def test_binomial_deviance():
# Check binomial deviance loss.
# Check against alternative definitions in ESLII.
bd = BinomialDeviance(2)
# pred has the same BD for y in {0, 1}
assert_equal(bd(np.array([0.0]), np.array([0.0])),
bd(np.array([1.0]), np.array([0.0])))
assert_almost_equal(
bd(np.array([1.0, 1.0, 1.0]), np.array([100.0, 100.0, 100.0])), 0.0)
assert_almost_equal(
bd(np.array([1.0, 0.0, 0.0]), np.array([100.0, -100.0, -100.0])), 0)
# check if same results as alternative definition of deviance (from ESLII)
alt_dev = lambda y, pred: np.mean(
np.logaddexp(0.0, -2.0 * (2.0 * y - 1) * pred))
test_data = [(np.array([1.0, 1.0, 1.0]), np.array([100.0, 100.0, 100.0])),
(np.array([0.0, 0.0, 0.0]), np.array([100.0, 100.0, 100.0])),
(np.array([0.0, 0.0, 0.0]), np.array([-100.0, -100.0,
-100.0])),
(np.array([1.0, 1.0, 1.0]), np.array([-100.0, -100.0,
-100.0]))]
for datum in test_data:
assert_almost_equal(bd(*datum), alt_dev(*datum))
# check the gradient against the
alt_ng = lambda y, pred: (2 * y - 1) / (1 + np.exp(2 * (2 * y - 1) * pred))
for datum in test_data:
assert_almost_equal(bd.negative_gradient(*datum), alt_ng(*datum))
def test_binomial_deviance():
# Check binomial deviance loss.
# Check against alternative definitions in ESLII.
bd = BinomialDeviance(2)
# pred has the same BD for y in {0, 1}
assert_equal(bd(np.array([0.0]), np.array([0.0])),
bd(np.array([1.0]), np.array([0.0])))
assert_almost_equal(bd(np.array([1.0, 1.0, 1.0]),
np.array([100.0, 100.0, 100.0])),
0.0)
assert_almost_equal(bd(np.array([1.0, 0.0, 0.0]),
np.array([100.0, -100.0, -100.0])), 0)
# check if same results as alternative definition of deviance (from ESLII)
alt_dev = lambda y, pred: np.mean(np.logaddexp(0.0, -2.0 *
(2.0 * y - 1) * pred))
test_data = [(np.array([1.0, 1.0, 1.0]), np.array([100.0, 100.0, 100.0])),
(np.array([0.0, 0.0, 0.0]), np.array([100.0, 100.0, 100.0])),
(np.array([0.0, 0.0, 0.0]),
np.array([-100.0, -100.0, -100.0])),
(np.array([1.0, 1.0, 1.0]),
np.array([-100.0, -100.0, -100.0]))]
for datum in test_data:
assert_almost_equal(bd(*datum), alt_dev(*datum))
# check the gradient against the
alt_ng = lambda y, pred: (2 * y - 1) / (1 + np.exp(2 * (2 * y - 1) * pred))
for datum in test_data:
assert_almost_equal(bd.negative_gradient(*datum), alt_ng(*datum))
def test_gradient_boosting_estimator_with_binomial_deviance_loss():
np.random.seed(0)
X, y = make_classification(n_classes=2)
loss_function = BinomialDeviance(2)
model = Booster(Earth(max_degree=2, use_fast=True, max_terms=10), loss_function)
model.fit(X, y)
assert_greater(np.sum(model.predict(X)==y) / float(y.shape[0]), .90)
assert_true(np.all(0<=model.predict_proba(X)))
assert_true(np.all(1>=model.predict_proba(X)))
def test_sklearn2code_export():
np.random.seed(0)
X, y = make_classification(n_classes=2)
X = DataFrame(X, columns=['x%d' % i for i in range(X.shape[1])])
loss_function = BinomialDeviance(2)
model = Booster(Earth(max_degree=2, use_fast=True, max_terms=10), loss_function)
model.fit(X, y)
code = sklearn2code(model, ['predict', 'predict_proba', 'transform'], numpy_flat)
module = exec_module('test_module', code)
assert_correct_exported_module(model, module, ['predict', 'predict_proba', 'transform'], dict(X=X), X)
def __init__(self):
BinomialDeviance.__init__(self,2)