# 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 VotingClassifierImpl():
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.ensemble.VotingClassifier(**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 transform(self, X):
        return self._wrapped_model.transform(X)

    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': 'Soft Voting/Majority Rule classifier for unfitted estimators.',
    'allOf': [{
        'type': 'object',
        'required': ['estimators', 'voting', 'weights', 'n_jobs', 'flatten_transform'],
        'relevantToOptimizer': ['voting'],
        'additionalProperties': False,
        'properties': {
            'estimators': {
                'type': 'array',
                'items': {
                    'type': 'array',
                    'laleType': 'tuple',
                    'items': [
                    {   'type':'string'},
                    {   'anyOf': [
                        {   'laleType': 'operator'},
                        {   'enum': [None]}]}]},
                'description': 'List of (string, estimator) tuples. Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones.'},
            'voting': {
                'enum': ['hard', 'soft'],
                'default': 'hard',
                'description': "If 'hard', uses predicted class labels for majority rule voting."},
            'weights': {
                'anyOf': [{
                    'type': 'array',
                    'items': {
                        'type': 'number'},
                }, {
                    'enum': [None]}],
                'default': None,
                'description': 'Sequence of weights (`float` or `int`) to weight the occurrences of'},
            'n_jobs': {
                'anyOf': [{
                    'type': 'integer'}, {
                    'enum': [None]}],
                'default': None,
                'description': 'The number of jobs to run in parallel for ``fit``.'},
            'flatten_transform': {
                'type': 'boolean',
                'default': True,
                'description': "Affects shape of transform output only when voting='soft'"},
        }}, {
        'description': "Parameter: flatten_transform > only when voting='soft' if voting='soft' and flatten_transform=true",
        'anyOf': [{
            'type': 'object',
            'properties': {
                'voting': {
                    'enum': ['soft']},
            }}, {
            'type': 'object',
            'properties': {
                'flatten_transform': {
                    'enum': [True]},
            }}]}],
}
_input_fit_schema = {
    'description': 'Fit the estimators.',
    'type': 'object',
    'required': ['X', 'y'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'Training vectors, where n_samples is the number of samples and'},
        'y': {
            'type': 'array',
            'items': {
                'type': 'number'},
            'description': 'Target values.'},
        'sample_weight': {
            'anyOf': [{
                'type': 'array',
                'items': {
                    'type': 'number'},
            }, {
                'enum': [None]}],
            'description': 'Sample weights. If None, then samples are equally weighted.'},
    },
}
_input_transform_schema = {
    'description': 'Return class labels or probabilities for X for each estimator.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'Training vectors, where n_samples is the number of samples and'},
    },
}
_output_transform_schema = {
    'description': "If `voting='soft'` and `flatten_transform=True`:",
    'type': 'array',
    'items': {
        'type': 'array',
        'items': { 'anyOf':[{
            'type': 'number'},{
            'type': 'array',
            'items': {'type':'number'}
            }]}}}

_input_predict_schema = {
    'description': 'Predict class labels for X.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The input samples.'},
    },
}
_output_predict_schema = {
    'description': 'Predicted class labels.',
    'type': 'array',
    'items': {
        'type': 'number'},
}
_input_predict_proba_schema = {
    'description': 'Compute probabilities of possible outcomes for samples in X.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'The input samples.'},
    },
}
_output_predict_proba_schema = {
    'description': 'Weighted average probability for each class per sample.',
    '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': """`Voting classifier`_ from scikit-learn for voting ensemble.

.. _`Voting classifier`: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn-ensemble-votingclassifier
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.voting_classifier.html',
    'import_from': 'sklearn.ensemble',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer', 'estimator'],
        '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_transform': _input_transform_schema,
        'output_transform': _output_transform_schema,
        'input_decision_function': _input_decision_function_schema,
        'output_decision_function': _output_decision_function_schema,
}}

VotingClassifier : lale.operators.IndividualOp
VotingClassifier = lale.operators.make_operator(VotingClassifierImpl, _combined_schemas)

if sklearn.__version__ >= '0.21':
    # old: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.VotingClassifier.html
    # new: https://scikit-learn.org/0.23/modules/generated/sklearn.ensemble.VotingClassifier.html
    from lale.schemas import JSON
    VotingClassifier = VotingClassifier.customize_schema(
        estimators=JSON({
            'type': 'array',
            'items': {
                'type': 'array',
                'laleType': 'tuple',
                'items': [
                {   'type':'string'},
                {   'anyOf': [
                    {   'laleType': 'operator'},
                    {   'enum': [None, 'drop']}]}]},
            'description': 'List of (string, estimator) tuples. Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones.'}))

lale.docstrings.set_docstrings(VotingClassifierImpl, VotingClassifier._schemas)