class TrainModel: def __init__(self,run_id,data_path): self.run_id = run_id self.data_path = data_path self.logger = Logger(self.run_id, 'TrainModel', 'training') self.loadValidate = LoadValidate(self.run_id, self.data_path,'training') self.preProcess = Preprocessor(self.run_id, self.data_path,'training') self.modelTuner = ModelTuner(self.run_id, self.data_path, 'training') self.fileOperation = FileOperation(self.run_id, self.data_path, 'training') self.cluster = KMeansCluster(self.run_id, self.data_path) def training_model(self): try: self.logger.info('Start of Training') self.logger.info('Run_id:' + str(self.run_id)) #Load, validations and transformation self.loadValidate.validate_trainset() #preprocessing activities self.X, self.y = self.preProcess.preprocess_trainset() columns = {"data_columns":[col for col in self.X.columns]} with open('apps/database/columns.json','w') as f: f.write(json.dumps(columns)) #create clusters number_of_clusters = self.cluster.elbow_plot(self.X) # Divide the data into clusters self.X= self.cluster.create_clusters(self.X, number_of_clusters) # create a new column in the dataset consisting of the corresponding cluster assignments. self.X['Labels'] = self.y # getting the unique clusters from our data set list_of_clusters = self.X['Cluster'].unique() # parsing all the clusters and look for the best ML algorithm to fit on individual cluster for i in list_of_clusters: cluster_data=self.X[self.X['Cluster']==i] # filter the data for one cluster # Prepare the feature and Label columns cluster_features=cluster_data.drop(['Labels','Cluster'],axis=1) cluster_label= cluster_data['Labels'] # splitting the data into training and test set for each cluster one by one x_train, x_test, y_train, y_test = train_test_split(cluster_features, cluster_label, test_size=0.2, random_state=0) #getting the best model for each of the clusters best_model_name, best_model = self.modelTuner.get_best_model(x_train, y_train, x_test, y_test) #saving the best model to the directory. save_model=self.fileOperation.save_model(best_model,best_model_name+str(i)) self.logger.info('End of Training') except Exception: self.logger.exception('Unsuccessful End of Training') raise Exception
class TrainModel: """ ***************************************************************************** * * filename: TrainModel.py * version: 1.0 * author: * creation date: * * * * * description: Class to training the models * **************************************************************************** """ def __init__(self, run_id, data_path): self.run_id = run_id self.data_path = data_path self.logger = Logger(self.run_id, 'TrainModel', 'training') self.loadValidate = LoadValidate(self.run_id, self.data_path, 'training') self.preProcess = Preprocessor(self.run_id, self.data_path, 'training') self.modelTuner = ModelTuner(self.run_id, self.data_path, 'training') self.fileOperation = FileOperation(self.run_id, self.data_path, 'training') # self.cluster = KMeansCluster(self.run_id, self.data_path) def training_model(self): """ * method: trainingModel * description: method to training the model * return: none * * * Parameters * none: """ try: self.logger.info('Start of Training') self.logger.info('Run_id:' + str(self.run_id)) # Load, validations and transformation self.loadValidate.validate_trainset() # preprocessing activities self.X, self.y = self.preProcess.preprocess_trainset() columns = {"data_columns": [col for col in self.X.columns]} with open('apps/database/columns.json', 'w') as f: f.write(json.dumps(columns)) # splitting the data into training and test set for each cluster one by one x_train, x_test, y_train, y_test = train_test_split( self.X, self.y, test_size=0.2, random_state=0) # getting the best model for each of the clusters best_model_name, best_model = self.modelTuner.get_best_model( x_train, y_train, x_test, y_test) # saving the best model to the directory. save_model = self.fileOperation.save_model( best_model, best_model_name) self.logger.info('End of Training') except Exception: self.logger.exception('Unsuccessful End of Training') raise Exception