From 14b28433e2f651cbe700897b5b6e101b5df364a4 Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Wed, 29 Sep 2021 17:11:59 +0700 Subject: [PATCH 01/13] clean workspace, enable inheritance --- .github/workflows/python-package.yml | 39 --- .gitignore | 138 --------- CHANGELOG.md | 43 --- LICENSE | 22 -- README.md | 104 ------- build.sh | 7 - conftest.py | 0 create_test_resources.py | 36 --- dev_requirements.txt | 6 - .../{models => core}/__init__.py | 0 .../{models => core}/model.py | 109 ++++--- .../{models/etm.py => core/trainer.py} | 41 +-- .../requirements.txt | 0 lint.sh | 4 - publish.txt | 20 -- setup.py | 42 --- tests/__init__.py | 0 tests/integration/__init__.py | 0 tests/integration/test_etm.py | 278 ------------------ tests/resources/train_resources.test | Bin 147578 -> 0 bytes .../train_w2v_embeddings.wordvectors | Bin 203319 -> 0 bytes .../train_w2v_embeddings.wordvectors.bin | Bin 132698 -> 0 bytes .../train_w2v_embeddings.wordvectors.txt | 111 ------- tests/unit/__init__.py | 0 tests/unit/test_embedding.py | 29 -- tests/unit/test_preprocessing.py | 30 -- train_resources.test | Bin 126761 -> 0 bytes 27 files changed, 75 insertions(+), 984 deletions(-) delete mode 100644 .github/workflows/python-package.yml delete mode 100644 .gitignore delete mode 100644 CHANGELOG.md delete mode 100644 LICENSE delete mode 100644 README.md delete mode 100755 build.sh delete mode 100644 conftest.py delete mode 100644 create_test_resources.py delete mode 100644 dev_requirements.txt rename embedded_topic_model/{models => core}/__init__.py (100%) rename embedded_topic_model/{models => core}/model.py (79%) rename embedded_topic_model/{models/etm.py => core/trainer.py} (94%) rename requirements.txt => embedded_topic_model/requirements.txt (100%) delete mode 100755 lint.sh delete mode 100644 publish.txt delete mode 100644 setup.py delete mode 100644 tests/__init__.py delete mode 100644 tests/integration/__init__.py delete mode 100644 tests/integration/test_etm.py delete mode 100644 tests/resources/train_resources.test delete mode 100644 tests/resources/train_w2v_embeddings.wordvectors delete mode 100644 tests/resources/train_w2v_embeddings.wordvectors.bin delete mode 100644 tests/resources/train_w2v_embeddings.wordvectors.txt delete mode 100644 tests/unit/__init__.py delete mode 100644 tests/unit/test_embedding.py delete mode 100644 tests/unit/test_preprocessing.py delete mode 100644 train_resources.test diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml deleted file mode 100644 index 49711a5..0000000 --- a/.github/workflows/python-package.yml +++ /dev/null @@ -1,39 +0,0 @@ -# This workflow will install Python dependencies, run tests and lint with a variety of Python versions -# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions - -name: Python package - -on: - push: - branches: [ main ] - pull_request: - branches: [ main ] - -jobs: - build: - - runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.6, 3.7, 3.8] - - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install -r dev_requirements.txt - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - pytest diff --git a/.gitignore b/.gitignore deleted file mode 100644 index a81c8ee..0000000 --- a/.gitignore +++ /dev/null @@ -1,138 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index 465cb06..0000000 --- a/CHANGELOG.md +++ /dev/null @@ -1,43 +0,0 @@ -# Changelog - -This changelog was inspired by the [keep-a-changelog](https://github.com/olivierlacan/keep-a-changelog) project and follows [semantic versioning](https://semver.org). - -## [1.0.2] - 2021-06-23 - -### Changed - -- deactivates debug mode by default -- documents get_most_similar_words method - -## [1.0.1] - 2021-02-15 - -### Changed - -- optimizes original word2vec TXT file input for model training -- updates README.md - -## [1.0.0] - 2021-02-15 - -### Added - -- adds support for original word2vec pretrained embeddings files on both formats (BIN/TXT) - -### Changed - -- optimizes handling of gensim's word2vec mapping file for better memory usage - -## [0.1.1] - 2021-02-01 - -### Added - -- support for python 3.6 - -## [0.1.0] - 2021-02-01 - -### Added - -- ETM training with partially tested support for original ETM features. -- ETM corpus preprocessing scripts - including word2vec embeddings training - adapted from the original code. -- adds methods to retrieve document-topic and topic-word probability distributions from the trained model. -- adds docstrings for tested API methods. -- adds unit and integration tests for ETM and preprocessing scripts. diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 4dfc41c..0000000 --- a/LICENSE +++ /dev/null @@ -1,22 +0,0 @@ -MIT License - -Copyright (c) 2019 Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei -Copyright (c) 2021 Luiz Matos - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/README.md b/README.md deleted file mode 100644 index d9ec42e..0000000 --- a/README.md +++ /dev/null @@ -1,104 +0,0 @@ -# Embedded Topic Model -[![PyPI version](https://badge.fury.io/py/embedded-topic-model.svg)](https://badge.fury.io/py/embedded-topic-model) -[![Actions Status](https://github.com/lffloyd/embedded-topic-model/workflows/Python%20package/badge.svg)](https://github.com/lffloyd/embedded-topic-model/actions) -[![License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://github.com/lffloyd/embedded-topic-model/blob/main/LICENSE) - -This package was made to easily run embedded topic modelling on a given corpus. - -ETM is a topic model that marries the probabilistic topic modelling of Latent Dirichlet Allocation with the -contextual information brought by word embeddings-most specifically, word2vec. ETM models topics as points -in the word embedding space, arranging together topics and words with similar context. -As such, ETM can either learn word embeddings alongside topics, or be given pretrained embeddings to discover -the topic patterns on the corpus. - -ETM was originally published by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei on a article titled ["Topic Modeling in Embedding Spaces"](https://arxiv.org/abs/1907.04907) in 2019. This code is an adaptation of the [original](https://github.com/adjidieng/ETM) provided with the article. Most of the original code was kept here, with some changes here and there, mostly for ease of usage. - -With the tools provided here, you can run ETM on your dataset using simple steps. - -# Installation -You can install the package using ```pip``` by running: ```pip install -U embedded_topic_model``` - -# Usage -To use ETM on your corpus, you must first preprocess the documents into a format understandable by the model. -This package has a quick-use preprocessing script. The only requirement is that the corpus must be composed -by a list of strings, where each string corresponds to a document in the corpus. - -You can preprocess your corpus as follows: - -```python -from embedded_topic_model.utils import preprocessing -import json - -# Loading a dataset in JSON format. As said, documents must be composed by string sentences -corpus_file = 'datasets/example_dataset.json' -documents_raw = json.load(open(dataset, 'r')) -documents = [document['body'] for document in documents_raw] - -# Preprocessing the dataset -vocabulary, train_dataset, _, = preprocessing.create_etm_datasets( - documents, - min_df=0.01, - max_df=0.75, - train_size=0.85, -) -``` - -Then, you can train word2vec embeddings to use with the ETM model. This is optional, and if you're not interested -on training your embeddings, you can either pass a pretrained word2vec embeddings file for ETM or learn the embeddings -using ETM itself. If you want ETM to learn its word embeddings, just pass ```train_embeddings=True``` as an instance parameter. - -To pretrain the embeddings, you can do the following: - -```python -from embedded_topic_model.utils import embedding - -# Training word2vec embeddings -embeddings_mapping = embedding.create_word2vec_embedding_from_dataset(documents) -``` - -To create and fit the model using the training data, execute: - -```python -from embedded_topic_model.models.etm import ETM - -# Training an ETM instance -etm_instance = ETM( - vocabulary, - embeddings=embeddings_mapping, # You can pass here the path to a word2vec file or - # a KeyedVectors instance - num_topics=8, - epochs=300, - debug_mode=True, - train_embeddings=False, # Optional. If True, ETM will learn word embeddings jointly with - # topic embeddings. By default, is False. If 'embeddings' argument - # is being passed, this argument must not be True -) - -etm_instance.fit(train_dataset) -``` - -Also, to obtain the topics, topic coherence or topic diversity of the model, you can do as follows: - -```python -topics = etm_instance.get_topics(20) -topic_coherence = etm_instance.get_topic_coherence() -topic_diversity = etm_instance.get_topic_diversity() -``` - -# Citation -To cite ETM, use the original article's citation: - -``` -@article{dieng2019topic, - title = {Topic modeling in embedding spaces}, - author = {Dieng, Adji B and Ruiz, Francisco J R and Blei, David M}, - journal = {arXiv preprint arXiv: 1907.04907}, - year = {2019} -} -``` - -# Acknowledgements -Credits given to Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei for the original work. - -# License -Licensed under [MIT](LICENSE) license. diff --git a/build.sh b/build.sh deleted file mode 100755 index de72887..0000000 --- a/build.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -rm -rf build && rm -rf dist -pip install -r dev_requirements.txt || exit 1 -pip install -r requirements.txt || exit 1 -pytest || exit 1 -python setup.py sdist bdist_wheel || exit 1 diff --git a/conftest.py b/conftest.py deleted file mode 100644 index e69de29..0000000 diff --git a/create_test_resources.py b/create_test_resources.py deleted file mode 100644 index 45e654e..0000000 --- a/create_test_resources.py +++ /dev/null @@ -1,36 +0,0 @@ -from embedded_topic_model.utils import embedding, preprocessing -import os -import joblib - -sentences = [ - "Peanut butter and jelly caused the elderly lady to think about her past.", - "Toddlers feeding raccoons surprised even the seasoned park ranger.", - "You realize you're not alone as you sit in your bedroom massaging your calves after a long day of playing tug-of-war with Grandpa Joe in the hospital.", - "She wondered what his eyes were saying beneath his mirrored sunglasses.", - "He was disappointed when he found the beach to be so sandy and the sun so sunny.", - "Flesh-colored yoga pants were far worse than even he feared.", - "The wake behind the boat told of the past while the open sea for told life in the unknown future.", - "Improve your goldfish's physical fitness by getting him a bicycle.", - "Harrold felt confident that nobody would ever suspect his spy pigeon.", - "Nudist colonies shun fig-leaf couture.", -] -vocabulary, train_dataset, test_dataset = preprocessing.create_etm_datasets( - sentences, debug_mode=True) - -embeddings = embedding.create_word2vec_embedding_from_dataset( - sentences, - embedding_file_path='tests/resources/train_w2v_embeddings.wordvectors', - save_c_format_w2vec=True, - debug_mode=True, -) - -os.makedirs(os.path.dirname('tests/resources/train_resources.test'), exist_ok=True) -joblib.dump( - (vocabulary, - embeddings, - train_dataset, - test_dataset), - './train_resources.test', - compress=8) - -print('the end') diff --git a/dev_requirements.txt b/dev_requirements.txt deleted file mode 100644 index eda91c9..0000000 --- a/dev_requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -joblib==0.16.0 -flake8==3.8.4 -pytest==6.2.1 -autopep8==1.5.4 -bump2version==1.0.1 -twine==3.3.0 diff --git a/embedded_topic_model/models/__init__.py b/embedded_topic_model/core/__init__.py similarity index 100% rename from embedded_topic_model/models/__init__.py rename to embedded_topic_model/core/__init__.py diff --git a/embedded_topic_model/models/model.py b/embedded_topic_model/core/model.py similarity index 79% rename from embedded_topic_model/models/model.py rename to embedded_topic_model/core/model.py index 22ebcb7..a9ec4b2 100644 --- a/embedded_topic_model/models/model.py +++ b/embedded_topic_model/core/model.py @@ -3,56 +3,23 @@ from torch import nn -class Model(nn.Module): +class Model(torch.nn.Module): def __init__( - self, - device, - num_topics, - vocab_size, - t_hidden_size, - rho_size, - emsize, - theta_act, - embeddings=None, - train_embeddings=True, - enc_drop=0.5, - debug_mode=False): - super(Model, self).