# 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.discriminant_analysis
import lale.docstrings
import lale.operators

class QuadraticDiscriminantAnalysisImpl():
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(**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)

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

_hyperparams_schema = {
    'description': 'Quadratic Discriminant Analysis',
    'allOf': [{
        'type': 'object',
        'required': ['priors', 'store_covariance'],
        'relevantToOptimizer': ['reg_param', 'tol'],
        'additionalProperties': False,
        'properties': {
            'priors': {
                'anyOf': [{
                    'type': 'array',
                    'items': {
                        'type': 'number'},
                }, {
                    'enum': [None]}],
                'default': None,
                'description': 'Priors on classes'},
            'reg_param': {
                'type': 'number',
                'minimumForOptimizer': 0.0,
                'maximumForOptimizer': 1.0,
                'distribution': 'uniform',
                'default': 0.0,
                'description': 'Regularizes the covariance estimate as'},
            'store_covariance': {
                'type': 'boolean',
                'default': False,
                'description': 'If True the covariance matrices are computed and stored in the'},
            'tol': {
                'type': 'number',
                'minimumForOptimizer': 1e-08,
                'maximumForOptimizer': 0.01,
                'distribution': 'loguniform',
                'default': 0.0001,
                'description': 'Threshold used for rank estimation.'}
        }}],
}
_input_fit_schema = {
    'description': 'Fit the model according to the given training data and parameters.',
    'type': 'object',
    'required': ['X', 'y'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'Training vector, where n_samples is the number of samples and'},
        'y': {
            'anyOf': [
                {'type': 'array', 'items': {'type': 'number'}},
                {'type': 'array', 'items': {'type': 'string'}},
                {'type': 'array', 'items': {'type': 'boolean'}}],
            'description': 'Target values (integers)'},
    },
}
_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': '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': 'Return posterior probabilities of classification.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'Array of samples/test vectors.'},
    },
}
_output_predict_proba_schema = {
    'description': 'Posterior probabilities of classification per class.',
    '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 = {
    'description': """`Quadratic discriminant analysis`_ classifier with a quadratic decision boundary from scikit-learn.

.. _`Quadratic discriminant analysis`: https://scikit-learn.org/0.20/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn-discriminant-analysis-quadraticdiscriminantanalysis 
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.quadratic_discriminant_analysis.html',
    'import_from': 'sklearn.discriminant_analysis',
    '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(QuadraticDiscriminantAnalysisImpl, _combined_schemas)

QuadraticDiscriminantAnalysis = lale.operators.make_operator(QuadraticDiscriminantAnalysisImpl, _combined_schemas)