/
users_coverage.py
48 lines (39 loc) · 1.22 KB
/
users_coverage.py
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import numpy as np
from collections import defaultdict
from .base import Metric
from typing import Any
np.seterr(all="raise")
class UsersCoverage(Metric):
"""Users Coverage.
It represents the percentage of distinctusers that are interested
in at least k items recommended (k ≥ 1).
"""
def __init__(self, users_covered=defaultdict(bool), *args, **kwargs):
"""__init__.
Args:
args:
kwargs:
users_covered:
"""
super().__init__(*args, **kwargs)
self.users_covered = users_covered
def compute(self, uid: int):
"""compute.
Args:
uid (int): user id
"""
vals = np.array(list(self.users_covered.values()))
return np.sum(vals) / len(vals)
def update_recommendation(self, uid: int, item: int, reward: float):
"""update_consumption_history.
Args:
uid (int): user id
item (int): item id
reward (float): reward
"""
if self.users_covered[uid] is False and self.relevance_evaluator.is_relevant(
reward
):
self.users_covered[uid] = True
# else:
# self.users_covered[uid] = False