loopity_loop_CALL

loopity_loop_CALL.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Mar 11 11:15:50 2022
+
+@author: tanu
+"""
+#%%
+del(t3_res)
+t3_res = MultClassPipeSKF(input_df = numerical_features_df
+                          , y_targetF = target1
+                          , var_type = 'numerical'
+                          , skf_splits = 10)
+pp.pprint(t3_res)
+#print(t3_res)
+
+#%% Manually: mean for each model, each metric
+model_name = 'Logistic Regression'
+model_name = 'Naive Bayes'
+model_name = 'K-Nearest Neighbors'
+model_name = 'SVM'
+
+
+
+
+
+#%%
+model_metric = 'F1_score'
+
+log_reg_f1 = []
+for key in t3_res[model_name]:
+    log_reg_f1.append(t3_res[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 t3_res[model_name]:
+    log_reg_mcc.append(t3_res[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
+
+#%%
+
+
+#%% Classification Metrics we need to mean()
+classification_metrics = {
+    'F1_score': []
+    ,'MCC': []
+    ,'Precision': []
+    ,'Recall': []
+    ,'Accuracy': []
+    }
+# "mean() of the current metric across all folds for this model"
+
+# the output containing all the metrics across all folds for this model
+out={}
+# Just the mean() for each of the above metrics-per-model
+out_means={}
+
+# Build up out{} from t3_res, which came from loopity_loop
+for model in t3_res:
+    # NOTE: can't copy objects in Python!!!
+    out[model]={'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': []}
+    out_means[model]={} # just to make life easier
+    print(model)
+    for fold in t3_res[model]:
+        for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': []}:
+            metric_value = t3_res[model][fold][metric]
+            out[model][metric].append(metric_value)
+# now that we've built out{}, let's mean() each metric            
+for model in out:
+    for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': []}:
+        metric_mean = mean(out[model][metric])
+        # just some debug output
+        # print('model:', model
+        #       , 'metric: ', metric
+        #       , metric_mean
+        #       )
+        out[model].update({(metric+'_mean'): metric_mean })
+        out_means[model].update({(metric+'_mean'): metric_mean })
+
+out_scores = pd.DataFrame(out_means)