{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pretty-performer",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-02 18:03:59.214143: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-05-02 18:03:59.214163: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "from library.analysis import testSets, generators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaging-warehouse",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "crazy-taxation",
   "metadata": {},
   "outputs": [],
   "source": [
    "kScore = \"cohens kappa score\"\n",
    "f1Score = \"f1 score\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extensive-future",
   "metadata": {},
   "source": [
    "# Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "warming-department",
   "metadata": {},
   "outputs": [],
   "source": [
    "ignoreSet = [\"ozone_level\", \"yeast_me2\"]\n",
    "\n",
    "gans = generators.keys()\n",
    "algs = [\"LR\", \"GB\", \"KNN\", \"GAN\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-greek",
   "metadata": {},
   "source": [
    "# ProWRAS Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "needed-birmingham",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset  = [\n",
    "    \"abalone9-18\",\n",
    "    \"abalone_17_vs_7_8_9_10\",\n",
    "    \"car-vgood\",\"car_good\",\n",
    "    \"flare-F\",\n",
    "    \"hypothyroid\",\n",
    "    \"kddcup-guess_passwd_vs_satan\",\n",
    "    \"kr-vs-k-three_vs_eleven\",\n",
    "    \"kr-vs-k-zero-one_vs_draw\",\n",
    "    \"shuttle-2_vs_5\",\n",
    "    \"winequality-red-4\",\n",
    "    \"yeast4\",\n",
    "    \"yeast5\",\n",
    "    \"yeast6\",\n",
    "    \"ozone_level\",\n",
    "    \"yeast_me2\",\n",
    "    \"Average\"\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "brilliant-phoenix",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_ProWRAS_f1 = [0.384,0.347,0.818,0.641,0.301,0.553,1.0,0.94,0.9,1.0,0.141,0.308,0.714,0.545,0.556,0.339,0.538]\n",
    "knn_ProWRAS_k = [0.35,0.328,0.81,0.622,0.263,0.528,1.0,0.938,0.896,1.0,0.093,0.268,0.704,0.531,0.526,0.305,0.515]\n",
    "\n",
    "lr_ProWRAS_f1 = [0.488,0.315,0.407,0.103,0.341,0.446,0.99,0.928,0.853,1.0,0.158,0.308,0.591,0.326,0.347,0.295,0.472]\n",
    "lr_ProWRAS_k = [0.446,0.287,0.371,0.033,0.3,0.407,0.99,0.926,0.847,1.0,0.119,0.268,0.574,0.3,0.319,0.254,0.441]\n",
    "\n",
    "gb_ProWRAS_f1 = [0.385,0.335,0.959,0.863,0.320,0.803,0.998,0.995,0.969,1.0,0.156,0.335,0.735,0.514,0.329,0.225,0.600]\n",
    "gb_ProWRAS_k = [0.341,0.310,0.957,0.857,0.291,0.794,0.998,0.995,0.967,1.0,0.115,0.303,0.726,0.501,0.303,0.328,0.589]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ordered-roman",
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic = { \"ProWRAS\": {} }\n",
    "for (n, f1, k) in zip(dataset, lr_ProWRAS_f1, lr_ProWRAS_k):\n",
    "    if n in ignoreSet:\n",
    "        continue\n",
    "    \n",
    "    if n not in statistic[\"ProWRAS\"]:\n",
    "        statistic[\"ProWRAS\"][n] = {}\n",
    "    \n",
    "    statistic[\"ProWRAS\"][n][\"LR\"] = { kScore: k, f1Score: f1 }\n",
    "\n",
    "for (n, f1, k) in zip(dataset, gb_ProWRAS_f1, gb_ProWRAS_k):\n",
    "    if n in ignoreSet:\n",
    "        continue\n",
    "    \n",
    "    if n not in statistic[\"ProWRAS\"]:\n",
    "        statistic[\"ProWRAS\"][n] = {}\n",
    "    \n",
    "    statistic[\"ProWRAS\"][n][\"GB\"] = { kScore: k, f1Score: f1 }\n",
    "\n",
    "    \n",
    "for (n, f1, k) in zip(dataset, knn_ProWRAS_f1, knn_ProWRAS_k):\n",
    "    if n in ignoreSet:\n",
    "        continue\n",
    "    \n",
    "    if n not in statistic[\"ProWRAS\"]:\n",
    "        statistic[\"ProWRAS\"][n] = {}\n",
    "    \n",
    "    statistic[\"ProWRAS\"][n][\"KNN\"] = { kScore: k, f1Score: f1 }\n",
    "    \n",
    "dataset = list(filter(lambda n: n not in ignoreSet, dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selective-connecticut",
   "metadata": {},
   "source": [
    "# Load data from CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "intended-watts",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDiagnoseData(ganType, datasetName):\n",
    "    fileName = f\"data_result/{ganType}/folding_{datasetName}.csv\"\n",
    "    r = {}\n",
    "    try:\n",
