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metric.py
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metric.py
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import torch
def get_recall(indices, targets):
"""
Calculates the recall score for the given predictions and targets
Args:
indices (Bxk): torch.LongTensor. top-k indices predicted by the model.
targets (B): torch.LongTensor. actual target indices.
Returns:
recall (float): the recall score
"""
targets = targets.view(-1, 1).expand_as(indices)
hits = (targets == indices).nonzero()
if len(hits) == 0:
return 0
n_hits = (targets == indices).nonzero()[:, :-1].size(0)
recall = float(n_hits) / targets.size(0)
return recall
def get_mrr(indices, targets):
"""
Calculates the MRR score for the given predictions and targets
Args:
indices (Bxk): torch.LongTensor. top-k indices predicted by the model.
targets (B): torch.LongTensor. actual target indices.
Returns:
mrr (float): the mrr score
"""
tmp = targets.view(-1, 1)
targets = tmp.expand_as(indices)
hits = (targets == indices).nonzero()
ranks = hits[:, -1] + 1
ranks = ranks.float()
rranks = torch.reciprocal(ranks)
mrr = torch.sum(rranks).data / targets.size(0)
return mrr.item()
def evaluate(indices, targets, k=20):
"""
Evaluates the model using Recall@K, MRR@K scores.
Args:
logits (B,C): torch.LongTensor. The predicted logit for the next items.
targets (B): torch.LongTensor. actual target indices.
Returns:
recall (float): the recall score
mrr (float): the mrr score
"""
_, indices = torch.topk(indices, k, -1)
recall = get_recall(indices, targets)
mrr = get_mrr(indices, targets)
return recall, mrr