examples/kernel_shap_adult_categorical_preproc.ipynb

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   "source": [
    "# KernelSHAP: combining preprocessor and predictor"
   ]
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   "source": [
    "## Introduction"
   ]
  },
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   "source": [
    "In [this](kernel_shap_adult_lr.ipynb) example, we showed that the categorical variables can be handled by fitting the explainer on preprocessed data and passing preprocessed data to the `explain` call. To handle the categorical variables, we either group them explicitly or sum the estimated shap values for each encoded shap dimension. An alternative way is to define our black-box model to include the preprocessor, as shown in [this](anchor_tabular_adult.ipynb) example. We now show that these methods give the same results."
   ]
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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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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from alibi.explainers.kernel_shap import KernelShap\n",
    "from alibi.datasets import fetch_adult\n",
    "from scipy.special import logit\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fetch_adult` function returns a `Bunch` object containing the features, the targets, the feature names and a mapping of categorical variables to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'target_names', 'category_map'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult = fetch_adult()\n",
    "adult.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = adult.data\n",
    "target = adult.target\n",
    "target_names = adult.target_names\n",
    "feature_names = adult.feature_names\n",
    "category_map = adult.category_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for your own datasets you can use our utility function `gen_category_map` to create the category map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alibi.utils.data import gen_category_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "data_perm = np.random.permutation(np.c_[data, target])\n",
    "data = data_perm[:,:-1]\n",
    "target = data_perm[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 30000\n",
    "X_train,y_train = data[:idx,:], target[:idx]\n",
    "X_test, y_test = data[idx+1:,:], target[idx+1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create feature transformation pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create feature pre-processor. Needs to have 'fit' and 'transform' methods. Different types of pre-processing can be applied to all or part of the features. In the example below we will standardize ordinal features and apply one-hot-encoding to categorical features.\n",
    "\n",
    "Ordinal features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_features = [x for x in range(len(feature_names)) if x not in list(category_map.keys())]\n",
    "ordinal_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),\n",
    "                                      ('scaler', StandardScaler())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = list(category_map.keys())\n",
    "categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),\n",
    "                                          ('onehot', OneHotEncoder(drop='first', handle_unknown='error'))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in order to be able to interpret the coefficients corresponding to the categorical features, the option `drop='first'` has been passed to the `OneHotEncoder`. This means that for a categorical variable with `n` levels, the length of the code will be `n-1`. This is necessary in order to avoid introducing feature multicolinearity, which would skew the interpretation of the results. For more information about the issue about multicolinearity in the context of linear modelling see [[1]](#References).\n",
    "<a id='src_1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine and fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
       "                  transformer_weights=None,\n",
       "                  transformers=[('num',\n",
       "                                 Pipeline(memory=None,\n",
       "                                          steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=False,\n",
       "                                                                copy=True,\n",
       "                                                                fill_value=None,\n",
       "                                                                missing_values=nan,\n",
       "                                                                strategy='median',\n",
       "                                                                verbose=0)),\n",
       "                                                 ('scaler',\n",
