from sklearn.neural_network.multilayer_perceptron import MLPRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class MLPRegressorImpl():
def __init__(self, hidden_layer_sizes=(100,), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10):
self._hyperparams = {
'hidden_layer_sizes': hidden_layer_sizes,
'activation': activation,
'solver': solver,
'alpha': alpha,
'batch_size': batch_size,
'learning_rate': learning_rate,
'learning_rate_init': learning_rate_init,
'power_t': power_t,
'max_iter': max_iter,
'shuffle': shuffle,
'random_state': random_state,
'tol': tol,
'verbose': verbose,
'warm_start': warm_start,
'momentum': momentum,
'nesterovs_momentum': nesterovs_momentum,
'early_stopping': early_stopping,
'validation_fraction': validation_fraction,
'beta_1': beta_1,
'beta_2': beta_2,
'epsilon': epsilon,
'n_iter_no_change': n_iter_no_change}
[docs] def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
[docs] def predict(self, X):
return self._sklearn_model.predict(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for MLPRegressor Multi-layer Perceptron regressor.',
'allOf': [{
'type': 'object',
'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': ['activation', 'solver', 'alpha', 'batch_size', 'learning_rate', 'max_iter', 'shuffle', 'tol', 'nesterovs_momentum', 'epsilon'],
'additionalProperties': False,
'properties': {
'hidden_layer_sizes': {
'XXX TODO XXX': 'tuple, length = n_layers - 2, default (100,)',
'description': 'The ith element represents the number of neurons in the ith',
'type': 'array',
'typeForOptimizer': 'tuple',
'default': (100,)},
'activation': {
'enum': ['identity', 'logistic', 'tanh', 'relu'],
'default': 'relu',
'description': 'Activation function for the hidden layer.'},
'solver': {
'enum': ['lbfgs', 'sgd', 'adam'],
'default': 'adam',
'description': 'The solver for weight optimization.'},
'alpha': {
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'L2 penalty (regularization term) parameter.'},
'batch_size': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 128,
'distribution': 'uniform'}, {
'enum': ['auto']}],
'default': 'auto',
'description': 'Size of minibatches for stochastic optimizers.'},
'learning_rate': {
'enum': ['constant', 'invscaling', 'adaptive'],
'default': 'constant',
'description': 'Learning rate schedule for weight updates.'},
'learning_rate_init': {
'type': 'number',
'default': 0.001,
'description': 'The initial learning rate used. It controls the step-size'},
'power_t': {
'type': 'number',
'default': 0.5,
'description': 'The exponent for inverse scaling learning rate.'},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 200,
'description': 'Maximum number of iterations. The solver iterates until convergence'},
'shuffle': {
'type': 'boolean',
'default': True,
'description': 'Whether to shuffle samples in each iteration. Only used when'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'If int, random_state is the seed used by the random number generator;'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Tolerance for the optimization. When the loss or score is not improving'},
'verbose': {
'type': 'boolean',
'default': False,
'description': 'Whether to print progress messages to stdout.'},
'warm_start': {
'type': 'boolean',
'default': False,
'description': 'When set to True, reuse the solution of the previous'},
'momentum': {
'type': 'number',
'default': 0.9,
'description': 'Momentum for gradient descent update. Should be between 0 and 1. Only'},
'nesterovs_momentum': {
'type': 'boolean',
'default': True,
'description': "Whether to use Nesterov's momentum. Only used when solver='sgd' and"},
'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'},
'beta_1': {
'type': 'number',
'default': 0.9,
'description': 'Exponential decay rate for estimates of first moment vector in adam,'},
'beta_2': {
'type': 'number',
'default': 0.999,
'description': 'Exponential decay rate for estimates of second moment vector in adam,'},
'epsilon': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 1.35,
'distribution': 'loguniform',
'default': 1e-08,
'description': "Value for numerical stability in adam. Only used when solver='adam'"},
'n_iter_no_change': {
'type': 'integer',
'default': 10,
'description': 'Maximum number of epochs to not meet ``tol`` improvement.'},
}}, {
'description': "learning_rate, only used when solver='sgd'",
'anyOf': [{
'type': 'object',
'properties': {
'learning_rate': {
'enum': ['constant']},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['sgd']},
}}]}, {
'description': "learning_rate_init, 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': "power_t, only used when solver='sgd'",
'anyOf': [{
'type': 'object',
'properties': {
'power_t': {
'enum': [0.5]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['sgd']},
}}]}, {
'description': "shuffle, only used when solver='sgd' or 'adam'",
'anyOf': [{
'type': 'object',
'properties': {
'shuffle': {
'enum': [True]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['sgd', 'adam']},
}}]}, {
'description': "momentum, only used when solver='sgd'",
'anyOf': [{
'type': 'object',
'properties': {
'momentum': {
'enum': [0.9]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['sgd']},
}}]}, {
'XXX TODO XXX': "Parameter: nesterovs_momentum > only used when solver='sgd' and momentum > 0"}, {
'description': "early_stopping, only effective when solver='sgd' or 'adam'",
'anyOf': [{
'type': 'object',
'properties': {
'early_stopping': {
'enum': [False]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['sgd', 'adam']},
}}]}, {
'description': 'validation_fraction, 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, only used when solver='adam'",
'anyOf': [{
'type': 'object',
'properties': {
'beta_1': {
'enum': [0.9]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['adam']},
}}]}, {
'description': "beta_2, only used when solver='adam'",
'anyOf': [{
'type': 'object',
'properties': {
'beta_2': {
'enum': [0.999]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['adam']},
}}]}, {
'description': "epsilon, only used when solver='adam'",
'anyOf': [{
'type': 'object',
'properties': {
'epsilon': {
'enum': [1e-08]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['adam']},
}}]}, {
'description': "n_iter_no_change, 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',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'array-like or sparse matrix, shape (n_samples, n_features)'}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'The input data.'},
'y': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'The target values (class labels in classification, real numbers in'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the multi-layer perceptron model.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The input data.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The predicted values.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'type': 'object',
'tags': {
'pre': [],
'op': ['estimator'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_predict': _input_predict_schema,
'output_predict': _output_predict_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
MLPRegressor = lale.operators.make_operator(MLPRegressorImpl, _combined_schemas)