def _prepare_classifiers(self): if self.classifier_params is None: self.classifier_params = dict() self.classifier_name.set_params(**self.classifier_params) if self.select_features is not None: if self.selection_params is None: self.selection_params = { 'nfeatures': 0.1, 'min_count': 2, 'minfeatures': 100 } self.selection_params['method'] = self.select_features self.first_selector = MulticlassFeatureSelector( local=True, method=self.select_features, min_count=self.min_count, nfeatures=-1) feature_selector = MulticlassFeatureSelector( **self.selection_params) self.classifier_ = Pipeline([('selector', feature_selector), ('classifier', self.classifier_name)]) else: self.first_selector = ZeroFeatureRemover() self.classifier_ = self.classifier_name return self