# 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 lale.operators
import lale.docstrings
import sklearn.neural_network.multilayer_perceptron

class MLPClassifierImpl():
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.neural_network.multilayer_perceptron.MLPClassifier(**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 partial_fit(self, X, y=None, classes = None):
      if not hasattr(self, "_wrapped_model"):
        self._wrapped_model = sklearn.neural_network.multilayer_perceptron.MLPClassifier(
            **self._hyperparams)
      self._wrapped_model.partial_fit(X, y, classes = classes)
      return self

_hyperparams_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description':
        'Hyperparameter schema for the MLPClassifier model from scikit-learn.',
    'allOf': [{
        'description':
            'This first sub-object lists all constructor arguments with their '
            'types, one at a time, omitting cross-argument constraints.',
        'type': 'object',
        'additionalProperties': False,
        'required': [
            'hidden_layer_sizes', 'activation', 'solver', 'alpha',
            'batch_size', 'learning_rate', 'learning_rate_init', 'power_t',
            'max_iter', 'shuffle', 'random_state', 'tol', 'verbose',
            'warm_start', 'momentum', 'nesterovs_momentum', 'early_stopping',
            'validation_fraction', 'beta_1', 'beta_2', 'epsilon',
            'n_iter_no_change'],
        'relevantToOptimizer': [
            'hidden_layer_sizes', 'activation', 'solver', 'alpha',
            'batch_size', 'learning_rate', 'tol', 'momentum',
            'nesterovs_momentum', 'early_stopping', 'validation_fraction',
            'beta_1', 'beta_2', 'epsilon'],
        'properties': {
            'hidden_layer_sizes': {
                'description':
                    'The ith element represents the number of neurons in '
                    'the ith hidden layer.',
                'type': 'array',
                'laleType': 'tuple',
                'minItemsForOptimizer': 1,
                'maxItemsForOptimizer': 20,
                'items': {
                    'type': 'integer',
                    'minimumForOptimizer': 1,
                    'maximumForOptimizer': 500},
                'default': [100]},
            'activation': {
                'description': 'Activation function for the hidden layer.',
                'enum': ['identity', 'logistic', 'tanh', 'relu'],
                'default': 'relu'},
            'solver': {
                'description': 'The solver for weight optimization.',
                'enum': ['lbfgs', 'sgd', 'adam'],
                'default': 'adam'},
            'alpha': {
                'description': 'L2 penalty (regularization term) parameter.',
                'type': 'number',
                'distribution': 'loguniform',
                'minimumForOptimizer': 1e-10,
                'maximumForOptimizer': 1,
                'default': 0.0001},
            'batch_size': {
                'description':
                    'Size of minibatches for stochastic optimizers.',
                'anyOf': [{
                    'description': 'Size of minibatches',
                    'type': 'integer',
                    'distribution': 'uniform',
                    'minimumForOptimizer': 3,
                    'maximumForOptimizer': 128}, {
                    'description':
                        'Automatic selection, batch_size=min(200, n_samples)',
                    'enum': ['auto']}],
                'default': 'auto'},
            'learning_rate': {
                'description': 'Learning rate schedule for weight updates.',
                'enum': ['constant', 'invscaling', 'adaptive'],
                'default': 'constant'},
            'learning_rate_init': {
                'description':
                    'The initial learning rate used. It controls the '
                    'step-size in updating the weights.',
                'type': 'number',
                'default': 0.001},
            'power_t': {
                'description':
                    'The exponent for inverse scaling learning rate.',
                'type': 'number',
                'default': 0.5},
            'max_iter': {
                'description':
                    'Maximum number of iterations. The solver iterates until '
                    'convergence (determined by "tol") or this number of '
                    'iterations.',
                'type': 'integer',
                'distribution': 'uniform',
                'minimum': 1,
                'minimumForOptimizer': 10,
                'maximumForOptimizer': 1000,
                'default': 200},
            'shuffle': {
                'description': 'Whether to shuffle samples in each iteration.',
                'type': 'boolean',
                'default': True},
            'random_state': {
                'description': 'Random generator selection',
                'anyOf': [{
                    'description': 'seed used by the random number generators',
                    'type': 'integer'}, {
                    'description': 'Random number generator',
                    'type': 'object'}, {
                    'description': 'RandomState instance used by np.random',
                    'enum': [None]}],
                'default': None},
            'tol': {
                'description':
                    'Tolerance for the optimization. When the loss or score '
                    'is not improving by at least tol for n_iter_no_change '
                    'consecutive iterations, unless learning_rate is set to '
                    '"adaptive", convergence is considered to be reached and '
                    'training stops.',
                'type': 'number',
                'distribution': 'loguniform',
                'minimumForOptimizer': 1e-08,
                'maximumForOptimizer': 0.01,
                'default': 0.0001},
            'verbose': {
                'description':
                    'Whether to print progress messages to stdout.',
                'type': 'boolean',
                'default': False},
            'warm_start': {
                'description':
                    'When set to True, reuse the solution of the previous '
