def run_pam(args):
set_global_seeds(args['seed'])
dataset = DataLoader(args['dataset'])
X_train, X_test, X_val, y_train, y_test, y_val = dataset.prepare_train_test_val(
args)
model = Perceptron(feature_dim=X_train.shape[-1], margin=args['margin'])
model.fit(X_train, y_train)
return model.score(X_test, y_test)
def find_best_margin(args):
""" return `best_margin / 0.1` """
set_global_seeds(args['seed'])
dataset = DataLoader(args['dataset'])
X_train, X_test, X_val, y_train, y_test, y_val = dataset.prepare_train_test_val(
args)
results = []
for margin in MARGINS:
model = Perceptron(feature_dim=X_train.shape[-1], margin=margin)
model.fit(X_train, y_train)
results.append(model.score(X_val, y_val))
return results