def test_cuda_predict(self, reg_data):
Xtr, Ytr, Xts, Yts = reg_data
kernel = kernels.GaussianKernel(20.0)
def error_fn(t, p):
return torch.sqrt(torch.mean((t - p)**2)), "RMSE"
opt = FalkonOptions(use_cpu=False,
keops_active="no",
debug=True,
min_cuda_pc_size_64=1,
min_cuda_iter_size_64=1)
flk = Falkon(kernel=kernel,
penalty=1e-6,
M=500,
seed=10,
options=opt,
error_fn=error_fn)
flk.fit(Xtr, Ytr, Xts=Xts, Yts=Yts)
flk.to("cuda:0")
cuda_ts_preds = flk.predict(Xts.to("cuda:0"))
cuda_tr_preds = flk.predict(Xtr.to("cuda:0"))
assert cuda_ts_preds.device.type == "cuda"
assert cuda_ts_preds.shape == (Yts.shape[0], 1)
ts_err = error_fn(cuda_ts_preds.cpu(), Yts)[0]
tr_err = error_fn(cuda_tr_preds.cpu(), Ytr)[0]
assert tr_err < ts_err
assert ts_err < 2.5
def test_compare_cuda_cpu(self, reg_data):
Xtr, Ytr, Xts, Yts = reg_data
kernel = kernels.GaussianKernel(20.0)
def error_fn(t, p):
return torch.sqrt(torch.mean((t - p)**2)).item(), "RMSE"
opt_cpu = FalkonOptions(use_cpu=True, keops_active="no", debug=True)
flk_cpu = Falkon(kernel=kernel,
penalty=1e-6,
M=500,
seed=10,
options=opt_cpu,
maxiter=10,
error_fn=error_fn)
flk_cpu.fit(Xtr, Ytr, Xts=Xts, Yts=Yts)
opt_gpu = FalkonOptions(use_cpu=False, keops_active="no", debug=True)
flk_gpu = Falkon(kernel=kernel,
penalty=1e-6,
M=500,
seed=10,
options=opt_gpu,
maxiter=10,
error_fn=error_fn)
flk_gpu.fit(Xtr, Ytr, Xts=Xts, Yts=Yts)
np.testing.assert_allclose(flk_cpu.alpha_.numpy(),
flk_gpu.alpha_.numpy())
def main(path, semi_supervised, kernel_function, max_iterations, gpu):
# loading dataset as ndarray
dataset = np.load(path).astype(np.float32)
print("Dataset loaded ({} points, {} features per point)".format(
dataset.shape[0], dataset.shape[1] - 1))
# adjusting label's range {-1, 1}
dataset[:, 0] = (2 * dataset[:, 0]) - 1
# defining train and test set
x_train, x_test, y_train, y_test = train_test_split(dataset[:, 1:],
dataset[:, 0],
test_size=0.2,
random_state=None)
print("Train and test set defined (test: {} + , train: {} +, {} -)".format(
np.sum(y_test == 1.), np.sum(y_train == 1.), np.sum(y_train == -1.)))
# removing some labels (if semi_supervised > 0)
labels_removed = int(len(y_train) * semi_supervised)
if labels_removed > 0:
y_train[np.random.choice(len(y_train), labels_removed,
replace=False)] = 0
print("{} labels removed".format(labels_removed))
# removing the mean and scaling to unit variance
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
print("Standardization done")
# choosing kernel function
kernel = Kernel(kernel_function=kernel_function, gpu=gpu)
# fitting falkon
print("Starting falkon fitting routine...")
falkon = Falkon(nystrom_length=10000,
gamma=1e-6,
kernel_fun=kernel.get_kernel(),
kernel_param=4,
optimizer_max_iter=max_iterations,
gpu=gpu)
# parameters = {'nystrom_length': [10000, ], 'gamma': [1e-6, ], 'kernel_param': [4, ]}
# gsht = GridSearchCV(falkon, param_grid=parameters, scoring=make_scorer(roc_auc_score), cv=3, verbose=3)
# gsht.fit(x_train, y_train)
start_ = time()
falkon.fit(x_train, y_train)
print("Fitting time: {:.3f} seconds".format(time() - start_))
# printing some information of the best model
# print("Best model information: {} params, {:.3f} time (sec)".format(gsht.best_params_, gsht.refit_time_))
# testing falkon
print("Starting falkon testing routine...")
