Merge pull request #96 from AnFreTh/main

version 0.1.9

README.md

@@ -1,5 +1,8 @@
 <div align="center">
-  <img src="./docs/images/logos/STREAM_2.jpg" width="400"/>
+  <a href="#get-started">
+    <img src="./assets/stream-topic-gif.gif" width="800"/>
+  </a>
+
 
 
 [![PyPI](https://img.shields.io/pypi/v/stream_topic)](https://pypi.org/project/stream_topic)
@@ -24,14 +27,6 @@
 <p>We present STREAM, a Simplified Topic Retrieval, Exploration, and Analysis Module for User-Friendly and Interactive Topic Modeling and Visualization. Our paper can be found <a href="https://aclanthology.org/2024.acl-short.41.pdf">here</a>.</p>
 
 
-
-<table>
-  <tr>
-    <td><img src="./docs/images/gif1.gif" alt="First GIF" width="400"/></td>
-    <td><img src="./docs/images/gif2.gif" alt="Second GIF" width="400"/></td>
-  </tr>
-</table>
-
 <h2> Table of Contents </h2>
 
 
@@ -164,6 +159,10 @@ STREAM offers a variety of neural as well as non-neural topic models and we are
         <td><a href="https://arxiv.org/abs/1703.01488">NeuralLDA</a></td>
         <td>Autoencoding Variational Inference For Topic Models</td>
       </tr>
+      <tr>
+        <td><a href="https://arxiv.org/abs/2008.13537">NSTM</a></td>
+        <td>Neural Topic Model via Optimal Transport</td>
+      </tr>
     </tbody>
   </table>
 </div>

docs/api/models/models.rst

@@ -44,3 +44,6 @@ Models
 
 .. autoclass:: stream_topic.models.CBC
     :members:
+
+.. autoclass:: stream_topic.models.NSTM
+    :members:

docs/conf.py

@@ -62,7 +62,6 @@
     "plotly",
     "matplotlib",
     "gensim",
-    "octis",
     "nltk",
     "langdetect",
     "loguru",

docs/notebooks/examples.ipynb

@@ -273,9 +273,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "db",
+   "display_name": "Python (stream_topic_venv)",
    "language": "python",
-   "name": "python3"
+   "name": "stream_topic_venv"
   },
   "language_info": {
    "codemirror_mode": {

docs/notebooks/quickstart.ipynb

@@ -269,9 +269,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "db",
+   "display_name": "Python (stream_topic_venv)",
    "language": "python",
-   "name": "python3"
+   "name": "stream_topic_venv"
   },
   "language_info": {
    "codemirror_mode": {

requirements.txt

@@ -13,7 +13,6 @@ torch==2.4.0
 transformers==4.40.2
 setfit==1.0.3
 gensim==4.2.0
-octis==1.13.1
 umap-learn==0.5.6
 wordcloud==1.9.3
 

stream_topic/__version__.py

@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "0.1.8"
+__version__ = "0.1.9"

stream_topic/models/CEDC.py

@@ -191,7 +191,7 @@ def fit(
         only_nouns: bool = False,
         clean: bool = False,
         clean_threshold: float = 0.85,
-        expansion_corpus: str = "octis",
+        expansion_corpus: str = "brown",
         n_words: int = 20,
     ):
         """
@@ -210,7 +210,7 @@ def fit(
         clean_threshold : float, optional
             Threshold for cleaning topics based on similarity (default is 0.85).
         expansion_corpus : str, optional
-            Corpus for expanding topics (default is 'octis').
+            Corpus for expanding topics (default is 'brown').
         n_words : int, optional
             Number of top words to include in each topic (default is 20).
 

stream_topic/models/__init__.py

@@ -13,6 +13,7 @@
 from .ctmneg import CTMNeg
 from .tntm import TNTM
 from .nmf import NMFTM
+from .nstm import NSTM
 
