from sklearn.svm.classes import LinearSVC as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class LinearSVCImpl():
def __init__(self, penalty='l2', loss='squared_hinge', dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight='balanced', verbose=0, random_state=None, max_iter=1000):
self._hyperparams = {
'penalty': penalty,
'loss': loss,
'dual': dual,
'tol': tol,
'C': C,
'multi_class': multi_class,
'fit_intercept': fit_intercept,
'intercept_scaling': intercept_scaling,
'class_weight': class_weight,
'verbose': verbose,
'random_state': random_state,
'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 LinearSVC Linear Support Vector Classification.',
'allOf': [{
'type': 'object',
'required': ['penalty', 'loss', 'dual', 'tol', 'C', 'multi_class', 'fit_intercept', 'intercept_scaling', 'class_weight', 'verbose', 'random_state', 'max_iter'],
'relevantToOptimizer': ['penalty', 'loss', 'dual', 'tol', 'multi_class', 'fit_intercept', 'intercept_scaling', 'max_iter'],
'additionalProperties': False,
'properties': {
'penalty': {
'XXX TODO XXX': "string, 'l1' or 'l2' (default='l2')",
'description': "Specifies the norm used in the penalization. The 'l2'",
'enum': ['l2', 'squared_hinge'],
'default': 'l2'},
'loss': {
'XXX TODO XXX': "string, 'hinge' or 'squared_hinge' (default='squared_hinge')",
'description': "Specifies the loss function. 'hinge' is the standard SVM loss",
'enum': ['epsilon_insensitive', 'hinge', 'l2', 'squared_epsilon_insensitive', 'squared_hinge'],
'default': 'squared_hinge'},
'dual': {
'type': 'boolean',
'default': True,
'description': 'Select the algorithm to either solve the dual or primal'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Tolerance for stopping criteria.'},
'C': {
'type': 'number',
'default': 1.0,
'description': 'Penalty parameter C of the error term.'},
'multi_class': {
'XXX TODO XXX': "string, 'ovr' or 'crammer_singer' (default='ovr')",
'description': 'Determines the multi-class strategy if `y` contains more than',
'enum': ['auto', 'crammer_singer', 'liblinear', 'ovr'],
'default': 'ovr'},
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'Whether to calculate the intercept for this model. If set'},
'intercept_scaling': {
'type': 'number',
'minimumForOptimizer': 0.0,
'maximumForOptimizer': 1.0,
'distribution': 'uniform',
'default': 1,
'description': 'When self.fit_intercept is True, instance vector x becomes'},
'class_weight': {
'enum': ['dict', 'balanced'],
'default': 'balanced',
'description': 'Set the parameter C of class i to ``class_weight[i]*C`` for'},
'verbose': {
'type': 'integer',
'default': 0,
'description': 'Enable verbose output. Note that this setting takes advantage of a'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'The seed of the pseudo random number generator to use when shuffling'},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 1000,
'description': 'The maximum number of iterations to be run.'},
}}],
}
_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': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'Array of weights that are assigned to individual'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict class labels for samples in X.',
'type': 'object',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'array_like or sparse matrix, shape (n_samples, n_features)'}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'Samples.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predicted class label per sample.',
'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)
LinearSVC = lale.operators.make_operator(LinearSVCImpl, _combined_schemas)