Heatmap
¶
Visualize a heatmap of the topic's similarity matrix
Based on the cosine similarity matrix between topic embeddings, a heatmap is created showing the similarity between 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. |
None |
n_clusters |
int |
Create n clusters and order the similarity matrix by those clusters. |
None |
width |
int |
The width of the figure. |
800 |
height |
int |
The height of the figure. |
800 |
Returns:
Type | Description |
---|---|
fig |
A plotly figure |
Usage:
To visualize the similarity matrix of topics simply run:
topic_model.visualize_heatmap()
Or if you want to save the resulting figure:
fig = topic_model.visualize_heatmap()
fig.write_html("path/to/file.html")
Source code in bertopic\plotting\_heatmap.py
def visualize_heatmap(topic_model,
topics: List[int] = None,
top_n_topics: int = None,
n_clusters: int = None,
width: int = 800,
height: int = 800) -> go.Figure:
""" Visualize a heatmap of the topic's similarity matrix
Based on the cosine similarity matrix between topic embeddings,
a heatmap is created showing the similarity between 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_clusters: Create n clusters and order the similarity
matrix by those clusters.
width: The width of the figure.
height: The height of the figure.
Returns:
fig: A plotly figure
Usage:
To visualize the similarity matrix of
topics simply run:
```python
topic_model.visualize_heatmap()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_heatmap()
fig.write_html("path/to/file.html")
```
<iframe src="../../getting_started/visualization/heatmap.html"
style="width:1000px; height: 720px; border: 0px;""></iframe>
"""
# Select topic embeddings
if topic_model.topic_embeddings is not None:
embeddings = np.array(topic_model.topic_embeddings)
else:
embeddings = topic_model.c_tf_idf
# 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())
# Order heatmap by similar clusters of topics
if n_clusters:
if n_clusters >= len(set(topics)):
raise ValueError("Make sure to set `n_clusters` lower than "
"the total number of unique topics.")
embeddings = embeddings[[topic + 1 for topic in topics]]
distance_matrix = cosine_similarity(embeddings)
Z = linkage(distance_matrix, 'ward')
clusters = fcluster(Z, t=n_clusters, criterion='maxclust')
# Extract new order of topics
mapping = {cluster: [] for cluster in clusters}
for topic, cluster in zip(topics, clusters):
mapping[cluster].append(topic)
mapping = [cluster for cluster in mapping.values()]
sorted_topics = [topic for cluster in mapping for topic in cluster]
else:
sorted_topics = topics
# Select embeddings
indices = np.array([topics.index(topic) for topic in sorted_topics])
embeddings = embeddings[indices]
distance_matrix = cosine_similarity(embeddings)
# Create nicer labels
new_labels = [[[str(topic), None]] + topic_model.get_topic(topic) for topic in sorted_topics]
new_labels = ["_".join([label[0] for label in labels[:4]]) for labels in new_labels]
new_labels = [label if len(label) < 30 else label[:27] + "..." for label in new_labels]
fig = px.imshow(distance_matrix,
labels=dict(color="Similarity Score"),
x=new_labels,
y=new_labels,
color_continuous_scale='GnBu'
)
fig.update_layout(
title={
'text': "<b>Similarity Matrix",
'y': .95,
'x': 0.55,
'xanchor': 'center',
'yanchor': 'top',
'font': dict(
size=22,
color="Black")
},
width=width,
height=height,
hoverlabel=dict(
bgcolor="white",
font_size=16,
font_family="Rockwell"
),
)
fig.update_layout(showlegend=True)
fig.update_layout(legend_title_text='Trend')
return fig