Inprocess algorithms#

In-process algorithms take training data and make predictions.

Inprocess base#

Abstract Base Class of all algorithms in the framework.

class InAlgorithm#

Bases: Algorithm, ABC

Abstract Base Class for algorithms that run in the middle of the pipeline.

abstract fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

abstract property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

abstract property name: str#: Name of the algorithm.

abstract predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

abstract run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

final run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class InAlgorithmDC#

Bases: InAlgorithm, ABC

Base class for algorithms that are dataclasses.

abstract fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

abstract property name: str#: Name of the algorithm.

abstract predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

abstract run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class InAlgorithmNoParams#

Bases: InAlgorithm, ABC

Base class for algorithms without parameters.

abstract fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

abstract property name: str#: Name of the algorithm.

abstract predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

abstract run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Agarwal#

Implementation of Agarwal model.

class Agarwal(dir=<factory>, fairness=FairnessType.dp, classifier=ClassifierType.lr, eps=0.1, iters=50, C=None, kernel=None)#

Bases: InAlgorithmSubprocess

Agarwal class.

A wrapper around the Exponentiated Gradient method.

Parameters:

fairness (FairnessType) – Type of fairness to enforce.
classifier (ClassifierType) – Type of classifier to use.
eps (float) – Epsilon fo.
iters (int) – Number of iterations for the DP algorithm.
C (float | None) – C parameter for the SVM algorithm.
kernel (KernelType | None) – Kernel type for the SVM algorithm.
dir (Path) –

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#

Path to a (Python) executable.

By default, the Python executable that called this script is used.

fit(train, seed=888)#

Fit Algorithm in a subprocess on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property model_path: Path#: Path to where the model with be stored.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions in a subprocess on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm in a subprocess on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Distributionally-robust optimization#

Fairness without Demographics.

class DRO(dir=<factory>, eta=0.5, epochs=10, batch_size=32, network_size=<factory>)#

Bases: InAlgorithmSubprocess

Implementation of https://arxiv.org/abs/1806.08010 .

Parameters:

eta (float) – Tolerance.
epochs (int) – The number of epochs to train for.
batch_size (int) – The batch size.
network_size (List[int]) – The size of the network.
dir (Path) –

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#

Path to a (Python) executable.

By default, the Python executable that called this script is used.

fit(train, seed=888)#

Fit Algorithm in a subprocess on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property model_path: Path#: Path to where the model with be stored.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions in a subprocess on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm in a subprocess on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Base for installed model#

Installable model.

This is a kind of complicated model, but it’s incredibly useful. Say you find a paper from a few years ago with code. It’s not unreasonable that there might be dependency clashes, python clashes, clashes galore. This approach downloads a model, runs it in its own venv and makes everyone happy.

class InstalledModel(name, dir_name, top_dir, url=None, executable=None, *, use_pdm=False)#

Bases: SubprocessAlgorithmMixin, InAlgorithm, ABC

The model that does the magic.

Download code from given URL and create Pip environment with Pipfile found in the code.

Parameters:

name (str) – Name of the model.
dir_name (str) – Where to download the code to (can be chosen freely).
top_dir (str) – Top directory of the repository where the Pipfile can be found (this is usually simply the last part of the repository URL).
url (str | None) – URL of the repository. (Default: None)
executable (list[str] | None) – Path to a Python executable. (Default: None.
use_pdm (bool) – If True, will try to use pdm instead of pipenv. (Default: False)

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#: Python executable from the virtualenv associated with the model.

abstract fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

abstract property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

abstract predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

remove()#

Remove the directory that we created in _clone_directory().

Return type:: None

abstract run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Kamiran#

Kamiran and Calders 2012.

class Reweighting(classifier=ClassifierType.lr, C=None, kernel=None)#

Bases: InAlgorithm

An implementation of the Reweighing method from Kamiran&Calders, 2012.

Kamiran and Calders 2012.

Each sample is assigned an instance-weight based on the joing probability of S and Y which is used during training of a classifier.

Parameters:

classifier (ClassifierType) – The classifier to use.
C (float | None) – The C parameter for the classifier.
kernel (KernelType | None) – The kernel to use for the classifier if SVM selected.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

compute_instance_weights(train, *, balance_groups=False, upweight=False)#

Compute weights for all samples.

Parameters:

train (DataTuple) – The training data.
balance_groups (bool) –
Whether to balance the groups. When False, the groups are balanced as in Kamiran and Calders 2012. When True, the groups are numerically balanced. (Default: False)
upweight (bool) – If balance_groups is True, whether to upweight the groups, or to downweight them. Downweighting is done by multiplying the weights by the inverse of the group size and is more numerically stable for small group sizes. (Default: False)

Returns:

A dataframe with the instance weights for each sample in the training data.

Return type:

DataFrame

Kamishima#

Wrapper for calling Kamishima model.

class Kamishima(*, eta=1.0)#

Bases: InstalledModel

Model that calls Kamishima’s code.

Based on Algo-Fairness https://github.com/algofairness/fairness-comparison/blob/master/fairness/algorithms/kamishima/KamishimaAlgorithm.py

Parameters:: eta (float) – Tolerance.

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#: Python executable from the virtualenv associated with the model.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

remove()#

Remove the directory that we created in _clone_directory().

