import numpy as np import pandas as pd from packaging import version from scipy.sparse import csr_matrix from typing import Mapping, List, Tuple, Union from sklearn.metrics.pairwise import cosine_similarity from bertopic.representation._base import BaseRepresentation from sklearn import __version__ as sklearn_version class KeyBERTInspired(BaseRepresentation): def __init__(self, top_n_words: int = 10, nr_repr_docs: int = 5, nr_samples: int = 500, nr_candidate_words: int = 100, random_state: int = 42): """ Use a KeyBERT-like model to fine-tune the topic representations The algorithm follows KeyBERT but does some optimization in order to speed up inference. The steps are as follows. First, we extract the top n representative documents per topic. To extract the representative documents, we randomly sample a number of candidate documents per cluster which is controlled by the `nr_samples` parameter. Then, the top n representative documents are extracted by calculating the c-TF-IDF representation for the candidate documents and finding, through cosine similarity, which are closest to the topic c-TF-IDF representation. Next, the top n words per topic are extracted based on their c-TF-IDF representation, which is controlled by the `nr_repr_docs` parameter. Then, we extract the embeddings for words and representative documents and create topic embeddings by averaging the representative documents. Finally, the most similar words to each topic are extracted by calculating the cosine similarity between word and topic embeddings. Arguments: top_n_words: The top n words to extract per topic. nr_repr_docs: The number of representative documents to extract per cluster. nr_samples: The number of candidate documents to extract per cluster. nr_candidate_words: The number of candidate words per cluster. random_state: The random state for randomly sampling candidate documents. Usage: ```python from bertopic.representation import KeyBERTInspired from bertopic import BERTopic # Create your representation model representation_model = KeyBERTInspired() # Use the representation model in BERTopic on top of the default pipeline topic_model = BERTopic(representation_model=representation_model) ``` """ self.top_n_words = top_n_words self.nr_repr_docs = nr_repr_docs self.nr_samples = nr_samples self.nr_candidate_words = nr_candidate_words self.random_state = random_state 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 Arguments: topic_model: A BERTopic model documents: All input documents c_tf_idf: The topic c-TF-IDF representation topics: The candidate topics as calculated with c-TF-IDF Returns: updated_topics: Updated topic representations """ # We extract the top n representative documents per class _, representative_docs, repr_doc_indices, _ = topic_model._extract_representative_docs(c_tf_idf, documents, topics, self.nr_samples, self.nr_repr_docs) # We extract the top n words per class topics = self._extract_candidate_words(topic_model, c_tf_idf, topics) # We calculate the similarity between word and document embeddings and create # topic embeddings from the representative document embeddings sim_matrix, words = self._extract_embeddings(topic_model, topics, representative_docs, repr_doc_indices) # Find the best matching words based on the similarity matrix for each topic updated_topics = self._extract_top_words(words, topics, sim_matrix) return updated_topics def _extract_candidate_words(self, topic_model, c_tf_idf: csr_matrix, topics: Mapping[str, List[Tuple[str, float]]] ) -> Mapping[str, List[Tuple[str, float]]]: """ For each topic, extract candidate words based on the c-TF-IDF representation. Arguments: topic_model: A BERTopic model c_tf_idf: The topic c-TF-IDF representation topics: The top words per topic Returns: topics: The `self.top_n_words` per topic """ labels = [int(label) for label in sorted(list(topics.keys()))] # Scikit-Learn Deprecation: get_feature_names is deprecated in 1.0 # and will be removed in 1.2. Please use get_feature_names_out instead. if version.parse(sklearn_version) >= version.parse("1.0.0"): words = topic_model.vectorizer_model.get_feature_names_out() else: words = topic_model.vectorizer_model.get_feature_names() indices = topic_model._top_n_idx_sparse(c_tf_idf, self.nr_candidate_words) scores = topic_model._top_n_values_sparse(c_tf_idf, indices) sorted_indices = np.argsort(scores, 1) indices = np.take_along_axis(indices, sorted_indices, axis=1) scores = np.take_along_axis(scores, sorted_indices, axis=1) # Get top 30 words per topic based on c-TF-IDF score topics = {label: [(words[word_index], score) if word_index is not None and score > 0 else ("", 0.00001) for word_index, score in zip(indices[index][::-1], scores[index][::-1]) ] for index, label in enumerate(labels)} topics = {label: list(zip(*values[:self.nr_candidate_words]))[0] for label, values in topics.items()} return topics def _extract_embeddings(self, topic_model, topics: Mapping[str, List[Tuple[str, float]]], representative_docs: List[str], repr_doc_indices: List[List[int]] ) -> Union[np.ndarray, List[str]]: """ Extract the representative document embeddings and create topic embeddings. Then extract word embeddings and calculate the cosine similarity between topic embeddings and the word embeddings. Topic embeddings are the average of representative document embeddings. Arguments: topic_model: A BERTopic model topics: The top words per topic representative_docs: A flat list of representative documents repr_doc_indices: The indices of representative documents that belong to each topic Returns: sim: The similarity matrix between word and topic embeddings vocab: The complete vocabulary of input documents """ # Calculate representative docs embeddings and create topic embeddings repr_embeddings = topic_model._extract_embeddings(representative_docs, method="document", verbose=False) topic_embeddings = [np.mean(repr_embeddings[i[0]:i[-1]+1], axis=0) for i in repr_doc_indices] # Calculate word embeddings and extract best matching with updated topic_embeddings vocab = list(set([word for words in topics.values() for word in words])) word_embeddings = topic_model._extract_embeddings(vocab, method="document", verbose=False) sim = cosine_similarity(topic_embeddings, word_embeddings) return sim, vocab def _extract_top_words(self, vocab: List[str], topics: Mapping[str, List[Tuple[str, float]]], sim: np.ndarray ) -> Mapping[str, List[Tuple[str, float]]]: """ Extract the top n words per topic based on the similarity matrix between topics and words. Arguments: vocab: The complete vocabulary of input documents labels: All topic labels topics: The top words per topic sim: The similarity matrix between word and topic embeddings Returns: updated_topics: The updated topic representations """ labels = [int(label) for label in sorted(list(topics.keys()))] updated_topics = {} for i, topic in enumerate(labels): indices = [vocab.index(word) for word in topics[topic]] values = sim[:, indices][i] word_indices = [indices[index] for index in np.argsort(values)[-self.top_n_words:]] updated_topics[topic] = [(vocab[index], val) for val, index in zip(np.sort(values)[-self.top_n_words:], word_indices)][::-1] return updated_topics