explain models/shap_XGB_classification.ipynb

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    "# Shap explanation of XGBoost Classifier\n",
    "\n",
    "This demo is on XGBoost classification. Check out the similarities and differences with [RandomForestClassifier](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_RF_classification.ipynb) in the same repo."
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!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 \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in 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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       "      <td>3</td>\n",
       "      <td>15.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>3</td>\n",
       "      <td>24.000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24.150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2</td>\n",
       "      <td>36.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>3</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>3</td>\n",
       "      <td>22.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass    age  sibsp  parch   fare  is_male\n",
       "830       3 15.000      1      0 14.454        0\n",
       "565       3 24.000      2      0 24.150        1\n",
       "148       2 36.500      0      2 26.000        1\n",
       "105       3 28.000      0      0  7.896        1\n",
       "289       3 22.000      0      0  7.750        0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "np.set_printoptions(formatter={'float':lambda x:\"{:.4f}\".format(x)})\n",
    "import pandas as pd\n",
    "pd.options.display.float_format = \"{:.3f}\".format\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style='darkgrid', context='talk', palette='rainbow')\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import (RandomForestRegressor, \n",
    "                              RandomForestClassifier)\n",
    "import xgboost as xgb\n",
    "import shap #v0.39.0\n",
    "shap.initjs()\n",
    "\n",
    "# Import sample data\n",
    "df =  sns.load_dataset('titanic')\n",
    "df['is_male'] = df['sex'].map({'male': 1, 'female': 0}) # Encode\n",
    "# Keep numerical complete columns\n",
    "df = df.select_dtypes('number').dropna() \n",
    "X = df.drop(columns=['survived'])\n",
    "y = df['survived']\n",
    "# Partition data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=1)\n",
    "print(f\"Training features shape: {X_train.shape}\")\n",
    "print(f\"Training target shape: {y_train.shape}\\n\")\n",
    "print(f\"Test features shape: {X_test.shape}\")\n",
    "print(f\"Test target shape: {y_test.shape}\")\n",
    "display(X_train.head())\n",
    "# Train a simple model\n",
    "model = xgb.XGBClassifier(random_state=42)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the results below that\n",
    "* Just like in [regression](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_regression.ipynb) and [RF classifier](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_RF_classification.ipynb), shap_test.data is the same with X_test. \n",
    "* However, unlike for [RF classifier](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_RF_classification.ipynb), the XGBoost classifier doesn't have `values` and `base_values` for the negative and positive class respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of shap_test: 143\n",
      "\n",
      "Sample shap_test for the first case:\n",
      ".values =\n",
      "array([1.7204, -0.0535, 0.1124, 0.1107, -1.0535, 2.9162], dtype=float32)\n",
      "\n",
      ".base_values =\n",
      "-0.48516756\n",
      "\n",
      ".data =\n",
      "array([1.0000, 15.0000, 0.0000, 1.0000, 211.3375, 0.0000])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n"
     ]
    }
   ],
   "source": [
    "# get the explainer and create shap values\n",
    "explainer = shap.Explainer(model)\n",
    "shap_test = explainer(X_test)\n",
    "print(f\"Length of shap_test: {len(shap_test)}\\n\")\n",
    "print(f\"Sample shap_test for the first case:\\n{shap_test[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shap_test object contains shap values for the positive class only\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    shap_test.values.shape[2]\n",
    "    print(\"The shap_test object contains shap values for both class\")\n",
    "except IndexError:\n",
    "    print(\"The shap_test object contains shap values for the positive class only\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base Value\n",
    "\n",
    "Unlike for [RandomForest Clssifier](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_RF_classification.ipynb) where both the base_values and expected_value is the probability for both class. The XGBoost Classifier result relate to raw maginal contributions and need to be transformed to probabilities (see below).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected value: -0.49\n",
      "Average target value (training data): 0.41\n",
      "Base value: -0.49\n"
     ]
    }
   ],
   "source": [
    "print(f\"Expected value: {explainer.expected_value:.2f}\")\n",
    "print(f\"Average target value (training data): {y_train.mean():.2f}\")\n",
    "print(f\"Base value: {shap_test.base_values[0]:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shap Values\n",
    "Shap values are the marginal contributions for each feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>is_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>1.720</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.111</td>\n",
       "      <td>-1.053</td>\n",
       "      <td>2.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>-1.033</td>\n",
       "      <td>1.529</td>\n",
       "      <td>-2.234</td>\n",
