def _score_disc_bin():
n_values = len(attr.values)
if n_values == 2:
return _score_disc()
score, mapping = _tree_scorers.find_binarization_MSE(
col_x, col_y, n_values, self.min_samples_leaf)
# The score is already adjusted for missing attribute values, so
# we don't do it here
if score == 0:
return REJECT_ATTRIBUTE
mapping, branches = MappedDiscreteNode.branches_from_mapping(
data.X[:, attr_no], mapping, len(attr.values))
node = MappedDiscreteNode(attr, attr_no, mapping, None)
return score, node, branches, 2
def setUpClass(cls):
super().setUpClass()
WidgetOutputsTestMixin.init(cls)
tree = TreeLearner()
cls.model = tree(cls.data)
cls.model.instances = cls.data
cls.signal_name = "Tree"
cls.signal_data = cls.model
# Load a dataset that contains two variables with the same entropy
data_same_entropy = Table(
path.join(path.dirname(path.dirname(path.dirname(__file__))),
"tests", "datasets", "same_entropy.tab"))
cls.data_same_entropy = tree(data_same_entropy)
cls.data_same_entropy.instances = data_same_entropy
vara = DiscreteVariable("aaa", values=("e", "f", "g"))
root = DiscreteNode(vara, 0, np.array([42, 8]))
root.subset = np.arange(50)
varb = DiscreteVariable("bbb", values=tuple("ijkl"))
child0 = MappedDiscreteNode(varb, 1, np.array([0, 1, 0, 0]), (38, 5))
child0.subset = np.arange(16)
child1 = Node(None, 0, (13, 3))
child1.subset = np.arange(16, 30)
varc = ContinuousVariable("ccc")
child2 = NumericNode(varc, 2, 42, (78, 12))
child2.subset = np.arange(30, 50)
root.children = (child0, child1, child2)
child00 = Node(None, 0, (15, 4))
child00.subset = np.arange(10)
child01 = Node(None, 0, (10, 5))
child01.subset = np.arange(10, 16)
child0.children = (child00, child01)
child20 = Node(None, 0, (90, 4))
child20.subset = np.arange(30, 35)
child21 = Node(None, 0, (70, 9))
child21.subset = np.arange(35, 50)
child2.children = (child20, child21)
domain = Domain([vara, varb, varc], ContinuousVariable("y"))
t = [[i, j, k] for i in range(3) for j in range(4) for k in (40, 44)]
x = np.array((t * 3)[:50])
data = Table.from_numpy(domain, x, np.arange(len(x)))
cls.tree = TreeModel(data, root)
def _score_disc_bin():
"""Scoring for discrete attributes, with binarization"""
n_values = len(attr.values)
if n_values <= 2:
return _score_disc()
cont = contingency.Discrete(data, attr)
attr_distr = np.sum(cont, axis=0)
# Skip instances with missing value of the attribute
cls_distr = np.sum(cont, axis=1)
if np.sum(attr_distr) == 0: # all values are missing
return REJECT_ATTRIBUTE
best_score, best_mapping = _tree_scorers.find_binarization_entropy(
cont, cls_distr, attr_distr, self.min_samples_leaf)
if best_score <= 0:
return REJECT_ATTRIBUTE
best_score *= 1 - np.sum(cont.unknowns) / len(data)
mapping, branches = MappedDiscreteNode.branches_from_mapping(
col_x, best_mapping, n_values)
node = MappedDiscreteNode(attr, attr_no, mapping, None)
return best_score, node, branches, 2
def _score_disc_bin():
n_values = len(attr.values)
if n_values == 2:
return _score_disc()
score, mapping = _tree_scorers.find_binarization_MSE(
col_x, col_y, n_values, self.min_samples_leaf)
# The score is already adjusted for missing attribute values, so
# we don't do it here
if score == 0:
return REJECT_ATTRIBUTE
mapping, branches = MappedDiscreteNode.branches_from_mapping(
col_x, mapping, len(attr.values))
node = MappedDiscreteNode(attr, attr_no, mapping, None)
return score, node, branches, 2
def _score_disc_bin():
"""Scoring for discrete attributes, with binarization"""
n_values = len(attr.values)
if n_values <= 2:
return _score_disc()
cont = contingency.Discrete(data, attr)
attr_distr = np.sum(cont, axis=0)
# Skip instances with missing value of the attribute
cls_distr = np.sum(cont, axis=1)
if np.sum(attr_distr) == 0: # all values are missing
return REJECT_ATTRIBUTE
best_score, best_mapping = _tree_scorers.find_binarization_entropy(
cont, cls_distr, attr_distr, self.min_samples_leaf)
if best_score <= 0:
return REJECT_ATTRIBUTE
best_score *= 1 - np.sum(cont.unknowns) / len(data)
mapping, branches = MappedDiscreteNode.branches_from_mapping(
data.X[:, attr_no], best_mapping, n_values)
node = MappedDiscreteNode(attr, attr_no, mapping, None)
return best_score, node, branches, 2
