def cs_impl(cls, data=None):
hps = {
'sampling_strategy':
CatHP(choice,
items=['majority', 'not minority', 'not majority', 'all'])
}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {
'solver': CatHP(choice, a=['svd', 'lsqr', 'eigen']),
'shrinkage': CatHP(choice, a=[None, 'auto']),
'tol': RealHP(uniform, low=1e-6, high=0.5)
}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {'kernel': CatHP(choice,a=['linear','poly','rbf','sigmoid']),
'degree': IntHP(uniform,low=1,high=5), #ignored if kernel != poly
'gamma': RealHP(uniform,low=1e-3,high=1.0), #ignored if kernel != rbf or sigmoid
'C': RealHP(uniform,low=1e-1,high=1e4),
'epsilon': RealHP(uniform,low=1e-1,high=1e-4),
'max_iter': IntHP(uniform,low=100,high=1000)}
return ConfigSpace(name='SVR', hps=hps)
def cs_impl(cls):
hps = {
'n_neighbors': IntHP(uniform, low=1, high=15),
'weights': CatHP(choice, a=['uniform', 'distance']),
'algorithm': CatHP(choice, a=['kd_tree', 'ball_tree']),
'leaf_size': IntHP(uniform, low=15, high=100),
'p': IntHP(uniform, low=1, high=5)
} #default metric: 'minkowski'
return ConfigSpace(name='KNN', hps=hps)
def cs_impl(cls, config_spaces):
hps = [
CatHP('configs',
cls.sampling_function,
config_spaces=config_spaces[0]),
CatHP('reduce',
cls.sampling_function,
config_spaces=config_spaces[1])
]
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {
'sampling_strategy':
CatHP(choice,
items=[
'not minority', 'not majority', 'all', 'minority', 'auto'
]),
'k_neighbours':
IntHP(uniform, low=2, high=1000)
}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {
'alpha': RealHP(uniform, low=1e-5, high=10.0),
'kernel': CatHP(choice,
a=['linear', 'polynomial', 'rbf', 'sigmoid']),
'gamma': RealHP(uniform, low=1e-5,
high=10.0), #ignored if kernel = linear
'degree': IntHP(uniform, low=1, high=5)
} #ignored if kernel != polynomial
return ConfigSpace(name='KRR', hps=hps)
def cs_impl(cls):
hps = {
'n_neighbors': IntHP(uniform, low=2, high=1000),
'weights': CatHP(choice, a=['uniform', 'distance']),
'algorithm': CatHP(choice,
a=['auto', 'ball_tree', 'kd_tree', 'brute']),
'leaf_size': IntHP(uniform, low=2, high=1000),
'p': IntHP(uniform, low=1, high=6)
}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {
'n_estimators': IntHP(uniform, low=2, high=1000),
'criterion': CatHP(choice, a=['mse', 'mae']),
'max_features': CatHP(choice, a=['auto', 'sqrt', 'log2', None]),
'max_depth': IntHP(uniform, low=2, high=1000),
'min_samples_split': RealHP(uniform, low=1e-6, high=0.5),
'min_samples_leaf': RealHP(uniform, low=1e-6, high=0.5),
'min_weight_fraction_leaf': RealHP(uniform, low=0.0, high=0.5),
'min_impurity_decrease': RealHP(uniform, low=0.0, high=0.2)
}
return ConfigSpace(name='RF', hps=hps)
def cs_impl(cls):
hps = {
'C': RealHP(uniform, low=0.0, high=10000.0),
'kernel': CatHP(choice, a=['linear', 'poly', 'rbf', 'sigmoid']),
'degree': IntHP(uniform, low=0, high=1000),
'gamma': CatHP(choice, a=['scale', 'auto']),
'coef0': RealHP(uniform, low=0.0, high=1000.0),
'shrinking': CatHP(choice, a=[True, False]),
'probability': CatHP(choice, a=[True, False]),
'tol': RealHP(uniform, low=1e-6, high=0.5),
'max_iter': IntHP(uniform, low=-1, max=10000),
'decision_function_shape': CatHP(choice, a=['ovo', 'ovr'])
}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {
'penalty':
