def test_new_serialization():
arima = ARIMA(order=(0, 0, 0), suppress_warnings=True).fit(y)
# Serialize it, show there is no tmp_loc_
pkl_file = "file.pkl"
new_loc = "ts_wrapper.pkl"
try:
joblib.dump(arima, pkl_file)
# Assert it does NOT use the old-style pickling
assert not _uses_legacy_pickling(arima)
loaded = joblib.load(pkl_file)
assert not _uses_legacy_pickling(loaded)
preds = loaded.predict()
os.unlink(pkl_file)
# Now save out the arima_res_ piece separately, and show we can load
# it from the legacy method
arima.summary()
arima.arima_res_.save(fname=new_loc)
arima.tmp_pkl_ = new_loc
assert _uses_legacy_pickling(arima)
# Save/load it and show it works
joblib.dump(arima, pkl_file)
loaded2 = joblib.load(pkl_file)
assert_array_almost_equal(loaded2.predict(), preds)
# De-cache
arima._clear_cached_state()
assert not os.path.exists(new_loc)
# Show we get an OSError now
with pytest.raises(OSError) as ose:
joblib.load(pkl_file)
assert "Does it still" in str(ose), ose
finally:
_unlink_if_exists(pkl_file)
_unlink_if_exists(new_loc)
stepwise=True,
trace=1,
m=args.period,
information_criterion="aicc",
seasonal=args.seasonal,
error_action="ignore",
suppress_warnings=True,
)
elif args.fit:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
arima = ARIMA(order=args.order,
seasonal_order=args.seasonal_order)
arima.fit(train)
print(arima.summary())
residuals = arima.resid()
print("train lengths: data={} resid={}".format(
train_len, residuals.shape[0]))
len_delta = train_len - residuals.shape[0]
# Diagnostics plot
arima.plot_diagnostics(lags=50)
box_ljung(residuals, nlags=20).format()
plt.gcf().suptitle('Diagnostics Plot')
plt.figure()
plt.plot(df.value.index[len_delta:train_len],
np.abs(residuals), label="abs(residuals)")
plt.plot(df.value, label="data", alpha=0.5)
# fig, axes = plt.subplots(3, 1, sharex=True)
