doc/source/examples/kernel_shap_wine_intro.ipynb

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    "# Kernel SHAP explanation for SVM models "
   ]
  },
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   "metadata": {},
   "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": [
    "In this example, we show how to explain a multi-class classification model based on the SVM algorithm using the `KernelSHAP` method. We show how to perform instance-level (or *local*) explanations on this model as well as how to draw insights about the model behaviour in general by aggregating information from explanations across many instances (that is, perform *global explanations*)."
   ]
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from alibi.explainers import KernelShap\n",
    "from sklearn import svm\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = wine.data\n",
    "target = wine.target\n",
    "target_names = wine.target_names\n",
    "feature_names  = wine.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data into testing and training sets and normalize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training records: 142\n",
      "Testing records: 36\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, \n",
    "                                                    target, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=0,\n",
    "                                                   )\n",
    "print(\"Training records: {}\".format(X_train.shape[0]))\n",
    "print(\"Testing records: {}\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_norm = scaler.transform(X_train)\n",
    "X_test_norm = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting a support vector classifier (SVC) to the Wine dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "\n",
    "SVM, is a binary classifier, so multiple classifiers are fitted in order to support multiclass classification. The algorithm output is explained [here](#ovr_definition)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, gamma=0.1, random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "classifier = SVC(\n",
    "    kernel = 'rbf', \n",
    "    C=1, \n",
    "    gamma = 0.1, \n",
    "    decision_function_shape='ovr',  # n_cls trained with data from one class as postive and remainder of data as neg\n",
    "    random_state = 0, \n",
    ")\n",
    "classifier.fit(X_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion matrix for SVC classifier')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LgBeBszrY5nJJxwI3kx3+XxYRsyv93sys9lovIjWSqh7CR8TlwOUdzLsIuKid6fu2s/jZaShlm1OAKV0I08xyokclUDOzihGolxPoSpM0iOzwvq3dI2JRreMxs9pwDbQCUpLcpt5xmFltOYGamZWhx11EMjOrqMbKn06gZpYT8iG8mVnZanmfeymcQM0sPxqrAuoEamb50WiH8I1VHzYz60CpPTGVkmQlXSbpZUmzCqadK+nx1OfwTZIGFCvHCdTMcqOC3dlNAvZsM+1WYIuI2Ar4B3BysUKcQM0sN9RLJQ3FRMRU4JU2026JiOVp9O9k3WF2yudAzSw3unAOtFlS4XNxJkbExC5s6jDgN8UWcgI1s3zoWjvQhRExuqzNSP8NLAeuKrasE6iZ5YKAal+ET0/H+CxZx0RFHyruBGpmOVHde+El7QmcCHw8It4qZR1fRDKz3JBKG4qXo2vIHla5uaS5kg4HLiB7Msat6RHqFxcrxzVQM8sHQa8KdagcEQe2M/nSrpbjBGpmuSAql0ArxQnUzHKjwe7kdAI1s/xotHvhnUDNLB9KvEBUS06gZpYLWTvQxsqgTqBmlhPyRSQzs3K5BmpmVg6fAzUzK4/PgZqZrYQGy59OoGaWH76IZGZWDj8XvjFtu3Ez90w+st5hNKyB2x9b7xAa3uIHLqh3CN1eLfoD7SonUDPLier2B1oOJ1Azy40Gy59OoGaWH66BmpmVQRXsULlSnEDNLDcarQbqZyKZWW5U8JlIl0l6WdKsgmlrS7pV0j/T34HFynECNbPckFTSUIJJwJ5tpn0XuC0iNgVuS+OdcgI1s3wosfZZSv6MiKnAK20m7w1cnl5fDuxTrByfAzWzXFDX+gNtljS9YHxiREwsss6QiJifXr8IDCm2ESdQM8uNXqVfRFoYEaPL3U5EhKQoGk+5GzAzq7VKHcJ34CVJQ7PtaCjwcrEVnEDNLBekil5Eas/vgYPT64OB3xVbwYfwZpYblWpHL+kaYFeyc6VzgdOBs4HrJB0OPAt8vlg5HSZQSecDHZ4DiIjjuhizmdlKqVRD+og4sINZu3elnM5qoNM7mWdmVlOiSxeRaqLDBBoRlxeOS+obEW9VPyQzs/Y12K3wxS8iSfqopDnA42l8a0m/qHpkZmaFSryAVMv75Uu5Cv8z4FPAIoCIeBjYpYoxmZm1q8rNmLqspKvwEfF8m6zeUp1wzMzal6tzoAWel7QTEJJWAY4HHqtuWGZm79dg+bOkBHoMcB6wHvACcDPwtWoGZWbWVi47VI6IhcC4GsRiZtapRjuEL+Uq/MaS/iBpQeqA9HeSNq5FcGZmhVTiUCulXIW/GrgOGAqsC0wGrqlmUGZm7cljM6a+EXFFRCxPw5XAatUOzMysUHYVvrShVjq7F37t9PLPkr4LXEt2b/wXgCk1iM3M7F3qUofKNdHZRaQZZAmzNeKjC+YFcHK1gjIza0+jPZWzs3vhN6plIGZmnWk9hG8kJd2JJGkLYAQF5z4j4tfVCsrMrD25qYG2knQ6WcejI8jOfY4F7gacQM2sphorfZZ2FX5/sk5GX4yIQ4Gtgf5VjcrMrA0pa0hfylArpRzCL4mIFZKWS1qL7EFLw6ocV4/013vncPKPr6dlxQrG770T3zxkj3qHVHfnf28cn/rYFixc/Do7ffGsd6Yf+fmPc8QBO9OyIrj17lmcfn7Rx9f0CN39O5Snq/CtpksaAPyS7Mr8G8B91QyqJ2ppWcF3zrmOmy44lnWHDGC3g89l7C5b8qGNh9Y7tLq65o9/55fX3cnF3z/onWkf225TPv3xLdn5S2ezdNlymgeuWccIG0dP+A5VqnIp6ZvAEWQtih4FDo2It7taTtFD+Ij4akS8GhEXA58EDk6H8l0maYKkE8pZtwvbuCzdcjqrmtuptBmzn2HjYc1suH4zfVZpYt9PjmLKnY/UO6y6u/fBp1j87/c+COGw/XbmZ5ffytJlywFYuPiNeoTWcLr7d0iUdvhe7BBe0nrAccDoiNgC6A18sZyYOkygkka1HYC1gab0ulFNAvasdxBdNX/Ba6w3ZOA74+sOGcj8Ba/VMaLG9cHh6/DRbTbh1l+dwB//93i2HbFBvUNqCN3+O1RiZ8ol1lKbgNUlNQF9yXqa67LODuF/3Mm8AHYrVrikg4AT0vKPAE8VzDsSOAroAzwJjI+ItyQdQPaI0RbgtYjYRdJI4Fdp2V7AfhHxz3YDi5gqacMSYjsqbZ9hG/gHmCdNvXsxcK01+OShP2LUiOH86qzD2GafCfUOy2qgC82YmiUVPhhzYkRMBIiIeZJ+BDwHLAFuiYhbyomns4b0nyinwFYp6Z0K7BQRC9OtoYWPQr4xIn6Zlv0hcDhwPnAa8Kn0JgekZY8BzouIqyT1Iatyr5S0MycCbLfd6A4f31wrQwf3