WorkFlow II - Modeling & Analytics for  3.13 betting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import cross_validation, metrics\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('Data/train_df_0313.csv')\n",
    "y_train = pd.read_csv('Data/y_train_0313.csv')\n",
    "valid_df = pd.read_csv('Data/valid_df_0313.csv')\n",
    "y_valid = pd.read_csv('Data/y_valid_0313.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "IDcol = 'Player_ID'\n",
    "target = \"NewGameFanPTs\"\n",
    "predictors = [x for x in train_df.columns if x not in [target, IDcol, 'fullName','Rank_dup']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def modelfit(alg, train_df,y_train,predictors,useTrainCV=True, cv_folds=3,early_stopping_rounds=50): \n",
    "    #cross validation for selecting the number of estomators(trees)\n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgtrain = xgb.DMatrix(train_df[predictors].values, label= y_train.values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\\\n",
    "                          early_stopping_rounds=early_stopping_rounds, show_progress=True)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "        print \"number of estomators : \"+ str(cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(train_df[predictors], y_train, eval_metric='rmse')\n",
    "                \n",
    "    return alg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Will train until cv error hasn't decreased in 50 rounds.\n",
      "[0]\tcv-test-rmse:24.172467+0.112173473846\tcv-train-rmse:24.168204+0.0466935464277\n",
      "[1]\tcv-test-rmse:22.1777596667+0.120004069068\tcv-train-rmse:22.1684113333+0.0329733064294\n",
      "[2]\tcv-test-rmse:20.416215+0.119144962884\tcv-train-rmse:20.400468+0.0248184089068\n",
      "[3]\tcv-test-rmse:18.868921+0.117097978414\tcv-train-rmse:18.8516843333+0.0222227928988\n",
      "[4]\tcv-test-rmse:17.509451+0.111682329939\tcv-train-rmse:17.485916+0.0182428495033\n",
      "[5]\tcv-test-rmse:16.3282706667+0.113624207181\tcv-train-rmse:16.2969216667+0.0100884192463\n",
      "[6]\tcv-test-rmse:15.2973146667+0.10925826378\tcv-train-rmse:15.257802+0.0069159367165\n",
      "[7]\tcv-test-rmse:14.4104203333+0.104489014071\tcv-train-rmse:14.3633646667+0.00485324567229\n",
      "[8]\tcv-test-rmse:13.6435536667+0.0998589157439\tcv-train-rmse:13.5870303333+0.00308684329948\n",
      "[9]\tcv-test-rmse:12.9857263333+0.0953641252475\tcv-train-rmse:12.9209296667+0.00857969278134\n",
      "[10]\tcv-test-rmse:12.4250836667+0.093321014212\tcv-train-rmse:12.3526016667+0.0137299666747\n",
      "[11]\tcv-test-rmse:11.954057+0.0890076677296\tcv-train-rmse:11.873113+0.0179622661711\n",
      "[12]\tcv-test-rmse:11.5566336667+0.08196160899\tcv-train-rmse:11.467112+0.0195122731804\n",
      "[13]\tcv-test-rmse:11.222841+0.0766034087492\tcv-train-rmse:11.1245506667+0.0208159421171\n",
      "[14]\tcv-test-rmse:10.941785+0.0678711533618\tcv-train-rmse:10.835107+0.0213938112702\n",
      "[15]\tcv-test-rmse:10.711728+0.0604768677154\tcv-train-rmse:10.594782+0.0237518555486\n",
      "[16]\tcv-test-rmse:10.5174006667+0.0558539772045\tcv-train-rmse:10.3932963333+0.024627052095\n",
      "[17]\tcv-test-rmse:10.3565563333+0.0490866191702\tcv-train-rmse:10.224833+0.024194545997\n",
      "[18]\tcv-test-rmse:10.225951+0.0431972627837\tcv-train-rmse:10.084427+0.0250221838509\n",
      "[19]\tcv-test-rmse:10.11675+0.0403027929305\tcv-train-rmse:9.967064+0.0273543764079\n",
      "[20]\tcv-test-rmse:10.0277546667+0.0375886245411\tcv-train-rmse:9.868124+0.0272539260413\n",
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      "[26]\tcv-test-rmse:9.74580166667+0.0331544547166\tcv-train-rmse:9.52939566667+0.0246133908315\n",
      "[27]\tcv-test-rmse:9.722514+0.0338882056572\tcv-train-rmse:9.498968+0.0235260702342\n",
      "[28]\tcv-test-rmse:9.703797+0.0357773704735\tcv-train-rmse:9.47357333333+0.0237215803624\n",
      "[29]\tcv-test-rmse:9.68783566667+0.0385435237131\tcv-train-rmse:9.450705+0.022276398916\n",
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      "[31]\tcv-test-rmse:9.66584666667+0.0409229469152\tcv-train-rmse:9.410065+0.0218888079621\n",
      "[32]\tcv-test-rmse:9.656399+0.0406835326883\tcv-train-rmse:9.39338966667+0.021360233306\n",
