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Arguments:
topic_model: A fitted BERTopic instance.
docs: The documents you used when calling either `fit` or `fit_transform`
topics: A selection of topics to visualize.
Not to be confused with the topics that you get from `.fit_transform`.
For example, if you want to visualize only topics 1 through 5:
`topics = [1, 2, 3, 4, 5]`.
embeddings: The embeddings of all documents in `docs`.
reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
sample: The percentage of documents in each topic that you would like to keep.
Value can be between 0 and 1. Setting this value to, for example,
0.1 (10% of documents in each topic) makes it easier to visualize
millions of documents as a subset is chosen.
hide_annotations: Hide the names of the traces on top of each cluster.
hide_document_hover: Hide the content of the documents when hovering over
specific points. Helps to speed up generation of visualization.
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_documents(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:
```python
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_documents(docs, embeddings=embeddings)
# Or, if you have reduced the original embeddings already:
topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
fig.write_html("path/to/file.html")
```
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