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League Learning
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Learning on League of Legends Data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Written by Brian Perrett for funsies" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from __future__ import division\n", | |
| "import numpy as np\n", | |
| "import csv\n", | |
| "from sklearn.linear_model import SGDClassifier\n", | |
| "from sklearn.svm import SVC\n", | |
| "import ast" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "{'133': 'Quinn', '131': 'Diana', '134': 'Syndra', '24': 'Jax', '25': 'Morgana', '26': 'Zilean', '27': 'Singed', '20': 'Nunu', '21': 'Miss Fortune', '22': 'Ashe', '23': 'Tryndamere', '28': 'Evelynn', '29': 'Twitch', '161': \"Vel'Koz\", '4': 'Twisted Fate', '8': 'Vladimir', '120': 'Hecarim', '121': \"Kha'Zix\", '122': 'Darius', '267': 'Nami', '266': 'Aatrox', '126': 'Jayce', '127': 'Lissandra', '268': 'Azir', '59': 'Jarvan IV', '58': 'Renekton', '55': 'Katarina', '54': 'Malphite', '57': 'Maokai', '56': 'Nocturne', '51': 'Caitlyn', '50': 'Swain', '53': 'Blitzcrank', '412': 'Thresh', '115': 'Ziggs', '114': 'Fiora', '117': 'Lulu', '89': 'Leona', '111': 'Nautilus', '110': 'Varus', '113': 'Sejuani', '112': 'Viktor', '82': 'Mordekaiser', '83': 'Yorick', '80': 'Pantheon', '81': 'Ezreal', '119': 'Draven', '84': 'Akali', '85': 'Kennen', '429': 'Kalista', '3': 'Galio', '7': 'LeBlanc', '421': \"Rek'Sai\", '420': 'Illaoi', '245': 'Ekko', '102': 'Shyvana', '103': 'Ahri', '101': 'Xerath', '106': 'Volibear', '107': 'Rengar', '104': 'Graves', '105': 'Fizz', '39': 'Irelia', '38': 'Kassadin', '33': 'Rammus', '32': 'Amumu', '31': \"Cho'Gath\", '30': 'Karthus', '37': 'Sona', '36': 'Dr. Mundo', '35': 'Shaco', '34': 'Anivia', '432': 'Bard', '60': 'Elise', '61': 'Orianna', '62': 'Wukong', '63': 'Brand', '64': 'Lee Sin', '67': 'Vayne', '68': 'Rumble', '69': 'Cassiopeia', '254': 'Vi', '2': 'Olaf', '6': 'Urgot', '99': 'Lux', '98': 'Shen', '91': 'Talon', '90': 'Malzahar', '92': 'Riven', '223': 'Tahm Kench', '222': 'Jinx', '96': \"Kog'Maw\", '11': 'Master Yi', '10': 'Kayle', '13': 'Ryze', '12': 'Alistar', '15': 'Sivir', '14': 'Sion', '17': 'Teemo', '16': 'Soraka', '19': 'Warwick', '18': 'Tristana', '150': 'Gnar', '154': 'Zac', '157': 'Yasuo', '238': 'Zed', '236': 'Lucian', '48': 'Trundle', '86': 'Garen', '44': 'Taric', '45': 'Veigar', '42': 'Corki', '43': 'Karma', '40': 'Janna', '41': 'Gangplank', '1': 'Annie', '5': 'Xin Zhao', '9': 'Fiddlesticks', '201': 'Braum', '203': 'Kindred', '202': 'Jhin', '143': 'Zyra', '77': 'Udyr', '76': 'Nidalee', '75': 'Nasus', '74': 'Heimerdinger', '72': 'Skarner', '79': 'Gragas', '78': 'Poppy'}\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "with open(\"F:/Box Sync/Documents/Python Documents/riot/champids.txt\", \"r\") as f:\n", | |
| " champion_dict = ast.literal_eval(f.read())\n", | |
| "print(champion_dict)\n", | |
| "global champion_dict" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def getDataNoInGame(filename):\n", | |
| " features = []\n", | |
| " x = []\n", | |
| " y = []\n", | |
| " with open(filename, \"r\") as f:\n", | |
| " reader = csv.reader(f)\n", | |
| " r = 0\n", | |
| " for row in reader:\n", | |
| " if r == 0:\n", | |
| " features = row[1:-14]\n", | |
| " r += 1\n", | |
| " continue\n", | |
| " if len(row) == 0:\n", | |
| " pass\n", | |
| " else:\n", | |
| " x.append(list(map(int, row[1:-14])))\n", | |
| " y.append(list(map(int, row[-2]))[0])\n", | |
| "# features = x[0]\n", | |
| " return np.array(features), np.array(x), np.array(y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def getData(filename):\n", | |
| " \"\"\"\n", | |
| " Includes data like first baron and first inhibitor.\n", | |
| " \"\"\"\n", | |
| " features = []\n", | |
| " x = []\n", | |
| " y = []\n", | |
| " with open(filename, \"r\") as f:\n", | |
| " reader = csv.reader(f)\n", | |
| " r = 0\n", | |
| " for row in reader:\n", | |
| " if r == 0:\n", | |
| " features = row[1:-2]\n", | |
| " r += 1\n", | |
| " continue\n", | |
| " if len(row) == 0:\n", | |
| " pass\n", | |
| " else:\n", | |
| " x.append(list(map(int, row[1:-2])))\n", | |
| " y.append(list(map(int, row[-2]))[0])\n", | |
| "# features = x[0]\n", | |
| " return np.array(features), np.array(x), np.array(y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def testCorrect(trainsize, x, y):\n", | |
| " correct = 0\n", | |
| " for i, x1 in enumerate(x[trainsize:]):\n", | |
| " ans = clf.predict([x1])\n", | |
| " # print(ans[0], int(y[trainsize + i]))\n", | |
| " if int(ans[0]) == int(y[trainsize + i]):\n", | |
| " correct += 1\n", | |
| " print(\"{} of {}\".format(correct, len(x[trainsize:])))\n", | |
