text: # Copyright 2019 IBM Corporation
  #
  # 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 sklearn.preprocessing.data
  import lale.helpers
  import lale.operators
  
  class StandardScalerImpl():
  
      def __init__(self, copy=True, with_mean=True, with_std=True):
          self._hyperparams = {
              'copy': copy,
              'with_mean': with_mean,
              'with_std': with_std}
  
      def fit(self, X, y=None):
          self._sklearn_model = sklearn.preprocessing.data.StandardScaler(**self._hyperparams)
          self._sklearn_model.fit(X, y)
          return self
  
      def transform(self, X, copy=None):
          return self._sklearn_model.transform(X, copy)
  
  _hyperparams_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Standardize features by removing the mean and scaling to unit variance',
      'allOf': [{
          'type': 'object',
          'required': ['copy', 'with_mean', 'with_std'],
          'relevantToOptimizer': ['copy', 'with_mean', 'with_std'],
          'additionalProperties': False,
          'properties': {
              'copy': {
                  'type': 'boolean',
                  'default': True,
                  'description': 'If False, try to avoid a copy and do inplace scaling instead.'},
              'with_mean': {
                  'type': 'boolean',
                  'default': True,
                  'description': 'If True, center the data before scaling.'},
              'with_std': {
                  'type': 'boolean',
                  'default': True,
                  'description': 'If True, scale the data to unit variance (or equivalently,'},
          }}],
  }
  _input_fit_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Compute the mean and std to be used for later scaling.',
      'type': 'object',
      'required': ['X'],
      'properties': {
          'X': {
              'type': 'array',
              'items': {
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              },
              'description': 'The data used to compute the mean and standard deviation'},
          'y': {'description': 'Ignored'},
      },
  }
  _input_transform_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Perform standardization by centering and scaling',
      'type': 'object',
      'required': ['X'],
      'properties': {
          'X': {
              'type': 'array',
              'items': {
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              },
              'description': 'The data used to scale along the features axis.'},
          'copy': {
              'anyOf': [{
                  'type': 'boolean'}, {
                  'enum': [None]}],
              'default': None,
              'description': 'Copy the input X or not.'},
      },
  }
  _output_transform_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Perform standardization by centering and scaling',
      'type': 'array',
      'items': {'type': 'array', 'items': {'type': 'number'}}
  }
  _combined_schemas = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Combined schema for expected data and hyperparameters.',
      'type': 'object',
      'tags': {
          'pre': [],
          'op': ['transformer'],
          'post': []},
      'properties': {
          'hyperparams': _hyperparams_schema,
          'input_fit': _input_fit_schema,
          'input_predict': _input_transform_schema,
          'output': _output_transform_schema},
  }
  if (__name__ == '__main__'):
      lale.helpers.validate_is_schema(_combined_schemas)
  StandardScaler = lale.operators.make_operator(StandardScalerImpl, _combined_schemas)