    "        with open(fileName) as f:\n",
    "            newBlock = True\n",
    "            n = \"\"\n",
    "            for line in f:\n",
    "                line = line.strip()\n",
    "                if newBlock:\n",
    "                    n = line\n",
    "                    newBlock = False\n",
    "                elif line == \"---\":\n",
    "                    newBlock = True\n",
    "                else:\n",
    "                    parts = line.split(\";\")\n",
    "                    if parts[0] == \"avg\":\n",
    "                        r[n] = { f1Score: float(parts[5]), kScore: float(parts[6]) }\n",
    "    except FileNotFoundError as e:\n",
    "        print(f\"Missing file: {fileName}\")\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "classical-rescue",
   "metadata": {},
   "outputs": [],
   "source": [
    "for gan in gans:\n",
    "    if gan not in statistic:\n",
    "        statistic[gan] = {}\n",
    "    \n",
    "    for ds in dataset:\n",
    "        if ds != \"Average\":\n",
    "            statistic[gan][ds] = loadDiagnoseData(gan, ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "unable-entrance",
   "metadata": {},
   "outputs": [],
   "source": [
    "for gan in statistic.keys():\n",
    "    f1 = { n: 0.0 for n in algs }\n",
    "    k =  { n: 0.0 for n in algs }\n",
    "    c = 0\n",
    "\n",
    "    for ds in dataset:\n",
    "        if ds != \"Average\":\n",
    "            c += 1\n",
    "            for n in algs:\n",
    "                if n in statistic[gan][ds].keys():\n",
    "                    f1[n] += statistic[gan][ds][n][f1Score]\n",
    "                    k[n] += statistic[gan][ds][n][kScore]\n",
    "\n",
    "    avg = {}\n",
    "    for n in algs:\n",
    "        avg[n] = { f1Score: f1[n] / c, kScore: k[n] / c }\n",
    "    statistic[gan][\"Average\"] = avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "public-collins",
   "metadata": {},
   "source": [
    "# Show Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5fec8e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanupName(name):\n",
    "    return name.replace(\"folding_\", \"\").replace(\"imblearn_\", \"\").replace(\"kaggle_\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "combined-courage",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnose(algo, score):\n",
    "    def gr(n):\n",
    "        if n in resultList:\n",
    "            return resultList[n][algo].data[score]\n",
    "        else:\n",
    "            return 0.0\n",
    "    \n",
    "    def valueOf(d, g):\n",
    "        d = cleanupName(d)\n",
    "        if d not in statistic[g].keys():\n",
    "            print(f\"Missing '{d}' in '{g}'\")\n",
    "            return 0.0\n",
    "        \n",
    "        if algo in statistic[g][d].keys():\n",
    "            return statistic[g][d][algo][score]\n",
    "        else:\n",
    "            print(f\"Missing '{algo}' in ('{g}', '{d}')\")\n",
    "            return 0.0\n",
    "    \n",
    "    print(f\"{algo}: {score}\")\n",
    "    \n",
    "    gans = list(statistic.keys())\n",
    "\n",
    "    w = 0.8 / len(gans)\n",
    "    bar = list(range(len(testSets)))\n",
    "    plt.figure(figsize=(20, 18))\n",
    "    for g in gans:\n",
    "        values = [valueOf(d, g) for d in testSets]\n",
    "        plt.barh(bar, values, w, label=g)\n",
    "        print(values)\n",
    "        \n",
    "        \n",
    "        bar = [i - w for i in bar]\n",
    "\n",
    "    plt.ylabel(\"Dataset\")\n",
    "    plt.xlabel(score)\n",
    "    plt.yticks(range(len(testSets)), [cleanupName(name) for name in testSets])\n",
    "    #plt.yticks(rotation=\"vertical\")\n",
    "    plt.legend()\n",
    "    plt.savefig(f\"data_result/statistics/byAlgorithm/statistic-{algo}-{score}.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ce1621f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseAverage(score, onlyOneBar=False):\n",
    "    def gr(n):\n",
    "        if n in resultList:\n",
    "            return resultList[n][algo].data[score]\n",
    "        else:\n",
    "            return 0.0\n",
    "    \n",
    "    print(f\"Average: {score}\")\n",
    "    \n",
    "    gans = list(statistic.keys())\n",
