChI=1S/C2H5NO/c1-2-3-4/h2,4H,1H3/b3-2+ FZENGILVLUJGJX-NSCUHMNNSA-N -DTXSID7020005 InChI=1S/C2H5NO/c1-2(3)4/h1H3,(H2,3,4) DLFVBJFMPXGRIB-UHFFFAOYSA-N \ No newline at end of file diff --git a/tests/data/smiles.csv b/tests/data/smiles.csv old mode 100755 new mode 100644 From c998d386a3824132ebb81c043c99dba3ae4503fd Mon Sep 17 00:00:00 2001 From: Kyriakos Soulios Date: Thu, 11 Jul 2024 12:28:12 +0200 Subject: [PATCH 45/45] interpret, fixed parsing args,finetuneencoder --- dfpl/__main__.py | 38 +- dfpl/autoencoder.py | 255 ++++++++----- dfpl/feedforwardNN.py | 19 +- dfpl/options.py | 356 ++++++++++-------- dfpl/single_label_model.py | 293 +++++++------- dfpl/utils.py | 184 ++++++--- environment.yml | 6 +- example/interpret.json | 6 +- example/predict.json | 2 +- example/predictgnn.json | 22 +- example/train.json | 16 +- example/traingnn.json | 13 +- tests/generic_encoder/keras_metadata.pb | 6 + tests/generic_encoder/saved_model.pb | Bin 0 -> 63156 bytes .../variables/variables.data-00000-of-00001 | Bin 0 -> 11019275 bytes .../generic_encoder/variables/variables.index | Bin 0 -> 533 bytes 16 files changed, 692 insertions(+), 524 deletions(-) create mode 100755 tests/generic_encoder/keras_metadata.pb create mode 100755 tests/generic_encoder/saved_model.pb create mode 100755 tests/generic_encoder/variables/variables.data-00000-of-00001 create mode 100755 tests/generic_encoder/variables/variables.index diff --git a/dfpl/__main__.py b/dfpl/__main__.py index bc28ae9a..f9aa715e 100755 --- a/dfpl/__main__.py +++ b/dfpl/__main__.py @@ -3,7 +3,7 @@ import os from argparse import Namespace from os import path - +import wandb import chemprop from keras.models import load_model @@ -63,7 +63,7 @@ def interpretdmpnn(opts: options.GnnOptions) -> None: chemprop.interpret.interpret( args=opts, save_to_csv=True - ) # ,additional_columns=["ID"]) + ) def train(opts: options.Options): @@ -105,19 +105,20 @@ def train(opts: options.Options): autoencoder.load_weights( os.path.join(opts.ecModelDir, opts.ecWeightsFile) ) + # compress the fingerprints using the autoencoder df = ac.compress_fingerprints(df, encoder) if opts.visualizeLatent and opts.trainAC: ac.visualize_fingerprints( df, - train_indices=train_indices, - test_indices=test_indices, - save_as=f"{opts.ecModelDir}/UMAP_{opts.aeSplitType}.png", + save_as=f"{opts.ecModelDir}/TSNE_{opts.aeType}_{opts.aeSplitType}.png", ) elif opts.visualizeLatent: logging.info( "Visualizing latent space is only available if you train the autoencoder. Skipping visualization." ) + if opts.trainFNN and opts.finetuneEncoder: + sl.train_single_label_models(df=df, opts=opts) # train single label models if requested if opts.trainFNN and not opts.enableMultiLabel: @@ -218,33 +219,32 @@ def main(): traindmpnn(traingnn_opts) elif prog_args.method == "predictgnn": - predictgnn_opts = options.GnnOptions.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - predictgnn_opts, - test_path=makePathAbsolute(predictgnn_opts.test_path), - preds_path=makePathAbsolute(predictgnn_opts.preds_path), - ) + predictgnn_opts = options.PredictGnnOptions.fromCmdArgs(prog_args) createLogger("predictgnn.log") - predictdmpnn(fixed_opts) + predictdmpnn(predictgnn_opts) elif prog_args.method == "interpretgnn": - interpretgnn_opts = options.GnnOptions.fromCmdArgs(prog_args) - fixed_opts = dataclasses.replace( - interpretgnn_opts, - test_path=makePathAbsolute(interpretgnn_opts.test_path), - preds_path=makePathAbsolute(interpretgnn_opts.preds_path), - ) + interpretgnn_opts = options.InterpretGNNoptions.fromCmdArgs(prog_args) createLogger("interpretgnn.log") - interpretdmpnn(fixed_opts) + interpretdmpnn(interpretgnn_opts) elif prog_args.method == "train": if prog_args.configFile is None and prog_args.inputFile is None: parser.error("Either --configFile or --inputFile must be provided.") train_opts = options.Options.fromCmdArgs(prog_args) + # Access wandb configuration + # wandb.init(project="dfpl") + # config = wandb.config + fixed_opts = dataclasses.replace( train_opts, inputFile=makePathAbsolute(train_opts.inputFile), outputDir=makePathAbsolute(train_opts.outputDir), + # learningRate=config.learningRate, + # learningRateDecay=config.learningRateDecay, + # dropout=config.dropout, + # batchSize=config.batchSize, + # l2reg=config.l2reg ) createDirectory(fixed_opts.outputDir) createLogger(path.join(fixed_opts.outputDir, "train.log")) diff --git a/dfpl/autoencoder.py b/dfpl/autoencoder.py index e4790e25..d96cd48c 100755 --- a/dfpl/autoencoder.py +++ b/dfpl/autoencoder.py @@ -284,6 +284,7 @@ def train_full_ac( else: encoder.save(filepath=save_path) # Return the encoder model of the trained autoencoder + autoencoder.summary(print_fn=logging.info) return encoder, train_indices, test_indices @@ -312,99 +313,161 @@ def compress_fingerprints(dataframe: pd.DataFrame, encoder: Model) -> pd.DataFra return dataframe -def visualize_fingerprints( - df: pd.DataFrame, - train_indices: np.ndarray, - test_indices: np.ndarray, - save_as: str, -): - """ - Visualize fingerprints using UMAP and save the visualization to a file. - - This function takes a Pandas DataFrame containing fingerprint data, calculates - the appropriate number of samples based on the size of the dataset, applies UMAP - for dimensionality reduction, and saves the resulting visualization. - - Parameters: - - df (pd.DataFrame): A Pandas DataFrame containing fingerprint data. - - train_indices (np.ndarray): An array containing indices of training samples. - - test_indices (np.ndarray): An array containing indices of test samples. - - save_as (str): The filename or path where the UMAP visualization will be saved. - Note: - - If the DataFrame size exceeds 50,000 rows, the function logs a message and skips - the UMAP visualization step. - """ - if len(df) <= 10000: - num_samples = len(df) - elif len(df) > 50000: - logging.info( - "Cannot return the UMAP due to the large dataset size. Skipping the function." - ) - return - else: - num_samples = len(df) // 2 - - after_col = "fpcompressed" - # Calculate the number of samples to be taken from each set - train_samples = int(num_samples * len(train_indices) / len(df)) - test_samples = num_samples - train_samples - - # Assign train and test data points separately - train_data = df.loc[train_indices] - test_data = df.loc[test_indices] - - # Sample train and test data points - train_data_sampled = train_data.sample(n=train_samples, random_state=42) - test_data_sampled = test_data.sample(n=test_samples, random_state=42) - - # Concatenate the sampled train and test data - df_sampled = pd.concat([train_data_sampled, test_data_sampled]) - - # Convert the boolean values in the after_col column to floats - df_sampled[after_col] = df_sampled[after_col].apply( - lambda x: np.array(x, dtype=float) - ) - - df_sampled.loc[train_data_sampled.index, "set"] = "train" - df_sampled.loc[test_data_sampled.index, "set"] = "test" - # Apply UMAP - umap_model = umap.UMAP( - n_neighbors=15, min_dist=0.1, metric="euclidean", random_state=42 - ) - # Filter out the rows with invalid arrays - umap_results = umap_model.fit_transform(df_sampled[after_col].tolist()) - # Add UMAP results to the DataFrame - df_sampled["umap_x"] = umap_results[:, 0] - df_sampled["umap_y"] = umap_results[:, 1] - - # Define custom color palette - palette = {"train": "blue", "test": "red"} - - # Create the scatter plot - sns.set(style="white") - fig, ax = plt.subplots(figsize=(10, 8)) - split = save_as.split("_", 1) - part_after_underscore = split[1] - split_type = part_after_underscore.split(".")