def fit(self, encoded_data: EncodedData, label_name: str, cores_for_training: int = 2):
self.class_mapping = Util.make_class_mapping(encoded_data.labels[label_name])
self.feature_names = encoded_data.feature_names
self.label_name = label_name
mapped_y = Util.map_to_new_class_values(encoded_data.labels[label_name], self.class_mapping)
self.model = CacheHandler.memo_by_params(self._prepare_caching_params(encoded_data, encoded_data.labels[label_name], self.FIT, label_name),
lambda: self._fit(encoded_data.examples, mapped_y, cores_for_training))
def fit_by_cross_validation(self, encoded_data: EncodedData, number_of_splits: int = 5, label_name: str = None, cores_for_training: int = -1,
optimization_metric='balanced_accuracy'):
self.class_mapping = Util.make_class_mapping(encoded_data.labels[label_name])
self.feature_names = encoded_data.feature_names
self.label_name = label_name
mapped_y = Util.map_to_new_class_values(encoded_data.labels[label_name], self.class_mapping)
self.model = CacheHandler.memo_by_params(
self._prepare_caching_params(encoded_data, mapped_y, self.FIT_CV, label_name, number_of_splits),
lambda: self._fit_by_cross_validation(encoded_data.examples, mapped_y, number_of_splits, label_name, cores_for_training,
optimization_metric))
def fit(self,
encoded_data: EncodedData,
label: Label,
cores_for_training: int = 2):
self.label = label
self.class_mapping = Util.make_class_mapping(
encoded_data.labels[self.label.name])
self.feature_names = encoded_data.feature_names
mapped_y = Util.map_to_new_class_values(
encoded_data.labels[self.label.name], self.class_mapping)
self.model = self._fit(encoded_data.examples, mapped_y,
cores_for_training)
def fit_by_cross_validation(self,
encoded_data: EncodedData,
number_of_splits: int = 5,
label: Label = None,
cores_for_training: int = -1,
optimization_metric='balanced_accuracy'):
self.class_mapping = Util.make_class_mapping(
encoded_data.labels[label.name])
self.feature_names = encoded_data.feature_names
self.label = label
mapped_y = Util.map_to_new_class_values(
encoded_data.labels[self.label.name], self.class_mapping)
self.model = self._fit_by_cross_validation(encoded_data.examples,
mapped_y, number_of_splits,
label, cores_for_training,
optimization_metric)
