def test_default_configuration(self):
transformation, original = _test_preprocessing(SelectRates)
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], 3)
self.assertFalse((transformation == 0).all())
transformation, original = _test_preprocessing(SelectRates, make_sparse=True)
self.assertTrue(scipy.sparse.issparse(transformation))
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits")
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
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 if default[hp_name] is not None}
)
transformer = preprocessor.fit(X_train, Y_train)
transformation, original = transformer.transform(X_train), original_X_train
self.assertEqual(transformation.shape[0], original.shape[0])
# I don't know why its 52 here and not 32 which would be half of the
# number of features. Seems to be related to a runtime warning raised
# by sklearn
self.assertEqual(transformation.shape[1], 52)
def test_default_configuration(self):
transformation, original = _test_preprocessing(SelectRates)
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], 3)
self.assertFalse((transformation == 0).all())
transformation, original = _test_preprocessing(SelectRates,
make_sparse=True)
self.assertTrue(scipy.sparse.issparse(transformation))
self.assertEqual(transformation.shape[0], original.shape[0])
self.assertEqual(transformation.shape[1], int(original.shape[1] / 2))
# Custon preprocessing test to check if clipping to zero works
X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits')
original_X_train = X_train.copy()
ss = sklearn.preprocessing.StandardScaler()
X_train = ss.fit_transform(X_train)
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
if default[hp_name] is not None
})
transformer = preprocessor.fit(X_train, Y_train)
transformation, original = transformer.transform(
X_train), original_X_train
self.assertEqual(transformation.shape[0], original.shape[0])
# I don't know why its 52 here and not 32 which would be half of the
# number of features. Seems to be related to a runtime warning raised
# by sklearn
self.assertEqual(transformation.shape[1], 52)
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)