# 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 MultinomialNBImpl():

    def __init__(self, alpha=1.0, fit_prior=True, class_prior=None):
        self._hyperparams = {
            'alpha': alpha,
            'fit_prior': fit_prior,
            'class_prior': class_prior}
        self._wrapped_model = sklearn.naive_bayes.MultinomialNB(
            **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': 'Naive Bayes classifier for multinomial models',
    'allOf': [{
        'type': 'object',
        'required': ['alpha', 'fit_prior'],
        'relevantToOptimizer': ['alpha', 'fit_prior'],       
        'properties': {
            'alpha': {
                'type': 'number',
                'distribution':'loguniform',
                'minimumForOptimizer': 1e-10,
                'maximumForOptimizer': 1.0,
                'default': 1.0,
                'description': 'Additive (Laplace/Lidstone) smoothing parameter'},
            'fit_prior': {
                'type': 'boolean',
                'default': True,
                'description': 'Whether to learn class prior probabilities or not.'},
            'class_prior': {
                'anyOf': [{
                    'type': 'array',
                    'items': {'type': 'number'}}, {
                    'enum': [None]}],
                'default': None,
                'description': 'Prior probabilities of the classes. If specified the priors are not'},
        }}],
}
_input_fit_schema = {
    'description': 'Fit Naive Bayes classifier 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 and n_features is the number of features.'},
        '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',
    'required': ['X'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {'type': 'array', 'items': {'type': 'number'}}},
    },
}
_output_predict_schema = {
    'description': 'Perform classification on an array of test vectors X.',
    'anyOf': [
        {'type': 'array', 'items': {'type': 'number'}},
        {'type': 'array', 'items': {'type': 'string'}},
        {'type': 'array', 'items': {'type': 'boolean'}}]}

_input_predict_proba_schema = {
    'description': 'Perform classification on an array of test vectors X.',
    'type': 'object',
    'required': ['X'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {'type': 'array', 'items': {'type': 'number'}}},
    },
}
_output_predict_proba_schema = {
    'description': 'Perform classification on an array of test vectors X.',
    'type': 'array',
    'items': {'type': 'array', 'items': {'type': 'number'}},
    'description': 'Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.'
}
_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`Multinomial Naive Bayes`_ classifier from scikit-learn.

.. _`Multinomial Naive Bayes`: https://scikit-learn.org/0.20/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn-naive-bayes-multinomialnb
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.multinomial_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(MultinomialNBImpl, _combined_schemas)

MultinomialNB = lale.operators.make_operator(MultinomialNBImpl, _combined_schemas)