Source code for dgl.sampling.negative

"""Negative sampling APIs"""

from numpy.polynomial import polynomial

from .. import backend as F, utils
from .._ffi.function import _init_api
from ..heterograph import DGLGraph

__all__ = ["global_uniform_negative_sampling"]

def _calc_redundancy(
    k_hat, num_edges, num_pairs, r=3
):  # pylint: disable=invalid-name
    # pylint: disable=invalid-name
    # Calculates the number of samples required based on a lower-bound
    # of the expected number of negative samples, based on N draws from
    # a binomial distribution.  Solves the following equation for N:
    # k_hat = N*p_k - r * np.sqrt(N*p_k*(1-p_k))
    # where p_k is the probability that a node pairing is a negative edge
    # and r is the number of standard deviations to construct the lower bound
    # Credits to @zjost
    p_m = num_edges / num_pairs
    p_k = 1 - p_m

    a = p_k**2
    b = -p_k * (2 * k_hat + r**2 * p_m)
    c = k_hat**2

    poly = polynomial.Polynomial([c, b, a])
    N = poly.roots()[-1]
    redundancy = N / k_hat - 1.0
    return redundancy

[docs]def global_uniform_negative_sampling( g, num_samples, exclude_self_loops=True, replace=False, etype=None, redundancy=None, ): """Performs negative sampling, which generate source-destination pairs such that edges with the given type do not exist. Specifically, this function takes in an edge type and a number of samples. It returns two tensors ``src`` and ``dst``, the former in the range of ``[0, num_src)`` and the latter in the range of ``[0, num_dst)``, where ``num_src`` and ``num_dst`` represents the number of nodes with the source and destination node type respectively. It guarantees that no edge will exist between the corresponding pairs of ``src`` with the source node type and ``dst`` with the destination node type. .. note:: This negative sampler will try to generate as many negative samples as possible, but it may rarely return less than :attr:`num_samples` negative samples. This is more likely to happen when a graph is so small or dense that not many unique negative samples exist. Parameters ---------- g : DGLGraph The graph. num_samples : int The number of desired negative samples to generate. exclude_self_loops : bool, optional Whether to exclude self-loops from the negative samples. Only impacts the edge types whose source and destination node types are the same. Default: True. replace : bool, optional Whether to sample with replacement. Setting it to True will make things faster. (Default: False) etype : str or tuple of str, optional The edge type. Can be omitted if the graph only has one edge type. redundancy : float, optional Indicates how much more negative samples to actually generate during rejection sampling before finding the unique pairs. Increasing it will increase the likelihood of getting :attr:`num_samples` negative samples, but will also take more time and memory. (Default: automatically determined by the density of graph) Returns ------- tuple[Tensor, Tensor] The source and destination pairs. Examples -------- >>> g = dgl.graph(([0, 1, 2], [1, 2, 3])) >>> dgl.sampling.global_uniform_negative_sampling(g, 3) (tensor([0, 1, 3]), tensor([2, 0, 2])) """ if etype is None: etype = g.etypes[0] utype, _, vtype = g.to_canonical_etype(etype) exclude_self_loops = exclude_self_loops and (utype == vtype) redundancy = _calc_redundancy( num_samples, g.num_edges(etype), g.num_nodes(utype) * g.num_nodes(vtype) ) etype_id = g.get_etype_id(etype) src, dst = _CAPI_DGLGlobalUniformNegativeSampling( g._graph, etype_id, num_samples, 3, exclude_self_loops, replace, redundancy, ) return F.from_dgl_nd(src), F.from_dgl_nd(dst)
DGLGraph.global_uniform_negative_sampling = utils.alias_func( global_uniform_negative_sampling ) _init_api("dgl.sampling.negative", __name__)