1ead4fe94cc00561467641317befbc26a594933a,pmdarima/arima/tests/test_arima.py,,test_more_elaborate,#,398

Before Change

    // pickle this for the __get/setattr__ coverage.
    // since the only time this is tested is in parallel in auto.py,
    // this doesn"t actually get any coverage proof...
    fl = "some_temp_file.pkl"
    with open(fl, "wb") as p:
        pickle.dump(arima, p)

    // show we can predict with this even though it"s been pickled
    new_xreg = rs.rand(5, 4)
    _preds = arima.predict(n_periods=5, exogenous=new_xreg)

    // now unpickle
    with open(fl, "rb") as p:
        other = pickle.load(p)

    // show we can still predict, compare
    _other_preds = other.predict(n_periods=5, exogenous=new_xreg)
    assert_array_almost_equal(_preds, _other_preds)

    // now remove the pickle file
    os.unlink(fl)

    // now show that since we fit the ARIMA with an exogenous array,
    // we need to provide one for predictions otherwise it breaks.
    with pytest.raises(ValueError):

After Change

        y=hr, exogenous=xreg)
    _try_get_attrs(arima)

    with tempfile.TemporaryDirectory() as tdir:

        // pickle this for the __get/setattr__ coverage.
        // since the only time this is tested is in parallel in auto.py,
        // this doesn"t actually get any coverage proof...
        fl = os.path.join(tdir, "some_temp_file.pkl")
        with open(fl, "wb") as p:
            pickle.dump(arima, p)

        // show we can predict with this even though it"s been pickled
        new_xreg = rs.rand(5, 4)
        _preds = arima.predict(n_periods=5, exogenous=new_xreg)

        // now unpickle
        with open(fl, "rb") as p:
            other = pickle.load(p)

        // show we can still predict, compare
        _other_preds = other.predict(n_periods=5, exogenous=new_xreg)
        assert_array_almost_equal(_preds, _other_preds)

    // now show that since we fit the ARIMA with an exogenous array,
    // we need to provide one for predictions otherwise it breaks.
    with pytest.raises(ValueError):
        arima.predict(n_periods=5, exogenous=None)

    // show that if we DO provide an exogenous and it"s the wrong dims, we

Instances