def test_preprocessing_dtype(self):
# Dense
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris")
self.assertEqual(X_train.dtype, np.float32)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_train = X_train.astype(np.float64)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
# Sparse
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris",
make_sparse=True)
self.assertEqual(X_train.dtype, np.float32)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris",
make_sparse=True)
X_train = X_train.astype(np.float64)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(
random_state=1,
**{hp_name: default[hp_name]
for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
def test_preprocessing_dtype(self):
# Dense
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris")
self.assertEqual(X_train.dtype, np.float32)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(random_state=1, **{hp_name: default[hp_name] for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris")
X_train = X_train.astype(np.float64)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(random_state=1, **{hp_name: default[hp_name] for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
# Sparse
# np.float32
X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True)
self.assertEqual(X_train.dtype, np.float32)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(random_state=1, **{hp_name: default[hp_name] for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float32)
# np.float64
X_train, Y_train, X_test, Y_test = get_dataset("iris", make_sparse=True)
X_train = X_train.astype(np.float64)
configuration_space = SelectRates.get_hyperparameter_search_space()
default = configuration_space.get_default_configuration()
preprocessor = SelectRates(random_state=1, **{hp_name: default[hp_name] for hp_name in default})
preprocessor.fit(X_train, Y_train)
Xt = preprocessor.transform(X_train)
self.assertEqual(Xt.dtype, np.float64)