[0] - # Plot the UMAP results - for label, grp in df_sampled.groupby("set"): - set_label = label - color = palette[set_label] - alpha = ( - 0.09 if set_label == "train" else 0.9 - ) # Set different opacities for train and test - ax.scatter( - grp["umap_x"], grp["umap_y"], label=f"{set_label}", c=color, alpha=alpha - ) - - # Customize the plot - ax.set_title( - f"UMAP visualization of molecular fingerprints using {split_type} split", - fontsize=14, - ) - ax.set_xlabel("UMAP 1") - ax.set_ylabel("UMAP 2") - ax.legend(title="", loc="upper right") - sns.despine(ax=ax, offset=10) - save_path = os.path.join(os.getcwd(), save_as) - plt.savefig(save_path) +# def visualize_fingerprints( +# df: pd.DataFrame, +# train_indices: np.ndarray, +# test_indices: np.ndarray, +# save_as: str, +# ): +# """ +# Visualize fingerprints using UMAP and save the visualization to a file. +# +# This function takes a Pandas DataFrame containing fingerprint data, calculates +# the appropriate number of samples based on the size of the dataset, applies UMAP +# for dimensionality reduction, and saves the resulting visualization. +# +# Parameters: +# - df (pd.DataFrame): A Pandas DataFrame containing fingerprint data. +# - train_indices (np.ndarray): An array containing indices of training samples. +# - test_indices (np.ndarray): An array containing indices of test samples. +# - save_as (str): The filename or path where the UMAP visualization will be saved. +# Note: +# - If the DataFrame size exceeds 50,000 rows, the function logs a message and skips +# the UMAP visualization step. +# """ +# if len(df) <= 10000: +# num_samples = len(df) +# elif len(df) > 50000: +# logging.info( +# "Cannot return the UMAP due to the large dataset size. Skipping the function." +# ) +# return +# else: +# num_samples = len(df) // 2 +# +# after_col = "fpcompressed" +# # Calculate the number of samples to be taken from each set +# train_samples = int(num_samples * len(train_indices) / len(df)) +# test_samples = num_samples - train_samples +# +# # Assign train and test data points separately +# train_data = df.loc[train_indices] +# test_data = df.loc[test_indices] +# +# # Sample train and test data points +# train_data_sampled = train_data.sample(n=train_samples, random_state=42) +# test_data_sampled = test_data.sample(n=test_samples, random_state=42) +# +# # Concatenate the sampled train and test data +# df_sampled = pd.concat([train_data_sampled, test_data_sampled]) +# +# # Convert the boolean values in the after_col column to floats +# df_sampled[after_col] = df_sampled[after_col].apply( +# lambda x: np.array(x, dtype=float) +# ) +# +# df_sampled.loc[train_data_sampled.index, "set"] = "train" +# df_sampled.loc[test_data_sampled.index, "set"] = "test" +# # Apply UMAP +# umap_model = umap.UMAP( +# n_neighbors=15, min_dist=0.1, metric="euclidean", random_state=42 +# ) +# # Filter out the rows with invalid arrays +# umap_results = umap_model.fit_transform(df_sampled[after_col].tolist()) +# # Add UMAP results to the DataFrame +# df_sampled["umap_x"] = umap_results[:, 0] +# df_sampled["umap_y"] = umap_results[:, 1] +# +# # Define custom color palette +# palette = {"train": "blue", "test": "red"} +# +# # Create the scatter plot +# sns.set(style="white") +# fig, ax = plt.subplots(figsize=(10, 8)) +# split = save_as.split("_", 1) +# part_after_underscore = split[1] +# split_type = part_after_underscore.split(".")[0] +# # Plot the UMAP results +# for label, grp in df_sampled.groupby("set"): +# set_label = label +# color = palette[set_label] +# alpha = ( +# 0.09 if set_label == "train" else 0.9 +# ) # Set different opacities for train and test +# ax.scatter( +# grp["umap_x"], grp["umap_y"], label=f"{set_label}", c=color, alpha=alpha +# ) +# +# # Customize the plot +# ax.set_title( +# f"UMAP visualization of molecular fingerprints using {split_type} split", +# fontsize=14, +# ) +# ax.set_xlabel("UMAP 1") +# ax.set_ylabel("UMAP 2") +# ax.legend(title="", loc="upper right") +# sns.despine(ax=ax, offset=10) +# save_path = os.path.join(os.getcwd(), save_as) +# plt.savefig(save_path) +import math +from sklearn.cluster import MiniBatchKMeans +from sklearn.manifold import TSNE +from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score +import umap +import matplotlib.pyplot as plt +from sklearn.neighbors import NearestNeighbors + +def knn_preservation(original_fp, compressed_fp, n_neighbors=5): + original_nn = NearestNeighbors(n_neighbors=n_neighbors).fit(original_fp) + compressed_nn = NearestNeighbors(n_neighbors=n_neighbors).fit(compressed_fp) + + original_neighbors = original_nn.kneighbors(original_fp, return_distance=False) + compressed_neighbors = compressed_nn.kneighbors(compressed_fp, return_distance=False) + + intersection = np.array([len(np.intersect1d(original_neighbors[i], compressed_neighbors[i])) for i in range(len(original_fp))]) + preservation_score = np.mean(intersection / n_neighbors) + return preservation_score +def knn_preservation(original_fp, compressed_fp, n_neighbors=5): + original_nn = NearestNeighbors(n_neighbors=n_neighbors).fit(original_fp) + compressed_nn = NearestNeighbors(n_neighbors=n_neighbors).fit(compressed_fp) + + original_neighbors = original_nn.kneighbors(original_fp, return_distance=False) + compressed_neighbors = compressed_nn.kneighbors(compressed_fp, return_distance=False) + + intersection = np.array([len(np.intersect1d(original_neighbors[i], compressed_neighbors[i])) for i in range(len(original_fp))]) + preservation_score = np.mean(intersection / n_neighbors) + return preservation_score + +def visualize_fingerprints(dataframe, n_clusters=7, save_as=None): + # Extract original and compressed fingerprints + original_fp = np.array(dataframe['fp'].to_list()) + compressed_fp = np.array(dataframe['fpcompressed'].to_list()) + + original_tsne = TSNE(n_components=2, random_state=42).fit_transform(original_fp) + compressed_tsne = TSNE(n_components=2, random_state=42).fit_transform(compressed_fp) + + + # Cluster using MiniBatchKMeans + kmeans = MiniBatchKMeans(n_clusters=n_clusters, random_state=42) + original_clusters = kmeans.fit_predict(original_tsne) + compressed_clusters = kmeans.fit_predict(compressed_tsne) + + # Calculate metrics + ars = adjusted_rand_score(original_clusters, compressed_clusters) + ami = adjusted_mutual_info_score(original_clusters, compressed_clusters) + knn_preservation_score = knn_preservation(original_fp, compressed_fp) + + # Visualize + plt.figure(figsize=(12, 6)) + plt.subplot(1, 2, 1) + plt.scatter(original_tsne[:, 0], original_tsne[:, 1], c=original_clusters, cmap='Spectral', s=5) + plt.title('Original TSNE Visualization') + + plt.subplot(1, 2, 2) + plt.scatter(compressed_tsne[:, 0], compressed_tsne[:, 1], c=compressed_clusters, cmap='Spectral', s=5) + plt.title('Deterministic Autoencoder Latent Space TSNE Visualization') + metrics_text = f"ARS: {ars:.2f}\nAMI: {ami:.2f}\nkNN: {knn_preservation_score:.2f}" + plt.text(0.95, 0.02, metrics_text, verticalalignment='bottom', horizontalalignment='right', transform=plt.gca().transAxes, color='black', fontsize=9, bbox=dict(facecolor='white', alpha=0.8, edgecolor='black')) + + plt.suptitle('Deterministic