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import numpy as np
import pandas as pd
from umap import UMAP
from typing import List, Union
from sklearn.preprocessing import MinMaxScaler
import plotly.express as px
import plotly.graph_objects as go
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)
def _plotly_topic_visualization(df: pd.DataFrame,
topic_list: List[str],
title: str,
width: int,
height: int):
""" Create plotly-based visualization of topics with a slider for topic selection """
def get_color(topic_selected):
if topic_selected == -1:
marker_color = ["#B0BEC5" for _ in topic_list]
else:
marker_color = ["red" if topic == topic_selected else "#B0BEC5" for topic in topic_list]
return [{'marker.color': [marker_color]}]
# Prepare figure range
x_range = (df.x.min() - abs((df.x.min()) * .15), df.x.max() + abs((df.x.max()) * .15))
y_range = (df.y.min() - abs((df.y.min()) * .15), df.y.max() + abs((df.y.max()) * .15))
# Plot topics
fig = px.scatter(df, x="x", y="y", size="Size", size_max=40, template="simple_white", labels={"x": "", "y": ""},
hover_data={"Topic": True, "Words": True, "Size": True, "x": False, "y": False})
fig.update_traces(marker=dict(color="#B0BEC5", line=dict(width=2, color='DarkSlateGrey')))
# Update hover order
fig.update_traces(hovertemplate="<br>".join(["<b>Topic %{customdata[0]}</b>",
"%{customdata[1]}",
"Size: %{customdata[2]}"]))
# Create a slider for topic selection
steps = [dict(label=f"Topic {topic}", method="update", args=get_color(topic)) for topic in topic_list]
sliders = [dict(active=0, pad={"t": 50}, steps=steps)]
# Stylize layout
fig.update_layout(
title={
'text': f"{title}",
'y': .95,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top',
'font': dict(
size=22,
color="Black")
},
width=width,
height=height,
hoverlabel=dict(
bgcolor="white",
font_size=16,
font_family="Rockwell"
),
xaxis={"visible": False},
yaxis={"visible": False},
sliders=sliders
)
# Update axes ranges
fig.update_xaxes(range=x_range)
fig.update_yaxes(range=y_range)
# Add grid in a 'plus' shape
fig.add_shape(type="line",
x0=sum(x_range) / 2, y0=y_range[0], x1=sum(x_range) / 2, y1=y_range[1],
line=dict(color="#CFD8DC", width=2))
fig.add_shape(type="line",
x0=x_range[0], y0=sum(y_range) / 2, x1=x_range[1], y1=sum(y_range) / 2,
line=dict(color="#9E9E9E", width=2))
fig.add_annotation(x=x_range[0], y=sum(y_range) / 2, text="D1", showarrow=False, yshift=10)
fig.add_annotation(y=y_range[1], x=sum(x_range) / 2, text="D2", showarrow=False, xshift=10)
fig.data = fig.data[::-1]
return fig
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