    "\n",
    "    w = 0.8 / len(gans)\n",
    "    if onlyOneBar:\n",
    "        barType = \"O\"\n",
    "        bar = range(len(algs))\n",
    "    else:\n",
    "        barType = \"M\"\n",
    "        bar = [0.8 + i - w for i in range(len(algs)) ]\n",
    "    plt.figure(figsize=(20, 18))\n",
    "    for g in gans:\n",
    "        values = [\n",
    "                 (statistic[g][\"Average\"][algo][score] if algo in statistic[g][\"Average\"].keys() else 0.0)\n",
    "                 for algo in algs\n",
    "                 ]\n",
    "        plt.barh(bar, values, w, label=g)\n",
    "        \n",
    "        if not onlyOneBar:\n",
    "            bar = [i - w for i in bar]\n",
    "\n",
    "    plt.ylabel(\"Dataset\")\n",
    "    plt.xlabel(score)\n",
    "    plt.yticks(range(len(algs)), algs)\n",
    "    plt.legend()\n",
    "    plt.savefig(f\"data_result/statistics/average/statistic-Average-{score}-{barType}.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2002b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseDataset(dataset):\n",
    "    def gr(n):\n",
    "        if n in resultList:\n",
    "            return resultList[n][algo].data[score]\n",
    "        else:\n",
    "            return 0.0\n",
    "    \n",
    "    print(f\"{dataset}\")\n",
    "    \n",
    "    gans = list(statistic.keys())\n",
    "    scores = [(a, s) for a in algs for s in (f1Score, kScore)]\n",
    "\n",
    "    w = 0.8 / len(scores)\n",
    "    bar = list(range(len(gans)))\n",
    "    plt.figure(figsize=(20, 18))\n",
    "    for (algo, score) in scores:\n",
    "        values = [\n",
    "                 (statistic[g][dataset][algo][score] if algo in statistic[g][dataset] else 0.0) if dataset in statistic[g] else 0.0\n",
    "                 for g in gans\n",
    "                 ]\n",
    "        plt.barh(bar, values, w, label=f\"{algo}: {score}\")\n",
    "        \n",
    "        \n",
    "        bar = [i - w for i in bar]\n",
    "\n",
    "    plt.xlabel(\"score\")\n",
    "    plt.ylabel(\"Generator\")\n",
    "    plt.yticks(range(len(gans)), gans)\n",
    "    #plt.yticks(rotation=\"vertical\")\n",
    "    plt.legend()\n",
    "    plt.savefig(f\"data_result/statistics/byDataset/statistic-{dataset}.pdf\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b03185",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "for a in algs:\n",
    "    showDiagnose(a, f1Score)\n",
    "    showDiagnose(a, kScore)\n",
    "    \n",
    "showDiagnoseAverage(f1Score)\n",
    "showDiagnoseAverage(kScore)\n",
    "\n",
    "for t in testSets:\n",
    "    showDiagnoseDataset(cleanupName(t))\n",
    "\n",
    "showDiagnoseDataset(\"Average\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63841c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getValueOf(gan, dataset, algo, score):\n",
    "    if dataset not in statistic[gan].keys():\n",
    "        #print(f\"Missing '{dataset}' in '{gan}'\")\n",
    "        return None\n",
    "\n",
    "    if algo not in statistic[gan][dataset].keys():\n",
    "        #print(f\"Missing '{algo}' in ('{gan}', '{dataset}')\")\n",
    "        return None\n",
    "    \n",
    "    if score not in statistic[gan][dataset][algo].keys():\n",
    "        #print(f\"Missing '{score}' in ('{gan}', '{dataset}', '{algo}')\")\n",
    "        return None\n",
    "    \n",
    "    return statistic[gan][dataset][algo][score]\n",
    "    \n",
    "    \n",
    "    \n",
    "def calcTable(algo, score, ignore=[]):\n",
    "    table = []\n",
    "    \n",
    "    def calc(gc, g):\n",
    "        n = 0\n",
    "        for d in testSets:\n",
    "            d = cleanupName(d)\n",
    "            if d not in ignore:\n",
    "                vc = getValueOf(gc, d, algo, score)\n",
    "                v = getValueOf(g, d, algo, score)\n",
    "                if vc is not None and v is not None and vc >= v:\n",
    "                    n += 1\n",
    "        return n\n",
    "    \n",
    "    for gc in gans:\n",
    "        table.append([calc(gc, g) for g in gans])\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "177774b0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[14, 13, 9, 11, 9, 11, 7, 11, 7],\n",
       " [3, 14, 6, 11, 6, 2, 2, 2, 2],\n",
