Change target_labels name for the SupervisedScorer

[YanisLalou]

In this example:

X, y, sample_domain = da_dataset.pack_train(as_sources=['s'], as_targets=['t'])
estimator = make_da_pipeline(
    ReweightDensityAdapter(),
    LogisticRegression().set_score_request(sample_weight=True),
)
cv = ShuffleSplit(n_splits=3, test_size=0.3, random_state=0)
_, target_labels, _ = da_dataset.pack(as_sources=['s'], as_targets=['t'], train=False)
scoring = SupervisedScorer()
scores = cross_validate(
    estimator,
    X,
    y,
    cv=cv,
    params={'sample_domain': sample_domain, 'target_labels': target_labels},
    scoring=scoring,
)

The target_labels corresponds to the labels of the SOURCE + TARGET samples.
Calling this array target_labels is super confusing...

We could call it unmasked_labels, all_labels, source_target_labels or even y_test.