# 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.linear_model
import lale.docstrings
import lale.operators

class RidgeClassifierImpl():

    def __init__(self, alpha=None, fit_intercept=None, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver=None, class_weight=None, random_state=None):
        self._hyperparams = {
            'alpha': alpha,
            'fit_intercept': fit_intercept,
            'normalize': normalize,
            'copy_X': copy_X,
            'max_iter': max_iter,
            'tol': tol,
            'solver': solver,
            'class_weight':class_weight,
            'random_state': random_state}
        self._wrapped_model = sklearn.linear_model.RidgeClassifier(**self._hyperparams)

    def fit(self, X, y, **fit_params):
        self._wrapped_model.fit(X, y, **fit_params)
        return self

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

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

_hyperparams_schema = {
    'description': 'Classifier using Ridge regression.',
    'allOf': [{
        'type': 'object',
        'required': ['alpha', 'fit_intercept', 'solver'],
        'relevantToOptimizer': ['alpha', 'fit_intercept', 'normalize', 'copy_X', 'max_iter', 'tol', 'solver'],
        'additionalProperties': False,
        'properties': {
            'alpha': {
                'description': 'Regularization strength; larger values specify stronger regularization.',
                'anyOf': [
                {   'type': 'number',
                    'minimum': 0.0,
                    'exclusiveMinimum': True,
                    'minimumForOptimizer': 1e-05,
                    'maximumForOptimizer': 10.0,
                    'distribution': 'loguniform'},
                {   'type': 'array',
                    'description': 'Penalties specific to the targets.',
                    'items': {
                        'type': 'number',
                        'minimum': 0.0,
                        'exclusiveMinimum': True},
                    'forOptimizer': False}],
                'default': 1.0},
            'fit_intercept': {
                'type': 'boolean',
                'default': True,
                'description': 'Whether to calculate the intercept for this model.'},
            'normalize': {
                'type': 'boolean',
                'default': False,
                'description': 'This parameter is ignored when ``fit_intercept`` is set to False.'},
            'copy_X': {
                'type': 'boolean',
                'default': True,
                'description': 'If True, X will be copied; else, it may be overwritten.'},
            'max_iter': {
                'anyOf': [{
                    'type': 'integer',
                    'minimumForOptimizer': 10,
                    'maximumForOptimizer': 1000}, {
                    'enum': [None]}],
                'default': None,
                'description': 'Maximum number of iterations for conjugate gradient solver.'},
            'tol': {
                'type': 'number',
                'minimumForOptimizer': 1e-08,
                'maximumForOptimizer': 0.01,
                'distribution': 'loguniform',
                'default': 0.001,
                'description': 'Precision of the solution.'},
            'solver': {
                'enum': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'],
                'default': 'auto',
                'description': 'Solver to use in the computational routines.'},
            'class_weight': {
                'anyOf': [{
                    'type': 'object'}, #dict, list of dicts, 
                    {'enum': ['balanced', None]}],
                'description': 'Weights associated with classes in the form ``{class_label: weight}``.',
                'default': None},
            'random_state': {
                'anyOf': [
                {   'type': 'integer'},
                {   'laleType': 'numpy.random.RandomState'},
                {   'enum': [None]}],
                'default': None,
                'description': 'The seed of the pseudo random number generator to use when shuffling'},
        }}]}

_input_fit_schema = {
    'description': 'Fit Ridge regression model',
    'type': 'object',
    'required': ['X', 'y'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'Training data'},
        'y': {
            'anyOf': [
            {   'type': 'array',
                'items': {
                    'type': 'array',
                    'items': {
                        'type': 'number'},
                }},
            {   'type': 'array',
                'items': {
                    'type': 'number'},
            },
            {   'type': 'array',
                'items': {
                    'type': 'string'},
            },
                {'type': 'array', 'items': {'type': 'boolean'}}],
            'description': 'Target values'},
        'sample_weight': {
            'anyOf': [{
                'type': 'number'}, {
                'type': 'array',
                'items': {
                    'type': 'number'},
            }, {'enum': [None]}],
            'description': 'Individual weights for each sample'},
    },
}
_input_predict_schema = {
    'description': 'Predict class labels for samples in X.',
    'type': 'object',
    'properties': {
        'X': {
            'anyOf': [{
                'type': 'array',
                'items': {'type': 'number'}}, {
                'type': 'array',
                'items': {
                    'type': 'array',
                    'items': {
                        'type': 'number'},
                }}],
            'description': 'Samples.'},
    },
}
_output_predict_schema = {
    'description': 'Predicted class label per sample.',
    'anyOf': [
        {'type': 'array', 'items': {'type': 'number'}},
        {'type': 'array', 'items': {'type': 'string'}},
        {'type': 'array', 'items': {'type': 'boolean'}}]}

_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': """`Ridge classifier`_ from scikit-learn.

.. _`Ridge classifier`: https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.RidgeClassifier.html#sklearn-linear-model-ridgeclassifier
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.ridge_classifier.html',
    'import_from': 'sklearn.linear_model',
    '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_decision_function': _input_decision_function_schema,
        'output_decision_function': _output_decision_function_schema,
}}

lale.docstrings.set_docstrings(RidgeClassifierImpl, _combined_schemas)

RidgeClassifier = lale.operators.make_operator(RidgeClassifierImpl, _combined_schemas)