def test_parallel_fit():
"""The goal of this test is to check that results of _parallel_fit is the
same for different controlled param_grid
"""
X, y = make_regression(n_samples=100, n_features=20,
n_informative=5, noise=0.2, random_state=42)
train = range(80)
test = range(80, len(y_classification))
outputs = []
estimator = svr
svr_params = [[1e-1, 1e0, 1e1], [1e-1, 1e0, 5e0, 1e1]]
scorer = check_scoring(estimator, 'r2') # define a scorer
# Define a screening selector
selector = check_feature_screening(screening_percentile=None,
mask_img=None, is_classification=False)
for params in svr_params:
param_grid = {}
param_grid['C'] = np.array(params)
outputs.append(list(_parallel_fit(estimator=estimator, X=X, y=y,
train=train, test=test,
param_grid=param_grid,
is_classification=False,
scorer=scorer, mask_img=None,
class_index=1,
selector=selector,
clustering_percentile=100)))
# check that every element of the output tuple is the same for both tries
for a, b in zip(outputs[0], outputs[1]):
if isinstance(a, np.ndarray):
np.testing.assert_array_almost_equal(a, b)
else:
assert a == b
def test_feature_screening():
# dummy
mask_img_data = np.zeros((182, 218, 182))
mask_img_data[30:-30, 30:-30, 30:-30] = 1
affine = np.eye(4)
mask_img = nibabel.Nifti1Image(mask_img_data, affine=affine)
for is_classif in [True, False]:
for screening_percentile in [100, None, 20, 101, -1, 10]:
if screening_percentile == 100 or screening_percentile is None:
assert check_feature_screening(screening_percentile, mask_img,
is_classif) == None
elif screening_percentile == 101 or screening_percentile == -1:
pytest.raises(ValueError, check_feature_screening,
screening_percentile, mask_img, is_classif)
elif screening_percentile == 20:
assert isinstance(
check_feature_screening(screening_percentile, mask_img,
is_classif), BaseEstimator)
def test_feature_screening():
# dummy
mask_img_data = np.zeros((182, 218, 182))
mask_img_data[30:-30, 30:-30, 30:-30] = 1
affine = np.eye(4)
mask_img = nibabel.Nifti1Image(mask_img_data, affine=affine)
for is_classif in [True, False]:
for screening_percentile in [100, None, 20, 101, -1, 10]:
if screening_percentile == 100 or screening_percentile is None:
assert_equal(check_feature_screening(
screening_percentile, mask_img, is_classif), None)
elif screening_percentile == 101 or screening_percentile == -1:
assert_raises(ValueError, check_feature_screening,
screening_percentile, mask_img, is_classif)
elif screening_percentile == 20:
assert_true(isinstance(check_feature_screening(
screening_percentile, mask_img, is_classif),
BaseEstimator))