Autoencoder Latent Space') + plt.savefig(save_as) \ No newline at end of file diff --git a/dfpl/feedforwardNN.py b/dfpl/feedforwardNN.py index bf4241aa..3f8ad261 100755 --- a/dfpl/feedforwardNN.py +++ b/dfpl/feedforwardNN.py @@ -288,7 +288,7 @@ def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: "f1_trained", ] ) # F1 scores of predictions - + all_scores_data = [] fold_no = 1 # split the data @@ -359,6 +359,16 @@ def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: ) idx = hist.history["val_loss"].index(min(hist.history["val_loss"])) + row_data = { + "fold_no": fold_no, + "loss": hist.history["loss"][idx], + "val_loss": hist.history["val_loss"][idx], + "acc": hist.history["accuracy"][idx], + "val_acc": hist.history["val_accuracy"][idx], + "f1_random": scores[0], + "f1_trained": scores[1], + } + all_scores_data.append(row_data) row_df = pd.DataFrame( [ [ @@ -383,12 +393,13 @@ def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: ) logging.info(row_df) - all_scores = all_scores.append(row_df, ignore_index=True) + all_scores = all_scores_data.append(row_data)#, ignore_index=True) fold_no += 1 del model logging.info(all_scores) + all_scores = pd.DataFrame(all_scores_data) # finalize model # 1. provide best performing fold variant @@ -397,11 +408,11 @@ def train_nn_models_multi(df: pd.DataFrame, opts: options.Options) -> None: fold_no = all_scores.iloc[idx2]["fold_no"] model_name = ( - "multi_compressed-" + str(opts.compressFeatures) + ".Fold-" + str(fold_no) + "multi_compressed-" + str(opts.compressFeatures) + ".Fold-" + str(int(fold_no)) ) checkpoint_path = opts.outputDir + "/" + model_name + ".checkpoint.model.hdf5" best_model_file = checkpoint_path.replace( - "Fold-" + str(fold_no) + ".checkpoint.", "best.FNN-" + "Fold-" + str(int(fold_no)) + ".checkpoint.", "best.FNN-" ) file = re.sub( diff --git a/dfpl/options.py b/dfpl/options.py index 1aba859c..347e6058 100755 --- a/dfpl/options.py +++ b/dfpl/options.py @@ -3,11 +3,11 @@ import argparse from dataclasses import dataclass from pathlib import Path -from typing import Optional +from typing import Optional, Literal, List import jsonpickle import torch -from chemprop.args import TrainArgs +from chemprop.args import TrainArgs, PredictArgs, InterpretArgs from dfpl.utils import parseCmdArgs @@ -36,6 +36,7 @@ class Options: verbose: int = 2 trainAC: bool = False trainFNN: bool = True + finetuneEncoder: bool = False compressFeatures: bool = False sampleFractionOnes: float = 0.5 sampleDown: bool = False @@ -85,8 +86,8 @@ class GnnOptions(TrainArgs): total_epochs: int = 30 save: bool = True - configFile: str = "./example/traingnn.json" - data_path: str = "./example/data/tox21.csv" + configFile: str = "" + data_path: str = "" use_compound_names: bool = False save_dir: str = "" no_cache: bool = False @@ -96,14 +97,103 @@ class GnnOptions(TrainArgs): num_lrs: int = 2 minimize_score: bool = False num_tasks: int = 12 - preds_path: str = "./tox21dmpnn.csv" + preds_path: str = "" test_path: str = "" - save_preds: bool = True - calibration_method: str = "none" - uncertainty_method: str = "none" + save_preds: bool = False + calibration_method: str = "" + uncertainty_method: str = "" calibration_path: str = "" - evaluation_methods: str = "none" + evaluation_methods: str = "" evaluation_scores_path: str = "" + wabTracking: bool = False + split_sizes: List[float] = None + show_individual_scores: bool = False + + # save_smiles_splits: bool = False + @classmethod + def fromCmdArgs(cls, args: argparse.Namespace, json_config: Optional[dict] = None): + # Initialize with JSON config if provided + if json_config: + opts = cls(**json_config) + else: + opts = cls() + + # Update with command-line arguments + for key, value in vars(args).items(): + if value is not None: + setattr(opts, key, value) + + return opts + +class PredictGnnOptions(PredictArgs): + """ + Dataclass to hold all options used for training the graph models + """ + + configFile: str = "./example/predictgnn.json" + calibration_atom_descriptors_path: str = None + calibration_features_path: str = None + calibration_interval_percentile: float = 95 + calibration_method: Literal[ + "zscaling", + "tscaling", + "zelikman_interval", + "mve_weighting", + "platt", + "isotonic", + ] = None + calibration_path: str = None + calibration_phase_features_path: str = None + drop_extra_columns: bool = False + dropout_sampling_size: int = 10 + evaluation_methods: List[str] = None + evaluation_scores_path: str = None + # no_features_scaling: bool = True + individual_ensemble_predictions: bool = False + preds_path: str = None + regression_calibrator_metric: Literal["stdev", "interval"] = None + test_path: str = None + uncertainty_dropout_p: float = 0.1 + uncertainty_method: Literal[ + "mve", + "ensemble", + "evidential_epistemic", + "evidential_aleatoric", + "evidential_total", + "classification", + "dropout", + ] = None + + @classmethod + def fromCmdArgs(cls, args: argparse.Namespace, json_config: Optional[dict] = None): + # Initialize with JSON config if provided + if json_config: + opts = cls(**json_config) + else: + opts = cls() + + # Update with command-line arguments + for key, value in vars(args).items(): + if value is not None: + setattr(opts, key, value) + + return opts + + +class InterpretGNNoptions(InterpretArgs): + """ + Dataclass to hold all options used for training the graph models + """ + + configFile: str = "./example/interpret.json" + data_path: str = "./example/data/smiles.csv" + batch_size: int = 500 + c_puct: float = 10.0 + max_atoms: int = 20 + min_atoms: int = 8 + prop_delta: float = 0.5 + property_id: List[int] = None + rollout: int = 20 @classmethod def fromCmdArgs(cls, args: argparse.Namespace, json_config: Optional[dict] = None): @@ -137,15 +227,15 @@ def createCommandlineParser() -> argparse.ArgumentParser: parser_predict_gnn = subparsers.add_parser( "predictgnn", help="Predict with your GNN models" ) + parser_predict_gnn.set_defaults(method="predictgnn") + parsePredictGnn(parser_predict_gnn) + parser_interpret_gnn = subparsers.add_parser( "interpretgnn", help="Interpret your GNN models" ) parser_interpret_gnn.set_defaults(method="interpretgnn") parseInterpretGnn(parser_interpret_gnn) - parser_predict_gnn.set_defaults(method="predictgnn") - parsePredictGnn(parser_predict_gnn) - parser_train = subparsers.add_parser( "train", help="Train new models with your data" ) @@ -409,6 +499,12 @@ def parseInputTrain(parser: argparse.ArgumentParser) -> None: help="Number of epochs that should be used for the FNN training", default=100, ) + training_args.add_argument( + "--finetuneEncoder", + action="store_true", + help="Finetune the encoder with the FNN", + default=False, + ) # TODO CHECK IF ALL LOSSES MAKE SENSE HERE training_args.add_argument( "--lossFunction", @@ -619,7 +715,6 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: "evidential_total", "classification", "dropout", - "spectra_roundrobin", "dirichlet", ], help="Method to use for uncertainty estimation", @@ -652,16 +747,17 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: general_args.add_argument("--split_key_molecule", type=int) general_args.add_argument("--pytorch_seed", type=int) general_args.add_argument("--cache_cutoff", type=float) - general_args.add_argument("--save_preds", type=bool) + general_args.add_argument("--save_preds", action="store_true", default=False) + general_args.add_argument("--wabTracking", action="store_true", default=False) general_args.add_argument( "--cuda", action="store_true", default=False, help="Turn on cuda" ) - general_args.add_argument( - "--save_smiles_splits", - action="store_true", - default=False, - help="Save smiles for each train/val/test splits for prediction convenience later", - ) + # general_args.add_argument( + # "--save_smiles_splits", + # action="store_true", + # default=False, + # help="Save smiles for each train/val/test splits for prediction convenience later", + # ) general_args.add_argument( "--test", action="store_true", @@ -703,13 +799,6 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: type=str, help="Input JSON file that contains all information for training/predicting.", ) - files_args.add_argument( - "--config_path", - type=str, - metavar="FILE", - help="Path to a .json file containing arguments. Any arguments present in the config" - "file will override arguments specified via the command line or by the defaults.", - ) files_args.add_argument( "--save_dir", type=str, @@ -1027,7 +1116,6 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: model_args.add_argument( "--show_individual_scores", action="store_true", - default=True, help="Show all scores for individual targets, not just average, at the end", ) model_args.add_argument("--aggregation", choices=["mean", "sum", "norm"]) @@ -1146,171 +1234,137 @@ def parseTrainGnn(parser: argparse.ArgumentParser) -> None: def parsePredictGnn(parser: argparse.ArgumentParser) -> None: general_args = parser.add_argument_group("General Configuration") - data_args = parser.add_argument_group("Data Configuration") files_args = parser.add_argument_group("Files") - training_args = parser.add_argument_group("Training Configuration") uncertainty_args = parser.add_argument_group("Uncertainty Configuration") - uncertainty_args.add_argument( - "--evaluation_methods", - type=str, - metavar="STRING", - choices=["nll", "spearman", "ence", "miscalibration_area"], - help="Method to use for evaluation", - default="none", - ) - uncertainty_args.add_argument( - "--evaluation_scores_path", + general_args.add_argument( + "--checkpoint_path", type=str, metavar="FILE", - help="Path to file with evaluation scores", + help="Path to model checkpoint (.pt file)", ) + # general_args.add_argument( + # "--no_features_scaling", + # action="store_true", + # help="Turn on scaling of features", + # ) files_args.add_argument( "-f", "--configFile", - metavar="FILE", - type=str, - help="Input JSON file that contains all information for training/predicting.", - ) - general_args.add_argument( - "--gpu", - type=int, - metavar="INT", - choices=list(range(torch.cuda.device_count())), - help="Which GPU to use", - ) - general_args.add_argument( - "--num_workers", - type=int, - metavar="INT", - help="Number of workers for the parallel data loading 0 means sequential", - ) - general_args.add_argument( - "--no_cache", - type=bool, - metavar="BOOL", - default=False, - help="Turn off caching mol2graph computation", - ) - general_args.add_argument( - "--no_cache_mol", - type=bool, - metavar="BOOL", - default=False, - help="Whether to not cache the RDKit molecule for each SMILES string to reduce memory\ - usage cached by default", - ) - general_args.add_argument( - "--empty_cache", - type=bool, - metavar="BOOL", - help="Whether to empty all caches before training or predicting. This is necessary if\ - multiple jobs are run within a single script and the atom or bond features change", - ) - files_args.add_argument( - "--preds_path", type=str, metavar="FILE", - help="Path to CSV file where predictions will be saved", - default="", + help="Path to a .json file containing arguments. Any arguments present in the config" + "file will override arguments specified via the command line or by the defaults.", ) files_args.add_argument( - "--checkpoint_dir", + "--test_path", type=str, - metavar="DIR", - help="Directory from which to load model checkpoints" - "(walks directory and ensembles all models that are found)", - default="./ckpt", + help="Path to CSV file containing testing data for which predictions will be made.", ) files_args.add_argument( - "--checkpoint_path", + "--preds_path", type=str, - metavar="DIR", - help="Path to model checkpoint (.pt file)", + help="Path to CSV or PICKLE file where predictions will be saved.", ) files_args.add_argument( - "--checkpoint_paths", + "--calibration_path", type=str, - metavar="FILE", - nargs="*", - help="Path to model checkpoint (.pt file)", + help="Path to data file to be used for uncertainty calibration.", ) files_args.add_argument( - "--data_path", + "--calibration_features_path", type=str, - metavar="FILE", - help="Path to CSV file containing testing data for which predictions will be made", - default="", + nargs="+", + help="Path to features data to be used with the uncertainty calibration dataset.", ) + files_args.add_argument("--calibration_phase_features_path", type=str, help="") files_args.add_argument( - "--test_path", + "--calibration_atom_descriptors_path", type=str, - metavar="FILE", - help="Path to CSV file containing testing data for which predictions will be made", - default="", + help="Path to the extra atom descriptors.", ) files_args.add_argument( - "--features_path", + "--calibration_bond_descriptors_path", type=str, - metavar="FILE", - nargs="*", - help="Path to features to use in FNN (instead of features_generator)", + help="Path to the extra bond descriptors that will be used as bond features to featurize a given molecule.", ) - files_args.add_argument( - "--atom_descriptors_path", + + general_args.add_argument( + "--drop_extra_columns", + action="store_true", + help="Whether to drop all columns from the test data file besides the SMILES columns and the new prediction columns.", + ) + + uncertainty_args.add_argument( + "--uncertainty_method", type=str, - metavar="FILE", - help="Path to the extra atom descriptors.", + choices=[ + "mve", + "ensemble", + "evidential_epistemic", + "evidential_aleatoric", + "evidential_total", + "classification", + "dropout", + "spectra_roundrobin", + "dirichlet", + ], + help="The method of calculating uncertainty.", ) - data_args.add_argument( - "--use_compound_names", - action="store_true", - default=False, - help="Use when test data file contains compound names in addition to SMILES strings", + uncertainty_args.add_argument( + "--calibration_method", + type=str, + nargs="+", + choices=[ + "zscaling", + "tscaling", + "zelikman_interval", + "mve_weighting", + "platt", + "isotonic", + ], + help="Methods used for calibrating the uncertainty calculated with uncertainty method.", ) - data_args.add_argument( - "--no_features_scaling", + uncertainty_args.add_argument( + "--individual_ensemble_predictions", action="store_true", default=False, - help="Turn off scaling of features", + help="Whether to save individual ensemble predictions.", ) - data_args.add_argument( - "--max_data_size", - type=int, - metavar="INT", - help="Maximum number of data points to load", - ) - data_args.add_argument( - "--smiles_columns", + uncertainty_args.add_argument( + "--evaluation_methods", type=str, - metavar="STRING", - help="List of names of the columns