def METHOD_NAME(testDir):
    testData = {}
    hpoDir = os.path.join(testDir,'hpo')
    bestTrialRetrainDir = os.path.join(testDir,'best_trial_retrain')
    transferLearningDir = os.path.join(testDir,'transfer_learning')
    modelPredictDir = os.path.join(testDir,'model_predict')
    if os.path.exists(hpoDir):
        df = pd.read_csv(os.path.join(hpoDir,'hpo_result.csv'))
        for col in ['datetime_start', 'datetime_complete','duration']:
            if col in df:
                df = df.drop([col], axis=1)
        testData['hpo'] = df
    if os.path.exists(bestTrialRetrainDir):
        bestTrialResultsDir = glob.glob(os.path.join(bestTrialRetrainDir,'trial_*'))[0]
        bestTrialNr = bestTrialResultsDir.split('trial_')[-1]
        df = pd.read_csv(os.path.join(bestTrialResultsDir,"best_trial_retrain_model.csv"))
        if 'time' in df:
            df = df.drop(['time'], axis=1)
        testData['bestTrialRetrain'+'_trial_'+str(bestTrialNr)] = df
    if os.path.exists(transferLearningDir):
        transferLearningResultsDir = glob.glob(os.path.join(transferLearningDir,'trial_*'))[0]
        transferLearningTrialNr = transferLearningResultsDir.split('trial_')[-1]
        df = pd.read_csv(os.path.join(transferLearningResultsDir,"transfer_learning_model.csv"))
        if 'time' in df:
            df = df.drop(['time'], axis=1)
        testData['transferLearning'+'_trial_'+str(transferLearningTrialNr)] = df
    if os.path.exists(modelPredictDir):
        df = pd.read_csv(os.path.join(modelPredictDir,'model_predict.csv'))
        testData['modelPredict'] = df
    return testData