@pytest.mark.parametrize("regres", [KNearestNeighborRegressor])
@pytest.mark.parametrize("mean", ["faulty", 42])
@pytest.mark.parametrize("dist", ["not_real", 27])
def test_metric_dict_error(regres, mean, dist, get_covmats, get_targets):
with pytest.raises((TypeError, KeyError)):
n_matrices, n_channels = 6, 3
targets = get_targets(n_matrices)
covmats = get_covmats(n_matrices, n_channels)
clf = regres(metric={"mean": mean, "distance": dist})
clf.fit(covmats, targets).predict(covmats)
@pytest.mark.parametrize("regres", [KNearestNeighborRegressor])
@pytest.mark.parametrize("mean", get_means())
@pytest.mark.parametrize("dist", get_distances())
def test_metric_dist(regres, mean, dist, get_covmats, get_targets):
n_matrices, n_channels = 4, 3
targets = get_targets(n_matrices)
covmats = get_covmats(n_matrices, n_channels)
clf = regres(metric={"mean": mean, "distance": dist})
clf.fit(covmats, targets).predict(covmats)
@pytest.mark.parametrize("regres", regs)
@pytest.mark.parametrize("metric", [42, "faulty", {"foo": "bar"}])
def test_metric_wrong_keys(regres, metric, get_covmats, get_targets):
with pytest.raises((TypeError, KeyError, ValueError)):
n_matrices, n_channels = 6, 3
targets = get_targets(n_matrices)
covmats = get_covmats(n_matrices, n_channels)
def get_dist_func():
dist_func = [
distance_riemann,
distance_logeuclid,
distance_euclid,
distance_logdet,
distance_kullback,
distance_kullback_right,
distance_kullback_sym,
distance_wasserstein,
]
for df in dist_func:
yield df
@pytest.mark.parametrize("dist", get_distances())
def test_check_distance_str(dist):
_check_distance_method(dist)
@pytest.mark.parametrize("dist", get_dist_func())
def test_check_distance_func(dist):
_check_distance_method(dist)
def test_check_distance_error():
with pytest.raises(ValueError):
_check_distance_method("universe")
with pytest.raises(ValueError):
_check_distance_method(42)