heloc.ipynb

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    "# HELOC Credit Approval \n",
    "This notebook is ispired by the [AIX360](https://aix360.mybluemix.net/) [HELOC Credit Approval Tutorial](https://github.com/gianluigilopardo/AIX360/blob/master/examples/tutorials/HELOC.ipynb), which shows different explainability methods for a credit approval process.\n",
    "Here [XGBoost](https://github.com/dmlc/xgboost) is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the *Data Scientist explanations* methods provided in the above notebook. The first ones come directly with XGBoost and the other is based on [SHAP](https://github.com/slundberg/shap).\n",
    "\n",
    "The dataset is from the [FICO Explainable Machine Learning Challenge](https://community.fico.com/s/explainable-machine-learning-challenge) and it is about credit approval."
   ]
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   "execution_count": 1,
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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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    }
   ],
   "source": [
    "# math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# models\n",
    "from sklearn.model_selection import train_test_split\n",
    "from rulefit import RuleFit\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# metrics\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# visual\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import plot_tree\n",
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files/Graphviz/bin/'\n",
    "\n",
    "# utils\n",
    "import random\n",
    "SEED = 42\n",
    "random.seed(SEED)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# explainability \n",
    "import shap\n",
    "# print the JS visualization code to the notebook\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "binding-satin",
   "metadata": {},
   "outputs": [
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       "<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>RiskPerformance</th>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <th>AverageMInFile</th>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bad</td>\n",
       "      <td>55</td>\n",
       "      <td>144</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>-8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bad</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>15</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>100</td>\n",
       "      <td>-7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>-8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bad</td>\n",
       "      <td>67</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>-7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bad</td>\n",
       "      <td>66</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bad</td>\n",
       "      <td>81</td>\n",
       "      <td>333</td>\n",
       "      <td>27</td>\n",
       "      <td>132</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>-7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RiskPerformance  ExternalRiskEstimate  MSinceOldestTradeOpen  \\\n",
       "0             Bad                    55                    144   \n",
       "1             Bad                    61                     58   \n",
       "2             Bad                    67                     66   \n",
       "3             Bad                    66                    169   \n",
       "4             Bad                    81                    333   \n",
       "\n",
       "   MSinceMostRecentTradeOpen  AverageMInFile  NumSatisfactoryTrades  \\\n",
       "0                          4              84                     20   \n",
       "1                         15              41                      2   \n",
       "2                          5              24                      9   \n",
       "3                          1              73                     28   \n",
       "4                         27             132                     12   \n",
       "\n",
       "   NumTrades60Ever2DerogPubRec  NumTrades90Ever2DerogPubRec  \\\n",
       "0                            3                            0   \n",
       "1                            4                            4   \n",
       "2                            0                            0   \n",
       "3                            1                            1   \n",
       "4                            0                            0   \n",
       "\n",
       "   PercentTradesNeverDelq  MSinceMostRecentDelq  MaxDelq2PublicRecLast12M  \\\n",
       "0                      83                     2                         3   \n",
       "1                     100                    -7                         0   \n",
       "2                     100                    -7                         7   \n",
       "3                      93                    76                         6   \n",
       "4                     100                    -7                         7   \n",
       "\n",
       "   MaxDelqEver  NumTotalTrades  NumTradesOpeninLast12M  PercentInstallTrades  \\\n",
       "0            5              23                       1                    43   \n",
       "1            8               7                       0                    67   \n",
       "2            8               9                       4                    44   \n",
       "3            6              30                       3                    57   \n",
       "4            8              12                       0                    25   \n",
       "\n",
       "   MSinceMostRecentInqexcl7days  NumInqLast6M  NumInqLast6Mexcl7days  \\\n",
       "0                             0             0                      0   \n",
