Auto_ViML_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pdb\n",
    "import copy\n",
    "import sys\n",
    "sys.path.append('../ram_code/')\n",
    "datapath = '../data_sets/'\n",
    "sep = ','"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 32)\n"
     ]
    },
    {
     "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>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>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>89869</td>\n",
       "      <td>B</td>\n",
       "      <td>14.76</td>\n",
       "      <td>14.74</td>\n",
       "      <td>94.87</td>\n",
       "      <td>668.7</td>\n",
       "      <td>0.08875</td>\n",
       "      <td>0.07780</td>\n",
       "      <td>0.04608</td>\n",
       "      <td>0.03528</td>\n",
       "      <td>...</td>\n",
       "      <td>17.27</td>\n",
       "      <td>17.93</td>\n",
       "      <td>114.20</td>\n",
       "      <td>880.8</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>0.2009</td>\n",
       "      <td>0.21510</td>\n",
       "      <td>0.12510</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.08187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>88147202</td>\n",
       "      <td>B</td>\n",
       "      <td>12.62</td>\n",
       "      <td>23.97</td>\n",
       "      <td>81.35</td>\n",
       "      <td>496.4</td>\n",
       "      <td>0.07903</td>\n",
       "      <td>0.07529</td>\n",
       "      <td>0.05438</td>\n",
       "      <td>0.02036</td>\n",
       "      <td>...</td>\n",
       "      <td>14.20</td>\n",
       "      <td>31.31</td>\n",
       "      <td>90.67</td>\n",
       "      <td>624.0</td>\n",
       "      <td>0.1227</td>\n",
       "      <td>0.3454</td>\n",
       "      <td>0.39110</td>\n",
       "      <td>0.11800</td>\n",
       "      <td>0.2826</td>\n",
       "      <td>0.09585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>906878</td>\n",
       "      <td>B</td>\n",
       "      <td>13.66</td>\n",
       "      <td>19.13</td>\n",
       "      <td>89.46</td>\n",
       "      <td>575.3</td>\n",
       "      <td>0.09057</td>\n",
       "      <td>0.11470</td>\n",
       "      <td>0.09657</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>...</td>\n",
       "      <td>15.14</td>\n",
       "      <td>25.50</td>\n",
       "      <td>101.40</td>\n",
       "      <td>708.8</td>\n",
       "      <td>0.1147</td>\n",
       "      <td>0.3167</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.2744</td>\n",
       "      <td>0.08839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>901088</td>\n",
       "      <td>M</td>\n",
       "      <td>20.44</td>\n",
       "      <td>21.78</td>\n",
       "      <td>133.80</td>\n",
       "      <td>1293.0</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.09799</td>\n",
       "      <td>0.07785</td>\n",
       "      <td>...</td>\n",
       "      <td>24.31</td>\n",
       "      <td>26.37</td>\n",
       "      <td>161.20</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>0.1327</td>\n",
       "      <td>0.2376</td>\n",
       "      <td>0.27020</td>\n",
       "      <td>0.17650</td>\n",
       "      <td>0.2609</td>\n",
       "      <td>0.06735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>898690</td>\n",
       "      <td>B</td>\n",
       "      <td>11.47</td>\n",
       "      <td>16.03</td>\n",
       "      <td>73.02</td>\n",
       "      <td>402.7</td>\n",
       "      <td>0.09076</td>\n",
       "      <td>0.05886</td>\n",
       "      <td>0.02587</td>\n",
       "      <td>0.02322</td>\n",
       "      <td>...</td>\n",
       "      <td>12.51</td>\n",
       "      <td>20.79</td>\n",
       "      <td>79.67</td>\n",
       "      <td>475.8</td>\n",
       "      <td>0.1531</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>0.09823</td>\n",
       "      <td>0.06548</td>\n",
       "      <td>0.2851</td>\n",
       "      <td>0.08763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "512     89869         B        14.76         14.74           94.87      668.7   \n",
       "457  88147202         B        12.62         23.97           81.35      496.4   \n",
       "439    906878         B        13.66         19.13           89.46      575.3   \n",
       "298    901088         M        20.44         21.78          133.80     1293.0   \n",
       "37     898690         B        11.47         16.03           73.02      402.7   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "512          0.08875           0.07780         0.04608              0.03528   \n",
