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import os
import sys
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from src.exception import CustomException
from src.logger import logging
from src.utils import load_object
from src.pipeline.kmeans_clustering import predict_cluster
class PredictPipeline:
def __init__(self):
self.model_path = os.path.join("artifacts", "model.pkl")
self.preprocessor_path = os.path.join('artifacts', 'preprocessor.pkl')
self.model = self.load_model()
self.preprocessor = self.load_preprocessor()
def load_model(self):
try:
return load_object(file_path=self.model_path)
except Exception as e:
raise CustomException(e, sys)
def load_preprocessor(self):
try:
return load_object(file_path=self.preprocessor_path)
except Exception as e:
raise CustomException(e, sys)
def predicts(self, features):
try:
data_scaled = self.preprocessor.transform(features)
preds = self.model.predict(data_scaled)
return preds
except Exception as e:
raise CustomException(e, sys)
def add_clusters(self, age, work_experience, household_size, living_standards):
try:
predict = predict_cluster(age, work_experience, household_size, living_standards)
return predict
except Exception as e:
raise CustomException(e, sys)
class CustomData:
def __init__(self, primary_mode_of_transportation: str, education_level: str, occupation: str, marital_status: str, living_standards: str, gender: str,
homeownership_status: str, location: str, type_of_housing: str, employment_status: str, work_experience: int, number_of_dependents: int, household_size: int, age: int):
self.primary_mode_of_transportation = primary_mode_of_transportation
self.education_level = education_level
self.occupation = occupation
self.marital_status = marital_status
self.living_standards = living_standards
self.gender = gender
self.homeownership_status = homeownership_status
self.location = location
self.type_of_housing = type_of_housing
self.employment_status = employment_status
self.work_experience = work_experience
self.number_of_dependents = number_of_dependents
self.household_size = household_size
self.age = age
def get_data_as_data_frame(self):
try:
custom_data_input_dict = {
"Primary_Mode_of_Transportation": [self.primary_mode_of_transportation],
"Education_Level": [self.education_level],
"Occupation": [self.occupation],
"Marital_Status": [self.marital_status],
"Living_Standards": [self.living_standards],
"Gender": [self.gender],
"Homeownership_Status": [self.homeownership_status],
"Location": [self.location],
"Type_of_Housing": [self.type_of_housing],
"Employment_Status": [self.employment_status],
"Work_Experience": [self.work_experience],
"Number_of_Dependents": [self.number_of_dependents],
"Household_Size": [self.household_size],
"Age": [self.age],
}
data_df = pd.DataFrame(custom_data_input_dict)
predict_pipeline = PredictPipeline()
#print("Data Input")
#print(data_df)
cluster_value = predict_pipeline.add_clusters(self.age, self.work_experience, self.household_size, self.living_standards)
data_df["Cluster"] = float(cluster_value)
#print("After Cluster")
#print(data_df)
data_df['Age_Group'] = pd.cut(data_df['Age'], bins=[15, 30, 45, 60, 75], labels=['15-30', '31-45', '46-60', '61-75'])
data_df['Age_Group'] = data_df['Age_Group'].astype(object)
#print("After Age Group")
#print(data_df)
return data_df
except Exception as e:
raise CustomException(e, sys)
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