CatHP(choice, a=['l1', 'l2', 'elasticnet', 'none']),
'dual':
CatHP(uniform, a=[True, False]),
'tol':
RealHP(uniform, low=1e-6, high=0.5),
'C':
RealHP(uniform, low=0.0, high=10000.0),
'fit_intercept':
CatHP(uniform, a=[True, False]),
'solver':
CatHP(choice, a=['newton-cg', 'lbfgs', 'liblinear', 'sag',
'saga']),
'max_iter':
IntHP(uniform, low=1, max=10000)
}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
# Sw
# cs = ConfigSpace('Switch')
# st = cs.start()
# st.add_children([a.start, b.start, c.start])
# cs.finish([a.end,b.end,c.end])
hps = {
'criterion': CatHP(choice, a=['gini', 'entropy']),
'splitter': CatHP(choice, a=['best']),
'class_weight': CatHP(choice, a=[None, 'balanced']),
'max_features': CatHP(choice, a=['auto', 'sqrt', 'log2', None]),
'max_depth': IntHP(uniform, low=2, high=1000),
'min_samples_split': RealHP(uniform, low=1e-6, high=0.3),
'min_samples_leaf': RealHP(uniform, low=1e-6, high=0.3),
'min_weight_fraction_leaf': RealHP(uniform, low=0.0, high=0.3),
'min_impurity_decrease': RealHP(uniform, low=0.0, high=0.2)
}
return ConfigSpace(name='DT', hps=hps)
def cs_impl(cls):
hps = {
'n_estimators': IntHP(uniform, low=2, high=1000),
'criterion': CatHP(choice, a=['mse', 'mae', 'friedman_mse']),
'max_features': CatHP(choice, a=['auto', 'sqrt', 'log2', None]),
'max_depth': IntHP(uniform, low=2, high=100),
'min_samples_split': RealHP(uniform, low=1e-6, high=0.5),
'min_samples_leaf': RealHP(uniform, low=1e-6, high=0.5),
'min_weight_fraction_leaf': RealHP(uniform, low=0.0, high=0.5),
'min_impurity_decrease': RealHP(uniform, low=0.0, high=0.2),
'loss': CatHP(choice, a=['ls', 'lad', 'huber', 'quantile']),
'learning_rate': RealHP(uniform, low=1e-4, high=1e-1),
'subsample': RealHP(uniform, low=0.1, high=0.9),
'alpha': RealHP(uniform, low=0.1,
high=0.9), #ignored if loss != huber or quantile
'validation_fraction': RealHP(uniform, low=0.1,
high=0.9), # Using Early Stopping #
'n_iter_no_change': IntHP(uniform, low=5,
high=20) # #
}
return ConfigSpace(name='GB', hps=hps)
def cs_impl(cls, config_spaces):
hps = {'configs': CatHP(cls.sampling_function,
config_spaces=config_spaces)}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {'alpha': RealHP(uniform, low=1e-5, high=10.0)}
return ConfigSpace(name='LASSO', hps=hps)
def cs_impl(cls):
hps = {'feature_range': CatHP(choice, items=[(-1, 1), (0, 1)])}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {'@nb_type': CatHP(choice, items=['GaussianNB', 'BernoulliNB'])}
return ConfigSpace(name='NB', hps=hps)
def cs_impl(cls):
hps = [CatHP('function', choice, itens=['mean'])]
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
hps = {'function': CatHP(choice, items=['mean'])}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(self):
hps = {
'@nb_type': CatHP(choice, items=["MultinomialNB", "ComplementNB"])
}
return ConfigSpace(name='NB', hps=hps)
def cs_impl(cls, data=None):
hps = {
'@with_mean/std':
CatHP(choice, items=[(True, False), (False, True), (True, True)])
}
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
# TODO: cirar funcao no data
hps = [CatHP('field', choice, itens=['X', 'Y', 'Z'])]
return ConfigSpace(name=cls.__name__, hps=hps)
def cs_impl(cls):
# TODO: cirar funcao no data
hps = {'field': CatHP(choice, items=['X', 'Y', 'Z'])}
return ConfigSpace(name=cls.__name__, hps=hps)