__init__() - - # define hyperparameters + self, num_topics: int, vocab_size: int, rho_size: int, + train_embeddings: bool, embeddings = None + ) -> None: + super().__init__() + # define hyperparameters self.num_topics = num_topics self.vocab_size = vocab_size - self.t_hidden_size = t_hidden_size self.rho_size = rho_size - self.enc_drop = enc_drop - self.emsize = emsize - self.t_drop = nn.Dropout(enc_drop) - self.debug_mode = debug_mode - self.theta_act = self.get_activation(theta_act) - - self.device = device # define the word embedding matrix \rho if train_embeddings: self.rho = nn.Linear(rho_size, vocab_size, bias=False) else: - num_embeddings, emsize = embeddings.size() self.rho = embeddings.clone().float().to(self.device) - - # define the matrix containing the topic embeddings - # nn.Parameter(torch.randn(rho_size, num_topics)) - self.alphas = nn.Linear(rho_size, num_topics, bias=False) - - # define variational distribution for \theta_{1:D} via amortizartion - self.q_theta = nn.Sequential( - nn.Linear(vocab_size, t_hidden_size), - self.theta_act, - nn.Linear(t_hidden_size, t_hidden_size), - self.theta_act, - ) - self.mu_q_theta = nn.Linear(t_hidden_size, num_topics, bias=True) - self.logsigma_q_theta = nn.Linear(t_hidden_size, num_topics, bias=True) - + def get_activation(self, act): if act == 'tanh': act = nn.Tanh() @@ -75,7 +42,7 @@ def get_activation(self, act): if self.debug_mode: print('Defaulting to tanh activation') return act - + def reparameterize(self, mu, logvar): """Returns a sample from a Gaussian distribution via reparameterization. """ @@ -85,6 +52,64 @@ def reparameterize(self, mu, logvar): return eps.mul_(std).add_(mu) else: return mu + + def get_beta(self, *args): + pass + + def get_theta(self, *args): + pass + + def encode(self, *args): + pass + + def decode(self, *args): + pass + + +class ETM(Model): + def __init__( + self, + vocab_size: int, + num_topics: int = 50, + t_hidden_size: int = 800, + rho_size: int = 100, + theta_act: str = 'relu', + train_embeddings=True, + embeddings=None, + enc_drop=0.5, + debug_mode=False + ) -> None: + super(Model, self).__init__( + num_topics, vocab_size, rho_size, + train_embeddings, embeddings + ) + + # define hyperparameters + self.t_hidden_size = t_hidden_size + self.enc_drop = enc_drop + self.t_drop = nn.Dropout(enc_drop) + self.debug_mode = debug_mode + self.theta_act = self.get_activation(theta_act) + + # define the word embedding matrix \rho + if train_embeddings: + self.rho = nn.Linear(rho_size, vocab_size, bias=False) + else: + self.rho = embeddings.clone().float().to(self.device) + + # define the matrix containing the topic embeddings + # nn.Parameter(torch.randn(rho_size, num_topics)) + self.alphas = nn.Linear(rho_size, num_topics, bias=False) + + # define variational distribution for \theta_{1:D} via amortizartion + self.q_theta = nn.Sequential( + nn.Linear(vocab_size, t_hidden_size), + self.theta_act, + nn.Linear(t_hidden_size, t_hidden_size), + self.theta_act, + ) + self.mu_q_theta = nn.Linear(t_hidden_size, num_topics, bias=True) + self.logsigma_q_theta = nn.Linear(t_hidden_size, num_topics, bias=True) def encode(self, bows): """Returns paramters of the variational distribution for \theta. @@ -140,3 +165,5 @@ def forward(self, bows, normalized_bows, theta=None, aggregate=True): if aggregate: recon_loss = recon_loss.mean() return recon_loss, kld_theta + + diff --git a/embedded_topic_model/models/etm.py b/embedded_topic_model/core/trainer.py similarity index 94% rename from embedded_topic_model/models/etm.py rename to embedded_topic_model/core/trainer.py index 4a21ddc..ab6d577 100644 --- a/embedded_topic_model/models/etm.py +++ b/embedded_topic_model/core/trainer.py @@ -8,27 +8,22 @@ from torch import optim from gensim.models import KeyedVectors -from embedded_topic_model.models.model import Model +from embedded_topic_model.core.model import Model from embedded_topic_model.utils import data from embedded_topic_model.utils import embedding from embedded_topic_model.utils import metrics -class ETM(object): +class Trainer: """ Creates an embedded topic model instance. The model hyperparameters are: vocabulary (list of str): training dataset vocabulary + model (embedded_topic_model.core.model.Model): model to train embeddings (str or KeyedVectors): KeyedVectors instance containing word-vector mapping for embeddings, or its path use_c_format_w2vec (bool): wheter input embeddings use word2vec C format. Both BIN and TXT formats are supported model_path (str): path to save trained model. If None, the model won't be automatically saved batch_size (int): input batch size for training - num_topics (int): number of topics - rho_size (int): dimension of rho - emb_size (int): dimension of embeddings - t_hidden_size (int): dimension of hidden space of q(theta) - theta_act (str): tanh, softplus, relu, rrelu, leakyrelu, elu, selu, glu) - train_embeddings (int): whether to fix rho or train it lr (float): learning rate lr_factor (float): divide learning rate by this... epochs (int): number of epochs to train. 150 for 20ng 100 for others @@ -50,16 +45,12 @@ class ETM(object): def __init__( self, - vocabulary, + vocabulary: list, + model: Model, embeddings=None, use_c_format_w2vec=False, model_path=None, batch_size=1000, - num_topics=50, - rho_size=300, - emb_size=300, - t_hidden_size=800, - theta_act='relu', train_embeddings=False, lr=0.005, lr_factor=4.0, @@ -83,11 +74,6 @@ def __init__( self.vocabulary_size = len(self.vocabulary) self.model_path = model_path self.batch_size = batch_size - self.num_topics = num_topics - self.rho_size = rho_size - self.emb_size = emb_size - self.t_hidden_size = t_hidden_size - self.theta_act = theta_act self.lr_factor = lr_factor self.epochs = epochs self.seed = seed @@ -102,8 +88,7 @@ def __init__( self.eval_batch_size = eval_batch_size self.eval_perplexity = eval_perplexity self.debug_mode = debug_mode - self.device = torch.device( - 'cuda' if torch.cuda.is_available() else 'cpu') + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') np.random.seed(self.seed) torch.manual_seed(self.seed) @@ -113,19 +98,7 @@ def __init__( self.embeddings = None if train_embeddings else self._initialize_embeddings( embeddings, use_c_format_w2vec=use_c_format_w2vec) - self.model = Model( - self.device, - self.num_topics, - self.vocabulary_size, - self.t_hidden_size, - self.rho_size, - self.emb_size, - self.theta_act, - self.embeddings, - train_embeddings, - self.enc_drop, - self.debug_mode).to( - self.device) + self.model = model self.optimizer = self._get_optimizer(optimizer_type, lr, wdecay) def __str__(self): diff --git a/requirements.txt b/embedded_topic_model/requirements.txt similarity index 100% rename from requirements.txt rename to embedded_topic_model/requirements.txt diff --git a/lint.sh b/lint.sh deleted file mode 100755 index 1529aca..0000000 --- a/lint.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/bash - -flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics -flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics diff --git a/publish.txt b/publish.txt deleted file mode 100644 index 971fcc0..0000000 --- a/publish.txt +++ /dev/null @@ -1,20 +0,0 @@ -# Bump version -bumpversion --current-version setup.py embedded_topic_model/__init__.py - -# Pack -python setup.py sdist bdist_wheel - -# Check packing -tar tzf dist/embedded_topic_model-1.0.0.tar.gz - -# Check dist -twine check dist/* - -# Upload to TestPyPI -twine upload --repository-url https://test.pypi.org/legacy/ dist/* - -# Install and check package -pip install -U embedded_topic_model - -# Upload to PyPI -twine upload dist/* diff --git a/setup.py b/setup.py deleted file mode 100644 index 80dbea2..0000000 --- a/setup.py +++ /dev/null @@ -1,42 +0,0 @@ -from setuptools import setup, find_packages - -with open('README.md') as readme_file: - readme = readme_file.read() - -with open('CHANGELOG.md') as changelog_file: - changelog = changelog_file.read() - -with open('requirements.txt') as f: - requirements = f.read().splitlines() - -with open('dev_requirements.txt') as f: - dev_requirements = f.read().splitlines() - -setup( - author='Luiz F. Matos', - author_email='lfmatosmelo@id.uff.br', - python_requires='>=3.6', - classifiers=[ - 'Intended Audience :: Developers', - 'License :: OSI Approved :: MIT License', - 'Natural Language :: English', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - ], - description='A package to run embedded topic modelling', - install_requires=requirements, - license='MIT license', - long_description=readme + changelog, - long_description_content_type='text/markdown', - include_package_data=True, - keywords='embedded_topic_model', - name='embedded_topic_model', - packages=find_packages(include=['embedded_topic_model', 'embedded_topic_model.models', 'embedded_topic_model.utils']), - setup_requires=dev_requirements, - test_suite='tests', - tests_require=dev_requirements, - url='https://github.com/lffloyd/embedded-topic-model', - version='1.0.2', - zip_safe=False, -) diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/integration/test_etm.py b/tests/integration/test_etm.py deleted file mode 100644 index 1ba2017..0000000 --- a/tests/integration/test_etm.py +++ /dev/null @@ -1,278 +0,0 @@ -from embedded_topic_model.models import etm -import joblib -import torch - - -class TestETM: - def test_etm_training_with_preprocessed_dummy_dataset(self): - vocabulary, embeddings, train_dataset, _ = joblib.load( - 