Source code for gpytorch.models.exact_gp

#!/usr/bin/env python3

import warnings
from copy import deepcopy

import torch

from .. import settings
from ..distributions import MultivariateNormal
from ..likelihoods import _GaussianLikelihoodBase
from ..utils.warnings import GPInputWarning
from .exact_prediction_strategies import prediction_strategy
from .gp import GP

[docs]class ExactGP(GP): r""" The base class for any Gaussian process latent function to be used in conjunction with exact inference. :param torch.Tensor train_inputs: (size n x d) The training features :math:`\mathbf X`. :param torch.Tensor train_targets: (size n) The training targets :math:`\mathbf y`. :param ~gpytorch.likelihoods.GaussianLikelihood likelihood: The Gaussian likelihood that defines the observational distribution. Since we're using exact inference, the likelihood must be Gaussian. The :meth:`forward` function should describe how to compute the prior latent distribution on a given input. Typically, this will involve a mean and kernel function. The result must be a :obj:`~gpytorch.distributions.MultivariateNormal`. Calling this model will return the posterior of the latent Gaussian process when conditioned on the training data. The output will be a :obj:`~gpytorch.distributions.MultivariateNormal`. Example: >>> class MyGP(gpytorch.models.ExactGP): >>> def __init__(self, train_x, train_y, likelihood): >>> super().__init__(train_x, train_y, likelihood) >>> self.mean_module = gpytorch.means.ZeroMean() >>> self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel()) >>> >>> def forward(self, x): >>> mean = self.mean_module(x) >>> covar = self.covar_module(x) >>> return gpytorch.distributions.MultivariateNormal(mean, covar) >>> >>> # train_x = ...; train_y = ... >>> likelihood = gpytorch.likelihoods.GaussianLikelihood() >>> model = MyGP(train_x, train_y, likelihood) >>> >>> # test_x = ...; >>> model(test_x) # Returns the GP latent function at test_x >>> likelihood(model(test_x)) # Returns the (approximate) predictive posterior distribution at test_x """ def __init__(self, train_inputs, train_targets, likelihood): if train_inputs is not None and torch.is_tensor(train_inputs): train_inputs = (train_inputs,) if train_inputs is not None and not all(torch.is_tensor(train_input) for train_input in train_inputs): raise RuntimeError("Train inputs must be a tensor, or a list/tuple of tensors") if not isinstance(likelihood, _GaussianLikelihoodBase): raise RuntimeError("ExactGP can only handle Gaussian likelihoods") super(ExactGP, self).__init__() if train_inputs is not None: self.train_inputs = tuple(tri.unsqueeze(-1) if tri.ndimension() == 1 else tri for tri in train_inputs) self.train_targets = train_targets else: self.train_inputs = None self.train_targets = None self.likelihood = likelihood self.prediction_strategy = None @property def train_targets(self): return self._train_targets @train_targets.setter def train_targets(self, value): object.__setattr__(self, "_train_targets", value) def _apply(self, fn): if self.train_inputs is not None: self.train_inputs = tuple(fn(train_input) for train_input in self.train_inputs) self.train_targets = fn(self.train_targets) return super(ExactGP, self)._apply(fn) def _clear_cache(self): # The precomputed caches from test time live in prediction_strategy self.prediction_strategy = None
[docs] def local_load_samples(self, samples_dict, memo, prefix): """ Replace the model's learned hyperparameters with samples from a posterior distribution. """ # Pyro always puts the samples in the first batch dimension num_samples = next(iter(samples_dict.values())).size(0) self.train_inputs = tuple(tri.unsqueeze(0).expand(num_samples, *tri.shape) for tri in self.train_inputs) self.train_targets = self.train_targets.unsqueeze(0).expand(num_samples, *self.train_targets.shape) super().local_load_samples(samples_dict, memo, prefix)
