class GEM(AutoSklearnPreprocessingAlgorithm):
def __init__(self, N, precond, random_state=None):
self.N = N
self.precond = precond
def fit(self, X, Y):
self.preprocessor = GEMImpl(self.N, self.precond)
self.preprocessor.fit(X, Y)
return self
def transform(self, X):
return self.preprocessor.transform(X)
@staticmethod
def get_properties(dataset_properties=None):
return {
'shortname': 'GEM',
'name': 'Generalized Eigenvector extraction',
'handles_regression': False,
'handles_classification': True,
'handles_multiclass': True,
'handles_multilabel': False,
'is_deterministic': True,
'input': (DENSE, UNSIGNED_DATA),
'output': (INPUT, UNSIGNED_DATA)
}
@staticmethod
def get_hyperparameter_search_space(dataset_properties=None):
N = UniformIntegerHyperparameter("N", 5, 20, default=10)
precond = UniformFloatHyperparameter("precond", 0, 0.5, default=0.1)
cs = ConfigurationSpace()
cs.add_hyperparameter(N)
cs.add_hyperparameter(precond)
return cs
class GEM(AutoSklearnPreprocessingAlgorithm):
def __init__(self, N, precond, random_state=None):
self.N = N
self.precond = precond
def fit(self, X, Y):
self.preprocessor = GEMImpl(self.N, self.precond)
self.preprocessor.fit(X, Y)
return self
def transform(self, X):
return self.preprocessor.transform(X)
@staticmethod
def get_properties(dataset_properties=None):
return {'shortname': 'GEM',
'name': 'Generalized Eigenvector extraction',
'handles_missing_values': False,
'handles_nominal_values': False,
'handles_numerical_features': True,
'prefers_data_scaled': True,
'prefers_data_normalized': True,
'handles_regression': False,
'handles_classification': True,
'handles_multiclass': True,
'handles_multilabel': False,
'is_deterministic': True,
'handles_sparse': False,
'handles_dense': True,
'input': (DENSE, UNSIGNED_DATA),
'output': (INPUT, UNSIGNED_DATA),
'preferred_dtype': None}
@staticmethod
def get_hyperparameter_search_space(dataset_properties=None):
N = UniformIntegerHyperparameter("N", 5, 20, default=10)
precond = UniformFloatHyperparameter("precond", 0, 0.5, default=0.1)
cs = ConfigurationSpace()
cs.add_hyperparameter(N)
cs.add_hyperparameter(precond)
return cs
def fit(self, X, Y):
self.preprocessor = GEMImpl(self.N, self.precond)
self.preprocessor.fit(X, Y)
return self
def fit(self, X, Y):
self.preprocessor = GEMImpl(self.N, self.precond)
self.preprocessor.fit(X, Y)
return self