def test_do_inference(self):
# Given
data_preprocessor = DataPreprocessor()
device = "cpu"
inferencer = Inferencer(data_preprocessor, device)
data_dimensions = (BASE_GRAPH_NODE_FEATURES.size(), BASE_GRAPH.size(), BASE_GRAPH.view(-1).size())
model = ModelSelector.load_model("RNN")
model = model(time_steps=1,
number_of_nodes=data_dimensions[1][0],
number_of_node_features=data_dimensions[0][1],
fully_connected_layer_input_size=data_dimensions[1][0] * data_dimensions[0][1],
fully_connected_layer_output_size=data_dimensions[2][0])
all_neighbors = to.tensor([[1, 2, -1, -1],
[0, 2, -1, -1],
[0, 1, 3, -1],
[2, -1, -1, -1]])
dataset = GraphDataset("")
dataset.enable_test_mode()
tag = 'tag'
dataset.dataset = [(BASE_GRAPH_NODE_FEATURES, all_neighbors, BASE_GRAPH.view(-1), tag)]
inference_data, _, _ = DataPreprocessor().train_validation_test_split(dataset, 1, 0.0, 0.0)
output_label_pairs_expected = [BASE_GRAPH.view(-1), BASE_GRAPH.view(-1)]
# When
output_label_pairs = inferencer.do_inference(model, inference_data)
# Then
self.assertEqual(output_label_pairs[0][0].squeeze().size(), output_label_pairs_expected[0].size())
self.assertEqual(output_label_pairs[0][1].squeeze().size(), output_label_pairs_expected[1].size())
def test_extract_data_dimensions(self):
# Given
dataset_length = 1
features = BASE_GRAPH_NODE_FEATURES
all_neighbors = to.tensor([[1, 2, -1, -1], [0, 2, -1, -1],
[0, 1, 3, -1], [2, -1, -1, -1]])
labels = BASE_GRAPH.view(-1)
dataset = GraphDataset("")
dataset.enable_test_mode()
dataset.dataset = [(features, all_neighbors, labels, i)
for i in range(dataset_length)]
data_dimensions_expected = (features.size(), labels.size())
# When
data_dimensions = self.data_preprocessor.extract_data_dimensions(
dataset)
# Then
self.assertEqual(data_dimensions_expected, data_dimensions)
def test_do_evaluate(self):
# Given
data_dimensions = (BASE_GRAPH_NODE_FEATURES.size(),
BASE_GRAPH.view(-1).size())
self.model_trainer.instantiate_attributes(
data_dimensions, self.configuration_dictionary)
all_neighbors = to.tensor([[1, 2, -1, -1], [0, 2, -1, -1],
[0, 1, 3, -1], [2, -1, -1, -1]])
dataset = GraphDataset("")
dataset.enable_test_mode()
tag = 'tag'
dataset.dataset = [(BASE_GRAPH_NODE_FEATURES, all_neighbors,
BASE_GRAPH.view(-1), tag)]
training_data, _, _ = DataPreprocessor().train_validation_test_split(
dataset, 1, 0.0, 0.0)
# When
validation_loss = self.model_trainer.do_evaluate(
evaluation_data=training_data, epoch=1)
# Then
self.assertTrue(validation_loss > 0.0)
def test_train_validation_test_split(self):
# Given
dataset_length = 10
features = BASE_GRAPH_NODE_FEATURES
all_neighbors = to.tensor([[1, 2, -1, -1], [0, 2, -1, -1],
[0, 1, 3, -1], [2, -1, -1, -1]])
labels = BASE_GRAPH.view(-1)
dataset = GraphDataset("")
dataset.enable_test_mode()
dataset.dataset = [(features, all_neighbors, labels, i)
for i in range(dataset_length)]
train_validation_test_split_expected = [7, 2, 1]
# When
train_validation_test_split = self.data_preprocessor.train_validation_test_split(
dataset, batch_size=1, validation_split=0.2, test_split=0.1)
train_validation_test_split = [
len(dataset) for dataset in train_validation_test_split
]
# Then
self.assertEqual(train_validation_test_split_expected,
train_validation_test_split)