       " [5, 8, 14, 9, 7, 7, 5, 7, 6],\n",
       " [3, 3, 5, 14, 3, 3, 2, 2, 3],\n",
       " [5, 9, 7, 11, 14, 6, 5, 5, 4],\n",
       " [3, 12, 7, 12, 8, 14, 0, 6, 1],\n",
       " [8, 13, 9, 12, 9, 14, 14, 13, 9],\n",
       " [4, 13, 7, 12, 9, 10, 1, 14, 3],\n",
       " [9, 14, 8, 12, 11, 14, 6, 12, 14]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tables = {}\n",
    "ignore = [ \"webpage\"\n",
    "         , \"mammography\"\n",
    "         , \"protein_homo\"\n",
    "         , \"ozone_level\"\n",
    "         , \"creditcard\"\n",
    "         ]\n",
    "for a in algs:\n",
    "    tables[a + \" - \" + f1Score] = calcTable(a, f1Score, ignore)\n",
    "    tables[a + \" - \" + kScore] = calcTable(a, kScore, ignore)\n",
    "    \n",
    "tables[algs[0] + \" - \" + f1Score]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "befa2db4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "453f491d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAN - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAN - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = matplotlib.colors.ListedColormap([\n",
    "    (1.0, x / 255.0, 0.0)\n",
    "    for x in range(256)\n",
    "    ] + [\n",
    "    ((255 - x) / 255.0, (255 - x) / 255.0, x / 255.0)\n",
    "    for x in range(256)\n",
    "    ])\n",
    "\n",
    "cmap.set_extremes(bad=cmap(0.0), under=cmap(0.0), over=cmap(1.0))\n",
    "\n",
    "for k in tables.keys():\n",
    "    print(k)\n",
    "    labels = list(gans)\n",
    "    t = tables[k]\n",
    "    if k[0:3] == \"GAN\":\n",
    "        labels = labels[-4:]\n",
    "        t = [r[-4:] for r in t[-4:]]\n",
    "        f = plt.figure(figsize=(5, 4))\n",
    "        f.add_axes([0.4, 0.45, 0.6, 0.5])\n",
    "    else:\n",
    "        f = plt.figure(figsize=(7, 6))\n",
    "        f.add_axes([0.27, 0.25, 0.7, 0.74])\n",
    "    p = plt.imshow(t, cmap=cmap)\n",
    "    plt.colorbar(p)\n",
    "    plt.xticks(range(len(labels)), labels, rotation=\"vertical\")\n",
    "    plt.yticks(range(len(labels)), labels)\n",
    "    plt.savefig(f\"data_result/statistics/successCount/statistic-{k}.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4397945",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Table:\n",
    "    def __init__(self, heading):\n",
    "        self.heading = [str(h) for h in heading]\n",
    "        self.sizes = [len(h) for h in self.heading]\n",
    "        self.rows = []\n",
    "        \n",
    "    def add(self, row):\n",
    "        row = [str(r) for r in row]\n",
    "        self.rows.append(row)\n",
    "        self.sizes = [max(a,len(b)) for (a, b) in zip(self.sizes, row)]\n",
    "        \n",
    "    def separator(self):\n",
    "        return \"|\".join([\"-\" * n for n in self.sizes])\n",
    "    \n",
    "    def showRow(self, row):\n",
    "        def pad(n, t):\n",
    "            while len(t) < n:\n",
    "                t += \" \"\n",
    "            return t\n",
    "        \n",
    "        return \"|\".join([pad(n, t) for (n,t) in zip(self.sizes, row)])\n",
    "    \n",
    "    def show(self):\n",
    "        print(self.showRow(self.heading))\n",
    "        print(self.separator())\n",
    "        for row in self.rows:\n",
    "            print(self.showRow(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d0e2faa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tableRow(algo, dataset):\n",
    "    row = []\n",
    "    for gan in (list(gans)[-4:] if algo[0:3] == \"GAN\" else gans):\n",
    "        v = getValueOf(gan, dataset, algo, f1Score)\n",
    "        w = getValueOf(gan, dataset, algo, kScore)\n",
    "        row.append((v, w))\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c820b02",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "### LR\n",
      "\n",
      "dataset ($f_1~$score$~/~\\kappa~$score)|ProWRAS          |Repeater         |SimpleGAN        |ctGAN            |CTAB GAN         \n",
      "--------------------------------------|-----------------|-----------------|-----------------|-----------------|-----------------\n",
      "abalone 17 vs 7 8 9 10                | 0.340  /  0.314 | 0.275  /  0.244 | 0.339  /  0.327 | 0.172  /  0.140 | 0.189  /  0.153 \n",
