def test_score_predictions():
"""Test the score predictions function in UoI Poisson."""
X = np.array([[np.log(2), -1, -3],
[np.log(3), -2, -4],
[np.log(4), -3, -5],
[np.log(5), -4, -6]])
y = 1. / np.log([2., 3., 4., 5.])
support = np.array([True, False, False])
n_samples = y.size
# create fitter by hand
fitter = Poisson()
fitter.coef_ = np.array([1])
fitter.intercept_ = 0
uoi_fitter = UoI_Poisson()
# test log-likelihood
ll = uoi_fitter._score_predictions(
metric='log',
fitter=fitter,
X=X, y=y, support=support)
assert_allclose(ll, -2.5)
# test information criteria
total_ll = ll * n_samples
aic = uoi_fitter._score_predictions(
metric='AIC',
fitter=fitter,
X=X, y=y, support=support)
assert_allclose(aic, 2 * total_ll - 2)
aicc = uoi_fitter._score_predictions(
metric='AICc',
fitter=fitter,
X=X, y=y, support=support)
assert_allclose(aicc, aic - 2)
bic = uoi_fitter._score_predictions(
metric='BIC',
fitter=fitter,
X=X, y=y, support=support)
assert_allclose(bic, 2 * total_ll - np.log(y.size))
# test invalid metric
assert_raises(ValueError,
uoi_fitter._score_predictions,
'fake',
fitter,
X, y, support)
def test_predict():
"""Test the predict function in the Poisson class"""
# design matrix
X = np.array([[np.log(2.5), -1, -3], [np.log(3.5), -2, -4],
[np.log(4.5), -3, -5], [np.log(5.5), -4, -6]])
poisson = Poisson()
# test for NotFittedError
assert_raises(NotFittedError, poisson.predict, X)
# create "fit"
poisson.coef_ = np.array([1, 0, 0])
poisson.intercept_ = 0
y_pred = poisson.predict(X)
y_mode = np.array([2, 3, 4, 5])
# test for predict
assert_almost_equal(y_pred, y_mode)