def testVariableImportance(self):
X, y, c = ExamplesGenerator().generateBinaryExamples(numExamples=100, verbose=True)
treeRankForest = TreeRankForest(self.leafRanklearner)
treeRankForest.setFeatureSize(0.5)
treeRankForest.setNumTrees(20)
treeRankForest.setSampleSize(1.0)
treeRankForest.learnModel(X, y)
weightVector = treeRankForest.variableImportance(X, y)
#Seems to work, sort of
print(c)
print(weightVector)
print(numpy.argsort(c))
print(numpy.argsort(weightVector))
def testPredict2(self):
#Test on Gauss2D dataset
dataDir = PathDefaults.getDataDir()
fileName = dataDir + "Gauss2D_learn.csv"
XY = numpy.loadtxt(fileName, skiprows=1, usecols=(1,2,3), delimiter=",")
X = XY[:, 0:2]
y = XY[:, 2]
y = y*2 - 1
fileName = dataDir + "Gauss2D_test.csv"
testXY = numpy.loadtxt(fileName, skiprows=1, usecols=(1,2,3), delimiter=",")
testX = testXY[:, 0:2]
testY = testXY[:, 2]
testY = testY*2-1
X = Standardiser().standardiseArray(X)
testX = Standardiser().standardiseArray(testX)
numTrees = 5
minSplit = 50
maxDepths = range(3, 10)
trainAucs = numpy.array([0.7252582, 0.7323278, 0.7350289, 0.7372529, 0.7399985, 0.7382176, 0.7395104, 0.7386347])
testAucs = numpy.array([0.6806122, 0.6851614, 0.6886183, 0.6904147, 0.6897266, 0.6874600, 0.6875980, 0.6878801])
i = 0
#The results are approximately the same, but not exactly
for maxDepth in maxDepths:
treeRankForest = TreeRankForest(self.leafRanklearner)
treeRankForest.setMaxDepth(maxDepth)
treeRankForest.setMinSplit(minSplit)
treeRankForest.setNumTrees(numTrees)
treeRankForest.learnModel(X, y)
trainScores = treeRankForest.predict(X)
testScores = treeRankForest.predict(testX)
print(Evaluator.auc(trainScores, y), Evaluator.auc(testScores, testY))
self.assertAlmostEquals(Evaluator.auc(trainScores, y), trainAucs[i], 1)
self.assertAlmostEquals(Evaluator.auc(testScores, testY), testAucs[i], 1)
i+=1
class MetabolomicsExpHelper(object):
def __init__(self, dataDict, YCortisol, YTesto, YIgf1, ages, numProcesses=1, runCortisol=True, runTestosterone=True, runIGF1=True):
"""
Create a new object for run the metabolomics experiments
"""
self.dataDict = dataDict
self.runCartTreeRank = False
self.runRbfSvmTreeRank = False
self.runL1SvmTreeRank = False
self.runCartTreeRankForest = False
self.runRbfSvmTreeRankForest = False
self.runL1SvmTreeRankForest = False
self.runRankBoost = False
self.runRankSVM = False
self.runCortisol = runCortisol
self.runTestosterone = runTestosterone
self.runIGF1 = runIGF1
self.YCortisol = YCortisol
self.YTesto = YTesto
self.YIgf1 = YIgf1
self.ages = ages
self.outerFolds = 3
self.innerFolds = 5
self.leafRankFolds = 3
self.resultsDir = PathDefaults.getOutputDir() + "metabolomics/"
self.numProcesses = numProcesses
#General params
Cs = 2.0**numpy.arange(-5, 7, 2, dtype=numpy.float)
gammas = 2.0**numpy.arange(-5, 3, 2, dtype=numpy.float)
depths = numpy.array([2, 4, 8])
numTrees = 20
sampleSize = 1.0
maxDepth = 10
featureSize = 0.5
#CART TreeRank
leafRankParamDict = {}
leafRankParamDict["setMaxDepth"] = depths
leafRankLearner = DecisionTree(leafRankParamDict, self.leafRankFolds)
self.cartTreeRank = TreeRank(leafRankLearner, numProcesses=numProcesses)
self.cartTreeRankParams = {}
self.cartTreeRankParams["setMaxDepth"] = depths
#RBF SVM TreeRank
leafRankParamDict = {}
leafRankParamDict["setC"] = Cs
leafRankParamDict["setGamma"] = gammas
leafRankLearner = SVMLeafRank(leafRankParamDict, self.leafRankFolds)
leafRankLearner.setKernel("rbf")
leafRankLearner.processes = 1
self.rbfSvmTreeRank = TreeRank(leafRankLearner, numProcesses=numProcesses)
self.rbfSvmTreeRankParams = {}
self.rbfSvmTreeRankParams["setMaxDepth"] = depths
#Linear L1 SVM TreeRank
leafRankParamDict = {}
leafRankParamDict["setC"] = Cs
leafRankLearner = SVMLeafRank(leafRankParamDict, self.leafRankFolds)
leafRankLearner.setKernel("linear")
leafRankLearner.setPenalty("l1")
leafRankLearner.processes = 1
self.l1SvmTreeRank = TreeRank(leafRankLearner, numProcesses=numProcesses)
self.l1SvmTreeRankParams = {}
self.l1SvmTreeRankParams["setMaxDepth"] = depths
#CART TreeRankForest
leafRankParamDict = {}
leafRankParamDict["setMaxDepth"] = depths
leafRankLearner = DecisionTree(leafRankParamDict, self.leafRankFolds)
