"""
This is the implementation of the Recall metric.
It proceeds from a user-wise computation, and average the values over the users.
"""
__version__ = '0.3.1'
__author__ = 'Vito Walter Anelli, Claudio Pomo'
__email__ = 'vitowalter.anelli@poliba.it, claudio.pomo@poliba.it'
import numpy as np
from elliot.evaluation.metrics.base_metric import BaseMetric
[docs]class Recall(BaseMetric):
r"""
Recall-measure
This class represents the implementation of the Recall recommendation metric.
For further details, please refer to the `link <https://en.wikipedia.org/wiki/Precision_and_recall#Recall>`_
.. math::
\mathrm {Recall@K} = \frac{|Rel_u\cap Rec_u|}{Rel_u}
:math:`Rel_u` is the set of items relevant to user :math:`U`,
:math:`Rec_u` is the top K items recommended to users.
We obtain the result by calculating the average :math:`Recall@K` of each user.
To compute the metric, add it to the config file adopting the following pattern:
.. code:: yaml
simple_metrics: [Recall]
"""
def __init__(self, recommendations, config, params, eval_objects):
"""
Constructor
:param recommendations: list of recommendations in the form {user: [(item1,value1),...]}
:param config: SimpleNameSpace that represents the configuration of the experiment
:param params: Parameters of the model
:param eval_objects: list of objects that may be useful for the computation of the different metrics
"""
super().__init__(recommendations, config, params, eval_objects)
self._cutoff = self._evaluation_objects.cutoff
self._relevance = self._evaluation_objects.relevance.binary_relevance
[docs] @staticmethod
def name():
"""
Metric Name Getter
:return: returns the public name of the metric
"""
return "Recall"
def __user_recall(self, user_recommendations, user, cutoff):
"""
Per User Recall
:param user_recommendations: list of user recommendation in the form [(item1,value1),...]
:param cutoff: numerical threshold to limit the recommendation list
:param user_relevant_items: list of user relevant items in the form [item1,...]
:return: the value of the Recall metric for the specific user
"""
return sum([self._relevance.get_rel(user, i) for i, _ in user_recommendations[:cutoff]]) / len(self._relevance.get_user_rel(user))
# def eval(self):
# """
# Evaluation Function
# :return: the overall averaged value of Recall
# """
# return np.average(
# [Recall.__user_recall(u_r, self._cutoff, self._relevant_items[u])
# for u, u_r in self._recommendations.items() if len(self._relevant_items[u])]
# )
[docs] def eval_user_metric(self):
"""
Evaluation Function
:return: the overall averaged value of Recall per user
"""
return {u: self.__user_recall(u_r, u, self._cutoff)
for u, u_r in self._recommendations.items() if len(self._relevance.get_user_rel(u))}