from sklearn.linear_model.passive_aggressive import PassiveAggressiveRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class PassiveAggressiveRegressorImpl():
def __init__(self, C=1.0, fit_intercept=True, max_iter=None, tol=None, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, shuffle=True, verbose=0, loss='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False, n_iter=None):
self._hyperparams = {
'C': C,
'fit_intercept': fit_intercept,
'max_iter': max_iter,
'tol': tol,
'early_stopping': early_stopping,
'validation_fraction': validation_fraction,
'n_iter_no_change': n_iter_no_change,
'shuffle': shuffle,
'verbose': verbose,
'loss': loss,
'epsilon': epsilon,
'random_state': random_state,
'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 PassiveAggressiveRegressor Passive Aggressive Regressor',
'allOf': [{
'type': 'object',
'required': ['C', 'fit_intercept', 'max_iter', 'tol', 'early_stopping', 'validation_fraction', 'n_iter_no_change', 'shuffle', 'verbose', 'loss', 'epsilon', 'random_state', 'warm_start', 'average', 'n_iter'],
'relevantToOptimizer': ['fit_intercept', 'max_iter', 'tol', 'shuffle', 'loss', 'epsilon', 'n_iter'],
'additionalProperties': False,
'properties': {
'C': {
'type': 'number',
'default': 1.0,
'description': 'Maximum step size (regularization). Defaults to 1.0.'},
'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'},
'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.'},
'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'},
'loss': {
'enum': ['huber', 'squared_epsilon_insensitive', 'squared_loss', 'epsilon_insensitive'],
'default': 'epsilon_insensitive',
'description': 'The loss function to be used:'},
'epsilon': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 1.35,
'distribution': 'loguniform',
'default': 0.1,
'description': 'If the difference between the current prediction and the correct label'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'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'},
'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': '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 Passive Aggressive algorithm.',
'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.'},
},
}
_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)
PassiveAggressiveRegressor = lale.operators.make_operator(PassiveAggressiveRegressorImpl, _combined_schemas)