def test_snaive_abnormal_input(data, exception):
'''
test snaive raises correct exceptions on abnormal input
'''
model = b.SNaive(1)
with pytest.raises(exception):
model.fit(data)
def test_snaive_fit_predict(data, horizon, expected):
'''
test the correct number of error metric functions are returned.
'''
model = b.SNaive(1)
# fit_predict for point forecasts only
preds = model.fit_predict(pd.Series(data), horizon)
assert len(preds) == expected
def test_snaive_call_predict_before_fit():
'''
test SNaive raises correct exceptions when
predict is called before fit
'''
model = b.SNaive(7)
with pytest.raises(UnboundLocalError):
model.predict(10)
def test_snaive_forecast_output(data, period, expected):
'''
test naive1 carries forward the last value in the series
'''
model = b.SNaive(period)
model.fit(data)
# point forecasts only
preds = model.predict(period)
assert np.array_equal(preds, expected)
def test_snaive_forecast_input_dataframe(data, horizon, expected):
'''
test the correct number of error metric functions are returned.
'''
model = b.SNaive(1)
model.fit(pd.DataFrame(data))
# point forecasts only
preds = model.predict(horizon)
assert len(preds) == expected
def test_snaive_pi_horizon(data, horizon, alpha, expected):
'''
test the correct forecast horizon is returned for prediction
interval for SNaive
'''
model = b.SNaive(1)
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_snaive_fitted_values_length(training_length):
'''
test SNaive .fittedvalues length is as expected
'''
np.random.seed(1066)
train = np.random.poisson(lam=50, size=training_length)
model = b.SNaive(7)
model.fit(train)
expected = training_length
assert len(model.fittedvalues) == expected
def test_snaive_fitted_values_nan_length(period):
'''
test SNaive .fittedvalues has the correct number of NaNs
i.e. = to the seasonal period.
'''
np.random.seed(1066)
train = np.random.poisson(lam=50, size=200)
model = b.SNaive(period)
model.fit(train)
expected = period
n_nan = np.isnan(model.fittedvalues).sum()
assert n_nan == expected
def test_snaive_prediction_interval_95_low():
'''
test snaive 95% lower prediction interval
intervals are matched from R forecast package
'''
np.random.seed(1066)
train = np.random.poisson(lam=50, size=100)
low = [25.12464, 50.12464, 42.12464, 23.12464, 16.89199]
# high = [64.87536, 89.87536, 81.87536, 62.87536, 73.10801]
# quarterly data
model = b.SNaive(period=4)
model.fit(train)
_, intervals = model.predict(5, return_predict_int=True, alpha=[0.05])
print(intervals[0].T[0])
assert pytest.approx(intervals[0].T[0]) == low
def test_snaive_prediction_interval_80_high():
'''
test snaive 80% upper prediction interval
intervals are matched from R forecast package
'''
np.random.seed(1066)
train = np.random.poisson(lam=50, size=100)
# low = [32.00420, 57.00420, 49.00420, 30.00420, 26.62116]
high = [57.99580, 82.99580, 74.99580, 55.99580, 63.37884]
# quarterly data
model = b.SNaive(period=4)
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[1]) == high
'''
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
@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