05.01_Evaluate_Final_Model_on_Test_Set.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"white\", palette=\"colorblind\", font_scale=1.35, \n",
    "        rc={\"figure.figsize\":(12,9)})\n",
    "\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv(\"processed_data/rb_test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 40 columns):\n",
      "Player                92 non-null object\n",
      "Pos                   92 non-null object\n",
      "College               92 non-null object\n",
      "Ht                    92 non-null int64\n",
      "Wt                    92 non-null int64\n",
      "Forty                 92 non-null float64\n",
      "Vertical              81 non-null float64\n",
      "Bench                 70 non-null float64\n",
      "Broad Jump            78 non-null float64\n",
      "Cone                  59 non-null float64\n",
      "Shuttle               58 non-null float64\n",
      "Year                  92 non-null int64\n",
      "Pfr_ID                82 non-null object\n",
      "Sref_Cfb_ID           84 non-null object\n",
      "Team_Drafted          55 non-null object\n",
      "G                     84 non-null float64\n",
      "Rush_Att              84 non-null float64\n",
      "Rush_Yds              84 non-null float64\n",
      "Rush_TD               84 non-null float64\n",
      "Rec                   84 non-null float64\n",
      "Rec_Yds               84 non-null float64\n",
      "Rec_TD                84 non-null float64\n",
      "Plays                 84 non-null float64\n",
      "Scrim_Yds             84 non-null float64\n",
      "Scrim_TD              84 non-null float64\n",
      "Col_Yrs_Played        84 non-null float64\n",
      "Col_Last_Yr_Played    84 non-null float64\n",
      "Rush_Yds_per_Att      84 non-null float64\n",
      "Rec_Yds_per_Rec       84 non-null float64\n",
      "Scrim_Avg             84 non-null float64\n",
      "Rush_Att_per_G        84 non-null float64\n",
      "Rush_Yds_per_G        84 non-null float64\n",
      "Rush_TD_per_G         84 non-null float64\n",
      "Rec_per_G             84 non-null float64\n",
      "Rec_Yds_per_G         84 non-null float64\n",
      "Rec_TD_per_G          84 non-null float64\n",
      "Plays_per_G           84 non-null float64\n",
      "Scrim_Yds_per_G       84 non-null float64\n",
      "Scrim_TD_per_G        84 non-null float64\n",
      "AV                    92 non-null float64\n",
      "dtypes: float64(31), int64(3), object(6)\n",
      "memory usage: 28.8+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2012, 2013, 2014])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.Year.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load in best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"models/best_model_04.01.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quantile_loss(y_true, y_pred, quantile=0.95):\n",
    "    \"\"\"\n",
    "    Quantile Loss function to be used to score different qunatile regression \n",
    "    models. The code below is based on the logic from this blog post:\n",
    "    https://www.lokad.com/pinball-loss-function-definition\n",
    "    \"\"\"\n",
    "    loss = np.nanmean(\n",
    "            # if real value (y_true) >= prediction (y_pred)\n",
    "            # then calculate (real value - prediction) * quantile\n",
    "            ((y_true >= y_pred) * (y_true - y_pred) * quantile) +\n",
    "            # otherwise, if real value < prediction\n",
    "            # then caclulate (prediction - real value) * (1 - quantile)\n",
    "            (y_true < y_pred) * (y_pred - y_true) * (1 - quantile)\n",
    "    )\n",
    "\n",
    "    return loss\n",
    "\n",
    "# create scorer\n",
    "quant_95_scorer = make_scorer(quantile_loss, greater_is_better=False, \n",
    "                              quantile=.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate on Test Set\n",
    "## First set up test data for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create yearly average rushing stats\n",
    "rush_per_yr_cols = [\"Rush_Att_per_Yr\", \"Rush_Yds_per_Yr\", \"Rush_TD_per_Yr\"]\n",
    "test_df[rush_per_yr_cols] = (test_df.loc[:, \"Rush_Att\":\"Rush_TD\"]\n",
    "                                      .div(test_df.Col_Yrs_Played, axis=0)\n",
    "                                      .fillna(0))\n",
    "\n",
    "features = [\"Rush_Att\",\n",
    "            \"Rush_Yds\",\n",
    "            \"Rush_TD\",\n",
    "            \"Rush_Yds_per_Att\",\n",
    "            \"Rush_Att_per_G\",\n",
    "            \"Rush_Yds_per_G\",\n",
    "            \"Rush_TD_per_G\",\n",
    "            \"Rush_Att_per_Yr\",\n",
