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Model Overview

Turftopic contains implementations of a number of contemporary topic models. Some of these models might be similar to each other in a lot of aspects, but they might be different in others. It is quite important that you choose the right topic model for your use case.

Looking for Model Performance?

If you are interested in seeing how these models perform on a bunch of datasets, and would like to base your model choice on evaluations, make sure to check out the Model Leaderboard tab:


Model Summary Strengths Weaknesses
Topeax Density peak detection + Gaussian mixture approximation Cluster quality, Topic quality, Stability, Automatic n-topics Underestimates N topics, Slower, No inference for new documents
KeyNMF Keyword importance estimation + matrix factorization Reliability, Topic quality, Scalability to large corpora and long documents Automatic topic number detection, Multilingual performance, Sometimes includes stop words
SensTopic(BETA) Regularized Semi-nonnegative matrix factorization in embedding space Very fast, High quality topics and clusters, Can assign multiple soft clusters to documents, GPU support Automatic n-topics is not too good
GMM Soft clustering with Gaussian Mixtures and soft-cTF-IDF Reliability, Speed, Cluster quality Manual n-topics, Lower quality keywords, Curse of dimensionality
FASTopic Neural topic modelling with Dual Semantic-relation Reconstruction High quality topics and clusters, GPU support Very slow, Memory hungry, Manual n-topics
\(S^3\) Semantic axis discovery in embedding space Fastest, Human-readable topics Axes can be very unintuitive, Manual n-topics
BERTopic and Top2Vec Embed -> Reduce -> Cluster Flexible, Feature rich Slow, Unreliable and unstable, Wildly overestimates number of clusters, Low topic and cluster quality
AutoEncodingTopicModel Discover topics by generating BoW with a variational autoencoder GPU-support Slow, Sometimes low quality topics

Different models will naturally be good at different things, because they conceptualize topics differently for instance:

  • BERTopic, Top2Vec, GMM and Topeax find clusters of documents and treats those as topics
  • KeyNMF, SensTopic, FASTopic and AutoEncodingTopicModel conceptualize topics as latent nonnegative factors that generate the documents.
  • SemanticSignalSeparation(\(S^3\)) conceptualizes topics as semantic axes, along which topics are distributed.

You can find a detailed overview of how each of these models work in their respective tabs.

Model Features

Some models are also capable of being used in a dynamic context, some can be fitted online, some can detect the number of topics for you and some can detect topic hierarchies. You can find an overview of these features in the table below.

Model šŸ”¢ Multiple Topics per Document #āƒ£ Detecting Number of Topics šŸ“ˆ Dynamic Modeling šŸŒ² Hierarchical Modeling ā­ Inference over New Documents šŸŒ Cross-Lingual šŸŒŠ Online Fitting
KeyNMF āœ” āœ” āœ” āœ” āœ” āœ” āœ”
SensTopic āœ” āœ” āœ” āŒ āœ” āœ” āŒ
Topeax āŒ āœ” āœ” āŒ āŒ āŒ āŒ
SemanticSignalSeparation āœ” āŒ āœ” āŒ āœ” āœ” āŒ
ClusteringTopicModel āŒ āœ” āœ” āœ” āŒ āœ” āŒ
GMM āœ” āŒ āœ” āŒ āœ” āœ” āŒ
AutoEncodingTopicModel āœ” āŒ āŒ āŒ āœ” āœ” āŒ
FASTopic āœ” āŒ āŒ āŒ āœ” āœ” āŒ

Model API Reference

turftopic.base.ContextualModel

Bases: BaseEstimator, TransformerMixin, TopicContainer

Base class for contextual topic models in Turftopic.

Source code in turftopic/base.py 
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class ContextualModel(BaseEstimator, TransformerMixin, TopicContainer):
    """Base class for contextual topic models in Turftopic."""

    @property
    def has_negative_side(self) -> bool:
        return False

    def encode_documents(self, raw_documents: Iterable[str]) -> np.ndarray:
        """Encodes documents with the sentence encoder of the topic model.

