Documents with DataMapPlot
¶
Visualize documents and their topics in 2D as a static plot for publication using DataMapPlot.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
topic_model |
A fitted BERTopic instance. |
required | |
docs |
List[str]
|
The documents you used when calling either |
required |
topics |
List[int]
|
A selection of topics to visualize.
Not to be confused with the topics that you get from |
None
|
embeddings |
ndarray
|
The embeddings of all documents in |
None
|
reduced_embeddings |
ndarray
|
The 2D reduced embeddings of all documents in |
None
|
custom_labels |
Union[bool, str]
|
If bool, whether to use custom topic labels that were defined using
|
False
|
title |
str
|
Title of the plot. |
'Documents and Topics'
|
sub_title |
Union[str, None]
|
Sub-title of the plot. |
None
|
width |
int
|
The width of the figure. |
1200
|
height |
int
|
The height of the figure. |
1200
|
**datamap_kwds |
All further keyword args will be passed on to DataMapPlot's
|
{}
|
Returns:
Name | Type | Description |
---|---|---|
figure |
Figure
|
A Matplotlib Figure object. |
Examples:
To visualize the topics simply run:
topic_model.visualize_document_datamap(docs)
Do note that this re-calculates the embeddings and reduces them to 2D. The advised and preferred pipeline for using this function is as follows:
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP
# Prepare embeddings
docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=False)
# Train BERTopic
topic_model = BERTopic().fit(docs, embeddings)
# Reduce dimensionality of embeddings, this step is optional
# reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
# Run the visualization with the original embeddings
topic_model.visualize_document_datamap(docs, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_document_datamap(docs, reduced_embeddings=reduced_embeddings)
Or if you want to save the resulting figure:
fig = topic_model.visualize_document_datamap(docs, reduced_embeddings=reduced_embeddings)
fig.savefig("path/to/file.png", bbox_inches="tight")
Source code in bertopic\plotting\_datamap.py
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