def compute_item_metric(test: ss.csr_matrix,
mat_rank: ss.csr_matrix,
cand_count: np.ndarray,
cutoff: int = 200):
rel_count = (test != 0).sum(axis=1)
istopk = mat_rank.max() < test.shape[1] * 0.5
recall, precision, map = Eval.compute_recall_precision(
mat_rank, rel_count, cutoff)
ndcg = Eval.compute_ndcg(test, mat_rank, cutoff)
if not istopk:
auc, mpr = Eval.compute_auc(mat_rank, rel_count, cand_count)
return {
'item_recall': recall,
'item_prec': precision,
'item_map': map,
'item_ndcg': ndcg,
'item_mpr': mpr,
'item_auc': auc
}
else:
return {
'item_recall': recall,
'item_prec': precision,
'item_map': map,
'item_ndcg': ndcg
}
