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import time
import torch
from torch import nn
import gpytorch
from .prior import Batch
from .utils import get_batch_to_dataloader
from ..utils import default_device
# We will use the simplest form of GP model, exact inference
class ExactGPModel(gpytorch.models.ExactGP):
def __init__(self, train_x, train_y, likelihood):
super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
def forward(self, x):
mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
def get_model(x, y, hyperparameters):
likelihood = gpytorch.likelihoods.GaussianLikelihood(noise_constraint=gpytorch.constraints.GreaterThan(1.e-9))
model = ExactGPModel(x, y, likelihood)
model.likelihood.noise = torch.ones_like(model.likelihood.noise) * hyperparameters["noise"]
model.covar_module.outputscale = torch.ones_like(model.covar_module.outputscale) * hyperparameters["outputscale"]
model.covar_module.base_kernel.lengthscale = torch.ones_like(model.covar_module.base_kernel.lengthscale) * \
hyperparameters["lengthscale"]
return model, likelihood
@torch.no_grad()
def get_batch(batch_size, seq_len, num_features, device=default_device, hyperparameters=None,
equidistant_x=False, fix_x=None, **kwargs):
if isinstance(hyperparameters, (tuple, list)):
hyperparameters = {"noise": hyperparameters[0]
, "outputscale": hyperparameters[1]
, "lengthscale": hyperparameters[2]
, "is_binary_classification": hyperparameters[3]
# , "num_features_used": hyperparameters[4]
, "normalize_by_used_features": hyperparameters[5]
, "order_y": hyperparameters[6]
, "sampling": hyperparameters[7]
}
elif hyperparameters is None:
hyperparameters = {"noise": .1, "outputscale": .1, "lengthscale": .1}
if 'verbose' in hyperparameters and hyperparameters['verbose']:
print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
, "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size, 'sampling': hyperparameters['sampling']})
observation_noise = hyperparameters.get("observation_noise", True)
# hyperparameters = {k: hyperparameters[k]() if callable(hyperparameters[k]) else hyperparameters[k] for k in
# hyperparameters.keys()}
assert not (equidistant_x and (fix_x is not None))
with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations', (True, True, True))):
if equidistant_x:
assert num_features == 1
x = torch.linspace(0, 1., seq_len).unsqueeze(0).repeat(batch_size, 1).unsqueeze(-1)
elif fix_x is not None:
assert fix_x.shape == (seq_len, num_features)
x = fix_x.unsqueeze(0).repeat(batch_size, 1, 1).to(device)
else:
if hyperparameters.get('sampling','uniform') == 'uniform':
x = torch.rand(batch_size, seq_len, num_features, device=device)
elif hyperparameters.get('sampling','uniform') == 'normal':
x = torch.randn(batch_size, seq_len, num_features, device=device)
elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith('uniform_'):
left_border, right_border = [float(v) for v in hyperparameters['sampling'][len('uniform_'):].split('_')]
x = torch.rand(batch_size, seq_len, num_features, device=device) * (right_border - left_border) + left_border
elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith('clustered_'):
dist_std, local_dist_std, base_likelihood = [float(v) for v in hyperparameters['sampling'][len('clustered_'):].split('_')]
def first_sample(dist):
return dist().unsqueeze(0)
def append_sample(samples, dist, local_dist, base_likelihood):
if samples is None:
return first_sample(dist)
num_samples, batch_size, num_features = samples.shape
use_base = torch.rand(batch_size) < base_likelihood
sample_mean = torch.where(use_base[:, None].repeat(1, num_features),
torch.zeros(batch_size, num_features),
samples[torch.randint(num_samples, (batch_size,)),
torch.arange(batch_size), :])
return torch.cat((samples, (local_dist() + sample_mean).unsqueeze(0)), 0)
def create_sample(num_samples, dist, local_dist, base_likelihood):
samples = None
for i in range(num_samples):
samples = append_sample(samples, dist, local_dist, base_likelihood)
return samples[torch.randperm(num_samples)]
x = create_sample(seq_len, lambda: torch.randn(batch_size, num_features)*dist_std,
lambda: torch.rand(batch_size, num_features)*local_dist_std, base_likelihood)\
.transpose(0,1).to(device)
elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith(
'gmix_'):
blob_width, n_centers_max, stddev = [float(v) for v in
hyperparameters['sampling'][len('gmix_'):].split('_')]
n_centers_max = int(n_centers_max)
def get_x(batch_size, n_samples, num_features, blob_width, n_centers_max, stddev, device):
n_centers = torch.randint(1, n_centers_max, tuple(), device=device)
centers = torch.rand((batch_size, n_centers, num_features), device=device) * blob_width - blob_width / 2
center_assignments = torch.randint(n_centers, (batch_size, n_samples,), device=device)
noise = torch.randn((batch_size, n_samples, num_features), device=device) * stddev
return centers.gather(1, center_assignments[..., None].repeat(1, 1,
num_features)) + noise # centers: (b, m, f), ass: (b,n)
x = get_x(batch_size, seq_len, num_features, blob_width, n_centers_max, stddev, device)
elif isinstance(hyperparameters['sampling'], str) and hyperparameters['sampling'].startswith(
'grep_'):
stddev, = [float(v) for v in hyperparameters['sampling'][len('grep_'):].split('_')]
x = torch.randn(batch_size, seq_len//2, num_features, device=device) * stddev
x = x.repeat(1,2,1)
x = x[:,torch.randperm(x.shape[1]),:]
else:
x = torch.randn(batch_size, seq_len, num_features, device=device) * hyperparameters.get('sampling', 1.)