      "abalone9 18                           | 0.568  /  0.535 | 0.458  /  0.409 | 0.562  /  0.540 | 0.315  /  0.267 | 0.471  /  0.427 \n",
      "car good                              | 0.100  /  0.029 | 0.106  /  0.035 | 0.073  /  0.013 | 0.107  /  0.036 | 0.106  /  0.035 \n",
      "car vgood                             | 0.395  /  0.357 | 0.362  /  0.320 | 0.192  /  0.153 | 0.322  /  0.276 | 0.367  /  0.328 \n",
      "flare F                               | 0.276  /  0.226 | 0.263  /  0.210 | 0.328  /  0.311 | 0.332  /  0.289 | 0.346  /  0.308 \n",
      "hypothyroid                           | 0.400  /  0.353 | 0.357  /  0.304 | 0.414  /  0.393 | 0.289  /  0.229 | 0.248  /  0.207 \n",
      "kddcup guess passwd vs satan          | 0.997  /  0.996 | 0.997  /  0.996 | 0.981  /  0.980 | 1.000  /  1.000 | 0.996  /  0.996 \n",
      "kr vs k three vs eleven               | 0.965  /  0.964 | 0.942  /  0.941 | 0.943  /  0.941 | 0.575  /  0.558 | 0.702  /  0.691 \n",
      "kr vs k zero one vs draw              | 0.744  /  0.732 | 0.650  /  0.632 | 0.758  /  0.748 | 0.401  /  0.364 | 0.739  /  0.727 \n",
      "shuttle 2 vs 5                        | 0.998  /  0.998 | 0.998  /  0.998 | 0.983  /  0.982 | 0.980  /  0.980 | 0.991  /  0.991 \n",
      "winequality red 4                     | 0.143  /  0.090 | 0.113  /  0.056 | 0.013  /  0.009 | 0.108  /  0.059 | 0.127  /  0.080 \n",
      "yeast4                                | 0.243  /  0.197 | 0.236  /  0.188 | 0.292  /  0.279 | 0.231  /  0.188 | 0.282  /  0.252 \n",
      "yeast5                                | 0.592  /  0.574 | 0.574  /  0.555 | 0.533  /  0.520 | 0.530  /  0.510 | 0.612  /  0.598 \n",
      "yeast6                                | 0.291  /  0.262 | 0.242  /  0.210 | 0.406  /  0.396 | 0.226  /  0.195 | 0.399  /  0.382 \n",
      "\n",
      "dataset ($f_1~$score$~/~\\kappa~$score)|convGAN majority 5|convGAN majority full|convGAN proximary 5|convGAN proximary full\n",
      "--------------------------------------|------------------|---------------------|-------------------|----------------------\n",
      "abalone 17 vs 7 8 9 10                | 0.296  /  0.266  | 0.332  /  0.304     | 0.305  /  0.276   | 0.335  /  0.308      \n",
      "abalone9 18                           | 0.523  /  0.483  | 0.564  /  0.531     | 0.518  /  0.478   | 0.580  /  0.548      \n",
      "car good                              | 0.107  /  0.036  | 0.109  /  0.038     | 0.108  /  0.037   | 0.107  /  0.037      \n",
      "car vgood                             | 0.379  /  0.339  | 0.407  /  0.370     | 0.381  /  0.341   | 0.380  /  0.340      \n",
      "flare F                               | 0.267  /  0.216  | 0.309  /  0.264     | 0.272  /  0.221   | 0.306  /  0.261      \n",
      "hypothyroid                           | 0.366  /  0.314  | 0.369  /  0.318     | 0.363  /  0.311   | 0.399  /  0.351      \n",
      "kddcup guess passwd vs satan          | 0.991  /  0.991  | 0.993  /  0.992     | 0.997  /  0.996   | 0.997  /  0.996      \n",
      "kr vs k three vs eleven               | 0.950  /  0.948  | 0.956  /  0.955     | 0.952  /  0.950   | 0.955  /  0.954      \n",
      "kr vs k zero one vs draw              | 0.708  /  0.694  | 0.795  /  0.786     | 0.711  /  0.697   | 0.748  /  0.737      \n",
      "shuttle 2 vs 5                        | 0.994  /  0.994  | 0.998  /  0.998     | 0.994  /  0.994   | 0.998  /  0.998      \n",
      "winequality red 4                     | 0.129  /  0.074  | 0.134  /  0.079     | 0.129  /  0.073   | 0.131  /  0.076      \n",
      "yeast4                                | 0.244  /  0.199  | 0.277  /  0.235     | 0.246  /  0.200   | 0.274  /  0.232      \n",
      "yeast5                                | 0.589  /  0.571  | 0.606  /  0.589     | 0.584  /  0.566   | 0.612  /  0.596      \n",
      "yeast6                                | 0.296  /  0.268  | 0.342  /  0.316     | 0.293  /  0.264   | 0.326  /  0.300      \n",
      "\n",
      "### GB\n",
      "\n",
      "dataset ($f_1~$score$~/~\\kappa~$score)|ProWRAS          |Repeater         |SimpleGAN        |ctGAN            |CTAB GAN         \n",
      "--------------------------------------|-----------------|-----------------|-----------------|-----------------|-----------------\n",
      "abalone 17 vs 7 8 9 10                | 0.339  /  0.321 | 0.333  /  0.311 | 0.240  /  0.231 | 0.195  /  0.178 | 0.211  /  0.199 \n",
      "abalone9 18                           | 0.399  /  0.361 | 0.388  /  0.352 | 0.395  /  0.370 | 0.324  /  0.292 | 0.371  /  0.340 \n",
      "car good                              | 0.845  /  0.840 | 0.839  /  0.831 | 0.659  /  0.646 | 0.533  /  0.504 | 0.773  /  0.765 \n",
      "car vgood                             | 0.973  /  0.972 | 0.938  /  0.935 | 0.896  /  0.891 | 0.812  /  0.803 | 0.974  /  0.973 \n",
      "flare F                               | 0.132  /  0.113 | 0.283  /  0.235 | 0.171  /  0.156 | 0.326  /  0.285 | 0.184  /  0.167 \n",
      "hypothyroid                           | 0.789  /  0.779 | 0.781  /  0.768 | 0.783  /  0.773 | 0.742  /  0.727 | 0.803  /  0.794 \n",
      "kddcup guess passwd vs satan          | 1.000  /  1.000 | 1.000  /  1.000 | 0.992  /  0.992 | 1.000  /  1.000 | 1.000  /  1.000 \n",
      "kr vs k three vs eleven               | 0.995  /  0.995 | 0.995  /  0.995 | 0.997  /  0.997 | 0.748  /  0.739 | 0.995  /  0.995 \n",
      "kr vs k zero one vs draw              | 0.961  /  0.960 | 0.944  /  0.942 | 0.972  /  0.971 | 0.636  /  0.617 | 0.949  /  0.948 \n",
      "shuttle 2 vs 5                        | 1.000  /  1.000 | 1.000  /  1.000 | 0.986  /  0.986 | 1.000  /  1.000 | 1.000  /  1.000 \n",
      "winequality red 4                     | 0.096  /  0.070 | 0.127  /  0.094 | 0.073  /  0.060 | 0.106  /  0.081 | 0.091  /  0.076 \n",
      "yeast4                                | 0.352  /  0.330 | 0.375  /  0.347 | 0.352  /  0.335 | 0.254  /  0.225 | 0.285  /  0.271 \n",
      "yeast5                                | 0.708  /  0.699 | 0.714  /  0.705 | 0.692  /  0.684 | 0.630  /  0.617 | 0.687  /  0.678 \n",
      "yeast6                                | 0.544  /  0.533 | 0.478  /  0.464 | 0.398  /  0.387 | 0.346  /  0.329 | 0.428  /  0.417 \n",
      "\n",
      "dataset ($f_1~$score$~/~\\kappa~$score)|convGAN majority 5|convGAN majority full|convGAN proximary 5|convGAN proximary full\n",
      "--------------------------------------|------------------|---------------------|-------------------|----------------------\n",
      "abalone 17 vs 7 8 9 10                | 0.294  /  0.273  | 0.331  /  0.318     | 0.320  /  0.299   | 0.318  /  0.304      \n",
      "abalone9 18                           | 0.348  /  0.307  | 0.327  /  0.301     | 0.374  /  0.335   | 0.328  /  0.304      \n",
      "car good                              | 0.718  /  0.709  | 0.839  /  0.832     | 0.714  /  0.704   | 0.845  /  0.839      \n",
      "car vgood                             | 0.950  /  0.948  | 0.982  /  0.982     | 0.936  /  0.933   | 0.977  /  0.976      \n",
      "flare F                               | 0.205  /  0.190  | 0.157  /  0.140     | 0.179  /  0.165   | 0.149  /  0.133      \n",
      "hypothyroid                           | 0.789  /  0.778  | 0.797  /  0.786     | 0.793  /  0.781   | 0.809  /  0.798      \n",
      "kddcup guess passwd vs satan          | 1.000  /  1.000  | 1.000  /  1.000     | 1.000  /  1.000   | 1.000  /  1.000      \n",
      "kr vs k three vs eleven               | 0.997  /  0.997  | 0.995  /  0.995     | 0.995  /  0.995   | 0.995  /  0.995      \n",
      "kr vs k zero one vs draw              | 0.957  /  0.956  | 0.959  /  0.958     | 0.957  /  0.955   | 0.960  /  0.958      \n",
      "shuttle 2 vs 5                        | 0.998  /  0.998  | 1.000  /  1.000     | 0.998  /  0.998   | 1.000  /  1.000      \n",
      "winequality red 4                     | 0.136  /  0.097  | 0.111  /  0.071     | 0.126  /  0.087   | 0.119  /  0.079      \n",
      "yeast4                                | 0.253  /  0.238  | 0.347  /  0.323     | 0.258  /  0.243   | 0.365  /  0.342      \n",
      "yeast5                                | 0.723  /  0.715  | 0.712  /  0.703     | 0.721  /  0.713   | 0.713  /  0.704      \n",
      "yeast6                                | 0.493  /  0.483  | 0.540  /  0.530     | 0.485  /  0.476   | 0.535  /  0.524      \n",
      "\n",
      "### KNN\n",
      "\n",
      "dataset ($f_1~$score$~/~\\kappa~$score)|ProWRAS          |Repeater         |SimpleGAN        |ctGAN            |CTAB GAN          \n",
