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
