Individual Figures

If you are preparing individual figures for a publication or report, you might need to modify the appearance or resolution of the figures you producing.

It might also be the case that it’s only certain figures you’re interested in.

topicwizard comes with an interface that allows you to do just that. If you have a TopicData object, you can manually produce individual figures.

These figures are like any other interactive Plotly figure, therefore you can manipulate them as such, and export them as HTML or a number of image formats. For an extensive overview of how you can manipulate plots produced by topicwizard consult Plotly’s documentation.

Topic Map

You can display a semantic map of topics in your model.

from topicwizard.figures import topic_map

topic_map(topic_data)

Word Barplots

You can display a joint plot of all topics, where word importances are displayed on a bar chart. You can specify the relevance metric with the alpha keyword parameter.

from topicwizard.figures import topic_barcharts

topic_barcharts(topic_data)

If you find that too many words get displayed, you can reduce that with the top_n keyword.

topic_barcharts(topic_data, top_n=5)

Word Clouds

You can produce a joint word cloud plot of all topics. You can specify the relevance metric with the alpha keyword parameter.

from topicwizard.figures import topic_wordclouds

topic_wordclouds(topic_data)

Word Map

The word map that you can display with a dedicated function is slightly different from the one in the app as here you can’t select words to highlight.

Instead you can specify a cutoff in Z-values over which words will be labelled on the graph.

Words are also distinctively colored according to the most relevant topic as you cannot select the individual words for inspection.

You can either choose to let UMAP discover the axis and project the words into 2D space, which is good for exploring words’ distances and relations to each other in the model, as well as potential clusters of words in the topic model.

from topicwizard.figures import word_map

word_map(topic_data)

Or you can display words with given topics as axes. This is especially useful for models like Semantic Signal Separation or Latent Semantic Analysis, where words with the lowest importance for a topic also cary information, as a topic is assumed to be an axis of semantic space.

from topicwizard.figures import word_map

word_map(
  topic_data,
  topic_axes=(
     "9_api_apis_register_automatedsarcasmgenerator",
     "4_study_studying_assessments_exams"
  )
)

Important Topics

You can visualize most relevant topics for a given set of words with barcharts, these behave virtually the same as in the app, but no associations are selected by default.

So for example if we would like to know which topics contain the words “supreme” and “court”, we can do so:

from topicwizard.figures import word_association_barchart

word_association_barchart(topic_data, ["supreme", "court"])

Document Map

You can display a map of documents as a self-contained plot. This can be advantageous when you want to see how different documents relate to each other in your corpus, and to the underlying topics discovered by the model.

This plot is not entirely identical to the one in the app, as documents cannot be selected or searched for.

Different topics are clearly outlined with discrete colors.

from topicwizard.figures import document_map

document_map(topic_data)

Topic Distribution

You can display topic distributions for a given document or list of documents on a bar chart.

from topicwizard.figures import document_topic_distribution

document_topic_distribution(
    topic_data,
    "New cure against type 2 diabetes in development.",
)

You can also display topic distribution over time in a single document on a line chart. (or an entire corpus if you join the texts.)

This works by taking windows of tokens from the document and running them through the pipeline. You can specify window and step size in number of tokens if you find the results have to high or to low resolution.

from topicwizard.figures import document_topic_timeline

document_topic_timeline(
    topic_data,
    "New cure against type 2 diabetes in development.",
)

Group Map

You can display the group map as a standalone plot, with the groups being colored according to dominant topic.

from topicwizard.figures import group_map

group_map(topic_data, group_labels)

Group Topic Barcharts

You can create a joint plot of the topic content of all groups. These will be displayed as bar charts.

from topicwizard.figures import group_topic_barcharts

group_topic_barcharts(corpus, group_labels, pipeline=pipeline, top_n=5)

Group Word Clouds

You can create word clouds for each of the group labels. This will only take word counts into account and not relevance.

from topicwizard.figures import group_wordclouds

group_wordclouds(corpus, group_labels, pipeline=pipeline)