def test_adagrad_hinge_multiclass():
clf = AdaGradClassifier(alpha=1e-2,
n_iter=100,
loss="hinge",
random_state=0)
clf.fit(X, y)
assert_almost_equal(clf.score(X, y), 0.960, 3)
def test_adagrad_elastic_hinge():
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_adagrad_hinge_multiclass():
clf = AdaGradClassifier(alpha=1e-2,
n_iter=100,
loss="hinge",
random_state=0)
clf.fit(X, y)
assert not hasattr(clf, "predict_proba")
np.testing.assert_almost_equal(clf.score(X, y), 0.940, 3)
def test_adagrad_elastic_hinge():
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, "predict_proba")
assert clf.score(X_bin, y_bin) == 1.0
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_adagrad_elastic_smooth_hinge(bin_train_data):
X_bin, y_bin = bin_train_data
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
loss="smooth_hinge",
n_iter=10,
random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, "predict_proba")
assert clf.score(X_bin, y_bin) == 1.0
def test_adagrad_hinge_multiclass():
clf = AdaGradClassifier(alpha=1e-2, n_iter=100, loss="hinge", random_state=0)
clf.fit(X, y)
assert not hasattr(clf, "predict_proba")
assert_almost_equal(clf.score(X, y), 0.960, 3)
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_adagrad_elastic_smooth_hinge():
clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, loss="smooth_hinge", n_iter=10, random_state=0)
clf.fit(X_bin, y_bin)
assert not hasattr(clf, "predict_proba")
assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_adagrad_elastic_hinge():
clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, n_iter=10, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(clf.score(X_bin, y_bin), 1.0)
