def test_train_and_test_various_datasets(self, given_dataset):
X_train_set, y_train_set, X_test, y_test, stats = MainTransformer.get_training_and_test_set(given_dataset,
'Pollutant',
'Uncertainty',
size=0.5,
normalize=True)
gp = GaussianProcesses()
gp.train(X_train_set, y_train_set, stats=stats)
assert gp.stats['n_instances_trained'] == X_train_set.shape[0]
assert gp.stats['dataset_stats'] == stats
predictions = gp.predict(X_test, uncertainty=True)
assert len(predictions) == X_test.shape[0]
def test_train_and_test(self, uncertainty):
X_train_set, y_train_set, X_test, y_test, stats = MainTransformer.get_training_and_test_set(dataset_gp,
'Pollutant',
'Uncertainty',
size=0.5,
normalize=True)
gp = GaussianProcesses()
gp.train(X_train_set, y_train_set, stats=stats)
assert gp.stats['n_instances_trained'] == X_train_set.shape[0]
assert gp.stats['dataset_stats'] == stats
predictions = gp.predict(X_test, uncertainty=uncertainty)
assert len(predictions) == X_test.shape[0]
if uncertainty:
values_without_uncertainty = list(filter(lambda x: len(x) != 2, predictions))
assert len(values_without_uncertainty) == 0
if not isinstance(uncertainty, bool):
assert len(list(filter(lambda x: not isinstance(x, tuple), predictions))) == X_test.shape[0]