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src/a/z/AZOrange-HEAD/azorange/AZutilities/competitiveWorkflow.py   AZOrange(Download)
        MLStatistics = {}
        learners = {}
        smilesAttr = dataUtilities.getSMILESAttr(trainData) 
        for ml in mlList:
            learner = MLMETHODS[ml](name = ml)
        learner = AZorngConsensus.ConsensusLearner(learners = consensusLearners, expression = expression)
        log(logFile, "  Training Consensus Learner")
        smilesAttr = dataUtilities.getSMILESAttr(trainData)
        if smilesAttr:
            log(logFile,"Found SMILES attribute:"+smilesAttr)
            if MLMETHODS[ML](name = ML).specialType == 1:  # If is a special model and has a built-in optimizaer
                log(logFile, "       This is a special model")
                smilesAttr = dataUtilities.getSMILESAttr(trainData)
                if smilesAttr:
                    log(logFile,"Found SMILES attribute:"+smilesAttr)
                MLMethods[MLMethod["MLMethod"]] = MLMethod
 
        smilesAttr = dataUtilities.getSMILESAttr(trainData)
        if smilesAttr:
            trainData = dataUtilities.attributeDeselectionData(trainData, [smilesAttr])

src/a/z/AZOrange-HEAD/azorange/AZutilities/getUnbiasedAccuracy.py   AZOrange(Download)
                trainData = self.data.select(DataIdxs,foldN,negate=1)
                testData = self.data.select(DataIdxs,foldN)
                smilesAttr = dataUtilities.getSMILESAttr(trainData)
                if smilesAttr:
                    self.__log("Found SMILES attribute:"+smilesAttr)
 
                    testData = self.data.select(DataIdxs,foldN+1)  # fold 0 if for the train Bias!!
                    smilesAttr = dataUtilities.getSMILESAttr(testData)
                    if smilesAttr:
                        self.__log("Found SMILES attribute:"+smilesAttr)

src/a/z/AZOrange-HEAD/azorange/AZutilities/getBBRCDesc.py   AZOrange(Download)
            return None
 
        smilesName = dataUtilities.getSMILESAttr(self.data)
        print "SMILES attr detected: ",smilesName
        for idx,ex in enumerate(self.data):
                Returns the data including the new features. 
    """
    smilesName = dataUtilities.getSMILESAttr(data)
    if not smilesName or type(smarts) != list or not len(smarts): 
        print "Please check the input parameters"

src/a/z/AZOrange-HEAD/azorange/AZutilities/getCinfonyDesc.py   AZOrange(Download)
def getSMILESAttr(data):
    # Check that the data contains a SMILES attribute
    smilesName = dataUtilities.getSMILESAttr(data)
    if not smilesName:
        print "Warning: The data set does not contain any known smiles attribute!"

src/a/z/AZOrange-HEAD/azorange/AZutilities/SimBoostedQSAR.py   AZOrange(Download)
def getSMILESAttr(data):
    # Check that the data contains a SMILES attribute
    smilesName = dataUtilities.getSMILESAttr(data)
    if not smilesName:
        print "Warning: The data set does not contain any known smiles attribute!"