Source code for gpytorch.models.deep_gps.deep_gp

#!/usr/bin/env python3

import warnings

import torch
from linear_operator.operators import BlockDiagLinearOperator

from ... import settings
from ...distributions import MultitaskMultivariateNormal
from ...likelihoods import Likelihood
from ..approximate_gp import ApproximateGP
from import GP

class _DeepGPVariationalStrategy(object):
    def __init__(self, model):
        self.model = model

    def sub_variational_strategies(self):
        if not hasattr(self, "_sub_variational_strategies_memo"):
            self._sub_variational_strategies_memo = [
                module.variational_strategy for module in self.model.modules() if isinstance(module, ApproximateGP)
        return self._sub_variational_strategies_memo

    def kl_divergence(self):
        return sum(strategy.kl_divergence().sum() for strategy in self.sub_variational_strategies)

[docs]class DeepGPLayer(ApproximateGP): """ Represents a layer in a deep GP where inference is performed via the doubly stochastic method of Salimbeni et al., 2017. Upon calling, instead of returning a variational distribution q(f), returns samples from the variational distribution. See the documentation for __call__ below for more details below. Note that the behavior of __call__ will change to be much more elegant with multiple batch dimensions; however, the interface doesn't really change. :param ~gpytorch.variational.VariationalStrategy variational_strategy: Strategy for changing q(u) -> q(f) (see other VI docs) :param int input_dims`: Dimensionality of input data expected by each GP :param int output_dims: (default None) Number of GPs in this layer, equivalent to output dimensionality. If set to `None`, then the output dimension will be squashed. Forward data through this hidden GP layer. The output is a MultitaskMultivariateNormal distribution (or MultivariateNormal distribution is output_dims=None). If the input is >=2 dimensional Tensor (e.g. `n x d`), we pass the input through each hidden GP, resulting in a `n x h` multitask Gaussian distribution (where all of the `h` tasks represent an output dimension and are independent from one another). We then draw `s` samples from these Gaussians, resulting in a `s x n x h` MultitaskMultivariateNormal distribution. If the input is a >=3 dimensional Tensor, and the `are_samples=True` kwarg is set, then we assume that the outermost batch dimension is a samples dimension. The output will have the same number of samples. For example, a `s x b x n x d` input will result in a `s x b x n x h` MultitaskMultivariateNormal distribution. The goal of these last two points is that if you have a tensor `x` that is `n x d`, then >>> hidden_gp2(hidden_gp(x)) will just work, and return a tensor of size `s x n x h2`, where `h2` is the output dimensionality of hidden_gp2. In this way, hidden GP layers are easily composable. """ def __init__(self, variational_strategy, input_dims, output_dims): super(DeepGPLayer, self).__init__(variational_strategy) self.input_dims = input_dims self.output_dims = output_dims def forward(self, x): raise NotImplementedError def __call__(self, inputs, are_samples=False, **kwargs): deterministic_inputs = not are_samples if isinstance(inputs, MultitaskMultivariateNormal): inputs = torch.distributions.Normal(loc=inputs.mean, scale=inputs.variance.sqrt()).rsample() deterministic_inputs = False if settings.debug.on(): if not torch.is_tensor(inputs): raise ValueError( "`inputs` should either be a MultitaskMultivariateNormal or a Tensor, got " f"{inputs.__class__.__Name__}" ) if inputs.size(-1) != self.input_dims: raise RuntimeError( f"Input shape did not match self.input_dims. Got total feature dims [{inputs.size(-1)}]," f" expected [{self.input_dims}]" ) # Repeat the input for all possible outputs if self.output_dims is not None: inputs = inputs.unsqueeze(-3) inputs = inputs.expand(*inputs.shape[:-3], self.output_dims, *inputs.shape[-2:]) # Now run samples through the GP output = ApproximateGP.__call__(self, inputs, **kwargs) if self.output_dims is not None: mean = output.loc.transpose(-1, -2) covar = BlockDiagLinearOperator(output.lazy_covariance_matrix, block_dim=-3) output = MultitaskMultivariateNormal(mean, covar, interleaved=False) # Maybe expand inputs? if deterministic_inputs: output = output.expand(torch.Size([settings.num_likelihood_samples.value()]) + output.batch_shape) return output
[docs]class DeepGP(GP): """ A container module to build a DeepGP. This module should contain :obj:`~gpytorch.models.deep.DeepGPLayer` modules, and can also contain other modules as well. """ def __init__(self): super().__init__() self.variational_strategy = _DeepGPVariationalStrategy(self) def forward(self, x): raise NotImplementedError
class DeepLikelihood(Likelihood): """ A wrapper to make a GPyTorch likelihood compatible with Deep GPs Example: >>> deep_gaussian_likelihood = gpytorch.likelihoods.DeepLikelihood(gpytorch.likelihood.GaussianLikelihood) """ def __init__(self, base_likelihood): super().__init__() warnings.warn( "DeepLikelihood is now deprecated. Use a standard likelihood in conjunction with a " "gpytorch.mlls.DeepApproximateMLL. See the DeepGP example in our documentation.", DeprecationWarning, ) self.base_likelihood = base_likelihood def expected_log_prob(self, observations, function_dist, *params, **kwargs): return self.base_likelihood.expected_log_prob(observations, function_dist, *params, **kwargs).mean(dim=0) def log_marginal(self, observations, function_dist, *params, **kwargs): return self.base_likelihood.log_marginal(observations, function_dist, *params, **kwargs).mean(dim=0) def forward(self, *args, **kwargs): pass def __call__(self, *args, **kwargs): return self.base_likelihood.__call__(*args, **kwargs)