Beispiel #1
0
    def testModelSelectRBF(self):
        folds = 3
        rankSVM = RankSVM()
        rankSVM.setKernel("rbf")

        #logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
        rankSVM.modelSelectRBF(self.X, self.y, folds)
Beispiel #2
0
    def testEvaluateCvOuter(self):
        folds = 3 
        rankSVM = RankSVM()
        (bestParams, allMetrics, bestMetaDicts) = rankSVM.evaluateCvOuter(self.X, self.y, folds)

        self.assertEquals(len(allMetrics[0]), folds)
        self.assertEquals(len(allMetrics[2]), folds)

        #for i in allMetrics[1]:
        #    print(i)

        #Now try the RBF version
        rankSVM.setKernel("rbf")
        (bestParams, allMetrics, bestMetaDicts) = rankSVM.evaluateCvOuter(self.X, self.y, folds)
    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
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()
Beispiel #5
0
 def testStr(self):
     rankSVM = RankSVM()
Beispiel #6
0
    def testSetC(self):
        rankSVM = RankSVM()
        rankSVM.setC(100.0)
        rankSVM.learnModel(self.X, self.y)
        predY = rankSVM.predict(self.X)
        auc1 = Evaluator.auc(predY, self.y)

        rankSVM.setC(0.1)
        rankSVM.learnModel(self.X, self.y)
        predY = rankSVM.predict(self.X)
        auc2 = Evaluator.auc(predY, self.y)

        self.assertTrue(auc1 != auc2)
Beispiel #7
0
 def testPredict(self):
     rankSVM = RankSVM()
     rankSVM.learnModel(self.X, self.y)
     predY = rankSVM.predict(self.X)
Beispiel #8
0
 def testLearnModel(self):
     rankSVM = RankSVM()
     rankSVM.learnModel(self.X, self.y)
Beispiel #9
0
 def testInit(self):
     rankSVM = RankSVM()