'tests/resources/train_resources.test') - - etm_instance = etm.ETM( - vocabulary, - embeddings, - num_topics=3, - epochs=50, - train_embeddings=False, - ) - - expected_no_topics = 3 - expected_no_documents = 10 - expected_t_w_dist_sums = torch.ones(expected_no_topics) - expected_d_t_dist_sums = torch.ones(expected_no_documents) - - etm_instance.fit(train_dataset) - - topics = etm_instance.get_topics() - - assert len(topics) == expected_no_topics, \ - "no. of topics error: exp = {}, result = {}".format(expected_no_topics, len(topics)) - - coherence = etm_instance.get_topic_coherence() - - assert coherence != 0.0, \ - 'zero coherence returned' - - diversity = etm_instance.get_topic_diversity() - - assert diversity != 0.0, \ - 'zero diversity returned' - - t_w_mtx = etm_instance.get_topic_word_matrix() - - assert len(t_w_mtx) == expected_no_topics, \ - "no. of topics in topic-word matrix error: exp = {}, result = {}".format(expected_no_topics, len(t_w_mtx)) - - t_w_dist = etm_instance.get_topic_word_dist() - assert len(t_w_dist) == expected_no_topics, \ - "topic-word distribution error: exp = {}, result = {}".format(expected_no_topics, len(t_w_dist)) - - similar_words = etm_instance.get_most_similar_words(["boat", "day", "sun"], 5) - assert len(similar_words) == 3, \ - "get_most_similar_words error: expected {} keys but {} were returned for method call".format(3, len(similar_words)) - for key in similar_words.keys(): - assert 0 <= len(similar_words[key]) <= 5, \ - "get_most_similar_words error: expected <= {} elements but got {} for {} key".format(5, len(similar_words[key]), key) - - - t_w_dist_below_zero_elems = t_w_dist[t_w_dist < 0] - assert len(t_w_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the topic-word distribution' - - t_w_dist_sums = torch.sum(t_w_dist, 1) - assert torch.allclose( - t_w_dist_sums, expected_t_w_dist_sums), "t_w_dist_sums error: exp = {}, result = {}".format( - expected_t_w_dist_sums, t_w_dist_sums) - - d_t_dist = etm_instance.get_document_topic_dist() - assert len(d_t_dist) == expected_no_documents, \ - "document-topics distribution error: exp = {}, result = {}".format(expected_no_documents, len(d_t_dist)) - - d_t_dist_below_zero_elems = d_t_dist[d_t_dist < 0] - assert len(d_t_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the document-topic distribution' - - d_t_dist_sums = torch.sum(d_t_dist, 1) - assert torch.allclose( - d_t_dist_sums, expected_d_t_dist_sums), "d_t_dist_sums error: exp = {}, result = {}".format( - expected_d_t_dist_sums, d_t_dist_sums) - - def test_etm_training_with_preprocessed_dummy_dataset_and_embeddings_file( - self): - vocabulary, _, train_dataset, _ = joblib.load( - 'tests/resources/train_resources.test') - - etm_instance = etm.ETM( - vocabulary, - embeddings='tests/resources/train_w2v_embeddings.wordvectors', - num_topics=3, - epochs=50, - train_embeddings=False, - ) - - expected_no_topics = 3 - expected_no_documents = 10 - expected_t_w_dist_sums = torch.ones(expected_no_topics) - expected_d_t_dist_sums = torch.ones(expected_no_documents) - - etm_instance.fit(train_dataset) - - topics = etm_instance.get_topics() - - assert len(topics) == expected_no_topics, \ - "no. of topics error: exp = {}, result = {}".format(expected_no_topics, len(topics)) - - coherence = etm_instance.get_topic_coherence() - - assert coherence != 0.0, \ - 'zero coherence returned' - - diversity = etm_instance.get_topic_diversity() - - assert diversity != 0.0, \ - 'zero diversity returned' - - t_w_mtx = etm_instance.get_topic_word_matrix() - - assert len(t_w_mtx) == expected_no_topics, \ - "no. of topics in topic-word matrix error: exp = {}, result = {}".format(expected_no_topics, len(t_w_mtx)) - - t_w_dist = etm_instance.get_topic_word_dist() - assert len(t_w_dist) == expected_no_topics, \ - "topic-word distribution error: exp = {}, result = {}".format(expected_no_topics, len(t_w_dist)) - - t_w_dist_below_zero_elems = t_w_dist[t_w_dist < 0] - assert len(t_w_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the topic-word distribution' - - t_w_dist_sums = torch.sum(t_w_dist, 1) - assert torch.allclose( - t_w_dist_sums, expected_t_w_dist_sums), "t_w_dist_sums error: exp = {}, result = {}".format( - expected_t_w_dist_sums, t_w_dist_sums) - - d_t_dist = etm_instance.get_document_topic_dist() - assert len(d_t_dist) == expected_no_documents, \ - "document-topics distribution error: exp = {}, result = {}".format(expected_no_documents, len(d_t_dist)) - - d_t_dist_below_zero_elems = d_t_dist[d_t_dist < 0] - assert len(d_t_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the document-topic distribution' - - d_t_dist_sums = torch.sum(d_t_dist, 1) - assert torch.allclose( - d_t_dist_sums, expected_d_t_dist_sums), "d_t_dist_sums error: exp = {}, result = {}".format( - expected_d_t_dist_sums, d_t_dist_sums) - - def test_etm_training_with_preprocessed_dummy_dataset_and_c_wordvec_txt_embeddings_file( - self): - vocabulary, _, train_dataset, _ = joblib.load( - 'tests/resources/train_resources.test') - - etm_instance = etm.ETM( - vocabulary, - embeddings='tests/resources/train_w2v_embeddings.wordvectors.txt', - num_topics=3, - epochs=50, - train_embeddings=False, - use_c_format_w2vec=True, - ) - - expected_no_topics = 3 - expected_no_documents = 10 - expected_t_w_dist_sums = torch.ones(expected_no_topics) - expected_d_t_dist_sums = torch.ones(expected_no_documents) - - etm_instance.fit(train_dataset) - - topics = etm_instance.get_topics() - - assert len(topics) == expected_no_topics, \ - "no. of topics error: exp = {}, result = {}".format(expected_no_topics, len(topics)) - - coherence = etm_instance.get_topic_coherence() - - assert coherence != 0.0, \ - 'zero coherence returned' - - diversity = etm_instance.get_topic_diversity() - - assert diversity != 0.0, \ - 'zero diversity returned' - - t_w_mtx = etm_instance.get_topic_word_matrix() - - assert len(t_w_mtx) == expected_no_topics, \ - "no. of topics in topic-word matrix error: exp = {}, result = {}".format(expected_no_topics, len(t_w_mtx)) - - t_w_dist = etm_instance.get_topic_word_dist() - assert len(t_w_dist) == expected_no_topics, \ - "topic-word distribution error: exp = {}, result = {}".format(expected_no_topics, len(t_w_dist)) - - t_w_dist_below_zero_elems = t_w_dist[t_w_dist < 0] - assert len(t_w_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the topic-word distribution' - - t_w_dist_sums = torch.sum(t_w_dist, 1) - assert torch.allclose( - t_w_dist_sums, expected_t_w_dist_sums), "t_w_dist_sums error: exp = {}, result = {}".format( - expected_t_w_dist_sums, t_w_dist_sums) - - d_t_dist = etm_instance.get_document_topic_dist() - assert len(d_t_dist) == expected_no_documents, \ - "document-topics distribution error: exp = {}, result = {}".format(expected_no_documents, len(d_t_dist)) - - d_t_dist_below_zero_elems = d_t_dist[d_t_dist < 0] - assert len(d_t_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the document-topic distribution' - - d_t_dist_sums = torch.sum(d_t_dist, 1) - assert torch.allclose( - d_t_dist_sums, expected_d_t_dist_sums), "d_t_dist_sums error: exp = {}, result = {}".format( - expected_d_t_dist_sums, d_t_dist_sums) - - def test_etm_training_with_preprocessed_dummy_dataset_and_c_wordvec_bin_embeddings_file( - self): - vocabulary, _, train_dataset, _ = joblib.load( - 'tests/resources/train_resources.test') - - etm_instance = etm.ETM( - vocabulary, - embeddings='tests/resources/train_w2v_embeddings.wordvectors.bin', - num_topics=3, - epochs=50, - train_embeddings=False, - use_c_format_w2vec=True, - ) - - expected_no_topics = 3 - expected_no_documents = 10 - expected_t_w_dist_sums = torch.ones(expected_no_topics) - expected_d_t_dist_sums = torch.ones(expected_no_documents) - - etm_instance.fit(train_dataset) - - topics = etm_instance.get_topics() - - assert len(topics) == expected_no_topics, \ - "no. of topics error: exp = {}, result = {}".format(expected_no_topics, len(topics)) - - coherence = etm_instance.get_topic_coherence() - - assert coherence != 0.0, \ - 'zero coherence returned' - - diversity = etm_instance.get_topic_diversity() - - assert diversity != 0.0, \ - 'zero diversity returned' - - t_w_mtx = etm_instance.get_topic_word_matrix() - - assert len(t_w_mtx) == expected_no_topics, \ - "no. of topics in topic-word matrix error: exp = {}, result = {}".format(expected_no_topics, len(t_w_mtx)) - - t_w_dist = etm_instance.get_topic_word_dist() - assert len(t_w_dist) == expected_no_topics, \ - "topic-word distribution error: exp = {}, result = {}".format(expected_no_topics, len(t_w_dist)) - - t_w_dist_below_zero_elems = t_w_dist[t_w_dist < 0] - assert len(t_w_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the topic-word distribution' - - t_w_dist_sums = torch.sum(t_w_dist, 1) - assert torch.allclose( - t_w_dist_sums, expected_t_w_dist_sums), "t_w_dist_sums error: exp = {}, result = {}".format( - expected_t_w_dist_sums, t_w_dist_sums) - - d_t_dist = etm_instance.get_document_topic_dist() - assert len(d_t_dist) == expected_no_documents, \ - "document-topics distribution error: exp = {}, result = {}".format(expected_no_documents, len(d_t_dist)) - - d_t_dist_below_zero_elems = d_t_dist[d_t_dist < 0] - assert len(d_t_dist_below_zero_elems) == 0, \ - 'there are elements smaller than 0 in the document-topic distribution' - - d_t_dist_sums = torch.sum(d_t_dist, 1) - assert torch.allclose( - d_t_dist_sums, expected_d_t_dist_sums), "d_t_dist_sums error: exp = {}, result = {}".format( - expected_d_t_dist_sums, d_t_dist_sums) diff --git a/tests/resources/train_resources.test b/tests/resources/train_resources.test deleted file mode 100644 index 35f61e88bd98ec55373ec31f1b1d590dfdbe97b4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 147578 zcmV(@K-Rx_+GM?TKoouV{y(y36jwJI=Yzb*^*9$l%c1qBwYER20Q0%sbMOPX6gX$UoGQ z-YY}uKO+PEEg9s)$iURUXY@*+`p?wQGI?c9{m0<2P(S~0e?LoRz7iJVXUQUe3HSE( z4GRm6uw<3PMEHAK?Brj8{^6Eva+Jt0OLqPk6>7=xpA&~$a>|E(-Z7S3UYSz=xWih~ANiptr7LM_F-GN=B-*E?j8 ze}tvD93X69>S&dauf~K$SxS0k`Fqq5@0g&}bCi;I0sj7ef8VKcEdzqW!^8fLT4_0| zcZ8*koYcqP+c(fsR(|0hk~**}etAV2@mNJ~3r&^z_++H=!Ek%5*D@|&p8;Lxzap_Yyex=&bCq{TJu zXrcbzso!&x6AuiGi3mzvewI%1#ev?Tkr5Vm`FB8ARO&r-mRku8i?np{kF$6<$hCL! z_YO@xU{~M%VLl;2KGj2`1`LepJ21#MIK)4j>)$uc!