def run_fold(fold_i, C_list=[1.0]):
train = tfd.TFD('train', fold=fold_i)
valid = tfd.TFD('valid', fold=fold_i)
test = tfd.TFD('test', fold=fold_i)
preproc = Standardize(train.X / 255.)
train.X = preproc.apply(train.X / 255.)
valid.X = preproc.apply(valid.X / 255.)
test.X = preproc.apply(test.X / 255.)
train_y = process_labels(train.y)
valid_y = process_labels(valid.y)
test_y = process_labels(test.y)
svm = LinearSVC(C=1.0, loss='l2', penalty='l2')
(svm, valid_error) = cross_validate_svm(svm, (train.X, train_y), (valid.X, valid_y), C_list)
svm = retrain_svm(svm, (train.X, train_y), (valid.X, valid_y))
test_error = compute_test_error(svm, (test.X, test_y))
print 'Fold %i: valid_error = %f\t test_error = %f' % (fold_i, valid_error, test_error)
return (valid_error, test_error, svm.C)
def run_fold(path, fold_i, C_list=[1.0]):
train = pickle.load(open('%s/fold%i/train.pkl' % (path, fold_i)))
valid = pickle.load(open('%s/fold%i/valid.pkl' % (path, fold_i)))
test = pickle.load(open('%s/fold%i/test.pkl' % (path, fold_i)))
preproc = Standardize(train.X)
train.X = preproc.apply(train.X)
valid.X = preproc.apply(valid.X)
test.X = preproc.apply(test.X)
train_y = process_labels(train.y)
valid_y = process_labels(valid.y)
test_y = process_labels(test.y)
svm = LinearSVC(C=1.0, loss='l2', penalty='l2')
if len(C_list) == 1:
svm.set_params(C = C_list[0])
svm = retrain_svm(svm, (train.X, train_y), (valid.X, valid_y))
valid_error = -1.
else:
(svm, valid_error) = cross_validate_svm(svm, (train.X, train_y), (valid.X, valid_y), C_list)
svm = retrain_svm(svm, (train.X, train_y), (valid.X, valid_y))
test_error = compute_test_error(svm, (test.X, test_y))
print 'Fold %i: valid_error = %f\t test_error = %f' % (fold_i, valid_error, test_error)
return (valid_error, test_error, svm.C)
