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specify number of parallel jobs for CrossvalMultipleRegularizations #39

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Nov 19, 2023
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specify number of parallel jobs for CrossvalMultipleRegularizations #39

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13 changes: 9 additions & 4 deletions pysaliency/baseline_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -355,12 +355,17 @@ def _normalize_regularization_factors(args):


class CrossvalMultipleRegularizations(object):
""" Class for computing crossvalidation scores of a fixation KDE with multiple regularization models"""
def __init__(self, stimuli, fixations, regularization_models: OrderedDict, crossvalidation, verbose=False):
"""Class for computing crossvalidation scores of a fixation KDE with multiple regularization models

n_jobs: number of parallel jobs to use in cross_val_score
verbose: verbosity level for cross_val_score
"""
def __init__(self, stimuli, fixations, regularization_models: OrderedDict, crossvalidation, n_jobs=None, verbose=False):
self.stimuli = stimuli
self.fixations = fixations

self.cv = crossvalidation
self.n_jobs = n_jobs
self.verbose = verbose

X_areas = fixations_to_scikit_learn(
Expand Down Expand Up @@ -413,13 +418,13 @@ def score(self, log_bandwidth, *args, **kwargs):


class CrossvalGoldMultipleRegularizations(CrossvalMultipleRegularizations):
def __init__(self, stimuli, fixations, regularization_models, verbose=False):
def __init__(self, stimuli, fixations, regularization_models, n_jobs=None, verbose=False):
if fixations.subject_count > 1:
crossvalidation_factory = ScikitLearnImageSubjectCrossValidationGenerator
else:
crossvalidation_factory = ScikitLearnWithinImageCrossValidationGenerator

super().__init__(stimuli, fixations, regularization_models, crossvalidation_factory=crossvalidation_factory, verbose=verbose)
super().__init__(stimuli, fixations, regularization_models, crossvalidation_factory=crossvalidation_factory, n_jobs=n_jobs, verbose=verbose)


# baseline models
Expand Down
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