^6E}x`n6CsegDji)W<-OSd?S 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-0.0027022401 -0.010373784 0.0077007045 0.008652481 0.0015398384 0.0033250463 0.005076749 diff --git a/tests/unit/__init__.py b/tests/unit/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/unit/test_embedding.py b/tests/unit/test_embedding.py deleted file mode 100644 index bc7761c..0000000 --- a/tests/unit/test_embedding.py +++ /dev/null @@ -1,29 +0,0 @@ -from embedded_topic_model.utils import embedding -from gensim.models import KeyedVectors - - -def test_create_word2vec_embedding_from_dataset(): - documents = [ - "Peanut butter and jelly caused the elderly lady to think about her past.", - "Toddlers feeding raccoons surprised even the seasoned park ranger.", - "You realize you're not alone as you sit in your bedroom massaging your calves after a long day of playing tug-of-war with Grandpa Joe in the hospital.", - "She wondered what his eyes were saying beneath his mirrored sunglasses.", - "He was disappointed when he found the beach to be so sandy and the sun so sunny.", - "Flesh-colored yoga pants were far worse than even he feared.", - "The wake behind the boat told of the past while the open sea for told life in the unknown future.", - "Improve your goldfish's physical fitness by getting him a bicycle.", - "Harrold felt confident that nobody would ever suspect his spy pigeon.", - "Nudist colonies shun fig-leaf couture.", - ] - - dimensionality = 240 - embeddings = embedding.create_word2vec_embedding_from_dataset( - documents, dim_rho=dimensionality) - - assert isinstance( - embeddings, KeyedVectors), "embeddings isn't KeyedVectors instance" - - vector = embeddings['Peanut'] - - assert len(vector) == dimensionality, "lenght of 'Peanut' vector doesn't match: exp = {}, result = {}".format( - dimensionality, len(vector)) diff --git a/tests/unit/test_preprocessing.py b/tests/unit/test_preprocessing.py deleted file mode 100644 index 468f3d2..0000000 --- a/tests/unit/test_preprocessing.py +++ /dev/null @@ -1,30 +0,0 @@ -from embedded_topic_model.utils import preprocessing - - -def test_create_etm_datasets(): - documents = [ - "Peanut butter and jelly caused the elderly lady to think about her past.", - "Toddlers feeding raccoons surprised even the seasoned park ranger.", - "You realize you're not alone as you sit in your bedroom massaging your calves after a long day of playing tug-of-war with Grandpa Joe in the hospital.", - "She wondered what his eyes were saying beneath his mirrored sunglasses.", - "He was disappointed when he found the beach to be so sandy and the sun so sunny.", - "Flesh-colored yoga pants were far worse than even he feared.", - "The wake behind the boat told of the past while the open sea for told life in the unknown future.", - "Improve your goldfish's physical fitness by getting him a bicycle.", - "Harrold felt confident that nobody would ever suspect his spy pigeon.", - "Nudist colonies shun fig-leaf couture.", - ] - - no_documents_in_train = 7 - no_documents_in_test = 3 - - vocabulary, train_dataset, test_dataset = preprocessing.create_etm_datasets( - documents, train_size=0.7) - - assert isinstance(vocabulary, list), "vocabulary isn't list" - - assert len(train_dataset['tokens']) == no_documents_in_train and len( - train_dataset['counts']) == no_documents_in_train, "lengths of tokens and counts for training dataset doesn't match" - - assert len(test_dataset['test']['tokens']) == no_documents_in_test and len( - test_dataset['test']['counts']) == no_documents_in_test, "lengths of tokens and counts for testing dataset doesn't match" diff --git a/train_resources.test b/train_resources.test deleted file mode 100644 index 3c491b8561e9da4e6c1cd8d0a35093eba2ef5b4e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 126761 zcmV(%K;pl6+BA20JXPQKzL}IFWGF+XOd+YbdtH?xN<=D@WKIbe5uwZq$s7&JkRem4 zaQ8ZCAT(&8LQ16(jVev*=i~kT-9PSYoxRsu&$EVo&f5F=HC;G7nvPR=czA+$2L%TC z277sOcpU{Np56Qc{Jl7Qjsg>MnA=V-4!@)Dgy`)X;_nq4%n_K}2y_eD$r1cd$2Vj< 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a/README.md b/README.md new file mode 100644 index 0000000..d9ec42e --- /dev/null +++ b/README.md @@ -0,0 +1,104 @@ +# Embedded Topic Model +[![PyPI version](https://badge.fury.io/py/embedded-topic-model.svg)](https://badge.fury.io/py/embedded-topic-model) +[![Actions Status](https://github.com/lffloyd/embedded-topic-model/workflows/Python%20package/badge.svg)](https://github.com/lffloyd/embedded-topic-model/actions) +[![License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://github.com/lffloyd/embedded-topic-model/blob/main/LICENSE) + +This package was made to easily run embedded topic modelling on a given corpus. + +ETM is a topic model that marries the probabilistic topic modelling of Latent Dirichlet Allocation with the +contextual information brought by word embeddings-most specifically, word2vec. ETM models topics as points +in the word embedding space, arranging together topics and words with similar context. +As such, ETM can either learn word embeddings alongside topics, or be given pretrained embeddings to discover +the topic patterns on the corpus. + +ETM was originally published by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei on a article titled ["Topic Modeling in Embedding Spaces"](https://arxiv.org/abs/1907.04907) in 2019. This code is an adaptation of the [original](https://github.com/adjidieng/ETM) provided with the article. Most of the original code was kept here, with some changes here and there, mostly for ease of usage. + +With the tools provided here, you can run ETM on your dataset using simple steps. + +# Installation +You can install the package using ```pip``` by running: ```pip install -U embedded_topic_model``` + +# Usage +To use ETM on your corpus, you must first preprocess the documents into a format understandable by the model. +This package has a quick-use preprocessing script. The only requirement is that the corpus must be composed +by a list of strings, where each string corresponds to a document in the corpus. + +You can preprocess your corpus as follows: + +```python +from embedded_topic_model.utils import preprocessing +import json + +# Loading a dataset in JSON format. As said, documents must be composed by string sentences +corpus_file = 'datasets/example_dataset.json' +documents_raw = json.load(open(dataset, 'r')) +documents = [document['body'] for document in documents_raw] + +# Preprocessing the dataset +vocabulary, train_dataset, _, = preprocessing.create_etm_datasets( + documents, + min_df=0.01, + max_df=0.75, + train_size=0.85, +) +``` + +Then, you can train word2vec embeddings to use with the ETM model. This is optional, and if you're not interested +on training your embeddings, you can either pass a pretrained word2vec embeddings file for ETM or learn the embeddings +using ETM itself. If you want ETM to learn its word embeddings, just pass ```train_embeddings=True``` as an instance parameter. + +To pretrain the embeddings, you can do the following: + +```python +from embedded_topic_model.utils import embedding + +# Training word2vec embeddings +embeddings_mapping = embedding.create_word2vec_embedding_from_dataset(documents) +``` + +To create and fit the model using the training data, execute: + +```python +from embedded_topic_model.models.etm import ETM + +# Training an ETM instance +etm_instance = ETM( + vocabulary, + embeddings=embeddings_mapping, # You can pass here the path to a word2vec file or + # a KeyedVectors instance + num_topics=8, + epochs=300, + debug_mode=True, + train_embeddings=False, # Optional. If True, ETM will learn word embeddings jointly with + # topic embeddings. By default, is False. If 'embeddings' argument + # is being passed, this argument must not be True +) + +etm_instance.fit(train_dataset) +``` + +Also, to obtain the topics, topic coherence or topic diversity of the model, you can do as follows: + +```python +topics = etm_instance.get_topics(20) +topic_coherence = etm_instance.get_topic_coherence() +topic_diversity = etm_instance.get_topic_diversity() +``` + +# Citation +To cite ETM, use the original article's citation: + +``` +@article{dieng2019topic, + title = {Topic modeling in embedding spaces}, + author = {Dieng, Adji B and Ruiz, Francisco J R and Blei, David M}, + journal = {arXiv preprint arXiv: 1907.04907}, + year = {2019} +} +``` + +# Acknowledgements +Credits given to Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei for the original work. + +# License +Licensed under [MIT](LICENSE) license. From 7e551994238232ac4d79487f80b7ffd8c4440a76 Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 10:41:46 +0700 Subject: [PATCH 03/13] add ProdETM --- embedded_topic_model/core/model.py | 60 +++++++++++++++---- embedded_topic_model/core/trainer.py | 6 +- .../requirements.txt => requirements.txt | 0 3 files changed, 51 insertions(+), 15 deletions(-) rename embedded_topic_model/requirements.txt => requirements.txt (100%) diff --git a/embedded_topic_model/core/model.py b/embedded_topic_model/core/model.py index a9ec4b2..cfa19e0 100644 --- a/embedded_topic_model/core/model.py +++ b/embedded_topic_model/core/model.py @@ -3,13 +3,14 @@ from torch import nn -class Model(torch.nn.Module): +class Model(nn.Module): def __init__( self, num_topics: int, vocab_size: int, rho_size: int, train_embeddings: bool, embeddings = None ) -> None: super().__init__() - # define hyperparameters + + # define hyperparameters self.num_topics = num_topics self.vocab_size = vocab_size self.rho_size = rho_size @@ -66,7 +67,7 @@ def decode(self, *args): pass -class ETM(Model): +class Etm(Model): def __init__( self, vocab_size: int, @@ -79,9 +80,9 @@ def __init__( enc_drop=0.5, debug_mode=False ) -> None: - super(Model, self).__init__( - num_topics, vocab_size, rho_size, - train_embeddings, embeddings + super().__init__( + num_topics=num_topics, vocab_size=vocab_size, rho_size=rho_size, + train_embeddings=train_embeddings, embeddings=embeddings ) # define hyperparameters @@ -133,9 +134,8 @@ def get_beta(self): logit = self.alphas(self.rho.weight) except BaseException: logit = self.alphas(self.rho) - beta = F.softmax( - logit, dim=0).transpose( - 1, 0) # softmax over vocab dimension + logit = logit.transpose(1, 0) + beta = F.softmax(logit, dim=0) # softmax over vocab dimension return beta def get_theta(self, normalized_bows): @@ -149,7 +149,7 @@ def decode(self, theta, beta): preds = torch.log(res + 1e-6) return preds - def forward(self, bows, normalized_bows, theta=None, aggregate=True): + def forward(self, bows, normalized_bows, theta=None): # get \theta if theta is None: theta, kld_theta = self.get_theta(normalized_bows) @@ -162,8 +162,44 @@ def forward(self, bows, normalized_bows, theta=None, aggregate=True): # get prediction loss preds = self.decode(theta, beta) recon_loss = -(preds * bows).sum(1) - if aggregate: - recon_loss = recon_loss.mean() + recon_loss = recon_loss.mean() return recon_loss, kld_theta +class ProdEtm(Etm): + def __init__( + self, + vocab_size: int, + num_topics: int = 50, + t_hidden_size: int = 800, + rho_size: int = 100, + theta_act: str = 'relu', + train_embeddings=True, + embeddings=None, + enc_drop=0.5, + debug_mode=False + ) -> None: + super().