def __init__(self,run_id,data_path):
self.run_id = run_id
self.data_path = data_path
self.logger = Logger(self.run_id, 'PredictModel', 'prediction')
self.loadValidate = LoadValidate(self.run_id, self.data_path,'prediction')
self.preProcess = Preprocessor(self.run_id, self.data_path,'prediction')
self.fileOperation = FileOperation(self.run_id, self.data_path, 'prediction')
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)
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 PredictModel:
"""
**************************************************************************
*
* filename: PredictModel.py
* version: 1.0
* creation date: 05-MAY-2020
*
* change history:
*
*
* description: Class to prediction the result
*
**************************************************************************
"""
def __init__(self, run_id, data_path):
self.run_id = run_id
self.data_path = data_path
self.logger = Logger(self.run_id, 'PredictModel', 'prediction')
self.loadValidate = LoadValidate(self.run_id, self.data_path,
'prediction')
self.preProcess = Preprocessor(self.run_id, self.data_path,
'prediction')
self.fileOperation = FileOperation(self.run_id, self.data_path,
'prediction')
def batch_predict_from_model(self):
"""
* method: batch_predict_from_model
* description: method to prediction the results
* return: none
*
* Parameters
* none:
"""
try:
self.logger.info('Start of Prediction')
self.logger.info('run_id:' + str(self.run_id))
#validations and transformation
self.loadValidate.validate_predictset()
#preprocessing activities
self.X = self.preProcess.preprocess_predictset()
#load model
kmeans = self.fileOperation.load_model('KMeans')
#cluster selection
clusters = kmeans.predict(self.X.drop(['empid'], axis=1))
self.X['clusters'] = clusters
clusters = self.X['clusters'].unique()
y_predicted = []
for i in clusters:
self.logger.info('clusters loop started')
cluster_data = self.X[self.X['clusters'] == i]
cluster_data_new = cluster_data.drop(['empid', 'clusters'],
axis=1)
model_name = self.fileOperation.correct_model(i)
model = self.fileOperation.load_model(model_name)
y_predicted = model.predict(cluster_data_new)
#result = pd.DataFrame(list(zip(y_predicted)), columns=['Predictions'])
#result.to_csv(self.data_path+'_results/'+'Predictions.csv', header=True, mode='a+')
result = pd.DataFrame({
"EmpId": cluster_data['empid'],
"Prediction": y_predicted
})
result.to_csv(self.data_path + '_results/' + 'Predictions.csv',
header=True,
mode='a+',
index=False)
self.logger.info('End of Prediction')
except Exception:
self.logger.exception('Unsuccessful End of Prediction')
raise Exception
def single_predict_from_model(self, data):
"""
* method: single_predict_from_model
* description: method to prediction the results
* return: none
*
* Parameters
* none:
"""
try:
self.logger.info('Start of Prediction')
self.logger.info('run_id:' + str(self.run_id))
#preprocessing activities
self.X = self.preProcess.preprocess_predict(data)
#load model
kmeans = self.fileOperation.load_model('KMeans')
#cluster selection
clusters = kmeans.predict(self.X.drop(['empid'], axis=1))
self.X['clusters'] = clusters
clusters = self.X['clusters'].unique()
y_predicted = []
for i in clusters:
self.logger.info('clusters loop started')
cluster_data = self.X[self.X['clusters'] == i]
cluster_data_new = cluster_data.drop(['empid', 'clusters'],
axis=1)
model_name = self.fileOperation.correct_model(i)
model = self.fileOperation.load_model(model_name)
self.logger.info('Shape of Data ' +
str(cluster_data_new.shape))
self.logger.info('Shape of Data ' +
str(cluster_data_new.info()))
y_predicted = model.predict(cluster_data_new)
#result = pd.DataFrame(list(zip(y_predicted)), columns=['Predictions'])
#result.to_csv(self.data_path+'_results/'+'Predictions.csv', header=True, mode='a+')
#result = pd.DataFrame({"EmpId": cluster_data['empid'],"Prediction": y_predicted})
#result.to_csv(self.data_path+'_results/'+'Predictions.csv', header=True, mode='a+',index=False)
self.logger.info('Output : ' + str(y_predicted))
self.logger.info('End of Prediction')
return int(y_predicted[0])
except Exception:
self.logger.exception('Unsuccessful End of Prediction')
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
class PredictModel:
def __init__(self,run_id,data_path):
self.run_id = run_id
self.data_path = data_path
self.logger = Logger(self.run_id, 'PredictModel', 'prediction')
self.loadValidate = LoadValidate(self.run_id, self.data_path,'prediction')
self.preProcess = Preprocessor(self.run_id, self.data_path,'prediction')
self.fileOperation = FileOperation(self.run_id, self.data_path, 'prediction')
def batch_predict_from_model(self):
try:
self.logger.info('Start of Prediction')
self.logger.info('run_id:' + str(self.run_id))
#validations and transformation
self.loadValidate.validate_predictset()
#preprocessing activities
self.X = self.preProcess.preprocess_predictset()
#load model
kmeans = self.fileOperation.load_model('KMeans')
#cluster selection
clusters = kmeans.predict(self.X.drop(['empid'],axis=1))
self.X['clusters'] = clusters
clusters = self.X['clusters'].unique()
y_predicted=[]
for i in clusters:
self.logger.info('clusters loop started')
cluster_data = self.X[self.X['clusters'] == i]
cluster_data_new = cluster_data.drop(['empid','clusters'], axis=1)
model_name = self.fileOperation.correct_model(i)
model = self.fileOperation.load_model(model_name)
y_predicted = model.predict(cluster_data_new)
result = pd.DataFrame({"EmpId": cluster_data['empid'],"Prediction": y_predicted})
result.to_csv(self.data_path+'_results/'+'Predictions.csv', header=True, mode='a+',index=False)
self.logger.info('End of Prediction')
except Exception:
self.logger.exception('Unsuccessful End of Prediction')
raise Exception
def single_predict_from_model(self,data):
try:
self.logger.info('Start of Prediction')
self.logger.info('run_id:' + str(self.run_id))
#preprocessing activities
self.X = self.preProcess.preprocess_predict(data)
#load model
kmeans = self.fileOperation.load_model('KMeans')
#cluster selection
clusters = kmeans.predict(self.X.drop(['empid'],axis=1))
self.X['clusters'] = clusters
clusters = self.X['clusters'].unique()
y_predicted=[]
for i in clusters:
self.logger.info('clusters loop started')
cluster_data = self.X[self.X['clusters'] == i]
cluster_data_new = cluster_data.drop(['empid','clusters'], axis=1)
model_name = self.fileOperation.correct_model(i)
model = self.fileOperation.load_model(model_name)
self.logger.info('Shape of Data '+str(cluster_data_new.shape))
self.logger.info('Shape of Data ' + str(cluster_data_new.info()))
y_predicted = model.predict(cluster_data_new)
self.logger.info('Output : '+str(y_predicted))
self.logger.info('End of Prediction')
return int(y_predicted[0])
except Exception:
self.logger.exception('Unsuccessful End of Prediction')
raise Exception