import numpy as np
import sklearn
from sklearn.dummy import DummyClassifier
from sklearn.metrics import accuracy_score, make_scorer
from fairlearn.postprocessing import ThresholdOptimizer
from fairlearn.metrics import demographic_parity_difference


sklearn.set_config(enable_metadata_routing=True)

rng = np.random.default_rng(0)
X = rng.normal(size=(10, 3))
y = rng.integers(0, 2, size=X.shape[0])
sensitive = rng.integers(0, 2, size=X.shape[0])

classifier = (
    ThresholdOptimizer(estimator=DummyClassifier(), predict_method="auto")
    .set_fit_request(sensitive_features=True)
    .set_predict_request(sensitive_features=True)
    .fit(X, y, sensitive_features=sensitive)
)

scoring = make_scorer(accuracy_score)
scoring(classifier, X, y, sensitive_features=sensitive) # TypeError: predict() missing 1 argument -- how could I pass `sensitive_features to predict() ?

# passing arguments to the score function (demographic_parity_difference) is OK
classifier = DummyClassifier().fit(X, y)
scoring = make_scorer(
    demographic_parity_difference, greater_is_better=False
).set_score_request(sensitive_features=True)

scoring(classifier, X, y, sensitive_features=sensitive)