戒酒的李白
132 lines
4.5 KiB
Python
132 lines
4.5 KiB
Python
import numpy as np
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from typing import List, Union
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import plotly.graph_objects as go
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def visualize_term_rank(
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topic_model,
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topics: List[int] = None,
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log_scale: bool = False,
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custom_labels: Union[bool, str] = False,
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title: str = "<b>Term score decline per Topic</b>",
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width: int = 800,
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height: int = 500,
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) -> go.Figure:
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"""Visualize the ranks of all terms across all topics.
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Each topic is represented by a set of words. These words, however,
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do not all equally represent the topic. This visualization shows
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how many words are needed to represent a topic and at which point
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the beneficial effect of adding words starts to decline.
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Arguments:
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topic_model: A fitted BERTopic instance.
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topics: A selection of topics to visualize. These will be colored
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red where all others will be colored black.
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log_scale: Whether to represent the ranking on a log scale
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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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fig: A plotly figure
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Examples:
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To visualize the ranks of all words across
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all topics simply run:
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```python
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topic_model.visualize_term_rank()
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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_term_rank()
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fig.write_html("path/to/file.html")
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```
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<iframe src="../../getting_started/visualization/term_rank.html"
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style="width:1000px; height: 530px; border: 0px;""></iframe>
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<iframe src="../../getting_started/visualization/term_rank_log.html"
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style="width:1000px; height: 530px; border: 0px;""></iframe>
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Reference:
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This visualization was heavily inspired by the
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"Term Probability Decline" visualization found in an
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analysis by the amazing [tmtoolkit](https://tmtoolkit.readthedocs.io/).
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Reference to that specific analysis can be found
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[here](https://wzbsocialsciencecenter.github.io/tm_corona/tm_analysis.html).
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"""
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topics = [] if topics is None else topics
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topic_ids = topic_model.get_topic_info().Topic.unique().tolist()
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topic_words = [topic_model.get_topic(topic) for topic in topic_ids]
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values = np.array([[value[1] for value in values] for values in topic_words])
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indices = np.array([[value + 1 for value in range(len(values))] for values in topic_words])
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# Create figure
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lines = []
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for topic, x, y in zip(topic_ids, indices, values):
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if not any(y > 1.5):
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# labels
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if isinstance(custom_labels, str):
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label = f"{topic}_" + "_".join(list(zip(*topic_model.topic_aspects_[custom_labels][topic]))[0][:3])
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elif topic_model.custom_labels_ is not None and custom_labels:
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label = topic_model.custom_labels_[topic + topic_model._outliers]
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else:
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label = f"<b>Topic {topic}</b>:" + "_".join([word[0] for word in topic_model.get_topic(topic)])
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label = label[:50]
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# line parameters
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color = "red" if topic in topics else "black"
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opacity = 1 if topic in topics else 0.1
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if any(y == 0):
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y[y == 0] = min(values[values > 0])
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y = np.log10(y, out=y, where=y > 0) if log_scale else y
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line = go.Scatter(
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x=x,
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y=y,
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name="",
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hovertext=label,
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mode="lines+lines",
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opacity=opacity,
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line=dict(color=color, width=1.5),
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)
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lines.append(line)
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fig = go.Figure(data=lines)
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# Stylize layout
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fig.update_xaxes(range=[0, len(indices[0])], tick0=1, dtick=2)
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fig.update_layout(
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showlegend=False,
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template="plotly_white",
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title={
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"text": f"{title}",
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"y": 0.9,
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"x": 0.5,
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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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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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)
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fig.update_xaxes(title_text="Term Rank")
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if log_scale:
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fig.update_yaxes(title_text="c-TF-IDF score (log scale)")
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else:
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fig.update_yaxes(title_text="c-TF-IDF score")
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return fig
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