[docs] def set_train_data(self, inputs=None, targets=None, strict=True): """ Set training data (does not re-fit model hyper-parameters). :param torch.Tensor inputs: The new training inputs. :param torch.Tensor targets: The new training targets. :param bool strict: (default True) If `True`, the new inputs and targets must have the same shape, dtype, and device as the current inputs and targets. Otherwise, any shape/dtype/device are allowed. """ if inputs is not None: if torch.is_tensor(inputs): inputs = (inputs,) inputs = tuple(input_.unsqueeze(-1) if input_.ndimension() == 1 else input_ for input_ in inputs) if strict: for input_, t_input in zip(inputs, self.train_inputs or (None,)): for attr in {"shape", "dtype", "device"}: expected_attr = getattr(t_input, attr, None) found_attr = getattr(input_, attr, None) if expected_attr != found_attr: msg = "Cannot modify {attr} of inputs (expected {e_attr}, found {f_attr})." msg = msg.format(attr=attr, e_attr=expected_attr, f_attr=found_attr) raise RuntimeError(msg) self.train_inputs = inputs if targets is not None: if strict: for attr in {"shape", "dtype", "device"}: expected_attr = getattr(self.train_targets, attr, None) found_attr = getattr(targets, attr, None) if expected_attr != found_attr: msg = "Cannot modify {attr} of targets (expected {e_attr}, found {f_attr})." msg = msg.format(attr=attr, e_attr=expected_attr, f_attr=found_attr) raise RuntimeError(msg) self.train_targets = targets self.prediction_strategy = None
[docs] def get_fantasy_model(self, inputs, targets, **kwargs): """ Returns a new GP model that incorporates the specified inputs and targets as new training data. Using this method is more efficient than updating with `set_train_data` when the number of inputs is relatively small, because any computed test-time caches will be updated in linear time rather than computed from scratch. .. note:: If `targets` is a batch (e.g. `b x m`), then the GP returned from this method will be a batch mode GP. If `inputs` is of the same (or lesser) dimension as `targets`, then it is assumed that the fantasy points are the same for each target batch. :param torch.Tensor inputs: (`b1 x ... x bk x m x d` or `f x b1 x ... x bk x m x d`) Locations of fantasy observations. :param torch.Tensor targets: (`b1 x ... x bk x m` or `f x b1 x ... x bk x m`) Labels of fantasy observations. :return: An `ExactGP` model with `n + m` training examples, where the `m` fantasy examples have been added and all test-time caches have been updated. :rtype: ~gpytorch.models.ExactGP """ if self.prediction_strategy is None: raise RuntimeError( "Fantasy observations can only be added after making predictions with a model so that " "all test independent caches exist. Call the model on some data first!" ) model_batch_shape = self.train_inputs[0].shape[:-2] if self.train_targets.dim() > len(model_batch_shape) + 1: raise RuntimeError("Cannot yet add fantasy observations to multitask GPs, but this is coming soon!") if not isinstance(inputs, list): inputs = [inputs] inputs = [i.unsqueeze(-1) if i.ndimension() == 1 else i for i in inputs] target_batch_shape = targets.shape[:-1] input_batch_shape = inputs[0].shape[:-2] tbdim, ibdim = len(target_batch_shape), len(input_batch_shape) if not (tbdim == ibdim + 1 or tbdim == ibdim): raise RuntimeError( f"Unsupported batch shapes: The target batch shape ({target_batch_shape}) must have either the " f"same dimension as or one more dimension than the input batch shape ({input_batch_shape})" ) # Check whether we can properly broadcast batch dimensions try: torch.broadcast_shapes(model_batch_shape, target_batch_shape) except RuntimeError: raise RuntimeError( f"Model batch shape ({model_batch_shape}) and target batch shape " f"({target_batch_shape}) are not broadcastable." ) if len(model_batch_shape) > len(input_batch_shape): input_batch_shape = model_batch_shape if len(model_batch_shape) > len(target_batch_shape): target_batch_shape = model_batch_shape # If input has no fantasy batch dimension but target does, we can save memory and computation by not # computing the covariance for each element of the batch. Therefore we don't expand the inputs to the # size of the fantasy model here - this is done below, after the evaluation and fast fantasy update train_inputs = [tin.expand(input_batch_shape + tin.shape[-2:]) for tin in self.train_inputs] train_targets = self.train_targets.expand(target_batch_shape + self.train_targets.shape[-1:]) full_inputs = [[train_input, input.expand(input_batch_shape + input.shape[-2:])], dim=-2) for train_input, input in zip(train_inputs, inputs) ] full_targets =[train_targets, targets.expand(target_batch_shape + targets.shape[-1:])], dim=-1) try: fantasy_kwargs = {"noise": kwargs.pop("noise")} except KeyError: fantasy_kwargs = {} full_output = super(ExactGP, self).