KG-Evaluation__Bachelor-Thesis__/metrics/ranking.py

import torch

from models.base_model import ERModel


def _rank(scores: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
    return (scores > scores[target]).sum() + 1


def simple_ranking(model: ERModel, triples: torch.Tensor) -> torch.Tensor:
    h, r, t = triples[:, 0], triples[:, 1], triples[:, 2]
    E = model.num_entities
    ranks = []

    with torch.no_grad():
        for hi, ri, ti in zip(h, r, t):
            tails = torch.arange(E, device=triples.device)
            triples = torch.stack([hi.repeat(E), ri.repeat(E), tails], dim=1)
            scores = model(triples)
            ranks.append(_rank(scores, ti))

    return torch.tensor(ranks, device=triples.device, dtype=torch.float32)


def mrr(r: torch.Tensor) -> torch.Tensor:
    return (1 / r).mean()


def hits_at_k(r: torch.Tensor, k: int = 10) -> torch.Tensor:
    return (r <= k).float().mean()