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 index a81c8ee..50d9ea3 100644 --- a/.gitignore +++ b/.gitignore @@ -136,3 +136,6 @@ dmypy.json # Cython debug symbols cython_debug/ + +# Other files +main.py \ No newline at end of file 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 index d9ec42e..088ee0a 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. @@ -59,22 +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.models.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: 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/core/layers.py b/embedded_topic_model/core/layers.py new file mode 100644 index 0000000..3bb7c3b --- /dev/null +++ b/embedded_topic_model/core/layers.py @@ -0,0 +1,86 @@ +import torch +import torch.nn.functional as F +from torch import nn + + +class SVDropout2D(nn.Module): + """A sparse variational dropout apply to a 2D Tensor. + """ + 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.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.dim != 2, \ + "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: + 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): + 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=0.5, bias=True): + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.threshold = threshold / (1-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)) + 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 * (torch.exp(self.log_alpha) < self.threshold).float()) + self.bias + else: return F.linear(x, self.W * (torch.exp(self.log_alpha) < 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_alpha) - 0.5 * torch.log1p(torch.exp(-self.log_alpha)) + kl = - kl.mean() + return kl + \ No newline at end of file diff --git a/embedded_topic_model/core/nets.py b/embedded_topic_model/core/nets.py new file mode 100644 index 0000000..9110f04 --- /dev/null +++ b/embedded_topic_model/core/nets.py @@ -0,0 +1,256 @@ +import torch +import torch.nn.functional as F +from torch import nn +from embedded_topic_model.core.layers import SVDropout2D + + +class BaseModel(nn.Module): + def __init__( + 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.rho_size = rho_size + + # define the word embedding matrix \rho + if train_embeddings: + self.rho = nn.Linear(self.rho_size, self.vocab_size, bias=False) + else: + self.rho = embeddings.clone().float().to(self.device) + + def get_activation(self, act): + if act == 'tanh': + act = nn.Tanh() + elif act == 'relu': + act = nn.ReLU() + elif act == 'softplus': + act = nn.Softplus() + elif act == 'rrelu': + act = nn.RReLU() + elif act == 'leakyrelu': + act = nn.LeakyReLU() + elif act == 'elu': + act = nn.ELU() + elif act == 'selu': + act = nn.SELU() + elif act == 'glu': + act = nn.GLU() + else: + act = nn.Tanh() + 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. + """ + if self.training: + std = torch.exp(0.5 * logvar) + eps = torch.randn_like(std) + 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(BaseModel): + 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__( + 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.theta_act = self.get_activation(theta_act) + + # define the word embedding matrix \rho + if train_embeddings: + 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(self.rho_size, self.num_topics, bias=False) + + # define variational distribution for \theta_{1:D} via amortization + self.q_theta = nn.Sequential( + nn.Linear(self.vocab_size, self.t_hidden_size), + self.theta_act, + nn.Linear(self.t_hidden_size, self.t_hidden_size), + self.theta_act, + ) + 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. + + input: bows + batch of bag-of-words...tensor of shape bsz x V + output: mu_theta, log_sigma_theta + """ + q_theta = self.q_theta(bows) + if self.enc_drop > 0: + q_theta = self.t_drop(q_theta) + mu_theta = self.mu_q_theta(q_theta) + logsigma_theta = self.logsigma_q_theta(q_theta) + kl_theta = -0.5 * \ + torch.sum(1 + logsigma_theta - mu_theta.pow(2) - logsigma_theta.exp(), dim=-1).mean() + return mu_theta, logsigma_theta, kl_theta + + def get_beta(self): + try: + logit = self.alphas(self.rho.weight) + except BaseException: + logit = self.alphas(self.rho) + logit = logit.transpose(1, 0) + beta = F.softmax(logit, dim=0) # softmax over vocab dimension + return beta + + def get_theta(self, normalized_bows): + mu_theta, logsigma_theta, kld_theta = self.encode(normalized_bows) + z = self.reparameterize(mu_theta, logsigma_theta) + theta = F.softmax(z, dim=-1) + return theta, kld_theta + + def decode(self, theta, beta): + res = torch.mm(theta, beta) + preds = torch.log(res + 1e-6) + return preds + + 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 + return recon_loss, kld_loss + + +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: + 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 + + +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.topic_dropout = SVDropout2D(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) + return beta + + 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.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/models/etm.py b/embedded_topic_model/core/topic_models.py similarity index 91% rename from embedded_topic_model/models/etm.py rename to embedded_topic_model/core/topic_models.py index 4a21ddc..199c416 100644 --- a/embedded_topic_model/models/etm.py +++ b/embedded_topic_model/core/topic_models.py @@ -8,27 +8,20 @@ from torch import optim from gensim.models import KeyedVectors -from embedded_topic_model.models.model import Model -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 ETM(object): +class TopicModel: """ Creates an embedded topic model instance. The model hyperparameters are: vocabulary (list of str): training dataset vocabulary + 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 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 @@ -50,20 +43,16 @@ class ETM(object): def __init__( self, - vocabulary, + vocabulary: list, + model: BaseModel, 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', + 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, @@ -79,15 +68,12 @@ 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 - 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 +88,6 @@ 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) @@ -113,19 +97,6 @@ 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.