y_pred = falkon.predict(x_test)
accuracy = accuracy_score(y_test, np.sign(y_pred))
auc = roc_auc_score(y_test, y_pred)
print("Accuracy: {:.3f} - AUC: {:.3f}".format(accuracy, auc))
def main(path, kernel_function, max_iterations, gpu):
# loading dataset as ndarray
dataset = np.load(path).astype(np.float32)
print("Dataset loaded ({} points, {} features per point)".format(
dataset.shape[0], dataset.shape[1] - 1))
dataset = dataset[1:, :]
np.random.shuffle(dataset)
# defining train and test set
# x_train, x_test, y_train, y_test = train_test_split(dataset[:, 1:], dataset[:, 0], test_size=51630, random_state=None)
x_train = dataset[0:15000, 1:]
x_test = dataset[15000:16000, 1:]
y_train = dataset[0:15000, 0]
y_test = dataset[15000:16000, 0]
print(x_test)
print("Train and test set defined")
# removing the mean and scaling to unit variance
#x_scaler = StandardScaler()
y_scaler = StandardScaler()
#x_scaler.fit(x_train)
y_scaler.fit(y_train.reshape(-1, 1))
#x_train = x_scaler.transform(x_train)
#x_test = x_scaler.transform(x_test)
y_train = y_scaler.transform(y_train.reshape(-1, 1)).reshape(-1)
y_test = y_scaler.transform(y_test.reshape(-1, 1)).reshape(-1)
print("Standardization done")
# choosing kernel function
kernel = Kernel(kernel_function=kernel_function, gpu=gpu)
# fitting falkon
print("Starting falkon fit routine...")
falkon = Falkon(nystrom_length=10000,
gamma=1e-19,
kernel_fun=kernel.get_kernel(),
kernel_param=19,
optimizer_max_iter=max_iterations,
gpu=gpu)
start_ = time()
falkon.fit(x_train, y_train, sample_weights=1.)
print("Fitting time: {:.3f} seconds".format(time() - start_))
# testing falkon
print("Starting falkon testing routine...")
y_pred = falkon.predict(x_test)
print(np.round(inv_transform(y_scaler, y_pred)))
print(np.round(inv_transform(y_scaler, y_test)))
x = (np.equal(np.round(inv_transform(y_scaler, y_pred)),
np.round(inv_transform(y_scaler, y_test))))
print(collections.Counter(x))
mse = mean_squared_error(inv_transform(y_scaler, y_test),
inv_transform(y_scaler, y_pred))
print("Mean squared error: {:.3f}".format(mse))
def test_classif(self, cls_data):
X, Y = cls_data
kernel = kernels.GaussianKernel(2.0)
def error_fn(t, p):
return 100 * torch.sum(t * p <= 0).to(
torch.float32) / t.shape[0], "c-err"
opt = FalkonOptions(use_cpu=True, keops_active="no", debug=True)
flk = Falkon(kernel=kernel,
penalty=1e-6,
M=500,
seed=10,
options=opt,
error_fn=error_fn)
flk.fit(X, Y)
preds = flk.predict(X)
err = error_fn(preds, Y)[0]
assert err < 5
def test_regression(self, reg_data):
Xtr, Ytr, Xts, Yts = reg_data
kernel = kernels.GaussianKernel(20.0)
def error_fn(t, p):
return torch.sqrt(torch.mean((t - p)**2)).item(), "RMSE"
opt = FalkonOptions(use_cpu=True, keops_active="no", debug=True)
flk = Falkon(kernel=kernel,
penalty=1e-6,
M=500,
seed=10,
options=opt,
maxiter=10)
flk.fit(Xtr, Ytr, Xts=Xts, Yts=Yts)
assert flk.predict(Xts).shape == (Yts.shape[0], 1)
ts_err = error_fn(flk.predict(Xts), Yts)[0]
tr_err = error_fn(flk.predict(Xtr), Ytr)[0]
assert tr_err < ts_err
assert ts_err < 2.5
def test_multiclass(self, multicls_data):
X, Y = multicls_data
kernel = kernels.GaussianKernel(10.0)
def error_fn(t, p):
t = torch.argmax(t, dim=1)
p = torch.argmax(p, dim=1)
return torch.mean((t.reshape(-1, ) != p.reshape(-1, )).to(
torch.float64)), "multic-err"
opt = FalkonOptions(use_cpu=True, keops_active="no", debug=True)
flk = Falkon(kernel=kernel,
penalty=1e-6,
M=500,
seed=10,
options=opt,
error_fn=error_fn)
flk.fit(X, Y)
preds = flk.predict(X)
err = error_fn(preds, Y)[0]
assert err < 0.23
class FALKONWrapper(ca.ClassifierAbstract):
def __init__(self, cfg_path=None, is_rpn=False, is_segmentation=False):
if cfg_path is not None:
self.cfg = yaml.load(open(cfg_path), Loader=yaml.FullLoader)
if is_rpn:
self.cfg = self.cfg['RPN']
if not is_segmentation:
opts = self.cfg['ONLINE_REGION_CLASSIFIER']['CLASSIFIER']
else:
opts = self.cfg['ONLINE_SEGMENTATION']['CLASSIFIER']
if 'sigma' in opts:
self.sigma = opts['sigma']
else:
print(
'Sigma not given for creating Falkon, default value is used.')
