nimare.annotate.gclda.GCLDAModel

class GCLDAModel(count_df, coordinates_df, mask='mni152_2mm', n_topics=100, n_regions=2, symmetric=True, alpha=0.1, beta=0.01, gamma=0.01, delta=1.0, dobs=25, roi_size=50.0, seed_init=1)

Bases: NiMAREBase

Generate a generalized correspondence latent Dirichlet allocation (GCLDA) topic model.

This model was originally described in Rubin et al.[1].

Changed in version 0.0.8:

• [ENH] Support symmetric GC-LDA topics with more than two subregions.

Parameters:

• 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.

• coordinates_df (pandas.DataFrame) – A DataFrame with a list of foci in the dataset. The index is ‘id’, used for identifying studies. Additional columns include ‘x’, ‘y’ and ‘z’ (foci in standard space).

• n_topics (int, optional) – Number of topics to generate in model. As a good rule of thumb, the number of topics should be less than the number of studies in the dataset. Otherwise, there can be errors during model training. The default is 100.

• n_regions (int, optional) – Number of subregions per topic (>=1). The default is 2.

• alpha (float, optional) – Prior count on topics for each document. The default is 0.1.

• beta (float, optional) – Prior count on word-types for each topic. The default is 0.01.

• gamma (float, optional) – Prior count added to y-counts when sampling z assignments. The default is 0.01.

• delta (float, optional) – Prior count on subregions for each topic. The default is 1.0.

• dobs (int, optional) – Spatial region ‘default observations’ (# observations weighting Sigma estimates in direction of default ‘roi_size’ value). The default is 25.

• roi_size (float, optional) – Default spatial ‘region of interest’ size (default value of diagonals in covariance matrix for spatial distribution, which the distributions are biased towards). The default is 50.0.

• symmetric (bool, optional) – Whether or not to use symmetry constraint on subregions. Symmetry requires n_regions = 2. The default is False.

• seed_init (int, optional) – Initial value of random seed. The default is 1.

Variables:

• p_topic_g_voxel ((V x T) numpy.ndarray) – Probability of each topic (T) give a voxel (V)

• p_voxel_g_topic ((V x T) numpy.ndarray) – Probability of each voxel (V) given a topic (T)

• p_topic_g_word ((W x T) numpy.ndarray) – Probability of each topic (T) given a word (W)

• p_word_g_topic ((W x T) numpy.ndarray) – Probability of each word (W) given a topic (T)

References

[1]

Timothy N Rubin, Oluwasanmi Koyejo, Krzysztof J Gorgolewski, Michael N Jones, Russell A Poldrack, and Tal Yarkoni. Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition. PLoS computational biology, 13(10):e1005649, 2017. URL: https://doi.org/10.1371/journal.pcbi.1005649, doi:10.1371/journal.pcbi.1005649.

See also

nimare.decode.continuous.gclda_decode_map

GCLDA map decoding

nimare.decode.discrete.gclda_decode_roi

GCLDA ROI decoding

nimare.decode.encode.encode_gclda

GCLDA text-to-map encoding

Methods

compute_log_likelihood([model, update_vectors])	Compute log-likelihood of a model object given current model.
fit([n_iters, loglikely_freq])	Run multiple iterations.
get_params([deep])	Get parameters for this estimator.
get_probability_distributions()	Get conditional probability of selecting each voxel in the brain mask given each topic.
load(filename[, compressed])	Load a pickled class instance from file.
save(filename[, compress])	Pickle the class instance to the provided file.
set_params(**params)	Set the parameters of this estimator.

compute_log_likelihood(model=None, update_vectors=True)

Compute log-likelihood of a model object given current model.

Computes the log-likelihood of data in any model object (either train or test) given the posterior predictive distributions over peaks and word-types for the model, using the method described in Newman et al.[2]. Note that this is not computing the joint log-likelihood of model parameters and data.

Parameters:

• model (GCLDAModel, optional) – The model for which log-likelihoods will be calculated. If not provided, log-likelihood will be calculated for the current model (self). Default is None.

• update_vectors (bool, optional) – Whether to update model’s log-likelihood vectors or not. Default is True.

Returns:

• x_loglikely (float) – Total log-likelihood of all peak tokens.

• w_loglikely (float) – Total log-likelihood of all word tokens.

• tot_loglikely (float) – Total log-likelihood of peak + word tokens.

References

[2]

David Newman, Arthur Asuncion, Padhraic Smyth, and Max Welling. Distributed algorithms for topic models. Journal of Machine Learning Research, 2009. URL: http://jmlr.org/papers/v10/newman09a.html.

fit(n_iters=5000, loglikely_freq=10)

Run multiple iterations.

Changed in version 0.0.8: [ENH] Remove verbose parameter.

Parameters:

• n_iters (int, default=5000) – Number of iterations to run. Default is 5000.

• loglikely_freq (int, optional) – The frequency with which log-likelihood is updated. Default value is 1 (log-likelihood is updated every iteration).

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 – Parameter names mapped to their values.

Return type:

dict

get_probability_distributions()

Get conditional probability of selecting each voxel in the brain mask given each topic.

Returns:

• p_topic_g_voxel (numpy.ndarray of numpy.float64) – A voxel-by-topic array of conditional probabilities: p(topic|voxel). For cell ij, the value is the probability of topic j being selected given voxel i is active.

• p_voxel_g_topic (numpy.ndarray of numpy.float64) – A voxel-by-topic array of conditional probabilities: p(voxel|topic). For cell ij, the value is the probability of voxel i being selected given topic j has already been selected.

• p_topic_g_word (numpy.ndarray of numpy.float64) – A word-by-topic array of conditional probabilities: p(topic|word). For cell ij, the value is the probability of topic i being selected given word j is present.

• p_word_g_topic (numpy.ndarray of numpy.float64) – A word-by-topic array of conditional probabilities: p(word|topic). For cell ij, the value is the probability of word j being selected given topic i has already been selected.

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 – Loaded class object.

Return type:

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.

Return type:

self