aif360.metrics.ClassificationMetric

class aif360.metrics.ClassificationMetric(dataset, classified_dataset, unprivileged_groups=None, privileged_groups=None)

Class for computing metrics based on two BinaryLabelDatasets.

The first dataset is the original one and the second is the output of the classification transformer (or similar).

Parameters:

• dataset (BinaryLabelDataset) – Dataset containing ground-truth labels.
• classified_dataset (BinaryLabelDataset) – Dataset containing predictions.
• privileged_groups (list(dict)) – Privileged groups. Format is a list of dicts where the keys are protected_attribute_names and the values are values in protected_attributes. Each dict element describes a single group. See examples for more details.
• unprivileged_groups (list(dict)) – Unprivileged groups in the same format as privileged_groups.

Raises:

TypeError – dataset and classified_dataset must be BinaryLabelDataset types.

Methods

accuracy	\(ACC = (TP + TN)/(P + N)\).
average_abs_odds_difference	Average of absolute difference in FPR and TPR for unprivileged and privileged groups:
average_odds_difference	Average of difference in FPR and TPR for unprivileged and privileged groups:
base_rate	Compute the base rate, \(Pr(Y = 1) = P/(P+N)\), optionally conditioned on protected attributes.
between_all_groups_coefficient_of_variation	The between-group coefficient of variation is two times the square root of the between_all_groups_generalized_entropy_index() with \(\alpha = 2\).
between_all_groups_generalized_entropy_index	Between-group generalized entropy index that uses all combinations of groups based on self.dataset.protected_attributes.
between_all_groups_theil_index	The between-group Theil index is the between_all_groups_generalized_entropy_index() with \(\alpha = 1\).
between_group_coefficient_of_variation	The between-group coefficient of variation is two times the square root of the between_group_generalized_entropy_index() with \(\alpha = 2\).
between_group_generalized_entropy_index	Between-group generalized entropy index that uses self.privileged_groups and self.unprivileged_groups as the only two groups.
between_group_theil_index	The between-group Theil index is the between_group_generalized_entropy_index() with \(\alpha = 1\).
binary_confusion_matrix	Compute the number of true/false positives/negatives, optionally conditioned on protected attributes.
coefficient_of_variation	The coefficient of variation is two times the square root of the generalized_entropy_index() with \(\alpha = 2\).
consistency	Individual fairness metric from [1]_ that measures how similar the labels are for similar instances.
difference	Compute difference of the metric for unprivileged and privileged groups.
differential_fairness_bias_amplification	Bias amplification is the difference in smoothed EDF between the classifier and the original dataset.
disparate_impact
equal_opportunity_difference	Alias of true_positive_rate_difference().
error_rate	\(ERR = (FP + FN)/(P + N)\)
error_rate_difference	Difference in error rates for unprivileged and privileged groups, \(ERR_{D = \text{unprivileged}} - ERR_{D = \text{privileged}}\).
error_rate_ratio	Ratio of error rates for unprivileged and privileged groups, \(\frac{ERR_{D = \text{unprivileged}}}{ERR_{D = \text{privileged}}}\).
false_discovery_rate	\(FDR = FP/(TP + FP)\)
false_discovery_rate_difference	\(FDR_{D = \text{unprivileged}} - FDR_{D = \text{privileged}}\)
false_discovery_rate_ratio	\(\frac{FDR_{D = \text{unprivileged}}}{FDR_{D = \text{privileged}}}\)
false_negative_rate	\(FNR = FN/P\)
false_negative_rate_difference	\(FNR_{D = \text{unprivileged}} - FNR_{D = \text{privileged}}\)
false_negative_rate_ratio	\(\frac{FNR_{D = \text{unprivileged}}}{FNR_{D = \text{privileged}}}\)
false_omission_rate	\(FOR = FN/(TN + FN)\)
false_omission_rate_difference	\(FOR_{D = \text{unprivileged}} - FOR_{D = \text{privileged}}\)
false_omission_rate_ratio	\(\frac{FOR_{D = \text{unprivileged}}}{FOR_{D = \text{privileged}}}\)
false_positive_rate	\(FPR = FP/N\)
