Interpreting and Visualizing Models
Interpreting topic models can be challenging. Luckily Turftopic comes loaded with a bunch of utilities you can use for interpreting your topic models.
from turftopic import KeyNMF
model = KeyNMF(10)
topic_data = model.prepare_topic_data(corpus)
Topic Tables
The easiest way you can investigate topics in your fitted model is to use the built-in pretty printing utilities, that you can call on every fitted model or TopicData
object.
Interpret your models with topic tables
model.print_topics()
# or
topic_data.print_topics()
Topic ID | Top 10 Words |
---|---|
0 | armenians, armenian, armenia, turks, turkish, genocide, azerbaijan, soviet, turkey, azerbaijani |
1 | sale, price, shipping, offer, sell, prices, interested, 00, games, selling |
2 | christians, christian, bible, christianity, church, god, scripture, faith, jesus, sin |
3 | encryption, chip, clipper, nsa, security, secure, privacy, encrypted, crypto, cryptography |
.... |
# Print highest ranking documents for topic 0
model.print_representative_documents(0, corpus, document_topic_matrix)
# since topic_data already stores the corpus and the doc-topic-matrix, you only need to give a topic ID
topic_data.print_representative_documents(0)
Document | Score |
---|---|
Poor 'Poly'. I see you're preparing the groundwork for yet another retreat from your... | 0.40 |
Then you must be living in an alternate universe. Where were they? An Appeal to Mankind During the... | 0.40 |
It is 'Serdar', 'kocaoglan'. Just love it. Well, it could be your head wasn't screwed on just right... | 0.39 |
document = "I think guns should definitely banned from all public institutions, such as schools."
model.print_topic_distribution(document)
# or
topic_data.print_topic_distribution(document)
Topic name | Score |
---|---|
7_gun_guns_firearms_weapons | 0.05 |
17_mail_address_email_send | 0.00 |
3_encryption_chip_clipper_nsa | 0.00 |
19_baseball_pitching_pitcher_hitter | 0.00 |
11_graphics_software_program_3d | 0.00 |
You can also export tables as pandas DataFrames by removing the print_
prefix, and postfixing the method with _df
or export tables in a given format, by using the export_<something>
method instead of print_<something>
.
model.topics_df()
model.topic_distribution_df("something something")
topic_data.representative_documents_df(5)
model.export_topics(format="markdown")
model.export_topic_distribution("something something", format="markdown")
topic_data.export_representative_documents(5, format="markdown")
model.export_topics(format="latex")
model.export_topic_distribution("something something", format="latex")
topic_data.export_representative_documents(5, format="latex")
model.export_topics(format="csv")
model.export_topic_distribution("something something", format="csv")
topic_data.export_representative_documents(5, format="csv")
Visualization with topicwizard
Turftopic comes with a number of model-specific visualization utilities, which you can check out on the models page. We do provide a general overview here, as well as instructions on how to use topicwizard with Turftopic for interactive topic interpretation.
To use topicwizard you will first have to install it:
pip install topic-wizard
Web App
The easiest way to investigate any topic model interactively is to use the topicwizard web app.
You can launch the app either using a TopicData
or a model object and a representative sample of documents.
topic_data.visualize_topicwizard()
import topicwizard
topicwizard.visualize(corpus=documents, model=model)
Figures
You can also produce individual interactive figures using the Figures API in topicwizard.
Almost all figures in the Figures API can be called on the figures
submodule of any TopicData
object.
Interpret your models using interactive figures
topic_data.figures.topic_map()
topic_data.figures.topic_barcharts()
topic_data.figures.word_map()
topic_data.figures.topic_wordclouds()
topic_data.figures.document_map()
Datamapplot (Clustering models)
You can interactively explore clusters using datamapplot directly in Turftopic!
You will first have to install datamapplot
for this to work:
pip install turftopic[datamapplot]
from turftopic import ClusteringTopicModel
from turftopic.namers import OpenAITopicNamer
model = ClusteringTopicModel(feature_importance="centroid").fit(corpus)
namer = OpenAITopicNamer("gpt-4o-mini")
model.rename_topics(namer)
fig = model.plot_clusters_datamapplot()
fig.save("clusters_visualization.html")
fig
Info
If you are not running Turftopic from a Jupyter notebook, make sure to call fig.show()
. This will open up a new browser tab with the interactive figure.
Naming Topics
Topics in Turftopic by default are named based on the highest ranking keywords for a given topic. You might however want to get more fitting names for your topics either automatically or assigning them manually. See a our detailed guide about Namers to learn how you can use LLMs to assign names to topics.