       "1                             0             0                      0   \n",
       "2                             0             4                      4   \n",
       "3                             0             5                      4   \n",
       "4                             0             1                      1   \n",
       "\n",
       "   NetFractionRevolvingBurden  NetFractionInstallBurden  \\\n",
       "0                          33                        -8   \n",
       "1                           0                        -8   \n",
       "2                          53                        66   \n",
       "3                          72                        83   \n",
       "4                          51                        89   \n",
       "\n",
       "   NumRevolvingTradesWBalance  NumInstallTradesWBalance  \\\n",
       "0                           8                         1   \n",
       "1                           0                        -8   \n",
       "2                           4                         2   \n",
       "3                           6                         4   \n",
       "4                           3                         1   \n",
       "\n",
       "   NumBank2NatlTradesWHighUtilization  PercentTradesWBalance  \n",
       "0                                   1                     69  \n",
       "1                                  -8                      0  \n",
       "2                                   1                     86  \n",
       "3                                   3                     91  \n",
       "4                                   0                     80  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'heloc_dataset_v1.csv'\n",
    "df = pd.read_csv(path) \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "modified-context",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset has 10459 instances and 23 features.\n"
     ]
    }
   ],
   "source": [
    "print(\"The dataset has \" + str(df.shape[0])  + \" instances and \" + str(df.shape[1]-1) + \" features.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "western-yield",
   "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>ExternalRiskEstimate</th>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <th>AverageMInFile</th>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "      <td>10459.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.425758</td>\n",
       "      <td>184.205373</td>\n",
       "      <td>8.543455</td>\n",
       "      <td>73.843293</td>\n",
       "      <td>19.428052</td>\n",
       "      <td>0.042738</td>\n",
       "      <td>-0.142843</td>\n",
       "      <td>86.661536</td>\n",
       "      <td>6.762406</td>\n",
       "      <td>4.928291</td>\n",
       "      <td>5.510183</td>\n",
       "      <td>20.856965</td>\n",
       "      <td>1.253083</td>\n",
       "      <td>32.166460</td>\n",
       "      <td>-0.325366</td>\n",
       "      <td>0.868152</td>\n",
       "      <td>0.812602</td>\n",
       "      <td>31.629888</td>\n",
       "      <td>39.158906</td>\n",
       "      <td>3.185008</td>\n",
       "      <td>0.976097</td>\n",
       "      <td>0.018071</td>\n",
       "      <td>62.079166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.121621</td>\n",
       "      <td>109.683816</td>\n",
       "      <td>13.301745</td>\n",
       "      <td>38.782803</td>\n",
       "      <td>13.004327</td>\n",
       "      <td>2.513910</td>\n",
       "      <td>2.367397</td>\n",
       "      <td>25.999584</td>\n",
       "      <td>20.501250</td>\n",
       "      <td>3.756275</td>\n",
       "      <td>3.971182</td>\n",
       "      <td>14.580905</td>\n",
       "      <td>3.068684</td>\n",
       "      <td>20.128634</td>\n",
       "      <td>6.067556</td>\n",
       "      <td>3.179304</td>\n",
       "      <td>3.143698</td>\n",
       "      <td>30.060140</td>\n",
       "      <td>42.101601</td>\n",
       "      <td>4.413173</td>\n",
       "      <td>4.060995</td>\n",
       "      <td>3.358135</td>\n",
       "      <td>27.711565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>249.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>803.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>471.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ExternalRiskEstimate  MSinceOldestTradeOpen  MSinceMostRecentTradeOpen  \\\n",
       "count          10459.000000           10459.000000               10459.000000   \n",
       "mean              67.425758             184.205373                   8.543455   \n",
       "std               21.121621             109.683816                  13.301745   \n",
       "min               -9.000000              -9.000000                  -9.000000   \n",
       "25%               63.000000             118.000000                   3.000000   \n",
       "50%               71.000000             178.000000                   5.000000   \n",
       "75%               79.000000             249.500000                  11.000000   \n",
       "max               94.000000             803.000000                 383.000000   \n",
       "\n",
       "       AverageMInFile  NumSatisfactoryTrades  NumTrades60Ever2DerogPubRec  \\\n",
       "count    10459.000000           10459.000000                 10459.000000   \n",
       "mean        73.843293              19.428052                     0.042738   \n",
       "std         38.782803              13.004327                     2.513910   \n",
       "min         -9.000000              -9.000000                    -9.000000   \n",
       "25%         52.000000              12.000000                     0.000000   \n",
       "50%         74.000000              19.000000                     0.000000   \n",
