wehub-resource-sync
127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
"""Negative sampling APIs"""
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from numpy.polynomial import polynomial
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from .. import backend as F, utils
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from .._ffi.function import _init_api
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from ..heterograph import DGLGraph
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__all__ = ["global_uniform_negative_sampling"]
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def _calc_redundancy(
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k_hat, num_edges, num_pairs, r=3
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): # pylint: disable=invalid-name
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# pylint: disable=invalid-name
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# Calculates the number of samples required based on a lower-bound
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# of the expected number of negative samples, based on N draws from
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# a binomial distribution. Solves the following equation for N:
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#
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# k_hat = N*p_k - r * np.sqrt(N*p_k*(1-p_k))
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#
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# where p_k is the probability that a node pairing is a negative edge
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# and r is the number of standard deviations to construct the lower bound
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#
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# Credits to @zjost
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p_m = num_edges / num_pairs
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p_k = 1 - p_m
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a = p_k**2
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b = -p_k * (2 * k_hat + r**2 * p_m)
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c = k_hat**2
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poly = polynomial.Polynomial([c, b, a])
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N = poly.roots()[-1]
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redundancy = N / k_hat - 1.0
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return redundancy
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def global_uniform_negative_sampling(
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g,
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num_samples,
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exclude_self_loops=True,
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replace=False,
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etype=None,
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redundancy=None,
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):
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"""Performs negative sampling, which generate source-destination pairs such that
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edges with the given type do not exist.
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Specifically, this function takes in an edge type and a number of samples. It
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returns two tensors ``src`` and ``dst``, the former in the range of ``[0, num_src)``
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and the latter in the range of ``[0, num_dst)``, where ``num_src`` and ``num_dst``
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represents the number of nodes with the source and destination node type respectively.
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It guarantees that no edge will exist between the corresponding pairs of ``src``
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with the source node type and ``dst`` with the destination node type.
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.. note::
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This negative sampler will try to generate as many negative samples as possible, but
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it may rarely return less than :attr:`num_samples` negative samples.
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This is more likely to happen when a graph is so small or dense that not many
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unique negative samples exist.
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Parameters
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----------
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g : DGLGraph
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The graph.
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num_samples : int
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The number of desired negative samples to generate.
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exclude_self_loops : bool, optional
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Whether to exclude self-loops from the negative samples. Only impacts the
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edge types whose source and destination node types are the same.
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Default: True.
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replace : bool, optional
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Whether to sample with replacement. Setting it to True will make things
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faster. (Default: False)
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etype : str or tuple of str, optional
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The edge type. Can be omitted if the graph only has one edge type.
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redundancy : float, optional
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Indicates how much more negative samples to actually generate during rejection sampling
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before finding the unique pairs.
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Increasing it will increase the likelihood of getting :attr:`num_samples` negative
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samples, but will also take more time and memory.
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(Default: automatically determined by the density of graph)
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Returns
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-------
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tuple[Tensor, Tensor]
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The source and destination pairs.
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Examples
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--------
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>>> g = dgl.graph(([0, 1, 2], [1, 2, 3]))
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>>> dgl.sampling.global_uniform_negative_sampling(g, 3)
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(tensor([0, 1, 3]), tensor([2, 0, 2]))
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"""
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if etype is None:
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etype = g.etypes[0]
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utype, _, vtype = g.to_canonical_etype(etype)
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exclude_self_loops = exclude_self_loops and (utype == vtype)
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redundancy = _calc_redundancy(
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num_samples, g.num_edges(etype), g.num_nodes(utype) * g.num_nodes(vtype)
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)
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etype_id = g.get_etype_id(etype)
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src, dst = _CAPI_DGLGlobalUniformNegativeSampling(
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g._graph,
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etype_id,
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num_samples,
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3,
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exclude_self_loops,
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replace,
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redundancy,
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)
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return F.from_dgl_nd(src), F.from_dgl_nd(dst)
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DGLGraph.global_uniform_negative_sampling = utils.alias_func(
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global_uniform_negative_sampling
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)
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_init_api("dgl.sampling.negative", __name__)
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