final_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vade Kamenitsa-Hale\n",
    "\n",
    "Code for final project:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from sportsreference.nfl.roster import Roster, Player\n",
    "from sportsreference.nfl.teams import Teams\n",
    "import csv\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "BridTe00\n",
      "MartTe00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PryoTe00\n",
      "BakeTe00\n",
      "BradTe00\n",
      "HanrTe00\n",
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      "WhitTo20\n",
      "BradTo00\n",
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      "OMalTo20\n",
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      "BoykTr00\n",
      "SiemTr00\n",
      "AikmTr00\n",
      "SmitTr02\n",
      "TaylTr00\n",
      "LeemTu20\n",
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      "DetmTy00\n",
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      "KeckVa00\n",
      "HuffVe20\n",
      "EatoVi20\n",
      "EvanVi00\n",
      "FerrVi00\n",
      "OlivVi20\n",
      "YounVi00\n",
      "TestVi00\n",
      "CartVi00\n",
      "WilsWa00\n",
      "RichWa00\n",
      "RankWa20\n",
      "BrigWa20\n",
      "MoonWa00\n",
      "RabbWa00\n",
      "ClarWa00\n",
      "GiftWa20\n",
      "CureWi00\n",
      "FurrWi00\n",
      "GrieWi00\n",
      "GillWi21\n",
      "ThroWi20\n",
      "TottWi20\n",
      "TittY.00\n",
      "BussYo20\n",
      "DyseZa00\n",
      "RobiZa00\n",
      "MettZa00\n",
      "BratZe00\n"
     ]
    }
   ],
   "source": [
    "f = open('/data/QB_data.csv')\n",
    "csv_f = csv.reader(f)\n",
    "\n",
    "QBs = []\n",
    "QB_ids = []\n",
    "for row in csv_f:\n",
    "    name = row[0].split(\"\\\\\")\n",
    "    QBs.append(name)\n",
    "\n",
    "for QB in QBs[1:]:\n",
    "    QB_ids.append(QB[1])\n",
    "\n",
    "QB_ids.remove('JohnAl20')\n",
    "QBdataset = pd.DataFrame()\n",
    "for QB_id in QB_ids:\n",
    "    print(QB_id)\n",
    "    QBdataset = pd.concat([QBdataset, Player(QB_id).dataframe])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BrowAB00\n",
      "WhitA.00\n",
      "JoneA.20\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WilsJe01\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FarmTe20\n",
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      "BrooTo21\n",
      "BursTo20\n",
      "CartTo00\n",
      "CherTo20\n",
      "CollTo01\n",
      "DaviTo00\n",
      "DollTo20\n",
      "DorsTo00\n",
      "FiamTo00\n",
      "FishTo00\n",
      "GalbTo00\n",
      "GreeTo01\n",
      "HollTo00\n",
      "HuntTo01\n",
      "HuntTo20\n",
      "JeffTo20\n",
      "JordTo00\n",
      "LoriTo00\n",
      "MumfTo20\n",
      "NathTo00\n",
      "PaigTo00\n",
      "PollTo00\n",
      "ReedTo00\n",
      "SmitTo00\n",
      "TaylTo00\n",
      "VinsTo00\n",
      "ClemTo21\n",
      "TyleTo00\n",
      "JohnTr00\n",
      "CadeTr00\n",
      "HenrTr00\n",
      "HomeTr00\n",
      "JervTr00\n",
      "MinoTr00\n",
      "PrenTr00\n",
      "ReecTr00\n",
      "StepTr01\n",
      "WillTr00\n",
      "WillTr06\n",
      "MaddTr01\n",
      "MasoTr00\n",
      "StepTr00\n",
      "RichTr00\n",
      "CannTr00\n",
      "CobbTr20\n",
      "EdmuTr00\n",
      "WattTr00\n",
      "WillTr03\n",
      "DaviTr01\n",
      "DaviTr00\n",
      "FlemTr00\n",
      "HambTr00\n",
      "PopeTr00\n",
      "CaniTr00\n",
      "FredTu00\n",
      "DoutTy20\n",
      "JohnTy02\n",
      "ErviTy00\n",
      "GaffTy00\n",
      "VargTy00\n",
      "SuttTy00\n",
      "AnthTy00\n",
      "MontTy00\n",
      "RushTy20\n",
      "WheaTy00\n",
      "ThomTy00\n",
      "JohnUn20\n",
      "FergVa00\n",
      "SikaVa00\n",
