def test_fit_linear_binary_l1r_log_loss(bin_dense_train_data):
bin_dense, bin_target = bin_dense_train_data
clf = CDClassifier(C=1.0, random_state=0, penalty="l1", loss="log")
clf.fit(bin_dense, bin_target)
check_predict_proba(clf, bin_dense)
acc = clf.score(bin_dense, bin_target)
np.testing.assert_almost_equal(acc, 0.995)
def test_fit_linear_binary_l2r_modified_huber():
clf = CDClassifier(C=1.0, random_state=0, penalty="l2",
loss="modified_huber")
clf.fit(bin_dense, bin_target)
check_predict_proba(clf, bin_dense)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0)
def test_sag_proba():
n_samples = 10
X, y = make_classification(n_samples, random_state=0)
sag = SAGClassifier(eta=1e-3, alpha=0.0, beta=0.0, max_iter=10,
loss='log', random_state=0)
sag.fit(X, y)
check_predict_proba(sag, X)
def test_adagrad_elastic_log():
clf = AdaGradClassifier(alpha=0.1,
l1_ratio=0.85,
loss="log",
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert clf.score(X_bin, y_bin) == 1.0
check_predict_proba(clf, X_bin)
def test_binary_linear_sgd(data, clf, request):
X, y = request.getfixturevalue(data)
clf.fit(X, y)
assert clf.score(X, y) > 0.934
assert list(clf.classes_) == [0, 1]
if clf.loss in {'log', 'modified_huber'}:
check_predict_proba(clf, X)
else:
assert not hasattr(clf, 'predict_proba')
def test_binary_linear_sgd():
for data in (bin_dense, bin_csr):
for clf in (
SGDClassifier(random_state=0, loss="hinge", fit_intercept=True, learning_rate="pegasos"),
SGDClassifier(random_state=0, loss="hinge", fit_intercept=False, learning_rate="pegasos"),
SGDClassifier(random_state=0, loss="hinge", fit_intercept=True, learning_rate="invscaling"),
SGDClassifier(random_state=0, loss="hinge", fit_intercept=True, learning_rate="constant"),
SGDClassifier(
random_state=0, loss="squared_hinge", eta0=1e-2, fit_intercept=True, learning_rate="constant"
),
SGDClassifier(random_state=0, loss="log", fit_intercept=True, learning_rate="constant"),
SGDClassifier(random_state=0, loss="modified_huber", fit_intercept=True, learning_rate="constant"),
):
clf.fit(data, bin_target)
assert_greater(clf.score(data, bin_target), 0.934)
assert_equal(list(clf.classes_), [0, 1])
if clf.loss in ("log", "modified_huber"):
check_predict_proba(clf, data)
else:
assert not hasattr(clf, "predict_proba")
def test_binary_linear_sgd():
for data in (bin_dense, bin_csr):
for clf in (
SGDClassifier(random_state=0,
loss="hinge",
fit_intercept=True,
learning_rate="pegasos"),
SGDClassifier(random_state=0,
loss="hinge",
fit_intercept=False,
learning_rate="pegasos"),
SGDClassifier(random_state=0,
loss="hinge",
fit_intercept=True,
learning_rate="invscaling"),
SGDClassifier(random_state=0,
loss="hinge",
fit_intercept=True,
learning_rate="constant"),
SGDClassifier(random_state=0,
loss="squared_hinge",
eta0=1e-2,
fit_intercept=True,
learning_rate="constant"),
SGDClassifier(random_state=0,
loss="log",
fit_intercept=True,
learning_rate="constant"),
SGDClassifier(random_state=0,
loss="modified_huber",
fit_intercept=True,
learning_rate="constant"),
):
clf.fit(data, bin_target)
assert_greater(clf.score(data, bin_target), 0.934)
assert_equal(list(clf.classes_), [0, 1])
if clf.loss in ('log', 'modified_huber'):
check_predict_proba(clf, data)
else:
assert not hasattr(clf, 'predict_proba')
def test_adagrad_elastic_log():
clf = AdaGradClassifier(alpha=0.1, l1_ratio=0.85, loss="log", n_iter=10, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
check_predict_proba(clf, X_bin)
def test_fit_linear_binary_l1r_log_loss():
clf = CDClassifier(C=1.0, random_state=0, penalty="l1", loss="log")
clf.fit(bin_dense, bin_target)
check_predict_proba(clf, bin_dense)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.995)
