{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "pretty-performer", "metadata": {}, "outputs": [], "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": "20ba8039", "metadata": {}, "source": [ "convGanMax = {}\n", "for g in gans:\n", " if g[0:7] == \"convGAN\":\n", " for d in statistic[g].keys():\n", " if d not in convGanMax.keys():\n", " convGanMax[d] = {}\n", " for a in statistic[g][d].keys():\n", " if a not in convGanMax[d].keys():\n", " convGanMax[d][a] = {}\n", " for s in statistic[g][d][a].keys():\n", " x = statistic[g][d][a][s]\n", " if x is None:\n", " x = 0.0\n", " if s not in convGanMax[d][a].keys():\n", " convGanMax[d][a][s] = x\n", " else:\n", " convGanMax[d][a][s] = max(x, convGanMax[d][a][s])\n", "\n", "gs = []\n", "for g in list(gans):\n", " if g[0:7] == \"convGAN\":\n", " del statistic[g]\n", " else:\n", " gs.append(g)\n", "\n", "statistic[\"convGAN\"] = convGanMax\n", "gs.append(\"convGAN\")\n", "gans = gs" ] }, { "cell_type": "markdown", "id": "public-collins", "metadata": {}, "source": [ "# Show Statistics" ] }, { "cell_type": "code", "execution_count": 12, "id": "removable-sterling", "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", " gans = list(statistic.keys())\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", " gans = list(statistic.keys())\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", " gans = list(statistic.keys())\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": [ "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": 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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HyvEC9gZmATtLWqHn9E8AxwGHNndFERHNkbrTw8093BZJ2oQqoW5re76kNYH/AL5v+9uSngGsYvv2pZxmW+B227+SdAmwG/C/Pdu/Ctwg6fNDtOUg4CCA568x6kuKiGhc7uHGcOwAfNv2fADb9/dtF7DoTzBJry893zskbVNWzwTOKN/PoG9YufSET2aIV7vbPs72DNsznrnKqK8nIqJh3RlSTg+3XYsl1H62H5D0kKT1bd9u+wLgAknfB5aXNBXYC9hD0uHlfGtJeobtB3tO9WVgDvDN2q4kIqIFA0PKXZAebrsuAt4saS2AMqT8IPCMnn0+CxwrafWyj4CB+7SvA663/Tzb02y/ADiLauLVIqXn/C3gnfVdSkREO7rSw03CbZHtm4AjgEslXU91//YM4DBJ15VJU8cCPwSukXQDcAVwXfnMBL7Td9qzgP0GCfdFILOVI2KSGf2EqUyaWsbYPgk4qW/1xn3LXyiffrMGOd85wDnl+yo96+8GVhpLWyMiJpomCl9IOhR4F9UtwHnAgbYfGel50sONiIhOq3NIWdJzqCadzrD9MmAq8JbRtDM93IiI6LBGhoaXA1aU9DjVSOHvRnuSiIiITqp7SNn2XZK+APwGeBi40PaFozlXhpQjIqLTxjhpam1Js3s+By1+bq0BvAFYH3g2sLKkt46mnenhRkREh4359Xzzbc9YyvbXUVXzuxdA0v8C2wD/PdJASbgREdFZDcxS/g2wtaSVqIaUdwRmj+ZESbgREdFpdU6asn2NpG9TVet7gqoGwnGjOVcSbkREdNiYh5SHjmD/G/BvYz1PEm5ERHRaV2opJ+FGRERnNVFparwk4cZT3MM6HMO+rcR+39HHtBJ3Injfwe1eu45pr5dw3nm7tRYbYFfOby32+ezaWmxo99rHJ3T9Q8rjJQk3IiI6LUPKERERNcuQckREREPSw42IiKhd7uFGRETULkPKERERDcmQckRERM2kDClHREQ0Ij3ciIiIBqSHGxERUbMMKUdERDSkK0PKU9puQNdIepakMyT9StLPJF0s6a+S5kq6X9Lt5fsPy/6bS7Kk1/edZ0HZ73pJcyRts5SYG0r6fol5bYm5Xd8+35V0Vd+6j5e2rdOz7i/j85OIiJgYpkxZOOpPo+1sNFrHSRLwHeAS2xvY3hg4FHi97enAOcBhtqfbfl05bCZweflnr4fLfpsBHwE+u4SYKwDnAseVmK8A3ge8sGef1YEtgNUlrd93ivnAP4/2miMiJjYjjf7TpAwpj8xrgcdtf31ghe25S9q5JOi9gZ2AH0tawfYjg+y6KvDHJZxmf+Aq2+f0xLwRuLFnn72A7wF3A29h8eR9AjBL0pG271/KtUVEdE6XCl+khzsyLwOuHcH+2wK32/4VcAnQ+w6yFcuQ8s3A8cCnlnCOTYA5Q8SZCZxePv096b9QJd1DRtDuiIjOyJByQJX8zijfz2DxZDgwpLwRsAtwcukRL5Wk70i6UdL/luV1gRcBl9v+JfCEpJf1HXY0cICkVZdy3oMkzZY0+y9/eXjYFxgREcOThDsyNwGvGM6OkqZSDfX+P0l3AMcAu0p6Rv++tq8C1gaeKemI0vOd2xNzi5593wjMAtYsq/YF1gBuL3GmUQ0r957/T8BpwD8uqb22j7M9w/aMVVZZcTiXGBExMSwcw6dBSbgj8yPg6ZL+fmCFpC0lvWaQfV8HXG/7eban2X4BcBawZ/+OkjYCpgL32T689Hynl82nAdtK2qPnkJV6vs8EdikxplH9QbBYwi3+A3g3uW8fEZONx/BpUBLuCNg28EZgp/KIzk3Ax4HfDbL7TKoZzb3OAvYr31fs6cmeCRxge8EgMR8GdgfeI+m28ujPvwKfljQNeD5wdc/+twMPSHpl33nml/Y8fUQXHRExkZnO9HDT2xkh278D3ryEbbMG+96z7hyqR4ewPXUEMW9m8QlXvZ4zyP4DQ9DX9K3/APCB4caNiOiEbkxSTsKNiIiO60ahqSTciIjosIEh5Q5Iwo2IiG5LDzciIqIB6eFGRETULEPKERERDcmQckRERM3Sw42IiGhIergRERENSA83IiKiZhlSji77859X5fzzd20l9vtuOaaVuIsc3G74NvmWId8OWRtt2O6YoGnv2jmvvdDA5PhvPkPKERERDUgPNyIiogHp4UZERNSsQ/dw8z7ciIjotprfhytpdUnflnSzpJ9LetVompkebkREdFv9Q8pHAf9ne29JywMrjeYkSbgREdFdNQ8pS1oV2A6YBWD7MeCx0ZwrQ8oREdFtHsMH1pY0u+dzUN/ZXwjcC3xT0nWSjpe08miamR5uRER029h6uPNtz1jK9uWALYD32b5G0lHAh4GPjTRQergREdFdA0PK9U2auhO40/Y1ZfnbVAl4xJJwIyKi28Y2pLz0U9t/AH4r6SVl1Y7Az0bTzAwpR0REt9X/HO77gFPLDOXbgANHc5JJ08OVtJWkSyTdImmOpHMlvbxvn+slnd637kRJd0l6elleW9IdNbXxeEkbj/YYSR8dRcyPl+ubWz67jfQcERET1lh6t8N8nMj2XNszbG9qe0/bfxxNUydFD1fSusC3gP1sX1nWvRrYAJhXll9K9QfGdpJWtv1QzykWAO8Ajq2znbbfNZL9JU3tO+ajwGdGEfpLtr8wiuMiIia+VJp6Kklvl3RD6WmeIukFki4q6y6S9Pyy34mSjpZ0paTbJO1d1p/Z20Mr++0FvBc4aSDZAti+3PbZPeH3A04BLgT26Gval4FDJS31D5AS71hJF5d2vUbSCaXyyIk9+x1bppffJOkTPesvkTSjfJ8paZ6kGyUd2bPPXyR9UtI1wKsGjpH0OWDF0ks9VdKnJB3Sc9wRkibDez8iIkam5kpT46WxhCtpE+BwYAfbmwGHAF8BTra9KXAqcHTPIesBrwZ2Bz5X1p0B7FvOtzzVzevzgE2AOUM0YV/gTOB0YGbftt8AlwNvG8alrAHsABwKfA/4Uon/cknTyz6Hl2nmmwKvkbRp7wkkPRs4spxnOrClpD3L5pWBG22/0vblA8fY/jDwsO3ptvcHvgEcUM43BXgL1c9wMO8tf9ScIGmNYVxjREQ3NDCkPF6a7OHuAHzb9nwA2/cDrwJOK9tPoUqwA862vdD2z4B1y7rzgR3K/dZdgctsP9wfSNI1pdd5VFneErjX9q+Bi4AtBkk8nwEOY+ifyfdsm2qo+m7b82wvBG4CppV93ixpDnAdVTLuv2+7JXCJ7XttP0GVKLcr2xYAZw3RBmzfAdwnaXNgZ+A62/cNsuuxVEPr04HfA18c7HySDhp48Puxxx4YKnxExMSRHu5TiKH/nujd/mjfsdh+BLgEeD1Vj/WMsv0mep6Lsv1KqoeSVyurZgIblclQvwJWBfZaLLB9KzAXePOioNUw7VxJcwdp18K+Ni4ElpO0PvBBYMfScz8XWKHvOpf2tutHbC9YyvZex1OVGzsQOKG0+ZulzeeV67rb9oLyR8F/AVsNdiLbx5VJATOWX37VYYaPiJgA0sN9iouoen5rAUhaE7iSaigUYH+qYd2hnEGVYP4GuKCs+yowS9I2PfutVOJMAfYBNrU9zfY04A08dVgZ4AiqZAmA7cPLEO704VxgsSrwEPDnMplr10H2uYZqqHltSVNLWy4dxrkfl/S0nuXvALtQ9ZgvKG0+sLR5NwBJ6/Xs/0bgxhFcS0TExFZ/4Ytx09gsZds3SToCuFTSAqrh1oOBEyQdRlWrcjjPNl0InAycU4pIY/sPkvYFjpT0HOAeYD7wSaqh2rts39VzjsuAjfuS0UAb5zDKKiLlHNdLuo6q130bcMVTd/HvJX0EuJiqt3ue7e8O4/THATdImmN7f9uPSboY+NNSesWfL/eWDdwBvHvkVxURMYF1ZJZyo48F2T4JOKlv9Q6D7Derb3mVnu+PA2sNcszVwGuWEHrrvn0XUE3KgvIGiJ5tb1rCORZrV7mH+rIlbFvsnD3WAu4v+5zGk/eve2Os0re8fc/3DwEfGlguvfetqXrwS2rzcCaCRUR0V8NDw6M1aQpfTHSSfgDMs337OJ1vY+BW4CLbt4zHOSMiOidDytHP9k7jfL6fUb02KiJi2daRHm4SbkREdFvu4UZERNRsYEi5A5JwIyKi2zKkHBER0YD0cCMiIhqQHm5ERETNcg83IiKiIUm4ERERDciQckRERM0ypBwxOgdzdLsNaDH8+ecP9mKpBuPv2m78NqnNLtJu7YUGOProg9sLfvAx43Oe9HAjIiIakB5uREREzTKkHBER0ZAMKUdERDQgPdyIiIiamfRwIyIiGpEebkRERAOScCMiImqWIeWIiIiGpIcbERHRgCTciIiImnVoSHlK2w0YDklbSbpE0i2S5kg6V9LL+/a5XtLpfetOlHSXpKeX5bUl3VFTG4+XtPFoj5H00VHE3EjSXEnXSdpgKfudKGnv8v0SSTNGGisiYsJaOIZPgyZ8wpW0LvAt4KO2N7S9BfBZYIOefV5KdS3bSVq57xQLgHfU3U7b77L9s+HuL2lq3zEjTrjAnsB3bW9u+1ejOD4iovs8hk+Dxi3hSnq7pBtKT/MUSS+QdFFZd5Gk55f9TpR0tKQrJd3W0/M6U9JuPec7UdJewHuBk2xfObDN9uW2z+4Jvx9wCnAhsEdf074MHCppqcPnJd6xki4u7XqNpBMk/VzSiT37HStptqSbJH2iZ/2inqOkmZLmSbpR0pE9+/xF0iclXQO8auAYSZ8DViy91VMlfUrSIT3HHSFpsVd6lJ/V+4F3lTZPk3Rjz/YPSvr40q45IqLzBmopLys9XEmbAIcDO9jeDDgE+Apwsu1NgVNZ/MVn6wGvBnYHPlfWnQHsW863PLAjcB6wCTBniCbsC5wJnA7M7Nv2G+By4G3DuJQ1gB2AQ4HvAV8q8V8uaXrZ53DbM4BNgddI2rT3BJKeDRxZzjMd2FLSnmXzysCNtl9p+/KBY2x/GHjY9nTb+wPfAA4o55sCvIXqZ0jPMecBXwe+ZPu1w7i2pZJ0UPlDYvZjjz0w1tNFRDRnWUq4VMnl27bnA9i+H3gVcFrZfgpVgh1wtu2FZTh13bLufGCHcr91V+Ay2w/3B5J0Tel1HlWWtwTutf1r4CJgC0lr9B32GeAwhr7e79k2MA+42/Y82wuBm4BpZZ83S5oDXEeVjPvv224JXGL7XttPUCXK7cq2BcBZQ7QB23cA90naHNgZuM72fUMdNxa2j7M9w/aM5Zdftc5QERHjaxkbUhZDN713+6N9x2L7EeAS4PVUPdYzyvabgC0WncR+JfAxYLWyaiawUZkM9StgVWCvxQLbtwJzgTcvCloN086VNHeQdi3sa+NCYDlJ6wMfBHYsPfdzgRX6rlODXPuAR2wvWMr2XscDs4ADgRNKm79Z2nzeIPs/weL/PvvbFREx+SxrQ8pUPcs3S1oLQNKawJVUQ6EA+1MN6w7lDKoE8zfABWXdV4FZkrbp2W+lEmcKsA+wqe1ptqcBb+Cpw8oAR1AlSwBsH16GcKcP5wKLVYGHgD+XyVy7DrLPNVRDzWtLmlracukwzv24pKf1LH8H2IWqx3xBafOBpc27DXL83cA6ktYqowS7D/uqIiK6rIEerqSp5YmQ74+2mePyHK7tmyQdAVwqaQHVcOvBwAmSDgPupUqkQ7kQOBk4x/Zj5dx