containing SMILES strings.By default, uses the first\ - number_of_molecules columns.", + nargs="+", + help="The methods used for evaluating the uncertainty performance if the test data provided includes targets. Available methods are [nll, miscalibration_area, ence, spearman] or any available classification or multiclass metric.", ) - data_args.add_argument( - "--number_of_molecules", - type=int, - metavar="INT", - help="Number of molecules in each input to the model.This must equal the length of\ - smiles_columns if not None", + uncertainty_args.add_argument( + "--evaluation_scores_path", + type=str, + help="Location to save the results of uncertainty evaluations.", ) - - data_args.add_argument( - "--atom_descriptors", - type=bool, - metavar="Bool", - help="Use or not atom descriptors", + uncertainty_args.add_argument( + "--uncertainty_dropout_p", + type=float, + default=0.1, + help="The probability to use for Monte Carlo dropout uncertainty estimation.", ) - - data_args.add_argument( - "--bond_features_size", + uncertainty_args.add_argument( + "--dropout_sampling_size", type=int, - metavar="INT", - help="Size of the extra bond descriptors that will be used as bond features to featurize a\ - given molecule", + default=10, + help="The number of samples to use for Monte Carlo dropout uncertainty estimation. Distinct from the dropout used during training.", ) - training_args.add_argument( - "--batch_size", type=int, metavar="INT", default=50, help="Batch size" + uncertainty_args.add_argument( + "--calibration_interval_percentile", + type=float, + default=95, + help="Sets the percentile used in the calibration methods. Must be in the range (1,100).", + ) + uncertainty_args.add_argument( + "--regression_calibrator_metric", + type=str, + choices=["stdev", "interval"], + help="Regression calibrators can output either a stdev or an inverval.", ) diff --git a/dfpl/single_label_model.py b/dfpl/single_label_model.py index 75ff8df9..d1ee3feb 100755 --- a/dfpl/single_label_model.py +++ b/dfpl/single_label_model.py @@ -21,13 +21,13 @@ ) from sklearn.model_selection import StratifiedKFold, train_test_split from tensorflow.keras import metrics, optimizers, regularizers -from tensorflow.keras.layers import AlphaDropout, Dense, Dropout +from tensorflow.keras.layers import AlphaDropout, Dense, Dropout, InputLayer from tensorflow.keras.losses import ( BinaryCrossentropy, BinaryFocalCrossentropy, MeanSquaredError, ) -from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.models import Model, Sequential, load_model from dfpl import callbacks as cb from dfpl import options @@ -180,79 +180,119 @@ def prepare_nn_training_data( # This function defines a feedforward neural network (FNN) with the given input size, options, and output bias def build_fnn_network( - input_size: int, opts: options.Options, output_bias=None + input_size: int, opts: options.Options, output_bias=None, encoder: Model = None ) -> Model: + # Assuming the last layer of the encoder is the input to the FNN + input_size = encoder.layers[-1].output_shape[-1] + # Rename encoder layers for clarity + for i, layer in enumerate(encoder.layers): + layer._name = f'encoder_layer_{i + 1}' + + # Start model with encoder output + x = encoder.output + # Set the output bias if it is provided if output_bias is not None: output_bias = tf.keras.initializers.Constant(output_bias) - # Define the number of hidden layers based on the input size - my_hidden_layers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} - if not str(input_size) in my_hidden_layers.keys(): - raise ValueError("Wrong input-size. Must be in {2048, 1024, 999, 512, 256}.") + # Define the number of hidden layers dynamically based on the input size nhl = int(math.log2(input_size) / 2 - 1) + my_hidden_layers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} + if str(input_size) not in my_hidden_layers.keys(): + raise ValueError("Input size not supported. Must be in {2048, 1024, 999, 512, 256}.") - # Create a sequential model - model = Sequential() - - # Add the first hidden layer - if opts.activationFunction == "relu": - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation="relu", - kernel_regularizer=regularizers.l2(opts.l2reg), - kernel_initializer="he_uniform", - ) - ) - model.add(Dropout(opts.dropout)) - elif opts.activationFunction == "selu": - model.add( - Dense( - units=int(input_size / 2), - input_dim=input_size, - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout)) - else: - logging.error("Only 'relu' and 'selu' activation is supported") - sys.exit(-1) + # Add hidden layers + for i in range(nhl): + units = int(input_size / 2 ** (i + 1)) + dropout_rate = opts.dropout / (2 * i + 1) if i > 0 else opts.dropout - # Add additional hidden layers - for i in range(1, nhl): - factor_units = 2 ** (i + 1) - factor_dropout = 2 * i if opts.activationFunction == "relu": - model.add( - Dense( - units=int(input_size / factor_units), - activation="relu", - kernel_regularizer=regularizers.l2(opts.l2reg), - kernel_initializer="he_uniform", - ) - ) - model.add(Dropout(opts.dropout / factor_dropout)) + x = Dense(units, activation="relu", kernel_regularizer=regularizers.l2(opts.l2reg), + kernel_initializer="he_uniform")(x) + x = Dropout(dropout_rate)(x) elif opts.activationFunction == "selu": - model.add( - Dense( - units=int(input_size / factor_units), - activation="selu", - kernel_initializer="lecun_normal", - ) - ) - model.add(AlphaDropout(opts.dropout / factor_dropout)) + x = Dense(units, activation="selu", kernel_initializer="lecun_normal")(x) + x = AlphaDropout(dropout_rate)(x) else: - logging.error("Only 'relu' and 'selu' activation is supported") + logging.error("Unsupported activation function. Only 'relu' and 'selu' are supported.") sys.exit(-1) - # Add the output layer with a sigmoid activation function and the output bias if provided - model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) + # Add the output layer + outputs = Dense(units=1, activation="sigmoid", bias_initializer=output_bias)(x) + + # Create the model + model = Model(inputs=encoder.input, outputs=outputs) model.summary() + return model + # # Define the number of hidden layers based on the input size + # my_hidden_layers = {"2048": 6, "1024": 5, "999": 5, "512": 4, "256": 3} + # if not str(input_size) in my_hidden_layers.keys(): + # raise ValueError("Wrong input-size. Must be in {2048, 1024, 999, 512, 256}.") + # nhl = int(math.log2(input_size) / 2 - 1) + # + # # Create a sequential model + # model = Sequential() + # + # # Add the first hidden layer + # if opts.activationFunction == "relu": + # model.add( + # Dense( + # units=int(input_size / 2), + # input_dim=input_size, + # activation="relu", + # kernel_regularizer=regularizers.l2(opts.l2reg), + # kernel_initializer="he_uniform", + # ) + # ) + # model.add(Dropout(opts.dropout)) + # elif opts.activationFunction == "selu": + # model.add( + # Dense( + # units=int(input_size / 2), + # input_dim=input_size, + # activation="selu", + # kernel_initializer="lecun_normal", + # ) + # ) + # model.add(AlphaDropout(opts.dropout)) + # else: + # logging.error("Only 'relu' and 'selu' activation is supported") + # sys.exit(-1) + # + # # Add additional hidden layers + # for i in range(1, nhl): + # factor_units = 2 ** (i + 1) + # factor_dropout = 2 * i + # if opts.activationFunction == "relu": + # model.add( + # Dense( + # units=int(input_size / factor_units), + # activation="relu", + # kernel_regularizer=regularizers.l2(opts.l2reg), + # kernel_initializer="he_uniform", + # ) + # ) + # model.add(Dropout(opts.dropout / factor_dropout)) + # elif opts.activationFunction == "selu": + # model.add( + # Dense( + # units=int(input_size / factor_units), + # activation="selu", + # kernel_initializer="lecun_normal", + # ) + # ) + # model.add(AlphaDropout(opts.dropout / factor_dropout)) + # else: + # logging.error("Only 'relu' and 'selu' activation is supported") + # sys.exit(-1) + # + # # Add the output layer with a sigmoid activation function and the output bias if provided + # model.add(Dense(units=1, activation="sigmoid", bias_initializer=output_bias)) + # model.summary() + # return model + # This function defines a shallow neural network (SNN) with the given input size, options, and output bias def build_snn_network( @@ -347,10 +387,13 @@ def define_single_label_model( else: logging.error(f"Your selected optimizer is not supported: {opts.optimizer}.") sys.exit("Unsupported optimizer") - + if opts.finetuneEncoder: + encoder = load_model(opts.ecModelDir) # Ensure this loads the correct model + else: + encoder = None # Set the type of neural network according to the option selected if opts.fnnType == "FNN": - model = build_fnn_network(input_size, opts, output_bias) + model = build_fnn_network(input_size, opts, output_bias,encoder=encoder) elif opts.fnnType == "SNN": model = build_snn_network(input_size, opts, output_bias) else: @@ -494,8 +537,10 @@ def fit_and_evaluate_model( y_test: np.ndarray, fold: int, target: str, - opts: options.Options, + opts: options.Options ) -> pd.DataFrame: + + # Print info about training logging.info(f"Training of fold number: {fold}") @@ -549,6 +594,15 @@ def fit_and_evaluate_model( # Evaluate model callback_model = define_single_label_model(input_size=x_train.shape[1], opts=opts) callback_model.load_weights(filepath=checkpoint_model_weights_path) + callback_model.save_weights( + path.join( + opts.outputDir, + f"{target}_single-labeled_Fold-{fold}.model.weights.hdf5", + ) + ) + callback_model.save( + filepath=path.join(opts.outputDir, f"{target}-{fold}_saved_model") + ) performance = evaluate_model( x_test=x_test, y_test=y_test, @@ -558,6 +612,7 @@ def fit_and_evaluate_model( fold=fold, ) + return performance @@ -600,7 +655,6 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: :param df: The dataframe containing x matrix and at least one column for a y target. """ - # find target columns targets = [c for c in df.columns if c not in ["smiles", "fp", "fpcompressed"]] if opts.wabTracking and opts.wabTarget != "": # For W&B tracking, we only train one target that's specified as wabTarget "ER". @@ -631,7 +685,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: # for single 'folds' and when sweeping on W&B, we fix the random state if opts.wabTracking and not opts.aeWabTracking: wandb.init( - project=f"FFN_{opts.split_type}", + project=f"May_FFN_{opts.split_type}", group=f"{target}", name=f"{target}_single_fold", ) @@ -643,7 +697,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: reinit=True, ) x_train, x_test, y_train, y_test = train_test_split( - x, y, stratify=y, test_size=opts.testSize, random_state=1 + x, y, stratify=y, test_size=opts.testSize, random_state=0 ) logging.info( f"Splitting train/test data with fixed random initializer" @@ -664,32 +718,19 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ) performance_list.append(performance) # save complete model - trained_model = define_single_label_model( - input_size=len(x[0]), opts=opts - ) - # trained_model.load_weights - # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join(opts.outputDir, f"{target}_saved_model") - ) + elif 1 < opts.kFolds < 10: # int(x.shape[0] / 100): # do a kfold cross-validation kfold_c_validator = StratifiedKFold( - n_splits=opts.kFolds, shuffle=True, random_state=42 + n_splits=opts.kFolds, shuffle=True, random_state=0 ) fold_no = 1 # split the data for train, test in kfold_c_validator.split(x, y): if opts.wabTracking and not opts.aeWabTracking: wandb.init( - project=f"FNN_{opts.threshold}_{opts.split_type}", + project=f"May_FNN_{opts.threshold}_{opts.split_type}", group=f"{target}", name=f"{target}-{fold_no}", reinit=True, @@ -721,28 +762,15 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ) performance_list.append(performance) - # save complete model - trained_model = define_single_label_model( - input_size=len(x[0]), opts=opts - ) - # trained_model.load_weights - # (path.join(opts.outputDir, f"{target}_single-labeled_Fold-0.model.weights.hdf5")) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_single-labeled_Fold-{fold_no}.model.weights.hdf5", - ) - ) - # create output directory and store complete model - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}-{fold_no}_saved_model" - ) - ) if opts.wabTracking: wandb.finish() - fold_no += 1 + train_indices_path = os.path.join(opts.outputDir, f"fold_{fold_no}_train_indices.csv") + test_indices_path = os.path.join(opts.outputDir, f"fold_{fold_no}_test_indices.csv") + # Save the indices to CSV files + pd.DataFrame(train, columns=['Index']).to_csv(train_indices_path, index=False) + pd.DataFrame(test, columns=['Index']).to_csv(test_indices_path, index=False) + fold_no += 1 # select and copy best model - how to define the best model? best_fold = pd.concat(performance_list, ignore_index=True).sort_values( by=["p_1", "r_1", "MCC"], ascending=False, ignore_index=True @@ -750,11 +778,11 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: # rename the fold to best fold src = os.path.join( opts.outputDir, - f"{target}_single-labeled_Fold-{best_fold}.model.weights.hdf5", + f"{target}_{opts.split_type}_single-labeled_Fold-{best_fold}.model.weights.hdf5", ) dst = os.path.join( opts.outputDir, - f"{target}_single-labeled_Best_Fold-{best_fold}.model.weights.hdf5", + f"{target}_{opts.split_type}_single-labeled_Best_Fold-{best_fold}.model.weights.hdf5", ) os.rename(src, dst) @@ -771,7 +799,6 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: # Rename source directory to destination directory os.rename(src_dir, dst_dir) - # save complete model else: logging.info( "Your selected number of folds for Cross validation is out of range. " @@ -781,7 +808,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ( pd.concat(performance_list, ignore_index=True).to_csv( path_or_buf=path.join( - opts.outputDir, "single_label_random_model.evaluation.csv" + opts.outputDir, f"single_label_{opts.split_type}_model.evaluation.csv" ) ) ) @@ -808,7 +835,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ) if opts.wabTracking and not opts.aeWabTracking: wandb.init( - project=f"FFN_{opts.split_type}", + project=f"May_FFN_{opts.split_type}", group=f"{target}", name=f"{target}_single_fold", ) @@ -829,20 +856,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: opts=opts, ) performance_list.append(performance) - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}_scaffold_saved_model_0" - ) - ) + elif opts.kFolds > 1: for fold_no in range(1, opts.kFolds + 1): print(f"Splitting data with seed {fold_no}") @@ -857,7 +871,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ) if opts.wabTracking and not opts.aeWabTracking: wandb.init( - project=f"FFN_{opts.split_type}", + project=f"May_FFN_{opts.split_type}", group=f"{target}", name=f"{target}-{fold_no}", reinit=True, @@ -880,20 +894,6 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ) performance_list.append(performance) - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-{fold_no}.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join( - opts.outputDir, f"{target}_scaffold_saved_model_{fold_no}" - ) - ) if opts.wabTracking: wandb.finish() fold_no += 1 @@ -904,20 +904,20 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: # rename the fold to best fold src = os.path.join( opts.outputDir, - f"{target}_scaffold_single-labeled_Fold-{best_fold}.model.weights.hdf5", + f"{target}_{opts.split_type}_single-labeled_Fold-{best_fold}.model.weights.hdf5", ) dst = os.path.join( opts.outputDir, - f"{target}_scaffold_single-labeled_BEST_Fold-{best_fold}.model.weights.hdf5", + f"{target}_{opts.split_type}_single-labeled_BEST_Fold-{best_fold}.model.weights.hdf5", ) os.rename(src, dst) src_dir = os.path.join( - opts.outputDir, f"{target}_scaffold_saved_model_{best_fold}" + opts.outputDir, f"{target}_{opts.split_type}_saved_model_{best_fold}" ) dst_dir = os.path.join( opts.outputDir, - f"{target}_scaffold_saved_model_BEST_FOLD_{best_fold}", + f"{target}_{opts.split_type}_saved_model_BEST_FOLD_{best_fold}", ) if path.isdir(dst_dir): @@ -935,7 +935,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ( pd.concat(performance_list, ignore_index=True).to_csv( path_or_buf=path.join( - opts.outputDir, "single_label_scaffold_model.evaluation.csv" + opts.outputDir, f"single_label_{opts.split_type}_model.evaluation.csv" ) ) ) @@ -958,7 +958,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: ) if opts.wabTracking and not opts.aeWabTracking: wandb.init( - project=f"FFN_{opts.split_type}AE_{opts.aeSplitType}", + project=f"May_FFN_{opts.split_type}AE_{opts.aeSplitType}", group=f"{target}", name=f"{target}_single_fold", ) @@ -978,18 +978,7 @@ def train_single_label_models(df: pd.DataFrame, opts: options.Options) -> None: opts=opts, ) performance_list.append(performance) - trained_model = define_single_label_model( - input_size=len(x_train[0]), opts=opts - ) - trained_model.save_weights( - path.join( - opts.outputDir, - f"{target}_weight_single-labeled_Fold-0.model.weights.hdf5", - ) - ) - trained_model.save( - filepath=path.join(opts.outputDir, f"{target}_weight_saved_model_0") - ) + elif opts.kFolds > 1: raise Exception( f"Unsupported number of folds: {opts.kFolds} for {opts.split_type} split.\ diff --git a/dfpl/utils.py b/dfpl/utils.py index 56068532..78ad47e4 100755 --- a/dfpl/utils.py +++ b/dfpl/utils.py @@ -61,7 +61,6 @@ def parseCmdArgs(cls: Type[T], args: argparse.Namespace) -> T: return result - def makePathAbsolute(p: str) -> str: path = pathlib.Path(p) if path.is_absolute(): @@ -75,38 +74,100 @@ def createDirectory(directory: str): if not os.path.exists(path): os.makedirs(path) - -def createArgsFromJson(jsonFile: str): +def parse_cli_list(value: str): + # Simple parser for lists passed as comma-separated values + return value.split(',') + +def parse_cli_boolean(cli_args, cli_arg_key): + # Determines boolean value based on command line presence + if cli_arg_key in cli_args: + return True # Presence of flag implies True + return False + +# def createArgsFromJson(jsonFile: str): +# arguments = [] +# ignore_elements = ["py/object"] +# cli_args = sys.argv[1:] # Skipping the script name itself +# +# with open(jsonFile, "r") as f: +# data = json.load(f) +# +# processed_cli_keys = [] # To track which CLI keys have been processed +# +# for key, value in data.items(): +# if key not in ignore_elements: +# cli_arg_key = f"--{key}" +# if cli_arg_key in cli_args: +# processed_cli_keys.append(cli_arg_key) +# arg_index = cli_args.index(cli_arg_key) + 1 +# if isinstance(value, bool): +# value = parse_cli_boolean(cli_args, cli_arg_key) +# elif arg_index < len(cli_args): +# cli_value = cli_args[arg_index] +# if isinstance(value, list): +# value = parse_cli_list(cli_value) +# else: +# value = cli_value # Override JSON value with command-line value +# if isinstance(value, bool) and value: +# arguments.append(cli_arg_key) +# elif isinstance(value, list): +# arguments.append(cli_arg_key) +# arguments.extend(map(str, value)) # Ensure all elements are strings +# else: +# arguments.extend([cli_arg_key, str(value)]) +# +# return arguments +def createArgsFromJson(jsonFile: str) -> List[str]: arguments = [] ignore_elements = ["py/object"] + cli_args = sys.argv[1:] # Skipping the script name itself with open(jsonFile, "r") as f: data = json.load(f) - # Check each key in the JSON file against command-line arguments + processed_cli_keys = [] # To track which CLI keys have been processed + for key, value in data.items(): if key not in ignore_elements: - # Prepare the command-line argument format cli_arg_key = f"--{key}" - - # Check if this argument is provided in the command line - if cli_arg_key in sys.argv: - # Find the index of the argument in sys.argv and get its value - arg_index = sys.argv.index(cli_arg_key) + 1 - if arg_index < len(sys.argv): - cli_value = sys.argv[arg_index] - value = cli_value # Override JSON value with command-line value - - # Append the argument and its value to the list - if key == "extra_metrics" and isinstance(value, list): + if cli_arg_key in cli_args: + processed_cli_keys.append(cli_arg_key) + arg_index = cli_args.index(cli_arg_key) + 1 + if isinstance(value, bool): + value = parse_cli_boolean(cli_args, cli_arg_key) + elif arg_index < len(cli_args) and not cli_args[arg_index].startswith('--'): + cli_value = cli_args[arg_index] + if isinstance(value, list): + value = parse_cli_list(cli_value) + else: + value = cli_value # Override JSON value with command-line value + if isinstance(value, bool): + if value: + arguments.append(cli_arg_key) + elif isinstance(value, list): arguments.append(cli_arg_key) - arguments.extend(value) + arguments.extend(map(str, value)) # Ensure all elements are strings else: arguments.extend([cli_arg_key, str(value)]) + i = 0 + while i < len(cli_args): + arg = cli_args[i] + if arg.startswith("--"): + key = arg.lstrip("--") + if key not in data: + value = True if i + 1 >= len(cli_args) or cli_args[i + 1].startswith("--") else cli_args[i + 1] + if isinstance(value, bool): + if value: + arguments.append(arg) + else: + arguments.extend([arg, str(value)]) + i += 1 if isinstance(value, bool) else 2 + else: + i += 1 + else: + i += 1 return arguments - - def make_mol(s: str, keep_h: bool, add_h: bool, keep_atom_map: bool): """ Builds an RDKit molecule from a SMILES string. @@ -135,49 +196,6 @@ def make_mol(s: str, keep_h: bool, add_h: bool, keep_atom_map: bool): return mol -def generate_scaffold( - mol: Union[str, Chem.Mol, Tuple[Chem.Mol, Chem.Mol]], include_chirality: bool = True -) -> str: - """ - Computes the Bemis-Murcko scaffold for a SMILES string, an RDKit molecule, or an InChI string or InChIKey. - - :param mol: A SMILES, RDKit molecule, InChI string, or InChIKey string. - :param include_chirality: Whether to include chirality in the computed scaffold. - :return: The Bemis-Murcko scaffold for the molecule. - """ - if isinstance(mol, str): - if mol.startswith("InChI="): - mol = inchi_to_mol(mol) - else: - mol = make_mol(mol, keep_h=False, add_h=False, keep_atom_map=False) - elif