def test_train_and_test(self):
cnn = ConvolutionalNeuralNetwork(24)
# just testing, don't care about overfitting
X_train_set, y_train_set, _, _, stats = MainTransformer.get_training_and_test_set(dataset,
'Pollutant',
'Uncertainty',
size=1,
normalize=True)
cnn.train(X_train_set, y_train_set, stats=stats)
predictions = cnn.predict(X_train_set)
assert len(predictions) == X_train_set.shape[0]
def test_train_and_test_no_uncertainty_not_enough_instances(self):
cnn = ConvolutionalNeuralNetwork(24)
# just testing, don't care about overfitting
X_train_set, y_train_set, X_test, _, stats = MainTransformer.get_training_and_test_set(dataset,
'Pollutant',
'Uncertainty',
size=0.95,
normalize=True)
cnn.train(X_train_set, y_train_set, stats=stats)
predictions = cnn.predict(X_test, uncertainty=False)
n_none_predictions = len(list(filter(lambda x: x[0] is None and x[1] is None, predictions)))
assert n_none_predictions == len(X_test)
def test_predict_not_enough_instances(self):
global cnn
cnn = ConvolutionalNeuralNetwork(24)
X_train_set, y_train_set, X_test, _, stats = MainTransformer.get_training_and_test_set(dataset,
'Pollutant',
'Uncertainty',
size=0.95,
normalize=True)
cnn.train(X_train_set, y_train_set, stats=stats)
predictions = cnn.predict(X_test=X_test, uncertainty=True)
n_none_predictions = len(list(filter(lambda x: x[0] is None and x[1] is None, predictions)))
assert X_test.shape[0] == n_none_predictions