Barchart

Visualize a barchart of selected topics

Parameters:

Name	Type	Description	Default
topic_model		A fitted BERTopic instance.	required
topics	List[int]	A selection of topics to visualize.	None
top_n_topics	int	Only select the top n most frequent topics.	8
n_words	int	Number of words to show in a topic	5
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.	'<b>Topic Word Scores</b>'
width	int	The width of each figure.	250
height	int	The height of each figure.	250
autoscale	bool	Whether to automatically calculate the height of the figures to fit the whole bar text	False

Returns:

Name	Type	Description
fig	Figure	A plotly figure

Examples:

To visualize the barchart of selected topics simply run:

topic_model.visualize_barchart()

Or if you want to save the resulting figure:

fig = topic_model.visualize_barchart()
fig.write_html("path/to/file.html")

Source code in bertopic\plotting\_barchart.py

def visualize_barchart(topic_model,
                       topics: List[int] = None,
                       top_n_topics: int = 8,
                       n_words: int = 5,
                       custom_labels: Union[bool, str] = False,
                       title: str = "<b>Topic Word Scores</b>",
                       width: int = 250,
                       height: int = 250,
                       autoscale: bool=False) -> go.Figure:
    """ Visualize a barchart of selected topics

    Arguments:
        topic_model: A fitted BERTopic instance.
        topics: A selection of topics to visualize.
        top_n_topics: Only select the top n most frequent topics.
        n_words: Number of words to show in a topic
        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.
        width: The width of each figure.
        height: The height of each figure.
        autoscale: Whether to automatically calculate the height of the figures to fit the whole bar text

    Returns:
        fig: A plotly figure

    Examples:

    To visualize the barchart of selected topics
    simply run:

    ```python
    topic_model.visualize_barchart()
    ```

    Or if you want to save the resulting figure:

    ```python
    fig = topic_model.visualize_barchart()
    fig.write_html("path/to/file.html")
    ```
    <iframe src="../../getting_started/visualization/bar_chart.html"
    style="width:1100px; height: 660px; border: 0px;""></iframe>
    """
    colors = itertools.cycle(["#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#009E73", "#F0E442"])

    # Select topics based on top_n and topics args
    freq_df = topic_model.get_topic_freq()
    freq_df = freq_df.loc[freq_df.Topic != -1, :]
    if topics is not None:
        topics = list(topics)
    elif top_n_topics is not None:
        topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
    else:
        topics = sorted(freq_df.Topic.to_list()[0:6])

    # Initialize figure
    if isinstance(custom_labels, str):
        subplot_titles = [[[str(topic), None]] + topic_model.topic_aspects_[custom_labels][topic] for topic in topics]
        subplot_titles = ["_".join([label[0] for label in labels[:4]]) for labels in subplot_titles]
        subplot_titles = [label if len(label) < 30 else label[:27] + "..." for label in subplot_titles]
    elif topic_model.custom_labels_ is not None and custom_labels:
        subplot_titles = [topic_model.custom_labels_[topic + topic_model._outliers] for topic in topics]
    else:
        subplot_titles = [f"Topic {topic}" for topic in topics]
    columns = 4
    rows = int(np.ceil(len(topics) / columns))
    fig = make_subplots(rows=rows,
                        cols=columns,
                        shared_xaxes=False,
                        horizontal_spacing=.1,
                        vertical_spacing=.4 / rows if rows > 1 else 0,
                        subplot_titles=subplot_titles)

    # Add barchart for each topic
    row = 1
    column = 1
    for topic in topics:
        words = [word + "  " for word, _ in topic_model.get_topic(topic)][:n_words][::-1]
        scores = [score for _, score in topic_model.get_topic(topic)][:n_words][::-1]

        fig.add_trace(
            go.Bar(x=scores,
                   y=words,
                   orientation='h',
                   marker_color=next(colors)),
            row=row, col=column)

        if autoscale:
            if len(words) > 12:
                height = 250 + (len(words) - 12) * 11

            if len(words) > 9:
                fig.update_yaxes(
                    tickfont=dict(size=(height - 140) // len(words))
                )

        if column == columns:
            column = 1
            row += 1
        else:
            column += 1

    # Stylize graph
    fig.update_layout(
        template="plotly_white",
        showlegend=False,
        title={
            'text': f"{title}",
            'x': .5,
            'xanchor': 'center',
            'yanchor': 'top',
            'font': dict(
                size=22,
                color="Black")
        },
        width=width*4,
        height=height*rows if rows > 1 else height * 1.3,
        hoverlabel=dict(
            bgcolor="white",
            font_size=16,
            font_family="Rockwell"
        ),
    )

    fig.update_xaxes(showgrid=True)
    fig.update_yaxes(showgrid=True)

    return fig