lr=0.01,
explainer_hidden=[10],
conceptizator='identity_bool')
start = time.time()
trainer.fit(model, train_loader, val_loader)
print(f"Concept mask: {model.model[0].concept_mask}")
model.freeze()
model_results = trainer.test(model, test_dataloaders=test_loader)
for j in range(n_classes):
n_used_concepts = sum(model.model[0].concept_mask[j] > 0.5)
print(f"Extracted concepts: {n_used_concepts}")
results, f = model.explain_class(val_loader,
val_loader,
test_loader,
topk_explanations=5,
x_to_bool=None,
max_accuracy=True,
concept_names=concept_names)
end = time.time() - start
results['model_accuracy'] = model_results[0]['test_acc']
results['extraction_time'] = end
results_list.append(results)
extracted_concepts = []
all_concepts = model.model[0].concept_mask[0] > 0.5
common_concepts = model.model[0].concept_mask[0] > 0.5
for j in range(n_classes):
n_used_concepts = sum(model.model[0].concept_mask[j] > 0.5)
print(f"Extracted concepts: {n_used_concepts}")
print(f"Explanation: {f[j]['explanation']}")
lr=0.01,
explainer_hidden=[20, 20],
temperature=tau,
l1=l1)
start = time.time()
trainer.fit(model, train_loader, val_loader)
print(f"Gamma: {model.model[0].concept_mask}")
model.freeze()
model_results = trainer.test(model, test_dataloaders=test_loader)
for j in range(n_classes):
n_used_concepts = sum(model.model[0].concept_mask[j] > 0.5)
print(f"Extracted concepts: {n_used_concepts}")
results, f = model.explain_class(val_loader,
train_loader,
test_loader,
topk_explanations=10,
concept_names=concept_names)
end = time.time() - start
results['model_accuracy'] = model_results[0]['test_acc']
results['extraction_time'] = end
results['tau'] = tau
results['lambda'] = l1
results_list.append(results)
results_df = pd.DataFrame(results_list)
results_df.to_csv(
os.path.join(base_dir,
f'results_aware_vdem_l_{l1}_tau_{tau}.csv'))
l1=0.0001,
temperature=0.7,
lr=0.01,
explainer_hidden=[10])
start = time.time()
trainer.fit(model, train_loader, val_loader)
print(f"Concept mask: {model.model[0].concept_mask}")
model.freeze()
model_results = trainer.test(model, test_dataloaders=test_loader)
for j in range(n_classes):
n_used_concepts = sum(model.model[0].concept_mask[j] > 0.5)
print(f"Extracted concepts: {n_used_concepts}")
results, f = model.explain_class(val_loader,
train_loader,
test_loader,
topk_explanations=50,
concept_names=concept_names,
verbose=True)
end = time.time() - start
results['model_accuracy'] = model_results[0]['test_acc']
results['extraction_time'] = end
results_list.append(results)
extracted_concepts = []
all_concepts = model.model[0].concept_mask[0] > 0.5
common_concepts = model.model[0].concept_mask[0] > 0.5
for j in range(n_classes):
n_used_concepts = sum(model.model[0].concept_mask[j] > 0.5)
print(f"Extracted concepts: {n_used_concepts}")
print(f"Explanation: {f[j]['explanation']}")
print(f"Explanation accuracy: {f[j]['explanation_accuracy']}")