.archive_benchmarks/ensemble/ovo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import rankdata\n",
    "from imblearn.metrics import geometric_mean_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from multi_imbalance.ensemble.ovo import OVO\n",
    "from multi_imbalance.utils import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "minority = dict()\n",
    "minority['1czysty-cut'] = [1, 2]\n",
    "minority['2delikatne-cut'] = [1, 2]\n",
    "minority['3mocniej-cut'] = [1, 2]\n",
    "minority['4delikatne-bezover-cut'] = [1, 2]\n",
    "minority['balance-scale'] = [0]\n",
    "minority['car'] = [3, 1]\n",
    "minority['cleveland'] = [4, 3, 2, 1]\n",
    "minority['cleveland_v2'] = [3, 2, 1]\n",
    "minority['cmc'] = [1]\n",
    "minority['dermatology'] = [5]\n",
    "minority['flare'] = [4, 1]\n",
    "minority['glass'] = [4, 2, 5]\n",
    "minority['hayes-roth'] = [0]\n",
    "minority['new_ecoli'] = [3, 2, 4]\n",
    "minority['new_led7digit'] = [1, 4]\n",
    "minority['new_vehicle'] = [2, 0]\n",
    "minority['new_winequality-red'] = [3, 2]\n",
    "minority['new_yeast'] = [2, 3, 4, 5, 6]\n",
    "minority['thyroid-newthyroid'] = [2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = data.load_arff_datasets()\n",
    "datasets_names = [dsn for dsn, _ in datasets.items()]\n",
    "\n",
    "binary_classifiers = ['tree', 'KNN', 'NB']\n",
    "preprocessings = [None, 'globalCS', 'SMOTE', 'SOUP']\n",
    "preprocessing_between_strategies = ['all', 'maj-min']\n",
    "\n",
    "results_g_mean = dict()\n",
    "results_gmean_min = dict()\n",
    "\n",
    "methods=[(bc,pre,pre_btwn) for bc in binary_classifiers for pre in preprocessings for pre_btwn in preprocessing_between_strategies]\n",
    "\n",
    "for res in (results_g_mean, results_gmean_min):\n",
    "    res['dataset'] = datasets_names\n",
    "    for method in methods:\n",
    "        res[method] = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1czysty-cut tree None all\n",
      "g-mean: 0.94\n",
      "gmean-min: 0.929\n",
      "1czysty-cut tree None maj-min\n",
      "g-mean: 0.94\n",
      "gmean-min: 0.929\n",
      "1czysty-cut tree globalCS all\n",
      "g-mean: 0.942\n",
      "gmean-min: 0.931\n",
      "1czysty-cut tree globalCS maj-min\n",
      "g-mean: 0.942\n",
      "gmean-min: 0.931\n",
      "1czysty-cut tree SMOTE all\n",
      "g-mean: 0.951\n",
      "gmean-min: 0.945\n",
      "1czysty-cut tree SMOTE maj-min\n",
      "g-mean: 0.951\n",
      "gmean-min: 0.945\n",
      "1czysty-cut tree SOUP all\n",
      "g-mean: 0.956\n",
      "gmean-min: 0.975\n",
      "1czysty-cut tree SOUP maj-min\n",
      "g-mean: 0.956\n",
      "gmean-min: 0.975\n",
      "1czysty-cut KNN None all\n",
      "g-mean: 0.959\n",
      "gmean-min: 0.952\n",
      "1czysty-cut KNN None maj-min\n",
      "g-mean: 0.959\n",
      "gmean-min: 0.952\n",
      "1czysty-cut KNN globalCS all\n",
      "g-mean: 0.965\n",
      "gmean-min: 0.975\n",
      "1czysty-cut KNN globalCS maj-min\n",
      "g-mean: 0.965\n",
      "gmean-min: 0.975\n",
      "1czysty-cut KNN SMOTE all\n",
      "g-mean: 0.966\n",
      "gmean-min: 0.973\n",
      "1czysty-cut KNN SMOTE maj-min\n",
      "g-mean: 0.966\n",
      "gmean-min: 0.973\n",
      "1czysty-cut KNN SOUP all\n",
      "g-mean: 0.955\n",
      "gmean-min: 1.0\n",
      "1czysty-cut KNN SOUP maj-min\n",
      "g-mean: 0.955\n",
      "gmean-min: 1.0\n",
      "1czysty-cut NB None all\n",
      "g-mean: 0.716\n",
      "gmean-min: 0.609\n",
      "1czysty-cut NB None maj-min\n",
      "g-mean: 0.716\n",
      "gmean-min: 0.609\n",
      "1czysty-cut NB globalCS all\n",
      "g-mean: 0.94\n",
      "gmean-min: 0.998\n",
      "1czysty-cut NB globalCS maj-min\n",
      "g-mean: 0.941\n",
      "gmean-min: 0.998\n",
      "1czysty-cut NB SMOTE all\n",
      "g-mean: 0.94\n",
      "gmean-min: 0.994\n",
      "1czysty-cut NB SMOTE maj-min\n",
      "g-mean: 0.939\n",
      "gmean-min: 0.994\n",
      "1czysty-cut NB SOUP all\n",
      "g-mean: 0.932\n",
      "gmean-min: 0.998\n",
      "1czysty-cut NB SOUP maj-min\n",
      "g-mean: 0.932\n",
      "gmean-min: 0.998\n",
      "2delikatne-cut tree None all\n",
      "g-mean: 0.693\n",
      "gmean-min: 0.607\n",
      "2delikatne-cut tree None maj-min\n",
      "g-mean: 0.693\n",
      "gmean-min: 0.607\n",
      "2delikatne-cut tree globalCS all\n",
      "g-mean: 0.703\n",
      "gmean-min: 0.621\n",
      "2delikatne-cut tree globalCS maj-min\n",
      "g-mean: 0.693\n",
      "gmean-min: 0.605\n",
      "2delikatne-cut tree SMOTE all\n",
      "g-mean: 0.718\n",
      "gmean-min: 0.651\n",
      "2delikatne-cut tree SMOTE maj-min\n",
      "g-mean: 0.714\n",
      "gmean-min: 0.645\n",
      "2delikatne-cut tree SOUP all\n",
      "g-mean: 0.789\n",
      "gmean-min: 0.776\n",
      "2delikatne-cut tree SOUP maj-min\n",
      "g-mean: 0.791\n",
      "gmean-min: 0.776\n",
      "2delikatne-cut KNN None all\n",
      "g-mean: 0.681\n",
      "gmean-min: 0.589\n",
      "2delikatne-cut KNN None maj-min\n",
      "g-mean: 0.681\n",
      "gmean-min: 0.589\n",
      "2delikatne-cut KNN globalCS all\n",
      "g-mean: 0.73\n",
      "gmean-min: 0.678\n",
      "2delikatne-cut KNN globalCS maj-min\n",
      "g-mean: 0.725\n",
      "gmean-min: 0.671\n",
      "2delikatne-cut KNN SMOTE all\n",
      "g-mean: 0.724\n",
      "gmean-min: 0.668\n",
      "2delikatne-cut KNN SMOTE maj-min\n",
      "g-mean: 0.723\n",
      "gmean-min: 0.667\n",
      "2delikatne-cut KNN SOUP all\n",
      "g-mean: 0.793\n",
      "gmean-min: 0.812\n",
      "2delikatne-cut KNN SOUP maj-min\n",
      "g-mean: 0.785\n",
      "gmean-min: 0.8\n",
      "2delikatne-cut NB None all\n",
      "g-mean: 0.483\n",
      "gmean-min: 0.336\n",
      "2delikatne-cut NB None maj-min\n",
      "g-mean: 0.483\n",
      "gmean-min: 0.336\n",
      "2delikatne-cut NB globalCS all\n",
      "g-mean: 0.812\n",
      "gmean-min: 0.842\n",
      "2delikatne-cut NB globalCS maj-min\n",
      "g-mean: 0.812\n",
      "gmean-min: 0.84\n",
      "2delikatne-cut NB SMOTE all\n",
      "g-mean: 0.817\n",
      "gmean-min: 0.836\n",
      "2delikatne-cut NB SMOTE maj-min\n",
      "g-mean: 0.819\n",
      "gmean-min: 0.839\n",
      "2delikatne-cut NB SOUP all\n",
      "g-mean: 0.807\n",
      "gmean-min: 0.846\n",
      "2delikatne-cut NB SOUP maj-min\n",
      "g-mean: 0.806\n",
      "gmean-min: 0.844\n",
      "3mocniej-cut tree None all\n",
      "g-mean: 0.487\n",
      "gmean-min: 0.373\n",
      "3mocniej-cut tree None maj-min\n",
      "g-mean: 0.487\n",
      "gmean-min: 0.373\n",
      "3mocniej-cut tree globalCS all\n",
      "g-mean: 0.481\n",
      "gmean-min: 0.364\n",
      "3mocniej-cut tree globalCS maj-min\n",
      "g-mean: 0.477\n",
      "gmean-min: 0.363\n",
      "3mocniej-cut tree SMOTE all\n",
      "g-mean: 0.483\n",
      "gmean-min: 0.395\n",
      "3mocniej-cut tree SMOTE maj-min\n",
      "g-mean: 0.492\n",
      "gmean-min: 0.404\n",
      "3mocniej-cut tree SOUP all\n",
      "g-mean: 0.606\n",
      "gmean-min: 0.577\n",
      "3mocniej-cut tree SOUP maj-min\n",
      "g-mean: 0.581\n",
      "gmean-min: 0.541\n",
      "3mocniej-cut KNN None all\n",
      "g-mean: 0.436\n",
      "gmean-min: 0.31\n",
      "3mocniej-cut KNN None maj-min\n",
      "g-mean: 0.436\n",
      "gmean-min: 0.31\n",
      "3mocniej-cut KNN globalCS all\n",
      "g-mean: 0.528\n",
      "gmean-min: 0.447\n",
      "3mocniej-cut KNN globalCS maj-min\n",
      "g-mean: 0.503\n",
      "gmean-min: 0.415\n",
      "3mocniej-cut KNN SMOTE all\n",
      "g-mean: 0.49\n",
      "gmean-min: 0.411\n",
      "3mocniej-cut KNN SMOTE maj-min\n",
      "g-mean: 0.49\n",
      "gmean-min: 0.409\n",
      "3mocniej-cut KNN SOUP all\n",
      "g-mean: 0.575\n",
      "gmean-min: 0.588\n",
      "3mocniej-cut KNN SOUP maj-min\n",
      "g-mean: 0.549\n",
      "gmean-min: 0.557\n",
      "3mocniej-cut NB None all\n",
      "g-mean: 0.01\n",
      "gmean-min: 0.001\n",
      "3mocniej-cut NB None maj-min\n",
      "g-mean: 0.01\n",
      "gmean-min: 0.001\n",
      "3mocniej-cut NB globalCS all\n",
      "g-mean: 0.637\n",
      "gmean-min: 0.709\n",
      "3mocniej-cut NB globalCS maj-min\n",
      "g-mean: 0.614\n",
      "gmean-min: 0.665\n",
      "3mocniej-cut NB SMOTE all\n",
      "g-mean: 0.651\n",
      "gmean-min: 0.711\n",
      "3mocniej-cut NB SMOTE maj-min\n",
      "g-mean: 0.625\n",
      "gmean-min: 0.665\n",
      "3mocniej-cut NB SOUP all\n",
      "g-mean: 0.612\n",
      "gmean-min: 0.704\n",
      "3mocniej-cut NB SOUP maj-min\n",
      "g-mean: 0.592\n",
      "gmean-min: 0.665\n",
      "4delikatne-bezover-cut tree None all\n",
      "g-mean: 0.767\n",
      "gmean-min: 0.719\n",
      "4delikatne-bezover-cut tree None maj-min\n",
      "g-mean: 0.767\n",
      "gmean-min: 0.719\n",
      "4delikatne-bezover-cut tree globalCS all\n",
      "g-mean: 0.755\n",
      "gmean-min: 0.697\n",
      "4delikatne-bezover-cut tree globalCS maj-min\n",
      "g-mean: 0.755\n",
      "gmean-min: 0.697\n",
      "4delikatne-bezover-cut tree SMOTE all\n",
      "g-mean: 0.795\n",
      "gmean-min: 0.764\n",
      "4delikatne-bezover-cut tree SMOTE maj-min\n",
      "g-mean: 0.795\n",
      "gmean-min: 0.764\n",
      "4delikatne-bezover-cut tree SOUP all\n",
