doc/source/examples/distributed_kernel_shap_adult_lr.ipynb

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   "source": [
    "# Distributed KernelSHAP"
   ]
  },
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   "metadata": {},
   "source": [
    "## Introduction"
   ]
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    "In this example, KernelSHAP is used to explain a batch of instances on multiple cores. To run this example, please run ``pip install alibi[ray]`` first.\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "Warning\n",
    "\n",
    "Windows support for the `ray` Python library is [still experimental](https://docs.ray.io/en/latest/installation.html#windows-support). Using `KernelShap` in parallel is not currently supported on Windows platforms.\n",
    "\n",
    "\n",
    "</div>"
   ]
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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 pprint\n",
    "import shap\n",
    "import ray\n",
    "shap.initjs()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from alibi.explainers import KernelShap\n",
    "from alibi.datasets import fetch_adult\n",
    "from collections import defaultdict\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\n",
    "from timeit import default_timer as timer\n",
    "from typing import Dict, List, Tuple"
   ]
  },
  {
   "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\n",
    "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(transformers=[('num',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(strategy='median')),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 [0, 8, 9, 10]),\n",
       "                                ('cat',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(strategy='median')),\n",
       "                                                 ('onehot',\n",
       "                                                  OneHotEncoder(drop='first'))]),\n",
       "                                 [1, 2, 3, 4, 5, 6, 7, 11])])"
      ]
     },
     "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": [
    "### 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": [
    "Applying the `sklearn` processing pipeline modifies the column order of the original dataset. The new feature ordering is necessary in order to corectly plot visualisations, and is inferred from the `preprocessor` object below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_feats_idx  = preprocessor.transformers_[0][2]\n",
    "categorical_feats_idx  = preprocessor.transformers_[1][2]\n",
    "scaler = preprocessor.transformers_[0][1].named_steps['scaler']\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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original order:\n",
      "['Age',\n",
      " 'Workclass',\n",
      " 'Education',\n",
      " 'Marital Status',\n",
      " 'Occupation',\n",
      " 'Relationship',\n",
      " 'Race',\n",
      " 'Sex',\n",
      " 'Capital Gain',\n",
      " 'Capital Loss',\n",
      " 'Hours per week',\n",
      " 'Country']\n",
      "\n",
      "New features order:\n",
      "['Age',\n",
