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predictFromModel.py
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predictFromModel.py
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import pandas as pd
import numpy as np
from file_operations import file_methods
from data_preprocessing import preprocessing
from data_ingestion import data_loader_prediction
from application_logging import logger
from Prediction_Raw_Data_Validation.predictionDataValidation import Prediction_Data_validation
class prediction:
def __init__(self,path):
self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
self.log_writer = logger.App_Logger()
self.pred_data_val = Prediction_Data_validation(path)
def predictionFromModel(self):
try:
self.pred_data_val.deletePredictionFile() #deletes the existing prediction file from last run!
self.log_writer.log(self.file_object,'Start of Prediction')
data_getter=data_loader_prediction.Data_Getter_Pred(self.file_object,self.log_writer)
data=data_getter.get_data()
preprocessor = preprocessing.Preprocessor(self.file_object, self.log_writer)
# check if missing values are present in the dataset
is_null_present, cols_with_missing_values = preprocessor.is_null_present(data)
# if missing values are there, replace them appropriately.
if (is_null_present):
data = preprocessor.impute_missing_values(data, cols_with_missing_values) # missing value imputation
# Proceeding with more data pre-processing steps
X = preprocessor.scale_numerical_columns(data)
file_loader=file_methods.File_Operation(self.file_object,self.log_writer)
kmeans=file_loader.load_model('KMeans')
clusters=kmeans.predict(X)#drops the first column for cluster prediction
X['clusters']=clusters
clusters=X['clusters'].unique()
predictions=[]
for i in clusters:
cluster_data= X[X['clusters']==i]
cluster_data = cluster_data.drop(['clusters'],axis=1)
model_name = file_loader.find_correct_model_file(i)
model = file_loader.load_model(model_name)
result=(model.predict(cluster_data))
final= pd.DataFrame(list(zip(result)),columns=['Predictions'])
path="Prediction_Output_File/Predictions.csv"
final.to_csv("Prediction_Output_File/Predictions.csv",header=True,mode='a+') #appends result to prediction file
self.log_writer.log(self.file_object,'End of Prediction')
except Exception as ex:
self.log_writer.log(self.file_object, 'Error occured while running the prediction!! Error:: %s' % ex)
raise ex
return path