self.sigma = 5
if 'lambda' in opts:
self.lam = opts['lambda']
else:
print(
'Lambda not given for creating Falkon, default value is used.')
self.lam = 0.001
self.kernel = None
self.nyst_centers = opts['M']
def train(self, X, y, sigma=None, lam=None):
# Set sigma and lambda
if sigma is None:
sigma = self.sigma
if lam is None:
lam = self.lam
# Initialize kernel
self.kernel = kernels.GaussianKernel(sigma=sigma)
# Compute indices of nystrom centers
indices = self.compute_indices_selection(y)
center_selector = MyCenterSelector(indices)
opt = FalkonOptions(min_cuda_iter_size_32=0,
min_cuda_iter_size_64=0,
keops_active="no")
# Initialize FALKON model
self.model = Falkon(kernel=self.kernel,
penalty=lam,
M=len(indices),
center_selection=center_selector,
options=opt)
# Train FALKON model
if self.model is not None:
self.model.fit(X, y)
else:
print('Model is None in trainRegionClassifier function')
sys.exit(0)
return copy.deepcopy(self.model)
def predict(self, model, X_np, y=None):
# Predict values
if y is not None:
predictions = model.predict(X_np, y)
else:
predictions = model.predict(X_np)
return predictions
def test(self):
pass
def compute_indices_selection(self, y):
# Choose at most M/2 nystrom centers from positive training examples
positive_indices = (y == 1).nonzero()
if positive_indices.size()[0] > int(self.nyst_centers / 2):
positive_indices = positive_indices[torch.randint(
positive_indices.size()[0], (int(self.nyst_centers / 2), ))]
# Fill the centers with negative examples training examples
negative_indices = (y == -1).nonzero()
if negative_indices.size(
)[0] > self.nyst_centers - positive_indices.size()[0]:
negative_indices = negative_indices[torch.randint(
negative_indices.size()[0],
(self.nyst_centers - positive_indices.size()[0], ))]
indices = torch.cat((positive_indices, negative_indices), dim=0)
return indices.squeeze().tolist()
def main(path, n_labeled, kernel_function, max_iterations, gpu):
# loading dataset as ndarray
dataset = np.load(path).astype(np.float32)
print("Dataset loaded ({} points, {} features per point)".format(dataset.shape[0], dataset.shape[1] - 1))
# adjusting label's range {-1, 1}
dataset[:, 0] = (2 * dataset[:, 0]) - 1
# defining train and test set
x_train, x_test, y_train, y_test = train_test_split(dataset[:, 1:], dataset[:, 0], test_size=50000, random_state=None)
print("Train and test set defined (test: {} + , train: {} +, {} -)".format(np.sum(y_test == 1.), np.sum(y_train == 1.), np.sum(y_train == -1.)))
# removing the mean and scaling to unit variance
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
print("Standardization done")
# defining labeled and unlabeled set
labeled = np.random.choice(np.where(y_train == 1)[0], size=int(n_labeled/2), replace=False)
labeled = np.concatenate((labeled, np.random.choice(np.where(y_train == -1)[0], size=int(n_labeled/2), replace=False)), axis=0)
x_labeled = x_train[labeled, :].copy() # train
y_labeled = y_train[labeled].copy() # train
x_train = np.delete(x_train, obj=labeled, axis=0)
y_train = np.delete(y_train, obj=labeled)
x_unlabeled = x_train.copy() # test
y_unlabeled = y_train.copy() # test
# choosing kernel function
kernel = Kernel(kernel_function=kernel_function, gpu=gpu)
# fitting falkon (semi-supervised scenario)
best_score = -np.infty
best_gamma, best_ker_param = None, None
print("First training...")
