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import ctypes as ct
import numpy as np
from Orange.base import Learner, Model
__all__ = ['SimpleTreeLearner']
from . import _simple_tree
_tree = ct.cdll.LoadLibrary(_simple_tree.__file__)
DiscreteNode = 0
ContinuousNode = 1
PredictorNode = 2
Classification = 0
Regression = 1
IntVar = 0
FloatVar = 1
c_int_p = ct.POINTER(ct.c_int)
c_double_p = ct.POINTER(ct.c_double)
class SIMPLE_TREE_NODE(ct.Structure):
pass
SIMPLE_TREE_NODE._fields_ = [
('type', ct.c_int),
('children_size', ct.c_int),
('split_attr', ct.c_int),
('split', ct.c_float),
('children', ct.POINTER(ct.POINTER(SIMPLE_TREE_NODE))),
('dist', ct.POINTER(ct.c_float)),
('n', ct.c_float),
('sum', ct.c_float),
]
_tree.build_tree.restype = ct.POINTER(SIMPLE_TREE_NODE)
_tree.new_node.restype = ct.POINTER(SIMPLE_TREE_NODE)
class SimpleTreeNode:
pass
class SimpleTreeLearner(Learner):
"""
Classification or regression tree learner.
Uses gain ratio for classification and mean square error for
regression. This learner was developed to speed-up random
forest construction, but can also be used as a standalone tree learner.
min_instances : int, optional (default = 2)
Minimal number of data instances in leaves. When growing the three,
new nodes are not introduced if they would result in leaves
with fewer instances than min_instances. Instance count is weighed.
max_depth : int, optional (default = 1024)
Maximal depth of tree.
max_majority : float, optional (default = 1.0)
Maximal proportion of majority class. When this is
exceeded, induction stops (only used for classification).
skip_prob : string, optional (default = 0.0)
Data attribute will be skipped with probability ``skip_prob``.
- if float, then skip attribute with this probability.
- if "sqrt", then `skip_prob = 1 - sqrt(n_features) / n_features`
- if "log2", then `skip_prob = 1 - log2(n_features) / n_features`
bootstrap : data table, optional (default = False)
A bootstrap dataset.
seed : int, optional (default = 42)
Random seed.
"""
name = 'simple tree'
def __init__(self, min_instances=2, max_depth=32, max_majority=0.95,
skip_prob=0.0, bootstrap=False, seed=42):
super().__init__()
self.min_instances = min_instances
self.max_depth = max_depth
self.max_majority = max_majority
self.skip_prob = skip_prob
self.bootstrap = bootstrap
self.seed = seed
def fit_storage(self, data):
return SimpleTreeModel(self, data)
class SimpleTreeModel(Model):
def __init__(self, learner, data):
X = np.ascontiguousarray(data.X)
Y = np.ascontiguousarray(data.Y)
W = np.ascontiguousarray(data.W)
self.num_attrs = X.shape[1]
self.dom_attr = data.domain.attributes
self.cls_vars = list(data.domain.class_vars)
if len(data.domain.class_vars) != 1:
n_cls = len(data.domain.class_vars)
raise ValueError("Number of classes should be 1: {}".format(n_cls))
if data.domain.has_discrete_class:
self.type = Classification
self.cls_vals = len(data.domain.class_var.values)
elif data.domain.has_continuous_class:
self.type = Regression
self.cls_vals = 0
else:
raise ValueError("Only Continuous and Discrete "
"variables are supported")
if isinstance(learner.skip_prob, (float, int)):
skip_prob = learner.skip_prob
elif learner.skip_prob == 'sqrt':
skip_prob = 1.0 - np.sqrt(X.shape[1]) / X.shape[1]
elif learner.skip_prob == 'log2':
skip_prob = 1.0 - np.log2(X.shape[1]) / X.shape[1]
else:
raise ValueError(
"skip_prob not valid: {}".format(learner.skip_prob))
attr_vals = []
domain = []
for attr in data.domain.attributes:
if attr.is_discrete:
attr_vals.append(len(attr.values))
domain.append(IntVar)
elif attr.is_continuous:
attr_vals.append(0)
domain.append(FloatVar)
else:
raise ValueError("Only Continuous and Discrete "
"variables are supported")
attr_vals = np.array(attr_vals, dtype=np.int32)
domain = np.array(domain, dtype=np.int32)
self.node = _tree.build_tree(
X.ctypes.data_as(c_double_p),
Y.ctypes.data_as(c_double_p),
W.ctypes.data_as(c_double_p),
X.shape[0],
W.size,
learner.min_instances,
learner.max_depth,
ct.c_float(learner.max_majority),
ct.c_float(skip_prob),
self.type,
self.num_attrs,
self.cls_vals,
