Visualize topics, their sizes, and their corresponding words
This visualization is highly inspired by LDAvis, a great visualization
technique typically reserved for LDA.
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
|
None
|
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
|
|
'<b>Intertopic Distance Map</b>'
|
width |
int
|
|
650
|
height |
int
|
The height of the figure.
|
650
|
Examples:
To visualize the topics simply run:
topic_model.visualize_topics()
Or if you want to save the resulting figure:
fig = topic_model.visualize_topics()
fig.write_html("path/to/file.html")
Source code in bertopic\plotting\_topics.py
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88 | def visualize_topics(topic_model,
topics: List[int] = None,
top_n_topics: int = None,
custom_labels: Union[bool, str] = False,
title: str = "<b>Intertopic Distance Map</b>",
width: int = 650,
height: int = 650) -> go.Figure:
""" Visualize topics, their sizes, and their corresponding words
This visualization is highly inspired by LDAvis, a great visualization
technique typically reserved for LDA.
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
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 the figure.
height: The height of the figure.
Examples:
To visualize the topics simply run:
```python
topic_model.visualize_topics()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics()
fig.write_html("path/to/file.html")
```
<iframe src="../../getting_started/visualization/viz.html"
style="width:1000px; height: 680px; border: 0px;""></iframe>
"""
# 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())
# Extract topic words and their frequencies
topic_list = sorted(topics)
frequencies = [topic_model.topic_sizes_[topic] for topic in topic_list]
if isinstance(custom_labels, str):
words = [[[str(topic), None]] + topic_model.topic_aspects_[custom_labels][topic] for topic in topic_list]
words = ["_".join([label[0] for label in labels[:4]]) for labels in words]
words = [label if len(label) < 30 else label[:27] + "..." for label in words]
elif custom_labels and topic_model.custom_labels_ is not None:
words = [topic_model.custom_labels_[topic + topic_model._outliers] for topic in topic_list]
else:
words = [" | ".join([word[0] for word in topic_model.get_topic(topic)[:5]]) for topic in topic_list]
# Embed c-TF-IDF into 2D
all_topics = sorted(list(topic_model.get_topics().keys()))
indices = np.array([all_topics.index(topic) for topic in topics])
if topic_model.topic_embeddings_ is not None:
embeddings = topic_model.topic_embeddings_[indices]
embeddings = UMAP(n_neighbors=2, n_components=2, metric='cosine', random_state=42).fit_transform(embeddings)
else:
embeddings = topic_model.c_tf_idf_.toarray()[indices]
embeddings = MinMaxScaler().fit_transform(embeddings)
embeddings = UMAP(n_neighbors=2, n_components=2, metric='hellinger', random_state=42).fit_transform(embeddings)
# Visualize with plotly
df = pd.DataFrame({"x": embeddings[:, 0], "y": embeddings[:, 1],
"Topic": topic_list, "Words": words, "Size": frequencies})
return _plotly_topic_visualization(df, topic_list, title, width, height)
|