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- #
- # Copyright 2021 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 numpy as np
- from sklearn.linear_model import LogisticRegression
- from ..component_converter import ComponentConverterBase
- class LRComponentConverter(ComponentConverterBase):
- @staticmethod
- def get_target_modules():
- return ['HomoLR']
- def convert(self, model_dict):
- param_obj = model_dict["HomoLogisticRegressionParam"]
- meta_obj = model_dict["HomoLogisticRegressionMeta"]
- sk_lr_model = LogisticRegression(penalty=meta_obj.penalty.lower(),
- tol=meta_obj.tol,
- fit_intercept=meta_obj.fit_intercept,
- max_iter=meta_obj.max_iter)
- coefficient = np.empty((1, len(param_obj.header)))
- for index in range(len(param_obj.header)):
- coefficient[0][index] = param_obj.weight[param_obj.header[index]]
- sk_lr_model.coef_ = coefficient
- sk_lr_model.intercept_ = np.array([param_obj.intercept])
- # hard-coded 0-1 classification as HomoLR only supports this for now
- sk_lr_model.classes_ = np.array([0., 1.])
- sk_lr_model.n_iter_ = [param_obj.iters]
- return sk_lr_model
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