Examples
from turftopic import KeyNMF
from turftopic.namers import OpenAITopicNamer
namer = OpenAITopicNamer("gpt-4o-mini")
model.rename_topics(namer)
model.print_topics()
Topic ID | Topic Name | Highest Ranking |
---|---|---|
0 | Operating Systems and Software | windows, dos, os, ms, microsoft, unix, nt, memory, program, apps |
1 | Atheism and Belief Systems | atheism, atheist, atheists, belief, religion, religious, theists, beliefs, believe, faith |
2 | Computer Architecture and Performance | motherboard, ram, memory, cpu, bios, isa, speed, 486, bus, performance |
... |
from turftopic import SemanticSignalSeparation
model = SemanticSignalSeparation(10).fit(corpus)
model.rename_topics({0: "New name for topic 0", 5: "New name for topic 5"})
API Reference
turftopic.container.TopicContainer
Bases: ABC
Base class for classes that contain topical information.
Source code in turftopic/container.py
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|
topic_names: list[str]
property
Names of the topics based on the highest scoring 4 terms.
export_representative_documents(topic_id, raw_documents=None, document_topic_matrix=None, top_k=5, show_negative=None, format='csv')
Exports the highest ranking documents in a topic as a text table.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
topic_id |
ID of the topic to display. |
required | |
raw_documents |
List of documents to consider. |
None
|
|
document_topic_matrix |
Document topic matrix to use. This is useful for transductive methods, as they cannot infer topics from text. |
None
|
|
top_k |
Top K documents to show. |
5
|
|
show_negative |
Optional[bool]
|
Indicates whether lowest ranking documents should also be shown. |
None
|
format |
str
|
Specifies which format should be used. 'csv', 'latex' and 'markdown' are supported. |
'csv'
|
Source code in turftopic/container.py
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|
export_topic_distribution(text=None, topic_dist=None, top_k=10, format='csv')
Exports topic distribution as a text table.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
text |
Text to infer topic distribution for. |
None
|
|
topic_dist |
Already inferred topic distribution for the text. This is useful for transductive methods, as they cannot infer topics from text. |
None
|
|
top_k |
int
|
Top K topics to show. |
10
|
format |
Specifies which format should be used. 'csv', 'latex' and 'markdown' are supported. |
'csv'
|
Source code in turftopic/container.py
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|
export_topics(top_k=10, show_scores=False, show_negative=None, format='csv')
Exports top K words from topics in a table in a given format. Returns table as a pure string.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words to return for each topic. |
10
|
show_scores |
bool
|
Indicates whether to show importance scores for each word. |
False
|
show_negative |
Optional[bool]
|
Indicates whether the most negative terms should also be displayed. |
None
|
format |
str
|
Specifies which format should be used. 'csv', 'latex' and 'markdown' are supported. |
'csv'
|
Source code in turftopic/container.py
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|
export_topics_over_time(top_k=5, show_scores=False, date_format='%Y %m %d', format='csv')
Pretty prints topics in the model in a table.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words to return for each topic. |
5
|
show_scores |
bool
|
Indicates whether to show importance scores for each word. |
False
|
format |
Specifies which format should be used. 'csv', 'latex' and 'markdown' are supported. |
'csv'
|
Source code in turftopic/container.py
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|
get_time_slices()
Returns starting and ending datetime of each timeslice in the model.
Source code in turftopic/container.py
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|
get_top_documents(raw_documents=None, document_topic_matrix=None, top_k=10, positive=True)
Returns list of top documents for each topic.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of documents to return per topic. |
10
|
positive |
bool
|
Indicates whether the highest or lowest scoring documents should be returned. |
True
|
Source code in turftopic/container.py
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|
get_top_images(top_k=True, positive=True)
Returns list of top images for each topic.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of images to return. |
True
|
positive |
bool
|
Indicates whether the highest or lowest scoring images should be returned. |
True
|
Source code in turftopic/container.py
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|
get_top_words(top_k=10, positive=True)
Returns list of top words for each topic.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of words to return. |
10
|
positive |
bool
|
Indicates whether the highest or lowest scoring terms should be returned. |
True
|
Source code in turftopic/container.py
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|
get_topics(top_k=10)
Returns high-level topic representations in form of the top K words in each topic.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words to return for each topic. |
10
|
Returns:
Type | Description |
---|---|
list[tuple]
|
List of topics. Each topic is a tuple of topic ID and the top k words. Top k words are a list of (word, word_importance) pairs. |
Source code in turftopic/container.py
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|
get_topics_over_time(top_k=10)
Returns high-level topic representations in form of the top K words in each topic.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words to return for each topic. |
10
|
Returns:
Type | Description |
---|---|
list[list[tuple]]
|
List of topics over each time slice in the dynamic model. Each time slice is a list of topics. Each topic is a tuple of topic ID and the top k words. Top k words are a list of (word, word_importance) pairs. |
Source code in turftopic/container.py
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|
plot_multimodal_topics(top_k=10, grid_size=4, raw_documents=None, document_topic_matrix=None)
Plots all multimodal topics in a model along with top documents individually, and provides a slider to switch between them.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words and documents to display. |
10
|
grid_size |
int
|
The square root of the number of images you want to display for a given topic. For instance if grid_size==4, all topics will have 16 images displayed, since the joint image will have 4 columns and 4 rows. |
4
|
raw_documents |
List of documents to consider. |
None
|
|
document_topic_matrix |
Document topic matrix to use. This is useful for transductive methods, as they cannot infer topics from text. |
None
|
Source code in turftopic/container.py
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|
plot_topics_over_time(top_k=6, color_discrete_sequence=None, color_discrete_map=None)
Displays topics over time in the fitted dynamic model on a dynamic HTML figure.