for gamma in [1e-6]:
for ker_param in [4]:
falkon = Falkon(nystrom_length=x_labeled.shape[0], gamma=gamma, kernel_fun=kernel.get_kernel(), kernel_param=ker_param, optimizer_max_iter=max_iterations, gpu=gpu)
falkon.fit(x_labeled, y_labeled)
score = accuracy_score(y_labeled, np.sign(falkon.predict(x_labeled)))
best_score, best_gamma, best_ker_param = (score, gamma, ker_param) if (score > best_score) else (best_score, best_gamma, best_ker_param)
print(" -> [debug info] best score {:.3f} -- best gamma {} -- best kernel_param {}".format(best_score, best_gamma, best_ker_param))
falkon = Falkon(nystrom_length=x_labeled.shape[0], gamma=best_gamma, kernel_fun=kernel.get_kernel(), kernel_param=best_ker_param, optimizer_max_iter=max_iterations, gpu=gpu)
falkon.fit(x_labeled, y_labeled)
functional_margin = falkon.predict(x_test)
print(np.sum(functional_margin >= 0), np.sum(functional_margin < 0))
print("Starting falkon testing routine...")
accuracy = accuracy_score(y_test, np.sign(functional_margin))
auc = roc_auc_score(y_test, functional_margin)
print("Accuracy: {:.3f} - AUC: {:.3f}".format(accuracy, auc))
print("Annealing loop...")
functional_margin = falkon.predict(x_unlabeled)
falkon = Falkon(nystrom_length=10000, gamma=best_gamma, kernel_fun=kernel.get_kernel(), kernel_param=best_ker_param, optimizer_max_iter=max_iterations, gpu=gpu)
balance_constraint = (2 * 0.5) - 1 # 2r - 1
start_ = time()
for idx, weight in enumerate([0.1, 0.25, 0.5, 0.75, 1.]):
print(" -> iteration {}".format(idx+1))
lam0 = ((2/x_unlabeled.shape[0])*np.sum(functional_margin)) - (2*balance_constraint)
y_u, lam, iter = labelling(functional_margin, balance_constraint, lam0, 1., int(x_unlabeled.shape[0]*0.005))
print(" -> [debug info] balance constraint {:.2}".format(np.divide(np.sum(y_u), x_unlabeled.shape[0])))
print(" -> [debug info] lambda from {:.3e} to {:.3e} in {} iterations".format(lam0, lam, iter+1))
print(" -> [debug info] wrong labels {}".format(np.sum(y_u != y_unlabeled)))
sample_weights = ([1.] * x_labeled.shape[0]) + ([weight] * x_unlabeled.shape[0])
falkon.fit(np.vstack((x_labeled, x_unlabeled)), np.concatenate((y_labeled, y_u)).astype(np.float32), sample_weights=sample_weights)
functional_margin = falkon.predict(x_unlabeled)
print("Annealing done in {:.3} seconds".format(time()-start_))
# testing semi-supervised falkon
print("Starting falkon testing routine...")
functional_margin = falkon.predict(x_test)
accuracy = accuracy_score(y_test, np.sign(functional_margin))
auc = roc_auc_score(y_test, functional_margin)
print("Accuracy: {:.3f} - AUC: {:.3f}".format(accuracy, auc))
def main(path, n_labeled, kernel_function, max_iterations, gpu):
# loading dataset as ndarray
dataset = np.load(path).astype(np.float32)
print("Dataset loaded ({} points, {} features per point)".format(dataset.shape[0], dataset.shape[1] - 1))
scaler = StandardScaler()
scaler.fit(dataset[:, 1:])
dataset[:, 1:] = scaler.transform(dataset[:, 1:])
print("Standardization done")
# defining labeled, unlabeled and validation set
labeled = np.random.choice(np.where(dataset[:, 0] == 1)[0], size=int(n_labeled/2), replace=False)
labeled = np.concatenate((labeled, np.random.choice(np.where(dataset[:, 0] == -1)[0], size=int(n_labeled/2), replace=False)), axis=0)
x_labeled = dataset[labeled, 1:].copy() # train
y_labeled = dataset[labeled, 0].copy() # train
dataset = np.delete(dataset, obj=labeled, axis=0)
# validation = np.random.choice(np.where(dataset[:, 0] == 1)[0], size=int(dataset.shape[0]*0.06), replace=False)
# validation = np.concatenate((validation, np.random.choice(np.where(dataset[:, 0] == -1)[0], size=int(dataset.shape[0]*0.06), replace=False)), axis=0)
# x_validation = dataset[validation, 1:].copy() # validation
# y_validation = dataset[validation, 0].copy() # validation
# dataset = np.delete(dataset, obj=validation, axis=0)
x_unlabeled = dataset[:, 1:].copy() # test
y_unlabeled = dataset[:, 0].copy() # test
# print("train: {} - validation: {} - test: {}".format(x_labeled.shape[0], x_validation.shape[0], x_unlabeled.shape[0]))
# choosing kernel function
kernel = Kernel(kernel_function=kernel_function, gpu=gpu)
print(x_labeled.shape, y_labeled.shape, x_unlabeled.shape, y_unlabeled.shape)
# fitting falkon (semi-supervised scenario)
best_score = -np.infty
best_gamma, best_ker_param = None, None
print("First training...")