      " 'Capital Gain',\n",
      " 'Capital Loss',\n",
      " 'Hours per week',\n",
      " 'Workclass',\n",
      " 'Education',\n",
      " 'Marital Status',\n",
      " 'Occupation',\n",
      " 'Relationship',\n",
      " 'Race',\n",
      " 'Sex',\n",
      " 'Country']\n"
     ]
    }
   ],
   "source": [
    "pp = pprint.PrettyPrinter()\n",
    "print(\"Original order:\")\n",
    "pp.pprint(feature_names)\n",
    "print(\"\")\n",
    "print(\"New features order:\")\n",
    "pp.pprint(perm_feat_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a utility to reorder the columns of an input array so that the features have the same ordering as that induced by the preprocessor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def permute_columns(X: np.ndarray, feat_names: List[str], perm_feat_names: List[str]) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Permutes the original dataset so that its columns (ordered according to feat_names) have the order \n",
    "    of the variables after transformation with the sklearn preprocessing pipeline (perm_feat_names).\n",
    "    \"\"\"\n",
    "    \n",
    "    perm_X = np.zeros_like(X)\n",
    "    perm = []\n",
    "    for i, feat_name in enumerate(perm_feat_names):\n",
    "        feat_idx = feat_names.index(feat_name)\n",
    "        perm_X[:, i] = X[:, feat_idx]\n",
    "        perm.append(feat_idx)\n",
    "    return perm_X, perm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The categorical variables will be grouped to reduce shap values variance, as shown in [this](kernel_shap_adult_lr.ipynb) example. To do so, the dimensionality of each categorical variable is extracted from the preprocessor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    feature_names  encoded_dim\n",
      "0       Workclass            8\n",
      "1       Education            6\n",
      "2  Marital Status            3\n",
      "3      Occupation            8\n",
      "4    Relationship            5\n",
      "5            Race            4\n",
      "6             Sex            1\n",
      "7         Country           10\n",
      "The dimensionality of the encoded categorical features is 45.\n"
     ]
    }
   ],
   "source": [
    "# get feature names for the encoded categorical features\n",
    "ohe = preprocessor.transformers_[1][1].named_steps['onehot']\n",
    "fts = [feature_names[x] for x in categorical_features]\n",
    "cat_enc_feat_names = ohe.get_feature_names(fts)\n",
    " # compute encoded dimension; -1 as ohe is setup with drop='first'\n",
    "feat_enc_dim = [len(cat_enc) - 1 for cat_enc in ohe.categories_]\n",
    "d = {'feature_names': fts , 'encoded_dim': feat_enc_dim}\n",
    "df = pd.DataFrame(data=d)\n",
    "print(df)\n",
    "total_dim = df['encoded_dim'].sum() \n",
    "print(\"The dimensionality of the encoded categorical features is {}.\".format(total_dim))\n",
    "assert total_dim == len(cat_enc_feat_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select a subset of test instances to explain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_set(X, y, fraction, random_state=0):\n",
