shap_outubro_rosa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data.filter(regex=r\"_mean\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   fractal_dimension_mean  \n",
       "0                 0.07871  \n",
       "1                 0.05667  \n",
       "2                 0.05999  \n",
       "3                 0.09744  \n",
       "4                 0.05883  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['diagnosis'].map({\"M\": 0, \"B\": 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xval, ytrain, yval = train_test_split(features, y, train_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((284, 10), (285, 10), (284,), (285,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape, Xval.shape, ytrain.shape, yval.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=-1, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "mdl = RandomForestClassifier(n_estimators=1000, n_jobs=-1, random_state=0)\n",
    "mdl.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.85      0.90       104\n",
      "           1       0.92      0.98      0.95       181\n",
      "\n",
      "    accuracy                           0.93       285\n",
      "   macro avg       0.94      0.91      0.93       285\n",
      "weighted avg       0.94      0.93      0.93       285\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "p = mdl.predict(Xval)\n",
    "print(classification_report(yval, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mario/anaconda3/lib/python3.7/site-packages/lightgbm/__init__.py:46: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "  \"You can install the OpenMP library by the following command: ``brew install libomp``.\", UserWarning)\n"
     ]
    },
    {
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strict\";function r(t){this._root=t,this._startText=this.getText(),this._fallbackText=null}var i=n(3),o=n(18),a=n(172);i(r.prototype,{destructor:function(){this._root=null,this._startText=null,this._fallbackText=null},getText:function(){return\"value\"in this._root?this._root.value:this._root[a()]},getData:function(){if(this._fallbackText)return this._fallbackText;var t,e,n=this._startText,r=n.length,i=this.getText(),o=i.length;for(t=0;t<r&&n[t]===i[t];t++);var a=r-t;for(e=1;e<=a&&n[r-e]===i[o-e];e++);var u=e>1?1-e:void 0;return this._fallbackText=i.slice(t,u),this._fallbackText}}),o.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";var r=n(21),i=r.injection.MUST_USE_PROPERTY,o=r.injection.HAS_BOOLEAN_VALUE,a=r.injection.HAS_NUMERIC_VALUE,u=r.injection.HAS_POSITIVE_NUMERIC_VALUE,c=r.injection.HAS_OVERLOADED_BOOLEAN_VALUE,s={isCustomAttribute:RegExp.prototype.test.bind(new RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var 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=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.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()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.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,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;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&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},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?d.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&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_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=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}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))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[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===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);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&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"),null!=e.dangerouslySetInnerHTML&&(null!=e.children&&g(\"60\"),\"object\"==typeof e.dangerouslySetInnerHTML&&z in e.dangerouslySetInnerHTML||g(\"61\")),null!=e.style&&\"object\"!=typeof e.style&&g(\"62\",r(t)))}function o(t,e,n,r){if(!(r instanceof D)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===q,u=o?i._node:i._ownerDocument;B(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;k.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;N.postMountWrapper(t)}function c(){var t=this;O.postMountWrapper(t)}function s(){var t=this;A.postMountWrapper(t)}function l(){L.track(this)}function f(){var t=this;t._rootNodeID||g(\"63\");var e=j(t);switch(e||g(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(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 g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={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\"},K={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},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,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(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+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\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_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(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.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=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[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&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.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=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,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(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},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(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[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 j(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\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},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(97),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+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({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})},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:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);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=l.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=p},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(162),c=(n(2),!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){a.getNodeFromInstance(t).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;return{start:o,end:o+r}}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(405),l=n(172),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(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),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+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){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),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(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "# load JS visualization code to notebook\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.TreeExplainer(mdl)\n",
    "shap_values = explainer.shap_values(Xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<shap.explainers.tree.TreeExplainer at 0x7fb3631c1470>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284, 10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_values[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iEI2MTJH2BSX5258VPDPF'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 0.6207816881360486, \"outValue\": 0.013999969174619431, \"link\": \"identity\", \"featureNames\": [\"radius_mean\", \"texture_mean\", \"perimeter_mean\", \"area_mean\", \"smoothness_mean\", \"compactness_mean\", \"concavity_mean\", \"concave points_mean\", \"symmetry_mean\", \"fractal_dimension_mean\"], \"features\": {\"0\": {\"effect\": 0.0031293909506468548, \"value\": 14.58}, \"1\": {\"effect\": -0.05823270115627364, \"value\": 21.53}, \"2\": {\"effect\": -0.01762614189005328, \"value\": 97.41}, \"3\": {\"effect\": 0.0019664740883486055, \"value\": 644.8}, \"4\": {\"effect\": -0.02488766079394183, \"value\": 0.1054}, \"5\": {\"effect\": -0.05998354243943518, \"value\": 0.1868}, \"6\": {\"effect\": -0.18853364104469694, \"value\": 0.1425}, \"7\": {\"effect\": -0.22566303746551664, \"value\": 0.08782999999999999}, \"8\": {\"effect\": -0.026463562782876558, \"value\": 0.2252}, \"9\": {\"effect\": -0.01048729642763061, \"value\": 0.06924}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iEI2MTJH2BSX5258VPDPF')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(explainer.expected_value[1], shap_values[1][0,:], Xtrain.iloc[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iRSO3TJ67OIPSOY78454X'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
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       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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   "source": [
    "shap.force_plot(explainer.expected_value[1], shap_values[1][1,:], Xtrain.iloc[1,:])"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "    document.getElementById('iH3MW83M8A3NGC454DIWL')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(explainer.expected_value[1], shap_values[1], Xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values[1], Xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot(\"area_mean\", shap_values[1], Xtrain, interaction_index=\"concave points_mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}