model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
model.to(device)
# trained_model = ExactGPModel(train_x, train_y, likelihood).cuda()
# trained_model.eval()
successful_sample = False
while not successful_sample:
try:
with gpytorch.settings.prior_mode(True):
model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
model.to(device)
d = model(x)
if observation_noise:
target_sample = sample = likelihood(d).sample().transpose(0, 1)
else:
target_sample = d.sample().transpose(0, 1) # this will be the target for the loss
sample = likelihood(target_sample).sample() # this will be the input to the Transformer
successful_sample = True
except RuntimeError: # This can happen when torch.linalg.eigh fails. Restart with new init resolves this.
print('GP Sampling unsuccessful, retrying.. ')
print(x)
print(hyperparameters)
if bool(torch.any(torch.isnan(x)).detach().cpu().numpy()):
print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
, "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size})
if hyperparameters.get('improvement_classification', False):
single_eval_pos = kwargs['single_eval_pos']
max_so_far = sample[:single_eval_pos].max(0).values
sample[single_eval_pos:] = (sample > max_so_far).float()[single_eval_pos:]
return Batch(x=x.transpose(0, 1), y=sample, target_y=target_sample)
DataLoader = get_batch_to_dataloader(get_batch)
def get_model_on_device(x,y,hyperparameters,device):
model, likelihood = get_model(x, y, hyperparameters)
model.to(device)
return model, likelihood
@torch.no_grad()
def evaluate(x, y, y_non_noisy, use_mse=False, hyperparameters={}, get_model_on_device=get_model_on_device, device=default_device, step_size=1, start_pos=0):
start_time = time.time()
losses_after_t = [.0] if start_pos == 0 else []
all_losses_after_t = []
with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations',(True,True,True))), gpytorch.settings.fast_pred_var(False):
for t in range(max(start_pos, 1), len(x), step_size):
loss_sum = 0.
model, likelihood = get_model_on_device(x[:t].transpose(0, 1), y[:t].transpose(0, 1), hyperparameters, device)
model.eval()
# print([t.shape for t in model.train_inputs])
# print(x[:t].transpose(0,1).shape, x[t].unsqueeze(1).shape, y[:t].transpose(0,1).shape)
f = model(x[t].unsqueeze(1))
l = likelihood(f)
means = l.mean.squeeze()
varis = l.covariance_matrix.squeeze()
# print(l.variance.squeeze(), l.mean.squeeze(), y[t])
assert len(means.shape) == len(varis.shape) == 1
assert len(means) == len(varis) == x.shape[1]
if use_mse:
c = nn.MSELoss(reduction='none')
ls = c(means, y[t])
else:
ls = -l.log_prob(y[t].unsqueeze(1))
losses_after_t.append(ls.mean())
all_losses_after_t.append(ls.flatten())
return torch.stack(all_losses_after_t).to('cpu'), torch.tensor(losses_after_t).to('cpu'), time.time() - start_time
if __name__ == '__main__':
hps = (.1,.1,.1)
for redo_idx in range(1):
print(
evaluate(*get_batch(1000, 10, hyperparameters=hps, num_features=10), use_mse=False, hyperparameters=hps))
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