| ################################################################################ |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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 typing |
| |
| from pyflink.ml.core.param import Param, BooleanParam |
| from pyflink.ml.core.wrapper import JavaWithParams |
| from pyflink.ml.lib.feature.common import JavaFeatureEstimator, JavaFeatureModel |
| from pyflink.ml.lib.param import HasInputCol, HasOutputCol |
| |
| |
| class _StandardScalerParams( |
| JavaWithParams, |
| HasInputCol, |
| HasOutputCol |
| ): |
| """ |
| Params for :class:`StandardScaler`. |
| """ |
| |
| WITH_MEAN: Param[bool] = BooleanParam( |
| "with_mean", |
| "Whether centers the data with mean before scaling.", |
| False) |
| |
| WITH_STD: Param[bool] = BooleanParam( |
| "with_std", |
| "Whether scales the data with standard deviation.", |
| True) |
| |
| def __init__(self, java_params): |
| super(_StandardScalerParams, self).__init__(java_params) |
| |
| def set_with_mean(self, value: bool): |
| return typing.cast(_StandardScalerParams, self.set(self.WITH_MEAN, value)) |
| |
| def set_with_std(self, value: bool): |
| return typing.cast(_StandardScalerParams, self.set(self.WITH_STD, value)) |
| |
| def get_with_mean(self) -> bool: |
| return self.get(self.WITH_MEAN) |
| |
| def get_with_std(self) -> bool: |
| return self.get(self.WITH_STD) |
| |
| @property |
| def with_mean(self): |
| return self.get_with_mean() |
| |
| @property |
| def with_std(self): |
| return self.get_with_std() |
| |
| |
| class StandardScalerModel(JavaFeatureModel, _StandardScalerParams): |
| """ |
| A Model which classifies data using the model data computed by :class:`StandardScaler`. |
| """ |
| |
| def __init__(self, java_model=None): |
| super(StandardScalerModel, self).__init__(java_model) |
| |
| @classmethod |
| def _java_model_package_name(cls) -> str: |
| return "standardscaler" |
| |
| @classmethod |
| def _java_model_class_name(cls) -> str: |
| return "StandardScalerModel" |
| |
| |
| class StandardScaler(JavaFeatureEstimator, _StandardScalerParams): |
| """ |
| An Estimator which implements the standard scaling algorithm. |
| |
| Standardization is a common requirement for machine learning training because they may behave |
| badly if the individual features of a input do not look like standard normally distributed data |
| (e.g. Gaussian with 0 mean and unit variance). |
| |
| This estimator standardizes the input features by removing the mean and scaling each dimension |
| to unit variance. |
| """ |
| |
| def __init__(self): |
| super(StandardScaler, self).__init__() |
| |
| @classmethod |
| def _create_model(cls, java_model) -> StandardScalerModel: |
| return StandardScalerModel(java_model) |
| |
| @classmethod |
| def _java_estimator_package_name(cls) -> str: |
| return "standardscaler" |
| |
| @classmethod |
| def _java_estimator_class_name(cls) -> str: |
| return "StandardScaler" |