Model/model_data/simulation_system/MLModel/BaseMLModel.py

"""
BaseMLModel class
This is the base protocol:
- Any implementation will be its own seperate file
Key tasks:
- Template Model class for different model types
- Save model
- Load Model 
- Generate Inference
"""

from pathlib import Path
from typing import Protocol, NamedTuple
import pandas as pd


class MLModel(Protocol):
    '''
    Base ML Model protocol
    '''

    def load_model(self, filepath: Path) -> None:
        """
        Providing a path, this function will load the model to be used. Will load to internal variable
        """

    def save_model(self, output_filepath: Path) -> None:
        """
        Providing a path, this function will save the model to be used.
        """

    def train_model(
            self, 
            data: pd.DataFrame,
            target_column: str,
            hyperparameter: dict
            ) -> None:
        """
        For the given data and hyperparameters (specified to the model), a model is trained
        """

    def generate_predictions(self, data: pd.DataFrame) -> pd.DataFrame:
        """
        For the given dataframe, model is loaded and predictions are generated
        """

    def model_evaluation(self, validation_data: pd.DataFrame, target_column: str, metrics_location: Path = None) -> NamedTuple:
        """
        For any validation data, a set of predictions and metrics are return
        """

    def optimise_model_for_deployment(self):
        """
        Perfomance post processing on Model to ensure ready for deployment
        """
     
    def model_metadata(self) -> dict:
        """
        Extract out model metadata as dictionary
        """