 __all__ = [
     "BERTopicTM",
@@ -30,4 +31,5 @@
     "CTMNeg",
     "TNTM",
     "NMFTM",
+    "NSTM",
 ]

stream_topic/models/neural_base_models/nstm_base.py

@@ -0,0 +1,210 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from ...utils.sinkhorn_loss import sinkhorn_loss
+
+
+class NSTMBase(nn.Module):
+    """
+    Neural Topic Model via Optimal Transport (NSTM). Based on the paper presented at ICLR 2021 by
+    He Zhao, Dinh Phung, Viet Huynh, Trung Le, and Wray Buntine.
+
+    This model learns topic embeddings using an encoder and leverages optimal transport
+    via the Sinkhorn loss for topic and word distributions.
+
+    Parameters
+    ----------
+    dataset : Dataset
+        A dataset object containing the bag-of-words (BoW) matrix used for training.
+    n_topics : int, optional
+        The number of topics to be learned by the model, by default 50.
+    encoder_dim : int, optional
+        The dimension of the encoder's hidden layer, by default 128.
+    dropout : float, optional
+        The dropout rate for the encoder, by default 0.1.
+    pretrained_WE : numpy.ndarray, optional
+        Pretrained word embeddings as a numpy array. If None, the embeddings will be randomly initialized, by default None.
+    train_WE : bool, optional
+        Whether to fine-tune (train) the word embeddings during model training, by default True.
+    encoder_activation : callable, optional
+        The activation function for the encoder, by default nn.ReLU().
+    embed_size : int, optional
+        The size of the word embedding vectors, by default 256.
+    recon_loss_weight : float, optional
+        The weight given to the reconstruction loss, by default 0.07.
+    sinkhorn_alpha : float, optional
+        The scaling factor for the Sinkhorn loss, by default 20.
+
+    Attributes
+    ----------
+    recon_loss_weight : float
+        The weight of the reconstruction loss in the final loss computation.
+    sinkhorn_alpha : float
+        The scaling factor applied to the Sinkhorn loss for optimal transport.
+    encoder : nn.Sequential
+        The neural network that encodes bag-of-words input into topic distribution.
+    word_embeddings : nn.Parameter
+        The word embeddings matrix, either pretrained or initialized randomly.
+    topic_embeddings : nn.Parameter
+        The matrix of learned topic embeddings.
+
+    Methods
+    -------
+    get_beta():
+        Computes the normalized topic-word distribution matrix.
+    get_theta(x):
+        Computes the topic distribution (theta) for the input BoW vector.
+    forward(x):
+        Executes the forward pass, returning the topic distribution, topic-word distribution, and the transport cost matrix.
+    compute_loss(x):
+        Computes the overall loss, combining reconstruction and Sinkhorn losses.
+    """
+
+    def __init__(
+        self,
+        dataset,
+        n_topics: int = 50,
+        encoder_dim: int = 128,
+        dropout: float = 0.1,
+        pretrained_WE=None,
+        train_WE: bool = True,
+        encoder_activation: callable = nn.ReLU(),
+        embed_size: int = 256,
+        recon_loss_weight=0.07,
+        sinkhorn_alpha=20,
+    ):
+        """
+        Initializes the Neural Topic Model.
+
+        Parameters
+        ----------
+        dataset : Dataset
+            A dataset object containing the BoW matrix as `dataset.bow`.
+        n_topics : int, optional
+            Number of topics to be learned, by default 50.
+        encoder_dim : int, optional
+            Hidden dimension size for the encoder, by default 128.
+        dropout : float, optional
+            Dropout rate for regularization in the encoder, by default 0.1.
+        pretrained_WE : np.ndarray, optional
+            Pretrained word embeddings (optional), by default None.
+        train_WE : bool, optional
+            Whether the word embeddings are trainable, by default True.
+        encoder_activation : callable, optional
+            Activation function for the encoder layers, by default nn.ReLU().
+        embed_size : int, optional
