examples/kernel_shap_wine_lr.ipynb

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    "# Kernel SHAP explanation for multinomial logistic regression models "
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    "## Introduction"
   ]
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    "In a previous [example](kernel_shap_wine_intro.ipynb), we showed how the KernelSHAP algorithm can be aplied to explain the output of an arbitrary classification model so long the model outputs probabilities or operates in margin space. We also showcased the powerful visualisations in the `shap` library that can be used for model investigation. In this example we focus on understanding, in a simple setting, how conclusions drawn from the analysis of the KernelShap output relate to conclusions drawn from interpreting the model directly. To make this possible, we fit a logistic regression model on the Wine dataset."
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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",
    "\n",
    "from alibi.explainers import KernelShap\n",
    "from scipy.special import logit\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation: load and split Wine dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = wine.data\n",
    "target = wine.target\n",
    "target_names = wine.target_names\n",
    "feature_names  = wine.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data into testing and training sets and normalize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training records: 142\n",
      "Testing records: 36\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, \n",
    "                                                    target, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=0,\n",
    "                                                   )\n",
    "print(\"Training records: {}\".format(X_train.shape[0]))\n",
    "print(\"Testing records: {}\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_norm = scaler.transform(X_train)\n",
    "X_test_norm = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting a multinomial logistic regression classifier to the Wine dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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=100,\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(multi_class='multinomial',\n",
    "                                random_state=0,\n",
    "                                )\n",
    "classifier.fit(X_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion matrix for the logistic regression classifier')"
      ]
     },
     "execution_count": 9,
     "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_norm, \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": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the logistic regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='src_2'></a>\n",
    "One way to arrive at the multinomial logistic regression model is to consider modelling a categorical response \n",
    "variable $y \\sim \\text{Cat} (y| \\beta x)$ where $\\beta$ is $K \\times D$ matrix of distribution parameters with $K$ being the number of classes and $D$ the feature dimensionality. Because the probability of outcome $k$ being observed given $x$, $p_{k} = p(y=k|x, \\mathbf{\\beta})$, is bounded by $[0, 1]$, the logistic regression assumes that a linear relationship exists between the *logit* transformation of the output and the input. This can be formalised as follows:\n",
    "\n",
    "\\begin{equation}\n",
    "\\log \\left( \\frac{p_{k}}{1 - p_{k}} \\right) = \\beta_{0,k} + \\beta_{1,k} x_{1}  + \\beta_{2,k} x_{2} + \\cdots + \\beta_{D,k} x_{D} = \\mathbf{\\beta}_k \\cdot x\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "The RHS is a function of the expected value of the categorical distribution (sometimes referred to as a _link function_ in the literature). The coefficients $\\beta$ of the linear relations used to fit the logit transformation are estimated jointly given a set of training examples $\\mathcal{D}=  \\{(x_i, y_i)\\}_{i=1}^N$. \n",
    "\n",
    "For each class, the vector of coefficients $\\mathbb{\\beta}_k$ can be used to interpret the model *globally*; in the absence of interaction terms, the coefficient of a predictor (i.e.,  independent variable) represents the *change in log odds* when the predictor changes by one unit while all other variables are kept at fixed values. Equivalently, the exponentiated coefficient is equivalent to a change in odds. Since the transformation from odds to outcome probabilities is monotonic, a change in odds also implies a change in the outcome probability in the same direction. Thus, the magnitudes of the feature coefficients measure the effect of a predictor on the output and thus one can globally interpret the logistic regression model.\n",
    "\n",
    "However, the log odds ratios and odds ratios are known to be sensitive to *unobserved heterogenity*, that is, omission of a variable with good explanatory power from a logistic regression model *assumed true*. While we will not be concerned directly with this issue and refer the interested reader to [[2]](#References), we will be using the *estimated percentage unit effect* (or the *marginal effect*)\n",
