sklearn_utils/preprocessing/functional_enrichment_analysis.py
def transform(self, X, y=None):
        '''
        :X: list of dict
        :y: labels
        '''
        return [{
            new_feature: self._fisher_pval(x, old_features)
            for new_feature, old_features in self.feature_groups.items()
            if len(set(x.keys()) & set(old_features))
        } for x in X]
def transform(self, X, y=None):
        '''
        :X: list of dict
        :y: labels
        '''
        return [{
            new_feature: self._fisher_pval(x, old_features)
            for new_feature, old_features in self.feature_groups.items()
            if len(set(x.keys()) & set(old_features))
        } for x in X]
def transform ( self , X , y = None ) : return [ { new_feature : self . _fisher_pval ( x , old_features ) for new_feature , old_features in self . feature_groups . items ( ) if len ( set ( x . keys ( ) ) & set ( old_features ) ) } for x in X ]
:X: list of dict
        :y: labels
: X : list of dict : y : labels