        Parameters
        ----------
        raw_documents: iterable of str
            Textual documents to encode.

        Return
        ------
        ndarray of shape (n_documents, n_dimensions)
            Matrix of document embeddings.
        """
        if not hasattr(self.encoder_, "encode"):
            return self.encoder.get_text_embeddings(list(raw_documents))
        return self.encoder_.encode(raw_documents)

    @abstractmethod
    def fit_transform(
        self, raw_documents, y=None, embeddings: Optional[np.ndarray] = None
    ) -> np.ndarray:
        """Fits model and infers topic importances for each document.

        Parameters
        ----------
        raw_documents: iterable of str
            Documents to fit the model on.
        y: None
            Ignored, exists for sklearn compatibility.
        embeddings: ndarray of shape (n_documents, n_dimensions), optional
            Precomputed document encodings.

        Returns
        -------
        ndarray of shape (n_documents, n_topics)
            Document-topic matrix.
        """
        pass

    def fit(
        self, raw_documents, y=None, embeddings: Optional[np.ndarray] = None
    ):
        """Fits model on the given corpus.

        Parameters
        ----------
        raw_documents: iterable of str
            Documents to fit the model on.
        y: None
            Ignored, exists for sklearn compatibility.
        embeddings: ndarray of shape (n_documents, n_dimensions), optional
            Precomputed document encodings.
        """
        self.fit_transform(raw_documents, y, embeddings)
        return self

    def get_vocab(self) -> np.ndarray:
        """Get vocabulary of the model.

        Returns
        -------
        ndarray of shape (n_vocab)
            All terms in the vocabulary.
        """
        return self.vectorizer.get_feature_names_out()

    def get_feature_names_out(self) -> np.ndarray:
        """Get topic ids.

        Returns
        -------
        ndarray of shape (n_topics)
            IDs for each output feature of the model.
            This is useful, since some models have outlier
            detection, and this gets -1 as ID, instead of
            its index.
        """
        n_topics = self.components_.shape[0]
        try:
            classes = self.classes_
        except AttributeError:
            classes = list(range(n_topics))
        return np.asarray(classes)

    def prepare_topic_data(
        self,
        corpus: List[str],
        embeddings: Optional[np.ndarray] = None,
    ) -> TopicData:
        """Produces topic inference data for a given corpus, that can be then used and reused.
        Exists to allow visualizations out of the box with topicwizard.

        Parameters
        ----------
        corpus: list of str
            Documents to infer topical content for.
        embeddings: ndarray of shape (n_documents, n_dimensions)
            Embeddings of documents.

        Returns
        -------
        TopicData
            Information about topical inference in a dictionary.
        """
        if embeddings is None:
            embeddings = self.encode_documents(corpus)
        try:
            document_topic_matrix = self.transform(
                corpus, embeddings=embeddings
            )
        except (AttributeError, NotFittedError):
            document_topic_matrix = self.fit_transform(
                corpus, embeddings=embeddings
            )
        dtm = self.vectorizer.transform(corpus)  # type: ignore
        try:
            classes = self.classes_
        except AttributeError:
            classes = list(range(self.components_.shape[0]))
        res = TopicData(
            corpus=corpus,
            document_term_matrix=dtm,
            vocab=self.get_vocab(),
            document_topic_matrix=document_topic_matrix,
            document_representation=embeddings,
            topic_term_matrix=self.components_,  # type: ignore
            transform=getattr(self, "transform", None),
            topic_names=self.topic_names,
            classes=classes,
            has_negative_side=self.has_negative_side,
            hierarchy=getattr(self, "hierarchy", None),
        )
        return res

    def to_disk(self, out_dir: Union[Path, str]):
        """Persists model to directory on your machine.