       "75%         95.000000              27.000000                     1.000000   \n",
       "max        383.000000              79.000000                    19.000000   \n",
       "\n",
       "       NumTrades90Ever2DerogPubRec  PercentTradesNeverDelq  \\\n",
       "count                 10459.000000            10459.000000   \n",
       "mean                     -0.142843               86.661536   \n",
       "std                       2.367397               25.999584   \n",
       "min                      -9.000000               -9.000000   \n",
       "25%                       0.000000               87.000000   \n",
       "50%                       0.000000               96.000000   \n",
       "75%                       0.000000              100.000000   \n",
       "max                      19.000000              100.000000   \n",
       "\n",
       "       MSinceMostRecentDelq  MaxDelq2PublicRecLast12M   MaxDelqEver  \\\n",
       "count          10459.000000              10459.000000  10459.000000   \n",
       "mean               6.762406                  4.928291      5.510183   \n",
       "std               20.501250                  3.756275      3.971182   \n",
       "min               -9.000000                 -9.000000     -9.000000   \n",
       "25%               -7.000000                  4.000000      5.000000   \n",
       "50%               -7.000000                  6.000000      6.000000   \n",
       "75%               14.000000                  7.000000      8.000000   \n",
       "max               83.000000                  9.000000      8.000000   \n",
       "\n",
       "       NumTotalTrades  NumTradesOpeninLast12M  PercentInstallTrades  \\\n",
       "count    10459.000000            10459.000000          10459.000000   \n",
       "mean        20.856965                1.253083             32.166460   \n",
       "std         14.580905                3.068684             20.128634   \n",
       "min         -9.000000               -9.000000             -9.000000   \n",
       "25%         12.000000                0.000000             20.000000   \n",
       "50%         20.000000                1.000000             31.000000   \n",
       "75%         29.000000                3.000000             44.000000   \n",
       "max        104.000000               19.000000            100.000000   \n",
       "\n",
       "       MSinceMostRecentInqexcl7days  NumInqLast6M  NumInqLast6Mexcl7days  \\\n",
       "count                  10459.000000  10459.000000           10459.000000   \n",
       "mean                      -0.325366      0.868152               0.812602   \n",
       "std                        6.067556      3.179304               3.143698   \n",
       "min                       -9.000000     -9.000000              -9.000000   \n",
       "25%                       -7.000000      0.000000               0.000000   \n",
       "50%                        0.000000      1.000000               1.000000   \n",
       "75%                        1.000000      2.000000               2.000000   \n",
       "max                       24.000000     66.000000              66.000000   \n",
       "\n",
       "       NetFractionRevolvingBurden  NetFractionInstallBurden  \\\n",
       "count                10459.000000              10459.000000   \n",
       "mean                    31.629888                 39.158906   \n",
       "std                     30.060140                 42.101601   \n",
       "min                     -9.000000                 -9.000000   \n",
       "25%                      5.000000                 -8.000000   \n",
       "50%                     25.000000                 47.000000   \n",
       "75%                     54.000000                 79.000000   \n",
       "max                    232.000000                471.000000   \n",
       "\n",
       "       NumRevolvingTradesWBalance  NumInstallTradesWBalance  \\\n",
       "count                10459.000000              10459.000000   \n",
       "mean                     3.185008                  0.976097   \n",
       "std                      4.413173                  4.060995   \n",
       "min                     -9.000000                 -9.000000   \n",
       "25%                      2.000000                  1.000000   \n",
       "50%                      3.000000                  2.000000   \n",
       "75%                      5.000000                  3.000000   \n",
       "max                     32.000000                 23.000000   \n",
       "\n",
       "       NumBank2NatlTradesWHighUtilization  PercentTradesWBalance  \n",
       "count                        10459.000000           10459.000000  \n",
       "mean                             0.018071              62.079166  \n",
       "std                              3.358135              27.711565  \n",
       "min                             -9.000000              -9.000000  \n",
       "25%                              0.000000              47.000000  \n",
       "50%                              0.000000              67.000000  \n",
       "75%                              1.000000              82.000000  \n",
       "max                             18.000000             100.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "isolated-monitoring",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns = 'RiskPerformance')\n",
    "features = X.columns\n",
    "y = df['RiskPerformance'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=SEED, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suburban-clinton",
   "metadata": {},
   "source": [
    "## XGBoost\n",
    "The idea of boosting approach in machine learning is to create highly accurate predictions by combining many low-accurate rules. ”Wisdom of the crowds” is the basis concept behind Boosting, which takes a large amount of its foundations in the game theory field.\n",