| " print(correct/len(x[trainsize:]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def getTopFeatures(clf, n):\n", | |
| " indeces = clf.coef_[0].argsort()[-10:][::-1]\n", | |
| " for ind in indeces:\n", | |
| " if \"champ\" in features[ind]:\n", | |
| " champ = features[ind].split(\"champ\")\n", | |
| " f = \"{}{}\".format(champ[0], champion_dict[champ[1]])\n", | |
| " else:\n", | |
| " f = features[ind]\n", | |
| " print(clf.coef_[0][ind], f)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def getWorstFeatures(clf, n):\n", | |
| " indeces = clf.coef_[0].argsort()[:10]\n", | |
| " for ind in indeces:\n", | |
| " if \"champ\" in features[ind]:\n", | |
| " champ = features[ind].split(\"champ\")\n", | |
| " f = \"{}{}\".format(champ[0], champion_dict[champ[1]])\n", | |
| " else:\n", | |
| " f = features[ind]\n", | |
| " print(clf.coef_[0][ind], f)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "\n", | |
| "### Diamond" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Stochastic Gradient Descent" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "features, x, y = getData(\"diamond.csv\")\n", | |
| "trainsize = int(.6 * len(x))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "4050" | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "len(x)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", | |
| " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", | |
| " learning_rate='optimal', loss='log', n_iter=50, n_jobs=1,\n", | |
| " penalty='l2', power_t=0.5, random_state=None, shuffle=True,\n", | |
| " verbose=0, warm_start=False)" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SGDClassifier(loss=\"log\", penalty=\"l2\", n_iter=50)\n", | |
| "clf.fit(x[:trainsize], y[:trainsize])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "1411 of 1620\n", | |
| "0.870987654321\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<h4>Reading the top features list</h4>\n", | |
| "<ul>\n", | |
| " <li>t100 = Team 1</li>\n", | |
| " <li>t200 = Team 2</li>\n", | |
| " <li>Basically, if you see a t200 on the topfeatures list, it means you want the other team to have that feature</li>\n", | |
| "</ul>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(4.7953455844123329, 't100firstinhibitor')\n", | |
| "(2.8398065867578812, 't200:p10:Tryndamere')\n", | |
| "(2.7132403942563212, 't100:p1:Akali')\n", | |
| "(2.6592542376319486, 't200:p8:Shaco')\n", | |
| "(2.6454619154157348, \"t200:p10:Vel'Koz\")\n", | |
| "(2.5592554082180508, 't100:p2:Vayne')\n", | |
| "(2.5542158265325039, 't200:p6:Braum')\n", | |
| "(2.4720206064685226, 't200:p10:Veigar')\n", | |
| "(2.4410791155160378, 't100firstbaron')\n", | |
| "(2.4019936685062784, 't100:p2:Fizz')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getTopFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The SGD algorithm put the largest weight on who took first tower, which makes a lot of sense, but I'm surprised it isn't something else, like first baron, or inhibitor." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wow okay it totally is first inhibitor, I had an off by one error in my indexing. Everything makes sense now." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Support Vector Machine" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", | |
| " decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n", | |
| " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", | |
| " tol=0.001, verbose=False)" | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SVC(kernel=\"linear\")\n", | |
| "clf.fit(x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "1582 of 1620\n", | |
| "0.976543209877\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The SVM was way slower, but with almost perfect predictions" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(1.9221614242489031, 't100firstinhibitor')\n", | |
| "(1.4134330937206883, 't200:p8:Shaco')\n", | |
| "(1.3393736042535727, 't200:p6:Xerath')\n", | |
| "(1.2869091698333024, 't100:p4:Nami')\n", | |
| "(1.2636548312007148, 't100:p2:Sejuani')\n", | |
| "(1.2519879847705975, 't100:p1:Akali')\n", | |
| "(1.2457964324805502, 't100:p2:Vayne')\n", | |
| "(1.2235508451634636, 't200:p9:Karthus')\n", | |
| "(1.1686349919970627, 't100:p1:Taric')\n", | |
| "(1.1549587773247338, 't200:p8:Dr. Mundo')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getTopFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Basically, it looks like if the other team has a shaco on their team, it's a good thing for you. Both algorithms agreed there." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Bronze" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# SGD" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "features, x, y = getData(\"bronze.csv\")\n", | |