    "    \"\"\"\n",
    "    Given a set X, associated labels y, splits 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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 128\n",
      "Number of class 0: 96\n",
      "Number of class 1: 32\n"
     ]
    }
   ],
   "source": [
    "fraction_explained = 0.05 \n",
    "X_explain, y_explain = split_set(X_test, \n",
    "                                 y_test, \n",
    "                                 fraction_explained, \n",
    "                                 )\n",
    "X_explain_proc = preprocessor.transform(X_explain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a version of the dataset to be explained that has the same feature ordering as that of the feature matrix after applying the preprocessing (for plotting purposes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_X_explain, _ = permute_columns(X_explain, feature_names, perm_feat_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit a binary logistic regression classifier to the Adult dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=500, multi_class='multinomial', random_state=0)"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test_proc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion matrix for the logistic regression classifier')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AfiDpv5L+I2mPmN4JmJm03KyYVl16Wl7Cdc6VBGUfTNtJmpA0PdTMhmZYpwnQFtgT2AN4WNK2uecy806cc66ohRF2sg64FWbWJ8ddzAIei6P0vinpO6AdMBvYOmm5zjGNNOnV8ioF51zxk1BZdo8aegLYP+xKPYBmQAUwGjhWUnNJ3YDuwJvAW0B3Sd0kNSNcWBudaSdewnXOlYQcSriZtjMS6E+oepgFXA0MA4bFpmKrgUGxtDtZ0sOEi2GVwNlmtiZu5xxgLFAODDOzyZn27QHXOVcS8hVwzey4amadUM3yg4HBVaSPAcbksm8PuM65kpCvgFufPOA654qf4qPEecB1zhU9IS/hOudcoZSVlX6jKg+4zrmS4CVc55wrBK/Ddc65wvESrnPOFYBfNHPOuQKqxW27RcMDrnOu+MmrFJxzrmA84DrnXIF4wHXOuQLwi2bOOVdIpR9vPeA650qA/NZe55wrGK9ScM65Qin9eOtjmpWSv/z+eD4ZeyOvjbp8bVqvHp0YN+wiXvr7pbww/BJ69+wCQPcuHRh770XMe3UI55xw4HrbOePY/rw26nJe+8cVnHlc/0IeQqN2znUj6H7wpez183WDB/z+tsfpe/T17HPcDZxw8VCWLV8JwOpvKzn72gfZ+9jB7PuLG3nl7U/qK9tFQ1JWjyy2M0zSgjicTuq8iySZpHZxWpJulzRN0vuSeictO0jS1PgYlM0xFG3AldRf0jJJ78bHVUnzBkj6OJ6ES5PSx0vqE593iyfikPrIf10Y+dQbHH3eneulXXvuQG6552n2O/4mbvy/p7j2vIEALPnyKy699RHuGPHCesvvtF1HBg3cmwMH/YEf/OJGDtm3F906tyvUITRqxx2+J4/efvZ6afv325HXRl3OqyMvZ7tttuBP948DYPjjrwLw2qgrePyOc7jyz4/z3XffFTzPxSLbYJtltcP9wIAq9rE1cDAwIyn5UMLAkd2B04G/xmXbEsZC6wf0Ba6W1CbTjgsacCU1k7RxDqu8bGa7x8d1cRvlwJ2EE9ETOE5Sz5T9dAaeAS4ys7F5yn69e+2dT1ny5cr10sxgk41bALBpq42Yt3AZABVLVvDOhzP4tnLNesv36LolEyZ9zqpvvmXNmu94deI0frL/7gXJf2O3T+/tabNpy/XSDthzJ5o0KQdgj17dmDN/KQAfT5/HD/bYAYD2bTehdauNeGfKDBqzfAVcM3sJWFzFrCHAJYAlpR0JPGDBG8BmkjoChwDPmtliM1sCPEsVQTxVQQKupJ0k3Qp8DPSo5eb6AtPM7DMzWw2MIpyUhI7AOOAKM8s4bHGpu/xPj3LdeQOZ9NT1XPebo7juzifTLj/l0znstfv2tGm9MRs1b8qP9t6ZTh0yfjG7Ahgx+nUO2juUHXp178QzL31AZeUavphdwbsfzWT2/CX1nMP6lcMw6e0kTUh6nJ5x29KRwGwzey9lVidgZtL0rJhWXXpadXbRLJZkjwFOjUn3AdeY2fI4fwhxHPgUo8zspvh8L0nvAXOA38ZhiKs60H5J08OBK83s0TR5O53w8wCatsrxyIrLKT/9AZf/6TH+9eK7DDzoe9z+++M56uw7ql3+k8/nc9sDz/LYX85m5arVTPpkFmsa8U/VYvHHYc/QpEkZxxy6BwAnHLEXn3w+n/1/eQtbd2xL3127Ud4AmkXVRg6tFCrMrE8O220JXE6oTqhTddlKYS7wPnCamX2