Z95Li98Zf+GlxQwd7MYO7Zn38qv84W8PATBzzrOsiGDQgDVZ9GrPPpTvCd+hUpoNJQsjYnR7MyQNBPYGNgJeBSZL+nLq56Na8XTZbsDk1J8oEfFKm/lbSLpL0qNk/Y2OTNPvASalGmprorwPOEXSScDwiFhSxbjrYtSI4Tz13AKenbeQpcuWc+OtMxm7y1b1DqshTbnjEXYevRkAm2ywDn1WaerxyRO6/3dIQO9eKmko4r+Af0XEgohYBtwI7FROTCXdiVQlk4B9IuJhSYeQNdYnIo6RtAPwGWCGpO0i4mpJ09K0KZKOjojb6xR3VTQ19eacEz/PfsddSEtLMG6vHfnwJt3n6mm5LvnhIYzZblMGDViTWX/8AWdPnMKVv7+PC04bx73XnsLSZS18ZcIV9Q6zIfSE71CFWjE9B+woqS/ZIfzuwPTOV2lfNRPo7cBNkn4SEYsKendq1Q+Yn56zNA6YByBpk4iYBkyTNBYYJqk/8HRE/FzSBsBWqfxuZY8xI9ljzMjiC/YgR5w6qd3pR5/mG+Ha052/Q9kFopXPoBExTdL1wExgOfAg6XReV5VyK6fIEtzGEXFGSmAfiIj7iwQ5W9KZwJ2SWlKQzxQs8j1gGrAg/e2Xpp8raVOyGvttwMPAScB4ScuAF4Gz6ICka8hqs82S5gKnR8Slxd6nmTW+SrWjj4jTyS5Wr5RSaqC/AFaQndM8A3gduAHYvtiKEXE5cHkH8y4CLmpn+r7tLH52GoqKiANLWc7M8qfB+hIpKYHuEBGjJD0IEBGL05VwM7OaEdDUYBm0lAS6TFJvsracSBpMViOtG0mDyA7v29o9IhbVOh4zq40Gy58lJdCfAzcB66RzmvuTte+sm5Qkt6lnDGZWW6pxT0ulKOW58FdJmkF2qV9kTY8eq3pkZmZtNFj+LOkq/AbAW8AfCqdFxHPVDMzMrK0G682upEP4P/Huw+VWI7v96QnevXPIzKzqsmciNVYGLeUQfsvC8dQT01erFpGZWXsEvat583kZunwnUkTMTLdampnVlBrsqUilnAP9VsFoL2AUZfadZ2ZWrrw+1rhfwevlZOdEb6hOOGZmHctVAk0N6PtFRFUfw2FmVorcPBdeUlNELJc0ppYBmZm1J2+H8PeTne98SNLvgcnAm60zI+LGKsdmZvYuUUpnyTVVyjnQ1YBFZL0xtbYHDbJenM3MaiJvNdB10hX4WbybOFvV/RlCZtbzNNgp0E4TaG9gTWi34ZUTqJnVmOiVo3ag8yPijJpFYmbWCdF4NdDOboxqsFDNrEcTNPVSSUPRoqQBkq6X9LikxyR9tJyQOquB7l5OgWZm1VDhGuh5wF8iYv/0hI2+5RTSYQJt5znuZmZ1VYnemNJTfncBDgGIiKXA0rLiWelozMxqJHu0cfGB7Km80wuGowqK2YjsacC/kvSgpEskrVFOPE6gZpYLIktYpQzAwogYXTAUPve9iewmoYsiYluyG4S+W05MTqBmlg/K7oUvZShiLjA3Iqal8evJEmqXOYGaWS4I6C2VNHQmIl4Enpe0eZq0OzCnnJi63KGymVm9VLBt5deBq9IV+KeBQ8spxAnUzHKjUs2YIuIhYPTKluMEamY5UdL5zZpyAjWzXGi9Ct9InEDNLDdy91hjs8UPXFDvEBre1TOfrXcIDW3RW2Xd6PNeytEjPczMGokP4c3MVoJroGZmZWqs9OkEamY50mAVUCdQM8uH1ls5G4kTqJnlhFCDHcQ7gZpZbjRYBdQJ1MzyIWvG1FgZ1AnUzPJBroGamZXNCdTMrAy+Cm9mthJ8Fd7MrEwNVgF1AjWz/Gi0GmijdW5iZtYuAb1U2lBSeVLv9Fz4P5Ybk2ugZpYPUqU7VD4eeAxYq9wCXAM1s9xQiUPRcqT1gc8Al6xMPK6BmlkuZIfwFauB/gw4Eei3MoW4BmpmudGFGmizpOkFw1HvlCF9Fng5ImasbDyugZpZfpReAV0YER09930MsJekTwOrAWtJujIivtzVcFwDNbPcUIn/OhMRJ0fE+hGxIfBF4PZykie4BmpmOVJqE6VacQI1s/yocAKNiDuAO8pd3wnUzHIhu0DUWFVQJ1Azywf3B2pmVr4Gy59OoGaWIw2WQZ1AzSwnKn4v/EpzAjWzXCj1PvdacgI1s/xosAzqBGpmueFmTNahv947h5N/fD0tK1Ywfu+d+OYhe9Q7pIbjfdS5t956m6uuuJn58xaC4MsH7cnGm6xX77AqpsFOgTqBNoqWlhV855zruOmCY1l3yAB2O/hcxu6yJR/aeGi9Q2sY3kfFXf+b2xkxciOOPHpvli9vYenSZfUOqXIasB1oTTsTkTRB0glVLH+YpL9JmiNptqTjq7WtSpsx+xk2HtbMhus302eVJvb95Cim3PlIvcNqKN5HnVuy5D88+c+57DRmSwCamnrTt+9qdY6qsirRmUgldbca6HLg2xExU1I/YIakWyNiTr0DK2b+gtdYb8jAd8bXHTKQGbOeqV9ADcj7qHMLF77Kmv1W54rL/8y8uQvYYIMh7P+F3Vh11T71Dq0iRA+rgUo6SNIjkh6WdEWbeUdKeiDNu0FS3zT9AEmz0vSpadpISfdLeiiVt2l724uI+RExM71+nex5J93nBJBZJ1a0BM8/9xI7f3wbTj71YPqsugq3/OX+eodVUZV6pEelVC2BShoJnArsFhFbkz3AqdCNEbF9mvcYcHiafhrwqTR9rzTtGOC8iNgGGA3MLWH7GwLbAtM6mH9Ua2/VCxYu6NJ7q4ahg/sz76XF74y/8NJihg7uX8eIGo/3UecGDFyTAQP7sdFG6wKw7ajNef65l+ocVYU1WAatZg10N2ByRCwEiIhX2szfQtJdkh4FxgEj0/R7gEmSjgR6p2n3AadIOgkYHhFLOtuwpDWBG4BvRMS/21smIiZGxOiIGD24eXA576+iRo0YzlPPLeDZeQtZumw5N946k7G7bFXvsBqK91Hn+vdfk4ED+/HSi9lP7YnHn+UDQwfVOarK8jnQd00C9omIhyUdAuwKEBHHSNqB7Il5MyRtFxFXS5qWpk2RdHRE3N5eoZJWIUueV0XEjTV4HxXR1NSbc078PPsddyEtLcG4vXbkw5v46nIh76PiDvji7ky69I8sb2mhuXkA4w8eW++QKqondah8O3CTpJ9ExCJJa7eZ3w+YnxLeOGAegKRNImIaME3SWGCYpP7A0xHxc0kbAFul8t9DkoBLgcci4ifVe2vVsceYkewxZmTxBXsw76PODRs2hJP++6B6h1E9PSWBRsRsSWcCd0pqAR4EnilY5Htk5ycXpL+tjxc9N10kEnAb8DBwEjBe0jLgReCsDjY7BhgPPCrpoTTtlIiYUqn3ZWb10eM6VI6Iy4HLO5h3EXBRO9P3bWfxs9NQbHt303D/R5lZRVSoIb2kYcCvgSFAABMj4rxyyupu7UDNrBurUO2oYu3Fc5lAJQ0iO7xva/eIWFTreMysRiqQQSNiPjA/vX5dUmt78Z6RQFOS3KbecZhZLVW+Q+Vi7cWLyWUCNbOep4tt5JslTS8YnxgRE99TXgntxYtxAjWz/Cg9gy6MiNEdFlOh9uJOoGaWG5VoxlTJ9uI17c7OzGxlSKUNRbS2F98tdVD0kKRPlxOPa6Bmlg+qzK2clWwv7gRqZjnSWPfJOIGaWS40YofKTqBmlhsNlj+dQM0sP1wDNTMrU4/qjcnMrJJcAzUzK0OJbTxrygnUzHLDh/BmZuVqrPzpBGpm+dFg+dMJ1Mzyw+dAzczKoCp0qLyy3BuTmVmZXAM1s9xosAqoE6iZ5YebMZmZlcMN6c3MyuPu7MzMVoIP4c3MytRoNVA3YzKz3FCJQ9FypD0lPSHpSUnfLTceJ1Azy48KZFBJvYELgbHACOBASSPKCccJ1MxyQyX+K+IjwJMR8XRELAWuBfYuJx6fAwVmzpyxcPVV9Gy94yjQDCysdxANzPunuEbbR8NXtoAHZ864uW8fNZe4+GqSpheMT4yIien1esDzBfPmAjuUE5MTKBARg+sdQyFJ0yNidL3jaFTeP8V1x30UEXvWO4a2fAhvZj3NPGBYwfj6aVqXOYGaWU/zALCppI0k9QG+CPy+nIJ8CN+YJhZfpEfz/inO+6gDEbFc0rHAzUBv4LKImF1OWYqIigZnZtZT+BDezKxMTqBmZmVyAjUzK5MTaJVJmiDphCpvoyL39dZLjfbRZZJeljSrmtuphmrvH0nDJP1N0hxJsyUdX61tdTdOoDlXyft6u7lJQMM1xG4Qy4FvR8QIYEfga/4OlcYJtMIkHSTpEUkPS7qizbwjJT2Q5t0gqW+afoCkWWn61DRtpKT7JT2Uytu0g01W7L7eWqnDPiIipgKvVPWNVUit909EzI+Imen168BjZLc7WjER4aFCAzAS+AfQnMbXBiYAJ6TxQQXL/hD4enr9KLBeej0g/T0fGJde9wFW72Cb+wOXFIyPBy6o975opH1UUN6GwKx674NG3T8F++g5YK1674s8DK6BVtZuwOSIWAgQEW1rPFtIukvSo8A4sh8LwD3AJElHkjXsBbgPOEXSScDwiFhS/fBrwvuoc3XbP5LWBG4AvhER/67M2+nenEBraxJwbERsCXwfWA0gIo4BTiW7P3eGpEERcTWwF7AEmCJptw7KrNh9vQ1iEpXfR93JJKqwfyStQpY8r4qIG6v7FroPJ9DKuh04QNIgAElrt5nfD5ifvqzjWidK2iQipkXEacACYJikjYGnI+LnwO+ArTrYZsXu662ReuyjPKn5/pEk4FLgsYj4ScXfUTfme+ErKCJmSzoTuFNSC/Ag8EzBIt8DppF9waeR/RgAzk0n+AXcBjwMnASMl7QMeBE4q4NtVuy+3lqoxz4CkHQNsCvQLGkucHpEXFrBt1YRddo/Y8jOnT8q6aE07ZSImFKp99Vd+V54M7My+RDezKxMPoTPiXRO7LZ2Zu0eEYtqHU8j8j7qnPdP5fkQ3sysTD6ENzMrkxOomVmZnECtKEkt6X7qWZImt95/XWZZkyTtn15fok46rZC0q6SdytjGM9L7H3/b0fQ2y7zRxW1VvScpa1xOoFaKJRGxTURsASwFjimcKamsi5ERcUREzOlkkV2BLidQs1pxArWuugv4YKod3iXp98AcSb0lnZt6CnpE0tGQ3eUi6QJl/ZX+FVintSBJd0ganV7vKWlm6k3oNkkbkiXqb6ba786SBqceiB5Iw5i07iBJtyjry/ISssbknZL0W0kz0jpHtZn30zT9NkmD07RNJP0lrXOXpA9VZG9arrkZk5Us1TTHAn9Jk0YBW0TEv1ISei0itpe0KnCPpFuAbYHNyfoqHQLMAS5rU+5g4JfALqmstSPiFUkXA29ExI/SclcDP42IuyVtQHb31YeB04G7I+IMSZ8BDi/h7RyWtrE68ICkG1JTnjWA6RHxTUmnpbKPJXvK5TER8U9JOwC/IOv4w3owJ1ArxeoFt/jdRXbf9E7A/RHxrzR9D2Cr1vObQH9gU2AX4JqIaAFekHR7O+XvCExtLaudHoha/RcwIrt1G4C1lPUgtAuwb1r3T5IWl/CejpP0ufR6WIp1EbAC+E2afiVwY9rGTsDkgm2vWsI2rJtzArVSLImIbQonpETyZuEksr4pb26z3KcrGEcvYMeIeLudWEomaVeyZPzRiHhL0h2kXo3aEWm7r7bdB2Y+B2qVcjPwldRLEJI2k7QGMBX4QjpHOhT4RDvr/h3YRdJGad3WHohe593OMgBuAb7eOiJpm/RyKvClNG0sMLBIrP2BxSl5foisBtyqF1kn1aQy7059Y/5L0gFpG5K0dZFtWA/gBGqVcgnZ+c2Zyh7c9r9kRzg3Af9M835N1snve0TEAuAossPlh3n3EPoPwOdaLyIBxwGj00WqObzbGuD7ZAl4Ntmh/HNFYv0L0CTpMeBssgTe6k3gI+k97AackaaPAw5P8c2mwR+bYrXhWznNzMrkGqiZWZmcQM3MyuQEamZWJidQM7MyOYGamZXJCdTMrExOoGZmZfo/w77fGq9YdFAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Confusion matrix for SVC classifier'\n",
    "disp = plot_confusion_matrix(classifier, \n",
    "                             X_test_norm, \n",
    "                             y_test,\n",
    "                             display_labels=target_names,\n",
    "                             cmap=plt.cm.Blues,\n",
    "                             normalize=None,\n",
    "                            )\n",
    "disp.ax_.set_title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix show the classifier is perfect - let's understand what patterns in the data help the SVC perform so well!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply KernelSHAP to explain the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ovr_definition'></a>\n",
    "\n",
    "The model needs access to a function that takes as an input samples and returns predictions to be explained. For an input $z$ the decision function of an _binary_ SVM classifier is given by:\n",
    "\n",
    "$$\n",
    "\\text{class}(z) = \\text{sign}(\\beta z + b)\n",
    "$$\n",
    "\n",
    "where $\\beta$ is the best separating hyperplane (linear combination of support vectors, the training points closest to the separating hyperplane) and $b$ is the bias of the model. \n",
    "\n",
    "For the 'one-vs-rest' SVM, `nclass` binary SVM algorithms are fitted using each class as the positive class and the remainder as negative class. The classification decision is taken by assigning the label from the classifier with the maximum absolute decision score. Therefore, to explain our model we could consider explaining the SVM model which outputs the highest decision score. Click here to go back to [source](#intro).\n",
    "\n",
    "To do so, the KernelSHAP explainer must receive a callable that returns a set of scores when called with an input $X$, in this case the `decision_function` attribute of our classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_fcn = classifier.decision_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 142 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={\n",
       "    'name': 'KernelShap',\n",
       "    'type': ['blackbox'],\n",
       "    'task': 'classification',\n",
       "    'explanations': ['local', 'global'],\n",
       "    'params': {\n",
       "        'groups': None,\n",
       "        'group_names': None,\n",
       "        'weights': None,\n",
       "        'kwargs': {},\n",
       "        'summarise_background': False\n",
       "    }\n",
       "})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "svm_explainer = KernelShap(pred_fcn)\n",
    "svm_explainer.fit(X_train_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='source_1'></a>\n",
    "<a id='f_1'></a>\n",
    "\n",
    "Note that the explainer is fit to the classifier training set . This training set is used for two purposes:\n",
    "\n",
    "   - To determine the model output when all inputs are missing ($\\phi_0$ in eq. $(5)$ of [[1]](#References). Because the SVM model does not accept arbitrary inputs, this quantity is approximated by averaging the decision score for each class, across the samples in `X_train_norm` as shown below and it is stored as the `expected_value` attribute of the explainer\n",
    "   - The values of the features in the $N \\times D$ `X_train_norm` dataset are used to replace the values missing during the feature attribution ($\\phi_i$) estimation process. Specifically, `nsamples` copies of `X_train_norm` are tiled to create a dataset where, for each copy, a subset of features $z'$ of size $s = |z'|$ are replaced by the values in the instance to be explained and the complement of this subset is left to the background dataset value. These background values simulate the effect of *missing values*, since most models cannot cope with arbitrary patterns of missing values at inference time. Therefore, when computing the shap value of a particular feature, $\\phi_i$, `nsamples` regression targets ($f(h_x(z'))$ in eq $(5)$ of [[1]](#References)) are computed as the *expected* prediction of the model to be explained when a given subset of features is missing as opposed to replacing the missing feature with a single value. Note that the averaging operation can be replaced by weighted averaging by specifying the `weights` argument to the `fit` method. <sup>[(a)](#Footnotes)</sup>   \n",
