# 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
import lale.docstrings
import lale.operators

class RobustScalerImpl():

    def __init__(self, with_centering=True, with_scaling=True, quantile_range=(0.25,0.75), copy=None):
        self._hyperparams = {
            'with_centering': with_centering,
            'with_scaling': with_scaling,
            'quantile_range': quantile_range,
            'copy': copy}
        self._wrapped_model = sklearn.preprocessing.RobustScaler(**self._hyperparams)

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

    def transform(self, X):
        return self._wrapped_model.transform(X)
_hyperparams_schema = {
    'description': 'Scale features using statistics that are robust to outliers.',
    'allOf': [{
        'type': 'object',
        'required': ['quantile_range', 'copy'],
        'relevantToOptimizer': ['with_centering', 'with_scaling', 'quantile_range'],
        'additionalProperties': False,
        'properties': {
            'with_centering': {
                'type': 'boolean',
                'default': True,
                'description': 'If True, center the data before scaling.'},
            'with_scaling': {
                'type': 'boolean',
                'default': True,
                'description': 'If True, scale the data to interquartile range.'},
            'quantile_range': {
                'type': 'array',
                'laleType': 'tuple',
                'minItemsForOptimizer': 2,
                'maxItemsForOptimizer': 2,
                'items': [{
                    'type': 'number',
                    'minimumForOptimizer': 0.001,
                    'maximumForOptimizer': 0.3},{
                    'type': 'number',
                    'minimumForOptimizer': 0.7,
                    'maximumForOptimizer': 0.999}],
                'default': [0.25, 0.75],
                'description': 'Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR'},
            'copy': {
                'type': 'boolean',
                'default': True,
                'description': 'If False, try to avoid a copy and do inplace scaling instead.'},
        }}]
}
_input_fit_schema = {
    'description': 'Compute the median and quantiles to be used for scaling.',
    'type': 'object',
    'required': ['X'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The data used to compute the median and quantiles'},
        'y': {}}}
_input_transform_schema = {
    'description': 'Center and scale the data.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The data used to scale along the specified axis.'},
    },
}
_output_transform_schema = {
    'description': 'Center and scale the data.',
    'type': 'array',
    'items': {
        'type': 'array',
        'items': {'type': 'number'}}

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

.. _`Robust scaler`: https://scikit-learn.org/0.20/modules/generated/sklearn.preprocessing.RobustScaler.html#sklearn-preprocessing-robustscaler
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.robust_scaler.html',
    'import_from': 'sklearn.preprocessing',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema}}

lale.docstrings.set_docstrings(RobustScalerImpl, _combined_schemas)

RobustScaler = lale.operators.make_operator(RobustScalerImpl, _combined_schemas)