90 lines
No EOL
2.6 KiB
Python
90 lines
No EOL
2.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Mar 11 11:15:50 2022
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@author: tanu
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"""
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#%%
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del(t3_res)
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t3_res = MultClassPipeSKF(input_df = numerical_features_df
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, y_targetF = target1
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, var_type = 'numerical'
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, skf_splits = 10)
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pp.pprint(t3_res)
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#print(t3_res)
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#%% Manually: mean for each model, each metric
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model_name = 'Logistic Regression'
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model_name = 'Naive Bayes'
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model_name = 'K-Nearest Neighbors'
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model_name = 'SVM'
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#%%
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model_metric = 'F1_score'
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log_reg_f1 = []
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for key in t3_res[model_name]:
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log_reg_f1.append(t3_res[model_name][key][model_metric])
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log_reg_f1M = mean(log_reg_f1)
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print('key:', key, model_metric, ':', log_reg_f1)
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print(log_reg_f1M)
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log_reg_f1df = pd.DataFrame({model_name: [log_reg_f1M]}, index = [model_metric])
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log_reg_f1df
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#%%
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model_metric = 'MCC'
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log_reg_mcc = []
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for key in t3_res[model_name]:
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log_reg_mcc.append(t3_res[model_name][key][model_metric])
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log_reg_mccM = mean(log_reg_mcc)
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print('key:', key, model_metric, ':', log_reg_mcc)
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print(log_reg_mccM)
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log_reg_mccdf = pd.DataFrame({model_name: [log_reg_mccM]}, index = [model_metric])
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log_reg_mccdf
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#%%
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model_metric = 'Precision'
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log_reg_pres = []
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for key in t3_res[model_name]:
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log_reg_pres.append(t3_res[model_name][key][model_metric])
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log_reg_presM = mean(log_reg_pres)
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print('key:', key, model_metric, ':', log_reg_pres)
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print(log_reg_presM)
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log_reg_presdf = pd.DataFrame({model_name: [log_reg_presM]}, index = [model_metric])
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log_reg_presdf
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#%%
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model_metric = 'Recall'
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log_reg_recall = []
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for key in t3_res[model_name]:
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log_reg_recall.append(t3_res[model_name][key][model_metric])
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log_reg_recallM = mean(log_reg_recall)
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print('key:', key, model_metric, ':', log_reg_recall)
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print(log_reg_recallM)
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log_reg_recalldf = pd.DataFrame({model_name: [log_reg_recallM]}, index = [model_metric])
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log_reg_recalldf
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#%%
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model_metric = 'Accuracy'
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log_reg_accu = []
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for key in t3_res[model_name]:
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log_reg_accu.append(t3_res[model_name][key][model_metric])
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log_reg_accuM = mean(log_reg_accu)
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print('key:', key, model_metric, ':', log_reg_accu)
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print(log_reg_accuM)
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log_reg_accudf = pd.DataFrame({model_name: [log_reg_accuM]}, index = [model_metric])
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log_reg_accudf
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#%%
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model_metric = 'ROC_AUC'
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log_reg_roc_auc = []
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for key in t3_res[model_name]:
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log_reg_roc_auc.append(t3_res[model_name][key][model_metric])
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log_reg_roc_aucM = mean(log_reg_roc_auc)
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print('key:', key, model_metric, ':', log_reg_roc_auc)
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print(log_reg_roc_aucM)
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log_reg_roc_aucdf = pd.DataFrame({model_name: [log_reg_roc_aucM]}, index = [model_metric])
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log_reg_roc_aucdf |