odtlearn.fair_oct

Module Contents

Classes

FairConstrainedOCT	Helper class that provides a standard way to create an ABC using
FairSPOCT	Helper class that provides a standard way to create an ABC using
FairCSPOCT	Helper class that provides a standard way to create an ABC using
FairPEOCT	Helper class that provides a standard way to create an ABC using
FairEOppOCT	Helper class that provides a standard way to create an ABC using
FairEOddsOCT	Helper class that provides a standard way to create an ABC using
FairOCT	Helper class that provides a standard way to create an ABC using

class odtlearn.fair_oct.FairConstrainedOCT(solver, _lambda, depth, time_limit, num_threads, verbose)

Bases: odtlearn.constrained_oct.ConstrainedOCT

Helper class that provides a standard way to create an ABC using inheritance.

fit(X, y, protect_feat, legit_factor)

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features)

The training input samples.

yarray-like, shape (n_samples,)

The target values (class labels in classification).

protect_featarray-like, shape (n_samples,1) or (n_samples, n_p)

The protected feature columns (Race, gender, etc); Can have one or more columns

legit_factorarray-like, shape (n_samples,)

The legitimate factor column(e.g., prior number of criminal acts)

Returns:

selfobject

Returns self.

predict(X)

Classify test points using the FairTree classifier

Parameters:

Xarray-like, shape (n_samples, n_features)

The input samples.

Returns:

yndarray, shape (n_samples,)

The label for each sample is the label of the closest sample seen during fit.

class odtlearn.fair_oct.FairSPOCT(solver, positive_class, depth=1, time_limit=60, _lambda=0, obj_mode='acc', fairness_bound=1, num_threads=None, verbose=False)

Bases: FairConstrainedOCT

Helper class that provides a standard way to create an ABC using inheritance.

calc_metric(protect_feat, y)

This function returns the statistical parity value for any given protected level and outcome value

Parameters:

• protect_feat – array-like, shape (n_samples,1) or (n_samples, n_p) The protected feature columns (Race, gender, etc); We could have one or more columns

• y – array-like, shape (n_samples,) The target values (class labels in classification).

Return sp_dict:

a dictionary with key =(p,t) and value = P(Y=t|P=p)

where p is a protected level and t is an outcome value

class odtlearn.fair_oct.FairCSPOCT(solver, positive_class, depth=1, time_limit=60, _lambda=0, obj_mode='acc', fairness_bound=1, num_threads=None, verbose=False)

Bases: FairConstrainedOCT

Helper class that provides a standard way to create an ABC using inheritance.

calc_metric(protect_feat, legit_factor, y)

Returns the conditional statistical parity value for any given protected level, legitimate feature value and outcome value

Parameters:

• protect_feat – array-like, shape (n_samples,1) or (n_samples, n_p) The protected feature columns (Race, gender, etc); We could have one or more columns

• legit_fact – array-like, shape (n_samples,) The legitimate factor column(e.g., prior number of criminal acts)

• y – array-like, shape (n_samples,) The target values (class labels in classification).

Return csp_dict:

a dictionary with key =(p, f, t) and value = P(Y=t|P=p, L=f) where p is a protected level and t is an outcome value and l is the value of the legitimate feature

class odtlearn.fair_oct.FairPEOCT(solver, positive_class, depth=1, time_limit=60, _lambda=0, obj_mode='acc', fairness_bound=1, num_threads=None, verbose=False)

Bases: FairConstrainedOCT

Helper class that provides a standard way to create an ABC using inheritance.

calc_metric(protect_feat, y, y_pred)

This function returns the false positive and true positive rate value for any given protected level, outcome value and prediction value

Parameters:

• protect_feat – array-like, shape (n_samples,1) or (n_samples, n_p) The protected feature columns (Race, gender, etc); We could have one or more columns

• y – array-like, shape (n_samples,) The true target values (class labels in classification).

• y_pred – array-like, shape (n_samples,) The predicted values (class labels in classification).