false_positive_rate_difference	\(FPR_{D = \text{unprivileged}} - FPR_{D = \text{privileged}}\)
false_positive_rate_ratio	\(\frac{FPR_{D = \text{unprivileged}}}{FPR_{D = \text{privileged}}}\)
generalized_binary_confusion_matrix	Compute the number of generalized true/false positives/negatives, optionally conditioned on protected attributes.
generalized_entropy_index	Generalized entropy index is proposed as a unified individual and group fairness measure in [3].
generalized_false_negative_rate	\(GFNR = GFN/P\)
generalized_false_positive_rate	\(GFPR = GFP/N\)
generalized_true_negative_rate	\(GTNR = GTN/N\)
generalized_true_positive_rate	Return the ratio of generalized true positives to positive examples in the dataset, \(GTPR = GTP/P\), optionally conditioned on protected attributes.
mean_difference	Alias of statistical_parity_difference().
negative_predictive_value	\(NPV = TN/(TN + FN)\)
num_false_negatives	\(FN = \sum_{i=1}^n \mathbb{1}[y_i = \text{favorable}]\mathbb{1}[\hat{y}_i = \text{unfavorable}]\)
num_false_positives	\(FP = \sum_{i=1}^n \mathbb{1}[y_i = \text{unfavorable}]\mathbb{1}[\hat{y}_i = \text{favorable}]\)
num_generalized_false_negatives	Return the generalized number of false negatives, \(GFN\), the weighted sum of 1 - predicted scores where true labels are ‘favorable’, optionally conditioned on protected attributes.
num_generalized_false_positives	Return the generalized number of false positives, \(GFP\), the weighted sum of predicted scores where true labels are ‘unfavorable’, optionally conditioned on protected attributes.
num_generalized_true_negatives	Return the generalized number of true negatives, \(GTN\), the weighted sum of 1 - predicted scores where true labels are ‘unfavorable’, optionally conditioned on protected attributes.
num_generalized_true_positives	Return the generalized number of true positives, \(GTP\), the weighted sum of predicted scores where true labels are ‘favorable’, optionally conditioned on protected attributes.
num_instances	Compute the number of instances, \(n\), in the dataset conditioned on protected attributes if necessary.
num_negatives	Compute the number of negatives, \(N = \sum_{i=1}^n \mathbb{1}[y_i = 0]\), optionally conditioned on protected attributes.
num_positives	Compute the number of positives, \(P = \sum_{i=1}^n \mathbb{1}[y_i = 1]\), optionally conditioned on protected attributes.
num_pred_negatives	\(\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{unfavorable}]\)
num_pred_positives	\(\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{favorable}]\)
num_true_negatives	\(TN = \sum_{i=1}^n \mathbb{1}[y_i = \text{unfavorable}]\mathbb{1}[\hat{y}_i = \text{unfavorable}]\)
num_true_positives	Return the number of instances in the dataset where both the predicted and true labels are ‘favorable’, \(TP = \sum_{i=1}^n \mathbb{1}[y_i = \text{favorable}]\mathbb{1}[\hat{y}_i = \text{favorable}]\), optionally conditioned on protected attributes.
performance_measures	Compute various performance measures on the dataset, optionally conditioned on protected attributes.
positive_predictive_value	\(PPV = TP/(TP + FP)\)
power	Alias of num_true_positives().
precision	Alias of positive_predictive_value().
ratio	Compute ratio of the metric for unprivileged and privileged groups.
recall	Alias of true_positive_rate().
rich_subgroup	Audit dataset with respect to rich subgroups defined by linear thresholds of sensitive attributes
selection_rate	\(Pr(\hat{Y} = \text{favorable})\)
sensitivity	Alias of true_positive_rate().
smoothed_empirical_differential_fairness	Smoothed EDF from [#foulds18]_.
specificity	Alias of true_negative_rate().
statistical_parity_difference
theil_index	The Theil index is the generalized_entropy_index() with \(\alpha = 1\).
true_negative_rate	\(TNR = TN/N\)
true_positive_rate	Return the ratio of true positives to positive examples in the dataset, \(TPR = TP/P\), optionally conditioned on protected attributes.
true_positive_rate_difference	\(TPR_{D = \text{unprivileged}} - TPR_{D = \text{privileged}}\)

