def __init__( self, train_X: Tensor, train_Y: Tensor, train_iteration_fidelity: bool = True, train_data_fidelity: bool = True, likelihood: Optional[Likelihood] = None, ) -> None: self._set_dimensions(train_X=train_X, train_Y=train_Y) num_fidelity = train_iteration_fidelity + train_data_fidelity ard_num_dims = train_X.shape[-1] - num_fidelity active_dimsX = list(range(train_X.shape[-1] - num_fidelity)) rbf_kernel = RBFKernel( ard_num_dims=ard_num_dims, batch_shape=self._aug_batch_shape, lengthscale_prior=GammaPrior(3.0, 6.0), active_dims=active_dimsX, ) exp_kernel = ExponentialDecayKernel( batch_shape=self._aug_batch_shape, lengthscale_prior=GammaPrior(3.0, 6.0), offset_prior=GammaPrior(3.0, 6.0), power_prior=GammaPrior(3.0, 6.0), ) ds_kernel = DownsamplingKernel( batch_shape=self._aug_batch_shape, offset_prior=GammaPrior(3.0, 6.0), power_prior=GammaPrior(3.0, 6.0), ) if train_iteration_fidelity and train_data_fidelity: active_dimsS1 = [train_X.shape[-1] - 1] active_dimsS2 = [train_X.shape[-1] - 2] exp_kernel.active_dims = torch.tensor(active_dimsS1) ds_kernel.active_dims = torch.tensor(active_dimsS2) kernel = rbf_kernel * exp_kernel * ds_kernel elif train_iteration_fidelity or train_data_fidelity: active_dimsS = [train_X.shape[-1] - 1] if train_iteration_fidelity: exp_kernel.active_dims = torch.tensor(active_dimsS) kernel = rbf_kernel * exp_kernel else: ds_kernel.active_dims = torch.tensor(active_dimsS) kernel = rbf_kernel * ds_kernel else: raise UnsupportedError( "You should have at least one fidelity parameter.") covar_module = ScaleKernel( kernel, batch_shape=self._aug_batch_shape, outputscale_prior=GammaPrior(2.0, 0.15), ) super().__init__(train_X=train_X, train_Y=train_Y, covar_module=covar_module) self.to(train_X)
def test_subset_computes_active_downsampling_function_batch(self): a = torch.tensor([[0.1, 0.2, 0.2], [0.3, 0.4, 0.2], [0.5, 0.5, 0.5]]).view( 3, 3, 1 ) a_p = torch.tensor([[0.1, 0.2, 0.2], [0.3, 0.4, 0.2], [0.5, 0.5, 0.5]]).view( 3, 3, 1 ) a = torch.cat((a, a_p), 2) b = torch.tensor([[0.5, 0.6, 0.1], [0.7, 0.8, 0.2], [0.6, 0.6, 0.5]]).view( 3, 3, 1 ) power = 1 offset = 1 kernel = DownsamplingKernel(batch_shape=torch.Size([3]), active_dims=[0]) kernel.initialize(power=power, offset=offset) kernel.eval() res = kernel(a, b).evaluate() actual = torch.zeros(3, 3, 3) diff = torch.tensor([[0.45, 0.36, 0.81], [0.4, 0.32, 0.72], [0.4, 0.32, 0.72]]) actual[0, :, :] = offset + diff.pow(1 + power) diff = torch.tensor( [[0.21, 0.14, 0.56], [0.18, 0.12, 0.48], [0.24, 0.16, 0.64]] ) actual[1, :, :] = offset + diff.pow(1 + power) diff = torch.tensor([[0.2, 0.2, 0.25], [0.2, 0.2, 0.25], [0.2, 0.2, 0.25]]) actual[2, :, :] = offset + diff.pow(1 + power) self.assertLess(torch.norm(res - actual), 1e-5)
def test_last_dim_is_batch(self): a = ( torch.tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.5]]) .view(3, 2) .transpose(-1, -2) ) b = ( torch.tensor([[0.5, 0.6], [0.7, 0.8], [0.6, 0.6]]) .view(3, 2) .transpose(-1, -2) ) power = 1 offset = 1 kernel = DownsamplingKernel() kernel.initialize(power=power, offset=offset) kernel.eval() res = kernel(a, b, last_dim_is_batch=True).evaluate() actual = torch.zeros(3, 2, 2) diff = torch.tensor([[0.45, 0.36], [0.4, 0.32]]) actual[0, :, :] = offset + diff.pow(1 + power) diff = torch.tensor([[0.21, 0.14], [0.18, 0.12]]) actual[1, :, :] = offset + diff.pow(1 + power) diff = torch.tensor([[0.2, 0.2], [0.2, 0.2]]) actual[2, :, :] = offset + diff.pow(1 + power) self.assertLess(torch.norm(res - actual), 1e-5)
