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 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