+            Size of the word embeddings, by default 256.
+        recon_loss_weight : float, optional
+            Weight of the reconstruction loss, by default 0.07.
+        sinkhorn_alpha : float, optional
+            Scaling factor for the Sinkhorn loss, by default 20.
+        """
+        super().__init__()
+
+        vocab_size = dataset.bow.shape[1]
+
+        self.recon_loss_weight = recon_loss_weight
+        self.sinkhorn_alpha = sinkhorn_alpha
+
+        self.encoder = nn.Sequential(
+            nn.Linear(vocab_size, encoder_dim),
+            encoder_activation,
+            nn.Dropout(dropout),
+            nn.Linear(encoder_dim, n_topics),
+            nn.BatchNorm1d(n_topics),
+        )
+
+        if pretrained_WE is not None:
+            self.word_embeddings = nn.Parameter(torch.from_numpy(pretrained_WE).float())
+        else:
+            self.word_embeddings = nn.Parameter(
+                torch.randn((vocab_size, embed_size)) * 1e-03
+            )
+
+        self.word_embeddings.requires_grad = train_WE
+
+        self.topic_embeddings = nn.Parameter(
+            torch.randn((n_topics, self.word_embeddings.shape[1])) * 1e-03
+        )
+
+    def get_beta(self):
+        """
+        Computes the normalized topic-word distribution matrix (beta) by taking the dot product
+        of the normalized topic embeddings and word embeddings.
+
+        Returns
+        -------
+        torch.Tensor
+            The topic-word distribution matrix of shape (n_topics, vocab_size).
+        """
+        word_embedding_norm = F.normalize(self.word_embeddings)
+        topic_embedding_norm = F.normalize(self.topic_embeddings)
+        beta = torch.matmul(topic_embedding_norm, word_embedding_norm.T)
+        return beta
+
+    def get_theta(self, x):
+        """
+        Computes the document-topic distribution (theta) for a given bag-of-words input using the encoder.
+
+        Parameters
+        ----------
+        x : torch.Tensor
+            Input tensor representing the bag-of-words (BoW) data of shape (batch_size, vocab_size).
+
+        Returns
+        -------
+        torch.Tensor
+            The document-topic distribution of shape (batch_size, n_topics).
+        """
+        theta = self.encoder(x)
+        theta = F.softmax(theta, dim=-1)
+        return theta
+
+    def forward(self, x):
+        """
+        Performs the forward pass of the model, which computes the document-topic distribution (theta),
+        the topic-word distribution (beta), and the optimal transport distance matrix (M).
+
+        Parameters
+        ----------
+        x : dict
+            A dictionary containing the input bag-of-words tensor under the key "bow".
+
+        Returns
+        -------
+        tuple
+            A tuple containing:
+            - theta (torch.Tensor): Document-topic distribution of shape (batch_size, n_topics).
+            - beta (torch.Tensor): Topic-word distribution of shape (n_topics, vocab_size).
+            - M (torch.Tensor): Distance matrix of shape (n_topics, vocab_size).
+        """
+        x = x["bow"]
+        theta = self.get_theta(x)
+        beta = self.get_beta()
+        M = 1 - beta
+        return theta, beta, M
+
+    def compute_loss(self, x):
+        """
+        Computes the total loss for a given input by combining the reconstruction loss and the Sinkhorn loss.
+
+        Parameters
+        ----------
+        x : dict
+            A dictionary containing the input bag-of-words tensor under the key "bow".
+
+        Returns
+        -------
+        torch.Tensor
+            The total loss, averaged over the batch.
+        """
+        theta, beta, M = self.forward(x)
+        sh_loss = sinkhorn_loss(
+            M, theta.T, F.softmax(x["bow"], dim=-1).T, lambda_sh=self.sinkhorn_alpha
+        )
+        recon = F.softmax(torch.matmul(theta, beta), dim=-1)
+        recon_loss = -(x["bow"] * recon.log()).sum(axis=1)
+
+        loss = self.recon_loss_weight * recon_loss + sh_loss
+        loss = loss.mean()
+        return loss