Source code for qbiocode.evaluation.model_evaluation

# ====== Base class imports ======

import time
from typing import Literal
import pandas as pd

# ====== Scikit-learn imports ======

from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.metrics import f1_score, accuracy_score, roc_auc_score

from qbiocode.utils.helper_fn import print_results



def modeleval(y_test, y_predicted, beg_time, params, args, model:str, verbose = True, average='weighted'):
    """
    Evaluates the model performance using accuracy, F1 score, and AUC.

    Args:
        y_test (array-like): True labels for the test set.
        y_predicted (array-like): Predicted labels by the model.
        beg_time (float): Start time for measuring execution time.
        params (dict): Model parameters used during training.
        args (dict): Additional arguments, including grid search flag.
        model (str): Name of the model being evaluated.
        verbose (bool): If True, prints the evaluation results.
        average (str): Type of averaging to use for F1 score calculation.
            Default is 'weighted'.

    Returns:
        pd.DataFrame: DataFrame containing the evaluation results, including accuracy, F1 score, AUC, and model parameters.
    """
    # Calculate evaluation metrics
    auc = roc_auc_score(y_test, y_predicted)
    accuracy = accuracy_score(y_test, y_predicted, normalize=True)
    f1 = f1_score(y_test, y_predicted, average=average)
    compile_time = time.time() - beg_time
    params = params
    if verbose==True:
        print_results(model, accuracy, f1, compile_time, params)
    
    if args['grid_search'] == True: 
        return pd.DataFrame({'y_test_' + model: [y_test], 
                         'y_predicted_' + model: [y_predicted],
                         'results_' + model: [{'model':model,'accuracy': accuracy, 'f1_score': f1,'time': compile_time, 'auc': auc, 'BestParams_GridSearch': params}]})
    else: 
        return pd.DataFrame({'y_test_' + model: [y_test], 
                    'y_predicted_' + model: [y_predicted],
                    'results_' + model: [{'model':model,'accuracy': accuracy, 'f1_score': f1,'time': compile_time, 'auc': auc, 'Model_Parameters': params}]})