{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "pretty-performer", "metadata": {}, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors\n", "from PIL import Image, ImageDraw, ImageFont\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\"\n", "\n", "kScoreSd = kScore + \" - SD\"\n", "f1ScoreSd = f1Score + \" - SD\"" ] }, { "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 = [g.replace(\"SimpleGAN\", \"GAN\") for g in generators.keys()][:-1]\n", "algs = [\"LR\", \"RF\", \"GB\", \"KNN\", \"DoC\"]" ] }, { "cell_type": "code", "execution_count": 4, "id": "edf5b592", "metadata": {}, "outputs": [], "source": [ "testSets = [t for t in testSets if t[0:7] == \"folding\"]" ] }, { "cell_type": "code", "execution_count": 5, "id": "needed-birmingham", "metadata": {}, "outputs": [], "source": [ "def cleanupName(name):\n", " return name.replace(\"folding_\", \"\").replace(\"imblearn_\", \"\").replace(\"kaggle_\", \"\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "8106bddf", "metadata": {}, "outputs": [], "source": [ "dataset = [cleanupName(d) for d in testSets]" ] }, { "cell_type": "code", "execution_count": 7, "id": "96a601db", "metadata": {}, "outputs": [], "source": [ "def ganName(name):\n", " return name.replace(\n", " \"ConvGeN-majority-5\", \"ConvGeN(5,maj)\").replace(\n", " \"ConvGeN-majority-full\", \"ConvGeN(min,maj)\").replace(\n", " \"ConvGeN-proximity-5\", \"ConvGeN(5,prox)\").replace(\n", " \"ConvGeN-proximity-full\", \"ConvGeN(min,prox)\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "d35b280b", "metadata": {}, "outputs": [], "source": [ "def filterConvGen(name):\n", " return not name.startswith(\"ConvGeN\") or name == \"ConvGeN(min,maj)\"" ] }, { "cell_type": "markdown", "id": "selective-connecticut", "metadata": {}, "source": [ "# Load data from CSV files" ] }, { "cell_type": "code", "execution_count": 9, "id": "intended-watts", "metadata": {}, "outputs": [], "source": [ "def loadDiagnoseData(ganType, datasetName):\n", " fileName = f\"data_result/{ganType}/{datasetName}.csv\"\n", " r = {}\n", " try:\n", " f1List = []\n", " kList = []\n", " indizes = [str(x) for x in range(100)]\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", " if n == \"GAN\" or n == \"DoG\":\n", " n = \"DoC\"\n", " newBlock = False\n", " elif line == \"---\":\n", " newBlock = True\n", " f1List = []\n", " kList = []\n", " else:\n", " parts = line.split(\";\")\n", " if parts[0] == \"avg\":\n", " f1Avg = float(parts[5])\n", " f1Sd = math.sqrt(sum([(f1Avg - x)*(f1Avg - x) for x in f1List]) / len(f1List))\n", " kAvg = float(parts[6])\n", " kSd = math.sqrt(sum([(kAvg - x)*(kAvg - x) for x in kList]) / len(kList))\n", " r[n] = { f1Score: f1Avg, kScore: kAvg, f1ScoreSd: f1Sd, kScoreSd: kSd }\n", " elif parts[0] in indizes:\n", " f1List.append(float(parts[5]))\n", " kList.append(float(parts[6]))\n", " except FileNotFoundError as e:\n", " print(f\"Missing file: {fileName}\")\n", " return r" ] }, { "cell_type": "code", "execution_count": 10, "id": "classical-rescue", "metadata": {}, "outputs": [], "source": [ "statistic = { }\n", "\n", "for gan in gans:\n", " if ganName(gan) not in statistic:\n", " statistic[ganName(gan)] = {}\n", " \n", " for ds in testSets:\n", " if ds != \"Average\":\n", " statistic[ganName(gan)][cleanupName(ds)] = loadDiagnoseData(gan, ds)\n", " \n", " d = cleanupName(ds)\n", " if d not in dataset:\n", " dataset.append(d)\n", "\n", "gans = list(statistic.keys())" ] }, { "cell_type": "code", "execution_count": 11, "id": "unable-entrance", "metadata": {}, "outputs": [], "source": [ "for gan in gans:\n", " f1 = { n: 0.0 for n in algs }\n", " k = { n: 0.0 for n in algs }\n", " f1Sd = { n: 0.0 for n in algs }\n", " kSd = { 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", " f1Sd[n] += statistic[gan][ds][n][f1ScoreSd]\n", " kSd[n] += statistic[gan][ds][n][kScoreSd]\n", "\n", " avg = {}\n", " for n in algs:\n", " avg[n] = { f1Score: f1[n] / c, f1ScoreSd: f1Sd[n] / c, kScore: k[n] / c, kScoreSd: kSd[n] / c }\n", " statistic[gan][\"Average\"] = avg" ] }, { "cell_type": "markdown", "id": "public-collins", "metadata": {}, "source": [ "# Show Statistics" ] }, { "cell_type": "code", "execution_count": 12, "id": "extra-taiwan", "metadata": {}, "outputs": [], "source": [ "def drawTransparentRect(img, rect, color, opacity=1.0):\n", " def mix(v, a, b):\n", " return max(0, min(255, int((v * a) + ((1.0 - v) * b))))\n", "\n", " def mixPixel(v, a, b):\n", " return (mix(v, a[0], b[0]), mix(v, a[1], b[1]), mix(v, a[2], b[2]))\n", "\n", "\n", " ((x0, y0), (x1, y1)) = rect\n", " \n", " for y in range(y0, y1):\n", " for x in range(x0, x1):\n", " p = (x, y)\n", " c = mixPixel(opacity, color, img.getpixel(p))\n", " img.putpixel(p, c)\n", " " ] }, { "cell_type": "code", "execution_count": 13, "id": "5fec8e83", "metadata": {}, "outputs": [], "source": [ "def drawDiagram(size, rowNames, data, colNames=[], colors=None, border=20, barIndent=10, fontSize=20, markers=[0.25, 0.5, 0.75, 1.00], maxY=1.0):\n", " silver = (204, 204, 204)\n", " black = (0,0,0)\n", " white = (255, 255, 255)\n", " \n", " defaultColors = [ (31,119,180)\n", " , (255,127,14)\n", " , (44,160,44)\n", " , (214,40,40)\n", " , (148,103,189)\n", " , (140,86,75)\n", " , (227,119,194)\n", " , (127,127,127)\n", " , (40,40,214)\n", " ]\n", "\n", " defaultColors = [ (209,188,115) # Gold\n", " , (145,196,223) # bright blue\n", " , (83,119,202) # blue\n", " , (224,138,86) # orange\n", " , (131,202,112) # green\n", " , (199,102,99) # red\n", " , (143,110,176) # violet\n", " , (134,99,66) # brown\n", " , (207,131,189) # pink\n", " ]\n", " \n", " def y(v):\n", " return v / maxY\n", "\n", " def med(v):\n", " if type(v) is tuple:\n", " return v[0]\n", " return v\n", "\n", " \n", " if colors is None:\n", " colors = defaultColors\n", "\n", " print((len(data[0]), len(colNames), len(data), len(rowNames)))\n", "\n", " font = ImageFont.truetype(\"FreeSans\", fontSize)\n", " \n", " markerSize = 0\n", " for m in markers:\n", " markerSize = max(markerSize, font.getsize(f\"{m:0.2f}\")[0])\n", "\n", " areaTop = 2 * border + markerSize\n", "\n", " barStep = (size[0] - border - areaTop) // len(data)\n", " barSize = max(border, barStep - border)\n", " barIndent = min(barIndent, barSize / (1 + len(data[0])))\n", " barIndent = barSize / (2 + len(data[0]))\n", " \n", " print((size[0], barSize, barSize * len(data)))\n", " \n", " # Create new Image\n", " w = max(size[0], size[1])\n", " img = Image.new(\"RGB\", (w,w))\n", " d = ImageDraw.Draw(img)\n", " \n", " # Set background to white.\n", " d.rectangle(((0,0), (w,w)), fill=white)\n", " \n", " # draw row names\n", " height = size[1]\n", " left = w - height\n", " textSize = 0\n", " for (n, name) in enumerate(rowNames):\n", " s = font.getsize(name)\n", " offset = int(border + barSize - s[1] + 1.5) // 2\n", " textSize = max(textSize, s[0])\n", " pos = (left + border, areaTop + offset + (barStep * n))\n", " d.text(pos, name, fill=black, font=font)\n", " \n", " \n", " # Calculate sizes for bar drawing.\n", " barLength = height - (4 * border) - textSize\n", " areaSize = (barLength, barSize)\n", " areaLeft = left + (2 * border) + textSize\n", " \n", " # Draw Lines for bar height comparing.\n", " markerPos = [areaLeft + int(y(v) * barLength) for v in markers]\n", " for p in markerPos:\n", " d.line(((p, border), (p, size[0] - border)), fill=silver)\n", " \n", " # Draw bars.