def test_drift_abnormal_input(data, exception):
'''
test drift raises correct exceptions on abnormal input
'''
model = b.Drift()
with pytest.raises(exception):
model.fit(data)
def test_drift_fit_predict(data, horizon, expected):
'''
test the correct number of error metric functions are returned.
'''
model = b.Drift()
# fit_predict for point forecasts only
preds = model.fit_predict(pd.Series(data), horizon)
assert len(preds) == expected
def test_drift_call_predict_before_fit():
'''
test Drift raises correct exceptions when
predict is called before fit
'''
model = b.Drift()
with pytest.raises(UnboundLocalError):
model.predict(10)
def test_drift_forecast_horizon(data, horizon, expected):
'''
test the correct number of error metric functions are returned.
'''
model = b.Drift()
model.fit(np.array(data))
# point forecasts only
preds = model.predict(horizon)
assert len(preds) == expected
def test_drift_forecast_output_longer_horizon(data, period, expected):
'''
test drift forecast predictions
'''
model = b.Drift()
model.fit(data)
# point forecasts only
preds = model.predict(period)
assert np.array_equal(preds, expected)
def test_drift_pi_horizon(data, horizon, alpha, expected):
'''
test the correct forecast horizon is returned for prediction
interval for Drift
'''
model = b.Drift()
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_drift_fitted_values_length(training_length):
'''
test drift .fittedvalues
'''
np.random.seed(1066)
train = np.random.poisson(lam=50, size=training_length)
model = b.Drift()
model.fit(train)
expected = training_length
assert len(model.fittedvalues) == expected
def test_drift_fitted_values_nan_length():
'''
test Drift .fittedvalues has the correct number of NaNs
i.e. = 1
'''
np.random.seed(1066)
train = np.random.poisson(lam=50, size=200)
model = b.Drift()
model.fit(train)
expected = 1
n_nan = np.isnan(model.fittedvalues).sum()
assert n_nan == expected
def test_drift_prediction_interval_95_high():
'''
test drift 95% lower prediction interval
intervals are matched from R forecast package
'''
np.random.seed(1066)
train = np.random.poisson(lam=50, size=100)
# low = [22.2100359, 13.2828923, 6.2277574, 0.1124247, -5.4196405]
high = [63.70916, 72.55549, 79.52982, 85.56434, 91.01560]
# quarterly data
model = b.Drift()
model.fit(train)
_, intervals = model.predict(5, return_predict_int=True, alpha=[0.05])
print(intervals[0].T[1])
# not ideal due to not adjusting for drift i think,
assert pytest.approx(intervals[0].T[1], rel=1e-6, abs=1.2) == high
'''
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)),