def test_effect_of_lambda2(lambda2):
# relative difference in gradients should be the same
weights = range(-5, 0) + range(1, 6)
clf = OGDLR(lambda2=lambda2)
grads = clf._get_grads(0, 0, weights)
# gradient is proportional to weights
frac = [gr / w for gr, w in zip(grads, weights)]
assert np.allclose(frac[0], frac)
# contingencey test: should fail
abso = [gr * np.sign(w) for gr, w in zip(grads, weights)]
with pytest.raises(AssertionError):
assert np.allclose(abso[0], abso)
def test_effect_of_lambda2(lambda2):
# relative difference in gradients should be the same
weights = range(-5, 0) + range(1, 6)
clf = OGDLR(lambda2=lambda2)
grads = clf._get_grads(0, 0, weights)
# gradient is proportional to weights
frac = [gr / w for gr, w in zip(grads, weights)]
assert np.allclose(frac[0], frac)
# contingencey test: should fail
abso = [gr * np.sign(w) for gr, w in zip(grads, weights)]
with pytest.raises(AssertionError):
assert np.allclose(abso[0], abso)
def test_effect_of_lambda1(lambda1):
# gradients should be the same regardless of magnitude of weights
weights = range(-5, 0) + range(1, 6)
clf = OGDLR(lambda1=lambda1)
grads = clf._get_grads(0, 0, weights)
# gradient only depends on sign of weight
abso = [gr * np.sign(w) for gr, w in zip(grads, weights)]
assert np.allclose(abso[0], abso)
# contingency test: should fail
frac = [gr / w for gr, w in zip(grads, weights)]
with pytest.raises(AssertionError):
assert np.allclose(frac[0], frac)
def test_effect_of_lambda1(lambda1):
# gradients should be the same regardless of magnitude of weights
weights = range(-5, 0) + range(1, 6)
clf = OGDLR(lambda1=lambda1)
grads = clf._get_grads(0, 0, weights)
# gradient only depends on sign of weight
abso = [gr * np.sign(w) for gr, w in zip(grads, weights)]
assert np.allclose(abso[0], abso)
# contingency test: should fail
frac = [gr / w for gr, w in zip(grads, weights)]
with pytest.raises(AssertionError):
assert np.allclose(frac[0], frac)