    "   \n",
    "For the above reason, this is sometimes referred to as the *background dataset*; a larger dataset increases the runtime of the algorithm, so for large datasets, a subset of it should be used. An option to deal with the runtime issue while still providing meaningful values for missing values is to summarise the dataset using the `shap.kmeans` function. This function wraps the `sklearn` k-means clustering implementation, while ensuring that the clusters returned have values that are found in the training data. In addition, the samples are weighted according to the cluster sizes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.11022302e-16  4.44089210e-16 -4.44089210e-16]\n"
     ]
    }
   ],
   "source": [
    "# expected_values attribute stores average scores across training set for every binary SVM \n",
    "mean_scores_train = pred_fcn(X_train_norm).mean(axis=0)\n",
    "# are stored in the expected value attibute of the explainer ...\n",
    "print(mean_scores_train - svm_explainer.expected_value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=36.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "svm_explanation = svm_explainer.explain(X_test_norm, l1_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='source_2'></a>\n",
    "\n",
    "In cases where the feature space has higher dimensionality, only a small fraction of the missing subsets can be enumerated for a given number of samples `nsamples`. If the the fraction of the subsets enumerated falls below a fraction (`0.2` for version `0.3.2`) and the regularisation is set to `auto`, a least angle regression with the AIC information criterion for selecting the regularisation coefficient $\\alpha$ is performed in order to select features. The regularisation has no effect if the fraction is greater than this threshold and `l1_reg` is not set to `auto`. Other options for regularisations are:\n",
    "- `l1_reg=\"num_features(10)\"`: in this case, the LARS algorithm [[2](#References)] is used to which 10 features to estimate the shap values for.\n",
    "- `l1_reg=\"bic\"`: in this case, the least angle regression is run with the Bayes Information Criterion \n",
    "- `l1_reg=0.02`: if a float is specified, the $\\ell_1$-regularised regression coefficient is set to this value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the SVM algorithm returns a score for each of the $3$ classes, the shap_values are computed for each class in turn. Moreover, the attributions are computed for each data point to be explained and for each feature, resulting in a $N_e \\times D$ matrix of shap values for each classs, where $N_e$ is the number of instances to be explained and $D$ is the number of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output type: <class 'list'>\n",
      "Output size: 3\n",
      "Class output size: (36, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"Output type:\", type(svm_explanation.shap_values))\n",
    "print(\"Output size:\", len(svm_explanation.shap_values))\n",
    "print(\"Class output size:\", svm_explanation.shap_values[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a given instance, we can visualise the attributions using a force plot. Let's choose the first example in the testing set as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted class for the X_test_norm[0] is 0.\n",
      "OVR decision function values are [ 2.24071294  0.85398239 -0.21510456].\n"
     ]
    }
   ],
   "source": [
    "idx =  0\n",
    "instance = X_test_norm[idx][None, :]\n",
    "pred = classifier.predict(instance)\n",
    "scores =  classifier.decision_function(instance)\n",
    "class_idx = pred.item()\n",
    "print(\"The predicted class for the X_test_norm[{}] is {}.\".format(idx, *pred))\n",
    "print(\"OVR decision function values are {}.\".format(*scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that class $0$ is predicted because the SVM model trained with class $0$ as a positive class and classes $1$ and $2$ combined as a negative class returned the largest score.\n",
    "\n",
    "To create this force plot, we have provided the plotting function with four inputs:\n",
    "- the expected predicted score by the class-0 SVM assuming all inputs are missing. This is marked as the base value on the force plot\n",
    "- the feature attributions for the instance to be explained \n",
    "- the instance to be explained \n",