       "                                                  StandardScaler(copy=True,\n",
       "                                                                 with_mean=True,\n",
       "                                                                 with_std=True))],\n",
       "                                          verbose=False),\n",
       "                                 [0, 8, 9, 10]),\n",
       "                                ('cat',\n",
       "                                 Pipeline(memory=None,\n",
       "                                          steps=[('imputer',\n",
       "                                                  SimpleImputer(add_indicator=False,\n",
       "                                                                copy=True,\n",
       "                                                                fill_value=None,\n",
       "                                                                missing_values=nan,\n",
       "                                                                strategy='median',\n",
       "                                                                verbose=0)),\n",
       "                                                 ('onehot',\n",
       "                                                  OneHotEncoder(categories='auto',\n",
       "                                                                drop='first',\n",
       "                                                                dtype=<class 'numpy.float64'>,\n",
       "                                                                handle_unknown='error',\n",
       "                                                                sparse=True))],\n",
       "                                          verbose=False),\n",
       "                                 [1, 2, 3, 4, 5, 6, 7, 11])],\n",
       "                  verbose=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor = ColumnTransformer(transformers=[('num', ordinal_transformer, ordinal_features),\n",
    "                                               ('cat', categorical_transformer, categorical_features)])\n",
    "preprocessor.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit a binary logistic regression classifier to the preprocessed Adult dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_proc = preprocessor.transform(X_train)\n",
    "X_test_proc = preprocessor.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
       "                   multi_class='multinomial', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(multi_class='multinomial',\n",
    "                                random_state=0,\n",
    "                                max_iter=500,\n",
    "                                verbose=0,\n",
    "                               )\n",
    "classifier.fit(X_train_proc, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test_proc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion matrix for the logistic regression classifier')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Confusion matrix for the logistic regression classifier'\n",
    "disp = plot_confusion_matrix(classifier, \n",
    "                             X_test_proc, \n",
    "                             y_test,\n",
    "                             display_labels=target_names,\n",
    "                             cmap=plt.cm.Blues,\n",
    "                             normalize=None,\n",
    "                            )\n",
    "disp.ax_.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.8546875\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy: ', accuracy_score(y_test, classifier.predict(X_test_proc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explaining the model with an explainer fitted on the preprocessed data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed up computation, we will use a background dataset with only `100` samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_example_idx = 0\n",
    "stop_example_idx = 100\n",
    "background_data = slice(start_example_idx, stop_example_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we group the categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_groups(num_feats_names, cat_feats_names, feat_enc_dim):\n",
    "    \"\"\"\n",
    "    Given a list with numerical feat. names, categorical feat. names\n",
    "    and a list specifying the lengths of the encoding for each cat.\n",
    "    varible, the function outputs a list of group names, and a list\n",
    "    of the same len where each entry represents the column indices that\n",
    "    the corresponding categorical feature \n",
    "    \"\"\"\n",
    "    \n",
    "    group_names = num_feats_names + cat_feats_names\n",
    "    groups = []\n",
    "    cat_var_idx = 0\n",
    "    \n",
    "    for name in group_names: \n",