UOtPMLsiw/kSgi5mtkHQY8AShHiWT54ATJN1vZiurWsDMhgJDAcpabmFVLVMqjju8H5feGr5bnnjuHW674hcZ1xkx+nVGjH4dgN+f9RPmLFhal1l0GTz0rzcY98oknrjrvLVBpUmTcm648Kdrlzn4lFvZbpst6iuL9a9uO6/ZDugGvBf30RmYKKkvMBvYOmnZzjFtNtA/JX18ph3V5Vfm0YRMPSbpKkldkmdKGpJ0QSz5cSmAmX1pZivi8zFA03jlsLoTkHAL8BbwiKQG3+xt7sJl7NM7fA/tt0cPPpu5MOM67dqEUn3nDm04fP/deOSZCXWaR1e95177kNsffI6Hbj2Dli2arU1f+fVqvlr1DQAv/ncKTZqUseO2Hesrm/VOgJTdI1dm9oGZbWFmXc2sK+FXc28zmweMBn4ZWyvsCSwzs7nAWOBgSW3ixbKDY1padRaQzGwcME7S5sAJwJOSKggl3s8zlXAlbQnMNzOL3zRlwCJgKdBdUjdCoD0WSC3WnQ88BNwr6SQzK+lSbMI9/3sS+3y/O5tv1opJT13PTUPHcP7gh7jxoqNpUl7G16srOf+GkQBssfkmvDD8EjbZuAVmxpnH9mevnw9m+Vdf88DNp9Gm9cZUVq7h4lse5ssVq+r5yBqHU6+4j1ffnsqipSvY+cdXcunphzHk/nF8s7pybTVQn126MuSy46hYvJyfnnsnZWWiY/vN+Nu1WbU6asDy15eCpJGE0mk7SbOAq83s3moWHwMcBkwDVgInA5jZYknXEwp3ANeZWVUX4tbfdyFjUQycc81sZhbLngP8GqgEVgEXmtlrcd5hwJ+BcmCYmQ2O6eMJdb0TJDUDngLeM7OLq9tPWcstrPkOx9TquFxhLXmr+jpqV3z26deHt9+eUKto2WLLHtZl0F+yWvaTWwa8nUsdbiEV9Ce3mb2Zw7J3AFV+smIVw5gq0vsnPV9NASrBnXMFUMPqgmLT4Os4nXOlT0CZD7HjnHOF4SVc55wrkDpsFlYwHnCdc8XP63Cdc64whLwDcuecKxQv4TrnXIF4Ha5zzhWC1+E651xhhL4USj/iesB1zpWEBhBvPeA650qD32nmnHOFULf94RaMB1znXNFL9Idb6jzgOudKQP76w61PHnCdcyWhAcRbD7jOuRKghnHRrPRvTnbONXiJdrjZPDJuSxomaYGkSUlpf5D0kaT3JT0uabOkeZdJmibpY0mHJKUPiGnTEmMxZuIB1zlXEvIVcIH7gQEpac8CvcxsV+AT4LK4z56EcRN3juvcJalcUjlwJ3Ao0BM4Li6blgdc51xJyNeovWb2ErA4JW2cmVXGyTcIo4EDHAmMMrNvzGw6YTDJvvExzcw+i8N5jYrLpuUB1zlXEnIo4baTNCHpcXqOuzoFeDo+7wQkD3o7K6ZVl56WXzRzzhW/3DqvqajpqL2SriCMFP73mqyfiQdc51zRCx2Q120rBUknAYcDB5qZxeTZwNZJi3WOaaRJr5ZXKTjnSkKZlNWjJiQNAC4BjjCzlUmzRgPHSmouqRvQHXgTeAvoLqmbpGaEC2ujM+3HS7jOuZKQrxsfJI0E+hPqemcBVxNaJTQHno31wG+Y2ZlmNlnSw8CHhKqGs81sTdzOOcBYoBwYZmaTM+3bA65zrugpj53XmNlxVSTfm2b5wcDgKtLHAGNy2bcHXOdcSWgAN5pVH3Al/QWw6uab2Xl1kiPnnKtCQ7i1N10Jd0LBcuGcc2mI0FKh1FUbcM1sePK0pJYpV++cc65gGkABN3OzMEl7SfoQ+ChO7ybprjrPmXPOJWR5l1mx95mbTTvcPwOHAIsAzOw9YL86zJNzzm0gX30p1KesWimY2cyUb441dZMd55zbkKDGNzUUk2wC7kxJewMmqSnwG2BK3WbLOefW1xBaKWRTpXAmcDahJ5w5wO5x2jnnCiLb6oRiLwRnLOGaWQVwfAHy4pxz1WoIVQrZtFLYVtK/JC2Mw1I8KWnbQmTOOecSlOWjmGVTpfAQ8DDQEdgKeAQYWZeZcs65VI2lWVhLM3vQzCrjYwTQoq4z5pxzCaGVQnaPYpauL4W28enTcUTKUYS+FX5Ojj3kOOdcrajuOyAvhHQXzd4mBNjEUZ6RNM+Io1o651whFHt1QTbS9aXQrZAZcc656iSqFEpdVneaSepFGHt9bd2tmT1QV5lyzrlUDaGEm02zsKuBv8TH/sAtwBF1nC/nnFtPvpqFSRoWm7hOSkprK+lZSVPj/zYxXZJulzRN0vuSeietMyguP1XSoGyOIZtWCkcDBwLzzOxkYDegdTYbd865fJCgvExZPbJwPzAgJe1S4Hkz6w48H6cBDiUMHNkdOB34a8iP2hLGQusH9AWuTgTpdLIJuKvM7DugUtKmwALWHx7YOefqXL7a4ZrZS8DilOQjgUQf4MOBgUnpD1jwBrCZpI6EHhSfNbPFZrYEeJYNg/gGsqnDnSBpM+BuQsuFFcDrWaznnHN5k0MVbjtJySPWDDWzoRnW6WBmc+PzeUCH+LwTMDNpuVkxrbr0tLLpS+Gs+PRvkp4BNjWz9zOt55xz+SKUS18KFWbWp6b7MjOTVO14jrWR7saH3unmmdnEusiQc85toO57ApsvqaOZzY1VBgti+mzWr0LtHNNmA/1T0sdn2km6Eu6taeYZcECmjZeCXXfcmnH/GVLf2XA5mLnIh9YrJasrv8vLduq4WdhoYBBwU/z/ZFL6OZJGES6QLYtBeSxwQ9KFsoPJ4mawdDc+7F+LzDvnXN4IKM9TwJU0klA6bSdpFqG1wU3Aw5JOBb4AjomLjwEOA6YBK4GTAcxssaTrgbficteZWeqFuA1kdeODc87Vt3zdaWZmx1Uz68AqljWqGXDBzIYBw3LZtwdc51xJaDS39jrnXH0Kw+eUfsTN5tZeSTpB0lVxehtJfes+a845t05D6A83mzvN7gL2AhL1HsuBO+ssR845V4VGMYgk0M/Mekt6B8DMlkhqVsf5cs65tQQ0KfZomoVsAu63ksoJbW+R1B7IT8M655zLUgOIt1kF3NuBx4EtJA0m9B52ZZ3myjnnkkg53dpbtLLpS+Hvkt4mtFETMNDMptR5zpxzLkkDiLeZA66kbQh3WPwrOc3MZtRlxpxzLlmxt0DIRjZVCv9m3WCSLYBuwMfAznWYL+ecW0uQbefiRS2bKoVdkqdjL2JnVbO4c87lXwm0sc1GzneamdlESf3qIjPOOVcdZTViWXHLpg73wqTJMqA3MKfOcuSccyka0zDpmyQ9ryTU6f6zbrLjnHNVa/ABN97wsImZ/bZA+XHOuSo1hM5r0g2x08TMKiXtU8gMOedcqjBMen3novbSHcKb8f+7kkZLOlHS/yQehcicc84llMW7zTI9siHpAkmTJU2SNFJSC0ndJP1X0jRJ/0j0GSOpeZyeFud3rfExZLFMC2ARYQyzw4GfxP/OOVcQiYtm+eieUVIn4Dygj5n1AsqBY4GbgSFmtj2wBDg1rnIqsCSmD4nL1Ui6gLtFbKEwCfgg/p8c/0+q6Q6dc64m8tw9YxNgI0lNgJbAXEKh8tE4fzgwMD4/Mk4T5x+oGlYop7toVg60giobv9XJmO3OOVc1UZZ9O9x2kiYkTQ81s6GJCTObLemPwAxgFTAOeBtYamaVcbFZQKf4vBMwM65bKWkZsDlQketRpAu4c83sulw36Jxz+SZyKr1WmFmfarcVhjY/ktBNwVLgEWBA7XKYnXQBt/TbYDjnGgZBk/w1xD0ImG5mCwEkPQbsA2yWaJ0FdAZmx+VnA1sDs2IVRGvCda2cpavD3WDIYOecqw+JEm6e6nBnAHtKahnrYg8EPgReJPT3DTAIeDI+Hx2nifNfiMOn56zaEq6ZLa7JBp1zri7kqwNyM/uvpEeBiYS7Z98BhhLuoh0l6X9j2r1xlXuBByVNAxYTWjTUiA+T7pwrCfm80czMrgauTkn+DNhgRHIz+xr4WT726wHXOVf0RHY3DRQ7D7jOueKn/FUp1CcPuM65ohfuNPOA65xzBVH64dYDrnOuRDSAAq4HXOdcKVDD7g/XOeeKhbdScM65AvKLZs45Vwhq4EPsOOdcsfAqBeecKyAv4TrnXIGUfrj1gOucKwECyr2E65xzhdEA4q0HXOdcKRBqAJUKHnCdcyXBS7jOOVcAoVlY6UfchtC0zTnX0GU5nlm2pWBJm0l6VNJHkqZI2ktSW0nPSpoa/7eJy0rS7ZKmSXpfUu+aHoYHXOdcSSiTsnpk6TbgGTPbEdgNmAJcCjxvZt2B5+M0wKFA9/g4HfhrjY+hpis651yhhA7Is3tk3JbUGtiPOEikma02s6XAkcDwuNhwYGB8fiTwgAVvEIZT71iT4/CA65wrCcryD2gnaULS4/SUTXUDFgL3SXpH0j2SNgY6mNncuMw8oEN83gmYmbT+rJiWM79o5pwrCTm0Uqgwsz5p5jcBegPnxiHTb2Nd9QEAZmaSrEYZzbBjV4LmzF/CBTc8RMXi5Ujwi5/sxSk/++Ha+UNHvcjgu0bzzujrabtZK8a9/AG33vs0ZWWivLyMq889ij123bYej6Bx+Wb1t5z827+x+ttK1qz5joN+sAtnn3gwZsZfho/l2Zffp6ysjGN+vCfHD9yXL5ev5KohjzBzziKaN2vKtRf+jO5dt6zvw6hXeWyHOwuYZWb/jdOPEgLufEkdzWxurDJYEOfPBrZOWr9zTMtZUQdcSfcDPwSWxaSTzOxdhV4sbgMOA1bG9ImSugJPmVmvuP6vgDOBg8xsSaHzX5fKy8u48qwj2GWHrVmx8msOP+1P7LvHDvTouiVz5i/h5bc+plOHNmuX3+f7PfjRvr2QxJRP53D21cN5YcRl9XgEjUuzpk245+bTablRc76tXMOgi+5i3z47MH3mAuYtXMqTd/+WsrIyFi1dAcDdo15gh2234s9XDWL6zAUMvvMJ7rkp9Zdx45Gow80HM5snaaakHczsY+BA4MP4GATcFP8/GVcZDZwjaRTQD1iWVPWQk3qtw000u8jgYjPbPT7ejWkZrxpKOhE4FzikoQVbgA7tWrPLDuFLt1XLFmzfpQPzF4bvpevueILLfv2T9X6Cbdyy+drellauWl3w/DZ2kmi5UXMAKivXUFm5Bkk8/NQbnHn8QZSVhY/i5pu1AuCzGQvou/v2AHTbegvmzF/MoiXL6yfzxSDLFgo5tFI4F/i7pPeB3YEbCIH2R5KmAgfFaYAxwGfANOBu4KyaHkZ9l3AnSHqDcLXwRTPLts5k7VVD4I3Ypm7tVUNJxxB+IhxoZhV5z3WRmTl3MZOnzmL3nl0Y9/IHbNmuNT2337BO/5mX3ueWof+mYskK7rv5V/WQ08ZtzZrvOPbc25gxZxHH/mRvdt1xG2bOXcQz/3mPF16bRJvWrbj010fQpVN7emzbkedf/YDv9+rGBx/PYO78pcyvWMbmbTap78OoN/m87SEW3qqq5z2wimUNODsf+63vVgo9gJHAOcCHki6XtFXKMoNjY+MhkprHtHRXDbsAdwAHm9m8qnYq6fTEFcxFFaUdj79a+Q1n/v4+rjr3KJqUl3HniOe48NRDq1x2wH678sKIy7h78Cnceu+YAufUlZeX8chdF/DsiCuY9PEMpn4+j9XfVtK8WRNG/eU3/HRAX6760yMAnHrM/ixf8TU/O2sII598jR2322ptKbgxClUKeS3h1ot6fQXNbI2ZPWVm/0NoF7ctMENS37jIZcCOwB5AW+B3WWx2ITADOCbNfoeaWR8z67N5u3a1Oob69G3lGs78/X0M/NH3OfSHu/LF7Apmzl3Moaf8gX2OuY65C5fx49NuZcGiL9dbr9/u2zFjziIWx/pCV1ibttqIPXbbjlcnfEyHdq05cJ9dADhwn15MnR7KCK02bsH1Fx3DI3ddwOCLf86SZV/Recu29ZnteqcsH8Ws3r8yJbWWdAahYro7cArwPoCZzY2Njb8B7gMSgTjdVcOVhItpZ0o6vgCHUC/MjEtuHsX2XTrwq5/3B2DH7bZi4ujrefXhq3j14avo2L41/77nIrbYfFM+n7WQRI3NBx/PZPW3a2jTeuN6PILGZfHSFXy5YhUAX3/zLa9PnEq3rdtzwN4789Z7nwIw4f3P6NIpFAC+XLGKb7+tBOCfz7xJ71260WrjFvWT+WLRACJuvdbhShoB7AU8AvzSzKamzE800RDhro9JcVaVVw1jKwXMbIGkAcB4SRVmNrYwR1Q4Ez6YzmNjJ7