      "g-mean: 0.892\n",
      "gmean-min: 0.953\n",
      "4delikatne-bezover-cut tree SOUP maj-min\n",
      "g-mean: 0.892\n",
      "gmean-min: 0.953\n",
      "4delikatne-bezover-cut KNN None all\n",
      "g-mean: 0.782\n",
      "gmean-min: 0.734\n",
      "4delikatne-bezover-cut KNN None maj-min\n",
      "g-mean: 0.782\n",
      "gmean-min: 0.734\n",
      "4delikatne-bezover-cut KNN globalCS all\n",
      "g-mean: 0.808\n",
      "gmean-min: 0.805\n",
      "4delikatne-bezover-cut KNN globalCS maj-min\n",
      "g-mean: 0.807\n",
      "gmean-min: 0.805\n",
      "4delikatne-bezover-cut KNN SMOTE all\n",
      "g-mean: 0.817\n",
      "gmean-min: 0.817\n",
      "4delikatne-bezover-cut KNN SMOTE maj-min\n",
      "g-mean: 0.817\n",
      "gmean-min: 0.817\n",
      "4delikatne-bezover-cut KNN SOUP all\n",
      "g-mean: 0.887\n",
      "gmean-min: 0.983\n",
      "4delikatne-bezover-cut KNN SOUP maj-min\n",
      "g-mean: 0.885\n",
      "gmean-min: 0.983\n",
      "4delikatne-bezover-cut NB None all\n",
      "g-mean: 0.705\n",
      "gmean-min: 0.606\n",
      "4delikatne-bezover-cut NB None maj-min\n",
      "g-mean: 0.705\n",
      "gmean-min: 0.606\n",
      "4delikatne-bezover-cut NB globalCS all\n",
      "g-mean: 0.889\n",
      "gmean-min: 0.967\n",
      "4delikatne-bezover-cut NB globalCS maj-min\n",
      "g-mean: 0.889\n",
      "gmean-min: 0.967\n",
      "4delikatne-bezover-cut NB SMOTE all\n",
      "g-mean: 0.894\n",
      "gmean-min: 0.96\n",
      "4delikatne-bezover-cut NB SMOTE maj-min\n",
      "g-mean: 0.894\n",
      "gmean-min: 0.96\n",
      "4delikatne-bezover-cut NB SOUP all\n",
      "g-mean: 0.875\n",
      "gmean-min: 0.967\n",
      "4delikatne-bezover-cut NB SOUP maj-min\n",
      "g-mean: 0.874\n",
      "gmean-min: 0.967\n",
      "balance-scale tree None all\n",
      "g-mean: 0.236\n",
      "gmean-min: 0.065\n",
      "balance-scale tree None maj-min\n",
      "g-mean: 0.236\n",
      "gmean-min: 0.065\n",
      "balance-scale tree globalCS all\n",
      "g-mean: 0.24\n",
      "gmean-min: 0.065\n",
      "balance-scale tree globalCS maj-min\n",
      "g-mean: 0.24\n",
      "gmean-min: 0.065\n",
      "balance-scale tree SMOTE all\n",
      "g-mean: 0.301\n",
      "gmean-min: 0.085\n",
      "balance-scale tree SMOTE maj-min\n",
      "g-mean: 0.301\n",
      "gmean-min: 0.085\n",
      "balance-scale tree SOUP all\n",
      "g-mean: 0.666\n",
      "gmean-min: 0.631\n",
      "balance-scale tree SOUP maj-min\n",
      "g-mean: 0.666\n",
      "gmean-min: 0.631\n",
      "balance-scale KNN None all\n",
      "g-mean: 0.163\n",
      "gmean-min: 0.023\n",
      "balance-scale KNN None maj-min\n",
      "g-mean: 0.163\n",
      "gmean-min: 0.023\n",
      "balance-scale KNN globalCS all\n",
      "g-mean: 0.148\n",
      "gmean-min: 0.023\n",
      "balance-scale KNN globalCS maj-min\n",
      "g-mean: 0.145\n",
      "gmean-min: 0.023\n",
      "balance-scale KNN SMOTE all\n",
      "g-mean: 0.244\n",
      "gmean-min: 0.085\n",
      "balance-scale KNN SMOTE maj-min\n",
      "g-mean: 0.244\n",
      "gmean-min: 0.085\n",
      "balance-scale KNN SOUP all\n",
      "g-mean: 0.729\n",
      "gmean-min: 0.816\n",
      "balance-scale KNN SOUP maj-min\n",
      "g-mean: 0.71\n",
      "gmean-min: 0.758\n",
      "balance-scale NB None all\n",
      "g-mean: 0.098\n",
      "gmean-min: 0.001\n",
      "balance-scale NB None maj-min\n",
      "g-mean: 0.098\n",
      "gmean-min: 0.001\n",
      "balance-scale NB globalCS all\n",
      "g-mean: 0.737\n",
      "gmean-min: 0.793\n",
      "balance-scale NB globalCS maj-min\n",
      "g-mean: 0.737\n",
      "gmean-min: 0.793\n",
      "balance-scale NB SMOTE all\n",
      "g-mean: 0.722\n",
      "gmean-min: 0.707\n",
      "balance-scale NB SMOTE maj-min\n",
      "g-mean: 0.722\n",
      "gmean-min: 0.707\n",
      "balance-scale NB SOUP all\n",
      "g-mean: 0.644\n",
      "gmean-min: 0.791\n",
      "balance-scale NB SOUP maj-min\n",
      "g-mean: 0.644\n",
      "gmean-min: 0.791\n",
      "car tree None all\n",
      "g-mean: 0.927\n",
      "gmean-min: 0.888\n",
      "car tree None maj-min\n",
      "g-mean: 0.927\n",
      "gmean-min: 0.888\n",
      "car tree globalCS all\n",
      "g-mean: 0.952\n",
      "gmean-min: 0.924\n",
      "car tree globalCS maj-min\n",
      "g-mean: 0.948\n",
      "gmean-min: 0.924\n",
      "car tree SMOTE all\n",
      "g-mean: 0.958\n",
      "gmean-min: 0.933\n",
      "car tree SMOTE maj-min\n",
      "g-mean: 0.954\n",
      "gmean-min: 0.933\n",
      "car tree SOUP all\n",
      "g-mean: 0.93\n",
      "gmean-min: 0.955\n",
      "car tree SOUP maj-min\n",
      "g-mean: 0.936\n",
      "gmean-min: 0.955\n",
      "car KNN None all\n",
      "g-mean: 0.44\n",
      "gmean-min: 0.277\n",
      "car KNN None maj-min\n",
      "g-mean: 0.44\n",
      "gmean-min: 0.277\n",
      "car KNN globalCS all\n",
      "g-mean: 0.769\n",
      "gmean-min: 0.86\n",
      "car KNN globalCS maj-min\n",
      "g-mean: 0.711\n",
      "gmean-min: 0.77\n",
      "car KNN SMOTE all\n",
      "g-mean: 0.313\n",
      "gmean-min: 0.15\n",
      "car KNN SMOTE maj-min\n",
      "g-mean: 0.312\n",
      "gmean-min: 0.153\n",
      "car KNN SOUP all\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g-mean: 0.701\n",
      "gmean-min: 0.831\n",
      "car KNN SOUP maj-min\n",
      "g-mean: 0.701\n",
      "gmean-min: 0.884\n",
      "car NB None all\n",
      "g-mean: 0.856\n",
      "gmean-min: 0.93\n",
      "car NB None maj-min\n",
      "g-mean: 0.856\n",
      "gmean-min: 0.93\n",
      "car NB globalCS all\n",
      "g-mean: 0.856\n",
      "gmean-min: 0.93\n",
      "car NB globalCS maj-min\n",
      "g-mean: 0.856\n",
      "gmean-min: 0.93\n",
      "car NB SMOTE all\n",
      "g-mean: 0.015\n",
      "gmean-min: 0.001\n",
      "car NB SMOTE maj-min\n",
      "g-mean: 0.03\n",
      "gmean-min: 0.001\n",
      "car NB SOUP all\n",
      "g-mean: 0.856\n",
      "gmean-min: 0.93\n",
      "car NB SOUP maj-min\n",
      "g-mean: 0.856\n",
      "gmean-min: 0.93\n",
      "cleveland tree None all\n",
      "g-mean: 0.141\n",
      "gmean-min: 0.095\n",
      "cleveland tree None maj-min\n",
      "g-mean: 0.141\n",
      "gmean-min: 0.095\n",
      "cleveland tree globalCS all\n",
      "g-mean: 0.068\n",
      "gmean-min: 0.038\n",
      "cleveland tree globalCS maj-min\n",
      "g-mean: 0.183\n",
      "gmean-min: 0.133\n",
      "cleveland tree SMOTE all\n",
      "g-mean: 0.078\n",
      "gmean-min: 0.045\n",
      "cleveland tree SMOTE maj-min\n",
      "g-mean: 0.173\n",
      "gmean-min: 0.122\n",
      "cleveland tree SOUP all\n",
      "g-mean: 0.157\n",
      "gmean-min: 0.114\n",
      "cleveland tree SOUP maj-min\n",
      "g-mean: 0.169\n",
      "gmean-min: 0.128\n",
      "cleveland KNN None all\n",
      "g-mean: 0.119\n",
      "gmean-min: 0.084\n",
      "cleveland KNN None maj-min\n",
      "g-mean: 0.119\n",
      "gmean-min: 0.084\n",
      "cleveland KNN globalCS all\n",
      "g-mean: 0.219\n",
      "gmean-min: 0.167\n",
      "cleveland KNN globalCS maj-min\n",
      "g-mean: 0.119\n",
      "gmean-min: 0.088\n",
      "cleveland KNN SMOTE all\n",
      "g-mean: 0.174\n",
      "gmean-min: 0.132\n",
      "cleveland KNN SMOTE maj-min\n",
      "g-mean: 0.117\n",
      "gmean-min: 0.088\n",
      "cleveland KNN SOUP all\n",
      "g-mean: 0.243\n",
      "gmean-min: 0.199\n",
      "cleveland KNN SOUP maj-min\n",
      "g-mean: 0.119\n",
      "gmean-min: 0.09\n",
      "cleveland NB None all\n",
      "g-mean: 0.095\n",
      "gmean-min: 0.076\n",
      "cleveland NB None maj-min\n",
      "g-mean: 0.095\n",
      "gmean-min: 0.076\n",
      "cleveland NB globalCS all\n",
      "g-mean: 0.092\n",
      "gmean-min: 0.077\n",
      "cleveland NB globalCS maj-min\n",
      "g-mean: 0.095\n",
      "gmean-min: 0.079\n",
      "cleveland NB SMOTE all\n",
      "g-mean: 0.065\n",
      "gmean-min: 0.045\n",
      "cleveland NB SMOTE maj-min\n",
      "g-mean: 0.069\n",
      "gmean-min: 0.046\n",
      "cleveland NB SOUP all\n",
      "g-mean: 0.203\n",
      "gmean-min: 0.174\n",
      "cleveland NB SOUP maj-min\n",
      "g-mean: 0.112\n",
      "gmean-min: 0.09\n",
      "cleveland_v2 tree None all\n",
      "g-mean: 0.074\n",
      "gmean-min: 0.033\n",
      "cleveland_v2 tree None maj-min\n",
      "g-mean: 0.074\n",
      "gmean-min: 0.033\n",
      "cleveland_v2 tree globalCS all\n",
      "g-mean: 0.083\n",
      "gmean-min: 0.038\n",
      "cleveland_v2 tree globalCS maj-min\n",
      "g-mean: 0.071\n",
      "gmean-min: 0.032\n",
      "cleveland_v2 tree SMOTE all\n",
      "g-mean: 0.139\n",
      "gmean-min: 0.082\n",
      "cleveland_v2 tree SMOTE maj-min\n",
      "g-mean: 0.072\n",
      "gmean-min: 0.032\n",
      "cleveland_v2 tree SOUP all\n",
      "g-mean: 0.073\n",
      "gmean-min: 0.034\n",
      "cleveland_v2 tree SOUP maj-min\n",
      "g-mean: 0.082\n",
      "gmean-min: 0.04\n",
      "cleveland_v2 KNN None all\n",
      "g-mean: 0.1\n",
      "gmean-min: 0.064\n",
      "cleveland_v2 KNN None maj-min\n",
      "g-mean: 0.1\n",
      "gmean-min: 0.064\n",
      "cleveland_v2 KNN globalCS all\n",
      "g-mean: 0.202\n",
      "gmean-min: 0.145\n",
      "cleveland_v2 KNN globalCS maj-min\n",
