gradec.model.LDAModel

class gradec.model.LDAModel(n_topics, max_iter=1000, alpha=None, beta=0.001, text_column='abstract', n_cores=1)[source]

Generate a latent Dirichlet allocation (LDA) topic model.

This class is a light wrapper around scikit-learn tools for tokenization and LDA.

Parameters:

  • n_topics (int) – Number of topics for topic model. This corresponds to the model’s n_components parameter. Must be an integer >= 1.

  • max_iter (int, optional) – Maximum number of iterations to use during model fitting. Default = 1000.

  • alpha (float or None, optional) – The alpha value for the model. This corresponds to the model’s doc_topic_prior parameter. Default is None, which evaluates to 1 / n_topics, as was used in :footcite:t:`poldrack2012discovering`.

  • beta (float or None, optional) – The beta value for the model. This corresponds to the model’s topic_word_prior parameter. If None, it evaluates to 1 / n_topics. Default is 0.001, which was used in :footcite:t:`poldrack2012discovering`.

  • text_column (str, optional) – The source of text to use for the model. This should correspond to an existing column in the texts attribute. Default is “abstract”.

  • n_cores (int, optional) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is 1.

Variables:

model (LatentDirichletAllocation) –

Notes

Adapted from: https://github.com/neurostuff/NiMARE/blob/main/nimare/annotate/lda.py.

Latent Dirichlet allocation was first developed in :footcite:t:`blei2003latent`, and was first applied to neuroimaging articles in :footcite:t:`poldrack2012discovering`.

References

See also

CountVectorizer

Used to build a vocabulary of terms and their associated counts from texts in the self.text_column of the Dataset’s texts attribute.

LatentDirichletAllocation

Used to train the LDA model.

fit(dset, counts_df=None)[source]

Fit the LDA topic model to text from a Dataset.

Parameters:

  • dset (Dataset) – A Dataset with, at minimum, text available in the self.text_column column of its texts attribute.

  • count_df (pandas.DataFrame) – A DataFrame with feature counts for the model. The index is ‘id’, used for identifying studies. Other columns are features (e.g., unigrams and bigrams from Neurosynth), where each value is the number of times the feature is found in a given article.

Returns:

dset (Dataset) – A new Dataset with an updated annotations attribute.

Variables:

distributions (dict) –

A dictionary containing additional distributions produced by the model, including:

  • p_topic_g_word: numpy.ndarray of shape (n_topics, n_tokens) containing the topic-term weights for the model.

  • p_topic_g_word_df: pandas.DataFrame of shape (n_topics, n_tokens) containing the topic-term weights for the model.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params (dict) – Parameter names mapped to their values.

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

  • filename (str) – Name of file containing object.

  • compressed (bool, default=True) – If True, the file is assumed to be compressed and gzip will be used to load it. Otherwise, it will assume that the file is not compressed. Default = True.

Returns:

obj (class object) – Loaded class object.

save(filename, compress=True)

Pickle the class instance to the provided file.

Parameters:

  • filename (str) – File to which object will be saved.

  • compress (bool, optional) – If True, the file will be compressed with gzip. Otherwise, the uncompressed version will be saved. Default = True.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:

self