adult_income_SHAP.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "1213e4ff",
   "metadata": {},
   "source": [
    "# 1. The Dataset: Adult Income Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "907fd25b",
   "metadata": {},
   "outputs": [
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     "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>Age</th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>NativeCountry</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>30-60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0-30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>30-60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>30-60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>30-60</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age          WorkClass   Education  EducationNum        MaritalStatus  \\\n",
       "0   39          State-gov   Bachelors            13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors            13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad             9             Divorced   \n",
       "3   53            Private        11th             7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors            13   Married-civ-spouse   \n",
       "\n",
       "           Occupation    Race   Gender  CapitalLoss HoursPerWeek  \\\n",
       "0        Adm-clerical   White     Male            0        30-60   \n",
       "1     Exec-managerial   White     Male            0         0-30   \n",
       "2   Handlers-cleaners   White     Male            0        30-60   \n",
       "3   Handlers-cleaners   Black     Male            0        30-60   \n",
       "4      Prof-specialty   Black   Female            0        30-60   \n",
       "\n",
       "    NativeCountry  Income  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from random import shuffle\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(\"adult_income_orig.csv\")\n",
    "\n",
    "df.columns = [\n",
    "        \"Age\", \"WorkClass\", \"fnlwgt\", \"Education\", \"EducationNum\",\n",
    "        \"MaritalStatus\", \"Occupation\", \"Relationship\", \"Race\", \"Gender\",\n",
    "        \"CapitalGain\", \"CapitalLoss\", \"HoursPerWeek\", \"NativeCountry\", \"Income\"]\n",
    "\n",
    "def convert_hours(x):\n",
    "    if x < 30:\n",
    "        return \"0-30\"\n",
    "    elif x < 60:\n",
    "        return \"30-60\"\n",
    "    elif x < 90:\n",
    "        return \"60-90\"\n",
    "    else:\n",
    "        return \"90+\"\n",
    "\n",
    "df['HoursPerWeek'] = df['HoursPerWeek'].apply(lambda x: convert_hours(x))\n",
    "\n",
    "cols = [\n",
    "        \"Age\", \"WorkClass\", \"Education\",\"EducationNum\", \"MaritalStatus\", \"Occupation\", \"Race\", \"Gender\",\n",
    "        \"CapitalGain\", \"CapitalLoss\", \"HoursPerWeek\", \"NativeCountry\", \"Income\"]\n",
    "df = df[cols]\n",
    "\n",
    "#df.drop('Education', inplace=True, axis=1)\n",
    "#df.drop('EducationNum', inplace=True, axis=1)\n",
    "#df.drop('NativeCountry', inplace=True, axis=1)\n",
    "#df.drop('Gender', inplace=True, axis=1)\n",
    "#df.drop('Age', inplace=True, axis=1)\n",
    "#df.drop('MaritalStatus', inplace=True, axis=1)\n",
    "df.drop('CapitalGain', inplace=True, axis=1)\n",
    "\n",
    "\n",
    "train_cols = df.columns[0:-1]\n",
    "label = df.columns[-1]\n",
    "X_df = df[train_cols]\n",
    "y_df = df[label]\n",
    "\n",
    "X_display = X_df.copy()\n",
    "\n",
    "#Converting strings to integers and floats for categorical data\n",
    "categorical_cols = [\"WorkClass\", \"Education\", \"MaritalStatus\", \"Occupation\", \"Race\", \"Gender\", \"NativeCountry\", \"HoursPerWeek\"]\n",
    "\n",
    "#categorical_cols = [\"WorkClass\", \"MaritalStatus\", \"Occupation\", \"Race\", \"Gender\", \"NativeCountry\", \"HoursPerWeek\"]\n",
    "#categorical_cols = [\"WorkClass\", \"Education\", \"MaritalStatus\", \"Occupation\", \"Race\", \"Gender\", \"HoursPerWeek\"]\n",
    "#categorical_cols = [\"WorkClass\", \"Education\", \"MaritalStatus\", \"Occupation\", \"Race\", \"NativeCountry\", \"HoursPerWeek\"]\n",
    "#categorical_cols = [\"WorkClass\", \"Education\", \"Occupation\", \"Race\", \"Gender\", \"NativeCountry\", \"HoursPerWeek\"]\n",
    "\n",
    "for col in categorical_cols:\n",
    "    X_df[col] = pd.Categorical(X_df[col])\n",
    "    X_df[col] = X_df[col].cat.codes\n",
    "\n",
    "\n",
    "#Converting the response / output variable to a binary class\n",
    "y_df = y_df.apply(lambda x: 0 if x == \" <=50K\" else 1)\n",
    "\n",
    "dataset = {\n",
    "        'X': X_df,\n",
    "        'y': y_df,\n",
    "}\n",
    "\n",
    "#Top 5 rows of the original dataset:\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258349aa",
   "metadata": {},
   "source": [
    "# 2. The Model: LightGBM, with explanation tool SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "91982d45",
   "metadata": {},
   "outputs": [
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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},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\"):void 0,Array.isArray(c)&&(c.length<=1?void 0: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",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0: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 t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
    },
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     "text": [
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001296 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 253\n",
      "[LightGBM] [Info] Number of data points in the train set: 24420, number of used features: 11\n",
      "[LightGBM] [Info] Start training from score 0.245823\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# print the JS visualization code to the notebook\n",
    "shap.initjs()\n",
    "\n",
    "# create a train/test split\n",
    "seed = 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset['X'],dataset['y'], test_size=0.25, random_state=seed)\n",
    "\n",
    "d_train = lgb.Dataset(X_train, label=y_train)\n",
    "d_test = lgb.Dataset(X_test, label=y_test)\n",
    "\n",
    "# train a lightgbm for the training dataset\n",
    "model = lgb.train({}, d_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2c3edcbe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model on the training set is:  0.86011\n",
      "The accuracy of the model on the test set is:  0.8526\n"
     ]
    }
   ],
   "source": [
    "#Training accuracy\n",
    "train_pred = model.predict(X_train).tolist()\n",
    "train_pred = [0 if x<=0.5 else 1 for x in train_pred]\n",
    "accuracy_train = round(sum(train_pred == y_train) / len(train_pred), 5)\n",
    "\n",
    "#Test set accuracy\n",
    "predictions = model.predict(X_test).tolist()\n",
    "predictions = [0 if x <= 0.5 else 1 for x in predictions]\n",
    "\n",
    "accuracy_test = round(sum(predictions == y_test) / len(predictions), 5)\n",
    "\n",
    "print(\"The accuracy of the model on the training set is: \", accuracy_train)\n",
    "print(\"The accuracy of the model on the test set is: \", accuracy_test)\n",
    "\n",
    "indices = [index for index, value in enumerate(zip(predictions, y_test.tolist())) if value[0] != value[1]]\n",
    "shuffle(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6bd330e",
   "metadata": {},
   "source": [
    "### Visualize Global Explanations: What the model learned overall from training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8350ecbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Age</th>\n",
       "      <th>WorkClass</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationNum</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>CapitalLoss</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>NativeCountry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>38</td>\n",
       "      <td>4</td>\n",
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       "      <td>28</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  WorkClass  Education  EducationNum  MaritalStatus  Occupation  \\\n",
       "0       39          7          9            13              4           1   \n",
       "1       50          6          9            13              2           4   \n",
       "2       38          4         11             9              0           6   \n",
       "3       53          4          1             7              2           6   \n",
       "4       28          4          9            13              2          10   \n",
       "...    ...        ...        ...           ...            ...         ...   \n",
       "32556   27          4          7            12              2          13   \n",
       "32557   40          4         11             9              2           7   \n",
       "32558   58          4         11             9              6           1   \n",
       "32559   22          4         11             9              4           1   \n",
       "32560   52          5         11             9              2           4   \n",
       "\n",
       "       Race  Gender  CapitalLoss  HoursPerWeek  NativeCountry  \n",
       "0         4       1            0             1             39  \n",
       "1         4       1            0             0             39  \n",
       "2         4       1            0             1             39  \n",
       "3         2       1            0             1             39  \n",
       "4         2       0            0             1              5  \n",
       "...     ...     ...          ...           ...            ...  \n",
