def supervised_reduction(method=None, dataset=None):
np.random.seed(1)
sklearn.utils.check_random_state(1)
dataset_path = 'data'
train_data, train_labels, test_data, test_labels = dataset_loader(
dataset_path, dataset, seed=1)
scaler = StandardScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)
if dataset == 'yale':
regularizer_weight = 0.0001
else:
regularizer_weight = 1
n_classes = len(np.unique(train_labels))
if method == 'lda':
proj = LinearDiscriminantAnalysis(n_components=n_classes - 1)
proj.fit(train_data, train_labels)
elif method == 's-lda':
proj = LinearSEF(train_data.shape[1],
output_dimensionality=(n_classes - 1))
proj.cuda()
loss = proj.fit(data=train_data,
target_labels=train_labels,
epochs=100,
target='supervised',
batch_size=256,
regularizer_weight=regularizer_weight,
learning_rate=0.001,
verbose=False)
elif method == 's-lda-2x':
# SEF output dimensions are not limited
proj = LinearSEF(train_data.shape[1],
output_dimensionality=2 * (n_classes - 1))
proj.cuda()
loss = proj.fit(data=train_data,
target_labels=train_labels,
epochs=100,
target='supervised',
batch_size=256,
regularizer_weight=regularizer_weight,
learning_rate=0.001,
verbose=False)
acc = evaluate_svm(proj.transform(train_data), train_labels,
proj.transform(test_data), test_labels)
print("Method: ", method, " Test accuracy: ", 100 * acc, " %")
