def test__data_at_node(self):
tree = dt._create_decision_node(self.x, self.labels_train)
assert np.all(
self.x[self.x[:,2] < 0] == dt._data_at_node(tree, tree.children[0],
self.x)
)
assert np.all(
self.x[self.x[:,2] > 0] == dt._data_at_node(tree, tree.children[1],
self.x)
)
def test_min_obs_split(self):
min_obs = 5
tree = dt._create_decision_node(X_TRAIN, self.labels_train,
min_obs_split=min_obs)
for desc in tree.descendents():
if len(dt._data_at_node(tree, desc, X_TRAIN)) < min_obs:
assert desc.split is None
def test_min_obs_split(self):
min_obs = 5
tree = dt._create_decision_node(X_TRAIN,
self.labels_train,
min_obs_split=min_obs)
for desc in tree.descendents():
if len(dt._data_at_node(tree, desc, X_TRAIN)) < min_obs:
assert desc.split is None
def test__data_at_node(self):
tree = dt._create_decision_node(self.x, self.labels_train)
assert np.all(self.x[self.x[:, 2] < 0] == dt._data_at_node(
tree, tree.children[0], self.x))
assert np.all(self.x[self.x[:, 2] > 0] == dt._data_at_node(
tree, tree.children[1], self.x))