⬆️ Bump sphinx-notes/pages from 2 to 3

.github/workflows/documentation.yaml

@@ -16,7 +16,7 @@ jobs:
           python -m pip install --upgrade pip
           pip install -e .[dev]
       - name: Build and Commit
-        uses: sphinx-notes/pages@v2
+        uses: sphinx-notes/pages@v3
       - name: Push changes
         uses: ad-m/github-push-action@v0.6.0
         with:

tweetopic/__init__.py

@@ -1,3 +1,3 @@
-from tweetopic.dmm import DMM  # noqa: F401
 from tweetopic.btm import BTM  # noqa: F401
+from tweetopic.dmm import DMM  # noqa: F401
 from tweetopic.pipeline import TopicPipeline  # noqa: F401

tweetopic/_btm.py

@@ -1,17 +1,19 @@
-"""Module for utility functions for fitting BTMs"""
+"""Module for utility functions for fitting BTMs."""
 
 import random
 from typing import Dict, Tuple, TypeVar
 
 import numba
 import numpy as np
 from numba import njit
+
 from tweetopic._prob import norm_prob, sample_categorical
 
 
 @njit
 def doc_unique_biterms(
-    doc_unique_words: np.ndarray, doc_unique_word_counts: np.ndarray
+    doc_unique_words: np.ndarray,
+    doc_unique_word_counts: np.ndarray,
 ) -> Dict[Tuple[int, int], int]:
     (n_max_unique_words,) = doc_unique_words.shape
     biterm_counts = dict()
@@ -42,7 +44,7 @@ def doc_unique_biterms(
 
 @njit
 def nb_add_counter(dest: Dict[T, int], source: Dict[T, int]):
-    """Adds one counter dict to another in place with Numba"""
+    """Adds one counter dict to another in place with Numba."""
     for key in source:
         if key in dest:
             dest[key] += source[key]
@@ -52,25 +54,28 @@ def nb_add_counter(dest: Dict[T, int], source: Dict[T, int]):
 
 @njit
 def corpus_unique_biterms(
-    doc_unique_words: np.ndarray, doc_unique_word_counts: np.ndarray
+    doc_unique_words: np.ndarray,
+    doc_unique_word_counts: np.ndarray,
 ) -> Dict[Tuple[int, int], int]:
     n_documents, _ = doc_unique_words.shape
     biterm_counts = doc_unique_biterms(
-        doc_unique_words[0], doc_unique_word_counts[0]
+        doc_unique_words[0],
+        doc_unique_word_counts[0],
     )
     for i_doc in range(1, n_documents):
         doc_unique_words_i = doc_unique_words[i_doc]
         doc_unique_word_counts_i = doc_unique_word_counts[i_doc]
         doc_biterms = doc_unique_biterms(
-            doc_unique_words_i, doc_unique_word_counts_i
+            doc_unique_words_i,
+            doc_unique_word_counts_i,
         )
         nb_add_counter(biterm_counts, doc_biterms)
     return biterm_counts
 
 
 @njit
 def compute_biterm_set(
-    biterm_counts: Dict[Tuple[int, int], int]
+    biterm_counts: Dict[Tuple[int, int], int],
 ) -> np.ndarray:
     return np.array(list(biterm_counts.keys()))
 
@@ -115,7 +120,12 @@ def add_biterm(
     topic_biterm_count: np.ndarray,
 ) -> None:
     add_remove_biterm(
-        True, i_biterm, i_topic, biterms, topic_word_count, topic_biterm_count
+        True,
+        i_biterm,
+        i_topic,
+        biterms,
+        topic_word_count,
+        topic_biterm_count,
     )
 
 
@@ -128,7 +138,12 @@ def remove_biterm(
     topic_biterm_count: np.ndarray,
 ) -> None:
     add_remove_biterm(
-        False, i_biterm, i_topic, biterms, topic_word_count, topic_biterm_count
+        False,
+        i_biterm,
+        i_topic,
+        biterms,
+        topic_word_count,
+        topic_biterm_count,
     )
 
 
@@ -146,7 +161,11 @@ def init_components(
         i_topic = random.randint(0, n_components - 1)
         biterm_topic_assignments[i_biterm] = i_topic
         add_biterm(
-            i_biterm, i_topic, biterms, topic_word_count, topic_biterm_count
+            i_biterm,
+            i_topic,
+            biterms,
+            topic_word_count,
+            topic_biterm_count,
         )
     return biterm_topic_assignments, topic_word_count, topic_biterm_count
 
