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@MaartenGr MaartenGr released this 11 Sep 10:36
· 137 commits to master since this release
v0.12.0
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Learn about vigilant mode.

Highlights

  • Perform online/incremental topic modeling with .partial_fit
  • Expose c-TF-IDF model for customization with bertopic.vectorizers.ClassTfidfTransformer
    • The parameters bm25_weighting and reduce_frequent_words were added to potentially improve representations:
  • Expose attributes for easier access to internal data
  • Added many tests with the intention of making development a bit more stable

Documentation

Fixes

  • Fixed iteratively merging topics (#632 and (#648)
  • Fixed 0th topic not showing up in visualizations (#667)
  • Fixed lowercasing not being optional (#682)
  • Fixed spelling (#664 and (#673)
  • Fixed 0th topic not shown in .get_topic_info by @oxymor0n in #660
  • Fixed spelling by @domenicrosati in #674
  • Add custom labels and title options to barchart @leloykun in #694

Online/incremental topic modeling

Online topic modeling (sometimes called "incremental topic modeling") is the ability to learn incrementally from a mini-batch of instances. Essentially, it is a way to update your topic model with data on which it was not trained before. In Scikit-Learn, this technique is often modeled through a .partial_fit function, which is also used in BERTopic.

At a minimum, the cluster model needs to support a .partial_fit function in order to use this feature. The default HDBSCAN model will not work as it does not support online updating.

from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer
from bertopic import BERTopic

# Prepare documents
all_docs = fetch_20newsgroups(subset="all",  remove=('headers', 'footers', 'quotes'))["data"]
doc_chunks = [all_docs[i:i+1000] for i in range(0, len(all_docs), 1000)]

# Prepare sub-models that support online learning
umap_model = IncrementalPCA(n_components=5)
cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)

topic_model = BERTopic(umap_model=umap_model,
                       hdbscan_model=cluster_model,
                       vectorizer_model=vectorizer_model)

# Incrementally fit the topic model by training on 1000 documents at a time
for docs in doc_chunks:
    topic_model.partial_fit(docs)

Only the topics for the most recent batch of documents are tracked. If you want to be using online topic modeling, not for a streaming setting but merely for low-memory use cases, then it is advised to also update the .topics_ attribute as variations such as hierarchical topic modeling will not work afterward:

# Incrementally fit the topic model by training on 1000 documents at a time and tracking the topics in each iteration
topics = []
for docs in doc_chunks:
    topic_model.partial_fit(docs)
    topics.extend(topic_model.topics_)

topic_model.topics_ = topics

c-TF-IDF

Explicitly define, use, and adjust the ClassTfidfTransformer with new parameters, bm25_weighting and reduce_frequent_words, to potentially improve the topic representation:

from bertopic import BERTopic
from bertopic.vectorizers import ClassTfidfTransformer

ctfidf_model = ClassTfidfTransformer(bm25_weighting=True)
topic_model = BERTopic(ctfidf_model=ctfidf_model)

Attributes

After having fitted your BERTopic instance, you can use the following attributes to have quick access to certain information, such as the topic assignment for each document in topic_model.topics_.

Attribute Type Description
topics_ List[int] The topics that are generated for each document after training or updating the topic model. The most recent topics are tracked.
probabilities_ List[float] The probability of the assigned topic per document. These are only calculated if an HDBSCAN model is used for the clustering step. When calculate_probabilities=True, then it is the probabilities of all topics per document.
topic_sizes_ Mapping[int, int] The size of each topic.
topic_mapper_ TopicMapper A class for tracking topics and their mappings anytime they are merged, reduced, added, or removed.
topic_representations_ Mapping[int, Tuple[int, float]] The top n terms per topic and their respective c-TF-IDF values.
c_tf_idf_ csr_matrix The topic-term matrix as calculated through c-TF-IDF. To access its respective words, run .vectorizer_model.get_feature_names() or .vectorizer_model.get_feature_names_out()
topic_labels_ Mapping[int, str] The default labels for each topic.
custom_labels_ List[str] Custom labels for each topic as generated through .set_topic_labels.
topic_embeddings_ np.ndarray The embeddings for each topic. It is calculated by taking the weighted average of word embeddings in a topic based on their c-TF-IDF values.
representative_docs_ Mapping[int, str] The representative documents for each topic if HDBSCAN is used.
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