    "\n",
    "$$\n",
    "\\beta_{j,k} \\times p_{i,k}(1 - p_{i, k})\n",
    "$$\n",
    "\n",
    "as a means of estimating the effect of a predictor $j$ on individual $i$ ($x_{i, j})$ with respect to predicting the $k^{th}$ class and thus *locally* interpret the model. The average marginal effect is more robust measure of effects in situations where effects are compared across different groups or models. Consider a logistic model where an independent variable $x_1$ is used to predict an outcome and a logistic model where $x_2$, known to be uncorrelated with $x_1$, is also included. Since the two models assign different probabilities to the different outcomes and since the distribution of the outcome across values of $x_1$ should be the same across the two models (due to the independence assumption), we expected the second model will scale the coeffcient of $\\beta_1$. Hence, the log-odds and odds ratios are not robust to unobserved heterogeneity so directly comparing the two across models or groups can be misleading. As discussed in [[2]](#References), the marginal effect is generally robust to the effect.\n",
    "\n",
    "\n",
    "The average marginal effect (AME) of a predictor\n",
    "\n",
    "$$\n",
    "\\frac{1}{n} \\sum_{i=1}^{n}\\beta_{j,k} \\times p_{i,k} (1 - p_{i,k})\n",
    "$$\n",
    "\n",
    "is equivalent to simply using $\\beta_{j,k}$ to *globally* explain the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def issorted(arr, reverse=False):\n",
    "    \"\"\"\n",
    "    Checks if a numpy array is sorted.\n",
    "    \"\"\"\n",
    "    \n",
    "    if reverse:\n",
    "        return np.all(arr[::-1][:-1] <=arr[::-1][1:])\n",
    "    \n",
    "    return np.all(arr[:-1] <= arr[1:])\n",
    "\n",
    "def get_importance(class_idx, beta, feature_names, intercepts=None):\n",
    "    \"\"\"\n",
    "    Retrive and sort abs magnitude of coefficients from model.\n",
    "    \"\"\"\n",
    "    \n",
    "    # sort the absolute value of model coef from largest to smallest\n",
    "    srt_beta_k = np.argsort(np.abs(beta[class_idx, :]))[::-1]\n",
    "    feat_names = [feature_names[idx] for idx in srt_beta_k]\n",
    "    feat_imp = beta[class_idx, srt_beta_k]\n",
    "    \n",
    "    # include bias among feat importances \n",
    "    if intercepts is not None: \n",
    "        intercept = intercepts[class_idx]\n",
    "        bias_idx = len(feat_imp) - (np.searchsorted(np.abs(feat_imp)[::-1], np.abs(intercept)))\n",
    "#         bias_idx = np.searchsorted(np.abs(feat_imp)[::-1], np.abs(intercept)) + 1\n",
    "        feat_imp = np.insert(feat_imp, bias_idx, intercept.item(), )\n",
    "        intercept_idx = np.where(feat_imp == intercept)[0][0]\n",
    "        feat_names.insert(intercept_idx, 'bias')\n",
    "\n",
    "    return feat_imp, feat_names\n",
    "\n",
    "def plot_importance(feat_imp, feat_names, **kwargs):\n",
    "    \"\"\"\n",
    "    Create a horizontal barchart of feature effects, sorted by their magnitude.\n",
    "    \"\"\"\n",
    "    \n",
    "    left_x, right_x = kwargs.get(\"left_x\"), kwargs.get(\"right_x\")\n",
    "    eps_factor = kwargs.get(\"eps_factor\", 4.5)\n",
    "    xlabel = kwargs.get(\"xlabel\", None)\n",
    "    ylabel = kwargs.get(\"ylabel\", None)\n",
    "    labels_fontsize = kwargs.get(\"labels_fontsize\", 15)\n",
    "    tick_labels_fontsize = kwargs.get(\"tick_labels_fontsize\", 15)\n",
    "    \n",
    "    # plot\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    y_pos = np.arange(len(feat_imp))\n",
    "    ax.barh(y_pos, feat_imp)\n",
    "    \n",
    "    # set lables\n",
    "    ax.set_yticks(y_pos)\n",
    "    ax.set_yticklabels(feat_names, fontsize=tick_labels_fontsize)\n",
    "    ax.invert_yaxis()                  # labels read top-to-bottom\n",
    "    ax.set_xlabel(xlabel, fontsize=labels_fontsize)\n",
    "    ax.set_ylabel(ylabel, fontsize=labels_fontsize)\n",
    "    ax.set_xlim(left=left_x, right=right_x)\n",
    "    \n",
    "    # add text\n",
    "    for i, v in enumerate(feat_imp):\n",
    "        eps = 0.03\n",
    "        if v < 0:\n",
    "            eps = -eps_factor*eps\n",
    "        ax.text(v + eps, i + .25, str(round(v, 3)))\n",
    "    \n",
    "    return ax, fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now retrieve the estimated coefficients, and plot them sorted by their maginitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = classifier.coef_\n",
    "intercepts = classifier.intercept_\n",
    "all_coefs = np.concatenate((beta, intercepts[:, None]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_idx = 0\n",
    "feat_imp, feat_names = get_importance(class_idx, \n",
    "                                      beta, \n",
    "                                      feature_names,\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, class_0_fig = plot_importance(feat_imp, \n",
    "                                 feat_names, \n",
    "                                 left_x=-1.,\n",
    "                                 right_x=1.25,\n",