for gamma in [1e-6]:
for ker_param in [0.5]:
falkon = Falkon(nystrom_length=x_labeled.shape[0], gamma=gamma, kernel_fun=kernel.get_kernel(), kernel_param=ker_param, optimizer_max_iter=max_iterations, gpu=gpu)
falkon.fit(x_labeled, y_labeled)
score = accuracy_score(y_labeled, np.sign(falkon.predict(x_labeled)))
best_score, best_gamma, best_ker_param = (score, gamma, ker_param) if (score > best_score) else (best_score, best_gamma, best_ker_param)
print(" -> [debug info] best score {:.3f} -- best gamma {} -- best kernel_param {}".format(best_score, best_gamma, best_ker_param))
falkon = Falkon(nystrom_length=x_labeled.shape[0], gamma=best_gamma, kernel_fun=kernel.get_kernel(), kernel_param=best_ker_param, optimizer_max_iter=max_iterations, gpu=gpu)
falkon.fit(x_labeled, y_labeled)
functional_margin = falkon.predict(x_unlabeled)
print(np.sum(functional_margin >= 0), np.sum(functional_margin < 0))
print("Starting falkon testing routine...")
accuracy = accuracy_score(y_unlabeled, np.sign(functional_margin))
auc = roc_auc_score(y_unlabeled, functional_margin)
print("Accuracy: {:.3f} - AUC: {:.3f}".format(accuracy, auc))
# plot_2d_dataset(x_labeled, x_validation, y_labeled, falkon.predict(x_validation), filepath='./fig.png')
# plot_2d_dataset(x_labeled, x_unlabeled, y_labeled, functional_margin, filepath='./fig0.png')
print("Annealing loop...")
falkon = Falkon(nystrom_length=10000, gamma=best_gamma, kernel_fun=kernel.get_kernel(), kernel_param=best_ker_param, optimizer_max_iter=max_iterations, gpu=gpu)
balance_constraint = (2 * 0.5) - 1 # 2r - 1
start_ = time()
for idx, weight in enumerate([0.1, 0.25, 0.5, 1.]):
print(" -> iteration {}".format(idx+1))
lam0 = ((2/x_unlabeled.shape[0])*np.sum(functional_margin)) - (2*balance_constraint)
y_u, lam, iter = labelling(functional_margin, balance_constraint, lam0, 1., int(x_unlabeled.shape[0]*0.005))
print(" -> [debug info] balance constraint {:.2}".format(np.divide(np.sum(y_u), x_unlabeled.shape[0])))
print(" -> [debug info] lambda from {:.3e} to {:.3e} in {} iterations".format(lam0, lam, iter+1))
print(" -> [debug info] wrong labels {}".format(np.sum(y_u != y_unlabeled)))
sample_weights = ([1.] * x_labeled.shape[0]) + ([weight] * x_unlabeled.shape[0])
falkon.fit(np.vstack((x_labeled, x_unlabeled)), np.concatenate((y_labeled, y_u)).astype(np.float32), sample_weights=sample_weights)
functional_margin = falkon.predict(x_unlabeled)
print("Annealing done in {:.3} seconds".format(time()-start_))
# testing semi-supervised falkon
print("Starting falkon testing routine...")
