def readCross(num,type,numtrees):
filename=resultFile+'_'+type+'_'+num+'_all.csv'
loader=CSVLoader()
loader.setSource(File(filename))
data=loader.getDataSet()
#print data.numAttributes()
data.setClassIndex(data.numAttributes()-1)
rf=RF()
rf.setNumTrees(numtrees)
#pred_output = PredictionOutput( classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
buffer = StringBuffer() # buffer for the predictions
output=PlainText()
output.setHeader(data)
output.setBuffer(buffer)
output.setOutputDistribution(True)
attRange = Range() # attributes to output
outputDistributions = Boolean(True)
evaluator=Evaluation(data)
evaluator.crossValidateModel(rf,data,10, Random(1),[output,attRange,outputDistributions])
print evaluator.toSummaryString()
print evaluator.toClassDetailsString()
print evaluator.toMatrixString()
return [evaluator.weightedPrecision(),evaluator.weightedRecall(),evaluator.weightedFMeasure(),evaluator.weightedMatthewsCorrelation(),evaluator.weightedFalseNegativeRate(),evaluator.weightedFalsePositiveRate(),evaluator.weightedTruePositiveRate(),evaluator.weightedTrueNegativeRate(),evaluator.weightedAreaUnderROC()]
def myGridSearch(data,NTreeBounds,NFeaturesBounds):
best_acc = -float('inf')
bestrandomforest = None
class bestValues(object):
t = float('nan')
f = float('nan')
for t in range(NTreeBounds[0],NTreeBounds[1]+NTreeBounds[2],NTreeBounds[2]):
for f in range(NFeaturesBounds[0],NFeaturesBounds[1]+NFeaturesBounds[2],NFeaturesBounds[2]):
randomforest = RandomForest()
randomforest.setNumTrees(int(t))
randomforest.setNumFeatures(int(f))
evaluation = Evaluation(data)
output = output = util.get_buffer_for_predictions()[0]
attRange = Range() # no additional attributes output
outputDistribution = Boolean(False) # we don't want distribution
random = Random(1)
numFolds = min(10,data.numInstances())
evaluation.crossValidateModel(randomforest,data,numFolds,random,[output, attRange, outputDistribution])
acc = evaluation.pctCorrect()
if (acc>best_acc):
bestrandomforest = randomforest
best_acc = acc
bestValues.t = t
bestValues.f = f
print "Best accuracy:", best_acc
print "Best values: NTreeBounds = ", bestValues.t, ", NFeaturesBounds = ", bestValues.f
print "-----------------------------------------"
return bestrandomforest, bestValues.t, bestValues.f, best_acc
def readFeature(num_features,type,select_feature,numtrees):
#filename1=resultFileTest
#filename2=resultFileTest2
filename1=resultFile+'_'+type+'_'+num_features+'_'+select_feature+'_train.csv'
filename2=resultFile+'_'+type+'_'+num_features+'_'+select_feature+'_test.csv'
#print filename1
loader=CSVLoader()
loader.setSource(File(filename1))
data=loader.getDataSet()
#print data.numAttributes()
data.setClassIndex(data.numAttributes()-1)
rf=RF()
rf.setNumTrees(numtrees)
rf.buildClassifier(data)
#print rf
loader.setSource(File(filename2))
test_data=Instances(loader.getDataSet())
test_data.setClassIndex(test_data.numAttributes()-1)
''' num=test_data.numInstances()
print num
for i in xrange(num):
r1=rf.distributionForInstance(test_data.instance(i))
r2=rf.classifyInstance(test_data.instance(i))
ptrixrint r1
print r2'''
buffer = StringBuffer() # buffer for the predictions
output=PlainText()
output.setHeader(test_data)
output.setBuffer(buffer)
attRange = Range() # attributes to output
outputDistribution = Boolean(True)
evaluator=Evaluation(data)
evaluator.evaluateModel(rf,test_data,[output,attRange,outputDistribution])
#print evaluator.evaluateModel(RF(),['-t',filename1,'-T',filename2,'-I',str(numtrees)])
#evaluator1=Evaluation(test_data)
print evaluator.toSummaryString()
print evaluator.toClassDetailsString()
print evaluator.toMatrixString()
return [evaluator.precision(1),evaluator.recall(1),evaluator.fMeasure(1),evaluator.matthewsCorrelationCoefficient(1),evaluator.numTruePositives(1),evaluator.numFalsePositives(1),evaluator.numTrueNegatives(1),evaluator.numFalseNegatives(1),evaluator.areaUnderROC(1)]
def RandomForest_ParamFinder(data):
# possible set for Number of trees
NTreeBounds = [1,20,1]
# possible set for number of features
NFeaturesBounds = [0,20,1]
if (data.numInstances()>10): # grid search does 10-fold cross validation; hence number of samples must be more than 10
gridsearch = GridSearch()
acctag = gridsearch.getEvaluation()
acctag = SelectedTag('ACC',acctag.getTags())
gridsearch.setEvaluation(acctag)
allfilters = AllFilters()
gridsearch.setFilter(allfilters)
gridsearch.setGridIsExtendable(Boolean(True))
randomforest = RandomForest()
gridsearch.setClassifier(randomforest)
gridsearch.setXProperty(String('classifier.numTrees'))
gridsearch.setYProperty(String('classifier.numFeatures'))
gridsearch.setXExpression(String('I'))
gridsearch.setYExpression(String('I'))
gridsearch.setXMin(NTreeBounds[0])
gridsearch.setXMax(NTreeBounds[1])
gridsearch.setXStep(NTreeBounds[2])
gridsearch.setYMin(NFeaturesBounds[0])
gridsearch.setYMax(NFeaturesBounds[1])
gridsearch.setYStep(NFeaturesBounds[2])
gridsearch.setYBase(10)
print "searching for random-forest NumTrees = [", NTreeBounds[0], ",", NTreeBounds[1], "], NumFeatures = [ ", NFeaturesBounds[0], ",", NFeaturesBounds[1], "] ...."