| "trainsize = int(.6 * len(x))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "2394" | |
| ] | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "len(x)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", | |
| " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", | |
| " learning_rate='optimal', loss='log', n_iter=50, n_jobs=1,\n", | |
| " penalty='l2', power_t=0.5, random_state=None, shuffle=True,\n", | |
| " verbose=0, warm_start=False)" | |
| ] | |
| }, | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SGDClassifier(loss=\"log\", penalty=\"l2\", n_iter=50)\n", | |
| "clf.fit(x[:trainsize], y[:trainsize])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "811 of 958\n", | |
| "0.846555323591\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(4.0780341544599672, 't100firstinhibitor')\n", | |
| "(2.5687310089850075, 't100:p3:Lux')\n", | |
| "(2.5534177132363873, 't200:p6:Teemo')\n", | |
| "(2.5102583295827352, 't100:p4:Yasuo')\n", | |
| "(2.4331208700436329, 't100:p3:Jax')\n", | |
| "(2.4205954521501969, 't100:p3:Talon')\n", | |
| "(2.4112080514270793, 't200:p10:Hecarim')\n", | |
| "(2.374823489169323, 't200:p6:Annie')\n", | |
| "(2.2954659333231957, 't200:p7:Tryndamere')\n", | |
| "(2.2550787553853411, 't200:p9:Blitzcrank')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getTopFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# SVM" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", | |
| " decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n", | |
| " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", | |
| " tol=0.001, verbose=False)" | |
| ] | |
| }, | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SVC(kernel=\"linear\")\n", | |
| "clf.fit(x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "944 of 958\n", | |
| "0.985386221294\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(1.5563313625313706, 't200:p10:Hecarim')\n", | |
| "(1.3725625528065544, 't100firstinhibitor')\n", | |
| "(1.3466930431097506, 't100:p4:Nasus')\n", | |
| "(1.2287842619682321, 't100:p5:Draven')\n", | |
| "(1.2132124630750809, 't200:p10:Nidalee')\n", | |
| "(1.2053695635800641, 't200:p7:Volibear')\n", | |
| "(1.1892381567730586, 't200:p6:Talon')\n", | |
| "(1.161076445045945, 't200:p6:Gnar')\n", | |
| "(1.1101318204266222, 't100:p5:Sion')\n", | |
| "(1.0891531280511229, 't100:p1:Annie')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getTopFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(-1.6203523849847805, 't200:p10:Illaoi')\n", | |
| "(-1.4782681058126228, 't200firstinhibitor')\n", | |
| "(-1.3142544008570698, \"t200:p6:Kog'Maw\")\n", | |
| "(-1.2448348290728046, 't200:p7:Azir')\n", | |
| "(-1.2402458430467633, 't200:p9:Twisted Fate')\n", | |
| "(-1.1827097915564944, 't200:p8:Karthus')\n", | |
| "(-1.1575872702935666, 't100:p1:Zed')\n", | |
| "(-1.1551235540340952, 't200:p8:Sejuani')\n", | |
| "(-1.0832806158864661, 't100:p1:Nunu')\n", | |
| "(-1.0397595238540016, 't100:p1:Xerath')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getWorstFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Apparently hecarim is not good on this patch or something." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Challenger" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "features, x, y = getData(\"challenger.csv\")\n", | |
| "trainsize = int(.8 * len(x))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", | |
| " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", | |
| " learning_rate='optimal', loss='log', n_iter=50, n_jobs=1,\n", | |
| " penalty='l2', power_t=0.5, random_state=None, shuffle=True,\n", | |
| " verbose=0, warm_start=False)" | |
| ] | |
| }, | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SGDClassifier(loss=\"log\", penalty=\"l2\", n_iter=50)\n", | |
| "clf.fit(x[:trainsize], y[:trainsize])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "62 of 71\n", | |
| "0.87323943662\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(9.2586255948382767, 't100firstinhibitor')\n", | |
| "(3.7565552243529998, 't100firstbaron')\n", | |
| "(3.3940741582075091, 't200:p6:Lucian')\n", | |
| "(3.3369911715648146, 't200:p9:Riven')\n", | |
| "(3.3343600665079878, 't100:p4:Wukong')\n", | |
| "(3.3325219357224443, 't100:p5:Vayne')\n", | |
| "(3.1399380092940716, 't200:p10:spell112')\n", | |
| "(2.9670288920162071, 't100:p4:Janna')\n", | |
| "(2.7963696663082223, 't200:p8:Ezreal')\n", | |
| "(2.7907344689232145, 't200:p10:Corki')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getTopFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(-10.689015417418142, 't200firstinhibitor')\n", | |
| "(-6.1745348150526489, 't200firstbaron')\n", | |