Djth059JQ/AHDxr37MAXv1rHL5p//zPv8c+xZNm5TTvHlT7rzmlw1iyJJSUbF4OVfe+g/WrPmO78w4ZL9d+WG/nnxv525cdvNIHnz8ZVq2aMY1FxwNwPQZC7jy1n8AsH2XLbk2pjdmxV5dkA1lf52qDnYuHQGMMbPKaua/ALQnfG+9C5xpZitiAL4DGEAo0Z5sZhOqaBa2G+EK41Fm9mZV+9i99/dt3H/eyO+BuTq1bOW39Z0Fl4P/OXhfPnhvYq2i5U67fM8eeHJ8Vsv23W6ztzPc+FBv6rWEa2ajM8w/oJr0Kq8amtnnQK+k6feo4S14zrkiU/oF3HpvFuaccxmF6tnSj7gecJ1zxS+Hvm6LmQdc51xJaADx1gOuc64UqEG0qvGA65wrCQ0g3nrAdc4VvxK4pyErHnCdc6WhAURcD7jOuZLgzcKcc65AvA7XOecKwdvhOudc4TSEKoV6757ROecyEaGEm80jq+1J5XGI9KfidDdJ/5U0TdI/JDWL6c3j9LQ4v2ttjsMDrnOuJOS5O9zfAFOSpm8GhpjZ9sAS4NSYfiqwJKYPicvVmAdc51xpyFPEldQZ+DFwT5wWcABhuHSA4YT+tyGMnzg8Pn8UOFC1uOXN63CdcyUhhw7I20makDQ91MyGJk3/GbgESIzIuTmwNKlf7uQxEteOn2hmlZKWxeVrNBiiB1znXEnIoVhZUV0H5JIOBxaY2duS+uclYznwgOucKw35aaSwD3CEpMOAFsCmwG3AZpKaxFJu8hiJifETZ0lqArQGFtV0516H65wreokOyLP5S8fMLjOzzmbWFTgWeMHMjgdeBBIDxw0CnozPR8dp4vwXrBbjknnAdc4VvyybhNXi5ojfARdKmkaoo703pt8LbB7TLwQurc1heJWCc64k5Pu2BzMbD4yPzz8D+laxzNfAz/K1Tw+4zrkS4B2QO+dcwTSAeOsB1zlX/LwDcuecK6QGEHE94DrnSkJD6C3MA65zriR4Ha5zzhWCoMwDrnPOFUrpR1wPuM65opfogLzUecB1zpWEBhBvPeA650qDl3Cdc65A/NZe55wrkNIPtx5wnXMloJZdLxYND7jOuZLgd5o551yhlH689YDrnCsNDSDe+hA7zrlSIMqU3SPjlqStJb0o6UNJkyX9Jqa3lfSspKnxf5uYLkm3S5om6X1JvWt6FB5wnXNFL3GnWZ7GNKsELjKznsCewNmSehLGK3vezLoDz7Nu/LJDge7xcTrw15oehwdc51yjYmZzzWxifL4cmAJ0Ao4EhsfFhgMD4/MjgQcseIMwpHrHmuzb63CdcyUhh2Zh7SRNSJoeamZDq96mugLfA/4LdDCzuXHWPKBDfN4JmJm02qyYNpccecB1zpWEHJqFVZhZn4zbk1oB/wTON7Mvk+9kMzOTZDXKaBpepeCcK35Z1t9mWwqW1JQQbP9uZo/F5PmJqoL4f0FMnw1snbR655iWMw+4zrmil8+LZgpF2XuBKWb2p6RZo4FB8fkg4Mmk9F/G1gp7AsuSqh5y4lUKzrmSkMc7zfYBTgQ+kPRuTLscuAl4WNKpwBfAMXHeGOAwYBqwEji5pjv2gOucKwn56kvBzF6h+vsoDqxieQPOzse+PeA650pCQ7jTzAOuc640NICI6wHXOVf0BFndtlvsFKonGi9JCwkV5A1NO6CivjPhctJQX7MuZta+NhuQ9Azh/GSjwswG1GZ/daXRB9yGStKEbBp/u+Lhr1nD5+1wnXOuQDzgOudcgXjAbbiq7KzDFTV/zRo4r8N1zrkC8RKuc84ViAdc55wrEA+4DZCk/pKWSXo3Pq5KmjdA0sdxfKZLk9LHS+oTn3eL4zodUh/5bwwk3S9petJrtHtMr3L8LEldJU1KWv9Xkt5OjLvlSoPfaVYiJDUDmprZV1mu8rKZHZ6yjXLgTuBHhF7r35I02sw+TFqmM/AMYcynsfnJfeMjqY2ZLcmw2MVm9mhKWvL4Wf0I42f1S9n2icC5wAFZ7MMVES/hFjlJO0m6FfgY6FHLzfUFppnZZ2a2GhhFGK8poSMwDrjCzEbXcl+N3QRJf5d0gJTTPalpx8+SdAxhcMODzawh3pXWoHnALUKSNpZ0sqRXgLuBD4FdzeydOH9I0k/R5MelSZvZS9J7kp6WtHNMq25spoThwB1VlLpc7noAI4FzgA8lXS5pq5RlBsdqgyGSmse0dK9RF+AOQrCdV4d5d3XEqxSK01zgfeA0M/sodaaZXZBh/YmE+9dXSDoMeILwEzWT54ATJN1vZitzzLNLYmZrgKeApyS1B24EZkja28zeBC4jDFTYjND+9nfAdRk2uxBYTOgYe0hd5d3VHS/hFqejCWMmPSbpKkldkmdmKuGa2ZdmtiI+HwM0ldSOzGMz3QK8BTwiyb+Ma0lSa0lnEIZo6Q6cQvgiTQzVbWb2DXAfoboH0r9GKwkjD5wp6fgCHILLM/9QFSEzGweMk7Q5cALwpKQKQon380wlXElbAvPjyKN9CV+si4ClQHdJ3Qgf4mOBX6Ssfj7wEHCvpJPM74ypEUkjgL2AR4BfmtnUlPkdzWxurN8dCCRaIIwGzpE0inCxbFlcriuAmS2QNAAYL6nCL2yWFg+4RczMFgG3AbfFwLkmy1WPBn4tqRJYBRwbA2elpHOAsUA5MMzMJqfs0yQNIvwcvgW4OD9H0+g8DJxkZpXVzP97rGoQ8C5wZkzPOH6WmU2XdAQwRtJRsYrClQC/tdc55wrE63Cdc65APOA651yBeMB1zrkC8YDrnHMF4gHXOecKxAOuS0vSmnhTxSRJj0hqWYtt3S/p6Pj8Hkk90yzbX9LeNdjH5/Emj6zSU5ZZkeO+rpH021zz6BovD7guk1VmtruZ9QJWs669KAA1vSPNzE5L7qWsCv2BnAOuc8XMA67LxcvA9rH0+bKk0YSOWcol/UHSW7EzljNgbd+udyj0v/scsEViQ1q//90BkibGznaej3dVnQlcEEvXP5DUXtI/4z7ekrRPXHdzSeMkTZZ0D+FGgrQkPaHQl+xkSaenzBsS05+PNyYgaTtJz8R1Xpa0Y17Opmt0/E4zl5VYkj2U0FcuQG+gV7zr6XTCLah7xF6vXpU0DvgesAPQE+