Added bespoke wrappers for

graph kernels that allow node/
edge label checking.

gauche/gp.py

@@ -2,65 +2,44 @@
 from functools import lru_cache
 
 import torch
-import gpytorch
 
-from gpytorch import Module, settings
+from gpytorch import settings
 from gpytorch.distributions import MultivariateNormal
 from gpytorch.likelihoods import _GaussianLikelihoodBase
 from gpytorch.models.exact_prediction_strategies import prediction_strategy
 from gpytorch.models import ExactGP
 
-Softplus = torch.nn.Softplus()
 
-
-class Inputs:
+class NonTensorialInputs:
     def __init__(self, data):
         self.data = data
 
     def append(self, new_data):
         self.data.extend(new_data.data)
 
+    def __iter__(self):
+        return iter(self.data)
 
-class GraphKernel(Module):
-    """
-    A class suporting externel kernels.
-    The external kernel must have a method `fit_transform`, which, when
-    evaluated on an `Inputs` instance `X`, returns a scaled kernel matrix
-    v * k(X, X).
-
-    As gradients are not propagated through to the external kernel, outputs are
-    cached to avoid repeated computation.
-    """
-
-    def __init__(self, graph_kernel, dtype=torch.float):
-        super().__init__()
-        self._scale_variance = torch.nn.Parameter(torch.tensor([0.1], dtype=dtype))
-        self.kernel = graph_kernel
-
-    def scale(self, S):
-        return Softplus(self._scale_variance) * S
-
-    def forward(self, X):
-        return self.scale(self.kern(X))
+    def __len__(self):
+        return len(self.data)
 
-    @lru_cache(maxsize=5)
-    def kern(self, X):
-        return torch.tensor(self.kernel.fit_transform(X.data)).float()
+    def __getitem__(self, idx):
+        return self.data[idx]
 
 
 class SIGP(ExactGP):
     """
-    A reimplementation of gpytorch(==1.7.0)'s ExactGP that allows for non-tensorial inputs.
-    The inputs to this class may be a gauche.gp.Inputs instance, with graphs stored within
-    the object's .data attribute.
+    A reimplementation of gpytorch's ExactGP that allows for non-tensorial inputs.
+    The inputs to this class may be a gauche.NonTensorialInputs instance, with graphs
+    stored within the object's .data attribute.
 
     In the longer term, if ExactGP can be refactored such that the validation checks ensuring
     that the inputs are torch.Tensors are optional, this class should subclass ExactGP without
     performing those checks.
     """
 
     def __init__(self, train_inputs, train_targets, likelihood):
-        if train_inputs is not None and type(train_inputs) is Inputs:
+        if train_inputs is not None and type(train_inputs) is NonTensorialInputs:
             train_inputs = (train_inputs,)
         if not isinstance(likelihood, _GaussianLikelihoodBase):
             raise RuntimeError("SIGP can only handle Gaussian likelihoods")

