# 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.naive_bayes
import lale.docstrings
import lale.operators

class GaussianNBImpl():

    def __init__(self, priors=None, var_smoothing=1e-09):
        self._hyperparams = {
            'priors': priors,
            'var_smoothing': var_smoothing}
        self._wrapped_model = sklearn.naive_bayes.GaussianNB(**self._hyperparams)

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

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

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

_hyperparams_schema = {
    'description': 'Gaussian Naive Bayes (GaussianNB)',
    'allOf': [{
        'type': 'object',
        'required': ['priors'],
        'relevantToOptimizer': [],
        'additionalProperties': False,
        'properties': {
            'priors': {
                'anyOf': [{
                    'type': 'array',
                    'items': {
                        'type': 'number'},
                }, {
                    'enum': [None]}],
                'default': None,
                'description': 'Prior probabilities of the classes. If specified the priors are not'},
            'var_smoothing': {
                'type': 'number',
                'default': 1e-09,
                'description': 'Portion of the largest variance of all features that is added to'},
        }}],
}
_input_fit_schema = {
    'description': 'Fit Gaussian Naive Bayes according to X, y',
    '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'},
        'y': {
            'anyOf': [
                {'type': 'array', 'items': {'type': 'number'}},
                {'type': 'array', 'items': {'type': 'string'}},
                {'type': 'array', 'items': {'type': 'boolean'}}],
            'description': 'Target values.'},
        'sample_weight': {
            'anyOf': [{
                'type': 'array',
                'items': {
                    'type': 'number'},
            }, {
                'enum': [None]}],
            'default': None,
            'description': 'Weights applied to individual samples (1. for unweighted).'},
    },
}
_input_predict_schema = {
    'description': 'Perform classification on an array of test vectors X.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            }},
    },
}
_output_predict_schema = {
    'description': 'Predicted target values for X',
    'anyOf': [
        {'type': 'array', 'items': {'type': 'number'}},
        {'type': 'array', 'items': {'type': 'string'}},
        {'type': 'array', 'items': {'type': 'boolean'}}]}

_input_predict_proba_schema = {
    'description': 'Return probability estimates for the test vector X.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            }},
    },
}
_output_predict_proba_schema = {
    'description': 'Returns the probability of the samples for each class in',
    'type': 'array',
    'items': {
        'type': 'array',
        'items': {
            'type': 'number'},
    },
}
_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`Gaussian Naive Bayes`_ classifier from scikit-learn.

.. _`Gaussian Naive Bayes`: https://scikit-learn.org/0.20/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn-naive-bayes-gaussiannb
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.gaussian_naive_bayes.html',
    'import_from': 'sklearn.naive_bayes',
    '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}}

lale.docstrings.set_docstrings(GaussianNBImpl, _combined_schemas)

GaussianNB = lale.operators.make_operator(GaussianNBImpl, _combined_schemas)