def test_corrupted_classif(loss, weighting, k, c, multi_class):
clf = RobustWeightedClassifier(
loss=loss,
max_iter=100,
weighting=weighting,
k=k,
c=c,
multi_class=multi_class,
random_state=rng,
)
clf.fit(X_cc, y_cc)
score = clf.score(X_cc, y_cc)
assert score > 0.8
def test_not_robust_classif(loss, weighting, multi_class):
clf = RobustWeightedClassifier(
loss=loss,
max_iter=100,
weighting=weighting,
k=0,
c=1e7,
burn_in=0,
multi_class=multi_class,
random_state=rng,
)
clf_not_rob = SGDClassifier(loss=loss, random_state=rng)
clf.fit(X_c, y_c)
clf_not_rob.fit(X_c, y_c)
pred1 = clf.predict(X_c)
pred2 = clf_not_rob.predict(X_c)
assert np.mean((pred1 > 0) == (pred2 > 0)) > 0.8
assert clf.score(X_c, y_c) == np.mean(pred1 == y_c)
