Verbosity parameter in CrossvalMultipleRegularizations

Signed-off-by: Matthias Kümmmerer <[email protected]>

pysaliency/baseline_utils.py

@@ -356,11 +356,12 @@ 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):
+    def __init__(self, stimuli, fixations, regularization_models: OrderedDict, crossvalidation, verbose=False):
         self.stimuli = stimuli
         self.fixations = fixations
 
         self.cv = crossvalidation
+        self.verbose = verbose
 
         X_areas = fixations_to_scikit_learn(
             self.fixations, normalize=stimuli,
@@ -406,19 +407,19 @@ def score(self, log_bandwidth, *args, **kwargs):
             bandwidth=10**log_bandwidth,
             regularizations=10**log_regularizations,
             regularizing_log_likelihoods=self.regularization_log_likelihoods),
-            self.X, cv=self.cv, verbose=1).sum() / len(self.X) / np.log(2)
+            self.X, cv=self.cv, verbose=self.verbose).sum() / len(self.X) / np.log(2)
         val += np.log2(self.mean_area)
         return val
 
 
 class CrossvalGoldMultipleRegularizations(CrossvalMultipleRegularizations):
-    def __init__(self, stimuli, fixations, regularization_models):
+    def __init__(self, stimuli, fixations, regularization_models, verbose=False):
         if fixations.subject_count > 1:
             crossvalidation_factory = ScikitLearnImageSubjectCrossValidationGenerator
         else:
             crossvalidation_factory = ScikitLearnWithinImageCrossValidationGenerator
 
-        super().__init__(stimuli, fixations, regularization_models, crossvalidation_factory=crossvalidation_factory)
+        super().__init__(stimuli, fixations, regularization_models, crossvalidation_factory=crossvalidation_factory, verbose=verbose)
 
 
 # baseline models