__init__( + vocab_size, num_topics, t_hidden_size, rho_size, + theta_act, train_embeddings, embeddings, enc_drop, debug_mode + ) + + def get_beta(self): + try: + # torch.mm(self.rho, self.alphas) + logit = self.alphas(self.rho.weight) + except BaseException: + logit = self.alphas(self.rho) + beta = logit.transpose(1, 0) + return beta + + def get_theta(self, normalized_bows): + mu_theta, logsigma_theta, kld_theta = self.encode(normalized_bows) + theta = self.reparameterize(mu_theta, logsigma_theta) + return theta, kld_theta + + def decode(self, theta, beta): + res = torch.mm(theta, beta) + res = F.softmax(res, dim=-1) + preds = torch.log(res + 1e-6) + return preds \ No newline at end of file diff --git a/embedded_topic_model/core/trainer.py b/embedded_topic_model/core/trainer.py index ab6d577..d06718c 100644 --- a/embedded_topic_model/core/trainer.py +++ b/embedded_topic_model/core/trainer.py @@ -336,11 +336,11 @@ def get_topics(self, top_n_words=10) -> List[str]: with torch.no_grad(): topics = [] - gammas = self.model.get_beta() + betas = self.model.get_beta() for k in range(self.num_topics): - gamma = gammas[k] - top_words = list(gamma.cpu().numpy().argsort() + beta = betas[k] + top_words = list(beta.cpu().numpy().argsort() [-top_n_words:][::-1]) topic_words = [self.vocabulary[a] for a in top_words] topics.append(topic_words) diff --git a/embedded_topic_model/requirements.txt b/requirements.txt similarity index 100% rename from embedded_topic_model/requirements.txt rename to requirements.txt From b925dd754978a890665d41f21e8d56954c1aeaaa Mon Sep 17 00:00:00 2001 From: Lam Bui Date: Thu, 30 Sep 2021 10:59:27 +0700 Subject: [PATCH 04/13] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index d9ec42e..49b043d 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ ETM was originally published by Adji B. Dieng, Francisco J. R. Ruiz, and David M With the tools provided here, you can run ETM on your dataset using simple steps. # Installation -You can install the package using ```pip``` by running: ```pip install -U embedded_topic_model``` +You can use this package by cloning this repository. Installation via pip will be updated soon. # Usage To use ETM on your corpus, you must first preprocess the documents into a format understandable by the model. @@ -59,7 +59,7 @@ embeddings_mapping = embedding.create_word2vec_embedding_from_dataset(documents) To create and fit the model using the training data, execute: ```python -from embedded_topic_model.models.etm import ETM +from embedded_topic_model.core.etm import ETM # Training an ETM instance etm_instance = ETM( From aab6484fe66b23a3568c0a54ee23fc17c34b1c55 Mon Sep 17 00:00:00 2001 From: Lam Bui Date: Thu, 30 Sep 2021 11:04:47 +0700 Subject: [PATCH 05/13] Update README.md --- README.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 49b043d..088ee0a 100644 --- a/README.md +++ b/README.md @@ -59,22 +59,22 @@ embeddings_mapping = embedding.create_word2vec_embedding_from_dataset(documents) To create and fit the model using the training data, execute: ```python -from embedded_topic_model.core.etm import ETM - -# Training an ETM instance -etm_instance = ETM( +from embedded_topic_model.core.model import ProdEtm, Model +from embedded_topic_model.core.trainer import Trainer + +# Declare model architecture +prodetm = ProdEtm( + len(vocabulary), + num_topics=50, + train_embeddings=True +) +# Declare a trainer to train/eval model +topic_model = Trainer( vocabulary, - embeddings=embeddings_mapping, # You can pass here the path to a word2vec file or - # a KeyedVectors instance - num_topics=8, - epochs=300, - debug_mode=True, - train_embeddings=False, # Optional. If True, ETM will learn word embeddings jointly with - # topic embeddings. By default, is False. If 'embeddings' argument - # is being passed, this argument must not be True + prodetm ) -etm_instance.fit(train_dataset) +topic_model.fit(train_dataset) ``` Also, to obtain the topics, topic coherence or topic diversity of the model, you can do as follows: From 3dde6c0843c09ce2fd7a9cad257262d0713eae70 Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 11:34:34 +0700 Subject: [PATCH 06/13] add example usage --- embedded_topic_model/core/trainer.py | 12 +-- example.ipynb | 151 +++++++++++++++++++++++++++ 2 files changed, 157 insertions(+), 6 deletions(-) create mode 100644 example.ipynb diff --git a/embedded_topic_model/core/trainer.py b/embedded_topic_model/core/trainer.py index d06718c..188161b 100644 --- a/embedded_topic_model/core/trainer.py +++ b/embedded_topic_model/core/trainer.py @@ -70,7 +70,9 @@ def __init__( eval_perplexity=False, debug_mode=False, ): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.vocabulary = vocabulary + self.model = model.to(self.device) self.vocabulary_size = len(self.vocabulary) self.model_path = model_path self.batch_size = batch_size @@ -88,7 +90,6 @@ def __init__( self.eval_batch_size = eval_batch_size self.eval_perplexity = eval_perplexity self.debug_mode = debug_mode - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') np.random.seed(self.seed) torch.manual_seed(self.seed) @@ -98,7 +99,6 @@ def __init__( self.embeddings = None if train_embeddings else self._initialize_embeddings( embeddings, use_c_format_w2vec=use_c_format_w2vec) - self.model = model self.optimizer = self._get_optimizer(optimizer_type, lr, wdecay) def __str__(self): @@ -145,14 +145,14 @@ def _initialize_embeddings( print('Reading embeddings from word2vec file...') vectors = KeyedVectors.load(embeddings, mmap='r') - model_embeddings = np.zeros((self.vocabulary_size, self.emb_size)) + model_embeddings = np.zeros((self.vocabulary_size, self.model.rho_size)) for i, word in enumerate(self.vocabulary): try: model_embeddings[i] = vectors[word] except KeyError: model_embeddings[i] = np.random.normal( - scale=0.6, size=(self.emb_size, )) + scale=0.6, size=(self.model.rho_size, )) return torch.from_numpy(model_embeddings).to(self.device) def _get_optimizer(self, optimizer_type, learning_rate, wdecay): @@ -338,7 +338,7 @@ def get_topics(self, top_n_words=10) -> List[str]: topics = [] betas = self.model.get_beta() - for k in range(self.num_topics): + for k in range(self.model.num_topics): beta = betas[k] top_words = list(beta.cpu().numpy().argsort() [-top_n_words:][::-1]) @@ -453,7 +453,7 @@ def get_topic_word_matrix(self) -> List[List[str]]: topics = [] - for i in range(self.num_topics): + for i in range(self.model.num_topics): words = list(beta[i].cpu().numpy()) topic_words = [self.vocabulary[a] for a, _ in enumerate(words)] topics.append(topic_words) diff --git a/example.ipynb b/example.ipynb new file mode 100644 index 0000000..7f4f9fe --- /dev/null +++ b/example.ipynb @@ -0,0 +1,151 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of Embedded Topic Model.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "kTHll-QePRBV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f9115c79-d11e-4bbc-f5b9-b8608ea8a50f" + }, + "source": [ + "# %cd drive/MyDrive/LamBT/\n", + "# !git clone https://github.com/bui-thanh-lam/embedded-topic-model.git\n", + "# %cd embedded-topic-model\n", + "%cd drive/MyDrive/LamBT/embedded-topic-model/\n", + "!pip install -r requirements.txt -q\n", + "!pip install datasets -q" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/drive/MyDrive/LamBT/embedded-topic-model\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Tdi7rCJB0etu", + "outputId": "b3df9360-2918-4fad-8133-422a354a5013" + }, + "source": [ + "%%writefile example.py\n", + "from datasets import load_dataset\n", + "from embedded_topic_model.utils import preprocessing\n", + "from embedded_topic_model.core.model import ProdEtm, Model\n", + "from embedded_topic_model.core.trainer import Trainer\n", + "\n", + "\n", + "# Load dataset from Huggingface's datasets\n", + "dataset = load_dataset('ag_news', split='test')\n", + "dataset = [d[\"text\"] for d in dataset]\n", + "\n", + "\n", + "# Preprocessing the dataset\n", + "vocabulary, train_dataset, test_dataset, = preprocessing.create_etm_datasets(\n", + " dataset, \n", + " min_df=0.01, \n", + " max_df=0.75, \n", + " train_size=0.85, \n", + ")\n", + "\n", + "\n", + "# Declare model architecture\n", + "prodetm = ProdEtm(\n", + " len(vocabulary),\n", + " num_topics=50,\n", + " train_embeddings=True\n", + ")\n", + "# Declare a trainer to train/eval model\n", + "topic_model = Trainer(\n", + " vocabulary,\n", + " prodetm,\n", + " debug_mode=True\n", + ")\n", + "\n", + "topic_model.fit(train_dataset)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Overwriting example.py\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0hHHVUVP8DVF", + "outputId": "025d3489-8b78-4535-b0cb-4b33bc62d547" + }, + "source": [ + "!python example.py" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using custom data configuration default\n", + "Reusing dataset ag_news (/root/.cache/huggingface/datasets/ag_news/default/0.0.0/bc2bcb40336ace1a0374767fc29bb0296cdaf8a6da7298436239c54d79180548)\n", + "Topics before training: [['beat', 'meeting', 'deal', 'country', 'should', 'sports', 'target', 'if', 'had', 'south'], ['prices', 'profit', 'little', 'points', 'as', 'near', 'her', 'troops', 'air', 'bill'], ['announced', 'companies', 'sports', 'won', 'growth', 'most', 'against', 'long', 'led', 'rise'], ['al', 'for', 'being', 'to', 'president', 'economy', 'both', 'three', 'software', 'what'], ['again', 'still', 'agreed', '2004', 'national', 'might', 'her', 'may', 'set', 'system'], ['percent', 'he', 'agreement', 'day', 'union', 'region', '39', 'race', 'might', 'night'], ['last', 'his', 'rise', 'market', 'million', 'left', 'bill', 'billion', 'during', '2004'], ['jobs', 'sales', 'this', 'third', 'nuclear', '2004', 'first', 'home', 'air', 'street'], ['media', 'their', 'games', 'north', 'biggest', 'its', 'study', 'last', 'agency', 'market'], ['phone', 'products', 'who', 'free', 'presidential', 'agreement', 'as', '12', 'into', 'bill'], ['bill', 'foreign', 'gold', 'agency', 'research', 'that', 'home', 'right', 'announced', 'presidential'], ['global', 'left', 'off', 'states', 'report', 'killed', 'software', 'police', 'north', 'where'], ['ahead', 'old', 'so', 'record', 'products', 'commission', 'court', 'used', 'said', 'police'], ['world', '39', 'give', 'investor', 'left', 'killed', 'through', 'four', 'now', 'title'], ['giant', 'next', 'reported', 'are', 'target', 'region', 'network', 'time', 'only', 'as'], ['after', 'strong', 'minister', 'close', 'case', 'new', 'says', 'election', 'union', 'buy'], ['charges', 'are', 'commission', 'put', 'nearly', 'news', 'group', 'year', 'him', 'while'], ['half', 'have', 'lower', 'while', 'left', 'key', 'plans', 'low', 'it', 'least'], ['fourth', 'loss', 'winning', 'war', 'chief', 'against', 'troops', 'in', 'early', 'video'], ['rise', 'study', 'put', 'on', 'former', 'has', 'first', 'million', 'across', '25'], ['share', 'sales', 'close', 'after', 'south', 'ahead', 'charges', 'windows', 'products', 'being'], ['in', 'being', 'buy', 'past', 'player', 'jobs', 'could', 'only', 'used', 'half'], ['talks', 'software', 'union', 'so', 'head', 'deal', 'dollar', 'home', 'percent', 'jobs'], ['long', 'windows', 'title', 'key', 'business', 'public', 'round', 'rose', 'court', 'target'], ['southern', 'update', 'best', 'people', 'announced', 'head', 'hit', 'stock', 'public', 'still'], ['west', 'union', 'half', 'al', 'wall', 'over', 'according', 'fell', 'general', 'top'], ['across', 'line', 'systems', 'press', 'race', 'during', 'quote', 'states', 'move', 'been'], ['federal', 'market', 'deal', 'across', 'company', 'loss', 'software', 'home', 'days', 