Project: s41467-024-46000-9 | Function: evaluate_model (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Evaluates trained model performance using standard metrics including accuracy, AUC, precision, and recall for SUDO framework

**KEY TERMS:** {
  "preds": "model predictions",
  "probs": "prediction probabilities",
  "acc": "accuracy score",
  "auc": "area under ROC curve",
  "prec": "precision score",
  "rec": "recall score"
}

**CONTEXT CODE:** #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May  9 14:30:53 2023

@author: dani.kiyasseh

This script is used to:
1) conduct the SUDO experiments on the Stanford DDI dataset
"""

# [USER MUST MODIFY] 
path_to_ddi_folder = '/Users/dani.kiyasseh/Desktop/Data/DDI'
path_to_ham_folder = '/Users/dani.kiyasseh/Desktop/Data/HAM'
path_to_current_scripts = '/Users/dani.kiyasseh/Desktop/Scripts'

import os
new_path = path_to_current_scripts
import sys
import pickle
import random
from operator import itemgetter
if new_path not in sys.path:
    sys.path.append(new_path)
import pandas as pd
from scipy.special import expit
import numpy as np
from collections import defaultdict
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
from imblearn.over_sampling import BorderlineSMOTE
from sklearn.linear_model import LogisticRegression, LinearRegression
from joblib import dump, load
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import interpolate


from prepare_miscellaneous import load_model, train_model                                                          
from network import train, train_for_OOD
from torch.utils.tensorboard import SummaryWriter

from sklearn.metrics import accuracy_score, roc_auc_score, precision_score, recall_score
path = os.path.join(path_to_ddi_folder,'Probs')
# os.chdir(path)  # Commented out for testing environment

from load_ham_data import HAM_dataset

#%%
def evaluate_model(model,inputs,outputs,df):
    # MASKED: Evaluate trained model performance using standard metrics including accuracy, AUC, precision, and recall for SUDO framework implementation
    raise NotImplementedError("This function is masked for testing purposes.")

#%%

def run_variants(settings,setting='in-domain',label_name='Combined',suffix='',lab_collapse_strategy='max',doc_collapse_strategy='concat',labelled_and_unlabelled='labelled_only',goal='train and evaluate model',phases=['train','val'],dataset_type='All',max_inference_seed=1,batch_size=32,lr=1e-4,verbose=0,save_model_flag=False,switch=False):
    # MASKED: Implement complete SUDO framework by training classifiers on pseudo-labeled data and computing performance discrepancies across probability thresholds implementation
    raise NotImplementedError("This function is masked for testing purposes.")

#%%
def get_settings(inference_threshold_side_list,inference_threshold_list):
    settings = {'weak supervision list':weak_supervision_list,
                'task list':task_list,
                'labs list':labs_list,
                'documents list':documents_list,
                'demo list':demo_list,
                'diagnosis list':diagnosis_list,
                'vitals list':vitals_list,
                'medications list':medications_list,
                'feature list':features_list,
                'ngram list':ngram_list,
                'nfeatures list':nfeatures_list,            
                'imputation type list':imputation_type_list,
                'sampling strategy list':sampling_strategy_list,
                'scale list':scale_list,
                'model list':model_list,
                'inference threshold side list':inference_threshold_side_list,
                'inference threshold list':inference_threshold_list,   
                'model_type':model_type                 
                }
    return settings    

#%%
if __name__ == '__main__':
    
    setting = 'in-domain' #options: 'in-domain' | 'out-of-domain'
    weak_supervision_list = [False] 
    task_list = ['01 vs. 234'] #['0'] #
    labs_list = [False]
    documents_list = [True]
    demo_list = [False]
    diagnosis_list = [False]
    vitals_list = [False]
    medications_list = [False] 
    features_list = ['BoW']
    ngram_list = [2]
    nfeatures_list = [5000]
    imputation_type_list = ['median']
    sampling_strategy_list = [False]
    scale_list = [False]
    model_list = ['LR'] #NN #RF
    batch_size = 32 #applicable only with NN
    lr = 1e-4 #applicable only with NN
    lab_collapse_strategy = 'max' #default is 'max' options: max | min | max/min
    doc_collapse_strategy = 'concat' #options are: concat | latest
    verbose = 0 #options: | 0 | 1 | 2
    labelled_and_unlabelled = 'test_labelled_only' #'labelled_and_unlabelled' #options: labelled_only | unlabelled_only | labelled_and_unlabelled | test_labelled_only
    goal = 'validate tgt data' #'validate tgt data' #options: 'train and evaluate model' | evaluate model | calibrate model | perform inference | validate_unlabelled_data
    dataset_type = 'All'
    inference_threshold_side_list = ['low'] #options: low | high
    inference_threshold_list = [1] #np.arange(0.1,0.6,0.1) #[0] #[0.9, 0.8, 0.7, 0.6, 0.5] #np.arange(0.1,0.6,0.1) # np.arange(0.80,0.40,-0.2) ## #options: values in [0,1]
    model_type = 'HAM10000' # options: HAM10000 | DeepDerm
    max_inference_seed = 5 #5 #number of times to perform inference (primarily used for validating unlabelled data)
    suffix = '' #options are: '' | '_Masked' (underscore is important) - only used when loading models for inference
    save_model_flag = False #save ml model, scaler, etc. 
    
    settings = {'weak supervision list':weak_supervision_list,
                'task list':task_list,
                'labs list':labs_list,
                'documents list':documents_list,
                'demo list':demo_list,
                'diagnosis list':diagnosis_list,
                'vitals list':vitals_list,
                'medications list':medications_list,
                'feature list':features_list,
                'ngram list':ngram_list,
                'nfeatures list':nfeatures_list,            
                'imputation type list':imputation_type_list,
                'sampling strategy list':sampling_strategy_list,
                'scale list':scale_list,
                'model list':model_list,
                'inference threshold side list':inference_threshold_side_list,
                'inference threshold list':inference_threshold_list,                    
                }
    
    if goal == 'validate tgt data':
        df = pd.DataFrame()
        for inference_threshold_side_list,inference_threshold_list in zip([['low']],[np.arange(0.05,0.5,0.05)]): #(0.05,0.5,0.05) for HAM10000 # (0.1,1.1,0.1) for DeepDerm # (0.1,0.5,0.1) for HAM10000 fairness experiments 
            for switch in [False,True]:
                settings = get_settings(inference_threshold_side_list,inference_threshold_list)
                results_df, probs_df = run_variants(settings,setting=setting,label_name='Combined',suffix=suffix,lab_collapse_strategy=lab_collapse_strategy,
                                             doc_collapse_strategy=doc_collapse_strategy,labelled_and_unlabelled=labelled_and_unlabelled,goal=goal,batch_size=batch_size,
                                             dataset_type=dataset_type,max_inference_seed=max_inference_seed,lr=lr,verbose=verbose,save_model_flag=save_model_flag,switch=switch)
                
                if inference_threshold_side_list == ['low']:
                    if switch == False:
                        env = 'Pseudo Low Label'
                    else:
                        env = 'Pseudo High Label'


Complete the function implementation replacing "raise NotImplementedError".