BERTopic/bertopic/plotting/_datamap.py

import numpy as np
import pandas as pd
from typing import List, Union
from umap import UMAP
from warnings import warn
try:
    import datamapplot
    from matplotlib.figure import Figure
except ImportError:
    warn("Data map plotting is unavailable unless datamapplot is installed.")
    # Create a dummy figure type for typing
    class Figure (object):
        pass

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