"""
This is the implementation of the Bias Disparity 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
import pandas as pd
from collections import Counter
from . import BiasDisparityBR, BiasDisparityBS
from elliot.evaluation.metrics.base_metric import BaseMetric
from elliot.evaluation.metrics.metrics_utils import ProxyMetric
[docs]class BiasDisparityBD(BaseMetric):
r"""
Bias Disparity - Standard
This class represents the implementation of the Bias Disparity recommendation metric.
For further details, please refer to the `paper <https://arxiv.org/pdf/1811.01461>`_
.. math::
\mathrm {BD(G, C)}=\frac{B_{R}(G, C)-B_{S}(G, C)}{B_{S}(G, C)}
To compute the metric, add it to the config file adopting the following pattern:
.. code:: yaml
complex_metrics:
- metric: BiasDisparityBD
user_clustering_name: Happiness
user_clustering_file: ../data/movielens_1m/u_happy.tsv
item_clustering_name: ItemPopularity
item_clustering_file: ../data/movielens_1m/i_pop.tsv
"""
def __init__(self, recommendations, config, params, eval_objects, additional_data):
"""
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, additional_data)
self._item_clustering_path = self._additional_data.get("item_clustering_file", False)
if self._item_clustering_path:
self._item_clustering = pd.read_csv(self._item_clustering_path, sep="\t", header=None)
self._item_n_clusters = self._item_clustering[1].nunique()
self._item_clustering = dict(zip(self._item_clustering[0], self._item_clustering[1]))
self._item_clustering_name = self._additional_data['item_clustering_name']
else:
self._item_n_clusters = 1
self._item_clustering = {}
self._item_clustering_name = ""
self._user_clustering_path = self._additional_data.get("user_clustering_file", False)
if self._user_clustering_path:
self._user_clustering = pd.read_csv(self._user_clustering_path, sep="\t", header=None)
self._user_n_clusters = self._user_clustering[1].nunique()
self._user_clustering = dict(zip(self._user_clustering[0], self._user_clustering[1]))
self._user_clustering_name = self._additional_data['user_clustering_name']
else:
self._user_n_clusters = 1
self._user_clustering = {}
self._user_clustering_name = ""
self._category_sum = np.zeros((self._user_n_clusters,self._item_n_clusters))
self._total_sum = np.zeros(self._user_n_clusters)
self.process()
[docs] def name(self):
"""
Metric Name Getter
:return: returns the public name of the metric
"""
return f"BiasDisparityBD_users:{self._user_clustering_name}_items:{self._item_clustering_name}"
[docs] def process(self):
"""
Evaluation function
:return: the overall value of Bias Disparity
"""
BR = BiasDisparityBR(self._recommendations, self._config, self._params, self._evaluation_objects, self._additional_data).get_BR()
BS = BiasDisparityBS(self._recommendations, self._config, self._params, self._evaluation_objects, self._additional_data).get_BS()
BD = (BR - BS) / BS
self._metric_objs_list = []
for u_group in range(self._user_n_clusters):
for i_category in range(self._item_n_clusters):
self._metric_objs_list.append(ProxyMetric(name= f"BiasDisparityBD_users:{self._user_clustering_name}-{u_group}_items:{self._item_clustering_name}-{i_category}",
val=BD[u_group, i_category],
needs_full_recommendations=False))
[docs] def get(self):
return self._metric_objs_list