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metrics.py
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metrics.py
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import numpy as np
def user_hitrate(rank, ground_truth, k=20):
"""
:param rank: shape [n_recommended_items]
:param ground_truth: shape [n_relevant_items]
:param k: number of top recommended items
:return: single hitrate
"""
return len(set(rank[:k]).intersection(set(ground_truth)))
def hitrate(rank, ground_truth, k=20):
"""
:param rank: shape [n_users, n_recommended_items]
:param ground_truth: shape [n_users, n_relevant_items]
:param k: number of top recommended items
:return: shape [n_users]
"""
return np.array([
user_hitrate(user_rank, user_ground_truth, k)
for user_rank, user_ground_truth in zip(rank, ground_truth)
])
def user_precision(rank, ground_truth, k=20):
"""
:param rank: shape [n_recommended_items]
:param ground_truth: shape [n_relevant_items]
:param k: number of top recommended items
:return: single precision
"""
return user_hitrate(rank, ground_truth, k) / len(rank[:k])
def precision(rank, ground_truth, k=20):
"""
:param rank: shape [n_users, n_recommended_items]
:param ground_truth: shape [n_users, n_relevant_items]
:param k: number of top recommended items
:return: shape [n_users]
"""
return np.array([
user_precision(user_rank, user_ground_truth, k)
for user_rank, user_ground_truth in zip(rank, ground_truth)
])
def user_recall(rank, ground_truth, k=20):
"""
:param rank: shape [n_recommended_items]
:param ground_truth: shape [n_relevant_items]
:param k: number of top recommended items
:return: single recall
"""
return user_hitrate(rank, ground_truth, k) / len(set(ground_truth))
def recall(rank, ground_truth, k=20):
"""
:param rank: shape [n_users, n_recommended_items]
:param ground_truth: shape [n_users, n_relevant_items]
:param k: number of top recommended items
:return: shape [n_users]
"""
return np.array([
user_recall(user_rank, user_ground_truth, k)
for user_rank, user_ground_truth in zip(rank, ground_truth)
])
def user_ap(rank, ground_truth, k=20):
"""
:param rank: shape [n_recommended_items]
:param ground_truth: shape [n_relevant_items]
:param k: number of top recommended items
:return: single ap
"""
return np.sum([
user_precision(rank, ground_truth, idx + 1)
for idx, item in enumerate(rank[:k]) if item in ground_truth
]) / len(rank[:k])
def ap(rank, ground_truth, k=20):
"""
:param rank: shape [n_users, n_recommended_items]
:param ground_truth: shape [n_users, n_relevant_items]
:param k: number of top recommended items
:return: shape [n_users]
"""
return np.array([
user_ap(user_rank, user_ground_truth, k)
for user_rank, user_ground_truth in zip(rank, ground_truth)
])
def map(rank, ground_truth, k=20):
"""
:param rank: shape [n_users, n_recommended_items]
:param ground_truth: shape [n_users, n_relevant_items]
:param k: number of top recommended items
:return: single map
"""
return np.mean([ap(rank, ground_truth, k)])
def user_ndcg(rank, ground_truth, k=20):
"""
:param rank: shape [n_recommended_items]
:param ground_truth: shape [n_relevant_items]
:param k: number of top recommended items
:return: single ndcg
"""
dcg = 0
idcg = 0
for idx, item in enumerate(rank[:k]):
dcg += 1.0 / np.log2(idx + 2) if item in ground_truth else 0.0
idcg += 1.0 / np.log2(idx + 2)
return dcg / idcg
def ndcg(rank, ground_truth, k=20):
"""
:param rank: shape [n_users, n_recommended_items]
:param ground_truth: shape [n_users, n_relevant_items]
:param k: number of top recommended items
:return: shape [n_users]
"""
return np.array([
user_ndcg(user_rank, user_ground_truth, k)
for user_rank, user_ground_truth in zip(rank, ground_truth)
])
def user_mrr(rank, ground_truth, k=20):
"""
:param rank: shape [n_recommended_items]
:param ground_truth: shape [n_relevant_items]
:param k: number of top recommended items
:return: single mrr
"""
for idx, item in enumerate(rank[:k]):
if item in ground_truth:
return 1 / (idx + 1)
return 0
def mrr(rank, ground_truth, k=20):
"""
:param rank: shape [n_users, n_recommended_items]
:param ground_truth: shape [n_users, n_relevant_items]
:param k: number of top recommended items
:return: shape [n_users]
"""
return np.array([
user_mrr(user_rank, user_ground_truth, k)
for user_rank, user_ground_truth in zip(rank, ground_truth)
])
metric_dict = {
'Hitrate': hitrate,
'Precision': precision,
'Recall': recall,
'MAP': map,
'NDCG': ndcg,
'MRR': mrr}