# 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 PassiveAggressiveClassifierImpl():
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.linear_model.PassiveAggressiveClassifier(**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 decision_function(self, X):
        return self._wrapped_model.decision_function(X)

_hyperparams_schema = {
    'description': 'Passive Aggressive Classifier',
    'allOf': [{
        'type': 'object',
        'additionalProperties': False,
        'required': ['C', 'fit_intercept', 'max_iter', 'tol', 'early_stopping', 
            'shuffle', 'loss', 'average'],
        'relevantToOptimizer': ['C', 'fit_intercept', 'max_iter', 'tol', 'early_stopping', 
            'shuffle', 'loss', 'average'],         
        'properties': {
            'C': {
                'type': 'number',
                'description': 'Maximum step size (regularization). Defaults to 1.0.',
                'default': 1.0,
                'distribution': 'loguniform',
                'minimumForOptimizer': 1e-5,
                'maximumForOptimizer': 10},
            'fit_intercept': {
                'type': 'boolean',
                'default': False,
                'description': 'Whether the intercept should be estimated or not. If False, the'
                'the data is assumed to be already centered.'},
            'max_iter': {
                'anyOf': [
                {   'type': 'integer',
                    'minimumForOptimizer': 5,
                    'maximumForOptimizer': 1000,
                    'distribution': 'uniform'},
                {    'enum': [None]}],
                'default': 5,
                'description': 'The maximum number of passes over the training data (aka epochs).'},
            'tol': {
                'anyOf': [{
                    'type': 'number',
                    'minimumForOptimizer': 1e-08,
                    'maximumForOptimizer': 0.01,
                    'distribution': 'loguniform'}, {
                    'enum': [None]}],
                'default': None, #default value is 1e-3 from sklearn 0.21.
                'description': 'The stopping criterion. If it is not None, the iterations will stop'},
            'early_stopping': {
                'type': 'boolean',
                'default': False,
                'description': 'Whether to use early stopping to terminate training when validation.'},
            'validation_fraction': {
                'type': 'number',
                'default': 0.1,
                'description': 'The proportion of training data to set aside as validation set for'},
            'n_iter_no_change': {
                'type': 'integer',
                'minimumForOptimizer': 5,
                'maximumForOptimizer': 10,
                'default': 5,
                'description': 'Number of iterations with no improvement to wait before early stopping.'},
            'shuffle': {
                'type': 'boolean',
                'default': True,
                'description': 'Whether or not the training data should be shuffled after each epoch.'},
            'verbose': {
                'anyOf': [{
                    'type': 'integer'}, {
                    'enum': [None]}],
                'default': 0,
                'description': 'The verbosity level'},
            'loss': {
                'enum': ['hinge', 'squared_hinge'],
                'default': 'hinge',
                'description': 'The loss function to be used:'},
            'n_jobs': {
                'anyOf': [{
                    'type': 'integer'}, {
                    'enum': [None]}],
                'default': None,
                'description': 'The number of CPUs to use to do the OVA (One Versus All, for'},
            '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'},
            'warm_start': {
                'type': 'boolean',
                'default': False,
                'description': 'When set to True, reuse the solution of the previous call to' 
                ' fit as initialization, otherwise, just erase the previous solution.'},
            'class_weight': {
                'anyOf': [{
                    'type': 'object'}, {
                    'enum': ['balanced', None]}],
                'default': None,
                'description': 'Preset for the class_weight fit parameter.'},
            'average': {
                'anyOf': [{
                    'type': 'boolean'}, {
                    'type': 'integer',
                    'forOptimizer': False}],
                'default': False,
                'description': 'When set to True, computes the averaged SGD weights and stores the'},
            'n_iter': {
                'anyOf': [
                {   'type': 'integer',
                    'minimum': 1},
                {   'enum': [None]}],
                'default': None,
                'description': 'The number of passes over the training data (aka epochs).'}}}]}

_input_fit_schema = {
    'description': 'Fit linear model with Passive Aggressive algorithm.',
    'type': 'object',
    'required': ['X', 'y'],
    'properties': {
        'X': {
            'description': 'Training data',
            'type': 'array',
            'items': {
                'type': 'array',
                'items': { 'type': 'number'}}},
        'y': {
            'description': 'Target values',
            'anyOf': [
                {'type': 'array', 'items': {'type': 'number'}},
                {'type': 'array', 'items': {'type': 'string'}},
                {'type': 'array', 'items': {'type': 'boolean'}}]
            },
        'coef_init': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': { 'type': 'number'}},
            'description': 'The initial coefficients to warm-start the optimization.'},
        'intercept_init': {
            'type': 'array',
            'items': {
                    'type': 'number'},
            'description': 'The initial intercept to warm-start the optimization.'},
    },
}
_input_predict_schema = {
    'description': 'Predict class labels for samples in X.',
    'type': 'object',
    'required': ['X'],
    'properties': {
        'X': {
            'description': 'Test data',
            'type': 'array',
            'items': {
                'type': 'array',
                'items': { 'type': 'number'}}},
    },
}
_output_predict_schema = {
    'description': 'Predict class labels for samples in X.',
    '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': """`Passive aggressive`_ classifier from scikit-learn.

.. _`Passive aggressive`: https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn-linear-model-passiveaggressiveclassifier
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.passive_aggressive_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,
}}

PassiveAggressiveClassifier : lale.operators.IndividualOp
PassiveAggressiveClassifier = lale.operators.make_operator(PassiveAggressiveClassifierImpl, _combined_schemas)

# old: https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html
from lale.schemas import Int

if sklearn.__version__ >= '0.21':
    PassiveAggressiveClassifier = PassiveAggressiveClassifier.customize_schema(
        max_iter=Int(
            minForOptimizer=5,
            maxForOptimizer=1000,
            distribution='uniform',
            desc='The maximum number of passes over the training data (aka epochs).',
            default=1000))

if sklearn.__version__ >= '0.22':
    PassiveAggressiveClassifier = PassiveAggressiveClassifier.customize_schema(
        n_iter=None)

lale.docstrings.set_docstrings(PassiveAggressiveClassifierImpl, PassiveAggressiveClassifier._schemas)