hA6PVsWMp22xN6RNsvbqutmS2W9DdghZn9MS73EDDEzF6RtA3hbrmdgKuBV8zsOkk/Bk7N4nBOifvYiNAn8D/jXX0bAxPM7AKFTtuvJvT2NRQ408ymSuoH3AUcUIPT6Bo5D7guk40kvRufvwzcS/ip/6aZTY/pBwO7JupngdaEzlr2A0bGnrPmSHqhiu3vCbyU2JaZLa4mHwcBPbWua9lNJbWK+/ifuO6/JWXTIfd5ko6Kz7eOeV0EfAf8I6aPIHQe1Coe7yNJ+26OczXgAddlssrMdk9OiIEneeQJAeemdqSi0DVkvpQBe5rZ11XkJWuS+hOC915mtlLSeKBFNYtb3O/S1HPgXE14Ha7Lh7GEznKaAkjqIWlj4CXg57GOtyOwfxXrvgHsF3swQ1LbmL4c2CRpuXGEYWWIy+0en75E7PFM0qFApjG+WgNLYrDdkVDCTigjdPxD3OYrZvYlMF3Sz+I+JGm3DPtwrkoecF0+3EOon52oMNDh/xF+PT0OTI3zHgBeT13RzBYCpxN+vr/Hup/0/wKOSlw0A84D+sSLch+yrrXEtYSAPZlQtTAjQ16fAZpImgLcRAj4CV8BfeMxHMC6DsGPB06N+ZvM+sMSOZc17y3MOecKxEu4zjlXIB5wnXOuQDzgOudcgXjAdc65AvGA65xzBeIB1znnCsQDrnPOFcj/AzVis3M2dIrLAAAAAElFTkSuQmCC\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": 22,
   "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": [
    "## Running KernelSHAP in sequential mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A background dataset is selected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "Groups are specified by creating a list where each sublist contains the column indices that a given variable occupies in the preprocessed feature matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_groups(num_feats_names: List[str], cat_feats_names: List[str], feat_enc_dim: List[int]) -> Tuple[List[str], List[List[int]]]:\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()\n",
    "\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": [
    "Initialise and run the explainer sequentially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_fcn = classifier.predict_proba\n",
    "seq_lr_explainer = KernelShap(pred_fcn, link='logit', feature_names=perm_feat_names)\n",
    "seq_lr_explainer.fit(X_train_proc_d[background_data, :], group_names=group_names, groups=groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_runs = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_explanations, s_times = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for run in range(n_runs):\n",
    "    t_start = timer()\n",
    "    explanation = seq_lr_explainer.explain(sparse2ndarray(X_explain_proc))\n",
    "    t_elapsed = timer() - t_start\n",
    "    s_times.append(t_elapsed)\n",
    "    s_explanations.append(explanation.shap_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running KernelSHAP in distributed mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only change needed to distribute the computation is to pass a dictionary containing the number of (physical) CPUs available to distribute the computation to the `KernelShap` constructor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distrib_opts_factory(n_cpus: int) -> Dict[str, int]:\n",
    "    return {'n_cpus': n_cpus}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu_range = range(2, 5)\n",
    "distrib_avg_times = dict(zip(cpu_range, [0.0]*len(cpu_range)))\n",
    "distrib_min_times = dict(zip(cpu_range, [0.0]*len(cpu_range))) \n",
    "distrib_max_times = dict(zip(cpu_range, [0.0]*len(cpu_range))) \n",
    "d_explanations = defaultdict(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n_cpu in cpu_range:\n",