      "[33]\tcv-test-rmse:9.64908333333+0.042861944771\tcv-train-rmse:9.379742+0.0207644537772\n",
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      "[110]\tcv-test-rmse:9.60647366667+0.046116058348\tcv-train-rmse:8.93261433333+0.0227762546282\n",
      "[111]\tcv-test-rmse:9.607403+0.0470538301381\tcv-train-rmse:8.92806533333+0.0230492394427\n",
      "[112]\tcv-test-rmse:9.60760633333+0.045948244452\tcv-train-rmse:8.923498+0.0229923352011\n",
      "[113]\tcv-test-rmse:9.60631566667+0.0471423354133\tcv-train-rmse:8.91746066667+0.0241772712319\n",
      "[114]\tcv-test-rmse:9.60693033333+0.0468962660158\tcv-train-rmse:8.91389566667+0.0236442894351\n",
      "[115]\tcv-test-rmse:9.60761066667+0.0470271663498\tcv-train-rmse:8.910892+0.0242027065015\n",
      "[116]\tcv-test-rmse:9.60809633333+0.047468510842\tcv-train-rmse:8.90635566667+0.0243337145313\n",
      "[117]\tcv-test-rmse:9.60734133333+0.0478548197805\tcv-train-rmse:8.902719+0.0242595887434\n",
      "[118]\tcv-test-rmse:9.607303+0.0484238610673\tcv-train-rmse:8.898685+0.0230441756777\n",
      "[119]\tcv-test-rmse:9.60865666667+0.0508051403589\tcv-train-rmse:8.89329266667+0.0235573894187\n",
      "[120]\tcv-test-rmse:9.60920333333+0.050993719001\tcv-train-rmse:8.88864166667+0.0230749866354\n",
      "[121]\tcv-test-rmse:9.610877+0.0506676540671\tcv-train-rmse:8.884743+0.0214522605957\n",
      "[122]\tcv-test-rmse:9.613071+0.0521843105924\tcv-train-rmse:8.87994033333+0.0235089896612\n",
      "[123]\tcv-test-rmse:9.61315766667+0.0529130904366\tcv-train-rmse:8.87563266667+0.0241063767129\n",
      "[124]\tcv-test-rmse:9.61257366667+0.0536050858304\tcv-train-rmse:8.87046866667+0.0243399532365\n",
      "[125]\tcv-test-rmse:9.612307+0.0535146260381\tcv-train-rmse:8.86452966667+0.024355299729\n",
      "[126]\tcv-test-rmse:9.61215033333+0.0539668819699\tcv-train-rmse:8.85913366667+0.0261484531261\n",
      "[127]\tcv-test-rmse:9.61315733333+0.0529634931648\tcv-train-rmse:8.856526+0.02699233569\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of estomators : 79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[128]\tcv-test-rmse:9.61406433333+0.0526699222285\tcv-train-rmse:8.85462266667+0.0274364029388\n",
      "Stopping. Best iteration: 78\n"
     ]
    }
   ],
   "source": [
    "xgb1 = XGBRegressor(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=3,\n",
    "        min_child_weight=8,\n",
    "        gamma=3,\n",
    "        objective= 'reg:linear',\n",
    "        subsample=0.95,\n",
    "        colsample_bytree=0.7,\n",
    "        nthread=-1,\n",
    "        scale_pos_weight=1,\n",
    "        reg_alpha=0.9753,\n",
    "        reg_lambda = 0.7083,\n",
    "        seed=27)\n",
    "model1 = modelfit(xgb1, train_df, y_train, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('Data/xgboost_model1_0313.pickle', 'wb') as handle:\n",
    "  pickle.dump(model1,handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_performance(xgbmodel,train_df, y_train, valid_df, y_valid,predictors):\n",
    "    train_predictions = xgbmodel.predict(train_df[predictors])\n",
    "    print \"\\nModel Report\"\n",
    "    print \"RMSE(Train) : %.4g\" % metrics.mean_squared_error(y_train, train_predictions)**0.5\n",
    "    print \"R^2 Score (Train): %f\" % metrics.r2_score(y_train, train_predictions)\n",
    "    test_result = xgbmodel.predict(valid_df[predictors])\n",
    "    print \"RMSE(Test) : %.4g\" % metrics.mean_squared_error(y_valid, test_result)**0.5\n",
    "    print 'R^2 Score (Test): %f' % metrics.r2_score(y_valid, test_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "RMSE(Train) : 9.201\n",
      "R^2 Score (Train): 0.540187\n",
      "RMSE(Test) : 9.769\n",
      "R^2 Score (Test): 0.477352\n"
     ]
    }
   ],
   "source": [
    "get_performance(model1,train_df, y_train, valid_df, y_valid, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_importance(xgbmodel):\n",
    "    feat_imp = pd.Series(xgbmodel.booster().get_fscore())\n",
    "    feat_imp.sort(ascending=False)\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    feat_imp[:30].plot(kind='barh', title='Feature Importances').invert_yaxis()\n",
