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 main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
import Orange
app = QApplication(list(argv))
argv = app.arguments()
if len(argv) > 1:
filename = argv[1]
else:
filename = 'iris'
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 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
model = TreeLearner(max_depth=1000)(data)
else:
from Orange.regression.tree import TreeRegressionLearner
model = TreeRegressionLearner(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_classify(self):
rep = OWReport.get_instance()
data = Table("titanic")
widgets = self.clas_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_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 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 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()
