def test_naive_asgd_with_feedback():
n_points = 1e3
n_features = 1e2
X, y = get_fake_data(n_points, n_features, 42)
Xtst, ytst = get_fake_data(n_points, n_features, 43)
clf = ASGD(n_features, sgd_step_size0=1e-3, l2_regularization=1e-6,
n_iterations=4, feedback=True, dtype=np.float32)
clf.fit(X, y)
ytrn_preds = clf.predict(X)
ytst_preds = clf.predict(Xtst)
ytrn_acc = (ytrn_preds==y).mean()
ytst_acc = (ytst_preds==y).mean()
assert_equal(ytrn_acc, 0.707)
assert_equal(ytst_acc, 0.505)
def test_naive_asgd():
rstate = RandomState(42)
X, y = get_fake_data(N_POINTS, N_FEATURES, rstate)
Xtst, ytst = get_fake_data(N_POINTS, N_FEATURES, rstate)
clf = BinaryASGD(*DEFAULT_ARGS, rstate=rstate, **DEFAULT_KWARGS)
clf.fit(X, y)
ytrn_preds = clf.predict(X)
ytst_preds = clf.predict(Xtst)
ytrn_acc = (ytrn_preds == y).mean()
ytst_acc = (ytst_preds == y).mean()
assert_equal(ytrn_acc, 0.72)
assert_equal(ytst_acc, 0.522)
def test_naive_asgd():
rstate = RandomState(42)
X, y = get_fake_data(N_POINTS, N_FEATURES, rstate)
Xtst, ytst = get_fake_data(N_POINTS, N_FEATURES, rstate)
clf = BinaryASGD(*DEFAULT_ARGS, rstate=rstate, **DEFAULT_KWARGS)
clf.fit(X, y)
ytrn_preds = clf.predict(X)
ytst_preds = clf.predict(Xtst)
ytrn_acc = (ytrn_preds == y).mean()
ytst_acc = (ytst_preds == y).mean()
assert_equal(ytrn_acc, 0.72)
assert_equal(ytst_acc, 0.522)
def test_naive_asgd_with_feedback():
raise SkipTest("FIXME: feedback support is buggy")
rstate = RandomState(43)
X, y = get_fake_data(N_POINTS, N_FEATURES, rstate)
Xtst, ytst = get_fake_data(N_POINTS, N_FEATURES, rstate)
clf = BinaryASGD(*DEFAULT_ARGS,
feedback=True, rstate=rstate, **DEFAULT_KWARGS)
clf.fit(X, y)
ytrn_preds = clf.predict(X)
ytst_preds = clf.predict(Xtst)
ytrn_acc = (ytrn_preds == y).mean()
ytst_acc = (ytst_preds == y).mean()
assert_equal(ytrn_acc, 0.709)
assert_equal(ytst_acc, 0.449)
def test_naive_asgd_with_feedback():
raise SkipTest("FIXME: feedback support is buggy")
rstate = RandomState(43)
X, y = get_fake_data(N_POINTS, N_FEATURES, rstate)
Xtst, ytst = get_fake_data(N_POINTS, N_FEATURES, rstate)
clf = BinaryASGD(*DEFAULT_ARGS,
feedback=True,
rstate=rstate,
**DEFAULT_KWARGS)
clf.fit(X, y)
ytrn_preds = clf.predict(X)
ytst_preds = clf.predict(Xtst)
ytrn_acc = (ytrn_preds == y).mean()
ytst_acc = (ytst_preds == y).mean()
assert_equal(ytrn_acc, 0.709)
assert_equal(ytst_acc, 0.449)
