"""
This is the implementation of the NumRetrieved 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 NumRetrieved(BaseMetric):
r"""
Number of Recommendations Retrieved
This class represents the implementation of the Number of Recommendations Retrieved recommendation metric.
For further details, please refer to the `link <https://github.com/RankSys/RankSys/blob/master/RankSys-metrics/src/main/java/es/uam/eps/ir/ranksys/metrics/basic/NumRetrieved.java>`_
.. code:: yaml
simple_metrics: [NumRetrieved]
"""
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 "NumRetrieved"
@staticmethod
def __user_num_retrieved(user_recommendations, cutoff):
"""
Per User NumRetrieved
: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 Precision metric for the specific user
"""
return len(user_recommendations[:cutoff])
# def eval(self):
# """
# Evaluation function
# :return: the overall averaged value of NumRetrieved
# """
# return np.average(
# [NumRetrieved.__user_num_retrieved(u_r, self._cutoff)
# 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 NumRetrieved
"""
return {u: NumRetrieved.__user_num_retrieved(u_r, self._cutoff)
for u, u_r in self._recommendations.items() if len(self._relevance.get_user_rel(u))}