# 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.svm
import lale.docstrings
import lale.operators

class LinearSVCImpl():
    def __init__(self, penalty=None, loss=None, dual=True, tol=0.0001, C=1.0, multi_class=None, fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000):
        self._hyperparams = {
            'penalty': penalty,
            'loss': loss,
            'dual': dual,
            'tol': tol,
            'C': C,
            'multi_class': multi_class,
            'fit_intercept': fit_intercept,
            'intercept_scaling': intercept_scaling,
            'class_weight': class_weight,
            'verbose': verbose,
            'random_state': random_state,
            'max_iter': max_iter}
        self._wrapped_model = sklearn.svm.LinearSVC(**self._hyperparams)

    def fit(self, X, y=None, sample_weight=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 = {
    'allOf': [
    {   'type': 'object',
        'additionalProperties': False,
        'required': [
            'penalty', 'loss', 'dual', 'tol', 'C', 'multi_class',
            'fit_intercept', 'intercept_scaling', 'class_weight',
            'verbose', 'random_state', 'max_iter'],
        'relevantToOptimizer': [
            'penalty', 'loss', 'dual', 'tol', 'C', 'multi_class',
            'fit_intercept'],
        'properties': {
            'penalty': {
                'description': 'Norm used in the penalization.',
                'enum': ['l1', 'l2'],
                'default': 'l2'},
            'loss': {
                'description': 'Loss function.',
                'enum': ['hinge', 'squared_hinge'],
                'default': 'squared_hinge'},
            'dual': {
                'type': 'boolean',
                'default': True,
                'description': 'Select the algorithm to either solve the dual or primal optimization problem.'},
            'tol': {
                'type': 'number',
                'distribution': 'loguniform',
                'minimum': 0.0,
                'exclusiveMinimum': True,
                'maximumForOptimizer': 0.01,
                'default': 0.0001,
                'description': 'Tolerance for stopping criteria.'},
            'C': {
                'description': 'Penalty parameter C of the error term.',
                'type': 'number',
                'distribution': 'loguniform',
                'minimum': 0.0,
                'exclusiveMinimum': True,
                'default': 1.0,
                'minimumForOptimizer': 0.03125,
                'maximumForOptimizer': 32768},
            'multi_class': {
                'description': 'Determines the multi-class strategy if `y` contains more than two classes.',
                'enum': ['ovr', 'crammer_singer'],
                'default': 'ovr'},
            'fit_intercept': {
                'type': 'boolean',
                'default': True,
                'description': 'Whether to calculate the intercept for this model.'},
            'intercept_scaling': {
                'type': 'number',
                'description':
                    'Append a constant feature with constant value '
                    'intercept_scaling to the instance vector.',
                'minimum': 0.0,
                'exclusiveMinimum': True,
                'maximumForOptimizer': 1.0,
                'default': 1.0},
            'class_weight': {
                'anyOf': [
                    { 'description': 'By default, all classes have weight 1.',
                      'enum': [None]},
                    { 'description': 'Adjust weights by inverse frequency.',
                      'enum': ['balanced']},
                    { 'description': 'Dictionary mapping class labels to weights.',
                      'type': 'object',
                      'additionalProperties': {'type': 'number'},
                      'forOptimizer': False}],
                'default': None},
            'verbose': {
                'type': 'integer',
                'default': 0,
                'description': 'Enable verbose output.'},
            'random_state': {
                'description':
                'Seed of pseudo-random number generator.',
                'anyOf': [
                {   'laleType': 'numpy.random.RandomState'},
                {   'description': 'RandomState used by np.random',
                    'enum': [None]},
                {   'description': 'Explicit seed.',
                    'type': 'integer'}],
                'default': None},
            'max_iter': {
                'type': 'integer',
                'minimumForOptimizer': 10,
                'maximumForOptimizer': 1000,
                'default': 1000,
                'description': 'The maximum number of iterations to be run.'}}},
    {   'description':
            'The combination of penalty=`l1` and loss=`hinge` is not supported',
        'anyOf': [
        {   'type': 'object',
            'properties': {'penalty': {'enum': ['l2']}}},
        {   'type': 'object',
            'properties': {'loss': {'enum': ['squared_hinge']}}}]},
    {   'description': 'The combination of penalty=`l2` and loss=`hinge` '
                       'is not supported when dual=False.',
        'anyOf': [
        {   'type': 'object',
            'properties': {'penalty': {'enum': ['l1']}}},
        {   'type': 'object',
            'properties': {'loss': {'enum': ['squared_hinge']}}},
        {   'type': 'object',
            'properties': {'dual': {'enum': [True]}}}]},
    {   'description': 'The combination of penalty=`l1` and '
                       'loss=`squared_hinge` is not supported when dual=True.',
        'anyOf': [
        {   'type': 'object',
            'properties': {'penalty': {'enum': ['l2']}}},
        {   'type': 'object',
            'properties': {'loss': {'enum': ['hinge']}}},
        {   'type': 'object',
            'properties': {'dual': {'enum': [False]}}}]}]}

_input_fit_schema = {
    'type': 'object',
    'required': ['X', 'y'],
    'properties': {
        'X': {
            'type': 'array',
            'description': 'The outer array is over samples aka rows.',
            'items': {
                'type': 'array',
                'description': 'The inner array is over features aka columns.',
                'items': {
                    'type': 'number'}}},
        'y': {
            'description': 'The predicted classes.',
            'anyOf': [
            {   'type': 'array', 'items': {'type': 'number'}},
            {   'type': 'array', 'items': {'type': 'string'}},
            {   'type': 'array', 'items': {'type': 'boolean'}}]},
        'sample_weight': {
            'anyOf': [
            {   'type': 'array',
                'items': {'type': 'number'}},
            {   'enum': [None],
                'description': 'Samples are equally weighted.'}],
            'description': 'Sample weights.'}}}

_input_predict_schema = {
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'description': 'The outer array is over samples aka rows.',
            'items': {
                'type': 'array',
                'description': 'The inner array is over features aka columns.',
                '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'],
    'properties': {
        'X': {
            'type': 'array',
            'description': 'The outer array is over samples aka rows.',
            'items': {
                'type': 'array',
                'description': 'The inner array is over features aka columns.',
                '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': """`Linear Support Vector Classification`_ from scikit-learn.

.. _`Linear Support Vector Classification`: https://scikit-learn.org/0.20/modules/generated/sklearn.svm.LinearSVC.html#sklearn-svm-linearsvc
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.linear_svc.html',
    'import_from': 'sklearn.svm',
    '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(LinearSVCImpl, _combined_schemas)

LinearSVC = lale.operators.make_operator(LinearSVCImpl, _combined_schemas)