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.optimizer = self._get_optimizer(optimizer_type, lr, wdecay) def __str__(self): @@ -172,14 +143,14 @@ 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): @@ -277,8 +248,8 @@ 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( - 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. @@ -363,11 +334,11 @@ 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() + for k in range(self.model.num_topics): + 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) @@ -428,7 +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: @@ -480,7 +451,7 @@ 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) @@ -611,3 +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) diff --git a/embedded_topic_model/models/model.py b/embedded_topic_model/models/model.py deleted file mode 100644 index 22ebcb7..0000000 --- a/embedded_topic_model/models/model.py +++ /dev/null @@ -1,142 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - - -class Model(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 = 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() - elif act == 'relu': - act = nn.ReLU() - elif act == 'softplus': - act = nn.Softplus() - elif act == 'rrelu': - act = nn.RReLU() - elif act == 'leakyrelu': - act = nn.LeakyReLU() - elif act == 'elu': - act = nn.ELU() - elif act == 'selu': - act = nn.SELU() - elif act == 'glu': - act = nn.GLU() - else: - act = nn.Tanh() - 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. - """ - if self.training: - std = torch.exp(0.5 * logvar) - eps = torch.randn_like(std) - return eps.mul_(std).add_(mu) - else: - return mu - - def encode(self, bows): - """Returns paramters of the variational distribution for \theta. - - input: bows - batch of bag-of-words...tensor of shape bsz x V - output: mu_theta, log_sigma_theta - """ - q_theta = self.q_theta(bows) - if self.enc_drop > 0: - q_theta = self.t_drop(q_theta) - mu_theta = self.mu_q_theta(q_theta) - logsigma_theta = self.logsigma_q_theta(q_theta) - kl_theta = -0.5 * \ - torch.sum(1 + logsigma_theta - mu_theta.pow(2) - logsigma_theta.exp(), dim=-1).mean() - return mu_theta, logsigma_theta, kl_theta - - 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 = F.softmax( - logit, dim=0).transpose( - 1, 0) # softmax over vocab dimension - return beta - - def get_theta(self, normalized_bows): - mu_theta, logsigma_theta, kld_theta = self.encode(normalized_bows) - z = self.reparameterize(mu_theta, logsigma_theta) - theta = F.softmax(z, dim=-1) - return theta, kld_theta - - def decode(self, theta, beta): - res = torch.mm(theta, beta) - preds = torch.log(res + 1e-6) - return preds - - def forward(self, bows, normalized_bows, theta=None, aggregate=True): - # 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) - if aggregate: - recon_loss = recon_loss.mean() - return recon_loss, kld_theta 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)) 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 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 35f61e8..0000000 Binary files a/tests/resources/train_resources.test and /dev/null differ diff --git a/tests/resources/train_w2v_embeddings.wordvectors b/tests/resources/train_w2v_embeddings.wordvectors deleted file mode 100644 index 9e5a90e..0000000 Binary files a/tests/resources/train_w2v_embeddings.wordvectors and /dev/null differ diff --git a/tests/resources/train_w2v_embeddings.wordvectors.bin b/tests/resources/train_w2v_embeddings.wordvectors.bin deleted file mode 100644 index b7ac994..0000000 Binary files a/tests/resources/train_w2v_embeddings.wordvectors.bin and /dev/null differ diff --git a/tests/resources/train_w2v_embeddings.wordvectors.txt b/tests/resources/train_w2v_embeddings.wordvectors.txt deleted file mode 100644 index 3349acc..0000000 --- a/tests/resources/train_w2v_embeddings.wordvectors.txt +++ /dev/null @@ -1,111 +0,0 @@ -110 300 -the 0.0056834337 0.007942483 -0.0012892423 0.022210808 -0.018627882 0.013231802 0.03182268 -0.0033102883 0.025002789 -0.020107863 -0.045107644 -0.0049397605 -0.016535718 -0.023544418 -0.016965088 0.015440295 0.018569143 0.020933136 -0.004087028 0.0009811288 0.02012508 0.021033848 -0.006488473 0.0074373884 -0.027565436 0.0045513967 -0.023385469 -0.02470901 0.013055978 0.020055756 -0.021442719 0.019213758 0.013320217 -0.013713907 -0.056604225 0.01588953 0.009455284 0.014193045 0.014461204 0.04582504 -0.008604304 0.0014215112 0.016555527 0.001794178 -0.02547464 0.0029120648 -0.017132675 0.012363753 -0.007399795 -0.052322038 -0.05756216 0.01136384 0.006460232 0.015553656 -0.027772589 0.015196331 0.007402782 0.007956603 0.010393624 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0.005169999 -0.014200663 -0.0026283052 -0.002015982 -0.0044092424 -0.0037659572 -0.013751734 0.003790984 -0.002288181 0.0055356408 0.0069480278 0.003090104 0.0016296576 -0.0049129296 0.0073469565 -0.0020063722 -0.008434455 -0.0024753225 0.0034662527 0.002412396 -0.007820853 0.0073197437 -0.0010403228 0.00083339185 0.0018516361 -0.004004517 -0.002191321 0.012810168 0.0069067376 0.0058804667 0.011715483 -0.0015076813 0.0035184557 -0.004254999 -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 3c491b8..0000000 Binary files a/train_resources.test and /dev/null differ