from sklearn.svm.classes import SVR as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class SVRImpl():
def __init__(self, kernel='rbf', degree=3, gamma='auto_deprecated', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=(- 1)):
self._hyperparams = {
'kernel': kernel,
'degree': degree,
'gamma': gamma,
'coef0': coef0,
'tol': tol,
'C': C,
'epsilon': epsilon,
'shrinking': shrinking,
'cache_size': cache_size,
'verbose': verbose,
'max_iter': max_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 SVR Epsilon-Support Vector Regression.',
'allOf': [{
'type': 'object',
'required': ['kernel', 'degree', 'gamma', 'coef0', 'tol', 'C', 'epsilon', 'shrinking', 'cache_size', 'verbose', 'max_iter'],
'relevantToOptimizer': ['kernel', 'degree', 'gamma', 'tol', 'epsilon', 'shrinking', 'cache_size', 'max_iter'],
'additionalProperties': False,
'properties': {
'kernel': {
'enum': ['linear', 'poly', 'sigmoid', 'rbf'],
'default': 'rbf',
'description': 'Specifies the kernel type to be used in the algorithm.'},
'degree': {
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 3,
'distribution': 'uniform',
'default': 3,
'description': "Degree of the polynomial kernel function ('poly')."},
'gamma': {
'anyOf': [{
'type': 'number',
'forOptimizer': False}, {
'enum': ['auto_deprecated']}],
'default': 'auto_deprecated',
'description': "Kernel coefficient for 'rbf', 'poly' and 'sigmoid'."},
'coef0': {
'type': 'number',
'default': 0.0,
'description': 'Independent term in kernel function.'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.001,
'description': 'Tolerance for stopping criterion.'},
'C': {
'type': 'number',
'default': 1.0,
'description': 'Penalty parameter C of the error term.'},
'epsilon': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 1.35,
'distribution': 'loguniform',
'default': 0.1,
'description': 'Epsilon in the epsilon-SVR model. It specifies the epsilon-tube'},
'shrinking': {
'type': 'boolean',
'default': True,
'description': 'Whether to use the shrinking heuristic.'},
'cache_size': {
'type': 'number',
'minimumForOptimizer': 0.0,
'maximumForOptimizer': 1.0,
'distribution': 'uniform',
'default': 200,
'description': 'Specify the size of the kernel cache (in MB).'},
'verbose': {
'type': 'boolean',
'default': False,
'description': 'Enable verbose output. Note that this setting takes advantage of a'},
'max_iter': {
'XXX TODO XXX': 'int, optional (default=-1)',
'description': 'Hard limit on iterations within solver, or -1 for no limit.',
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': (- 1)},
}}, {
'XXX TODO XXX': "Parameter: coef0 > only significant in 'poly' and 'sigmoid'"}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the SVM model according to the given training data.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training vectors, where n_samples is the number of samples'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Target values (class labels in classification, real numbers in'},
'sample_weight': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Per-sample weights. Rescale C per sample. Higher weights'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Perform regression on samples in X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'For kernel="precomputed", the expected shape of X is'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Perform regression on samples in X.',
'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)
SVR = lale.operators.make_operator(SVRImpl, _combined_schemas)