        Parameters
        ----------
        out_dir: Path | str
            Directory to save the model to.
        """
        out_dir = Path(out_dir)
        out_dir.mkdir(exist_ok=True)
        package_versions = get_package_versions()
        with out_dir.joinpath("package_versions.json").open("w") as ver_file:
            ver_file.write(json.dumps(package_versions))
        joblib.dump(self, out_dir.joinpath("model.joblib"))

    def push_to_hub(self, repo_id: str):
        """Uploads model to HuggingFace Hub

        Parameters
        ----------
        repo_id: str
            Repository to upload the model to.
        """
        api = HfApi()
        api.create_repo(repo_id, exist_ok=True)
        with tempfile.TemporaryDirectory() as tmp_dir:
            readme_path = Path(tmp_dir).joinpath("README.md")
            with readme_path.open("w") as readme_file:
                readme_file.write(create_readme(self, repo_id))
            self.to_disk(tmp_dir)
            api.upload_folder(
                folder_path=tmp_dir,
                repo_id=repo_id,
                repo_type="model",
            )

encode_documents(raw_documents)

Encodes documents with the sentence encoder of the topic model.

Parameters:

Name Type Description Default
raw_documents Iterable[str]

Textual documents to encode.

 required
Return

ndarray of shape (n_documents, n_dimensions) Matrix of document embeddings.

Source code in turftopic/base.py 
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def encode_documents(self, raw_documents: Iterable[str]) -> np.ndarray:
    """Encodes documents with the sentence encoder of the topic model.

    Parameters
    ----------
    raw_documents: iterable of str
        Textual documents to encode.

    Return
    ------
    ndarray of shape (n_documents, n_dimensions)
        Matrix of document embeddings.
    """
    if not hasattr(self.encoder_, "encode"):
        return self.encoder.get_text_embeddings(list(raw_documents))
    return self.encoder_.encode(raw_documents)

fit(raw_documents, y=None, embeddings=None)

Fits model on the given corpus.

Parameters:

Name Type Description Default
raw_documents

Documents to fit the model on.

 required
y

Ignored, exists for sklearn compatibility.

 None
embeddings Optional[ndarray]

Precomputed document encodings.

 None
Source code in turftopic/base.py 
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def fit(
    self, raw_documents, y=None, embeddings: Optional[np.ndarray] = None
):
    """Fits model on the given corpus.

    Parameters
    ----------
    raw_documents: iterable of str
        Documents to fit the model on.
    y: None
        Ignored, exists for sklearn compatibility.
    embeddings: ndarray of shape (n_documents, n_dimensions), optional
        Precomputed document encodings.
    """
    self.fit_transform(raw_documents, y, embeddings)
    return self

fit_transform(raw_documents, y=None, embeddings=None) abstractmethod

Fits model and infers topic importances for each document.

Parameters:

Name Type Description Default
raw_documents

Documents to fit the model on.

 required
y

Ignored, exists for sklearn compatibility.

 None
embeddings Optional[ndarray]

Precomputed document encodings.

 None

Returns:

Type Description
ndarray of shape (n_documents, n_topics)

Document-topic matrix.
Source code in turftopic/base.py 
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@abstractmethod
def fit_transform(
    self, raw_documents, y=None, embeddings: Optional[np.ndarray] = None
) -> np.ndarray:
    """Fits model and infers topic importances for each document.

    Parameters
    ----------
    raw_documents: iterable of str
        Documents to fit the model on.
    y: None
        Ignored, exists for sklearn compatibility.
    embeddings: ndarray of shape (n_documents, n_dimensions), optional
        Precomputed document encodings.

    Returns
    -------
    ndarray of shape (n_documents, n_topics)
        Document-topic matrix.
    """
    pass

get_feature_names_out()

Get topic ids.

Returns:

Type Description
ndarray of shape (n_topics)

IDs for each output feature of the model. This is useful, since some models have outlier detection, and this gets -1 as ID, instead of its index.
Source code in turftopic/base.py 
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def get_feature_names_out(self) -> np.ndarray:
    """Get topic ids.

    Returns
    -------
    ndarray of shape (n_topics)
        IDs for each output feature of the model.
        This is useful, since some models have outlier
        detection, and this gets -1 as ID, instead of
        its index.
    """
    n_topics = self.components_.shape[0]
    try:
        classes = self.classes_
    except AttributeError:
        classes = list(range(n_topics))
    return np.asarray(classes)

get_vocab()

Get vocabulary of the model.

