def get_gpr_model(X, y, model=None): """ Fit a gpr model to the data or update the model to new data Params :: X: (sx1) Tensor: Covariates y: (sx1) Tensor: Observations model: PyTorch SingleTaskGP model: If model is passed, X and y are used to update it. If None then model is trained on X and y. Default is None Return :: model: PyTorch SingleTaskGP model: Trained or updated model. Returned in train mode mll: PyTorch MarginalLogLikelihood object: Returned in train mode """ if model is None: # set up model model = SingleTaskGP(X, y) else: # update model with new observations model = model.condition_on_observations(X, y) mll = ExactMarginalLogLikelihood(model.likelihood, model).to(X) # begin training model.train() mll.train() fit_gpytorch_model(mll) return model, mll
def get_gpr_model(X, y, model=None): """Fit a gpr model to the data or update the model to new data. Parameters ---------- X: (sx1) Tensor Covariates y: (sx1) Tensor Observations model: PyTorch SingleTaskGP model If model is passed, X and y are used to update it. If None then model is trained on X and y. Default is None. Returns ------- model: PyTorch SingleTaskGP model Trained or updated model. Returned in train mode. mll: PyTorch MarginalLogLikelihood object This is the loss used to train hyperparameters. Returned in train mode. """ if model is None: # set up model print('X', X.shape) print('y', y.shape) model = SingleTaskGP(X, y) else: # update model with new observations model = model.condition_on_observations(X, y) mll = ExactMarginalLogLikelihood(model.likelihood, model).to(X) # begin training model.train() mll.train() fit_gpytorch_model(mll) return model, mll
def gp_torch_train(train_x: Tensor, train_y: Tensor, n_inducing_points: int, tkwargs: Dict[str, Any], init, scale: bool, covar_name: str, gp_file: Optional[str], save_file: str, input_wp: bool, outcome_transform: Optional[OutcomeTransform] = None, options: Dict[str, Any] = None) -> SingleTaskGP: assert train_y.ndim > 1, train_y.shape assert gp_file or init, (gp_file, init) likelihood = gpytorch.likelihoods.GaussianLikelihood() if init: # build hyp print("Initialize GP hparams...") print("Doing Kmeans init...") assert n_inducing_points > 0, n_inducing_points kmeans = MiniBatchKMeans(n_clusters=n_inducing_points, batch_size=min(10000, train_x.shape[0]), n_init=25) start_time = time.time() kmeans.fit(train_x.cpu().numpy()) end_time = time.time() print(f"K means took {end_time - start_time:.1f}s to finish...") inducing_points = torch.from_numpy(kmeans.cluster_centers_.copy()) output_scale = None if scale: output_scale = train_y.var().item() lscales = torch.empty(1, train_x.shape[1]) for i in range(train_x.shape[1]): lscales[0, i] = torch.pdist(train_x[:, i].view( -1, 1)).median().clamp(min=0.01) base_covar_module = query_covar(covar_name=covar_name, scale=scale, outputscale=output_scale, lscales=lscales) covar_module = InducingPointKernel(base_covar_module, inducing_points=inducing_points, likelihood=likelihood) input_warp_tf = None if input_wp: # Apply input warping # initialize input_warping transformation input_warp_tf = CustomWarp( indices=list(range(train_x.shape[-1])), # use a prior with median at 1. # when a=1 and b=1, the Kumaraswamy CDF is the identity function concentration1_prior=LogNormalPrior(0.0, 0.75**0.5), concentration0_prior=LogNormalPrior(0.0, 0.75**0.5), ) model = SingleTaskGP(train_x, train_y, covar_module=covar_module, likelihood=likelihood, input_transform=input_warp_tf, outcome_transform=outcome_transform) else: # load model output_scale = 1 # will be overwritten when loading model lscales = torch.ones( train_x.shape[1]) # will be overwritten when loading model base_covar_module = query_covar(covar_name=covar_name, scale=scale, outputscale=output_scale, lscales=lscales) covar_module = InducingPointKernel(base_covar_module, inducing_points=torch.empty( n_inducing_points, train_x.shape[1]), likelihood=likelihood) input_warp_tf = None if input_wp: # Apply input warping # initialize input_warping transformation input_warp_tf = Warp( indices=list(range(train_x.shape[-1])), # use a prior with median at 1. # when a=1 and b=1, the Kumaraswamy CDF is the identity function concentration1_prior=LogNormalPrior(0.0, 0.75**0.5), concentration0_prior=LogNormalPrior(0.0, 0.75**0.5), ) model = SingleTaskGP(train_x, train_y, covar_module=covar_module, likelihood=likelihood, input_transform=input_warp_tf, outcome_transform=outcome_transform) print("Loading GP from file") state_dict = torch.load(gp_file) model.load_state_dict(state_dict) print("GP regression") start_time = time.time() model.to(**tkwargs) model.train() mll = ExactMarginalLogLikelihood(model.likelihood, model) # set approx_mll to False since we are using an exact marginal log likelihood # fit_gpytorch_model(mll, optimizer=fit_gpytorch_torch, approx_mll=False, options=options) fit_gpytorch_torch(mll, options=options, approx_mll=False, clip_by_value=True if input_wp else False, clip_value=10.0) end_time = time.time() print(f"Regression took {end_time - start_time:.1f}s to finish...") print("Save GP model...") torch.save(model.state_dict(), save_file) print("Done training of GP.") model.eval() return model