/kLQvcKSk5wD3APOBT1IN1d5l+66ec1wGbCxpvUHaOIee3vIorvN6SddR9bpvA6546i7+vaSPABdT9XbPs/3dYZz+OOAGSXNs72/7MUkXA38aTq/Y9uOSPkmV8G8Hbh7BpUVEdFczPdVDgJ9TdbxGZdwKX9g+CTipb/UOg+w3q295lZ7vjwNrDXLM1cBrlhB66759F1BNyoJqSLZ325uWcI7F2lXuob5sCdsWO2ePtYD7yz6n8eT9694Yq/Qtb9/z/UPAhwaWS+99a6oe/JLa/PG+5aNZfHLaYO3fvn97RERnDQwp10jSc4G/pRop/cBozzPhn8PtAkk/AObZvn2czrcxcCtwke1bxuOcERGT1tiGlNceeEKjfA4aJMKXgX9hjKk9pR3Hge2dxvl8PwNeOJ7njIiYtMbWw51fHvUclKTdgXtsXytp+7EESsKNiIjuqv/xnm2BPUqxoRWAVSX9t+23jvREGVKOiIhuq/GxINsfsf3c8hTMW4AfjSbZQnq4ERHRdXk9X0RERM0arBhl+xKqAk2jkoQbERHdlh5uREREAzryAvok3IiI6K4GCl+MlyTciIjotiTciJE7+taD223A+9oLffBTq3I2alfOby22b1naS7YasGF7odXyeOj7Dj6mtdjj9n97hpQjIiJqliHliIiIhqSHGxER0YD0cCMiImqWIeWIiIiGZEg5IiKiAenhRkRENCA93IiIiJrlHm5ERERDknAjIiIakCHliIiImnVoSHlK2w0YDklbSbpE0i2S5kg6V9LL+/a5XtLpfetOlHSXpKeX5bUl3dFg05F0vKSNm4zZF//j5Wcwt3x2a6stERG18Bg+DZrwPVxJ6wLfAvazfWVZ92pgA2BeWX4p1R8P20la2fZDPadYALwDOHYc2rKc7SdGcoztd4017iDtmGp7wQgO+ZLtL4x3OyIiJoRlrYcr6e2Sbig9zVMkvUDSRWXdRZKeX/Y7UdLRkq6UdJukvcv6M3t7X2W/vYD3AicNJFsA25fbPrsn/H7AKcCFwB59TfsycKikpf5xUeJ9XdKPJf1S0u5l/SxJ/yPpe8CFktaUdHa5rqslbSppOUk/lbR9Oeazko4o3y+RNKN8/4ukIyVdK+mHPT332yTtUfaZVtowp3y2Keu3l3SxpNOAeZI+JemQnvYfIanlV+1ERDRsYEh5tJ8GjUvClbQJcDiwg+3NgEOArwAn294UOBUWe/fYesCrgd2Bz5V1ZwD7lvMtD+wInAdsAswZogn7AmcCpwMz+7b9BrgceNswLmUa8Brgb4GvS1qhrH8VcIDtHYBPANeV6/poucYngFnAsZJ2AnYp+/VbGbjE9iuAB4FPAzsBbwQ+Wfa5B9jJ9hblunp/blsBh9veGPgGcACApCnAW6h+zoN5b/kD4QRJawzj5xAR0R0dGVIerx7uDsC3bc8HsH0/VZI6rWw/hSrBDjjb9kLbPwPWLevOB3Yo91t3BS6z/XB/IEnXSPq5pKPK8pbAvbZ/DVwEbDFIUvkMcBhDX++3SrtuAW4DNirrf1CuiXIdp5Tr/BGwlqTVbN9U1n8PeIftxwY5/2PA/5Xv84BLbT9evk8r658G/JekecD/AL33f39i+/YS+w7gPkmbAztT/RFw3yAxj6Uafp8O/B744mAXLukgSbMlzX7ssQcG/eFERExIy1IPFxBD/63Qu/3RvmOx/QhwCfB6qp7dGWX7TcAWi05ivxL4GLBaWTUT2KhMhvoVsCqw12KB7VuBucCbFwWthmDnSpq7hDb2LvfeEx7sTdkD+70c+BNP/hHR73HbA/supPwcbC/kyfvphwJ3A5sBM4Dle47vbQfA8VQ96wOBEwAkfbNc13nl3HfbXlBi/BdVL/mpF2AfZ3uG7RnLL7/qEpofETHBjKV329Ee7kXAmyWtBSBpTeBKqmFOgP2phnWHcgZV8vgb4IKy7qvArIF7mcVKJc4UYB9gU9vTbE8D3sBTh5UBjgA+OLBg+3Db021P79lnH0lTJG0AvBD4xSDnuaxcD+We7XzbD0h6E7AWsB1wtKTVh3G9g1kN+H1JkG8Dpi5l3+9QDV9vSfl52T6wXNdupY3r9ez/RuDGUbYrImJi6kgPd1xmKdu+qUwSulTSAuA64GDgBEmHAfdSJdKhXAicDJwzMCRr+w+S9gWOlPQcqnuc86nueW4H3GX7rp5zXAZs3JdoBto4h57e8iB+AVxK1UN9j+1HpKd0aD8OfFPSDcBfgQMkrU11L3pH27+V9BXgKMo91hH6GnCWpH2Ai3lqr7b3mh6TdDHwp6XMWv68pOlUf8vdAbx7FG2KiJi4OjJLWU+OcC7bJJ0IfN/2t9tuy3CVHv4cYJ9y33lcrLbaht5226OH3rEG59HyY8Lvay/0wee38zMfcPSuLU5y37C90G3HV8tlkjzoXbJmCK61PWMs55ixtjz7DWNowwljb8NwdaLwRTxVKaZxK3DReCbbiIjOWZaGlCcD27PabsNIlBneL2y7HRERrevIQG0SbkREdFeHaikn4UZERLcl4UZERDQgQ8oRERE1y5ByREREQ9LDjYiIaEB6uBERETXLkHJERERDMqQcXfXooytwyy3t1Lo7eNd2yxtueGt7RbvOP3/X1mIDnL9re/Hb/vd+y9Et1nZssaImtF1acpzKSnakh5vSjhER0V01v55P0vMkXVzew36TpENG29T0cCMiotvq7eE+Afyz7TmSngFcK+kHpbzuiCThRkREt9WYcG3/Hvh9+f6gpJ8DzwGScCMiYhkyzKHh8SBpGrA5cM1ojk/CjYiIbhtbD3dtSbN7lo+zfVz/TpJWAc4C3m/7gdEESsKNiIhuG1sPd/5QL6CX9DSqZHuq7f8dbaAk3IiI6K6aC19IEvAN4Oe2/2Ms58pjQRER0W0Lx/AZ2rbA24AdJM0tn91G08z0cCMiottqnDRl+3LGqUJHEm5ERHRXailHREQ0pCO1lFu/hytpK0mXSLpF0hxJ50p6ed8+10s6vW/diZLukvT0sry2pDsabDqSjpe0cZMx++JvVO4nXCdpg6Xsd6Kkvcv3SyQtdUZeRESn1HsPd9y0mnAlrQt8C/io7Q1tbwF8FtigZ5+XUrVzO0kr951iAfCOcWrLiHv7tt81mvJeQ7Rj6gh23xP4ru3Nbf9qPNsREdEJA0PKkyXhSnq7pBtKT/MUSS+QdFFZd5Gk55f9TpR0tKQrJd3W06s6s3dWV9lvL+C9wEm2rxzYZvty22f3hN8POAW4ENijr2lfBg4dKlmWeF+X9GNJv5S0e1k/S9L/SPoecKGkNSWdXa7rakmbSlpO0k8lbV+O+aykI8r3Rb1FSX+RdKSkayX9sKfnfpukPco+00ob5pTPNmX99qU49mnAPEmf6i2QLekISQf3XdNuwPuBd5Vjp0m6sWf7ByV9fGk/l4iISaHGlxeMpyETrqRNgMOBHWxvBhwCfAU42famwKlA77u11gNeDewOfK6sOwPYt5xveWBH4DxgE2DOEE3YFzgTOB2Y2bftN8DlVFO2hzINeA3wt8DXJa1Q1r8KOMD2DsAngOvKdX20XOMTwCzgWEk7AbuU/fqtDFxi+xXAg8CngZ2ANwKfLPvcA+xUevL7svjPbSvgcNsbUz3zdQCApCnAW6h+zovYPg/4OvAl268dxvVHRExOHenhDmcYdQfg27bnA9i+X9KrgDeV7acAn+/Z/2zbC4GflSFjgPOBo8v91l2Ay2w/XD1P/CRJ1wCrAhfaPkTSlsC9tn8t6U7gBElr2P5jz2GfAc4Bzh3iOr5V2nWLpNuAjcr6H9i+v3x/NbBXuc4fSVpL0mq2b5J0CvA94FW2Hxvk/I8B/1e+zwMetf24pHlUyR7gacBXJE2nGg5/cc/xP7F9e4l9h6T7JG0OrEv1R8B9Q1zfmEg6CDgIYLnlnl1nqIiI8TWJJk2JoS+nd/ujfcdi+xHgEuD1VD27M8r2m4AtFp3EfiXwMWC1smomsFGZDPUrqmS812KB7VuBucCbFwWthmDnSpq7hDb2Lj/U394l7Pdy4E9UCXAwj9se2Hch5edQkvzAHzaHAncDmwEzgOV7ju9tB8DxVD3rA4ETACR9s1zXeYPEf4LF/32uMMg+S2T7ONszbM+YOnXNkRwaEdGeSXYP9yLgzZLWApC0JnAl1TAnwP5Uw7pDOYMqefwNcEFZ91Vg1sC9zGKlEmcKsA+wqe1ptqcBb+Cpw8oARwAfHFiwfbjt6ban9+yzj6QpZTbvC4FfDHKey8r1UO7Zzrf9gKQ3AWsB21H11FcfxvUOZjXg9yUJvw1Y2gSp71CNBmxJ+XnZPrBc12BVTu4G1im98qdTDelHREx+HUm4Qw4pl+HUI4BLJS0ArgMOphrePQy4lyqRDuVC4GTgnIEhWdt/kLQvcKSk51Dd45xPdc9zO+Au23f1nOMyYGNJ6w3Sxjn09JYH8QvgUqoe6ntsP9I/pA18HPimpBuAvwIHSFqb6l70jrZ/K+krwFGUe6wj9DXgLEn7ABfz1F5t7zU9Juli4E+2Fwx14jJ8/Umq10bdDtw8ivZFRHRPR4aUh/UojO2TgJP6Vu8wyH6z+pZX6fn+OFUvsf+Yq6kmMw1m6759F1BNyoJquLV325tYuitsH9p3zInAiT3L91P1ovu9uGefo3u+b9/zvfdaP94XZ5Xyz1uATXs2faSsv4RqyH2R0sPfmqqXP6hB4hzN4hOxBtbPGqzNERGd16FKU60XvoinUlVM41bgopKkIyJiSTryWNAyUdqxv+c90ZViGi9sux0REZ3QkR7uMpFwIyJikurQkHISbkREdNtkmjQVERExYaWHGxERUbMWJj+NVhJuRER0W3q4ERERDUjCjYiIqFmGlCMiIhqSHm5EREQDOtLD1ZNvlIuoSLoX+PUYTrE21Uso2tBm7LbjL6ux246fax+9F9h+5lgaMGN5efazRn+8fsu1tmeMpQ3DlR5uPMVY/weQNLup/4AnUuy24y+rsduOn2tv79oXyZByREREAzoyUJuEGxER3ZVayrGMO24Zjd12/GU1dtvxc+1t60gPN5OmIiKis2Y8TZ69xuiP172ZNBURETG0DClHREQ0pCMDtVPabkB0nyrPa7sdEbGMWjiGT4PSw40xs21JZwOvaDKupC2B39r+Q1l+O7AXVdGOj9u+v8bYz1/adtu/qSt2tEfSFkvbbntOU21p0oS/7o70cJNwY7xcLWlL2z9tMOZ/Aq8DkLQd8DngfcB0qtmTe9cY+1yq/83Vs87AM4F1gKk1xkbS/1vKZtv+VI2xH+TJX3ED12+q3yfL267t94qkXWz/X/m+GvAfwJbAjcChtu+uK3bxxaVsM7BDXYFbvvbWrntIHbqHm1nKMS4k/Qx4CXAH8BDVL2Lb3rTGmNfb3qx8/ypwr+2Pl+W5tqfXFXuQtkwDPkT1B8DRto+pOd4/D7J6JeBdwFq2V6kzfl9bngH8I/Bu4Du2B2vbeMWaY3uL8v144A/AfwFvAl5je8+6YrdtWb72pZkxVZ694uiP10OZpRzds2sLMadKWs72E8COwEE92xr5b1vShsDhwCupegEH23687ri2F/U4SsI7BHgHcAZL742MG0mrA+8H3g6cBmxp+74mYhczev6o+pKkA+oOKOlNS9tu+3/rbkPR6LVPoOteQgNajT5sSbgxLmz/WtKrgQ1tf1PSM4G6e1mnA5dKmg88DPwYQNKLgD/XGVjSy6gS7SbA54F32l5QZ8xB2rAm8AFgf+AkYAvbf2wg7trAPwP7AicAm9uu9efdYx1JH6AaQVlVkvzkMF0Tk0D/binbDNSZeNq89jave+k6NKSchBvjQtK/ATOohpW/CTwN+G9g27pi2j5C0kXAesCFfb983ltX3OJ64LdU93K3AraSnryda/vgOoNL+neqocTjgJfb/kud8fr8GriX6t/zX4F39l37f9QY+7+AZ5TvJ1G9reZeSc8C5tYYFwDbB9YdYylau/aWr3toHenh5h5ujAtJc4HNgTm2Ny/rbqjzHu4S2rEy8EZgpu2/rTHOUofwbJ9UV+wSfyHwKPAEi/+6Gbh3vmqNsT/Okn/F2fYn64o9USxp0tpkv/aJeN0zpsizx9B11OND38OVtAtwFNVkyONtf240sdLDjfHyWHk8yLAo8TVC0vLAbsB+wC7AWcDX64y5pIQqaQWWPvw2XvFbe4Z+YGLaYMqjWo2S9H3buzcc9qGe7ysAuwM/b7gNbVz7hLjuxdQ8pCxpKvBVYCfgTuCnks6x/bORnisJN8bLtyT9J7C6pL+nmsBzfJ0BJe0EzAReD1wMnAJs1fTwV/kfcueetvwY+J8m21DasTKwJ7Bfnb37QeJuDLyF6vr/THVroUnPaTjeYpPWACR9ATin6XbQ8LVPoOteXL0DtVsBt9q+DUDSGcAbgCTcaIftL5QE+ADVfdz/Z/sHNYe9gCq5vdr27QCSjqo55iLl2d/9gL8FfkJ1v3p9239tsA2N9+5L3BdQJdiZVMPaL6CaOXtH3bEHcV0LMfutBLywhbhtX3tb173ItXCBFrL2GE6xgqTZPcvH2e59C9JzqOZrDLiT6qmEEUvCjXEh6UjbHwJ+MMi6uryCqmf1Q0m3UT0SU2vBiQGS7gR+AxwLHGb7QUm3N5Vs2+zdS7oSWI3q57237VvKtd9Rd+yeNrwXONX2H22/o6m4PfHn8WS/aipVwZNG7mO2ee1tXveS2N6l5hAaZN2o+tRJuDFedqIq/NBr10HWjRvb11H9hf8hSdtSJaDlJZ1PVYChznd1nkU1fLsvsEDSd2l2rmSbvft7gecC61L9wr2F5ueJPovqXtocqkeTLuiZpV4bSeuXn3fvfdMngLvL8+BNaPzaJ8h1t+VOoLdW/HOB343mRJmlHGMi6R+oqgy9EPhVz6ZnAFfYfmvD7ZlCVe3p723vU3MsAa+lSvS7UfX63gmcW/djOpI2p+rd7w0M9O7/n+0X1Bm3J/5qVHWrZwIvAlYHXm/7J03EL20Q1b3zA6nuG38L+IbtXy31wLHFvNb2KyRdZHvHuuIMox2NXvtEue42SFoO+CVVcZ27gJ9SzZO4acTnSsKNsSi/eNcAPgt8uGfTg3W+PKAn/nOonsO9wfZjktahqn40y/az647f046nUd1DnQnsbHss95RGGnugd78X1fOYdffu++OvS9XTfwvwPNuNvTlK0mZUSWcXqqH1rYEf2P6XmuJdB5xNVULzS/3ba34Gub8tjV37RLruNkjaDfgy1TD6CbaPGNV5knBjPJWEt8LAsmt8a46k91NVe7oVeDrVc3L/AZwMfN7272uM/Qbguba/WpavoXppAVQ9zVPqir2UNg307me2VahA0gts/7qBOAcDBwDzqWbDn2378fIzuMX2BjXFfQnVrYT3M8jkNNufqCNuXxsav/aJcN2TQRJujAtJf0eV7J4N3EM1a/XntjepMebPqO5h3q/qdXm3AtvZvrqumD2xrwDeYvu3ZXku1ZDTysA36x52K48irTgwdC1pa2D5svk62w/WGPvVwAttn1yWvw2sWTZ/2vaP6ord04ZPUg2hPiW5S3qp7VqfDZW0q+3z64yxlNifoOplNX7tbV73ZJAX0Md4+TTVkNYvba9PlXyuqDnmIwPD1qUn/csmkm2x/ECyLS63fV9pRxNFP46kunc+4HTgMOBjVL3+On0C6H2M4iUl9seBWoZyB7F+f8KRdApA3cm2xFiUdCR9v+54PbGmAHstaRSh7mtv67oni8xSjvHyuO37JE2RNMX2xZKOrDnmcyUd3bO8Tu+y661nvEbvgu3e2s3PrDHugB2p3oU64E+2/65MpvlxzbFX7auyc4vtawEkfbbm2AMWGzkpPf5XNBS7X2PFJ2wvlHS9pOfXebtmmBovONJ1SbgxXv4kaRWqX/anSrqH6rGBOh3Wt3xtzfF6XSPp723/V+9KSe+mKoJRtyl9j2N8CKpCxuXfQ51W712w3fvqtnXrDCzpI8BHgRUlPTCwGniM6kUObWi6+MR6wE2SfkJPqUXbezTcjraLbnRO7uHGuChlBR+muk2xP9UjMqe6xvejSpoOXN/E85eDxF6Hatbmo8CcsvoVVJO39rR9d83xf05V6OLBvvWrAdfY3qjG2N8Dvm773L71uwP/4AbKSkr6rO2P1B1nKfEXFZ9oIfZrBltv+9IGYrd23ZNBEm6Mm1Lub0PbP5S0EjC15sk7s4H1qRLeFcCVwNW2H1jqgePbhh14cnjzpiYmDJW4H6CakfyegaHF8vM/FrjIfTVvxzn2i6heS3gli/+xsQ2wu+1f1hh7I9s3S9pisO225wy2voZ2fJrqMahGC2+0bVm97vGShBvjQtULCw4C1rS9gaQNqXpBdc/WXYmquPg25bMl8Aeqohv/uLRju07Se6iGV1emqvT0EPA528c2EPvpVCMZi/7YAE6z/UjNcY+zfZCkiwfZbNs71Bm/ry2NF94ocbcGjgFeSjUzfSrwkGt8JWNf/FauezJIwo1xUR6L2YpqOHPgfbjzbL+8ofgrU82S3hZ4O9U9zlaLqjdB0gupHsPSwGhCTxm+SanM1H2V7bpnwQ+nLY0W3igxZ1P1Mv+HKuG9nWpk6aN1xRykDY1f92SQSVMxXh4tlZ6AReXQ6q7vuh9Vr3Y61b3UnwLXUD2b+4c6Y08g37bdP7z6bRqesStpziDtqEWZqfsF4FVNxBvMIMUnDustPkHNj0fZvlXSVNsLgG+qeqFE7dq+7q5Lwo3xcqmkgdmjO1E9I/q9mmMeB9xMVfnmsjrvHU40kjaiGs5dXVLvLOFV6an01WSTGo53oaS9gP9t6R7i2sCb+p+HLX8M1P1C+L+qei3jXEmfB35PM89+A6xFe9fdeRlSjnFR/sJ9J9W9HVG9zeb4On8ZlmcvN+PJ+7cvofrlcxVwVVMTmNpQSkvuCewBfJcnE96DwOm2r2q4PZ+2/a8NxnuQKsksoJodL6p7uE3dxzzF9tuGWldT7BdQ3UZ4GnAo1RMBX7N9a81xp1DVLH9ZnXEmsyTcGDflr+6NqIaSf2H7sYbjr0v19pxDqSoRNfJu3DZJuhB4s+0/leU1gC+6+fekrg3ct6zMWO0fQi9//M2zvXGLzaqdpFOBj0yAohudlCHlGBeS/pZqaPdXVL2N9SW9u866q5I25cne7TZUMzavoprB2fqEmoY8cyDZAtj+o6pX99WmzJL9HHA/8CngFKoh1imS3m77/+qM39OOPYDtyuIltmsvNTgRCm+UodtPUdUrX45me/cTpehGJ6WHG+NC0s1Uz2DeWpY3oHovbJ0FGHqfv71ySfVlJzNJ1wPbDxQikLQmcGmds8PLLNmPUg1lHgfsavvqcl/59IFZ6nWS9DmqR8BOLatmAtfa/vCSjxrX+K0V3pB0K/Amqh51o7/A2yy6MRkk4ca4kHSZ7e16lkX1i3+7pRw2nvGXB15cFn9h+/Em4rZN0tuBj1DNTDbwZuAI1/h6QElzbU8v339u+6U9265rKOHeAEy3vbAsT6V6S9KmNcdtvfBGeQZ5x4Frj+7IkHKMl5sknUf1ELyBfYCfDsygtf2/dQWWtD1wEnAH1fDa8yQdYPuyumJOFLZPLj3OHaiu/U1e/MUCdej9Rf9wf5Nqjt1rdaphbah62034AFWBl8EqeZnq30Pd/gU4T9KlVI/DVcEbeAl820U3ui493BgXkr65lM2ucxKPpGuB/Wz/oiy/mGpos623x0xqkhZQ3b8TsCLw14FNwAq2n9ZAG2ZS3Ue+uMTdjmoyzxkNxG618EaZKPcXYB49f/y4gZfAT4SiG12WhBudJ+mG/qHEwdbF5CJpPar7uKKqcNZYsRNJV9lupfCGpNm2Z7QZu/f/L0lX2t6mjfZ0TV5AH+NC0oslXSTpxrK8qaSmnsu8VtI3JG1fPv9Fs6/qW+ZJWlnS/pLOHXrvMcXZqPxzC6oZs3cCvwWeLWnz8oxqEy6UtJcGSqs164eSdm4hLvQV3ZB0KM0V3ei89HBjXJT7SYcB/9lTS/nGJh6SV1VI/5+AV1P1di6jKgTw6FIPjDEpv3h3A/ajqql7FlXlp9oqjGnpLy+AqhLS9XUXoGiz8EZP7EeBxxuO3UrRjckiCTfGhaSf2t6yd5Zq72zWGuOm+k3DSunOmcDrqe6hngkcY3tam+0aIOlC2231ACOWKLOUY7zML8/eGkDS3lRlFmtVarheL+n5qX7TmAuAH1O9JOJ2AElHNd0ISS8DNqandrTtk5tKtk0X3pggjyS1WXSj85JwY7z8E1URhI0k3QXcTvW+1Cak+k2zXkE1U/WHkm4DzqB6PKQxkv4N2J4q4Z4H7ApcDpzcUPz+whuHSHp1zYU3JsIjSV+mpaIbk0GGlGNcqXov7RSq+1r72j51iEPGI2aq37RE0rZUw8t7AXOB79iuvcShpHlUL664zvZmpY728bb/ru7YJX4rhTfalqIbY5NZyjEmklaV9BFJXyn39v5K9b7MW6mqHtUZewVJ76cqsrERcIXtSwc+dcZelpXnQAGwfYXt9wLPoer9NPWozMPll/4TklalmsjzwoZiD1i953tThTeQ9KmS4AeWVx3iOfjxNFB04yOSPjDwaSh252VIOcbqFOCPVC8N+Huq/yGXB/a0Pbfm2CdRzdL8MdWQ4sbAITXHDHhm/4qS/C4onybMlrQ6MPAI2F+AnzQUG+CzwHWlx7eo8EZDsZcDfiLpQOBZVJWfjmko9hFUP+sVqP4/jxHIkHKMiaR5A4Xyy1/d84Hn236w4djLAT/pfWVa1KPct/3gkrbXWcZzMJKmAavavqHhuG0W3ngd8D2qP3a3a+qxnDaLbkwG6eHGWC16SYDtBZJubyLZDhL7iXZqECyTVgN2Bwb7gRtoJOGqej3jNMrvMUkvqjvZDzJT+M7yz2eXBHx/3W+tkrQdcBTwSeDlwFckvcP27+qMW/xQ0s62Lxx61+iXHm6MSU9dXVi8tm7tjwu0GXtZpr6Xr7fUhhOATYGbeLKecK01u0vc1gtvlNn4swZeUqHqBSGfcY2vwuyJ3VrRjckgCTciRkTSQ8DO/cX7Jf0N8Dvbv2qgDT+zvXHdcUaj7sIbkqaW0aRnUCW7v0hay/Z9dcWM8ZEh5YgYqWuAwW4bPEw1U7mJR3OukrRxA68iXKIWC2+8VNIpwJpVM3Qv1Vt7aku4E6HoxmSQHm5EjMjSamT3TmSruQ3bUU0a+gPV8ObA0GYjz8EuqfCG7b0biH0lcLjti8vy9lRDyrW9sWeIoXTbbqLoRucl4UbEiEi61faLRrptvNtAVXmp/52wtU5Y6onfWuENSdfb3myodTHxpPBFRIzUTyX9ff9KSe+kudci/sb2ObZvt/3rgU9DsaHdwhu3SfqYpGnl869UpVRr13LRjc7LPdyIGKn3A9+RtD9PJtgZVIUQ3thQG26WdBrVsPKi1zA2+Axwm4U33gF8gicfv7oMOLCh2G0W3ei8DClHxKhIei0wcC/3Jts/ajD2YL2q2h8LWkJbptFQ4Y3Su7zA9uvqjrWUNrRSdGMySMKNiBiF/sIb0EwPW9I5wNts/7nuWIPE3g44FvhvqqIbawJNFd3ovAwpR0SntVGIY0mFN2imytYjwDxJP2Dx11Ee3EDsLwD79BXd+BHVy0NiCOnhRkSnSbrO9uYNx2yt8IakAwZbb/ukBmKn6MYYpIcbEV13bgsxWyu8YfskSctT9SoN/ML2Yw2Fb7zoxmSSHm5ExAi1WXhD0m7AfwK/KnHXB95t+/wGYjdedGMyScKNiM6QdDtVr27Rqp5l296goXa0VnhD0s3A7gOzgyVtAJzb0MsLUnRjDDKkHBFd0v8u1inAm6nez3tdg+34je1zGozX656+R3Fuoyq80YTbJH0MOKUsv5WGim5MBunhRkTnSJoCvA04DJhLNazZ2P1USV8DVqeFwhuSjgVeAHyLqne/D/AL4Iq62yBpDaqiG68uqy4DPmH7j3XFnEyScCOiMyQ9jarS0qHA5cBnm3gd4CDtaK3wxhClFGtrw0QoutF1SbgR0RmS7gSeoHoN4G/6tzdY2nGZ1GbRjckg93Ajokt+SDWMuln59Gqq8MRi2ii80WLsNotudF4SbkR0hu1ZS9pWXpHXBrUUt43Y59LOc8+TQhJuRHSWpNWAvYD9gJcCz2mhGW0moEZjt1x0o/NyDzciOkXSisAeVEl2C+AZwJ7AZeUdtVGTNotuTAZJuBHRGZJOBbYDLgTOoCqcf6vt9RuK31rhjYlQ9KPNohuTQYaUI6JLXkb1HtafAzeXQvpN9hraLLwxEYp+tFl0o/OScCOiM2xvJmkjquHkH0q6B3iGpGfZ/kMD8e+DQQtv/G3dhTfajN3jJknnsXjRjZ+W1/TlsawhZEg5IjpL0gyq5Ls3cGfdRfTbLLwxEYp+tFV0Y7JIwo2IzpMkYDvbl9Ycp7XCGyn60X1JuBHRGZI2ATYYeHGApC8Bq5XNX7E9p+b4J7L4xKVetfbw2ow9mDYLfnRVEm5EdIak71ENpV5Zln8GfAxYCdjL9p4ttm1d23cvK7ElXWd78yZjdt2UthsQETEC6w0k2+IB22fZPgVYu+nGSFpN0jsk/RCotXc9kWIXqTg1QunhRkRnSPqF7ZcsYdsvbb+4gTa0VngjRT+6LY8FRUSX/E7SK21f07tS0tbA7+oO3ld44ys8WXjjkkkeu/WiG5NBEm5EdMmHgDPLBKKBYdRXAAcA+zYQv83CG23GnghFNzov93AjojNs/wR4JTAVmFU+U4Cty7a6429GlWhWpSq88WNK4Y1JHvu+Unjjj8DuwMXAq6iKbuxVd/zJIvdwI6JzJL0ROM/2oy23o9HCG23FnghFNyaDJNyI6JxS8WgH4DKqlxhcYPuJFtvTSOGNtmKn6Mb4SMKNiE4qva5dqe7dvhr4ge131RyztcIbLcc+kQlUdKOrknAjorNK0t0FOBD4G9vPrDlea4U3JmrRjzYLfnRNJk1FROdI2qX0um6luod5PLBeA6HbLLwxYYp+TICiG52Ux4IiootmUd27fXfDE6ee0btge+uexXUmceylFt2oO/ZkkR5uRHSO7bfYPruFWcq/k/TK/pUNFd5oLXYpuvFLYGeqohvTgD/aviQVroYvPdyI6JzywvMjqXp2Kh/bXrXm0G0W3mgzdptFNyaNTJqKiM6RdCvwd7Z/3kLsdYD3ApuUVTcBX21i4lDLsTeiGk7eF7gH2Ah4ue0/1B17skjCjYjOkXSF7W1bjN9a4Y2JUPSjzYIfXZaEGxGdI+ko4FnA2cCixNNUAYY2C29MpKIfbRb86KIk3IjonJJ0+jVagKGNwhttxW6z6MZkkoQbETFKTRfeaCv2RC260TV5LCgiOkfScyV9R9I9ku6WdJak5zYYv63CG23FnjBFN7osPdyI6BxJPwBOA04pq94K7G97p4bin0F1//T8picvtRFb0i9sv2QJ235p+8VNtKPrknAjonMkzbU9fah1MT4kXQx82PY1feu3Bj5ne/tWGtYxKXwREV00X9JbgdPL8kzgvqaCt1h4o63YbRbdmDTSw42IzpH0fKoSg6+iem3clcDBtp/yrtaa4rdZeKOV2G0W3Zgs0sONiC76FHCA7T8CSFoT+ALQ1GNBd7eRbNuMbfseSdcBR7RZdKPLknAjoos2HUi2ALbvl7R5g/FnSzqTdgpvtBl7D+DLklovutFFSbgR0UVTJK3R18Nt8vfZqsBfqd6eM8BAE0mvtdi2D+wpurEf8DVJjRX86Lrcw42IzpH0duAjwLepks2bqYY6T1nqgTEu2iz40WUpfBERnWP7ZGAv4G7gXuBNTSbbNgtvtBy7tYIfk0F6uBERI9Rm4Y2WY7dW8GMySMKNiBihNgtvpOhHd2VIOSJi5OZLequkqeXzVporvNFabElvknSLpD9LekDSg5IeaCL2ZJAebkTECLVZeKPl2K0V/JgM8lhQRMTItVl4o83YbRb86Lwk3IiIkWuz8EabsdssutF5SbgRESPXZuGNNmO3WfCj85JwIyJG7ovAlZIWK7wx2WPbPrCJOJNVJk1FRIyCpI2BHahej3eR7Z9N9tilwMYxwLZUyf5y4BDbdzYRv+uScCMiYljaLLoxGSThRkTEsKToxtik8EVERAxXmwU/Oi893IiIGJY2i25MBkm4ERExLJJOAt7fX3TDdhNFNzovQ8oRETFcTym6ATRVdKPzknAjImK4pkhaY2Ch4aIbnZcfVEREDFebBT86L/dwIyJi2Nos+NF1SbgRERENyD3ciIiIBiThRkRENCAJNyIiogFJuBEREQ1Iwo2IiGjA/wcEQJqLGiqNvAAAAABJRU5ErkJggg==\n", "text/plain": [ "