    "    plt.ylabel('Feature Importance Score')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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8dH9EzO7jcMrS2NjoH7wc/A9XPs6fmZn1Jhe9vSQi/gT05EE5MzMzM+slA/pB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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdbb090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ggplot import *\n",
    "def plot_predictions(xgbmodel,predictors,pred_label,data,data_label): \n",
    "    data[pred_label] = xgbmodel.predict(data[predictors])\n",
    "    data['NewGameFanPTs'] = data_label\n",
    "    plot_obj = ggplot(aes(x='NewGameFanPTs', y=pred_label, colour='Rank_dup'), data=data)+geom_point()+\\\n",
    "    ggtitle(pred_label+\" Prediction Performance\")+ geom_abline(intercept = 0, slope = 1)\n",
    "    return(plot_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oCSFENWQsKMA/9ntOtupInfC67g5HCOEmkugJIUQ1dGRJLFatnqj7B7o7FCGE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\nAgIuEidfdBg+4Klo2R3mea/Tmo94o8il9QCsBIx2eNSu0ucGedAj0HZ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VSBKtU9Eye0p1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J4qpBdnZvkWkq97JDtWAGFAEB+QNPWuZOFXJjZczUQpxZ6HLUba/XNHOxW7H0uy6B6TPY9YDDZ7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\nIC5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r9Le8nv4nbJm1U9GEjiUjpEcbewQGIe6iVj/PsMz66oVFJS8mdM6491On91Op9WEK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\nerIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daa4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285747925)>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_predictions(model1,predictors,'Train_pred',train_df,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1TyORvsXBqb0qig6ixhgJ6Kyv8XWlp1SS1jsQKkQkQFMMclRtgoMf27DmCowW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52GOuxd212xBhyRPip7GrGmldP71L/S2xcl8/kQo8aizZiJduEi96GrrsMuHo154DD6lEn4Ljgv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KRC1LzBszj4dVb9brYcqo0Rdkr0WEqdLnssr6IDvt/+Tgh/sQaMwQS5HiKAOJ59DQmWtsZIX1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\n9RcDeA0Rq8FHUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089a5610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (279054765)>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_predictions(model1,predictors,'Test_pred',valid_df,y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('Data/experiment_test_df_0313.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_predictions = model1.predict(test_df[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'fullName', u'Player_ID', u'LastFanPTs', u'AvgFanPTs', u'AvgPTS',\n",
       "       u'LastPT', u'AvgMIN', u'LastMIN', u'AvgFGM', u'LastFGM', \n",
       "       ...\n",
       "       u'OpponentTeam_CHI', u'OpponentTeam_SAS', u'OpponentTeam_MEM',\n",
       "       u'OpponentTeam_WAS', u'OpponentTeam_DAL', u'GameMonth_1',\n",
       "       u'GameMonth_2', u'GameMonth_11', u'GameMonth_12', u'GameMonth_10'],\n",
       "      dtype='object', length=110)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_result_df = pd.DataFrame({'fullName':test_df['fullName'],'Player_ID':test_df['Player_ID'],\\\n",
    "                           'prediction':test_predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>fullName</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>203521</td>\n",
       "      <td>Matthew Dellavedova</td>\n",
       "      <td>16.859592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202389</td>\n",
       "      <td>Timofey Mozgov</td>\n",
       "      <td>12.560592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201168</td>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>12.859308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2747</td>\n",
       "      <td>J.R. Smith</td>\n",
       "      <td>21.988348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203471</td>\n",
       "      <td>Dennis Schroder</td>\n",
       "      <td>16.945889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_ID             fullName  prediction\n",
       "0     203521  Matthew Dellavedova   16.859592\n",
       "1     202389       Timofey Mozgov   12.560592\n",
       "2     201168       Tiago Splitter   12.859308\n",