leafRankLearner.processes = 1
self.cartTreeRankForest = TreeRankForest(leafRankLearner, numProcesses=numProcesses)
self.cartTreeRankForest.setNumTrees(numTrees)
self.cartTreeRankForest.setSampleSize(sampleSize)
self.cartTreeRankForest.setFeatureSize(featureSize)
self.cartTreeRankForestParams = {}
self.cartTreeRankForestParams["setMaxDepth"] = numpy.array([maxDepth])
self.cartTreeRankForestParams["setSampleSize"] = numpy.array([0.5, 0.75, 1.0])
self.cartTreeRankForestParams["setFeatureSize"] = numpy.array([0.5, 0.75, 1.0])
#RBF SVM TreeRankForest
leafRankParamDict = {}
leafRankParamDict["setC"] = Cs
leafRankParamDict["setGamma"] = gammas
leafRankLearner = SVMLeafRank(leafRankParamDict, self.leafRankFolds)
leafRankLearner.setKernel("rbf")
leafRankLearner.processes = 1
self.rbfSvmTreeRankForest = TreeRankForest(leafRankLearner, numProcesses=numProcesses)
self.rbfSvmTreeRankForest.setNumTrees(numTrees)
self.rbfSvmTreeRankForest.setSampleSize(sampleSize)
self.rbfSvmTreeRankForest.setFeatureSize(featureSize)
self.rbfSvmTreeRankForestParams = {}
self.rbfSvmTreeRankForestParams["setMaxDepth"] = numpy.array([maxDepth])
self.rbfSvmTreeRankForestParams["setSampleSize"] = numpy.array([0.5, 0.75, 1.0])
self.rbfSvmTreeRankForestParams["setFeatureSize"] = numpy.array([0.5, 0.75, 1.0])
#L1 SVM TreeRankForest
leafRankParamDict = {}
leafRankParamDict["setC"] = Cs
leafRankLearner = SVMLeafRank(leafRankParamDict, self.leafRankFolds)
leafRankLearner.setKernel("linear")
leafRankLearner.setPenalty("l1")
leafRankLearner.processes = 1
self.l1SvmTreeRankForest = TreeRankForest(leafRankLearner, numProcesses=numProcesses)
self.l1SvmTreeRankForest.setNumTrees(numTrees)
self.l1SvmTreeRankForest.setSampleSize(sampleSize)
self.l1SvmTreeRankForest.setFeatureSize(featureSize)
self.l1SvmTreeRankForestParams = {}
self.l1SvmTreeRankForestParams["setMaxDepth"] = numpy.array([maxDepth])
self.l1SvmTreeRankForestParams["setSampleSize"] = numpy.array([0.5, 0.75, 1.0])
self.l1SvmTreeRankForestParams["setFeatureSize"] = numpy.array([0.5, 0.75, 1.0])
#RankBoost
self.rankBoost = RankBoost(numProcesses=numProcesses)
self.rankBoostParams = {}
self.rankBoostParams["setIterations"] = numpy.array([10, 50, 100])
self.rankBoostParams["setLearners"] = numpy.array([5, 10, 20])
#RankSVM
self.rankSVM = RankSVM(numProcesses=numProcesses)
self.rankSVM.setKernel("rbf")
self.rankSVMParams = {}
self.rankSVMParams["setC"] = 2.0**numpy.arange(0, 3, dtype=numpy.float)
self.rankSVMParams["setGamma"] = 2.0**numpy.arange(-3, 0, dtype=numpy.float)
#Store all the label vectors and their missing values
self.hormoneDict = {}
if self.runCortisol:
self.hormoneDict["Cortisol"] = YCortisol
if self.runTestosterone:
self.hormoneDict["Testosterone"] = YTesto
if self.runIGF1:
self.hormoneDict["IGF1"] = YIgf1
def saveResult(self, X, Y, learner, paramDict, fileName):
"""
Save a single result to file, checking if the results have already been computed
"""
filelock = FileLock(fileName)
gc.collect()
if not filelock.isLocked() and not filelock.fileExists():
filelock.lock()
try:
logging.debug("Computing file " + fileName)
logging.debug("Shape of examples: " + str(X.shape) + ", number of +1: " + str(numpy.sum(Y==1)) + ", -1: " + str(numpy.sum(Y==-1)))
#idxFull = Sampling.crossValidation(self.outerFolds, X.shape[0])
idxFull = StratifiedKFold(Y, self.outerFolds)
errors = numpy.zeros(self.outerFolds)
for i, (trainInds, testInds) in enumerate(idxFull):
logging.debug("Outer fold: " + str(i))
trainX, trainY = X[trainInds, :], Y[trainInds]
testX, testY = X[testInds, :], Y[testInds]
#idx = Sampling.crossValidation(self.innerFolds, trainX.shape[0])
idx = StratifiedKFold(trainY, self.innerFolds)
logging.debug("Initial learner is " + str(learner))
bestLearner, cvGrid = learner.parallelModelSelect(trainX, trainY, idx, paramDict)
bestLearner = learner.getBestLearner(cvGrid, paramDict, trainX, trainY, idx, best="max")
logging.debug("Best learner is " + str(bestLearner))
bestLearner.learnModel(trainX, trainY)
predY = bestLearner.predict(testX)
errors[i] = Evaluator.auc(predY, testY)
logging.debug("Mean auc: " + str(numpy.mean(errors)))
