def eval_reconstruction_slow()

in hype/graph.py [0:0]

• 23 lines of code
• 4 McCabe index (conditional complexity)

def eval_reconstruction_slow(adj, lt, distfn):
    ranks = []
    ap_scores = []

    for s, s_types in adj.items():
        s_e = lt[s].expand_as(lt)
        _dists = distfn(s_e, lt).data.cpu().numpy().flatten()
        _dists[s] = 1e+12
        _labels = np.zeros(lt.size(0))
        _dists_masked = _dists.copy()
        _ranks = []
        for o in s_types:
            _dists_masked[o] = np.Inf
            _labels[o] = 1
        for o in s_types:
            d = _dists_masked.copy()
            d[o] = _dists[o]
            r = np.argsort(d)
            _ranks.append(np.where(r == o)[0][0] + 1)
        ranks += _ranks
        ap_scores.append(
            average_precision_score(_labels, -_dists)
        )
    return np.mean(ranks), np.mean(ap_scores)