       "3       2747           J.R. Smith   21.988348\n",
       "4     203471      Dennis Schroder   16.945889"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_result_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###We need to combine our predictions with salary costs as inputs for our second stage optimizer problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('Data/SalaryHistory031316/2016-03-13.csv','rb') as data_file:\n",
    "    playcost_0313 = pd.read_csv(data_file,sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 14)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playcost_0313.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "playcost_0313['DK Salary'] = playcost_0313['DK Salary'].astype(str).map(\n",
    "    lambda ele: ele if ele == 'nan' else int(ele.replace('$', '').replace(',', '')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "playcost_0313['fullName'] = playcost_0313['Name'].astype(str).map(lambda nn: nn.split(', ')[1]+' '\\\n",
    "                                                      + nn.split(', ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_result_df.fullName = test_result_df.fullName.map(lambda x: x.replace('.',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "playcost_0313.fullName = playcost_0313.fullName.map(lambda x: x.replace('.',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names_tofix = {'Louis Williams':'Lou Williams','Larry Nance':'Larry Nance Jr'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "playcost_0313.fullName = playcost_0313.fullName.map(lambda x: names_tofix[x] if x in names_tofix.keys() else x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Anthony Brown',\n",
       " 'Branden Dawson',\n",
       " 'CJ Wilcox',\n",
       " 'Caron Butler',\n",
       " 'Chris Johnson',\n",
       " 'Chris McCullough',\n",
       " 'Cleanthony Early',\n",
       " 'Damien Inglis',\n",
       " 'Dante Exum',\n",
       " 'Duje Dukan',\n",
       " 'Eric Moreland',\n",
       " 'Glenn Robinson III',\n",
       " 'James Anderson',\n",
       " 'James Jones',\n",
       " 'Joe Ingles',\n",
       " \"Johnny O'Bryant\",\n",
       " 'Jordan McRae',\n",
       " 'Joseph Young',\n",
       " 'Kevin Seraphin',\n",
       " 'Kirk Hinrich',\n",
       " 'Lamar Patterson',\n",
       " 'Louis Amundson',\n",
       " 'Luc Mbah a Moute',\n",
       " 'Markel Brown',\n",
       " 'Maurice Williams',\n",
       " 'Mike Muscala',\n",
       " 'Pablo Prigioni',\n",
       " 'Rakeem Christmas',\n",
       " 'Rashad Vaughn',\n",
       " 'Robert Sacre',\n",
       " 'Sasha Kaun',\n",
       " 'Sasha Vujacic',\n",
       " 'Sean Kilpatrick',\n",
       " 'Sergey Karasev',\n",
       " 'Seth Curry',\n",
       " 'Shayne Whittington',\n",
       " 'Solomon Hill',\n",
       " 'Steve Novak',\n",
       " 'Tarik Black',\n",
       " 'Tibor Pleiss',\n",
       " 'Tim Hardaway Jr',\n",
       " 'Tyler Ennis',\n",
       " 'Walter Tavares'}"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(playcost_0313.fullName) - set(test_result_df.fullName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ntest_result_df = pd.merge(test_result_df,playcost_0313[['fullName','DK Salary','Pos','Team']],on='fullName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 3)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playcost_0313[['fullName','DK Salary','Pos']].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 6)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ntest_result_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>fullName</th>\n",
       "      <th>prediction</th>\n",
       "      <th>DK Salary</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>203521</td>\n",
       "      <td>Matthew Dellavedova</td>\n",
       "      <td>16.859592</td>\n",
       "      <td>3300</td>\n",
       "      <td>PG</td>\n",
       "      <td>cle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202389</td>\n",
       "      <td>Timofey Mozgov</td>\n",
       "      <td>12.560592</td>\n",
       "      <td>3200</td>\n",
       "      <td>C</td>\n",
       "      <td>cle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201168</td>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>12.859308</td>\n",
