def test_ensemble_nystrom_full_prec_one_learner():
# test if keep all the dimensions is the nystrom kernel matrix equals to the exact kernel
n_sample = 150
n_feat = n_sample
input_val1 = torch.DoubleTensor(
np.random.normal(size=[n_sample, n_feat])).double()
input_val2 = input_val1
# input_val2 = torch.DoubleTensor(np.random.normal(size=[n_sample - 1, n_feat] ) ).double()
# get exact gaussian kernel
kernel = GaussianKernel(sigma=10.0)
kernel_mat = kernel.get_kernel_matrix(input_val1, input_val2)
# nystrom method
approx = Nystrom(n_feat, kernel=kernel)
approx.setup(input_val1)
feat = approx.get_feat(input_val1)
approx_kernel_mat = approx.get_kernel_matrix(input_val1, input_val2)
# ensembleed nystrom method
approx_ensemble = EnsembleNystrom(n_feat, n_learner=1, kernel=kernel)
approx_ensemble.setup(input_val1)
feat_ensemble = approx_ensemble.get_feat(input_val1)
approx_kernel_mat_ensemble = approx_ensemble.get_kernel_matrix(
input_val1, input_val2)
np.testing.assert_array_almost_equal(
np.sum(feat.cpu().numpy()**2), np.sum(feat_ensemble.cpu().numpy()**2))
np.testing.assert_array_almost_equal(
np.sum(approx_kernel_mat.cpu().numpy()**2),
np.sum(approx_kernel_mat_ensemble.cpu().numpy()**2))
print("single learner ensembled nystrom test passed!")
def test_ensemble_nystrom_full_prec_three_learner():
# test if keep all the dimensions is the nystrom kernel matrix equals to the exact kernel
n_sample = 150
n_feat = n_sample
input_val1 = torch.DoubleTensor(
np.random.normal(size=[n_sample, n_feat])).double()
input_val2 = input_val1
# input_val2 = torch.DoubleTensor(np.random.normal(size=[n_sample - 1, n_feat] ) ).double()
# get exact gaussian kernel
kernel = GaussianKernel(sigma=10.0)
kernel_mat = kernel.get_kernel_matrix(input_val1, input_val2)
# nystrom method
approx = Nystrom(n_feat, kernel=kernel)
approx.setup(input_val1)
feat = approx.get_feat(input_val1)
approx_kernel_mat = approx.get_kernel_matrix(input_val1, input_val2)
# ensembleed nystrom method
approx_ensemble = EnsembleNystrom(n_feat, n_learner=3, kernel=kernel)
approx_ensemble.setup(input_val1)
feat_ensemble = approx_ensemble.get_feat(input_val1)
assert feat_ensemble.size(0) == n_sample
assert feat_ensemble.size(1) == n_feat
approx_kernel_mat_ensemble = approx_ensemble.get_kernel_matrix(
input_val1, input_val2)
print("single learner ensembled nystrom test passed!")
def test_ensemble_nystrom_low_prec():
# test if keep all the dimensions is the nystrom kernel matrix equals to the exact kernel
n_sample = 150
n_feat = n_sample
input_val1 = torch.DoubleTensor(
np.random.normal(size=[n_sample, n_feat])).double()
input_val2 = input_val1
# input_val2 = torch.DoubleTensor(np.random.normal(size=[n_sample - 1, n_feat] ) ).double()
# get exact gaussian kernel
kernel = GaussianKernel(sigma=10.0)
kernel_mat = kernel.get_kernel_matrix(input_val1, input_val2)
# setup quantizer
quantizer = Quantizer(4,
torch.min(input_val1),
torch.max(input_val1),
rand_seed=2,
use_cuda=False)
# nystrom method
approx = Nystrom(n_feat, kernel=kernel)
approx.setup(input_val1)
feat = approx.get_feat(input_val1)
approx_kernel_mat = approx.get_kernel_matrix(input_val1, input_val2,
quantizer, quantizer)
# ensembleed nystrom method
approx_ensemble = EnsembleNystrom(n_feat, n_learner=1, kernel=kernel)
approx_ensemble.setup(input_val1)
feat_ensemble = approx_ensemble.get_feat(input_val1)
approx_kernel_mat_ensemble = approx_ensemble.get_kernel_matrix(
input_val1,
input_val2,
quantizer,
quantizer,
consistent_quant_seed=True)
approx_kernel_mat_ensemble = approx_ensemble.get_kernel_matrix(
input_val1,
input_val2,
quantizer,
quantizer,
consistent_quant_seed=True)
print("single learner ensembled nystrom quantizerd version test passed!")
shuffle=False)
# setup gaussian kernel
n_input_feat = X_train.shape[1]
kernel = GaussianKernel(sigma=args.kernel_sigma)
if args.approx_type == "exact":
print("exact kernel mode")
# raise Exception("SGD based exact kernel is not implemented yet!")
kernel_approx = kernel
quantizer = None
elif args.approx_type == "nystrom":
print("fp nystrom mode")
kernel_approx = Nystrom(args.n_feat,
kernel=kernel,
rand_seed=args.random_seed)
kernel_approx.setup(X_train)
quantizer = None
elif args.approx_type == "ensemble_nystrom":
print("ensembled nystrom mode with ", args.n_ensemble_nystrom,
"learner")
kernel_approx = EnsembleNystrom(args.n_feat,
n_learner=args.n_ensemble_nystrom,
kernel=kernel,
rand_seed=args.random_seed)
kernel_approx.setup(X_train)
if args.do_fp_feat:
quantizer = None
else:
# decide on the range of representation from training sample based features
train_feat = kernel_approx.get_feat(X_train)
min_val = torch.min(train_feat)