/
epc.py
48 lines (39 loc) · 1.39 KB
/
epc.py
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import numpy as np
from collections import defaultdict
from .base import Metric
np.seterr(all="raise")
class EPC(Metric):
"""Expected Popularity Complement.
EPC is a metric that measures the ability of a system to recommend
relevant items that reside in the long-tail.
"""
def __init__(self, items_normalized_popularity, *args, **kwargs):
"""__init__.
Args:
args:
kwargs:
items_normalized_popularity
"""
super().__init__(*args, **kwargs)
self.users_num_items_recommended = defaultdict(int)
self.users_prob_not_seen_cumulated = defaultdict(float)
self.items_normalized_popularity = items_normalized_popularity
def compute(self, uid: int):
"""compute.
Args:
uid (int): user id
"""
C_2 = 1.0 / self.users_num_items_recommended[uid]
sum_2 = self.users_prob_not_seen_cumulated[uid]
EPC = C_2 * sum_2
return EPC
def update_recommendation(self, uid: int, item: int, reward: float):
"""update_recommendation.
Args:
uid (int): user id
item (int): item id
reward (float): reward
"""
self.users_num_items_recommended[uid] += 1
probability_seen = self.items_normalized_popularity[item]
self.users_prob_not_seen_cumulated[uid] += 1 - probability_seen