'say'], ['profit', 'no', 'end', 'music', 'demand', 'due', 'charges', 'win', 'update', 'who'], ['al', 'network', 'into', 'media', 'first', 'record', 'for', 'leader', 'took', 'long'], ['windows', 'players', 'based', 'officials', 'wins', 'long', 'four', 'around', 'nearly', 'latest'], ['biggest', 'study', 'is', 'state', 'next', 'government', 'search', 'can', 'including', 'men'], ['day', 'hit', 'executive', 'nations', '12', 'were', 'today', 'troops', 'country', 'only'], ['what', 'this', 'air', 'way', 'another', 'state', 'said', 'west', 'and', 'are'], ['around', 'economic', 'new', 'wall', 'attacks', 'can', 'out', 'record', '12', 'firm'], ['investor', 'over', 'championship', 'prices', 'low', 'first', 'agency', 'space', 'would', 'charges'], ['should', 'target', 'firm', 'off', 'agency', 'million', 'were', 'here', 'race', 'union'], ['news', 'who', 'red', 'help', 'victory', 'do', 'again', 'technology', 'charges', 'percent'], ['army', 'leader', 'open', 'years', 'home', 'again', 'will', 'points', 'start', 'music'], ['com', 'even', 'plan', 'oil', 'southern', 'they', 'earnings', 'people', 'shares', 'may'], ['first', 'oil', 'windows', 'even', 'few', '39', 'face', 'network', 'international', 'troops'], ['lower', 'killing', 'court', 'and', 'after', 'pay', 'third', 'four', 'public', 'wall'], ['reports', 'from', 'that', 'chief', 'billion', 'of', 'commission', 'red', 'which', 'end'], ['released', 'between', 'will', 'again', 'federal', 'under', 'country', 'even', 'called', 'quarter'], ['night', 'mobile', 'data', 'bill', 'year', 'say', 'open', 'go', 'bank', 'wireless'], ['foreign', 'for', 'days', 'be', 'fourth', 'head', 'media', 'had', 'won', 'earnings'], ['bill', 'over', 'software', 'technology', 'yesterday', 'now', 'lower', 'pay', 'under', 'search'], ['series', 'stock', 'most', 'agreed', 'has', 'from', 'would', 'mobile', 'next', 'beat'], ['com', 'biggest', 'is', 'prime', 'right', 'city', 'player', 'play', 'military', 'new'], ['bill', 'near', 'economy', 'left', 'here', 'buy', 'global', 'most', 'this', 'give']]\n", + "Epoch 1 - Learning Rate: 0.005 - KL theta: 0.5 - Rec loss: 73.49 - NELBO: 73.99\n", + "Epoch 2 - Learning Rate: 0.005 - KL theta: 2.88 - Rec loss: 64.52 - NELBO: 67.4\n", + "Epoch 3 - Learning Rate: 0.005 - KL theta: 1.76 - Rec loss: 64.84 - NELBO: 66.6\n", + "Epoch 4 - Learning Rate: 0.005 - KL theta: 2.19 - Rec loss: 63.62 - NELBO: 65.81\n", + "Epoch 5 - Learning Rate: 0.005 - KL theta: 2.35 - Rec loss: 63.17 - NELBO: 65.52\n", + "Epoch 6 - Learning Rate: 0.005 - KL theta: 2.17 - Rec loss: 63.26 - NELBO: 65.43\n", + "Epoch 7 - Learning Rate: 0.005 - KL theta: 2.21 - Rec loss: 63.33 - NELBO: 65.54\n", + "Epoch 8 - Learning Rate: 0.005 - KL theta: 2.23 - Rec loss: 63.13 - NELBO: 65.36\n", + "Epoch 9 - Learning Rate: 0.005 - KL theta: 2.2 - Rec loss: 62.92 - NELBO: 65.12\n", + "Epoch 10 - Learning Rate: 0.005 - KL theta: 2.14 - Rec loss: 62.9 - NELBO: 65.04\n", + "Topics: [['second', 'million', 'beat', 'long', 'run', 'after', 'lead', 'like', 'record', 'to'], ['to', 'of', 'in', 'and', 'for', 'on', 'with', 'at', 'as', 'that'], ['to', 'and', 'for', 'of', 'in', 'at', 'on', 'be', 'after', 'it'], ['of', 'to', 'in', 'and', 'on', 'for', 'with', 'at', 'that', 'its'], ['39', 'quote', 'united', 'windows', 'west', 'cup', 'profile', 'wall', 'al', 'states'], ['for', 'to', 'of', 'on', 'and', 'in', 'is', 'has', 'said', 'that'], ['windows', 'quote', 'cup', 'profile', 'al', '39', 'united', 'commission', 'west', 'com'], ['on', 'in', 'leaders', 'to', 'last', 'all', 'network', 'second', 'at', 'over'], ['no', 'use', 'take', 'services', 'sports', 'strong', 'wireless', 'winning', 'free', 'region'], ['free', 'five', '10', 'ago', 'systems', 'who', 'windows', 'attacks', 'army', 'is'], ['of', 'than', 'in', 'to', 'on', 'as', 'from', 'and', 'after', 'for'], ['to', 'and', 'in', 'for', 'of', 'has', 'on', 'said', 'that', 'its'], ['for', 'on', 'between', 'be', 'what', 'is', 'said', 'war', 'its', 'to'], ['to', 'of', 'and', 'for', 'in', 'on', 'with', 'by', 'at', 'that'], ['windows', 'cup', 'wall', 'quote', 'profile', 'united', '39', 'street', 'al', 'red'], ['data', 'and', 'phone', 'southern', 'in', 'what', 'as', 'plans', 'least', 'to'], ['charges', 'commission', 'drug', 'nearly', 'wall', 'put', 'bill', 'region', 'where', 'united'], ['quote', 'windows', '39', 'cup', 'wall', 'profile', 'united', 'street', 'investor', 'com'], ['to', 'of', 'in', 'and', 'on', 'for', 'with', 'at', 'as', 'that'], ['today', 'coach', 'man', 'online', 'if', 'take', 'study', 'due', 'update', 'as'], ['in', 'of', 'as', 'run', 'to', 'and', 'by', 'its', 'on', 'at'], ['to', 'of', 'in', 'and', 'for', 'on', 'that', 'at', 'as', 'with'], ['39', 'quote', 'windows', 'al', 'united', 'profile', 'west', 'com', 'cup', 'wall'], ['windows', 'target', 'street', 'quote', 'south', '39', 'nations', 'web', 'sports', 'league'], ['39', 'quote', 'commission', 'windows', 'wall', 'cup', 'com', 'lower', 'north', 'west'], ['quote', '39', 'wall', 'windows', 'united', 'cup', 'west', 'profile', 'al', 'com'], ['race', 'executive', 'took', 'deal', 'way', 'right', 'left', 'could', 'big', 'top'], ['quote', 'windows', 'al', '39', 'wall', 'united', 'profile', 'cup', 'commission', 'com'], ['in', 'to', 'and', 'on', 'of', 'from', 'for', 'as', 'by', 'its'], ['and', 'on', 'to', 'in', 'for', 'of', 'at', 'first', 'that', 'with'], ['in', 'to', 'of', 'from', 'at', 'on', 'for', 'and', 'with', 'its'], ['him', 'plan', 'wins', 'not', 'home', 'cup', 'al', 'rise', 'new', 'investor'], ['video', 'is', 'target', 'shares', 'for', 'most', 'there', 'says', 'two', 'of'], ['to', 'of', 'in', 'and', 'for', 'on', 'that', 'at', 'with', 'its'], ['to', 'in', 'as', 'of', 'and', 'over', 'on', 'from', 'at', 'has'], ['four', 'of', 'to', 'has', 'open', 'over', 'may', 'for', 'plans', 'its'], ['quote', '39', 'united', 'cup', 'windows', 'wall', 'profile', 'west', 'al', 'investor'], ['quote', 'profile', '39', 'windows', 'united', 'al', 'cup', 'red', 'west', 'wall'], ['profile', '39', 'united', 'quote', 'windows', 'al', 'cup', 'wall', 'investor', 'west'], ['profile', 'com', 'united', 'states', 'south', 'player', 'net', 'media', 'west', 'windows'], ['low', 'city', 'plan', 'nuclear', 'may', 'lower', 'was', 'close', 'he', 'time'], ['of', 'to', 'in', 'on', 'and', 'for', 'its', 'that', 'at', 'over'], ['least', 'five', 'low', 'so', 'union', 'business', 'quote', 'reports', 'due', 'can'], ['in', 'to', 'of', 'and', 'at', 'that', 'it', 'as', 'first', 'by'], ['to', 'and', 'of', 'has', 'that', 'on', 'for', 'is', 'in', 'who'], ['39', 'quote', 'windows', 'cup', 'wall', 'profile', 'west', 'united', 'al', 'com'], ['financial', 'plan', 'today', 'market', 'found', 'left', 'least', 'software', 'biggest', 'music'], ['windows', 'stock', '39', 'update', 'bank', 'west', 'per', 'old', 'cup', 'united'], ['quote', 'cup', '39', 'windows', 'profile', 'united', 'wall', 'com', 'west', 'street'], ['leaders', 'so', 'this', 'public', 'bill', 'called', 'case', '39', 'announced', 'chief']]\n", + "Epoch 11 - Learning Rate: 0.005 - KL theta: 2.19 - Rec loss: 62.83 - NELBO: 65.02\n", + "Epoch 12 - Learning Rate: 0.005 - KL theta: 2.19 - Rec loss: 62.48 - NELBO: 64.67\n", + "Epoch 13 - Learning Rate: 0.005 - KL theta: 2.23 - Rec loss: 62.52 - NELBO: 64.75\n", + "Epoch 14 - Learning Rate: 0.005 - KL theta: 2.2 - Rec loss: 62.15 - NELBO: 64.35\n", + "Epoch 15 - Learning Rate: 0.005 - KL theta: 2.09 - Rec loss: 62.27 - NELBO: 64.36\n", + "Epoch 16 - Learning Rate: 0.005 - KL theta: 2.09 - Rec loss: 61.88 - NELBO: 63.97\n", + "Epoch 17 - Learning Rate: 0.005 - KL theta: 1.99 - Rec loss: 61.6 - NELBO: 63.59\n", + "Epoch 18 - Learning Rate: 0.005 - KL theta: 1.94 - Rec loss: 61.57 - NELBO: 63.51\n", + "Epoch 19 - Learning Rate: 0.005 - KL theta: 1.75 - Rec loss: 61.27 - NELBO: 63.02\n" + ] + } + ] + } + ] +} \ No newline at end of file From ab7019b4e71810ed81b905cbf1ff19cb9178a9fd Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 13:22:02 +0700 Subject: [PATCH 07/13] add dropout --- embedded_topic_model/core/layer.py | 46 ++++++++++++++++++++++++++ embedded_topic_model/core/model.py | 49 ++++++++++++++++++++++++---- embedded_topic_model/core/trainer.py | 6 ++-- 3 files changed, 92 insertions(+), 9 deletions(-) create mode 100644 embedded_topic_model/core/layer.py diff --git a/embedded_topic_model/core/layer.py b/embedded_topic_model/core/layer.py new file mode 100644 index 0000000..7b85071 --- /dev/null +++ b/embedded_topic_model/core/layer.py @@ -0,0 +1,46 @@ +import torch +import torch.nn.functional as F +from torch import nn + + +class LinearSVD(nn.Module): + """A sparse variational dropout apply to linear layer. + """ + def __init__(self, in_features, out_features, threshold=1, bias=True): + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.threshold = threshold + self._bias = bias + + self.W = nn.Parameter(torch.Tensor(out_features, in_features)) + self.log_sigma = nn.Parameter(torch.Tensor(out_features, in_features)) + if self._bias: self.bias = nn.Parameter(torch.Tensor(1, out_features)) + + self._init_weights() + + def _init_weights(self): + if self._bias: self.bias.data.zero_() + self.W.data.normal_(0, 0.02) + self.log_sigma.data.fill_(-5) + + def forward(self, x): + self.log_alpha = self.log_sigma * 2.0 - 2.0 * torch.log(1e-16 + torch.abs(self.W)) + self.log_alpha = torch.clamp(self.log_alpha, -10, 10) + + if self.training: + if self._bias: lrt_mean = F.linear(x, self.W) + self.bias + else: lrt_mean = F.linear(x, self.W) + lrt_std = torch.sqrt(F.linear(x * x, torch.exp(self.log_sigma * 2.0)) + 1e-8) + eps = lrt_std.data.new(lrt_std.size()).normal_() + return lrt_mean + lrt_std * eps + + if self._bias: return F.linear(x, self.W * (self.log_alpha < self.threshold).float()) + self.bias + else: return F.linear(x, self.W * (self.log_alpha < self.threshold).float()) + + @property + def kl_loss(self): + k1, k2, k3 = torch.Tensor([0.63576]).cuda(), torch.Tensor([1.8732]).cuda(), torch.Tensor([1.48695]).cuda() + kl = k1 * torch.sigmoid(k2 + k3 * self.log_alpha) - 0.5 * torch.log1p(torch.exp(-self.log_alpha)) + kl = - torch.sum(kl) + return kl \ No newline at end of file diff --git a/embedded_topic_model/core/model.py b/embedded_topic_model/core/model.py index cfa19e0..b9cb5cc 100644 --- a/embedded_topic_model/core/model.py +++ b/embedded_topic_model/core/model.py @@ -1,9 +1,10 @@ import torch import torch.nn.functional as F from torch import nn +from embedded_topic_model.core.layer import LinearSVD -class Model(nn.Module): +class BaseModel(nn.Module): def __init__( self, num_topics: int, vocab_size: int, rho_size: int, train_embeddings: bool, embeddings = None @@ -67,7 +68,7 @@ def decode(self, *args): pass -class Etm(Model): +class Etm(BaseModel): def __init__( self, vocab_size: int, @@ -130,7 +131,6 @@ def encode(self, bows): def get_beta(self): try: - # torch.mm(self.rho, self.alphas) logit = self.alphas(self.rho.weight) except BaseException: logit = self.alphas(self.rho) @@ -163,7 +163,8 @@ def forward(self, bows, normalized_bows, theta=None): preds = self.decode(theta, beta) recon_loss = -(preds * bows).sum(1) recon_loss = recon_loss.mean() - return recon_loss, kld_theta + kld_loss = kld_theta + return recon_loss, kld_loss class ProdEtm(Etm): @@ -186,7 +187,6 @@ def __init__( def get_beta(self): try: - # torch.mm(self.rho, self.alphas) logit = self.alphas(self.rho.weight) except BaseException: logit = self.alphas(self.rho) @@ -202,4 +202,41 @@ def decode(self, theta, beta): res = torch.mm(theta, beta) res = F.softmax(res, dim=-1) preds = torch.log(res + 1e-6) - return preds \ No newline at end of file + return preds + + +class DropProdEtm(Etm): + def __init__( + self, + vocab_size: int, + num_topics: int = 50, + t_hidden_size: int = 800, + rho_size: int = 100, + theta_act: str = 'relu', + train_embeddings=True, + embeddings=None, + enc_drop=0.5, + debug_mode=False + ) -> None: + super().