# 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.

from sklearn.ensemble import BaggingClassifier as SKLModel
import lale.operators
import lale.docstrings
from lale.sklearn_compat import make_sklearn_compat

class BaggingClassifierImpl():
    def __init__(self, base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0):
        self._hyperparams = {
            'base_estimator': make_sklearn_compat(base_estimator),
            'n_estimators': n_estimators,
            'max_samples': max_samples,
            'max_features': max_features,
            'bootstrap': bootstrap,
            'bootstrap_features': bootstrap_features,
            'oob_score': oob_score,
            'warm_start': warm_start,
            'n_jobs': n_jobs,
            'random_state': random_state,
            'verbose': verbose}
        self._wrapped_model = SKLModel(**self._hyperparams)

    def fit(self, X, y=None):
        if (y is not None):
            self._wrapped_model.fit(X, y)
        else:
            self._wrapped_model.fit(X)
        return self

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

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

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

_hyperparams_schema = {
    'description': 'A Bagging classifier.',
    'allOf': [{
        'type': 'object',
        'required': ['base_estimator', 'n_estimators', 'max_samples', 'max_features', 'bootstrap', 'bootstrap_features', 'oob_score', 'warm_start', 'n_jobs', 'random_state', 'verbose'],
        'relevantToOptimizer': ['n_estimators', 'bootstrap'],
        'additionalProperties': False,
        'properties': {
            'base_estimator': {
                'anyOf': [{
                    'laleType': 'operator'}, {
                    'enum': [None]}],
                'default': None,
                'description': 'The base estimator to fit on random subsets of the dataset.'},
            'n_estimators': {
                'type': 'integer',
                'minimumForOptimizer': 10,
                'maximumForOptimizer': 100,
                'distribution': 'uniform',
                'default': 10,
                'description': 'The number of base estimators in the ensemble.'},
            'max_samples': {
                'anyOf': [{
                    'type': 'integer'}, {
                    'type': 'number'}],
                'default': 1.0,
                'description': 'The number of samples to draw from X to train each base estimator.'},
            'max_features': {
                'anyOf': [{
                    'type': 'integer'}, {
                    'type': 'number'}],
                'default': 1.0,
                'description': 'The number of features to draw from X to train each base estimator.'},
            'bootstrap': {
                'type': 'boolean',
                'default': True,
                'description': 'Whether samples are drawn with replacement. If False, sampling'},
            'bootstrap_features': {
                'type': 'boolean',
                'default': False,
                'description': 'Whether features are drawn with replacement.'},
            'oob_score': {
                'type': 'boolean',
                'default': False,
                'description': 'Whether to use out-of-bag samples to estimate'},
            'warm_start': {
                'type': 'boolean',
                'default': False,
                'description': 'When set to True, reuse the solution of the previous call to fit'},
            'n_jobs': {
                'anyOf': [{
                    'type': 'integer'}, {
                    'enum': [None]}],
                'default': None,
                'description': 'The number of jobs to run in parallel for both `fit` and `predict`.'},
            '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;'},
            'verbose': {
                'type': 'integer',
                'default': 0,
                'description': 'Controls the verbosity when fitting and predicting.'},
        }}],
}
_input_fit_schema = {
    'description': 'Build a Bagging ensemble of estimators from the training',
    'type': 'object',
    'required': ['y', 'X'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The training input samples. Sparse matrices are accepted only if'},
        'y': {
            'type': 'array',
            'items': {
                'type': 'number'},
            'description': 'The target values (class labels in classification, real numbers in'},
        'sample_weight': {
            'anyOf': [{
                'type': 'array',
                'items': {
                    'type': 'number'},
            }, {
                'enum': [None]}],
            'description': 'Sample weights. If None, then samples are equally weighted.'},
    },
}
_input_predict_schema = {
    'description': 'Predict class for X.',
    'type': 'object',
    'required': ['X'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The training input samples. Sparse matrices are accepted only if'},
    },
}
_output_predict_schema = {
    'description': 'The predicted classes.',
    'type': 'array',
    'items': {
        'type': 'number'},
}
_input_predict_proba_schema = {
    'description': 'Predict class probabilities for X.',
    'type': 'object',
    'required': ['X'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The training input samples. Sparse matrices are accepted only if'},
    },
}
_output_predict_proba_schema = {
    'description': 'The class probabilities of the input samples. The order of the',
    'type': 'array',
    'items': {
        'type': 'array',
        'items': {
            'type': 'number'},
    },
}

_input_decision_function_schema = {
  '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_decision_function_schema = {
    'description': 'Confidence scores for samples for each class in the model.',
    'anyOf': [
    {   'description': 'In the multi-way case, score per (sample, class) combination.',
        'type': 'array',
        'items': {'type': 'array', 'items': {'type': 'number'}}},
    {   'description': 'In the binary case, score for `self._classes[1]`.',
        'type': 'array',
        'items': {'type': 'number'}}]}

_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`Bagging classifier`_ from scikit-learn for bagging ensemble.

.. _`Bagging classifier`: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn-ensemble-baggingclassifier
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.bagging_classifier.html',
    'import_from': 'sklearn.ensemble',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['estimator', 'classifier'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_predict': _input_predict_schema,
        'output_predict': _output_predict_schema,
        'input_predict_proba': _input_predict_proba_schema,
        'output_predict_proba': _output_predict_proba_schema,
        'input_decision_function': _input_decision_function_schema,
        'output_decision_function': _output_decision_function_schema,
}}

lale.docstrings.set_docstrings(BaggingClassifierImpl, _combined_schemas)

BaggingClassifier = lale.operators.make_operator(BaggingClassifierImpl, _combined_schemas)