src/evaluation.py

import logging
from abc import ABC, abstractmethod

import numpy as np
from sklearn.metrics import mean_squared_error, r2_score

class Evaluation(ABC):
    """
    Abstract class for all evaluations strategy
    """
    @abstractmethod
    def calculate_scores(self, y_true: np.ndarray, y_pred: np.ndarray):
        """
        Calculates the scores for the model
        Args:
            y_true: True labels
            y_pred: Predicted labels
        Returns:
            None
        """
        pass

class MSE(Evaluation):
    """
    Mean Squared Error evaluation
    """
    def calculate_scores(self, y_true: np.ndarray, y_pred: np.ndarray):
        try:
            logging.info("Calculating MSE")
            mse = mean_squared_error(y_true, y_pred)
            logging.info(f"MSE: {mse}")
            return mse
        except Exception as e:
            logging.error(f"Error in training model: {e}")
            raise e

class R2(Evaluation):
    """
    R2 score evaluation
    """
    def calculate_scores(self, y_true: np.ndarray, y_pred: np.ndarray):
        try:
            logging.info("Calculating R2 score")
            r2 = r2_score(y_true, y_pred)
            logging.info(f"R2 score: {r2}")
            return r2
        except Exception as e:
            logging.error(f"Error in training model: {e}")
            raise e

class RMSE(Evaluation):
    """
    Root Mean Squared Error evaluation
    """
    def calculate_scores(self, y_true: np.ndarray, y_pred: np.ndarray):
        try:
            logging.info("Calculating RMSE")
            rmse = mean_squared_error(y_true, y_pred, squared=False)
            logging.info(f"RMSE: {rmse}")
            return rmse
        except Exception as e:
            logging.error(f"Error in training model: {e}")
            raise e