def test_average_abnormal_input(data, exception): ''' test average raises correct exceptions on abnormal input ''' model = b.Average() with pytest.raises(exception): model.fit(data)
def test_average_fit_predict(data, horizon, expected): ''' test the correct number of error metric functions are returned. ''' model = b.Average() # fit_predict for point forecasts only preds = model.fit_predict(pd.Series(data), horizon) assert len(preds) == expected
def test_average_forecast_output_longer_horizon(data, period, expected): ''' test naive1 carries forward the last value in the series ''' model = b.Average() model.fit(data) # point forecasts only preds = model.predict(period) assert np.array_equal(preds, expected)
def test_average_forecast_output(data, expected): ''' test the correct number of error metric functions are returned. ''' model = b.Average() model.fit(pd.DataFrame(data)) # point forecasts only preds = model.predict(1) assert preds[0] == expected
def test_average_forecast_input_dataframe(data, horizon, expected): ''' test the average baseline class accept dataframe ''' model = b.Average() model.fit(pd.DataFrame(data)) # point forecasts only preds = model.predict(horizon) assert len(preds) == expected
def test_average_forecast_input_series(data, horizon, expected): ''' test the average class accepts pandas series ''' model = b.Average() model.fit(pd.Series(data)) # point forecasts only preds = model.predict(horizon) assert len(preds) == expected
def test_average_forecast_input_numpy(data, horizon, expected): ''' test the average class accepts numpy array ''' model = b.Average() model.fit(np.array(data)) # point forecasts only preds = model.predict(horizon) assert len(preds) == expected
def test_average_pi_horizon(data, horizon, alpha, expected): ''' test the correct forecast horizon is returned for prediction interval for Average ''' model = b.Average() model.fit(pd.Series(data)) # point forecasts only _, intervals = model.predict(horizon, return_predict_int=True, alpha=alpha) assert len(intervals[0]) == expected
def test_average_fitted_values_length(training_length): ''' test Average forecaster .fittedvalues length is as expected ''' np.random.seed(1066) train = np.random.poisson(lam=50, size=training_length) model = b.Average() model.fit(train) expected = training_length assert len(model.fittedvalues) == expected
def test_average_fitted_values_nan_length(): ''' test Average forecast .fittedvalues has the correct number of NaNs i.e. = 1 ''' np.random.seed(1066) train = np.random.poisson(lam=50, size=200) model = b.Average() model.fit(train) expected = 0 n_nan = np.isnan(model.fittedvalues).sum() assert n_nan == expected
def test_average_prediction_interval_low(): ''' test average 80% lower prediction interval ''' np.random.seed(1066) train = np.random.poisson(lam=50, size=100) low = [40.97369, 40.97369, 40.97369, 40.97369, 40.97369] # high = [59.34631, 59.34631, 59.34631, 59.34631, 59.34631] model = b.Average() model.fit(train) _, intervals = model.predict(5, return_predict_int=True, alpha=[0.2]) print(intervals[0].T[1]) assert pytest.approx(intervals[0].T[0]) == low
''' model = b.Average() model.fit(pd.Series(data)) # point forecasts only _, intervals = model.predict(horizon, return_predict_int=True, alpha=alpha) assert len(intervals[0]) == expected @pytest.mark.parametrize( "model, data, horizon, alpha, expected", [(b.Naive1(), [1, 2, 3, 4, 5], 12, [0.2, 0.05], 2), (b.Naive1(), [1, 2, 3, 4, 5], 24, [0.2, 0.10, 0.05], 3), (b.SNaive(1), [1, 2, 3], 8, [0.8], 1), (b.SNaive(1), [1, 2, 3, 4, 5], 24, [0.2, 0.10, 0.05], 3), (b.Naive1(), [1, 2, 3], 8, None, 2), (b.SNaive(1), [1, 2, 3], 8, None, 2), (b.Average(), [1, 2, 3], 8, None, 2), (b.Drift(), [1, 2, 3], 8, None, 2), (b.Drift(), [1, 2, 3], 8, [0.8], 1), (b.Drift(), [1, 2, 3], 8, None, 2), (b.Average(), [1, 2, 3, 4, 5], 24, [0.2, 0.10, 0.05], 3)]) def test_naive_pi_set_number(model, data, horizon, alpha, expected): ''' test the correct number of Prediction intervals are returned for prediction interval for all Naive forecasting classes ''' model.fit(pd.Series(data)) # point forecasts only _, intervals = model.predict(horizon, return_predict_int=True, alpha=alpha) assert len(intervals) == expected @pytest.mark.parametrize("data, period, expected", [(np.arange(1, 7), 6, np.arange(7, 13)),