You will need to
pip install plotly
to use this method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words per topic to display on the figure. |
6
|
color_discrete_sequence |
Optional[Iterable[str]]
|
Color palette to use in the plot. Example:
|
None
|
color_discrete_map |
Optional[dict[str, str]]
|
Topic names mapped to the colors that should be associated with them. |
None
|
Returns:
Type | Description |
---|---|
Figure
|
Plotly graph objects Figure, that can be displayed or exported as HTML or static image. |
Source code in turftopic/container.py
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|
plot_topics_with_images(n_cols=3, grid_size=4)
Plots the most important images for each topic, along with keywords.
Note that you will need to pip install plotly
to use plots in Turftopic.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
n_cols |
int
|
Number of columns you want to have in the grid of topics. |
3
|
grid_size |
int
|
The square root of the number of images you want to display for a given topic. For instance if grid_size==4, all topics will have 16 images displayed, since the joint image will have 4 columns and 4 rows. |
4
|
Returns:
Type | Description |
---|---|
Figure
|
Plotly figure containing top images and keywords for topics. |
Source code in turftopic/container.py
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|
print_representative_documents(topic_id, raw_documents=None, document_topic_matrix=None, top_k=5, show_negative=None)
Pretty prints the highest ranking documents in a topic.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
topic_id |
ID of the topic to display. |
required | |
raw_documents |
List of documents to consider. |
None
|
|
document_topic_matrix |
Document topic matrix to use. This is useful for transductive methods, as they cannot infer topics from text. |
None
|
|
top_k |
Top K documents to show. |
5
|
|
show_negative |
Optional[bool]
|
Indicates whether lowest ranking documents should also be shown. |
None
|
Source code in turftopic/container.py
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|
print_topic_distribution(text=None, topic_dist=None, top_k=10)
Pretty prints topic distribution in a document.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
text |
Text to infer topic distribution for. |
None
|
|
topic_dist |
Already inferred topic distribution for the text. This is useful for transductive methods, as they cannot infer topics from text. |
None
|
|
top_k |
int
|
Top K topics to show. |
10
|
Source code in turftopic/container.py
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print_topics(top_k=10, show_scores=False, show_negative=None)
Pretty prints topics in the model in a table.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words to return for each topic. |
10
|
show_scores |
bool
|
Indicates whether to show importance scores for each word. |
False
|
show_negative |
Optional[bool]
|
Indicates whether the most negative terms should also be displayed. |
None
|
Source code in turftopic/container.py
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print_topics_over_time(top_k=5, show_scores=False, date_format='%Y %m %d')
Pretty prints topics in the model in a table.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words to return for each topic. |
5
|
show_scores |
bool
|
Indicates whether to show importance scores for each word. |
False
|
Source code in turftopic/container.py
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rename_topics(names)
Rename topics in a model manually or automatically, using a namer.
Examples:
model.rename_topics(["Automobiles", "Telephones"])
# Or:
model.rename_topics({-1: "Outliers", 2: "Christianity"})
# Or:
namer = OpenAITopicNamer()
model.rename_topics(namer)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
names |
Union[list[str], dict[int, str], TopicNamer]
|
Should be a list of topic names, or a mapping of topic IDs to names. |
required |
Source code in turftopic/container.py
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representative_documents_df(topic_id, raw_documents=None, document_topic_matrix=None, top_k=5, show_negative=None)
Collects highest ranking documents in a topic to a dataframe.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
topic_id |
ID of the topic to display. |
required | |
raw_documents |
List of documents to consider. |
None
|
|
document_topic_matrix |
Document topic matrix to use. This is useful for transductive methods, as they cannot infer topics from text. |
None
|
|
top_k |
Top K documents to show. |
5
|
|
show_negative |
Optional[bool]
|
Indicates whether lowest ranking documents should also be shown. |
None
|
Source code in turftopic/container.py
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topic_distribution_df(text=None, topic_dist=None, top_k=10)
Extracts topic distribution into a dataframe.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
text |
Text to infer topic distribution for. |
None
|
|
topic_dist |
Already inferred topic distribution for the text. This is useful for transductive methods, as they cannot infer topics from text. |
None
|
|
top_k |
int
|
Top K topics to show. |
10
|
Source code in turftopic/container.py
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topics_df(top_k=10, show_scores=False, show_negative=None)
Extracts topics into a pandas dataframe.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
top_k |
int
|
Number of top words to return for each topic. |
10
|
show_scores |
bool
|
Indicates whether to show importance scores for each word. |
False
|
show_negative |
Optional[bool]
|
Indicates whether the most negative terms should also be displayed. |
None
|
Source code in turftopic/container.py
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