__init__(dataset, classified_dataset, unprivileged_groups=None, privileged_groups=None)

Parameters:

• dataset (BinaryLabelDataset) – Dataset containing ground-truth labels.
• classified_dataset (BinaryLabelDataset) – Dataset containing predictions.
• privileged_groups (list(dict)) – Privileged groups. Format is a list of dicts where the keys are protected_attribute_names and the values are values in protected_attributes. Each dict element describes a single group. See examples for more details.
• unprivileged_groups (list(dict)) – Unprivileged groups in the same format as privileged_groups.

Raises:

TypeError – dataset and classified_dataset must be BinaryLabelDataset types.

accuracy(privileged=None)

\(ACC = (TP + TN)/(P + N)\).

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

average_abs_odds_difference()

Average of absolute difference in FPR and TPR for unprivileged and privileged groups:

\[\tfrac{1}{2}\left[|FPR_{D = \text{unprivileged}} - FPR_{D = \text{privileged}}| + |TPR_{D = \text{unprivileged}} - TPR_{D = \text{privileged}}|\right]\]

A value of 0 indicates equality of odds.

average_odds_difference()

Average of difference in FPR and TPR for unprivileged and privileged groups:

\[\tfrac{1}{2}\left[(FPR_{D = \text{unprivileged}} - FPR_{D = \text{privileged}}) + (TPR_{D = \text{unprivileged}} - TPR_{D = \text{privileged}}))\right]\]

A value of 0 indicates equality of odds.

between_all_groups_coefficient_of_variation()

The between-group coefficient of variation is two times the square root of the between_all_groups_generalized_entropy_index() with \(\alpha = 2\).

between_all_groups_generalized_entropy_index(alpha=2)

Between-group generalized entropy index that uses all combinations of groups based on self.dataset.protected_attributes. See _between_group_generalized_entropy_index().

Parameters:alpha (int) – See generalized_entropy_index().

between_all_groups_theil_index()

The between-group Theil index is the between_all_groups_generalized_entropy_index() with \(\alpha = 1\).

between_group_coefficient_of_variation()

The between-group coefficient of variation is two times the square root of the between_group_generalized_entropy_index() with \(\alpha = 2\).

between_group_generalized_entropy_index(alpha=2)

Between-group generalized entropy index that uses self.privileged_groups and self.unprivileged_groups as the only two groups. See _between_group_generalized_entropy_index().

Parameters:alpha (int) – See generalized_entropy_index().

between_group_theil_index()

The between-group Theil index is the between_group_generalized_entropy_index() with \(\alpha = 1\).

binary_confusion_matrix(privileged=None)

Compute the number of true/false positives/negatives, optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Returns:dict – Number of true positives, false positives, true negatives, false negatives (optionally conditioned).

coefficient_of_variation()

The coefficient of variation is two times the square root of the generalized_entropy_index() with \(\alpha = 2\).

differential_fairness_bias_amplification(concentration=1.0)

Bias amplification is the difference in smoothed EDF between the classifier and the original dataset. Positive values mean the bias increased due to the classifier.

Parameters:concentration (float, optional) – Concentration parameter for Dirichlet smoothing. Must be non-negative.

disparate_impact()

\[\frac{Pr(\hat{Y} = 1 | D = \text{unprivileged})} {Pr(\hat{Y} = 1 | D = \text{privileged})}\]

equal_opportunity_difference()

Alias of true_positive_rate_difference().

error_rate(privileged=None)

\(ERR = (FP + FN)/(P + N)\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

error_rate_difference()

Difference in error rates for unprivileged and privileged groups, \(ERR_{D = \text{unprivileged}} - ERR_{D = \text{privileged}}\).

error_rate_ratio()

Ratio of error rates for unprivileged and privileged groups, \(\frac{ERR_{D = \text{unprivileged}}}{ERR_{D = \text{privileged}}}\).

false_discovery_rate(privileged=None)

\(FDR = FP/(TP + FP)\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

false_discovery_rate_difference()

\(FDR_{D = \text{unprivileged}} - FDR_{D = \text{privileged}}\)

false_discovery_rate_ratio()

\(\frac{FDR_{D = \text{unprivileged}}}{FDR_{D = \text{privileged}}}\)

false_negative_rate(privileged=None)

\(FNR = FN/P\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

false_negative_rate_difference()

\(FNR_{D = \text{unprivileged}} - FNR_{D = \text{privileged}}\)

false_negative_rate_ratio()

\(\frac{FNR_{D = \text{unprivileged}}}{FNR_{D = \text{privileged}}}\)

false_omission_rate(privileged=None)

\(FOR = FN/(TN + FN)\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

false_omission_rate_difference()

\(FOR_{D = \text{unprivileged}} - FOR_{D = \text{privileged}}\)

false_omission_rate_ratio()

\(\frac{FOR_{D = \text{unprivileged}}}{FOR_{D = \text{privileged}}}\)

false_positive_rate(privileged=None)

\(FPR = FP/N\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

false_positive_rate_difference()

\(FPR_{D = \text{unprivileged}} - FPR_{D = \text{privileged}}\)

false_positive_rate_ratio()

\(\frac{FPR_{D = \text{unprivileged}}}{FPR_{D = \text{privileged}}}\)

generalized_binary_confusion_matrix(privileged=None)

Compute the number of generalized true/false positives/negatives, optionally conditioned on protected attributes. Generalized counts are based on scores and not on the hard predictions.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Returns:dict – Number of generalized true positives, generalized false positives, generalized true negatives, generalized false negatives (optionally conditioned).

generalized_entropy_index(alpha=2)

Generalized entropy index is proposed as a unified individual and group fairness measure in [3]. With \(b_i = \hat{y}_i - y_i + 1\):

\[\begin{split}\mathcal{E}(\alpha) = \begin{cases} \frac{1}{n \alpha (\alpha-1)}\sum_{i=1}^n\left[\left(\frac{b_i}{\mu}\right)^\alpha - 1\right],& \alpha \ne 0, 1,\\ \frac{1}{n}\sum_{i=1}^n\frac{b_{i}}{\mu}\ln\frac{b_{i}}{\mu},& \alpha=1,\\ -\frac{1}{n}\sum_{i=1}^n\ln\frac{b_{i}}{\mu},& \alpha=0. \end{cases}\end{split}\]

Parameters:alpha (int) – Parameter that regulates the weight given to distances between values at different parts of the distribution.