      "WillVa00\n",
      "MuelVa00\n",
      "DunbVa00\n",
      "HebrVa00\n",
      "MoreVe00\n",
      "HaynVe00\n",
      "NyvaVi00\n",
      "BallVi00\n",
      "KingVi00\n",
      "AndeVi20\n",
      "FloyVi20\n",
      "JoneVi20\n",
      "EzerVi00\n",
      "ClemVi00\n",
      "KendVi00\n",
      "ThomVi00\n",
      "WillVi00\n",
      "WorkVi00\n",
      "AlexVi20\n",
      "RobiVi00\n",
      "LeacVo00\n",
      "LundWa00\n",
      "EaslWa00\n",
      "GarrWa00\n",
      "AberWa00\n",
      "DeanWa20\n",
      "HolmWa20\n",
      "LandWa20\n",
      "PaytWa00\n",
      "WillWa02\n",
      "WillWa01\n",
      "WalsWa00\n",
      "LoviWa20\n",
      "MarsWa20\n",
      "WillWa00\n",
      "DunnWa00\n",
      "GallWa00\n",
      "MorrWa00\n",
      "MoslWa00\n",
      "PatrWa00\n",
      "WilsWa02\n",
      "HayeWe00\n",
      "SmalWe00\n",
      "TyleWe00\n",
      "BendWe00\n",
      "HillWe00\n",
      "OursWe00\n",
      "HaslWi20\n",
      "MontWi00\n",
      "JackWi01\n",
      "TaufWi00\n",
      "HarrWi01\n",
      "ReavWi20\n",
      "AndrWi00\n",
      "BellWi20\n",
      "GreeWi01\n",
      "HowaWi00\n",
      "PoweWi00\n",
      "AsbuWi00\n",
      "BeltWi00\n",
      "ElliWi00\n",
      "JoynWi20\n",
      "McClWi00\n",
      "ParkWi00\n",
      "RodgWi00\n",
      "ShelWi00\n",
      "SpenWi00\n",
      "TurrWi20\n",
      "CrenWi00\n",
      "McGaWi00\n",
      "PerrWi00\n",
      "OlivWi00\n",
      "PippWo20\n",
      "DantWo00\n",
      "BennWo00\n",
      "CampWo00\n",
      "GreeWo00\n",
      "ThomWo00\n",
      "OmonXa00\n",
      "JourYo20\n",
      "StacZa00\n",
      "BaumZa00\n",
      "ZennZa00\n",
      "DixoZa00\n",
      "TiptZu01\n"
     ]
    }
   ],
   "source": [
    "f2 = open('/data/RB_data.csv')\n",
    "csv_f2 = csv.reader(f2)\n",
    "\n",
    "RBs = []\n",
    "RB_ids = []\n",
    "for row in csv_f2:\n",
    "    name = row[0].split(\"\\\\\")\n",
    "    RBs.append(name)\n",
    "\n",
    "for RB in RBs[1:]:\n",
    "    RB_ids.append(RB[1])\n",
    "RB_ids.remove('CoxxMi00')\n",
    "RBdataset = pd.DataFrame()\n",
    "for RB_id in RB_ids:\n",
    "    print(RB_id)\n",
    "    RBdataset = pd.concat([RBdataset, Player(RB_id).dataframe])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_QB_HOF = [\n",
    "    Player('FavrBr00'),\n",
    "    Player('MariDa00'),\n",
    "    Player('ElwaJo00'),\n",
    "    Player('MoonWa00'),\n",
    "    Player('TarkFr00'),\n",
    "    Player('MontJo00'),\n",
    "    Player('FoutDa00'),\n",
    "    Player('AikmTr00'),\n",
    "    Player('KellJi00'),\n",
    "    Player('UnitJo00'),\n",
    "    Player('YounSt00'),\n",
    "    Player('WarnKu00'),\n",
    "    Player('JurgSo00'),\n",
    "    Player('TittY.00'),\n",
    "    Player('StabKe00'),\n",
    "    Player('DawsLe00'),\n",
    "    Player('BradTe00'),\n",
    "    Player('GrieBo00'),\n",
    "    Player('BlanGe00'),\n",
    "    Player('NamaJo00'),\n",
    "    Player('LaynBo00'),\n",
    "    Player('StarBa00'),\n",
    "    Player('BaugSa00'),\n",
    "    Player('StauRo00'),\n",
    "    Player('VanBNo00'),\n",
    "    Player('GrahOt00'),\n",
    "    Player('LuckSi00'),\n",
    "    Player('WateBo00'),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_RB_HOF = [\n",
    "    Player('DaviTe00'),\n",
    "    Player('CsonLa00'),\n",
    "    Player('JameEd00'),\n",
    "    Player('HarrFr00'),\n",
    "    Player('RiggJo00'),\n",
    "    Player('FaulMa00'),\n",
    "    Player('ThomTh00'),\n",
    "    Player('CampEa00'),\n",
    "    Player('DickEr00'),\n",
    "    Player('SandBa00'),\n",
    "    Player('SmitEm00'),\n",
    "    Player('LittFl00'),\n",
    "    Player('MartCu00'),\n",
    "    Player('DorsTo00'),\n",