    "            \"Rush_Yds_per_Yr\",\n",
    "            \"Rush_TD_per_Yr\",\n",
    "            \"Ht\",\n",
    "            \"Wt\",\n",
    "            \"Forty\",\n",
    "            \"Vertical\",\n",
    "            \"Bench\",\n",
    "            \"Broad Jump\",\n",
    "            \"Cone\",\n",
    "            \"Shuttle\"]\n",
    "\n",
    "target = \"AV\"\n",
    "\n",
    "X = test_df.loc[:, features]\n",
    "y = test_df.loc[:, target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measure Quantile Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.1834754812823665"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_95_scorer(model, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measure the Coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8804347826086957"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y <= y_pred).sum() / y.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Predictions for 2018 NFL RB draft class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refit the model on the whole data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rb_df = pd.read_csv(\"processed_data/merged_rb_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rb_complete_df = rb_df.loc[rb_df.Year <= 2014].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create yearly average rushing stats\n",
    "rb_complete_df[rush_per_yr_cols] = (rb_complete_df.loc[:, \"Rush_Att\":\"Rush_TD\"]\n",
    "                                      .div(rb_complete_df.Col_Yrs_Played, axis=0)\n",
    "                                      .fillna(0))\n",
    "\n",
    "\n",
    "X_complete = rb_complete_df.loc[:, features]\n",
    "y_complete = rb_complete_df.loc[:, target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)), ('scaler', None), ('estimator', LGBMRegressor(alpha=0.95, boosting_type='gbdt', class_weight=None,\n",
       "       colsample_bytree=1.0, learning_rate=0.3, max_depth=1,\n",
       "       min_child_samples=20, min_child_wei...    reg_lambda=0.0, silent=True, subsample=1.0,\n",
       "       subsample_for_bin=200000, subsample_freq=1))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_complete, y_complete)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up 2018 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rb_2018_df = rb_df.loc[rb_df.Year==2018].copy()\n",
    "# create yearly average rushing stats\n",
    "rb_2018_df[rush_per_yr_cols] = (rb_2018_df.loc[:, \"Rush_Att\":\"Rush_TD\"]\n",
    "                                      .div(rb_2018_df.Col_Yrs_Played, axis=0)\n",
    "                                      .fillna(0))\n",
    "\n",
    "\n",
    "X_2018 = rb_2018_df.loc[:, features]\n",
    "y_2018 = rb_2018_df.loc[:, target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 31 entries, 509 to 539\n",
      "Data columns (total 44 columns):\n",
      "Unnamed: 0            31 non-null int64\n",
      "Player                31 non-null object\n",
      "Pos                   31 non-null object\n",
      "College               31 non-null object\n",
      "Ht                    31 non-null int64\n",
      "Wt                    31 non-null int64\n",
      "Forty                 26 non-null float64\n",
      "Vertical              28 non-null float64\n",
      "Bench                 29 non-null float64\n",
      "Broad Jump            24 non-null float64\n",
      "Cone                  13 non-null float64\n",
      "Shuttle               18 non-null float64\n",
      "Year                  31 non-null int64\n",
      "Pfr_ID                19 non-null object\n",
      "Sref_Cfb_ID           25 non-null object\n",
      "Team_Drafted          14 non-null object\n",
      "G                     25 non-null float64\n",
      "Rush_Att              25 non-null float64\n",
      "Rush_Yds              25 non-null float64\n",
      "Rush_TD               25 non-null float64\n",
      "Rec                   25 non-null float64\n",
      "Rec_Yds               25 non-null float64\n",
      "Rec_TD                25 non-null float64\n",
      "Plays                 25 non-null float64\n",
      "Scrim_Yds             25 non-null float64\n",
      "Scrim_TD              25 non-null float64\n",
      "Col_Yrs_Played        25 non-null float64\n",
      "Col_Last_Yr_Played    25 non-null float64\n",
      "Rush_Yds_per_Att      25 non-null float64\n",
      "Rec_Yds_per_Rec       25 non-null float64\n",
      "Scrim_Avg             25 non-null float64\n",
      "Rush_Att_per_G        25 non-null float64\n",
      "Rush_Yds_per_G        25 non-null float64\n",
      "Rush_TD_per_G         25 non-null float64\n",
      "Rec_per_G             25 non-null float64\n",
