def score(self, epoch_counter):
"""
Scoring on the test set.
"""
print("\n\nModel evaluation.\n")
start_time = time.time()
self.model.eval()
self.scores = []
self.ground_truth = []
for test_graph_pair in tqdm(self.testing_graphs):
data = process_pair(test_graph_pair)
self.ground_truth.append(calculate_normalized_ged(data))
data = self.transfer_to_torch(data)
target = data["target"]
prediction = self.model(data)
print("\n" + str(test_graph_pair) + "- " + "Similarity/Target: " +
str(prediction) + " / " + str(target))
self.scores.append(calculate_loss(prediction, target))
print("--- %s seconds ---" % (time.time() - start_time))
model_error = self.print_evaluation()
print('\n\n >>>>>>>>>>>>>>>>>>\t' + str(model_error) + '\n')
with open("./outputFiles/test/test_error_graph.txt",
"a") as test_error_writer:
test_error_writer.write(
str(epoch_counter) + ',' + str(model_error) + '\n')
test_error_writer.close()
def score(self):
"""
Scoring on the test set.
"""
print("\n\nModel evaluation.\n")
self.model.eval()
self.scores = []
self.ground_truth = []
preds = []
truths = []
for graph_pair in tqdm(self.testing_graphs):
data = process_pair(graph_pair)
self.ground_truth.append(calculate_normalized_ged(data))
data = self.transfer_to_torch(data)
target = data["target"]
prediction = self.model(data)
self.scores.append(calculate_loss(prediction, target))
preds.append(0 if prediction.item() < 0.5 else 1)
truths.append(int(data["target"].item()))
self.print_evaluation()
plot_confusion_matrix(np.array(truths),
np.array(preds),
np.array([0, 1]),
title='SimGNN confusion matrix')
def get_train_baseline_error(self):
"""
Calculates the baseline error of the training data
"""
self.train_ground_truth = []
for graph_pair in tqdm(self.training_graphs):
data = process_pair(graph_pair)
self.train_ground_truth.append(calculate_normalized_ged(data))
norm_ged_mean = np.mean(self.train_ground_truth)
base_train_error = np.mean([(n - norm_ged_mean)**2
for n in self.train_ground_truth])
print("\nBaseline Training error: " + str(round(base_train_error, 5)))
def load_model_parallel(self, pairList):
#print("Parallel Execution of funcGNN from pretrained model")
#self.model = funcGNN(self.args, self.number_of_labels)
#self.model.load_state_dict(torch.load('./model_state.pth'))
#self.model.eval()
data = process_pair(pairList)
self.ground_truth.append(calculate_normalized_ged(data))
data = self.transfer_to_torch(data)
target = data["target"]
prediction = self.model(data)
#print("\n" + str(pairList) + "- " + "Similarity/Target: " + str(prediction) + " / " + str(target))
self.scores.append(calculate_loss(prediction, target))
def score(self):
"""
Scoring on the test set.
"""
print("\n\nModel evaluation.\n")
self.scores = []
self.ground_truth = []
for graph_pair in tqdm(self.testing_graphs):
data = process_pair(graph_pair)
self.ground_truth.append(calculate_normalized_ged(data))
data = self.transfer_to_torch(data)
target = self.model(data)
prediction = self.model(data)
self.scores.append(calculate_loss(prediction, target))
self.print_evaluation()
def load_model(self):
print("\nSerial Execution of funcGNN from pretrained model")
start_time = time.time()
self.model = funcGNN(self.args, self.number_of_labels)
self.model.load_state_dict(torch.load('./model_state.pth'))
self.model.eval()
self.scores = []
self.ground_truth = []
for test_graph_pair in tqdm(self.random_graphs):
data = process_pair(test_graph_pair)
self.ground_truth.append(calculate_normalized_ged(data))
data = self.transfer_to_torch(data)
target = data["target"]
prediction = self.model(data)
#print("\n" + str(test_graph_pair) + "- " + "Similarity/Target: " + str(prediction) + " / " + str(target))
self.scores.append(calculate_loss(prediction, target))
self.scores.append(
torch.nn.functional.mse_loss(prediction, data["target"]))
print("--- %s seconds ---" % (time.time() - start_time))
def score(self):
"""
Scoring on the test set.
"""
print("\n\nModel evaluation.\n")
if path.isfile(self.args.saved_model):
self.model=torch.load(self.args.saved_model)
self.model.train(False)
self.model.eval()
self.scores = []
self.ground_truth = []
for graph_pair in tqdm(self.testing_graphs):
data = process_pair(graph_pair)
data_org = data.copy()
self.ground_truth.append(calculate_normalized_ged(data))
data = self.transfer_to_torch(data)
target = data["target"]
prediction = self.model(data)
print(f'Test target: {data_org["ged"]} {reverse_normalized_ged(-math.log(target),data_org)}, prediction: {reverse_normalized_ged(-math.log(prediction),data_org)}')
self.scores.append(calculate_loss(prediction, target))
self.print_evaluation()
