def check_persistence_via_pickle(Estimator):
# Check that we can pickle all estimators
estimator = _construct_instance(Estimator)
set_random_state(estimator)
fit_args = _make_args(estimator, "fit")
estimator.fit(*fit_args)
# Generate results before pickling
results = dict()
args = dict()
for method in NON_STATE_CHANGING_METHODS:
if hasattr(estimator, method):
args[method] = _make_args(estimator, method)
results[method] = getattr(estimator, method)(*args[method])
# Pickle and unpickle
pickled_estimator = pickle.dumps(estimator)
unpickled_estimator = pickle.loads(pickled_estimator)
# Compare against results after pickling
for method in results:
unpickled_result = getattr(unpickled_estimator, method)(*args[method])
_assert_array_almost_equal(
results[method],
unpickled_result,
decimal=6,
err_msg="Results are not the same after pickling",
)
def check_fit_idempotent(Estimator):
# Check that calling fit twice is equivalent to calling it once
estimator = _construct_instance(Estimator)
set_random_state(estimator)
# Fit for the first time
fit_args = _make_args(estimator, "fit")
estimator.fit(*fit_args)
results = dict()
args = dict()
for method in NON_STATE_CHANGING_METHODS:
if hasattr(estimator, method):
args[method] = _make_args(estimator, method)
results[method] = getattr(estimator, method)(*args[method])
# Fit again
set_random_state(estimator)
estimator.fit(*fit_args)
for method in NON_STATE_CHANGING_METHODS:
if hasattr(estimator, method):
new_result = getattr(estimator, method)(*args[method])
_assert_array_almost_equal(
results[method],
new_result,
# err_msg=f"Idempotency check failed for method {method}",
)
def test_load_UCR_UEA_dataset_download(tmpdir):
# tmpdir is a pytest fixture
extract_path = tmpdir.mkdtemp()
name = "ArrowHead"
actual_X, actual_y = load_UCR_UEA_dataset(name,
return_X_y=True,
extract_path=extract_path)
data_path = os.path.join(extract_path, name)
assert os.path.exists(data_path)
# check files
files = [
f"{name}.txt",
f"{name}_TEST.arff",
f"{name}_TEST.ts",
f"{name}_TEST.txt",
f"{name}_TRAIN.arff",
f"{name}_TRAIN.ts",
f"{name}_TRAIN.txt",
# "README.md",
]
for file in os.listdir(data_path):
assert file in files
files.remove(file)
assert len(files) == 0
# check data
expected_X, expected_y = load_arrow_head(return_X_y=True)
_assert_array_almost_equal(actual_X, expected_X, decimal=4)
np.testing.assert_array_equal(expected_y, actual_y)
def check_transform_inverse_transform_equivalent(Estimator):
estimator = _construct_instance(Estimator)
X = _make_args(estimator, "fit")[0]
Xt = estimator.fit_transform(X)
Xit = estimator.inverse_transform(Xt)
_assert_array_almost_equal(X, Xit)