      "Rec_Yds_per_G         25 non-null float64\n",
      "Rec_TD_per_G          25 non-null float64\n",
      "Plays_per_G           25 non-null float64\n",
      "Scrim_Yds_per_G       25 non-null float64\n",
      "Scrim_TD_per_G        25 non-null float64\n",
      "AV                    31 non-null float64\n",
      "Rush_Att_per_Yr       31 non-null float64\n",
      "Rush_Yds_per_Yr       31 non-null float64\n",
      "Rush_TD_per_Yr        31 non-null float64\n",
      "dtypes: float64(34), int64(4), object(6)\n",
      "memory usage: 10.9+ KB\n"
     ]
    }
   ],
   "source": [
    "rb_2018_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "av_pred_2018 = model.predict(X_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rb_2018_df[\"AV_pred_95\"] = av_pred_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>AV_pred_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>Saquon Barkley</td>\n",
       "      <td>51.875928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Royce Freeman</td>\n",
       "      <td>38.674701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Jordan Wilkins</td>\n",
       "      <td>9.835766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Darrel Williams</td>\n",
       "      <td>7.508391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Player  AV_pred_95\n",
       "511   Saquon Barkley   51.875928\n",
       "519    Royce Freeman   38.674701\n",
       "..               ...         ...\n",
       "537   Jordan Wilkins    9.835766\n",
       "538  Darrel Williams    7.508391\n",
       "\n",
       "[31 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rb_2018_df[[\"Player\", \"AV_pred_95\"]].sort_values(\"AV_pred_95\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = model.named_steps[\"estimator\"]\n",
    "X_2018_imp = model.named_steps[\"imputer\"].transform(X_2018)\n",
    "X_2018_imp_df = pd.DataFrame(X_2018_imp, columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_explainer = shap.TreeExplainer(estimator)\n",
    "shap_values = shap_explainer.shap_values(X_2018_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values_df = pd.DataFrame(shap_values, columns=features+[\"mean\"])\n",
    "col_ids = ['Player', 'Pfr_ID', 'Sref_Cfb_ID']\n",
    "shap_values_df[col_ids] = rb_2018_df[col_ids].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barkley_mask = shap_values_df.Player==\"Saquon Barkley\"\n",
    "(shap_values_df.loc[barkley_mask, features]\n",
    "               .T\n",
    "               .plot\n",
    "               .barh(legend=False, title=\"Saquon Barkley Feature Contributions\"));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    51.875928\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double check that SHAP values add up\n",
    "shap_values_df.loc[barkley_mask, features+['mean']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "change:{phasedRegistrationNames:{bubbled:\"onChange\",captured:\"onChangeCapture\"},dependencies:[\"topBlur\",\"topChange\",\"topClick\",\"topFocus\",\"topInput\",\"topKeyDown\",\"topKeyUp\",\"topSelectionChange\"]}},S=null,N=null,A=null,P=null,O=!1;b.canUseDOM&&(O=k(\"change\")&&(!document.documentMode||document.documentMode>8));var I=!1;b.canUseDOM&&(I=k(\"input\")&&(!document.documentMode||document.documentMode>11));var D={get:function(){return P.get.call(this)},set:function(t){A=\"\"+t,P.set.call(this,t)}},R={eventTypes:T,extractEvents:function(t,e,n,i){var o,a,u=e?x.getNodeFromInstance(e):window;if(r(u)?O?o=c:a=s:E(u)?I?o=h:(o=v,a=d):g(u)&&(o=y),o){var l=o(t,e);if(l){var f=C.getPooled(T.change,l,n,i);return f.type=\"change\",_.accumulateTwoPhaseDispatches(f),f}}a&&a(t,u,e)}};t.exports=R},function(t,e,n){\"use strict\";var r=n(2),i=n(20),o=n(6),a=n(322),u=n(9),c=(n(0),{dangerouslyReplaceNodeWithMarkup:function(t,e){if(o.canUseDOM?void 0:r(\"56\"),e?void 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p=n.relatedTarget||n.toElement;f=p?i.getClosestInstanceFromNode(p):null}else l=null,f=e;if(l===f)return null;var h=null==l?c:i.getNodeFromInstance(l),d=null==f?c:i.getNodeFromInstance(f),v=o.getPooled(a.mouseLeave,l,n,u);v.type=\"mouseleave\",v.target=h,v.relatedTarget=d;var g=o.getPooled(a.mouseEnter,f,n,u);return g.type=\"mouseenter\",g.target=d,g.relatedTarget=h,r.accumulateEnterLeaveDispatches(v,g,l,f),[v,g]}};t.exports=u},function(t,e,n){\"use strict\";var r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r=n(80),i=n(350),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t,e){}function o(t){return!(!t.prototype||!t.prototype.isReactComponent)}function a(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var u=n(2),c=n(3),s=n(26),l=n(85),f=n(15),p=n(86),h=n(40),d=(n(10),n(164)),v=n(24),g=n(38),y=(n(0),n(79)),m=n(94),_=(n(1),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=h.