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_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 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")
np.testing.assert_almost_equal(clf.score(X, y), 0.940, 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 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_callback():
class Callback(object):
def __init__(self, X, y):
self.X = X
self.y = y
self.acc = []
def __call__(self, clf, t):
alpha1 = clf.l1_ratio * clf.alpha
alpha2 = (1 - clf.l1_ratio) * clf.alpha
_proj_elastic_all(clf.eta, t, clf.g_sum_[0], clf.g_norms_[0], alpha1, alpha2, 0, clf.coef_[0])
score = clf.score(self.X, self.y)
self.acc.append(score)
cb = Callback(X_bin, y_bin)
clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, n_iter=10, callback=cb, random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(cb.acc[-1], 1.0)
def test_adagrad_callback():
class Callback(object):
def __init__(self, X, y):
self.X = X
self.y = y
self.acc = []
def __call__(self, clf, t):
alpha1 = clf.l1_ratio * clf.alpha
alpha2 = (1 - clf.l1_ratio) * clf.alpha
_proj_elastic_all(clf.eta, t, clf.g_sum_[0], clf.g_norms_[0],
alpha1, alpha2, 0, clf.coef_[0])
score = clf.score(self.X, self.y)
self.acc.append(score)
cb = Callback(X_bin, y_bin)
clf = AdaGradClassifier(alpha=0.5,
l1_ratio=0.85,
n_iter=10,
callback=cb,
random_state=0)
clf.fit(X_bin, y_bin)
assert_equal(cb.acc[-1], 1.0)
def test_adagrad_classes_binary():
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X_bin, y_bin)
assert_equal(list(clf.classes_), [-1, 1])
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_classes_binary(bin_train_data):
X_bin, y_bin = bin_train_data
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X_bin, y_bin)
assert list(clf.classes_) == [-1, 1]
def test_adagrad_classes_multiclass():
clf = AdaGradClassifier()
assert not hasattr(clf, "classes_")
clf.fit(X, y)
assert_equal(list(clf.classes_), [0, 1, 2])
def test_adagrad_classes_binary():
clf = AdaGradClassifier()
assert not hasattr(clf, "classes_")
clf.fit(X_bin, y_bin)
assert_equal(list(clf.classes_), [-1, 1])
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_classes_multiclass():
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X, y)
assert_equal(list(clf.classes_), [0, 1, 2])
def test_adagrad_classes_multiclass(train_data):
X, y = train_data
clf = AdaGradClassifier()
assert not hasattr(clf, 'classes_')
clf.fit(X, y)
assert list(clf.classes_) == [0, 1, 2]
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)