    "    Player('KellLe00'),\n",
    "    Player('BettJe00'),\n",
    "    Player('SayeGa00'),\n",
    "    Player('MatsOl00'),\n",
    "    Player('SimpO.00'),\n",
    "    Player('TomlLa00'),\n",
    "    Player('PaytWa00'),\n",
    "    Player('AlleMa00'),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "QBdataset['HOF'] = False\n",
    "counter = 0\n",
    "for player in QBdataset['player_id']:\n",
    "    for QBHOF in test_QB_HOF:\n",
    "        if QBHOF.player_id == player:\n",
    "            QBdataset['HOF'][counter] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "RBdataset['HOF'] = False\n",
    "counter = 0\n",
    "for player in RBdataset['player_id']:\n",
    "    for RBHOF in test_RB_HOF:\n",
    "        if RBHOF.player_id == player:\n",
    "            RBdataset['HOF'][counter] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in QBdataset:\n",
    "    QBdataset[col] = pd.Categorical(QBdataset[col])\n",
    "    command = \"QBdataset[col] = QBdataset.\" + col + \".cat.codes\"\n",
    "    exec(command)\n",
    "        \n",
    "QBdataset['HOF'] = QBdataset['HOF'].astype('bool')\n",
    "QBdataset.fillna(0)\n",
    "FIELDS_TO_DROP = ['name', 'birth_date', 'height', 'weight', 'position', 'team_abbreviation', 'HOF',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = QBdataset.drop(FIELDS_TO_DROP, 1).drop_duplicates()\n",
    "y = QBdataset['HOF'].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "4693/4693 [==============================] - 0s 44us/step - loss: 4.9192 - accuracy: 0.9642\n",
      "Epoch 2/20\n",
      "4693/4693 [==============================] - 0s 11us/step - loss: 0.3354 - accuracy: 0.9955\n",
      "Epoch 3/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1391 - accuracy: 0.9977\n",
      "Epoch 4/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.2864 - accuracy: 0.9994\n",
      "Epoch 5/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0896 - accuracy: 0.9998\n",
      "Epoch 6/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0025 - accuracy: 0.9996\n",
      "Epoch 7/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1837 - accuracy: 0.9994\n",
      "Epoch 8/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.2952 - accuracy: 0.9996\n",
      "Epoch 9/20\n",
      "4693/4693 [==============================] - 0s 11us/step - loss: 0.2513 - accuracy: 0.9996\n",
      "Epoch 10/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1145 - accuracy: 0.9994\n",
      "Epoch 11/20\n",
      "4693/4693 [==============================] - 0s 11us/step - loss: 0.1700 - accuracy: 0.9998\n",
      "Epoch 12/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1240 - accuracy: 0.9994\n",
      "Epoch 13/20\n",
      "4693/4693 [==============================] - 0s 11us/step - loss: 0.0549 - accuracy: 0.9996\n",
      "Epoch 14/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1756 - accuracy: 0.9998\n",
      "Epoch 15/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0993 - accuracy: 0.9994\n",
      "Epoch 16/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0880 - accuracy: 0.9998\n",
      "Epoch 17/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1262 - accuracy: 0.9998\n",
      "Epoch 18/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.2359 - accuracy: 0.9996\n",
      "Epoch 19/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1545 - accuracy: 0.9994\n",
      "Epoch 20/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.1425 - accuracy: 0.9994\n",
      "1565/1565 [==============================] - 0s 33us/step\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(104, input_dim=104, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train,\n",
    "          epochs=20,\n",
