def test_transform(self):
main_transformer = MainTransformer(config=config)
result_weather = main_transformer.add_transformer(
Transformers.WEATHER_TRANSFORMER)
result_pollutant = main_transformer.add_transformer(
Transformers.POLLUTANT_TRANSFORMER)
weather_size = len(config['weather'].keys())
pollutant_size = len(config['pollutant'].keys())
main_transformer.transform()
dataset = main_transformer.get_dataset()
assert weather_size + pollutant_size == dataset.shape[1]
def test_train_and_test(self, uncertainty):
global dataset
data_transformer = MainTransformer(config=ConfigReader.CONFIG)
data_transformer.add_transformer(Transformers.WEATHER_TRANSFORMER)
data_transformer.add_transformer(Transformers.POLLUTANT_TRANSFORMER)
data_transformer.transform()
dataset = data_transformer.get_dataset()
complete_dataset = dataset.dropna(inplace=False)
MainTransformer.periodic_f(complete_dataset)
X_train_set, y_train_set, X_test, y_test, stats = MainTransformer.get_training_and_test_set(dataset,
'Pollutant',
'Uncertainty',
size=0.8,
normalize=True)
print(np.array(X_train_set))
print(y_train_set)
# X_train_set, y_train_set, X_test, y_test, stats = MainTransformer.get_training_and_test_set(complete_dataset,
# 'Pollutant',
# 'Uncertainty',
# size=0.8,
# normalize=True)
#
# print(np.array(X_train_set))
# print(X_train_set)
# print(y_train_set)
gp = SparseGaussianProcesses()
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]