       "      <td>3000</td>\n",
       "      <td>C</td>\n",
       "      <td>atl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2747</td>\n",
       "      <td>JR Smith</td>\n",
       "      <td>21.988348</td>\n",
       "      <td>4800</td>\n",
       "      <td>SG</td>\n",
       "      <td>cle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203471</td>\n",
       "      <td>Dennis Schroder</td>\n",
       "      <td>16.945889</td>\n",
       "      <td>4200</td>\n",
       "      <td>PG</td>\n",
       "      <td>atl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_ID             fullName  prediction  DK Salary Pos Team\n",
       "0     203521  Matthew Dellavedova   16.859592       3300  PG  cle\n",
       "1     202389       Timofey Mozgov   12.560592       3200   C  cle\n",
       "2     201168       Tiago Splitter   12.859308       3000   C  atl\n",
       "3       2747             JR Smith   21.988348       4800  SG  cle\n",
       "4     203471      Dennis Schroder   16.945889       4200  PG  atl"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ntest_result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>fullName</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2590</td>\n",
       "      <td>Mo Williams</td>\n",
       "      <td>11.922332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2746</td>\n",
       "      <td>Josh Smith</td>\n",
       "      <td>13.326095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>202362</td>\n",
       "      <td>Lance Stephenson</td>\n",
       "      <td>21.427237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>200745</td>\n",
       "      <td>Andrea Bargnani</td>\n",
       "      <td>11.052099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2207</td>\n",
       "      <td>Joe Johnson</td>\n",
       "      <td>29.154091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>201978</td>\n",
       "      <td>Chase Budinger</td>\n",
       "      <td>12.222034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player_ID          fullName  prediction\n",
       "10       2590       Mo Williams   11.922332\n",
       "19       2746        Josh Smith   13.326095\n",
       "28     202362  Lance Stephenson   21.427237\n",
       "38     200745   Andrea Bargnani   11.052099\n",
       "39       2207       Joe Johnson   29.154091\n",
       "82     201978    Chase Budinger   12.222034"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_result_df[~test_result_df.fullName.isin(ntest_result_df.fullName)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we can't match all the malary cost, actually, there are players not playing today. So we finish the step of combining our predictions with salary costs as inputs for our second stage optimizer problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>fullName</th>\n",
       "      <th>prediction</th>\n",
       "      <th>DK Salary</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>203521</td>\n",
       "      <td>Matthew Dellavedova</td>\n",
       "      <td>16.859592</td>\n",
       "      <td>3300</td>\n",
       "      <td>PG</td>\n",
       "      <td>cle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202389</td>\n",
       "      <td>Timofey Mozgov</td>\n",
       "      <td>12.560592</td>\n",
       "      <td>3200</td>\n",
       "      <td>C</td>\n",
       "      <td>cle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201168</td>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>12.859308</td>\n",
       "      <td>3000</td>\n",
       "      <td>C</td>\n",
       "      <td>atl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2747</td>\n",
       "      <td>JR Smith</td>\n",
       "      <td>21.988348</td>\n",
       "      <td>4800</td>\n",
       "      <td>SG</td>\n",
       "      <td>cle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203471</td>\n",
       "      <td>Dennis Schroder</td>\n",
       "      <td>16.945889</td>\n",
       "      <td>4200</td>\n",
       "      <td>PG</td>\n",
       "      <td>atl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_ID             fullName  prediction  DK Salary Pos Team\n",
       "0     203521  Matthew Dellavedova   16.859592       3300  PG  cle\n",
