# 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.ensemble
import lale.docstrings
import lale.operators

class GradientBoostingRegressorImpl():
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.ensemble.GradientBoostingRegressor(**self._hyperparams)

    def fit(self, X, y, **fit_params):
        self._wrapped_model.fit(X, y, **fit_params)
        return self

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

_hyperparams_schema = {
    'description': 'Gradient Boosting for regression.',
    'allOf': [{
        'type': 'object',
        'required': ['init', 'presort'],
        'relevantToOptimizer': ['loss', 'n_estimators', 'min_samples_split', 'min_samples_leaf', 'max_depth', 'max_features', 'alpha', 'presort', 'n_iter_no_change', 'tol'],
        'additionalProperties': False,
        'properties': {
            'loss': {
                'enum': ['ls', 'lad', 'huber', 'quantile'],
                'default': 'ls',
                'description': "loss function to be optimized. 'ls' refers to least squares"},
            'learning_rate': {
                'type': 'number',
                'minimumForOptimizer': 0.01,
                'maximumForOptimizer': 1.0,
                'distribution': 'loguniform',
                'default': 0.1,
                'description': 'learning rate shrinks the contribution of each tree by `learning_rate`.'},
            'n_estimators': {
                'type': 'integer',
                'minimumForOptimizer': 10,
                'maximumForOptimizer': 100,
                'distribution': 'uniform',
                'default': 100,
                'description': 'The number of boosting stages to perform. Gradient boosting'},
            'subsample': {
                'type': 'number',
                'minimumForOptimizer': 0.01,
                'maximumForOptimizer': 1.0,
                'distribution': 'uniform',
                'default': 1.0,
                'description': 'The fraction of samples to be used for fitting the individual base'},
            'criterion': {
                'enum': ['friedman_mse', 'mse', 'mae'],
                'default': 'friedman_mse',
                'description': 'The function to measure the quality of a split. Supported criteria'},
            'min_samples_split': {
                'anyOf': [{
                    'type': 'integer',
                    'minimumForOptimizer': 2,
                    'maximumForOptimizer': 20,
                    'distribution': 'uniform'}, {
                    'type': 'number',
                    'minimumForOptimizer': 0.01,
                    'maximumForOptimizer': 0.5}],
                'default': 2,
                'description': 'The minimum number of samples required to split an internal node:'},
            'min_samples_leaf': {
                'anyOf': [{
                    'type': 'integer',
                    'minimumForOptimizer': 1,
                    'maximumForOptimizer': 20,
                    'distribution': 'uniform'}, {
                    'type': 'number',
                    'minimumForOptimizer': 0.01,
                    'maximumForOptimizer': 0.5}],
                'default': 1,
                'description': 'The minimum number of samples required to be at a leaf node.'},
            'min_weight_fraction_leaf': {
                'type': 'number',
                'default': 0.0,
                'description': 'The minimum weighted fraction of the sum total of weights (of all'},
            'max_depth': {
                'type': 'integer',
                'minimumForOptimizer': 3,
                'maximumForOptimizer': 5,
                'default': 3,
                'description': 'maximum depth of the individual regression estimators. The maximum'},
            'min_impurity_decrease': {
                'type': 'number',
                'default': 0.0,
                'description': 'A node will be split if this split induces a decrease of the impurity'},
            'min_impurity_split': {
                'anyOf':[
                {'type': 'number'},{
                    'enum': [None]
                }],
                'default': None,
                'description': 'Threshold for early stopping in tree growth. A node will split'},
            'init': {
                'anyOf': [{
                    'type': 'object'}, {
                    'enum': ['zero', None]}],
                'default': None,
                'description': 'An estimator object that is used to compute the initial'},
            'random_state': {
                'anyOf': [
                {   'type': 'integer'},
                {   'laleType': 'numpy.random.RandomState'},
                {   'enum': [None]}],
                'default': None,
                'description': 'If int, random_state is the seed used by the random number generator;'},
            'max_features': {
                'anyOf': [
                {   'type': 'integer',
                    'minimum': 1,
                    'forOptimizer': False},
                {   'type': 'number',
                    'minimum': 0.0,
                    'exclusiveMinimum': True,
                    'maximum': 1.0,
                    'exclusiveMaximum': True,
                    'distribution': 'uniform'},
                {   'enum': ['auto', 'sqrt', 'log2', None]}],
                'default': None,
                'description': 'The number of features to consider when looking for the best split.'},
            'alpha': {
                'type': 'number',
                'minimumForOptimizer': 1e-10,
                'maximumForOptimizer': 1.0,
                'distribution':'loguniform',
                'default': 0.9,
                'description': 'The alpha-quantile of the huber loss function and the quantile'},
            'verbose': {
                'type': 'integer',
                'default': 0,
                'description': 'Enable verbose output. If 1 then it prints progress and performance'},
            'max_leaf_nodes': {
                'anyOf': [{
                    'type': 'integer'}, {
                    'enum': [None]}],
                'default': None,
                'description': 'Grow trees with ``max_leaf_nodes`` in best-first fashion.'},
            'warm_start': {
                'type': 'boolean',
                'default': False,
                'description': 'When set to ``True``, reuse the solution of the previous call to fit'},
            'presort': {
                'anyOf': [{
                    'type': 'boolean'}, {
                    'enum': ['auto']}],
                'default': 'auto',
                'description': 'Whether to presort the data to speed up the finding of best splits in'},
            'validation_fraction': {
                'type': 'number',
                'default': 0.1,
                'description': 'The proportion of training data to set aside as validation set for'},
            'n_iter_no_change': {
                'anyOf': [{
                    'type': 'integer',
                    'minimumForOptimizer': 5,
                    'maximumForOptimizer': 10}, {
                    'enum': [None]}],
                'default': None,
                'description': '``n_iter_no_change`` is used to decide if early stopping will be used'},
            'tol': {
                'type': 'number',
                'minimumForOptimizer': 1e-08,
                'maximumForOptimizer': 0.01,
                'distribution': 'loguniform',
                'default': 0.0001,
                'description': 'Tolerance for the early stopping. When the loss is not improving'},
        }}]}