attr_vals.ctypes.data_as(c_int_p),
domain.ctypes.data_as(c_int_p),
learner.bootstrap,
learner.seed)
def predict(self, X):
X = np.ascontiguousarray(X)
if self.type == Classification:
p = np.zeros((X.shape[0], self.cls_vals))
_tree.predict_classification(
X.ctypes.data_as(c_double_p),
X.shape[0],
self.node,
self.num_attrs,
self.cls_vals,
p.ctypes.data_as(c_double_p))
return p.argmax(axis=1), p
elif self.type == Regression:
p = np.zeros(X.shape[0])
_tree.predict_regression(
X.ctypes.data_as(c_double_p),
X.shape[0],
self.node,
self.num_attrs,
p.ctypes.data_as(c_double_p))
return p
else:
assert False, "Invalid prediction type"
def __del__(self):
if hasattr(self, "node"):
_tree.destroy_tree(self.node, self.type)
def __getstate__(self):
dict = self.__dict__.copy()
del dict['node']
py_node = self.__to_python(self.node)
return dict, py_node
def __setstate__(self, state):
dict, py_node = state
self.__dict__.update(dict)
self.node = self.__from_python(py_node)
# for pickling a tree
def __to_python(self, node):
n = node.contents
py_node = SimpleTreeNode()
py_node.type = n.type
py_node.children_size = n.children_size
py_node.split_attr = n.split_attr
py_node.split = n.split
py_node.children = [
self.__to_python(n.children[i]) for i in range(n.children_size)]
if self.type == Classification:
py_node.dist = [n.dist[i] for i in range(self.cls_vals)]
else:
py_node.n = n.n
py_node.sum = n.sum
return py_node
# for unpickling a tree
def __from_python(self, py_node):
node = _tree.new_node(py_node.children_size, self.type, self.cls_vals)
n = node.contents
n.type = py_node.type
n.children_size = py_node.children_size
n.split_attr = py_node.split_attr
n.split = py_node.split
for i in range(n.children_size):
n.children[i] = self.__from_python(py_node.children[i])
if self.type == Classification:
for i in range(self.cls_vals):
n.dist[i] = py_node.dist[i]
else:
n.n = py_node.n
n.sum = py_node.sum
return node
# for comparing two trees
def dumps_tree(self, node):
n = node.contents
xs = ['{', str(n.type)]
if n.type != PredictorNode:
xs.append(str(n.split_attr))
if n.type == ContinuousNode:
xs.append('{:.5f}'.format(n.split))
elif self.type == Classification:
for i in range(self.cls_vals):
xs.append('{:.2f}'.format(n.dist[i]))
else:
xs.append('{:.5f} {:.5f}'.format(n.n, n.sum))
for i in range(n.children_size):
xs.append(self.dumps_tree(n.children[i]))
xs.append('}')
return ' '.join(xs)
def to_string(self, node=None, level=0):
"""Return a text-based representation of the tree.
Parameters
----------
node : LP_SIMPLE_TREE_NODE, optional (default=None)
Tree node. Used to construct representation of the
tree under this node.
If not provided, node is considered root node.
level : int, optional (defaul=0)
Level of the node. Used for line indentation.
Returns
-------
tree : str
Text-based representation of the tree.
"""
if node is None:
if self.node is None:
return '(null node)'
else:
node = self.node
n = node.contents
if self.type == Classification:
format_str = format_leaf = format_node = None
else:
format_str = f"({self.domain.class_var.format_str}: %s)"
format_leaf = " --> " + format_str
format_node = "%s " + format_str
if n.children_size == 0:
if self.type == Classification:
node_cont = [round(n.dist[i], 1)
for i in range(self.cls_vals)]
index = node_cont.index(max(node_cont))
major_class = self.cls_vars[0].values[index]
return ' --> %s (%s)' % (major_class, node_cont)
else:
return format_leaf % (n.sum / n.n, n.n)
else:
attr = self.dom_attr[n.split_attr]
node_desc = attr.name
indent = '\n' + ' ' * level
if self.type == Classification:
node_cont = [round(n.dist[i], 1)
for i in range(self.cls_vals)]
ret_str = indent + '%s (%s)' % (node_desc, node_cont)
else:
ret_str = indent + format_node % (node_desc, n.sum / n.n, n.n)
for i in range(n.children_size):
if attr.is_continuous:
split = '<=' if i % 2 == 0 else '>'
split += attr.format_str % n.split
ret_str += indent + ': %s' % split
else:
ret_str += indent + ': %s' % attr.values[i]
ret_str += self.to_string(n.children[i], level + 1)
return ret_str
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