       "      <td>-0.289</td>\n",
       "      <td>-2.745</td>\n",
       "      <td>-0.593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>1.200</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.219</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>0.090</td>\n",
       "      <td>3.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.176</td>\n",
       "      <td>-0.831</td>\n",
       "      <td>0.192</td>\n",
       "      <td>-0.149</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>3.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>-0.891</td>\n",
       "      <td>-1.525</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.207</td>\n",
       "      <td>-0.865</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>1.837</td>\n",
       "      <td>-0.140</td>\n",
       "      <td>0.137</td>\n",
       "      <td>-0.268</td>\n",
       "      <td>2.583</td>\n",
       "      <td>3.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-1.237</td>\n",
       "      <td>0.663</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>-0.399</td>\n",
       "      <td>2.293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>-0.366</td>\n",
       "      <td>-1.392</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>-0.311</td>\n",
       "      <td>-1.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>2.221</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-0.173</td>\n",
       "      <td>1.484</td>\n",
       "      <td>2.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>-0.648</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.026</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>-0.252</td>\n",
       "      <td>-1.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass    age  sibsp  parch   fare  is_male\n",
       "689   1.720 -0.054  0.112  0.111 -1.053    2.916\n",
       "278  -1.033  1.529 -2.234 -0.289 -2.745   -0.593\n",
       "506   1.200  0.682  0.219 -0.046  0.090    3.239\n",
       "9     1.176 -0.831  0.192 -0.149 -0.096    3.047\n",
       "494  -0.891 -1.525  0.004 -0.038  0.207   -0.865\n",
       "..      ...    ...    ...    ...    ...      ...\n",
       "581   1.837 -0.140  0.137 -0.268  2.583    3.229\n",
       "44   -1.237  0.663 -0.012 -0.194 -0.399    2.293\n",
       "234  -0.366 -1.392 -0.001 -0.090 -0.311   -1.535\n",
       "781   2.221  0.178  0.067 -0.173  1.484    2.926\n",
       "884  -0.648  0.032  0.026 -0.044 -0.252   -1.034\n",
       "\n",
       "[143 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_df = pd.DataFrame(shap_test.values, \n",
    "                       columns=shap_test.feature_names, \n",
    "                       index=X_test.index)\n",
    "shap_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like in [regression explanation](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_regression.ipynb) and [RF classifier](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_RF_classification.ipynb), adding up shap values and the base values should give us the predicted value, which is confirmed below. Note that we need to output raw margin by defining `output_margin = True` when making the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isclose(model.predict(X_test, output_margin=True), \n",
    "           explainer.expected_value + shap_df.sum(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To transform raw margin to probabilities, we need to employ sigmoid function. The raw_margin is positive when the predicted probability is higher than 0.5, and negative when the predicted probability is lower than 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_margin</th>\n",
       "      <th>predicted_probability</th>\n",
       "      <th>sig_transform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.267</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-5.851</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.899</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.854</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.593</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   raw_margin  predicted_probability  sig_transform\n",
       "0       3.267                  0.963          0.963\n",
       "1      -5.851                  0.003          0.003\n",
       "2       4.899                  0.993          0.993\n",
       "3       2.854                  0.946          0.946\n",
       "4      -3.593                  0.027          0.027"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prob = model.predict_proba(X_test)\n",
    "y_pred_margin = model.predict(X_test, output_margin = True)\n",
    "sig_transform = pd.DataFrame({\n",
    "    'raw_margin': y_pred_margin,\n",
    "    'predicted_probability': y_prob[:,1]\n",
    "})\n",
    "# transform the margin into probability using sigmoid function\n",
    "from scipy.special import expit\n",
    "sig_transform['sig_transform'] = expit(sig_transform['raw_margin'])\n",
    "sig_transform.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shap Built-in Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global | Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.bar(shap_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\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>feature_name</th>\n",
       "      <th>mean_absolute_shap_value</th>\n",
       "      <th>max_shap_magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>is_male</td>\n",
       "      <td>1.577</td>\n",
       "      <td>3.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pclass</td>\n",
       "      <td>0.999</td>\n",
       "      <td>2.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>0.992</td>\n",
       "      <td>5.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fare</td>\n",
