def test_model_error_returns_nan():
with mock.patch('sklearn.base.clone', lambda x: x):
mock_model = mock.MagicMock()
def mock_fit(*args, **kwargs):
raise ValueError()
mock_model.fit = mock_fit
with pytest.warns(ModelFitWarning):
scores = cross_val_score(mock_model,
y,
scoring='mean_squared_error',
cv=SlidingWindowForecastCV(
window_size=100, step=24, h=1),
verbose=0)
assert np.isnan(scores).all()
# if the error_score is 'raise', we will raise
with pytest.raises(ValueError):
cross_val_score(mock_model,
y,
scoring='mean_squared_error',
cv=SlidingWindowForecastCV(window_size=100,
step=24,
h=1),
verbose=0,
error_score='raise')
def test_error_action_validation():
est = ARIMA(order=(1, 1, 2), seasonal_order=(0, 1, 1, 12))
with pytest.raises(ValueError) as ve:
cross_validate(
est, y, error_score=None, scoring='mean_squared_error',
cv=SlidingWindowForecastCV(window_size=100, step=24, h=1))
assert 'error_score should be' in pytest_error_str(ve)
def test_cross_val_predict_error():
cv = SlidingWindowForecastCV(step=24, h=1)
with pytest.raises(ValueError):
cross_val_predict(ARIMA(order=(2, 1, 0), maxiter=3), y, cv=cv)
from pmdarima.preprocessing import FourierFeaturizer
from pmdarima.model_selection._split import RollingForecastCV, \
SlidingWindowForecastCV
from pmdarima.model_selection._validation import cross_val_score, \
_check_scoring, cross_validate, cross_val_predict, _check_averaging
from pmdarima.datasets import load_wineind
import pytest
import numpy as np
from unittest import mock
y = load_wineind()
exogenous = np.random.RandomState(1).rand(y.shape[0], 2)
@pytest.mark.parametrize('cv', [
SlidingWindowForecastCV(window_size=100, step=24, h=1),
RollingForecastCV(initial=150, step=12, h=1),
])
@pytest.mark.parametrize('est', [
ARIMA(order=(2, 1, 1)),
ARIMA(
order=(1, 1, 2), seasonal_order=(0, 1, 1, 12), suppress_warnings=True),
Pipeline([("fourier", FourierFeaturizer(m=12)),
("arima", ARIMA(order=(2, 1, 0), maxiter=3))])
])
@pytest.mark.parametrize('verbose', [0, 2, 4])
@pytest.mark.parametrize('exog', [None, exogenous])
def test_cv_scores(cv, est, verbose, exog):
scores = cross_val_score(est,
y,
exogenous=exog,