def test_criterions(self):
X = numpy.array([[10., 12., 13.]]).T
y = numpy.array([20., 22., 23.])
c1 = MSE(1, X.shape[0])
c2 = LinearRegressorCriterion(X)
self.assertNotEmpty(c1)
self.assertNotEmpty(c2)
w = numpy.ones((y.shape[0],))
self.assertEqual(w.sum(), X.shape[0])
ind = numpy.arange(y.shape[0]).astype(numpy.int64)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 0, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 0, y.shape[0])
# https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_criterion.pyx#L886
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
self.assertGreater(i1, i2)
self.assertGreater(i2, 0)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
X = numpy.array([[1., 2., 3.]]).T
y = numpy.array([1., 2., 3.])
c1 = MSE(1, X.shape[0])
c2 = LinearRegressorCriterion(X)
w = numpy.ones((y.shape[0],))
ind = numpy.arange(y.shape[0]).astype(numpy.int64)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 0, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 0, y.shape[0])
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
self.assertGreater(i1, i2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
X = numpy.array([[1., 2., 10., 11.]]).T
y = numpy.array([0.9, 1.1, 1.9, 2.1])
c1 = MSE(1, X.shape[0])
c2 = LinearRegressorCriterion(X)
w = numpy.ones((y.shape[0],))
ind = numpy.arange(y.shape[0]).astype(numpy.int64)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 0, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 0, y.shape[0])
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
self.assertGreater(i1, i2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
X = numpy.array([[1., 2., 10., 11.]]).T
y = numpy.array([0.9, 1.1, 1.9, 2.1])
c1 = MSE(1, X.shape[0])
c2 = LinearRegressorCriterion(X)
w = numpy.ones((y.shape[0],))
ind = numpy.array([0, 3, 2, 1], dtype=ind.dtype)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 1, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 1, y.shape[0])
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
self.assertGreater(i1, i2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
for i in range(2, 4):
_test_criterion_update(c1, i)
_test_criterion_update(c2, i)
left1, right1 = _test_criterion_node_impurity_children(c1)
left2, right2 = _test_criterion_node_impurity_children(c2)
self.assertGreater(left1, left2)
self.assertGreater(right1, right2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_impurity_improvement(c1, 0.)
p2 = _test_criterion_impurity_improvement(c2, 0.)
self.assertGreater(p1, p2 - 1.)
dest = numpy.empty((2, ))
c2.node_beta(dest)
self.assertGreater(dest[0], 0)
self.assertGreater(dest[1], 0)
def test_criterions(self):
X = numpy.array([[1., 2.]]).T
y = numpy.array([1., 2.])
c1 = MSE(1, X.shape[0])
c2 = SimpleRegressorCriterion(X)
self.assertNotEmpty(c1)
self.assertNotEmpty(c2)
w = numpy.ones((y.shape[0], ))
self.assertEqual(w.sum(), X.shape[0])
ind = numpy.arange(y.shape[0]).astype(numpy.int64)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 0, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 0, y.shape[0])
# https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_criterion.pyx#L886
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
self.assertEqual(i1, i2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
X = numpy.array([[1., 2., 3.]]).T
y = numpy.array([1., 2., 3.])
c1 = MSE(1, X.shape[0])
c2 = SimpleRegressorCriterion(X)
w = numpy.ones((y.shape[0], ))
ind = numpy.arange(y.shape[0]).astype(numpy.int64)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 0, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 0, y.shape[0])
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
self.assertAlmostEqual(i1, i2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
X = numpy.array([[1., 2., 10., 11.]]).T
y = numpy.array([0.9, 1.1, 1.9, 2.1])
c1 = MSE(1, X.shape[0])
c2 = SimpleRegressorCriterion(X)
w = numpy.ones((y.shape[0], ))
ind = numpy.arange(y.shape[0]).astype(numpy.int64)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 0, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 0, y.shape[0])
_test_criterion_check(c1)
_test_criterion_check(c2)
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
_test_criterion_check(c1)
_test_criterion_check(c2)
assert_criterion_equal(c1, c2)
self.assertAlmostEqual(i1, i2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
_test_criterion_check(c2)
assert_criterion_equal(c1, c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
_test_criterion_check(c2)
assert_criterion_equal(c1, c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
for i in range(1, 4):
_test_criterion_check(c2)
_test_criterion_update(c1, i)
_test_criterion_update(c2, i)
_test_criterion_check(c2)
assert_criterion_equal(c1, c2)
left1, right1 = _test_criterion_node_impurity_children(c1)
left2, right2 = _test_criterion_node_impurity_children(c2)
self.assertAlmostEqual(left1, left2)
self.assertAlmostEqual(right1, right2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
try:
# scikit-learn >= 0.24
p1 = _test_criterion_impurity_improvement(
c1, 0., left1, right1)
p2 = _test_criterion_impurity_improvement(
c2, 0., left2, right2)
except TypeError:
# scikit-learn < 0.24
p1 = _test_criterion_impurity_improvement(c1, 0.)
p2 = _test_criterion_impurity_improvement(c2, 0.)
self.assertAlmostEqual(p1, p2)
X = numpy.array([[1., 2., 10., 11.]]).T
y = numpy.array([0.9, 1.1, 1.9, 2.1])
c1 = MSE(1, X.shape[0])
c2 = SimpleRegressorCriterion(X)
w = numpy.ones((y.shape[0], ))
ind = numpy.array([0, 3, 2, 1], dtype=ind.dtype)
ys = y.astype(float).reshape((y.shape[0], 1))
_test_criterion_init(c1, ys, w, 1., ind, 1, y.shape[0])
_test_criterion_init(c2, ys, w, 1., ind, 1, y.shape[0])
i1 = _test_criterion_node_impurity(c1)
i2 = _test_criterion_node_impurity(c2)
self.assertAlmostEqual(i1, i2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
p1 = _test_criterion_proxy_impurity_improvement(c1)
p2 = _test_criterion_proxy_impurity_improvement(c2)
self.assertTrue(numpy.isnan(p1), numpy.isnan(p2))
for i in range(2, 4):
_test_criterion_update(c1, i)
_test_criterion_update(c2, i)
left1, right1 = _test_criterion_node_impurity_children(c1)
left2, right2 = _test_criterion_node_impurity_children(c2)
self.assertAlmostEqual(left1, left2)
self.assertAlmostEqual(right1, right2)
v1 = _test_criterion_node_value(c1)
v2 = _test_criterion_node_value(c2)
self.assertEqual(v1, v2)
try:
# scikit-learn >= 0.24
p1 = _test_criterion_impurity_improvement(
c1, 0., left1, right1)
p2 = _test_criterion_impurity_improvement(
c2, 0., left2, right2)
except TypeError:
# scikit-learn < 0.24
p1 = _test_criterion_impurity_improvement(c1, 0.)
p2 = _test_criterion_impurity_improvement(c2, 0.)
self.assertAlmostEqual(p1, p2)