    "                                 xlabel = f\"Feature effects (class {class_idx})\",\n",
    "                                 ylabel = \"Features\"\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that these effects are with respect to the model bias (displayed below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.24013981,  0.66712652, -0.90726633])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows that features such as `proline`, `flavanoids`, `od280/od315_of_diluted_wines`, `alcohol` increase the odds of *any* sample being classified as `class_0` whereas the `alcalinity_of_ash` decreases them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp, feat_names = get_importance(1,            # class_idx \n",
    "                                      beta, \n",
    "                                      feature_names,\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below shows that, however, `alcalinity_of_ash` increases the odds of a wine being in `class_1`. Predictors such as `proline`, `alcohol` or `ash`, which increase the odds of predicting a wine as a member of `class_0`, decrease the odds of predicting it as a member of `class_1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, class_1_fig = plot_importance(feat_imp, \n",
    "                                 feat_names, \n",
    "                                 left_x=-1.5,\n",
    "                                 right_x=1,\n",
    "                                 eps_factor = 5,  # controls text distance from end of bar for negative examples\n",
    "                                 xlabel = \"Feature effects (class {})\".format(1),\n",
    "                                 ylabel = \"Features\"\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp, feat_names = get_importance(2, # class_idx\n",
    "                                      beta, \n",
    "                                      feature_names,\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, for `class_2`, the `color_intensity`, `ash` are the features that increase the `class_2` odds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, class_2_fig = plot_importance(feat_imp, \n",
    "                                 feat_names, \n",
    "                                 left_x=-1.25,\n",
    "                                 right_x=1,\n",
    "                                 xlabel = \"Feature effects (class {})\".format(2),\n",
    "                                 ylabel = \"Features\"\n",
    "#                                   eps_factor = 5.\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply KernelSHAP to explain the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='src_1'></a>\n",
    "Note that the *local accuracy* property of SHAP (eq. (5) in [[1]](#References))  requires\n",
    "\n",
    "$$\n",
    "f(x) = g(x') = \\phi_0 + \\sum_{j=1}^D \\phi_j x_j'.\n",
    "$$\n",
    "\n",
    "Hence, sum of the feature importances, $\\phi_j$, should be equal to the model output, $f(x)$. By passing `link='logit'` to the explainer, we ensure that $\\phi_0$, the *base value* (see _**Local explanation**_ section [here](kernel_shap_wine_intro.ipynb)) will be calculated in the correct units. Note that here $x' \\in \\mathbb{R}^D$ represents a *simplified input* for which the shap value is computed. A simple example of a simplified input in the image domain, justified by the dimensionality of the input space, is a *superpixel mask*: we formulate the task of explaining the outcome of an image prediction task as determining the effects of each superpixel in a segmenented image upon the outcome. The interested reader is referred to [[1]](#References) for more details about simplified inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 142 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={\n",
       "    'name': 'KernelShap',\n",
       "    'type': ['blackbox'],\n",
       "    'explanations': ['local', 'global'],\n",
       "    'params': {\n",
       "        'groups': None,\n",
       "        'group_names': None,\n",
       "        'weights': None,\n",
       "        'summarise_background': False\n",
       "    }\n",
       "})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_fcn = classifier.predict_proba\n",
    "lr_explainer = KernelShap(pred_fcn, link='logit')\n",
    "lr_explainer.fit(X_train_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-6.66133815e-16  0.00000000e+00  4.44089210e-16]\n"
     ]
    }
   ],
   "source": [
    "# passing the logit link function to the explainer ensures the units are consistent ...\n",
    "mean_scores_train = logit(pred_fcn(X_train_norm).mean(axis=0))\n",