\n", " for (n, row) in enumerate(data):\n", " area = ((areaLeft, areaTop + (n * barStep) + (border // 2)), areaSize)\n", "\n", " indices = list(range(len(row)))\n", " indices.sort(key= lambda i: 1.0 - med(row[i]))\n", "\n", " for (n, i) in enumerate(indices):\n", " v = y(med(row[i]))\n", " c = colors[i]\n", " offset = barIndent * n\n", " tl = (area[0][0], area[0][1] + offset)\n", " br = (tl[0] + int(v * area[1][0]), area[1][1] + tl[1] - offset)\n", " rect = (tl, br)\n", " d.rectangle(rect, fill=c, outline=black)\n", "\n", " for (n, row) in enumerate(data):\n", " area = ((areaLeft, areaTop + (n * barStep) + (border // 2)), areaSize)\n", "\n", " indices = list(range(len(row)))\n", " indices.sort(key= lambda i: 1.0 - med(row[i]))\n", "\n", " for (n, i) in enumerate(indices):\n", " v = y(med(row[i]))\n", " c = colors[i]\n", " offset = barIndent * n\n", " tl = (area[0][0], area[0][1] + offset)\n", " br = (tl[0] + int(v * area[1][0]), area[1][1] + tl[1] - offset)\n", "\n", " if type(row[i]) is tuple and (row[i][0] != 0.0 or row[i][1] != row[i][0] or row[i][2] != row[i][0]):\n", " vLower = y(row[i][1])\n", " vUpper = y(row[i][2])\n", " xCenter = (tl[1] + br[1]) // 2\n", " yUpper = tl[0] + int(vUpper * area[1][0])\n", " yLower = tl[0] + int(vLower * area[1][0])\n", "\n", " #d.line(((br[0], tl[1]), (br[0], br[1])), fill=black, width=5)\n", " d.line(((yLower, xCenter), (yUpper, xCenter)), fill=white, width=5)\n", " d.line(((yLower, xCenter - border), (yLower, xCenter + border)), fill=white, width=5)\n", " d.line(((yUpper, xCenter - border), (yUpper, xCenter + border)), fill=white, width=5)\n", "\n", " #d.line(((br[0], tl[1]), (br[0], br[1])), fill=c, width=3)\n", " d.line(((yLower, xCenter), (yUpper, xCenter)), fill=c, width=3)\n", " d.line(((yLower, xCenter - border), (yLower, xCenter + border)), fill=c, width=3)\n", " d.line(((yUpper, xCenter - border), (yUpper, xCenter + border)), fill=c, width=3)\n", "\n", "\n", " # Draw axis.\n", " d.line(((areaLeft, areaTop), (areaLeft, size[0] - border)), fill=black)\n", " d.line(((areaLeft, areaTop), (w - border, areaTop)), fill=black)\n", " \n", " # Draw y-axis text.\n", " img = img.rotate(90)\n", " d = ImageDraw.Draw(img)\n", "\n", " for (m, p) in zip(markers, markerPos):\n", " d.text((border, size[1] - (p - left)), f\"{m:0.2f}\", fill=black, font=font)\n", " \n", " # Draw legend.\n", " if len(colNames) > 0:\n", " colNameWidth = 0\n", " colNameHeight = fontSize * len(colNames)\n", " for c in colNames:\n", " colNameWidth = max(colNameWidth, font.getsize(c)[0])\n", " \n", " rWidth = int(fontSize * 0.75)\n", " rHeight = fontSize // 2\n", " rPadd = (fontSize - rHeight) // 2\n", " \n", " tl = (size[0] - (int(2.5 * border) + colNameWidth + rWidth), 0)\n", " br = (size[0], int(1.2 * border) + colNameHeight)\n", " drawTransparentRect(img, (tl, br), white, 0.75)\n", "\n", " for (n, c) in enumerate(colNames):\n", " t = border + (fontSize * n)\n", " l = size[0] - border - colNameWidth\n", " d.rectangle(((l - border - rWidth, t + rPadd - 1), (l - border, t + rPadd + rHeight)), fill=colors[n], outline=black)\n", " d.text((l, t), c, fill=black, font=font)\n", " \n", " return img.crop((0, 0, size[0], size[1]))" ] }, { "cell_type": "code", "execution_count": 14, "id": "038344f7", "metadata": {}, "outputs": [], "source": [ "def showDiagnose(algo, score):\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", " data = [[valueOf(d, g) for g in gans] for d in testSets] \n", " img = drawDiagram((1024, 1024), [cleanupName(d) for d in testSets], data, colNames=gans)\n", " img.save(f\"data_result/statistics/byAlgorithm/statistic-{algo}-{score}.png\")\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "8904b4b3", "metadata": {}, "outputs": [], "source": [ "def showDiagnoseAverage(score, onlyOneBar=False):\n", " def valueOf(g, algo):\n", " if algo in statistic[g][\"Average\"].keys():\n", " if not g.startswith(\"ConvGeN\"):\n", " return statistic[g][\"Average\"][algo][score]\n", " else:\n", " a = statistic[g][\"Average\"][algo][score]\n", " sd = statistic[g][\"Average\"][algo][score + \" - SD\"]\n", " return (a, a - sd, a + sd)\n", " else:\n", " return 0.0\n", "\n", " print(f\"Average: {score}\")\n", " \n", " data = [[valueOf(g, algo) for g in gans] for algo in algs]\n", " img = drawDiagram((1024, 1024), algs, data, colNames=gans, maxY=0.80)\n", " img.save(f\"data_result/statistics/average/statistic-Algo-Average-{score}.png\")" ] }, { "cell_type": "code", "execution_count": 16, "id": "d1f4961a", "metadata": {}, "outputs": [], "source": [ "def showDiagnoseDataset(dataset):\n", " print(f\"{dataset}\")\n", " \n", " def valueOf(algo, score, g):\n", " if dataset in statistic[g]:\n", " if algo in statistic[g][dataset]:\n", " if score in statistic[g][dataset][algo]:\n", " return statistic[g][dataset][algo][score]\n", " \n", " return 0.0\n", " \n", " scores = [f1Score, kScore]\n", " \n", " for score in scores:\n", " data = [[valueOf(algo, score, g) for algo in algs] for g in gans]\n", " img = drawDiagram((1024, 1024), gans, data, colNames=algs, maxY=0.80)\n", " img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.png\")\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "magnetic-romance", "metadata": {}, "outputs": [], "source": [ "def showDiagnoseDatasetAverage():\n", " print(\"Average\")\n", " \n", " dataset = \"Average\"\n", " \n", " def valueOf(algo, score, g):\n", " if dataset in statistic[g]:\n", " if algo in statistic[g][dataset]:\n", " if score in statistic[g][dataset][algo]:\n", " if algo != \"DoC\":\n", " return statistic[g][dataset][algo][score]\n", " else:\n", " a = statistic[g][dataset][algo][score]\n", " sd = statistic[g][\"Average\"][algo][score + \" - SD\"]\n", " return (a, a - sd, a + sd)\n", " \n", " return 0.0\n", " \n", " scores = [f1Score, kScore]\n", " \n", " for score in scores:\n", " data = [[valueOf(algo, score, g) for algo in algs] for g in gans]\n", " img = drawDiagram((1024, 1024), gans, data, colNames=algs, maxY=0.80)\n", " img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.png\")\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "57fe8925", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR: f1 score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "LR: cohens kappa score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "RF: f1 score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "RF: cohens kappa score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "GB: f1 score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "GB: cohens kappa score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "KNN: f1 score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "KNN: cohens kappa score\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "DoC: f1 score\n", "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('Repeater', 'abalone9-18')\n", "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n", "Missing 'DoC' in ('GAN', 'abalone9-18')\n", "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n", "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n", "Missing 'DoC' in ('Repeater', 'car_good')\n", "Missing 'DoC' in ('ProWRAS', 'car_good')\n", "Missing 'DoC' in ('GAN', 'car_good')\n", "Missing 'DoC' in ('CTGAN', 'car_good')\n", "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n", "Missing 'DoC' in ('Repeater', 'car-vgood')\n", "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n", "Missing 'DoC' in ('GAN', 'car-vgood')\n", "Missing 'DoC' in ('CTGAN', 'car-vgood')\n", "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n", "Missing 'DoC' in ('Repeater', 'flare-F')\n", "Missing 'DoC' in ('ProWRAS', 'flare-F')\n", "Missing 'DoC' in ('GAN', 'flare-F')\n", "Missing 'DoC' in ('CTGAN', 'flare-F')\n", "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n", "Missing 'DoC' in ('Repeater', 'hypothyroid')\n", "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n", "Missing 'DoC' in ('GAN', 