       "32556     4       0            0             1             39  \n",
       "32557     4       1            0             1             39  \n",
       "32558     4       0            0             1             39  \n",
       "32559     4       1            0             0             39  \n",
       "32560     4       0            0             1             39  \n",
       "\n",
       "[32561 rows x 11 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['X']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5973ec54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x424.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.params['objective'] = 'binary'\n",
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(dataset['X'])\n",
    "shap.summary_plot(shap_values, dataset['X'], plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b742320c",
   "metadata": {},
   "source": [
    "### Visualize Global Explanations for Specific Input Features: What the model learned about a feature from the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4557a9e0",
   "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": [
    "#Visualizing one feature here, but you can visualize all features by uncommenting the code below\n",
    "shap.dependence_plot(X_train.columns[0], shap_values[1], dataset['X'], display_features=X_display)\n",
    "\n",
    "# VISUALIZE ALL INPUT FEATURES [Uncomment code below]\n",
    "\n",
    "# for name in X_train.columns:\n",
    "#     shap.dependence_plot(name, shap_values[1], dataset['X'], display_features=X_display)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d438b01",
   "metadata": {},
   "source": [
    "### Visualize Local Explanations: How an individual prediction was made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33d92eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values_test = explainer.shap_values(X_test)\n",
    "\n",
    "def get_display_features(testidx):\n",
    "    testcase = X_test.iloc[testidx, :]\n",
    "\n",
    "    idx = dataset['X'].index[(dataset['X']['Age'] == testcase['Age']) & (dataset['X']['WorkClass'] == testcase['WorkClass']) & \n",
    "                     (dataset['X']['Education'] == testcase['Education']) & (dataset['X']['MaritalStatus'] == testcase['MaritalStatus']) &\n",
    "                    (dataset['X']['EducationNum'] == testcase['EducationNum']) & \n",
    "                    (dataset['X']['Occupation'] == testcase['Occupation']) & \n",
    "                    (dataset['X']['Race'] == testcase['Race']) & (dataset['X']['Gender'] == testcase['Gender']) & \n",
    "                    (dataset['X']['CapitalGain'] == testcase['CapitalGain']) & (dataset['X']['CapitalLoss'] == testcase['CapitalLoss']) &\n",
    "                    (dataset['X']['HoursPerWeek'] == testcase['HoursPerWeek']) & (dataset['X']['NativeCountry'] == testcase['NativeCountry'])\n",
    "                    ]\n",
    "    return idx\n",
    "\n",
    "#Enter a test index you want to test\n",
    "TEST_IDX = 10\n",
    "idx = get_display_features(TEST_IDX)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36779f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iBSTNQ9N34JTD7G0LPU90'>\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\": [\"f(x)\"], \"baseValue\": 0.24582309581775705, \"outValue\": 0.5910547291978365, \"link\": \"identity\", \"featureNames\": [\"Age\", \"WorkClass\", \"Education\", \"EducationNum\", \"MaritalStatus\", \"Occupation\", \"Race\", \"Gender\", \"CapitalGain\", \"CapitalLoss\", \"HoursPerWeek\", \"NativeCountry\"], \"features\": {\"0\": {\"effect\": 0.03977254032024581, \"value\": 19.0}, \"1\": {\"effect\": 0.01772838131501191, \"value\": \" Self-emp-not-inc\"}, \"2\": {\"effect\": -0.005722365922895502, \"value\": \" HS-grad\"}, \"3\": {\"effect\": -0.016915874851009236, \"value\": 9.0}, \"4\": {\"effect\": 0.19626281233768714, \"value\": \" Never-married\"}, \"5\": {\"effect\": 0.0950250248229441, \"value\": \" Adm-clerical\"}, \"6\": {\"effect\": -0.010085346550943352, \"value\": \" Asian-Pac-Islander\"}, \"7\": {\"effect\": 0.009478881805203397, \"value\": \" Female\"}, \"8\": {\"effect\": -0.024204633500913352, \"value\": 0.0}, \"9\": {\"effect\": -0.007674433808059149, \"value\": 0.0}, \"10\": {\"effect\": 0.04708232369963019, \"value\": \"30-60\"}, \"11\": {\"effect\": 0.0044843237131775866, \"value\": \" United-States\"}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iBSTNQ9N34JTD7G0LPU90')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x7f89da677df0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(explainer.expected_value[1], shap_values[1][TEST_IDX,:], X_display.iloc[idx[0],:])"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "file_extension": ".py",
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