def test_prox_sdca_smooth_hinge_elastic(): clf = ProxSDCA_Classifier(alpha=0.5, l1_ratio=0.85, loss="smooth_hinge", random_state=0) clf.fit(X_bin, y_bin) assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_hinge_multiclass(): clf = ProxSDCA_Classifier(alpha=1e-2, max_iter=100, loss="hinge", random_state=0) clf.fit(X, y) assert_almost_equal(clf.score(X, y), 0.947, 3)
def test_prox_sdca_absolute_l1_only(): clf = ProxSDCA_Classifier(alpha=0.5, l1_ratio=1.0, loss="absolute", tol=1e-2, max_iter=200, random_state=0) clf.fit(X_bin, y_bin) assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_prox_sdca_callback(): class Callback(object): def __init__(self, X, y): self.X = X self.y = y self.acc = [] def __call__(self, clf): score = clf.score(self.X, self.y) self.acc.append(score) cb = Callback(X_bin, y_bin) clf = ProxSDCA_Classifier(alpha=0.5, l1_ratio=0.85, loss="hinge", callback=cb, random_state=0) clf.fit(X_bin, y_bin) assert_equal(cb.acc[0], 0.5) assert_equal(cb.acc[-1], 1.0)
def test_sdca_absolute(): clf = ProxSDCA_Classifier(loss="absolute", random_state=0) clf.fit(X_bin, y_bin) assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_sdca_squared(): clf = ProxSDCA_Classifier(loss="squared", random_state=0) clf.fit(X_bin, y_bin) assert_equal(clf.score(X_bin, y_bin), 1.0)