Documents with DataMapPlot

Visualize documents and their topics in 2D as a static plot for publication using DataMapPlot.

Parameters:

Name	Type	Description	Default
topic_model		A fitted BERTopic instance.	required
docs	List[str]	The documents you used when calling either fit or fit_transform	required
topics	List[int]	A selection of topics to visualize. Not to be confused with the topics that you get from .fit_transform. For example, if you want to visualize only topics 1 through 5: topics = [1, 2, 3, 4, 5]. Documents not in these topics will be shown as noise points.	None
embeddings	ndarray	The embeddings of all documents in docs.	None
reduced_embeddings	ndarray	The 2D reduced embeddings of all documents in docs.	None
custom_labels	Union[bool, str]	If bool, whether to use custom topic labels that were defined using topic_model.set_topic_labels. If str, it uses labels from other aspects, e.g., "Aspect1".	False
title	str	Title of the plot.	'Documents and Topics'
sub_title	Optional[str]	Sub-title of the plot.	None
width	int	The width of the figure.	1200
height	int	The height of the figure.	1200
**datamap_kwds		All further keyword args will be passed on to DataMapPlot's create_plot function. See the DataMapPlot documentation for more details.	{}

Returns:

Type	Description
figure	A Matplotlib Figure object.

Examples:

To visualize the topics simply run:

topic_model.visualize_document_datamap(docs)

Do note that this re-calculates the embeddings and reduces them to 2D. The advised and preferred pipeline for using this function is as follows:

from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP

# Prepare embeddings
docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=False)

# Train BERTopic
topic_model = BERTopic().fit(docs, embeddings)

# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)

# Run the visualization with the original embeddings
topic_model.visualize_document_datamap(docs, embeddings=embeddings)

# Or, if you have reduced the original embeddings already:
topic_model.visualize_document_datamap(docs, reduced_embeddings=reduced_embeddings)

Or if you want to save the resulting figure:

fig = topic_model.visualize_document_datamap(docs, reduced_embeddings=reduced_embeddings)
fig.savefig("path/to/file.png", bbox_inches="tight")

Source code in bertopic\plotting\_datamap.py

def visualize_document_datamap(
    topic_model,
    docs: List[str],
    topics: List[int] = None,
    embeddings: np.ndarray = None,
    reduced_embeddings: np.ndarray = None,
    custom_labels: Union[bool, str] = False,
    title: str = "Documents and Topics",
    sub_title: Union[str, None] = None,
    width: int = 1200,
    height: int = 1200,
    **datamap_kwds,
) -> Figure:
    """Visualize documents and their topics in 2D as a static plot for publication using
    DataMapPlot.

    Arguments:
        topic_model:  A fitted BERTopic instance.
        docs: The documents you used when calling either `fit` or `fit_transform`
        topics: A selection of topics to visualize.
                Not to be confused with the topics that you get from `.fit_transform`.
                For example, if you want to visualize only topics 1 through 5:
                `topics = [1, 2, 3, 4, 5]`. Documents not in these topics will be shown
                as noise points.
        embeddings:  The embeddings of all documents in `docs`.
        reduced_embeddings:  The 2D reduced embeddings of all documents in `docs`.
        custom_labels:  If bool, whether to use custom topic labels that were defined using
                       `topic_model.set_topic_labels`.
                       If `str`, it uses labels from other aspects, e.g., "Aspect1".
        title: Title of the plot.
        sub_title: Sub-title of the plot.
        width: The width of the figure.
        height: The height of the figure.
        **datamap_kwds:  All further keyword args will be passed on to DataMapPlot's
                         `create_plot` function. See the DataMapPlot documentation
                         for more details.

    Returns:
        figure: A Matplotlib Figure object.

    Examples:
    To visualize the topics simply run:

    ```python
    topic_model.visualize_document_datamap(docs)
    ```

    Do note that this re-calculates the embeddings and reduces them to 2D.
    The advised and preferred pipeline for using this function is as follows:

    ```python
    from sklearn.datasets import fetch_20newsgroups
    from sentence_transformers import SentenceTransformer
    from bertopic import BERTopic
    from umap import UMAP

    # Prepare embeddings
    docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
    embeddings = sentence_model.encode(docs, show_progress_bar=False)

    # Train BERTopic
    topic_model = BERTopic().fit(docs, embeddings)

    # Reduce dimensionality of embeddings, this step is optional
    # reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)

    # Run the visualization with the original embeddings
    topic_model.visualize_document_datamap(docs, embeddings=embeddings)

    # Or, if you have reduced the original embeddings already:
    topic_model.visualize_document_datamap(docs, reduced_embeddings=reduced_embeddings)
    ```

    Or if you want to save the resulting figure:

    ```python
    fig = topic_model.visualize_document_datamap(docs, reduced_embeddings=reduced_embeddings)
    fig.savefig("path/to/file.png", bbox_inches="tight")
    ```
    <img src="../../getting_started/visualization/datamapplot.png",
         alt="DataMapPlot of 20-Newsgroups", width=800, height=800></img>
    """
    topic_per_doc = topic_model.topics_

    df = pd.DataFrame({"topic": np.array(topic_per_doc)})
    df["doc"] = docs
    df["topic"] = topic_per_doc

    # Extract embeddings if not already done
    if embeddings is None and reduced_embeddings is None:
        embeddings_to_reduce = topic_model._extract_embeddings(df.doc.to_list(), method="document")
    else:
        embeddings_to_reduce = embeddings

    # Reduce input embeddings
    if reduced_embeddings is None:
        umap_model = UMAP(n_neighbors=15, n_components=2, min_dist=0.15, metric="cosine").fit(embeddings_to_reduce)
        embeddings_2d = umap_model.embedding_
    else:
        embeddings_2d = reduced_embeddings

    unique_topics = set(topic_per_doc)

    # Prepare text and names
    if isinstance(custom_labels, str):
        names = [[[str(topic), None]] + topic_model.topic_aspects_[custom_labels][topic] for topic in unique_topics]
        names = [" ".join([label[0] for label in labels[:4]]) for labels in names]
        names = [label if len(label) < 30 else label[:27] + "..." for label in names]
    elif topic_model.custom_labels_ is not None and custom_labels:
        names = [topic_model.custom_labels_[topic + topic_model._outliers] for topic in unique_topics]
    else:
        names = [
            f"Topic-{topic}: " + " ".join([word for word, value in topic_model.get_topic(topic)][:3])
            for topic in unique_topics
        ]

    topic_name_mapping = {topic_num: topic_name for topic_num, topic_name in zip(unique_topics, names)}
    topic_name_mapping[-1] = "Unlabelled"

    # If a set of topics is chosen, set everything else to "Unlabelled"
    if topics is not None:
        selected_topics = set(topics)
        for topic_num in topic_name_mapping:
            if topic_num not in selected_topics:
                topic_name_mapping[topic_num] = "Unlabelled"

    # Map in topic names and plot
    named_topic_per_doc = pd.Series(topic_per_doc).map(topic_name_mapping).values

    figure, axes = datamapplot.create_plot(
        embeddings_2d,
        named_topic_per_doc,
        figsize=(width / 100, height / 100),
        dpi=100,
        title=title,
        sub_title=sub_title,
        **datamap_kwds,
    )

    return figure