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