       "1     202389       Timofey Mozgov   12.560592       3200   C  cle\n",
       "2     201168       Tiago Splitter   12.859308       3000   C  atl\n",
       "3       2747             JR Smith   21.988348       4800  SG  cle\n",
       "4     203471      Dennis Schroder   16.945889       4200  PG  atl"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ntest_result_df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##Run our optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ntest_result_df.rename(columns={'fullName':'Name','prediction':'DK Pts'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ntest_result_df.drop_duplicates('Player_ID',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pulp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def LineUpOptimizer(specificDate):\n",
    "    # initialize variables\n",
    "    playerPositions = specificDate['Pos'].tolist()\n",
    "    teams = specificDate['Team'].tolist()\n",
    "    playerNames = specificDate['Name'].tolist()\n",
    "    #playerTeams = tuple(specificDate['Team'].unique())\n",
    "    playerCosts = tuple(specificDate['DK Salary'])\n",
    "    playerPoints = tuple(specificDate['DK Pts'])\n",
    "    salaryCap = 50000\n",
    "    numOfPlayers = range(len(playerCosts))\n",
    "    \n",
    "    # formulate linear programming problem\n",
    "    problem = pulp.LpProblem(\"Optimal Lineup\", pulp.LpMaximize)\n",
    "    # create variable to represent each player\n",
    "    playerInLineup = pulp.LpVariable.dicts(\"Players\", [i for i in numOfPlayers], 0, 1, cat=\"Binary\")\n",
    "    # create objective: maximize sum of player points\n",
    "    problem += pulp.lpSum(playerInLineup[i] * playerPoints[i] for i in numOfPlayers)\n",
    "    \n",
    "    # constraint: each player can only be chosen at most once\n",
    "    for i in numOfPlayers:\n",
    "        problem += pulp.lpSum(playerInLineup[i]) <= 1\n",
    "    # constraints: sum of player costs must be less than or equal to the salary cap\n",
    "    problem += sum(playerInLineup[i] * playerCosts[i] for i in numOfPlayers) <= salaryCap\n",
    "    # constraint: teams must have 8 players\n",
    "    problem += pulp.lpSum(problem.variables()) == 8\n",
    "    # positions\n",
    "    # constraint: 1 <= number of PG <= Max 3\n",
    "    pointguards = tuple(specificDate['Pos'] == 'PG')\n",
    "    problem += pulp.lpSum(playerInLineup[i] * pointguards[i] for i in numOfPlayers) >= 1\n",
    "    problem += pulp.lpSum(playerInLineup[i] * pointguards[i] for i in numOfPlayers) <= 3\n",
    "\n",
    "    # constraint: 1 <= number of SG <= Max 3\n",
    "    shootguards = tuple(specificDate['Pos'] == 'SG')\n",
    "    problem += pulp.lpSum(playerInLineup[i] * shootguards[i] for i in numOfPlayers) >= 1\n",
    "    problem += pulp.lpSum(playerInLineup[i] * shootguards[i] for i in numOfPlayers) <= 3\n",
    "\n",
    "    # constraint: 1 <= number of SF <= Max 3\n",
    "    smallforward = tuple(specificDate['Pos'] == 'SF')\n",
    "    problem += pulp.lpSum(smallforward[i] * playerInLineup[i] for i in numOfPlayers) >= 1\n",
    "    problem += pulp.lpSum(smallforward[i] * playerInLineup[i] for i in numOfPlayers) <= 3\n",
    "\n",
    "    # constraint: 1 <= number of PF <= Max 3\n",
    "    powerfoward = tuple(specificDate['Pos'] == 'PF')\n",
    "    problem += pulp.lpSum(powerfoward[i] * playerInLineup[i] for i in numOfPlayers) >= 1\n",
    "    problem += pulp.lpSum(powerfoward[i] * playerInLineup[i] for i in numOfPlayers) <= 3\n",
    "\n",
    "    # constraint: 1 <= number of C <= Max 2\n",
    "    centers = tuple(specificDate['Pos'] == 'C')\n",
    "    problem += pulp.lpSum(playerInLineup[i] * centers[i] for i in numOfPlayers) <= 2\n",
    "    problem += pulp.lpSum(playerInLineup[i] * centers[i] for i in numOfPlayers) >= 1\n",
    "    \n",
    "    # output the calculated predicted lineup\n",
    "    cost = 0\n",
    "    points = 0\n",
    "\n",
    "    predicted = set()\n",
    "    if problem.solve() == 1:\n",
    "        for pos in range(len(numOfPlayers)):\n",
    "            if playerInLineup[pos].value() == 1:\n",
    "                name = playerNames[pos]\n",
    "                predicted.add(name)\n",
    "                cost += playerCosts[pos]\n",
    "                points += playerPoints[pos]\n",
    "                print '%25s, Position = %2s, Price = %5.f, Points = %3.2f, Team = %3s' \\\n",
    "                      % (playerNames[pos], playerPositions[pos], playerCosts[pos],\n",
    "                         playerPoints[pos], teams[pos])\n",
    "        print '\\nTotal Team Cost: %5d\\nTotal Team Points: %5.2f' % (cost, points)\n",
    "    else:\n",
    "        print 'Error finding solution'\n",
    "        \n",
    "    return(predicted,cost, points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Thabo Sefolosha, Position = SF, Price =  3200, Points = 18.48, Team = atl\n",
      "            Blake Griffin, Position = PF, Price =  8200, Points = 44.02, Team = lac\n",
      "              Paul George, Position = SF, Price =  8100, Points = 44.53, Team = ind\n",
      "           DeAndre Jordan, Position =  C, Price =  7700, Points = 41.31, Team = lac\n",
      "              Rajon Rondo, Position = PG, Price =  7800, Points = 44.64, Team = sac\n",
      "           Jerryd Bayless, Position = PG, Price =  4700, Points = 25.11, Team = mil\n",
      "  Michael Carter-Williams, Position = PG, Price =  5300, Points = 28.39, Team = mil\n",
      "              Kobe Bryant, Position = SG, Price =  5000, Points = 27.24, Team = lal\n",
      "\n",
      "Total Team Cost: 50000\n",
      "Total Team Points: 273.72\n"
     ]
    }
   ],
   "source": [
    "PredictedLineUp, cost, points = LineUpOptimizer(ntest_result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('Data/allplayerFantasyGameLogs_0314.pickle','rb') as data_file:\n",
    "    result_df = pickle.load(data_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result_df = result_df.ix['2016-03-13']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fullName</th>\n",
       "      <th>FanPTs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAME_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Jerryd Bayless</td>\n",
       "      <td>22.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Paul George</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Rajon Rondo</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>DeAndre Jordan</td>\n",
       "      <td>29.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   fullName  FanPTs\n",
       "GAME_DATE                          \n",
       "2016-03-13   Jerryd Bayless   22.25\n",
       "2016-03-13  Thabo Sefolosha   26.25\n",
       "2016-03-13      Paul George   22.50\n",
       "2016-03-13      Kobe Bryant   18.50\n",
       "2016-03-13      Rajon Rondo   23.50\n",
       "2016-03-13   DeAndre Jordan   29.25"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[result_df.fullName.isin(list(PredictedLineUp))][['fullName','FanPTs']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Why do we miss another two players?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ntest_result_df2 = ntest_result_df[~ntest_result_df.Name.isin([\"Blake Griffin\",\"Michael Carter-Williams\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Thabo Sefolosha, Position = SF, Price =  3200, Points = 18.48, Team = atl\n",
      "              Greg Monroe, Position =  C, Price =  6300, Points = 34.01, Team = mil\n",
      "              Paul George, Position = SF, Price =  8100, Points = 44.53, Team = ind\n",
      "              Rajon Rondo, Position = PG, Price =  7800, Points = 44.64, Team = sac\n",
      "              George Hill, Position = PG, Price =  4800, Points = 25.58, Team = ind\n",
      "              Kobe Bryant, Position = SG, Price =  5000, Points = 27.24, Team = lal\n",
      "         DeMarcus Cousins, Position = PF, Price = 10200, Points = 53.07, Team = sac\n",
      "            Arron Afflalo, Position = SG, Price =  4600, Points = 24.87, Team = nyk\n",
      "\n",
      "Total Team Cost: 50000\n",
      "Total Team Points: 272.43\n"
     ]
    }
   ],
   "source": [
    "PredictedLineUp2, cost2, points2 = LineUpOptimizer(ntest_result_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fullName</th>\n",