       "      <td>0.879</td>\n",
       "      <td>3.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sibsp</td>\n",
       "      <td>0.284</td>\n",
       "      <td>2.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>parch</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature_name  mean_absolute_shap_value  max_shap_magnitude\n",
       "5      is_male                     1.577               3.584\n",
       "0       pclass                     0.999               2.533\n",
       "1          age                     0.992               5.543\n",
       "4         fare                     0.879               3.613\n",
       "2        sibsp                     0.284               2.234\n",
       "3        parch                     0.105               0.621"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shap_func import get_SHAP_importance\n",
    "shap_feature_summary = get_SHAP_importance(model, X_test)\n",
    "shap_feature_summary.sort_values(by='mean_absolute_shap_value', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x390 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local | Waterfall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.waterfall(shap_test[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussions\n",
    "\n",
    "There you have it, shap explanation for XGBoost classifier expalained. With the sigmoid transformation, we can add up shap values across all individual features to get the final predicted probability.\n",
    "\n",
    "One question remains, however, why isn't the below equation True? We remember for [the RandomForest Classifier](https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_basic_RF_classification.ipynb), the `explainer.expected_value[1]` equals rate of the positive class in the train data, that is, `y_train.mean()`. How was the expected_value calculated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# question remains, why isn't the below true?\n",
    "expit(explainer.expected_value) == y_train.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The shap values for XGBoost Explainer Explained\n",
    "\n",
    "So I posted the above question on StackOverflow, below content is based on the answer I thankfully received there ([post and answer](https://stackoverflow.com/questions/77126001/calculation-of-expected-value-in-shap-explanations-of-xgboost-classifier)) to illustrate how the expected value can be calculated based on the background data or from the tree itself. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttest-logloss:0.54663\n",
      "[100]\ttest-logloss:0.36373\n",
      "[200]\ttest-logloss:0.31793\n",
      "[300]\ttest-logloss:0.30061\n",
      "[400]\ttest-logloss:0.29207\n",
      "[500]\ttest-logloss:0.28678\n",
      "[600]\ttest-logloss:0.28381\n",
      "[700]\ttest-logloss:0.28181\n",
      "[800]\ttest-logloss:0.28064\n",
      "[900]\ttest-logloss:0.27992\n",
      "[1000]\ttest-logloss:0.27928\n",
      "[1018]\ttest-logloss:0.27935\n"
     ]
    }
   ],
   "source": [
    "from shap.explainers._tree import XGBTreeModelLoader\n",
    "\n",
    "X, y = shap.datasets.adult()\n",
    "X_display, y_display = shap.datasets.adult(display=True)\n",
    "\n",
    "# create a train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)\n",
    "d_train = xgb.DMatrix(X_train, label=y_train)\n",
    "d_test = xgb.DMatrix(X_test, label=y_test)\n",
    "\n",
    "params = {\n",
    "    \"eta\": 0.01,\n",
    "    \"objective\": \"binary:logistic\",\n",
    "    \"subsample\": 0.5,\n",
    "    \"base_score\": np.mean(y_train),\n",
    "    \"eval_metric\": \"logloss\",\n",
    "}\n",
    "model = xgb.train(\n",
    "    params,\n",
    "    d_train,\n",
    "    num_boost_round=5000,\n",
    "    evals=[(d_test, \"test\")],\n",
    "    verbose_eval=100,\n",
    "    early_stopping_rounds=20,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the expected value from the background data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the background data\n",
    "background = X_train[:100]\n",
    "# Create a SHAP TreeExplainer using the model and the background dataset\n",
    "explainer = shap.TreeExplainer(model, background)\n",
    "# Get the expected value for the background dataset\n",
    "ev_background = explainer.expected_value\n",
    "# obtain predictions along with their contribution values\n",
    "d_train_background = xgb.DMatrix(background, y_train[:100])\n",
    "preds = model.predict(d_train_background, pred_contribs = True)\n",
    "# check that the mean across all predictions is the same with the expected value\n",
    "np.isclose(ev_background, preds.sum(1).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the expected value from the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explainer = shap.TreeExplainer(model)\n",
    "ev_trees = explainer.expected_value[0]\n",
    "\n",
    "# Create an XGBTreeModelLoader to load the XGBoost model's trees\n",
    "xgb_loader = XGBTreeModelLoader(model)\n",
    "ts = xgb_loader.get_trees()\n",
    "\n",
    "# Extract the leaf values from the XGBoost trees\n",
    "v = []\n",
    "for t in ts:\n",
    "    v.append(t.values[0][0])\n",
    "sv = sum(v)\n",
    "\n",
    "import struct\n",
    "from scipy.special import logit\n",
    "size = struct.calcsize('f')\n",
    "buffer = model.save_raw().lstrip(b'binf')\n",
    "v = struct.unpack('f', buffer[0:0+size])[0]\n",
    "# if objective \"binary:logistic\" or \"reg:logistic\" \n",
    "bv = logit(v)\n",
    "\n",
    "ev_trees_raw = sv+bv\n",
    "\n",
    "np.allclose(ev_trees, ev_trees_raw)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38-modelling",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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