def test_effect_of_beta():
# higher beta should lead to slower learning, thus lower weights
mean_abs_weights = []
for beta in [10**n for n in range(7)]:
clf = OGDLR(beta=beta)
clf.fit(X[:100], y[:100])
mean_abs_weights.append(np.abs(clf.weights()).mean())
assert np.allclose(mean_abs_weights[-1], 0, atol=1e-6)
assert all(np.diff(mean_abs_weights) < 0)
def test_effect_of_beta():
# higher beta should lead to slower learning, thus lower weights
mean_abs_weights = []
for beta in [10 ** n for n in range(7)]:
clf = OGDLR(beta=beta)
clf.fit(X[:100], y[:100])
mean_abs_weights.append(np.abs(clf.weights()).mean())
assert np.allclose(mean_abs_weights[-1], 0, atol=1e-6)
assert all(np.diff(mean_abs_weights) < 0)
def test_effect_of_alpha():
# higher alpha should lead to faster learning, thus higher weights
mean_abs_weights = []
for alpha in [0, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1]:
clf = OGDLR(alpha=alpha)
clf.fit(X[:100], y[:100])
mean_abs_weights.append(np.abs(clf.weights()).mean())
assert mean_abs_weights[0] == 0.
assert all(np.diff(mean_abs_weights) > 0)
def test_effect_of_alpha():
# higher alpha should lead to faster learning, thus higher weights
mean_abs_weights = []
for alpha in [0, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1]:
clf = OGDLR(alpha=alpha)
clf.fit(X[:100], y[:100])
mean_abs_weights.append(np.abs(clf.weights()).mean())
assert mean_abs_weights[0] == 0.
assert all(np.diff(mean_abs_weights) > 0)
