def __init__(self, loggerObj, log_file):
     self.loggerObj = loggerObj
     self.log_file = log_file
     self.clf = RandomForestClassifier()
     self.xgb = XGBClassifier(objective='binary:logistic')
     self.preprocess = dataPreprocessor.processData(self.loggerObj,
                                                    self.log_file)
 def __init__(self):
     try:
         self.train_model_logs = pd.read_csv('Logs\\TrainingLogs\\train_model_logs.csv')
     except:
         self.train_model_logs = pd.DataFrame(columns=['date', 'time', 'logs'])
     self.loggerObj = application_logger.logger()
     self.data_loader = trainingDataLoader.data_loader(self.loggerObj,self.train_model_logs)
     self.preprocess = dataPreprocessor.processData(self.loggerObj,self.train_model_logs)
     self.model_finder_obj = modelFinder.modelFinder(self.loggerObj,self.train_model_logs)
     self.save_modelObj = saveLoadModel.saveLoadModel(self.loggerObj,self.train_model_logs)
Ejemplo n.º 3
0
    def __init__(self):

        try:
            self.prediction_logs = pd.read_csv(
                'Logs\\Prediction Logs\\prediction_logs.csv')
            self.prediction_logs.drop('Unnamed :0', axis=1, inplace=True)
        except:
            self.prediction_logs = pd.DataFrame(
                columns=['date', 'time', 'logs'])

        self.loggerObj = application_logger.logger()
        self.data_loaderObj = predictionDataLoader.predictionDataLoader(
            logger_obj=self.loggerObj, log_file=self.prediction_logs)
        self.load_modelObj = saveLoadModel.saveLoadModel(
            loggerObj=self.loggerObj, log_file=self.prediction_logs)
        self.preprocessObj = dataPreprocessor.processData(
            logger_object=self.loggerObj, log_file=self.prediction_logs)