Source code for elliot.evaluation.metrics.base_metric

"""
This is the implementation of the Precision 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 typing as t
from abc import ABC, abstractmethod
import numpy as np


class BaseMetric(ABC):
    """
    This class represents the implementation of the Precision recommendation metric.
    Passing 'Precision' to the metrics list will enable the computation of the metric.
    """

    def __init__(self, recommendations, config, params, evaluation_objects, additional_data=None):
        """
        Constructor
        :param recommendations: list of recommendations in the form {user: [(item1,value1),...]}
        :param cutoff: numerical threshold to limit the recommendation list
        :param relevant_items: list of relevant items (binary) per user in the form {user: [item1,...]}
        """
        self._recommendations: t.Dict[int, t.List[t.Tuple[int, float]]] = recommendations
        self._config = config
        self._params = params
        self._evaluation_objects = evaluation_objects
        self._additional_data = additional_data

    @abstractmethod
    def name(self):
        pass

    def eval(self):
        return np.average(list(self.eval_user_metric().values()))

    @staticmethod
    def needs_full_recommendations():
        return False

    def get(self):
        return [self]