def test_for_older_version():
# Fit an ARIMA
arima = ARIMA(order=(0, 0, 0), trend='c', suppress_warnings=True)
# There are three possibilities here:
# 1. The model is serialized/deserialized BEFORE it has been fit.
# This means we should not get a warning.
#
# 2. The model is saved after being fit, but it does not have a
# pkg_version_ attribute due to it being an old (very old) version.
# We still warn for this
#
# 3. The model is saved after the fit, and it's version does not match.
# We warn for this.
for case, do_fit, expect_warning in [(1, False, False),
(2, True, True),
(3, True, True)]:
# Only fit it if we should
if do_fit:
arima.fit(y)
# If it's case 2, we remove the pkg_version_. If 3, we set it low
if case == 2:
delattr(arima, 'pkg_version_')
elif case == 3:
arima.pkg_version_ = '0.0.1' # will always be < than current
# Pickle it
pickle_file = 'model.pkl'
try:
joblib.dump(arima, pickle_file)
# Now unpickle it and show that we get a warning (if expected)
with warnings.catch_warnings(record=True) as w:
arm = joblib.load(pickle_file) # type: ARIMA
if expect_warning:
assert len(w) > 0
else:
assert not len(w)
# we can still produce predictions (only if we fit)
if do_fit:
arm.predict(n_periods=4)
finally:
os.unlink(pickle_file)
def test_for_older_version():
# Fit an ARIMA
arima = ARIMA(order=(0, 0, 0), trend='c', suppress_warnings=True)
# There are three possibilities here:
# 1. The model is serialized/deserialized BEFORE it has been fit.
# This means we should not get a warning.
#
# 2. The model is saved after being fit, but it does not have a
# pkg_version_ attribute due to it being an old (very old) version.
# We still warn for this
#
# 3. The model is saved after the fit, and it's version does not match.
# We warn for this.
for case, do_fit, expect_warning in [(1, False, False),
(2, True, True),
(3, True, True)]:
# Only fit it if we should
if do_fit:
arima.fit(y)
# If it's case 2, we remove the pkg_version_. If 3, we set it low
if case == 2:
delattr(arima, 'pkg_version_')
elif case == 3:
arima.pkg_version_ = '0.0.1' # will always be < than current
with tempfile.TemporaryDirectory() as tdir:
pickle_file = os.path.join(tdir, 'model.pkl')
joblib.dump(arima, pickle_file)
# Now unpickle it and show that we get a warning (if expected)
if expect_warning:
with pytest.warns(UserWarning):
arm = joblib.load(pickle_file) # type: ARIMA
else:
arm = joblib.load(pickle_file) # type: ARIMA
# we can still produce predictions (only if we fit)
if do_fit:
arm.predict(n_periods=4)