    "\n",
    "Boosting assumes the availability of a base or weak classifier, which it will see as a ”black-box” and that can be called repeatedly. This classification algorithm has to be at least better than taking random predictions (weak learning assumption). A boosting algorithm learns by repeatedly calling the base learning algorithm, but if this always works with the same data and in the same way, we can’t expect anything interesting. The key idea is to choose training sets for the base classifier in such a way to infer something new each time it is called.\n",
    "\n",
    "[XGBoost](https://xgboost.ai/) stands for ”eXtreme Gradient Boosting”. It is a scalable, fast and well-performing tree boosting system, widely used in the data science community. It is designed for optimizing computational resources as it performs different optimization improvements which make it better than other boosting technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "waiting-brain",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do cross validation to better choose (some) parameters\n",
    "# or if you trust me set it False\n",
    "CV = False\n",
    "\n",
    "if CV:\n",
    "    learning_rate =  [0.001, 0.01, 0.1]\n",
    "    max_depth = np.arange(3,6)\n",
    "    alpha = [0,2,10]\n",
    "    colsample_bytree = [0,0.2,0.5]\n",
    "    parameters = {'learning_rate': learning_rate, 'max_depth': max_depth,\n",
    "                 'alpha': alpha, 'colsample_bytree':colsample_bytree}\n",
    "    xgb_mod = xgb.XGBClassifier(objective='reg:logistic')\n",
    "    xgb_mod = GridSearchCV(xgb_mod, parameters, scoring='f1', cv=5)\n",
    "    xgb_mod.fit(X_train,y_train)\n",
    "    print(\"The best parameters for XGB Classifier are %s with a score of %0.2f\" % (xgb_mod.best_params_, xgb_mod.best_score_))\n",
    "    \n",
    "else: \n",
    "    xgb_mod = xgb.XGBClassifier(objective='reg:logistic', colsample_bytree=0.2, alpha=10,\n",
    "                            learning_rate=0.1, max_depth=4, n_estimators=100)\n",
    "    xgb_mod.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "revolutionary-mistress",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.746209534216637\n",
      "Test accuracy: 0.7297641810070108\n"
     ]
    }
   ],
   "source": [
    "xgb_pred = xgb_mod.predict(X_test)\n",
    "print('Training accuracy:', accuracy_score(y_train, xgb_mod.predict(X_train)))\n",
    "print('Test accuracy:', accuracy_score(y_test, xgb_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faced-dryer",
   "metadata": {},
   "source": [
    "By looking at *Test accuracy* we se that XGBoost classifier outperformes Boolean Rule and Logistic Rule Regression models in the HELOC example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "proved-notion",
   "metadata": {},
   "outputs": [],
   "source": [
    "if CV: xgb_mod = xgb_mod.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plastic-strength",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "integral-worker",
   "metadata": {},
   "source": [
    "What is more, it also provides different scoring systems to undestand features importance: \n",
    "* **Gain**: the average gain across all splits the feature is used in;\n",
    "* **Total gain**: the total gain across all splits the feature is used in;\n",
    "* **Coverage**: the average coverage across all splits the feature is used in;\n",
    "* **Total coverage**: the total coverage across all splits the feature is used in;\n",
    "* **Weight**: the number of times a feature is used to split the data across all trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "resident-hollow",
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_importance(xgb_mod, scoring_type):\n",
    "    features_scoring = xgb_mod.get_booster().get_score(importance_type = scoring_type)\n",
    "    features = list(features_scoring.keys())\n",
    "    scores = list(features_scoring.values())\n",
    "\n",
    "    data = pd.DataFrame(data=scores, index=features, columns=[\"score\"]).sort_values(by = \"score\", ascending=True).tail(10)\n",
    "    data.plot(kind='barh')\n",
    "    plt.grid(color='k', linewidth=0.4)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expressed-strike",
   "metadata": {},
   "source": [
    "#### Gain\n",
    "The Gain implies the relative contribution of the corresponding feature to the model calculated by taking each feature's contribution for each tree in the model. A higher value of this metric when compared to another feature implies it is more important for generating a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "persistent-qualification",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance(xgb_mod, 'gain')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "premier-conjunction",
   "metadata": {},
   "source": [
    "#### Total Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "strategic-oracle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance(xgb_mod, 'total_gain')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "usual-investor",
   "metadata": {},
   "source": [
    "#### Coverage\n",
    "The Coverage metric means the relative number of observations related to this feature. For example, if you have 100 observations, 4 features and 3 trees, and suppose feature1 is used to decide the leaf node for 10, 5, and 2 observations in tree1, tree2 and tree3 respectively; then the metric will count cover for this feature as 10+5+2 = 17 observations. This will be calculated for all the 4 features and the cover will be 17 expressed as a percentage for all features’ cover metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "violent-fourth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance(xgb_mod, 'cover')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nasty-direction",