    "- the feature names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
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   ],
   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[class_idx], \n",
    "    svm_explanation.shap_values[class_idx][idx, :] , \n",
    "    instance,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The force plot depicts the contribution of each feature to the process of moving the value of the decision score from the _base value_ (estimation of the decision score if all inputs were missing) to the value predicted by the classifier. We see that all features contribute to increasing the decision score, and that the largest increases are due to the `proline` feature with a value of `1.049` and the `flavanoids` feature with a value of `0.9778`. The lengths of the bars are the corresponding feature attributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, below we see that the `proline` and `alcohol` features contribute the to decreasing the decision score of the   SVM predicting class `1` as positive and that the `malic_acid` feature increases the decision score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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   ],
   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[1], \n",
    "    svm_explanation.shap_values[1][idx, :] , \n",
    "    instance,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative way to visualise local explanations for multi-output models is a _multioutput decision plot_. This plot can be especially useful when the number of features is large and the force plot might not be readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_labels(classifier, instance, class_names=None):\n",
    "    \"\"\"\n",
    "    Creates a set of legend labels based on the decision \n",
    "    scores of a classifier and, optionally, the class names.\n",
    "    \"\"\"\n",
    "\n",
    "    decision_scores = classifier.decision_function(instance)\n",
    "    \n",
    "    if not class_names:\n",
    "        class_names = [f'Class {i}' for i in range(decision_scores.shape[1])]\n",
    "    \n",
    "    for i, score in enumerate(np.nditer(decision_scores)):\n",
    "        class_names[i] = class_names[i] + ' ({})'.format(round(score.item(),3))\n",
    "        \n",
    "    return class_names\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = class_labels(classifier, instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = shap.multioutput_decision_plot(svm_explainer.expected_value.tolist(), \n",
    "                                   svm_explanation.shap_values,\n",
    "                                   idx, \n",
    "                                   feature_names=feature_names, \n",
    "                                   feature_order='importance',\n",
    "                                   highlight=[class_idx],\n",
    "                                   legend_labels=legend_labels,\n",
    "                                   return_objects=True,\n",
    "                                   legend_location='lower right')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision plots shows how the individual features influence contribute to the classification into each of the three classes (a *prediction path*). One sees that, for this example, the model can easily separate the three classes. It also shows, for example, that a wine with the given alcohol content is typical of class `0` (because the `alcohol` feature contributes positively to a classification of class `0` as negatively to classification in classes `1` and `2`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the feature ordering is determined by summing the shap value magnitudes corresponding to each feature across classes and then ordering the `feature_names` in descending order of cumulative magniture. The plot origin, marked by the gray vertical line, is the average base values across the classes. The dashed line represents the model prediction - in general we can highlight a particular class by passing the class index in the `highlight` list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose now that we want to analyse instance `5` but realise that the feature importances are different for this instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx =  5\n",
    "instance = X_test_norm[idx][None, :]\n",
    "pred = classifier.predict(instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance_shap = np.array(svm_explanation.shap_values)[:, idx, :]\n",
    "feature_order = np.argsort(np.sum(np.abs(instance_shap),axis=0))[::-1]\n",
    "feat_importance = [feature_names[i] for i in feature_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['flavanoids', 'alcalinity_of_ash', 'od280/od315_of_diluted_wines', 'alcohol', 'ash', 'total_phenols', 'proline', 'magnesium', 'proanthocyanins', 'hue', 'malic_acid', 'nonflavanoid_phenols', 'color_intensity']\n"
     ]
    }
   ],