                    'call to fit as initialization, otherwise, just erase '
                    'the previous solution.',
                'type': 'boolean',
                'default': False},
            'momentum': {
                'description': 'Momentum for gradient descent update.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'default': 0.9},
            'nesterovs_momentum': {
                'description': "Whether to use Nesterov's momentum.",
                'type': 'boolean',
                'default': True},
            'early_stopping': {
                'description':
                    'Whether to use early stopping to terminate training when '
                    'validation score is not improving. If set to true, it '
                    'will automatically set aside 10% of training data as '
                    'validation and terminate training when validation score '
                    'is not improving by at least tol for n_iter_no_change '
                    'consecutive epochs.',
                'type': 'boolean',
                'default': False},
            'validation_fraction': {
                'description':
                    'The proportion of training data to set aside as '
                    'validation set for early stopping.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'default': 0.1},
            'beta_1': {
                'description':
                    'Exponential decay rate for estimates of first moment '
                    'vector in adam.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'exclusiveMaximum': True,
                'default': 0.9},
            'beta_2': {
                'description':
                    'Exponential decay rate for estimates of second moment '
                    'vector in adam.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'exclusiveMaximum': True,
                'default': 0.999},
            'epsilon': {
                'description': 'Value for numerical stability in adam.',
                'type': 'number',
                'distribution': 'loguniform',
                'minimumForOptimizer': 1e-08,
                'maximumForOptimizer': 1.35,
                'default': 1e-08},
            'n_iter_no_change': {
                'description':
                    'Maximum number of epochs to not meet tol improvement.',
                'type': 'integer',
                'default': 10,
                'minimum': 1},
        }},
    {   'description':
            'If the solver is "lbfgs", the classifier will not use minibatch',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'solver': {
                    'not': {
                        'enum': ['lbfgs']},
                }},
        }, {
            'type': 'object',
            'property': {
                'batch_size': 'auto'},
        }]},
    {   'description': 'The learning rate is only used when solver="sgd".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'learning_rate': {
                    'enum': ['constant']},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description':
            'The initial learning rate is only used when '
            'solver="sgd" or "adam".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'learning_rate_init': {
                    'enum': [0.001]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]},
    {   'description':
            'The exponent for inverse scaling learning rate is used in '
            'updating effective learning rate when the learning_rate is '
            'set to "invscaling" and when solver="sgd".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'power_t': {
                    'enum': [0.5]},
            }}, {
            'type': 'object',
            'properties': {
                'learning_rate': {
                    'enum': ['invscaling']},
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description': 'Shuffle is only used when solver="sgd" or "adam".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'shuffle': {
                    'enum': [True]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]},
    {   'description': 'The momentum  is only used when solver="sgd".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'momentum': {
                    'enum': [0.9]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description':
            "The Nesterov's momentum is only used when solver='sgd' "
            "and momentum > 0.",
        'anyOf': [{
            'type': 'object',
            'properties': {
                'nesterovs_momentum': {
                    'enum': [True]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description':
            'Early stopping is only effective when solver="sgd" or "adam"',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'early_stopping': {
                    'enum': [False]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]},
    {   'description':
            'The validation fraction is only used if early stopping is True',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'validation_fraction': {
                    'enum': [0.1]},
            }}, {
            'type': 'object',
            'properties': {
                'early_stopping': {
                    'enum': [True]},
            }}]},
    {   'description': 'beta_1 is only used for solver adam',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'beta_1': {
                    'enum': [0.9]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['adam']},
            }}]},
    {   'description': 'beta_2 is only used for solver adam',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'beta_2': {
                    'enum': [0.999]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['adam']},
            }}]},
    {   'description': 'epsilon is only used for solver adam',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'epsilon': {
                    'enum': [1e-08]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['adam']},
            }}]},
    {   'description':
            'n_iter_no_change is only effective when solver="sgd" or "adam"',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'n_iter_no_change': {
                    'enum': [10]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]}]}

_input_fit_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Fit the model to data matrix X and target(s) y.',
    'type': 'object',
    'required': ['X', 'y'],
    'additionalProperties': False,
    'properties': {
        'X': {
            'description': 'Features; the outer array is over samples.',
            'type': 'array',
            'items': {
                'type': 'array',
                'items': { 'type': 'number'}}},
        'y': {
            'description': 'Target class labels; the array is over samples.',
            'anyOf': [
            {   'type': 'array',
                'items': {
                    'type': 'array',
                    'items': { 'type': 'number'}}},
            {   'type': 'array',
                'items': {
                    'type': 'number'}},
            {   'type': 'array',
                'items': {
                    'type': 'string'}}]}}}

_input_predict_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Predict using the multi-layer perceptron classifier',
    '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_predict_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Predict using the multi-layer perceptron classifier',
    'anyOf': [
    {   'type': 'array',
        'items': {
            'type': 'number'}},
    {   'type': 'array',
        'items': {
            'type': 'string'}}]}

_input_predict_proba_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_predict_proba_schema = {
    'type': 'array',
    'items': {
        'type': 'array',
        'items': {'type': 'number'}}}

_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`Multi-layer perceptron`_ dense deep neural network from scikit-learn for classification.

.. _`Multi-layer perceptron`: https://scikit-learn.org/0.20/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn-neural-network-mlpclassifier
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.mlp_classifier.html',
    'type': 'object',
    'tags': {
        'pre': ['~categoricals'],
        'op': ['estimator', 'classifier', '~interpretable'],
        'post': ['probabilities']},
    '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(MLPClassifierImpl, _combined_schemas)

MLPClassifier = lale.operators.make_operator(MLPClassifierImpl, _combined_schemas)

# 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 lale.operators
import lale.docstrings
import sklearn.neural_network.multilayer_perceptron

class MLPClassifierImpl():
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.neural_network.multilayer_perceptron.MLPClassifier(**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 partial_fit(self, X, y=None, classes = None):
      if not hasattr(self, "_wrapped_model"):
        self._wrapped_model = sklearn.neural_network.multilayer_perceptron.MLPClassifier(
            **self._hyperparams)
      self._wrapped_model.partial_fit(X, y, classes = classes)
      return self

