Basics & Model Specification of AdversarialFairnessClassifier

This example shows how to use AdversarialFairnessClassifier on the UCI Adult dataset.

First, we cover a most basic application of adversarial mitigation. We start by loading and preprocessing the dataset:

from fairlearn.datasets import fetch_adult

X, y = fetch_adult(return_X_y=True)
pos_label = y[0]

z = X["sex"]  # In this example, we consider 'sex' the sensitive feature.

As with other machine learning methods, it is wise to take a train-test split of the data in order to validate the model on unseen data:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test, Z_train, Z_test = train_test_split(
    X, y, z, test_size=0.2, random_state=12345, stratify=y
)

The UCI adult dataset cannot be fed into a neural network (yet), as we have many columns that are not numerical in nature. To resolve this issue, we could for instance use one-hot encodings to preprocess categorical columns. Additionally, let’s preprocess the numeric columns to a standardized range. For these tasks, we can use functionality from scikit-learn (sklearn.preprocessing). We also use an imputer to get rid of NaN’s:

import sklearn
from numpy import number
from sklearn.compose import make_column_selector, make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler

sklearn.set_config(enable_metadata_routing=True)

ct = make_column_transformer(
    (
        Pipeline(
            [
                ("imputer", SimpleImputer(strategy="mean")),
                ("normalizer", StandardScaler()),
            ]
        ),
        make_column_selector(dtype_include=number),
    ),
    (
        Pipeline(
            [
                ("imputer", SimpleImputer(strategy="most_frequent")),
                ("encoder", OneHotEncoder(drop="if_binary", sparse_output=False)),
            ]
        ),
        make_column_selector(dtype_include="category"),
    ),
)

Now, we can use AdversarialFairnessClassifier to train on the UCI Adult dataset. As our predictor and adversary models, we use for simplicity the default constructors for fully connected neural networks with sigmoid activations implemented in Fairlearn. We initialize neural network constructors by passing a list \(h_1, h_2, \dots\) that indicate the number of nodes \(h_i\) per hidden layer \(i\). You can also put strings in this list to indicate certain activation functions, or just pass an initialized activation function directly.

The specific fairness objective that we choose for this example is demographic parity, so we also set objective = "demographic_parity". We generally follow sklearn API, but in this case we require some extra kwargs. In particular, we should specify the number of epochs, batch size, whether to shuffle the rows of data after every epoch, and optionally after how many seconds to show a progress update:

from fairlearn.adversarial import AdversarialFairnessClassifier

mitigator = AdversarialFairnessClassifier(
    backend="torch",
    predictor_model=[50, "leaky_relu"],
    adversary_model=[3, "leaky_relu"],
    batch_size=2**8,
    progress_updates=0.5,
    random_state=123,
)

We now put the above model in a Pipeline with the transformation step. Note that we use scikit-learn’s metadata routing to pass the sensitive feature:

.. GENERATED FROM PYTHON SOURCE LINES 107-112

from sklearn.pipeline import make_pipeline

pipeline = make_pipeline(ct, mitigator.set_fit_request(sensitive_features=True))

Then, we can fit the data to our model:

pipeline.fit(X_train, y_train, sensitive_features=Z_train)

Pipeline(steps=[('columntransformer',
                 ColumnTransformer(transformers=[('pipeline-1',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer()),
                                                                  ('normalizer',
                                                                   StandardScaler())]),
                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7d0172834860>),
                                                 ('pipeline-2',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='most_frequent')),
                                                                  ('encoder',
                                                                   OneHotEncoder(drop='if_binary',
                                                                                 sparse_output=False))]),
                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7d0172836390>)])),
                ('adversarialfairnessclassifier',
                 AdversarialFairnessClassifier(adversary_model=[3,
                                                                'leaky_relu'],
                                               backend='torch', batch_size=256,
                                               predictor_model=[50,
                                                                'leaky_relu'],
                                               progress_updates=0.5,
                                               random_state=123))])

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Finally, we evaluate the predictions. In particular, we trained the predictor for demographic parity, so we are not only interested in the accuracy, but also in the selection rate. MetricFrames are a great resource here:

predictions = pipeline.predict(X_test)

from sklearn.metrics import accuracy_score

from fairlearn.metrics import MetricFrame, selection_rate

mf = MetricFrame(
    metrics={"accuracy": accuracy_score, "selection_rate": selection_rate},
    y_true=y_test == pos_label,
    y_pred=predictions == pos_label,
    sensitive_features=Z_test,
)

Then, to display the result:

print(mf.by_group)

# The above statistics tell us that the accuracy of our model is quite good,
# 90% for females and 72% for males. However, the selection rates differ, so there
# is a large demographic disparity here. When using adversarial fairness
# out-of-the-box, users may not yield such
# good training results after the first attempt. In general, training
# adversarial networks is hard, and users may need to tweak the
# hyperparameters continuously. Besides general scikit-learn algorithms
# that finetune estimators,
# :ref:`adversarial_Example_2` will demonstrate some problem-specific
# techniques we can use such as using dynamic hyperparameters,
# validation, and early stopping to improve adversarial training.

        accuracy  selection_rate
sex
Female  0.906308        0.978664
Male    0.723336        0.484927