def run_binary():
from data_loader import toy_data_binary, \
moon_dataset, \
data_loader_mnist
datasets = [(toy_data_binary(), 'Synthetic data'),
(moon_dataset(), 'Two Moon data'),
(data_loader_mnist(), 'Binarized MNIST data')]
for data, name in datasets:
print(name)
X_train, X_test, y_train, y_test = toy_data_binary()
if name == 'Binarized MNIST data':
y_train = [0 if yi < 5 else 1 for yi in y_train]
y_test = [0 if yi < 5 else 1 for yi in y_test]
y_train = np.asarray(y_train)
y_test = np.asarray(y_test)
for loss_type in ["perceptron", "logistic"]:
w, b = sol.binary_train(X_train, y_train, loss="logistic")
train_preds = sol.binary_predict(X_train, w, b, loss=loss_type)
preds = sol.binary_predict(X_test, w, b, loss=loss_type)
print(loss_type + ' train acc: %f, test acc: %f' %
(accuracy_score(y_train, train_preds),
accuracy_score(y_test, preds)))
print()
def run_binary():
from data_loader import toy_data_binary, \
data_loader_mnist
print('Performing binary classification on synthetic data')
X_train, X_test, y_train, y_test = toy_data_binary()
w, b = binary_train(X_train, y_train)
train_preds = binary_predict(X_train, w, b)
preds = binary_predict(X_test, w, b)
print(
'train acc: %f, test acc: %f' %
(accuracy_score(y_train, train_preds), accuracy_score(y_test, preds)))
print('Performing binary classification on binarized MNIST')
X_train, X_test, y_train, y_test = data_loader_mnist()
binarized_y_train = [0 if yi < 5 else 1 for yi in y_train]
binarized_y_test = [0 if yi < 5 else 1 for yi in y_test]
w, b = binary_train(X_train, binarized_y_train)
train_preds = binary_predict(X_train, w, b)
preds = binary_predict(X_test, w, b)
print('train acc: %f, test acc: %f' %
(accuracy_score(binarized_y_train, train_preds),
accuracy_score(binarized_y_test, preds)))
