# 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 lale.operators
import lale.docstrings
import sklearn.preprocessing

class MinMaxScalerImpl:
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.preprocessing.MinMaxScaler(
            **self._hyperparams)

    def fit(self, X, y=None):
        self._wrapped_model.fit(X)
        return self

    def transform(self, X):
        return self._wrapped_model.transform(X)

    def partial_fit(self, X, y=None):
      if not hasattr(self, "_wrapped_model"):
        self._wrapped_model = sklearn.preprocessing.MinMaxScaler(
            **self._hyperparams)
      self._wrapped_model.partial_fit(X)
      return self

_input_schema_fit = {
  'description': 'Input data schema for training.',
  'type': 'object',
  'required': ['X'],
  'additionalProperties': False,
  'properties': {
    'X': {
      'description': 'Features; the outer array is over samples.',
      'type': 'array',
      'items': {'type': 'array', 'items': {'type': 'number'}}},
    'y': {}}}

_input_transform_schema = {
  'description': 'Input data schema for predictions.',
  'type': 'object',
  'required': ['X'],
  'additionalProperties': False,
  'properties': {
    'X': {
      'description': 'Features; the outer array is over samples.',
      'type': 'array',
      'items': {'type': 'array', 'items': {'type': 'number'}}}}}

_output_transform_schema = {
  'description': 'Output data schema for transformed data.',
  'type': 'array',
  'items': {'type': 'array', 'items': {'type': 'number'}}}

_hyperparams_schema = {
  'description': 'Hyperparameter schema.',
  'allOf': [
    { 'description':
        'This first sub-object lists all constructor arguments with their '
        'types, one at a time, omitting cross-argument constraints.',
      'type': 'object',
      'additionalProperties': False,
      'required': ['feature_range', 'copy'],
      'relevantToOptimizer': [], #['feature_range'],
      'properties': {
        'feature_range': {
          'description': 'Desired range of transformed data.',
          'type': 'array',
          'laleType': 'tuple',
          'minItems': 2,
          'maxItems': 2,
          'default': [0, 1]},
        'copy': {
          'description':
            'Set to False to perform inplace row normalization and avoid '
            'a copy (if the input is already a numpy array).',
          'type': 'boolean',
          'default': True}}}]}

_combined_schemas = {
  '$schema': 'http://json-schema.org/draft-04/schema#',
  'description': """`Min-max scaler`_ transformer from scikit-learn.

.. _`Min-max scaler`: https://scikit-learn.org/0.20/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn-preprocessing-minmaxscaler
""",
  'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.min_max_scaler.html',
  'import_from': 'sklearn.preprocessing',
  'type': 'object',
  'tags': {
    'pre': ['~categoricals'],
    'op': ['transformer', 'interpretable'],
    'post': []},
  'properties': {
    'hyperparams': _hyperparams_schema,
    'input_fit': _input_schema_fit,
    'input_transform': _input_transform_schema,
    'output_transform': _output_transform_schema}}

lale.docstrings.set_docstrings(MinMaxScalerImpl, _combined_schemas)

MinMaxScaler = lale.operators.make_operator(MinMaxScalerImpl, _combined_schemas)