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support ROC and AUC for multi-class classification (#170)

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* support ROC and AUC for multi-class classification

* add a test case to cover ROC and AUC for multi-class classification
This commit is contained in:
すずまる
2021-08-23 07:16:10 +09:00
committed by GitHub
parent 10082b9262
commit 6270353458
7 changed files with 52 additions and 14 deletions
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4
notebook/automl_in_sklearn_pipeline.ipynb
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@@ -200,7 +200,7 @@
"source": [
"settings = {\n",
" \"time_budget\": 60, # total running time in seconds\n",
" \"metric\": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc','f1','log_loss','mae','mse','r2']\n",
" \"metric\": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1','log_loss','mae','mse','r2']\n",
" \"task\": 'classification', # task type \n",
" \"estimator_list\":['xgboost','catboost','lgbm'],\n",
" \"log_file_name\": 'airlines_experiment.log', # flaml log file\n",
@@ -962,4 +962,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
}
}

2
notebook/flaml_automl.ipynb
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@@ -121,7 +121,7 @@
"source": [
"settings = {\n",
" \"time_budget\": 300, # total running time in seconds\n",
" \"metric\": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc','f1','log_loss','mae','mse','r2']\n",
" \"metric\": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc','roc_auc_ovr','roc_auc_ovo','f1','log_loss','mae','mse','r2']\n",
" \"task\": 'classification', # task type \n",
" \"log_file_name\": 'airlines_experiment.log', # flaml log file\n",
"}"

2
notebook/flaml_azureml.ipynb
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@@ -141,7 +141,7 @@
"source": [
"settings = {\n",
" \"time_budget\": 60, # total running time in seconds\n",
" \"metric\": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc','f1','log_loss','mae','mse','r2']\n",
" \"metric\": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc','roc_auc_ovr','roc_auc_ovo','f1','log_loss','mae','mse','r2']\n",
" \"estimator_list\": ['lgbm', 'rf', 'xgboost'], # list of ML learners\n",
" \"task\": 'classification', # task type \n",
" \"sample\": False, # whether to subsample training data\n",