_input_fit_schema = {
    'description': 'Fit the gradient boosting model.',
    'type': 'object',
    'required': ['X', 'y'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The input samples. Internally, it will be converted to'},
        'y': {
            'type': 'array',
            'items': {
                'type': 'number'},
            'description': 'Target values (strings or integers in classification, real numbers'},
        'sample_weight': {
            'anyOf': [{
                'type': 'array',
                'items': {
                    'type': 'number'},
            }, {
                'enum': [None]}],
            'default': None,
            'description': 'Sample weights. If None, then samples are equally weighted. Splits'},
        'monitor': {
            'anyOf': [
            {   'laleType': 'callable'},
            {   'enum': [None]}],
            'default': None,
            'description': 'The monitor is called after each iteration with the current the current iteration, a reference to the estimator and the local variables of _fit_stages as keyword arguments callable(i, self, locals()).'},
    },
}
_input_predict_schema = {
    'description': 'Predict regression target for X.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The input samples. Internally, it will be converted to'},
    },
}
_output_predict_schema = {
    'description': 'The predicted values.',
    'type': 'array',
    'items': {
        'type': 'number'},
}
_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`Gradient boosting regressor`_ random forest from scikit-learn.

.. _`Gradient boosting regressor`: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn-ensemble-gradientboostingregressor
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.gradient_boosting_regressor.html',
    'import_from': 'sklearn.ensemble',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['estimator', 'regressor'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_predict': _input_predict_schema,
        'output_predict': _output_predict_schema}}

GradientBoostingRegressor : lale.operators.IndividualOp
GradientBoostingRegressor = lale.operators.make_operator(GradientBoostingRegressorImpl, _combined_schemas)

if sklearn.__version__ >= '0.22':
    # old: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
    # new: https://scikit-learn.org/0.23/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
    from lale.schemas import AnyOf, Bool, Enum, Float
    GradientBoostingRegressor = GradientBoostingRegressor.customize_schema(
        presort=AnyOf(
            types=[Bool(), Enum(['deprecated'])],
            desc='This parameter is deprecated and will be removed in v0.24.',
            default='deprecated'),
        ccp_alpha=Float(
            desc='Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed.',
            default=0.0,
            forOptimizer=True,
            min=0.0,
            maxForOptimizer=0.1))

lale.docstrings.set_docstrings(GradientBoostingRegressorImpl, GradientBoostingRegressor._schemas)