_hyperparams_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description':
        'Hyperparameter schema for the MLPClassifier model from scikit-learn.',
    'allOf': [{
        'description':
            'This first sub-object lists all constructor arguments with their '
            'types, one at a time, omitting cross-argument constraints.',
        'type': 'object',
        'additionalProperties': False,
        'required': [
            'hidden_layer_sizes', 'activation', 'solver', 'alpha',
            'batch_size', 'learning_rate', 'learning_rate_init', 'power_t',
            'max_iter', 'shuffle', 'random_state', 'tol', 'verbose',
            'warm_start', 'momentum', 'nesterovs_momentum', 'early_stopping',
            'validation_fraction', 'beta_1', 'beta_2', 'epsilon',
            'n_iter_no_change'],
        'relevantToOptimizer': [
            'hidden_layer_sizes', 'activation', 'solver', 'alpha',
            'batch_size', 'learning_rate', 'tol', 'momentum',
            'nesterovs_momentum', 'early_stopping', 'validation_fraction',
            'beta_1', 'beta_2', 'epsilon'],
        'properties': {
            'hidden_layer_sizes': {
                'description':
                    'The ith element represents the number of neurons in '
                    'the ith hidden layer.',
                'type': 'array',
                'laleType': 'tuple',
                'minItemsForOptimizer': 1,
                'maxItemsForOptimizer': 20,
                'items': {
                    'type': 'integer',
                    'minimumForOptimizer': 1,
                    'maximumForOptimizer': 500},
                'default': [100]},
            'activation': {
                'description': 'Activation function for the hidden layer.',
                'enum': ['identity', 'logistic', 'tanh', 'relu'],
                'default': 'relu'},
            'solver': {
                'description': 'The solver for weight optimization.',
                'enum': ['lbfgs', 'sgd', 'adam'],
                'default': 'adam'},
            'alpha': {
                'description': 'L2 penalty (regularization term) parameter.',
                'type': 'number',
                'distribution': 'loguniform',
                'minimumForOptimizer': 1e-10,
                'maximumForOptimizer': 1,
                'default': 0.0001},
            'batch_size': {
                'description':
                    'Size of minibatches for stochastic optimizers.',
                'anyOf': [{
                    'description': 'Size of minibatches',
                    'type': 'integer',
                    'distribution': 'uniform',
                    'minimumForOptimizer': 3,
                    'maximumForOptimizer': 128}, {
                    'description':
                        'Automatic selection, batch_size=min(200, n_samples)',
                    'enum': ['auto']}],
                'default': 'auto'},
            'learning_rate': {
                'description': 'Learning rate schedule for weight updates.',
                'enum': ['constant', 'invscaling', 'adaptive'],
                'default': 'constant'},
            'learning_rate_init': {
                'description':
                    'The initial learning rate used. It controls the '
                    'step-size in updating the weights.',
                'type': 'number',
                'default': 0.001},
            'power_t': {
                'description':
                    'The exponent for inverse scaling learning rate.',
                'type': 'number',
                'default': 0.5},
            'max_iter': {
                'description':
                    'Maximum number of iterations. The solver iterates until '
                    'convergence (determined by "tol") or this number of '
                    'iterations.',
                'type': 'integer',
                'distribution': 'uniform',
                'minimum': 1,
                'minimumForOptimizer': 10,
                'maximumForOptimizer': 1000,
                'default': 200},
            'shuffle': {
                'description': 'Whether to shuffle samples in each iteration.',
                'type': 'boolean',
                'default': True},
            'random_state': {
                'description': 'Random generator selection',
                'anyOf': [{
                    'description': 'seed used by the random number generators',
                    'type': 'integer'}, {
                    'description': 'Random number generator',
                    'type': 'object'}, {
                    'description': 'RandomState instance used by np.random',
                    'enum': [None]}],
                'default': None},
            'tol': {
                'description':
                    'Tolerance for the optimization. When the loss or score '
                    'is not improving by at least tol for n_iter_no_change '
                    'consecutive iterations, unless learning_rate is set to '
                    '"adaptive", convergence is considered to be reached and '
                    'training stops.',
                'type': 'number',
                'distribution': 'loguniform',
                'minimumForOptimizer': 1e-08,
                'maximumForOptimizer': 0.01,
                'default': 0.0001},
            'verbose': {
                'description':
                    'Whether to print progress messages to stdout.',
                'type': 'boolean',
                'default': False},
            'warm_start': {
                'description':
                    'When set to True, reuse the solution of the previous '
                    'call to fit as initialization, otherwise, just erase '
                    'the previous solution.',
                'type': 'boolean',
                'default': False},
            'momentum': {
                'description': 'Momentum for gradient descent update.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'default': 0.9},
            'nesterovs_momentum': {
                'description': "Whether to use Nesterov's momentum.",
                'type': 'boolean',
                'default': True},
            'early_stopping': {
                'description':
                    'Whether to use early stopping to terminate training when '
                    'validation score is not improving. If set to true, it '
                    'will automatically set aside 10% of training data as '
                    'validation and terminate training when validation score '
                    'is not improving by at least tol for n_iter_no_change '
                    'consecutive epochs.',
                'type': 'boolean',
                'default': False},
            'validation_fraction': {
                'description':
                    'The proportion of training data to set aside as '
                    'validation set for early stopping.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'default': 0.1},
            'beta_1': {
                'description':
                    'Exponential decay rate for estimates of first moment '
                    'vector in adam.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'exclusiveMaximum': True,
                'default': 0.9},
            'beta_2': {
                'description':
                    'Exponential decay rate for estimates of second moment '
                    'vector in adam.',
                'type': 'number',
                'minimum': 0,
                'maximum': 1,
                'exclusiveMaximum': True,
                'default': 0.999},
            'epsilon': {
                'description': 'Value for numerical stability in adam.',
                'type': 'number',
                'distribution': 'loguniform',
                'minimumForOptimizer': 1e-08,
                'maximumForOptimizer': 1.35,
                'default': 1e-08},
            'n_iter_no_change': {
                'description':
                    'Maximum number of epochs to not meet tol improvement.',
                'type': 'integer',
                'default': 10,
                'minimum': 1},
        }},
    {   'description':
            'If the solver is "lbfgs", the classifier will not use minibatch',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'solver': {
                    'not': {
                        'enum': ['lbfgs']},
                }},
        }, {
            'type': 'object',
            'property': {
                'batch_size': 'auto'},
        }]},
    {   'description': 'The learning rate is only used when solver="sgd".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'learning_rate': {
                    'enum': ['constant']},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description':
            'The initial learning rate is only used when '
            'solver="sgd" or "adam".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'learning_rate_init': {
                    'enum': [0.001]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]},
    {   'description':
            'The exponent for inverse scaling learning rate is used in '
            'updating effective learning rate when the learning_rate is '
            'set to "invscaling" and when solver="sgd".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'power_t': {
                    'enum': [0.5]},
            }}, {
            'type': 'object',
            'properties': {
                'learning_rate': {
                    'enum': ['invscaling']},
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description': 'Shuffle is only used when solver="sgd" or "adam".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'shuffle': {
                    'enum': [True]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]},
    {   'description': 'The momentum  is only used when solver="sgd".',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'momentum': {
                    'enum': [0.9]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description':
            "The Nesterov's momentum is only used when solver='sgd' "
            "and momentum > 0.",
        'anyOf': [{
            'type': 'object',
            'properties': {
                'nesterovs_momentum': {
                    'enum': [True]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd']},
            }}]},
    {   'description':
            'Early stopping is only effective when solver="sgd" or "adam"',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'early_stopping': {
                    'enum': [False]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]},
    {   'description':
            'The validation fraction is only used if early stopping is True',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'validation_fraction': {
                    'enum': [0.1]},
            }}, {
            'type': 'object',
            'properties': {
                'early_stopping': {
                    'enum': [True]},
            }}]},
    {   'description': 'beta_1 is only used for solver adam',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'beta_1': {
                    'enum': [0.9]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['adam']},
            }}]},
    {   'description': 'beta_2 is only used for solver adam',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'beta_2': {
                    'enum': [0.999]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['adam']},
            }}]},
    {   'description': 'epsilon is only used for solver adam',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'epsilon': {
                    'enum': [1e-08]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['adam']},
            }}]},
    {   'description':
            'n_iter_no_change is only effective when solver="sgd" or "adam"',
        'anyOf': [{
            'type': 'object',
            'properties': {
                'n_iter_no_change': {
                    'enum': [10]},
            }}, {
            'type': 'object',
            'properties': {
                'solver': {
                    'enum': ['sgd', 'adam']},
            }}]}]}

