def test_predict_nans(): """ Tests that the predictions are not NaNs """ model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Laplace()) x = model.fit() x.summary() assert(len(model.predict(h=5, oos_data=data_oos).values[np.isnan(model.predict(h=5, oos_data=data_oos).values)]) == 0)
def test_predict_length(): """ Tests that the length of the predict dataframe is equal to no of steps h """ model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2) x = model.fit() x.summary() assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_2_predict_nonconstant(): """ We should not really have predictions that are constant (should be some difference)... This captures bugs with the predict function not iterating forward """ model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2) x = model.fit() predictions = model.predict(h=10, oos_data=data_oos, intervals=False) assert (not np.all(predictions.values == predictions.values[0]))
def test_predict_nonconstant(): """ We should not really have predictions that are constant (should be some difference)... This captures bugs with the predict function not iterating forward """ model = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1, family=Poisson()) x = model.fit('BBVI', iterations=200, quiet_progress=True) predictions = model.predict(h=10, oos_data=data_oos, intervals=False) assert (not np.all(predictions.values == predictions.values[0]))
def test_2_predict_intervals_mh(): """ Tests prediction intervals are ordered correctly """ model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2, family=Laplace()) x = model.fit('M-H', nsims=200, quiet_progress=True) predictions = model.predict(h=10, oos_data=data_oos, intervals=True) assert(np.all(predictions['99% Prediction Interval'].values > predictions['95% Prediction Interval'].values)) assert(np.all(predictions['95% Prediction Interval'].values > predictions[model.data_name].values)) assert(np.all(predictions[model.data_name].values > predictions['5% Prediction Interval'].values)) assert(np.all(predictions['5% Prediction Interval'].values > predictions['1% Prediction Interval'].values))
def test_predict_intervals_bbvi(): """ Tests prediction intervals are ordered correctly """ model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=t()) x = model.fit('BBVI', iterations=100, quiet_progress=True) predictions = model.predict(h=10, oos_data=data_oos, intervals=True) assert(np.all(predictions['99% Prediction Interval'].values > predictions['95% Prediction Interval'].values)) assert(np.all(predictions['95% Prediction Interval'].values > predictions[model.data_name].values)) assert(np.all(predictions[model.data_name].values > predictions['5% Prediction Interval'].values)) assert(np.all(predictions['5% Prediction Interval'].values > predictions['1% Prediction Interval'].values))
def test_predict_intervals(): """ Tests prediction intervals are ordered correctly """ model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Poisson()) x = model.fit() predictions = model.predict(h=10, oos_data=data_oos, intervals=True) assert (np.all(predictions['99% Prediction Interval'].values >= predictions['95% Prediction Interval'].values)) assert (np.all(predictions['95% Prediction Interval'].values >= predictions['5% Prediction Interval'].values)) assert (np.all(predictions['5% Prediction Interval'].values >= predictions['1% Prediction Interval'].values))
def a_test2_predict_intervals_mh(): """ Tests prediction intervals are ordered correctly """ model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2, family=Exponential()) x = model.fit('M-H', nsims=400) predictions = model.predict(h=10, oos_data=data_oos, intervals=True) assert (np.all(predictions['99% Prediction Interval'].values >= predictions['95% Prediction Interval'].values)) assert (np.all(predictions['95% Prediction Interval'].values >= predictions['5% Prediction Interval'].values)) assert (np.all(predictions['5% Prediction Interval'].values >= predictions['1% Prediction Interval'].values))