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added loopity loop function call to extract mean values for each model's metric from nested dict (2/3 levels)

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Tanushree Tunstall 2022-03-14 18:46:59 +00:00
parent 29306e77ee
commit a1631ea54b
3 changed files with 321 additions and 8 deletions
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#!/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
#%%
model_metric = 'Precision'
log_reg_pres = []
for key in t3_res[model_name]:
log_reg_pres.append(t3_res[model_name][key][model_metric])
log_reg_presM = mean(log_reg_pres)
print('key:', key, model_metric, ':', log_reg_pres)
print(log_reg_presM)
log_reg_presdf = pd.DataFrame({model_name: [log_reg_presM]}, index = [model_metric])
log_reg_presdf
#%%
model_metric = 'Recall'
log_reg_recall = []
for key in t3_res[model_name]:
log_reg_recall.append(t3_res[model_name][key][model_metric])
log_reg_recallM = mean(log_reg_recall)
print('key:', key, model_metric, ':', log_reg_recall)
print(log_reg_recallM)
log_reg_recalldf = pd.DataFrame({model_name: [log_reg_recallM]}, index = [model_metric])
log_reg_recalldf
#%%
model_metric = 'Accuracy'
log_reg_accu = []
for key in t3_res[model_name]:
log_reg_accu.append(t3_res[model_name][key][model_metric])
log_reg_accuM = mean(log_reg_accu)
print('key:', key, model_metric, ':', log_reg_accu)
print(log_reg_accuM)
log_reg_accudf = pd.DataFrame({model_name: [log_reg_accuM]}, index = [model_metric])
log_reg_accudf
#%%
model_metric = 'ROC_AUC'
log_reg_roc_auc = []
for key in t3_res[model_name]:
log_reg_roc_auc.append(t3_res[model_name][key][model_metric])
log_reg_roc_aucM = mean(log_reg_roc_auc)
print('key:', key, model_metric, ':', log_reg_roc_auc)
print(log_reg_roc_aucM)
log_reg_roc_aucdf = pd.DataFrame({model_name: [log_reg_roc_aucM]}, index = [model_metric])
log_reg_roc_aucdf