def test_lars_path_readonly_data():
# When using automated memory mapping on large input, the
# fold data is in read-only mode
# This is a non-regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/4597
splitted_data = train_test_split(X, y, random_state=42)
with TempMemmap(splitted_data) as (X_train, X_test, y_train, y_test):
# The following should not fail despite copy=False
_lars_path_residues(X_train, y_train, X_test, y_test, copy=False)
def test_lars_path_readonly_data():
# When using automated memory mapping on large input, the
# fold data is in read-only mode
# This is a non-regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/4597
splitted_data = train_test_split(X, y, random_state=42)
with TempMemmap(splitted_data) as (X_train, X_test, y_train, y_test):
# The following should not fail despite copy=False
_lars_path_residues(X_train, y_train, X_test, y_test, copy=False)
def test_lars_path_readonly_data():
# When using automated memory mapping on large input, the
# fold data is in read-only mode
# This is a non-regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/4597
splitted_data = train_test_split(X, y, random_state=42)
temp_folder = tempfile.mkdtemp()
try:
fpath = op.join(temp_folder, 'data.pkl')
joblib.dump(splitted_data, fpath)
X_train, X_test, y_train, y_test = joblib.load(fpath, mmap_mode='r')
# The following should not fail despite copy=False
_lars_path_residues(X_train, y_train, X_test, y_test, copy=False)
finally:
shutil.rmtree(temp_folder)
def test_lars_path_readonly_data():
# When using automated memory mapping on large input, the
# fold data is in read-only mode
# This is a non-regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/4597
splitted_data = train_test_split(X, y, random_state=42)
temp_folder = tempfile.mkdtemp()
try:
fpath = op.join(temp_folder, 'data.pkl')
joblib.dump(splitted_data, fpath)
X_train, X_test, y_train, y_test = joblib.load(fpath, mmap_mode='r')
# The following should not fail despite copy=False
_lars_path_residues(X_train, y_train, X_test, y_test, copy=False)
finally:
# try to release the mmap file handle in time to be able to delete
# the temporary folder under windows
del X_train, X_test, y_train, y_test
try:
shutil.rmtree(temp_folder)
except shutil.WindowsError:
warnings.warn("Could not delete temporary folder %s" % temp_folder)
X = y.reshape(-1, 1)
lars = linear_model.LassoLarsIC(normalize=False)
assert_no_warnings(lars.fit, X, y)
assert_true(np.any(np.isinf(lars.criterion_)))
def test_lars_path_readonly_data():
# When using automated memory mapping on large input, the
# fold data is in read-only mode
# This is a non-regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/4597
splitted_data = train_test_split(X, y, random_state=42)
with TempMemmap(splitted_data) as (X_train, X_test, y_train, y_test):
# The following should not fail despite copy=False
<<<<<<< HEAD
_lars_path_residues(X_train, y_train, X_test, y_test, copy=False)
finally:
# try to release the mmap file handle in time to be able to delete
# the temporary folder under windows
del X_train, X_test, y_train, y_test
try:
shutil.rmtree(temp_folder)
except shutil.WindowsError:
warnings.warn("Could not delete temporary folder %s" % temp_folder)
def test_lars_path_positive_constraint():
# this is the main test for the positive parameter on the lars_path method
# the estimator classes just make use of this function
# we do the test on the diabetes dataset