gridsearch.buildClassifier(data)
bestValues = gridsearch.getValues()
# ----------------------- Evaluation
bestrandomforest = RandomForest()
bestrandomforest.setNumTrees(int(bestValues.x))
bestrandomforest.setNumFeatures(int(bestValues.y))
evaluation = Evaluation(data)
output = output = util.get_buffer_for_predictions()[0]
attRange = Range() # no additional attributes output
outputDistribution = Boolean(False) # we don't want distribution
random = Random(1)
numFolds = min(10,data.numInstances())
evaluation.crossValidateModel(bestrandomforest,data,numFolds,random,[output, attRange, outputDistribution])
acc = evaluation.pctCorrect()
print "best accuracy: ", acc
print "best random-forest classifier with NumTrees=",bestValues.x , ", NumFeatures = ", bestValues.y
OptRndFrst = bestrandomforest
OptRndFrstp1 = bestValues.x
OptRndFrstp2 = bestValues.y
OptRndFrstAcc = acc
else:
OptRndFrst, OptRndFrstp1, OptRndFrstp2, OptRndFrstAcc = myGridSearch(data,NTreeBounds,NFeaturesBounds)
Description = 'Random-Forest classifier: OptNumTrees = ' + str(OptRndFrstp1) + \
', OptNumFeatures = ' + str(OptRndFrstp2) + ', OptAcc = ' + str(OptRndFrstAcc)
print "-----------------------------------------"
return OptRndFrst, OptRndFrstp1, OptRndFrstp2, OptRndFrstAcc, Description
def main():
#create the training & test sets, skipping the header row with [1:]
dataset = genfromtxt(open('Data/train.csv','r'), delimiter=',', dtype='f8')[1:]
target = [x[0] for x in dataset]
train = [x[1:] for x in dataset]
test = genfromtxt(open('Data/test.csv','r'), delimiter=',', dtype='f8')[1:]
#create and train the random forest
#multi-core CPUs can use: rf = RandomForestClassifier(n_estimators=100, n_jobs=2)
rf = RandomForest.setNumTrees(100)
rf.Evaluation(train, target)
savetxt('Data/submission2.csv', rf.predict(test), delimiter=',', fmt='%f')
def random_forest(trainData,testData,params,exparams):
numTrees = int(float(params[0]))
numFeatures = int(float(params[1]))
randomforest = RandomForest()
randomforest.setNumTrees(numTrees)
randomforest.setNumFeatures(numFeatures)
randomforest.buildClassifier(trainData) # only a trained classifier can be evaluated
# evaluate it on the training
evaluation = Evaluation(trainData)
(trainOutput, trainBuffer) = util.get_buffer_for_predictions(trainData)
attRange = Range() # no additional attributes output
outputDistribution = Boolean(False) # we don't want distribution
evaluation.evaluateModel(randomforest, trainData, [trainOutput, attRange, outputDistribution])
print "--> Evaluation:\n"
print evaluation.toSummaryString()
trainSummary = makeTrainEvalSummary(evaluation)
# evaluate it on testing
evaluation = Evaluation(testData)
(testOutput, testBuffer) = util.get_buffer_for_predictions(testData)
attRange = Range() # no additional attributes output
outputDistribution = Boolean(False) # we don't want distribution
evaluation.evaluateModel(randomforest, testData, [testOutput, attRange, outputDistribution])
return trainBuffer, testBuffer, trainSummary