_input_fit_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Fit the model to data matrix X and target(s) y.',
    'type': 'object',
    'required': ['X', 'y'],
    'additionalProperties': False,
    'properties': {
        'X': {
            'description': 'Features; the outer array is over samples.',
            'type': 'array',
            'items': {
                'type': 'array',
                'items': { 'type': 'number'}}},
        'y': {
            'description': 'Target class labels; the array is over samples.',
            'anyOf': [
            {   'type': 'array',
                'items': {
                    'type': 'array',
                    'items': { 'type': 'number'}}},
            {   'type': 'array',
                'items': {
                    'type': 'number'}},
            {   'type': 'array',
                'items': {
                    'type': 'string'}}]}}}

_input_predict_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Predict using the multi-layer perceptron classifier',
    '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_predict_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Predict using the multi-layer perceptron classifier',
    'anyOf': [
    {   'type': 'array',
        'items': {
            'type': 'number'}},
    {   'type': 'array',
        'items': {
            'type': 'string'}}]}

_input_predict_proba_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_predict_proba_schema = {
    'type': 'array',
    'items': {
        'type': 'array',
        'items': {'type': 'number'}}}

_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`Multi-layer perceptron`_ dense deep neural network from scikit-learn for classification.

.. _`Multi-layer perceptron`: https://scikit-learn.org/0.20/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn-neural-network-mlpclassifier
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.mlp_classifier.html',
    'type': 'object',
    'tags': {
        'pre': ['~categoricals'],
        'op': ['estimator', 'classifier', '~interpretable'],
        'post': ['probabilities']},
    '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(MLPClassifierImpl, _combined_schemas)

MLPClassifier = lale.operators.make_operator(MLPClassifierImpl, _combined_schemas)

# 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. #', #', #', #', #', `Multi-layer perceptron`_ dense deep neural network from scikit-learn for classification.  .. _`Multi-layer perceptron`: https://scikit-learn.org/0.20/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn-neural-network-mlpclassifier