def getLearningCurve(self, learners):
pb = OWGUI.ProgressBar(self, iterations=self.steps * self.folds)
curve = orngTest.learningCurveN(learners,
self.data,
folds=self.folds,
proportions=self.curvePoints,
callback=pb.advance)
pb.finish()
return curve
def getLearningCurve(self, learners):
pb = OWGUI.ProgressBar(self, iterations=self.steps*self.folds)
if not self.testdata:
curve = orngTest.learningCurveN(learners, self.data, folds=self.folds, proportions=self.curvePoints, callback=pb.advance)
else:
curve = orngTest.learningCurveWithTestData(learners,
self.data, self.testdata, times=self.folds, proportions=self.curvePoints, callback=pb.advance)
pb.finish()
return curve
print "#iter %i, #classifiers %i" % (len(
res.classifiers), len(res.classifiers[0]))
print
##print "\nLearning with 100% class noise"
##classnoise = orange.Preprocessor_addClassNoise(proportion=1.0)
##res = orngTest.proportionTest(learners, data, 0.7, 100, pps = [("L", classnoise)])
##printResults(res)
print "\nGood old 10-fold cross validation"
res = orngTest.crossValidation(learners, data)
printResults(res)
print "\nLearning curve"
prop = orange.frange(0.2, 1.0, 0.2)
res = orngTest.learningCurveN(learners, data, folds=5, proportions=prop)
for i in range(len(prop)):
print "%5.3f:" % prop[i],
printResults(res[i])
print "\nLearning curve with pre-separated data"
indices = orange.MakeRandomIndices2(data, p0=0.7)
train = data.select(indices, 0)
test = data.select(indices, 1)
res = orngTest.learningCurveWithTestData(learners,
train,
test,
times=5,
proportions=prop)
for i in range(len(prop)):
print "%5.3f:" % prop[i],
print "#iter %i, #classifiers %i" % (len(res.classifiers), len(res.classifiers[0]) if len(res.classifiers) > 0 else -1)
print
##print "\nLearning with 100% class noise"
##classnoise = orange.Preprocessor_addClassNoise(proportion=1.0)
##res = orngTest.proportionTest(learners, data, 0.7, 100, pps = [("L", classnoise)])
##printResults(res)
print "\nGood old 10-fold cross validation"
res = orngTest.crossValidation(learners, data)
printResults(res)
print "\nLearning curve"
prop = orange.frange(0.2, 1.0, 0.2)
res = orngTest.learningCurveN(learners, data, folds = 5, proportions = prop)
for i in range(len(prop)):
print "%5.3f:" % prop[i],
printResults(res[i])
print "\nLearning curve with pre-separated data"
indices = orange.MakeRandomIndices2(data, p0 = 0.7)
train = data.select(indices, 0)
test = data.select(indices, 1)
res = orngTest.learningCurveWithTestData(learners, train, test, times = 5, proportions = prop)
for i in range(len(prop)):
print "%5.3f:" % prop[i],
printResults(res[i])
print "\nLearning and testing on pre-separated data"
