Add BERTopic.

LLMTopicDetection_BERTopic/bertopic/vectorizers/_ctfidf.py

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+from typing import List
+from sklearn.feature_extraction.text import TfidfTransformer
+from sklearn.preprocessing import normalize
+from sklearn.utils import check_array
+import numpy as np
+import scipy.sparse as sp
+
+
+class ClassTfidfTransformer(TfidfTransformer):
+    """A Class-based TF-IDF procedure using scikit-learns TfidfTransformer as a base.
+
+    ![](../algorithm/c-TF-IDF.svg)
+
+    c-TF-IDF can best be explained as a TF-IDF formula adopted for multiple classes
+    by joining all documents per class. Thus, each class is converted to a single document
+    instead of set of documents. The frequency of each word **x** is extracted
+    for each class **c** and is **l1** normalized. This constitutes the term frequency.
+
+    Then, the term frequency is multiplied with IDF which is the logarithm of 1 plus
+    the average number of words per class **A** divided by the frequency of word **x**
+    across all classes.
+
+    Arguments:
+        bm25_weighting: Uses BM25-inspired idf-weighting procedure instead of the procedure
+                        as defined in the c-TF-IDF formula. It uses the following weighting scheme:
+                        `log(1+((avg_nr_samples - df + 0.5) / (df+0.5)))`
+        reduce_frequent_words: Takes the square root of the bag-of-words after normalizing the matrix.
+                               Helps to reduce the impact of words that appear too frequently.
+        seed_words: Specific words that will have their idf value increased by
+                    the value of `seed_multiplier`.
+                    NOTE: This will only increase the value of words that have an exact match.
+        seed_multiplier: The value with which the idf values of the words in `seed_words`
+                         are multiplied.
+
+    Examples:
+    ```python
+    transformer = ClassTfidfTransformer()
+    ```
+    """
+
+    def __init__(
+        self,
+        bm25_weighting: bool = False,
+        reduce_frequent_words: bool = False,
+        seed_words: List[str] = None,
+        seed_multiplier: float = 2,
+    ):
+        self.bm25_weighting = bm25_weighting
+        self.reduce_frequent_words = reduce_frequent_words
+        self.seed_words = seed_words
+        self.seed_multiplier = seed_multiplier
+        super(ClassTfidfTransformer, self).__init__()
+
+    def fit(self, X: sp.csr_matrix, multiplier: np.ndarray = None):
+        """Learn the idf vector (global term weights).
+
+        Arguments:
+            X: A matrix of term/token counts.
+            multiplier: A multiplier for increasing/decreasing certain IDF scores
+        """
+        X = check_array(X, accept_sparse=("csr", "csc"))
+        if not sp.issparse(X):
+            X = sp.csr_matrix(X)
+        dtype = np.float64
+
+        if self.use_idf:
+            _, n_features = X.shape
+
+            # Calculate the frequency of words across all classes
+            df = np.squeeze(np.asarray(X.sum(axis=0)))
+
+            # Calculate the average number of samples as regularization
+            avg_nr_samples = int(X.sum(axis=1).mean())
+
+            # BM25-inspired weighting procedure
+            if self.bm25_weighting:
+                idf = np.log(1 + ((avg_nr_samples - df + 0.5) / (df + 0.5)))
+
+            # Divide the average number of samples by the word frequency
+            # +1 is added to force values to be positive
+            else:
+                idf = np.log((avg_nr_samples / df) + 1)
+
+            # Multiplier to increase/decrease certain idf scores
+            if multiplier is not None:
+                idf = idf * multiplier
+
+            self._idf_diag = sp.diags(
+                idf,
+                offsets=0,
+                shape=(n_features, n_features),
+                format="csr",
+                dtype=dtype,
+            )
+
+        return self
+
+    def transform(self, X: sp.csr_matrix):
+        """Transform a count-based matrix to c-TF-IDF.
+
+        Arguments:
+            X (sparse matrix): A matrix of term/token counts.
+
+        Returns:
+            X (sparse matrix): A c-TF-IDF matrix
+        """
+        if self.use_idf:
+            X = normalize(X, axis=1, norm="l1", copy=False)
+
+            if self.reduce_frequent_words:
+                X.data = np.sqrt(X.data)
+
+            X = X * self._idf_diag
+
+        return X