| "(-3.7201689568111775, 't200:p10:spell114')\n", | |
| "(-3.3219388866008033, 't100:p3:Gragas')\n", | |
| "(-2.729922620612883, 't100:p2:spell13')\n", | |
| "(-2.6749337303808884, 't100:p1:Viktor')\n", | |
| "(-2.6670562367840533, \"t100:p1:Rek'Sai\")\n", | |
| "(-2.5987716570977666, 't100:p4:Bard')\n", | |
| "(-2.4199281798587533, 't200:p7:spell17')\n", | |
| "(-2.294666173835473, 't100:p3:spell112')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getWorstFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This may just be that my challenger dataset is relatively smaller than the others, but it is interesting to see the large weight on inhibitor and baron. \n", | |
| "\n", | |
| "It is much larger than it was in Bronze which would imply that getting first baron or inhib for challenger games is much more indicitive of winning." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# SVM" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", | |
| " decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n", | |
| " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", | |
| " tol=0.001, verbose=False)" | |
| ] | |
| }, | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SVC(kernel=\"linear\")\n", | |
| "clf.fit(x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "71 of 71\n", | |
| "1.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(0.84376448830477058, 't100firstinhibitor')\n", | |
| "(0.39022169412339591, 't200:p10:Corki')\n", | |
| "(0.35319143837353273, 't200:p9:Lee Sin')\n", | |
| "(0.34980695636668141, 't100:p4:Wukong')\n", | |
| "(0.34157096431957373, 't200:p10:Ekko')\n", | |
| "(0.33817091259529097, 't200:p7:spell11')\n", | |
| "(0.33729562183535411, 't100firstbaron')\n", | |
| "(0.30714224877813301, 't200:p7:Ahri')\n", | |
| "(0.2891027284345008, 't200:p7:spell121')\n", | |
| "(0.28865677196628964, 't100firstdragon')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getTopFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "source": [ | |
| "# What if we didn't know about barons and inhibs?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Diamond" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### SGD" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "features, x, y = getDataNoInGame(\"diamond.csv\")\n", | |
| "trainsize = int(.6 * len(x))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", | |
| " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", | |
| " learning_rate='optimal', loss='log', n_iter=50, n_jobs=1,\n", | |
| " penalty='l2', power_t=0.5, random_state=None, shuffle=True,\n", | |
| " verbose=0, warm_start=False)" | |
| ] | |
| }, | |
| "execution_count": 35, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SGDClassifier(loss=\"log\", penalty=\"l2\", n_iter=50)\n", | |
| "clf.fit(x[:trainsize], y[:trainsize])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "844 of 1620\n", | |
| "0.520987654321\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SGD has a much harder time discerning who wins, which means that the champions are more or less balanced.\n", | |
| "Only classified 52 % of them correctly. Basically a coin flip." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### SVM" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", | |
| " decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n", | |
| " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", | |
| " tol=0.001, verbose=False)" | |
| ] | |
| }, | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "clf = SVC(kernel=\"linear\")\n", | |
| "clf.fit(x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "1270 of 1620\n", | |
| "0.783950617284\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "testCorrect(trainsize, x, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wow almost 80% correct on the test set?? Imbalance?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(1.8183912726840334, 't200:p9:Rumble')\n", | |
| "(1.6730483627122275, 't200:p6:Yorick')\n", | |
| "(1.6678794419393672, 't100:p1:Akali')\n", | |
| "(1.6447259451347747, 't100:p1:Taric')\n", | |
| "(1.6005601262474496, 't200:p6:Teemo')\n", | |
| "(1.5721088539867876, 't200:p9:Zac')\n", | |
| "(1.4964099580946537, 't200:p8:Twitch')\n", | |
| "(1.4839422463911134, 't200:p9:Heimerdinger')\n", | |
| "(1.4432712419660707, 't200:p8:Sejuani')\n", | |
| "(1.4190523825849422, 't100:p3:Illaoi')\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "getTopFeatures(clf, 10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Suggests that Rumble and Yorick are not good picks right now. Whereas Akali is." | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.11" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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