# # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import copy import functools import numpy as np from federatedml.protobuf.generated.feature_scale_meta_pb2 import ScaleMeta from federatedml.protobuf.generated.feature_scale_param_pb2 import ScaleParam from federatedml.protobuf.generated.feature_scale_param_pb2 import ColumnScaleParam from federatedml.feature.feature_scale.base_scale import BaseScale class MinMaxScale(BaseScale): """ Transforms features by scaling each feature to a given range,e.g.between minimum and maximum. The transformation is given by: X_scale = (X - X.min) / (X.max - X.min), while X.min is the minimum value of feature, and X.max is the maximum """ def __init__(self, params): super().__init__(params) self.mode = params.mode self.column_range = None @staticmethod def __scale(data, max_value_list, min_value_list, scale_value_list, process_cols_list): """ Scale operator for each column. The input data type is data_instance """ features = np.array(data.features, dtype=float) for i in process_cols_list: value = features[i] if value > max_value_list[i]: value = max_value_list[i] elif value < min_value_list[i]: value = min_value_list[i] features[i] = (value - min_value_list[i]) / scale_value_list[i] _data = copy.deepcopy(data) _data.features = features return _data def fit(self, data): """ Apply min-max scale for input data Parameters ---------- data: data_instance, input data Returns ---------- fit_data:data_instance, data after scale """ self.column_min_value, self.column_max_value = self._get_min_max_value(data) self.scale_column_idx = self._get_scale_column_idx(data) self.header = self._get_header(data) self.column_range = [] for i in range(len(self.column_max_value)): scale = self.column_max_value[i] - self.column_min_value[i] if scale < 0: raise ValueError("scale value should large than 0") elif np.abs(scale - 0) < 1e-6: scale = 1 self.column_range.append(scale) f = functools.partial(MinMaxScale.__scale, max_value_list=self.column_max_value, min_value_list=self.column_min_value, scale_value_list=self.column_range, process_cols_list=self.scale_column_idx) fit_data = data.mapValues(f) return fit_data def transform(self, data): """ Transform input data using min-max scale with fit results Parameters ---------- data: data_instance, input data Returns ---------- transform_data:data_instance, data after transform """ self.column_range = [] for i in range(len(self.column_max_value)): scale = self.column_max_value[i] - self.column_min_value[i] if scale < 0: raise ValueError("scale value should large than 0") elif np.abs(scale - 0) < 1e-6: scale = 1 self.column_range.append(scale) f = functools.partial(MinMaxScale.__scale, max_value_list=self.column_max_value, min_value_list=self.column_min_value, scale_value_list=self.column_range, process_cols_list=self.scale_column_idx) transform_data = data.mapValues(f) return transform_data def _get_meta(self, need_run): if self.header: scale_column = [self.header[i] for i in self.scale_column_idx] else: scale_column = ["_".join(["col", str(i)]) for i in self.scale_column_idx] if not self.data_shape: self.data_shape = -1 meta_proto_obj = ScaleMeta(method="min_max_scale", mode=self.mode, area="null", scale_column=scale_column, feat_upper=self._get_upper(self.data_shape), feat_lower=self._get_lower(self.data_shape), need_run=need_run ) return meta_proto_obj def _get_param(self): min_max_scale_param_dict = {} if self.header: scale_column_idx_set = set(self.scale_column_idx) for i, header in enumerate(self.header): if i in scale_column_idx_set: param_obj = ColumnScaleParam(column_upper=self.column_max_value[i], column_lower=self.column_min_value[i]) min_max_scale_param_dict[header] = param_obj param_proto_obj = ScaleParam(col_scale_param=min_max_scale_param_dict, header=self.header) return param_proto_obj