References

[3]

(1, 2) T. Speicher, H. Heidari, N. Grgic-Hlaca, K. P. Gummadi, A. Singla, A. Weller, and M. B. Zafar, “A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual and Group Unfairness via Inequality Indices,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018.

generalized_false_negative_rate(privileged=None)

\(GFNR = GFN/P\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

generalized_false_positive_rate(privileged=None)

\(GFPR = GFP/N\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

generalized_true_negative_rate(privileged=None)

\(GTNR = GTN/N\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

generalized_true_positive_rate(privileged=None)

Return the ratio of generalized true positives to positive examples in the dataset, \(GTPR = GTP/P\), optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

negative_predictive_value(privileged=None)

\(NPV = TN/(TN + FN)\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_false_negatives(privileged=None)

\(FN = \sum_{i=1}^n \mathbb{1}[y_i = \text{favorable}]\mathbb{1}[\hat{y}_i = \text{unfavorable}]\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_false_positives(privileged=None)

\(FP = \sum_{i=1}^n \mathbb{1}[y_i = \text{unfavorable}]\mathbb{1}[\hat{y}_i = \text{favorable}]\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_generalized_false_negatives(privileged=None)

Return the generalized number of false negatives, \(GFN\), the weighted sum of 1 - predicted scores where true labels are ‘favorable’, optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_generalized_false_positives(privileged=None)

Return the generalized number of false positives, \(GFP\), the weighted sum of predicted scores where true labels are ‘unfavorable’, optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be must be provided at initialization to condition on them.

num_generalized_true_negatives(privileged=None)

Return the generalized number of true negatives, \(GTN\), the weighted sum of 1 - predicted scores where true labels are ‘unfavorable’, optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_generalized_true_positives(privileged=None)

Return the generalized number of true positives, \(GTP\), the weighted sum of predicted scores where true labels are ‘favorable’, optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_pred_negatives(privileged=None)

\(\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{unfavorable}]\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_pred_positives(privileged=None)

\(\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{favorable}]\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_true_negatives(privileged=None)

\(TN = \sum_{i=1}^n \mathbb{1}[y_i = \text{unfavorable}]\mathbb{1}[\hat{y}_i = \text{unfavorable}]\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

num_true_positives(privileged=None)

Return the number of instances in the dataset where both the predicted and true labels are ‘favorable’, \(TP = \sum_{i=1}^n \mathbb{1}[y_i = \text{favorable}]\mathbb{1}[\hat{y}_i = \text{favorable}]\), optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

performance_measures(privileged=None)

Compute various performance measures on the dataset, optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Returns:dict – True positive rate, true negative rate, false positive rate, false negative rate, positive predictive value, negative predictive value, false discover rate, false omission rate, and accuracy (optionally conditioned).

positive_predictive_value(privileged=None)

\(PPV = TP/(TP + FP)\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

power(privileged=None)

Alias of num_true_positives().

precision(privileged=None)

Alias of positive_predictive_value().

recall(privileged=None)

Alias of true_positive_rate().

selection_rate(privileged=None)

\(Pr(\hat{Y} = \text{favorable})\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

sensitivity(privileged=None)

Alias of true_positive_rate().

specificity(privileged=None)

Alias of true_negative_rate().

statistical_parity_difference()

\[Pr(\hat{Y} = 1 | D = \text{unprivileged}) - Pr(\hat{Y} = 1 | D = \text{privileged})\]

theil_index()

The Theil index is the generalized_entropy_index() with \(\alpha = 1\).

true_negative_rate(privileged=None)

\(TNR = TN/N\)

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

true_positive_rate(privileged=None)

Return the ratio of true positives to positive examples in the dataset, \(TPR = TP/P\), optionally conditioned on protected attributes.

Parameters:privileged (bool, optional) – Boolean prescribing whether to condition this metric on the privileged_groups, if True, or the unprivileged_groups, if False. Defaults to None meaning this metric is computed over the entire dataset. Raises:AttributeError – privileged_groups or unprivileged_groups must be provided at initialization to condition on them.

true_positive_rate_difference()

\(TPR_{D = \text{unprivileged}} - TPR_{D = \text{privileged}}\)