accuracy = accuracy_score(y_unlabeled, np.sign(functional_margin))
auc = roc_auc_score(y_unlabeled, functional_margin)
print("Accuracy: {:.3f} - AUC: {:.3f}".format(accuracy, auc))
def main(path, kernel_function, max_iterations, gpu):
# loading dataset as ndarray
dataset = np.load(path).astype(np.float32)
print("Dataset loaded ({} points, {} features per point)".format(
dataset.shape[0], dataset.shape[1] - 1))
# defining train and test set
x_train = dataset[0:463715, 1:]
x_test = dataset[463715:515345, 1:]
y_train = dataset[0:463715, 0]
y_test = dataset[463715:515345, 0]
print("Train and test set defined")
# creating the unsupervised part of the dataset (using x_train)
labeled_ids, unlabeled_ids = train_test_split(range(x_train.shape[0]),
test_size=0.7,
random_state=42)
x_labeled, y_labeled = x_train[labeled_ids, :], y_train[labeled_ids]
x_unlabeled, y_unlabeled = x_train[
unlabeled_ids, :], y_train[unlabeled_ids]
print("Labeled examples {}, Unlabeled examples {}".format(
x_labeled.shape[0], x_unlabeled.shape[0]))
# labels binarization (-1 from 1922 to 2002, 1 from 2002 to 2011) -- balanced (labeled) dataset
y_labeled, y_test = (y_labeled >= 2002).astype(
np.float32), (y_test >= 2002).astype(np.float32)
y_unlabeled = (y_unlabeled >= 2002).astype(np.float32)
y_labeled, y_unlabeled = (2 * y_labeled) - 1, (2 * y_unlabeled) - 1
y_test = (2 * y_test) - 1
# removing the mean and scaling to unit variance
x_scaler = StandardScaler()
x_scaler.fit(x_train) # using labeled + unlabeled part
x_labeled, x_unlabeled = x_scaler.transform(x_labeled), x_scaler.transform(
x_unlabeled)
x_test = x_scaler.transform(x_test)
print("Standardization done")
# choosing kernel function
kernel = Kernel(kernel_function=kernel_function, gpu=gpu)
# training
print("First training...")
falkon = Falkon(nystrom_length=round(np.sqrt(x_labeled.shape[0])),
gamma=1e-6,
kernel_fun=kernel.get_kernel(),
kernel_param=6,
optimizer_max_iter=max_iterations,
gpu=gpu)
falkon.fit(x_labeled, y_labeled)
functional_margin = falkon.predict(x_test)
# initial Accuracy, AUC_ROC
accuracy = accuracy_score(y_test, np.sign(functional_margin))
auc_roc = roc_auc_score(y_test, functional_margin)
print("Accuracy: {:.4f} - AUC: {:.4f}".format(accuracy, auc_roc))
print("Annealing loop...")
functional_margin = falkon.predict(x_unlabeled)
falkon = Falkon(nystrom_length=10000,
gamma=1e-6,
kernel_fun=kernel.get_kernel(),
kernel_param=6,
optimizer_max_iter=max_iterations,
gpu=gpu)
balance_constraint = (2 * 0.5) - 1 # 2r - 1
tic = time()
for idx, weight in enumerate([0.1, 0.15, 0.25, 1.]):
print(" -> iteration {}".format(idx + 1))
lam0 = ((2 / x_unlabeled.shape[0]) *
np.sum(functional_margin)) - (2 * balance_constraint)
y_u, lam, _iter = labelling(functional_margin,
balance_constraint, lam0, 1.,
int(x_unlabeled.shape[0] * 0.005))
print(" -> [debug info] balance constraint {:.2}".format(
np.divide(np.sum(y_u), x_unlabeled.shape[0])))
print(
" -> [debug info] lambda from {:.3e} to {:.3e} in {} iterations".
format(lam0, lam, _iter + 1))
print(" -> [debug info] wrong labels {}".format(
np.sum(y_u != y_unlabeled)))
sample_weights = ([1.] * x_labeled.shape[0]) + ([weight] *
x_unlabeled.shape[0])
falkon.fit(np.vstack((x_labeled, x_unlabeled)),
np.concatenate((y_labeled, y_u)).astype(np.float32),
sample_weights=sample_weights)
functional_margin = falkon.predict(x_unlabeled)
print("Annealing done in {:.3} seconds".format(time() - tic))
# testing falkon
print("Starting falkon testing routine...")
y_pred = falkon.predict(x_test)
functional_margin = falkon.predict(x_test)
accuracy = accuracy_score(y_test, np.sign(functional_margin))
auc_roc = roc_auc_score(y_test, functional_margin)
print("Accuracy: {:.3f} - AUC_ROC: {:.3f}".format(accuracy, auc_roc))