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\n", 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c3MNtkaQpVB3qG20/IGk14N+BH9j+rqQVgRfYvmOI07wRuMP2bZIuBXYG/qdn+zeAGyV9aZi2HAQcBPDytUd9SRERjcs93BiJ7YHv2n4AwPZDfdsFPPMrmKS3lsz3TklbldXTgdPK99PoG1YumfCJwMFDNcT2TNvTbE9bc7VRX09ERMO6M6ScDLddC3So/Wz/UdKjktaxfYftC4ALJP0AWFbSUsAewK6SDi/nW13SirYf6TnV14DZwHdqu5KIiBYMDCl3QTLcdl0M7C1pdYAypPwIsGLPPl8AjpG0StlHwMB92jcDN9h+me3Jtl8BnEU18eoZJXM+A3h/fZcSEdGOrmS46XBbZHsecARwmaQbqO7fngYcJun6MmnqGOBHwLWSbgSuBK4vn+nA9/pOexaw7yDhvgJktnJETDCjnzCVSVNLGNsnACf0rd6gb/nL5dPvgEHOdw5wTvn+gp719wLLL05bIyLGmy4VvkiHGxERnZYONyIionbNDw2PVjrciIjorAwpR0RENCQZbkRERO3yer6IiIjaZUg5IiKiIRlSjoiIqF2GlCMiIhqRDDciIqJmuYcb3TYPmNJO6Jvn9Ve1bNYG825uLfa8eS390AtNaS9L8Dy1FhuAM1uM/ZkWYwN8tuX4iy1DyhEREY3IkHJERETNMqQcERHRkGS4ERERtcs93IiIiNplSDkiIqIhGVKOiIiomZQh5YiIiEYkw42IiGhAMtyIiIiaZUg5IiKiIV0ZUp7UdgO6RtKLJJ0m6TZJN0u6RNKfJc2R9JCkO8r3H5X9N5ZkSW/tO8/8st8NkmZL2mqImK+S9IMS87oSc5u+fb4v6eq+dTNK217Ys+5PY/OTiIgYHyZNenrUn5GQdKikeZJuknSqpOVG1c7RHLSkkiTge8ClttezvQFwKPBW21OBc4DDbE+1/eZy2HTgivLfXo+V/TYCPgl8YSExlwPOA2aWmJsC/wCs27PPKsAmwCqS1uk7xQPAP472miMixjcjjf4zHEkvAQ4Gptl+HbAU8K7RtDRDyovmTcCTtr81sML2nIXtXDroPYG3AD+WtJztxwfZdSXg4YWcZj/gatvn9MS8CbipZ589gHOBe6n+R+jtvI8DDpB0pO2Hhri2iIjOaajwxdLA8yU9CSwP/HY0J0mGu2heB1y3CPu/EbjD9m3ApcDOPdueX4aUfw4cC3x+IeeYAsweJs504NTy6c+k/0TV6R6yCO2OiOiMOoeUbd8NfBm4C7gH+IPtC0fVztEcFCM2HTitfD+NBTvDgSHl9YGdgBNLRjwkSd8r9xH+pyyvBbwSuML2L4GnJL2u77Cjgf0lrTTEeQ+SNEvSrPtHfHkREZ23xsC/feVzUO9GSasC7wDWAV4MrCDp3aMJlCHlRTOPaoh4WJKWohrq3VXS4YCA1SWtaPuR3n1tXy1pDWBNSYcAf13WTy0xt+nZ952SplH9xgWwD7AqcEfpr1eiGlb+555jfi/pFODvFtZe2zOBmQDTujLlLyICYPFGlB+wPW2I7W+mGqm8H6AkO1sB/72ogZLhLpr/A54n6W8GVkjaTNK2g+z7ZuAG2y+zPdn2K4CzgN36d5S0PtWN+AdtH14y36ll8ynAGyXt2nPI8j3fpwM7lRiTgU0Z/Ib+vwMfJL9kRcRE48X4DO8uYAtJy5dRyB2An42mmelwF4FtA+8E3lIe0ZkHzGDwG+jTqWY09zoL2Ld8H7iHOwc4Hdjf9vxBYj4G7AJ8SNLt5dGffwb+RdJk4OXANT373wH8UdIb+s7zQGnP8xbpoiMixjNTZbij/Qx3evta4LtUc2nmUvWbM0fT1GQ7i8j2b4G9F7LtgMG+96w7h+rRIWwvtQgxf86CE656vWSQ/TcpX6/tW/9R4KMjjRsR0Qk1T1K2/RngM4t7nnS4ERHRbR2ZdZIONyIiumtgSLkD0uFGRES3JcONiIhoQDLciIiImmVIOSIioiEZUo6IiKhZMtyIiIiGJMONiIhoQDLciIiImmVIOTptbarXHCyJprQXet68FoO3HF9T2h0T9F7DvhmzPje3Fxqo3kfWdRlSjoiIaEAy3IiIiAYkw42IiKhZ7uFGREQ0JB1uREREAzKkHBERUbMMKUdERDQkGW5EREQDkuFGRETULEPKERERDcmQckRERAM6kuFOarsBY0XS5pIulXSLpNmSzpP0+r59bpB0at+64yXdLel5ZXkNSXfW1MZjJW0w2mMkfWoUMWeU65tTPjsv6jkiIsYtL+anQRMiw5W0FnAGsK/tq8q6rYH1gLll+bVUv2BsI2kF24/2nGI+8D7gmDrbafsDi7K/pKX6jvkU8K+jCP1V218exXEREeNfMtznkvReSTeWTPMkSa+QdHFZd7Gkl5f9jpd0tKSrJN0uac+y/vTeDK3stwfwYeCEgc4WwPYVts/uCb8vcBJwIbBrX9O+BhwqachfQEq8YyRdUtq1raTjJP1M0vE9+x0jaZakeZI+27P+UknTyvfpkuZKuknSkT37/EnS5yRdC2w5cIykLwLPL1nqyZI+L+mQnuOOkHTwkH8AERET0dOL8WlQYx2upCnA4cD2tjcCDgG+Dpxoe0PgZODonkPWBrYGdgG+WNadBuxTzrcssANwPtVL1WYP04R9gNOBU4HpfdvuAq4A3jOCS1kV2B44FDgX+GqJ/3pJU8s+h9ueBmwIbCtpw94TSHoxcGQ5z1RgM0m7lc0rADfZfoPtKwaOsf0J4DHbU23vB3wb2L+cbxLwLqqf4WA+XH6pOU7SqiO4xoiIbujQkHKTGe72wHdtPwBg+yFgS+CUsv0kqg52wNm2n7Z9M7BWWfdDYPtyv/VtwOW2H+sPJOnaknUeVZY3A+63/SvgYmCTQTqefwUOY/ifybm2TTVUfa/tubafpnqr5OSyz96SZgPXU3XG/fdtNwMutX2/7aeoOsptyrb5wFnDtAHbdwIPStoY2BG43vaDg+x6DNXQ+lTgHuArg51P0kElK591/5+Hix4RMY4kw30OMfzvE73b/9J3LLYfBy4F3kqVsZ5Wts8DNnnmJPYbgE8DK5dV04H1y2So24CVgD0WCGzfCswB9n4maDVMO0fSnEHa9XRfG58Glpa0DvAxYIeSuZ8HLNd3nUO97fpx2/OH2N7rWOAA4EDguNLm75Q2n1+u617b88svBf8FbD7YiWzPtD3N9rQ1lx9h9IiI8SAZ7nNcTJX5rQ4gaTXgKqqhUID9qIZ1h3MaVQfzV8AFZd03gAMkbdWz3/IlziRgL2BD25NtTwbewXOHlQGOoOosAbB9eBnCnTqSCyxWAh4F/lAmc71tkH2upRpqXkPSUqUtl43g3E9KWqZn+XvATlQZ8wWlzQeWNu8MIGntnv3fCdy0CNcSETG+DRS+6ECG29gsZdvzJB0BXCZpPtVw68HAcZIOA+6n6kiHcyFwInCO7SfKuX8naR/gSEkvAe4DHgA+RzVUe7ftu3vOcTmwQV9nNNDG2fRky6O4zhskXU+Vdd8OXPncXXyPpE8Cl1Blu+fb/v4ITj8TuFHSbNv72X5C0iXA74fIir9U7i0buBP44KJfVUTEONaRWcqNPhZk+wTghL7V2w+y3wF9yy/o+f4ksPogx1wDbLuQ0Fv07TufalIWVEOyvdt2X8g5FmhXuYf6uoVsW+CcPVYHHir7nMKz9697Y7ygb3m7nu8fBz4+sFyy9y2oMviFtXkkE8EiIrqrI5WmJkzhi/FO0kXAXNt3jNH5NgBuBS62fctYnDMionMypBz9bL9ljM93M7DuWJ4zIqKTOpLhpsONiIhuyz3ciIiImuX1fBEREQ3JkHJEREQDkuFGREQ0IBluREREzXIPNyIioiHpcCMiIhqQIeWIiIiaZUg5uuzh56/KmRu8uZXYZ85YaFnoCW+vvc5sNf4ZZ+w9/E41afvadWZ7KdIG825uLTbAjBkz2gt+5hj9uSfDjYiIaEBHMty8vCAiIrqrgZcXSFpF0ncl/VzSzyRtOZqmJsONiIhuq39I+Sjgf23vKWlZYPnRnCQdbkREdFuNQ8qSVgK2obw73fYTwBOjOVeGlCMioru8mJ/hrQvcD3xH0vWSjpW0wmiamg43IiK6bfHu4a4haVbP56C+sy8NbAIcY3tj4FHgE6NpZoaUIyKi2xZvSPkB29OG2P4b4De2ry3L32WUHW4y3IiI6K6ah5Rt/w74taTXlFU7AKN6eDoZbkREdFv9z+H+A3BymaF8O3DgaE6SDjciIrqt5g7X9hxgqGHnEUmHGxER3TXy2cat68Q9XEmbS7pU0i2SZks6T9Lr+/a5QdKpfeuOl3S3pOeV5TUk3VlTG4+VtMFoj5H0qVHEXF/SnDJVfb0h9jte0p7l+6WSFvs3tYiIcaPmSlNjZdx3uJLWAs4APmX7VbY3Ab4ArNezz2uprmWbQZ6Pmg+8r+522v6A7RHfSJe0VN8xi9zhArsB37e9se3bRnF8RET31fsc7pgZsw5X0nsl3VgyzZMkvULSxWXdxZJeXvY7XtLRkq6SdHtP5nW6pJ17zne8pD2ADwMn2L5qYJvtK2yf3RN+X+Ak4EJg176mfQ04VNKQw+cl3jGSLint2lbScaVu5vE9+x1TntWaJ+mzPeufyRwlTZc0V9JNko7s2edPkj4n6Vpgy4FjJH0ReH7JVk+W9HlJh/Qcd4Skg/vauzPwEeADpc2TJd3Us/1jkmYMdc0REZ3XQC3lsTImHa6kKcDhwPa2NwIOAb4OnGh7Q+Bk4OieQ9YGtgZ2Ab5Y1p0G7FPOtyzV1OvzgSnA7GGasA9wOnAqML1v213AFcB7RnApqwLbA4cC5wJfLfFfL2lq2efw8szWhsC2kjbsPYGkFwNHlvNMBTaTtFvZvAJwk+032L5i4BjbnwAesz3V9n7At4H9y/kmAe+i+hnSc8z5wLeAr9p+0wiubUiSDhp48PuPf/zL4p4uIqI5S1KHS9W5fNf2AwC2HwK2BE4p20+i6mAHnG376TKculZZ90Ng+3K/9W3A5bYf6w8k6dqSdR5VljcD7rf9K+BiYBNJq/Yd9q/AYQx/vefaNjAXuNf2XNtPA/OAyWWfvSXNBq6n6oz779tuBlxq+37bT1F1lNuUbfOBs4ZpA7bvBB6UtDGwI3C97QeHO25x2J5pe5rtaSut9Lw6Q0VEjK0lbEhZDN/03u29KZQAbD8OXAq8lSpjPa1sn0dVVouy3xuATwMrl1XTgfXLZKjbgJWAPRYIbN8KzAGeecN2GaadI2nOIO16uq+NTwNLS1oH+BiwQ8nczwOW67tODXLtAx63PX+I7b2OpSqWfSBwXGnzd0qbzx9k/6dY8M+zv10RERPPkjakTJVZ7i1pdQBJqwFXUQ2FAuxHNaw7nNOoOpi/Ai4o674BHCBpq579li9xJgF7ARvanmx7MvAOnjusDHAEVWcJgO3DyxDu1JFcYLESVR3NP5TJXG8bZJ9rqYaa15C0VGnLZSM495OSlulZ/h6wE1XGfEFp84GlzTsPcvy9wAslrV5GCXYZ8VVFRHRZRzLcMXkO1/Y8SUcAl0maTzXcejBwnKTDqN60MJLKHBcCJwLnlFcgYft3kvYBjpT0EuA+4AHgc1RDtXfbvrvnHJcDG0hae5A2zqYnWx7Fdd4g6XqqrPt24Mrn7uJ7JH0SuIQq2z3f9vdHcPqZwI2SZtvez/YTki4Bfj+SrNj2k5I+R9Xh3wH8fBEuLSKiuxrOVEdrzApf2D4BOKFv9faD7HdA3/ILer4/Caw+yDHXANsuJPQWffvOp5qUBeX9hT3bdl/IORZoV7mH+rqFbFvgnD1WBx4q+5zCs/eve2O8oG95u57vHwc+PrBcsvctqDL4hbV5Rt/y0Sw4OW2w9m/Xvz0iorMGhpQ7YNw/h9sFki4C5tq+Y4zOtwFwK3Cx7VvG4pwRERPWkjSkvKSz/ZYxPt/NVC89joiI4XQkw02HGxER3dWhWsrpcCMiotuS4UZERDQgHW5ERETNMqQcERHRkGS4ERERDUiGGxERUbMOFb5IhxsREd2WDje6atXHHmavm89sJfaUGfNaiTsezJs3pdX4M2bMaC32mWcutIJpI86Yt/fwO9VEN7c7HroX7fxdH1MZUo6IiKhZhpQjIiIakgw3IiKiAclwIyIiapYh5YiIiIZkSDkiIqIByXAjIiIakAw3IiKiZrmHGxER0ZB0uBEREQ3IkHJERETNOjSkPKntBoyEpM0lXSrpFkmzJZ0n6fV9+9wg6dS+dcdLulvS88ryGpLubLDpSDpW0gZNxuyLP6P8DOaUz85ttSUiohZejE+Dxn2GK2kt4AxgX9tXlXVbA+sBc8vya6l+edhG0gq2H+05xXzgfcAxY9CWpW0/tSjH2P7A4sYdpB1L2Z6/CId81faXx7odERHjwpKW4Up6r6QbS6Z5kqRXSLq4rLtY0svLfsdLOlrSVZJul7RnWX96b/ZV9tsD+DBwwkBnC2D7Cttn94TfFzgJuBDYta9pXwMOlTTkLxcl3rck/VjSLyXtUtYfIOlMSecCF0paTdLZ5bqukbShpKUl/VTSduWYL0g6ony/VNK08v1Pko6UdJ2kH/Vk7rdL2rXsM7m0YXb5bFXWbyfpEkmnAHMlfV7SIT3tP0LSwSP5s4qImDAGhpRH+2nQmHS4kqYAhwPb294IOAT4OnCi7Q2Bk4Gjew5ZG9ga2AX4Yll3GrBPOd+ywA7A+cAUYPYwTdgHOB04FZjet+0u4ArgPSO4lMnAtsBfA9+StFxZvyWwv+3tgc8C15fr+lS5xqeAA4BjJL0F2Kns128F4FLbmwKPAP8CvAV4J/C5ss99wFtsb1Kuq/fntjlwuO0NgG8D+wNImgS8i+rnPJgPl18QjpO06gh+DhER3dGRIeWxynC3B75r+wEA2w9RdVKnlO0nUXWwA862/bTtm4G1yrofAtuX+61vAy63/Vh/IEnXSvqZpKPK8mbA/bZ/BVwMbDJIp/KvwGEMf71nlHbdAtwOrF/WX1SuiXIdJ5Xr/D9gdUkr255X1p8LvM/2E4Oc/wngf8v3ucBltp8s3yeX9csA/yVpLnAm0Hv/9ye27yix7wQelLQxsCPVLwEPDhLzGKrh96nAPcBXBrtwSQdJmiVp1v1/HvRnExExPi1JGS4ghv9doXf7X/qOxfbjwKXAW6kyu9PK9nnAJs+cxH4D8Glg5bJqOrB+mQx1G7ASsMcCge1bgTnAM2+ZLkOwcyTNWUgbe5d77wlriGt7PfB7nv0lot+Ttgf2fZryc7D9NM/eTz8UuBfYCJgGLNtzfG87AI6lyqwPBI4DkPSdcl3nl3Pfa3t+ifFfVFnycy/Anml7mu1pay6/kNZHRIw3i5PddjTDvRjYW9LqAJJWA66iGuYE2I9qWHc4p1F1Hn8FXFDWfQM4YOBeZrF8iTMJ2AvY0PZk25OBd/DcYWWAI4CPDSzYPtz2VNtTe/bZS9IkSesB6wK/GOQ8l5frodyzfcD2HyXtDqwObAMcLWmVEVzvYFYG7ikd5HuApYbY93tUw9ebUX5etg8s17VzaePaPfu/E7hplO2KiBifOpLhjsksZdvzyiShyyTNB64HDgaOk3QYcD9VRzqcC4ETgXMGhmRt/07SPsCRkl5CdY/zAap7ntsAd9u+u+cclwMb9HU0A22cTU+2PIhfAJdRZagfsv249JyEdgbwHUk3An8G9pe0BtW96B1s/1rS14GjKPdYF9E3gbMk7QVcwnOz2t5rekLSJcDvh5i1/CVJU6l+l7sT+OAo2hQRMX51ZJbymD0WZPsE4IS+1dsPst8Bfcsv6Pn+JFWW2H/MNVSTmQazRd++86kmZUE13Nq7bfeFnGPAlbYP7TvmeOD4nuWHqLLofq/u2efonu/b9XzvvdYZfXFeUP57C7Bhz6ZPlvWXUg25P6Nk+FtQZfmDsj2SyWIREd3UwtDwaHWi8EU8VymmcStwcemkIyKWTEvSkPJE0J95j3dlhve6bbcjIqJ1Hclw0+FGRER3daiWcjrciIjoto50uLmHGxER3dbAc7iSlpJ0vaQfjLaZyXAjIqK7mhtSPgT4GVVxpVFJhhsREd1Wc4Yr6aVUNfaPXZxmJsONiIhuW7wMdw1Js3qWZ9qe2bfP14B/AlZcnEDpcCMiorsWf0j5AdvTFraxvKr1PtvXDbyCdbTS4UZERLfV+xzuG4Fdy/valwNWkvTftt+9qCdKhxvPtRpDFIus1xln7D38ThPUlCnzWo0/Y8aM1mLffPMGw+9Uo73OPLO12GfMa/f/ee3dZtWIwV6+Ngo1Tpqy/UlKid2S4X5sNJ0tpMONiIgu61At5XS4ERHRbQ0VvhjsJTKLIh1uRER0W0cqTaXDjYiI7sqQckREREOS4UZERDQgGW5ERETN8nq+iIiIhqTDjYiIaECGlCMiImqWIeWIiIiGdCTDbf19uJI2l3SppFskzZZ0nqTX9+1zg6RT+9YdL+luSc8ry2tIurPBpiPpWEmtFYGVtL6kOZKul7TeEPsdL2nP8v1SSQt9M0ZEROc8vRifBrXa4UpaCzgD+JTtV9neBPgCsF7PPq+lauc2klboO8V84H1j1JZFzvZtf8D2zWMRv6cdSy3C7rsB37e9se3bxrIdERGdMDCkPFE6XEnvlXRjyTRPkvQKSReXdRdLennZ73hJR0u6StLtPVnV6eXVRvTstwfwYeAE21cNbLN9he2ze8LvC5wEXAjs2te0rwGHDtdZlnjfkvRjSb8s7zdE0gGSzpR0LnChpNUknV2u6xpJG0paWtJPB96DKOkLko4o35/JFiX9SdKRkq6T9KOezP12SbuWfSaXNswun63K+u0kXSLpFGCupM9LOqSn/UdIOrjvmnYGPgJ8oBw7WdJNPds/JmnGUD+XiIgJwYvxadCwHa6kKcDhwPa2NwIOAb4OnGh7Q+Bk4OieQ9YGtgZ2Ab5Y1p0G7FPOtyywA3A+MAWYPUwT9gFOB04Fpvdtuwu4AnjPcNcBTAa2Bf4a+Jak5cr6LYH9bW8PfBa4vlzXp8o1PgUcABwj6S3ATmW/fisAl9reFHgE+BfgLcA7gc+Vfe4D3lIy+X1Y8Oe2OXC47Q2AbwP7A0iaBLyL6uf8DNvnA98Cvmr7TSO4/oiIiakjGe5IhlG3B75r+wEA2w9J2hLYvWw/CfhSz/5n234auLkMGQP8EDi63G/dCbjc9mPSgu9ClHQtsBJwoe1DJG0G3G/7V5J+AxwnaVXbD/cc9q/AOcB5w1zHGaVdt0i6HVi/rL/I9kPl+9bAHuU6/0/S6pJWtj1P0knAucCWtp8Y5PxPAP9bvs8F/mL7SUlzqTp7gGWAr0uaSjUc/uqe439i+44S+05JD0raGFiL6peAB4e5vsUi6SDgIICXr11npIiIMTaBJk2J4S+nd/tf+o7F9uNUrzR6K1Vmd1rZPg/Y5JmT2G8APg2sXFZNB9Yvk6Fuo+qM91ggsH0rMAd45i3OZQh2jqQ5C2lj7/Kj/e1dyH6vB35P1QEO5knbA/s+Tfk5lE5+4BebQ4F7gY2AacCyPcf3tgPgWKrM+kDgOABJ3ynXdf4g8Z9iwT/P5QbZZ6Fsz7Q9zfa0NVdblCMjIlo0we7hXgzsLWl1AEmrAVdRDXMC7Ec1rDuc06g6j78CLijrvgEcMHAvs1i+xJkE7AVsaHuy7cnAO3jusDLAEcDHBhZsH257qu2pPfvsJWlSmc27LvCLQc5zebkeyj3bB2z/UdLuwOrANlSZ+iojuN7BrAzcUzrh9wBDTZD6HtVowGaUn5ftA8t17TzI/vcCLyxZ+fOohvQjIia+jnS4ww4pl+HUI4DLJM0HrgcOphrePQy4n6ojHc6FwInAOQNDsrZ/J2kf4EhJL6G6x/kA1T3PbYC7bd/dc47LgQ0kLTDoWdo4m55seRC/AC6jylA/ZPvx/iFtYAbwHUk3An8G9pe0BtW96B1s/1rS14GjKPdYF9E3gbMk7QVcwnOz2t5rekLSJcDvbc8f7sRl+PpzwLXAHcDPR9G+iIju6ciQsp4dBZ24JB0P/MD2d9tuy0iVDH82sJftW5qMPe118qwzmoz4rBlnzGgn8DgwZcq8VuPPmzeltdg339za4+wAnDFj7+F3qsmZ8/ZqLTbA3nu39JcdAF1ne7HqAkxbXp71msVowRwWuw0j1Xrhi3guVcU0bgUubrqzjYjonI48FrRElHa0fUDbbVgUpZjGum23IyKiE1JLOSIiomZ5eUFERERDOjIVKR1uRER0WzLciIiImrUw+Wm00uFGRES3JcONiIhoQDrciIiImmVIOSIioiHJcCMiIhrQkQx3iailHItG0v3ArxbjFGtQvYSiDW3Gbjv+khq77fi59tF7he01F6cB05aVZ71o9Mfr183VUk6GG8+xuH8BJM1q6n/g8RS77fhLauy24+fa27v2Z2RIOSIiogEdGahNhxsREd2VWsqxhJu5hMZuO/6SGrvt+Ln2tnUkw82kqYiI6Kxpy8izVh398bo/k6YiIiKGlyHliIiIhnRkoHZS2w2I7lPlZW23IyKWUE8vxqdByXBjsdm2pLOBTZuMK2kz4Ne2f1eW3wvsQVW0Y4bth2qM/fKhttu+q67Y0R5Jmwy13fbsptrSpHF/3R3JcNPhxli5RtJmtn/aYMz/BN4MIGkb4IvAPwBTqWZP7llj7POo/pqrZ52BNYEXAkvVGBtJ/2+Izbb9+RpjP8Kz/8QNXL+p/j1Z1nZt/65I2sn2/5bvKwP/DmwG3AQcavveumIXXxlim4Ht6wrc8rW3dt3D6tA93MxSjjEh6WbgNcCdwKNU/xDb9oY1xrzB9kbl+zeA+23PKMtzbE+tK/YgbZkMfJzqF4Cjbf9HzfH+cZDVywMfAFa3/YI64/e1ZUXg74APAt+zPVjbxirWbNublO/HAr8D/gvYHdjW9m51xW7bknztQ5m2lDzr+aM/Xo9mlnJ0z9taiLmUpKVtPwXsABzUs62R/7clvQo4HHgDVRZwsO0n645r+5mMo3R4hwDvA05j6GxkzEhaBfgI8F7gFGAz2w82EbuY1vNL1Vcl7V93QEm7D7Xd9v/U3Yai0WsfR9e9kAa0Gn3E0uHGmLD9K0lbA6+y/R1JawJ1Z1mnApdJegB4DPgxgKRXAn+oM7Ck11F1tFOALwHvtz2/zpiDtGE14KPAfsAJwCa2H24g7hrAPwL7AMcBG9uu9efd44WSPko1grKSJPnZYbomJoG+fYhtBurseNq89jave2gdGlJOhxtjQtJngGlUw8rfAZYB/ht4Y10xbR8h6WJgbeDCvn98PlxX3OIG4NdU93I3BzaXnr2da/vgOoNL+jeqocSZwOtt/6nOeH1+BdxP9ef8Z+D9fdf+7zXG/i9gxfL9BKq31dwv6UXAnBrjAmD7wLpjDKG1a2/5uofXkQw393BjTEiaA2wMzLa9cVl3Y533cBfSjhWAdwLTbf91jXGGHMKzfUJdsUv8p4G/AE+x4D83A/fOV6ox9gwW/k+cbX+urtjjxcImrU30ax+P1z1tkjxrMVJHPTn0PdzyyOOJwIuocumZto8aTaxkuDFWniiPBxme6fgaIWlZYGdgX2An4CzgW3XGXFiHKmk5hh5+G6v4rT1DPzAxbTDlUa1GSfqB7V0aDvtoz/flgF2AnzXchjaufVxc9wLqH1J+CvhH27PLfInrJF1k++ZFPVE63BgrZ0j6T2AVSX9DNYHn2DoDSnoLMB14K3AJcBKwedPDX5KWAnbsacuPgTObbENpxwrAbsC+dWb3g8TdAHgX1fX/gerWQpNe0nC8BSatAUj6MnBO0+2g4WsfR9e9oBoHam3fA9xTvj8i6WdUP/d0uNEO218uHeAfqe7j/j/bF9Uc9gKqzm1r23cASBrVUM9olGd/9wX+GvgJ1f3qdWz/ucE2NJ7dl7ivoOpgp1NlAK+gmjl7Z92xB3F9CzH7LQ+s20Lctq+9ret+xnVwgZ5mjcU4xXKSZvUsz7Q96FuQyuN/GwPXjiZQOtwYE5KOtP1x4KJB1tVlU6rM6keSbqd6JKbWghMDJP0GuAs4Bjis/OZ7R1OdbZvZvaSrgJWpft572r6lXPuddcfuacOHgZNtP2z7fU3F7Yk/l2fzqqWoCp40ch+zzWtv87oXxvZOTcSR9AKqX2g/YvuPozlHOtwYK2+hKvzQ622DrBsztq+n+g3/45LeSNUBLSvph1QFGOp8V+dZVMO3+wDzJX2fZudKtpnd3w+8FFiL6h/cW2h+nuiLgJ9Kmk31aNIFPbPUayNpnfLz7r1v+hRwb3kevAmNX/s4ue7WSFqG6u/8yYvzzHFmKcdikfS3VFWG1gVu69m0InCl7Xc33J5JVNWe/sb2XjXHEvAmqo5+Z6qs7/3AeXU/piNpY6rsfk9gILv/f7ZfUWfcnvgrU9Wtng68ElgFeKvtnzQRv7RBVPfOD6S6b3wG8G3btw154OLFvM72ppIutr1DXXFG0I5Gr328XHcbys/6BOAh2x9ZrHOlw43FUf7hXRX4AvCJnk2PuMaXB/TEfwnVc7g32n5C0gupqh8dYPvFdcfvaccyVPdQpwM72l6ce0qLGnsgu9+D6nnMurP7/vhrUWX67wJeZruxN0dJ2oiq09mJamh9C+Ai2/9UU7zrgbOpSmh+tX97zc8g97elsWsfT9fdtFLQ58fAXJ6dD/0p2+cv8rnS4cZYKh3ecgPLrvGtOZI+QlXt6VbgecBRVAXdTwS+VGYX1hX7HcBLbX+jLF9L9dICqDLNk+qKPUSbBrL76W0VKpD0Ctu/aiDOwcD+wANUs+HPtv1k+RncYnu9muK+hupWwkcYZHKa7c/WEbevDY1f+3i47okgHW6MCUlvp+rsXgzcRzVr9We2p9QY82aqe5gPqXpd3q3ANravqStmT+wrgXfZ/nVZnkNVz3kF4Dt1D7uVR5GePzB0LWkLYNmy+Xrbj9QYe2tgXdsnluXvAquVzf9i+//qit3Ths9RDaE+p3OX9FrbtT4bKulttn9YZ4whYn8WOK6Na2/zuieCvIA+xsq/UA1p/dL2OlSdz5U1x3x8YNi6ZNK/bKKzLZYd6GyLK2w/WNrRRNGPI6nunQ84FTgM+DRV1l+nzwK9j1G8psSeAdQylDuIdfo7HEknAdTd2ZYYz3Q6kn5Qd7yeWJOAPRY2ilD3tbd13RNFZinHWHnS9oOSJkmaZPsSSUfWHPOlko7uWX5h77LrrWe8au+C7d7azWvWGHfADlTvQh3we9tvLxM8flxz7JX6quzcYvs6AElfqDn2gAVGTkrGv2lDsfs1VnzC9tOSbpD08jpv14xQ4wVHui4dboyV35fn1H4MnCzpPqrHBup0WN/ydTXH63WtpL+x/V+9KyV9kKoIRt0m9T2O8XGoChmXP4c6rdK7YLv31W1r1RlY0ieBTwHPlzTwLKSAJ6he5NCGpotPrA3Mk/QTekot2t614Xa0XXSjc3IPN8ZEKSv4GNVtiv2oHpE52TW+H1XSVOCGJp6/HCT2C6lmbf4FmF1Wb0o1eWs32/fWHP9nVIUuHulbvzJwre31a4x9LvAt2+f1rd8F+Fs3UFZS0hdsf7LuOEPEf6b4RAuxtx1sve3LGojd2nVPBOlwY8yUcn+vsv0jScsDS9U8eWcWsA5Vh3clcBVwzWirwIyyDdvz7PDmvCYmDJW4H6WakfyhgaHF8vM/BrjYfTVvxzj2K6leS3gVC/6ysRWwi+1f1hh7fds/l7TJYNttzx5sfQ3t+Beqx6AaLbzRtiX1usdKOtwYE6peWHAQsJrt9SS9iioLqnu27vJU76Pdqnw2A35HVXTj74Y6tuskfYhqeHUFqkpPjwJftH1MA7GfRzWS8cwvG8Apth+vOe5M2wdJumSQzba9fZ3x+9rSeOGNEncL4D+A11LNTF8KeNQ1vpKxL34r1z0RpMONMVEei9mcajhz4H24c22/vqH4K1DNkn4j8F6qe5ytFlVvgqR1qR7D0sBoQk8ZvgmpzNTd0nbds+BH0pZGC2+UmLOosswzqTq891KNLH2qrpiDtKHx654IMmkqxspfSqUnACQtTc31dSXtS5XVTqW6l/pTqrd4bG37d3XGHke+a7t/ePW7NDxjV9LsQdpRizJT98vAlk3EG8wgxScO6y0+Qc2PR9m+VdJStucD31H1QonatX3dXZcON8bKZZIGZo++heoZ0XNrjjkT+DlV5ZvL67x3ON5IWp9qOHcVSb2zhFeip9JXk01qON6FkvYA/qele4hrALv3Pw9bfhmo+4Xwf1b1WsY5kr5E9a7WJp79Blid9q678zKkHGOi/Ib7fqp7O6J6m82xdf5jWJ693Ihn79++huofn6uBq5uawNSGUlpyN2BX4Ps82+E9Apxq++qG2/Mvtv+5wXiPUHUy86lmx4vqHm5T9zFPsv2e4dbVFPsVVLcRlgEOpXoi4Ju2b6057iSqmuWvqzPORJYON8ZM+a17faqh5F/YfqLh+GtRvT3nUKpKRI28G7dNki4E9rb9+7K8KvAVN/+e1DWAB5eUGav9Q+jll7+5tjdosVm1k3Qy8MlxUHSjkzKkHGNC0l9TDe3eRpVtrCPpg3XWXZW0Ic9mt1tRzdi8mmoGZ+sTahqy5kBnC2D7YVWv7qtNmSX7ReAh4PPASVRDrJMkvdf2/9YZv6cduwLblMVLbddeanA8FN4oQ7efp6pXvjTNZvfjpehGJyXDjTEh6edUz2DeWpbXo3ovbJ0FGHqfv71qYfVlJzJJNwDbDRQikLQacFmds8PLLNlPUQ1lzgTeZvuacl/51IFZ6nWS9EWqR8BOLqumA9fZ/sTCjxrT+K0V3pB0K7A7VUbd6D/gbRbdmAjS4caYkHS57W16lkX1D/82Qxw2lvGXBV5dFn9h+8km4rZN0nuBT1LNTDawN3CEa3w9oKQ5tqeW7z+z/dqebdc31OHeCEy1/XRZXorqLUkb1hy39cIb5RnkHQauPbojQ8oxVuZJOp/qIXgDewE/HZhBa/t/6gosaTvgBOBOquG1l0na3/bldcUcL2yfWDLO7amufXcv+GKBOvT+Q/9Yf5Nqjt1rFaphbaiy7SZ8lKrAy2CVvEz151C3fwLOl3QZ1eNwVfAGXgLfdtGNrkuGG2NC0neG2Ow6J/FIug7Y1/YvyvKrqYY223p7zIQmaT7V/TsBzwf+PLAJWM72Mg20YTrVfeRLStxtqCbznNZA7FYLb5SJcn8C5tLzy48beAn8eCi60WXpcKPzJN3YP5Q42LqYWCStTXUfV1QVzhordiLpatutFN6QNMv2tDZj9/79knSV7a3aaE/X5AX0MSYkvVrSxZJuKssbSmrquczrJH1b0nbl8180+6q+JZ6kFSTtJ+m84fderDjrl/9uQjVj9jfAr4EXS9q4PKPahAsl7aGB0mrN+pGkHVuIC31FNyQdSnNFNzovGW6MiXI/6TDgP3tqKd/UxEPyqgrp/z2wNVW2czlVIYC/DHlgLJbyD+/OwL5UNXXPoqr8VFuFMQ398gKoKiHdUHcBijYLb/TE/gvwZMOxWym6MVGkw40xIemntjfrnaXaO5u1xripftOwUrpzOvBWqnuopwP/YXtym+0aIOlC221lgBELlVnKMVYeKM/eGkDSnlRlFmtVarjeIOnlqX7TmAuAH1O9JOIOAElHNd0ISa8DNqCndrTtE5vqbJsuvDFOHklqs+hG56XDjbHy91RFENaXdDdwB9X7UpuQ6jfN2pRqpuqPJN0OnEb1eEhjJH0G2I6qwz0feBtwBXBiQ/H7C28cImnrmgtvjIdHkr5GS0U3JoIMKceYUvVe2klU97X2sX3yMIeMRcxUv2mJpDdSDS/vAcwBvme79hKHkuZSvbjietsblTrax9p+e92xS/xWCm+0LUU3Fk9mKcdikbSSpE9K+nq5t/dnqvdl3kpV9ajO2MtJ+ghVkY31gSttXzbwqTP2kqw8BwqA7Sttfxh4CVX209SjMo+Vf/SfkrQS1USedRuKPWCVnu9NFd5A0udLBz+wvNIwz8GPpYGiG5+U9NGBT0OxOy9DyrG4TgIepnppwN9Q/YVcFtjN9pyaY59ANUvzx1RDihsAh9QcM2DN/hWl87ugfJowS9IqwMAjYH8CftJQbIAvANeXjO+ZwhsNxV4a+ImkA4EXUVV++o+GYh9B9bNejurveSyCDCnHYpE0d6BQfvmt+wHg5bYfaTj20sBPel+ZFvUo920/trDtdZbxHIykycBKtm9sOG6bhTfeDJxL9cvuNk09ltNm0Y2JIBluLK5nXhJge76kO5robAeJ/VQ7NQiWSCsDuwCD/cANNNLhqno942TKv2OSXll3Zz/ITOHflP++uHTAD9X91ipJ2wBHAZ8DXg98XdL7bP+2zrjFjyTtaPvC4XeNfslwY7H01NWFBWvr1v64QJuxl2Tqe/l6S204DtgQmMez9YRrrdld4rZeeKPMxj9g4CUVql4Q8q+u8VWYPbFbK7oxEaTDjYhFIulRYMf+4v2S/gr4re3bGmjDzbY3qDvOaNRdeEPSUmU0aUWqzu5Pkla3/WBdMWNsZEg5IhbVtcBgtw0eo5qp3MSjOVdL2qCBVxEuVIuFN14r6SRgtaoZup/qrT21dbjjoejGRJAMNyIWyVA1snsnstXchm2oJg39jmp4c2Bos5HnYBdWeMP2ng3Evgo43PYlZXk7qiHl2t7YM8xQum03UXSj89LhRsQikXSr7Vcu6raxbgNV5aX+d8LWOmGpJ35rhTck3WB7o+HWxfiTwhcRsah+Kulv+ldKej/NvRbxLtvn2L7D9q8GPg3FhnYLb9wu6dOSJpfPP1OVUq1dy0U3Oi/3cCNiUX0E+J6k/Xi2g51GVQjhnQ214eeSTqEaVn7mNYwNPgPcZuGN9wGf5dnHry4HDmwodptFNzovQ8oRMSqS3gQM3MudZ/v/Gow9WFZV+2NBC2nLZBoqvFGyywtsv7nuWEO0oZWiGxNBOtyIiFHoL7wBzWTYks4B3mP7D3XHGiT2NsAxwH9TFd1YDWiq6EbnZUg5IjqtjUIcCyu8QTNVth4H5kq6iAVfR3lwA7G/DOzVV3Tj/6heHhLDSIYbEZ0m6XrbGzccs7XCG5L2H2y97RMaiJ2iG4shGW5EdN15LcRsrfCG7RMkLUuVVRr4he0nGgrfeNGNiSQZbkTEImqz8IaknYH/BG4rcdcBPmj7hw3EbrzoxkSSDjciOkPSHVRZ3TOrepZte72G2tFa4Q1JPwd2GZgdLGk94LyGXl6QohuLIUPKEdEl/e9inQTsTfV+3usbbMddts9pMF6v+/oexbmdqvBGE26X9GngpLL8bhoqujERJMONiM6RNAl4D3AYMIdqWLOx+6mSvgmsQguFNyQdA7wCOIMqu98L+AVwZd1tkLQqVdGNrcuqy4HP2n64rpgTSTrciOgMSctQVVo6FLgC+EITrwMcpB2tFd4YppRibW0YD0U3ui4dbkR0hqTfAE9RvQbwrv7tDZZ2XCK1WXRjIsg93Ijokh9RDaNuVD69mio8sYA2Cm+0GLvNohudlw43IjrD9gEL21ZekdcGtRS3jdjn0c5zzxNCOtyI6CxJKwN7APsCrwVe0kIz2uyAGo3dctGNzss93IjoFEnPB3al6mQ3AVYEdgMuL++ojZq0WXRjIkiHGxGdIelkYBvgQuA0qsL5t9pep6H4rRXeGA9FP9osujERZEg5IrrkdVTvYf0Z8PNSSL/JrKHNwhvjoehHm0U3Oi8dbkR0hu2NJK1PNZz8I0n3AStKepHt3zUQ/0EYtPDGX9ddeKPN2D3mSTqfBYtu/LS8pi+PZQ0jQ8oR0VmSplF1vnsCv6m7iH6bhTfGQ9GPtopuTBTpcCOi8yQJ2Mb2ZTXHaa3wRop+dF863IjoDElTgPUGXhwg6avAymXz123Prjn+8Sw4calXrRlem7EH02bBj65KhxsRnSHpXKqh1KvK8s3Ap4HlgT1s79Zi29ayfe+SElvS9bY3bjJm101quwEREYtg7YHOtvij7bNsnwSs0XRjJK0s6X2SfgTUml2Pp9hFKk4tomS4EdEZkn5h+zUL2fZL269uoA2tFd5I0Y9uy2NBEdElv5X0BtvX9q6UtAXw27qD9xXe+DrPFt64dILHbr3oxkSQDjciuuTjwOllAtHAMOqmwP7APg3Eb7PwRpuxx0PRjc7LPdyI6AzbPwHeACwFHFA+k4Atyra6429E1dGsRFV448eUwhsTPPaDpfDGw8AuwCXAllRFN/aoO/5EkXu4EdE5kt4JnG/7Ly23o9HCG23FHg9FNyaCdLgR0Tml4tH2wOVULzG4wPZTLbankcIbbcVO0Y2xkQ43IjqpZF1vo7p3uzVwke0P1ByztcIbLcc+nnFUdKOr0uFGRGeVTncn4EDgr2yvWXO81gpvjNeiH20W/OiaTJqKiM6RtFPJum6luod5LLB2A6HbLLwxbop+jIOiG52Ux4IioosOoLp3+8GGJ06t2Ltge4uexRdO4NhDFt2oO/ZEkQw3IjrH9rtsn93CLOXfSnpD/8qGCm+0FrsU3fglsCNV0Y3JwMO2L02Fq5FLhhsRnVNeeH4kVWan8rHtlWoO3WbhjTZjt1l0Y8LIpKmI6BxJtwJvt/2zFmK/EPgwMKWsmgd8o4mJQy3HXp9qOHkf4D5gfeD1tn9Xd+yJIh1uRHSOpCttv7HF+K0V3hgPRT/aLPjRZelwI6JzJB0FvAg4G3im42mqAEObhTfGU9GPNgt+dFE63IjonNLp9Gu0AEMbhTfait1m0Y2JJB1uRMQoNV14o63Y47XoRtfksaCI6BxJL5X0PUn3SbpX0lmSXtpg/LYKb7QVe9wU3eiyZLgR0TmSLgJOAU4qq94N7Gf7LQ3FP43q/ukPm5681EZsSb+w/ZqFbPul7Vc30Y6uS4cbEZ0jaY7tqcOti7Eh6RLgE7av7Vu/BfBF29u10rCOSeGLiOiiByS9Gzi1LE8HHmwqeIuFN9qK3WbRjQkjGW5EdI6kl1OVGNyS6rVxVwEH237Ou1prit9m4Y1WYrdZdGOiSIYbEV30eWB/2w8DSFoN+DLQ1GNB97bR2bYZ2/Z9kq4Hjmiz6EaXpcONiC7acKCzBbD9kKSNG4w/S9LptFN4o83YuwJfk9R60Y0uSocbEV00SdKqfRluk/+erQT8mertOQMMNNHptRbb9oE9RTf2Bb4pqbGCH12Xe7gR0TmS3gt8EvguVWezN9VQ50lDHhhjos2CH12WwhcR0Tm2TwT2AO4F7gd2b7KzbbPwRsuxWyv4MREkw42IWERtFt5oOXZrBT8mgnS4ERGLqM3CGyn60V0ZUo6IWHQPSHq3pKXK5900V3ijtdiSdpd0i6Q/SPqjpEck/bGJ2BNBMtyIiEXUZuGNlmO3VvBjIshjQRERi67Nwhttxm6z4EfnpcONiFh0bRbeaDN2m0U3Oi8dbkTEomuz8Eabsdss+NF56XAjIhbdV4CrJC1QeGOix7Z9YBNxJqpMmoqIGAVJGwDbU70e72LbN0/02KXAxn8Ab6Tq7K8ADrH9mybid1063IiIGJE2i25MBOlwIyJiRFJ0Y/Gk8EVERIxUmwU/Oi8ZbkREjEibRTcmgnS4ERExIpJOAD7SX3TDdhNFNzovQ8oRETFSzym6ATRVdKPz0uFGRMRITZK06sBCw0U3Oi8/qIiIGKk2C350Xu7hRkTEiLVZ8KPr0uFGREQ0IPdwIyIiGpAONyIiogHpcCMiIhqQDjciIqIB6XAjIiIa8P8BjIMe+mPFRfUAAAAASUVORK5CYII=\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 - 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": [ "
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\n", "text/plain": [ "
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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", " heading = \"\\\\begin{table*}[ht]\\\\scriptsize\"\n", " heading += \"\\\\caption{\" + caption + \"}\\\\label{\" + key + \"}\"\n", " heading += \"\\\\centering\\\\tabularnewline\"\n", " heading += \"\\\\begin{tabular}{\" + columnConfig + \"}\\hline\"\n", " footer = \"\\hline\\end{tabular}\\end{table*}\"\n", " \n", " print(heading)\n", " print(\" & \".join([\"\\\\textbf{\" + h + \"}\" for h in self.heading]))\n", " print(\"\\\\tabularnewline\")\n", " print(\"\\\\hline\")\n", " for row in self.rows:\n", " print(\" & \".join(row))\n", " print(\"\\\\tabularnewline\")\n", " print(footer)" ] }, { "cell_type": "code", "execution_count": 22, "id": "d0e2faa0", "metadata": {}, "outputs": [], "source": [ "def tableRow(algo, dataset):\n", " row = []\n", " for gan in (list(gans)[-4:] if algo[0:3] == \"DoG\" 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": 23, "id": "8c820b02", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "% ### LR\n", "\n", "\\begin{table*}[ht]\\scriptsize\\caption{LR}\\label{tab:results:LR}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n", "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximary 5} & \\textbf{convGAN proximary full}\n", "\\tabularnewline\n", "\\hline\n", "abalone 17 vs 7 8 9 10 & 0.275 / 0.244 & 0.336 / 0.309 & 0.336 / 0.317 & 0.182 / 0.152 & 0.186 / 0.151 & 0.292 / 0.262 & 0.337 / 0.310 & 0.292 / 0.261 & 0.344 / 0.318 \n", "\\tabularnewline\n", "abalone9 18 & 0.458 / 0.409 & 0.566 / 0.534 & 0.514 / 0.481 & 0.341 / 0.296 & 0.517 / 0.479 & 0.538 / 0.500 & 0.575 / 0.544 & 0.541 / 0.503 & 0.574 / 0.543 \n", "\\tabularnewline\n", "car good & 0.106 / 0.035 & 0.100 / 0.028 & 0.112 / 0.043 & 0.109 / 0.038 & 0.108 / 0.037 & 0.108 / 0.038 & 0.109 / 0.038 & 0.107 / 0.036 & 0.110 / 0.039 \n", "\\tabularnewline\n", "car vgood & 0.362 / 0.320 & 0.394 / 0.356 & 0.336 / 0.304 & 0.325 / 0.279 & 0.379 / 0.342 & 0.373 / 0.332 & 0.422 / 0.387 & 0.377 / 0.337 & 0.400 / 0.362 \n", "\\tabularnewline\n", "flare F & 0.263 / 