       "457          0.07903           0.07529         0.05438              0.02036   \n",
       "439          0.09057           0.11470         0.09657              0.04812   \n",
       "298          0.09150           0.11310         0.09799              0.07785   \n",
       "37           0.09076           0.05886         0.02587              0.02322   \n",
       "\n",
       "              ...             radius_worst  texture_worst  perimeter_worst  \\\n",
       "512           ...                    17.27          17.93           114.20   \n",
       "457           ...                    14.20          31.31            90.67   \n",
       "439           ...                    15.14          25.50           101.40   \n",
       "298           ...                    24.31          26.37           161.20   \n",
       "37            ...                    12.51          20.79            79.67   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "512       880.8            0.1220             0.2009          0.21510   \n",
       "457       624.0            0.1227             0.3454          0.39110   \n",
       "439       708.8            0.1147             0.3167          0.36600   \n",
       "298      1780.0            0.1327             0.2376          0.27020   \n",
       "37        475.8            0.1531             0.1120          0.09823   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "512               0.12510          0.3109                  0.08187  \n",
       "457               0.11800          0.2826                  0.09585  \n",
       "439               0.14070          0.2744                  0.08839  \n",
       "298               0.17650          0.2609                  0.06735  \n",
       "37                0.06548          0.2851                  0.08763  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Download the Breast Cancer data set from: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)\n",
    "df = pd.read_csv(datapath+'breast_cancer.csv',sep=sep, index_col=None)\n",
    "df = df.sample(frac=1.0, random_state=0)\n",
    "target = 'diagnosis'\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = int(0.9*df.shape[0])\n",
    "train = df[:num]\n",
    "test = df[num:]\n",
    "sample_submission=''\n",
    "scoring_parameter = 'f1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train (Size: 512,32) has Single_Label with target: ['diagnosis']\n",
      "\"\n",
      " ################### Binary-Class ##################### \" \n",
      "Shuffling the data set before training\n",
      " Class  -> Counts -> Percent\n",
      "     B:     322  ->   62.9%\n",
      "     M:     190  ->   37.1%\n",
      "Selecting 2-Class Classifier...\n",
      "    Using GridSearchCV for Hyper Parameter tuning...\n",
      "String or Multi Class target diagnosis transformed to numeric.\n",
      "Classifying variables in data set...\n",
      "    Number of Numeric Columns =  30\n",
      "    Number of Integer-Categorical Columns =  0\n",
      "    Number of String-Categorical Columns =  0\n",
      "    Number of Factor-Categorical Columns =  0\n",
      "    Number of String-Boolean Columns =  0\n",
      "    Number of Numeric-Boolean Columns =  0\n",
      "    Number of Discrete String Columns =  0\n",
      "    Number of NLP String Columns =  0\n",
      "    Number of Date Time Columns =  0\n",
      "    Number of ID Columns =  1\n",
      "    Number of Columns to Delete =  0\n",
      "    31 Predictors classified...\n",
      "        This does not include the Target column(s)\n",
      "    1 variables removed since they were some ID or low-information variables\n",
      "Test data has no missing values...\n",
      "Number of numeric variables = 30\n",
      "    Number of variables removed due to high correlation = 11 \n",
      "\n",
      "Target Ready for Modeling: diagnosis\n",
      "Starting Feature Engg, Extraction and Model Training for target diagnosis and 19 predictors\n",
      "Number of numeric variables = 19\n",
      "    Number of variables removed due to high correlation = 4 \n",
      "    Adding 0 categorical variables to reduced numeric variables  of 15\n",
      "Selected No. of variables = 15 \n",
      "Finding Important Features...\n",
      "        in 15 variables\n",
      "        in 12 variables\n",
      "        in 9 variables\n",
      "        in 6 variables\n",
      "        in 3 variables\n",
      "    Found 15 important features\n",
      "Leaving Top 15 continuous variables as is...\n",
      "    No Entropy Binning specified\n",