__call__(*full_inputs, **kwargs) # Copy model without copying training data or prediction strategy (since we'll overwrite those) old_pred_strat = self.prediction_strategy old_train_inputs = self.train_inputs old_train_targets = self.train_targets old_likelihood = self.likelihood self.prediction_strategy = None self.train_inputs = None self.train_targets = None self.likelihood = None new_model = deepcopy(self) self.prediction_strategy = old_pred_strat self.train_inputs = old_train_inputs self.train_targets = old_train_targets self.likelihood = old_likelihood new_model.likelihood = old_likelihood.get_fantasy_likelihood(**fantasy_kwargs) new_model.prediction_strategy = old_pred_strat.get_fantasy_strategy( inputs, targets, full_inputs, full_targets, full_output, **fantasy_kwargs ) # if the fantasies are at the same points, we need to expand the inputs for the new model if tbdim == ibdim + 1: new_model.train_inputs = [fi.expand(target_batch_shape + fi.shape[-2:]) for fi in full_inputs] else: new_model.train_inputs = full_inputs new_model.train_targets = full_targets return new_model
def __call__(self, *args, **kwargs): train_inputs = list(self.train_inputs) if self.train_inputs is not None else [] inputs = [i.unsqueeze(-1) if i.ndimension() == 1 else i for i in args] # Training mode: optimizing if if self.train_inputs is None: raise RuntimeError( "train_inputs, train_targets cannot be None in training mode. " "Call .eval() for prior predictions, or call .set_train_data() to add training data." ) if settings.debug.on(): if not all(torch.equal(train_input, input) for train_input, input in zip(train_inputs, inputs)): raise RuntimeError("You must train on the training inputs!") res = super().__call__(*inputs, **kwargs) return res # Prior mode elif settings.prior_mode.on() or self.train_inputs is None or self.train_targets is None: full_inputs = args full_output = super(ExactGP, self).__call__(*full_inputs, **kwargs) if settings.debug().on(): if not isinstance(full_output, MultivariateNormal): raise RuntimeError("ExactGP.forward must return a MultivariateNormal") return full_output # Posterior mode else: if settings.debug.on(): if all(torch.equal(train_input, input) for train_input, input in zip(train_inputs, inputs)): warnings.warn( "The input matches the stored training data. Did you forget to call model.train()?", GPInputWarning, ) # Get the terms that only depend on training data if self.prediction_strategy is None: train_output = super().__call__(*train_inputs, **kwargs) # Create the prediction strategy for self.prediction_strategy = prediction_strategy( train_inputs=train_inputs, train_prior_dist=train_output, train_labels=self.train_targets, likelihood=self.likelihood, ) # Concatenate the input to the training input full_inputs = [] batch_shape = train_inputs[0].shape[:-2] for train_input, input in zip(train_inputs, inputs): # Make sure the batch shapes agree for training/test data if batch_shape != train_input.shape[:-2]: batch_shape = torch.broadcast_shapes(batch_shape, train_input.shape[:-2]) train_input = train_input.expand(*batch_shape, *train_input.shape[-2:]) if batch_shape != input.shape[:-2]: batch_shape = torch.broadcast_shapes(batch_shape, input.shape[:-2]) train_input = train_input.expand(*batch_shape, *train_input.shape[-2:]) input = input.expand(*batch_shape, *input.shape[-2:]) full_inputs.append([train_input, input], dim=-2)) # Get the joint distribution for training/test data full_output = super(ExactGP, self).__call__(*full_inputs, **kwargs) if settings.debug().on(): if not isinstance(full_output, MultivariateNormal): raise RuntimeError("ExactGP.forward must return a MultivariateNormal") full_mean, full_covar = full_output.loc, full_output.lazy_covariance_matrix # Determine the shape of the joint distribution batch_shape = full_output.batch_shape joint_shape = full_output.event_shape tasks_shape = joint_shape[1:] # For multitask learning test_shape = torch.Size([joint_shape[0] - self.prediction_strategy.train_shape[0], *tasks_shape]) # Make the prediction with settings.cg_tolerance(settings.eval_cg_tolerance.value()): predictive_mean, predictive_covar = self.prediction_strategy.exact_prediction(full_mean, full_covar) # Reshape predictive mean to match the appropriate event shape predictive_mean = predictive_mean.view(*batch_shape, *test_shape).contiguous() return full_output.__class__(predictive_mean, predictive_covar)