Return type:: None

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Logistic regression#

Wrapper around Sci-Kit Learn Logistic Regression.

class LR(C=<factory>)#

Bases: InAlgorithmDC

Logistic regression with hard predictions.

This is a wrapper around Sci-Kit Learn’s LogisticRegression. See the sklearn documentation for details.

Parameters:: C (float) – The regularization parameter.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

SoftPrediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class LRCV(n_splits=3)#

Bases: InAlgorithmDC

Kind of a cheap hack for now, but gives a proper cross-valudeted LR.

Parameters:: n_splits (int) – The number of splits for the cross-validation.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Majority#

Simply returns the majority label from the train set.

class Majority#

Bases: InAlgorithmNoParams

Simply returns the majority label from the train set.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Manual methods#

Manually specified (i.e. not learned) models.

class Corels#

Bases: InAlgorithmNoParams

CORELS (Certifiably Optimal RulE ListS) algorithm for the COMPAS dataset.

This algorithm uses if-statements to make predictions. It only works on COMPAS with s as sex.

From this paper: https://arxiv.org/abs/1704.01701

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

MLP#

Wrapper for SKLearn implementation of MLP.

class MLP(hidden_layer_sizes=<factory>, batch_size=32, lr=0.001)#

Bases: InAlgorithmDC

Multi-layer perceptron.

This is a wraper around the SKLearn implementation of the MLP. Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html

Parameters:

hidden_layer_sizes (Tuple[int, ...]) – The number of neurons in each hidden layer.
batch_size (int) –
lr (float) –

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Oracle#

Perfect predictors.

class DPOracle#

Bases: InAlgorithmNoParams

A perfect Demographic Parity Predictor.

Can only be used if test is a DataTuple, rather than the usual TestTuple. This model isn’t intended for general use, but can be useful if you want to either do a sanity check, or report potential values.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class Oracle#

Bases: InAlgorithmNoParams

A perfect predictor.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

SVM#

Wrapper for SKLearn implementation of SVM.

class SVM(C=<factory>, kernel=<factory>)#

Bases: InAlgorithmDC

A wrapper around the SciKitLearn Support Vector Classifier (SVC) model.

Documentation for the underlying classifier can be found here.

Parameters:

C (float) – The penalty parameter of the error term.
kernel (KernelType) – The kernel to use.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

Zafar#

Algorithms by Zafar et al. for Demographic Parity.

class ZafarAccuracy(*, gamma=0.5)#

Bases: _ZafarAlgorithmBase

Zafar with fairness.

Parameters:: gamma (float) –

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#: Python executable from the virtualenv associated with the model.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

remove()#

Remove the directory that we created in _clone_directory().

Return type:: None

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class ZafarBaseline#

Bases: _ZafarAlgorithmBase

Zafar without fairness.

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#: Python executable from the virtualenv associated with the model.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

remove()#

Remove the directory that we created in _clone_directory().

Return type:: None

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class ZafarEqOdds(*, tau=5.0, mu=1.2, eps=0.0001)#

Bases: ZafarEqOpp

Zafar for Equalised Odds.

Parameters:

tau (float) –
mu (float) –
eps (float) –

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#: Python executable from the virtualenv associated with the model.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

remove()#

Remove the directory that we created in _clone_directory().

Return type:: None

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class ZafarEqOpp(*, tau=5.0, mu=1.2, eps=0.0001)#

Bases: _ZafarAlgorithmBase

Zafar for Equality of Opportunity.

Parameters:

tau (float) –
mu (float) –
eps (float) –

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#: Python executable from the virtualenv associated with the model.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

remove()#

Remove the directory that we created in _clone_directory().

Return type:: None

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

class ZafarFairness(*, C=0.001)#

Bases: _ZafarAlgorithmBase

Zafar with fairness.

Parameters:: C (float) –

call_script(cmd_args, env=None, cwd=None)#

Call a (Python) script as a separate process.

Parameters:

cmd_args (list[str]) – List of strings that are passed as commandline arguments to the executable.
env (dict[str, str] | None) – Environment variables specified as a dictionary; e.g. {"PATH": "/usr/bin"}.
cwd (Path | None) – If not None, change working directory to the given path before running command.

Raises:

RuntimeError – If the called script failed or timed out.

Return type:

None

property executable: list[str]#: Python executable from the virtualenv associated with the model.

fit(train, seed=888)#

Fit Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
seed (int) – Random seed for model initialization.

Returns:

Self, but trained.

Return type:

Self

property hyperparameters: Dict[str, bool | int | float | str]#: Return list of hyperparameters.

property name: str#: Name of the algorithm.

predict(test)#

Make predictions on the given data.

Parameters:: test (SubgroupTuple | DataTuple) – Data to evaluate on.
Returns:: Predictions on the test data.
Return type:: Prediction

remove()#

Remove the directory that we created in _clone_directory().

Return type:: None

run(train, test, seed=888)#

Run Algorithm on the given data.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction

run_test(train, test, seed=888)#

Run with reduced training set so that it finishes quicker.

Parameters:

train (DataTuple) – Data tuple of the training data.
test (SubgroupTuple | DataTuple) – Data to evaluate on.
seed (int) – Random seed for model initialization.

Returns:

Predictions on the test data.

Return type:

Prediction