chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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/**
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* Copyright (c) 2020 by Contributors
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* @file dgl/sampling/negative.h
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* @brief Negative sampling.
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*/
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#ifndef DGL_SAMPLING_NEGATIVE_H_
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#define DGL_SAMPLING_NEGATIVE_H_
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#include <dgl/array.h>
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#include <dgl/base_heterograph.h>
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#include <utility>
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namespace dgl {
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namespace sampling {
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/**
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* @brief Given an edge type, uniformly sample source-destination pairs that do
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* not have an edge in between using rejection sampling.
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*
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* @note This function may not return the same number of elements as the given
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* number of samples.
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* @note This function requires sorting the CSR or CSC matrix of the graph
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* in-place. It prefers CSC over CSR.
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*
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* @param hg The graph.
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* @param etype The edge type.
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* @param num_samples The number of negative examples to sample.
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* @param num_trials The number of rejection sampling trials.
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* @param exclude_self_loops Do not include the examples where the source equals
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* the destination.
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* @param replace Whether to sample with replacement.
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* @param redundancy How much redundant negative examples to take in case of
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* duplicate examples.
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* @return The pair of source and destination tensors.
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*/
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std::pair<IdArray, IdArray> GlobalUniformNegativeSampling(
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HeteroGraphPtr hg, dgl_type_t etype, int64_t num_samples, int num_trials,
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bool exclude_self_loops, bool replace, double redundancy);
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}; // namespace sampling
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}; // namespace dgl
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#endif // DGL_SAMPLING_NEGATIVE_H_
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@@ -0,0 +1,131 @@
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/**
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* Copyright (c) 2020 by Contributors
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* @file dgl/sampling/neighbor.h
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* @brief Neighborhood-based sampling.
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*/
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#ifndef DGL_SAMPLING_NEIGHBOR_H_
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#define DGL_SAMPLING_NEIGHBOR_H_
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#include <dgl/array.h>
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#include <dgl/base_heterograph.h>
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#include <tuple>
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#include <vector>
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namespace dgl {
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namespace sampling {
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/**
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* @brief Sample from the neighbors of the given nodes and return the sampled
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* edges as a graph.
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*
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* When sampling with replacement, the sampled subgraph could have parallel
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* edges.
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*
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* For sampling without replace, if fanout > the number of neighbors, all the
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* neighbors will be sampled.
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*
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* @param hg The input graph.
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* @param nodes Node IDs of each type. The vector length must be equal to the
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* number of node types. Empty array is allowed.
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* @param fanouts Number of sampled neighbors for each edge type. The vector
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* length should be equal to the number of edge types, or one if they all have
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* the same fanout.
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* @param dir Edge direction.
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* @param probability A vector of 1D float arrays, indicating the transition
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* probability of each edge by edge type. An empty float array assumes uniform
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* transition.
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* @param exclude_edges Edges IDs of each type which will be excluded during
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* sampling. The vector length must be equal to the number of edges types. Empty
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* array is allowed.
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* @param replace If true, sample with replacement.
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* @return Sampled neighborhoods as a graph. The return graph has the same
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* schema as the original one.
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*/
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HeteroSubgraph SampleNeighbors(
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const HeteroGraphPtr hg, const std::vector<IdArray>& nodes,
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const std::vector<int64_t>& fanouts, EdgeDir dir,
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const std::vector<FloatArray>& probability,
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const std::vector<IdArray>& exclude_edges, bool replace = true);
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/**
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* @brief Sample from the neighbors of the given nodes and convert a graph into
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* a bipartite-structured graph for message passing.
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*
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* Specifically, we create one node type \c ntype_l on the "left" side and
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* another node type \c ntype_r on the "right" side for each node type \c ntype.
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* The nodes of type \c ntype_r would contain the nodes designated by the
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* caller, and node type \c ntype_l would contain the nodes that has an edge
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* connecting to one of the designated nodes.
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*
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* The nodes of \c ntype_l would also contain the nodes in node type \c ntype_r.
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* When sampling with replacement, the sampled subgraph could have parallel
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* edges.
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*
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* For sampling without replace, if fanout > the number of neighbors, all the
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* neighbors will be sampled.
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*
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* Non-deterministic algorithm, requires nodes parameter to store unique Node
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* IDs.
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*
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* @tparam IdType Graph's index data type, can be int32_t or int64_t
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* @param hg The input graph.
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* @param nodes Node IDs of each type. The vector length must be equal to the
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* number of node types. Empty array is allowed.
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* @param mapping External parameter that should be set to a vector of IdArrays
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* filled with -1, required for mapping of nodes in returned
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* graph
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* @param fanouts Number of sampled neighbors for each edge type. The vector
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* length should be equal to the number of edge types, or one if they all have
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* the same fanout.
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* @param dir Edge direction.
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* @param probability A vector of 1D float arrays, indicating the transition
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* probability of each edge by edge type. An empty float array assumes uniform
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* transition.
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* @param exclude_edges Edges IDs of each type which will be excluded during
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* sampling. The vector length must be equal to the number of edges types. Empty
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* array is allowed.
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* @param replace If true, sample with replacement.
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* @return Sampled neighborhoods as a graph. The return graph has the same
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* schema as the original one.
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*/
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template <typename IdType>
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std::tuple<HeteroGraphPtr, std::vector<IdArray>, std::vector<IdArray>>
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SampleNeighborsFused(
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const HeteroGraphPtr hg, const std::vector<IdArray>& nodes,
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const std::vector<IdArray>& mapping, const std::vector<int64_t>& fanouts,
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EdgeDir dir, const std::vector<NDArray>& prob_or_mask,
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const std::vector<IdArray>& exclude_edges, bool replace = true);
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/**
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* Select the neighbors with k-largest weights on the connecting edges for each
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* given node.
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*
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* If k > the number of neighbors, all the neighbors are sampled.
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*
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* @param hg The input graph.
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* @param nodes Node IDs of each type. The vector length must be equal to the
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* number of node types. Empty array is allowed.
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* @param k The k value for each edge type. The vector length should be equal to
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* the number of edge types, or one if they all have the same fanout.
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* @param dir Edge direction.
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* @param weight A vector of 1D float arrays, indicating the weights associated
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* witheach edge.
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* @param ascending If true, elements are sorted by ascending order, equivalent
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* to find the K smallest values. Otherwise, find K largest values.
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* @return Sampled neighborhoods as a graph. The return graph has the same
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* schema as the original one.
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*/
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HeteroSubgraph SampleNeighborsTopk(
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const HeteroGraphPtr hg, const std::vector<IdArray>& nodes,
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const std::vector<int64_t>& k, EdgeDir dir,
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const std::vector<FloatArray>& weight, bool ascending = false);
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HeteroSubgraph SampleNeighborsBiased(
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const HeteroGraphPtr hg, const IdArray& nodes, const int64_t fanouts,
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const NDArray& bias, const NDArray& tag_offset, const EdgeDir dir,
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const bool replace);
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} // namespace sampling
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} // namespace dgl
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#endif // DGL_SAMPLING_NEIGHBOR_H_
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@@ -0,0 +1,89 @@
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/**
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* Copyright (c) 2019 by Contributors
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* @file dgl/samplinig/randomwalks.h
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* @brief Random walk functions.
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*/
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#ifndef DGL_SAMPLING_RANDOMWALKS_H_
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#define DGL_SAMPLING_RANDOMWALKS_H_
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#include <dgl/array.h>
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#include <dgl/base_heterograph.h>
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#include <tuple>
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#include <utility>
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#include <vector>
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namespace dgl {
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namespace sampling {
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/**
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* @brief Metapath-based random walk.
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* @param hg The heterograph.
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* @param seeds A 1D array of seed nodes, with the type the source type of the
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* first edge type in the metapath.
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* @param metapath A 1D array of edge types representing the metapath.
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* @param prob A vector of 1D float arrays, indicating the transition
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* probability of each edge by edge type. An empty float array assumes uniform
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* transition.
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* @return A pair of
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* 1. One 2D array of shape (len(seeds), len(metapath) + 1) with node
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* IDs. The paths that terminated early are padded with -1.
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* 2. One 2D array of shape (len(seeds), len(metapath)) with edge IDs.
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* The paths that terminated early are padded with -1.
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* 3. One 1D array of shape (len(metapath) + 1) with node type IDs.
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*/
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std::tuple<IdArray, IdArray, TypeArray> RandomWalk(
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const HeteroGraphPtr hg, const IdArray seeds, const TypeArray metapath,
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const std::vector<FloatArray> &prob);
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/**
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* @brief Metapath-based random walk with restart probability.
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* @param hg The heterograph.
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* @param seeds A 1D array of seed nodes, with the type the source type of the
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* first edge type in the metapath.
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* @param metapath A 1D array of edge types representing the metapath.
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* @param prob A vector of 1D float arrays, indicating the transition
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* probability of each edge by edge type. An empty float array assumes uniform
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* transition.
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* @param restart_prob Restart probability.
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* @return A pair of
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* 1. One 2D array of shape (len(seeds), len(metapath) + 1) with node
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* IDs. The paths that terminated early are padded with -1.
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* 2. One 2D array of shape (len(seeds), len(metapath)) with edge IDs.
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* The paths that terminated early are padded with -1.
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* 3. One 1D array of shape (len(metapath) + 1) with node type IDs.
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*/
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std::tuple<IdArray, IdArray, TypeArray> RandomWalkWithRestart(
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const HeteroGraphPtr hg, const IdArray seeds, const TypeArray metapath,
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const std::vector<FloatArray> &prob, double restart_prob);
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/**
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* @brief Metapath-based random walk with stepwise restart probability. Useful
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* for PinSAGE-like models.
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* @param hg The heterograph.
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* @param seeds A 1D array of seed nodes, with the type the source type of the
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* first edge type in the metapath.
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* @param metapath A 1D array of edge types representing the metapath.
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* @param prob A vector of 1D float arrays, indicating the transition
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* probability of each edge by edge type. An empty float array assumes uniform
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* transition.
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* @param restart_prob Restart probability array which has the same number of
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* elements as \c metapath, indicating the probability to terminate after
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* transition.
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* @return A pair of
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* 1. One 2D array of shape (len(seeds), len(metapath) + 1) with node
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* IDs. The paths that terminated early are padded with -1.
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* 2. One 2D array of shape (len(seeds), len(metapath)) with edge IDs.
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* The paths that terminated early are padded with -1.
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* 3. One 1D array of shape (len(metapath) + 1) with node type IDs.
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*/
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std::tuple<IdArray, IdArray, TypeArray> RandomWalkWithStepwiseRestart(
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const HeteroGraphPtr hg, const IdArray seeds, const TypeArray metapath,
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const std::vector<FloatArray> &prob, FloatArray restart_prob);
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}; // namespace sampling
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}; // namespace dgl
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#endif // DGL_SAMPLING_RANDOMWALKS_H_
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