      "--------------------------------------|-----------------|-----------------|-----------------|-----------------|------------------\n",
      "abalone 17 vs 7 8 9 10                | 0.337  /  0.314 | 0.315  /  0.291 | 0.000  /  0.000 | 0.122  /  0.117 | 0.111  /  0.105  \n",
      "abalone9 18                           | 0.395  /  0.347 | 0.342  /  0.286 | 0.000  /  0.000 | 0.213  /  0.196 | 0.132  /  0.118  \n",
      "car good                              | 0.720  /  0.706 | 0.357  /  0.312 | 0.282  /  0.270 | 0.247  /  0.190 | 0.390  /  0.349  \n",
      "car vgood                             | 0.805  /  0.797 | 0.413  /  0.375 | 0.572  /  0.562 | 0.308  /  0.260 | 0.634  /  0.614  \n",
      "flare F                               | 0.294  /  0.250 | 0.245  /  0.191 | 0.025  /  0.016 | 0.293  /  0.250 | 0.262  /  0.219  \n",
      "hypothyroid                           | 0.616  /  0.593 | 0.568  /  0.540 | 0.574  /  0.559 | 0.610  /  0.586 | 0.608  /  0.588  \n",
      "kddcup guess passwd vs satan          | 0.991  /  0.991 | 0.991  /  0.991 | 0.970  /  0.969 | 0.991  /  0.991 | 0.991  /  0.991  \n",
      "kr vs k three vs eleven               | 0.932  /  0.929 | 0.901  /  0.898 | 0.940  /  0.938 | 0.638  /  0.624 | 0.816  /  0.810  \n",
      "kr vs k zero one vs draw              | 0.887  /  0.882 | 0.840  /  0.833 | 0.866  /  0.862 | 0.784  /  0.774 | 0.795  /  0.787  \n",
      "shuttle 2 vs 5                        | 0.956  /  0.955 | 0.951  /  0.951 | 0.930  /  0.929 | 0.949  /  0.948 | 0.916  /  0.915  \n",
      "winequality red 4                     | 0.072  /  0.020 | 0.069  /  0.016 | 0.000  /  0.000 | 0.043  /  0.000 | 0.040  /  -0.010 \n",
      "yeast4                                | 0.367  /  0.336 | 0.329  /  0.293 | 0.092  /  0.088 | 0.298  /  0.267 | 0.269  /  0.234  \n",
      "yeast5                                | 0.689  /  0.677 | 0.670  /  0.657 | 0.580  /  0.571 | 0.599  /  0.582 | 0.706  /  0.696  \n",
      "yeast6                                | 0.460  /  0.442 | 0.409  /  0.387 | 0.502  /  0.495 | 0.343  /  0.319 | 0.433  /  0.415  \n",
      "\n",
      "dataset ($f_1~$score$~/~\\kappa~$score)|convGAN majority 5|convGAN majority full|convGAN proximary 5|convGAN proximary full\n",
      "--------------------------------------|------------------|---------------------|-------------------|----------------------\n",
      "abalone 17 vs 7 8 9 10                | 0.287  /  0.258  | 0.316  /  0.296     | 0.287  /  0.257   | 0.320  /  0.299      \n",
      "abalone9 18                           | 0.366  /  0.310  | 0.161  /  0.143     | 0.342  /  0.283   | 0.194  /  0.175      \n",
      "car good                              | 0.545  /  0.517  | 0.459  /  0.424     | 0.568  /  0.542   | 0.495  /  0.464      \n",
      "car vgood                             | 0.689  /  0.673  | 0.831  /  0.824     | 0.677  /  0.660   | 0.705  /  0.689      \n",
      "flare F                               | 0.263  /  0.213  | 0.296  /  0.253     | 0.275  /  0.226   | 0.312  /  0.270      \n",
      "hypothyroid                           | 0.580  /  0.552  | 0.596  /  0.570     | 0.582  /  0.555   | 0.626  /  0.603      \n",
      "kddcup guess passwd vs satan          | 0.982  /  0.982  | 0.991  /  0.991     | 0.987  /  0.987   | 0.991  /  0.991      \n",
      "kr vs k three vs eleven               | 0.895  /  0.892  | 0.909  /  0.907     | 0.893  /  0.890   | 0.911  /  0.908      \n",
      "kr vs k zero one vs draw              | 0.855  /  0.848  | 0.871  /  0.866     | 0.859  /  0.853   | 0.848  /  0.841      \n",
      "shuttle 2 vs 5                        | 0.959  /  0.958  | 0.936  /  0.935     | 0.958  /  0.958   | 0.943  /  0.942      \n",
      "winequality red 4                     | 0.077  /  0.019  | 0.063  /  0.005     | 0.070  /  0.012   | 0.058  /  -0.000     \n",
      "yeast4                                | 0.320  /  0.282  | 0.339  /  0.303     | 0.331  /  0.293   | 0.334  /  0.298      \n",
      "yeast5                                | 0.641  /  0.626  | 0.640  /  0.625     | 0.647  /  0.632   | 0.651  /  0.637      \n",
      "yeast6                                | 0.344  /  0.318  | 0.402  /  0.380     | 0.339  /  0.313   | 0.394  /  0.371      \n",
