{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "pretty-performer", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-06-13 16:56:18.932546: 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", "2022-06-13 16:56:18.932568: 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 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\"" ] }, { "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()]\n", "algs = [\"LR\", \"RF\", \"GB\", \"KNN\", \"DoG\"]" ] }, { "cell_type": "code", "execution_count": 4, "id": "edf5b592", "metadata": {}, "outputs": [], "source": [ "testSets = [t for t in testSets if t[0:7] == \"folding\"]" ] }, { "cell_type": "markdown", "id": "seasonal-greek", "metadata": {}, "source": [ "# ProWRAS Data" ] }, { "cell_type": "code", "execution_count": 5, "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": "markdown", "id": "d7e8179d", "metadata": {}, "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": "6121f46b", "metadata": {}, "outputs": [], "source": [ "statistic = { }" ] }, { "cell_type": "markdown", "id": "e825147c", "metadata": {}, "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": "a23177bd", "metadata": {}, "outputs": [], "source": [ "def cleanupName(name):\n", " return name.replace(\"folding_\", \"\").replace(\"imblearn_\", \"\").replace(\"kaggle_\", \"\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "304d69ce", "metadata": {}, "outputs": [], "source": [ "dataset = [cleanupName(d) for d in testSets]" ] }, { "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", " 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\":\n", " n = \"DoG\"\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": 10, "id": "classical-rescue", "metadata": {}, "outputs": [], "source": [ "for gan in gans:\n", " if gan not in statistic:\n", " statistic[gan] = {}\n", " \n", " for ds in testSets:\n", " if ds != \"Average\":\n", " statistic[gan][cleanupName(ds)] = loadDiagnoseData(gan, ds)\n", " \n", " d = cleanupName(ds)\n", " if d not in dataset:\n", " dataset.append(d)" ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "id": "extra-taiwan", "metadata": {}, "outputs": [], "source": [ "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", "def drawTransparentRect(img, rect, color, opacity=1.0):\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]):\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", " \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(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 - row[i])\n", "\n", " for (n, i) in enumerate(indices):\n", " v = 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", "\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", " return statistic[g][\"Average\"][algo][score] \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)\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", " 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)\n", " img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.png\")\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "57fe8925", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR: f1 score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "LR: cohens kappa score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "RF: f1 score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "RF: cohens kappa score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "GB: f1 score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "GB: cohens kappa score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "KNN: f1 score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "KNN: cohens kappa score\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "DoG: f1 score\n", "Missing 'DoG' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('ctGAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('Repeater', 'abalone9-18')\n", "Missing 'DoG' in ('ProWRAS', 'abalone9-18')\n", "Missing 'DoG' in ('GAN', 'abalone9-18')\n", "Missing 'DoG' in ('ctGAN', 'abalone9-18')\n", "Missing 'DoG' in ('CTAB-GAN', 'abalone9-18')\n", "Missing 'DoG' in ('Repeater', 'car_good')\n", "Missing 'DoG' in ('ProWRAS', 'car_good')\n", "Missing 'DoG' in ('GAN', 'car_good')\n", "Missing 'DoG' in ('ctGAN', 'car_good')\n", "Missing 'DoG' in ('CTAB-GAN', 'car_good')\n", "Missing 'DoG' in ('Repeater', 'car-vgood')\n", "Missing 