    "print(mean_scores_train - lr_explainer.expected_value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6c2306462cf4bf396379bcf7708d9b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=36), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "lr_explanation = lr_explainer.explain(X_test_norm, l1_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the dimensionality of the feature space is relatively small, we opted not to regularise the regression that computes the Shapley values. For more information about the regularisation options available for higher dimensional data see the introductory example [here](kernel_shap_wine_intro.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Locally explaining multi-output models with KernelShap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaining the logitstic regression model globally with KernelSHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise the impact of the features on the decision scores associated with class `class_idx`, we can use a summary plot. In this plot, the features are sorted by the sum of their SHAP values magnitudes across all instances in `X_test_norm`. Therefore, the features with the highest impact on the decision score for class `class_idx` are displayed at the top of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(lr_explanation.shap_values[class_idx], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the logistic regression model uses a linear predictor function, the exact shap values for each class $k$ can be computed exactly according to ([[1]](#References))\n",
    "$$\n",
    "\\phi_{i,j}(f, x_i) = \\beta_{j,k}(x_{i,j} - \\mathbb{E}_{\\mathcal{D}}[x_{j}]).\n",
    "$$\n",
    "\n",
    "Here we introduced an additional index $i$ to emphasize that we compute a shap value for *each predictor* and *each instance* in a set to be explained.This allows us to check the accuracy of the SHAP estimate. Note that we have already applied the normalisation so the expectation is not subtracted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_shap = beta[:, None, :]*X_test_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_name = 'alcohol'\n",
    "feat_idx = feature_names.index(feat_name)\n",
    "x = np.linspace(-3, 4, 1000)\n",
    "plt.scatter(exact_shap[class_idx,...][:, feat_idx], lr_explanation.shap_values[class_idx][:, feat_idx])\n",
    "plt.plot(x, x, linestyle='dashed', color='red')\n",
    "plt.xlabel(r'Exact $\\phi_j$', fontsize=18)\n",
    "plt.ylabel(r'Estimated $\\phi_j$', fontsize=18)\n",
    "plt.title(fr\"Comparison of estimated and exact shap values for feature '{feat_name}'\")\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below shows that the exact shap values and the estimate values give rise to similar ranking of the features, and only the order of the `flavanoids` and `alcohol`features is swapped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(exact_shap[class_idx, ...], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An simlar plot can be create for the logistic regression model by plotting the marginal effects. Note that the plot labelling cannot be changed, so the x axis is incorrectly labeled as `SHAP value` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = classifier.predict_proba(X_test_norm)\n",
    "prb = p * (1. - p) \n",
    "marg_effects = all_coefs[:, None, :] * prb.T[..., None]\n",
    "assert (all_coefs[0, 0] * prb[:, 0] - marg_effects[0, :, 0]).sum() == 0.0\n",
    "avg_marg_effects = np.mean(marg_effects, axis=1) # nb: ranking of the feature coefs should be preserved\n",
    "mask = np.ones_like(X_test_norm)  # the effect (postive vs negative) on the output depend on the sign of the input\n",
    "mask[X_test_norm < 0] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(marg_effects[class_idx, :, :-1]*mask, X_test_norm, feature_names)  # exclude bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the ranking of the marginal effects is the same as that provided the ranking the raw coefficients (see below). However, this effect measure allows us to assess the effects at instance level. Note that both the approximate computation and the exact method yield the same group of features as the most important, although their rankings are not identical. It is important to note that the exact effects ranking and absolute values is a function of the entire data (due to the dependence of the model coefficients) whereas the approximate computation is *local*: the explanation model is fitted locally around each instance. We also notice that the approximate and exact shap value computation both identify the same relationship between the feature value and the effect on the evidence of a sample belonging to `class_idx`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_0_fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the 6 most important features for this classification in `class_0`, we see that both the `KernelSHAP` method and the logistic regression rank the `proline` feature as the one with the most significant effect. While the order of the subsequent 5 features is permuted, the effects of these features are also very similar so, in effect, similar conclusions would be drawn from analysing either output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#src_1) Lundberg, S.M. and Lee, S.I., 2017. A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774).\n",
    "\n",
    "[[2]](#src_2) Mood, C., 2017. \"Logistic regression: Uncovering unobserved heterogeneity.\""
   ]
  }
 ],
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   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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