def main():
import sys
import Orange
argv = sys.argv
if len(argv) > 1:
filename = argv[1]
else:
filename = 'iris'
app = QtGui.QApplication(argv)
ow = OWPythagorasTree()
data = Orange.data.Table(filename)
if data.domain.has_discrete_class:
from Orange.classification.tree import TreeLearner
else:
from Orange.regression.tree import TreeLearner
model = TreeLearner(max_depth=1000)(data)
model.instances = data
ow.set_tree(model)
ow.show()
ow.raise_()
ow.handleNewSignals()
app.exec_()
sys.exit(0)
def test_report_widgets_model(self):
rep = OWReport.get_instance()
data = Table("titanic")
widgets = self.model_widgets
w = self.create_widget(OWTreeGraph)
clf = TreeLearner(max_depth=3)(data)
clf.instances = data
w.ctree(clf)
w.create_report_html()
rep.make_report(w)
self._create_report(widgets, rep, data)
def test_report_widgets_classify(self):
rep = OWReport.get_instance()
data = Table("zoo")
widgets = self.clas_widgets
w = OWClassificationTreeGraph()
clf = TreeLearner(max_depth=3)(data)
clf.instances = data
w.ctree(clf)
w.create_report_html()
rep.make_report(w)
self.assertEqual(len(widgets) + 1, 8)
self._create_report(widgets, rep, data)
def set_learner(self):
self.learner = TreeLearner(
criterion=self.scores[self.attribute_score][1],
max_depth=self.max_depth,
min_samples_split=self.min_internal,
min_samples_leaf=self.min_leaf,
preprocessors=self.preprocessors)
self.learner.name = self.model_name
self.classifier = None
if self.data is not None:
self.error(1)
if not self.learner.check_learner_adequacy(self.data.domain):
self.error(1, self.learner.learner_adequacy_err_msg)
else:
self.classifier = self.learner(self.data)
self.classifier.name = self.model_name
self.classifier.instances = self.data
self.send("Learner", self.learner)
self.send("Classification Tree", self.classifier)
def main():
from AnyQt.QtWidgets import QApplication
import sys
app = QApplication(sys.argv)
ow = OWPythagorasTree()
data = Table(sys.argv[1] if len(sys.argv) > 1 else 'iris')
if data.domain.has_discrete_class:
from Orange.classification.tree import SklTreeLearner as TreeLearner
else:
from Orange.regression.tree import SklTreeRegressionLearner as TreeLearner
model = TreeLearner(max_depth=1000)(data)
model.instances = data
ow.set_tree(model)
ow.show()
ow.raise_()
ow.handleNewSignals()
app.exec_()
def main():
import sys
import Orange
from Orange.classification.tree import TreeLearner
argv = sys.argv
if len(argv) > 1:
filename = argv[1]
else:
filename = 'iris'
app = QtGui.QApplication(argv)
ow = OWPythagorasTree()
data = Orange.data.Table(filename)
clf = TreeLearner(max_depth=1000)(data)
ow.set_tree(clf)
ow.show()
ow.raise_()
ow.handleNewSignals()
app.exec_()
sys.exit(0)
def toggle_node_color(self):
colors = self.scene.colors
for node in self.scene.nodes():
distr = node.get_distribution()
total = numpy.sum(distr)
if self.target_class_index:
p = distr[self.target_class_index - 1] / total
color = colors[self.target_class_index - 1].light(200 - 100 * p)
else:
modus = node.majority()
p = distr[modus] / (total or 1)
color = colors[int(modus)].light(400 - 300 * p)
node.backgroundBrush = QBrush(color)
self.scene.update()
if __name__ == "__main__":
from Orange.classification.tree import TreeLearner
a = QApplication(sys.argv)
ow = OWClassificationTreeGraph()
data = Table("iris")
clf = TreeLearner(max_depth=3)(data)
clf.instances = data
ow.ctree(clf)
ow.show()
ow.raise_()
a.exec_()
ow.saveSettings()
def paint(self, painter, option, widget=None):
rect = self.rect()
if self.isSelected():
option.state ^= QStyle.State_Selected
painter.setFont(self.document().defaultFont())
draw_text = str(self.split_condition())
painter.drawText(QPointF(4, -self.line_descent - 1), draw_text)
painter.save()
painter.setBrush(self.backgroundBrush)
if self.isSelected():
painter.setPen(QPen(QBrush(Qt.black), 2))
else:
painter.setPen(QPen(Qt.gray))
if self.is_leaf():
painter.drawRect(rect.adjusted(-3, 0, 0, 0))
else:
painter.drawRoundedRect(rect.adjusted(-3, 0, 0, 0), 4, 4)
painter.restore()
painter.setClipRect(rect)
return QGraphicsTextItem.paint(self, painter, option, widget)
if __name__ == "__main__":
from Orange.classification.tree import TreeLearner
a = QApplication(sys.argv)
ow = OWClassificationTreeGraph()
ow.ctree(TreeLearner(max_depth=3)(Table('iris')))
ow.show()
a.exec_()
ow.saveSettings()
