def saveDot(self, filename=None):
if filename==None:
filename = QFileDialog.getSaveFileName(self, "Save to ...", "tree.dot", "Dot Tree File (.DOT)")
filename = unicode(filename)
if not filename:
return
orngTree.printDot(self.tree, filename)
def print_tree(self, data, visual):
"""
Decision Tree from the data set
"""
tree = learner(data)
orngTree.printTxt(tree)
if visual: # visual equals true allows plotting a visual tree. A .dot file is written into the current directory.
orngTree.printDot(tree, fileName='tree.dot', internalNodeShape="ellipse", leafShape="box")
def saveDot(self, filename=None):
if filename == None:
filename = str(
QFileDialog.getSaveFileName(self, "Save to ...", "tree.dot",
"Dot Tree File (.DOT)"))
if not filename:
return
orngTree.printDot(self.tree, filename)
def print_tree(self, data, visual):
"""
Decision Tree from the data set
"""
tree = learner(data)
orngTree.printTxt(tree)
if visual: # visual equals true allows plotting a visual tree. A .dot file is written into the current directory.
orngTree.printDot(tree,
fileName='tree.dot',
internalNodeShape="ellipse",
leafShape="box")
def report_tree(self, name):
filename = name + '.tsv'
stream = file(filename, 'wt')
# header
stream.write('\t'.join(self.names) + '\n')
stream.write('\t'.join(self.types) + '\n')
stream.write('class\n')
# rows
for row in self.rows:
if row[0] == 'skip':
continue
row += [''] * (len(self.names) - len(row))
stream.write('\t'.join(row) + '\n')
stream.close()
# See http://www.ailab.si/orange/doc/ofb/c_otherclass.htm
try:
import orange
import orngTree
except ImportError:
sys.stderr.write(
'Install Orange from http://www.ailab.si/orange/ for a classification tree.\n'
)
return None
data = orange.ExampleTable(filename)
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
orngTree.printTxt(tree, maxDepth=4)
text_tree = orngTree.dumpTree(tree)
file(name + '.txt', 'wt').write(text_tree)
orngTree.printDot(tree,
fileName=name + '.dot',
nodeShape='ellipse',
leafShape='box')
return text_tree
def summary(self):
sys.stdout.write("%u tests, %u passed, %u skipped, %u failed\n\n" %
(self.tests, self.passed, self.skipped, self.failed))
sys.stdout.flush()
name, ext = os.path.splitext(os.path.basename(sys.argv[0]))
filename = name + '.tsv'
stream = file(filename, 'wt')
# header
stream.write('\t'.join(self.names) + '\n')
stream.write('\t'.join(self.types) + '\n')
stream.write('class\n')
# rows
for row in self.rows:
row += [''] * (len(self.names) - len(row))
stream.write('\t'.join(row) + '\n')
stream.close()
# See http://www.ailab.si/orange/doc/ofb/c_otherclass.htm
try:
import orange
import orngTree
except ImportError:
sys.stderr.write(
'Install Orange from http://www.ailab.si/orange/ for a classification tree.\n'
)
return
data = orange.ExampleTable(filename)
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
orngTree.printTxt(tree, maxDepth=4)
file(name + '.txt', 'wt').write(orngTree.dumpTree(tree))
orngTree.printDot(tree,
fileName=name + '.dot',
nodeShape='ellipse',
leafShape='box')
def report_tree(self, name):
filename = name + '.tsv'
stream = file(filename, 'wt')
# header
stream.write('\t'.join(self.names) + '\n')
stream.write('\t'.join(self.types) + '\n')
stream.write('class\n')
# rows
for row in self.rows:
if row[0] == 'skip':
continue
row += ['']*(len(self.names) - len(row))
stream.write('\t'.join(row) + '\n')
stream.close()
# See http://www.ailab.si/orange/doc/ofb/c_otherclass.htm
try:
import orange
import orngTree
except ImportError:
sys.stderr.write('Install Orange from http://www.ailab.si/orange/ for a classification tree.\n')
return None
data = orange.ExampleTable(filename)
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
orngTree.printTxt(tree, maxDepth=4)
text_tree = orngTree.dumpTree(tree)
file(name + '.txt', 'wt').write(text_tree)
orngTree.printDot(tree, fileName=name+'.dot', nodeShape='ellipse', leafShape='box')
return text_tree
def classifier(personalrecords, features , attlist ,lowage, upage ,estimation):
data = orange.ExampleTable(createAttributes(attlist), getallfeatures(personalrecords,attlist,
lowage, upage, 'MONTHS'))
bayes = orange.BayesLearner()
bayesWithEstimation = orange.BayesLearner(m=estimation)
tree = orngTree.TreeLearner(mForPruning=2)
bayes.name = "bayes"
bayesWithEstimation.name = "bayesWithEstimation"