   "source": [
    "print(feat_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to create a multi-output decision plot with the same feature order and scale. This is possible, since by passing the `return_objects=True` to the plotting function in the example above, we retrieved the feature indices and the axis limits and can reuse them to display the decision plot for instance `5` with the same feature order as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.multioutput_decision_plot(svm_explainer.expected_value.tolist(), \n",
    "                               svm_explanation.shap_values,\n",
    "                               idx, \n",
    "                               feature_names=feature_names, \n",
    "                               feature_order=r.feature_idx,\n",
    "                               highlight=[pred.item()],\n",
    "                               legend_labels=legend_labels,\n",
    "                               legend_location='lower right',\n",
    "                               xlim=r.xlim,\n",
    "                               return_objects=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown above, the force plot allows us to understand how the individual features contribute to a classification output *given* an instance. However, the particular explanation does not tell us about the model behaviour in general. Below, we show how such insights can be drawn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stacked force plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way we can do this is to stack the force plot for a number of instances, which can be achieved by calling the `force_plot` function with the same arguments as before but replacing `instance` with the whole testing set, `X_test_norm`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iK08AFPPB3Y8UZ5L4AC1H'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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       "    document.getElementById('iK08AFPPB3Y8UZ5L4AC1H')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7fe4f476d990>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_idx = 0 # we explain the predicted label\n",
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[class_idx], \n",
    "    svm_explanation.shap_values[class_idx], \n",
    "    X_test_norm,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the default configuration, the `x` axis is represented by the `36` instances in `X_test_norm` whereas the `y` axis represents the decision score. Note that, like before, the decision score is for class `0`. For a given instance, the height in between two horizontal lines is equal to the shap value of the feature, and hovering over a plot shows a list of the features along with their values, sorted by shap values. As before, the blue shading shows a negative contribution to the decision score (moves the score *away* from the baseline value) whereas the pink shading shows a positive contribution to the decision score. Hovering over  the plot, tells us, for example, that to achieve a high decision score (equivalent to class `0` membership) the features `proline`  and `flavanoids` are generally the most important and that positive `proline` values lead to higher decision scores for belonging to this class whereas negative proline values provide evidence against this one belonging to `0` class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the relationship between decision scores and the values more clearly, we can permute the `x` axis so that the instances are sorted according to the value of the `proline` feature by selecting `proline` from the horizontal drop down menu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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       "    document.getElementById('iI82F07SNF9DHB8NVCCUY')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7fe4f4757910>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[class_idx], \n",
    "    svm_explanation.shap_values[class_idx], \n",
    "    X_test_norm,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also explore the effect of a particular feature across the testing dataset. For example, in the plot below, by selecting `flavanoids` from the top drop-down, the instances are ordered on the  `x` axis in increasing value of the `flavanoids` feature. \n",
    "\n",
    "Similarly, selecting `flavanoids effects` from the side drop-down will plot the shap value as opposed to the model output. The effect of this feature generally increases as its value increases and the large negative values of this feature reduce the decision score for classification as `0`. Note that the shap values are respresented with resepect to the base value for this class (0.798, as shown below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iMICUQ7C70V2NVF6KYSE6'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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       "    document.getElementById('iMICUQ7C70V2NVF6KYSE6')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7fe4f4757f10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[0], \n",
    "    svm_explanation.shap_values[0], \n",
    "    X_test_norm,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.79821894 1.41710253 0.69461514]\n"
     ]
    }
   ],
   "source": [
    "print(svm_explainer.expected_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise the impact of the features on the decision scores associated with class `0`, we can use a summary plot. In this plot, the features are sorted by the sum of their SHAP values magnitudes across all instances in `X_test_norm`. Therefore, the features with the highest impact on the decision score for class `class_idx` are displayed at the top of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(svm_explanation.shap_values[0], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the `proline` and `flavanoids` have the most impact on the model output; as the values of the features increase, their impact also increases and the model is more likely to predict class `0`. On the other hand, high values of the `nonflavonoid_phenols` have a negative impact on the model output, potentially contributing to the classification of the particular wine in a different class. To see this, we do a summary plot with respect to `class_2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UgUopj1LqUpwBQy/vbhBaawP8C7hPKTUYQCmVrJQ6TinVvkO2hRBC7DNMg0dXIEmm6KyeAB4CSoCzgZO01mWNbai1fhlncM6LQBHwa+BErfW6Norlb8C7wLtKqXJgBc7AJHn9CCGEaCNd7xd/pLlcdDqR5vKbtNYvdnQsnYC8QMU+TZrLRSfULhlg0PpFvfd7r3lyr880pSZGCCGEEEK0ObkZuxBCCCFEh9vrKy53IEmm6HS01v06OgYhhBBiT+oq98aMJc3lQgghhBCizUlNphBCCCFEh+t6NZmSZAohhBBCdDBpLhdCCCGEEKIZJMkUQgghhBBtTprLhRBCCCE6WFdsLpckUwghhBCiw3W9JFOay4UQQgghRJuTmkwhhBBCiA7WFZvLpSZTCLFPM8ZgAuHW7RsMYWw7Ou8PtT6OsI0dsne9YQfZnXMT7ccYQyBsOjqMRvlDnTOuzstq8Nj7SZIphNhn1c4rZFWvJ1ge/y8Kfj+tRfuaf3yAif8lJuu3hN/9iYpx/6Is/q9UTn4KUxtsUVn5UzfwUc/X+TD3NdY9t7JF+7Y3U+Wn+oh/Uxl/A1UH/QtTXtvRIYmI7zba5DwUIuG+ELdPb90XpfZy1cchEu4J0vvhAIsKO++XJ9G+JMkUQuyztt30HeH8KjBQ8s85+BcXNWs/U1yJ+fNbYBsorSbw6zcJz90MQOizFQRfm9+iOBbeoAlXhzFBm4V/mIUJd54P5eB/NeFvVgNgz1hH8MkZHRyR2O6Gr2yKapyn4d+m2+RXdo6awzlbbB6dY2OAjRVw67edKwHurAxWvUdXIEmm2GsopdYqpS5og3L6KaWMUipvN8qYppS6aXdjER3LlRDTLd0Cy+du3o4eN3hi3j4b7pfQsu7u7vjo/i6fG1yd5wPGSvDWX9BwXnSY2KeZxwVxzXz6trcEj7XTebHvkCRTCLHPyrn3cOIP6oGnVzK5Dx5J3MD0Zu1npSZgPXc59M6Esb3xvXYx3tNHYfVIIe6XB+I9Y3SL4tjvsYNIHpJKYr9kxj1zCJbVeT6UPReOx3PxAVjdU/Ccuz/eKyZ0dEgi4t+T3YzrBnkp8OTxbrISOsfzZni2xT1HuemZDIfmWdw9qZNkv51cV6zJlNHlQoi908p8mLUSDhoK/XJbVYQ7xUO3a0bhtvx47SrIL4YemY1vvGoLzFwBE4dgJyQSsBLxfPBHPEnAjytIuudYGNAdyqrgjR+gXw5MGNKsODIn5DBp9hQqfizAv7aCUFkAT1pcq84pVk2Rn00zCskYmELWsDQAipaWUbKqgl4Tc0jI8kW3nb6R0OZKkk4agCspemzL4ybhuXMJfLEKU1qDCdoUfbgWl89N5gm9dzvG3bFyTjn+6jBDJ6RR+FMxNUV+knJ8VG+uocehucRn+HZZRtG2IKuW1dB3QDzderTumhcWhVi80s+gvnH06u7l9Z/8zFoX4qz94jigr5ctxWF+WhVgeB8vfbtFP3a/WBKgOmA4YVQcHnc0qfAHDLPm15Ca7GLMsHjyKwzT1oQZ3c3FqG7RuiG3Mfxhfzi4r5vCGnh1YZjJA11k7qFkszJg+HC1oVeKxSG96h/zjxPd7J8WpiZg0y2xZeWuKzN8v9mgulsMzugayda+SpJM0akopa4Ffg30AkqAl4CbtNY7dOpRSo0B7gHGA25gjtb6mJh1/wT2j5TzDHB3g3ImKaX+DPQGfgAu1lrnR/bPAh4EJke2/QT4vda6uE1PWLTOT2vgkL9AtR9SE2HWPTCkZ4uKsMtrKZ74JK4V68hgHWCgWzrMvW/HRHP+Wjj4L1BVi0lJoCx+MKHCEHgs0jybiastheR4+Oo2uPjfsHgjWBY8exVcfGSz4il4bhkrL/0agISRGYyZ+XPcia1/i64t8fP2GdOoyq/Bclsc9+gEsCw++fUMTNiQ1D2B0/93JPEZPkofmUvh1V8A4Bvfjd7fn4cV0/ZafdsXVN/6JQCbcgZQVugs73XtSAb+c2KrY9wdnz+/ia9f3QJAv+QA4flbAbBsgzcYJqVPElOmHk1catOJY8HWAHf9dT3VVTbeOIs/3tKbfgPjWxRHfkGI6+7YQlW1IS7OIl2l8PQip0/tA9P8PPnzeF54t5KKGoPPC09el8mIvl5uebeKh750BlEdP8rLq79IBSBsG25+oJDFKwMAnDoljVuWedhSCV43fHxhHEcNcDN3U5hDH6umOgiJXqj2eMFlMSjTYvYVXlJ97ZucBcKGI14LM8e57DxytIvf7B9NgP/6vyoemeac30mjvbx0RWqzyl1RYjjgxTBlfqc7wPRz3Yzrtq8kml3vPKW5XHQ2G4ETgFTgVOAy4IqGGymlegBfRx79gO7A3yPr0oDPgK8iy0+KlHNdg2LOBg7HSWiTgNtj1r0EZADDI49s4IXdPz3RJt6b5SSYAOXV8NGcFhcRmr2Z8Ioi4inHIjJgYmspTFu048ZTNVQ5H5hWRQ3ewsgna8jgr40kJZW18Nw0J8EEMAZe+67Z8Wx7bXXddM2iEqoX7t73mS1ziqnKr3FCCRtWf7KZ1R9vwkRud1O1pYYtc5xjVLy6tG4//+ytBFaW1CvL/4ozkMlAXYIJUPDqajrKgq+jMVYvi06bSH/WyvVVbJtXssN+sRbPq6a6ykkIgwHDvNmVLY5jzsIaqqqdaxoIGPS86Oh727J44/taKmqc9f4gfDPfed6+NSdQt93HC4NUB5xtikrCdQkmwFtzA2yJhBUMw9uLne/J7y0JUR25iUF1EIgMFltZbJiT3/4DgJYXU5dgAry2rP5gtbfm+OumP1gQbPbtjD5eYyiL7FoTgvdWdp5BcO2tKzaXS5IpOhWt9Vta6zVaa6O1nouT2B3dyKYXAiu11ndrrau01gGt9eeRdScBAeBOrbVfa70E+Ac7Jqu3aa23aa3LgZcBBaCU6gkcB1yntS7RWpfgJKgnRpLbPaaiokKmG5seN6BuHsuC/fu3uJyanvFYyXEESYiW5fVQNSB7x+1jjmcsi5An2v7nIZpUVB/YH9Ji2gYj+zUnHu+olLppV7qX+AGpzd63semMQSm44qJv8SkD48kekV43745zkTEwhYqKCuLHdYsuz0nAk5dSr0wz2umOYAE+b/R+mQlj0loVW1tM9xgYvc6u9JjaR+MkM+54N6kDUnZaTmZumNjur336+1ocz4A+cfXKyMqJXnPLGMYO8NZbP6y3h4qKCsbkRWuKB+e6SIyzqKioID3VTVZGdN1+PV14XdEEbUSmk4CO6xndxoK6wWLJcdDDV9Xs+Fs73TsVsmNeOuO6WfW2GRnzTjkk18IXGfyzq/L3z7WiX/qA4am1O92+I6fFrlnGdI5bHggBoJQ6FyehG4DTnSMOmKG1nqSUWovTdP6iUupRIE1rfX4jZdwITNFaHxqz7BjgPa11olKqH7AG6K213hhZf0mk7EFKqQnADMCrtQ5F1nuAIDBBaz1TKTUN+FxrfWe7XIgoeYE25fXv4JvFMHksnHJgq4oIztpEzfM/