renamed hyperparams to gscv

earlier_versions/loopity_detangle.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 10 18:06:34 2022
+
+@author: tanu
+"""
+
+#%%
+models = [
+        ('Logistic Regression'  , log_reg) 
+        , ('K-Nearest Neighbors', knn) 
+        ]
+
+classification_metrics = {
+    'F1_score': []
+    ,'MCC': []
+    ,'Precision': []
+    ,'Recall': []
+    ,'Accuracy': []
+    ,'ROC_curve': []
+    }
+
+folds=[1,2]
+fold_no=1
+fold_dict={}
+for model_name, model in models:
+    fold_dict.update({model_name: {}})
+
+for f in folds:
+    fold=("fold_"+str(fold_no))
+    for model_name, model in models:
+        print("start of model", model_name, "fold: ", fold)
+        fold_dict[model_name].update({fold: {}})
+        fold_dict[model_name][fold].update(classification_metrics)
+
+        print("end of model", model_name, "fold: ", fold)
+        fold_dict[model_name][fold].update({'F1_score': random.randrange(1,10)})
+    fold_no +=1
+    pp.pprint(fold_dict)
+    
+
+#%%
+folds_f1=[]
+
+for model_name, model in models:
+    print("Calculating mean for F1_score for: ", model_name)
+    #for key in fold_dict['Logistic Regression']:
+        # wrap this in a classification_metric for loop
+    for key in fold_dict[model_name]:
+        folds_f1.append(fold_dict['Logistic Regression'][key]['F1_score'])
+        #folds_f1.append(folds_f1)
+        print('key:', key, 'F1scores:', folds_f1)
+mean(folds_f1)
+#%%
+scores_df = pd.DataFrame(columns=['Model', 'F1_Score', 'MCC', 'Precision', 'Recall', 'Accuracy', 'ROC_AUC'])
+
+# manually
+model_name = 'Logistic Regression'
+model_metric = 'F1_score'
+
+log_reg_f1 = []
+for key in fold_dict[model_name]:
+    log_reg_f1.append(fold_dict[model_name][key][model_metric])
+    log_reg_f1M = mean(log_reg_f1)
+    print('key:', key, model_metric, ':', log_reg_f1)
+print(log_reg_f1M)
+
+log_reg_f1df = pd.DataFrame({model_name: [log_reg_f1M]}, index = [model_metric])
+log_reg_f1df
+
+#%%
+model_metric = 'MCC'
+log_reg_mcc = []
+for key in fold_dict[model_name]:
+    log_reg_mcc.append(fold_dict[model_name][key][model_metric])
+    log_reg_mccM = mean(log_reg_mcc)
+    print('key:', key, model_metric, ':', log_reg_mcc)
+print(log_reg_mccM)
+
+log_reg_mccdf = pd.DataFrame({model_name: [log_reg_mccM]}, index = [model_metric])
+log_reg_mccdf