def test_compile_and_run_cont_sparse(self):
# pylint: disable=protected-access
model = TreeModel(self.data, self.root)
expected_values = np.vstack((np.arange(8), [42] * 8)).T
np.testing.assert_equal(model._values, expected_values)
self.assertEqual(model._thresholds[0], 13)
self.assertEqual(model._thresholds.shape, (8, ))
nan = float("nan")
x = sp.csr_matrix(
np.array([[nan, 0, 0], [13, nan, 0], [13, 0, 0], [13, 1, 0],
[13, 2, 0], [14, 2, nan], [14, 2, 2], [14, 2, 1]],
dtype=float))
np.testing.assert_equal(model.get_values(x), expected_values)
x = sp.csc_matrix(
np.array([[nan, 0, 0], [13, nan, 0], [13, 0, 0], [13, 1, 0],
[13, 2, 0], [14, 2, nan], [14, 2, 2], [14, 2, 1]],
dtype=float))
np.testing.assert_equal(model.get_values(x), expected_values)
x = sp.lil_matrix(
np.array([[nan, 0, 0], [13, nan, 0], [13, 0, 0], [13, 1, 0],
[13, 2, 0], [14, 2, nan], [14, 2, 2], [14, 2, 1]],
dtype=float))
np.testing.assert_equal(model.get_values(x), expected_values)
def test_compile_and_run_cont_sparse(self):
# pylint: disable=protected-access
model = TreeModel(self.data, self.root)
expected_values = np.vstack((np.arange(8), [42] * 8)).T
np.testing.assert_equal(model._values, expected_values)
self.assertEqual(model._thresholds[0], 13)
self.assertEqual(model._thresholds.shape, (8,))
nan = float("nan")
x = sp.csr_matrix(np.array(
[[nan, 0, 0],
[13, nan, 0],
[13, 0, 0],
[13, 1, 0],
[13, 2, 0],
[14, 2, nan],
[14, 2, 2],
[14, 2, 1]], dtype=float
))
np.testing.assert_equal(model.get_values(x), expected_values)
x = sp.csc_matrix(np.array(
[[nan, 0, 0],
[13, nan, 0],
[13, 0, 0],
[13, 1, 0],
[13, 2, 0],
[14, 2, nan],
[14, 2, 2],
[14, 2, 1]], dtype=float
))
np.testing.assert_equal(model.get_values(x), expected_values)
x = sp.lil_matrix(np.array(
[[nan, 0, 0],
[13, nan, 0],
[13, 0, 0],
[13, 1, 0],
[13, 2, 0],
[14, 2, nan],
[14, 2, 2],
[14, 2, 1]], dtype=float
))
np.testing.assert_equal(model.get_values(x), expected_values)
def test_null_nodes(self):
a = DiscreteVariable("d4", "ab")
y = ContinuousVariable("ey")
domain = Domain([a], y)
data = Table(domain)
values = np.array([[42., 43], [44, 45]])
root = DiscreteNode(a, 0, values[1])
root.children = [Node(None, -1, values[0]), None]
model = TreeModel(data, root)
x = np.array([[0.], [1]])
np.testing.assert_equal(model.get_values(x), values)
np.testing.assert_equal(model.get_values_in_python(x), values)
np.testing.assert_equal(model.get_values_by_nodes(x), values)
def test_compile_and_run_cont(self):
# I investigate, I have a warrant
# pylint: disable=protected-access
model = TreeModel(self.data, self.root)
expected_values = np.vstack((np.arange(8), [42] * 8)).T
np.testing.assert_equal(model._values, expected_values)
self.assertEqual(model._thresholds[0], 13)
self.assertEqual(model._thresholds.shape, (8, ))
nan = float("nan")
x = np.array(
[
[nan, 0, 0],
[13, nan, 0],
[13, 0, 0],
[13, 1, 0],
[13, 2, 0],
[14, 2, nan],
[14, 2, 2],
[14, 2, 1],
],
dtype=float,
)
np.testing.assert_equal(model.get_values(x), expected_values)
np.testing.assert_equal(model.get_values_in_python(x), expected_values)
np.testing.assert_equal(model.get_values_by_nodes(x), expected_values)
np.testing.assert_equal(model.predict(x), np.arange(8).astype(int))
v1 = ContinuousVariable("d1")
v2 = DiscreteVariable("d2", "abc")
v3 = DiscreteVariable("d3", "def")
y = DiscreteVariable("dy")
domain = Domain([v1, v2, v3], y)
data = Table(domain, np.zeros((10, 4)))
root = NumericNode(v1, 0, 13, np.array([0.0, 42]))
left = DiscreteNode(v2, 1, np.array([1, 42]))
left.children = [
Node(None, None, np.array([x, 42])) for x in [2, 3, 4]
]
right = MappedDiscreteNode(v3, 2, np.array([1, 1, 0]),
np.array([5, 42]))
right.children = [Node(None, None, np.array([x, 42])) for x in [6, 7]]
root.children = [left, right]
model = TreeModel(data, root)
normalized = expected_values / np.sum(expected_values,
axis=1)[:, np.newaxis]
np.testing.assert_equal(model.predict(x), normalized)
def test_compile_and_run_cont(self):
# I investigate, I have a warrant
# pylint: disable=protected-access
model = TreeModel(self.data, self.root)
expected_values = np.vstack((np.arange(8), [42] * 8)).T
np.testing.assert_equal(model._values, expected_values)
self.assertEqual(model._thresholds[0], 13)
self.assertEqual(model._thresholds.shape, (8,))
nan = float("nan")
x = np.array(
[[nan, 0, 0],
[13, nan, 0],
[13, 0, 0],
[13, 1, 0],
[13, 2, 0],
[14, 2, nan],
[14, 2, 2],
[14, 2, 1]], dtype=float
)
np.testing.assert_equal(model.get_values(x), expected_values)
np.testing.assert_equal(model.get_values_in_python(x), expected_values)
np.testing.assert_equal(model.get_values_by_nodes(x), expected_values)
np.testing.assert_equal(model.predict(x), np.arange(8).astype(int))
v1 = ContinuousVariable("d1")
v2 = DiscreteVariable("d2", "abc")
v3 = DiscreteVariable("d3", "def")
y = DiscreteVariable("dy")
domain = Domain([v1, v2, v3], y)
data = Table(domain, np.zeros((10, 4)))
root = NumericNode(v1, 0, 13, np.array([0., 42]))
left = DiscreteNode(v2, 1, np.array([1, 42]))
left.children = [Node(None, None, np.array([x, 42])) for x in [2, 3, 4]]
right = MappedDiscreteNode(v3, 2, np.array([1, 1, 0]),
np.array([5, 42]))
right.children = [Node(None, None, np.array([x, 42])) for x in [6, 7]]
root.children = [left, right]
model = TreeModel(data, root)
normalized = \
expected_values / np.sum(expected_values, axis=1)[:, np.newaxis]
np.testing.assert_equal(model.predict(x), normalized)