numpy.save(fileName, errors)
logging.debug("Saved results as : " + fileName)
finally:
filelock.unlock()
else:
logging.debug("File exists, or is locked: " + fileName)
def saveWeightVectorResults(self, X, Y, learner, paramDict, fileName):
"""
Save the results of the variable importance
"""
filelock = FileLock(fileName)
gc.collect()
if not filelock.isLocked() and not filelock.fileExists():
filelock.lock()
try:
logging.debug("Computing weights file " + fileName)
logging.debug("Shape of examples: " + str(X.shape) + ", number of +1: " + str(numpy.sum(Y==1)) + ", -1: " + str(numpy.sum(Y==-1)))
tempLearner = learner.copy()
logging.debug("Initial learner is " + str(tempLearner))
idx = StratifiedKFold(Y, self.innerFolds)
tempLearner.processes = self.numProcesses
bestLearner, cvGrid = tempLearner.parallelModelSelect(X, Y, idx, paramDict)
bestLearner = tempLearner.getBestLearner(cvGrid, paramDict, X, Y, idx, best="max")
logging.debug("Best learner is " + str(bestLearner))
bestLearner.learnModel(X, Y)
weightVector = bestLearner.variableImportance(X, Y)
numpy.save(fileName, weightVector)
logging.debug("Saved results as : " + fileName)
finally:
filelock.unlock()
else:
logging.debug("File exists, or is locked: " + fileName)
def saveResults(self):
"""
Compute the results and save them for a particular hormone. Does so for all
learners.
"""
metaUtils = MetabolomicsUtils()
logging.debug("Running on hormones: " + str(self.hormoneDict.keys()))
for hormoneName, hormoneConc in self.hormoneDict.items():
nonNaInds = numpy.logical_not(numpy.isnan(hormoneConc))
hormoneIndicators = metaUtils.createIndicatorLabel(hormoneConc, metaUtils.boundsDict[hormoneName])
for i in range(hormoneIndicators.shape[1]):
#Make labels -1/+1
Y = numpy.array(hormoneIndicators[nonNaInds, i], numpy.int)*2-1
for dataName, dataFeatures in self.dataDict.items():
X = dataFeatures[nonNaInds, :]
X = numpy.c_[X, self.ages[nonNaInds]]
X = Standardiser().standardiseArray(X)
if self.runCartTreeRank:
fileName = self.resultsDir + "CartTreeRank-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.cartTreeRank, self.cartTreeRankParams, fileName)
if self.runRbfSvmTreeRank:
fileName = self.resultsDir + "RbfSvmTreeRank-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.rbfSvmTreeRank, self.rbfSvmTreeRankParams, fileName)
if self.runL1SvmTreeRank:
fileName = self.resultsDir + "L1SvmTreeRank-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.l1SvmTreeRank, self.l1SvmTreeRankParams, fileName)
#For this SVM save the weight vector
weightsFileName = self.resultsDir + "WeightsL1SvmTreeRank-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveWeightVectorResults(X, Y, self.l1SvmTreeRank, self.l1SvmTreeRankParams, weightsFileName)
if self.runCartTreeRankForest:
fileName = self.resultsDir + "CartTreeRankForest-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.cartTreeRankForest, self.cartTreeRankForestParams, fileName)
if self.runRbfSvmTreeRankForest:
fileName = self.resultsDir + "RbfSvmTreeRankForest-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.rbfSvmTreeRankForest, self.rbfSvmTreeRankForestParams, fileName)
if self.runL1SvmTreeRankForest:
fileName = self.resultsDir + "L1SvmTreeRankForest-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.l1SvmTreeRankForest, self.l1SvmTreeRankForestParams, fileName)
#For this SVM save the weight vector
weightsFileName = self.resultsDir + "WeightsL1SvmTreeRankForest-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveWeightVectorResults(X, Y, self.l1SvmTreeRankForest, self.l1SvmTreeRankForestParams, weightsFileName)
if self.runRankBoost:
fileName = self.resultsDir + "RankBoost-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.rankBoost, self.rankBoostParams, fileName)
if self.runRankSVM:
fileName = self.resultsDir + "RankSVM-" + hormoneName + "-" + str(i) + "-" + dataName + ".npy"
self.saveResult(X, Y, self.rankSVM, self.rankSVMParams, fileName)
logging.debug("All done. See you around!")
def run(self):
logging.debug('module name:' + __name__)
logging.debug('parent process:' + str(os.getppid()))
logging.debug('process id:' + str(os.getpid()))
self.saveResults()