      "g-mean: 0.163\n",
      "gmean-min: 0.111\n",
      "cleveland_v2 KNN SMOTE all\n",
      "g-mean: 0.173\n",
      "gmean-min: 0.123\n",
      "cleveland_v2 KNN SMOTE maj-min\n",
      "g-mean: 0.167\n",
      "gmean-min: 0.115\n",
      "cleveland_v2 KNN SOUP all\n",
      "g-mean: 0.263\n",
      "gmean-min: 0.206\n",
      "cleveland_v2 KNN SOUP maj-min\n",
      "g-mean: 0.171\n",
      "gmean-min: 0.12\n",
      "cleveland_v2 NB None all\n",
      "g-mean: 0.057\n",
      "gmean-min: 0.034\n",
      "cleveland_v2 NB None maj-min\n",
      "g-mean: 0.057\n",
      "gmean-min: 0.034\n",
      "cleveland_v2 NB globalCS all\n",
      "g-mean: 0.058\n",
      "gmean-min: 0.037\n",
      "cleveland_v2 NB globalCS maj-min\n",
      "g-mean: 0.055\n",
      "gmean-min: 0.034\n",
      "cleveland_v2 NB SMOTE all\n",
      "g-mean: 0.152\n",
      "gmean-min: 0.115\n",
      "cleveland_v2 NB SMOTE maj-min\n",
      "g-mean: 0.07\n",
      "gmean-min: 0.036\n",
      "cleveland_v2 NB SOUP all\n",
      "g-mean: 0.139\n",
      "gmean-min: 0.101\n",
      "cleveland_v2 NB SOUP maj-min\n",
      "g-mean: 0.061\n",
      "gmean-min: 0.034\n",
      "cmc tree None all\n",
      "g-mean: 0.449\n",
      "gmean-min: 0.348\n",
      "cmc tree None maj-min\n",
      "g-mean: 0.449\n",
      "gmean-min: 0.348\n",
      "cmc tree globalCS all\n",
      "g-mean: 0.444\n",
      "gmean-min: 0.342\n",
      "cmc tree globalCS maj-min\n",
      "g-mean: 0.441\n",
      "gmean-min: 0.342\n",
      "cmc tree SMOTE all\n",
      "g-mean: 0.435\n",
      "gmean-min: 0.321\n",
      "cmc tree SMOTE maj-min\n",
      "g-mean: 0.435\n",
      "gmean-min: 0.321\n",
      "cmc tree SOUP all\n",
      "g-mean: 0.482\n",
      "gmean-min: 0.487\n",
      "cmc tree SOUP maj-min\n",
      "g-mean: 0.473\n",
      "gmean-min: 0.487\n",
      "cmc KNN None all\n",
      "g-mean: 0.469\n",
      "gmean-min: 0.42\n",
      "cmc KNN None maj-min\n",
      "g-mean: 0.469\n",
      "gmean-min: 0.42\n",
      "cmc KNN globalCS all\n",
      "g-mean: 0.445\n",
      "gmean-min: 0.523\n",
      "cmc KNN globalCS maj-min\n",
      "g-mean: 0.453\n",
      "gmean-min: 0.526\n",
      "cmc KNN SMOTE all\n",
      "g-mean: 0.468\n",
      "gmean-min: 0.49\n",
      "cmc KNN SMOTE maj-min\n",
      "g-mean: 0.463\n",
      "gmean-min: 0.49\n",
      "cmc KNN SOUP all\n",
      "g-mean: 0.486\n",
      "gmean-min: 0.553\n",
      "cmc KNN SOUP maj-min\n",
      "g-mean: 0.478\n",
      "gmean-min: 0.559\n",
      "cmc NB None all\n",
      "g-mean: 0.436\n",
      "gmean-min: 0.676\n",
      "cmc NB None maj-min\n",
      "g-mean: 0.436\n",
      "gmean-min: 0.676\n",
      "cmc NB globalCS all\n",
      "g-mean: 0.427\n",
      "gmean-min: 0.7\n",
      "cmc NB globalCS maj-min\n",
      "g-mean: 0.427\n",
      "gmean-min: 0.7\n",
      "cmc NB SMOTE all\n",
      "g-mean: 0.429\n",
      "gmean-min: 0.712\n",
      "cmc NB SMOTE maj-min\n",
      "g-mean: 0.42\n",
      "gmean-min: 0.712\n",
      "cmc NB SOUP all\n",
      "g-mean: 0.44\n",
      "gmean-min: 0.715\n",
      "cmc NB SOUP maj-min\n",
      "g-mean: 0.432\n",
      "gmean-min: 0.715\n",
      "dermatology tree None all\n",
      "g-mean: 0.932\n",
      "gmean-min: 0.9\n",
      "dermatology tree None maj-min\n",
      "g-mean: 0.932\n",
      "gmean-min: 0.9\n",
      "dermatology tree globalCS all\n",
      "g-mean: 0.93\n",
      "gmean-min: 0.9\n",
      "dermatology tree globalCS maj-min\n",
      "g-mean: 0.932\n",
      "gmean-min: 0.9\n",
      "dermatology tree SMOTE all\n",
      "g-mean: 0.956\n",
      "gmean-min: 0.9\n",
      "dermatology tree SMOTE maj-min\n",
      "g-mean: 0.932\n",
      "gmean-min: 0.9\n",
      "dermatology tree SOUP all\n",
      "g-mean: 0.939\n",
      "gmean-min: 0.95\n",
      "dermatology tree SOUP maj-min\n",
      "g-mean: 0.938\n",
      "gmean-min: 0.95\n",
      "dermatology KNN None all\n",
      "g-mean: 0.96\n",
      "gmean-min: 1.0\n",
      "dermatology KNN None maj-min\n",
      "g-mean: 0.96\n",
      "gmean-min: 1.0\n",
      "dermatology KNN globalCS all\n",
      "g-mean: 0.946\n",
      "gmean-min: 1.0\n",
      "dermatology KNN globalCS maj-min\n",
      "g-mean: 0.958\n",
      "gmean-min: 1.0\n",
      "dermatology KNN SMOTE all\n",
      "g-mean: 0.957\n",
      "gmean-min: 1.0\n",
      "dermatology KNN SMOTE maj-min\n",
      "g-mean: 0.958\n",
      "gmean-min: 1.0\n",
      "dermatology KNN SOUP all\n",
      "g-mean: 0.944\n",
      "gmean-min: 1.0\n",
      "dermatology KNN SOUP maj-min\n",
      "g-mean: 0.958\n",
      "gmean-min: 1.0\n",
      "dermatology NB None all\n",
      "g-mean: 0.914\n",
      "gmean-min: 1.0\n",
      "dermatology NB None maj-min\n",
      "g-mean: 0.914\n",
      "gmean-min: 1.0\n",
      "dermatology NB globalCS all\n",
      "g-mean: 0.914\n",
      "gmean-min: 1.0\n",
      "dermatology NB globalCS maj-min\n",
      "g-mean: 0.914\n",
      "gmean-min: 1.0\n",
      "dermatology NB SMOTE all\n",
      "g-mean: 0.914\n",
      "gmean-min: 1.0\n",
      "dermatology NB SMOTE maj-min\n",
      "g-mean: 0.914\n",
      "gmean-min: 1.0\n",
      "dermatology NB SOUP all\n",
      "g-mean: 0.919\n",
      "gmean-min: 1.0\n",
      "dermatology NB SOUP maj-min\n",
      "g-mean: 0.917\n",
      "gmean-min: 1.0\n",
      "flare tree None all\n",
      "g-mean: 0.431\n",
      "gmean-min: 0.282\n",
      "flare tree None maj-min\n",
      "g-mean: 0.431\n",
      "gmean-min: 0.282\n",
      "flare tree globalCS all\n",
      "g-mean: 0.5\n",
      "gmean-min: 0.359\n",
      "flare tree globalCS maj-min\n",
      "g-mean: 0.478\n",
      "gmean-min: 0.333\n",
      "flare tree SMOTE all\n",
      "g-mean: 0.348\n",
      "gmean-min: 0.17\n",
      "flare tree SMOTE maj-min\n",
      "g-mean: 0.352\n",
      "gmean-min: 0.171\n",
      "flare tree SOUP all\n",
      "g-mean: 0.557\n",
      "gmean-min: 0.521\n",
      "flare tree SOUP maj-min\n",
      "g-mean: 0.527\n",
      "gmean-min: 0.491\n",
      "flare KNN None all\n",
      "g-mean: 0.314\n",
      "gmean-min: 0.169\n",
      "flare KNN None maj-min\n",
      "g-mean: 0.314\n",
      "gmean-min: 0.169\n",
      "flare KNN globalCS all\n",
      "g-mean: 0.474\n",
      "gmean-min: 0.429\n",
      "flare KNN globalCS maj-min\n",
      "g-mean: 0.414\n",
      "gmean-min: 0.317\n",
      "flare KNN SMOTE all\n",
      "g-mean: 0.45\n",
      "gmean-min: 0.379\n",
      "flare KNN SMOTE maj-min\n",
      "g-mean: 0.381\n",
      "gmean-min: 0.289\n",
      "flare KNN SOUP all\n",
      "g-mean: 0.505\n",
      "gmean-min: 0.483\n",
      "flare KNN SOUP maj-min\n",
      "g-mean: 0.48\n",
      "gmean-min: 0.493\n",
      "flare NB None all\n",
      "g-mean: 0.22\n",
      "gmean-min: 0.471\n",
      "flare NB None maj-min\n",
      "g-mean: 0.22\n",
      "gmean-min: 0.471\n",
      "flare NB globalCS all\n",
      "g-mean: 0.216\n",
      "gmean-min: 0.546\n",
      "flare NB globalCS maj-min\n",
      "g-mean: 0.218\n",
      "gmean-min: 0.549\n",
      "flare NB SMOTE all\n",
      "g-mean: 0.253\n",
      "gmean-min: 0.489\n",
      "flare NB SMOTE maj-min\n",
      "g-mean: 0.225\n",
      "gmean-min: 0.491\n",
      "flare NB SOUP all\n",
      "g-mean: 0.259\n",
      "gmean-min: 0.423\n",
      "flare NB SOUP maj-min\n",
      "g-mean: 0.222\n",
      "gmean-min: 0.437\n",
      "glass tree None all\n",
      "g-mean: 0.449\n",
      "gmean-min: 0.33\n",
      "glass tree None maj-min\n",
      "g-mean: 0.449\n",
      "gmean-min: 0.33\n",
      "glass tree globalCS all\n",
      "g-mean: 0.589\n",
      "gmean-min: 0.512\n",
      "glass tree globalCS maj-min\n",
      "g-mean: 0.599\n",
      "gmean-min: 0.512\n",
      "glass tree SMOTE all\n",
      "g-mean: 0.641\n",
      "gmean-min: 0.582\n",
      "glass tree SMOTE maj-min\n",
      "g-mean: 0.644\n",
      "gmean-min: 0.582\n",
      "glass tree SOUP all\n",
      "g-mean: 0.588\n",
      "gmean-min: 0.56\n",
      "glass tree SOUP maj-min\n",
      "g-mean: 0.577\n",
      "gmean-min: 0.544\n",
      "glass KNN None all\n",
      "g-mean: 0.379\n",
      "gmean-min: 0.229\n",
      "glass KNN None maj-min\n",
      "g-mean: 0.379\n",
      "gmean-min: 0.229\n",
      "glass KNN globalCS all\n",
      "g-mean: 0.491\n",
      "gmean-min: 0.403\n",
      "glass KNN globalCS maj-min\n",
      "g-mean: 0.489\n",
      "gmean-min: 0.403\n",
      "glass KNN SMOTE all\n",
      "g-mean: 0.569\n",
      "gmean-min: 0.497\n",
      "glass KNN SMOTE maj-min\n",
      "g-mean: 0.563\n",
      "gmean-min: 0.497\n",
      "glass KNN SOUP all\n",
      "g-mean: 0.582\n",
      "gmean-min: 0.618\n",
      "glass KNN SOUP maj-min\n",
      "g-mean: 0.554\n",
      "gmean-min: 0.595\n",
      "glass NB None all\n",
      "g-mean: 0.211\n",
      "gmean-min: 0.216\n",
      "glass NB None maj-min\n",
      "g-mean: 0.211\n",
      "gmean-min: 0.216\n",
      "glass NB globalCS all\n",
      "g-mean: 0.319\n",
      "gmean-min: 0.424\n",
      "glass NB globalCS maj-min\n",
      "g-mean: 0.318\n",
      "gmean-min: 0.424\n",
      "glass NB SMOTE all\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g-mean: 0.351\n",
      "gmean-min: 0.396\n",
      "glass NB SMOTE maj-min\n",
      "g-mean: 0.348\n",