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return i(t,e),e};var 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o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=d.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return v.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";p.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(v.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,h.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return g;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!y(s,l)||!y(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(12),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){A.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),y=n(332),m=n(334),_=n(20),b=n(81),x=n(21),w=n(156),C=n(22),M=n(82),k=n(51),E=n(157),T=n(4),S=n(351),N=n(352),A=n(158),P=n(355),O=(n(10),n(364)),I=n(369),D=(n(9),n(54)),R=(n(0),n(93),n(79),n(95),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o);break;case\"select\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var m=_(h);this._createInitialChildren(t,o,r,m),p=m}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=m.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"select\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"textarea\":o=P.getHostProps(this,o),a=P.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":P.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&m.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(95),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(80),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(84),s=n(4),l=n(12),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(80),a=n(20),u=n(4),c=n(54),s=(n(0),n(95),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){\n",
       "if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(84),c=n(4),s=n(12),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 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i=n(3),o=n(12),a=n(53),u=n(9),c={initialize:u,close:function(){p.isBatchingUpdates=!1}},s={initialize:u,close:o.flushBatchedUpdates.bind(o)},l=[s,c];i(r.prototype,a,{getTransactionWrappers:function(){return l}});var f=new r,p={isBatchingUpdates:!1,batchedUpdates:function(t,e,n,r,i,o){var a=p.isBatchingUpdates;return p.isBatchingUpdates=!0,a?t(e,n,r,i,o):f.perform(t,null,e,n,r,i,o)}};t.exports=p},function(t,e,n){\"use strict\";function r(){C||(C=!0,m.EventEmitter.injectReactEventListener(y),m.EventPluginHub.injectEventPluginOrder(u),m.EventPluginUtils.injectComponentTree(p),m.EventPluginUtils.injectTreeTraversal(d),m.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),m.HostComponent.injectGenericComponentClass(f),m.HostComponent.injectTextComponentClass(v),m.DOMProperty.injectDOMPropertyConfig(i),m.DOMProperty.injectDOMPropertyConfig(s),m.DOMProperty.injectDOMPropertyConfig(b),m.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),m.Updates.injectReconcileTransaction(_),m.Updates.injectBatchingStrategy(g),m.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),y=n(361),m=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(85),u=n(159),c=n(51),s=n(161),l=n(12),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(85),p=(n(40),n(10),n(15),n(24)),h=n(342),d=(n(9),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],y=a[u];g===y?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(y,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(y)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(10),n(53)),l=n(87),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return 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    "shap.force_plot(shap_values_df.loc[barkley_mask, features+['mean']].values,\n",
    "                X_2018_imp_df.loc[barkley_mask, :])"
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   "execution_count": 28,
   "metadata": {},
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "shap.force_plot(shap_values, X_2018_imp_df)"
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   "source": [
    "# Possible Improvements to this analysis\n",
    "- Include more features (e.g. include interactions and ratios)\n",
    "- Add feature selection in modeling pipeline\n",
    "- Try more machine learning algorithms\n",
    "- Use other interpretability techniques such as ICE plots and PDPs\n",
    "- Predict other quantiles, possibly construct prediction intervals\n",
    "- Evaluate ranking of players instead of predicted AV quantile.\n",
    "- Fill in missing college Division II or III stats for players."
   ]
  }
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