def test_classif_binary(weighting):
clf = RobustWeightedClassifier(
max_iter=100,
weighting=weighting,
k=0,
c=1e7,
burn_in=0,
multi_class="binary",
random_state=rng,
)
clf_not_rob = SGDClassifier(loss="log", random_state=rng)
clf.fit(X_cb, y_cb)
clf_not_rob.fit(X_cb, y_cb)
norm_coef1 = np.linalg.norm(np.hstack([clf.coef_.ravel(), clf.intercept_]))
norm_coef2 = np.linalg.norm(
np.hstack([clf_not_rob.coef_.ravel(), clf_not_rob.intercept_])
)
coef1 = clf.coef_ / norm_coef1
coef2 = clf_not_rob.coef_ / norm_coef2
intercept1 = clf.intercept_ / norm_coef1
intercept2 = clf_not_rob.intercept_ / norm_coef2
assert np.linalg.norm(coef1 - coef2) < 0.5
assert np.linalg.norm(intercept1 - intercept2) < 0.5
assert len(clf.weights_) == len(X_cb)
def test_classif_corrupted_weights(weighting):
clf = RobustWeightedClassifier(
max_iter=100,
weighting=weighting,
k=5,
c=1,
burn_in=0,
multi_class="binary",
random_state=rng,
)
clf.fit(X_cc, y_cc)
assert np.mean(clf.weights_[:3]) < np.mean(clf.weights_[3:])
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_predict_proba(weighting):
clf = RobustWeightedClassifier(
max_iter=100,
weighting=weighting,
k=0,
c=1e7,
burn_in=0,
random_state=rng,
)
clf_not_rob = SGDClassifier(loss="log", random_state=rng)
clf.fit(X_c, y_c)
clf_not_rob.fit(X_c, y_c)
pred1 = clf.base_estimator_.predict_proba(X_c)[:, 1]
pred2 = clf_not_rob.predict_proba(X_c)[:, 1]
assert np.mean((pred1 > 1 / 2) == (pred2 > 1 / 2)) > 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.base_estimator_.decision_function(X_c)
pred2 = clf_not_rob.decision_function(X_c)
assert np.mean((pred1 > 0) == (pred2 > 0)) > 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)
def test_robust_estimator_unsupported_multiclass():
"""Test that warning message is thrown when unsupported weighting."""
model = RobustWeightedClassifier(multi_class="invalid")
msg = "No such multiclass method implemented."
with pytest.raises(ValueError, match=msg):
model.fit(X_cc, y_cc)
def test_robust_estimator_unsupported_weighting():
"""Test that warning message is thrown when unsupported weighting."""
model = RobustWeightedClassifier(weighting="invalid")
msg = "No such weighting scheme"
with pytest.raises(ValueError, match=msg):
model.fit(X_cc, y_cc)
def test_robust_estimator_unsupported_loss():
"""Test that warning message is thrown when unsupported loss."""
model = RobustWeightedClassifier(loss="invalid")
msg = "The loss invalid is not supported. "
with pytest.raises(ValueError, match=msg):
model.fit(X_cc, y_cc)
def test_robust_estimator_max_iter():
"""Test that warning message is thrown when max_iter is reached."""
model = RobustWeightedClassifier(max_iter=1)
msg = "Maximum number of iteration reached before"
with pytest.warns(UserWarning, match=msg):
model.fit(X_cc, y_cc)
def test_robust_estimator_unsupported_loss():
model = RobustWeightedClassifier(multi_class="binary")
msg = "y must be binary."
with pytest.raises(ValueError, match=msg):
model.fit(X_c, y_c)