How to use the modelbase.ModelBase.AllModelInfo function in modelbase

def __init__(self, model, dataset):
        self.model = model
        self.dataset = dataset
        self.TP, self.TN, self.FP, self.FN = 0, 0, 0, 0
        self.Losses = [0.0] * self.dataset.Length()
        '''
            Protoclass in ['REGRESS', 'CLASSFY', 'CLUASTER']
        '''
        self.Protoclass = ModelBase.AllModelInfo()[self.model.prototype]['modeltype']

def __init__(self, taskid, mdinfo, ds, method, classfeatureindex = None):
        '''
            classfeatureindex和训练得到的模型是紧密相关的，对classfeatureindex的改动会牵涉到对模型的改动，因此classfeatureindex一定要去db_model的classfeatureindex
        '''
        clsname = mdinfo.modeltype
        if (clsname.upper() in ModelBase.AllModelInfo().keys()): 

            # dataset is load from Dataset.GetDataset
            ds['view'].classfeatureindex = mdinfo.classfeatureindex

            md = ModelBase.AllModelInfo()[clsname.upper()]['cls'](dataset = ds['view'])

            self.dataset = ds['view']
            self.model = md
            self.Load(mdinfo.model_path)

            self.assessmodel = assessment.Assessment(self.model, self.dataset)

def __init__(self, taskid, mdinfo, ds):
        '''
            Instance and run Model according to given `model` and `dataset`

            mdinfo is a MLModel object(Database record)
            ds is {'info':dsinfo, 'view':dataset} or {'info':oldsinfo, 'view':dataset} while 
                dbds and oldsds is Dataset or OnlineDataset object
                lcdt is datasets.localdata.LocalData object
        '''
        clsname = mdinfo.modeltype
        # possibles = globals()
        # possibles.update(locals())
        if (clsname.upper() in ModelBase.AllModelInfo().keys()): # and (clsname in possibles.keys()):
            # dataset.classfeatureindex is determined by mdinfo(and when training md.classfeatureindex is determined by dataset.classfeatureindex)
            ds['view'].classfeatureindex = mdinfo.classfeatureindex

            # need to set args to __init__
            # md = possibles.get(clsname)(dataset = ds['view'])
            # print ModelBase.AllModelInfo()[clsname.upper()]
            md = ModelBase.AllModelInfo()[clsname.upper()]['cls'](dataset = ds['view'])
            md.positive = mdinfo.positive
            md.negative = mdinfo.negative
            md.classfeatureindex = mdinfo.classfeatureindex
            md.loss = {
                'QUAD': ModelBase.QuadLoss
                ,'BIN': ModelBase.BinLoss
                ,'ABS': ModelBase.AbsLoss
                ,'LOG': ModelBase.LogLoss
            }[mdinfo.loss]

def __init__(self, taskid, mdinfo, ds):
        clsname = mdinfo.modeltype
        if (clsname.upper() in ModelBase.AllModelInfo().keys()): 
            # dataset.classfeatureindex is determined by mdinfo(and when training md.classfeatureindex is determined by dataset.classfeatureindex)
            ds['view'].classfeatureindex = mdinfo.classfeatureindex

            md = ModelBase.AllModelInfo()[clsname.upper()]['cls'](dataset = ds['view'])

            self.dataset = ds['view']
            self.model = md
            self.Load(mdinfo.model_path)