      "gmean-min: 0.396\n",
      "glass NB SOUP all\n",
      "g-mean: 0.263\n",
      "gmean-min: 0.389\n",
      "glass NB SOUP maj-min\n",
      "g-mean: 0.235\n",
      "gmean-min: 0.389\n",
      "hayes-roth tree None all\n",
      "g-mean: 0.848\n",
      "gmean-min: 0.862\n",
      "hayes-roth tree None maj-min\n",
      "g-mean: 0.848\n",
      "gmean-min: 0.862\n",
      "hayes-roth tree globalCS all\n",
      "g-mean: 0.848\n",
      "gmean-min: 0.862\n",
      "hayes-roth tree globalCS maj-min\n",
      "g-mean: 0.848\n",
      "gmean-min: 0.862\n",
      "hayes-roth tree SMOTE all\n",
      "g-mean: 0.848\n",
      "gmean-min: 0.862\n",
      "hayes-roth tree SMOTE maj-min\n",
      "g-mean: 0.848\n",
      "gmean-min: 0.862\n",
      "hayes-roth tree SOUP all\n",
      "g-mean: 0.792\n",
      "gmean-min: 0.862\n",
      "hayes-roth tree SOUP maj-min\n",
      "g-mean: 0.806\n",
      "gmean-min: 0.862\n",
      "hayes-roth KNN None all\n",
      "g-mean: 0.55\n",
      "gmean-min: 0.6\n",
      "hayes-roth KNN None maj-min\n",
      "g-mean: 0.55\n",
      "gmean-min: 0.6\n",
      "hayes-roth KNN globalCS all\n",
      "g-mean: 0.645\n",
      "gmean-min: 0.631\n",
      "hayes-roth KNN globalCS maj-min\n",
      "g-mean: 0.597\n",
      "gmean-min: 0.677\n",
      "hayes-roth KNN SMOTE all\n",
      "g-mean: 0.625\n",
      "gmean-min: 0.615\n",
      "hayes-roth KNN SMOTE maj-min\n",
      "g-mean: 0.584\n",
      "gmean-min: 0.615\n",
      "hayes-roth KNN SOUP all\n",
      "g-mean: 0.629\n",
      "gmean-min: 0.692\n",
      "hayes-roth KNN SOUP maj-min\n",
      "g-mean: 0.596\n",
      "gmean-min: 0.615\n",
      "hayes-roth NB None all\n",
      "g-mean: 0.733\n",
      "gmean-min: 0.754\n",
      "hayes-roth NB None maj-min\n",
      "g-mean: 0.733\n",
      "gmean-min: 0.754\n",
      "hayes-roth NB globalCS all\n",
      "g-mean: 0.712\n",
      "gmean-min: 0.723\n",
      "hayes-roth NB globalCS maj-min\n",
      "g-mean: 0.735\n",
      "gmean-min: 0.738\n",
      "hayes-roth NB SMOTE all\n",
      "g-mean: 0.715\n",
      "gmean-min: 0.754\n",
      "hayes-roth NB SMOTE maj-min\n",
      "g-mean: 0.733\n",
      "gmean-min: 0.754\n",
      "hayes-roth NB SOUP all\n",
      "g-mean: 0.667\n",
      "gmean-min: 0.738\n",
      "hayes-roth NB SOUP maj-min\n",
      "g-mean: 0.714\n",
      "gmean-min: 0.738\n",
      "new_ecoli tree None all\n",
      "g-mean: 0.697\n",
      "gmean-min: 0.604\n",
      "new_ecoli tree None maj-min\n",
      "g-mean: 0.697\n",
      "gmean-min: 0.604\n",
      "new_ecoli tree globalCS all\n",
      "g-mean: 0.607\n",
      "gmean-min: 0.523\n",
      "new_ecoli tree globalCS maj-min\n",
      "g-mean: 0.698\n",
      "gmean-min: 0.613\n",
      "new_ecoli tree SMOTE all\n",
      "g-mean: 0.713\n",
      "gmean-min: 0.634\n",
      "new_ecoli tree SMOTE maj-min\n",
      "g-mean: 0.729\n",
      "gmean-min: 0.661\n",
      "new_ecoli tree SOUP all\n",
      "g-mean: 0.757\n",
      "gmean-min: 0.774\n",
      "new_ecoli tree SOUP maj-min\n",
      "g-mean: 0.757\n",
      "gmean-min: 0.793\n",
      "new_ecoli KNN None all\n",
      "g-mean: 0.715\n",
      "gmean-min: 0.654\n",
      "new_ecoli KNN None maj-min\n",
      "g-mean: 0.715\n",
      "gmean-min: 0.654\n",
      "new_ecoli KNN globalCS all\n",
      "g-mean: 0.766\n",
      "gmean-min: 0.789\n",
      "new_ecoli KNN globalCS maj-min\n",
      "g-mean: 0.759\n",
      "gmean-min: 0.773\n",
      "new_ecoli KNN SMOTE all\n",
      "g-mean: 0.75\n",
      "gmean-min: 0.741\n",
      "new_ecoli KNN SMOTE maj-min\n",
      "g-mean: 0.736\n",
      "gmean-min: 0.722\n",
      "new_ecoli KNN SOUP all\n",
      "g-mean: 0.779\n",
      "gmean-min: 0.818\n",
      "new_ecoli KNN SOUP maj-min\n",
      "g-mean: 0.771\n",
      "gmean-min: 0.803\n",
      "new_ecoli NB None all\n",
      "g-mean: 0.43\n",
      "gmean-min: 0.514\n",
      "new_ecoli NB None maj-min\n",
      "g-mean: 0.43\n",
      "gmean-min: 0.514\n",
      "new_ecoli NB globalCS all\n",
      "g-mean: 0.469\n",
      "gmean-min: 0.566\n",
      "new_ecoli NB globalCS maj-min\n",
      "g-mean: 0.467\n",
      "gmean-min: 0.566\n",
      "new_ecoli NB SMOTE all\n",
      "g-mean: 0.475\n",
      "gmean-min: 0.56\n",
      "new_ecoli NB SMOTE maj-min\n",
      "g-mean: 0.472\n",
      "gmean-min: 0.56\n",
      "new_ecoli NB SOUP all\n",
      "g-mean: 0.567\n",
      "gmean-min: 0.537\n",
      "new_ecoli NB SOUP maj-min\n",
      "g-mean: 0.493\n",
      "gmean-min: 0.436\n",
      "new_led7digit tree None all\n",
      "g-mean: 0.745\n",
      "gmean-min: 0.652\n",
      "new_led7digit tree None maj-min\n",
      "g-mean: 0.745\n",
      "gmean-min: 0.652\n",
      "new_led7digit tree globalCS all\n",
      "g-mean: 0.764\n",
      "gmean-min: 0.723\n",
      "new_led7digit tree globalCS maj-min\n",
      "g-mean: 0.764\n",
      "gmean-min: 0.723\n",
      "new_led7digit tree SMOTE all\n",
      "g-mean: 0.745\n",
      "gmean-min: 0.67\n",
      "new_led7digit tree SMOTE maj-min\n",
      "g-mean: 0.753\n",
      "gmean-min: 0.67\n",
      "new_led7digit tree SOUP all\n",
      "g-mean: 0.769\n",
      "gmean-min: 0.785\n",
      "new_led7digit tree SOUP maj-min\n",
      "g-mean: 0.759\n",
      "gmean-min: 0.761\n",
      "new_led7digit KNN None all\n",
      "g-mean: 0.754\n",
      "gmean-min: 0.697\n",
      "new_led7digit KNN None maj-min\n",
      "g-mean: 0.754\n",
      "gmean-min: 0.697\n",
      "new_led7digit KNN globalCS all\n",
      "g-mean: 0.736\n",
      "gmean-min: 0.705\n",
      "new_led7digit KNN globalCS maj-min\n",
      "g-mean: 0.76\n",
      "gmean-min: 0.704\n",
      "new_led7digit KNN SMOTE all\n",
      "g-mean: 0.749\n",
      "gmean-min: 0.681\n",
      "new_led7digit KNN SMOTE maj-min\n",
      "g-mean: 0.742\n",
      "gmean-min: 0.659\n",
      "new_led7digit KNN SOUP all\n",
      "g-mean: 0.743\n",
      "gmean-min: 0.734\n",
      "new_led7digit KNN SOUP maj-min\n",
      "g-mean: 0.749\n",
      "gmean-min: 0.734\n",
      "new_led7digit NB None all\n",
      "g-mean: 0.76\n",
      "gmean-min: 0.695\n",
      "new_led7digit NB None maj-min\n",
      "g-mean: 0.76\n",
      "gmean-min: 0.695\n",
      "new_led7digit NB globalCS all\n",
      "g-mean: 0.757\n",
      "gmean-min: 0.684\n",
      "new_led7digit NB globalCS maj-min\n",
      "g-mean: 0.756\n",
      "gmean-min: 0.684\n",
      "new_led7digit NB SMOTE all\n",
      "g-mean: 0.755\n",
      "gmean-min: 0.676\n",
      "new_led7digit NB SMOTE maj-min\n",
      "g-mean: 0.755\n",
      "gmean-min: 0.676\n",
      "new_led7digit NB SOUP all\n",
      "g-mean: 0.756\n",
      "gmean-min: 0.77\n",
      "new_led7digit NB SOUP maj-min\n",
      "g-mean: 0.749\n",
      "gmean-min: 0.725\n",
      "new_vehicle tree None all\n",
      "g-mean: 0.901\n",
      "gmean-min: 0.898\n",
      "new_vehicle tree None maj-min\n",
      "g-mean: 0.901\n",
      "gmean-min: 0.898\n",
      "new_vehicle tree globalCS all\n",
      "g-mean: 0.893\n",
      "gmean-min: 0.895\n",
      "new_vehicle tree globalCS maj-min\n",
      "g-mean: 0.893\n",
      "gmean-min: 0.895\n",
      "new_vehicle tree SMOTE all\n",
      "g-mean: 0.906\n",
      "gmean-min: 0.903\n",
      "new_vehicle tree SMOTE maj-min\n",
      "g-mean: 0.906\n",
      "gmean-min: 0.903\n",
      "new_vehicle tree SOUP all\n",
      "g-mean: 0.873\n",
      "gmean-min: 0.935\n",
      "new_vehicle tree SOUP maj-min\n",
      "g-mean: 0.88\n",
      "gmean-min: 0.946\n",
      "new_vehicle KNN None all\n",
      "g-mean: 0.919\n",
      "gmean-min: 0.91\n",
      "new_vehicle KNN None maj-min\n",
      "g-mean: 0.919\n",
      "gmean-min: 0.91\n",
      "new_vehicle KNN globalCS all\n",
      "g-mean: 0.922\n",
      "gmean-min: 0.947\n",
      "new_vehicle KNN globalCS maj-min\n",
      "g-mean: 0.922\n",
      "gmean-min: 0.947\n",
      "new_vehicle KNN SMOTE all\n",
      "g-mean: 0.923\n",
      "gmean-min: 0.936\n",
      "new_vehicle KNN SMOTE maj-min\n",
      "g-mean: 0.927\n",
      "gmean-min: 0.943\n",
      "new_vehicle KNN SOUP all\n",
      "g-mean: 0.883\n",
      "gmean-min: 0.952\n",
      "new_vehicle KNN SOUP maj-min\n",
      "g-mean: 0.886\n",
      "gmean-min: 0.956\n",
      "new_vehicle NB None all\n",
      "g-mean: 0.422\n",
      "gmean-min: 0.329\n",
      "new_vehicle NB None maj-min\n",
      "g-mean: 0.422\n",
      "gmean-min: 0.329\n",
      "new_vehicle NB globalCS all\n",
      "g-mean: 0.462\n",
      "gmean-min: 0.382\n",
      "new_vehicle NB globalCS maj-min\n",
      "g-mean: 0.464\n",
      "gmean-min: 0.386\n",
      "new_vehicle NB SMOTE all\n",
      "g-mean: 0.48\n",
      "gmean-min: 0.407\n",
      "new_vehicle NB SMOTE maj-min\n",
      "g-mean: 0.486\n",
      "gmean-min: 0.413\n",
      "new_vehicle NB SOUP all\n",
      "g-mean: 0.52\n",
      "gmean-min: 0.472\n",
      "new_vehicle NB SOUP maj-min\n",
      "g-mean: 0.458\n",
      "gmean-min: 0.391\n",
      "new_winequality-red tree None all\n",
      "g-mean: 0.39\n",
      "gmean-min: 0.23\n",
      "new_winequality-red tree None maj-min\n",
      "g-mean: 0.39\n",
      "gmean-min: 0.23\n",
      "new_winequality-red tree globalCS all\n",
      "g-mean: 0.338\n",
      "gmean-min: 0.191\n",
      "new_winequality-red tree globalCS maj-min\n",
