added loopity_loop to run multiple models with stratified k-fold, got stuck in infinite loops and nested dicts

my_data5.py

@@ -1,172 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Mar  3 17:08:18 2022
-
-@author: tanu
-"""
-from sklearn.linear_model import LogisticRegression
-from sklearn.naive_bayes import BernoulliNB
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.svm import SVC
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
-from sklearn.neural_network import MLPClassifier
-from sklearn.pipeline import Pipeline
-from xgboost import XGBClassifier
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.model_selection import train_test_split
-import os
-from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
-import pandas as pd
-#%%
-homedir = os.path.expanduser("~")
-os.chdir(homedir + "/git/ML_AI_training/test_data")
-
-#gene 'pncA'
-#drug = 'pyrazinamide'
-
-#==============
-# directories
-#==============
-datadir = homedir + '/git/Data/'
-indir = datadir + drug + '/input/'
-outdir = datadir + drug + '/output/'
-
-#=======
-# input
-#=======
-# this needs to be merged_df2 or merged_df3?
-infile_ml1 = outdir + gene.lower() + '_merged_df3.csv' 
-#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
-
-my_df = pd.read_csv(infile_ml1)
-my_df.dtypes
-my_df_cols = my_df.columns
-
-gene_baiscL = ['pnca']
-geneL_naL   = ['gid', 'rpob']
-geneL_ppi2L = ['alr', 'embb', 'katg', 'rpob']
-#%%============================================================================
-# GET Y
-# Y = my_df.loc[:,drug] #has NA
-dm_om_map = {'DM': 1, 'OM': 0}
-my_df['resistance'] = my_df['mutation_info_labels'].map(dm_om_map)
-
-# sanity check
-my_df['resistance'].value_counts()
-my_df['mutation_info_labels'].value_counts()
-Y = my_df['resistance']
-
-# GET X
-cols = my_df.columns
-X_stability = my_df[['ligand_distance'
-           , 'ligand_affinity_change'
-           , 'duet_stability_change'
-           , 'ddg_foldx'
-           , 'deepddg'
-           , 'ddg_dynamut2']]
-
-X_evol =  my_df[['consurf_score'
-           , 'snap2_score'
-           , 'snap2_accuracy_pc']]
-
-X_str =  my_df[['asa'
-           , 'rsa'
-           , 'kd_values'
-           , 'rd_values']]
-
-#%% try combinations
-X_vars = X_stability
-X_vars = X_evol
-X_vars = X_str
-
-X_vars = pd.concat([X_stability, X_evol, X_str], axis = 1)
-X_vars = pd.concat([X_stability, X_evol], axis = 1)
-X_vars = pd.concat([X_stability, X_str], axis = 1)
-X_vars = pd.concat([X_evol, X_str], axis = 1)
-
-#%%
-X_vars.shape[1]
-
-# TODO: stratified cross validate
-# Train-test Split
-rs = {'random_state': 42}
-X_train, X_test, y_train, y_test = train_test_split(X_vars, 
-                                                    Y, 
-                                                    test_size = 0.33, 
-                                                    random_state = 42)
-
-# Classification - Model Pipeline
-def modelPipeline(X_train, X_test, y_train, y_test):
-
-    log_reg = LogisticRegression(**rs)
-    nb = BernoulliNB()
-    knn = KNeighborsClassifier()
-    svm = SVC(**rs)
-    mlp = MLPClassifier(max_iter=500, **rs)
-    dt = DecisionTreeClassifier(**rs)
-    et = ExtraTreesClassifier(**rs)
-    rf = RandomForestClassifier(**rs)
-    xgb = XGBClassifier(**rs, verbosity=0)
-
-    clfs = [
-            ('Logistic Regression', log_reg), 
-            ('Naive Bayes', nb),
-            ('K-Nearest Neighbors', knn), 
-            ('SVM', svm), 
-            ('MLP', mlp), 
-            ('Decision Tree', dt), 
-            ('Extra Trees', et), 
-            ('Random Forest', rf), 
-            ('XGBoost', xgb)
-            ]
-
-
-    pipelines = []
-
-    scores_df = pd.DataFrame(columns=['Model', 'F1_Score', 'Precision', 'Recall', 'Accuracy', 'ROC_AUC'])
-
-
-    for clf_name, clf in clfs:
-
-        pipeline = Pipeline(steps=[
-                                   ('scaler', MinMaxScaler()),
-                                   ('classifier', clf)
-                                   ]
-                            )
-        pipeline.fit(X_train, y_train)
-
-        # Model predictions
-        y_pred  = pipeline.predict(X_test)
-        
-        # F1-Score
-        fscore  = f1_score(y_test, y_pred)
-        # Precision
-        pres    = precision_score(y_test, y_pred)
-        # Recall
-        rcall   = recall_score(y_test, y_pred)
-        # Accuracy
-        accu    = accuracy_score(y_test, y_pred)
-        # ROC_AUC
-        roc_auc = roc_auc_score(y_test, y_pred)
-
-
-        pipelines.append(pipeline)
-
-        scores_df = scores_df.append({
-                                      'Model'     : clf_name, 
-                                      'F1_Score'  : fscore,
-                                      'Precision' : pres,
-                                      'Recall'    : rcall,
-                                      'Accuracy'  : accu,
-                                      'ROC_AUC'   : roc_auc
-                                      
-                                      }, 
-                                     ignore_index = True)
-        
-    return pipelines, scores_df
-
-
-modelPipeline(X_train, X_test, y_train, y_test)