isinstance(mol, tuple): - mol = mol[0] - scaffold = MurckoScaffold.MurckoScaffoldSmiles( - mol=mol, includeChirality=include_chirality - ) - - return scaffold - - -def scaffold_to_smiles( - mols: List[str], use_indices: bool = False -) -> Dict[str, Union[Set[str], Set[int]]]: - """ - Computes the scaffold for each SMILES and returns a mapping from scaffolds to sets of smiles (or indices). - :param mols: A list of SMILES. - :param use_indices: Whether to map to the SMILES's index in :code:`mols` rather than - mapping to the smiles string itself. This is necessary if there are duplicate smiles. - :return: A dictionary mapping each unique scaffold to all SMILES (or indices) which have that scaffold. - """ - scaffolds = defaultdict(set) - for i, mol in tqdm(enumerate(mols), total=len(mols)): - scaffold = generate_scaffold(mol) - if use_indices: - scaffolds[scaffold].add(i) - else: - scaffolds[scaffold].add(mol) - - return scaffolds # def inchi_to_mol(inchi: str) -> Chem.Mol: @@ -243,7 +261,49 @@ def weight_split( test_df = sorted_data.iloc[test_indices].reset_index(drop=True) return train_df, val_df, test_df +def generate_scaffold( + mol: Union[str, Chem.Mol, Tuple[Chem.Mol, Chem.Mol]], include_chirality: bool = True +) -> str: + """ + Computes the Bemis-Murcko scaffold for a SMILES string, an RDKit molecule, or an InChI string or InChIKey. + :param mol: A SMILES, RDKit molecule, InChI string, or InChIKey string. + :param include_chirality: Whether to include chirality in the computed scaffold. + :return: The Bemis-Murcko scaffold for the molecule. + """ + if isinstance(mol, str): + if mol.startswith("InChI="): + mol = inchi_to_mol(mol) + else: + mol = make_mol(mol, keep_h=False, add_h=False, keep_atom_map=False) + elif isinstance(mol, tuple): + mol = mol[0] + scaffold = MurckoScaffold.MurckoScaffoldSmiles( + mol=mol, includeChirality=include_chirality + ) + + return scaffold + + +def scaffold_to_smiles( + mols: List[str], use_indices: bool = False +) -> Dict[str, Union[Set[str], Set[int]]]: + """ + Computes the scaffold for each SMILES and returns a mapping from scaffolds to sets of smiles (or indices). + :param mols: A list of SMILES. + :param use_indices: Whether to map to the SMILES's index in :code:`mols` rather than + mapping to the smiles string itself. This is necessary if there are duplicate smiles. + :return: A dictionary mapping each unique scaffold to all SMILES (or indices) which have that scaffold. + """ + scaffolds = defaultdict(set) + for i, mol in tqdm(enumerate(mols), total=len(mols)): + scaffold = generate_scaffold(mol) + if use_indices: + scaffolds[scaffold].add(i) + else: + scaffolds[scaffold].add(mol) + + return scaffolds def ae_scaffold_split( data: pd.DataFrame, diff --git a/environment.yml b/environment.yml index bc7f7595..f008b36b 100755 --- a/environment.yml +++ b/environment.yml @@ -8,17 +8,19 @@ dependencies: - conda>=4.12.0 - pip>=22.0.4 - pytest>=7.1.1 + - python=3.8 # application requirements - protobuf>=3.21.12 - jsonpickle>=2.1 - matplotlib>=3.5.1 - - numpy>=1.22.2 + - numpy=1.23.5 - rdkit>=2023.03.3 - scikit-learn>=1.0.2 - seaborn>=0.12.2 - tensorflow>=2.13 - wandb>=0.16.2 - - umap-learn>=0.5.3 + - umap-learn=0.5.5 + - numba>=0.51.0 - pip: - git+https://github.com/soulios/chemprop.git@cfcb67a diff --git a/example/interpret.json b/example/interpret.json index 297a7dc3..cc0cea00 100644 --- a/example/interpret.json +++ b/example/interpret.json @@ -1,10 +1,10 @@ { - "data_path": "tests/data/smiles.csv", - "checkpoint_path": "dmpnn-random/fold_0/model_0/model.pt", + "data_path": "tests/data/S_dataset.csv", + "checkpoint_path": "dmpnn-tox21-cvnotest8020-30-multilabel-bce/fold_0/model_0/model.pt", "preds_path": "interpretations.csv", "max_atoms": 20, "min_atoms": 8, "prop_delta": 0.5, - "property_id": 5, + "property_id": [1,2,3,4,5,6,7,8,9,10,11,12], "rollout": 20 } \ No newline at end of file diff --git a/example/predict.json b/example/predict.json index 7e2a6e8e..c9b9e1f1 100755 --- a/example/predict.json +++ b/example/predict.json @@ -5,7 +5,7 @@ "outputFile": "smiles.csv", "ecModelDir": "", "ecWeightsFile": "", - "fnnModelDir": "example/results_train/AR_saved_model", + "fnnModelDir": "example/results/AR_saved_model", "aeType": "deterministic", "compressFeatures": false, "trainFNN": false diff --git a/example/predictgnn.json b/example/predictgnn.json index 7b84d3ee..e4f58e37 100755 --- a/example/predictgnn.json +++ b/example/predictgnn.json @@ -1,25 +1,11 @@ { "py/object": "dfpl.options.GnnOptions", - "test_path": "tests/data/calibra.csv", + "checkpoint_path": "dmpnntox21-cv-8020/fold_1/model_0/model.pt", + "test_path": "tests/data/tox21.csv", "preds_path": "example/results_gnn.csv", - "evaluation_methods": "nll", - "evaluation_scores_path": "example/eval_gnn.csv", - "calibration_method": "zscaling", - "calibration_path": "tests/data/calibra.csv", - "calibration_interval_percentile": 95, - "calibration_atom_descriptors_path": "tests/data/atom_descriptors.csv", - "calibration_bond_features_path": "tests/data/bond_features.csv", - "calibration_features_path": "tests/data/features.csv", - "calibration_phase_features_path": "tests/data/phase_features.csv", - "calibration_phase_features_path": "tests/data/phase_features.csv", - "ensemble_variance": true, - "individual_ensemble_predictions": true, - "propertyensemble_size": 5, - "uncertainty_method": "mve", + "uncertainty_method": "dropout", "uncertainty_dropout_p": 0.1, - "dropout_sampling_size": 10, - "regression_calibrator_metric": "stdev", - "spectra_roundrobin": true + "dropout_sampling_size": 10 } diff --git a/example/train.json b/example/train.json index 317d5ec2..824d2418 100755 --- a/example/train.json +++ b/example/train.json @@ -1,8 +1,8 @@ { "py/object": "dfpl.options.Options", - "inputFile": "tests/data/S_dataset.csv", - "outputDir": "example/results_train/", - "ecModelDir": "", + "inputFile": "tests/data/tox21.csv", + "outputDir": "example/results/", + "ecModelDir": "ae/random_split_autoencoder/encoder_model/", "ecWeightsFile": "", "verbose": 2, @@ -12,11 +12,13 @@ "aeSplitType": "random", "aeBatchSize": 351, + "aeEpochs": 2, "aeOptimizer": "Adam", "aeActivationFunction": "relu", "aeLearningRate": 0.001, "aeLearningRateDecay": 0.96, "aeType": "deterministic", + "finetuneEncoder": false, "visualizeLatent": false, "type": "smiles", @@ -26,7 +28,7 @@ "enableMultiLabel": false, "sampleFractionOnes": 0, "sampleDown": false, - "gpu": "0", + "gpu": "", "trainFNN": true, "kFolds": 1, @@ -36,12 +38,8 @@ "optimizer": "Adam", "lossFunction": "bce", "epochs": 2, - "batchSize": 128, "activationFunction": "selu", - "dropout": 0.0107, - "learningRate": 0.0000022, - "learningRateDecay": 0.96, - "l2reg": 0.001, + "aeWabTracking": false, "wabTracking": false, diff --git a/example/traingnn.json b/example/traingnn.json index 00dc3c4b..b0e4fa96 100755 --- a/example/traingnn.json +++ b/example/traingnn.json @@ -1,14 +1,13 @@ { "py/object": "dfpl.options.GnnOptions", - "data_path": "tests/data/S_dataset.csv", - "save_dir": "dmpnn-random/", - "epochs": 2, - "num_folds": 1, + "data_path": "tests/data/tox21.csv", + "save_dir": "dmpnntox21-cv-8020/", + "epochs": 30, "metric": "accuracy", "loss_function": "binary_cross_entropy", - "split_type": "random", + "split_type": 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