戒酒的李白
135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
import pandas as pd
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from typing import List, Union
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import plotly.graph_objects as go
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from sklearn.preprocessing import normalize
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def visualize_topics_over_time(
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topic_model,
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topics_over_time: pd.DataFrame,
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top_n_topics: int = None,
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topics: List[int] = None,
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normalize_frequency: bool = False,
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custom_labels: Union[bool, str] = False,
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title: str = "<b>Topics over Time</b>",
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width: int = 1250,
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height: int = 450,
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) -> go.Figure:
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"""Visualize topics over time.
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Arguments:
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topic_model: A fitted BERTopic instance.
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topics_over_time: The topics you would like to be visualized with the
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corresponding topic representation
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top_n_topics: To visualize the most frequent topics instead of all
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topics: Select which topics you would like to be visualized
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normalize_frequency: Whether to normalize each topic's frequency individually
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custom_labels: If bool, whether to use custom topic labels that were defined using
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`topic_model.set_topic_labels`.
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If `str`, it uses labels from other aspects, e.g., "Aspect1".
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title: Title of the plot.
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width: The width of the figure.
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height: The height of the figure.
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Returns:
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A plotly.graph_objects.Figure including all traces
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Examples:
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To visualize the topics over time, simply run:
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```python
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topics_over_time = topic_model.topics_over_time(docs, timestamps)
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topic_model.visualize_topics_over_time(topics_over_time)
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```
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Or if you want to save the resulting figure:
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```python
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fig = topic_model.visualize_topics_over_time(topics_over_time)
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fig.write_html("path/to/file.html")
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```
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<iframe src="../../getting_started/visualization/trump.html"
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style="width:1000px; height: 680px; border: 0px;""></iframe>
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"""
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colors = [
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"#E69F00",
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"#56B4E9",
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"#009E73",
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"#F0E442",
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"#D55E00",
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"#0072B2",
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"#CC79A7",
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]
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# Select topics based on top_n and topics args
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freq_df = topic_model.get_topic_freq()
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freq_df = freq_df.loc[freq_df.Topic != -1, :]
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if topics is not None:
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selected_topics = list(topics)
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elif top_n_topics is not None:
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selected_topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
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else:
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selected_topics = sorted(freq_df.Topic.to_list())
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# Prepare data
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if isinstance(custom_labels, str):
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topic_names = [[[str(topic), None]] + topic_model.topic_aspects_[custom_labels][topic] for topic in topics]
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topic_names = ["_".join([label[0] for label in labels[:4]]) for labels in topic_names]
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topic_names = [label if len(label) < 30 else label[:27] + "..." for label in topic_names]
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topic_names = {key: topic_names[index] for index, key in enumerate(topic_model.topic_labels_.keys())}
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elif topic_model.custom_labels_ is not None and custom_labels:
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topic_names = {
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key: topic_model.custom_labels_[key + topic_model._outliers] for key, _ in topic_model.topic_labels_.items()
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}
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else:
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topic_names = {
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key: value[:40] + "..." if len(value) > 40 else value for key, value in topic_model.topic_labels_.items()
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}
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topics_over_time["Name"] = topics_over_time.Topic.map(topic_names)
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data = topics_over_time.loc[topics_over_time.Topic.isin(selected_topics), :].sort_values(["Topic", "Timestamp"])
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# Add traces
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fig = go.Figure()
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for index, topic in enumerate(data.Topic.unique()):
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trace_data = data.loc[data.Topic == topic, :]
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topic_name = trace_data.Name.values[0]
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words = trace_data.Words.values
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if normalize_frequency:
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y = normalize(trace_data.Frequency.values.reshape(1, -1))[0]
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else:
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y = trace_data.Frequency
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fig.add_trace(
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go.Scatter(
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x=trace_data.Timestamp,
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y=y,
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mode="lines",
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marker_color=colors[index % 7],
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hoverinfo="text",
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name=topic_name,
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hovertext=[f"<b>Topic {topic}</b><br>Words: {word}" for word in words],
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)
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)
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# Styling of the visualization
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fig.update_xaxes(showgrid=True)
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fig.update_yaxes(showgrid=True)
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fig.update_layout(
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yaxis_title="Normalized Frequency" if normalize_frequency else "Frequency",
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title={
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"text": f"{title}",
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"y": 0.95,
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"x": 0.40,
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"xanchor": "center",
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"yanchor": "top",
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"font": dict(size=22, color="Black"),
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},
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template="simple_white",
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width=width,
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height=height,
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hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"),
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legend=dict(
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title="<b>Global Topic Representation",
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),
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)
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return fig
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