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src/a/z/AZOrange-HEAD/doc/openExampleScripts/getAccParamOpt.py   AZOrange(Download)
def getDesc(trainDataFile):
 
    # Read the SAR for which to calculate descriptors
    sarData = dataUtilities.DataTable(trainDataFile) 
 

src/a/z/AZOrange-HEAD/doc/openExampleScripts/getTempAcc.py   AZOrange(Download)
 
    # Read the test data
    testData = dataUtilities.DataTable(testDataFile)
 
    # Full path to the model 

src/a/z/AZOrange-HEAD/doc/openExampleScripts/buildOptParamModel.py   AZOrange(Download)
 
    # Load the data on which to train the model
    trainData = dataUtilities.DataTable(trainDataFile)
 
    # Build the model with optimized parameters

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvSVM.py   AZOrange(Download)
                return False
 
            impData = dataUtilities.DataTable(self.imputer.defaults.domain)
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            scalizer = None
 
        impData = dataUtilities.DataTable(str(os.path.join(thePath,"ImputeData.tab")),createNewOn=orange.Variable.MakeStatus.OK)
        loadedsvm = ml.CvSVM()
        loadedsvm.load(os.path.join(thePath,"model.svm"))

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBayes.py   AZOrange(Download)
                return False
 
            impData = dataUtilities.DataTable(self.imputer.defaults.domain)
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            return None
 
        impData = dataUtilities.DataTable(str(os.path.join(thePath,"ImputeData.tab")),createNewOn=orange.Variable.MakeStatus.OK)
        loadedbayes = ml.CvNormalBayesClassifier()
        loadedbayes.load(os.path.join(thePath,"model.bayes"))

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngRF.py   AZOrange(Download)
        if self.useBuiltInMissValHandling:
            #Create the imputer empty since we will not be using it
            impData = dataUtilities.DataTable(trainData.domain)
            CvMatrices = dataUtilities.ExampleTable2CvMat(trainData)
        else:
                else:
                    # Save a data set with one row containing the impute values
                    impData = dataUtilities.DataTable(self.imputer.defaults.domain)
                    impData.append(self.imputer.defaults)
                # Remove the meta attributes from the imputer data. We don't need to store them along with the model
    ##scPA 
    try:
        impData = dataUtilities.DataTable(impDataPath,createNewOn=orange.Variable.MakeStatus.OK)
        classVar = impData.domain.classVar
 
    # Read data set of orange type
    fileName = "train.tab"
    data = dataUtilities.DataTable(fileName)
 
    # Create a RF model from data

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvANN.py   AZOrange(Download)
                return False
 
            impData = dataUtilities.DataTable(self.imputer.defaults.domain)
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            return None
 
        impData = dataUtilities.DataTable(str(os.path.join(thePath,"ImputeData.tab")),createNewOn=orange.Variable.MakeStatus.OK)
        loadedann = ml.CvANN_MLP()
        loadedann.load(os.path.join(thePath,"model.ann"))

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBoost.py   AZOrange(Download)
                return False
 
            impData = dataUtilities.DataTable(self.imputer.defaults.domain)
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            return None
 
        impData = dataUtilities.DataTable(str(os.path.join(thePath,"ImputeData.tab")),createNewOn=orange.Variable.MakeStatus.OK)
        loadedboost = ml.CvBoost()
        loadedboost.load(os.path.join(thePath,"model.boost"))
 
if __name__ == "__main__":
    trainData = dataUtilities.DataTable("../../tests/source/data/BinClass_No_metas_Test.tab")
    learner = CvBoostLearner()
    learner.priors = {"POS":0.7,  "NEG":0.3}

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngPLS.py   AZOrange(Download)
                    return False 
                # Save a data set with one row containing the impute values
                impData = dataUtilities.DataTable(self.imputer.defaults.domain)
                impData.append(self.imputer.defaults)
                # Remove the meta attributes from the imputer data. We don't need to store them along with the model
 
    try:
        impData = dataUtilities.DataTable(str(filePath)+"/ImputeData.tab",createNewOn=orange.Variable.MakeStatus.OK)
        classVar = impData.domain.classVar
    except:

src/a/z/AZOrange-HEAD/azorange/AZutilities/similarityMetrics.py   AZOrange(Download)
def rmClass(data):
 
    newDomain = orange.Domain(data.domain.attributes)
    newData = dataUtilities.DataTable(newDomain, data)
    return newData
        newAttr = orange.EnumVariable("dummyClass", values["dummyClass"])
        newDomain = orange.domain(data.domain.attributes, newAttr)
        newData = dataUtilities.DataTable(newDomain, data)
        data = newData
    return data
        testData = dataUtilities.attributeDeselectionData(predictor.exToPred,["SMILEStoPred"])
        if not dataTableFile:
            trainData = dataUtilities.DataTable(predictor.trainDataPath)
            domain = trainData.domain
        else:
            dat = exFixed1
 
        tab = dataUtilities.DataTable(domain)
        tab.append(dat)
 
    dataFile = "trainData.txt"
    testDataFile = "testData.txt"
    data = dataUtilities.DataTable(dataFile) 
    testData = dataUtilities.DataTable(testDataFile)
 

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