      "g-mean: 0.354\n",
      "gmean-min: 0.21\n",
      "new_winequality-red tree SMOTE all\n",
      "g-mean: 0.395\n",
      "gmean-min: 0.247\n",
      "new_winequality-red tree SMOTE maj-min\n",
      "g-mean: 0.407\n",
      "gmean-min: 0.261\n",
      "new_winequality-red tree SOUP all\n",
      "g-mean: 0.434\n",
      "gmean-min: 0.336\n",
      "new_winequality-red tree SOUP maj-min\n",
      "g-mean: 0.441\n",
      "gmean-min: 0.353\n",
      "new_winequality-red KNN None all\n",
      "g-mean: 0.299\n",
      "gmean-min: 0.164\n",
      "new_winequality-red KNN None maj-min\n",
      "g-mean: 0.299\n",
      "gmean-min: 0.164\n",
      "new_winequality-red KNN globalCS all\n",
      "g-mean: 0.449\n",
      "gmean-min: 0.383\n",
      "new_winequality-red KNN globalCS maj-min\n",
      "g-mean: 0.43\n",
      "gmean-min: 0.35\n",
      "new_winequality-red KNN SMOTE all\n",
      "g-mean: 0.436\n",
      "gmean-min: 0.374\n",
      "new_winequality-red KNN SMOTE maj-min\n",
      "g-mean: 0.423\n",
      "gmean-min: 0.348\n",
      "new_winequality-red KNN SOUP all\n",
      "g-mean: 0.454\n",
      "gmean-min: 0.496\n",
      "new_winequality-red KNN SOUP maj-min\n",
      "g-mean: 0.432\n",
      "gmean-min: 0.465\n",
      "new_winequality-red NB None all\n",
      "g-mean: 0.388\n",
      "gmean-min: 0.267\n",
      "new_winequality-red NB None maj-min\n",
      "g-mean: 0.388\n",
      "gmean-min: 0.267\n",
      "new_winequality-red NB globalCS all\n",
      "g-mean: 0.468\n",
      "gmean-min: 0.534\n",
      "new_winequality-red NB globalCS maj-min\n",
      "g-mean: 0.47\n",
      "gmean-min: 0.54\n",
      "new_winequality-red NB SMOTE all\n",
      "g-mean: 0.407\n",
      "gmean-min: 0.559\n",
      "new_winequality-red NB SMOTE maj-min\n",
      "g-mean: 0.43\n",
      "gmean-min: 0.56\n",
      "new_winequality-red NB SOUP all\n",
      "g-mean: 0.463\n",
      "gmean-min: 0.55\n",
      "new_winequality-red NB SOUP maj-min\n",
      "g-mean: 0.464\n",
      "gmean-min: 0.565\n",
      "new_yeast tree None all\n",
      "g-mean: 0.248\n",
      "gmean-min: 0.133\n",
      "new_yeast tree None maj-min\n",
      "g-mean: 0.248\n",
      "gmean-min: 0.133\n",
      "new_yeast tree globalCS all\n",
      "g-mean: 0.221\n",
      "gmean-min: 0.129\n",
      "new_yeast tree globalCS maj-min\n",
      "g-mean: 0.203\n",
      "gmean-min: 0.096\n",
      "new_yeast tree SMOTE all\n",
      "g-mean: 0.228\n",
      "gmean-min: 0.115\n",
      "new_yeast tree SMOTE maj-min\n",
      "g-mean: 0.252\n",
      "gmean-min: 0.137\n",
      "new_yeast tree SOUP all\n",
      "g-mean: 0.335\n",
      "gmean-min: 0.234\n",
      "new_yeast tree SOUP maj-min\n",
      "g-mean: 0.334\n",
      "gmean-min: 0.267\n",
      "new_yeast KNN None all\n",
      "g-mean: 0.272\n",
      "gmean-min: 0.152\n",
      "new_yeast KNN None maj-min\n",
      "g-mean: 0.272\n",
      "gmean-min: 0.152\n",
      "new_yeast KNN globalCS all\n",
      "g-mean: 0.44\n",
      "gmean-min: 0.389\n",
      "new_yeast KNN globalCS maj-min\n",
      "g-mean: 0.454\n",
      "gmean-min: 0.415\n",
      "new_yeast KNN SMOTE all\n",
      "g-mean: 0.403\n",
      "gmean-min: 0.356\n",
      "new_yeast KNN SMOTE maj-min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g-mean: 0.413\n",
      "gmean-min: 0.378\n",
      "new_yeast KNN SOUP all\n",
      "g-mean: 0.452\n",
      "gmean-min: 0.445\n",
      "new_yeast KNN SOUP maj-min\n",
      "g-mean: 0.447\n",
      "gmean-min: 0.456\n",
      "new_yeast NB None all\n",
      "g-mean: 0.091\n",
      "gmean-min: 0.237\n",
      "new_yeast NB None maj-min\n",
      "g-mean: 0.091\n",
      "gmean-min: 0.237\n",
      "new_yeast NB globalCS all\n",
      "g-mean: 0.082\n",
      "gmean-min: 0.243\n",
      "new_yeast NB globalCS maj-min\n",
      "g-mean: 0.082\n",
      "gmean-min: 0.237\n",
      "new_yeast NB SMOTE all\n",
      "g-mean: 0.095\n",
      "gmean-min: 0.241\n",
      "new_yeast NB SMOTE maj-min\n",
      "g-mean: 0.093\n",
      "gmean-min: 0.236\n",
      "new_yeast NB SOUP all\n",
      "g-mean: 0.111\n",
      "gmean-min: 0.246\n",
      "new_yeast NB SOUP maj-min\n",
      "g-mean: 0.097\n",
      "gmean-min: 0.242\n",
      "thyroid-newthyroid tree None all\n",
      "g-mean: 0.892\n",
      "gmean-min: 0.854\n",
      "thyroid-newthyroid tree None maj-min\n",
      "g-mean: 0.892\n",
      "gmean-min: 0.854\n",
      "thyroid-newthyroid tree globalCS all\n",
      "g-mean: 0.916\n",
      "gmean-min: 0.891\n",
      "thyroid-newthyroid tree globalCS maj-min\n",
      "g-mean: 0.916\n",
      "gmean-min: 0.891\n",
      "thyroid-newthyroid tree SMOTE all\n",
      "g-mean: 0.923\n",
      "gmean-min: 0.912\n",
      "thyroid-newthyroid tree SMOTE maj-min\n",
      "g-mean: 0.923\n",
      "gmean-min: 0.912\n",
      "thyroid-newthyroid tree SOUP all\n",
      "g-mean: 0.939\n",
      "gmean-min: 0.939\n",
      "thyroid-newthyroid tree SOUP maj-min\n",
      "g-mean: 0.939\n",
      "gmean-min: 0.939\n",
      "thyroid-newthyroid KNN None all\n",
      "g-mean: 0.903\n",
      "gmean-min: 0.869\n",
      "thyroid-newthyroid KNN None maj-min\n",
      "g-mean: 0.903\n",
      "gmean-min: 0.869\n",
      "thyroid-newthyroid KNN globalCS all\n",
      "g-mean: 0.93\n",
      "gmean-min: 0.914\n",
      "thyroid-newthyroid KNN globalCS maj-min\n",
      "g-mean: 0.93\n",
      "gmean-min: 0.914\n",
      "thyroid-newthyroid KNN SMOTE all\n",
      "g-mean: 0.952\n",
      "gmean-min: 0.946\n",
      "thyroid-newthyroid KNN SMOTE maj-min\n",
      "g-mean: 0.952\n",
      "gmean-min: 0.946\n",
      "thyroid-newthyroid KNN SOUP all\n",
      "g-mean: 0.925\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid KNN SOUP maj-min\n",
      "g-mean: 0.925\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB None all\n",
      "g-mean: 0.943\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB None maj-min\n",
      "g-mean: 0.943\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB globalCS all\n",
      "g-mean: 0.941\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB globalCS maj-min\n",
      "g-mean: 0.941\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB SMOTE all\n",
      "g-mean: 0.941\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB SMOTE maj-min\n",
      "g-mean: 0.941\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB SOUP all\n",
      "g-mean: 0.941\n",
      "gmean-min: 0.927\n",
      "thyroid-newthyroid NB SOUP maj-min\n",
      "g-mean: 0.941\n",
      "gmean-min: 0.927\n"
     ]
    }
   ],
   "source": [
    "for dataset_name, dataset_values in datasets.items():\n",
    "    X, y = dataset_values.data, dataset_values.target\n",
    "    \n",
    "    dataset_name = dataset_name[5:]\n",
    "\n",
    "    for col_idx, col in enumerate(X.T):\n",
    "        if len(set(col)) > 2:\n",
    "            scaled = (col - np.mean(col)) / np.std(col)\n",
    "            X[:, col_idx] = scaled\n",
    "            \n",
    "    for binary_classifier, preprocessing, preprocessing_between_strategy in methods:       \n",
    "        method = binary_classifier, preprocessing, preprocessing_between_strategy\n",
    "        print(dataset_name, binary_classifier, preprocessing, preprocessing_between_strategy)\n",
    "        \n",
    "        g_mean, g_mean_min = list(), list()\n",
    "\n",
    "        for i in range(10):\n",
    "            skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=i)\n",
    "\n",
    "            for train_index, test_index in skf.split(X, y):\n",
    "                X_train, X_test = X[train_index], X[test_index]\n",
    "                y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "                ovo = OVO(binary_classifier=binary_classifier, preprocessing=preprocessing, n_neighbors=3,\n",
    "                          preprocessing_between=preprocessing_between_strategy)\n",
    "                ovo.fit(X_train, y_train, minority_classes=minority[dataset_name])\n",
    "                y_pred = ovo.predict(X_test)\n",
    "\n",
    "                g_mean.append(geometric_mean_score(y_test, y_pred, correction=0.001))\n",
    "                g_mean_min.append(geometric_mean_score(y_test, y_pred, correction=0.001, labels=minority[dataset_name]))\n",
    "                \n",
    "        method = (binary_classifier, preprocessing, preprocessing_between_strategy)\n",
    "\n",
    "        results_g_mean[method].append(round(np.mean(g_mean), 3))\n",
    "        results_gmean_min[method].append(round(np.mean(g_mean_min), 3))\n",
    "\n",
    "        print('g-mean:', results_g_mean[method][-1])\n",
    "        print('gmean-min:', results_gmean_min[method][-1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gmean = pd.DataFrame(results_g_mean)\n",
    "df_gmean_min = pd.DataFrame(results_gmean_min)\n",
    "\n",
    "for df in (df_gmean, df_gmean_min):\n",
    "    df.set_index('dataset')\n",
    "    df.columns = pd.MultiIndex.from_tuples(\n",
    "        [('classifier', 'preprocessing', 'preprocessing between')] + [(str(m[0]), str(m[1]), str(m[2])) for m in methods])\n",
    "    \n",
    "import os\n",
    "import datetime\n",
    "    \n",
    "current_date = str(datetime.datetime.today())[:-7].replace(' ', '_').replace(':','_')\n",
