如何更改 BERTopic 中的函数参数（visualize_topics_over_time）？

Question

我正在使用 BERTopic 执行主题建模，一切正常。但是，由于我强制算法使用 nr_topics=10 作为输出给我 10 个主题，并且当我使用超时可视化主题时 topic_model.visualize_topics_over_time(topics_over_time, top_n_topics=10, width=1250, height=450)，由于函数 visualize_topics_over_time 中只提到了 7 种颜色，因此主题重复了一些颜色。我尝试在我的 python 笔记本中使用其他颜色值执行相同的功能，但它给了我以下错误：

有人可以帮我更新功能，增加四种颜色吗？

Answer 1

要为函数添加颜色，您确实需要复制函数并将其更改为包含更多颜色：

import pandas as pd
from typing import List
import plotly.graph_objects as go
from sklearn.preprocessing import normalize


def visualize_topics_over_time(topic_model,
                               topics_over_time: pd.DataFrame,
                               top_n_topics: int = None,
                               topics: List[int] = None,
                               normalize_frequency: bool = False,
                               width: int = 1250,
                               height: int = 450) -> go.Figure:
    """ Visualize topics over time
    Arguments:
        topic_model: A fitted BERTopic instance.
        topics_over_time: The topics you would like to be visualized with the
                          corresponding topic representation
        top_n_topics: To visualize the most frequent topics instead of all
        topics: Select which topics you would like to be visualized
        normalize_frequency: Whether to normalize each topic's frequency individually
        width: The width of the figure.
        height: The height of the figure.
    Returns:
        A plotly.graph_objects.Figure including all traces
    Usage:
    To visualize the topics over time, simply run:
    ```python
    topics_over_time = topic_model.topics_over_time(docs, topics, timestamps)
    topic_model.visualize_topics_over_time(topics_over_time)
    ```
    Or if you want to save the resulting figure:
    ```python
    fig = topic_model.visualize_topics_over_time(topics_over_time)
    fig.write_html("path/to/file.html")
    ```
    <iframe src="../../getting_started/visualization/trump.html"
    style="width:1000px; height: 680px; border: 0px;""></iframe>
    """
    colors = ["#E69F00", "#56B4E9", "#009E73", "#F0E442", "#D55E00", "#0072B2", "#CC79A7"]  # DEFINE MORE COLORS HERE

    # Select topics
    if topics:
        selected_topics = topics
    elif top_n_topics:
        selected_topics = topic_model.get_topic_freq().head(top_n_topics + 1)[1:].Topic.values
    else:
        selected_topics = topic_model.get_topic_freq().Topic.values

    # Prepare data
    topic_names = {key: value[:40] + "..." if len(value) > 40 else value
                   for key, value in topic_model.topic_names.items()}
    topics_over_time["Name"] = topics_over_time.Topic.map(topic_names)
    data = topics_over_time.loc[topics_over_time.Topic.isin(selected_topics), :]

    # Add traces
    fig = go.Figure()
    for index, topic in enumerate(data.Topic.unique()):
        trace_data = data.loc[data.Topic == topic, :]
        topic_name = trace_data.Name.values[0]
        words = trace_data.Words.values
        if normalize_frequency:
            y = normalize(trace_data.Frequency.values.reshape(1, -1))[0]
        else:
            y = trace_data.Frequency
        fig.add_trace(go.Scatter(x=trace_data.Timestamp, y=y,
                                 mode='lines',
                                 marker_color=colors[index % 7],
                                 hoverinfo="text",
                                 name=topic_name,
                                 hovertext=[f'<b>Topic {topic}</b><br>Words: {word}' for word in words]))

    # Styling of the visualization
    fig.update_xaxes(showgrid=True)
    fig.update_yaxes(showgrid=True)
    fig.update_layout(
        yaxis_title="Normalized Frequency" if normalize_frequency else "Frequency",
        title={
            'text': "<b>Topics over Time",
            'y': .95,
            'x': 0.40,
            'xanchor': 'center',
            'yanchor': 'top',
            'font': dict(
                size=22,
                color="Black")
        },
        template="simple_white",
        width=width,
        height=height,
        hoverlabel=dict(
            bgcolor="white",
            font_size=16,
            font_family="Rockwell"
        ),
        legend=dict(
            title="<b>Global Topic Representation",
        )
    )
    return fig

那么，您可以使用如下函数：

import re
import pandas as pd
from bertopic import BERTopic

# Prepare data
trump = pd.read_csv('https://drive.google.com/uc?export=download&id=1xRKHaP-QwACMydlDnyFPEaFdtskJuBa6')
trump.text = trump.apply(lambda row: re.sub(r"http\S+", "", row.text).lower(), 1)
trump.text = trump.apply(lambda row: " ".join(filter(lambda x:x[0]!="@", row.text.split())), 1)
trump.text = trump.apply(lambda row: " ".join(re.sub("[^a-zA-Z]+", " ", row.text).split()), 1)
trump = trump.loc[(trump.isRetweet == "f") & (trump.text != ""), :]
timestamps = trump.date.to_list()
tweets = trump.text.to_list()

# Create topics over time
model = BERTopic(verbose=True)
topics, probs = model.fit_transform(tweets)
topics_over_time = model.topics_over_time(tweets, topics, timestamps)

# Visualize topics over time with the updated colors
visualize_topics_over_time(model, topics_over_time)

如何更改 BERTopic 中的函数参数（visualize_topics_over_time）？

How to change the function parameters (visualize_topics_over_time) in BERTopic?

python

plot