def profileLearnModel(self):
numExamples = 1000
numFeatures = 50
minSplit = 10
maxDepth = 20
generator = ExamplesGenerator()
X, y = generator.generateBinaryExamples(numExamples, numFeatures)
y = numpy.array(y, numpy.float)
learner = DecisionTreeLearner(minSplit=minSplit, maxDepth=maxDepth, pruneType="REP-CV")
#learner.learnModel(X, y)
#print("Done")
ProfileUtils.profile('learner.learnModel(X, y) ', globals(), locals())
print(learner.getTree().getNumVertices())
def profilePredict(self):
#Make the prdiction function faster
numExamples = 1000
numFeatures = 20
minSplit = 1
maxDepth = 20
generator = ExamplesGenerator()
X, y = generator.generateBinaryExamples(numExamples, numFeatures)
learner = DecisionTreeLearner(minSplit=minSplit, maxDepth=maxDepth)
learner.learnModel(X, y)
print(learner.getTree().getNumVertices())
ProfileUtils.profile('learner.predict(X)', globals(), locals())
print(learner.getTree().getNumVertices())
def profileModelSelect(self):
learner = DecisionTreeLearner(minSplit=5, maxDepth=30, pruneType="CART")
numExamples = 1000
numFeatures = 10
folds = 5
paramDict = {}
paramDict["setGamma"] = numpy.array(numpy.round(2**numpy.arange(1, 7.5, 0.5)-1), dtype=numpy.int)
X = numpy.random.rand(numExamples, numFeatures)
Y = numpy.array(numpy.random.rand(numExamples) < 0.1, numpy.int)*2-1
def run():
for i in range(5):
print("Iteration " + str(i))
idx = Sampling.crossValidation(folds, numExamples)
learner.parallelModelSelect(X, Y, idx, paramDict)
ProfileUtils.profile('run()', globals(), locals())