    "        if name in num_feats_names:\n",
    "            groups.append(list(range(len(groups), len(groups) + 1)))\n",
    "        else:\n",
    "            start_idx = groups[-1][-1] + 1 if groups else 0\n",
    "            groups.append(list(range(start_idx, start_idx + feat_enc_dim[cat_var_idx] )))\n",
    "            cat_var_idx += 1\n",
    "    \n",
    "    return group_names, groups\n",
    "            \n",
    "def sparse2ndarray(mat, examples=None):\n",
    "    \"\"\"\n",
    "    Converts a scipy.sparse.csr.csr_matrix to a numpy.ndarray.\n",
    "    If specified, examples is slice object specifying which selects a\n",
    "    number of rows from mat and converts only the respective slice.\n",
    "    \"\"\"\n",
    "    \n",
    "    if examples:\n",
    "        return mat[examples, :].toarray()\n",
    "    \n",
    "    return mat.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the indices of the categorical and the numerical features from the pipeline.\n",
    "numerical_feats_idx  = preprocessor.transformers_[0][2]\n",
    "categorical_feats_idx  = preprocessor.transformers_[1][2]\n",
    "num_feats_names = [feature_names[i] for i in numerical_feats_idx]\n",
    "cat_feats_names = [feature_names[i] for i in categorical_feats_idx]\n",
    "perm_feat_names = num_feats_names + cat_feats_names \n",
    "ohe = preprocessor.transformers_[1][1].named_steps['onehot']\n",
    "feat_enc_dim = [len(cat_enc) - 1 for cat_enc in ohe.categories_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the groups\n",
    "X_train_proc_d = sparse2ndarray(X_train_proc, examples=background_data)\n",
    "group_names, groups = make_groups(num_feats_names, cat_feats_names, feat_enc_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having created the groups, we are now ready to instantiate the explainer and explain our set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={'name': 'KernelShap', 'type': 'blackbox', 'explanations': ['local', 'global'], 'params': {'groups': [[0], [1], [2], [3], [4, 5, 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20], [21, 22, 23, 24, 25, 26, 27, 28], [29, 30, 31, 32, 33], [34, 35, 36, 37], [38], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48]], 'group_names': ['Age', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Workclass', 'Education', 'Marital Status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Country'], 'weights': None, 'summarise_background': False}})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_fcn = classifier.predict_proba\n",
    "grp_lr_explainer = KernelShap(pred_fcn, link='logit', feature_names=perm_feat_names, seed=0)\n",
    "grp_lr_explainer.fit(X_train_proc_d, group_names=group_names, groups=groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select only a small fraction of the testing set to explain for the purposes of this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_set(X, y, fraction, random_state=0):\n",
    "    \"\"\"\n",
    "    Given a set X, associated labels y, split\\\\s a fraction y from X.\n",
    "    \"\"\"\n",
    "    _, X_split, _, y_split = train_test_split(X, \n",
    "                                              y, \n",
    "                                              test_size=fraction, \n",
    "                                              random_state=random_state,\n",
    "                                             )\n",
    "    print(\"Number of records: {}\".format(X_split.shape[0]))\n",
    "    print(\"Number of class {}: {}\".format(0, len(y_split) - y_split.sum()))\n",
    "    print(\"Number of class {}: {}\".format(1, y_split.sum()))\n",
    "    \n",
    "    return X_split, y_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 26\n",
      "Number of class 0: 20\n",
      "Number of class 1: 6\n"
     ]
    }
   ],
   "source": [
    "fraction_explained = 0.01 \n",
    "X_explain, y_explain = split_set(X_test, \n",
    "                                 y_test, \n",
    "                                 fraction_explained, \n",
    "                                 )\n",
    "X_explain_proc = preprocessor.transform(X_explain)\n",
    "X_explain_proc_d = sparse2ndarray(X_explain_proc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_explanation = grp_lr_explainer.explain(X_explain_proc_d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaining with an explainer fitted on the raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain with an explainer fitted on the raw data, we make the preprocessor part of the predictor, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_fcn = lambda x: classifier.predict_proba(preprocessor.transform(x))\n",
