Add BERTopic.

2025-08-12 19:01:20 +08:00
parent e2323d579c
commit c5c530775e
256 changed files with 28666 additions and 0 deletions
@@ -0,0 +1,40 @@
+import pandas as pd
+from scipy.sparse import csr_matrix
+from sklearn.base import BaseEstimator
+from typing import Mapping, List, Tuple
+
+
+class BaseRepresentation(BaseEstimator):
+    """The base representation model for fine-tuning topic representations."""
+
+    def extract_topics(
+        self,
+        topic_model,
+        documents: pd.DataFrame,
+        c_tf_idf: csr_matrix,
+        topics: Mapping[str, List[Tuple[str, float]]],
+    ) -> Mapping[str, List[Tuple[str, float]]]:
+        """Extract topics.
+
+        Each representation model that inherits this class will have
+        its arguments (topic_model, documents, c_tf_idf, topics)
+        automatically passed. Therefore, the representation model
+        will only have access to the information about topics related
+        to those arguments.
+
+        Arguments:
+            topic_model: The BERTopic model that is fitted until topic
+                         representations are calculated.
+            documents: A dataframe with columns "Document" and "Topic"
+                       that contains all documents with each corresponding
+                       topic.
+            c_tf_idf: A c-TF-IDF representation that is typically
+                      identical to `topic_model.c_tf_idf_` except for
+                      dynamic, class-based, and hierarchical topic modeling
+                      where it is calculated on a subset of the documents.
+            topics: A dictionary with topic (key) and tuple of word and
+                    weight (value) as calculated by c-TF-IDF. This is the
+                    default topics that are returned if no representation
+                    model is used.
+        """
+        return topic_model.topic_representations_