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import numpy as np
from typing import List, Union
import plotly.graph_objects as go
def visualize_term_rank(topic_model,
topics: List[int] = None,
log_scale: bool = False,
custom_labels: Union[bool, str] = False,
title: str = "<b>Term score decline per Topic</b>",
width: int = 800,
height: int = 500) -> go.Figure:
""" Visualize the ranks of all terms across all topics
Each topic is represented by a set of words. These words, however,
do not all equally represent the topic. This visualization shows
how many words are needed to represent a topic and at which point
the beneficial effect of adding words starts to decline.
Arguments:
topic_model: A fitted BERTopic instance.
topics: A selection of topics to visualize. These will be colored
red where all others will be colored black.
log_scale: Whether to represent the ranking on a log scale
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.
Returns:
fig: A plotly figure
Examples:
To visualize the ranks of all words across
all topics simply run:
```python
topic_model.visualize_term_rank()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_term_rank()
fig.write_html("path/to/file.html")
```
<iframe src="../../getting_started/visualization/term_rank.html"
style="width:1000px; height: 530px; border: 0px;""></iframe>
<iframe src="../../getting_started/visualization/term_rank_log.html"
style="width:1000px; height: 530px; border: 0px;""></iframe>
Reference:
This visualization was heavily inspired by the
"Term Probability Decline" visualization found in an
analysis by the amazing [tmtoolkit](https://tmtoolkit.readthedocs.io/).
Reference to that specific analysis can be found
[here](https://wzbsocialsciencecenter.github.io/tm_corona/tm_analysis.html).
"""
topics = [] if topics is None else topics
topic_ids = topic_model.get_topic_info().Topic.unique().tolist()
topic_words = [topic_model.get_topic(topic) for topic in topic_ids]
values = np.array([[value[1] for value in values] for values in topic_words])
indices = np.array([[value + 1 for value in range(len(values))] for values in topic_words])
# Create figure
lines = []
for topic, x, y in zip(topic_ids, indices, values):
if not any(y > 1.5):
# labels
if isinstance(custom_labels, str):
label = f"{topic}_" + "_".join(list(zip(*topic_model.topic_aspects_[custom_labels][topic]))[0][:3])
elif topic_model.custom_labels_ is not None and custom_labels:
label = topic_model.custom_labels_[topic + topic_model._outliers]
else:
label = f"<b>Topic {topic}</b>:" + "_".join([word[0] for word in topic_model.get_topic(topic)])
label = label[:50]
# line parameters
color = "red" if topic in topics else "black"
opacity = 1 if topic in topics else .1
if any(y == 0):
y[y == 0] = min(values[values > 0])
y = np.log10(y, out=y, where=y > 0) if log_scale else y
line = go.Scatter(x=x, y=y,
name="",
hovertext=label,
mode="lines+lines",
opacity=opacity,
line=dict(color=color, width=1.5))
lines.append(line)
fig = go.Figure(data=lines)
# Stylize layout
fig.update_xaxes(range=[0, len(indices[0])], tick0=1, dtick=2)
fig.update_layout(
showlegend=False,
template="plotly_white",
title={
'text': f"{title}",
'y': .9,
'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"
),
)
fig.update_xaxes(title_text='Term Rank')
if log_scale:
fig.update_yaxes(title_text='c-TF-IDF score (log scale)')
else:
fig.update_yaxes(title_text='c-TF-IDF score')
return fig
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