elliot.evaluation.metrics.fairness.rsp package

Submodules

elliot.evaluation.metrics.fairness.rsp.rsp module

This is the implementation of the Ranking-based Statistical Parity (RSP) metric. It proceeds from a user-wise computation, and average the values over the users.

class elliot.evaluation.metrics.fairness.rsp.rsp.RSP(recommendations, config, params, eval_objects, additional_data)

Bases: elliot.evaluation.metrics.base_metric.BaseMetric

Ranking-based Statistical Parity

This class represents the implementation of the Ranking-based Statistical Parity (RSP) recommendation metric.

For further details, please refer to the paper

\[\mathrm {RSP}=\frac{{std}(P(R @ k \mid g=g_{1}), \ldots, P(R @ k \mid g=g_{A}))} {{mean}(P(R @ k \mid g=g_{1}), \ldots, P(R @ k \mid g=g_{A}))}\]

\(P(R @ k \mid g=g_{A})) = \frac{\sum_{u=1}^{N} \sum_{i=1}^{k} G_{g_{a}}(R_{u, i})} {\sum_{u=1}^{N} \sum_{i \in I \backslash I_{u}^{+}} G_{g_{a}}(i)}\)

\(\sum_{i=1}^{k} G_{g_{a}}(R_{u, i})\) calculates how many un-interacted items from group {g_a} are ranked in top-𝑘 for user u.

\(\sum_{i \in I \backslash I_{u}^{+}} G_{g_{a}}(i)\) calculates how many un-interacted items belong to group {g_a} for u

To compute the metric, add it to the config file adopting the following pattern:

complex_metrics:
 - metric: RSP
  clustering_name: ItemPopularity
  clustering_file: ../data/movielens_1m/i_pop.tsv

eval()

get()

name()

Metric Name Getter :return: returns the public name of the metric

process()

Evaluation function :return: the overall value of Ranking-based Statistical Parity (RSP)

Module contents