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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
import torch
from .. import py_graphlearn_torch as pywrap
[docs]class RandomNegativeSampler(object):
r""" Random negative Sampler.
Args:
graph: A ``graphlearn_torch.data.Graph`` object.
mode: Execution mode of sampling, 'CUDA' means sampling on
GPU, 'CPU' means sampling on CPU.
"""
def __init__(self, graph, mode='CUDA'):
self._mode = mode
if mode == 'CUDA':
self._sampler = pywrap.CUDARandomNegativeSampler(graph.graph_handler)
else:
self._sampler = pywrap.CPURandomNegativeSampler(graph.graph_handler)
[docs] def sample(self, req_num, trials_num=5, padding=False):
r""" Negative sampling.
Args:
req_num: The number of request(max) negative samples.
trials_num: The number of trials for negative sampling.
padding: Whether to patch the negative sampling results to req_num.
If True, after trying trials_num times, if the number of true negative
samples is still less than req_num, just random sample edges(non-strict
negative) as negative samples.
Returns:
negative edge_index(non-strict when padding is True).
"""
rows, cols = self._sampler.sample(req_num, trials_num, padding)
return torch.stack([rows, cols], dim=0)