    "lr_explainer = KernelShap(pred_fcn, link='logit', feature_names=feature_names, seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the same background dataset to fit the explainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={'name': 'KernelShap', 'type': 'blackbox', 'explanations': ['local', 'global'], 'params': {'groups': None, 'group_names': None, 'weights': None, 'summarise_background': False}})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_explainer.fit(X_train[background_data])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We explain the same dataset as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = lr_explainer.explain(X_explain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show that fitting the explainer on the raw data and combining the preprocessor with the classifier gives the same results as grouping the variables and fitting the explainer on the preprocessed data, we check to see that the same features are considered as most important when combining the two approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ranked_values(explanation):\n",
    "    \"\"\"\n",
    "    Retrives a tuple of (feature_effects, feature_names) for\n",
    "    each class explained. A feature's effect is its average\n",
    "    shap value magnitude across an array of instances.\n",
    "    \"\"\"\n",
    "    \n",
    "    ranked_shap_vals = []\n",
    "    for cls_idx in range(len(explanation.shap_values)):\n",
    "        this_ranking = (\n",
    "            explanation.raw['importances'][str(cls_idx)]['ranked_effect'],\n",
    "            explanation.raw['importances'][str(cls_idx)]['names']\n",
    "                       )\n",
    "        ranked_shap_vals.append(this_ranking)\n",
    "    \n",
    "    return ranked_shap_vals\n",
    "\n",
    "def compare_ranking(ranking_1, ranking_2, methods=None):\n",
    "    for i, (combined, grouped) in enumerate(zip(ranking_1, ranking_2)):\n",
    "        print(\"Class: {}\".format(i))\n",
    "        c_names, g_names = combined[1], grouped[1]\n",
    "        c_mag, g_mag = combined[0], grouped[0]\n",
    "        different = []\n",
    "        for i, (c_n, g_n) in enumerate(zip(c_names, g_names)):\n",
    "            if c_n != g_n:\n",
    "                different.append((i, c_n, g_n))\n",
    "        if different:\n",
    "            method_1 = methods[0] if methods else \"Method_1\"\n",
    "            method_2 = methods[1] if methods else \"Method_2\"\n",
    "            i, c_ns, g_ns = list(zip(*different))\n",
    "            data = {\"Rank\": i, method_1: c_ns, method_2: g_ns}\n",
    "            df = pd.DataFrame(data=data)\n",
    "            print(\"Found the following rank differences:\")\n",
    "            print(df)\n",
    "        else:\n",
    "            print(\"The methods provided the same ranking for the feature effects.\")\n",
    "            print(\"The ranking is: {}\".format(c_names))\n",
    "        print(\"\")\n",
    "        \n",
    "def reorder_feats(vals_and_names, src_vals_and_names):\n",
    "    \"\"\"Given a two tuples, each containing a list of ranked feature\n",
    "    shap values and the corresponding feature names, the function \n",
    "    reorders the values in vals according to the order specified in\n",
    "    the list of names contained in src_vals_and_names.\n",
    "    \"\"\"\n",
    "    \n",
    "    _, src_names = src_vals_and_names\n",
    "    vals, names = vals_and_names\n",
    "    reordered = np.zeros_like(vals)\n",
    "    \n",
    "    for i, name in enumerate(src_names):\n",
    "        alt_idx = names.index(name)\n",
    "        reordered[i] = vals[alt_idx]\n",
    "    \n",
    "    return reordered, src_names\n",
    "\n",
    "def compare_avg_mag_shap(class_idx, comparisons, baseline, **kwargs):\n",
    "    \"\"\"\n",
    "    Given a list of tuples, baseline, containing the feature values and a list with feature names \n",
    "    for each class and, comparisons, a list of lists with tuples with the same structure , the \n",
    "    function reorders the values of the features in comparisons entries according to the order \n",
    "    of the feature names provided in the baseline entries and displays the feature values for comparison.\n",
    "    \"\"\"\n",
    "    \n",
    "    methods = kwargs.get(\"methods\", [\"method_{}\".format(i) for i in range(len(comparisons) + 1)])\n",
    "    \n",
    "    n_features = len(baseline[class_idx][0])\n",
    "    \n",
    "    # bar settings\n",
    "    bar_width = kwargs.get(\"bar_width\", 0.05)\n",
    "    bar_space = kwargs.get(\"bar_space\", 2)\n",
    "    \n",
    "    # x axis \n",
    "    x_low = kwargs.get(\"x_low\", 0.0)\n",
    "    x_high = kwargs.get(\"x_high\", 1.0)\n",
    "    x_step = kwargs.get(\"x_step\", 0.05)\n",
    "    x_ticks = np.round(np.arange(x_low, x_high + x_step, x_step), 3)\n",
    "\n",
    "    # y axis (these are the y coordinate of start and end of each group \n",
    "    # of bars)\n",
    "    start_y_pos = np.array(np.arange(0, n_features))*bar_space\n",