Return eq_dict:

a dictionary with key =(p, t, t_pred) and value = P(Y_pred=t_pred|P=p, Y=t)

class odtlearn.fair_oct.FairEOppOCT(solver, positive_class, depth=1, time_limit=60, _lambda=0, obj_mode='acc', fairness_bound=1, num_threads=None, verbose=False)

Bases: FairConstrainedOCT

Helper class that provides a standard way to create an ABC using inheritance.

abstract calc_metric()

class odtlearn.fair_oct.FairEOddsOCT(solver, positive_class, depth=1, time_limit=60, _lambda=0, obj_mode='acc', fairness_bound=1, num_threads=None, verbose=False)

Bases: FairConstrainedOCT

Helper class that provides a standard way to create an ABC using inheritance.

class odtlearn.fair_oct.FairOCT(solver, positive_class, _lambda=0, depth=1, obj_mode='acc', fairness_type=None, fairness_bound=1, time_limit=60, num_threads=None, verbose=False)

Bases: odtlearn.flow_oct_ms.FlowOCTMultipleSink

Helper class that provides a standard way to create an ABC using inheritance.

fit(X, y, protect_feat, legit_factor)

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features)

The training input samples.

yarray-like, shape (n_samples,)

The target values (class labels in classification).

protect_featarray-like, shape (n_samples,1) or (n_samples, n_p)

The protected feature columns (Race, gender, etc); Can have one or more columns

legit_factorarray-like, shape (n_samples,)

The legitimate factor column(e.g., prior number of criminal acts)

Returns

——-

selfobject

Returns self.

predict(X)

Classify test points using the FairTree classifier Parameters ———- X : array-like, shape (n_samples, n_features)

The input samples.

Returns

yndarray, shape (n_samples,)

The label for each sample is the label of the closest sample seen during fit.

get_SP(protect_feat, y)

This function returns the statistical parity value for any given protected level and outcome value

Parameters:

• protect_feat – array-like, shape (n_samples,1) or (n_samples, n_p) The protected feature columns (Race, gender, etc); We could have one or more columns

• y – array-like, shape (n_samples,) The target values (class labels in classification).

Return sp_dict:

a dictionary with key =(p,t) and value = P(Y=t|P=p)

where p is a protected level and t is an outcome value

get_CSP(protect_feat, legit_factor, y)

This function returns the conditional statistical parity value for any given protected level, legitimate feature value and outcome value

Parameters:

• protect_feat – array-like, shape (n_samples,1) or (n_samples, n_p) The protected feature columns (Race, gender, etc); We could have one or more columns

• legit_fact – array-like, shape (n_samples,) The legitimate factor column(e.g., prior number of criminal acts)

• y – array-like, shape (n_samples,) The target values (class labels in classification).

Return csp_dict:

a dictionary with key =(p, f, t) and value = P(Y=t|P=p, L=f) where p is a protected level and t is an outcome value and l is the value of the legitimate feature

get_EqOdds(protect_feat, y, y_pred)

This function returns the false positive and true positive rate value for any given protected level, outcome value and prediction value

Parameters:

• protect_feat – array-like, shape (n_samples,1) or (n_samples, n_p) The protected feature columns (Race, gender, etc); We could have one or more columns

• y – array-like, shape (n_samples,) The true target values (class labels in classification).

• y_pred – array-like, shape (n_samples,) The predicted values (class labels in classification).

Return eq_dict:

a dictionary with key =(p, t, t_pred) and value = P(Y_pred=t_pred|P=p, Y=t)

get_CondEqOdds(protect_feat, legit_factor, y, y_pred)

This function returns the conditional false negative and true positive rate value for any given protected level, outcome value, prediction value and legitimate feature value

Parameters:

• protect_feat – array-like, shape (n_samples,1) or (n_samples, n_p) The protected feature columns (Race, gender, etc); We could have one or more columns

• legit_factor – array-like, shape (n_samples,) The legitimate factor column(e.g., prior number of criminal acts)

• y – array-like, shape (n_samples,) The true target values (class labels in classification).

• y_pred – array-like, shape (n_samples,) The predicted values (class labels in classification).

Return ceq_dict:

a dictionary with key =(p, f, t, t_pred) and value = P(Y_pred=t_pred|P=p, Y=t, L=f)

fairness_metric_summary(metric, new_data=None)