0.210 & 0.278 / 0.228 & 0.322 / 0.287 & 0.321 / 0.277 & 0.342 / 0.301 & 0.273 / 0.223 & 0.323 / 0.279 & 0.276 / 0.226 & 0.317 / 0.274 \n", "\\tabularnewline\n", "hypothyroid & 0.358 / 0.305 & 0.400 / 0.353 & 0.297 / 0.275 & 0.294 / 0.234 & 0.223 / 0.175 & 0.363 / 0.312 & 0.378 / 0.328 & 0.360 / 0.308 & 0.429 / 0.386 \n", "\\tabularnewline\n", "kddcup guess passwd vs satan & 0.997 / 0.996 & 0.996 / 0.996 & 0.997 / 0.996 & 0.998 / 0.998 & 0.997 / 0.996 & 0.989 / 0.989 & 0.995 / 0.995 & 0.994 / 0.994 & 0.998 / 0.998 \n", "\\tabularnewline\n", "kr vs k three vs eleven & 0.942 / 0.941 & 0.965 / 0.964 & 0.960 / 0.959 & 0.576 / 0.559 & 0.699 / 0.687 & 0.952 / 0.951 & 0.965 / 0.965 & 0.948 / 0.947 & 0.963 / 0.962 \n", "\\tabularnewline\n", "kr vs k zero one vs draw & 0.650 / 0.632 & 0.744 / 0.732 & 0.796 / 0.788 & 0.398 / 0.361 & 0.735 / 0.723 & 0.700 / 0.686 & 0.836 / 0.830 & 0.710 / 0.696 & 0.761 / 0.750 \n", "\\tabularnewline\n", "shuttle 2 vs 5 & 0.998 / 0.998 & 0.996 / 0.996 & 0.990 / 0.990 & 0.980 / 0.980 & 0.987 / 0.986 & 0.994 / 0.994 & 0.996 / 0.996 & 0.989 / 0.989 & 0.992 / 0.992 \n", "\\tabularnewline\n", "winequality red 4 & 0.113 / 0.056 & 0.143 / 0.090 & 0.017 / 0.011 & 0.109 / 0.059 & 0.144 / 0.097 & 0.130 / 0.075 & 0.135 / 0.080 & 0.126 / 0.071 & 0.131 / 0.076 \n", "\\tabularnewline\n", "yeast4 & 0.236 / 0.188 & 0.247 / 0.202 & 0.271 / 0.256 & 0.230 / 0.188 & 0.284 / 0.257 & 0.254 / 0.209 & 0.277 / 0.235 & 0.249 / 0.204 & 0.276 / 0.234 \n", "\\tabularnewline\n", "yeast5 & 0.574 / 0.555 & 0.592 / 0.575 & 0.551 / 0.539 & 0.510 / 0.489 & 0.614 / 0.600 & 0.582 / 0.564 & 0.620 / 0.604 & 0.577 / 0.559 & 0.601 / 0.584 \n", "\\tabularnewline\n", "yeast6 & 0.242 / 0.210 & 0.292 / 0.263 & 0.393 / 0.382 & 0.272 / 0.242 & 0.375 / 0.353 & 0.295 / 0.266 & 0.353 / 0.328 & 0.290 / 0.261 & 0.316 / 0.289 \n", "\\tabularnewline\n", "\\hline Average & 0.470 / 0.436 & 0.503 / 0.473 & 0.492 / 0.473 & 0.403 / 0.368 & 0.471 / 0.442 & 0.489 / 0.457 & 0.523 / 0.494 & 0.488 / 0.457 & 0.515 / 0.486 \n", "\\tabularnewline\n", "maximum & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 \n", "\\tabularnewline\n", "\\hline\\end{tabular}\\end{table*}\n", "\n", "% ### RF\n", "\n", "\\begin{table*}[ht]\\scriptsize\\caption{RF}\\label{tab:results:RF}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n", "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximary 5} & \\textbf{convGAN proximary full}\n", "\\tabularnewline\n", "\\hline\n", "abalone 17 vs 7 8 9 10 & 0.189 / 0.180 & 0.188 / 0.179 & 0.185 / 0.175 & 0.236 / 0.219 & 0.115 / 0.109 & 0.234 / 0.222 & 0.280 / 0.268 & 0.253 / 0.240 & 0.283 / 0.270 \n", "\\tabularnewline\n", "abalone9 18 & 0.311 / 0.288 & 0.338 / 0.311 & 0.309 / 0.287 & 0.398 / 0.366 & 0.252 / 0.235 & 0.324 / 0.291 & 0.348 / 0.326 & 0.335 / 0.302 & 0.359 / 0.337 \n", "\\tabularnewline\n", "car good & 0.900 / 0.896 & 0.557 / 0.548 & 0.672 / 0.664 & 0.533 / 0.504 & 0.669 / 0.660 & 0.634 / 0.624 & 0.659 / 0.650 & 0.655 / 0.646 & 0.647 / 0.638 \n", "\\tabularnewline\n", "car vgood & 0.982 / 0.982 & 0.904 / 0.901 & 0.924 / 0.921 & 0.812 / 0.803 & 0.944 / 0.942 & 0.910 / 0.907 & 0.929 / 0.927 & 0.904 / 0.901 & 0.921 / 0.918 \n", "\\tabularnewline\n", "flare F & 0.224 / 0.180 & 0.080 / 0.059 & 0.110 / 0.091 & 0.261 / 0.214 & 0.091 / 0.071 & 0.062 / 0.043 & 0.120 / 0.099 & 0.064 / 0.044 & 0.133 / 0.113 \n", "\\tabularnewline\n", "hypothyroid & 0.796 / 0.787 & 0.737 / 0.726 & 0.785 / 0.775 & 0.764 / 0.750 & 0.779 / 0.769 & 0.782 / 0.772 & 0.798 / 0.788 & 0.791 / 0.781 & 0.796 / 0.786 \n", "\\tabularnewline\n", "kddcup guess passwd vs satan & 1.000 / 1.000 & 1.000 / 1.000 & 0.993 / 0.992 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 \n", "\\tabularnewline\n", "kr vs k three vs eleven & 0.999 / 0.999 & 0.993 / 0.993 & 0.995 / 0.995 & 0.791 / 0.784 & 0.997 / 0.997 & 0.993 / 0.993 & 0.992 / 0.992 & 0.993 / 0.993 & 0.992 / 0.992 \n", "\\tabularnewline\n", "kr vs k zero one vs draw & 0.945 / 0.943 & 0.939 / 0.936 & 0.938 / 0.935 & 0.703 / 0.688 & 0.938 / 0.936 & 0.941 / 0.939 & 0.946 / 0.944 & 0.937 / 0.935 & 0.949 / 0.947 \n", "\\tabularnewline\n", "shuttle 2 vs 5 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 \n", "\\tabularnewline\n", "winequality red 4 & 0.014 / 0.009 & 0.000 / -0.008 & 0.026 / 0.023 & 0.132 / 0.102 & 0.007 / -0.000 & 0.079 / 0.062 & 0.086 / 0.055 & 0.067 / 0.050 & 0.070 / 0.041 \n", "\\tabularnewline\n", "yeast4 & 0.295 / 0.282 & 0.329 / 0.314 & 0.252 / 0.242 & 0.353 / 0.325 & 0.253 / 0.242 & 0.251 / 0.240 & 0.321 / 0.304 & 0.238 / 0.228 & 0.314 / 0.298 \n", "\\tabularnewline\n", "yeast5 & 0.736 / 0.728 & 0.705 / 0.697 & 0.700 / 0.692 & 0.651 / 0.638 & 0.665 / 0.656 & 0.691 / 0.684 & 0.738 / 0.730 & 0.705 / 0.698 & 0.727 / 0.718 \n", "\\tabularnewline\n", "yeast6 & 0.516 / 0.507 & 0.551 / 0.543 & 0.478 / 0.470 & 0.381 / 0.362 & 0.459 / 0.452 & 0.498 / 0.490 & 0.575 / 0.567 & 0.510 / 0.503 & 0.510 / 0.501 \n", "\\tabularnewline\n", "\\hline Average & 0.636 / 0.627 & 0.594 / 0.586 & 0.598 / 0.590 & 0.573 / 0.554 & 0.584 / 0.576 & 0.600 / 0.591 & 0.628 / 0.618 & 0.604 / 0.594 & 0.622 / 0.611 \n", "\\tabularnewline\n", "maximum & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 \n", "\\tabularnewline\n", "\\hline\\end{tabular}\\end{table*}\n", "\n", "% ### GB\n", "\n", "\\begin{table*}[ht]\\scriptsize\\caption{GB}\\label{tab:results:GB}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n", "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximary 5} & \\textbf{convGAN proximary full}\n", "\\tabularnewline\n", "\\hline\n", "abalone 17 vs 7 8 9 10 & 0.331 / 0.309 & 0.333 / 0.315 & 0.278 / 0.265 & 0.213 / 0.196 & 0.232 / 0.221 & 0.291 / 0.270 & 0.291 / 0.278 & 0.310 / 0.289 & 0.311 / 0.297 \n", "\\tabularnewline\n", "abalone9 18 & 0.382 / 0.345 & 0.384 / 0.344 & 0.338 / 0.308 & 0.398 / 0.370 & 0.389 / 0.361 & 0.367 / 0.329 & 0.318 / 0.292 & 0.343 / 0.304 & 0.318 / 0.292 \n", "\\tabularnewline\n", "car good & 0.839 / 0.831 & 0.853 / 0.847 & 0.831 / 0.824 & 0.533 / 0.504 & 0.764 / 0.756 & 0.714 / 0.705 & 0.864 / 0.858 & 0.733 / 0.723 & 0.869 / 0.864 \n", "\\tabularnewline\n", "car vgood & 0.938 / 0.935 & 0.973 / 0.972 & 0.982 / 0.981 & 0.812 / 0.803 & 0.974 / 0.973 & 0.933 / 0.931 & 0.982 / 0.982 & 0.951 / 0.949 & 0.982 / 0.982 \n", "\\tabularnewline\n", "flare F & 0.285 / 0.237 & 0.133 / 0.113 & 0.183 / 0.168 & 0.326 / 0.286 & 0.166 / 0.149 & 0.139 / 0.125 & 0.181 / 0.164 & 0.162 / 0.147 & 0.156 / 0.138 \n", "\\tabularnewline\n", "hypothyroid & 0.781 / 0.768 & 0.788 / 0.777 & 0.808 / 0.799 & 0.748 / 0.733 & 0.809 / 0.800 & 0.789 / 0.777 & 0.800 / 0.789 & 0.789 / 0.777 & 0.815 / 0.806 \n", "\\tabularnewline\n", "kddcup guess passwd vs satan & 1.000 / 1.000 & 1.000 / 1.000 & 0.987 / 0.986 & 1.000 / 1.000 & 1.000 / 1.000 & 0.995 / 0.995 & 1.000 / 1.000 & 0.996 / 0.996 & 1.000 / 1.000 \n", "\\tabularnewline\n", "kr vs k three vs eleven & 0.995 / 0.995 & 0.995 / 0.995 & 0.995 / 0.995 & 0.751 / 0.742 & 0.994 / 0.994 & 0.995 / 0.995 & 0.995 / 0.995 & 0.992 / 0.992 & 0.995 / 0.995 \n", "\\tabularnewline\n", "kr vs k zero one vs draw & 0.944 / 0.942 & 0.960 / 0.958 & 0.964 / 0.963 & 0.638 / 0.619 & 0.947 / 0.945 & 0.959 / 0.958 & 0.972 / 0.971 & 0.960 / 0.958 & 0.961 / 0.960 \n", "\\tabularnewline\n", "shuttle 2 vs 5 