'hypothyroid')\n", "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n", "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n", "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n", "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n", "Missing 'DoC' in ('GAN', 'winequality-red-4')\n", "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n", "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n", "Missing 'DoC' in ('Repeater', 'yeast4')\n", "Missing 'DoC' in ('ProWRAS', 'yeast4')\n", "Missing 'DoC' in ('GAN', 'yeast4')\n", "Missing 'DoC' in ('CTGAN', 'yeast4')\n", "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n", "Missing 'DoC' in ('Repeater', 'yeast5')\n", "Missing 'DoC' in ('ProWRAS', 'yeast5')\n", "Missing 'DoC' in ('GAN', 'yeast5')\n", "Missing 'DoC' in ('CTGAN', 'yeast5')\n", "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n", "Missing 'DoC' in ('Repeater', 'yeast6')\n", "Missing 'DoC' in ('ProWRAS', 'yeast6')\n", "Missing 'DoC' in ('GAN', 'yeast6')\n", "Missing 'DoC' in ('CTGAN', 'yeast6')\n", "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "DoC: cohens kappa score\n", "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoC' in ('Repeater', 'abalone9-18')\n", "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n", "Missing 'DoC' in ('GAN', 'abalone9-18')\n", "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n", "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n", "Missing 'DoC' in ('Repeater', 'car_good')\n", "Missing 'DoC' in ('ProWRAS', 'car_good')\n", "Missing 'DoC' in ('GAN', 'car_good')\n", "Missing 'DoC' in ('CTGAN', 'car_good')\n", "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n", "Missing 'DoC' in ('Repeater', 'car-vgood')\n", "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n", "Missing 'DoC' in ('GAN', 'car-vgood')\n", "Missing 'DoC' in ('CTGAN', 'car-vgood')\n", "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n", "Missing 'DoC' in ('Repeater', 'flare-F')\n", "Missing 'DoC' in ('ProWRAS', 'flare-F')\n", "Missing 'DoC' in ('GAN', 'flare-F')\n", "Missing 'DoC' in ('CTGAN', 'flare-F')\n", "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n", "Missing 'DoC' in ('Repeater', 'hypothyroid')\n", "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n", "Missing 'DoC' in ('GAN', 'hypothyroid')\n", "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n", "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n", "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n", "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n", "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n", "Missing 'DoC' in ('GAN', 'winequality-red-4')\n", "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n", "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n", "Missing 'DoC' in ('Repeater', 'yeast4')\n", "Missing 'DoC' in ('ProWRAS', 'yeast4')\n", "Missing 'DoC' in ('GAN', 'yeast4')\n", "Missing 'DoC' in ('CTGAN', 'yeast4')\n", "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n", "Missing 'DoC' in ('Repeater', 'yeast5')\n", "Missing 'DoC' in ('ProWRAS', 'yeast5')\n", "Missing 'DoC' in ('GAN', 'yeast5')\n", "Missing 'DoC' in ('CTGAN', 'yeast5')\n", "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n", "Missing 'DoC' in ('Repeater', 'yeast6')\n", "Missing 'DoC' in ('ProWRAS', 'yeast6')\n", "Missing 'DoC' in ('GAN', 'yeast6')\n", "Missing 'DoC' in ('CTGAN', 'yeast6')\n", "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n", "(6, 6, 14, 14)\n", "(1024, 46, 644)\n", "Average: f1 score\n", "(6, 6, 5, 5)\n", "(1024, 165, 825)\n", "Average: cohens kappa score\n", "(6, 6, 5, 5)\n", "(1024, 165, 825)\n", "abalone_17_vs_7_8_9_10\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "abalone9-18\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "car_good\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "car-vgood\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "flare-F\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "hypothyroid\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "kddcup-guess_passwd_vs_satan\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "kr-vs-k-three_vs_eleven\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "kr-vs-k-zero-one_vs_draw\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "shuttle-2_vs_5\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "winequality-red-4\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "yeast4\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "yeast5\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "yeast6\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "Average\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n", "(5, 5, 6, 6)\n", "(1024, 134, 804)\n" ] } ], "source": [ "gans = [g for g in statistic.keys() if filterConvGen(g)]\n", "\n", "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", "showDiagnoseDatasetAverage()\n", "\n", "gans = list(statistic.keys())" ] }, { "cell_type": "code", "execution_count": 19, "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": 20, "id": "177774b0", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[[14, 3, 6, 10, 5, 2, 2, 2, 1],\n", " [11, 14, 8, 11, 7, 11, 5, 12, 3],\n", " [9, 7, 14, 12, 8, 8, 3, 9, 4],\n", " [4, 3, 2, 14, 3, 3, 2, 3, 2],\n", " [10, 7, 7, 11, 14, 9, 5, 9, 5],\n", " [12, 3, 6, 11, 6, 14, 0, 9, 1],\n", " [12, 11, 11, 13, 9, 14, 14, 14, 10],\n", " [12, 2, 5, 11, 5, 6, 0, 14, 0],\n", " [13, 11, 10, 13, 9, 13, 4, 14, 14]]" ] }, "execution_count": 20, "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": 21, "id": "453f491d", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR - f1 score\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "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", 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\n", 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\n", "text/plain": [ "
" ] }, "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, 1.0) # 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] == \"DoC\":\n", " #continue\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": 22, "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))\n", " \n", " def showLatex(self, caption, key):\n", " \n", " columnConfig = \"|\".join([\"l\"] + [\"@{\\\\hskip3pt}c@{\\\\hskip3pt}\" for h in self.heading[1:]])\n", "\n", " text = \"\\\\begin{table*}[ht]\\\\scriptsize\"\n", " text += \"\\\\caption{\" + caption + \"}\\\\label{\" + key + \"}\"\n", " text += \"\\\\centering\\\\tabularnewline\\n\"\n", "\n", " text += \"\\\\begin{tabular}{\" + columnConfig + \"}\\\\hline\\n\"\n", " text += \" & \".join([\"\\\\textbf{\" + h + \"}\" for h in self.heading])\n", " text += \"\\n\\\\tabularnewline\\n\\\\hline\\n\"\n", " \n", " for row in self.rows:\n", " text += \" & \".join(row)\n", " text += \"\\n\\\\tabularnewline\\n\"\n", " \n", " text += \"\\hline\\end{tabular}\\end{table*}\\n\"\n", " \n", " return text" ] }, { "cell_type": "code", "execution_count": 23, "id": "d0e2faa0", "metadata": {}, "outputs": [], "source": [ "def tableRow(algo, dataset, myGans):\n", " row = []\n", " for gan in myGans:\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": 24, "id": "8c820b02", "metadata": { "scrolled": false }, "outputs": [], "source": [ "def p(f, bold=False):\n", " if f is None:\n", " text = \"?\"\n", " else:\n", " text = f\"{f:0.3f}\"\n", " \n", " if bold:\n", " return \" \\\\textbf{\" + text + \"} \"\n", " else:\n", " return \" \" + text + \" \"\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", "def pairMax(row):\n", " a = 0.0\n", " b = 0.0\n", " \n", " for (x,y) in row:\n", " a = max(a, x)\n", " b = max(b, y)\n", " \n", " return (a, b)\n", " \n", "with open(\"data_result/statistics/Tables.tex\", \"w\") as latexFile:\n", " for algo in algs:\n", " latexFile.write(\"\\n\")\n", " latexFile.write(\"% ### \" + algo + \"\\n\")\n", " latexFile.write(\"\\n\")\n", " f = \"\"\n", " heading = [\"dataset ($f_1~$score$~/~\\\\kappa~$score)\"]\n", " \n", " myGans = gans\n", " if algo[0:3] == \"DoC\":\n", " myGans = list(gans)[-4:]\n", "\n", " for g in myGans:\n", " heading.append(latex(g))\n", " table = Table(heading)\n", " \n", " avg = [[0.0, 0.0] for h in heading[1:]]\n", " mx = [[0.0, 0.0] for h in heading[1:]]\n", " cnt = 0\n", "\n", " for d in testSets:\n", " d = cleanupName(d)\n", " if d not in ignore:\n", " cnt += 1\n", " row = tableRow(algo, d, myGans)\n", " line = [latex(d)]\n", "\n", " m = pairMax(row)\n", "\n", " for (n, r) in enumerate(row):\n", " line.append(f\"{p(r[0], r[0] == m[0])} / {p(r[1], r[1] == m[1])}\")\n", " avg[n][0] += r[0] or 0.0\n", " avg[n][1] += r[1] or 0.0\n", " mx[n][0] = max(mx[n][0], r[0] or 0.0)\n", " mx[n][1] = max(mx[n][1], r[1] or 0.0)\n", " table.add(line)\n", "\n", " m = pairMax(avg)\n", " table.add([\"\\\\hline Average\"] + [f\"{p(a / cnt, a == m[0])} / {p(b / cnt, b == m[1])}\" for (a,b) in avg])\n", " #table.add([\"maximum\"] + [f\"{p(a)} / {p(b)}\" for (a,b) in mx])\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", " if tableB is not None:\n", " latexFile.write(table.showLatex(algo + \" (1)\", \"tab:results:\" + algo + \":A\") + \"\\n\")\n", " latexFile.write(\"\\n\")\n", " latexFile.write(tableB.showLatex(algo + \" (2)\", \"tab:results:\" + algo + \":B\") + \"\\n\")\n", " else:\n", " latexFile.write(table.showLatex(algo, \"tab:results:\" + algo + \":A\") + \"\\n\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "a3792b44", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "% ### LR\n", "% ### RF\n", "% ### GB\n", "% ### KNN\n", "% ### DoC\n" ] } ], "source": [ "for algo in algs:\n", " print(\"% ### \" + algo)\n", " heading = [\"dataset\"]\n", " for g in gans:\n", " if filterConvGen(g):\n", " heading.append(g)\n", " table = []\n", " \n", " avg = [[0.0, 0.0] for h in heading[1:]]\n", " cnt = 0\n", " \n", " for d in testSets:\n", " d = cleanupName(d)\n", " if d not in ignore:\n", " cnt += 1\n", " row = tableRow(algo, d, heading[1:])\n", " table.append([(d,d)] + row)\n", "\n", " for (n, r) in enumerate(row):\n", " avg[n][0] += r[0] or 0.0\n", " avg[n][1] += r[1] or 0.0\n", " \n", " table.append([(\"Average\", \"Average\")] + [(a / cnt, b / cnt) for (a,b) in avg])\n", " \n", " with open(f\"data_result/statistics/{algo}-f1.csv\", \"w\") as f:\n", " f.write((\",\".join(heading)) + \"\\n\")\n", " for row in table:\n", " f.write((\",\".join([str(x[0]) for x in row])) + \"\\n\")\n", "\n", " with open(f\"data_result/statistics/{algo}-kappa.csv\", \"w\") as f:\n", " f.write((\",\".join(heading)) + \"\\n\")\n", " for row in table:\n", " f.write((\",\".join([str(x[1]) for x in row])) + \"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 26, "id": "f1b75f48", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.480 - winequality-red-4\n", "17.141 - flare-F\n", "20.768 - abalone_17_vs_7_8_9_10\n", "22.928 - yeast4\n", "29.056 - abalone9-18\n", "33.003 - yeast6\n", "40.735 - car_good\n", "49.689 - hypothyroid\n", "51.936 - yeast5\n", "57.451 - car-vgood\n", "68.274 - kr-vs-k-zero-one_vs_draw\n", "74.547 - kr-vs-k-three_vs_eleven\n", "78.777 - shuttle-2_vs_5\n", "79.641 - kddcup-guess_passwd_vs_satan\n" ] } ], "source": [ "def dsWeight(d, score):\n", " w = 0.0\n", " for g in gans:\n", " for a in algs:\n", " x = getValueOf(g, d, a, score)\n", " if x is not None:\n", " w += x\n", " return w\n", "\n", "\n", "\n", "dataNames = [cleanupName(d) for d in testSets]\n", "dataNames.sort(key=lambda d: dsWeight(d, f1Score) + dsWeight(d, kScore))\n", "for d in dataNames:\n", " w = dsWeight(d, f1Score) + dsWeight(d, kScore)\n", " print(f\"{w:0.3f} - {d}\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 5 }