from sklearn.linear_model.huber import HuberRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class HuberRegressorImpl():
def __init__(self, epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05):
self._hyperparams = {
'epsilon': epsilon,
'max_iter': max_iter,
'alpha': alpha,
'warm_start': warm_start,
'fit_intercept': fit_intercept,
'tol': tol}
[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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for HuberRegressor Linear regression model that is robust to outliers.',
'allOf': [{
'type': 'object',
'required': ['epsilon', 'max_iter', 'alpha', 'warm_start', 'fit_intercept', 'tol'],
'relevantToOptimizer': ['epsilon', 'max_iter', 'alpha', 'fit_intercept', 'tol'],
'additionalProperties': False,
'properties': {
'epsilon': {
'XXX TODO XXX': 'float, greater than 1.0, default 1.35',
'description': 'The parameter epsilon controls the number of samples that should be',
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 1.35,
'distribution': 'loguniform',
'default': 1.35},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 100,
'description': 'Maximum number of iterations that scipy.optimize.fmin_l_bfgs_b'},
'alpha': {
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Regularization parameter.'},
'warm_start': {
'type': 'boolean',
'default': False,
'description': 'This is useful if the stored attributes of a previously used model'},
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'Whether or not to fit the intercept. This can be set to False'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 1e-05,
'description': 'The iteration will stop when'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the model according to the given training data.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training vector, where n_samples in the number of samples and'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Target vector relative to X.'},
'sample_weight': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Weight given to each sample.'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the linear model',
'type': 'object',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'array_like or sparse matrix, shape (n_samples, n_features)'}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'Samples.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Returns predicted values.',
'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)
HuberRegressor = lale.operators.make_operator(HuberRegressorImpl, _combined_schemas)