from sklearn.linear_model.stochastic_gradient import SGDRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class SGDRegressorImpl():
def __init__(self, loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate='invscaling', eta0=0.01, power_t=0.25, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, warm_start=False, average=False, n_iter=None):
self._hyperparams = {
'loss': loss,
'penalty': penalty,
'alpha': alpha,
'l1_ratio': l1_ratio,
'fit_intercept': fit_intercept,
'max_iter': max_iter,
'tol': tol,
'shuffle': shuffle,
'verbose': verbose,
'epsilon': epsilon,
'random_state': random_state,
'learning_rate': learning_rate,
'eta0': eta0,
'power_t': power_t,
'early_stopping': early_stopping,
'validation_fraction': validation_fraction,
'n_iter_no_change': n_iter_no_change,
'warm_start': warm_start,
'average': average,
'n_iter': n_iter}
[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 SGDRegressor Linear model fitted by minimizing a regularized empirical loss with SGD',
'allOf': [{
'type': 'object',
'required': ['loss', 'penalty', 'alpha', 'l1_ratio', 'fit_intercept', 'max_iter', 'tol', 'shuffle', 'verbose', 'epsilon', 'random_state', 'learning_rate', 'eta0', 'power_t', 'early_stopping', 'validation_fraction', 'n_iter_no_change', 'warm_start', 'average', 'n_iter'],
'relevantToOptimizer': ['loss', 'penalty', 'alpha', 'fit_intercept', 'max_iter', 'tol', 'shuffle', 'epsilon', 'learning_rate', 'eta0', 'n_iter'],
'additionalProperties': False,
'properties': {
'loss': {
'enum': ['epsilon_insensitive', 'huber', 'squared_epsilon_insensitive', 'squared_loss'],
'default': 'squared_loss',
'description': "The loss function to be used. The possible values are 'squared_loss',"},
'penalty': {
'XXX TODO XXX': "str, 'none', 'l2', 'l1', or 'elasticnet'",
'description': "The penalty (aka regularization term) to be used. Defaults to 'l2'",
'enum': ['elasticnet', 'l1', 'l2', 'none'],
'default': 'l2'},
'alpha': {
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Constant that multiplies the regularization term. Defaults to 0.0001'},
'l1_ratio': {
'type': 'number',
'default': 0.15,
'description': 'The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.'},
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'Whether the intercept should be estimated or not. If False, the'},
'max_iter': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None,
'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,
'description': 'The stopping criterion. If it is not None, the iterations will stop'},
'shuffle': {
'type': 'boolean',
'default': True,
'description': 'Whether or not the training data should be shuffled after each epoch.'},
'verbose': {
'type': 'integer',
'default': 0,
'description': 'The verbosity level.'},
'epsilon': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 1.35,
'distribution': 'loguniform',
'default': 0.1,
'description': 'Epsilon in the epsilon-insensitive loss functions; only if `loss` is'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'The seed of the pseudo random number generator to use when shuffling'},
'learning_rate': {
'enum': ['constant', 'optimal', 'invscaling'],
'default': 'invscaling',
'description': 'The learning rate schedule:'},
'eta0': {
'type': 'number',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 1.0,
'distribution': 'uniform',
'default': 0.01,
'description': "The initial learning rate for the 'constant', 'invscaling' or"},
'power_t': {
'type': 'number',
'default': 0.25,
'description': 'The exponent for inverse scaling learning rate [default 0.5].'},
'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',
'default': 5,
'description': 'Number of iterations with no improvement to wait before early stopping.'},
'warm_start': {
'type': 'boolean',
'default': False,
'description': 'When set to True, reuse the solution of the previous call to fit as'},
'average': {
'anyOf': [{
'type': 'boolean'}, {
'type': 'integer'}],
'default': False,
'description': 'When set to True, computes the averaged SGD weights and stores the'},
'n_iter': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 5,
'maximumForOptimizer': 1000,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None,
'description': 'The number of passes over the training data (aka epochs).'},
}}, {
'XXX TODO XXX': 'Parameter: max_iter > only impacts the behavior in the fit method'}, {
'description': "epsilon, only if loss is 'huber'",
'anyOf': [{
'type': 'object',
'properties': {
'epsilon': {
'enum': [0.1]},
}}, {
'type': 'object',
'properties': {
'loss': {
'enum': ['huber']},
}}]}, {
'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]},
}}]}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit linear model with Stochastic Gradient Descent.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training data'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Target values'},
'coef_init': {
'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.'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'Weights applied to individual samples (1. for unweighted).'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the linear model',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predicted target values per element in X.',
'XXX TODO XXX': '',
}
_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)
SGDRegressor = lale.operators.make_operator(SGDRegressorImpl, _combined_schemas)