n_seeds = 5
results_list = []
explanations = {i: [] for i in range(n_classes)}
for seed in range(n_seeds):
seed_everything(seed)
print(f'Seed [{seed + 1}/{n_seeds}]')
train_loader = DataLoader(train_data, batch_size=len(train_data))
val_loader = DataLoader(val_data, batch_size=len(val_data))
test_loader = DataLoader(test_data, batch_size=len(test_data))
checkpoint_callback = ModelCheckpoint(dirpath=base_dir, monitor='val_loss', save_top_k=1)
trainer = Trainer(max_epochs=500, gpus=1, auto_lr_find=True, deterministic=True,
check_val_every_n_epoch=1, default_root_dir=base_dir,
weights_save_path=base_dir, callbacks=[checkpoint_callback])
model = Explainer(n_concepts=n_concepts, n_classes=n_classes, l1=0, lr=0.01, explainer_hidden=[10])
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 = {}
results['model_accuracy'] = model_results[0]['test_acc']
results_list.append(results)
results_df = pd.DataFrame(results_list)
results_df.to_csv(os.path.join(base_dir, 'results_aware_cub.csv'))
checkpoint_callback_xc = ModelCheckpoint(dirpath=base_dir,
monitor='val_loss',
save_top_k=1)
trainer_xc = Trainer(max_epochs=200,
gpus=1,
auto_lr_find=True,
deterministic=True,
check_val_every_n_epoch=1,
default_root_dir=base_dir + '_xc',
weights_save_path=base_dir,
callbacks=[checkpoint_callback_xc])
model_xc = Explainer(n_concepts=x.shape[1],
n_classes=c.shape[1],
l1=0,
lr=0.01,
explainer_hidden=[100, 50],
temperature=5000,
loss=torch.nn.BCEWithLogitsLoss())
trainer_xc.fit(model_xc, train_loader_xc, val_loader_xc)
model_xc.freeze()
c_train_pred = model_xc.model(x_train)
c_val_pred = model_xc.model(x_val)
c_test_pred = model_xc.model(x_test)
# train C->Y
train_data = TensorDataset(c_train_pred.squeeze(), y_train)
val_data = TensorDataset(c_val_pred.squeeze(), y_val)
test_data = TensorDataset(c_test_pred.squeeze(), y_test)
train_loader = DataLoader(train_data, batch_size=train_size)
val_loader = DataLoader(val_data, batch_size=val_size)
y_trainval, y_test = torch.FloatTensor(y[trainval_index]), torch.FloatTensor(y[test_index])
x_train, x_val, y_train, y_val = train_test_split(x_trainval, y_trainval, test_size=0.2, random_state=42)
print(f'{len(y_train)}/{len(y_val)}/{len(y_test)}')
train_data = TensorDataset(x_train, y_train)
val_data = TensorDataset(x_val, y_val)
test_data = TensorDataset(x_test, y_test)
train_loader = DataLoader(train_data, batch_size=train_size)
val_loader = DataLoader(val_data, batch_size=val_size)
test_loader = DataLoader(test_data, batch_size=test_size)
checkpoint_callback = ModelCheckpoint(dirpath=base_dir, monitor='val_loss', save_top_k=1)
trainer = Trainer(max_epochs=200, gpus=1, auto_lr_find=True, deterministic=True,
check_val_every_n_epoch=1, default_root_dir=base_dir,
weights_save_path=base_dir, callbacks=[checkpoint_callback])
model = Explainer(n_concepts=n_concepts, n_classes=n_classes, l1=1e-3, lr=0.01,
explainer_hidden=[20], temperature=0.7)
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
checkpoint_callback = ModelCheckpoint(dirpath=base_dir,
monitor='val_loss',
save_top_k=1)
trainer = Trainer(max_epochs=10,
gpus=1,
auto_lr_find=True,
deterministic=True,
check_val_every_n_epoch=1,
default_root_dir=base_dir,
weights_save_path=base_dir,
callbacks=[checkpoint_callback])
model = Explainer(n_concepts=n_concepts,
n_classes=n_classes,
l1=0.0000001,
temperature=5,
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,
test_loader = DataLoader(test_data, batch_size=len(test_data))
checkpoint_callback = ModelCheckpoint(dirpath=base_dir,
monitor='val_loss',
save_top_k=1)
trainer = Trainer(max_epochs=500,
gpus=1,
auto_lr_find=True,
deterministic=True,
check_val_every_n_epoch=1,
default_root_dir=base_dir,
weights_save_path=base_dir,
callbacks=[checkpoint_callback])
model = Explainer(n_concepts=n_concepts,
n_classes=n_classes,
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,
checkpoint_callback_xc = ModelCheckpoint(dirpath=base_dir,
monitor='val_loss',
save_top_k=1)
trainer_xc = Trainer(max_epochs=200,
gpus=1,
auto_lr_find=True,
deterministic=True,
check_val_every_n_epoch=1,
default_root_dir=base_dir + '_xc',
weights_save_path=base_dir,
callbacks=[checkpoint_callback_xc])
model_xc = Explainer(n_concepts=x.shape[1],
n_classes=c.shape[1],
l1=0,
lr=0.01,
explainer_hidden=[100, 50],
temperature=5000,
loss=torch.nn.BCEWithLogitsLoss())
trainer_xc.fit(model_xc, train_loader_xc, val_loader_xc)
model_xc.freeze()
c_train_pred = model_xc.model(x_train)
c_val_pred = model_xc.model(x_val)
c_test_pred = model_xc.model(x_test)
# train C->Y
train_data = TensorDataset(c_train_pred.squeeze(), y_train)
val_data = TensorDataset(c_val_pred.squeeze(), y_val)
test_data = TensorDataset(c_test_pred.squeeze(), y_test)
train_loader = DataLoader(train_data, batch_size=train_size)
val_loader = DataLoader(val_data, batch_size=val_size)
n_seeds = 5
results_list = []
explanations = {i: [] for i in range(n_classes)}
for seed in range(n_seeds):
seed_everything(seed)
print(f'Seed [{seed + 1}/{n_seeds}]')
train_loader = DataLoader(train_data, batch_size=len(train_data))
val_loader = DataLoader(val_data, batch_size=len(val_data))
test_loader = DataLoader(test_data, batch_size=len(test_data))
checkpoint_callback = ModelCheckpoint(dirpath=base_dir, monitor='val_loss', save_top_k=1)
trainer = Trainer(max_epochs=10, gpus=1, auto_lr_find=True, deterministic=True,
check_val_every_n_epoch=1, default_root_dir=base_dir,
weights_save_path=base_dir, callbacks=[checkpoint_callback])
model = Explainer(n_concepts=n_concepts, n_classes=n_classes, l1=0, lr=0.01,
explainer_hidden=[10], conceptizator='identity_bool')
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 = {}
results['model_accuracy'] = model_results[0]['test_acc']
results_list.append(results)
results_df = pd.DataFrame(results_list)
results_df.to_csv(os.path.join(base_dir, 'results_aware_mnist.csv'))