__init__( + vocab_size, num_topics, t_hidden_size, rho_size, + theta_act, train_embeddings, embeddings, enc_drop, debug_mode + ) + self.alphas = LinearSVD(rho_size, num_topics, bias=False) + + def forward(self, bows, normalized_bows, theta=None): + # get \theta + if theta is None: + theta, kld_theta = self.get_theta(normalized_bows) + else: + kld_theta = None + + # get \beta + beta = self.get_beta() + + # get prediction loss + preds = self.decode(theta, beta) + recon_loss = -(preds * bows).sum(1) + recon_loss = recon_loss.mean() + kld_loss = kld_theta + self.alphas.kl_loss + return recon_loss, kld_loss diff --git a/embedded_topic_model/core/trainer.py b/embedded_topic_model/core/trainer.py index 188161b..03ba62c 100644 --- a/embedded_topic_model/core/trainer.py +++ b/embedded_topic_model/core/trainer.py @@ -8,7 +8,7 @@ from torch import optim from gensim.models import KeyedVectors -from embedded_topic_model.core.model import Model +from embedded_topic_model.core.model import BaseModel from embedded_topic_model.utils import data from embedded_topic_model.utils import embedding from embedded_topic_model.utils import metrics @@ -19,7 +19,7 @@ class Trainer: Creates an embedded topic model instance. The model hyperparameters are: vocabulary (list of str): training dataset vocabulary - model (embedded_topic_model.core.model.Model): model to train + model (embedded_topic_model.core.model.BaseModel): model to train embeddings (str or KeyedVectors): KeyedVectors instance containing word-vector mapping for embeddings, or its path use_c_format_w2vec (bool): wheter input embeddings use word2vec C format. Both BIN and TXT formats are supported model_path (str): path to save trained model. If None, the model won't be automatically saved @@ -46,7 +46,7 @@ class Trainer: def __init__( self, vocabulary: list, - model: Model, + model: BaseModel, embeddings=None, use_c_format_w2vec=False, model_path=None, From 179edbf95ddc757601483579e1da794476a36fd2 Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 13:44:30 +0700 Subject: [PATCH 08/13] fix kl loss dropout --- embedded_topic_model/core/layer.py | 2 +- embedded_topic_model/core/trainer.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedded_topic_model/core/layer.py b/embedded_topic_model/core/layer.py index 7b85071..a915e3d 100644 --- a/embedded_topic_model/core/layer.py +++ b/embedded_topic_model/core/layer.py @@ -42,5 +42,5 @@ def forward(self, x): def kl_loss(self): k1, k2, k3 = torch.Tensor([0.63576]).cuda(), torch.Tensor([1.8732]).cuda(), torch.Tensor([1.48695]).cuda() kl = k1 * torch.sigmoid(k2 + k3 * self.log_alpha) - 0.5 * torch.log1p(torch.exp(-self.log_alpha)) - kl = - torch.sum(kl) + kl = - torch.sum(kl).mean() return kl \ No newline at end of file diff --git a/embedded_topic_model/core/trainer.py b/embedded_topic_model/core/trainer.py index 03ba62c..058a81f 100644 --- a/embedded_topic_model/core/trainer.py +++ b/embedded_topic_model/core/trainer.py @@ -250,7 +250,7 @@ def _train(self, epoch): cur_real_loss = round(cur_loss + cur_kl_theta, 2) if self.debug_mode: - print('Epoch {} - Learning Rate: {} - KL theta: {} - Rec loss: {} - NELBO: {}'.format( + print('Epoch {} \t- Learning Rate: {} - KL Loss: {} - Rec Loss: {} - NELBO: {}'.format( epoch, self.optimizer.param_groups[0]['lr'], cur_kl_theta, cur_loss, cur_real_loss)) def _perplexity(self, test_data) -> float: From a77f8c83376092c0c16e8897ea27683015da08cb Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 14:30:12 +0700 Subject: [PATCH 09/13] restructure workspace --- embedded_topic_model/core/{layer.py => layers.py} | 10 ++++++---- embedded_topic_model/core/{model.py => nets.py} | 2 +- .../core/{trainer.py => topic_models.py} | 8 +++----- 3 files changed, 10 insertions(+), 10 deletions(-) rename embedded_topic_model/core/{layer.py => layers.py} (80%) rename embedded_topic_model/core/{model.py => nets.py} (99%) rename embedded_topic_model/core/{trainer.py => topic_models.py} (99%) diff --git a/embedded_topic_model/core/layer.py b/embedded_topic_model/core/layers.py similarity index 80% rename from embedded_topic_model/core/layer.py rename to embedded_topic_model/core/layers.py index a915e3d..e275b8c 100644 --- a/embedded_topic_model/core/layer.py +++ b/embedded_topic_model/core/layers.py @@ -12,6 +12,7 @@ def __init__(self, in_features, out_features, threshold=1, bias=True): self.out_features = out_features self.threshold = threshold self._bias = bias + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.W = nn.Parameter(torch.Tensor(out_features, in_features)) self.log_sigma = nn.Parameter(torch.Tensor(out_features, in_features)) @@ -35,12 +36,13 @@ def forward(self, x): eps = lrt_std.data.new(lrt_std.size()).normal_() return lrt_mean + lrt_std * eps - if self._bias: return F.linear(x, self.W * (self.log_alpha < self.threshold).float()) + self.bias + if self._bias: return F.linear(x, self.W * (torch.exp(self.log_alpha) < self.threshold).float()) + self.bias else: return F.linear(x, self.W * (self.log_alpha < self.threshold).float()) @property def kl_loss(self): - k1, k2, k3 = torch.Tensor([0.63576]).cuda(), torch.Tensor([1.8732]).cuda(), torch.Tensor([1.48695]).cuda() + k1, k2, k3 = torch.Tensor([0.63576]).to(self.device), torch.Tensor([1.8732]).to(self.device), torch.Tensor([1.48695]).to(self.device) kl = k1 * torch.sigmoid(k2 + k3 * self.log_alpha) - 0.5 * torch.log1p(torch.exp(-self.log_alpha)) - kl = - torch.sum(kl).mean() - return kl \ No newline at end of file + kl = - kl.mean() + return kl + \ No newline at end of file diff --git a/embedded_topic_model/core/model.py b/embedded_topic_model/core/nets.py similarity index 99% rename from embedded_topic_model/core/model.py rename to embedded_topic_model/core/nets.py index b9cb5cc..ea7eb41 100644 --- a/embedded_topic_model/core/model.py +++ b/embedded_topic_model/core/nets.py @@ -1,7 +1,7 @@ import torch import torch.nn.functional as F from torch import nn -from embedded_topic_model.core.layer import LinearSVD +from embedded_topic_model.core.layers import LinearSVD class BaseModel(nn.Module): diff --git a/embedded_topic_model/core/trainer.py b/embedded_topic_model/core/topic_models.py similarity index 99% rename from embedded_topic_model/core/trainer.py rename to embedded_topic_model/core/topic_models.py index 058a81f..3515c55 100644 --- a/embedded_topic_model/core/trainer.py +++ b/embedded_topic_model/core/topic_models.py @@ -8,13 +8,11 @@ from torch import optim from gensim.models import KeyedVectors -from embedded_topic_model.core.model import BaseModel -from embedded_topic_model.utils import data -from embedded_topic_model.utils import embedding -from embedded_topic_model.utils import metrics +from embedded_topic_model.core.nets import BaseModel +from embedded_topic_model.utils import data, embedding, metrics -class Trainer: +class TopicModel: """ Creates an embedded topic model instance. The model hyperparameters are: From 1f041d0697902ba14fde95dcdd7fe6efe0fd4b8d Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 14:56:04 +0700 Subject: [PATCH 10/13] fix legacy data loader --- embedded_topic_model/utils/data.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/embedded_topic_model/utils/data.py b/embedded_topic_model/utils/data.py index aeb8648..237ba30 100644 --- a/embedded_topic_model/utils/data.py +++ b/embedded_topic_model/utils/data.py @@ -26,9 +26,11 @@ def _fetch(path, name): counts_1 = scipy.io.loadmat(count_1_file)['counts'].squeeze() tokens_2 = scipy.io.loadmat(token_2_file)['tokens'].squeeze() counts_2 = scipy.io.loadmat(count_2_file)['counts'].squeeze() - return {'tokens': tokens, 'counts': counts, - 'tokens_1': tokens_1, 'counts_1': counts_1, - 'tokens_2': tokens_2, 'counts_2': counts_2} + return { + "test": {'tokens': tokens, 'counts': counts}, + "test1": {'tokens': tokens_1, 'counts': counts_1}, + "test2": {'tokens': tokens_2, 'counts': counts_2} + } return {'tokens': tokens, 'counts': counts} @@ -43,7 +45,7 @@ def get_data(path): return vocab, train, valid, test -def get_batch(tokens, counts, ind, vocab_size, device, emsize=300): +def get_batch(tokens, counts, ind, vocab_size, device): """fetch input data by batch.""" batch_size = len(ind) data_batch = np.zeros((batch_size, vocab_size)) From df7d9f9828740e3652ca7a18107d6bb801bd793f Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 16:56:35 +0700 Subject: [PATCH 11/13] add dropout to tensor --- embedded_topic_model/core/layers.py | 39 +++++++++++++++++++++-- embedded_topic_model/core/nets.py | 19 ++++++++--- embedded_topic_model/core/topic_models.py | 4 +-- 3 files changed, 53 insertions(+), 9 deletions(-) diff --git a/embedded_topic_model/core/layers.py b/embedded_topic_model/core/layers.py index e275b8c..8ca37ae 100644 --- a/embedded_topic_model/core/layers.py +++ b/embedded_topic_model/core/layers.py @@ -3,14 +3,47 @@ from torch import nn +class SVDropout2D(nn.Module): + """A sparse variational dropout apply to a 2D Tensor. + """ + def __init__(self, n_features, threshold=0.5): + super().__init__() + self.n_features = n_features + self.threshold = threshold / (1-threshold) + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.log_sigma = nn.Parameter(torch.Tensor(n_features)) + self._init_weights() + + def _init_weights(self): + self.log_sigma.data.fill_(-5) + + def forward(self, x): + assert x.shape[-1] == self.n_features, \ + "Mismatch tensor shape" + + if self.training: + std = torch.exp(self.log_sigme) + eps = std.data.new(std.size()).normal_() + return 1 + std * eps + + return torch.matmul(x, torch.diag(torch.exp(self.log_sigma < self.threshold).float())) + + @property + def kl_loss(self): + k1, k2, k3 = torch.Tensor([0.63576]).to(self.device), torch.Tensor([1.8732]).to(self.device), torch.Tensor([1.48695]).to(self.device) + kl = k1 * torch.sigmoid(k2 + k3 * self.log_sigma) - 0.5 * torch.log1p(torch.exp(-self.log_sigma)) + kl = - kl.mean() + return kl + + class LinearSVD(nn.Module): """A sparse variational dropout apply to linear layer. """ - def __init__(self, in_features, out_features, threshold=1, bias=True): + def __init__(self, in_features, out_features, threshold=0.5, bias=True): super().