Skip to content

New questions for a literature review #71

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
t-kimber wants to merge 1 commit into
base: main
Choose a base branch
from
Open

New questions for a literature review #71

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
251 changes: 250 additions & 1 deletion maxsmi/results_analysis/results_maxsmi.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -103,6 +103,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE values\n",
"-----------\n",
"\n",
"FreeSolv\n",
"CONV1D augmentation_with_duplication 70\n",
"1.032\n",
Expand All @@ -119,19 +122,265 @@
}
],
"source": [
"print(\"RMSE values\\n-----------\\n\")\n",
"for task in [\"FreeSolv\", \"ESOL\", \"Lipophilicity\"]:\n",
" print(task)\n",
" maxsmi_model = load_data(path_to_output,\n",
" task)\n",
" print(f\"{maxsmi_model.test[0][1]:.3f}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2 values\n",
"----------\n",
"\n",
"FreeSolv\n",
"CONV1D augmentation_with_duplication 70\n",
"0.935\n",
"\n",
"ESOL\n",
"CONV1D augmentation_with_reduced_duplication 70\n",
"0.926\n",
"\n",
"Lipophilicity\n",
"CONV1D augmentation_without_duplication 80\n",
"0.758\n",
"\n"
]
}
],
"source": [
"print(\"R^2 values\\n----------\\n\")\n",
"for task in [\"FreeSolv\", \"ESOL\", \"Lipophilicity\"]:\n",
" print(task)\n",
" maxsmi_model = load_data(path_to_output,\n",
" task)\n",
" print(f\"{maxsmi_model.test[0][2]:.3f}\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This values indeed correspond to the minimum value shown in the `results_tables` notebooks."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"From smiles to predictions\n",
"\n",
"\"\"\"\n",
"import argparse\n",
"import logging\n",
"import logging.handlers\n",
"import pandas\n",
"import warnings\n",
"import os\n",
"from datetime import datetime\n",
"import numpy\n",
"import rdkit\n",
"from rdkit.Chem import Draw\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from maxsmi.utils.utils_data import data_retrieval, smiles_in_training, data_checker\n",
"from maxsmi.utils.utils_smiles import (\n",
" validity_check,\n",
" smiles_to_canonical,\n",
" smiles_to_folder_name,\n",
" smiles_from_folder_name,\n",
" is_connected,\n",
" ALL_SMILES_DICT,\n",
")\n",
"from maxsmi.utils.utils_encoding import char_replacement\n",
"from maxsmi.utils.utils_prediction import (\n",
" retrieve_longest_smiles_from_optimal_model,\n",
" unlabeled_smiles_max_length,\n",
" character_check,\n",
" mixture_check,\n",
")\n",
"\n",
"from maxsmi.pytorch_utils.pytorch_models import model_type\n",
"from maxsmi.pytorch_utils.pytorch_data import AugmentSmilesData\n",
"from maxsmi.pytorch_utils.pytorch_evaluation import out_of_sample_prediction\n",
"from maxsmi.utils.utils_optimal_model import retrieve_optimal_model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"is_cuda = torch.cuda.is_available()\n",
"\n",
"if is_cuda:\n",
" device = torch.device(\"cuda\")\n",
" device_name = torch.cuda.get_device_name(device)\n",
" logging.info(f\"CUDA available: {is_cuda} with {device_name}\")\n",
"else:\n",
" device = torch.device(\"cpu\")\n",
" logging.info(f\"CUDA available: {is_cuda}\")\n",
"\n",
"time_execution_start = datetime.now()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def retrieve_nb_model_parameters(task):\n",
" data = data_retrieval(task)\n",
" print(f\"Shape of training data set before processing: {data.shape} \")\n",
" \n",
" longest_smiles = retrieve_longest_smiles_from_optimal_model(task)\n",
"\n",
" # Retrieve SMILES' dictionary\n",
" smi_dict = ALL_SMILES_DICT\n",
"\n",
" # Obtain longest of all smiles\n",
" max_length_smi = longest_smiles\n",
"\n",
" print(f\"Longest smiles in training data set: {max_length_smi} \")\n",
" (\n",
" ml_model,\n",
" augmentation_strategy,\n",
" augmentation_number,\n",
" ) = retrieve_optimal_model(task)\n",
" (ml_model_name, ml_model) = model_type(ml_model, device, smi_dict, max_length_smi)\n",
" print(f\"Summary of ml model used for the prediction: {ml_model} \")\n",
" \n",
" file_path = f\"{path_to_output}/prediction_models/{task}\"\n",
" ml_model.load_state_dict(\n",
" torch.load(f\"{file_path}/model_dict.pth\", map_location=device)\n",
" )\n",
" \n",
" total_params = sum(\n",
" param.numel() for param in ml_model.parameters()\n",
" )\n",
"\n",
" return total_params"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of training data set before processing: (1128, 2) \n",
"Longest smiles in training data set: 109 \n",
"Summary of ml model used for the prediction: Convolutional1DNetwork(\n",
" (convolution): Conv1d(48, 300, kernel_size=(10,), stride=(1,))\n",
" (fully_connected_1): Linear(in_features=30000, out_features=100, bias=True)\n",
" (fully_connected_out): Linear(in_features=100, out_features=1, bias=True)\n",
") \n"
]
},
{
"data": {
"text/plain": [
"3144501"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retrieve_nb_model_parameters(\"ESOL\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of training data set before processing: (642, 2) \n",
"Longest smiles in training data set: 76 \n",
"Summary of ml model used for the prediction: Convolutional1DNetwork(\n",
" (convolution): Conv1d(48, 300, kernel_size=(10,), stride=(1,))\n",
" (fully_connected_1): Linear(in_features=20100, out_features=100, bias=True)\n",
" (fully_connected_out): Linear(in_features=100, out_features=1, bias=True)\n",
") \n"
]
},
{
"data": {
"text/plain": [
"2154501"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retrieve_nb_model_parameters(\"FreeSolv\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of training data set before processing: (4200, 2) \n",
"Longest smiles in training data set: 268 \n",
"Summary of ml model used for the prediction: Convolutional1DNetwork(\n",
" (convolution): Conv1d(48, 300, kernel_size=(10,), stride=(1,))\n",
" (fully_connected_1): Linear(in_features=77700, out_features=100, bias=True)\n",
" (fully_connected_out): Linear(in_features=100, out_features=1, bias=True)\n",
") \n"
]
},
{
"data": {
"text/plain": [
"7914501"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retrieve_nb_model_parameters(\"lipophilicity\")"
]
}
],
"metadata": {
Expand All @@ -150,7 +399,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.10.4"
}
},
"nbformat": 4,
Expand Down