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DecisionTree.py
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DecisionTree.py
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import Utils
import Evaluation
from pyspark import SparkConf, SparkContext, mllib
from pyspark.mllib.tree import DecisionTree
import evaluation
def trainModel(trainingData):
print '\nTraining Decision Tree model started'
Utils.logTime()
model = DecisionTree.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, impurity='gini', maxDepth=5,maxBins=32)
print '\nTraining Decision Tree model finished'
Utils.logTime()
return model
def trainOptimalModel(trainingData, testData):
print "\nTraining optimal Decision Tree model started!"
Utils.logTime()
impurityVals = ['gini', 'entropy']
maxDepthVals = [3,4,5,6,7]
maxBinsVals = [8,16,32]
optimalModel = None
optimalMaxDepth = None
optimalImpurity = None
optimalBinsVal = None
minError = None
try:
for curImpurity in impurityVals:
for curMaxDepth in maxDepthVals:
for curMaxBins in maxBinsVals:
model = DecisionTree.trainClassifier(trainingData,
numClasses=2,
categoricalFeaturesInfo={},
impurity=curImpurity,
maxDepth=curMaxDepth,
maxBins=curMaxBins)
testErr, PR, ROC = Evaluation.evaluate(model, testData)
if testErr < minError or not minError:
minError = testErr
optimalImpurity = curImpurity
optimalMaxDepth = curMaxDepth
optimalBinsVal = curMaxBins
optimalModel = model
except:
msg = "\nException during model training with below parameters:"
msg += "\timpurity: " + str(curImpurity)
msg += "\tmaxDepth: " + str(curMaxDepth)
msg += "\tmaxBins: " + str(curMaxBins)
Utils.logMessage(msg)
logMessage(optimalModel, optimalMaxDepth, optimalImpurity, optimalBinsVal, minError)
return optimalModel
def logMessage(optimalModel,optimalMaxDepth, optimalImpurity, optimalBinsVal, minError):
msg = "\nTraining optimal Decision Tree model finished:"
msg += "\tMin Test Error : " + str(minError)
msg += "\toptimal impurity : " + str(optimalImpurity)
msg += "\toptimal max depth : " + str(optimalMaxDepth)
msg += "\toptimal bins val : " + str(optimalBinsVal)
Utils.logMessage(msg)
Utils.logTime()
#print "\toptimal model : " + optimalModel.toDebugString()