    "          batch_size=128)\n",
    "classification = model.evaluate(X_test, y_test, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in RBdataset:\n",
    "    RBdataset[col] = pd.Categorical(RBdataset[col])\n",
    "    command = \"RBdataset[col] = RBdataset.\" + col + \".cat.codes\"\n",
    "    exec(command)\n",
    "        \n",
    "RBdataset['HOF'] = RBdataset['HOF'].astype('bool')\n",
    "RBdataset.fillna(0)\n",
    "FIELDS_TO_DROP = ['name', 'birth_date', 'height', 'weight', 'position', 'team_abbreviation', 'HOF',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xr = QBdataset.drop(FIELDS_TO_DROP, 1).drop_duplicates()\n",
    "yr = QBdataset['HOF'].values\n",
    "Xr_train, Xr_test, yr_train, yr_test = train_test_split(Xr, yr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "4693/4693 [==============================] - 0s 43us/step - loss: 1.6210 - accuracy: 0.9789\n",
      "Epoch 2/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0845 - accuracy: 0.9981\n",
      "Epoch 3/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0197 - accuracy: 0.9991\n",
      "Epoch 4/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0240 - accuracy: 0.9991\n",
      "Epoch 5/20\n",
      "4693/4693 [==============================] - 0s 9us/step - loss: 1.1366e-08 - accuracy: 1.0000\n",
      "Epoch 6/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 2.6547e-10 - accuracy: 1.0000\n",
      "Epoch 7/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0146 - accuracy: 0.9994\n",
      "Epoch 8/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 3.1340e-11 - accuracy: 1.0000\n",
      "Epoch 9/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 7.5152e-07 - accuracy: 1.0000\n",
      "Epoch 10/20\n",
      "4693/4693 [==============================] - 0s 9us/step - loss: 3.9598e-08 - accuracy: 1.0000\n",
      "Epoch 11/20\n",
      "4693/4693 [==============================] - 0s 9us/step - loss: 2.9173e-07 - accuracy: 1.0000\n",
      "Epoch 12/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0205 - accuracy: 0.9998\n",
      "Epoch 13/20\n",
      "4693/4693 [==============================] - 0s 14us/step - loss: 6.4128e-09 - accuracy: 1.0000\n",
      "Epoch 14/20\n",
      "4693/4693 [==============================] - 0s 14us/step - loss: 1.9453e-06 - accuracy: 1.0000\n",
      "Epoch 15/20\n",
      "4693/4693 [==============================] - 0s 13us/step - loss: 1.2416e-09 - accuracy: 1.0000\n",
      "Epoch 16/20\n",
      "4693/4693 [==============================] - 0s 11us/step - loss: 7.7299e-06 - accuracy: 1.0000\n",
      "Epoch 17/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 1.3745e-10 - accuracy: 1.0000\n",
      "Epoch 18/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 1.2440e-10 - accuracy: 1.0000\n",
      "Epoch 19/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 1.1571e-09 - accuracy: 1.0000\n",
      "Epoch 20/20\n",
      "4693/4693 [==============================] - 0s 10us/step - loss: 0.0032 - accuracy: 0.9998\n",
      "1565/1565 [==============================] - 0s 26us/step\n"
     ]
    }
   ],
   "source": [
    "modelr = Sequential()\n",
    "modelr.add(Dense(104, input_dim=104, activation='relu'))\n",
    "modelr.add(Dropout(0.5))\n",
    "modelr.add(Dense(64, activation='relu'))\n",
    "modelr.add(Dropout(0.5))\n",
    "modelr.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "modelr.compile(loss='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "modelr.fit(Xr_train, yr_train,\n",
    "          epochs=20,\n",
    "          batch_size=128)\n",
    "classification = modelr.evaluate(Xr_test, yr_test, batch_size=128)"
   ]
  }
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