def test_computes_downsampling_function(self): a = torch.tensor([0.1, 0.2]).view(2, 1) b = torch.tensor([0.2, 0.4]).view(2, 1) power = 1 offset = 1 kernel = DownsamplingKernel() kernel.initialize(power=power, offset=offset) kernel.eval() diff = torch.tensor([[0.72, 0.54], [0.64, 0.48]]) actual = offset + diff.pow(1 + power) res = kernel(a, b).evaluate() self.assertLess(torch.norm(res - actual), 1e-5)
def test_diag_calculation(self): a = torch.tensor([0.1, 0.2]).view(2, 1) b = torch.tensor([0.2, 0.4]).view(2, 1) power = 1 offset = 1 kernel = DownsamplingKernel() kernel.initialize(power=power, offset=offset) kernel.eval() diff = torch.tensor([[0.72, 0.54], [0.64, 0.48]]) actual = offset + diff.pow(1 + power) res = kernel(a, b, diag=True) self.assertLess(torch.norm(res - torch.diag(actual)), 1e-5)
def test_computes_downsampling_function_batch(self): a = torch.tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.5]]).view(3, 2, 1) b = torch.tensor([[0.5, 0.6], [0.7, 0.8], [0.6, 0.6]]).view(3, 2, 1) power = 1 offset = 1 kernel = DownsamplingKernel(batch_shape=torch.Size([3])) kernel.initialize(power=power, offset=offset) kernel.eval() res = kernel(a, b).evaluate() actual = torch.zeros(3, 2, 2) diff = torch.tensor([[0.45, 0.36], [0.4, 0.32]]) actual[0, :, :] = offset + diff.pow(1 + power) diff = torch.tensor([[0.21, 0.14], [0.18, 0.12]]) actual[1, :, :] = offset + diff.pow(1 + power) diff = torch.tensor([[0.2, 0.2], [0.2, 0.2]]) actual[2, :, :] = offset + diff.pow(1 + power) self.assertLess(torch.norm(res - actual), 1e-5)
def test_initialize_offset_prior(self): kernel = DownsamplingKernel() kernel.offset_prior = NormalPrior(1, 1) self.assertTrue(isinstance(kernel.offset_prior, NormalPrior)) kernel2 = DownsamplingKernel(offset_prior=GammaPrior(1, 1)) self.assertTrue(isinstance(kernel2.offset_prior, GammaPrior))
def create_kernel_no_ard(self, **kwargs): return DownsamplingKernel(**kwargs)
def test_initialize_power_batch(self): kernel = DownsamplingKernel(batch_shape=torch.Size([2])) power_init = torch.tensor([1.0, 2.0]) kernel.initialize(power=power_init) actual_value = power_init.view_as(kernel.power) self.assertLess(torch.norm(kernel.power - actual_value), 1e-5)
def test_initialize_power(self): kernel = DownsamplingKernel() kernel.initialize(power=1) actual_value = torch.tensor(1.0).view_as(kernel.power) self.assertLess(torch.norm(kernel.power - actual_value), 1e-5)
def test_initialize_offset_batch(self): kernel = DownsamplingKernel(batch_shape=torch.Size([2])) off_init = torch.tensor([1.0, 2.0]) kernel.initialize(offset=off_init) actual_value = off_init.view_as(kernel.offset) self.assertLess(torch.norm(kernel.offset - actual_value), 1e-5)
def test_initialize_offset(self): kernel = DownsamplingKernel() kernel.initialize(offset=1) actual_value = torch.tensor(1.0).view_as(kernel.offset) self.assertLess(torch.norm(kernel.offset - actual_value), 1e-5)