      "Rows in Train data set = 460\n",
      "Features in Train data set = 15\n",
      "    Rows in held-out data set = 52\n",
      "Finding Best Model and Hyper Parameters for Target: diagnosis...\n",
      "Baseline Accuracy Needed for Model = 62.89%\n",
      "\n",
      "Using Linear Model, Estimated Training time = 1.0 mins\n",
      "    Hyper Tuned  f1 = 95.5%\n",
      "Model Best Parameters = {'C': 50, 'class_weight': 'balanced', 'solver': 'saga'}\n",
      "Finding Best Threshold for Highest F1 Score...\n",
      "    Found Optimal Threshold as 0.85 for Best F1: 1.00\n",
      "Linear Model Results on Held Out Data Set:\n",
      "    Accuracy Score = 100.0%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        28\n",
      "           1       1.00      1.00      1.00        24\n",
      "\n",
      "   micro avg       1.00      1.00      1.00        52\n",
      "   macro avg       1.00      1.00      1.00        52\n",
      "weighted avg       1.00      1.00      1.00        52\n",
      "\n",
      "[[28  0]\n",
      " [ 0 24]]\n",
      "    Time taken for Ensembling: 0.2 seconds\n",
      "\n",
      "Completed Ensemble predictions on held out data\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 1.00\n",
      "Weighted F1 score, averaged over all classes: 1.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After multiple models, Ensemble Model Results:\n",
      "    Accuracy Score = 98.1%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98        28\n",
      "           1       1.00      0.96      0.98        24\n",
      "\n",
      "   micro avg       0.98      0.98      0.98        52\n",
      "   macro avg       0.98      0.98      0.98        52\n",
      "weighted avg       0.98      0.98      0.98        52\n",
      "\n",
      "[[28  0]\n",
      " [ 1 23]]\n",
      "\n",
      "Single Model is better than Ensembling Models for this data set.\n",
      "    Time taken for this Target (in seconds) = 5\n"
     ]
    },
    {
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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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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 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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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      "Plotting Feature Importances to explain the output of model\n",
      "Train model on full data set and predict using Test Data here...\n",
      "Leaving Top 15 continuous variables as is...\n",
      "    No Entropy Binning specified\n",
      "Number of numeric variables = 15\n",
      "    Number of variables removed due to high correlation = 4 \n",
      "    Adding 0 categorical variables to reduced numeric variables  of 11\n",
      "Selected No. of variables = 11 \n",
      "Finding Important Features...\n",
      "        in 11 variables\n",
      "        in 9 variables\n",
      "        in 7 variables\n",
      "        in 5 variables\n",
      "        in 3 variables\n",
      "        in 1 variables\n",
      "    Found 11 important features\n",
      "Test Data predictions using Best Threshold = 0.85\n",
      "QuickML_Ensembling completed.\n",
      "    Time taken for Ensembling: 0.3 seconds\n",
      "\n",
      "Completed Ensemble predictions on held out data\n",
      "    Adding output files to current folder: ./diagnosis/diagnosis_Binary_Classification_test_modified.csv\n",
      "    Adding output files to current folder: ./diagnosis/diagnosis_Binary_Classification_submission.csv\n",
      "    Adding output files to current folder: ./diagnosis/diagnosis_Binary_Classification_train_modified.csv\n",
      "Time Taken in mins = 0.1 for the Entire Process\n"
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Auto_ViML import Auto_ViML\n",
    "#### If Boosting_Flag = True => XGBoost, Fase=>ExtraTrees, None=>Linear Model\n",
    "m, feats, trainm, testm = Auto_ViML(train, target, test, sample_submission,scoring_parameter=scoring_parameter,\n",
    "                                    hyper_param='GS',\n",
    "                                     Boosting_Flag=None,Binning_Flag=False,\n",
    "                                    Add_Poly=0, Stacking_Flag=False, Imbalanced_Flag=False, \n",
    "                                    verbose=1)                      "
   ]
  },
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   "metadata": {},
   "source": [
    "# "
   ]
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