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 1.000 / 1.000 & 0.994 / 0.994 & 1.000 / 1.000 \n", "\\tabularnewline\n", "winequality red 4 & 0.122 / 0.088 & 0.093 / 0.065 & 0.087 / 0.077 & 0.112 / 0.086 & 0.084 / 0.065 & 0.133 / 0.095 & 0.107 / 0.066 & 0.133 / 0.095 & 0.102 / 0.063 \n", "\\tabularnewline\n", "yeast4 & 0.378 / 0.350 & 0.354 / 0.331 & 0.289 / 0.274 & 0.276 / 0.248 & 0.332 / 0.317 & 0.257 / 0.242 & 0.351 / 0.329 & 0.261 / 0.245 & 0.333 / 0.311 \n", "\\tabularnewline\n", "yeast5 & 0.712 / 0.703 & 0.707 / 0.698 & 0.736 / 0.728 & 0.618 / 0.604 & 0.690 / 0.682 & 0.713 / 0.705 & 0.724 / 0.716 & 0.731 / 0.723 & 0.714 / 0.705 \n", "\\tabularnewline\n", "yeast6 & 0.475 / 0.461 & 0.539 / 0.529 & 0.474 / 0.465 & 0.355 / 0.336 & 0.463 / 0.453 & 0.490 / 0.481 & 0.564 / 0.554 & 0.481 / 0.472 & 0.520 / 0.510 \n", "\\tabularnewline\n", "\\hline Average & 0.656 / 0.640 & 0.651 / 0.639 & 0.639 / 0.631 & 0.556 / 0.538 & 0.632 / 0.623 & 0.627 / 0.615 & 0.654 / 0.642 & 0.631 / 0.619 & 0.648 / 0.637 \n", "\\tabularnewline\n", "maximum & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 \n", "\\tabularnewline\n", "\\hline\\end{tabular}\\end{table*}\n", "\n", "% ### KNN\n", "\n", "\\begin{table*}[ht]\\scriptsize\\caption{KNN}\\label{tab:results:KNN}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n", "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximary 5} & \\textbf{convGAN proximary full}\n", "\\tabularnewline\n", "\\hline\n", "abalone 17 vs 7 8 9 10 & 0.315 / 0.291 & 0.338 / 0.315 & 0.154 / 0.144 & 0.169 / 0.163 & 0.090 / 0.084 & 0.290 / 0.261 & 0.315 / 0.296 & 0.273 / 0.243 & 0.311 / 0.291 \n", "\\tabularnewline\n", "abalone9 18 & 0.342 / 0.286 & 0.383 / 0.334 & 0.161 / 0.144 & 0.212 / 0.197 & 0.153 / 0.140 & 0.361 / 0.305 & 0.136 / 0.120 & 0.347 / 0.290 & 0.165 / 0.149 \n", "\\tabularnewline\n", "car good & 0.357 / 0.312 & 0.720 / 0.707 & 0.465 / 0.439 & 0.220 / 0.160 & 0.375 / 0.332 & 0.558 / 0.531 & 0.509 / 0.479 & 0.534 / 0.505 & 0.585 / 0.561 \n", "\\tabularnewline\n", "car vgood & 0.413 / 0.375 & 0.805 / 0.797 & 0.718 / 0.707 & 0.297 / 0.249 & 0.618 / 0.597 & 0.659 / 0.641 & 0.879 / 0.874 & 0.700 / 0.684 & 0.794 / 0.784 \n", "\\tabularnewline\n", "flare F & 0.245 / 0.191 & 0.294 / 0.251 & 0.300 / 0.262 & 0.298 / 0.253 & 0.272 / 0.228 & 0.269 / 0.220 & 0.306 / 0.265 & 0.277 / 0.228 & 0.302 / 0.262 \n", "\\tabularnewline\n", "hypothyroid & 0.568 / 0.540 & 0.615 / 0.592 & 0.605 / 0.583 & 0.604 / 0.580 & 0.601 / 0.580 & 0.576 / 0.548 & 0.606 / 0.582 & 0.578 / 0.551 & 0.646 / 0.628 \n", "\\tabularnewline\n", "kddcup guess passwd vs satan & 0.991 / 0.991 & 0.991 / 0.991 & 0.991 / 0.991 & 0.991 / 0.991 & 0.991 / 0.991 & 0.982 / 0.982 & 0.991 / 0.991 & 0.985 / 0.985 & 0.991 / 0.991 \n", "\\tabularnewline\n", "kr vs k three vs eleven & 0.901 / 0.898 & 0.931 / 0.928 & 0.945 / 0.943 & 0.648 / 0.634 & 0.819 / 0.813 & 0.895 / 0.891 & 0.961 / 0.960 & 0.894 / 0.890 & 0.925 / 0.923 \n", "\\tabularnewline\n", "kr vs k zero one vs draw & 0.840 / 0.833 & 0.885 / 0.881 & 0.864 / 0.859 & 0.721 / 0.708 & 0.795 / 0.786 & 0.864 / 0.858 & 0.897 / 0.892 & 0.865 / 0.859 & 0.853 / 0.847 \n", "\\tabularnewline\n", "shuttle 2 vs 5 & 0.951 / 0.951 & 0.956 / 0.955 & 0.931 / 0.930 & 0.947 / 0.946 & 0.914 / 0.913 & 0.954 / 0.954 & 0.932 / 0.931 & 0.960 / 0.960 & 0.943 / 0.942 \n", "\\tabularnewline\n", "winequality red 4 & 0.069 / 0.016 & 0.074 / 0.022 & 0.037 / 0.013 & 0.046 / 0.002 & 0.045 / -0.005 & 0.074 / 0.016 & 0.058 / -0.001 & 0.074 / 0.016 & 0.059 / 0.000 \n", "\\tabularnewline\n", "yeast4 & 0.329 / 0.293 & 0.365 / 0.335 & 0.363 / 0.340 & 0.319 / 0.290 & 0.254 / 0.218 & 0.334 / 0.296 & 0.348 / 0.313 & 0.338 / 0.301 & 0.345 / 0.309 \n", "\\tabularnewline\n", "yeast5 & 0.670 / 0.657 & 0.691 / 0.679 & 0.677 / 0.667 & 0.583 / 0.565 & 0.678 / 0.667 & 0.650 / 0.636 & 0.646 / 0.631 & 0.640 / 0.624 & 0.659 / 0.645 \n", "\\tabularnewline\n", "yeast6 & 0.409 / 0.387 & 0.464 / 0.447 & 0.406 / 0.386 & 0.397 / 0.375 & 0.424 / 0.405 & 0.337 / 0.311 & 0.426 / 0.406 & 0.334 / 0.308 & 0.390 / 0.367 \n", "\\tabularnewline\n", "\\hline Average & 0.529 / 0.502 & 0.608 / 0.588 & 0.544 / 0.529 & 0.461 / 0.437 & 0.502 / 0.482 & 0.557 / 0.532 & 0.572 / 0.553 & 0.557 / 0.532 & 0.569 / 0.550 \n", "\\tabularnewline\n", "maximum & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.070 / 0.070 & 0.071 / 0.071 & 0.070 / 0.070 & 0.071 / 0.071 \n", "\\tabularnewline\n", "\\hline\\end{tabular}\\end{table*}\n", "\n", "% ### DoG\n", "\n", "\\begin{table*}[ht]\\scriptsize\\caption{DoG}\\label{tab:results:DoG}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n", "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximary 5} & \\textbf{convGAN proximary full}\n", "\\tabularnewline\n", "\\hline\n", "abalone 17 vs 7 8 9 10 & 0.306 / 0.278 & 0.393 / 0.372 & 0.317 / 0.289 & 0.366 / 0.345 \n", "\\tabularnewline\n", "abalone9 18 & 0.477 / 0.433 & 0.538 / 0.512 & 0.512 / 0.473 & 0.549 / 0.525 \n", "\\tabularnewline\n", "car good & 0.741 / 0.729 & 0.659 / 0.646 & 0.759 / 0.748 & 0.577 / 0.558 \n", "\\tabularnewline\n", "car vgood & 0.772 / 0.763 & 0.721 / 0.712 & 0.776 / 0.766 & 0.753 / 0.743 \n", "\\tabularnewline\n", "flare F & 0.321 / 0.279 & 0.223 / 0.193 & 0.302 / 0.262 & 0.267 / 0.234 \n", "\\tabularnewline\n", "hypothyroid & 0.627 / 0.604 & 0.703 / 0.688 & 0.625 / 0.602 & 0.690 / 0.674 \n", "\\tabularnewline\n", "kddcup guess passwd vs satan & 0.991 / 0.991 & 1.000 / 1.000 & 0.996 / 0.996 & 1.000 / 1.000 \n", "\\tabularnewline\n", "kr vs k three vs eleven & 0.993 / 0.992 & 0.995 / 0.995 & 0.994 / 0.994 & 0.996 / 0.996 \n", "\\tabularnewline\n", "kr vs k zero one vs draw & 0.957 / 0.955 & 0.951 / 0.949 & 0.965 / 0.964 & 0.941 / 0.939 \n", "\\tabularnewline\n", "shuttle 2 vs 5 & 1.000 / 1.000 & 0.996 / 0.996 & 0.994 / 0.994 & 0.998 / 0.998 \n", "\\tabularnewline\n", "winequality red 4 & 0.118 / 0.065 & 0.123 / 0.073 & 0.115 / 0.063 & 0.105 / 0.052 \n", "\\tabularnewline\n", "yeast4 & 0.312 / 0.274 & 0.308 / 0.271 & 0.311 / 0.273 & 0.334 / 0.299 \n", "\\tabularnewline\n", "yeast5 & 0.578 / 0.560 & 0.652 / 0.639 & 0.571 / 0.553 & 0.637 / 0.623 \n", "\\tabularnewline\n", "yeast6 & 0.366 / 0.342 & 0.491 / 0.475 & 0.358 / 0.334 & 0.382 / 0.359 \n", "\\tabularnewline\n", "\\hline Average & 0.611 / 0.590 & 0.625 / 0.609 & 0.614 / 0.594 & 0.614 / 0.596 \n", "\\tabularnewline\n", "maximum & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 & 0.071 / 0.071 \n", "\\tabularnewline\n", "\\hline\\end{tabular}\\end{table*}\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] == \"DoG\":\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", " 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)\n", " line = [latex(d)]\n", " for (n, r) in enumerate(row):\n", " line.append(f\"{p(r[0])} / {p(r[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", " table.add([\"\\\\hline Average\"] + [f\"{p(a / cnt)} / {p(b / cnt)}\" for (a,b) in avg])\n", " table.add([\"maximum\"] + [f\"{p(a / cnt)} / {p(b / cnt)}\" 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", " table.showLatex(algo, \"tab:results:\" + algo)\n", " if tableB is not None:\n", " print()\n", " tableB.showLatex(algo, \"tab:results:\" + algo)\n", " \n" ] }, { "cell_type": "code", "execution_count": 24, "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 }