'DoG' in ('ProWRAS', 'car-vgood')\n", "Missing 'DoG' in ('GAN', 'car-vgood')\n", "Missing 'DoG' in ('ctGAN', 'car-vgood')\n", "Missing 'DoG' in ('CTAB-GAN', 'car-vgood')\n", "Missing 'DoG' in ('Repeater', 'flare-F')\n", "Missing 'DoG' in ('ProWRAS', 'flare-F')\n", "Missing 'DoG' in ('GAN', 'flare-F')\n", "Missing 'DoG' in ('ctGAN', 'flare-F')\n", "Missing 'DoG' in ('CTAB-GAN', 'flare-F')\n", "Missing 'DoG' in ('Repeater', 'hypothyroid')\n", "Missing 'DoG' in ('ProWRAS', 'hypothyroid')\n", "Missing 'DoG' in ('GAN', 'hypothyroid')\n", "Missing 'DoG' in ('ctGAN', 'hypothyroid')\n", "Missing 'DoG' in ('CTAB-GAN', 'hypothyroid')\n", "Missing 'DoG' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('ctGAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('ctGAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('ctGAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('Repeater', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('ProWRAS', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('GAN', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('ctGAN', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('CTAB-GAN', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('Repeater', 'winequality-red-4')\n", "Missing 'DoG' in ('ProWRAS', 'winequality-red-4')\n", "Missing 'DoG' in ('GAN', 'winequality-red-4')\n", "Missing 'DoG' in ('ctGAN', 'winequality-red-4')\n", "Missing 'DoG' in ('CTAB-GAN', 'winequality-red-4')\n", "Missing 'DoG' in ('Repeater', 'yeast4')\n", "Missing 'DoG' in ('ProWRAS', 'yeast4')\n", "Missing 'DoG' in ('GAN', 'yeast4')\n", "Missing 'DoG' in ('ctGAN', 'yeast4')\n", "Missing 'DoG' in ('CTAB-GAN', 'yeast4')\n", "Missing 'DoG' in ('Repeater', 'yeast5')\n", "Missing 'DoG' in ('ProWRAS', 'yeast5')\n", "Missing 'DoG' in ('GAN', 'yeast5')\n", "Missing 'DoG' in ('ctGAN', 'yeast5')\n", "Missing 'DoG' in ('CTAB-GAN', 'yeast5')\n", "Missing 'DoG' in ('Repeater', 'yeast6')\n", "Missing 'DoG' in ('ProWRAS', 'yeast6')\n", "Missing 'DoG' in ('GAN', 'yeast6')\n", "Missing 'DoG' in ('ctGAN', 'yeast6')\n", "Missing 'DoG' in ('CTAB-GAN', 'yeast6')\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "DoG: cohens kappa score\n", "Missing 'DoG' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('ctGAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n", "Missing 'DoG' in ('Repeater', 'abalone9-18')\n", "Missing 'DoG' in ('ProWRAS', 'abalone9-18')\n", "Missing 'DoG' in ('GAN', 'abalone9-18')\n", "Missing 'DoG' in ('ctGAN', 'abalone9-18')\n", "Missing 'DoG' in ('CTAB-GAN', 'abalone9-18')\n", "Missing 'DoG' in ('Repeater', 'car_good')\n", "Missing 'DoG' in ('ProWRAS', 'car_good')\n", "Missing 'DoG' in ('GAN', 'car_good')\n", "Missing 'DoG' in ('ctGAN', 'car_good')\n", "Missing 'DoG' in ('CTAB-GAN', 'car_good')\n", "Missing 'DoG' in ('Repeater', 'car-vgood')\n", "Missing 'DoG' in ('ProWRAS', 'car-vgood')\n", "Missing 'DoG' in ('GAN', 'car-vgood')\n", "Missing 'DoG' in ('ctGAN', 'car-vgood')\n", "Missing 'DoG' in ('CTAB-GAN', 'car-vgood')\n", "Missing 'DoG' in ('Repeater', 'flare-F')\n", "Missing 'DoG' in ('ProWRAS', 'flare-F')\n", "Missing 'DoG' in ('GAN', 'flare-F')\n", "Missing 'DoG' in ('ctGAN', 'flare-F')\n", "Missing 'DoG' in ('CTAB-GAN', 'flare-F')\n", "Missing 'DoG' in ('Repeater', 'hypothyroid')\n", "Missing 'DoG' in ('ProWRAS', 'hypothyroid')\n", "Missing 'DoG' in ('GAN', 'hypothyroid')\n", "Missing 'DoG' in ('ctGAN', 'hypothyroid')\n", "Missing 'DoG' in ('CTAB-GAN', 'hypothyroid')\n", "Missing 'DoG' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('ctGAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n", "Missing 'DoG' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('ctGAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n", "Missing 'DoG' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('ctGAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n", "Missing 'DoG' in ('Repeater', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('ProWRAS', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('GAN', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('ctGAN', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('CTAB-GAN', 'shuttle-2_vs_5')\n", "Missing 'DoG' in ('Repeater', 'winequality-red-4')\n", "Missing 'DoG' in ('ProWRAS', 'winequality-red-4')\n", "Missing 'DoG' in ('GAN', 'winequality-red-4')\n", "Missing 'DoG' in ('ctGAN', 'winequality-red-4')\n", "Missing 'DoG' in ('CTAB-GAN', 'winequality-red-4')\n", "Missing 'DoG' in ('Repeater', 'yeast4')\n", "Missing 'DoG' in ('ProWRAS', 'yeast4')\n", "Missing 'DoG' in ('GAN', 'yeast4')\n", "Missing 'DoG' in ('ctGAN', 'yeast4')\n", "Missing 'DoG' in ('CTAB-GAN', 'yeast4')\n", "Missing 'DoG' in ('Repeater', 'yeast5')\n", "Missing 'DoG' in ('ProWRAS', 'yeast5')\n", "Missing 'DoG' in ('GAN', 'yeast5')\n", "Missing 'DoG' in ('ctGAN', 'yeast5')\n", "Missing 'DoG' in ('CTAB-GAN', 'yeast5')\n", "Missing 'DoG' in ('Repeater', 'yeast6')\n", "Missing 'DoG' in ('ProWRAS', 'yeast6')\n", "Missing 'DoG' in ('GAN', 'yeast6')\n", "Missing 'DoG' in ('ctGAN', 'yeast6')\n", "Missing 'DoG' in ('CTAB-GAN', 'yeast6')\n", "(9, 9, 14, 14)\n", "(1024, 46, 644)\n", "Average: f1 score\n", "(9, 9, 5, 5)\n", "(1024, 165, 825)\n", "Average: cohens kappa score\n", "(9, 9, 5, 5)\n", "(1024, 165, 825)\n", "abalone_17_vs_7_8_9_10\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "abalone9-18\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "car_good\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "car-vgood\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "flare-F\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "hypothyroid\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "kddcup-guess_passwd_vs_satan\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "kr-vs-k-three_vs_eleven\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "kr-vs-k-zero-one_vs_draw\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "shuttle-2_vs_5\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "winequality-red-4\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "yeast4\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "yeast5\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "yeast6\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "Average\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n", "(5, 5, 9, 9)\n", "(1024, 82, 738)\n" ] } ], "source": [ "gans = [g for g in statistic.keys() if not g.startswith(\"convGeN\") or g == \"convGeN-majority-full\"]\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", "showDiagnoseDataset(\"Average\")\n", "\n", "gans = list(statistic.keys())" ] }, { "cell_type": "code", "execution_count": 18, "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": 19, "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": 19, "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": 20, "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": [ "
" ] }, "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": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "RF - f1 score\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "RF - cohens kappa score\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "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": [ "
" ] }, "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": [ "
" ] }, "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": [ "
" ] }, "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": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DoG - f1 score\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DoG - cohens kappa score\n" ] }, { "data": { "image/png": "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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, 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] == \"DoG\":\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": 21, "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": 22, "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": 23, "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] == \"DoG\":\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": 24, "id": "a3792b44", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "% ### LR\n", "% ### RF\n", "% ### GB\n", "% ### KNN\n", "% ### DoG\n" ] } ], "source": [ "for algo in algs:\n", " print(\"% ### \" + algo)\n", " heading = [\"dataset\"]\n", " for g in gans:\n", " if not g.startswith(\"convGeN\") or g == \"convGeN-majority-full\":\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": 25, "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 (ipykernel)", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }