examples/interventional_tree_shap_adult_xgb.ipynb

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   "source": [
    "# Explaining Tree Models with Interventional Feature Perturbation Tree SHAP"
   ]
  },
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   "source": [
    "<div class=\"alert alert-info\">\n",
    "To enable SHAP support, you may need to run\n",
    "    \n",
    "```bash\n",
    "pip install alibi[shap]\n",
    "```\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to apply interventional Tree SHAP to compute shap values exactly for an `xgboost` model fitted to the `Adult` dataset (binary classification task). Furthermore, the shap values computed by Kernel SHAP, an approximate feature attribution method, are shown to converge to the interventional Tree SHAP contributions given a sufficiently large number of model evaluations."
   ]
  },
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   "execution_count": 52,
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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 json\n",
    "import pickle\n",
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "\n",
    "from alibi.datasets import fetch_adult\n",
    "from alibi.explainers import KernelShap, TreeShap\n",
    "from collections import defaultdict, Counter\n",
    "from functools import partial\n",
    "from itertools import product, zip_longest\n",
    "\n",
    "from scipy.special import expit\n",
    "invlogit=expit\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.utils import resample\n",
    "\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "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 features, targets, feature names and a mapping of categorical variables to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'target_names', 'category_map'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult = fetch_adult()\n",
    "adult.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 the utility function `gen_category_map` imported from `alibi.utils.data`  to create the 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": [
    "`xgboost` wraps arrays using  `DMatrix` objects, optimised for both memory efficiency and training speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wrap(arr):\n",
    "    return np.ascontiguousarray(arr)\n",
    "\n",
    "dtrain = xgb.DMatrix(\n",
    "    wrap(X_train), \n",
    "    label=wrap(y_train), \n",
    "    feature_names=feature_names, \n",
    ")\n",
    "\n",
    "dtest = xgb.DMatrix(wrap(X_test), label=wrap(y_test), feature_names=feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, a matrix that contains the raw string values for categorical variables (used for display) is created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _decode_data(X, feature_names, category_map):\n",
    "    \"\"\"\n",
    "    Given an encoded data matrix `X` returns a matrix where the \n",
    "    categorical levels have been replaced by human readable categories.\n",
    "    \"\"\"\n",
    "    \n",
    "    X_new = np.zeros(X.shape, dtype=object)\n",
    "    for idx, name in enumerate(feature_names):\n",
    "        categories = category_map.get(idx, None)\n",
    "        if categories:\n",
    "            for j, category in enumerate(categories):\n",
    "                encoded_vals = X[:, idx] == j\n",
    "                X_new[encoded_vals, idx] = category\n",
    "        else:\n",
    "            X_new[:, idx] = X[:, idx]\n",
    "        \n",
    "    return X_new\n",
    "            \n",
    "decode_data = partial(_decode_data,\n",
    "                     feature_names=feature_names,\n",
    "                     category_map=category_map)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_display = decode_data(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[52, 'Private', 'Associates', ..., 0, 60, 'United-States'],\n",
       "       [21, 'Private', 'High School grad', ..., 0, 20, 'United-States'],\n",
       "       [43, 'Private', 'Dropout', ..., 0, 50, 'United-States'],\n",
       "       ...,\n",
       "       [23, 'Private', 'High School grad', ..., 0, 40, 'United-States'],\n",
       "       [45, 'Local-gov', 'Doctorate', ..., 0, 45, 'United-States'],\n",
       "       [25, 'Private', 'High School grad', ..., 0, 48, 'United-States']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model fitted in the `xgboost` fitting [example](xgboost_model_fitting_adult.ipynb) will be explained. The confusion matrix of this model is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_conf_matrix(y_test, y_pred, class_names):\n",
    "    \"\"\"\n",
    "    Plots confusion matrix. Taken from:\n",
    "    http://queirozf.com/entries/visualizing-machine-learning-models-examples-with-scikit-learn-and-matplotlib\n",
    "    \"\"\"\n",
    "    \n",
    "    matrix = confusion_matrix(y_test,y_pred)\n",
    "\n",
    "\n",
    "    # place labels at the top\n",
    "    plt.gca().xaxis.tick_top()\n",
    "    plt.gca().xaxis.set_label_position('top')\n",
    "\n",
    "    # plot the matrix per se\n",
    "    plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "    # plot colorbar to the right\n",
    "    plt.colorbar()\n",
    "\n",
    "    fmt = 'd'\n",
    "\n",
    "    # write the number of predictions in each bucket\n",
    "    thresh = matrix.max() / 2.\n",
    "    for i, j in product(range(matrix.shape[0]), range(matrix.shape[1])):\n",
    "\n",
    "        # if background is dark, use a white number, and vice-versa\n",
    "        plt.text(j, i, format(matrix[i, j], fmt),\n",
    "             horizontalalignment=\"center\",\n",
    "             color=\"white\" if matrix[i, j] > thresh else \"black\")\n",
    "\n",
    "    tick_marks = np.arange(len(class_names))\n",
    "    plt.xticks(tick_marks, class_names, rotation=45)\n",
    "    plt.yticks(tick_marks, class_names)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label',size=14)\n",
    "    plt.xlabel('Predicted label',size=14)\n",
    "    plt.show()\n",
    "\n",
    "def predict(xgb_model, dataset, proba=False, threshold=0.5):\n",
    "    \"\"\"\n",
    "    Predicts labels given a xgboost model that outputs raw logits. \n",
    "    \"\"\"\n",
    "    \n",
    "    y_pred = model.predict(dataset)  # raw logits are predicted\n",
    "    y_pred_proba = invlogit(y_pred) \n",
    "    if proba:\n",
    "        return y_pred_proba\n",
    "    y_pred_class = np.zeros_like(y_pred)\n",
    "    y_pred_class[y_pred_proba >= threshold] = 1  # assign a label \n",
    "    \n",
    "    return y_pred_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgb.Booster()  \n",
    "model.load_model('adult_xgb.mdl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = predict(model, dtrain)\n",
    "y_pred_test = predict(model, dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_conf_matrix(y_test, y_pred_test, target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  87.75  %.\n",
      "Test  accuracy:  86.6797%.\n"
     ]
    }
   ],
   "source": [
    "print(f'Train accuracy:  {round(100*accuracy_score(y_train, y_pred_train), 4)}  %.')\n",
    "print(f'Test  accuracy:  {round(100*accuracy_score(y_test, y_pred_test), 4)}%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explaining xgboost with interventional Tree SHAP: global knowledge from local explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that the goal of shap values computation for an instance $x$ is to attribute the difference $f(x) - \\mathbb{E}_{\\mathcal{D}}[f(x)]$ to $M$ input features. Here $\\mathcal{D}$ represents the background data. Unlike the [path-dependent perturbation](path_dependent_tree_shap_adult_xgb.ipynb) algorithm which exploits the tree structure and cover information (derived from the training data) to obviate the need for a background dataset, the interventional perturbation algorithm follows a similar idea to [Kernel SHAP](https://docs.seldon.io/projects/alibi/en/latest/methods/KernelSHAP.html) and uses a background dataset to compute the expected value as the average of the leaves where the background samples fall plus the baseline model offset <sup>[(1)](#Footnotes) </sup> . As explained in the algorithm [overview](https://docs.seldon.io/projects/alibi/en/latest/methods/TreeSHAP.html), this allows explaining nonlinear transformations of the model output, so this method can be used to explain loss function fluctuations.\n",
    "\n",
    "As discussed in [[1]](#References) and detailed in the [overview](https://docs.seldon.io/projects/alibi/en/latest/methods/TreeSHAP.html), this perturbation method enforces the conditional independence $x_{S} \\perp x_{\\bar{S}}$ where $\\bar{S}$ is a subset of missing features. This section shows that this method is consistent with the path-dependent perturbation method, in the sense that it leads to very similar analysis conclusions assuming an appropriate background dataset is used.\n",
    "<a id='source_3'></a>\n",
    "<a id='f_3'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the background dataset contains $30, 000$ examples, the next part of the example is **long running**. In practice, sufficient accuracy can be achieved using a couple of hundred samples (the library authors recommend anywhere between 200 and 1000 examples), provided that the samples chosen represent the underlying distribution accurately (i.e., they cover the entire support of the distribution). Instead you can **load the results** by calling the `load_shap_values` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_explainer_interventional = TreeShap(model, model_output='raw', task='classification')  \n",
    "tree_explainer_interventional.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_shap_values():\n",
    "    \n",
    "    with open('data/shap_interv.pkl', 'rb') as f:\n",
    "        shap_interventional = pickle.load(f)\n",
    "    \n",
    "    return shap_interventional "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "interventional_shap_values = load_shap_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|===================| 2559/2560 [107:15<00:02]        "
     ]
    }
   ],
   "source": [
    "# Shows how the above values were computed\n",
    "# interventional_shap_explanation = tree_explainer_interventional.explain(X_test)\n",
    "# interventional_shap_values = interventional_shap_explanation.shap_values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, one notices that the local accuracy property does not hold for all examples. A number of issues are currently opened about this issue in the `shap` library github repository (see [here](https://github.com/slundberg/shap/issues/1151)). \n",
    "\n",
    "NB:The `0.5` offset is due to the fact that the `shap` library adds the model initial output value (set to `0.5`) to the expected value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "errs = np.abs(model.predict(dtest) - tree_explainer_interventional.expected_value - interventional_shap_values.sum(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.5: 2557, 0.84: 1, 0.58: 1, 0.36: 1})\n"
     ]
    }
   ],
   "source": [
    "print(Counter(np.round(errs, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error observed seems to be the same for all but 3 examples, and for the majority of the examples could be due to the way the expected value is calculated, as explained in the [White-box vs black-box model explanations: a comparison with Kernel SHAP \n",
    "](#convergence) section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dfzVw+8txrFKEHHjjigAl23ZRP6ic6Yfm4jiKp16t53cPVbMp6RANBBicTFGY8iiPRAl6HnHXpdF1STmKuOtSFI1ySFU1Cr8RKum6DGqs5gtv/I3KUBlrB49l6ZCpTFy3jSk7dqCwhIm2DCuypLB45BGhikEoFMPYjON6JD95NGV/+iROWM7JxIDSa9lHQl3e5ngftL876DMdSWx6gdb6I/hjaI4EtmQsmgk8k/7/CPAU8G1gOPAv/MHFzYOH/xc4DrjMGLNKa10AHAssNsZs7eVdkDfFAJJMWRriUJK79+NZ9IOtPHnzfB4OzaXOLaEw5XcVzV3/LouHT6EpFGZk1Wbq8svJi8cJolou9y7eVcvIzTsAaCjKZ+jWXRxSVUVhMkJOxr1qqihgp1OCa1Pk2yiTWEfwjnMo+eZxvfMECNH/SWLTDXLa0zu+CDzWwRVMFVrr19PLzwF+C+wE1gJ/AW7JWPdG4BrgX1rrUUAj8AbwlV6OXQwwAVdR0sV73YWnjSB04QguZCPz5s3ivt9uYdNTa2kcMoQSN8nkpgrGHjGEhuVbMM6QNgeYeE5rB1EwFodAih25+cRjiuHx2pYj92B2UWvziKsQKeuQ+9bXKZgzvMf2VwiR6aDPY3YjLTb9hNb6i8A3jDGT+zoWpMVG7MH8+fMBmDdvXucr7azlibkP8Oq0o0kG/QHNw7bsoKyqFgBPKSZvrGRIvCG9QYoS6giQIocYVRSzUo1hhK1iuvf1bo5tEiLr9NoHIK6+2OZ4H7K/Oeg/bNJi00e01scCFfitNTPwx9309tVOQhwY5cWc9PxFBL79XxZtzSUeCBDNyyF3c4ym3ByclEdRPJqxgUuIOMH0JdpBEgy1uxj547mS1AjRq7Lv8yWJTd8ZAzyEfwXUTuAfwG19GpEQPahgfDFn/u0sTk96NFbGcBzLc9NWkXQDJEIuESdI2PMTmSQOFcFSRid2ksIhPKGI2Wu+3sd7IIQ4GEli00eMMQ/hJzZCZDUn4FA4zB/EM+GKKay8fxW1toCVIwcxurKOoJeiOpxDfipO0nMZ/5OjKbm665fhCyH2nZUWGyGE2Hczbz2Gmbcew+KHN7L418vxIjUQTzKpaRfhyUMZ+fAlhKeW9XWYQoiDmCQ2QogDbsYnxjDjE2P6OgwhBrxsbLGR34oSQgghRNaQFhshhBBiwJIWGyHEQaDpkXeo/f1/uvcL6X3EWstvFqX42/IUiZTlzW0e1dH+H7cQ2cCi2vxlA2mxESLLrDz+Lt7ZVEzMyaHkR7/jw29eROGIvL4Oq0PWWtyfpjLuCJlqmbpyJtxzuhyihOhd2ZHMZJIWGyGySN36BhZvyKMpkE/KCVCVM5injvorq65/Fi/p9XV4u7lwfqrT21zf+z7c+FrygMYjhDj4SWIjRJb4y1Hz+cepz1IbKGzTBdUYKuL1f1SxYM799LefUPnryj0vv+mNAxOHEANVNnZFSWIjRBZY/+wW4lX+L2Rbx4WMnyGwyiHgJYlub6RxW6SvQhRCiANCEhshssD7v1uxx4HCcTeHivBQFhz9l37TavPUmtTeVxJC9CppsRFC9EtVy2vatNK0l3IClMRrKW3cxTuz7zqAkXXusme6lmCd87CMsxFCdJ1cciDEQa5yaTVek+0wsQmm4gBMqV/NkVULUUCyyqHh3sMoOP9IKC04wNH6PGvZ1tS1deev79VQhBjgsqOVJtNBl9horT8DXG+MmbUfZfwQOM4Yc2qPBSZEH9n65s4Ok5qxDRs4Zftr6UetjcwBPPKuuovt1xRTNL6Y3KV3QvDAHQriKUvO/+teN1RdNEVR2O2liIQYuLKl+ylTjx/NtNYvAScCnzbG/D1j/lHAG8AGY8y4fS3fGPN/wP9llHsfkDTGXLavZbantS4HfgycCRQD9cAi4BJjzDat9UnA88aYLj9/WutxwDpgtDFmc0/FKgagSAxSHhT4v5idU9Tx2/D4nW/gpC+mzuz0sUCjG2ZospbUqlpM+U3sGjGGo2+cSeEri2DFZpgxFpZtgdnj/HpK82FQAaRg7IuLqR9bAnOqYVAhbNgBm6pgziS/8KAL4RDWWqqjUBqGujjkBuBnb6f47msdBLsXxXdZIIkDDC+AFz+lGJyrSKYsrqMoy1VUR6ExYVlWaRlTbHlpI2xpgGgStjbC9EFwzREO+SHpgReimSQ2XbcMuBz4e8a8y9Pz9/lOYVrroDEmsZ+xdcUDQC1wuDFmh9Z6CH6S0z9GXYqBY3MlnHcHrNgKV54Bgwvh+j/778SPHw0Pf5Mlf1rd4aZxJ0SOt/vHRQEBz2IBF9C1S1ikLGu+tIHZNUv9lV5Y4v9/ZtFu289snrjx+U7Drjn7KJIvfcDKQcM4/YobaMzJ7eoe75GHn6xM+aOl9ePY9Y/ld//j8dwn4NRxktwIka16K7F5FPiS1nqCMWat1roQ+DhwK/Dl5pW01ucD3wHGA43A48C1xpjG9PL1wB+Bk4EjgUu11mHgBmPMJK319cBnMsoCv4VlOvBL4DD8Y/cbwNXGmDVdjP8Y/BanHQDp/39O1zMCeBpwtdYN6fW/bIy5X2v9J+BUoATYBNxijHkwvc576f8rtNYWuN0Yc3N6+nhjzGvp8k8iozUovV83AqOAJuBpY8zFXdwPcbC78W/wdjpxuf2ffmtI8/f4I6/DlipqVjR0uOk/xnyM4ZEKTq14maD1b4Sn8DfPtbE2606pW83TI05tTWz2U+FTb+Fay/fOPL/HkpqecsETHjuvlsRGiGzVW5/uKH530aXpxxcALwPb2q1XC1yInwgcn/67od06lwPXAgXAvzIXGGPuSNdzvzGmIP2Xwj92/xAYCYwDGvBbYbrqFeAnWusrtNaHa61bOveNMVuBs4BURp33pxe/BsxO789NwH1a62npZc1jgqakt7l5b0ForfOAv+AnToXABOAP3diPfVJfXy/T/WQ6EWubgOC2+8ju4Uooqxy25o1gWfEUf1VgYcl0Uh187JvcXMriNZ2W1V3NUXmq/yYQ/eH1lWmZ7sp0b8rGy717c8Tg74BntdY3AlfgtzqUZq5gjHk64+FqrfU9wOfal2OMeTc9HdFa77ViY8z7GQ9jWusfAYu11vnNrUF78WngK8AXgF+ky7gP+LYxJrqHejOTjr9qra8DTgI+6EKdnUkAU7XWi4wxu4BX96OsLiksLJTpfjIdvOUieG8jrNwKXzwdjpoMl9wFiRRcfiqMKGPi/4xkzT83d5rkeOlEZkvuMN4dNJvZNUvaLI/hsjlvBLOq3uto8+5zFJH/OQaeMFzx+nM8OPtYmnLCPVN2D/jbPP/56A+vr0zLdFeme1d2JDOZei2xMcYs0VpvAL4PDAX+jd9y00JrfRrwA2AqkIPfbbSjXVHru1u31noi8BPgKKCQ1sb7wfhdXnuLvQG4DbhNax3CH1/zF6AuHW9HdTr4rUSfBoal68wHyrsbf0YcTVrrj+C3WP2v1not8NOM7i2R7cYNgcU/bzvvMye0eXjohRP9xKYDrpckkIqxoPxY1heMBmtJEiCEf28YC+SQYtaECDx8B/z9v7CzDq46A37+BIwph10NEHLhwzNgUxXP12+CoMOpn/0kOA4s3wzlxTC0pKXe/PT/yTHLirhFoRheAE+v87j4CUtlfN+ejgBw2GB49QJF0jrsiliKcxSD8/yDc23MUtFoqWiwvF1hKc/zx1n/cQkcNRzuPMlF7aGVSwhx8Ovtazx/i991cpMxJpXZ2pJOGB4Drgf+aIyJaK2vBq5rV8befrmvo+W/BrYCM40xVVrr6cBi9iE1NcbEgce11s/jdzN1VucFwGXA6cAHxhhPa20y6uxsPxpp/R4AGNGu/peAl9LdYecAj2it3+zGeCGR5WJ1id1aa5T1GN60jek1yyhN1DGtfjUn7LTMH3UWbw7+EMdVvuWPuTlHw7++27rht89rnf7V5R3WF5k/358IBf3/08d2GltxjqI4pzW2sye47LwGnDuT3RqJ732j44SkNNx2XnN9U8rgxDGt8y+ZiRCiA9nS/ZSptxObh/AH0b7TwbIQEAaq00nNNODqfaijApirtXaMMc3JQxGwCqjRWg/GH+/SZVrrn6VjXwzEgRPwBzDfllGnq7Ueb4xZl1FnEtgJOFrri/HH1TyRXr4TP7k5BMg8vTbA57XWC/CTmmsz4hgKHIc/mLhWa908CELuRS9ajJi7e6OgVQ5b80cyo3Y5BSn/TngeiuJYHTvDg6n+4v9Q9uv2vb4HTuobLs5Pu/Y2znGQVhYhekk2Jja9OrLPGBM1xjxvjKnuYFkDcCVwR/rqoruBfeli+T1+i0eV1rom3bLxdfyByHX4Y1Ke2MP2HXGAP+F3i1UD9wB3Aj9Nx74yPe+tdJ2fBe4H3gRWA1uAaWSMhzHGRPC75R5Kb/O99KKrgUnALvzL4+9rF8eXgfVa63r85+jzxpj13dwfkcUC4QCFY3I7/K2ohNN67uJg2ZlTxoyi7X2a1ICfqFw2vWvr3nJc78YixMCm2v0d/FR/+UE80a/Im+Ig89i5L1C1uLbNvNGNmzm14uWWax22hodSUTyc6au+Q6gwuM91zU93Rc2bN29/QiaatOT+fO+tNvGvuwTd7DjgCrGPeu0DUKO+1eZ4X2JvP+g/bAfdTyoIIXY3/OihbRMba0kqlydHnEZDsIARTds4rGYZ4178yn4lNT0pHOja8VOSGiF6Tzaexfbfm0wIIbrsyOsOw00P0g14SYJenG15w9mRO4SIG2Zk41aWTJxD2bTSvZR0YOXvfRUhRC/KxvvYSGIjRBZQjuLipecy7+GTSCqX8mgloVQMrKUkXkPkB5/lpGVX9nWYu/nW0Xteft3eb1slhNgv2TfGRhIbIbLIkNllFI7OZ2v+SOJuDgWJes770xxmfOtDfR1ah75/bIDOepqOGQ4/OUl6y4UQ3SNHDSGyzCcXnMG6JzbC+h2MP3cCjBrU1yHtUeJalzveSpEfhC/NdnhpI4wpgsllct4lRG/Llu6nTJLYCJFllFJMmDcW6PzGef2JUopvHdV6KDp1XN/FIsRAk42JjZwSCSGEECJrSGIjhOhQUzzFgvVJEqm9/aqJEEL0H9IVJYRoI5Kw5P0i88Z5HgE8EtfJ4UKIbCNdUUKIrDfngd3vBpwEogn5iTIhso3cx0YIkfU+qOp4/vbGbLxHqRADndzHRgiR5To7KDTEDmgYQgixT6TTXAjRRmdDhROJAxqGEOIAyMZ2WElshBBtdHagi2bjEVCIAS5bxtVkkq4oIUSX/GlJX0cghOhpMnj4IKS1/q7Wev5+lnGf1vr3PRXTHup5Wmt9fW/XI8S++O1S2FSb7OswhBBij/q8K0prrYEbgGOBHKACeAq43RizbX/LN8bc2q6+l4DnjTG37G/Z7co9AvgucDyQB1QC7wB3G2Ne7GKsZ/VkTEL0tM89Ddfm93UUQoiekx2tNJn6NLHRWp8GzAd+AXzZGLNFaz0cuAw4EfhrX8bXVRn78Uvg68AmoAA4HTgX6FJik80iT62m6bEV5Bw1ksDcUVR+9l+ktjSAA7Y6ghuLgXJQ+SEihQVEqzyCYVAK4gRxRxYwJrSB/B1bUHkh+MoZcM1ZbHp9O/+4djFWObjxBIPrqwg2KSoKizCXzOFrFwziiJEuAHblVrzP3QtxD3XPJThzJ7aJ0cYSpO54Dru1BveqE3FmjOx0f1J/eRPv1dU4Zx2Ge+7sXnnObDwJP3kKu3kX6spTUDPHAOBd+yA89R6cOBX164tR6sAdmJZUAn2U2NiGGLEfv4itiZDz9RNwJg5uuzyRIv6TBXgbawh96Wjc2SNJPL2M5N8X4VXU44wrIzlnAg0/fR1bHKYhXEJsUyPDxltUjsuqD0IkolAwvYTccYUMu3A8pScO27+g//kGPP0uzD0ENlXB9ho4YiL8+114exXkh+FDE6CyHgpzscdPw25qhFgCLj0e7/43wFrcb52JGlrUvecrmsC74xnstlqcq0/GOWwEPPQqLFgCp82CTx7T9cKWbIC7/w0jSuH6cyEnCNE43PoIPPAKbNwJzXeoVkBZIUwZAa+v8AdtOQq89CCt0nx/uikGjgOugqkj/efhhcX+vo8og3XbIRL3n6MTpsG15yF5uDcAACAASURBVMDnfwk1jTBlJCRSUBCGbdWQnwPTRsNbq2DOJPhgEyzbDHlhSCahPuLXmTlOzFVQWuDHVt2YPtgkW/ehMBcu+TBsr4VnF0FVfeu244fAfV+BP78E63fC8BJ4bz1s2AnlRfCpY+GK0/znLJGEb50Lw8u69fodKNnS/ZRJWdt3IwK11quAV40xl+xhnfOB7wDjgUbgceBaY0xjevl64I/4ScRsYDlwpTHm7fTyHwLHGWNO1VrfBVyJf7+xBLDFGDNFa/1h4FZgcnrZC8A1xpgd6TLuA5LGmMs6iXE18FJny7uxLy+Rbk3SWo8D1gGfS28zGngd+HxPtGTtRY++KWJmK9uPuq/lwKbCDoFo22uHk7jYdM+ohyJOiBDxlo+cQwOjWde23N98kV/db/ECfuLixhOMXltJKH0juVVDSrjlghNZfV0e5XlgSz6Pqmvw6wsV4O78Daoot6W8xFf+hnfXS/6DQfmE1tyMKs6lvdQ/F5E87zf+A6UIvvR1nBMO2denp1PeVx+AXz6XjqcAtfoO7L0vwncfbl3pG2fi3HlBj9Y78u4kWyMdL8tx4B+HPA3AvHnzerTevWk6/y8k/7YIADWmlII130GlX3uA6HWPE//py/6DklxyH72YyGm/afnCtUCSAEH8L6+dlLKO0bgkmcQGVjCBzLNXJ8dh7pKPkTepewlFi1c/gBO/D904xqbIB0J+vHkhvCY/djVnHMG3vtOt6pNX/h/er1/xH5QXErz3E6hP3NG6wnM3wqmz9l5QXRNMvAoq6/zHV50Jd18BV/0G7n2mWzHtF0X/uoRHqT2/toMKW5Oh2ePh3Z/uV237s/GebFE3tdmJkfYHB32m02djbLTWk4FJwIN7WbUWuBAowe/mOR6/6yrTl4CvAmXAw8BTWuvdjkbGmKuBV4GbjTEFxpgp6UUx4GqgHJgBjMBvRerqfkwEHurC6l3Zl/Y+DZwAjMQ/V76pK3Htj/r6+h6dTize2Xq2BjjR3W+IknnW4A9hS7X5JAc7uAi55s0NeE7rW9jxPIIZd8cdVd1AXRzWV3tQH2lJagCceAS7pbZNnHbRptbCqxppWLmlw/2KvbkmI3CLfX9Lp/u+X9OLNmbE00Djis3w+uq2T8Jba3u83u2dJDUAqYzXscf3dy/T3qLW18NurMZWR9qsE38n4/mqiZB6bW1rKwL+ZezNSQ1AHv6OpghQTz7tvzu8mEflwop9jjn65vJuJTW+jENyU5zmb3L73uZux9Dm/byznviCdqO/31vftTK37GpNaoDkwvT7f9H6Lu1Rj+lPSQ3s/bXd1Xq8YfEG6utan8PuvpdE9/Tl4OHy9P8te1rJGPO0MWapMcYzxqwG7gE+3G61Pxhj3jHGxIHbgQjw0a4GYox5zRjztjEmaYypAO7ooI4u74fW+hytdY3WulZrHe3mvrT3I2NMpTGmDj8J1F3dr31VWFjYo9Ph08bjDPX7L1Q4gB1fTntORuKSwsHDbTmOeUADeW2Pa46i/OK5hL1kywEmFQjQVBBqWeWlyaOYOUQxfagDRXl4Mya0LPNGDEdNKm8Tp3vRUS3L1ZHjKJg9vsP9Cp9/pN/0DVCWj3PWYfv1/HQ2rS7K6CqYM578wyfClafQxpWn9Hi9e/rhhEDGl39P17u36eBFR7Q8ds+Yghqc3/Z1+dyR/lk04HxoFMGLNGpY63IHaAy09qNVUgpAPo0Mohqn3Z7nji9g+Klj9znm8HnHQFmB/yCU0eufE6QzyslI4CcNpTnZctLvze7E4GS+n4+eQOjyU/3uFYDiPPio7lqZk4bB3Mkt8wOfO9mfuOiETvejVxTt3nrapwr3Es/sca3TFx5PYVHruXZ330u9KRuviurLMTY70/9HAss6Wyk9fuUHwFT8wcUusKPdauubJ4wxVmu9ERjV1UDSA39vBWbhD/xV+GNkuqIy/X8UfjcYxpjHgRKt9XH4LUTd2Zf2MrudGoED827vQYFRRQxfdBmx/2wmOHMIgUml1Nz+BrENdaicAMkNtfDBNgKeJTBlMOqYsdQ8tZngjFKSO6MQziFvfAGRIaeQt24dFIXhwuNwJg/nqhcn8egVb7Hj/RrceJxYcYRYYZjIkeP58FdmcudhQXKD6S+HhbeT+vm/IZLEve4sVNBtE6f7xeNRs0dht9binDFtt+XNnNmjCb3/PbyFm3DmjkeNKu2V501dfhLMGg1bquGMGahQAHXWLLw3b4RH3oazZ+GcMLVX6u7MSWMOaHVt5NxwGu4JE7A1EQJnHbrb2KLQF47EnT4Mb3MtgdMno/JzyF/0DVKvrcPGk6iiMPnHjaf2zjdxRhYzqiSXwQu3Uzotj5Q6hjFrPBLbo5SeMgwVcCk9aSjBspx9D3jCMHjvZ/DGSn/8yPZaf4zNnEnw5kowq/0xIMdM8cff5ARw9CTs+mqIJbBnTsd53m/1UWcc1u3q3S+fjDpiLFTUoc6YhsoNwfv/z6/3yENgzO4nGB0KBuDFH/njgkaUwVHpJOeqs/z9+utr8MoHsLPWTyxL8uHw8XDcofCb52BHDQwrhW1VkBPyx/dEE1BRDQHXHytz9hFw6Ch4cbE/HmbSMHh3nV/m6MFw1hHwkQ/BDx7yW5BOnu6PtRlSDBsr/XKOnARPvANnzIY1FfDw6/7YnWgcVm6Filo/u62oAdeFySP8OAcVwovvQ0kerN3hlzt6sP/3o/Nh6SZ4aQk88jrUNUJRPlx+KtzwKXjuPX/9/PRYnxcXw/TR/r6fNhueW+Tv35mHd/v1O3CyI5nJ1GeJjTFmZXpsygXA8x2to7UOAY8B1wN/NMZEtNZXA9e1W3VcxjYKGANs7qTqjm6s+lf8LqxPGmPqtNYfxR8M3BUrgbXA+Z3tRzquru5LVnKHFZD38dYv4dJvH73H9cu/29mS49o8CuS4fOr+PZfVurKLe93Ze1zFOWr8Hpc3UxPKcSd08YthP6gjJ+42zzlyAhw5oYO1e9/fP6Z46dk+qRqAwAm7Px+Z3DljcOe0PnaGFuJ8fGabdQbdfFLrg/Nb35OTeiLA9kYNhk+kBzlPyBiIfN7R/l8H1KTh/n+AfUhoMjlz271Pxg3x/7orNwfOnbv7/LlT/L/OXH569+rZ25if2z675+UnTm8t54tndL3em/YwTu2wMf5g4Hu+uPuyeXPaPv5yuwtbT+udCwt6Ura00mTq68u9rwLma623A3cZY7ZqrYcAl+InC08CYaA6nQhMwx8L094lWut/Aovxr0rKS2/bkQp2P4YV4Y9/qddajwG+3dUdSLcQfRn4l9a6CrgLP6nKBY7KWDXUxX0Rot8qzOm4FUsIIfqLPr1BnzHmOfxT8GnAYq11PfAfYAjwsjGmAf8qpju01g3A3XQ82Pi3+JdaV+MPtj3bGFPbSbX/D//2OTVa66XpeVfgX2JeDzwK/KOb+/Hv9H5MBhYCDcBS/HvzfDi9Tlf3RYh+yfTsxVdCiH7AtvvLBn16uXdPSF/ufYMx5oG+jiWLHNxvCrFf1J0d31148Wdh+tAA8+f7vbQH+nJvIQawXusv2qBubXO8H2u/e9D3TfV1V5QQ4iCR3/nFPEKIg1Q2jrHJ+t+KEkL0jCb5mSghxEHgoG+xMcaM6+sYhBgIXDkNEiILSYuNECLL3dzJTwhNKpMrooTINtk4eFgSGyFEGzccE+Cj7W7n87HxEHCy78xOiIFO7jwshBgQ5n9cDg1CiIOTHL2EEEKIASpbWmkySWIjhBBCDFDZmNjIGBshRBuLV0U59esVnPD1Ct5eGd37BkKIg5YMHhZCZLWmiMc3b68kXJ+kqD7JD++o5O9PdfbrJEII0f9IYiOEaLF8bbRN/7QC/vxoPSkvW87lhBBtqXZ/Bz9JbIQQLdxOjmu7alIHNhAhxAEhl3sLIbKbAx5tz3iSQFJabITIStmSzGSSFhshRItv3rNrt8NcALj8lp19EY4QQnSbJDZCiBahmjiO9XabH23cfV57Np6i8Z8riLyzre22r6xlx5SbiKoL8NR5VE/4Brte3tBjMQsh9l02XhUlXVFCCCK1UT555RrOWLuC8sZGHjjyJFCtbTcx6/H9Ozdy83Vjdts2sbaarTN/i9eYwkMRI0hwRJhDNl3FilP/RmzBBvKJYp1S3h0yjaZEHqELnuRQtZMJm76PcuT8Soi+ko1dUZLYHCBa618DSWPM1X0dixDNFl36L55d5nHP7DlsGDuWY9Ys5cSKTW2SGguk3AALF9XxqWuW8ZkjEqRebmDlpT8jUB/DifqHRgdwsASI427dTnXgUorcEE0U4ZEDgQSnVSzAxVLplLPOmUS+ew3hSYUUP3Yl6rDdkyYhhOiufpvYaK2PBm4EjgZcYAXwS2PM/X0aWBdordcDNxhjHmieZ4z5Ut9FJPqzxTstr22xVEc8bn8L6hJtl5cEwVpoTEHYhVPGwCemwI4mRVWT5W8rYFcUsJAbBBQ0xSFlIeykGBpt5NCxYcZ/sJ5n6vIJx2J864VniQSC/PyUeRQdZomFcwH47eFHccEH73PY1g0sHTEWrCWlFEsK8/hIxTru+vsjuJ7l1rmnUF/gcFjDDsppfxO/JMNZh2s9f+QxO0niEIi3dmcN9nayxZtIPWUEVm+ncvqtVDKE+MiRlE4MMWxwLaEffAxmtfs1TiFEj5IWmwNEa3068DhwG/AZIAJ8FPiN1nqCMebGvoxPiJ7yq4Ue17y45/ErNRmJTkMSHl/r/3XUI17TLilqwKXSKWLDqijx8HjihUEAntw+g0vffIfhjUk8x+GIuiZMEWwoHcRVn7icoU0NKM/DOg4B4Ii6Jm598UnyE34FP/zPc1QylI7ue6GwuLTu0xbGMYTNkDHPAnFySBKkllImsIypLGHTlkZGb1mHArxHX8X59w1wxuF7fH6EEPsuW8bVZOqXiQ1wN/CQMeZHGfP+rrXOA36vtf4TsAG4HPgKMBaoBX5sjLkbQGt9HvBd4BD8xOgPxpjvaa0vxm9NmdRcsNb6Pvxuosu01uOAdemyrweGAC8DlxtjdqTX/ypwJTASqAb+L11mSms9HxiTjvPXwH+NMadn1pEuYyzwS+DYdHyPAN8xxkTSyy3wZeALwFRgKXCxMWb5/j65ov+46929D8rtCQ3hcJvHU3bupLKgCC9jfMuoaJwmx2FlYR6u67ZZP+x5xNz2h4uOzvQsRewiSS4BIgBsZBKD2Ua6+QYLrGQ6MXIZykYqGMcKZnA0CxhCRUupDhZ+8YQkNkL0omxssel3o/a01pOBScADHSx+EP9oehrwJeCH+AlGCXA48Ha6jLOA+9PLBwGTgae7GcrngBPwkxSvXTybgbOAIuBjwCXAZQDGmHnARuAyY0yBMeb0DvYxADwJVOAnZXPxE5w72616MfBxYDCwCfhVN/dhn9TX18v0AZqeWnbgDiof2rgRgJxEgpNWrWT61g3kxmP+QmsZFk8yLBon0cFg3nXhELedPI+YCpLCpY6STmqx5OCRoJQUQeopJESU9UxuOTP0UAxnEzN5nVoGA5DEb0lqpLClJA8FR0wE+s/rJdMy3RfTonuUtf2rIUprfSzwGnBoR60TWuvt+C0dnwHubm6habfOU8BSY8w3O1h2MV1rsTnVGPNCevkkYBUw0hiztYMy7wTGGGM+lX68nnZjbNrVcQywACgzxjSml58BPAbkGWNsusXmU8aYf6SXnw08YIwp3dPz10P615sii+2KWK5+IcVrm6EyApEu3ODXAcpzIWmhJgqdb2LBQn4sykmrV3HVa6+xpaiIk1avYlR9HdXhPKLBEMuHjWZYdSV3nzyPdweX835hHrPrI5THE9QEXZbmhxkZS1AeT3LYqlV8e8Fr7OnW6yVUEiJBgCoeHf4RhlY0McUuZTibWtbZyHg2MhmL3zJUSBWF1FDJUIayjbJQNUUfnwJ/+Rq0az0SYgDqtTOgxeqXbY73M+w1B30TTn/simq+E9hIoE1io7UO4bde7ATGASs7KWMc8M/9jGN9B9OjgK1a6wuAa4EJ+M9hCHijG2WPBnY0JzVpa4AwUA7sSM/LvCFII2SczoqsUJarePCjvf0xDAIfAj5EImVJxlIEH3uX0oUbefSZJraUDkavWwHW8n5RPimleKc4v00J6xyXofF64rlBntEj8WqCDG9oIJRMMrWyNqPp16LwgATvlE6nITeXkkG1DK3c1Ka8JgpRpChnEyGiDDksSP6S25jYy8+EEKKtbDyL7Y+JzSpgLXAh8EK7Zefjvw7P4Scbh6Sn22te1pEGIL/dvBH43UeZxuEnG83TAJu11qPxu6XOA542xsTTLTY6Y9u9DZzYBAzRWucZY5rS8yYAUaByL9sKsc+CriKYF4AL5xC4cA6fuRPWbI3zix8OpnRDNSk6vgop7HlUKHjm0bk8+2wVAPPmfQZrLZUXPkzjX1cCCoWlnlKiBDnst/OYcMEvGZKsIpH+HRqFl+5u8igNbGbwL85l0FVzDtwTIIRoIxvH2PS7xCbdDXM18JjWeh1wD/7g2rOBnwO3G2PWaa3vBr6rtX4XeBMoA8YbY94mPfhYa70AP/HJA2YYY/4DvIufVHwUeAp/jMwJ7D6m5/ta6yXpum8HXjDGbNVaH4rfG7ATSGit5wKfBZZlbFtB54kVwFvAauCnWutv4I8Ruhn4kzHmwIwmFSJt4ogQ//vbOdTUJfnLjbXsCOe0WZ6bSjGzronn7hpOTqjt+BulFOUPfZLyh2DnZU/Q9H8fYEcVMmHll1BKUTD3Nupm3UB1TR6LSmfQFMphUKSa8ccWM/WJiw7kbgohBoh+N3gYwBjzNPBh/IRjPX4rxveA64wx30uvdg/+5eB/wL8iaiEwJ739k/iDeW8FduHfA+fM9LI1wFeB36aXnYl/RVJ7DwCv4reuhICL0tsvw7+/zr+AGuDbwEPttr0FuEhrXa213m3QsjEmiX/5+ij8lqK38JOz67r0BAnRC0qKAhxR38johiZGR2JMaoySk/IYFY2Tl0rultS0V/77jzI2cj2jVl2JSt/gT40aTFHVrxmb+hlnr/kM5/33bE6v/jKHSFIjRL+Qjb/u3e8GD/e1jMHDo40xm/s4nL4ib4oB6lf3bubxd6C5zSauFI61nHxkkG9dMRSA+fPnAzBv3rw+ilKIAafXMo531V1tjveH26sP+uymX7bYCCH6RvnUYjKvQQpZS8Barvns4D6LSQjRe7KxxUYSGyFEi1mjAnR0cXVurlxyLYQ4OPS7wcN9zRiznl5s9hOiPxs8KNjmsQVywvJxECJ7Zd/nW1pshBAtyssCHK9bf36hvFTx6N2j+jAiIURvysauKGmxEUK08e0vyXgaIQaKbLxSRFpshBBCCJE1pMVGCCGEGKCypfspk7TYCCH2ybI/rODFS1+hdlVtX4cihNhHtt1fNpAWGyFEt9VdE2VhYhEBm+SJ57ZzxvxTGDxrUF+HJYToJk9abIQQA13sjTgkLTgOSTfE9Nr3ef6jz/Z1WEIIAUhiI4TohlQ0RewvHqjWs7wlRTMoj2xn57tVfRiZEGJfZOPl3pLYCCG6bNMLW3efqRTbc4fx1g8WHviAhBD7RcbYCCEGrFhNnP9c+UaHyywODRvqDnBEQoj9lS2tNJmkxUYI0SX//ZEh4Xbym1FKUeDGDmxAQgjRAUlshBB7FWtK8d6rjbipVMcrWEu4MNjxsrTU2p3YDZW9EJ0QYl9l4xgb6Yrqx7TW64EbjDEP9HUsYuB6757lvP2LZVBU0PmZkFJUrLPsfHUr5cePIBWJs2HUz0nuihOkiTwSJCjCQxEDhszKofDpy3GGlxzAPRFCtJct42oy7TWx0Vq/BDxvjLmlK/OFEB2LJCxbGzw21oMeqlhc6TE0F5JW8etF/tC9meWKxrjlnW2Wx9fC+GI4cSS4DlytHVIeDM51yA0qqptSzP8gybZ6j0PKHTwc1lelWLML3t6a4rBCj0/PdDE7FedMD/DQY5Us2QSJFMyKN3DisUUMHpXLqmURqlfWEktYSg8pIPVuJXXbmkhEIBhNMHhnNZ5VDNpejUWhsHT4i8BK8fL/LGDyrs0UEyOEIghEKWEjeTikGMJ2xrMO9Z5H7YhNJHEABwV4pCigmuSh4wlYizuyiJwnvwo5e24JEkLsu2xppcnUb1tstNZBY0xioNYv+rflVZYLnkyxqQ4CDtTGIOjA7CGwrAqqY5Da46lQeqG1/h+AZ1s3CipwFLsqPN7Z4s/6yX+TUBNtzSkc5RfjpbcJua1lJjzecxUPblCokMutiyBFOTl5SQ7bVU9ldYBH5zeSm6hhcH0DR61azux1a3nomONYMWo0Y6IRQtbDhoPsGD0EBdTGkwzbvBM35XWyS5bC+gTxgIObVHh4KBRVFGHTbT0lVBAghgXyaMIjjAUCNFHNMHYxiMCyKPlU0bQ8QSz8NQrZ1lrH2CF4m6pxPP+jqQBK8yHgwmmz4L6vQLDfHtaEEAdAjx0BtNYzgZ8DhwPVwB+B24wxKa31OGAdMNoYszm9/sX43SyT0o/Xp7c5GTgSuFRrvQL4FTADSAHLgbONMdUd1H8fEAQ84GPATuBmY8x9GescD9wGTEvHeA/wM2OM1VqfBDwPfAH4EVAOFLar4wjgJaDMGJPQWl8K/B44xRizQGs9FNgKDDfG7NBajwF+BhybLmI+8A1jTH26vEHAHcDpQBhYAHzFGLO9g/3LAx7Cf80+ZYxp7OBlEAfIVxd4LNrRdl40Ba9u6WZBSrUmKorWxEaRTnQy1nUc/wvcS4FVu2dOXjpJap6fshD3sAWhlmJiuERdFwWEUilykykac3N5cebhDKqv55yFhtvGjsNz3ZaEqzm8ZChAynVaE5uU5ydX6XvaKM8STng0MogEG8hB4ZJoc0bo4ickHiE8wi3lRygmQZ5fD3lESaJwiFOMx3Zc0nVu2MFuw5er0x+FB1+Fk6bD5aft/XkXQgDZ2WLTI4OHtdbFwHP4X8zDgLOBS4Bru1nU5eltCoB/AXcDzwJlwND0svgetv8U8Ex6/S8B92qtj0nHeBjwFPAT/KTlbOBq4LMZ27vAWfjJ2dAOyn8XiAFHpx+fCqwGTst4vCSd1ISBF4EPgAn4ydQo4BfpeBTwGP4593RgLFAPPNi+Uq31MOBl/KTpnN5Oaurr62V6L9OxZC/0TGcmORboqGFE2YwVMud3Umb7T7hS5KQTE9drW0ZtXj5x1z/Xqc8N71615xGKJ1tnuE6bG/UVNLQ2cFZTRogELpZB1OAfPj1qGJnuzmq7c167QJME03up8HZPZToX82PoL+8TmZbpnpjuTf3tPjZKqUOVUt9XSt2dfjxVKTWzW2VYu+ddSY+lOQr/Cz1TAfBDY8wtWusLgduBMcYYm97ui8C1xpgp3WmxMcbc1K7u1cAtxpj1e4nzPmCiMeb4jHkPAE3GmCu01ncBecaYSzKWfwM4yxhzarrFZgEw1hizcQ/1/ANYBtwIVADXANcZY+Zorf8E7DLGfENr/QngdmPMxIxtjwD+C+ThJ0+vAP+fvfuOk6o6Gzj+OzOzvcPuUpcmIqiowBFBxYKgsWI0xhJ7iy3GGGMSo4mJ2GKM3VeNRI0tiS0GFaOo2ALKQVECKh2Wugtsr1PO+8e9uzvbYLYxs8Pz/XzGuXPuveeeGca7z5zz3HNzjDF17vq+wLaGz8n9TF4AzgIeN8bcvbPPoBvFwvc7pn2+2XLKa0GKqyHBC/VB50MblQPLS5w4Y5cforU0pqso5fS4+MP2almJtVBS0/Y6cMbEwOlJaViXlgjpCeBRqJAlrcbPqJJK9t1eiScUJKu2Hg+QUV3N8eZz3tSawtw8CIXIqa4hr7yiKeQIhRi6ajPtSawLkF9Ug4cgSdQznE2N8ZYfHwESnCEtkkinklx24MESQlFJKn7SG99YAjV4UASB/nzT9BFkp0FpU1yvAFISoabe6a154yZIax2UCdHL9Vi3ygfqqWZnkqPtRVHrwlFKnYHTofEqcI61NlMppYG7rLXTIq0n0qGo29tJHm5QAKxtCGpcq9zyjljb4vVFwC3AJ1prP/Ac8HtjTKDlju3svxYY7y4PB6ZqrU8LW+8BCsNeh1q8bstc4ALgFaAUeBmnZ6gPcAzw47DjDdFal7bY3+L0ag0HkoCtWuvw9bXAEGCD+/pinGDnkV20S+xGEwcotlzlIxCyeN2YxAI+jyIQso3PZbUhvArK6yxJPsV76y3Ds2BLFRRXK4qqLGNzLX9eaCmphhF94fCB8OYKmFfoxCT75jnpMz892MOOyjQmDLRU1jtDUc8vruerrSH2zfdSElRs3erHW+tnc3oGyaEgEwcrPF5LQkUNJauqYUM1K5JT+LpfGufu78Hu8LNyax0Z35XzycQDCBZkMaq+nB1bApRmZpCcnExWbS0ACfWBprRha51grOEZqE/04k8AmxjEV+VlM3nkUI7T7+IJC3KS2Ew6pWSSTzGplOKjGqgjmSpCaenUk4mnfzJ951wIQ3OxJVWoPumoBB8Eg9gQKA/uh+6FQNB5FkL0Zn8AjrXWLlZKnemWfQUc2JFKuivHphAYqrVWYcHNCJqChEr3OS1sn4Ft1NOsf9oYswbnDzta67E4w1JrcHJx2jKsjdcNAcI6nB6hq3fyPmyL4Kwt7wIPA6cB77o5RB8DV+EELB+FHW+5MWa/tirRWq8DqnDyddrJxgTgV8BxwFyt9fFt5ReJ6PF5nD/XXtW6zOdR9E11/thmpzjrztm37Xpm7NP89Q2T296upSNHJkbY0kQgcyfrh7dZaq3l48dW8r8nvuXIFZ+yOGe8O2TW+kedJ2SZ+N3pZA7Poujvy1l50YcE6mCY/RY/mdSQgQVy2Ywamkvex5eQWBDZ5d6qX9h2Xi+qZQwjQY0QnRJj3fP5OIEMNDWtw6Nk3RXYvImTOHyT1voenLPkL4HHAYwx29w/5BdrrW/CyTe5jOapka1orS/ACR42DCIg4wAAIABJREFU4fSOBNxHeyZprc8G/gkcCZxOU/7Lo8CHWuu3gbdxRw6APGPMh5G+UWPMaq11IXAdcL5b/B5wGzA/LP/lDWCm+34fwgnuBgITjTGvAQZYDDygtb7VGLNda50HHGOM+XvYIQPAj3A+y3la62PbSi4WoicopTjiyr3Rpw/mH7qWVmFUQ4BjLakDfWQOzwIg/6xR5J81qtmmoZVbYFMJniPG9HzDhRARibHk4UU4ea9/Cys7C/i8I5V0S/KwMaYM58qeacBWnATev+FcEdTgAuAkoMwtnxVB1VOBRVrrSmA+Tr7J8zvZ/p/ACThXPM0CrjbGfOK28X/u8a8DNgNFwNM4icQdNRcnT+aDsNeZ7jPu8apxhqb2xbmaqwwnADrIXR8CTsX5N1ikta4APgOOankwY0zIGHOZu/9H7tVWQuw2qfkpjL7MSRdTto0ORqUIhXZ+gvSM7C9BjRAxJsZmHr4WmKmU+hBIU0r9B6fT4GcdqWSXycO9hZs8HDDGXBrttsSB+PhSiG73/JCXUDaEVa1/Ew3oW8/UL38UhVYJEfd6LOKYq55pdr6fZi+IanSjlErF6YQYipPO8oa1tnLnezUnM1kJISI28qIRhB6ax+r01p2GqcN3lsMjhIhFO0vwjAZrbTXO6EunSWAjhIjYqLNHsvD/PmfC9iUs6rN/sxyb4b86JLqNE0J0mPVEffipkVLqY9oZMbDWHhFpPXET2BhjLox2G4SIdzmjsygZn03xstzmKxT0m5gfnUYJITrNxk5cA85M/uH6A5fgTPUSsbgJbIQQu0fylalUXlPXNI+NhZM+PT7azRJC9HLW2mdalimlXgGewpnjJiIS2AghOsTj8ZB+fxJ95uVTX1rPYQ9NIjk7KdrNEkJ0QiwNRbVjI9ChWypIYCOE6DBPoocpD0U4i6AQImbZbpn0pXsopS5uUZSKMxnugo7UI4GNEEIIsYey3pjqsTmvxesqnPsr3teRSiSwEULsUjBk8cZ+l7UQohez1h7dHfVIYCOEaFd1RT0L936M0VuLAVAXjofT5LQhRLwIRfkHi1JqRCTbWWtXR1qnnKGEEO16fepLTN1a3DjtqX36CzhtYlTbJIToPjGQY7MSZ+6anUVYFoj4TrcS2Agh2mSDISabb6klkToSSKcGb8zNUyqE6IpoXxVlbfeHVhLYCCHaVHj4M/hJYx39UVgSqWcvNkW7WUIIsVMS2Agh2pS4YDXgY39WoXD6gmtJiHKrhBDdKZZmHlZK+YCrgCOBXMKGpzpyS4Xoj64JIWKOtRYFJBPAg3N28QAp+KPbMCFEt7Ie1ewRZfcBPwY+AiYArwD5wPsdqUQCGyFEK1sG/LHddX1e2bIbWyKE6Ekh1fwRZacBx1trHwAC7vOpQIcuA99jAhutdUBrfVQUj79Ua31mtI4vREfYrZVtXqKggBHPrO90vTX1QTbtCGBtmzfwFULs2VKBQne5RimVaq39FhjXkUp6bY6N1noeMBla9Y1PNsYs2f0tcmithwFrgAJjzIaGcmPMftFqk9hzLTxmDmXvFzkvvHB44Rn896B/ESryk3ZQNocsPBmPr/nvm5JZi6Exq6a1OqV44Ku9eM4GGJoJ04fAuP6KvikelGoKh6rqQpx293bWbg7gsUH8Hh94PCgbYvzmlfxu8evs+4/LKFIpZGb4SN5nQA99CkKI9sTA8FO4b4CDgc8BA9yqlCrHuV9UxHptYOO6zRgzM9qNECKaSj7eSuFD31BbWEXl1yWE6oL4+iYSKKpvvmEQPhn4UuPLqsWl3Dv6Hdb1y2LyskL6l1YwlC1kUIsP8OMliJck6pv13iRZGPbf7byg6qlJSuQeA9gQEIKQJbu6mszKevpVlLAtJRuPx0NQecFaCsqKKczOZ9GgfbjVcxpPTb6FO48+l/kFY8irWsRzr/2ZwqxcZh00jTHbN3LC6kUMqCrFm5kM3xtHYL9h/OdTy+LqfPB4QSmUF/KGpVBeVE9Kpo+B+2SwfUMN29fXkJWfyIUPjyUp1TnVbVhawbJ528gblsq4E/v19D+NEDEvlpKHgZ8CQXf5euD/gAzg8o5U0tsDmzZprTOAh4GTgQrgty3W3wocboyZFlY2D5jbEChprQ8A/oiTwOQFFhljprvrngKmAdk43WYzjTEvuFV95T5/p7W2wN3GmNu01muBm40xz7l1HOnWPxrYDNxnjHncXXcUMBf4EXAHTnb4f4BLjDEVXf+ERLyo+raMRcf8B/zNe1daBTVtWJefyY1XOP8LPD9tf964dxaZVbUAlJPKOvrjwTKGtaiw3hsvlqyAE9Q0cntqkgJBckr8eKxie1IfPIEQeJweIWUthdn5jbt8MWAks8ZN55Iv32X26ElsS8viH/tP4fIv3+G41Yu55oQrKCjfxuCy7bCjCl74hHdHJbG4YHyzqbpsEIpW1QBQWxGkZGNd47pt62t57MLF/PSfmpJNtTz/i6UE6p33EgxY9Iz+u/ychIhnVsVOZGOtXRi2vALn72yHxWuOzf3A3sC+OLc7n0EHZi3UWg8APnQfw4D+wN1hm3wCHIQT2PwBeFprva+77kD3eR9jTLox5rY26h8OvA08BvQFLgTu1FqfEbaZFzjWrW8UzhjjtZG+h66oqKiQ5V6yXLmkpFVQE6llQ/Mal0szUlhekNv4ehvZWDz4COJpY0hq6cC2eztSa/x43M0VNAY8ANbT/HSTW1nKg5NmkFNb2VhWkpIOwNjidQCsyW5+nIqk9F2/sRaqSp3R6uK11Y1BDcD6pSVN9cbQv6ksy3LL5T2FUuorpdQvlFIFXamnt/fY/EZrfUOLsj44PR0nGmO2AGitfwl8vwP1ngesNMbcGVY2t2HBGDMrrPzvbhuOApZFWP/ZwBfGmKfc1wu01o8DlwIvhW33K2NMJVCptf4XoDvwHjotIyNDlnvJcvaUfiTkJ+MvqqWjgmFxRlpNPUM2lTe+TiAAQB0JlJNKJtXN9k2va9EjZC0oRV1S0ynFQrOcG2VDWOUctH/FDlQohLIhtqZmAdCnupwfLfkQgDl7TSCnppITVi5qdpi8ym2syNu7WcC0K/1HpgEweL8MMvISqSiuR3lg/6ObgqZo/zvKsizvbLknxcCVUOFuxfn7+Dul1CLgBeAla+2OjlTS2wOb21vm2Git+wFJwNqw4jUdrHcYsLytFVprD86HfyZOT44F0oC8trZvRwHQ8oZeq3B6lhoEjTHFYa+rcMYahWiU1D+FSYtPpuhf6/Fvr6Nk3hZqVlfQ94RBFM8upH59Tbv7nvTZKjJq/Kzvl8WUlYUk1iZSQhKJhOjPNgD8+GiZRBwCkv1+vMEQiX6/MyQVCpFaXYfCQ1Uq9CmrxRcMUp2Y4vTUWEuKv57zv34fZS1rs/KYP2Rfrv/va7wz/EAmrV3GwMpt5FaXUZaQxN5VRTz131n49h8M5cmwXwGcoDl6SC4ZXwd471+lBJQP8DB8QiajDs1h5Wel5A5PJXdwCuXb6lm5oITB+2Vw7FXDAUjNSuDiR8ey9osy+g5JYcDeHe/9ESLexFLysLX2NeA1pVQGzqXfZwN/Vkq9Z609JdJ6entg05ZioB4nOFnllg1vsU0lTjASbmDY8lrgB+3UfzZOz8qxwDJjTEhrbWiaITGSm+kUAie0KBtB02VuQkQsaUAqBVeOdl7cfGBj+ZiHJ1M8dyNfTXc6G9Mn9WXS/JMomb+VopfXMuC8kUw7qK+79fjG/fzrStk+7F4KKGp1rIY71Z1y7jbumuplUEZCWK9MkvvcFDB8tryOCx/aTmplFVXJqbwz/CAOL/yGVVn5vH5rLoMGXdHiCKeTjDPtaHv0kaB/0kb5jIHNXh9xXuve7PScRPY/piO/QYSIbzGWPAyAtbZCKfUCUAok0Prv5U7FXWDjBhovAL/XWv8PqAHubLkZcLvWegJOsu8VNA9+nsMZ5vol8BDOJeVHGGPeAzKBAE4A5dFaX4iTB/OGu28xTnCzN7CBtr0I3KK1Ph+nq208zmyLV3b2fQvRlrxpg5hmL2hWljO5HzmT278iKGFodjsXejtBTcADKd4QgzN3nbZ2yKgkvnkoPOAYRFMamhBCNFHOr6SpwDk46SPrcP5GXtiRenp78vAtWuvKFo+TcC4ZWwN8CywBZtN0CRnGmHnAvTgJvJuBfsCnYes34eTMTMcJTrYCv3RXPwN8hnOr9Y04Ccofh+1bA9wCvKi1LtVa/6Zlo40xa3Ai0GuA7cCzwG+NMf/s4uchRLfwHDm0zXILfPnihN3bGCFEj7FKNXtE2Saci2o2AYdZa8dZa++x1nZoNEPJDKCiDfKl2MNVP7eYyvNeaVVugc//PRGAk08+eTe3Sog9Vo9FHM8OfanZ+f68dWdELbpRSh1irf2sq/X09h4bIUQPSDn7ACwS4QoR72Kpx6Y7ghqQwEYI0Qbl9ZD0i0Nbl0ehLUII0RES2Agh2pT9x+MlkBEizlnV/BEPJLARQrRv75xot0AI0YNCSjV7xAMJbIQQ7cpffj30SWp8nfHa2VFsjRCiu8Vaj41SarpSapZSarb7Wiulpnakjribx0YI0b3yt9/cvGD2qrY3FEKILlBK/QRnupYnaZoktwZ4EGid9NcO6bERQggh9lCxdFUUcB0wzVp7F02z+H8L7NORSqTHRgghhNhDxUAwEy6DplsLNcw2kYBzm6SISY+NEKJrXvwIkn8I2efCutb3lxJCxK4Yy7H5CPhVi7JrgQ86UokENkKIztu0Dc65H+oCUFYNw1re1FIIISL2E+D7Sqm1QIZS6jvgDOD6jlQiQ1FCiM7b66pot0AI0QXWE/1umjBbgYPdx1CcYanPrbWhne7VggQ2QojOqw1EuwVCiC6IlRwbpZQXqASyrbWfA593ti4ZihJCdM6qzdFugRCii6xHNXtErR3WBoHlQN+u1iU9NkKIzhl5ddvlgQD45NQihOiw54E3lFIPABsIuw+vtfb9SCuRs48QosNU3U6GoB6eA9edvPsaI4TovBgZinJd6T7f2qLcAiMirUQCGyFEh+Us3tj+yhueksBGiF4ilpKHrbXDu6MeCWyE2INV1lvyHw5S08Y1Bwf2hS8v9KLa+EU3+rlF7Vca7MYGCiF6VKwkD3cnCWx6kNZ6BHA3MAVIB0oAA5xpjOnQTIpCdEVdwPLCN0H+tBDWlNJmINPSV9vBc29TlOIBJg2ArIrR9An04TDK2t9ZnQbmj5CYAGOHdv0NCCHinlKqkLC8mnDW2iGR1iOBTc96C3gH5z4X5cAg4CQg/kJkEbPqApYxfw2yprxr9YSA/24GGM5XF/2SjTN3MRmfvtF5vuUM+IPcFVyIWGRVTF0cfW6L1wNwbor5945UIoFND9Fa98UJaE4zxjT8tN0APBa2zanALcBewGZgpjHmeXfdTOAU4BBjTI3WejTwGfADY8y7u++diN7us810Oahpqc7nI0SE80XcN1sCGyFiVIzl2HzYskwpNQ94G3gg0npiKlSLJ8aY7cBS4Emt9fla63211o3fIK31dGAWzt1M+wAXAA9rrY9wN/kdsA14RGudCrwM3L87gpqKigpZjqPlwRnQTu9up21PyyQQacfjiH7ttk2WZVmWd73ck2Ls7t5tqQM6lFSsrO3eE55oorXOxbnHxfeA/YFS4CFgJjAb+NwY84ew7R8CUowxl7qv+wNfAluA7cCxxpgOTS3dSfKliDP/+DbIRXMsNd2Q2JtXUcLha5fzyt/u3Xlos38BjB0Gt58Dw/t1/cBC7Ll6LOJ4aMJ/mp3vf7LouKhFN0qpP7QoSgVOAL621p4VcT0S2Owebq/LD4G/AD8Gfg4MA/xhm3mBj40xJ4Tt9wDO3U2nGmM6dIfTLpAvxR7gnNcDvLhi59so4FcHwx1HNo1az549m2NPeYqkne24/jEoyO+OZgohejKw0S0CGxPVwOapFkVVwGLgWWttXcT1SGCze2mtFwGfAiOBD4wx9+xk2yNxenZeAiYDBxtjqnZDM+VLsQcpqwvx/VdDLNsBlx8AWcmwVxacOqrtFLzZs2cz9o53GbZgQ/uV2ld7qLVC7JF6LNh48OB3mp3vr114bDQDm/7W2i2RlrdHkod7iNY6B7gRZ4ro73CChRk4Q1J3AW8AT2mtFwD/xemtGQsoY4zRWvcDXsTJCH8GJ3nq/4Dzd/NbEXEuK8nD+2d3LN2u8NT92w9sxhZ0Q6uEELtDLCUP49wrKrON8mU4uagRkeThnlMP5AOvAjuAYuBm4CfGmJeMMe8AlwP34CQJbwbuA9K11h6cgOgdY8xTbl7NucA0rfUlu/+tCNFc6b4D2l/5fsthciGEiEirKEsplYkz20TklchQlGiDfClEu2bPng3Ayf8thbtea71B8GXwyG8mIbpRj3Wr3D9pbrPz/XULpu32LpywifkGAptarO4LvGitvTTS+mQoSgjROXee13ZgE5uXjAoh2hAjl3ifixO8vQWcF1Zuga3W2u86UpkENkKI7hUbJ0ohRARiIbBpmJhPKZVrra3uan0S2AghOu+Fa+GcB6PdCiFEHLDWViulDsK5v2IuYUNw1trfRlqPDIQLITrv7KMgLWxGm8l7R60pQoiOi6WZh5VSl+NMhzIV+CXOlcI/x5keJWLSYyOE6JrKF2FrKSR4oU9GtFsjhOiAaAczLdwIfM9a+7FSqsRa+32l1PFAxLMOgwQ2Qoju0C872i0QQnRCjAU2+dbaj93lkFLKY62do5R6viOVSGAjhBBCiFiwQSk1zFq7FmeyvhlKqW0488JFTHJshBAR+2h9gL9s2YfCmtRoN0UI0Q2sRzV7RNkfgTHu8h+A54D3gd93pBLpsRFCROQ3r5Zwx6p0YDizS4fz7ydKeftyGYISojeLpaEoa+3TYctzlFI5QKK1trIj9UiPjRAiIv/8tJTM2urGeWr+U5LCz18qj3KrhBBdEUtXRQEopfoqpc5TSt1ora0HMpVSgztShwQ2QoiIjNuynvKUNOeFUijl4bFvo9smIUT8UEodiXPT6B8Bt7jFe+PcADpiEtgIISJS60uEsHvLZdf6CXkTmb8hEMVWCSG6IsZ6bO4HzrTWfg9oOLF8BkzsSCUS2Aghduknd6+jTymk1wVI8gfJrapj/MZSUuv9fLq0JtrNE0J0UowFNsOste+5yw2/ourpYD6wBDZCiF2q+HQjz0w5mMrkBOp9HoZvr8QTCjG4rJbH5vqprglFu4lCiN5vmVLquBZl04AlHalEAhshxC59M7hf47JVioqkBBItFFTUMbCylrN/timKrRNCdFaM9dj8HHheKfUMkKKUehx4GvhFRyrptYGN1vpprfWTXaxjjtb6xu5q006OM0xrbbXW7WZ27662CNEhlTXMH/RLjlnxVWORspaBFbWNrzPr/AxbUxiN1gkhuiiWAhtr7QLgAGAp8FdgDTDRWruwI/VEbR4brfU8YDLgB4I4b+B2Y8xLPXQ8C0wxxnzSUGaMOb4njtUZsdQWseeoensla2a8RmliCpWJSSgVJLG+nvz6jeR4ilEo9qlJYFFeU9KwJ2RRNqwSj5fVgwYx87713HxVf+dycAt4FGze4dw/Kj3F2bawGKprISMF6oKQk+Yse7279X0LIRw26p00oJTqb63dAmCt3YQzUV+nRXuCvtuMMTO11j7gGuAFrfWXxpiVUW6XEHEpuKOGbef+m9qP11NXHQQUGaEgGfV+IASEqPYmszFrJIsyxzNx03KGsJpzlr3JYxsPZumggQS9HuYP7cOgshqGllaTFLRYpdCPvYi9fgHdfp5UQF4WHD4afB549XMIhaBvBtzxI7h0encfUQixey0HMhteKKVetdae1tnKoh3YAGCMCWit/wLcBxwErNRap+JMqXw6kAV8DlzTXtCjtb4D5w6g+cBW4CFjzP3uuoZ+9He01iHg78aYS91eo7nGmJnudgfgXG42DijB6Qq70xgT1FoPw+lVOh/4NVAAzAcuMMZsdve/FvgZkAuUA88YY24Ka+bRWuv29m1sS9ixLsO522k+8CFwmTGmqIMfrxCNym77hNo5qwAoysyioLwsbK0H8JAaDLD3jmLKk5PJYSOplJHqh3mP3UzebX8FoDrRx7qcVEZtr3J2VYpFBfvxveULur/RFigqg1c/a15eXA6XPwbHj4dBfbv/uELsAaI9/ORq2YijulJZTOTYaK0TgSvdl8vd5yeB0cAkoD/OtexvaK0T2qlmGXA4kIETENyptT4OwBhzoLvNscaYdGPMpW20IQt4F/jAPd6JwMXA9S02PRM4AhgEpOEEX2itRwF3AScZYzKA/YB/R7LvTpzvbj8E5+f0c7vYvltUVFTIcpwu2yp/Y1lQeQgfUWopIRgky98U+OTWVnL5/Hc4+8tPUKEQKf5gs+2rExJ3UlsPsRZqnfcU7c9WlmW5p5Z7Uozk2OzsVNRhytpurS9ibg/FIUAdTjDiB642xszSWucCxcBQY8x6d3sPTi/KicaYT7TWTwOBtoIUd/uXgdXGmBvd161ybFr0kpwD3A0MMcZYd/2PgeuNMfuE9aJMNMYsdNdfDVxqjBmntR6Bk/B0AfCWMaYy7Dg73beNtjRsP80Y8567fiSwAhhkjOnpS1Ci86UQPc6/uoSiaS8SWFOKXym2p6TRr7qy1c+lioQEdmRaJm3/kgTqUMDGzBwGlZcA8MTEY3j64NPpU+NvvMXCuQtnc9bX7/Zc48cMBo8Hlq5vKrthBtxzQc8dU4jY0GMRx++OX9jsfP/7OQfv9uhGKVWN05nQcOx/ATPCXmOtfT/S+qI9FHW7+4c8B5gFTHWfh7vrv9Zah2+fgDOM04o7DHQZMBjnw0gBXuhAWwqAtQ1BjWtVG8fbHLZchROUYYxZrbX+EU7P05Na66+BPxhj3tnVvjuxto3lwYBcWys6JWFEDgNXXYmt9oNHkXHDO5T939cElIekUBAPzi+NzemJ9K3bQbEvg/xAkKOu+i2fPvrbxnpOWbaIZ/XphJSiItFLRl2A4qMOgsemQp0fUnywtQw2lUD/bCjo6+TE/PNT+G4zpCRCbgYMy4MR/SE1CTKSoaQKBvR1Eo/TEqGqDgbnQnUdpCU7By+rgkT31JWStNs/QyFEtyvCSf1osL3FawuMiLSyaAc2ABhjSrTWlwKrtNYzcPJPAPY2xhTvan+t9WE4vS3HAJ+5OTEv0zzK3VUvRCEwVGutwoKbEW55pO/jVeBVd2jtCuB1rXVXBv+H4QRXDcsAG7pQnxAopVBpzrBRv0dOpN8jJ7baZrT7vOPjFcz8neHoZSsxg0agN64GYHneCLLqnBnPq5J8lKck8pN7J+/64Dee3rHG9nGfG4IagKy0jtUhhGiX7bnOoMjbYO2w7qwvJgIbAGPMDq31n4E7gLE4vS2Paq2vM8Zs1FpnA0cD74YP87gycS4ZLwas1vpE4Hgg/NLxLTg30/qEtr2Jkzh8k9b6Hpxeo18Cj0fSfq31Pu4+HwE1QBlOMNWVKVlv0Vr/z63vbuC93TAMJUSjPlP25vfv780lJ3/K9Mtv5sr575AQ8vDVwAmN29T6vKTZuii2UgjRWTGSPNytYiJ5OMwDwACcpNnLcO7yOU9rXYEzpfIZtN3z8h/gWZwrp7YBPwBea7HNb4A/aK1LtNatghVjTBlwLM70zVvdOv8G/DnCticCv8MZbioFrgVON8bU7nSvnXsO+Bin1ygROLcLdQnRaR+NLKA0NZ23R43j9qknsz3VmZemzuthR5KPd2/Lj3ILhRCdESPJw90qasnDon1hycMFxphoDD3Jl0I08+cnVnLr1jwOX7ucOWPGoawlrT5ATmUNt0xP5LKp6dFuohDxrMcijptP/KLZ+X7mm+N7fXQTaz02QogY9LPzh5BZaxlUXA04v/L8HsVeWzZx6MgoXOYthOgW8dhjEzM5NkKI2KWSE8mp2sE+m0uY9ddXWDYwn2nLVjJ37EDGDN4v2s0TQnRSLNxSobtJYBODjDFr6cGuRyE6Y2t6EsV9vBy+aiOTV60lmFTNV4dOx+ORr6oQvVUoTnppwklgI4SIyI8PTeLBhKNYk5vFAVvW8cHoA3nvd8Oi3SwhhGhGAhshRERuOyGV8YPrePz1QdROS+C9iw6IdpOEEF0UL3k14SSwEUJE7PsHJOFbtyzazRBCdBMJbIQQQggRN+Ixx0Yu9xZCdEhdUCHTXwkhYpX02AghIjLi0QBrqgGOcwr+FCD4cy+eOPzFJ8SeIh4v95YeGyFERJygBpyZCJyz4eHPB6PVHCFEN7CoZo94ID02QohO+19xtFsghOgKybERQuyR/MG2k2qy5G4KQogYIz02QohdaieuodK/e9shhOhecrm3EGKPlORtuzxdziBC9GoS2Agh9kildW132VQGdnNDhBDdKhR/cc2ekWOjtb5Jaz27i3U8rbV+srvaJERvkpPc9qkiO2E3N0QIIXYhJnpstNYauBk4DEgCtgBvAXcbYzZ3tX5jzB0tjjcPmGuMmdnVunuyTiFi3fqaaLdACNEVMhTVA7TW04HZwAPA1caYjVrrAcClwJHA36PZPhFb6m95jdpnvsIzvC+JVxxO7Z3v4l+6HY9SeAemEKqow+LBU1UFwRC+ERmEfngEnlc+xbOxCDzg89cTTErD+9D5JJyre6ahL3wEi9fCaYfApH2ayt9aROjNL1g1p5ZtdelknjyE/TZ/DssKYdIouPUs2Kt/lw5tN+7APvo+KicVfjIdldR+t0poeRGVd8/jYf9givcv4OpLBjH8oyUE562gYn4RgQ1VpJw2Gu+hA8EeAC1OguE1B3fUUP7QIvAqsn6q8WQktTqef/kOKv76Nb4hmWRcMY5gUSXl57+Mqqoh6+AUPJnJVOYNp35DDWlnjiFpvPNZWGvhqY/gu82QmwFPfwjLt4DPAxdOgXeXwbZKGNoX1m6DrBT4189Q44e1aEAAHpkDRWWg94IFK6CyNhRLAAAgAElEQVTej91Qjk1IRp13GBwxCvvAXPAHYWIB9roXwCrUExfjOXoMVNTAg2856/crgFlzIS0JbjkD9h8CM1+Cpz6AwX2xD1wMP3ocNpVCViqUuZMBeT0wIg+umgYrt8LQXPjx0ShPGz1joRA89h9Yvw0uPgYSvPDEuzAgB676HvjaSYBqz9tfwPv/g6n7w/fGw7Pz4NE5sG8BPHwZpLT+dxPxKxQnc9eEUzbKc6NrrVcAHxtjLt7JNmcBvwaGA1XAv4HrjTFV7vq1wF+BY4GDgG+BK40xC931twKHG2Omaa0fBq4EAoAf2GiM2UdrfQxwBzDKXfcecK0xpsit42kgYIy5tJ02zqOdHhutdSpwJ3AakAJ84ta9Puz9/Q4YDFQDc4wxF2qtFTATuAjIALYD9xpjHtrJR9odYnLC/OBt/2Lbbw0NI6g+qgmSRALOpTl1JNMwcZxq/N/VkkEhKVS4r2j837iWviR+ezueffK7t6FPvw8XPewsJyfCV3+GUQPhw6Vw9G8pt33ZyBjAGd/O8y0n37/V2T4/C1Y9CukpnTq0DQSxY37t/LEEuGgKnr+2+ZXFllZTNfJ2rj/oOP4yyQnw+gXrWHLvbST5LSXk0vBpJXrLGHjX7W3Xc4Pz+2jToc9SN38jACnHDaf/22c22y5UVkvhqCcIFTl/3LNumkzNk4b6Ij/5rCCNEgDqSGUT+6PSEhi87FJ8Q7Kw974FN7zYsQ/Do6DkcVRm2Gf5k7/Aw3NabRoiFUuS83YnjoDPVje8u8bPABXCs+qPqKv/AnO+bNyz8X+X1CQ49wgn6GjcOxGI8Jr4W2ag/nB66/KbX4DbX3aW8zKdwGaT81lx3UlwX7unztY+WALH3ArWOkHqXefCL59tWj91LLz3+8jrE7tLj0UfV5y1vNn5/rG/j+r1kU5Uc2y01qOAkcALu9i0DDgHyAamuI+bW2xzBfBToA/wMvCW1jqzZUXGmGuAj4HbjDHpxpiGn9N1wDVAHjAWGIjTi9Qd7gMmuY+hwDZgttba6wY9z+L0VmUAI4BZ7n7TgQuAQ9x1hwCfdlOb2lVRURGTy/4PVtL8K6sa/2vDZsOl8bWz1oe/xR4OL3UE5n7X/W1esLzpILX11Mx374a9cAVYSw0Zjas9FoI2uWn7ojKqlq3tdBuq1m5pCmoAFqxqd3u7ahvB7bUsHjigcd1WbxKFWdn4SSD80/IEPezsBlHl5eXUfbap8XXdgk2tjlu2ZFNjUANQO38j9UX1ACRR2VieRDWKELbKT/0SZwbAwCfftnvsdoUsrC1u1obgp+3V486gbMF+Xdj2JlbB1xua//uG/waoriP4UefvfB745LvG5fA2Bz4Nq7O4vCmogca2RPw9+XxF07+jtfD2l4QLfbG6/X1lOWrLomOinTyc5z5v3NlGxpg5xpilxpiQMWYl8ChwTIvNZhljFhlj6oG7gRrgpEgbYoz5xBiz0BgTMMZsAf7YxjE6TGvtAc4HbjbGbHR7ma4DxgAT3c38wGitdR9jTJUx5mO3vB5IBvbTWicbY7YaY77oapt2JSMjIyaXE86fiKcxSGn4gxJyf1NbFKHGfZqWg9ST0rh1+HOAVHynHtD9bT7lYGgYUsjNJGX6eGf5e+MgLZkMtoPbPr/XQ4Kn6Y86oweRduDITrchbcQAOLRpf04d3+72akx/EvbK5vjvmv5Q70cVIypLSaQu7DO0JHjbnrCmYSgqMzOT1Bl7N5annrp3q+NmTSggYXTfprZ+fxSpY5z11eQ0lleThcWDd2A6SRMHAuA7/ZA2j9/QvjY7GVMSYFT/Zm3wnj65ab3XE7a3Oyqf5EMdu3/TNuGRcIIHJo2AUyeGrQ8bBhqQg/fsKc3b0IHfvr4fNNUb3mbf6Yc2bbTPQBg7tOm125aIvyfHjXN6EQFSEuHSaU7Plstz0oTI6pHl3brck0Kq+SMeRDvHpmFC9kHAN+1t5Obh/BYYjZNc7AWKWmy2tmHBGGO11utxhnYiorWegDMUdSCQinNKSo90/53IwwlOGn8KGWMqtdZFQIExZr7W+gTgeuB2rfVqnOGmF4wx87TWN+H0Tv1Taz0f+I0xxnRDu3od74VH0KdvBrX3f4Jn/0EkXDCRukc+wf/aUvB5SZ9SQHDNDqwvEe+27dhtlXhP2RfP935I9X++Qi1ahSdZ4a2uJZiaSeIzF+EZlNX9DT1hAsy/E5asg+kHwsA+Tvn+Q+GLP5Hy8Tckv76dbZtgwM8OJHdFP/hyDUwZA5dOh53kxOyK8njg3RvhpYXQJw118rj2t01NJOWz67npmYWM3/w1xXoEZ8zII+MH1xP8fC3Z/yvF//VW0i6bQOXQbJjfuo4+YZ1N+f+YQdXL36F8HlJP36fVtp6UBAZ8ei7Vr6/AV5BJyrRhZF4xjsqb30FVjiI0qS8eBZ7BQ8hdXU7K90bgzUt12nruYdihubBiCwzuA899DG8vgcwkuOsseH4+rCqCKaPhk+9gYA48ewUqucUw0G9+AONHwNZSOHgkLFiOSk7Es24b1pOEOmUcjO4Pr36BrQ/A5OFw/QtYq1APnYfqlwVPXgHHj3PydcYOhec/goxkuGwa5GfDQcPggbecIOSe8+GSWbCkEEb2g1VbwedzgomDh8MFh8N3W2BoLmrqvm3/Q11zAoweBIXbYMZEJ6fm1QXQP9vJkemIg4bDF3+CT7+Bw8bAmMHO45E5MG6Ek7Mj9ijxeEuFWMmx+cgYc0k76xOBEuBG4K/GmBqt9TXADcaYYe42a4FnjTG3uK8VsA74lTHmhfAcG3f9+8D74fkwbjteBu40xpRrrU8CZhtjlLv+aTqRY+P22FQDJxlj5rpl6cAO4EhjzPywbb3AKcArwN7GmFVh61KBW4GzjDFD2v9Eu0VM5tiI6PnvxgCHtZHi0jcRtl0b7d9HQsS9Hos+LvnRymbn+1nPj+z1kU4snJGuwsk32Qo8bIzZpLXOBy7B6eV4E6fHo8QNavbFyYVp6WKt9WvAEuBnOL0ub7ZzzC04uT3hMnFyeSq01kOAX3Xivfi01snhBcaYWq3134DbtNbLgFLgXpwE58+11v2Aw3GCojKtdam7a1BrfTBOD9VCnBygCpzEZiF2q+U72i6vk2+jECLGRDvHBmPMuzh/2PcFlmitK3ASZPOBD40xlThXMf1Ra10JPELbycZPAA/i9O6cCZxojClr57D34UyfU6q1XuqWXY5ziXkF8CrwUifezu9wcnsaH1rr/jiBlsEJUNYDA4BTjDFBnH+Dq4G17nt/BLjAGLMW50qoB3GSjbfjXPV1VifaJUSXHFXQ9qkiWSboE6JXi8ccm6gPRXUHdyjqZmPMc9FuS5zo/V8K0a0+KQww5R+tyz1A8IZY6PgVIq71WMhx4bmrmp3vn35ur14f3sgZSQixS4FQ2+XtFAsheol4TB6O+lCUECL2DeiO6wOFEGI3iIsem4aro4QQPSM/zUvjJHZhkltvKoToReKxxyYuAhshRM/KSW775Pfznc2bJ4SIefGSMBxOhqKEEBF59KiGpaaZfmdOkd9GQvRmIffOeqGwO+z1dnJWEkJE5Ert40oNs2fPBuDkk0+OcouEEKI1CWyEEEKIPZSVHBshhBBCxAvJsRFC7NHqt1bR75RCBp5SyBfZf8GGZC5HIXqzkFLNHvFAAhshRERsyLKk/9Mk4SeLbSSWlfFl3yej3SwhhGhGhqKEEBGpWboNHwFG8zlJ1BDCw4rScdFulhCiC+LlSqhwEtgIISLiX7yJDHaQRA0AHkLksCXKrRJCdEUw/uIaGYoSQkRmxQ2fUUsqVWRQQxoAtaSxZPq/otwyIURnxWOOjfTYCCEioorqqSWD73CmG06jhCqyWfVFBWOj3DYhhGgggY0QIiKqxVh8FTkAZFfVRKM5QohuIJd7CyH2SG9Mfp2itNa3vLRAdl3d7m+QEKJbxOMtFSSwaYfW+mmtdaeuZdVar9Van9vdbRIiGgKBEJu3Bsivah3AKKAyPY3KF5bs/oYJIbosqFSzRzzodUNRWuufAj8xxowMK7sWeAA43hjztluWApQAZxhjZkelsWLPUFMH32yA4f0gJz3arWlSUcOGLzaxIJDOlrIQ+43P4+jVy1j698W8WHAAg4ZnMXjeIopXb2dZYg459SGS/fWk1ZaSU12Jn0Q+HjSGWm8m167ZypIh+Wzok8Hxi1c1O0xtYgKv3Gwo+Pn7bEtL59FxGr8NUJ6cRF2ij8E+P6NHZjJ0cDonHJ5J1TfFfLS0nhOOzmZdpY/8bA/jx6RQUxWkdLuffv18qOVFeIZk4+mbFqUPTwjRW/W6wAaYC9yvtR5qjFnnlk0FlgLHAG+7ZYcBXmBeRyrXWit3PyF2rbQKDr8JlhZCbiZ8eBvsWxDtVkHhNioO/jWDt25nQk4eh14zk7tmPsjqtd8x4Rf3UZeQSHJhPR+8u5CT16/g3/tN5/4jTuaD4f1AKY7/eiHv9tmbgNeHspa6zHwylOWEDxe3OlS/HRX02xFiANtIYSNjt2xk7KVX47WWep+PVaEQH2z1kLgDnnx/Ayszs0Gl8tBfavn+1u0UZqUyoK+XjM1V1NWEKKgr5fw5s0nISCT9vcvxTRgchQ9QiD1DPObY9LrAxhizVGu9GSeI+avW2gscCVwG3BS26THA58aYCq31UOBBnGCnBngF+LUxpgZAa22B64DzgP2Ao1seV2t9M3A+cKIxZoXWehhwD3A4kIITWJ1ijNneYr9U4DngUCAVWAn80hjzrrt+GPA4cAhOysJq4BxjzHda62nuMfYC6oHFxphpnfzoRE94wzhBDcC2cpg1F+69KLptAnjuQzK2Ol/F4SXFnPnVfznj6wVcd+pF1CUkAlCbkMj9U07g788/gBlyEIv754DbFT1nxIFQUQ84N8lbn5bMBYu+xRcKtXNAD+VkkkIxA6oqUUC9zzm9hDzOiHe9H4pSUxqPsSElma1JCQwtq2ZzIInEGqfuwqRs1uX2Z2TRJuofX4DviR/0xCckhACCcZJXE6635ti8jxO4AEwAtgCvA3tprfu65ccAc7XWPuBNd5uhwCScAOdPLeq8BDgTSAe+bCjUWidorf8KnAAc6gY1qW4bioDRQC5wA07w0ZIHeBXYG+gLvAi8orXOc9ffAawH+rn1XASUuuv+hhOQZQGDgNsj+3i6pqKiQpYjXK7OTaWZIXmx0bYhuc2atT47l9qEREYVb25WPqhsBwADyreSVedvLM8O1uALBgBQ1jJt+XoO/noVg4pKaY8PZ/uAUgS8bXd6eoNN95byWEtaMORu33QqUjZERm21s82QnI6/d1mW5Thb7klB1fwRD5S1ve8mdlrrC4E7jTEDtNa/BgYbY67WWs8BZgHvAttxel6CwAdAH2NMlbv/ccC/gFRjjHV7bC4wxvwt7BhPA2lADk6gcV5YD88PcXJ6CowxgTbatxa42RjzXDvt3wacb4x5yz1OX+BGY8w3bdTzHPCwMWZ3TvHa+74U0fT4f+Dl+aBHwsyzoZ0/6rtb6S0vsea1JfyrYD8ePvRYphat5vlnH+CsUy7nvVEHMKBsB4+8NotRxZspTUpjwfCJPDvhYNLryvj5vNcJBhWzxp0AKblM/3ol45euaedIllQqyWMHIQWXnDCDhQMGgYLkQBAPlrLUFAbVVrNXVgIL/MmUeHwcWlFBQShAcb80bjwuhe1rati6oY7x3hL2m2fwHtCf5DuPRyX2uo5lIbpbj4UcU67Y3Ox8//FjA3p9eNNbzxjvAf211vvi5Nc85pZ/4L724ww5LQBOA4oaghrXKiAZyMPpdQFY28ZxpuD0loxvCGpcw4DVbQU1LblJzH8ETsTpkQkBGe6xAX4B3ALM1lqnAS/jDJNVAjNwhteWaK2LgSeMMffv6phiN/vxcc4jxmTfdgbjbjuDccDvAZgAD/2NVxu3yIR7bgOgABiLM55LKEQoeBCe95cw9omP+HphNWlbMmgv9cxDkFf1XuSOGseJDxzBS9mJJHpAeXbVIdy3+cujs92FYfAbuQeVELtDvMw2HK5XDkUZYwqB5TjBwmSaEoQbhqiOAT4yxviBQiDfHT5qMAKoBbaFlbWVPPAGcCPwgdb6wLDytcBwN79nV67HyQE6BsgyxmTjXK2l3PdSbIy51r3K6zDgKPeYGGO+MsacCeQDPwbu1FpPjeCYQnSex4MnIQGOG8+AV67juBU3UeZNor2OPB9V/HrBaVz+/LEMyk0myeeJIKgRQsSCeLzcuzeffd7DCRpWhCXsfoETBJyBc/UUwOc4Cbv3aq1TtdYDgduAp4wx7WVCNjLGPAT8GnhPaz3ZLX4TJ5/mPq11ltbaq7WepLXOaKOKTKAOZ2gsUWv9W6Dhpyla6zO11sPdq7HK3HoDWutErfUFWutcY4zFCYZCwC57iYToblMWnUnbsb+liiyUtzefSoTYcwVaPOJBbz4bzQX64/TSAOAGKh+55XPdsgBwEjAYJ0n3c+AznGTfiBhjngKuBN7UWh/jDmtNxenBX4ETtNwDJLSx+59xcnQ24QyBVdN82Gsc8CFQiXNl1Rc0JTafCXyrta4E/g38zhjzUaTtFqK7ZO6VRdvD/C1vtCCEENHVK5OHRY+TL4Vo5Qv1IBZfq0CmPjuBSSWXRaVNQuwheuz3w7iri5qd7798JL/X/1bpzT02QojdKP/2yahWMW+Ig4svjkp7hBBdF1DNH/FAAhshRERyTh/RutBTh9cXG5e3CyE6LoBq9ogHEtgIISKSmJPSosRS2De3zW2FECJaJLARQkQkIT+V0tw0nKujQtT5FFNelTt8CNGb+VXzRzzorRP0CSGiYGrxhbx578vU77CceutpqAQZhhKiN/PHydw14SSwEUJ0SGhUEj6QoEaIOODf9Sa9jgxFCSGEECJuSGAjhOiQAW/8j+N+8Az84O5oN0UI0UXVSjV7xAMJbIQQkTv1DiY8sZDE+hC88hl4Tot2i4QQXVCjmj/igeTYCCEi97ppPtOFzFEtRK9WHydz14STHhshhBBCxA0JbIQQXRMMRrsFQojOUi0ecUACGyFE10y7NdotEEJ0llLNH3FAAhshRNfMWxrtFgghRCMJbIQQQggRN+SqKCFExCxxMwwvhIC4GX4KJ4FNF2it5wGTcWalDgJrgNuNMS9Fs11CdFZ5TYD1ZdA3MUROmodfzLUsKw7yyhk+si+/v819JNgRoheLw/95JbDputuMMTO11j7gGuAFrfWXxpiV0W6YiE/WWn7xYYh/r7IcOlDx+HQPSb6ms9OCTZYz/h1kY2VT0BHxdDPWglKcsGglp5jvGJaSyJvHHUzelkxG58/g/4ZuI6+ynMRggIFlO/Bi8VqL9+46Qt7m945SQJIXat2LpjITID8N8lPhyeO8jOnb1ObPNluumhvEWnjoGC+HDYrDs60QMSn+/l+TwKabGGMCWuu/APcBBwErtdY/Ba4EBgElwPPAzcaYIIDWOg+4C5gOZAMrgHOMMd9prVOBPwCnA1nA58A1EjCJ11ZY7jVOqLKixHJAnuV63XRyOv31IJuqmrbv0Bx6SpFVVctpC78FILu6jh8s+IZ7ZhyG11oOXbe8MTFvW0oa5519Lc+9+CDDSopZndu/WVWWpqAGoNwP5aWwshQufjvI/B81nX7Omh1kbbmz/MPZQTZeIacmIUTnSPJwN9FaJ+IEMQDL3ecNwPFAJjADuBi41N3eA7yOE9Ac7D5fBFS4+z4JjAYmAf2Bz4A3tNYJPf1eKioqZDmGl8vqaKaovK7ZNhX1dEnIowiF/YgLepwXQ0uKm50wsmtr+HT4PvxNH0X/itIOHaOkJtS4XFFRQVlYmxveX7Q/Z1mW5VhZ7lFxOI+NslbmRO8sN8fmEKAOyMDJtbnaGDOrne3/BAwxxvxQaz0R+BTINcaUtdguFygGhhpj1rtlHpxenxONMZ/00FtqIF+KGFbttxz7cpBPN8LoPvDBmV76pzWdkWZ9HeSyd2yX/hGPWLaWGQu/ozw1iSeOmcDmnHT2L1zFokduITHkdMPUeb0k3/UiT//9YS488+qIkxBTfPCPkz2cvFdTmPTUkhCXvxvCWnh0mofLD5TfXEKE6bGQQ91Y1uxUYf+Y1evDG+nv7brb3RybHGAWMNV9Rmt9NnA9MALns04EFrj7DQOKWgY1ruHu89da6/DyBKCgu9+A6F1SExQfn+VlRy3kJIOnRUBxyQFezhptKa4JUeeHwZmwdJslLxXqg7B4K/RNgUHpUFIDry6Hd9ZAWT1sqoEAQT4aM4Rl/bMoSUkhmJhAkvJzk3mL4rQMsmurSQoEqPEl8sv3X+OMLz6m7vFr6JMKBRng8UAIGJYJXo+XSn+Q7TWwX66XuiAkeiAloXmbLxrr4Yf7KCyQntjrz6tC9CLx9/+b9Nh0gdtjM9cYM9N93QdYBVwIfAGsBU4D5hhj6t0eG22MOSqsx6avMaa8Rb35wFYg3xhTvJveTjj5UojWtpfB/7d352FylNXix7+nZ5JM9gVISCAQQhBZheSgIAS4IBcQAkTwAsoSUFmEq+AF5bKJ5IdBQBT1IiKSsCh6WUT2JdwbLqAsR0IAFSGBQBZCSEggy2SSmXl/f7zVSU1P90zPJDM9030+z9NPd1W9XXWqurrr1Pu+1bX5ac1GNwBV4b7Oj8e5ytGBNTaf5NTYDOj2mY7X925CZvYRcD3wQ2K/mgyxSWmdqu4NnJwuDvwVuEVVh6pqRlV3U9XhZrYY+B1wo6puBaCqg1R1oqr268x1cm69zQbmHV2Vd6xzrlsowz42nthsejcAw4kdgr9P7CC8HLgIuCtbyMwagaOAWuCVpMxUYl8dgG8A/wRmqOoK4DXgy3htiish3/mcKzfll9l4U5TLx3cKl1eQLzX/6Tt1PEw7vxThOFcpOq4p6qIVTZuiru7f7bMbr7FxzrVbAJh6XqnDcM659fyqKOdcuwmU5b1mnKsYZfj19cTGOVe0MvwNdK7Cld+32hMb51zxMsQ/qXHOlYfyy2u8j41zrg0a7mN1n6rYtyYD+P/XOOe6GK+xcc61yVO/PwWACRMmlDgS59xGK8M+cl5j45xzzrmy4YmNc84558qGN0U554r393c58qipycBUOPtQuPHMkobknNsI5dcS5TU2zrk22OX8pn+8/svHSxiMc27jld8tFbzGxjnnnKtU5ZHLNOE1Ns654vzi4Waj/KZizrmuxmtsnHPFeejFUkfgnNvUyrDGxhMb51xx3lpU6gicc5tc+WU23hTlnCvKuiUrSh2Cc25TK7++w57YOOeKs2CbkaUOwTnnWlVxTVGqqsClwL5AL2AR8AjwIzN7v4OXPQp4BxhpZvM7clnObWoD//Z2s3FlcoLnnCsjFZXYqOohwIPADcA5ZrZAVYcDXwcOAH5fyvgAVFWAKjOrL3UsrutqXFXHx0f/jobZS+n9rb3p+5391k9bfeMLrLxkOpkBvej7g4NoeHsZvY7ckR6f3ZpPVjfy7R8v4NXlENYFPpLeVDXGq5vWVWeo65mh95p6lvftQZ+1AQgsH9CLPee9yXOhoXkcwFWffgIQ7jpoZxZsNoCG6gyra6oJCBmgT0/YY6jQb+U6DhkZePh94aVlVYQqYXDvDGMGwdb9YUhvWL1OGDVQOGw7eGB2YFkdLFgRGFwjnLtnhh2HwC9fCdTWw9l7CINrYmpV3xi4eVZg6Rr4xu7Cln095XKuKGX4VZEQKueCTVV9C3jGzE4vML0PMAX4EtAbeBb4lpm9l0yfAUw3s/+Xek8AxpvZs6p6BTAeeIGYLAH80sy+n5T9GBgArCYeS35kZpOTeZwHnAzsAhwEPA1sbWaLk/cKsbbnMjO7Y9NskYIqZ6foppZsfz2Nby9bP9z/1on0Pm0sa5+dy/Lxv2n+hl7VDHn5bPb/XSN/GbQ5AJmGRraZ/zFVeeYf2PB7t7Y6w73//UP2WvhOs3LrEJ7mKF4btQU/PGE/GqqEZYN6JzMJTW6wl2loZKv3V7BgRH8aM5m8ZbKqM1Df2HRcn2o4cCQ8koSx15bw4knx3Oyc6Q3c+ErcbXcYDH+bVEWPqjL8xXaVqsN2ZvnBmia/9+H7Nd3+i1MxfWxU9VPAGOB3LRT7CbB38tgWWAI8qKr5fvsL2R94DxgBTAAuVtV9k2mfSZ53NLN+ZjY59b6vAccD/YCXgeeBU1PTDwEGAve0IZZ2WbFihb/u4q8b5y4nre7RNwH45LczyauunjXPz2dB/YZdubEqw9oe+X8C0r9sPesb2WXxvLzlqom1Om9tNZiaunpqa1KVwDkJS2NVhtre1RuSmhbkJjUAq+vhzws3DL+0CJYsj9vkmfkbfpvfWgZzFq9cP9wVPi9/7a835rVrm4pJbIAtkucF+SaqagY4BbjUzBaY2SpiLcpOwGfbsJw3zewmM6s3sxeAVwAt4n3XmdkcM2swszrgZmKyk/U14E4zq21DLO3Sv39/f93FX1ftPoy0mhN3B2Dg2XuTjwzoRe8DRzGmz4aMobq+kZ7rmmYQIecZYHVNNTZ8u7zzjeWEcW8uYl2PDH1q16UmNq34q65voM/qtWQa8mQtOWrynEoMqYHDU2EcvI2w+aC4TY4YvSGJGjsMdhjWb/1wV/i8/LW/3pjXrm0qqY/Nh8nzVsA/8kzfAqgB1veQNLOVqroYGAn8pcjl5HZAXgUUs4fOzRm+B7hBVfdL4j2atiVYrowNeeksVnzzQepfXUSfiw6g5pidAOix+3AGPHEqK7/5IDKsP/2uOJCGfy6l5yHbUzV6CI/8Z+CyqR8w681aQoPwdk0/GtcEBGjICKt7Zuhd18DSgT3p2QBV9YE1vTJ857CTefHXl+c9E3pIR1Pbq5q+a+v4uLqG/ivWsKp3D5D4AzN8gLDf1hmqVzZw6Ph+TF9Yz+MLq8j0yDBigLDjENiqHwypEWobYHhf4QvbwqPvwKp1gfkrYGAvOIhOjhIAABJ/SURBVG3XDCP7wxGjA2sa4Cuf3pDMTNm/inHDGlm6Bk78tFCV6fa16c51jjL8qlRMYmNmb6rqbOBEYHqeIh8CdcB2wBwAVe0HDAWy9fArgb7ZN6jqiDaG0dKpapNpZrZGVW8j1tTMAmaZ2attXJ4rU1JdxYCbj8k7reaQMdS8df6GEV8Ys/5lrx7CNWds2Y4l7sri23sztK5phaEAP31pfJHziD83JxZZ+uw98o//6s75f4mP27GSKqCd21TKL7OpmMQm8U1in5kPgF+Y2UJVHUpMHt4Gbgcmq+rfgeXAj4E3gOx/yRvwb6p6PbAGuKqNy/+QmMDsABRzuffNyTI/D1zbxmU5t0m9seduDH2+6W0VGqi8HxHnXNdWUac4ZvYksB+wM/Caqq4AniPWyjwNnE9MJF4idgAeDhxlZtnrXH9CTHTmEPvONL8rYMvLrwUuA+5S1eWqekkr5d8A/krsiFzyS9FdZdt+wTzqczoEf8SwAqWdc91CGf7zcEVd7t0dqeo0YK2ZndGJi/WdwjU3+Cs0LK+jKrV7fMwABoZppYvJucrQcZd7T65rern3Zb26fXrjtchdWHKJ+peBz5U6FucY3I/M8jVNRgnN/7TPOdeNdPs0prmKaorqTlT1HmIz1BQze73U8TjHz89sVpU3gFUlCcU55wrxGpsuysyOK3UMzjVxxDg/E3LOdXme2DjnnHOVKs8tTbo7PwFzzhVvxZ3JTRQSPz+thME45zZaGV4V5TU2zrni9evDQw/EZGbChAklDsY555rzGhvnnHPOlQ2vsXHOOecqVZk0P6V5YuOcc85VrPLLbLwpyjnnnHNlw2tsnHPOuUpVfhU2XmPjnHPOufLhNTbOOedcpfIaG+ecc865rssTG+ecc86VDW+Kcs455yqVN0U555xzznVdntg455xzriARmSsiu5Y6jmJ5U5RzzjlXqaT82qK8xsY555yrVJLzKPZtInuJyF9E5NXkea9k/BQRuTB5/W8i0igiQ5PhR0TkXzf5OuTGFkLo6GW4bkZEHgM2L3Ucramurt68vr5+SanjaI/uHDt07/i7c+zQvePvzrFDSeNfEkI4rATLBWJTFHBkCOH1ZLgnMBs4PYQwXUQOBqYCY4D9gQtCCIeJyK+A3YCfAfcCi4CRIYTVHRpwCMEf/uiWj3HjxlmpY6jE2Lt7/N059u4ef3eOvRzib+8DmAvsmhreDZidU2Z2Mr438BHQE3gdOAy4BRgPzOiMeL0pyjnnnHNtIUC+5p4QQqgFZgEnAu8D/wvsAxwM/E9nBOeJjXPOOefa4g2gl4j8C0Dy3AN4M5n+FPAD4KkQQh0wH5iUjO9wflWU685uLnUAG6E7xw7dO/7uHDt07/i7c+zQ/ePfGNNFpD41PBH4mYj0BVYBx4UQ1ibTngImsyGReQrYF3ixMwL1zsPOOeecKxveFOWcc865suGJjXPOOefKhvexcd2Gqk4DvgBk/0fibjO7qkDZy4id1QCmmdnkDg+wBar6X8SrAuqAlcC3zczylJsE/JR4eSXAO2Y2sZPCzI3lU8BtwGbAUuAUM3srp0wV8T8qDiNeJXG1md3S2bHmUtXNgDuA7YnbfDZwppl9mFNuGkXuU51JVecCa5IHwPfM7PGcMn2I/x0yDqgHLjCzhzozzlyqOgq4PzVqEDDAzIbklLsC+CawMBn1nJmd0xkx5lLV64BjgVHAbmb2ejK+1f0/KdclvwOVzBMb191cbWa/aKmAqu4PfBnI3tvkBVV92sz+r8OjK+xR4DwzW6eqRwJ/IB5085luZsd1XmgF3QT8l5ndqaonAb8CDsop81Xin3LtQDwAzFTV6WY2t1MjbS4A15jZDABVvRa4GvhanrKt7lMlclz2IFvABcAKMxujqjsAz6jqGDNb2UnxNZN87ntkh1X1pxQ+ztxuZhd0RlytuB+4AXgmZ3wx+z903e9AxfKmKFeOjif+aNaaWS1wezKuZMzsITNblwz+BdhaVbvs909VhwJjgbuSUXcBY1V1i5yixwO/NrPGpDbkfmJSWVJm9lE2qUk8D2xbonA6yvHEgy9JTYIBh5c0ohRV7Uk86N9a6lhaYmbPmtm89Lg27P/QRb8DlazL/rA6V8B3VPU1Vb1fVXcqUGYb4N3U8HvAyI4PrWjnAg+bWWOB6Qeo6iuq+n+qekRnBpYyElhgZg0AyfNCmm/Hrr6tSRLIs4EHChQpZp8qhd+q6quqeqOqDsozvatv+6OI+9DLBaafkKzfE6q6T2cGVoRi93/o+p9DxfGmKNdlqOrLxB+JfIYBlwDvm1mjqp4CPKaqo7M/PqXUWuzZGFX1BOArxPup5PMQ8Aczq1XVPYnreKCZ/WOTB105fk7s15Svuamr7lPjzWyeqvYi9rn6BXBSiWNqq9MpXFtzE3BV0jR7CPAnVd3JzJZ2XniuXHli47oMMxvbSpEFqbK3q+pPgK1perYE8Ywp3eywDTCPDlRE7KjqROAq4GAz+6DAfJakXs9U1WeBzwKdndjMA7ZS1Soza0g6SI6g+XbMbuuXkuHcs9eSSjqG7gBMyFdDZmbF7lOdKts0YmZ1qnoj+Wubsts+2yF6G+Lf15ecqo4ADgBOzjfdzBalXj+pqvOIfeKe7pwIW1Xs/g9d/DtQibwpynUbqrpV6vWhQAOpZCflbuAUVe2tqr2BU4D/7pwo80s6DF8PHNpSp8KcddwW2Bt4tcMDzGFmi4FXiPd7IXmemXtVEXFbf0NVM0n/g2OId/EtOVW9injF0DFmVlegTLH7VKdR1b6qOjB5LcAJxM8i193AmUm5HYC9gMc6K85WTCI2t+atgcnZ7nsQr0j6Z6dEVoQ27P/Qhb8DlcprbFx3cpuqDgMagU+Ao8ysHkBVbwEeMLMHzGyGqt5HvLOsEDsSl/pMcCqwFrhHVbPjDjazpenYgXNU9Wji5bsAF5vZzM4PF4CziNv8cmAZMUFEVR8BLk8uV78D+ByQvQz2SjN7uxTBpqnqLsDFxHvX/DnZ5u+Y2URVfQX4opktpIV9qoSGAfcmtQRVwN+Jl0aTE/u1wDRVnU1MyM4wsxUlijnXJOBb6RE5+80PVXUcMe61wMnpWpzOpKo/A74EbAlMV9WlZrYLBfb/5D1d/jtQyfyWCs4555wrG94U5Zxzzrmy4YmNc84558qGJzbOOeecKxue2DjnnHOubHhi45xzzrmy4YmNc2VCREaJSBCRrTt4OWeJyB2p4UdF5LsduUyXn4jMFpFJRZbtlP2jM4hILxF5S0Q+XepYXNfjiY2rOCIyWkTuFpFFIrJSROaJyB9FpGcyfZKIzM7zvkLjT0oOGJfnmTZDROqS5XwsIjNF5NiOWbOOJyJ9gSuBK7LjQgiHhxCuKVlQrUg+m/1KHUcl6IhtLSIHikiT/xYKIdQB1xH/y8e5JjyxcZXoEeB9YEegP7AP8Djxz/za4wzgI+DrIlKVZ/rkEEI/YDPiXYL/ICKfaueySu0k4LUQwpxSB+Iq3l3AQSIyptSBuK7FExtXUURkM2JCc1MI4eMQzQ8h3JScBbZ1fjsB44FTgeHA4YXKhhDqgRuJ/ya7W555nSsiM3PGbSciDSIyKhmemtQwrRCRv4vIV1qI7QoRmZ4zboaIXJoa3lVEHheRJSLynohMEZEeLazyMcCTheaZau44NYlvlYg8IiKDReRqEVmc1JSdk3r/pKRJ5Xsi8n5S5sfpOFpbbxHZXUQeE5EPReQjEXkyGT8rKfJEUmt2S4Ft1UdEbkiWsURE7heRbVLTZyQx3ZvEMEdEji60kVLrdL6IzE/ec52IbJbM4xMReSNduyEi1SJyuYi8nazDUyKya2p6DxG5PrUNv5dnueNF5Nnk/XNE5D9EpOiEXUSOFZFZSe3iLBGZmLtOOeWnZbdpoW0tInOT9Xo2GW8isle+eaTGzZVYEzoCeBSoSt67UkROBQghfEK8P9NRxa6fqwye2LiKEkJYCvwNuEVEThGRndvyw5/HmcQajIeINUFnFCoosanrHGAdMCtPkd8CO4nIHqlxk4AZIYS5yfCzwB7AIGKT0DQR2bk9gYvIUOJNB+8j3uBvH+AQ4D9beNtY4l/8t+ZYYD/iDQFHAS8Ac5LlnAb8NJ04EG8iuA0wOoljAnBBanrB9RaR4cl6PJ0sa0vgRwAhhM8k7//XEEK/EMLXC8T7E+J9ufZOYlkCPChNa+BOJd7vayDxbtu3iUifFrbBtkm8o5Nt8e/Eg/S1wGDidp+aKn8h8W/7v0hMkp8BnhSRAcn0i4Ajgc8D2yXruv5mryKyC3EfvBbYAjgCOJcCN6LMJSL7EPfBi4i1ixcDd4nI54p5fyvb+izg28AQ4B7gkdR6tTTPhcSThYZknv1CCLelirxG3CedW88TG1eJDgRmAOcRb3T3gYhclpPgbCciy9MPYm3LeiJSQzxo3JqM+g3wRWneOfOS5P3zgaOBY0MIzfrqhBCWAX8iHvhJ4jk1NX9CCL8JISwNITSEEH5PvEHmge3YBhAPorNCCL8KIawNISwAppC6J04eg4n3VGrN5BDCR0ki+RCwLoTw6xBCfQjhUeK9d/ZMlW8ELgwh1CbNXNeQbAdodb1PBmaHEKaEEFYl69KkpqolIpIhrvOlIYQFIYRVxH1jJ+Kd1bP+EEJ4LoTQCNxMTHB2aGHWtcAPknhmEZPZl0IIz4cQGoA7gTEiMjApfxrwoxDCG0nt4ZXEeykdkUw/JZk+O4RQS0z80vfEORu4O4Twp2Q7vUFMwFr6PNNOA+4NITyafE4PA38ETi/y/S35TQjhryGEtcSks5aYpG2sT4jJknPreWLjKk4IYUkI4eIQwljiGfV3gctJHUiBd0IIg9IPkhsRpnwZ6Ec8QEE8W14M5NYKXJXMY2gI4fMhhAdbCG8q8NWkduegJL77IB6AReRKEfln0lSwHPgM8ey8PbYD9s1J3m4l1ngUsgxo9Uyb2Icpa3XOcHZc/9Tw4hDC6tTwXGBrKGq9RxFvdtleWwA1wPobF4YQVhI/y5Gpcu+npq9KXqbXIdfiJAnKyt0O2fXNzmNkTgyNxO2QjWHrZDgdw+LU/LYDTsz5PL9PrP0pRpPlJ+bQdBu019zsixBvUPgeyee7kQYQ+7c5t54nNq6ihRBWhxCmEWsA9mileK4zif1lXheRRcQamSHA1yR/J+JiPAGsIZ7NTgJ+n5ydA5xITJqOBQYnydYsCnd6Xgn0zRk3IvX6XWB6TgI3MOnoXMhMoF1NX60YmtOsM4q4PaH19Z5LyzUnrd3p90OgjpgYACAi/YChwLziwt8k5uXEkCFuh2wMC5Lh7PS+xBiz3gVuzfk8B4QQdmnP8hOjU8tvbX+Cwts6HbcQmx2zn2+T+YpINU3XK50c5tqVuE86t54nNq6iSOzEOkVip9keSYfNY4k/kM+0YT47A/sCE4kJUfbxWWKNxxfbE19yln478C3gS6SaoYhnp/XEA3FGRE4n1lwUYsBYERmXrOe5ND1w3Q6oiJwuIjVJzchoETmshXneD3yh7WvWqgxwtYj0FpHRxGaWbF+K1tb7TmBHiZ2P+ySf68Gp6YtoIfFJbfPJIjIiSbB+DLwBvLiJ1q8Y04Dvisinkhq7S4Bq4OFk+h3AhSKyvYj0JjbXpZPaG4ETRGRCat/eWUQOaMPyjxWRQ0WkSkQOJ+6D2X5AM4kJ6JHJvjIR2D9nHoW29ekiMlZih/ALgT6p9TLgYIkd5XsBVwHpDuyLiJ2HmyRdItKf+H17oMj1cxXCExtXadYSzwbvI1ZhfwhcCvx7COHuNsznTODlEMKDIYRFqcerwN3J9PaaChxAbA5LH1hvI3bCnU08e9+ZFpKxEMIM4gH6MWITyDDgudT0RcC/EK90mktsZvoj8Sy9kDuAzyTJx6b0LnGd3iGu42PEAze0st5JB9MDiR2f5wMfAOkrhi4BrhSRZSLyqwLLP594gH2J2EwyHDgq6QvTWa4lXsL8BHEdDiJ2xM32aZpC/FuC54nb6T3idgMghPA6sabvPOLnvZiYrBTVVBlC+DOxT9d1xH3hGuCkEMLzyfQ5xA7ANxO/O4cB9+bMptC2vhn4WTLf44EjQggfJ9N+S0xOXiY2fb1H/Jyzcb1JTNpeTJrYsp2hTwT+N4TwVjHr5yqHxOZO55wrjoicBewbQijqapsi5jeJ2HHX/4+kDInIXOLne2drZdswz17A68Tk8x+bar6uPFSXOgDnXPcSQrgJuKnUcbjKlVw11lK/KlfBvCnKOeecc2XDm6Kcc845Vza8xsY555xzZcMTG+ecc86VDU9snHPOOVc2PLFxzjnnXNnwxMY555xzZeP/A+5ZOFUL+5otAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(interventional_shap_values, X_test, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 1: Summary plot of the interventional perturbation Tree SHAP explanations for the test set \n",
    "<a id='figure_8'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(interventional_shap_values, X_test, feature_names, plot_type='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 2: Most important features as predicted by the interventional perturbation Tree SHAP algorithm\n",
    "<a id='figure_9'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One might be tempted to proceed to compare the feature rankings displayed above with the ranking provided by the path-dependent Tree SHAP [example](path_dependent_tree_shap_adult_xgb.ipynb). However, these algorithms have different ways of estimating the effect of missing features and:\n",
    "\n",
    "1. The length of the bar represents the average magnitude of the points in the summary plot above; each point is the average of the shap values computed for a given instance $x$ with respect to $R$ different background samples. Hence, one can consider that for each instance to be explained the shap value of the $j$th feature is a random variable, denoted by $\\Phi_{i,j}$. One way to define the importance of the $j$th feature, $I_j$, is \n",
    "\n",
    "$$\n",
    "I_j = \\frac{1}{N} \\sum \\limits_{i=1}^N |\\mathbb{E}[\\Phi_{i, j}]|,\n",
    "$$\n",
    "\n",
    "where the expectation is taken over the background distribution and $N$ is the number of instances explained. This corresponds to the notion of feature importance according to which a feature is important for explaining the model behaviour over a given dataset if:\n",
    "\n",
    "-  either the instances to explained are consistently affected by the feature, or the feature has a particularly large impact for certain subgroups and a small or moderate impact for the remainder. Traditional global explanation feature importances hide this information whereas the summary plot reveals why a particular feature was deemed important\n",
    "\n",
    "- locally, one also requires that cancellation effects are not significant. In other words, for a particular instance, a feature would be considered as not important if, across different backgrounds, cancellation effects result in a small  average for the effect.\n",
    "\n",
    "It should be noted that the error $I_j$ is inversely proportional to the square root of the size of the background dataset for a given dataset to be explained, so it is important to select a sufficient number of background samples in order to reduce the error of this estimate. \n",
    "\n",
    "2. The two methods explain the dataset with respect to different expected values, so the contributions will be different. This also arises because of the different set of conditional assumptions are made when estimating the individual contributions, as explained in the algorithm [overview](../doc/source/methods/TreeSHAP.ipynb).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of analysing feature importance rankings, it is perhaps more instructive to look at the dependence plots and see if the conclusions from the previous model interpretation hold. Although the decision plots in Figure 3 show the same patterns as their counterparts in the path-dependent [example](path_dependent_tree_shap_adult_xgb.ipynb), different variables are found to have the strongest interaction with the variables of interest so the colouring of the plot is different. This is expected since the different conditional independence assumptions give rise to different magnitudes for the shap values, and therefore the estimations for the Pearson coefficients will be affected.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _dependence_plot(features, shap_values, dataset, feature_names, category_map, display_features=None, **kwargs):\n",
    "    \"\"\" \n",
    "    Plots dependence plots of specified features in a grid.\n",
    "    \n",
    "    features: List[str], List[Tuple[str, str]]\n",
    "        Names of features to be plotted. If List[str], then shap \n",
    "        values are plotted as a function of feature value, coloured \n",
    "        by the value of the feature determined to have the strongest\n",
    "        interaction (empirically). If List[Tuple[str, str]], shap\n",
    "        interaction values are plotted.\n",
    "    display_features: np.ndarray, N x F\n",
    "        Same as dataset, but contains human readable values\n",
    "        for categorical levels as opposed to numerical values\n",
    "    \"\"\"\n",
    "    \n",
    "    def _set_fonts(fig, ax, fonts=None, set_cbar=False):\n",
    "        \"\"\"\n",
    "        Sets fonts for axis labels and colobar.\n",
    "        \"\"\"\n",
    "\n",
    "        ax.xaxis.label.set_size(xlabelfontsize)\n",
    "        ax.yaxis.label.set_size(ylabelfontsize)\n",
    "        ax.tick_params(axis='x', labelsize=xtickfontsize)\n",
    "        ax.tick_params(axis='y', labelsize=ytickfontsize)\n",
    "        if set_cbar:\n",
    "            fig.axes[-1].tick_params(labelsize=cbartickfontsize)\n",
    "            fig.axes[-1].tick_params(labelrotation=cbartickrotation)\n",
    "            fig.axes[-1].yaxis.label.set_size(cbarlabelfontsize)\n",
    "\n",
    "    # parse plotting args\n",
    "    figsize = kwargs.get(\"figsize\", (15, 10))\n",
    "    nrows = kwargs.get('nrows', len(features))\n",
    "    ncols = kwargs.get('ncols', 1)\n",
    "    xlabelfontsize = kwargs.get('xlabelfontsize', 14)\n",
    "    xtickfontsize = kwargs.get('xtickfontsize', 11)\n",
    "    ylabelfontsize = kwargs.get('ylabelfontsize', 14)\n",
    "    ytickfontsize = kwargs.get('ytickfontsize', 11)\n",
    "    cbartickfontsize = kwargs.get('cbartickfontsize', 14)\n",
    "    cbartickrotation = kwargs.get('cbartickrotation', 10)\n",
    "    cbarlabelfontsize = kwargs.get('cbarlabelfontsize', 14)\n",
    "    rotation_orig = kwargs.get('xticklabelrotation', 25)\n",
    "    \n",
    "    alpha = kwargs.get(\"alpha\", 1)\n",
    "    x_jitter_orig = kwargs.get(\"x_jitter\", 0.8)\n",
    "    grouped_features = list(zip_longest(*[iter(features)] * ncols))\n",
    "    \n",
    "    \n",
    "    fig, axes = plt.subplots(nrows, ncols,  figsize=figsize)\n",
    "    if nrows == len(features):\n",
    "        axes = list(zip_longest(*[iter(axes)] * 1))\n",
    "\n",
    "\n",
    "    for i, (row, group) in enumerate(zip(axes, grouped_features), start=1):\n",
    "        # plot each feature or interaction in a subplot\n",
    "        for ax, feature in zip(row, group):\n",
    "            # set x-axis ticks and labels and x-jitter for categorical variables\n",
    "            if not feature:\n",
    "                continue\n",
    "            if isinstance(feature, list) or isinstance(feature, tuple):\n",
    "                feature_index = feature_names.index(feature[0])\n",
    "            else:\n",
    "                feature_index = feature_names.index(feature)\n",
    "            if feature_index in category_map:\n",
    "                ax.set_xticks(np.arange(len(category_map[feature_index])))\n",
    "                if i == nrows:\n",
    "                    rotation = 90\n",
    "                else:\n",
    "                    rotation = rotation_orig\n",
    "                ax.set_xticklabels(category_map[feature_index], rotation=rotation, fontsize=22)\n",
    "                x_jitter = x_jitter_orig\n",
    "            else:\n",
    "                x_jitter = 0\n",
    "            \n",
    "            shap.dependence_plot(feature, \n",
    "                                 shap_values,\n",
    "                                 dataset,\n",
    "                                 feature_names=feature_names,\n",
    "                                 display_features=display_features,\n",
    "                                 interaction_index='auto',\n",
    "                                 ax=ax,\n",
    "                                 show=False,\n",
    "                                 x_jitter=x_jitter,\n",
    "                                 alpha=alpha\n",
    "                                )\n",
    "            if i!= nrows:\n",
    "                ax.tick_params('x', labelrotation=rotation_orig)\n",
    "            _set_fonts(fig, ax, set_cbar=True)\n",
    "    \n",
    "plot_dependence = partial(\n",
    "    _dependence_plot, \n",
    "    feature_names=feature_names,\n",
    "    category_map=category_map,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dependence(['Marital Status', 'Age', 'Hours per week', 'Occupation'], \n",
    "                interventional_shap_values, \n",
    "                X_test, \n",
    "                display_features=X_display, \n",
    "                nrows=2,\n",
    "                ncols=2,\n",
    "                figsize=(22, 10),\n",
    "                alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 3: Decision plots of the variables `Marital Status`, `Age`,  `Sex`, `Race`, `Occupation`, `Education` using the interventional perturbation Tree SHAP algorithm for the test set\n",
    "<a id='figure_10'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By changing, value of `feature` below, one can recolour the decision plots according to the interactions estimate from the path-dependent perturbation example. Generally, the same interaction patterns are observed, with the exception of `Age`, where the interaction with the `Capital Gain` feature is not conclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_dep_interactions = {\n",
    "    'Marital Status': 'Hours per week',\n",
    "    'Age': 'Capital Gain',\n",
    "    'Hours per week': 'Age',\n",
    "    'Occupation': 'Sex',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Occupation'\n",
    "x_jitter = 0.5 if feature in ['Occupation', 'Marital Status'] else 0\n",
    "shap.dependence_plot(feature, \n",
    "                     interventional_shap_values,\n",
    "                     X_test,\n",
    "                     feature_names=feature_names,\n",
    "                     display_features=X_display,\n",
    "                     interaction_index=path_dep_interactions[feature],\n",
    "                     alpha=0.5,\n",
    "                     x_jitter=x_jitter\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If interaction effects are of interest, these can be computed exactly using the path-dependent perturbation algorithm as opposed to approximated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### White-box vs black-box model explanations: a comparison with Kernel SHAP \n",
    "<a id='convergence'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main drawback of model-agnostic methods such as [Kernel SHAP](../doc/source/methods/KernelSHAP.ipynb) is their sample complexity, which leads to variability in the results obtained. Given enough samples, the feature attributions estimated Kernel SHAP algorithm approach their exact values and give rise to the same feature importance rankings, as shown below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, both the Tree SHAP and Kernel SHAP algorithms are used to explain `100` instances from the test set using a background \n",
    "dataset of `200` samples. For the Kernel SHAP algorithm, each explanation is computed `10` times to account for the variability in the estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_background_samples = 200\n",
    "n_explained = 100\n",
    "background_dataset, y_background = resample(X_train, y_train, n_samples=n_background_samples, replace=False, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_display_background = decode_data(background_dataset)\n",
    "X_explain = X_test[:n_explained, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_explainer = TreeShap(model, model_output='raw', task='classification')\n",
    "tree_explainer.fit(background_dataset)\n",
    "explanation = tree_explainer.explain(X_explain)\n",
    "tree_shap_values = explanation.shap_values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`xgboost` requires the model inputs to be a `DMatrix` instance, so `predict_fcn` needs to account for this transformation to avoid errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_fcn = lambda x: model.predict(xgb.DMatrix(x, feature_names=feature_names))\n",
    "kernel_explainer = KernelShap(predict_fcn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_explainer.fit(background_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assess convergence, Kernel SHAP is run with the numbers of samples specified in `n_samples` for `n_runs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_runs = 5\n",
    "n_samples = [50, 100, 500, 1000, 5000, 10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = defaultdict(list)\n",
    "times = defaultdict(list)\n",
    "\n",
    "for n_samp in n_samples:\n",
    "    print(f\"Number of samples {n_samp}\")\n",
    "    for run in range(n_runs):\n",
    "        t_start = timer()\n",
    "        exp = kernel_explainer.explain(X_explain, nsamples=n_samp)\n",
    "        t_end = timer()\n",
    "        times[str(n_samp)].append(t_end - t_start)\n",
    "        results[str(n_samp)].append(exp.shap_values[0])\n",
    "        \n",
    "        results['time'] = times\n",
    "\n",
    "        with open('data/kernel_convergence.pkl', 'wb') as f:\n",
    "            pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/kernel_convergence.pkl', 'rb') as f:\n",
    "    convergence_data = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compare the two algorithms, the mean absolute deviation from the ground truth provided by the Tree SHAP algorithm with interventional feature perturbation is computed. For each number of samples, either the maximum mean absolute deviation across the feature, or the mean of this quantity across the features is computed. This calculation can be performed for one instance, or averaged across an entire distribution. The plots below show that all these quantities approach to the ground truth values. A threshold of $1\\%$ from the effect of the most important feature (`Marital Status`) is depicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_errors(tree_shap_values, convergence_data, instance_idx=None):\n",
    "    \"\"\"\n",
    "    Compute the mean and max maximum absolute deviation of Kernel SHAP values\n",
    "    from Tree SHAP values for a specific instance or as an average over instances.\n",
    "    If instance_idx is set, then the errors are computed at instance level.\n",
    "    \"\"\"\n",
    "    \n",
    "    mad = []\n",
    "    for key in convergence_data:\n",
    "        if key != 'time':\n",
    "            mad.append(np.abs(tree_shap_values - np.mean(convergence_data[key], axis=0)))\n",
    "    \n",
    "    if instance_idx is not None:\n",
    "        err_max = [max(x[instance_idx, :]) for x in mad]\n",
    "        err_mean =[np.mean(x[instance_idx, :]).item() for x in mad]\n",
    "    else:\n",
    "        err_max = [max(x.mean(axis=0)) for x in mad]\n",
    "        err_mean =[np.mean(x.mean(axis=0)).item() for x in mad]\n",
    "    \n",
    "    return err_max, err_mean \n",
    "\n",
    "\n",
    "def plot_convergence(err_mean, err_max, n_samples, threshold, instance_idx=None):\n",
    "    \"\"\"\n",
    "    Plots the average error across the features and the maximum error across \n",
    "    features as a function of the number of samples Kernel SHAP uses to estimate\n",
    "    the contributions. \n",
    "    \"\"\"\n",
    "    \n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "    \n",
    "    ax1.loglog(n_samples, err_max, '--*')\n",
    "    ax1.plot([0] + n_samples, [threshold]*(len(n_samples)+1), '--', color='gray', linewidth='3')\n",
    "    ax1.grid(True)\n",
    "    ax1.set_ylabel('Estimation error (max over all features)')\n",
    "    ax1.set_xlabel('Number of samples')\n",
    "    \n",
    "    ax2.loglog(n_samples, err_mean, '--*')\n",
    "    ax2.plot([0] + n_samples, [threshold]*(len(n_samples)+1), '--', color='gray', linewidth='3')\n",
    "    ax2.grid(True)\n",
    "    ax2.set_ylabel('Estimation error (mean over all features)')\n",
    "    ax2.set_xlabel('Number of samples')\n",
    "    if instance_idx is not None:\n",
    "        plt.suptitle(f'Convergence of the Kernel SHAP algorithm to exact shap values (instance {instance_idx})')\n",
    "    else:\n",
    "        plt.suptitle('Convergence of the Kernel SHAP algorithm to exact shap values (mean)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0.01*np.max(np.mean(np.abs(tree_shap_values), axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_max, err_mean = get_errors(tree_shap_values, convergence_data, instance_idx=0)\n",
    "plot_convergence(err_max, err_mean, n_samples, threshold, instance_idx=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 4: Converge of Kernel SHAP to true values according to the maximum error (left) and mean error (right) for instance 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T4i6cioQa7lNK8jm8BIwoIkehPOTw8rkW5XI3nzGFkl/uFBljvBKRaji1MK1Vda3feYzJT0TexekM+ne/s5jwERHFuQ6t9jtLqLl3CRbhdCrf6neePCIyGGeEFK9Dg5oKygrZxnjkXlin4TQTeRKnprq72g+RiQDubfA9OLV2eRP+9FHVEk+mY8qPaC5kG1PeWXMRY7y7EOdW9Rac263DrIBtIkhDnGH10oFngT9YAdsYY/xjNdnGGGOMMcaEmNVkG2OMMcYYE2JWyDbGGGOMMSbErJBtjDHGGGNMiFkh2xhjjDHGmBCzQrYxxhhjjDEhZoVsY4wxxhhjQswK2cYYY4wxxoSYFbKNMcYYY4wJMStkG2OMMcYYE2JWyDbGGGOMMSbErJBtjDHGGGNMiFkh2xhjjDHGmBCL+EK2iLQQkVdE5H2/sxhjjDHGGONFWAvZIvKqiOwQkR/zbT9HRFaIyGoRuauoY6jqGlW9Npw5jTHGGGOMCaXYMB9/PPAc8HreBhGJAZ4HzgI2AfNEZBIQAzySb/9rVHVHmDMaY4wxxhgTUmEtZKvqTBFplm9zL2C1qq4BEJF3gAtV9RFgUGlfS0RGAaMAKleu3KNx48alPVTEyM3NJRCI+BY9ppTs+41upf1+V65cuUtV64UhUsSqW7euNmvWzO8YJywjI4OqVav6HcOEiX2/0a803/GCBQsKvWaHuya7IMnAxqD1TUDvwp4sInWAh4BuInK3Wxj/FVUdC4wFSE1N1fnz54cusU/S0tLo37+/3zFMmNj3G91K+/2KyPrQp4lszZo1w67ZJtLZ9xv9SvMdF3XN9qOQLQVs08KerKq7gRvCF8cYY4wxxpjQ8uNe9SYguC1HCrDFhxzGGGOMMcaEhR+F7HlAaxFpLiLxwDBgUigOLCKDRWTs/v37Q3E4Y4wxxhhjSiXcQ/hNAGYDbUVkk4hcq6rZwM3AF8AyYKKqLg3F66nqZFUdVaNGjVAczhhjjDHGmFIJayFbVS9X1UaqGqeqKar6irt9iqq2UdWWqvpQODN4tePAES59aTY7Dh7xO4oxxphi2DXbGBPpbPww17PTVjFv3R6enbrK7yjGGBMSIlLVnZsg6tg12xgT6fwYXSSitL33czKzc4+tvzlnA2/O2UCl2AArxpzrYzJjjCkZEQng9HMZDvQEMoFKIrITmAKMVdVyXSq1a7YxpryIqprs0nR8/PqOM7igaxJxMc7IgnExwoVdk/j6zjPCFdMYY8LlK6AlcDfQUFUbq2p94DTgO+BREfmdnwFPVN41Oz7G+fVVKTZg12xjTESKqppsVZ0MTE5NTR3pdZ/61RNIrBRLdo4iQFaOMmfNbg4czqJ+YkL4whpjTOgNVNWs/BtVdQ/wAfCBiMSVfazQybtmZ+U4tdmZ2bnExwTsem2MiTiearJFpL6IXCQiN4nINSLSy70tGRV2pWcy/OSmTLq5Lz2b1WJX+lHOefprxn29xu9oxhjjWV4BW0Raikgl9//9ReRWEakZ/JzyLO+aPWZIJwT430/bOXw0x+9YxhhznCJrskXkDOAuoDawCNgBJABDgJYi8j7wpKoeCHfQcHppROqx/793wynsSs/k8f+uoGW9agBk5eQSGxBECpqs0hhjIs4HQKqItAJewZmL4G3gPF9ThUjwNbtWlXhunrCQm95eyEsjehAXEzX1P8aYcq645iLnASNVdUP+B0QkFhgEnIVzQY8adatV4rGhnY+tPzPV6cX+4IWdaNsw0cdkxhjjSa6qZovIRcDTqvovEVnkd6hwOL9zI/Yd7sR3a/ag6ncaY4z5RZF/8qvqXwoqYLuPZavqx6oaMQXscM342KR2FVZsP8h5z37Ng5N/4sCRcn+31RgT3bJE5HLgKuBTd1u5botdlOG9m/LssK7ExwbIyMxGrbRtjIkAXttk/1FEqovjFRFZKCJnhztcSYVrxsdLezbmq9v7c2lqY177di1nPjGDmSt3hvQ1jDEmhK4G+gAPqepaEWkOvOlzprASEfZkHGXwc9/w/Fer/Y5jjDGeh/C7xm13fTZQD+cC/mjYUkWgWlXjeeTik/jkpr40q1OFeomVAKzGxBgTcVT1J+BOYKG7vlZVo/6aXbNyHF1SavLElyt587v1fscxxlRwXgvZeT3+zgNeU9Xvg7ZVKJ1TavLeDX1o36g6ALdP/J6/ffIj+w9ZExJjTGQQkcHAYuC/7npXEZnkb6rwCwSEfwztzJnt6nPfJz/y2ZKtfkcyxlRgXgvZC0TkS5xC9hcikgjkFrNP1MobZSQ3V6leOY43v1vPGU+m8e68DeTmWs22McZ39wO9gH0AqroYaO5noLISFxPg+Su606NJLW57dxHf/rzL70jGmArKayH7Wpyh/Hqq6iEgHqfJSIUWCAj3X9CRybecSou6Vbnzgx+4+N/fsmZnut/RjDEVW7aq5u8BXmFqACrHx/DK73syqHMSrevbiFDGGH94LWQr0AG41V2vijNedkQJ1+gixemYVIP3bujDU5d24cDhLKolRNVEmsaY8udHEbkCiBGR1iLyL+Bbv0OVpRqV4/jnZV2pl1iJrJxcNu877HckY0wF47WQ/QJOT/XL3fWDwPNhSXQCwjW6iBciwsXdU5g6uh/1ExPIzVWu+8883pqznhxrQmKMKVu3AB2BTJxJaPYDt/mayEf3fPQDQ//9rRW0jTFlymshu7eq3gQcAVDVvThNRkw+gYDTXvvAkSwOHMnmno9+ZMjzs1i0Ya/PyYwxFYGIxAAPqOo9qtrTXe5V1SN+Z/PL709pTnpmNiNemcPu9Ey/4xhjKgivhews98KtACJSjwrc8dGLmlXieXfUyTwzrCs7Dh7hohe+5Y73v7eJbIwxYaWqOUAPv3NEkg5J1Xnlqp5s3nuYq8fPIz0z2+9IxpgKwGsh+1ngI6C+iDwEfAM8HLZUUUJEuLBrMtNu78/1p7dg/rq9xMd4/ciNMabUFonIJBEZISIX5y1+h/JTr+a1eWF4d5ZuOcDodxf7HccYUwF46qGnqm+JyAJgAM742ENUdVlYk0WRapViufu89ow+uw2VYmM4kpXDLRMWMer0FvRsVtvveMaY6FMb2A2cGbRNgQ/9iRMZBrRvwD8v60rzOlX9jmKMqQCKLWSLSABYoqqdgOXhjxS9KsXGALB+9yGWbt7Pb1+czcXdkrnrvHbUT4y4wVqMMeWUqlb4IVYLc0GXpGP/n7V6F6e0rHNs7gNjjAmlYtsuqGou8L2INCmDPCfEryH8Sqptw0Sm3t6PG/u3ZPKSLQx4YgavfLPWRiExxoSEiLwmIq/mX8rw9VuIyCsi8n5ZvWZJfb1qJ8PHzeGJL1f4HcUYE6W8NhBuBCwVkWluO79JkThFr59D+JVUlfhY7jinHV/cdjrdm9Ziyg9bj5unfseBIzw85zA7DlbYAQGMMaX3KfCZu0wDqgOeZslyC+Q7ROTHfNvPEZEVIrJaRO4q6hiqukZVry1l9jJxaqu6XN6rMc9/9TPjvl7jdxxjTBTyOmvKA2FNUYG1qFeN8Vf3JD0zm0BA2Hkwk8e/WE52rrJqby7PTl3FmItO8jumMaYcUdUPgtdFZAIw1ePu44HngNeD9o/BmRvhLGATMM+taIkBHsm3/zWquqN0ycuOiDBmyEnsO5TFmM+WUatKPJf0SPE7ljEminjt+Dgj3EEqMhEhMSEOgFMenUZWzi/NRt6cs4E352ygUmyAFWPO9SuiMaZ8aw14avKnqjNFpFm+zb2A1aq6BkBE3gEuVNVHgEEhzFmmYgLC08O6cmD8PO76cAm9W9QmpVYVv2MZY6KEp0K2iBzEHSMbZxKaOCBDVauHK1hFNevOM/nrRz8wffkO8ppo16sWz6e3nOZvMGNMuZHvmg2wDbjzBA6ZDGwMWt8E9C7i9esADwHdRORutzCe/zmjgFEADRo0IC0t7QTinZjfNVN6Vo9n9fdzWX0Cx0lPT/f1fZjwsu83+oX6O/Zak50YvC4iQ3BqNkyI1a+eQIPqCSgQG4DsXKibWIkGNZzRR16c8TN9WtShc0oN6xFvjClQ/mt2CBR0sSm0p7aq7gZuKOqAqjoWGAuQmpqq/fv3P5F8JyzvPuGs1buoWSWOjkkl79uTlpaG3+/DhI99v9Ev1N+x1zbZx1HVj4vr+GJKb1d6JsN7N6VNYDsrcxuw0+38uCs9k2emruLRrOW0a5jIpamNGdItmdpVbYZ7Y8wvRGSaqg4oblsJbAIaB62nAFtKmy9SZeXk8tePfiAjM4cP/tCHpjaetjHmBHhtLhI8U1gASKWIWgxzYl4akQpAWtouruzf6dj2utUqMeeeAUz+fgsT523kwU9/4tHPl/PyVan0a1PPr7jGmAghIglAFaCuiNTilxro6kBSoTsWbx7QWkSaA5uBYcAVJ5I1EsXFBHjlqlR+++JsfvfKHD644RTqV7c5DIwxpeN1CL/BQctvgIPAheEKZQpXPSGO4b2b8snNp/Lf207jdyc3pWvjmgBM/n4LT325go17Dvmc0hjjk+uBBUA799+85ROc0UGK5Y5EMhtoKyKbRORaVc0Gbga+AJYBE1V16YmGjcS5DVrVT+S1q3uxO/0oV746l/2HsvyOZIwpp7w2FxmnqrOCN4hIXyCihmkSkcHA4FatWvkdpUy0a1idvw3ucGx98cZ9vDprLc9OX03fVnW4NLUxv+nYkIS4GB9TGmPKiqo+AzwjIreo6r9KeYzLC9k+BZhyIvkKOOZkYHJqaurIUB73RHVtXJOxI1K5evxc3pq7nhv7V4zfKcaY0PJak13QxbpUF/BwKk+T0YTDfYM68M2dZ/KngW1Yv/sQf3xnMSNfn+93LGNMGVPVf4lIJxG5VESuzFv8zlWenNq6Lh/d2JcbTm/pdxRjTDlVZE22iPQBTgHqicjooIeq40xCYCJMcs3K/HFga245sxWz1+wmbwCSfYeOcvX4eVzULZkLuyRTo0qcv0GNMWEjIn8H+gMdcGqfzwW+IWiCGVO8TslOhc2G3Yd4e+4G7vhNWwIBG9XJGONNcTXZ8UA1nMJ4YtByABga3mjmRAQCQt9WdTmlZV0Atu4/QmZWLn/7ZCm9Hp7KH99ZxLerd5Gba/1XjYlCQ4EBwDZVvRroAlTyN1L59b9l23lxxs88+OlPqNo10xjjTZE12e5MjzNEZLyqri+jTCYM2jeqzpQ/nsaPm/fz7ryNfLx4M58s3kLan/vTrG5VcnKVGKuhMSZaHFbVXBHJFpHqOP1nWvgdqry6pm8zNu89zKuz1lKnajy3DGjtdyRjTDngtePjIRF5HOgIHBvPSFXPDEsqEzadkmvQKbkG95zfnjlr99CsrjMO7K0TFpGemc1lPRszsH0D4mO9Ntc3xkSg+SJSE3gZZ3SRdGCuv5F+rbx0VhcR7j2/PfsOHeXJ/62kVtV4fndyU79jGWMinNeS1FvAcqA58ACwDmfcVFNOJcTFHDe2dtuGiazcfpAb31rIyY9M4/8+/YnVOw76mNAYU1qqeqOq7lPVF4GzgKvcZiMRpTx1Vg8EhMeGdubMdvX5YOEmsnNy/Y5kjIlwXmuy66jqKyLyx6AmJDPCGcyUrVsHtOamM1rx9aqdTJy/kddnr6NqpVhGn5VIVk4uR7JySEywzpLGlAciIsBwoIWqPigiTUSkl6pGXG12eRIXE+D5K7qTq0psjN3tM8YUzWshO280/q0icj7OdLop4Ylk/BITEPq3rU//tvXZnZ5JwB2aZPryHdz2zmLOO6kRl/VsTM9mtRCx9tvGRLAXgFzgTOBBnAnEPgB6+hkqGlSOdwbWysjMZvTExdzQryXdmtTyOZUxJhJ5/VN8jIjUAG4H/gyMA/4UtlTGd3WqVaJW1XgAWtStypBuyXyxdBuXvjSbM5+cwQtpq8nMzvE5pTGmEL1V9SbgCICq7sUZLcqESMbRbJZtPcjV4+exars1rTPG/JqnQraqfqqq+1X1R1U9Q1V7qOqkcIczkaF1g0Qeufgk5t4zgCd+24V61Srx7ryNxAWc02fl9oNkWftEYyJJlojEAAogIvVwarZNiNRPTOCNa3sRGwgw4pW5LN64l4fnHGbHwSN+RzPGRAhPhWwRaSMi00TkR3e9s4jcG95oJScig7F3NnsAACAASURBVEVk7P79+/2OEpWqxMcytEcKE2/ow2e3nkYgIGRm53DpS7Pp88h0Hvl8GT/vTPc7pjEGngU+AuqLyEM4E9E87G+kXyvv1+ymdary+jW9yDiazYhxc1m1N5dnp67yO5YxJkJ4bS7yMnA3bttsVV0CDAtXqNIqTz3Vy7tqlZzm/LGBAE8M7UK3JjUZ9/VaBjw5g6H//pZ56/b4nNCYikdEmgOo6lvAHcAjwFZgiKq+52e2gkTDNfuiF2Zx8Eg2BzOzUeDNORtodtdntL33c7+jGWN85rXjYxVVnZuvs1t2GPKYciYmIAzs0ICBHRqw4+ARPly4mYnzNpJ3pqzfncGu9KN0b1LTOksaE37vAz1EZJqqDsAZetWE0dd3nMGYKcv4cuk2jmTlkhAXoGbleK47rRlHs3NtzgFjKjCvhexdItKSX9r3DcWpHTHmmPqJCdzQryXXn/7LxHKvfrOW/8xeT+v61bg0tTEXdU+mbjWb3dmYMAmIyN+BNiIyOv+DqvqUD5miWv3qCSRWiiUzO5e4AGRm53I0J5cxny3ntVnruaFfC36b2piEuBi/oxpjypjXP7FvAl4C2onIZuA24IawpTLlmogcq7X+yzntePTik6iWEMtDU5Zx8sPT+Mt73/uc0JioNQxnRJFYILGAxYTBrvRMhvduyn0nJzC8d1N6NqvF+Kt70rBGAvd9spTT//GVTe5lTAVUZE22O/nMM0AjVR0oIlWBgKra1cJ4Uq1SLMN6NWFYryas2n6QifM3Hrt9qqqM+3otZ3dsQNM6VX1Oakz5p6orgMdEZImqWqPgMvLSiFQA0tJ2cWX/Tse292tTj9lrdjNx3sZj17jvN+6jZf1qx/q1GGOiV3E/5VcDzwD/Arqrakb4I5lo1bpBIvec3+HY+s87M3jk82VODXeL2lzWszHndmpkt1WNOUHlpYAtIoOBwa1atfI7SliICKe0rMspLesCcDQ7l5GvzyczO5dr+jbn932bUaOyzaRrTLQqrrnIMhFZB7QVkSVByw8isqQM8pko1qp+Nb69awB/PrsNW/Yd4U/vfk/Ph6ayZNM+v6MZY8pANIwuUhLxsQFevjKVns1q88+pKzn10ek8/sVy9mQc9TuaMSYMiqzJVtXLRaQh8AVwQdlEMhVJwxoJ3Hxma27s34o5a/fwyeLNtGngNB39YMEmDhzJYkjX5GOzTxpjTHnWpXFNxl2Vyk9bDvDcV6t4Ie1n+rSoy6mt6/odzRgTYsU2ClPVbUCXMshiKrBAQOjTsg59WtY5tm36ih18tmQrj0xZztkdG3BZz8b0bVmXQMCGAjSmICJycVGPq+qHZZXFFK1DUnVeGN6DdbsyaFqnCgCPf7GcjMwcru/XgkY1Kvuc0BhzoqznhYlYz1/RnZv6H2Di/I18vHgzny7ZyiXdU3jyUvubz5hCDC7iMQWskB1hmtX9pdN3+pFs3pqzgbfnbGBoagp/6NeSxrWr+JjOGHMirJBtIlqHpOrcf0FH7jq3Hf/7aTsNqicAsHnfYe76YAmXpjbm7I4NqBRrnSWNUdWr/c5gSu+BCztx3Wkt+PeMn3l//iYmztvIgxd24oreTfyOZowpBStkm3IhIS6GwV2Sjq1v3HOINTszuGXCImpWiWNI12Qu69mY9o2q+5jSGH8VNAFNMJuMJvI1rl2Fhy86iVvObMVLM9aQ2qwW4Myem5mde6zPijEm8hU3TvZk3FkeC6Kq1hnS+OLkFnX4+o4zmPXzLt6dt5G352zgje/WM++egdSuGo+qHjeN+44DR7h5wiKeu6Ib9RMTfExuTFhZCSxKNKpRmfsv6Hhs/Zmpq/ho8WbO6diQm89sRcekijEiizHlWXE12U+USQpjSiEQEE5rXY/TWtdjb8ZRFqzfS213FJJrxs+jZpV4fpuawsnN6/DstFXMW7eHZ6euYsxFJ/mc3JjwUNUH/M5gwuPeQR1IrlWZ8bPW8fmP2xjYvj63nNmaLo1r+h3NGFOI4obwm1FWQYw5EbWqxjOwQwMAcnKVlFpV+HjxZj5atPm45705ZwNvztlApdgAK8ac60dUY8JORBKAa4GOwLFbN6p6jW+hChDtk9GEUu2q8dx+dluuO60Fr3+7jldmreWzH7bSpXHNX925M8ZEhiIno8mbdKawpaxCeiUig0Vk7P79+/2OYnwUExD+b0gn5t0zkPsv6ECdoDG2E+ICXNg1ia/vPMPHhMaE3RtAQ+A3wAwgBTjoa6ICVLTJaEKhRuU4bhnQmm/uPJObznD+OJm5aheXvTSbWat3oVpoC09jTBkrrrnIoDJJESKqOhmYnJqaOtLvLMZ/CXEx/P6U5qzans7bczcQHxMgMzuXxEqx1i7bRLtWqvpbEblQVf8jIm/jTCpmokS1Sr/8+j6Umc3aXRkMHzeH7k1qcsuA1vRvU89qt43xWXHNRdaXVRBjwmVXeibDezflil5NeHvOenamZ/odyZhwy3L/3ScinYBtQDP/4phwOvekRpzRrj7vLdjEi2k/c/Vr8xjYvgHjrkr1O5oxFZqnIfxE5GTgX0B7IB6IATJU1cZLMxHvpRHOL5rnpq/ih837+fimvj4nMibsxopILeBeYBJQDbjP30gmnBLiYhhxclMuS23MR4s2ERfjtAbNysll2rIdnNWhATE2W64xZcrrONnPAcOA94BU4ErAeqqYcqVOtUp8v2k/P2zeT+cU65FvopeqjnP/OxNo4WcWU7biYwNc1vOXyWs+/3Ebt05YRMt6VbnpjFZc0CWJ2Jgiu2MZY0LE80+aqq4GYlQ1R1VfA6znmClXzu/ciEqxAd5fsMnvKMYYUybOP6kRz13RjbiYAKMnfs+Ap2bw7rwN5ORaB0ljws1rIfuQiMQDi0XkHyLyJ6BqGHMZE3LVE+I4p1NDPlm8hczsHL/jGGNM2MUEhEGdk5hy62mMHdGD6glxjP92PXktR2w0EmPCx2she4T73JuBDKAxcEm4QhkTLkN7pLD/cBbTlu3wO4oxYSEiARE5xe8cJrIEAsLZHRsy6ea+vHFtL0SE/YezOOufMxn39RoOH7WKB2NCzVMhW1XXq+oRVT2gqg+o6mi3+Ygx5copLety28DWdEyyPrsmOqlqLvCk3zlMZBIR6larBMDejKPUqRrPmM+Wcepj0/l32s+kZ2b7nNCY6GG9H0yFEhMQbhvYhqZ1rLWTiWpfisglEuEDJdsEYv5qVrcq717fh4nX96FDUnUe++9yTn1sOrttmFNjQsLr6CLGRJWZK3eSlZPLgPYN/I5iTDiMxuk3kyMihwEBNNKGXbUJxCJDr+a1eePa3izeuI8ZK3ZSx63p/u+PW+ndvA61gmbNNcZ4V2whW0RigEdV9S9lkMeYMvHMtFUcOJzFme3q26xoJuqoaqLfGUz507VxTbo2doY33ZNxlFsmLCIuJsCIk5ty3WktqJdYyeeExpQvxTYXUdUcoEek33Y0piSG9khh1Y50lmyy29Qm+ojjdyJyn7veWER6+Z3LlB+1q8bz2a2ncVaHBrz89RpOfWw6909ays6D1pTEGK+8tsleBHwiIiNE5OK8JZzBjAknGzPbRLkXgD7AFe56OvC8f3FMedSmQSLPDOvGtNv7c0GXJN6dt5GjObkA5No428YUy2shuzawGzgTGOwug8IVyphwyxsze9L3WziSZUNXmajTW1VvAo4AqOpewBrWmlJpXrcqj/+2C9/dPYDkmpUBuO71+fzlve9ZtyvD53TGRC5PHR9V9epwBzGmrA3tkcJ3a3azbncG7RpGVH8wY05UltufRgFEpB6Q628kU97VqBIHQHZOLk1qV2HC3A18sHATF3RJ4uYzW9GqvnUFMCaYp5psEWkjItNE5Ed3vbOI3BveaMaEV9+Wdfn2rgFWwDbR6FngI6C+iDwEfAM87G8kEy1iYwLcf0FHvr7zDK47rQVf/rSds/45k89/2Op3NGMiitch/F4G/gK8BKCqS0TkbWBMuIKdiC1btvDAAw8ct23YsGG0bdv2uG1PPvkk6enpno45cuRIkpKSjtuW/zWKMnr0aBITf/kr/+DBgzz11FPF7jdjxgwA/v73vx+3fcuWLbz88sueXrtatWrcfvvtx21bsWIF77zzjqf9GzVqxKhRo47btmDBAj799FNP+7dp04bLL7/8uG1paWnH3ltxunfvzuDBg4/bNnnyZBYuXOhp/379+tG/f//jtk2YMIGVK1d62n/QoEH06NHjuG1jx45l61Zvv1CKOve8fAZ+nXt57Nwr3bnXtGlTT68RDqr6logsAAbgDN83RFWX+RbIRKX6iQn89bz23NCvJeO/XUff1nUBmLNmNwlxMXRxRyoxpqLyWsiuoqpz8w0wYtNCGWNMBBKRZ4B3VdU6O5qwq101ntFntTm2/uSXK5m7bg+nt6nHrWe2IrVZbR/TGeMfrx0fd4lIS35p3zcUsPtCxhgTmRYC94rIahF5XERS/Q5kKo5Xfp/Knee0Y+nm/Qx9cTbDxs5mwfo9fscypsyJavHD8IhIC2AscAqwF1gLDFfV9eGNVzqpqak6f/58v2OcsLS0tF81czCh9/acDfz1ox/45Ka+ZXp7077f6Fba71dEFqhqSArFIlIbuAQYBjRR1dahOG6o2TU7Oh06ms3bczYwduYa7jinHUN7pJCdk0tMQMrlJGD2/Ua/0nzHRV2zvdZkr1fVgUA9oJ2qnhqpBWxjSmpQFxsz20StVkA7oBmw3N8ovyYig0Vk7P79NilUNKoSH8t1p7Vg5h1nMKSr06/ktVnruPD5WXy5dJuNtW2intdC9loRGQucjDOpgTFRw8bMNtFGRB4TkVXAg8BSoIeqDi5mtzKnqpNVdVSNGjX8jmLCKCEuhtgYp7jRqGYC+w5lMeqNBZz37Nd8umQLOVbYNlHKayG7LTAVuAmnwP2ciJwavljGlK3rT2/Jc1d0Iy7G64+EMRFtLdBHVc9R1VdVdZ/fgYwBGNQ5iem39+OpS7twNCeXm99exB3vL/E7ljFh4XUymsPARGCiiNQCngFmADFhzAaAiAwBzgfqA8+r6pfhfk1T8XRIsrGyTfRQ1RdF5AIROd3dNENVJ/sayhhXbEyAi7uncGHXZKb8sJWkmgkA7DhwhLQVOxnSLZl9h45y84RFPHdFN+onJvic2JjS8VxtJyL9ROQFnF7rCcClHvZ5VUR25E1iE7T9HBFZ4fZ8v6uoY6jqx6o6Evg9cJnXvMaU1Nb9h3l4yjK2HzjidxRjToiIPAL8EfjJXW51txkTMWICwuAuSfRo6gzx99GizdzxwRLOeCKNm95exLx1e3h26iqfUxpTep5qskVkLbAYpzb7L6qa4fH444HngNeDjhUDPA+cBWwC5onIJJxa8fy/BK5R1R3u/+919zMmLA4fzWHszDXUrhrPDf1a+h3HmBNxPtBVVXMBROQ/wCLgbl9TGVOEUae34IkvV7B532E27zsMwJtzNvDmnA1Uig2wYsy5Pic0pmS8TkbTRVUPlPTgqjpTRJrl29wLWK2qawBE5B3gQlV9BBiU/xjijPPzKPC5qhY6zZqIjAJGATRo0IC0tLSSxo046enpUfE+ypNWNQO8PnMFbXM3hH2IKft+o1sEfL81gbzBia1noYl4IsKsO89kzGfLmPLDVrJzlfjYAOd2asg957f3O54xJea1kF3drQnpizMhzTfAH1W1NGOeJQMbg9Y3Ab2LeP4twECghoi0UtUXC3qSqo7FGcub1NRUjYaxLG1MzrK3tcoG7v7wB2q16kbXMI+Zbd9vdPP5+30EWCQiX+FMq346VottyoH61RNITIglR5XYgJCVk0tipVhrl23KJa9tsl8DJgFJOIXkye620iioerDQ8XtU9VlV7aGqNxRWwDYmVM7vnDdm9sbin2xMhFLVCThDrn7oLn1U9R1/Uxnjza70TIb3bsqkm09leO+m7EzP9DuSMaXitSa7nqoGF6rHi8htpXzNTUDjoPUUYEspj2VMSFVPiOOibsnElMPZyIwJpqpbcSpHjClXXhrhTJ6XmZ1Du4aJDOrcyOdExpSO10L2LhH5HTDBXb8c2F3K15wHtBaR5sBmnOl+ryjlsYwJuUcv6ex3BGOMqfBiRHh66ip6NK3JyS3q+B3HmBLz2lzkGpwh+7YBW4Gh7rYiicgEYDbQVkQ2ici1qpoN3Ax8ASwDJqrq0tKEL+D1bIpeEzIb9xzyO4IxxlRYsTEBhnRNYvryHezNOOp3HGNKzFMhW1U3qOoFqlpPVeur6hBVXe9hv8tVtZGqxqlqiqq+4m6foqptVLWlqj50om8i6PVsil4TEq9+s5Z+j3/FDhsz25RTIhIjIkki0iRv8TuTMSV1cfcUsnKUyUusVakpf2wOaWMK0L9tPXLVmRzBmPJGRG4BtgP/Az5zl099DWVMKXRIqk67hol8sNCuxab8sUK2MQVoUa8aPZrW4v0Fm1AtdPAbYyLVH4G2qtpRVU9yF+tsYMqlS7qnECNw6Gi231GMKZGoKmRbm2wTSkN7pLBqRzpLNtn5ZMqdjYCduCYqXHdacz68sS9V4r2O1WBMZPBUyBaRN0SkRtB6UxGZFr5YpWNtsk0o5Y2Z/eHC0sy5ZIyv1gBpInK3iIzOW/wOZUxp5M2+u/9QFrm5dmfRlB9ea7K/AeaIyHkiMhKnnd/T4YtljP+qJ8Txn2t6ccc57fyOYkxJbcC5TscDiUGLMeXS3LV76PnQVL5bW9rRg40pe57uvajqSyKyFPgK2AV0U9VtYU1mTASwsVlNeaSqD/idwZhQOim5BvGxAT5cuJlTWtb1O44xnnhtLjICeBW4EhgPTBGRLmHMZUzEeH/BJh6c/JPfMYzxTETqicjjIjJFRKbnLX7nys/60RivKsfHcN5JDfn8h60cPprjdxxjPPHaXOQS4FRVnaCqdwM3AP8JX6zSsQu2CYe1u9IZ/+1aGzPblCdvAcuB5sADwDqc2XYjivWjMSVxcfcUMo7m8OVPdiPdlA9eJ6MZoqo7gtbnAr3ClqqU7IJtwuGS7ik2ZrYpb+q4k39lqeoMVb0GONnvUMaciF7NapNcszLvL7DO6KZ88NQmW0QSgGuBjkBC0EPFTq1uTHkXPGb2qNNbHOvpbkwEy3L/3Soi5wNbgBQf8xhzwgIB4R9DO5NUs7LfUYzxxGtzkTeAhsBvgBk4F+uD4QplTKSxMbNNOTPGHXb1duDPwDjgT/5GMubE9W1Vl+Z1q/odwxhPvBayW6nqfUCGqv4HOB84KXyxjIks53duxOlt6pFjsz+ackBVP1XV/ar6o6qeoao9VHWS37mMCYW5a/fw4OSfbDZeE/G8FrLzbj3uE5FOQA2gWVgSGROBqifE8fo1vejepJbfUYwploi0EZFpIvKju95ZRO71O5cxobBi+0FenbWWn7Ye8DuKMUXyWsgeKyK1gPuAScBPwD/ClqqUbHQRE247D2ayeke63zGMKc7LwN24FSSqugQY5msiY0JkcOdGxMUIHy60zugmsnkdXWScqu51e6m3UNX6qvpiuMOVlI0uYsJJVbn437MY85mNmW0iXhV3FKhg2b4kMSbEalaJZ0C7BnyyeDPZObl+xzGmUF4no6kpIreKyFMi8mzeEu5wxkQSEeGCLknMXLmT7TZmtolsu0SkJaAAIjIU2OpvJGNC5+LuyexKP8rMVTv9jmJMobw2F5mC0wb7B2BB0GJMhWJjZpty4ibgJaCdiGwGbgP+4G8kY0Knf9v6dE6pweGjVpNtIpencbKBBFUdHdYkxpQDwWNmX29jZpsIpaprgIEiUhUIqKoNuWqiSnxsgEk3n+p3DGOK5LWQ/YaIjAQ+BTLzNqrqnrCkMiaCDe2Rwl8/+oGfd6bTqn6i33GM+RURqQlciXMHMjbvj0FVvdXHWMaEXFZOLjsPZtoENSYieS1kHwUeB+7BbePn/tsiHKGMiWQXdEni9Db1SLaLuolcU4DvcJr42f10E7WuePk7BGHiDX38jmLMr3gtZI/GmZBmVzjDGFMeVK0US9VKXn90jPGFNfEzFUL/tvV5/IsVbNh9iCZ1qvgdx5jjeO34uBQ4FM4goWDjZJuysjs9k6tfm8vnP9iADSYivSEiI0WkkYjUzlv8DmVMqA3ployIdUY3kclrITsHWCwiL0XyEH42TrYpKzWrxLN820Emzt/odxRjCpLXxG82v4wGNd/XRMaEQXLNyvRpUYcPF22yadZNxPFayP4YeAj4FhvCzxhiAsLF3ZOZsXInO2zMbBN58pr4NVPV5u5ifWhMVLqoWzLrdx9i0cZ9fkcx5jieGpaq6n/CHcSY8uaS7ik8/9XPfLRoM9f3a+l3HGOClYsmfnm2bNnCAw888Kvtf//733/1vJdfftnTMatVq8btt99+3LYVK1bwzjvveNq/UaNGjBo16rhtCxYs4NNPPy1yvxkzZgDQpk0bLr/88uMeS0tLO/Z4cbp3787gwYOP2zZ58mQWLlzoaf9+/frRv3//47ZNmDCBlStXetp/0KBB9OjR47htY8eOZetWb03khg0bRtu2bY/b9uSTT5Kenu5p/5EjR5KUlHTctoLOkTxXV4bJr81nsrs+evRoEhN/Gf3p4MGDPPXUU55eGwo+92bMmOHp+/Pr3Mtj517pz72mTZt6eg2viqzJFpHJbjvnuAIeayEiD4rINSFNZEw50aJeNVLdMbPtNqWJMOWiiZ8xxkSz4mqyR+LcdnxaRPYAO4EEoDmwGnhOVT8Jb0RjItd1pzXn550ZZOcqcTE2MY2JGB+7izHGGJ+I1xo4EWkGNAIOAytVNWJvRaampur8+eW/j09aWtqvbrmY6GHfb3Qr7fcrIgtUNTX0iSKXXbPNicrJVU55dBonJddg3FU9w/Ia9v1Gv9J8x0Vds712fERV16nqbFVdHMkFbGPK2pGsHCZ/v4UjWTl+RzEVnDXxMxVVTEAY0i2ZtBU72ZWeWfwOxpQBz4Xs8sDGyTZ+mL9uL7dMWMTUZdv9jmLMSOA0YLmIzBORKSIyXUTWAi8BC1T1VX8jGhMeF3dLITtXmfz9Fr+jGANEWSHbxsk2fujTsg5JNRJ4f8Emv6OYCk5Vt6nqHaraEvgt8H84/Wo6qupZ1ofGRLO2DRPplFydDxfaxDQmMhRbyBaRGBF5syzCGFMeOWNmpzBz5U6225jZJkJYEz9TEQ3v3ZT2jRI5mp3rdxRjii9kq2oOUE9E4ssgjzHl0iU9UshVm9rXGGP8dHmvJvxjaBfiY6PqRr0ppzxNRgOsA2aJyCQgI2+jqnof2d2YKNa8blVSm9Ziwfq9fkcxxnciMgQ4H6gPPK+qX/ocyVQgqsqPmw/QIak6MQEbWtX4x+ufeluAT93nJwYtxhjXuKtSGTuiR/FPNCaMTrSJn4i8KiI7ROTHfNvPEZEVIrJaRO4q6hiq+rGqjgR+D1xW2izGlMb05TsY/Nw3zP55t99RTAXndVr1BwBEJNFZVW/zohpTgdSs4rSoys1VAlZ7YnyiqjkiUk9E4lX1aCkOMR54Dng9b4OIxADPA2cBm4B57p3NGOCRfPtfo6o73P/f6+5nTJnp26ouiQmxfLhwE6e2rut3HFOBearJFpFOIrII+BFYKiILRKRjeKMZU/5MnLeR0/7xlY2Zbfy2DqeJ330iMjpv8bKjqs4E9uTb3AtYrapr3IL7O8CFqvqDqg7Kt+wQx2PA56q6MJRvzJjiJMTFMKhzEp//uI2MzGy/45gKzGub7LHAaFX9CkBE+gMvA6eEKZcx5VKjmgls3neYqcu2M6hzkt9xTMW1xV3ymvidqGRgY9D6JqB3Ec+/BRgI1BCRVqr6Yv4niMgoYBRAgwYNSEtLC0FMf6Wnp0fF+4gGLSSHw1k5PP3+V/RN/tXcTKVi32/0C/V37LWQXTWvgA2gqmkiUjVkKYyJEqe0rEsjd8xsK2Qbv4ShiV9B7Z+0iNd/Fni2qAOq6licChxSU1M1Gqartmm3I0c/Vd5YlcaarGrc0z8006zb9xv9Qv0dey1krxGR+4A33PXfAWtDlsKYKOGMmZ3Mv9N+ZvuBIzSonuB3JFMBiUgnnOt1bXd9F3Clqi4t5SE3AY2D1lNwasqNiUgiwrirUmlcq4rfUUwF5nV0kWuAesCH7lIXuDpcoYwpzy7pbmNmG9/lNfFrqqpNgdtxmviV1jygtYg0d+dMGAZMCkFOY8KmTYNEKsfH+B3DVGCeZnwE/qqqt6pqd3e5TVUjbkBgERksImP379/vdxRTgbWoV42/nteOfm3q+R3FVFy/auIHeGriJyITgNlAWxHZJCLXqmo2cDPwBbAMmHgCteLBr2XXbBNWnyzezPVvzEe10NZNxoSN1xkfy8Xgv6o6WVVH1ahRw+8opoIbdXpL2jeq7ncMU3GtcUcWaeYu9+KxiZ+qXq6qjVQ1TlVTVPUVd/sUVW2jqi1V9aFQhLRrtgm3jMwcvli6nR83H/A7iqmAvDYXWSQik0RkhIhcnLeENZkx5dyC9Xv5aNEmv2OYisma+BkDnN+5EfGxAT5YaNdiU/a8dnysDewGzgzapjgXb2NMAd6YvY7py3dwbqdGJMRZu0BTNoKb+PmdxRi/1agcx1kdGjDp+y3cc3574mK81i0ac+K8tsleoqpX51uuKYN8xpRbQ3s05sCRbKYu2+53FFOBlKcmfsaUhUu6J7Mn4ygzVuz0O4qpYLy2yb6gDLIYE1X6tKxDkjtmtjFlzJr4GeM6rXU9BnVuRI0qoZmUxhivvDYX+VZEngPeBTLyNtp0ucYUzhkzO4UX0lbbmNmmrJWLJn4iMhgY3KpVK7+jmCgWFxPguSu6+x3DVEBeC9l506c/GLRNOf4CbozJ55IeKbw1Zz2rtqdbIduUiaAmfv/0O0txVHUyMDk1NXWk31lM9Nu6/zC704/SKdlGszFlw1MhW1XPCHcQY6JR87pVmXvPQOtsY8qMquaIyAVAxBeyjpg+yQAAH/BJREFUjSlLV782jyrxMXx4Y1+/o5gKwtNvfhFpICKviMjn7noHEbk2vNGMiQ5xMQFyc5WMzGy/o5iK41sReU5EThOR7nmL36GM8dOQbsks3LCPdbsyin+yMSHgtXptPM5MX0nu+krgtnAEMiba5OYq5z7zNY98vszvKKbiOAXoiNPE70l3ecLXRMb4bEjXZETgw0Wb/Y5iKgivhey6qjoRyOX/27vz+KjK64/jnzNZWULYIayC7IsKRNwR1CpuVal1rXSxWrVubdVqtcvPF/5+tbbV2s2lKi51qzt1a0UQVEQUQUCEiOwoi+wCIcmc3x8ziUMMySTM5M5Mvu/Xa17O3Hvnzpk8zpmHZ859HiC6xG5F0qISySChkDGwqIAX5qxhV5k+NpJ87j6mhpuuoZEmrXNhPkf2ac8zs1cRDmuZdUm+eDvZX5pZOyIXO2JmhwJbkhaVSIbRnNnSmNKlxM/MTjWze7Zs0deJNI5xw7uyZvNOFq/bFnQo0gTE28n+KfACsL+ZvQU8BFyRtKhEMozmzJZGNpE0KPFz90nufnFhoWZ7kMZx4pAi3rnhWAZ0bhV0KNIExNXJjs6HfTSROr8fAYPd/cNkBiaSSSrnzJ62eD1rt+4KOhzJfCrxE6lBfk4WHTWdqjSSeOfJrkzSC5IYi0hGO2dkdw7oVkjbFrlBhyKZTyV+InuxYXspVz8+h3NH9uDkA4qCDkcyWNyd7HSg1cMklXVr05xubZoHHYY0DdVL/DoAZwYbkkhqaNs8l0/Xb+ep91eqky1JlVErZKi+T1Ldlh1l/PG/i5m/WoOKkjwq8RPZu1DIOH1YV6aVbGDdNpXvSfLE3ck2s65mdriZjaq8JTMwkUwUCsE905bw+KwVQYciGc7dy919gbvPd/eyoOOpiWYXkaCMG96VirDzwpw1QYciGSzeFR9vBd4CbgKujd6uSWJcIhmpID+HsYM789wHq7nlnZ0aRZEmTb8+SlD6dCzgwG6FPDNbC9NI8sRbk3060N/dS5MZjEhTcOaI7jw3Zw0lpXDnayVMOGNo0CGJiDQ5F43qzWebd1ERdrJCFnQ4koHi7WR/CuQA6mSL7IP+N71MaXm46vEjM1fwyMwV5GWHWDThxAAjk0xjZl2BnsTkeXefFlxEIqnllAO61H2QyD6It5O9A5hjZpOJ6Wi7+5VJiUokQ02/bgwTXlrIy/M+o6zCyc8JccLgzlw/dkDQoUkGiZb4nQ18xFfzYzugTrZIjC9LIyvxnjy0iOysjJoLQlJAvJ3sF6I3EdkHHVvlU5CXTXnYyQlBaXkYA77197f58TF9OLu4uxK9JIJK/ETiML1kPVc9PofWzXM5ul+HoMORDBNXJ9vdHzSzXKBfdNOiVL1aXSTVbdheyvmH9KRfaC2Lw51YtuFLurVpzo3PzufBt5dx48mDlOxlX6nETyQOYwZ0pLBZDs/MXqW8KwkXVyfbzEYDDwLLAAO6m9l3Vd8nUn93X1AMwNSpGxg/eggA7s6rCz7n/17+mO/e/y7HDujIveOLCeliHGkYlfiJxCEvO4tTDyziqfdXsW1XGQX5OUGHJBkk3nKRPwDHu/siADPrBzwGjEhWYCJNiZkxdkgRxwzoxMPvLGfjl6VVHeytu8popcQv9aMSP5E4jRvejUfeWcHL8z/nrOLuQYcjGSTeTnZOZQcbwN0Xm5m+9UUSLDc7xIVH9qp6PGvZRr7/wCwuHb0/Fx7Zi/ycrACjk3SRLiV+ZnYqcGqfPn2CDkWasGHdW9OrfQveX7ZJnWxJqHivsHrPzO4zs9HR273A+8kMTESgY0Eeh+/fjtteXcQxv5/Kcx+sJhz2oMOSFBct8SsB/gr8DViciqv0ajEaSQVmxjOXHs6tZx4QdCiSYeLtZF8KLACuBK4iMi3UJckKSkQierZrwT3ji3n84kNp2zKXq5+Yw3cfeBd3dbSlVpUlfke7+yjgBOD2gGMSSVltWuQCKLdKQsU7u0gp8MfoTUQa2aG92/HCj4/k2Q9WUx4OY2aEw87qzTvp3rZ50OFJ6lGJn0g93fXGEv794RomXX4kZrroXPZdrZ1sM3vS3c8ys3lEFjLYg7vrtxWRRhIKGd8a0a3q8fNzV3PdUx8y/rD9uPKYvhQ2Vx9KqrxnZvcBD0cfn49K/ERq1aZ5DvNXb2XOys0M69Em6HAkA9RVLnJV9L+nAKfWcBORgByxf3vGDevG/W8t5ejfT+H+N5eyO2bJdmnSVOInUk8nDi0iLzvEM7NXBx2KZIhaO9nu/ln07mXuvjz2BlyW/PBEZG86tsrn1jMP4MUrjmJIl0Ju/vdHXPTQe0GHJSnA3Uvd/Y/uPs7dz3D327X6o0jtWuXncMLgzkz6cA2l5RVBhyMZIN4LH79Rw7YTExmIiDTMoC6tePjCkTzwvYP54VGR6f927q5g/uotAUcmjc3Mnoz+d56ZfVj9FnR8Iqlu3PCubN5RxpSP1wcdimSAumqyLyUyYt27WoIuAN5KZmAiEj8zY8yAjlWPH3h7Kbe9uohxw7px7Qn96VyYH2B00ohiS/xEpJ6O7NOey8f0oX/ngqBDkQxQ1+wijwIvA/8HXB+zfZu7b0xaVCKyT75zaE+27CzjgTeX8eK8NVw8an9+NKo3LfLiXX9K0lG1Er+fx+4zs1uBn3/9WSJSKTsrxDUn9A86DMkQddVkb3H3Ze5+brQOeyeRWUZamlmPRolQROqtVX4ON5w4kMk/O5pvDOrMnZNLuPapuUGHJY0nLUr8zOxUM7tnyxaVNknqcHfe+mQDb5ZsCDoUSXNxDWtFl779I9AFWAf0BBYCg5MXmojsq+5tm/Pnc4fx/SP2o3luZEn2tVt3UbJ2O0f2bR9wdJJo6Vbi5+6TgEnFxcUXBR2LSCUzY8KLC8nNDilPyj6J98LHCcChwGJ37wUcSyMlbDMbaGZ3mdlT0S8QEamn4T3aMKBzKwDue3Mp37lvJj+YOItP1m0LODJJsEeJTK/6AntOtzrC3b8TZGAi6eRbw7syd+VmPlm3PehQJI3F28kuc/cvgJCZhdx9CnBQXU8ys/vNbJ2Zza+2fayZLTKzT8zs+r09H8DdF7r7JcBZQHGc8YrIXvz0G/244cQBzFq6kRPumM4vn5vPF9s1u1smUImfSGJ888AuhAye/WBV0KFIGou3k73ZzFoC04B/mtmfgPI4njcRGBu7wcyygL8SqQ8cBJxrZoPMbKiZ/bvarWP0Od8E3gQmxxmviOxFfk4WPzp6f6ZeO5rzD+nBo++u4Pf/WVT3EyVtRGudS4ClwBvAMiIXsYtIHDq2yueovh14dvZqwuGvLXgtEpd4O9mnERkR+QnwCrCEOFZ8dPdpQPVZSEYCn7j7p+6+G3gcOM3d57n7KdVu66LnecHdDyeyNLCIJEC7lnncfNoQXr36KK4+rh8AH63ZyqS5a3DXl0qaC6zETyRTjBveld0VzqpNO4MORdJUXBc+uvuXAGbWCpi0j6/ZFVgZ83gVcMjeDjaz0cA4IA94qZbjLgYuBujUqRNTp07dxzCDt3379ox4H1KzVGvfhcDEBaVMXVnOHS/N5dwBufRpkxV0WGkr4PYtc/cvzKyqxC86hZ+IxOmkoUWcPLSI7Kx4xyNF9hTv7CI/Am4mMpodBoxInV/vBrym1bBtr8Nm7j4VmFrXSd39HuAegOLiYh89enQDQkstU6dOJRPeh9QsFdv3qFHO07NX8ftXFzFh5i5OOaCIn48dQPe2zYMOLe0E3L7VS/zWEV+Jn4hE5UQ71+GwE9ave9IA8f7z7BpgsLvv5+693b2Xuzekgw2RkevuMY+7AWsaeC4RSaCskHFWcXemXDOaK4/ty2sL1/LU+7rwJw01qMRPRPa0atMOjrj1df794Wd1HyxSTbzLvy0BdiToNWcBfc2sF7AaOAc4L0HnFpEEaJGXzU+/0Y9zR3ansFkOAFMWrWPVxh2cO7KHfj5NcQku8RNpsroUNiNkxtOzV3Hh/kFHI+km3k72DcDbZjYTqJrry92vrO1JZvYYMBpob2argF+7+31mdjnwKpAF3O/uCxoSfA2vdypwap8+fRJxOpEmr6iwWdX9l+d9xpPvreLBGcv5xUkDGNO/I2Y1VX9J0BJc4ifSZIVCxrjhXfnL65+wZp0xaMQuOhbkBx2WpIl4h6PuBl4H3gHej7nVKjpXa5G757h7N3e/L7r9JXfv5+77u/stDQ2+hteb5O4XFxYWJuqUIhJ167cO4J4LRlARdn4w8T0uuO9dPv58a9BhSc0SWeIn0qSdMawrDizZ4tz5WknQ4UgaiXcku9zdf5rUSEQkpZkZxw/uzOj+HfnnzOX8aXIJC1ZvrVpJUlJKIkv8kka/Pkqq63/Ty5SWh6sePzJzBY/MXEFedohFE04MMDJJB/F2sqdEp8ibxJ7lItXnwBaRDJebHeL7R/Ri3PBuFORFUsgj7yxn45e7ueio3jTL1bR/KaBBJX6Nzd0nAZOKi4svCjoWkZpMv24ME15ayMvzPqOswsnPCTFyv7aUloe5/b+LGd6zDQd1b1117YpIrHg72ZUXJt4Qsy3l6vs0KiLSeGK/VOav3sLjs1by6MwVXHtCf84Y1pVQSPXaAaos8ZtHpCZbRBqgY6t8CvKyKQ87OSEoLQ+TnRVi3bZS7ny9BHcwg74dW/KP8QfTo11zdu6uIC87pBwocS9G0yvZgSSCRkVEgvHbbx3AuOHduOXFj/jZv+bywNtLmXD6UA7q3jro0JoqlfiJJMiG7aWcf0hP+oXWsjjcifXbdvHK1aPYtquMuSu3MHvFJuas3EzHVnkA3P7aYp6YtZJhPVozvEcbhvdow4HdCynI12h3U1NrJ9vMjnH3181sXE373f2Z5IQlIulmZK+2PHvZEbwwdw2/e+VjSssqAHB3zULS+FTiJ5Igd19QDMDUqRsYP3pI1faC/ByO7NueI/u23+P4w3q3Y+vOMmav2MTUResBaN8yl1k3HoeZ8d6yjbRtkUuv9i2UGzNcXSPZRxP5ybGmRQwcUCdbRKqEQsbpw7py0tAicrMjkxfd8uJCysPOVcf2pU2L3IAjbDLSosRPJBONGdCRMQM6ArBlZxlzVm5m05e7qzrUP3/6Q5as/5I2zXMY1qMNw3u05og+7RnWo02QYUsS1NrJdvdfR+/e7O5LY/dFF5MREfmayg62u1NWEebhd5bzzOxVXHlsX8Yftl/VfkmOdCnxE8l0hc1yOLpfhz223fWdEby/fBOzV2xi9orNvP7xOpZu2MGwHm1wd255cSGDurRieI829GzXXKPdaSzeCx+fBoZX2/YUMCKx4YhIJjEz/ue0IZx3SE9ueWkhE15cyMPvLOePZx3IiJ5tgw4v46jETyT19e1UQN9OBZwzsgcAm3fsZme0vG7dtlIen7WS7aXlALRrkcuwHm34wRH7cXif9ns9p6SmumqyBwCDgcJqSbsVkHJLHml2EZHU1L9zAQ/9YCRvLF7Pba9+TPuWkQuEyivCWqI9sVTiJ5JmWjfPpfIS8U6t8pn76+NZvHZbZKR7+WY+WLGJrbvKAJizcjO/fG4+w3u0ZnjPyEWV3do002h3iqprJLs/cArQmj2T9jYg5Wbw0OwiIqnt6H4dGNW3fdUXwiWPzKZlXhbXjh1A19bN6ni21EUlfiLpLytkDCxqxcCiVpx/SE8gUnoHkYGJlnnZPPneKh6csRyADgV5/POHh9CvUwFbdpSRlxMiP0frFaSCumqynweeN7PD3H1GI8UkIhmssoNdEXb6d27JvdOX8vL8z/nhUb24dHQfWubFW8UmtVCJn0gGqcybxfu15bGLD6W8IszHn2/jg2hdd7c2kUGKu6ct4d7pnzKoqFXkosqekQsru7bWaHcQ4v02O8PMFgA7gVeAA4Gr3f2RpEUmIhktK2Rce8IAzjukJ7e98jF/nbKEJ2at5O4LihnRU1fZN0S6lfiJSMNkZ4UY0rWQIV0LueCwr7YfO7AjDsxevoknZq1k4tvLKMjPZu6vjscMppesp3luFoO7FGq0uxHE28k+3t2vM7MzgFXAt4EpgDrZIrJPurZuxh3nDON7R/TiL6+X0KdDSyAy9ZWWKq63tCrxE5HEGtGzbdVF5WUVYT7+bBtrtuysWn3ylhcX8vHn28jNCjG4a2QGk1H9OnxtBhRJjHg72ZXfdCcBj7n7Rv3sICKJdFD31vzjuwcDkVKSs++eQadW+dx48kD6dSoIOLr0oBI/EamUkxViaLdChnYrrNr20IUjqy6mnL1iEw+/s5wN20s5ul8H3J0bnplH304FDO/RmsFdCjXd6j6Kt5M9ycw+JlIucpmZdQB2JS+shtHsIiKZIezOmSO68afJJYy9YxrnjOzBT47rR4eCvKBDSxcq8RORr+lYkM/YIZ0ZO6QzALvLw2yLzlyyZWcZ00s28PislUBkvYMDuhZy8ajeHD+4c2Axp7O4/oni7tcDhwHF7l4G7ABOS2ZgDeHuk9z94sLCwroPFpGUlZMV4odH9WbatWMYf9h+PDlrJWN+P5V5q7YEHVq6ON7dtxIpHVkF9AOuDTYkEUk1udkh2kWnVG3dPJe3rj+Gd244lr+dP5zxh/Yk7E5FODKzybxVWzjit69zxWMfMPGtpXy4ajNlFeEgw095dc2TfZ27/y768Dh3/xeAu39pZjcCv0h2gCLSdLVpkctvvjmY8Yf1ZOLbyxhQFCkbWfHFDrq1aVZVZyhfoxI/EWmQzoX5nDS0iJOGFu2xPRSCA7sXMmvpRibNXQNAfk6IJy4+jAO7t2bD9lLc0S+OMeoayT4n5v4N1faNTXAsIiI16t2hJTefNoScrBDbS8sZ9/e3OeNvbzFr2UYA1m3dxVl3z2DdttSoYlu3dRf/O3NnkPFUlvgVA5NTtcRPRNLH4C6F/O38Ebzzi2N5+/pj+Mt5wzhvZE96d2gBwMMzlnPwLa8x6ndTuPrxD3hoxjLmr95CODoSHivVcjYkJ2/X1cm2vdyv6bGISNI1z8ni+hMH8PnWXXz7rhlc9s/3ueWlj5i1bCN3vlYSdHgA3Dm5hJJN4cDiSZcSPxFJT11aN+OUA7rwq1MHUZAf+eHspKFF/OKkAQwqasVbS77gV88v4Ky7ZxCOLqTz2kdree2jtWz8cjd3Ti5JqZwNycnbdV346Hu5X9NjEZGkC4WMM0d046ShnRn6m//w0rzPq/Y9MnMFj8xcQcjg9IO6Vm1vnpfFhNOHAnD/m0uZv3rP2u42LXL55SmDAPj71CWUrN22x/6i1vlce8IAAO54bTErvtixx/792rfgymP70v+mlykt/6pGsTKevOwQiyacmIB3X7t0K/HTxeoimaN/5wL6d46U9Lk7qzbtZPkXO8jOiozn/vn1EuZWu64mNkeed0gPtuwo22P/Ad0K+d4RkcVqf/HsPHbtrthj/8hebTlnZA8Afvbk3KqVMSsd1a89Zwzrxu7yMNc//eHXYj5uUCd+8sScpOXtujrZB5rZViKj1s2i94k+1sIGIhKY5rnZzLj+GH75/HwmL1xHedjJzwnRtXUzdpZVMGv5xqpjY+fbXrJ++x77AIpafbWk++K123iv2v7eX7asur/ws6189NnWPfbvKo8k/unXjeHkP7/Jhm2lOJF6xRMGd+bGkwfu8/uN0zlAZSf7BuBfMfvGkmKdbHefBEwqLi7WHN4iGcTM6N62Od3bNq/a9vjFh/HG4nX8aXIJiz7fRtghy+CUA7tw48kDueyR2aytVqrRImYF4A9WbGZ76Z6d8Nj67/eXb6SiWie7Z7tIKYvjX8v7AAOLWjH9ujH8+oUF/PejtVXfI4nK23Utq55WywFpVESkaenYKp/2LfOocCcvO0RpeZjDerdjwhlD9/qcW2rZB3D72QfVuv/uC4prjef4QZ149N0V5BiUlocpyMumY0GjjUmoxE9EUlKz3CzGDilieskGPv58G3nZIXZXfJUjn7r08Fqf//JVR9W6f+q1Y/a6Ly87i+nXHbPX/W1b5FLhTk4osXk73nmy04JGRUSang3bSzn/kJ6cN7IHj767gvUBX0hTGU+/0FoWhzs1djwq8RORlJZqOTs2pkTn7YzqZItI0xM7sjzh9CEBRhJRGc/UqRsYP7rR41GJn4iktFTL2ZC8vK1OtohIhki3Ej8RkUymRelFRERERBJMnWwRERERkQRTJ1tEREREJMHUyRYRERERSTB1skVEREREEsyqL0GZzioXowHOBkqAQmBLrU/aUzzH13VMbfv3tm9v29sDG+qIpzHV9++Z7HOmS/vubZ/aN7HPjff4xvoMN7R9e7p7hwY8L22Z2XpgOfpMJ5o+03Xvb4zPdDKlcxunWvtCw9p47znb3TP2BtyT6OPrOqa2/XvbV8v294L+G+7L3zPZ50yX9t3bPrVv47dvsto4Hdo3HW76TAf790z2OfWZVhuncvsmo40zvVxkUhKOr+uY2vbvbV994wxKMuLcl3OmS/vG+9pBawrtG89xmfwZTnX6TCeWPtN170/3z3Q6t3HGt29GlYtkGjN7z92L6z5S0pHaN7OpfZsetXlmU/tmvkS3caaPZKe7e4IOQJJK7ZvZ1L5Nj9o8s6l9M19C21gj2SIiIiIiCaaRbBERERGRBFMnW0REREQkwdTJFhERERFJMHWy04iZnW5m95rZ82Z2fNDxSGKZ2UAzu8vMnjKzS4OORxLPzFqY2ftmdkrQsUjyKWdnNuXszLevOVud7ICZ2f1mts7M5lfbPtbMFpnZJ2Z2PYC7P+fuFwHfI7KqpaS4erbvQne/BDgL0DRRaaA+7Rv1c+DJxo1SEkk5O7MpZ2e2xs7Z6mQHbyIwNnaDmWUBfwVOBAYB55rZoJhDborul9Q3kXq0r5l9E3gTmNy4YUoDTSTO9jWz44CPgLWNHaQk1ESUszPZRJSzM9lEGjFnq5MdMHefBmystnkk8Im7f+ruu4HHgdMs4lbgZXef3dixSv3Vp32jx7/g7ocD5zdupNIQ9WzfMcChwHnARWam/JuGlLMzm3J2ZmvsnJ29j/FKcnQFVsY8XgUcAlwBHAcUmlkfd78riOBkn9XYvmY2GhgH5AEvBRCXJEaN7evulwOY2feADe4eDiA2SQ7l7MymnJ3Zkpaz1clOTVbDNnf3O4E7GzsYSbi9te9UYGrjhiJJUGP7Vt1xn9h4oUgjUc7ObMrZmS1pOVs/V6amVUD3mMfdgDUBxSKJp/bNbGrfpkdtntnUvpktae2rTnZqmgX0NbNeZpYLnAO8EHBMkjhq38ym9m161OaZTe2b2ZLWvupkB8zMHgNmAP3NbJWZXeju5cDlwKvAQuBJd18QZJzSMGrfzKb2bXrU5plN7ZvZGrt9zd3rPkpEREREROKmkWwRERERkQRTJ1tEREREJMHUyRYRERERSTB1skVEREREEkydbBERERGRBFMnW0REREQkwdTJlkZnZm5mf4h5fI2Z/SZB555oZmcm4lx1vM63zWyhmU1J9mvVEccyM2sfZAwikvmUtxMah/J2E6FOtgShFBiXaknGzLLqcfiFwGXuPiZZ8YiIpBDlbZF6UidbglAO3AP8pPqO6iMaZrY9+t/RZvaGmT1pZovN7Ldmdr6ZvWtm88xs/5jTHGdm06PHnRJ9fpaZ3WZms8zsQzP7Ucx5p5jZo8C8GuI5N3r++WZ2a3Tbr4AjgbvM7LZqxxeZ2TQzmxN9zlHR7X83s/fMbIGZ/U/M8cvM7H/NbEZ0/3Aze9XMlpjZJTExTjOzZ83sIzO7y8y+9tk1s+9E/x5zzOzu6HvOiv5N50ffx9f+5iIicVDe/up45W2JS3bQAUiT9VfgQzP7XT2ecyAwENgIfAr8w91HmtlVwBXA1dHj9gOOBvYHpphZH2A8sMXdDzazPOAtM/tP9PiRwBB3Xxr7YmbWBbgVGAFsAv5jZqe7+81mdgxwjbu/Vy3G84BX3f0Wi4ywNI9uv9HdN0a3TTazA9z9w+i+le5+mJndDkwEjgDygQXAXTExDgKWA68A44CnYmIdCJwNHOHuZWb2N+D86Dm6uvuQ6HGt4/lDi4jUQHlbeVvqQSPZEgh33wo8BFxZj6fNcvfP3L0UWAJUJtt5RBJ0pSfdPezuJUSS+gDgeGC8mc0BZgLtgL7R49+tnqijDgamuvt6dy8H/gmMqitG4PsWqVUc6u7botvPMrPZwAfAYCKJt9ILMe9jprtvc/f1wK6Y5Pquu3/q7hXAY0RGZGIdS+RLZVb0PR4L9I6+/95m9mczGwtsrSN+EZEaKW8rb0v9aCRbgnQHMBt4IGZbOdF//JmZAbkx+0pj7odjHofZ8/9lr/Y6Dhhwhbu/GrvDzEYDX+4lPqvzHVR/IfdpZjYKOBl4OPqz5HTgGuBgd99kZhOJjHhUin0f1d9j5fuq6T1Vj/VBd7/ha2/C7EDgBODHwFnAD+r7vkREopS3I5S3pU4ayZbAuPtG4EkiF6NUWkbkX/YApwE5DTj1t80sFK336w0sAl4FLjWzHAAz62dmLeo4z0zgaDNrH/258FzgjdqeYGY9gXXufi9wHzAcaEXkC2GLmXUCTmzAexppZr2iNX1nA29W2z8ZONPMOkbjaGtmPS1ykVLI3Z8GfhmNR0SkQZS360V5u4nTSLYE7Q/A5TGP7wWeN7N3iSSgvY1W1GYRkaTaCbjE3XeZ2T+I/DQ5OzrSsh44vbaTuPtnZnYDMIXIiMNL7v58Ha89GrjWzMqA7cB4d19qZh8QqbP7FHirAe9pBvBbYCgwDXi2WqwfmdlNROoPQ0AZkRGQncADMRfcfG3ERESknpS346O83cSZe/VfL0QklUR/Gr3G3U8JOhYREamb8raAykVERERERBJOI9kiIiIiIgmmkWwRERERkQRTJ1tEREREJMHUyRYRERERSTB1skVEREREEkydbBERERGRBFMnW0REREQkwf4fheg2TwbGNnAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_err_max, e_err_mean = get_errors(tree_shap_values, convergence_data)\n",
    "plot_convergence(e_err_max, e_err_mean, n_samples, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 5: Converge of Kernel SHAP according to the maximum error (left) and mean error (right) averaged across 100 instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a high enough number of samples is selected, the algorithms yield the same global patterns, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_explained = 500\n",
    "X_explained = X_test[:n_explained, :]\n",
    "explanation_500_kernel = kernel_explainer.explain(X_explained, nsamples=1000)\n",
    "shap_values_500_kernel = explanation_500_kernel.shap_values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation_500_tree = tree_explainer.explain(X_explained)\n",
    "shap_values_500_tree = explanation_500_tree.shap_values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the local accuracy check reveals a large (compare value below vs shap values scale) difference between the expected sum of shap values and the actual sum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.5: 498, 0.84: 1, 0.58: 1})\n"
     ]
    }
   ],
   "source": [
    "errs = np.round(np.abs(model.predict(xgb.DMatrix(X_explained, feature_names=feature_names)) - tree_explainer.expected_value - shap_values_500_tree.sum(1)), 2)\n",
    "print(Counter(errs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "shap.summary_plot(shap_values_500_tree, X_explained, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the Tree SHAP values take a few seconds to compute, the Kernel SHAP takes a few minutes to provide estimates for the shap values. Note that this is also a consequence of the fact that the implementation of Tree SHAP is distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d8RwP/J+k/clhaiNg24j4N4CkI4EpklbpbPkiB6TvABdExBnzOH4zMzOr02zQOTUiTqmfIWlc3eSqwIQScjo9Xub3xISG6S8DxwN3SnoduBI4KSJmN7n9BOAD5fkawNaSdqlbvhDwTN30nIbprowFvgT8EpgGXEtuOXonsA1wQN3+VpM0rWH7GrnVaw1gUeBFSfXLXyWHu86gsw8whdy6ZWZmZj3QW2N0ngFWl5Tqws6avBkaZpbHJeu2WamLcubUT0TEk+Q/9Ehan9yN9SR5LE9XRnUx3RkYngIujYiD53IctYaw1pVbgXOBXYBbyxikO4D/JgeYP9Tt79GIeE9XhUh6CniFPN5nTlfrFEcDHwPGStqhq/FJZmZm1rXeuurqRvJg2mMkDZW0LnAUcAlAREwh/+HfR9KQElr2n1ehkr4kqTMQTQNml5/ubCJp97KPrYHP8Ga31PnAbpI6JC0iaWFJ60nasicHGhFPkAPcYeTQA3Ab8E3gT3XjZ24AFpF0jKSlJCVJK0v6dGdRwP3AD8ugZCSNkLRbwy5nA18EHgDGlQHPZmZm1oReCTplTMv25DEqL5IHBP+UfMVRpy+Rx61ML/MvaaLorYF7Jc0E/kQeqPuzuaz/c+AT5CuqLgEOjog7Sx0fLPs/DHgBmASMJg9M7qmxwBLkwded08N5c3wOEfEvclfWeuSrxaaTA9GGZfkc4FPk1+BeSTOAu4GtGncWEXMiYv+y/R/K1VxmZmY2D6lWm1dPzeBQLi+fHRH7tbourZbOnl2NF9WsB2pH9Nq3ZZjZ4JO6WzBgvzDQzMzMbEE56JiZmVllVabryt40ZsyYWkdHR6urYWZm1l/cdWVmZmbtx0HHzMzMKstBx8zMzCrLQcfMzMwqy0HHzMzMKstBx8zMzCrLQcfMzMwqy9+jU0G+BYRViW/tYGZN8PfomJmZWftx0DEzM7PKctAxMzOzynLQMTMzs8py0Oknkn4i6dxW18PMzKydDNirriRtCpwAbAoMAR4BfhQRl7e0Yk2QNAE4LiKubMX+fdWVVYmvujKzJgyuq64kbQ/cDvwJWBMYCZwB/EDSSa2sm5mZmQ0eA7JFR9JjwJ0R8eWG+XsDFwNrAU8B+wNfBVYHpgPfjYjzyrq7AMcAawOzgEsi4thSxnERsVZduaOB2RGxn6RRwJOl7CPJIev3wP4RMams/zXgIGBlYCrws1LmG5LGADsCrwGzgf+LiO3r91HKWB34EfCRUr9fAt+KiFlleQ04GPgy8G7gIWDviHh4XufPLTpWJW7RMbMmDJ4WHUnrkINMV90+V5EPZjvgQOBEcuBYBng/8OdSxg7A5WX5ssA6wM09rMpewBbAasCchvo8C+wADAd2BvYB9gOIiA7gaWC/iBgWEdt3cYwLAzcCE8khbRNy4Dm7YdW9gc8AywHPAD/u4TGYmZm1tYH4r9KI8vhc44KIeE3SFHIryxeBUyPizrJ4SvmB3Mrzk4i4oUz/E7iTnjkpIiYCSPom8JiklSLi+Yj4Zd1690m6AtgGuKDJsj9EbmnaOCJeAV6RdBxwnaRDIqKzReasiHi61GE0XYc/MzMz68ZADDqTy+PKwFu6aSQNJbduTAZGAY92U8Yo4H8XsB4Tuni+CvC8pN2Bw8njhxYGhgJ39aDsVYFJJeR0ehxYjBz0JpV5L9QtfwVYqgf7MDMza3sDrusKeAx4AvhCF8t2A2rAreTwsXY3Zcxt2UxgyYZ5K3Wx3qgunj8raVVyy8opwIoRsTRwHm/tH5zTzb47PQOMlLRE3bw1gVd5s1XKzMzMFtCAa9GJiJqkQ8jdOE8C55MH6+4I/AA4IyKelHQecIyk+4C7gXcCa0TEn8nB42pJt5ND0RLA+hHxR+A+csj4JHATeYzNFry9W+h4SQ+WfZ8B3BYRz0v6L3JAnAy8LmkTYE/gH3XbTqT7oAVwDzAe+J6kb5DHGJ0MXBYR8wpJZmZm1qSB2KJDRNxMHvOyBbl1ZgpwLHBERBxbVjsfOB24hHzF1V+AD5btbyQPDj4NeJn8HTwfL8seB74GXFiWfZx8xVOjK4E7yK0vQ4E9yvb/IH+/z6+BacDRwNUN254C7CFpqqS3DYKOiNnAJ8ldYU+Tg8/dwBFNnSAzMzNryoC8vLyV6i4vXzUinm1xdeaLLy+3KvHl5WbWhMFzebmZmZlZb3HQMTMzs8py11UFjRkzptbR0dHqapiZmfUXd12ZmZlZ+3HQMTMzs8py0DEzM7PKctAxMzOzynLQMTMzs8py0DEzM7PKctAxMzOzyvL36FSQbwFhreBbNZhZC/l7dMzMzKz9OOiYmZlZZTnomJmZWWU56JiZmVlltU3QkTRb0lYt3P9DknZt1f7NzMza0aC9TELSOGBT4PWGRZtGxAP9X6NM0ijgSWDViHi2c35EvKdVdTIzM2tXgzboFCdHxCmtroSZmZkNTIM96HRJ0lLAuUAHMAP4dsPyE4HNImLbunnjgLGdwUnSBsCZwEbAEODeiNiuLLsM2BZYBngGOCUiripF/bU8PiKpBpwRESdLmgAcFxFXljK2LOW/G3gBOCciLijLtgLGAl8ETgOWA24B9o2IGQt+hszMzNpDVcfo/ABYG1gP2ADYmRxWmiJpReD35WcUsAJwRt0qdwIbkoPOd4DRktYry95XHteNiGERcXIX5a8B/Ab4CbAssDdwuqTP1a02BNi+lLcO8H7g0GaPwczMzAZ/i86xko5omPdOckvIjhExEUDSUcCne1DunsD4iDi9bt7YzicRcUnd/GtKHbYC/t5k+bsDf4mIy8r0XZIuAPYDflG33tERMROYKek6QD04BjMzs7Y32IPOqY1jdCQtDywKTKib/WQPyx0FPNrVAkkLAScCu5JbemrAksCIHpS/KvBEw7zHyS1Pnd6IiMl1068AS/VgH2ZmZm2vil1Xk4HXyGGl0xoN68wkh5N6K9U9n0Du+urK7uSWl88A74iIZcjjcjrvszGniTo+00Wd1izzzczMrJcM9hadt4mIOZKuAk6S9CAwCzi9cTXgVEkbkUPKgbw1eFxJ7hY7Cvgx+RL2LSLiNmA4MJscqBaStDd5HM0NZdvJ5LCzNvAsXbsaOF7SXsBVwAeAA4CD5ve4zczM7O0Ge4vO8ZJmNvx8EvgaubvqYeABYAzwRudGETEO+B55QPALwPLAH+uWP08ec7MdOay8CBxVFl8O3A2MB54jD3i+o27bWcDxwNWSpkk6trHSEfEk8AngEOAl4Arg2xHx8wU8H2ZmZlYn1Wq1VtfBelk6e7ZfVOt3tSMq10BsZoNH6m7BYG/RMTMzM+uWg46ZmZlVloOOmZmZVZY71Svo+nVvpqOjo9XVMDMzazm36JiZmVllOeiYmZlZZTnomJmZWWU56JiZmVllOeiYmZlZZTnomJmZWWU56JiZmVll+V5XFeR7XQ1+vm+UmVmP+F5XZmZm1n4cdMzMzKyyHHTMzMysshx0zMzMrLIcdMzMzKyyfGlHH5K0JnAGsDkwDJgKBLBrRLzWyrqZmZm1A7fo9K2bgBeAdYGlgE2BW5jLZXBmZmbWe9yi00ckLUsOOLtExPQy+1ngJ3XrfAo4HngXORCdEhE/K8tOAXYCNo6IWZLeDdwNfDYibu2/IzEzMxu8/IWBfUjSg8A/yeEmgH9ERK0s2w64BvgU8EdA5NaenSPiD5KGALcCE4BDgHuAX0bECfPar78wcPDzFwaamfWIvzCwRbYCxgGHAfcDL0o6XlICvgb8MCLuiIg5EXEPcCWwF0BEvAF8AdiBHIQmAif1+xGYmZkNYm7R6SeSlgA+D1wEHAB8AxgFvF632hDgjoj4RN12PwQOBbaOiNub2ZdbdAY/t+iYmfVIty06Djr9TNK95BaatYDbI+Ksuay7JTAG+AV5IPMHI+KVee3DQWfwc9AxM+uRboOOP037iKR3AEcCPwMeAWrAzsB7ge8CNwCXSboL+D9ya876QIqIkLQ8cDW5i+ty4DfA/1C6tszMzGzePEan77wGjAR+BbwMTAaOA74aEb+IiN8CXwHOAqaQr7o6BxgmaSFyQPptRFwWEXOAPYBtJe3b/4diZmY2OLnrqoLcdTX4uevKzKxHfNWVmZmZtR8HHTMzM6sst49X0PXr3kxHR0erq2FmZtZybtExMzOzynLQMTMzs8py0DEzM7PKctAxMzOzynLQMTMzs8py0DEzM7PKctAxMzOzyvItICqoHW4B4VskmJlZHd8CwszMzNqPg46ZmZlVloOOmZmZVZaDjpmZmVXWoA06kkZLungBy7hZ0pG9Vae57GeUpJqkVVpdFzMzs3bSsktXJI0DNgVeB94AngROjYhf9NH+asDmEXFn57yI2KEv9jU/BlJdzMzMqqLVLTonR8QwYFlgNHCVpLVaWyUzMzOrigHxZSQRMVvSRcA5wIbAeElLAN8BPgMsDdwDHBIR47sqQ9JpwG7ASOBF4McR8YOy7K9ltd9KmgNcExH7lValsRFxSllvA+AHwPuBqcClwOkR8YakUeRWp72AbwGrAn8CvhQRL5TtDwW+DiwH/BO4PCKOqavmRyV1t+1/6lK3r/2BI8sx/R7YPyIm9fD0mpmZta1Wt+gAIGkocFCZfLQ8Xgy8G9gEWAG4G7hB0iLdFPN3YDNgKXJAOF3SxwAi4n1lne0jYlhE7NdFHZYGbgVuL/vbEdgHOLxh1V2BLYCVgSXJYQxJ6wDfBT4ZEUsB7wGub2bbudirrL8aMAe4ch7rm5mZWZ1Wt+gcK+kIcjh5HdgvIv4maTlgd2D1iHgRQNJJwGHAxsCdjQVFRH0I+J2kG4FtgFuarMuOwGvAKRFRA/4h6Qxy0Dmrbr2TImJKqdNVQGdomk3+Zsb3SHoqIqYBdzXso7ttu3NSREws638TeEzSShHxfJPHZGZm1tZa3aJzakQsQ+7quQnYusxfozz+TdI0SdOAl4FFyN0+byPpUEkPSJpa1u8ARvSgLqsCE0rI6fR4F/t7oe75K+SQRkQ8AXyR3Jr0vKQ7JW3fzLZzMaGL591euWVmZmZv1eoWHQAiYqqk/YDHJe1MHr8CsHZETJ7X9pI+ApxBbsG5u4ypuZa33vtiXvd/egZYXVKqCztrlvnNHsevgF+VrrgDgV9LWrbZ7bswihy2Op8DPLsA5ZmZmbWVVrfo/EdEvAx8HzgNmAJcBZwvaWUASctI+rSkYV1sPpx8ifpkoCZpR6Dxcu2JwNpzqcKNwGLAMZKGSloXOAq4pJn6S1pX0sfLIOrXgenkcDWnme27cbyk5SUNJwe529xtZWZm1rwBE3SKHwIrkgfh7g88AoyTNAN4APgcXbfM3AJcQb4yawrwWeB/G9Y5FvhO6dq6oLGAiJgObA9sS75q6xbgp+Tw1YyhwAnk7qlpwKHAZyLi1Sa378qVwB3kVqWhwB4LUJaZmVnbSbXavHp0rL/VXV6+akT0uKsqnT278i9q7YgB0etqZmYDQ+puwUBr0TEzMzPrNQ46ZmZmVlnuuqqgMWPG1Do6OlpdDTMzs/7iriszMzNrPw46ZmZmVlkOOmZmZlZZDjpmZmZWWQ46ZmZmVlkOOmZmZlZZDjpmZmZWWf4enQoajLeA8C0dzMxsAfh7dMzMzKz9OOiYmZlZZTnomJmZWWU56JiZmVlltUXQkXSMpDELWMZoSRf3Vp3MzMys7w2IS10kCTgO+AiwKDARuAk4IyJeWNDyI+K0hv2NA8ZGxCkLWnZflmlmZmYLpuUtOpK2A+4EHgE2jIjhwJbAS+XRzMzMbL4MhBad84GrIuKozhmlFefkzmlJuwHfAtYAXgGuBw6PiFfK8gnApcD2wIbAw8BBEfHnsvxEYLOI2FbSucDmwKaSjgaei4h1JW0DnAasA8wGbgMOjYhJC3qAkpYATgd2ARYnB7tDI+LpuuM7AVgF+Bdwc0TsLSkBpwBfBpYih7/vRcSPF7ROZmZm7aClLTqS1gHWAq6ax6rTgS8Ay5BDyubkrq56BwJfA94JXAvcJGl4Y0ERcQhwB3ByRAyLiHXLon8DhwAjgPWBlYAfzsdhdeUcYJPyszowBRgjaUgJQVcAB0fEUsCawCVlu+2ALwEbl2UbA3/spTqZmZlVXqtbdEaUx+fmtlJE3GtEc5oAAB8/SURBVFw3OV7S+cBeDatdEhH3Akg6A/hv4JPMO0R17uPOusmJks4ktxItEEkLlbruFBHPlXmHAS8DHwL+CrwOvFvS/RHxMjmIAbwGLAa8R9LkiHgReHFB62RmZtYuWj1GZ3J5XHluK0naTtIdkiZL+idwBm+GpE4TOp9ERA14mtwV1BRJG0m6RdLEso+ru9jH/BhBDitP1NVvJjAJWDUi/gV8Avg48LikeyV9oaw3DjiG3Ho1qdRPvVAnMzOzttDSoBMRjwLjgd27W0fSUOA64BpgtTJY+Sjefl+LUXXbJGA14Nluip3TxbxrgL8A65R9dFunHppM7hZbo65+w4CRwDOQA01E7AQsRx6Tc6Wkd5VlF0bEZsAK5NafX/VSvczMzCqv1V1XkLuYxkh6ETg3Ip6XNBLYl9wKciO5RWRqRMyStB55LE2jfST9L/AA8HVgibJtVyaSxwbVG04eCzRD0mrA0fNxLAtLWqx+RkS8KumnwMmS/g5MA75HHjB9j6Tlgc3Il6ZPlzStbPqGpA+SL7f/MzkszSAPlDYzM7MmtLrrioi4lfyHfj3gAUkzyANuRwK/L908BwFnSpoJnEfX424uBH4ETAV2BXaMiOnd7PYc8tf3TJP0UJn3FWA/cpj4FfCL+TicE4BZ9T+SViAHryAHlqeBFcljdt4gvwYHAxPKsZ8HfCkiJpCvtPoRefDyS+Srynabj3qZmZm1pVSr1VpdhwVWLi8/LiKubHVdBoJ09uxB96LWjhgIjYtmZjZINQ5n+Y+Wt+iYmZmZ9RUHHTMzM6usSvQXRMSoVtfBzMzMBp5KBB17q+vXvZmOjo5WV8PMzKzl3HVlZmZmleWgY2ZmZpXloGNmZmaV5aBjZmZmleWgY2ZmZpXloGNmZmaV5aBjZmZmlVWJe13ZWw2me135HldmZtYLfK8rMzMzaz8OOmZmZlZZDjpmZmZWWQ46ZmZmVlkOOt2QNFrSxfO57QRJe/R2nczMzKxnBl3QkfQ1SeMb5h0qqSbp43XzFpf0qiTfxtvMzKxNDbqgA4wF3iVp9bp5WwMPAdvUzfsIMAQY15PCJSVJvubZzMysAgbdH/SIeEjSC+RQc6mkIcCWwP7AMXWrbgPcExEzSij6ETn8zAJ+CXwrImYBSKoBhwF7Au8BPtq4X0nHAXsBO0bEY5JGAWcBmwGLk4PWThHxUsN2SwBXAh8GlgDGA0dFxK1l+SjgAmBjoAY8AXwhIh6RtG3Zx7uA14D7I2Lb+Tx1ZmZmbWcwtugA/I43W282AiYCvya39Cxb5m8DjC2tMzeWdVYHNiEHnrMbytwX2BUYBtzXOVPSIpIuBT4BfLiEnCVKHSYB7waWA44gh5FGCwG/AtYGlgWuBn4paURZfhrwNLB8KefLwLSy7KfkgLY0sDJwanOnx8zMzGAQtugUY4HTy/NtgN9FxOuS/g/4qKRbgQ8A3wA+RA4ZG0fEK8ArpXXmOkmHRETntwifHRGPl+dvSIIcMG4mB49tOluAgE+SW3G+FhGzy7w/dVXRiJhJbtHpdJako4APAjeRw9EKwJoR8Q/gb3XrvkZuzVk+IiYCtzd/iszMzGywBp3bgBUkrUcen/OTMv/2Mv06uYvqLmAXYFIJOZ0eBxYDRpBbZQAmdLGfzclh5wN1IQdgFPBEXcjplqTFgTOBHcktNnOApcq+Ab4JHA+MkbQkcC25W20msDO5O+4BSZOBCyPiB/Pap5mZmWWDsusqIp4BHiWHh015c8BxZ5fWNsAfIuJ14BlgZOlu6rQm8CowpW7enC52dQNwJHC7pPfVzZ8ArFHGB83L4eQxRNsAS0fEMsBUyn05ImJyRBwaEWuRu9S2KvskIv4aEbsCI4EDgNMlbd3EPs3MzIzB26IDuVXncOCxugHAfyGHgs+RW1EA7iEPAP6epG8AywAnA5dFRFfh5i0i4seSZgK3SeqIiD+Rx/ycCZwj6XhgJrkr6qGImNFQxHDg38BLwNDSbbVM50JJu5Y6TgCmk7urZksaCuwO3BgRUyRNJYexebYimZmZWTYoW3SKseSxLb/rnFGCyx/K/LFl3mzymJpVyIN+7wHuJg8ebkpEXAYcBNwoaZvSDbY1sCrwGDnEnAUs0sXm3yeP8Xme3GX2L97aTfZ+4PfksPQQOax1DpTeFXi4BK3rgRMi4g/N1tvMzKzdpVqtNu+1bFBJZ88eNC9q7YjB3KhoZmYDROpuwWBu0TEzMzObKwcdMzMzqyz3G1TQ9eveTEeHb/FlZmbmFh0zMzOrLAcdMzMzqywHHTMzM6ssBx0zMzOrLAcdMzMzqywHHTMzM6ssBx0zMzOrLN8CooIG0i0gfIsHMzPrB74FhJmZmbUfBx0zMzOrLAcdMzMzqywHHTMzM6ssBx0zMzOrLF8SswAkjQM2BV4H3gCeBE6NiF+0sl5mZmaWuUVnwZ0cEcOAZYHRwFWS1mptlczMzAzcotNrImK2pIuAc4ANgfGSvgYcBKwMTAV+BhwXEW8ASBoBfBfYDlgGeAz4QkQ8ImkJ4DvAZ4ClgXuAQyJifP8emZmZ2eDlFp1eImkoOdQAPFoenwV2AIYDOwP7APuV9RcCfk0OOB8sj18GZpRtLwbeDWwCrADcDdwgaZG+PhYzM7OqcIvOgjtW0hHAUuSxOvtFxN8AIuKXdevdJ+kKYBvgAkDkgLNcREwv6/wNQNJywO7A6hHxYpl3EnAYsDFwZ58flZmZWQU46Cy4UyPiFEnvAC4Bti6PSNodOBxYk3yuhwJ3le1GAZPqQk69Ncrj3yTVz18EWLW3D8DMzKyqHHR6SURMlbQf8LiknYG/AFcCuwA3R8Rrks4mt+QATABGShoeEf9sKO6p8rh2REzuh+qbmZlVksfo9KKIeBn4PnAaeVzOQsBk4HVJmwB71q8O3AtcLGmkpIUkrS9pxYiYBFwFnC9pZQBJy0j6tKRh/XlMZmZmg5mDTu/7IbAiefzNCeQBx9OAo4GrO1eKiDnATsAs4P6yzmXksT4A+wOPAOMkzQAeAD4HDJg7k5uZmQ10qVbz382qSWfPHjAvau0I946amVmfS90tcIuOmZmZVZaDjpmZmVWW+xUq6Pp1b6ajo6PV1TAzM2s5t+iYmZlZZTnomJmZWWU56JiZmVllOeiYmZlZZTnomJmZWWU56JiZmVllOeiYmZlZZfkWEBXU17eA8G0dzMxsgPEtIMzMzKz9OOiYmZlZZTnomJmZWWU56JiZmVllOeiYmZlZZbXd5TOSBBwHfARYFJgI3AScEREv9PG+RwFPAqtGxLN9uS8zMzNrsxYdSdsBdwKPABtGxHBgS+Cl8thykpKktgugZmZmfaGtvkdH0mPAHRGxTzfLlwBOB3YBFieHokMj4umyfBwwNiJOqdumBmweEXdKOhHYHLgb2K+s8j8RcUJZdzowHPgXUCO3Ip1cyjgM2BN4D7A18HtglYiYVLZN5Nag4yPiirkdp79Hx8zM2oy/R0fSOsBawFVzWe0cYJPyszowBRgjaUgPdrUF8DSwEtABHCPpI2XZ+8rjuhExLCJOrttuX2BXYBjwF+Au4Et1y7cDlgau7UFdzMzM2lo7/Ws+ojw+19VCSQsBewE7RcRzZd5hwMvAh4A/NbmfRyPiJ+X53ZLuBwT8cR7bnR0Rj5fnb0i6kDyW6Kwyb1/gyoiY1WQ9zMzM2l7btOgAk8vjyt0sHwEsBjzROSMiZgKTgFV7sJ/GAc2vAEs1sd2EhulrgRGSNpO0LLAzcFEP6mFmZtb22iboRMSjwHhg925WmQz8G1ijc4akYcBI4JkyayawZN3ylXpYjTnNLouIV4HLyS05ewJ/jYi/9XB/ZmZmba2duq4A/ps85uZF4NyIeF7SSHKYeAL4KXCypL8D04DvAQ8D95TtA/i8pO8DrwKn9nD/k8mBZm2gmcvLLyz7/DBvdmGZmZlZk9qmRQcgIm4FNgPWAx6QNIM8dmYk+Sqnr5ODxZ/JA4pXJI/ZeaMUcQ45+DwO3A/c2MP9zwKOB66WNE3SsfNY/2HgXvLA5mt6si8zMzNrs8vLByNJo4HXIuIrzW7jy8vNzKzNdHt5uf9iDWDlkvjPARu3ui5mZmaDUVt1XQ0mkq4ld1udHhEPtro+ZmZmg5FbdAaoiPhsq+tgZmY22DnoVND1695MR0dHq6thZmbWcu66MjMzs8py0DEzM7PKctAxMzOzynLQMTMzs8py0DEzM7PKctAxMzOzynLQMTMzs8py0DEzM7PKctAxMzOzynLQMTMzs8py0DEzM7PKctAxMzOzynLQMTMzs8py0DEzM7PKctAxMzOzynLQMTMzs8pKtVqt1XWwXrbooos++Nprr73a6noMJAsvvPBys2fPntLqegwkPidd83l5O5+Tt/M56VoLz8uUWq328a4WLNzfNbG+t/76678aEWp1PQYSSeFz8lY+J13zeXk7n5O38znp2kA8L+66MjMzs8py0DEzM7PKctCppgtbXYEByOfk7XxOuubz8nY+J2/nc9K1AXdePBjZzMzMKsstOmZmZlZZDjpmZmZWWb68fJCStA5wObAs8BKwV0Q81rDOEOBHwMeBGvDdiLi4v+vaX5o8J8cDuwGzy88xEXFLf9e1vzRzTurWXRe4Dzg/Io7ov1r2v2bPi6TPA8cDifw7tG1EvNifde0vTf7+jAQuA1YFhgK/Aw6NiNn9XN1+Iels4DPAKGD9iHiwi3Xa7XO2mXMyoD5n3aIzeP0EOC8i1gHOAy7oYp0vAmsBawObAidKGtVvNex/zZyTe4APRsT7gH2A/ydp8X6sY39r5px0flhfAFzXj3VrpXmeF0kCTgS2i4j3ApsB0/uzkv2smffKMcA/ImIDYH1gI2CX/qtiv7sO2AJ4ai7rtNvnbDPnZEB9zjroDELlv6oPAFeXWVcDH5A0omHVXYGLImJOREwmv0E/13817T/NnpOIuCUi/lUm/0b+T33ZfqtoP+rB+wTgaOAG4NF+ql7L9OC8fB04OyImAkTE9Iio5DeO9+Cc1IClJC0ELEpu1Xmu3yrazyLizoh4Zh6rtc3nLDR3Tgba56yDzuC0KvBcRLwBUB6fL/PrrcZbU/fTXaxTFc2ek3p7AY9HxLP9UL9WaOqcSNoA+BhwTr/XsDWafa+sB6wp6Q+S/iLpOEmpn+vaX5o9JycD6wAvABOBWyLij/1Z0QGonT5n50fLP2cddKwtSdqS/KG9e6vr0kqSFgEuAg7s/CNn/7EwsAGwHbAlsAOwZ0tr1HqfI/+HviKwMrCFpM+2tko2UA2Uz1kHncHpGWDlMq6ic3zFSmV+vaeB1eumV+tinapo9pwgaVPgSuBTEfFIv9ayfzVzTlYE3gXcJGkCcBiwv6QB96VfvajZ98pTwLUR8e+ImAH8GvhQv9a0/zR7Tr4K/Kx000wnn5OP9mtNB552+pxt2kD6nHXQGYQiYhJwP2+m5N2B+0r/cL1fkP9oLVT62j8F/LL/atp/mj0nkj4I/D/gsxHxl/6tZf9q5pxExNMRsVxEjIqIUcAPyOMNvtLvFe4nPfj9uQrYXlIqLV/bAH/tv5r2nx6ckyfJVxchaSiwLfC2q27aTNt8zjZroH3OOugMXgcCX5X0KPm/rAMBJN1UrhYBuAJ4AngMuAv4TkQ80YrK9pNmzsn5wOLABZLuLz/rt6a6/aKZc9KOmjkv1wCTgL+TQ8BDwCUtqGt/aeacHAZsLukB8jl5lNz1WUmSfiTpWWAVYKykh8r8tv2cbfKcDKjPWd8CwszMzCrLLTpmZmZWWQ46ZmZmVlkOOmZmZlZZDjpmZmZWWQ46ZmZmVlkOOtZyKaWPpZTuqJveKqU0oYVV6jcppdEppV6703FKaVRKqVY3PSKl9FRKabkmtj0wpXRFb9VlMEgpbZ5SmtbqerSjlNIePfk97+3fFZu7vvrdmI/X/YyU0skLsk8HHWuplFIi32PphHmsd1BK6cGU0j9TSlNTSpFS2rVu+YSU0h5dbPe2+Sl7tJQ1rGHZVimlWkppZvl5PqV0WUrpnQt2pK1Rq9Umk7/4bl7nd0ngO+S7dbeNWq12R61WW6bV9ehOSunElNLYVtejHfTVuU4pjUspHdfb5fa1xt+NFr4XvwscnFJaeX4LcNCxVtuefAfk27tbIaW0O/kP9b7A0uSvpv86MHU+9/lRYE1gDl3fg+WNWq02rFarDQM2AzYlf2PwYHUp8OWU0vC5rLMH8ECtVnu8n+r0FimlISklfx6Z2VvUarWpwM3AAfNbhj9Y2khp3TgupXR7aa14IKW0QUpp95TS+JTS9JTSxSmlheu2WS2ldG1K6YXyc2FKaam65aellJ4o5T2eUjqsbtmo0jqyZ0rp7ymlGSml36aUVqyr1qeAsbW5f3Plh4E/1Gq1u2vZrPLfxm/n81QcAPyG/I2mc/3lqdVqTwA3AO9vXJZSWrick50b5l+eUrq0PN8mpXR3aYWanFK6JqU0srv9lfO1Wd30Viml2Q37PKa0SE1LKf0xpbTRPI7hMWAK+ev6u/Mp4NaGunwtpfRwed2eTimdnlIaUpadnVL634b1P1rWXbJMvzeldEtKaUrd9ouUZZ3vjX1TSn8H/gWMTCntllL6a2lteyGldEFneWW7FVJKY8p79dGyfS2lNKpunf1L69/0lNJ9KaXtuzvoLs7v6JTSFSmlS8v5fa78fmyYUvpzOb7bU0or1W0zIaX07ZTSneX3IFJKH6xbPtf3QEppkfKaPlLKfzyl9JmUWyyPAbZKb7YwrtnNcWxZ9jG9vGYH1C3bKqU0O6W0ayl7ekrp5/W/x12UNz+fFRuklH5XjvOJsv2QuuUfKudmZkrpTvI/G/X7XKK8r55MKb2cUvpNSmmt7urYRZ2XTSn9tLxvJqb8e/jOuuVvad2tew+u0t25TintXY73qFLupJTS97p4H69SV+7eKaXx5fm5wObA8aXMLu/5lHJryW0pd9NMTim9lFI6PKW0ejmnM1JK96aU/qtumwX6Xal7r19U915/2/umPJ/r+Wk4lrd0MfbS634r+TNq/tRqNf+0yQ8wgfw15f8FLEK+4drjwIXAkuSb0U0CvlDWXwwYT+7SWBx4B3ATcGldmXuQW1gSsDUwC/hYWTYKqJGDwnLAcOCPwEV1298NHNpQz62ACXXTnwNeBU4h329omW6ObY95zQdGAP8GdgE2LPXbqGHfs+um1wIeqT/mhvLPBK6rmx4GzAQ2L9ObAR8k3wl7BeAPwNV1648GLq6brgGbzaU+p5VztiYwhNzKNQV4R/0576KeY4BT5vLeeBHYqWHeZ4A1ymv7/rLOAWXZesBrwIi69S8HLinPRwIvkYPkUPKdrgP4dsN747ZyXoaW49kBeA/5n7C1yLdfOL1uH7eR7yM0vOxjXClnVFn+FfJ79n2ljE+U12Otbo678fyOJr+HdyzbH1i2v578lfdLAL8DLmx4jz0PbFSO42hgMjC8yffAGeU4NyjnehVgg7LsRPI/AnP7vV6j1PnLZR+bAC8Dn6s7xhr59hXDgOXJnwPH9uJnxdLl/XE8sGjZ7gngm3XLXyrnZmg5HxN56+/5VeTPiuXLOicBDwOLdPW70kWdf0N+n7+j/NwI3DiXz4JR5bys0t25BvYGXgfOI38Gvot824tvdVVG3Tbj66bHAcfN4zU8sexnP978PXgDGNvwGvy2bpsF/V0ZTX7f7FTK2KXUYfVufje6Oz/jG+b953Xqjde9rLMRuQV+6NzOY7fnd3428s/g/Cm/6N+sm/5EeePX/7H6OXBOef5Z4PGGMjYiB4Uh3ezjWuDM8rzzQ+CDdcsPBu6rm34U2LuhjK3qfxHKvE8CvyJ/mL5B7up6b8OxvQJMa/iZw1s/3I4kf0B3fnj+BbigYd+1su1U8k0Mf0IX4aqs/1/kP/gjy/Q+wKNzeQ0+CUyqm/7Ph0KZ7jbokP8IzgC2aCjzgc5jpPug8zPg/LnU67X/3975h9h1VHH8c2IbmphuoyQBtdlki2uDtETTIIVICMRqJYINGsVo07LYNpWgWFvEWLRgpUFTgwVtUcqGtNi0/aMpSbFRFAULWqqFak0UQnbbrFmzJbsxm0it9vjH99zs7M179/3YZzZZ5wOPvW/mzo87M2fmzJlz3wJrGoyf7cATyfffAV+O60uRQrAqvt8J/LKU/hPEpJiMjdUNytwCPB/Xl0eaK5L4tUyevP8EbCrlsZc6Cw21FZ10cZwb+W9Iwr7A5DE8AHwr+W7oP1pvbDQG4t5xYF2de++hsaKzFXiuFHYfsL80plM5/y7wVEWeA7Q2V2xE/7HbkvjbgL/E9WejTdL4bxNyjjZCDnQn8bOAE4Q8UKHooM2WA71J2JUR9o7kmdpRdF4H5iZhnydkvJxHkqYdReflUtixGn0w2kFZ2Uky1iNsBPh4Hdmo1z5Vis6U+z3CeuO+RVXtWO9zxuyY+b/haHJ9GvmjjJTCCpN2D9BtZ3veO9qZDpnZF4FbkGAZ2vX8pKLMU0n+IGWiyndEBbrvQ1o/ZrYM/dO4fWbW4yEJyNrwaJrOEu9+M7Oo66Pu/kYEPwxsM7OvuPt4hP3Hm3RQdfcDZvYHZNn6HtpV9ydlXoOsMMvRomloV90OCyLtXkverEK7vctrJzlDF1La6nFWP5h8o+5A1qOL0G7rt8kt/WjR3wF8Chhy9+cirgdYVRo7hnarKQOlMq8DvgEsQ5aBt6AJH2QVAk2cBYOl/HqAH5jZA0nYRcARmufMeHX30xo2Z8lN+dhnIEnjZvYK0ScNxsBCZCH5awv1K7MYWU9SDgHpkWpZzstyWItW5orFaPFKx+WhCAe1xWApPh2PPfH3pWjvgouTPKoo7knzPJTEHaV9jrn76eT7AI3lrR3KdTxNxbjrgKzUKrOZcdEKner3LiY2oC2TfXQyVQyincv80ucSdx8ys1XI7H4bsCCUg71oIm+WF9ExSNO4+0G0uC5BJupmWYtMvH1xhj+MzKTz0I60XfqBm+Nc+VpgVxK3G1mN3uPuXdR2fk45hRa+gncm169F/IdK/fFWd9/WIN+rUFvXY1I/mNliZCq/F+2IL0Pm+7RvdwO9ZrYC7ez6k7hBtPtL63mZy8E75c2kzNnAnsi3O9rrq0mZQ/G3O0mfXhfl9pXKnefut1c8eydYWlyEQt3NhHJVNQZGUJ/21sn3zTrhKa8ysWAUXBHh54pXgSU2ebVK6zBUIz6tc7EI95b6bq67P9Zk+ZD0AxO+IEXcOPVlC+q39SIzm5t8X8pE3xabo3bybZsOyUqr1HqOcpvC5OfvVL9fhSxe/2qn4lnRyVSxDygcJS818S4zWx/xXegYaQRwM1uHzo1bYQ9SQOpiZn1mtsHit2DC8W8z8Gd3P95CWbci/4hlyD/nfUiA+pmCRz+abN4NPAD83N2HkrguZIY9aWbd6Ky6iheAm8xsdjgN3lFExK7o+8B2M+sFMLN5pt8hKk+uZwgFbCE676/HHiY7K89D88MI8IaZXQvcmCZw9zHgKaQMlRW8XcDK6LtLzGxWOC9eX1GH2cgvbNTd/2lm70Xm+KK8I+gYYFuMx0VA+bXdHcA9JudhM7M5ZvbBsAL+L+kzsxUmJ9W7kOXmmYirOwaiTx8EvmNy3i5k7Oq4ZRhZVWdXlP0YcI2ZbTI5q38AjeeHO/qE1TyD+m5rjN0r0cJb1GEfGlN3mZyvV6BjXgDc/RiyBP/Q4jViM5tvZuut9BMQtXD3vwE/A+6PdG8D7gd+6u6F1eIF4DMhMwuRP1FKvbaehcbcHJMz+J3IHw13f41Qrk1vDl6NrMblfJt2qm6STshKq9RqnxeRIvixkPH1wOokvlP9fh2ao9oiKzqZuoS5di3a6R9Ek/UvkIIAsB+9ufQ8sjZ8Ei18rbAf+LeZram4ZxQdkRwws1PIN2QM+To0RQj6DcB2dx9OP8gq9X4zW9li3QFw9xPouT+KXuVOuRWd6Z9EPkZPNshuC5oUjyMfiJ2l+G8CTwNPm9k/kMPoZqpluQ/YGfWsxyPA8pjIcfcDSVljaHGutbPuR8+9PxYbIv0weo3/BmTqH0VtVPOtoUgzDtyOFv1xZEEqH4NuRErEEeA3TLTn65HHj5GDeH+U+Qpa0C6uePZO8COk6I4Cn0Y+N0V7NxoDX0d9vSfu+TUTFp4nkUVi2PRmTNlyg7sfRv4bW5Dj5yPI6fuJjj1dA+JZP4yU5b8jud6FjnMLpXgdaptR1FYPlrK5BTn+/8rMTiLfsw3oyKIZPofa72B8xoBNSfzdaGN2FCkBu0vp67X1ILJMHEZzz7NojBXchOaiE/G8ZQVzB1L6x8zs5SafpZJOyEobnNU+rp+j+BIa/8eB65EDdFHPKfe7mc1H4/uhNustB6FMZjqJXf5Wd18d39eghXnpdNbrQiSsQIfd3eL7AuD3wMqSf0WttJuRM/GNVfedT5jZR5AyNsenaTIz+YHdXfYPy1z4mNnNqG87bZE555wPstIOZnYf8g9r2yKVnZEz0467P4t2SZkOE6b1JU3e+xBT2DWdC8xsOdrp/RGd9d8LPH4hTdyZzLlgpsiKu39tqnnko6vM+cgAF/YvEU8nY8jBeqbydnT8M47M8S8h03kmk5lMlpUgH11lMplMJpOZsWSLTiaTyWQymRlLVnQymUwmk8nMWLKik8lkMplMZsaSFZ1MJpPJZDIzlqzoZDKZTCaTmbH8F+oh/eY/CKbfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values_500_tree, X_explained, feature_names, plot_type='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 6: Feature importances estimated using the interventional feature perturbation Tree SHAP algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values_500_kernel, X_explained, feature_names, plot_type='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 7: Feature importances estimated using the Kernel SHAP algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max absolute deviation from ground truth: 0.1463.\n",
      "Min absolute deviation from ground truth: 0.0.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Max absolute deviation from ground truth: {np.round(np.max(np.abs(shap_values_500_tree - shap_values_500_kernel)), 4)}.\")\n",
    "print(f\"Min absolute deviation from ground truth: {np.round(np.min(np.abs(shap_values_500_tree - shap_values_500_kernel)), 4)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the errors incurred in estimating the shap values are relatively small, the feature importance rankings shown in Figures 6 and 7 are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average prediction on background data is the expected value of kernel explainer: True\n",
      "Average expected value for kernel explainer is the same as the tree explainer: False\n"
     ]
    }
   ],
   "source": [
    "average_prediction = model.predict(xgb.DMatrix(background_dataset, feature_names=feature_names)).mean()\n",
    "kernel_exp_value = kernel_explainer.expected_value\n",
    "tree_exp_value = tree_explainer.expected_value\n",
    "print(f\"Average prediction on background data is the expected value of kernel explainer: {np.abs(average_prediction - kernel_exp_value) < 1e-3}\")\n",
    "print(f\"Average expected value for kernel explainer is the same as the tree explainer: {np.abs(kernel_exp_value - tree_exp_value) < 1e-3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expected values of the two explainers appear to be different between the two explainers despite their use of the background dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference between the expected values is 0.5.\n"
     ]
    }
   ],
   "source": [
    "print(f\"The difference between the expected values is {np.round(np.abs(kernel_exp_value - tree_exp_value),2)}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model base score: 0.500000\n"
     ]
    }
   ],
   "source": [
    "print(f\"Model base score: {json.loads(model.save_config())['learner']['learner_model_param']['base_score']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This differences arises because Tree Shap takes into account `base_score` the initial model bias, used by `xgboost` to make predictions at the start of training. This parameter value does not matter so long the training process succeeds so it can be set to 0 to eliminate the differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Footnotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Footnotes'></a>\n",
    "\n",
    "\n",
    "[(1)](#f_3): The base offset represents the leaf value before training. It can be specified as part of `params` as `'base_score'` and defaults to `0.5`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#source_3) Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. and Lee, S.I., 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), pp.56-67."
   ]
  }
 ],
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