    "    opts = distrib_opts_factory(n_cpu)\n",
    "    distrib_lr_explainer = KernelShap(pred_fcn, link='logit', feature_names=perm_feat_names, distributed_opts=opts)\n",
    "    distrib_lr_explainer.fit(X_train_proc_d[background_data, :], group_names=group_names, groups=groups)\n",
    "    raw_times = []\n",
    "    for _ in range(n_runs):\n",
    "        t_start = timer()\n",
    "        d_explanations[n_cpu].append(distrib_lr_explainer.explain(sparse2ndarray(X_explain_proc), silent=True).shap_values)\n",
    "        t_elapsed = timer() - t_start\n",
    "        raw_times.append(t_elapsed)\n",
    "    distrib_avg_times[n_cpu] = np.round(np.mean(raw_times), 3)\n",
    "    distrib_min_times[n_cpu] = np.round(np.min(raw_times), 3)\n",
    "    distrib_max_times[n_cpu] = np.round(np.max(raw_times), 3)\n",
    "    ray.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distributed average times for 3 runs (n_cpus: avg_time):\n",
      "{2: 57.197, 3: 41.728, 4: 36.751}\n",
      "\n",
      "Sequential average time for 3 runs:\n",
      "119.656 s\n"
     ]
    }
   ],
   "source": [
    "print(f\"Distributed average times for {n_runs} runs (n_cpus: avg_time):\")\n",
    "print(distrib_avg_times)\n",
    "print(\"\")\n",
    "print(f\"Sequential average time for {n_runs} runs:\")\n",
    "print(np.round(np.mean(s_times), 3), \"s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running KernelSHAP in a distributed fashion improves the runtime as the results above show. However, the results above should not be interpreted as performance measurements since they were not run in a controlled environment. See our [blog post](https://www.seldon.io/how-seldons-alibi-and-ray-make-model-explainability-easy-and-scalable/) for a more thorough analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanations comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls = 0  # class of prediction explained\n",
    "run = 1  # which run to compare the result for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sequential\n",
    "shap.summary_plot(s_explanations[run][cls], perm_X_explain, perm_feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distributed\n",
    "n_cpu = 3\n",
    "shap.summary_plot(d_explanations[n_cpu][run][cls], perm_X_explain, perm_feat_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the results above one sees that the running the algorithm across multiple cores gave identical results, indicating its correctness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
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    "This example showed that batches of explanations can be explained much faster by simply passing ``distributed_opts={'n_cpus': k}`` to the ``KernelShap`` constructor (here ``k`` is the number of physical cores available). The significant runtime reduction makes it possible to explain larger datasets faster and combine shap values estimated with KernelSHAP into global explanations or use larger background datasets."
   ]
  }
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