def test_svrg_callback():
class Callback(object):
def __init__(self, X, y):
self.X = X
self.y = y
self.obj = []
def __call__(self, clf):
clf._finalize_coef()
y_pred = clf.decision_function(self.X).ravel()
loss = (np.maximum(1 - self.y * y_pred, 0) ** 2).mean()
coef = clf.coef_.ravel()
regul = 0.5 * clf.alpha * np.dot(coef, coef)
self.obj.append(loss + regul)
cb = Callback(X_bin, y_bin)
clf = SVRGClassifier(loss="squared_hinge", eta=1e-3, max_iter=20,
random_state=0, callback=cb)
clf.fit(X_bin, y_bin)
assert_true(np.all(np.diff(cb.obj) <= 0))
n_classes=2,
random_state=0)
etas = (1e-3, 1e-4, 1e-5)
n_inners = (0.25, 0.5, 1.0, 1.5)
y = y * 2 - 1
plt.figure()
for eta in etas:
print "eta =", eta
cb = Callback(X, y)
clf = SVRGClassifier(loss="squared_hinge", alpha=1e-5, eta=eta,
n_inner=1.0, max_iter=20, random_state=0, callback=cb)
clf.fit(X, y)
plt.plot(cb.times, cb.obj, label="eta=" + str(eta))
plt.xlabel("CPU time")
plt.ylabel("Objective value")
plt.legend()
plt.figure()
for n_inner in n_inners:
print "n_inner =", n_inner
cb = Callback(X, y)
clf = SVRGClassifier(loss="squared_hinge", alpha=1e-5, eta=1e-4,
n_inner=n_inner, max_iter=1000, random_state=0,
callback=cb, verbose=1)
clf.fit(X, y)
y = y * 2 - 1
plt.figure()
for eta in etas:
print "eta =", eta
cb = Callback(X, y)
clf = SVRGClassifier(loss="squared_hinge",
alpha=1e-5,
eta=eta,
n_inner=1.0,
max_iter=20,
random_state=0,
callback=cb)
clf.fit(X, y)
plt.plot(cb.times, cb.obj, label="eta=" + str(eta))
plt.xlabel("CPU time")
plt.ylabel("Objective value")
plt.legend()
plt.figure()
for n_inner in n_inners:
print "n_inner =", n_inner
cb = Callback(X, y)
clf = SVRGClassifier(loss="squared_hinge",
alpha=1e-5,
eta=1e-4,
n_inner=n_inner,
def test_multiclass_classes():
clf = SVRGClassifier()
clf.fit(X, y)
assert_equal(list(clf.classes_), [0, 1, 2])
def test_bin_classes():
clf = SVRGClassifier()
clf.fit(X_bin, y_bin)
assert_equal(list(clf.classes_), [-1, 1])
def test_svrg():
clf = SVRGClassifier(eta=1e-3, max_iter=20, random_state=0, verbose=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_multiclass_classes():
clf = SVRGClassifier()
clf.fit(X, y)
assert list(clf.classes_) == [0, 1, 2]
def test_bin_classes():
clf = SVRGClassifier()
clf.fit(X_bin, y_bin)
assert list(clf.classes_) == [-1, 1]
