Create training_pipeline/__init__.py

src/constants/training_pipeline/__init__.py

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+import os 
+import sys 
+import numpy as np 
+import pandas as pd 
+
+"""
+Defining common constant variable for training pipeline
+"""
+
+TARGET_COLUMN="Time_taken(min)"
+PIPELINE_NAME:str = "DeliveryTimePrediction"
+ARTIFACT_DIR:str = "Artifacts"
+FILE_NAME:str = "final_data.csv"
+
+TRAIN_FILE_NAME:str = "train.csv"
+TEST_FILE_NAME: str = "test.csv"
+
+
+SCHEMA_FILE_PATH=os.path.join("data_schema", "schema.yaml")
+
+SAVED_MODEL_DIR=os.path.join("saved_models")
+MODEL_FILE_NAME= "model.pkl"
+
+"""
+Data Ingestion related constant start with DATA_INGESTION VAR NAME
+"""
+
+DATA_INGESTION_COLLECTION_NAME: str ="DeliveryTimePrediction"
+DATA_INGESTION_DATABASE_NAME: str ="NIVASYSTEM"
+DATA_INGESTION_DIR_NAME: str = "data_ingestion"
+DATA_INGESTION_FEATURE_STORE_DIR: str = "feature_store"
+DATA_INGESTION_INGESTED_DIR: str = "ingested"
+DATA_INGESTION_TRAIN_TEST_SPLIT_RATION: float = 0.2
+
+"""
+Data Validation related constant start with DATA_VALIDATION VAR NAME
+"""
+DATA_VALIDATION_DIR_NAME:str = "data_validation"
+DATA_VALIDATION_VALID_DIR: str = "validated"
+DATA_VALIDATION_INVALID_DIR: str = "invalid"
+DATA_VALIDATION_DRIFT_REPORT_DIR: str = "drift_report"
+DATA_VALIDATION_DRIFT_REPORT_FILE_NAME:str = "report.yaml"
+PREPROCESSING_OBJECT_FILE_NAME:str="preprocessing.pkl"
+
+"""
+Data Transformation related constant start with DATA_TRANSFORMATION VAR NAME
+"""
+
+DATA_TRANSFORMATION_DIR_NAME: str = "data_transformation"
+DATA_TRANSFORMATION_TRANSFORMED_DATA_DIR: str="transformed"
+DATA_TRANSFORMATION_TRANSFORMED_OBJECT_DIR: str="transformed_object"
+
+## Knn imputer class to replace nan values
+DATA_TRANSFORMATION_IMPUTER_PARAMS: dict={
+    "missing_values": np.nan,
+    "n_neighbors": 3,
+    "weights": "uniform"
+}
+
+
+DATA_TRANSFORMATION_TRAIN_FILE_PATH:str = "train.py"
+DATA_TRANSFORMATION_TEST_FILE_PATH:str = "test.npy"
+
+"""
+Model Trainer related content startt with MODEL_TRAINER VAR NAME
+"""
+
+MODEL_TRAINER_DIR_NAME:str = "model_trainer"
+MODEL_TRAINER_TRAINED_MODEL_DIR:str="trained_model"
+MODEL_TRAINER_TRAINED_MODEL_NAME: str="model.pkl"
+MODEL_TRAINER_EXPECTED_SCORE: float=0.6
+MODEL_TRAINER_OVER_FITTING_UNDER_FITTING_THRESHOLD:float= 0.05
+MODEL_TRAINER_OVER_FIITING_UNDER_FITTING_THRESHOLD:float=0.05
+
+TRAINING_BUCKET_NAME="deliveryprediction"