elliot.evaluation.metrics.fairness.MAD package

Submodules

elliot.evaluation.metrics.fairness.MAD.ItemMADranking module

This is the implementation of the Item MAD ranking metric. It proceeds from a user-wise computation, and average the values over the users.

class elliot.evaluation.metrics.fairness.MAD.ItemMADranking.ItemMADranking(recommendations, config, params, eval_objects, additional_data)

Bases: elliot.evaluation.metrics.base_metric.BaseMetric

Item MAD Ranking-based

This class represents the implementation of the Item MAD ranking recommendation metric.

For further details, please refer to the paper

\[\mathrm {MAD}={avg}_{i, j}({MAD}(R^{(i)}, R^{(j)}))\]

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

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

eval()

Evaluation function :return: the overall averaged value of Item MAD ranking

get()

name()

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

elliot.evaluation.metrics.fairness.MAD.ItemMADrating module

This is the implementation of the Item MAD rating metric. It proceeds from a user-wise computation, and average the values over the users.

class elliot.evaluation.metrics.fairness.MAD.ItemMADrating.ItemMADrating(recommendations, config, params, eval_objects, additional_data)

Bases: elliot.evaluation.metrics.base_metric.BaseMetric

Item MAD Rating-based

This class represents the implementation of the Item MAD rating recommendation metric.

For further details, please refer to the paper

\[\mathrm {MAD}={avg}_{i, j}({MAD}(R^{(i)}, R^{(j)}))\]

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

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

eval()

Evaluation function :return: the overall averaged value of Item MAD rating

get()

name()

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

elliot.evaluation.metrics.fairness.MAD.UserMADranking module

This is the implementation of the User MAD ranking metric. It proceeds from a user-wise computation, and average the values over the users.

class elliot.evaluation.metrics.fairness.MAD.UserMADranking.UserMADranking(recommendations, config, params, eval_objects, additional_data)

Bases: elliot.evaluation.metrics.base_metric.BaseMetric

User MAD Ranking-based

This class represents the implementation of the User MAD ranking recommendation metric.

For further details, please refer to the paper

\[\mathrm {MAD}={avg}_{i, j}({MAD}(R^{(i)}, R^{(j)}))\]

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

complex_metrics:
- metric: UserMADranking
  clustering_name: Happiness
  clustering_file: ../data/movielens_1m/u_happy.tsv

compute_idcg(user: int, cutoff: int) → float

Method to compute Ideal Discounted Cumulative Gain :param gain_map: :param cutoff: :return:

compute_user_ndcg(user_recommendations: List, user: int, cutoff: int) → float

Method to compute normalized Discounted Cumulative Gain :param sorted_item_predictions: :param gain_map: :param cutoff: :return:

eval()

Evaluation function :return: the overall averaged value of User MAD ranking

get()

name()

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

elliot.evaluation.metrics.fairness.MAD.UserMADrating module

This is the implementation of the User MAD rating metric. It proceeds from a user-wise computation, and average the values over the users.

class elliot.evaluation.metrics.fairness.MAD.UserMADrating.UserMADrating(recommendations, config, params, eval_objects, additional_data)

Bases: elliot.evaluation.metrics.base_metric.BaseMetric

User MAD Rating-based

This class represents the implementation of the User MAD rating recommendation metric.

For further details, please refer to the paper

\[\mathrm {MAD}={avg}_{i, j}({MAD}(R^{(i)}, R^{(j)}))\]

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

complex_metrics:
- metric: UserMADrating
  clustering_name: Happiness
  clustering_file: ../data/movielens_1m/u_happy.tsv

eval()

Evaluation function :return: the overall averaged value of User MAD rating

get()

name()

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

Module contents

This is the Precision metric module.

This module contains and expose the recommendation metric.