      "\n",
      "### GAN\n",
      "\n",
      "dataset ($f_1~$score$~/~\\kappa~$score)|convGAN majority 5|convGAN majority full|convGAN proximary 5|convGAN proximary full\n",
      "--------------------------------------|------------------|---------------------|-------------------|----------------------\n",
      "abalone 17 vs 7 8 9 10                | 0.287  /  0.256  | 0.359  /  0.336     | 0.209  /  0.175   | 0.294  /  0.269      \n",
      "abalone9 18                           | 0.442  /  0.394  | 0.388  /  0.360     | 0.443  /  0.395   | 0.480  /  0.451      \n",
      "car good                              | 0.693  /  0.678  | 0.642  /  0.625     | 0.626  /  0.606   | 0.415  /  0.377      \n",
      "car vgood                             | 0.706  /  0.693  | 0.630  /  0.619     | 0.723  /  0.711   | 0.639  /  0.620      \n",
      "flare F                               | 0.313  /  0.270  | 0.221  /  0.192     | 0.165  /  0.106   | 0.268  /  0.223      \n",
      "hypothyroid                           | 0.585  /  0.558  | 0.710  /  0.694     | 0.380  /  0.333   | 0.389  /  0.339      \n",
      "kddcup guess passwd vs satan          | 0.984  /  0.983  | 0.993  /  0.992     | 0.996  /  0.996   | 0.998  /  0.998      \n",
      "kr vs k three vs eleven               | 0.986  /  0.985  | 0.995  /  0.995     | 0.979  /  0.978   | 0.971  /  0.971      \n",
      "kr vs k zero one vs draw              | 0.928  /  0.925  | 0.955  /  0.954     | 0.940  /  0.938   | 0.847  /  0.841      \n",
      "shuttle 2 vs 5                        | 1.000  /  1.000  | 0.981  /  0.981     | 0.685  /  0.678   | 0.743  /  0.738      \n",
      "winequality red 4                     | 0.095  /  0.040  | 0.103  /  0.049     | 0.089  /  0.031   | 0.090  /  0.032      \n",
      "yeast4                                | 0.292  /  0.252  | 0.276  /  0.234     | 0.193  /  0.147   | 0.290  /  0.250      \n",
      "yeast5                                | 0.570  /  0.551  | 0.614  /  0.599     | 0.353  /  0.322   | 0.556  /  0.539      \n",
      "yeast6                                | 0.344  /  0.318  | 0.423  /  0.402     | 0.221  /  0.190   | 0.388  /  0.366      \n"
     ]
    }
   ],
   "source": [
    "def p(f):\n",
    "    if f is None:\n",
    "        return \" ? \"\n",
    "    else:\n",
    "        return f\" {f:0.3f} \"\n",
    "\n",
    "def latex(text):\n",
    "    r = \"\"\n",
    "    for x in text:\n",
    "        if x == \"_\" or x == \"-\":\n",
    "            r += \" \"\n",
    "        else:\n",
    "            r += x\n",
    "    return r\n",
    "    \n",
    "for algo in algs:\n",
    "    print()\n",
    "    print(\"### \" + algo)\n",
    "    print()\n",
    "    f = \"\"\n",
    "    heading = [\"dataset ($f_1~$score$~/~\\\\kappa~$score)\"]\n",
    "    if algo[0:3] == \"GAN\":\n",
    "        for g in list(gans)[-4:]:\n",
    "            heading.append(latex(g))\n",
    "    else:\n",
    "        for g in gans:\n",
    "            heading.append(latex(g))\n",
    "    table = Table(heading)\n",
    "            \n",
    "    for d in testSets:\n",
    "        d = cleanupName(d)\n",
    "        if d not in ignore:\n",
    "            row = tableRow(algo, d)\n",
    "            line = [latex(d)]\n",
    "            for r in row:\n",
    "                line.append(f\"{p(r[0])} / {p(r[1])}\")\n",
    "            table.add(line)\n",
    "    \n",
    "    tableB = None\n",
    "    if len(table.heading) > 5:\n",
    "        heading = [table.heading[0]] + table.heading[6:]\n",
    "        tableB = Table(heading)\n",
    "        tableB.sizes = [table.sizes[0]] + table.sizes[6:]\n",
    "        tableB.rows = [\n",
    "            [r[0]] + r[6:]\n",
    "            for r in table.rows\n",
    "            ]\n",
    "        table.heading = table.heading[0:6]\n",
    "        table.sizes = table.sizes[0:6]\n",
    "        table.rows = [\n",
    "            r[0:6]\n",
    "            for r in table.rows\n",
    "            ]\n",
    "\n",
    "    table.show()\n",
    "    if tableB is not None:\n",
    "        print()\n",
    "        tableB.show()\n",
    "    \n"
   ]
  }
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