Returns:

Type Description
ndarray of shape (n_vocab)

All terms in the vocabulary.
Source code in turftopic/base.py 
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def get_vocab(self) -> np.ndarray:
    """Get vocabulary of the model.

    Returns
    -------
    ndarray of shape (n_vocab)
        All terms in the vocabulary.
    """
    return self.vectorizer.get_feature_names_out()

prepare_topic_data(corpus, embeddings=None)

Produces topic inference data for a given corpus, that can be then used and reused. Exists to allow visualizations out of the box with topicwizard.

Parameters:

Name Type Description Default
corpus List[str]

Documents to infer topical content for.

 required
embeddings Optional[ndarray]

Embeddings of documents.

 None

Returns:

Type Description
TopicData

Information about topical inference in a dictionary.
Source code in turftopic/base.py 
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def prepare_topic_data(
    self,
    corpus: List[str],
    embeddings: Optional[np.ndarray] = None,
) -> TopicData:
    """Produces topic inference data for a given corpus, that can be then used and reused.
    Exists to allow visualizations out of the box with topicwizard.

    Parameters
    ----------
    corpus: list of str
        Documents to infer topical content for.
    embeddings: ndarray of shape (n_documents, n_dimensions)
        Embeddings of documents.

    Returns
    -------
    TopicData
        Information about topical inference in a dictionary.
    """
    if embeddings is None:
        embeddings = self.encode_documents(corpus)
    try:
        document_topic_matrix = self.transform(
            corpus, embeddings=embeddings
        )
    except (AttributeError, NotFittedError):
        document_topic_matrix = self.fit_transform(
            corpus, embeddings=embeddings
        )
    dtm = self.vectorizer.transform(corpus)  # type: ignore
    try:
        classes = self.classes_
    except AttributeError:
        classes = list(range(self.components_.shape[0]))
    res = TopicData(
        corpus=corpus,
        document_term_matrix=dtm,
        vocab=self.get_vocab(),
        document_topic_matrix=document_topic_matrix,
        document_representation=embeddings,
        topic_term_matrix=self.components_,  # type: ignore
        transform=getattr(self, "transform", None),
        topic_names=self.topic_names,
        classes=classes,
        has_negative_side=self.has_negative_side,
        hierarchy=getattr(self, "hierarchy", None),
    )
    return res

push_to_hub(repo_id)

Uploads model to HuggingFace Hub

Parameters:

Name Type Description Default
repo_id str

Repository to upload the model to.

 required
Source code in turftopic/base.py 
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def push_to_hub(self, repo_id: str):
    """Uploads model to HuggingFace Hub

    Parameters
    ----------
    repo_id: str
        Repository to upload the model to.
    """
    api = HfApi()
    api.create_repo(repo_id, exist_ok=True)
    with tempfile.TemporaryDirectory() as tmp_dir:
        readme_path = Path(tmp_dir).joinpath("README.md")
        with readme_path.open("w") as readme_file:
            readme_file.write(create_readme(self, repo_id))
        self.to_disk(tmp_dir)
        api.upload_folder(
            folder_path=tmp_dir,
            repo_id=repo_id,
            repo_type="model",
        )

to_disk(out_dir)

Persists model to directory on your machine.

Parameters:

Name Type Description Default
out_dir Union[Path, str]

Directory to save the model to.

 required
Source code in turftopic/base.py 
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def to_disk(self, out_dir: Union[Path, str]):
    """Persists model to directory on your machine.

    Parameters
    ----------
    out_dir: Path | str
        Directory to save the model to.
    """
    out_dir = Path(out_dir)
    out_dir.mkdir(exist_ok=True)
    package_versions = get_package_versions()
    with out_dir.joinpath("package_versions.json").open("w") as ver_file:
        ver_file.write(json.dumps(package_versions))
    joblib.dump(self, out_dir.joinpath("model.joblib"))