    "\n",
    "directory='OVO-' + current_date\n",
    "os.makedirs(directory)\n",
    "\n",
    "df_gmean.to_csv(f'./{directory}/ovo_kfold_gmean.csv', index=False)\n",
    "df_gmean_min.to_csv(f'./{directory}/ovo_kfold_gmean_min.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## G-mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>classifier</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tree</th>\n",
       "      <th colspan=\"8\" halign=\"left\">KNN</th>\n",
       "      <th colspan=\"8\" halign=\"left\">NB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>preprocessing</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">globalCS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SMOTE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SOUP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">globalCS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SMOTE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SOUP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">globalCS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SMOTE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SOUP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>preprocessing between</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>arff\\1czysty-cut</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>arff\\2delikatne-cut</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>arff\\3mocniej-cut</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>arff\\4delikatne-bezover-cut</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>arff\\balance-scale</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>arff\\car</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>arff\\cleveland</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>arff\\cleveland_v2</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>arff\\cmc</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>arff\\dermatology</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>arff\\flare</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>arff\\glass</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>arff\\hayes-roth</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>arff\\new_ecoli</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>arff\\new_led7digit</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>arff\\new_vehicle</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>arff\\new_winequality-red</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>arff\\new_yeast</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.409</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>arff\\thyroid-newthyroid</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     classifier   tree                                  \\\n",
       "                  preprocessing   None         globalCS          SMOTE   \n",
       "          preprocessing between    all maj-min      all maj-min    all   \n",
       "0              arff\\1czysty-cut  0.943   0.943    0.944   0.944  0.953   \n",
       "1           arff\\2delikatne-cut  0.703   0.703    0.707   0.706  0.738   \n",
       "2             arff\\3mocniej-cut  0.486   0.486    0.485   0.484  0.504   \n",
       "3   arff\\4delikatne-bezover-cut  0.772   0.772    0.770   0.770  0.798   \n",
       "4            arff\\balance-scale  0.225   0.225    0.183   0.183  0.336   \n",
       "5                      arff\\car  0.933   0.933    0.958   0.954  0.959   \n",
       "6                arff\\cleveland  0.096   0.096    0.066   0.085  0.083   \n",
       "7             arff\\cleveland_v2  0.073   0.073    0.058   0.060  0.093   \n",
       "8                      arff\\cmc  0.431   0.431    0.429   0.432  0.430   \n",
       "9              arff\\dermatology  0.932   0.932    0.932   0.932  0.933   \n",
       "10                   arff\\flare  0.416   0.416    0.511   0.491  0.399   \n",
       "11                   arff\\glass  0.487   0.487    0.490   0.490  0.543   \n",
       "12              arff\\hayes-roth  0.841   0.841    0.843   0.843  0.840   \n",
       "13               arff\\new_ecoli  0.723   0.723    0.694   0.709  0.720   \n",
       "14           arff\\new_led7digit  0.755   0.755    0.767   0.767  0.764   \n",
       "15             arff\\new_vehicle  0.900   0.900    0.895   0.897  0.901   \n",
       "16     arff\\new_winequality-red  0.355   0.355    0.324   0.334  0.401   \n",
       "17               arff\\new_yeast  0.253   0.253    0.217   0.221  0.272   \n",
       "18      arff\\thyroid-newthyroid  0.903   0.903    0.903   0.903  0.901   \n",
       "\n",
       "                             KNN                                          \\\n",
       "             SOUP           None         globalCS          SMOTE           \n",
       "   maj-min    all maj-min    all maj-min      all maj-min    all maj-min   \n",
       "0    0.953  0.954   0.954  0.961   0.961    0.968   0.968  0.969   0.969   \n",
       "1    0.734  0.802   0.799  0.684   0.684    0.734   0.726  0.734   0.729   \n",
       "2    0.506  0.613   0.594  0.440   0.440    0.510   0.499  0.496   0.499   \n",
       "3    0.798  0.890   0.890  0.786   0.786    0.819   0.819  0.829   0.829   \n",
       "4    0.336  0.645   0.645  0.120   0.120    0.130   0.121  0.276   0.276   \n",
       "5    0.955  0.922   0.932  0.404   0.404    0.761   0.746  0.326   0.330   \n",
       "6    0.090  0.114   0.102  0.112   0.112    0.178   0.116  0.177   0.118   \n",
       "7    0.085  0.142   0.121  0.053   0.053    0.216   0.146  0.178   0.155   \n",
       "8    0.428  0.480   0.467  0.463   0.463    0.445   0.445  0.461   0.462   \n",
       "9    0.932  0.937   0.941  0.955   0.955    0.948   0.955  0.946   0.955   \n",
       "10   0.400  0.543   0.530  0.338   0.338    0.486   0.452  0.460   0.435   \n",
       "11   0.544  0.569   0.583  0.357   0.357    0.576   0.576  0.578   0.580   \n",
       "12   0.840  0.800   0.830  0.568   0.568    0.658   0.599  0.643   0.590   \n",
       "13   0.724  0.745   0.745  0.750   0.750    0.762   0.776  0.764   0.769   \n",
       "14   0.763  0.759   0.753  0.751   0.751    0.764   0.757  0.772   0.768   \n",
       "15   0.903  0.882   0.887  0.923   0.923    0.922   0.921  0.921   0.922   \n",
       "16   0.400  0.453   0.457  0.260   0.260    0.463   0.443  0.461   0.444   \n",
       "17   0.270  0.346   0.342  0.269   0.269    0.409   0.417  0.424   0.430   \n",
       "18   0.901  0.927   0.927  0.905   0.905    0.947   0.947  0.941   0.941   \n",
       "\n",
       "                      NB                                                 \\\n",
       "     SOUP           None         globalCS          SMOTE           SOUP   \n",
       "      all maj-min    all maj-min      all maj-min    all maj-min    all   \n",
       "0   0.954   0.954  0.714   0.714    0.935   0.935  0.936   0.936  0.931   \n",
       "1   0.805   0.786  0.476   0.476    0.820   0.815  0.825   0.821  0.813   \n",
       "2   0.585   0.558  0.010   0.010    0.630   0.614  0.638   0.622  0.609   \n",
       "3   0.890   0.888  0.717   0.717    0.891   0.891  0.896   0.896  0.881   \n",
       "4   0.699   0.705  0.099   0.099    0.744   0.744  0.713   0.713  0.650   \n",
       "5   0.706   0.704  0.857   0.857    0.857   0.857  0.015   0.030  0.857   \n",
       "6   0.146   0.121  0.068   0.068    0.059   0.065  0.083   0.071  0.096   \n",
       "7   0.240   0.170  0.062   0.062    0.069   0.059  0.145   0.069  0.080   \n",
       "8   0.480   0.477  0.443   0.443    0.424   0.431  0.423   0.424  0.435   \n",
       "9   0.945   0.953  0.908   0.908    0.908   0.908  0.908   0.908  0.918   \n",
       "10  0.491   0.468  0.214   0.214    0.225   0.225  0.230   0.222  0.302   \n",
       "11  0.599   0.584  0.219   0.219    0.278   0.276  0.363   0.360  0.291   \n",
       "12  0.645   0.600  0.710   0.710    0.699   0.728  0.687   0.719  0.653   \n",
       "13  0.790   0.794  0.406   0.406    0.431   0.422  0.482   0.467  0.434   \n",
       "14  0.754   0.756  0.737   0.737    0.737   0.737  0.731   0.731  0.747   \n",
       "15  0.878   0.877  0.433   0.433    0.475   0.476  0.484   0.489  0.516   \n",
       "16  0.458   0.452  0.379   0.379    0.446   0.446  0.410   0.421  0.452   \n",
       "17  0.448   0.440  0.085   0.085    0.069   0.070  0.102   0.102  0.103   \n",
       "18  0.942   0.942  0.936   0.936    0.942   0.942  0.942   0.942  0.941   \n",
       "\n",
       "            \n",
       "            \n",
       "   maj-min  \n",
       "0    0.931  \n",
       "1    0.807  \n",
       "2    0.596  \n",
       "3    0.880  \n",
       "4    0.650  \n",
       "5    0.857  \n",
       "6    0.073  \n",
       "7    0.065  \n",
       "8    0.434  \n",
       "9    0.912  \n",
       "10   0.218  \n",
       "11   0.296  \n",
       "12   0.708  \n",
       "13   0.376  \n",
       "14   0.749  \n",
       "15   0.460  \n",
       "16   0.452  \n",
       "17   0.094  \n",
       "18   0.941  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = 100\n",
    "pd.options.display.max_rows = 4000\n",
    "df_gmean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## G-mean for minority"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>classifier</th>\n",