gauche/kernels/graph_kernels/grakel_kernels.py

@@ -0,0 +1,284 @@
+from typing import List, Optional
+
+import torch
+import networkx as nx
+from functools import lru_cache
+from gpytorch import Module
+
+from grakel import graph_from_networkx
+from grakel.kernels import (
+    VertexHistogram,
+    EdgeHistogram,
+    WeisfeilerLehman,
+    NeighborhoodHash,
+    RandomWalk,
+    RandomWalkLabeled,
+    ShortestPath,
+    GraphletSampling,
+)
+
+
+class _GraphKernel(Module):
+    """
+    A base class suporting external graph kernels.
+    The external kernel must have a method `fit_transform`, which, when
+    evaluated on an `Inputs` instance `X`, returns a scaled kernel matrix
+    v * k(X, X).
+
+    As gradients are not propagated through to the external kernel, outputs are
+    cached to avoid repeated computation.
+    """
+
+    def __init__(
+        self,
+        dtype=torch.float,
+    ) -> None:
+        super().__init__()
+        self.node_label = None
+        self.edge_label = None
+        self._scale_variance = torch.nn.Parameter(torch.tensor([0.1], dtype=dtype))
+
+    def scale(self, S: torch.Tensor) -> torch.Tensor:
+        return torch.nn.functional.softplus(self._scale_variance) * S
+
+    def forward(self, X: torch.Tensor) -> torch.Tensor:
+        return self.scale(self.kernel(X))
+
+    def kernel(self, X: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError("Subclasses must implement this method.")
+
+
+class VertexHistogramKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the vertex histogram kernel.
+    This kernel requires node labels to be specified.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/vertex_histogram.html
+    for more details.
+    """
+
+    def __init__(
+        self,
+        node_label: str,
+        dtype=torch.float,
+    ):
+        super().__init__(dtype=dtype)
+        self.node_label = node_label
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: List[nx.Graph], **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(VertexHistogram(**grakel_kwargs).fit_transform(X)).float()
+
+
+class EdgeHistogramKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the edge histogram kernel.
+    This kernel requires edge labels to be specified.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/edge_histogram.html
+    for more details.
+    """
+
+    def __init__(self, edge_label, dtype=torch.float):
+        super().__init__(dtype=dtype)
+        self.edge_label = edge_label
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(EdgeHistogram(**grakel_kwargs).fit_transform(X)).float()
+
+
+class WeisfeilerLehmanKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the Weisfeiler-Lehman kernel.
+    This kernel needs node labels to be specified and
+    can optionally use edge labels for the base kernel.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/weisfeiler_lehman.html
+    for more details.
+    """
+
+    def __init__(
+        self, node_label: str, edge_label: Optional[str] = None, dtype=torch.float
+    ):
+        super().__init__(dtype=dtype)
+        self.node_label = node_label
+        self.edge_label = edge_label
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(WeisfeilerLehman(**grakel_kwargs).fit_transform(X)).float()
+
+
+class NeighborhoodHashKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the neighborhood hash kernel.
+    This kernel requires node labels to be specified.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/neighborhood_hash.html
+    for more details.
+    """
+
+    def __init__(self, node_label: str, dtype=torch.float):
+        super().__init__(dtype=dtype)
+        self.node_label = node_label
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(NeighborhoodHash(**grakel_kwargs).fit_transform(X)).float()
+
+
+class RandomWalkKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the random walk kernel.
+    This kernel only works on unlabelled graphs.
+    See RandomWalkLabeledKernel for labelled graphs.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/random_walk.html
+    for more details.
+    """
+
+    def __init__(self, dtype=torch.float):
+        super().__init__(dtype=dtype)
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(RandomWalk(**grakel_kwargs).fit_transform(X)).float()
+
+
+class RandomWalkLabeledKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the random walk kernel.
+    This kernel requires node labels to be specified.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/random_walk.html
+    for more details.
+    """
+
+    def __init__(self, node_label: str, dtype=torch.float):
+        super().__init__(dtype=dtype)
+        self.node_label = node_label
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(RandomWalkLabeled(**grakel_kwargs).fit_transform(X)).float()
+
+
+class ShortestPathKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the shortest path kernel.
+    This kernel only works on unlabelled graphs.
+    See ShortestPathLabeledKernel for labelled graphs.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/shortest_path.html
+    for more details.
+    """
+
+    def __init__(self, dtype=torch.float):
+        super().__init__(dtype=dtype)
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(
+            ShortestPath(**grakel_kwargs, with_labels=False).fit_transform(X)
+        ).float()
+
+
+class ShortestPathLabeledKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the shortest path kernel.
+    This kernel requires node labels to be specified.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/shortest_path.html
+    for more details.
+    """
+
+    def __init__(self, node_label: str, dtype=torch.float):
+        super().__init__(dtype=dtype)
+        self.node_label = node_label
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(
+            ShortestPath(**grakel_kwargs, with_labels=True).fit_transform(X)
+        ).float()
+
+
+class GraphletSamplingKernel(_GraphKernel):
+    """
+    A GraKel wrapper for the graphlet sampling kernel.
+    This kernel only works on unlabelled graphs.
+
+    See https://ysig.github.io/GraKeL/0.1a8/kernels/graphlet_sampling.html
+    for more details.
+    """
+
+    def __init__(self, dtype=torch.float):
+        super().__init__(dtype=dtype)
+
+    @lru_cache(maxsize=5)
+    def kernel(self, X: torch.Tensor, **grakel_kwargs) -> torch.Tensor:
+        # extract required data from the networkx graphs
+        # constructed with the Graphein utilities
+        # this is cheap and will be cached
+        X = graph_from_networkx(
+            X, node_labels_tag=self.node_label, edge_labels_tag=self.edge_label
+        )
+
+        return torch.tensor(GraphletSampling(**grakel_kwargs).fit_transform(X)).float()