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"""Tree learner widget"""
from collections import OrderedDict
from AnyQt.QtCore import Qt
from Orange.data import Table
from Orange.modelling.tree import TreeLearner
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.localization import pl
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from Orange.widgets.utils.widgetpreview import WidgetPreview
class OWTreeLearner(OWBaseLearner):
"""Tree algorithm with forward pruning."""
name = "Tree"
description = "A tree algorithm with forward pruning."
icon = "icons/Tree.svg"
replaces = [
"Orange.widgets.classify.owclassificationtree.OWClassificationTree",
"Orange.widgets.regression.owregressiontree.OWRegressionTree",
"Orange.widgets.classify.owclassificationtree.OWTreeLearner",
"Orange.widgets.regression.owregressiontree.OWTreeLearner",
]
priority = 30
keywords = "tree, classification tree"
LEARNER = TreeLearner
binary_trees = Setting(True)
limit_min_leaf = Setting(True)
min_leaf = Setting(2)
limit_min_internal = Setting(True)
min_internal = Setting(5)
limit_depth = Setting(True)
max_depth = Setting(100)
# Classification only settings
limit_majority = Setting(True)
sufficient_majority = Setting(95)
spin_boxes = (
("Min. number of instances in leaves: ",
"limit_min_leaf", "min_leaf", 1, 1000),
("Do not split subsets smaller than: ",
"limit_min_internal", "min_internal", 1, 1000),
("Limit the maximal tree depth to: ",
"limit_depth", "max_depth", 1, 1000))
classification_spin_boxes = (
("Stop when majority reaches [%]: ",
"limit_majority", "sufficient_majority", 51, 100),)
def add_main_layout(self):
box = gui.widgetBox(self.controlArea, 'Parameters')
# the checkbox is put into vBox for alignemnt with other checkboxes
gui.checkBox(box, self, "binary_trees", "Induce binary tree",
callback=self.settings_changed,
attribute=Qt.WA_LayoutUsesWidgetRect)
for label, check, setting, fromv, tov in self.spin_boxes:
gui.spin(box, self, setting, fromv, tov, label=label,
checked=check, alignment=Qt.AlignRight,
callback=self.settings_changed,
checkCallback=self.settings_changed, controlWidth=80)
def add_classification_layout(self, box):
for label, check, setting, minv, maxv in self.classification_spin_boxes:
gui.spin(box, self, setting, minv, maxv,
label=label, checked=check, alignment=Qt.AlignRight,
callback=self.settings_changed, controlWidth=80,
checkCallback=self.settings_changed)
def learner_kwargs(self):
# Pylint doesn't get our Settings
# pylint: disable=invalid-sequence-index
return dict(
max_depth=(None, self.max_depth)[self.limit_depth],
min_samples_split=(2, self.min_internal)[self.limit_min_internal],
min_samples_leaf=(1, self.min_leaf)[self.limit_min_leaf],
binarize=self.binary_trees,
preprocessors=self.preprocessors,
sufficient_majority=(1, self.sufficient_majority / 100)[
self.limit_majority])
def create_learner(self):
# pylint: disable=not-callable
return self.LEARNER(**self.learner_kwargs())
def get_learner_parameters(self):
from Orange.widgets.report import plural_w
items = OrderedDict()
items["Pruning"] = ", ".join(s for s, c in (
(f'at least {self.min_leaf} '
f'{pl(self.min_leaf, "instance")} in leaves',
self.limit_min_leaf),
(f'at least {self.min_internal} '
f'{pl(self.min_internal, "instance")} in internal nodes',
self.limit_min_internal),
(f'maximum depth {self.max_depth}',
self.limit_depth)
) if c) or "None"
if self.limit_majority:
items["Splitting"] = "Stop splitting when majority reaches %d%% " \
"(classification only)" % \
self.sufficient_majority
items["Binary trees"] = ("No", "Yes")[self.binary_trees]
return items
if __name__ == "__main__": # pragma: no cover
WidgetPreview(OWTreeLearner).run(Table("iris"))
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