__init__() self.in_features = in_features self.out_features = out_features - self.threshold = threshold + self.threshold = threshold / (1-threshold) self._bias = bias self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -37,7 +70,7 @@ def forward(self, x): return lrt_mean + lrt_std * eps if self._bias: return F.linear(x, self.W * (torch.exp(self.log_alpha) < self.threshold).float()) + self.bias - else: return F.linear(x, self.W * (self.log_alpha < self.threshold).float()) + else: return F.linear(x, self.W * (torch.exp(self.log_alpha) < self.threshold).float()) @property def kl_loss(self): diff --git a/embedded_topic_model/core/nets.py b/embedded_topic_model/core/nets.py index ea7eb41..f12ef55 100644 --- a/embedded_topic_model/core/nets.py +++ b/embedded_topic_model/core/nets.py @@ -1,7 +1,7 @@ import torch import torch.nn.functional as F from torch import nn -from embedded_topic_model.core.layers import LinearSVD +from embedded_topic_model.core.layers import SVDropout class BaseModel(nn.Module): @@ -85,12 +85,12 @@ def __init__( num_topics=num_topics, vocab_size=vocab_size, rho_size=rho_size, train_embeddings=train_embeddings, embeddings=embeddings ) + self.debug_mode = debug_mode # define hyperparameters self.t_hidden_size = t_hidden_size self.enc_drop = enc_drop self.t_drop = nn.Dropout(enc_drop) - self.debug_mode = debug_mode self.theta_act = self.get_activation(theta_act) # define the word embedding matrix \rho @@ -100,7 +100,6 @@ def __init__( self.rho = embeddings.clone().float().to(self.device) # define the matrix containing the topic embeddings - # nn.Parameter(torch.randn(rho_size, num_topics)) self.alphas = nn.Linear(rho_size, num_topics, bias=False) # define variational distribution for \theta_{1:D} via amortizartion @@ -222,7 +221,18 @@ def __init__( vocab_size, num_topics, t_hidden_size, rho_size, theta_act, train_embeddings, embeddings, enc_drop, debug_mode ) - self.alphas = LinearSVD(rho_size, num_topics, bias=False) + self.topic_dropout = SVDropout(num_topics) + + def get_beta(self): + try: + logit = self.alphas(self.rho.weight) + except BaseException: + logit = self.alphas(self.rho) + beta = self.topic_dropout(logit) + beta = beta.transpose(1, 0) + if self.debug_mode: + print(f"beta shape: {beta.shape}") + return beta def forward(self, bows, normalized_bows, theta=None): # get \theta @@ -233,6 +243,7 @@ def forward(self, bows, normalized_bows, theta=None): # get \beta beta = self.get_beta() + beta = self.topic_dropout(beta) # get prediction loss preds = self.decode(theta, beta) diff --git a/embedded_topic_model/core/topic_models.py b/embedded_topic_model/core/topic_models.py index 3515c55..19f26f5 100644 --- a/embedded_topic_model/core/topic_models.py +++ b/embedded_topic_model/core/topic_models.py @@ -48,11 +48,11 @@ def __init__( embeddings=None, use_c_format_w2vec=False, model_path=None, - batch_size=1000, + batch_size=32, train_embeddings=False, lr=0.005, lr_factor=4.0, - epochs=20, + epochs=100, optimizer_type='adam', seed=2019, enc_drop=0.0, From 7f5e77ca7f5ae2fd838c4386bc128ed6671b99fe Mon Sep 17 00:00:00 2001 From: bui-thanh-lam Date: Thu, 30 Sep 2021 17:28:41 +0700 Subject: [PATCH 12/13] fix minor bugs --- embedded_topic_model/core/layers.py | 10 ++++++---- embedded_topic_model/core/nets.py | 6 ++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/embedded_topic_model/core/layers.py b/embedded_topic_model/core/layers.py index 8ca37ae..b7bc074 100644 --- a/embedded_topic_model/core/layers.py +++ b/embedded_topic_model/core/layers.py @@ -6,7 +6,7 @@ class SVDropout2D(nn.Module): """A sparse variational dropout apply to a 2D Tensor. """ - def __init__(self, n_features, threshold=0.5): + def __init__(self, n_features, dim=1, threshold=0.5): super().__init__() self.n_features = n_features self.threshold = threshold / (1-threshold) @@ -18,14 +18,16 @@ def _init_weights(self): self.log_sigma.data.fill_(-5) def forward(self, x): - assert x.shape[-1] == self.n_features, \ + assert x.dim != 2, \ + "Must be a 2D Tensor" + assert x.shape[self.dim] == self.n_features, \ "Mismatch tensor shape" if self.training: - std = torch.exp(self.log_sigme) + std = torch.exp(self.log_sigma) eps = std.data.new(std.size()).normal_() return 1 + std * eps - + print(torch.diag(torch.exp(self.log_sigma < self.threshold).float())) return torch.matmul(x, torch.diag(torch.exp(self.log_sigma < self.threshold).float())) @property diff --git a/embedded_topic_model/core/nets.py b/embedded_topic_model/core/nets.py index f12ef55..8797ec2 100644 --- a/embedded_topic_model/core/nets.py +++ b/embedded_topic_model/core/nets.py @@ -1,7 +1,7 @@ import torch import torch.nn.functional as F from torch import nn -from embedded_topic_model.core.layers import SVDropout +from embedded_topic_model.core.layers import SVDropout2D class BaseModel(nn.Module): @@ -221,7 +221,7 @@ def __init__( vocab_size, num_topics, t_hidden_size, rho_size, theta_act, train_embeddings, embeddings, enc_drop, debug_mode ) - self.topic_dropout = SVDropout(num_topics) + self.topic_dropout = SVDropout2D(num_topics) def get_beta(self): try: @@ -230,8 +230,6 @@ def get_beta(self): logit = self.alphas(self.rho) beta = self.topic_dropout(logit) beta = beta.transpose(1, 0) - if self.debug_mode: - print(f"beta shape: {beta.shape}") return beta def forward(self, bows, normalized_bows, theta=None): From 8c384d2301c3a766a703a2dfbc1cd3b9e90c4584 Mon Sep 17 00:00:00 2001 From: Bui Thanh Lam Date: Fri, 1 Oct 2021 11:03:23 +0700 Subject: [PATCH 13/13] finish dropout --- .gitignore | 141 ++++++++++++++++++++++ embedded_topic_model/core/layers.py | 17 +-- embedded_topic_model/core/nets.py | 25 ++-- embedded_topic_model/core/topic_models.py | 18 ++- 4 files changed, 181 insertions(+), 20 deletions(-) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..50d9ea3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,141 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# Other files +main.py \ No newline at end of file diff --git a/embedded_topic_model/core/layers.py b/embedded_topic_model/core/layers.py index b7bc074..3bb7c3b 100644 --- a/embedded_topic_model/core/layers.py +++ b/embedded_topic_model/core/layers.py @@ -8,9 +8,10 @@ class SVDropout2D(nn.Module): """ def __init__(self, n_features, dim=1, threshold=0.5): super().__init__() + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.n_features = n_features + self.dim = dim self.threshold = threshold / (1-threshold) - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.log_sigma = nn.Parameter(torch.Tensor(n_features)) self._init_weights() @@ -22,13 +23,15 @@ def forward(self, x): "Must be a 2D Tensor" assert x.shape[self.dim] == self.n_features, \ "Mismatch tensor shape" - + if self.dim == 0: x = x.permute(1, 0) + if self.training: - std = torch.exp(self.log_sigma) - eps = std.data.new(std.size()).normal_() - return 1 + std * eps - print(torch.diag(torch.exp(self.log_sigma < self.threshold).float())) - return torch.matmul(x, torch.diag(torch.exp(self.log_sigma < self.threshold).float())) + sigma = torch.exp(self.log_sigma) + eps = sigma.data.new(sigma.size()).normal_() + a = torch.ones_like(sigma) + sigma * eps + return torch.matmul(x, torch.diag(a)) + + return torch.matmul(x, torch.diag((torch.exp(self.log_sigma) < self.threshold).float())) @property def kl_loss(self): diff --git a/embedded_topic_model/core/nets.py b/embedded_topic_model/core/nets.py index 8797ec2..9110f04 100644 --- a/embedded_topic_model/core/nets.py +++ b/embedded_topic_model/core/nets.py @@ -18,7 +18,7 @@ def __init__( # define the word embedding matrix \rho if train_embeddings: - self.rho = nn.Linear(rho_size, vocab_size, bias=False) + self.rho = nn.Linear(self.rho_size, self.vocab_size, bias=False) else: self.rho = embeddings.clone().float().to(self.device) @@ -95,22 +95,22 @@ def __init__( # define the word embedding matrix \rho if train_embeddings: - self.rho = nn.Linear(rho_size, vocab_size, bias=False) + self.rho = nn.Linear(self.rho_size, self.vocab_size, bias=False) else: self.rho = embeddings.clone().float().to(self.device) # define the matrix containing the topic embeddings - self.alphas = nn.Linear(rho_size, num_topics, bias=False) + self.alphas = nn.Linear(self.rho_size, self.num_topics, bias=False) - # define variational distribution for \theta_{1:D} via amortizartion + # define variational distribution for \theta_{1:D} via amortization self.q_theta = nn.Sequential( - nn.Linear(vocab_size, t_hidden_size), + nn.Linear(self.vocab_size, self.t_hidden_size), self.theta_act, - nn.Linear(t_hidden_size, t_hidden_size), + nn.Linear(self.t_hidden_size, self.t_hidden_size), self.theta_act, ) - self.mu_q_theta = nn.Linear(t_hidden_size, num_topics, bias=True) - self.logsigma_q_theta = nn.Linear(t_hidden_size, num_topics, bias=True) + self.mu_q_theta = nn.Linear(self.t_hidden_size, self.num_topics, bias=True) + self.logsigma_q_theta = nn.Linear(self.t_hidden_size, self.num_topics, bias=True) def encode(self, bows): """Returns paramters of the variational distribution for \theta. @@ -241,11 +241,16 @@ def forward(self, bows, normalized_bows, theta=None): # get \beta beta = self.get_beta() - beta = self.topic_dropout(beta) # get prediction loss preds = self.decode(theta, beta) recon_loss = -(preds * bows).sum(1) recon_loss = recon_loss.mean() - kld_loss = kld_theta + self.alphas.kl_loss + kld_loss = kld_theta + self.topic_dropout.kl_loss return recon_loss, kld_loss + + def decode(self, theta, beta): + res = torch.mm(theta, beta) + res = F.softmax(res, dim=-1) + preds = torch.log(res + 1e-6) + return preds diff --git a/embedded_topic_model/core/topic_models.py b/embedded_topic_model/core/topic_models.py index 19f26f5..199c416 100644 --- a/embedded_topic_model/core/topic_models.py +++ b/embedded_topic_model/core/topic_models.py @@ -248,8 +248,8 @@ def _train(self, epoch): cur_real_loss = round(cur_loss + cur_kl_theta, 2) if self.debug_mode: - print('Epoch {} \t- Learning Rate: {} - KL Loss: {} - Rec Loss: {} - NELBO: {}'.format( - epoch, self.optimizer.param_groups[0]['lr'], cur_kl_theta, cur_loss, cur_real_loss)) + print('Epoch {:<3} \t KL Loss: {:<10.2f} Rec Loss: {:<10.2f} \t NELBO: {:<10.2f}'.format( + epoch, cur_kl_theta, cur_loss, cur_real_loss)) def _perplexity(self, test_data) -> float: """Computes perplexity on document completion for a given testing data. @@ -399,7 +399,7 @@ def fit(self, train_data, test_data=None): if self.debug_mode: print(f'Topics before training: {self.get_topics()}') - for epoch in range(1, self.epochs): + for epoch in range(1, self.epochs + 1): self._train(epoch) if self.eval_perplexity: @@ -582,3 +582,15 @@ def _load_model(self, model_path): with open(model_path, 'rb') as file: self.model = torch.load(file) self.model = self.model.to(self.device) + + @property + def num_topics(self): + return self.model.num_topics + + @property + def model(self): + return self.__model + + @model.setter + def model(self, model: BaseModel): + self.__model = model.to(self.device)

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