tree.name = "tree"
learners = [bayes, bayesWithEstimation]
print '10 fold cross validattion'
results = orngTest.crossValidation(learners, data, folds=10)
# output the results
print "Learner CA IS Brier AUC"
for i in range(len(learners)):
print "%-8s %5.3f %5.3f %5.3f %5.3f" % (learners[i].name, \
orngStat.CA(results)[i], orngStat.IS(results)[i],
orngStat.BrierScore(results)[i], orngStat.AUC(results)[i])
if learners[i].name == 'bayes' or learners[i].name == 'bayesWithEstimation':
orngTree.printDot(tree, fileName='c:\\tree10.dot', internalNodeShape="ellipse", leafShape="box")
orngTree.printDot(bayes, fileName='c:\\bayes10.dot', internalNodeShape="ellipse", leafShape="box")
def summary(self):
sys.stdout.write("%u tests, %u passed, %u skipped, %u failed\n\n" % (self.tests, self.passed, self.skipped, self.failed))
sys.stdout.flush()
name, ext = os.path.splitext(os.path.basename(sys.argv[0]))
filename = name + '.tsv'
stream = file(filename, 'wt')
# header
stream.write('\t'.join(self.names) + '\n')
stream.write('\t'.join(self.types) + '\n')
stream.write('class\n')
# rows
for row in self.rows:
row += ['']*(len(self.names) - len(row))
stream.write('\t'.join(row) + '\n')
stream.close()
# See http://www.ailab.si/orange/doc/ofb/c_otherclass.htm
try:
import orange
import orngTree
except ImportError:
sys.stderr.write('Install Orange from http://www.ailab.si/orange/ for a classification tree.\n')
return
data = orange.ExampleTable(filename)
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
orngTree.printTxt(tree, maxDepth=4)
file(name+'.txt', 'wt').write(orngTree.dumpTree(tree))
orngTree.printDot(tree, fileName=name+'.dot', nodeShape='ellipse', leafShape='box')
#!/usr/bin/env python
#
# See also:
# http://www.ailab.si/orange/doc/ofb/c_otherclass.htm
import os.path
import sys
import orange
import orngTree
for arg in sys.argv[1:]:
name, ext = os.path.splitext(arg)
data = orange.ExampleTable(arg)
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
orngTree.printTxt(tree)
file(name + '.txt', 'wt').write(orngTree.dumpTree(tree) + '\n')
orngTree.printDot(tree,
fileName=name + '.dot',
nodeShape='ellipse',
leafShape='box')
# Description: Learn decision tree from data and output class probabilities for first few instances
# Category: modelling
# Uses: voting.tab
# Classes: orngTree.TreeLearner
# Referenced: c_otherclass.htm
import orange, orngTree
data = orange.ExampleTable("voting")
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
print "Possible classes:", data.domain.classVar.values
print "Probabilities for democrats:"
for i in range(5):
p = tree(data[i], orange.GetProbabilities)
print "%d: %5.3f (originally %s)" % (i+1, p[1], data[i].getclass())
print
orngTree.printTxt(tree)
orngTree.printDot(tree, fileName='tree.dot', internalNodeShape="ellipse", leafShape="box")
# Author: J Zabkar
# Version: 1.0
# Description: Demostration of use of orngTree module: prin out
# a tree in text and dot format
# Category: modelling
# Uses: iris
# Referenced: orngTree.htm
import orange, orngTree
data = orange.ExampleTable("iris.tab")
tree = orange.TreeLearner(data)
orngTree.printTree(tree)
orngTree.printDot(tree, fileName="tree1.dot")
#!/usr/bin/env python
#
# See also:
# http://www.ailab.si/orange/doc/ofb/c_otherclass.htm
import os.path
import sys
import orange
import orngTree
for arg in sys.argv[1:]:
name, ext = os.path.splitext(arg)
data = orange.ExampleTable(arg)
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
orngTree.printTxt(tree)
file(name+'.txt', 'wt').write(orngTree.dumpTree(tree) + '\n')
orngTree.printDot(tree, fileName=name+'.dot', nodeShape='ellipse', leafShape='box')
# Description: Learn decision tree from data and output class probabilities for first few instances
# Category: modelling
# Uses: voting.tab
# Classes: orngTree.TreeLearner
# Referenced: c_otherclass.htm
import orange, orngTree
data = orange.ExampleTable("voting")
tree = orngTree.TreeLearner(data, sameMajorityPruning=1, mForPruning=2)
print "Possible classes:", data.domain.classVar.values
print "Probabilities for democrats:"
for i in range(5):
p = tree(data[i], orange.GetProbabilities)
print "%d: %5.3f (originally %s)" % (i + 1, p[1], data[i].getclass())
print
orngTree.printTxt(tree)
orngTree.printDot(tree,
fileName='tree.dot',
internalNodeShape="ellipse",
leafShape="box")
