def profileDecisionTreeRegressor(self):
numExamples = 1000
numFeatures = 20
minSplit = 10
maxDepth = 20
generator = ExamplesGenerator()
X, y = generator.generateBinaryExamples(numExamples, numFeatures)
regressor = DecisionTreeRegressor(min_split=minSplit, max_depth=maxDepth, min_density=0.0)
ProfileUtils.profile('regressor.fit(X, y)', globals(), locals())
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 profileFindBestSplit(self):
numExamples = 1000
numFeatures = 100
minSplit = 1
maxDepth = 20
generator = ExamplesGenerator()
X, y = generator.generateBinaryExamples(numExamples, numFeatures)
X = numpy.array(X, order="F")
nodeInds = numpy.arange(X.shape[0])
argsortX = numpy.zeros(X.shape, numpy.int, order="F")
for i in range(X.shape[1]):
argsortX[:, i] = numpy.argsort(X[:, i])
argsortX[:, i] = numpy.argsort(argsortX[:, i])
def run():
for i in range(10):
findBestSplit3(minSplit, X, y, nodeInds, argsortX)
ProfileUtils.profile('run()', globals(), locals())
