def test_ogdlr_grad_numerically_l2(lambda2):
epsilon = 1e-6
clf = OGDLR(lambda2=lambda2)
clf.fit(X[:100], y[:100])
for xx, yy in zip(X[:10], y[:10]):
grad, grad_num = clf.numerical_grad(xx, yy, epsilon)
assert np.allclose(grad, grad_num, atol=epsilon)
def test_ogdlr_grad_numerically(alr):
epsilon = 1e-6
clf = OGDLR(alr_schedule=alr)
clf.fit(X[:100], y[:100])
for xx, yy in zip(X[:10], y[:10]):
grad, grad_num = clf.numerical_grad(xx, yy, epsilon)
assert np.allclose(grad, grad_num, atol=epsilon)
def test_ogdlr_grad_numerically_l2(lambda2):
epsilon = 1e-6
clf = OGDLR(lambda2=lambda2)
clf.fit(X[:100], y[:100])
for xx, yy in zip(X[:10], y[:10]):
grad, grad_num = clf.numerical_grad(xx, yy, epsilon)
assert np.allclose(grad, grad_num, atol=epsilon)
def test_ogdlr_grad_numerically(alr):
epsilon = 1e-6
clf = OGDLR(alr_schedule=alr)
clf.fit(X[:100], y[:100])
for xx, yy in zip(X[:10], y[:10]):
grad, grad_num = clf.numerical_grad(xx, yy, epsilon)
assert np.allclose(grad, grad_num, atol=epsilon)
def test_ogdlr_grad_numerically_l1_l2(args):
# test all combinations of lambda1 and lambda2 values of 0,
# 0.5, and 2 and for alpha = 0.02 and beta = 1.
epsilon = 1e-6
clf = OGDLR(*args)
clf.fit(X[:100], y[:100])
close = []
for xx, yy in zip(X[:10], y[:10]):
grad, grad_num = clf.numerical_grad(xx, yy, epsilon)
assert np.allclose(grad, grad_num, atol=epsilon)
def test_ogdlr_grad_numerically_l1_l2(args):
# test all combinations of lambda1 and lambda2 values of 0,
# 0.5, and 2 and for alpha = 0.02 and beta = 1.
epsilon = 1e-6
clf = OGDLR(*args)
clf.fit(X[:100], y[:100])
close = []
for xx, yy in zip(X[:10], y[:10]):
grad, grad_num = clf.numerical_grad(xx, yy, epsilon)
assert np.allclose(grad, grad_num, atol=epsilon)