@@ -360,7 +379,10 @@ def predict_docs(
         )
         biterms = doc_unique_biterms(words, word_counts)
         prob_topic_given_document(
-            pred, biterms, topic_distribution, topic_word_distribution
+            pred,
+            biterms,
+            topic_distribution,
+            topic_word_distribution,
         )
         predictions[i_doc, :] = pred
     return predictions

tweetopic/_dmm.py

@@ -1,12 +1,13 @@
-"""Module containing tools for fitting a Dirichlet Multinomial Mixture Model."""
+"""Module containing tools for fitting a Dirichlet Multinomial Mixture
+Model."""
 from __future__ import annotations
 
 from math import exp, log
 
 import numpy as np
 from numba import njit
 
-from tweetopic._prob import sample_categorical, norm_prob
+from tweetopic._prob import norm_prob, sample_categorical
 
 
 @njit
@@ -197,8 +198,7 @@ def _cond_prob(
     # I use logs instead of computing the products directly,
     # as it would quickly result in numerical overflow.
     log_norm_term = log(
-        (cluster_doc_count[i_cluster] + alpha)
-        / (n_docs - 1 + n_clusters * alpha),
+        (cluster_doc_count[i_cluster] + alpha) / (n_docs - 1 + n_clusters * alpha),
     )
     log_numerator = 0
     for i_unique in range(max_unique_words):

tweetopic/_doc.py

@@ -11,14 +11,12 @@ def init_doc_words(
     n_docs, _ = doc_term_matrix.shape
     doc_unique_words = np.zeros((n_docs, max_unique_words)).astype(np.uint32)
     doc_unique_word_counts = np.zeros((n_docs, max_unique_words)).astype(
-        np.uint32
+        np.uint32,
     )
     for i_doc in range(n_docs):
         unique_words = doc_term_matrix[i_doc].rows[0]  # type: ignore
         unique_word_counts = doc_term_matrix[i_doc].data[0]  # type: ignore
         for i_unique in range(len(unique_words)):
             doc_unique_words[i_doc, i_unique] = unique_words[i_unique]
-            doc_unique_word_counts[i_doc, i_unique] = unique_word_counts[
-                i_unique
-            ]
+            doc_unique_word_counts[i_doc, i_unique] = unique_word_counts[i_unique]
     return doc_unique_words, doc_unique_word_counts

tweetopic/btm.py

@@ -7,15 +7,19 @@
 import scipy.sparse as spr
 import sklearn
 from numpy.typing import ArrayLike
-from tweetopic._btm import (compute_biterm_set, corpus_unique_biterms,
-                            fit_model, predict_docs)
+
+from tweetopic._btm import (
+    compute_biterm_set,
+    corpus_unique_biterms,
+    fit_model,
+    predict_docs,
+)
 from tweetopic._doc import init_doc_words
 from tweetopic.exceptions import NotFittedException
 
 
 class BTM(sklearn.base.TransformerMixin, sklearn.base.BaseEstimator):
-    """Implementation of the Biterm Topic Model with Gibbs Sampling
-    solver.
+    """Implementation of the Biterm Topic Model with Gibbs Sampling solver.
 
     Parameters
     ----------
@@ -136,7 +140,8 @@ def fit(self, X: Union[spr.spmatrix, ArrayLike], y: None = None):
             max_unique_words=max_unique_words,
         )
         biterms = corpus_unique_biterms(
-            doc_unique_words, doc_unique_word_counts
+            doc_unique_words,
+            doc_unique_word_counts,
         )
         biterm_set = compute_biterm_set(biterms)
         self.topic_distribution, self.components_ = fit_model(
@@ -152,8 +157,7 @@ def fit(self, X: Union[spr.spmatrix, ArrayLike], y: None = None):
     # TODO: Something goes terribly wrong here, fix this
 
     def transform(self, X: Union[spr.spmatrix, ArrayLike]) -> np.ndarray:
-        """Predicts probabilities for each document belonging to each
-        topic.
+        """Predicts probabilities for each document belonging to each topic.
 
         Parameters
         ----------

tweetopic/pipeline.py

@@ -47,7 +47,8 @@ def fit(self, texts: Iterable[str]) -> TopicPipeline:
         return self
 
     def fit_transform(
-        self, texts: Iterable[str]
+        self,
+        texts: Iterable[str],
     ) -> Union[ArrayLike, spr.spmatrix]:
         """Fits vectorizer and topic model and transforms the given text.
 
@@ -65,7 +66,8 @@ def fit_transform(
         return self.topic_model.fit_transform(doc_term_matrix)
 
     def transform(
-        self, texts: Iterable[str]
+        self,
+        texts: Iterable[str],
     ) -> Union[ArrayLike, spr.spmatrix]:
         """Transforms given texts with the fitted pipeline.