   "metadata": {},
   "source": [
    "#### Total Coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "standard-climb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance(xgb_mod, 'total_cover')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solid-association",
   "metadata": {},
   "source": [
    "#### Weight\n",
    "It is the number of times a feature is used to split the data across all trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "later-illinois",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_importance(xgb_mod, 'weight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caroline-halloween",
   "metadata": {},
   "source": [
    "## SHAP\n",
    "[SHAP (SHapley Additive exPlanations)](https://github.com/slundberg/shap) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions.\n",
    "\n",
    "To get an overview of which features are most important for a model we can just take the mean absolute value of the SHAP values for each feature to get a standard bar plot (produces stacked bars for multi-class outputs), providing feature importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "homeless-majority",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explain the model's predictions using SHAP\n",
    "explainer = shap.Explainer(xgb_mod.get_booster())\n",
    "shap_values = explainer(X)\n",
    "shap.plots.bar(shap_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dying-verification",
   "metadata": {},
   "source": [
    "To go deeper, we can plot the SHAP values of every feature for every sample. The plot below sorts features by the sum of SHAP value magnitudes over all samples, and uses SHAP values to show the distribution of the impacts each feature has on the model output. The color represents the feature value (red high, blue low)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "unlike-handling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize the effects of all the features\n",
    "shap.plots.beeswarm(shap_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prepared-classic",
   "metadata": {},
   "source": [
    "If we look for local explanation, SHAP can show features each contributing to push the model output from the base value (the average model output over the training dataset we passed) to the model output. Features pushing the prediction higher are shown in red, those pushing the prediction lower are in blue.\n",
    "\n",
    "Let's look at the first prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "jewish-producer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first istance has been predicted as Bad\n"
     ]
    }
   ],
   "source": [
    "print('The first istance has been predicted as ' + xgb_pred[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "violent-discrimination",
   "metadata": {},
   "source": [
    "Bad means no credit approval, Good means credit approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bizarre-omega",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the first prediction's explanation\n",
    "shap.plots.waterfall(shap_values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "pending-tyler",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iHBRPOIG8F1CBFNO6B9M8'>\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\": -0.09160248190164566, \"outValue\": -1.6231626272201538, \"link\": \"identity\", \"featureNames\": [\"ExternalRiskEstimate\", \"MSinceOldestTradeOpen\", \"MSinceMostRecentTradeOpen\", \"AverageMInFile\", \"NumSatisfactoryTrades\", \"NumTrades60Ever2DerogPubRec\", \"NumTrades90Ever2DerogPubRec\", \"PercentTradesNeverDelq\", \"MSinceMostRecentDelq\", \"MaxDelq2PublicRecLast12M\", \"MaxDelqEver\", \"NumTotalTrades\", \"NumTradesOpeninLast12M\", \"PercentInstallTrades\", \"MSinceMostRecentInqexcl7days\", \"NumInqLast6M\", \"NumInqLast6Mexcl7days\", \"NetFractionRevolvingBurden\", \"NetFractionInstallBurden\", \"NumRevolvingTradesWBalance\", \"NumInstallTradesWBalance\", \"NumBank2NatlTradesWHighUtilization\", \"PercentTradesWBalance\"], \"features\": {\"0\": {\"effect\": -0.6123710870742798, \"value\": 55.0}, \"1\": {\"effect\": -0.03934474661946297, \"value\": 144.0}, \"2\": {\"effect\": 0.04439033940434456, \"value\": 4.0}, \"3\": {\"effect\": 0.1261497437953949, \"value\": 84.0}, \"4\": {\"effect\": -0.016560405492782593, \"value\": 20.0}, \"5\": {\"effect\": -0.022253654897212982, \"value\": 3.0}, \"6\": {\"effect\": 0.01815098710358143, \"value\": 0.0}, \"7\": {\"effect\": -0.3205746114253998, \"value\": 83.0}, \"8\": {\"effect\": -0.11909997463226318, \"value\": 2.0}, \"9\": {\"effect\": -0.04774387925863266, \"value\": 3.0}, \"10\": {\"effect\": -0.037659961730241776, \"value\": 5.0}, \"11\": {\"effect\": 0.022988924756646156, \"value\": 23.0}, \"12\": {\"effect\": 0.009870242327451706, \"value\": 1.0}, \"13\": {\"effect\": 0.0006481129676103592, \"value\": 43.0}, \"14\": {\"effect\": -0.17697429656982422, \"value\": 0.0}, \"15\": {\"effect\": 0.03726230934262276, \"value\": 0.0}, \"16\": {\"effect\": 0.014489652588963509, \"value\": 0.0}, \"17\": {\"effect\": 0.08082138746976852, \"value\": 33.0}, \"18\": {\"effect\": 0.0006725529674440622, \"value\": -8.0}, \"19\": {\"effect\": -0.22412370145320892, \"value\": 8.0}, \"20\": {\"effect\": -0.0367455780506134, \"value\": 1.0}, \"21\": {\"effect\": -0.2458564043045044, \"value\": 1.0}, \"22\": {\"effect\": 0.012303801253437996, \"value\": 69.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iHBRPOIG8F1CBFNO6B9M8')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x2aa3953a9a0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.plots.force(shap_values[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}