       "      <th colspan=\"8\" halign=\"left\">tree</th>\n",
       "      <th colspan=\"8\" halign=\"left\">KNN</th>\n",
       "      <th colspan=\"8\" halign=\"left\">NB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>preprocessing</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">globalCS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SMOTE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SOUP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">globalCS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SMOTE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SOUP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">globalCS</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SMOTE</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SOUP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>preprocessing between</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "      <th>all</th>\n",
       "      <th>maj-min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>arff\\1czysty-cut</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.978</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>arff\\2delikatne-cut</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>arff\\3mocniej-cut</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>arff\\4delikatne-bezover-cut</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>arff\\balance-scale</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>arff\\car</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>arff\\cleveland</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>arff\\cleveland_v2</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>arff\\cmc</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>arff\\dermatology</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.995</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.985</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>arff\\flare</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>arff\\glass</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>arff\\hayes-roth</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>arff\\new_ecoli</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>arff\\new_led7digit</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>arff\\new_vehicle</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>arff\\new_winequality-red</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>arff\\new_yeast</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>arff\\thyroid-newthyroid</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     classifier   tree                                  \\\n",
       "                  preprocessing   None         globalCS          SMOTE   \n",
       "          preprocessing between    all maj-min      all maj-min    all   \n",
       "0              arff\\1czysty-cut  0.930   0.930    0.932   0.932  0.946   \n",
       "1           arff\\2delikatne-cut  0.620   0.620    0.623   0.622  0.673   \n",
       "2             arff\\3mocniej-cut  0.371   0.371    0.369   0.368  0.420   \n",
       "3   arff\\4delikatne-bezover-cut  0.725   0.725    0.719   0.719  0.769   \n",
       "4            arff\\balance-scale  0.048   0.048    0.036   0.036  0.103   \n",
       "5                      arff\\car  0.899   0.899    0.936   0.937  0.939   \n",
       "6                arff\\cleveland  0.061   0.061    0.038   0.052  0.052   \n",
       "7             arff\\cleveland_v2  0.038   0.038    0.026   0.027  0.052   \n",
       "8                      arff\\cmc  0.329   0.329    0.332   0.332  0.325   \n",
       "9              arff\\dermatology  0.860   0.860    0.860   0.860  0.860   \n",
       "10                   arff\\flare  0.260   0.260    0.395   0.367  0.234   \n",
       "11                   arff\\glass  0.383   0.383    0.390   0.390  0.463   \n",
       "12              arff\\hayes-roth  0.829   0.829    0.825   0.825  0.828   \n",
       "13               arff\\new_ecoli  0.651   0.651    0.618   0.636  0.658   \n",
       "14           arff\\new_led7digit  0.684   0.684    0.734   0.734  0.712   \n",
       "15             arff\\new_vehicle  0.896   0.896    0.893   0.896  0.902   \n",
       "16     arff\\new_winequality-red  0.202   0.202    0.175   0.183  0.255   \n",
       "17               arff\\new_yeast  0.144   0.144    0.119   0.120  0.171   \n",
       "18      arff\\thyroid-newthyroid  0.875   0.875    0.877   0.877  0.878   \n",
       "\n",
       "                             KNN                                          \\\n",
       "             SOUP           None         globalCS          SMOTE           \n",
       "   maj-min    all maj-min    all maj-min      all maj-min    all maj-min   \n",
       "0    0.946  0.970   0.970  0.956   0.956    0.979   0.979  0.978   0.978   \n",
       "1    0.669  0.794   0.790  0.590   0.590    0.683   0.673  0.680   0.674   \n",
       "2    0.422  0.590   0.560  0.312   0.312    0.429   0.415  0.420   0.423   \n",
       "3    0.769  0.954   0.954  0.734   0.734    0.821   0.821  0.833   0.833   \n",
       "4    0.103  0.568   0.568  0.009   0.009    0.018   0.013  0.078   0.078   \n",
       "5    0.939  0.939   0.946  0.239   0.239    0.830   0.844  0.165   0.170   \n",
       "6    0.055  0.080   0.068  0.075   0.075    0.136   0.082  0.136   0.084   \n",
       "7    0.046  0.094   0.073  0.025   0.025    0.157   0.097  0.125   0.108   \n",
       "8    0.325  0.494   0.494  0.398   0.398    0.514   0.514  0.489   0.489   \n",
       "9    0.860  0.905   0.910  0.995   0.995    1.000   1.000  1.000   1.000   \n",
       "10   0.242  0.518   0.504  0.166   0.166    0.427   0.376  0.349   0.327   \n",
       "11   0.461  0.549   0.565  0.218   0.218    0.515   0.519  0.525   0.528   \n",
       "12   0.828  0.818   0.828  0.586   0.586    0.583   0.591  0.568   0.571   \n",
       "13   0.666  0.764   0.778  0.695   0.695    0.786   0.806  0.758   0.768   \n",
       "14   0.708  0.770   0.765  0.676   0.676    0.729   0.719  0.710   0.698   \n",
       "15   0.906  0.943   0.951  0.916   0.916    0.946   0.944  0.934   0.936   \n",
       "16   0.259  0.365   0.380  0.127   0.127    0.396   0.364  0.413   0.376   \n",
       "17   0.165  0.256   0.268  0.146   0.146    0.345   0.356  0.377   0.390   \n",
       "18   0.878  0.923   0.923  0.871   0.871    0.938   0.938  0.933   0.933   \n",
       "\n",
       "                      NB                                                 \\\n",
       "     SOUP           None         globalCS          SMOTE           SOUP   \n",
       "      all maj-min    all maj-min      all maj-min    all maj-min    all   \n",
       "0   1.000   1.000  0.607   0.607    0.993   0.993  0.991   0.991  0.995   \n",
       "1   0.831   0.802  0.330   0.330    0.852   0.843  0.847   0.839  0.855   \n",
       "2   0.608   0.577  0.001   0.001    0.703   0.668  0.698   0.665  0.702   \n",
       "3   0.988   0.989  0.620   0.620    0.969   0.969  0.965   0.965  0.975   \n",
       "4   0.717   0.732  0.001   0.001    0.796   0.796  0.698   0.698  0.796   \n",
       "5   0.846   0.868  0.931   0.931    0.931   0.931  0.001   0.001  0.931   \n",
       "6   0.111   0.088  0.053   0.053    0.046   0.055  0.061   0.050  0.073   \n",
       "7   0.185   0.123  0.041   0.041    0.050   0.041  0.108   0.041  0.051   \n",
       "8   0.538   0.537  0.683   0.683    0.704   0.704  0.718   0.718  0.714   \n",
       "9   0.995   0.995  0.985   0.985    0.985   0.985  0.985   0.985  1.000   \n",
       "10  0.473   0.457  0.390   0.390    0.458   0.451  0.391   0.383  0.457   \n",
       "11  0.635   0.627  0.203   0.203    0.384   0.384  0.528   0.528  0.557   \n",
       "12  0.585   0.589  0.688   0.688    0.671   0.677  0.668   0.675  0.646   \n",
       "13  0.822   0.828  0.485   0.485    0.539   0.520  0.545   0.520  0.373   \n",
       "14  0.764   0.762  0.686   0.686    0.688   0.690  0.664   0.664  0.724   \n",
       "15  0.955   0.953  0.344   0.344    0.399   0.400  0.411   0.417  0.469   \n",
       "16  0.497   0.491  0.266   0.266    0.496   0.498  0.557   0.544  0.536   \n",
       "17  0.423   0.432  0.246   0.246    0.240   0.247  0.235   0.243  0.241   \n",
       "18  0.949   0.949  0.914   0.914    0.928   0.928  0.928   0.928  0.928   \n",
       "\n",
       "            \n",
       "            \n",
       "   maj-min  \n",
       "0    0.995  \n",
       "1    0.845  \n",
       "2    0.669  \n",
       "3    0.975  \n",
       "4    0.796  \n",
       "5    0.931  \n",
       "6    0.054  \n",
       "7    0.042  \n",
       "8    0.714  \n",
       "9    1.000  \n",
       "10   0.421  \n",
       "11   0.575  \n",
       "12   0.657  \n",
       "13   0.302  \n",
       "14   0.712  \n",
       "15   0.395  \n",
       "16   0.552  \n",
       "17   0.249  \n",
       "18   0.928  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gmean_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks_gmean = dict()\n",
    "ranks_gmean_min = dict()\n",
    "\n",
    "for results, ranks in zip((results_g_mean, results_gmean_min), (ranks_gmean, ranks_gmean_min)):\n",
    "    for ds_idx, ds in enumerate(datasets_names):\n",
    "        ranks[ds] = dict()\n",
    "        for bc in binary_classifiers:\n",
    "            ranks[ds][bc] = rankdata(\n",