       "      <th>FanPTs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAME_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>15.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Greg Monroe</td>\n",
       "      <td>24.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>George Hill</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>DeMarcus Cousins</td>\n",
       "      <td>61.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Paul George</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-13</th>\n",
       "      <td>Rajon Rondo</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    fullName  FanPTs\n",
       "GAME_DATE                           \n",
       "2016-03-13     Arron Afflalo   15.25\n",
       "2016-03-13       Greg Monroe   24.25\n",
       "2016-03-13       George Hill   14.50\n",
       "2016-03-13   Thabo Sefolosha   26.25\n",
       "2016-03-13  DeMarcus Cousins   61.50\n",
       "2016-03-13       Paul George   22.50\n",
       "2016-03-13       Kobe Bryant   18.50\n",
       "2016-03-13       Rajon Rondo   23.50"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[result_df.fullName.isin(list(PredictedLineUp2))][['fullName','FanPTs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206.25"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(result_df[result_df.fullName.isin(list(PredictedLineUp2))][['fullName','FanPTs']].FanPTs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best0313_fromPred = ntest_result_df[ntest_result_df.Name.isin(['Giannis Antetokounmpo','Shelvin Mack','Khris Middleton',\\\n",
    "                                      'Thabo Sefolosha','Derrick Favors','Robin Lopez','Jerryd Bayless',\\\n",
    "                                      'Carmelo Anthony'])][['Name','DK Salary','DK Pts']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>DK Salary</th>\n",
       "      <th>DK Pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>3200</td>\n",
       "      <td>18.483992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Derrick Favors</td>\n",
       "      <td>7100</td>\n",
       "      <td>35.422897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Khris Middleton</td>\n",
       "      <td>7300</td>\n",
       "      <td>36.721508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Robin Lopez</td>\n",
       "      <td>5900</td>\n",
       "      <td>26.451281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>8300</td>\n",
       "      <td>40.693798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Jerryd Bayless</td>\n",
       "      <td>4700</td>\n",
       "      <td>25.110645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>8700</td>\n",
       "      <td>36.089252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>4600</td>\n",
       "      <td>22.466486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name  DK Salary     DK Pts\n",
       "12        Thabo Sefolosha       3200  18.483992\n",
       "24         Derrick Favors       7100  35.422897\n",
       "31        Khris Middleton       7300  36.721508\n",
       "39            Robin Lopez       5900  26.451281\n",
       "47        Carmelo Anthony       8300  40.693798\n",
       "57         Jerryd Bayless       4700  25.110645\n",
       "85  Giannis Antetokounmpo       8700  36.089252\n",
       "89           Shelvin Mack       4600  22.466486"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best0313_fromPred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241.43987"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best0313_fromPred['DK Pts'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, our system is not good enough. Due to the most uncertain and challenging task in predicting player's daily value, our Lineup may not be really optimal in real-life experiments. \n",
    "\n",
    "However, the good news is that we had successfully implemented our algorithm from end to end and figure out ways to test our algorithm. \n",
    "\n",
    "There are two approaches we think can help improve our algorithm for final prediction.\n",
    "- use the idea of \"bagging\" to improve our prediction model or the final lineup results.\n",
    "- incorporate more information about risk and uncertainty into our optimizer"
   ]
  }
 ],
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