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A little wrapper for the topic modeling functions of MALLET

Project description

little-mallet-wrapper

This is a little Python wrapper around the topic modeling functions of MALLET.

Currently under construction; please send feedback/requests to Maria Antoniak.

See demo.ipynb for a demonstration of how to use the functions in little-mallet-wrapper.

Documentation

print_dataset_stats(training_data)

Displays basic statistics about the training dataset.

Name Type Description
training_data list of strings Documents that will be used to train the topic model.

process_string(text, lowercase=True, remove_short_words=True, remove_stop_words=True, remove_punctuation=True, numbers='replace', stop_words=STOPS)

A simple string processor that prepares raw text for topic modeling.

Name Type Description
text string Individual document to process.
lowercase boolean Whether or not to lowercase the text.
remove_short_words boolean Whether or not to remove words with fewer than 2 characters.
remove_stop_words boolean Whether or not to remove stopwords.
remove_punctuation boolean Whether or not to remove punctuation (not A-Za-z0-9)
remove_numbers string 'replace' replaces all numbers with the normalized token NUM; 'remove' removes all numbers.
stop_words list of strings Custom list of words to remove.
RETURNS string Processed version of the input text.

import_data(path_to_mallet, path_to_training_data, path_to_formatted_training_data, training_data, use_pipe_from=None)

Imports the training data into MALLET formatted data that can be used for training.

Name Type Description
path_to_mallet string Path to your local MALLET installation: .../mallet-2.0.8/bin/mallet
path_to_training_data string Path to where the training data should be stored.
path_to_formatted_training_data string Path to where the MALLET formatted training data should be stored.
training_data list of strings Processed documents for training the topic model.
use_pipe_from string If you want to import the documents using the same model as a previous set of documents, include the path to the previous MALLET formatted training data.

train_topic_model(path_to_mallet, path_to_formatted_training_data, path_to_model, path_to_topic_key, path_to_topic_distributions, num_topics)

Trains an LDA topic model using MALLET.

Name Type Description
path_to_mallet string Path to your local MALLET installation: .../mallet-2.0.8/bin/mallet
path_to_formatted_training_data string Path to where the MALLET formatted training data is stored.
path_to_model string Path to where the model should be stored.
path_to_topic_key string Path to where the topic keys should be stored.
path_to_topic_distributions string Path to where the topic distributions should be stored.
num_topics integer The number of topics to use for training.

load_topic_keys(topic_keys_path)

Loads the most sets of most probable words for each topic after training a topic model.

Name Type Description
topic_keys_path string Path to where the topic keys are stored.
RETURNS list of lists of strings The 20 most probable words for each topic.

load_topic_distributions(topic_distributions_path)

Loads the topic distribution for each document after training a topic model.

Name Type Description
topic_distributions_path string Path to where the topic distributions are stored.
RETURNS list of lists of integers Topic distribution (list of probabilities) for each document.

get_top_docs(training_data, topic_distributions, topic_index, n=5)

Gets the documents with the highest probability for the target topic.

Name Type Description
training_data list of strings Processed documents that was used to train the topic model.
topic_distributions list of lists of integers Topic distribution (list of probabilities) for each document.
topic_index integer The index of the target topic.
n integer The number of documents to return.
RETURNS list of tuples (float, string) The topic probability and document text for the n documents with the highest probability for the target topic.

plot_categories_by_topics_heatmap(labels, topic_distributions, topic_keys, output_path=None, target_labels=None, dim=None)

If the dataset includes some time of categorical labels, creates a heatmap of the labels x topics.

Name Type Description
labels list of strings Document labels (e.g., authors of the documents, genres of the documents).
topic_distributions list of lists of integers Topic distribution (list of probabilities) for each document.
topic_keys list of lists of strings The 20 most probable words for each topic.
output_path string Path to where the resulting figure should be saved.
target_labels list of strings A subset of labels to use for plotting.
dim tuple of integers (x, y) dimensions for the resulting figure.

plot_categories_by_topic_boxplots(labels, topic_distributions, topic_keys, output_path=None, target_labels=None, dim=None)

If the dataset includes some time of categorical labels, creates a set of boxplots, one plot for each topic.

Name Type Description
labels list of strings Document labels (e.g., authors of the documents, genres of the documents).
topic_distributions list of lists of integers Topic distribution (list of probabilities) for each document.
topic_keys list of lists of strings The 20 most probable words for each topic.
output_path string Path to where the resulting figure should be saved.
target_labels list of strings A subset of labels to use for plotting.
dim tuple of integers (x, y) dimensions for the resulting figure.

divide_training_data(documents, num_chunks=10)

Given a dataset, divides each document into a set of equally sized chunks.

Name Type Description
documents list of strings Documents to split.
num_chunks integer How many times to split each document.
RETURNS tuple (list of strings, list of integers, list of floats) The divided documents, the indices of the input documents, and the positions within the documents (0-1.0).

infer_topics(path_to_mallet, path_to_original_model, path_to_new_formatted_training_data, path_to_new_topic_distributions)

Get topic distributions for a set of new documents using a model that has been trained on another set of documents.

Name Type Description
path_to_mallet string Path to your local MALLET installation: .../mallet-2.0.8/bin/mallet
path_to_original_model string Path to where the topic model was stored.
path_to_new_formatted_training_data string Path to where the MALLET formatted training data is stored.
path_to_new_topic_distributions string Path to where the topic distributions should be stored.

plot_topics_over_time(topic_distributions, topic_keys, times, topic_index, output_path=None)

Creates lineplots, one for each topic, showing the mean topic probability over document segments.

Name Type Description
topic_distributions list of lists of integers Topic distribution (list of probabilities) for each document.
topic_keys list of lists of strings The 20 most probable words for each topic.
times list of floats The division indices within the document.
topic_index integer The index of the target topic.
output_path string Path to where the resulting figure should be saved.

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