    "                [-results[method][ds_idx] for method in methods if method[0]==bc])\n",
    "\n",
    "\n",
    "avg_ranks_gmean=dict()\n",
    "avg_ranks_gmean_min =dict()\n",
    "\n",
    "for avg_ranks, ranks in zip((avg_ranks_gmean, avg_ranks_gmean_min), (ranks_gmean, ranks_gmean_min)):\n",
    "    for bc in binary_classifiers:\n",
    "        avg_ranks[bc]=dict()\n",
    "        for method_idx, method in enumerate([m for m in methods if m[0]==bc]):\n",
    "            avg_ranks[bc][method] = round(np.mean([ranks[ds][bc][method_idx] for ds in datasets_names]),3)\n",
    "            \n",
    "for avg_ranks in (avg_ranks_gmean, avg_ranks_gmean_min):\n",
    "    for bc in binary_classifiers:\n",
    "        for method in methods:\n",
    "            if method[0]==bc:\n",
    "                avg_ranks[bc][str(method[1:])] = avg_ranks[bc][method]\n",
    "                avg_ranks[bc].pop(method)\n",
    "\n",
    "df_gmean_avg_ranks = pd.DataFrame(avg_ranks_gmean).T\n",
    "df_gmean_min_avg_ranks = pd.DataFrame(avg_ranks_gmean_min).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean ranks in G-mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tree</th>\n",
       "      <th>KNN</th>\n",
       "      <th>NB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>('SOUP', 'all')</td>\n",
       "      <td>2.605</td>\n",
       "      <td>2.895</td>\n",
       "      <td>3.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('SOUP', 'maj-min')</td>\n",
       "      <td>2.816</td>\n",
       "      <td>3.474</td>\n",
       "      <td>4.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('SMOTE', 'all')</td>\n",
       "      <td>4.184</td>\n",
       "      <td>4.105</td>\n",
       "      <td>3.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('SMOTE', 'maj-min')</td>\n",
       "      <td>4.342</td>\n",
       "      <td>4.237</td>\n",
       "      <td>3.605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(None, 'all')</td>\n",
       "      <td>5.421</td>\n",
       "      <td>6.500</td>\n",
       "      <td>6.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(None, 'maj-min')</td>\n",
       "      <td>5.421</td>\n",
       "      <td>6.500</td>\n",
       "      <td>6.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('globalCS', 'maj-min')</td>\n",
       "      <td>5.447</td>\n",
       "      <td>4.737</td>\n",
       "      <td>4.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('globalCS', 'all')</td>\n",
       "      <td>5.763</td>\n",
       "      <td>3.553</td>\n",
       "      <td>4.342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          tree    KNN     NB\n",
       "('SOUP', 'all')          2.605  2.895  3.342\n",
       "('SOUP', 'maj-min')      2.816  3.474  4.553\n",
       "('SMOTE', 'all')         4.184  4.105  3.447\n",
       "('SMOTE', 'maj-min')     4.342  4.237  3.605\n",
       "(None, 'all')            5.421  6.500  6.184\n",
       "(None, 'maj-min')        5.421  6.500  6.184\n",
       "('globalCS', 'maj-min')  5.447  4.737  4.342\n",
       "('globalCS', 'all')      5.763  3.553  4.342"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gmean_avg_ranks.T.sort_values('tree')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean ranks in G-mean minority"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tree</th>\n",
       "      <th>KNN</th>\n",
       "      <th>NB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>('SOUP', 'maj-min')</td>\n",
       "      <td>1.605</td>\n",
       "      <td>2.132</td>\n",
       "      <td>3.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('SOUP', 'all')</td>\n",
       "      <td>1.921</td>\n",
       "      <td>1.921</td>\n",
       "      <td>2.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('SMOTE', 'maj-min')</td>\n",
       "      <td>4.211</td>\n",
       "      <td>4.842</td>\n",
       "      <td>5.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('SMOTE', 'all')</td>\n",
       "      <td>4.289</td>\n",
       "      <td>4.658</td>\n",
       "      <td>4.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(None, 'all')</td>\n",
       "      <td>5.842</td>\n",
       "      <td>7.132</td>\n",
       "      <td>6.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(None, 'maj-min')</td>\n",
       "      <td>5.842</td>\n",
       "      <td>7.132</td>\n",
       "      <td>6.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('globalCS', 'maj-min')</td>\n",
       "      <td>6.000</td>\n",
       "      <td>4.395</td>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('globalCS', 'all')</td>\n",
       "      <td>6.289</td>\n",
       "      <td>3.789</td>\n",
       "      <td>4.053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          tree    KNN     NB\n",
       "('SOUP', 'maj-min')      1.605  2.132  3.395\n",
       "('SOUP', 'all')          1.921  1.921  2.868\n",
       "('SMOTE', 'maj-min')     4.211  4.842  5.079\n",
       "('SMOTE', 'all')         4.289  4.658  4.132\n",
       "(None, 'all')            5.842  7.132  6.237\n",
       "(None, 'maj-min')        5.842  7.132  6.237\n",
       "('globalCS', 'maj-min')  6.000  4.395  4.000\n",
       "('globalCS', 'all')      6.289  3.789  4.053"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gmean_min_avg_ranks.T.sort_values('tree')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gmean</th>\n",
       "      <th>gmean_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>('KNN', 'SOUP', 'all')</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', 'SOUP', 'all')</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', 'SOUP', 'maj-min')</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('KNN', 'SOUP', 'maj-min')</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('KNN', 'globalCS', 'all')</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', 'SMOTE', 'all')</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', 'SMOTE', 'maj-min')</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('KNN', 'SMOTE', 'all')</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('KNN', 'globalCS', 'maj-min')</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('KNN', 'SMOTE', 'maj-min')</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', None, 'maj-min')</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', None, 'all')</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', 'globalCS', 'maj-min')</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('tree', 'globalCS', 'all')</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', 'globalCS', 'all')</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', 'globalCS', 'maj-min')</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', 'SOUP', 'all')</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', 'SOUP', 'maj-min')</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('KNN', None, 'maj-min')</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('KNN', None, 'all')</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', 'SMOTE', 'all')</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', 'SMOTE', 'maj-min')</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', None, 'all')</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>('NB', None, 'maj-min')</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 gmean  gmean_min\n",
       "('KNN', 'SOUP', 'all')            0.66       0.68\n",
       "('tree', 'SOUP', 'all')           0.66       0.65\n",
       "('tree', 'SOUP', 'maj-min')       0.66       0.65\n",
       "('KNN', 'SOUP', 'maj-min')        0.64       0.67\n",
       "('KNN', 'globalCS', 'all')        0.62       0.59\n",
       "('tree', 'SMOTE', 'all')          0.61       0.54\n",
       "('tree', 'SMOTE', 'maj-min')      0.61       0.54\n",
       "('KNN', 'SMOTE', 'all')           0.60       0.55\n",
       "('KNN', 'globalCS', 'maj-min')    0.60       0.58\n",
       "('KNN', 'SMOTE', 'maj-min')       0.59       0.55\n",
       "('tree', None, 'maj-min')         0.59       0.52\n",
       "('tree', None, 'all')             0.59       0.52\n",
       "('tree', 'globalCS', 'maj-min')   0.59       0.52\n",
       "('tree', 'globalCS', 'all')       0.59       0.52\n",
       "('NB', 'globalCS', 'all')         0.56       0.62\n",
       "('NB', 'globalCS', 'maj-min')     0.56       0.62\n",
       "('NB', 'SOUP', 'all')             0.56       0.63\n",
       "('NB', 'SOUP', 'maj-min')         0.55       0.62\n",
       "('KNN', None, 'maj-min')          0.53       0.46\n",
       "('KNN', None, 'all')              0.53       0.46\n",
       "('NB', 'SMOTE', 'all')            0.53       0.58\n",
       "('NB', 'SMOTE', 'maj-min')        0.52       0.57\n",
       "('NB', None, 'all')               0.45       0.45\n",
       "('NB', None, 'maj-min')           0.45       0.45"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_results = dict()\n",
    "for m in methods:\n",
    "    mean_results[str(m)] = [round(np.mean(results_g_mean[m]),2), round(np.mean(results_gmean_min[m]),2)]\n",
    "\n",
    "df_mean = pd.DataFrame(mean_results).T\n",
    "df_mean.columns = 'gmean gmean_min'.split()\n",
    "\n",
    "df_mean.sort_values(['gmean'], ascending=[False])"
   ]
  }
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