    "    end_y_pos = start_y_pos + bar_width*len(methods)\n",
    "    y_ticks = 0.5*(start_y_pos + end_y_pos)\n",
    "    \n",
    "    # figure \n",
    "    fig_x = kwargs.get(\"fig_x\", 10)\n",
    "    fig_y = kwargs.get(\"fig_y\", 7)\n",
    "    \n",
    "    # fontsizes \n",
    "    title_font = kwargs.get(\"title_fontsize\", 20)\n",
    "    legend_font = kwargs.get(\"legend_fontsize\", 20)\n",
    "    tick_labels_font = kwargs.get(\"tick_labels_fontsize\", 20)\n",
    "    axes_label_fontsize = kwargs.get(\"axes_label_fontsize\", 10)\n",
    "    \n",
    "    # labels \n",
    "    title = kwargs.get(\"title\", None)\n",
    "    ylabel = kwargs.get(\"ylabel\", None)\n",
    "    xlabel = kwargs.get(\"xlabel\", None)\n",
    "    \n",
    "    # process input data \n",
    "    methods = list(reversed(methods))\n",
    "    base_vals = baseline[class_idx][0] \n",
    "    ordering = baseline[class_idx][1]\n",
    "    comp_vals = []\n",
    "    \n",
    "    # reorder the features so that they match the order of the baseline (ordering)\n",
    "    for comparison in comparisons:\n",
    "        vals, ord_ = reorder_feats(comparison[class_idx], baseline[class_idx])\n",
    "        comp_vals.append(vals)\n",
    "        assert ord_ is ordering \n",
    "        \n",
    "    all_vals = [base_vals] + comp_vals\n",
    "    data = dict(zip(methods, all_vals))\n",
    "    df = pd.DataFrame(data=data, index=ordering)\n",
    "    \n",
    "    # plotting logic\n",
    "    fig, ax = plt.subplots(figsize=(fig_x, fig_y))\n",
    "\n",
    "    for i, col in enumerate(df.columns):\n",
    "        values = list(df[col])\n",
    "        y_pos = [y + bar_width*i for y  in start_y_pos] \n",
    "        ax.barh(y_pos, list(values), bar_width, label=col)\n",
    "    \n",
    "    # add ticks, legend and labels\n",
    "    ax.set_xticks(x_ticks)\n",
    "    ax.set_xticklabels([str(x) for x in x_ticks], rotation=45, fontsize=tick_labels_font)\n",
    "    ax.set_xlabel(xlabel, fontsize=axes_label_fontsize)\n",
    "    ax.set_yticks(y_ticks)\n",
    "    ax.set_yticklabels(ordering, fontsize=tick_labels_font)\n",
    "    ax.set_ylabel(ylabel, fontsize=axes_label_fontsize)\n",
    "    ax.invert_yaxis()  # labels read top-to-bottom\n",
    "    ax.legend(fontsize=legend_font)   \n",
    "\n",
    "    plt.grid(True)\n",
    "    plt.title(title, fontsize=title_font)\n",
    "\n",
    "    return ax, fig, df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class: 0\n",
      "The methods provided the same ranking for the feature effects.\n",
      "The ranking is: ['Marital Status', 'Capital Gain', 'Education', 'Occupation', 'Sex', 'Relationship', 'Age', 'Hours per week', 'Workclass', 'Capital Loss', 'Country', 'Race']\n",
      "\n",
      "Class: 1\n",
      "The methods provided the same ranking for the feature effects.\n",
      "The ranking is: ['Marital Status', 'Capital Gain', 'Education', 'Occupation', 'Sex', 'Relationship', 'Age', 'Hours per week', 'Workclass', 'Capital Loss', 'Country', 'Race']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ranked_grouped_shap_vals = get_ranked_values(grouped_explanation)\n",
    "ranked_shal_vals_raw = get_ranked_values(explanation)\n",
    "compare_ranking(ranked_grouped_shap_vals, ranked_shal_vals_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see that both methods returned the same feature importances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_idx = 0 \n",
    "\n",
    "ax, fig, _ = compare_avg_mag_shap(class_idx, \n",
    "                               [ranked_shal_vals_raw], \n",
    "                               ranked_grouped_shap_vals, \n",
    "                               methods=('raw_data', 'grouped'),\n",
    "                               bar_width=0.5,\n",
    "                               tick_labels_fontsize=12,\n",
    "                               legend_fontsize=12,\n",
    "                               title_fontsize=15,\n",
    "                               xlabel=\"Features  effects (class {})\".format(0),\n",
    "                               ylabel=\"Feature\",\n",
    "                               axes_label_fontsize=15,\n",
    "                               )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the shap values are very similar. The differences appear because the regression dataset generated in order to compute the shap values differes slightly between the two runs due to the difference in the order of the features in the background dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#src_1) *Mahto, K.K., 2019. \"One-Hot-Encoding, Multicollinearity and the Dummy Variable Trap\". Retrieved 02 Feb 2020*  [(link)](https://towardsdatascience.com/one-hot-encoding-multicollinearity-and-the-dummy-variable-trap-b5840be3c41a)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}