4SvaTFyGwTr9IDiqiUE7b34PXy2EY8cSSM/B/+YiPPv1ICEzBF8sgKNHw2kTnab8p7+E/rlw7Yngbt6gBxOyyX9oIbVrKuh2+VCS9sve9U67sGV2ESs/2EjmkFSGn90PgCWvraV4eTkDT+xFD+UcwwTClDyoCW2qJO3KsfhG1D+2qQ5Qc++32KW1uM/dn/zXN2DFu+n9xzFt0nd0Z6ZOnQrAlClT6i0P1IaZ/uZWaqvDTDgxm/UfbKC6sJaEJDeBkgADf96HnP2zdln+/DmVLPypikFDEzjwkOY16TY0a14NsxfUMnxwHPuNSeT371Qxb1OYM8Z6ufHoBL5fFOC7RX7GDvRy/AFOZlZRa3joyxqq/YarJsXTMz36PNm8NcT7X1WSluzi58elMH2DzduLw4zr6eKycdEuFK/PD/LNmjDHDHKzvspiRbHhojFuDui5Z+qPFm8zPDbPJi/F4vfjLbwx/UrLa20e+qKWmoDh6qMS6JHW/JimrrL5eI3hsDyLc4Z1yrqwdqlmrLCuq/d+n2Ie2OurMyXJFJ2GUqo3sBY4DfhIax1QSt0HKK31kQ2SzBuAM7TWO2QXSqnzcGou+2itTWTZL4HrtdZDm5Fk9gQ2AYO11isj64cAy4CeWut8STKF2DOaSjKF6ECSZDZTp/yKIPZZyTjPyUIgqJSaiNMs3pgXgaFKqRuVUolKqbhIbSXAB4AP+Etk+VDgRuDp5gShtd4MfArcr5RKV0plAPfjJL75rT47IYQQYh8iSaboNCJ9J/8GvAuUAn8CXmli283AkcCxOIOFtgB/jKwrwxkVfgywFWdk+PPAAy0I5wKgAqf2cmkknotadEJCCCFEM3XFgT/SXC5E5yYvULFPk+Zy0Qm1SwZYbv2h3vt9qrlvr8805T6ZQgghhBAdrKvUXsaS5nIhhBBCCNHmpCZTCCGEEKKDdcWaTEkyhRBCCCE6XNdLMqW5XAghhBBCtDmpyRRCCCGE6GBd8VYikmQKIYQQQnSwrtgnU5rLhRBCCCFEm5OaTCGEEEKIDtf1ajIlyRRCCCGE6GBdsblckkwhhBBCiA7WFZNM6ZMphBBCCCHanCSZQgghdskO2W2+b0vLDId37yYvTe1vjMG2u+INZIToWNJcLoQQokkV80uYfcoX1G6uof91Ixj69/Et2n/WU6v48clVxKd5OfmB/ek+Kp2qIj9vXjefwlVVDD4ymym3jcTlbrqp0Bh465/rmPtFMVk9fVxy+0AyuvlaFMf7rxfyyf+KSEnzcOUNefQZEA/AvAXVPPxoIYGAzUXnZ3HMUaktKleItiLN5UIIIfYpy2+aQ+3GarANa+5bROXSsmbvW1lYy4zHVmLChpriAN/9azkAs17dSOGqKgBWTNvGqunbdlpOeb6POZ8XYwxs2+Tnq9e2tugcthUE+OitImwbykpCvPtyQd26518sprraJhSC/75YRDAoNZpCtBVJMgVKqeeUUk+1Y/mVSqmD2qv83aWUekwp9e+OjkOIzsjlc0dnLHDFNf9jw+W2sGJqKN1eZ1+Pt36NjXsXZbpc9RO/hvvvitttYcXsEru/x9P0dkLsSQar3qMrkCRTtDutdbLW+ofmbq+UMkqpQ9szplha6yu11lfHHH+tUuqCPXV8ITqzofeMJ01l4euewLD7FIkDUpq9b2Kmj0l/HkFSto/sISkc/sdhABxwXh/6TcgkMcPL/mf0ov/EzJ2Wk9I9wBFndSM53UP/0ckcdW73Fp1DRpaXsy7tRmq6m979fZx+Ube6db+4LIeePbxkZbr5zS9z8Hi6xoe72BtZDR57P+mTKXaLUsoC3FrrUEfHIrqw4kr4chEM6QFj+sDiDbBoAxwxAnLT2/RQgc9XYmpCxJ04BMvd4Hv4jGWwuQSO3x8SW9YnsL1t/KGQUE2IPod3w+Vpu/qDxP4pjHn2EKoWlpCqsih5axVxvZNJOrAbK2eXUbutluRAgMwR6aQOTKEyv4ZVn+djfB6GHNudkT/LY+QBifD9cghXAskUrKlm9M97MfDADDwxtZjVZUHWzSsn0YRwl/nJOjAHgFA5DO3l5oC7BpLRJ7Fu+83fFxCqDpF3ZPddnvPhx2XQLc9HTY1NVo63bnmvHl7OPi2dnBwv/fq3/H+6pSTMvDVBRvT2UF1t2FIUQg33kZTgImwbPloWxucxhNxOjfDx/S38IfhoWYjcZItD+u34Mbx0m82CrYbD+7rolhxNNpYXG+YVGAamw6oy2D/XYlDG7icjtjF8vMbgtmByPwsrUp1bEzR8vNbQLdHi4F7tk/TYxvDx6six+0ePvS/qih01JMnsQpRSycCtwGlADrAB+BUwG7g7sjwBmA5co7Ve30Q5fYGHgEOAGuAt4M9a65rIegP8DrgQGAlMAmbsJC4DHKa1nq6UugS4KVL+DUAS8DrwG611WCk1L7Lbp0opG3hVa32FUioRuB04HUgDZgJXa61XRo4xLXKe/YDJQAFwndb63cj6/YGHgdFAGFgKnKS1LlFKPQeEIseZCvQBnlJKPQZ8DzwIPA/00loHIuWlAPnACVrrb5s6d9EGSqvggJthdQG4XXDrz+H2VyEYgl6ZMOe+Nks0K2/4mJp7pwMQd/pI0t48N7ryPx/Db55wpg8cDNP/D7yd4y105j8XM/fxFQD0Pao7xz8yoc3KLvkqn5+O/xQTCJPiDeIOhgEoPW8032310Gf1ZjwhG5fPxYQHDuSrW+cTqg5hgB+fSeeCfwwh6ehboKgSEuKYccf1fPlhJQB9xqZy/n0jsVwWNRUhnrlqAf7V5fRevRWXMfiyfAR+7WbT/xJYV70Aj8/FGY+Op/vINGbfv4j5jywFoPdR3TnmqUN2eh4fvFXE1DeKABgxNpFr/pyHv9bmzts3smljEMuCX/06l4kHN7+mdkNhiHPuLaG82pDsMsT7bQzQr6eHx/6czUVv+nljQQgSPRDJXy8fbbF8bS3frnGu4wMnx/P7w6PJ7TfrbI59IUAgDD2SYfYvffRIsfhhk81Rr4SpDYFlgXFBgtfim3PcqO67l5hd+pHN84udFOc3+1k8coybkG049s0w321ytvnnJBfXjm/7xs9LPwjz/MLIsce5eGSyexd7iL2JNJd3LU8DE4CjgVTgFJxE6EFgYuTRF9gGTFVK7fBqVkp5gA+ALZFtJ+Ikm/c12PRy4GwgGZjbwjj7At2AgcABwJnAOQBa67GRbSZHmtmviMw/CQyLxNMd+BF4XynljSn3YuB+nCT038B/I8kpwCPAp0Bm5NjXAYGGgWmtpwDrgSsix58MfAJUAafGbHousEESzD3ghxVOggkQtuG/3zgJJsCmYvh6cZsdyv/ivLrpwFuLMLXB6MqXvolOz1wBK7e02XF314r3NtZNr/tyC8GqtmtYKHhjLSZg48LUJZgA9mfrSKyqxRO5DZHtt1n6yhpC1c6xLcBfUMOGVxY5CSZATYCF04rrylg/r5yKbc7LcOOiCsq2+Ekpq8ZlnKTDX+SneqaFXe0kUSG/zcqvCwFY/W70O/KGL7cQqIj5XzVi5vTyuunF86qpLA+zfr2fTRud/YyBGT9UtujafLMoQHm1E6sVsOtqotZuDrF0bcBJMAFiamtfWmzqEkyAl+bWj/uNRWECkdX5lfDVWuf6vrXMUBspzhjAQE0I/rei9beWcsoyvLQkWoe2fXptGXUJJsDLS3bvOE0ee1HMsRe1/TH2JtInU3RaSqlc4CzgSq31Gq21idTyrcZJvm7SWm/SWlfh1EIOBw5spKgDgcE4tYBVWutNODWPl0Waxre7T2u9Smsd1lr7WxhuDXCL1tofifELQO3k3LKB83BqO7dGahNvA3rgJNXbvaa1/l5rbQNP4CSbgyPrAjg1lL211kGt9YzItdilSHlP4STW210eWdauKioqZHpYT0x89LtEcESPumnjdcOIvDY7lme/aNnuYTlUBmvr5gMjetVN21kpTi1qe5xvK6bTBifVTSf3TsCT6G6z8pP3c87TYBGbAoT7puH3ees18eXu16BvpddN0ugMYkfTdMuLi8aaFUdiupeKigqy+yTg9lrUJkTX44K4fgas6FFyBidTUVFB5vC0umUpfZPwJnt2ei55faO1hZnZHhKSXCQm+omPj8bWJ7JNc6/P0DxP3amFYs4xKd6iT3cPQ7Miccfcn3NsLnRLjp7iyJxowllRUcF+MbWSHheMyrWc5bkNko7I7Pblrf3/WpbF6Kzof3Z0phNPjyTITYjGPTIjtMO+uztdWVnJ2NzoKe3uuezp6bbWFZPMztHWI9pCv8jf5Q2W5+A01KzZvkBrXamUKgB6Aw0H5PQGChskYKuA+EhZ2+/9sXY3Yi3QWodj5quAnbVR9Y/8na9UvVzUG4l3u/ztE1rrqsi228u9FLgZmK6UCgIvAre1oC/p08DNSqk+OMnrfsBJzdy31VJSUmS6fy7WZ3+Gl76D4T3xXj0ZnvgMflqDdebBMLJPmx0r5eUzqf7Ht1AdJOGPh+KO2Sbu4V9Cn1zYXIzrN8dDamLnuD7A5PsPZO6TKwhVhxhz6aC6fm1tUX6vXw4FoGJOEenjMgj8VEBc72SG/nIU1jsF1CxLJSvkp/uB2Qw6pz+Z+2Uy/+W1GJ+HcVcMovcBmZB6PXwwFw4ewvFnTSDttU3UlIVQP++BJ85FSlwKpMB5/xjBwi+3kbApi1QTpOdxeeja2XTPCJAdGEDP0WkMPdYZ9HPYfQew4PFlBKtCjLpiMJZl7fRcLrwyiW49nNsVHXNSBm63Ra+8dG74czzfflNBbq6Xycentej6qBR48Io0pi/2s/8AL9WlYTZvC3PiIQlkprr54hdJ3PtNENsC47PweSxunOCiuDKZh74LkJtsccORvnplXj4ObAOz8w2nDXcxppsLSOGCURAIw8x8Q7dk2FoDB/e0OHOoa7f/1x+d6eWemTZuC/40wUnyk+Ispp3j4d9zbbolWtxwoK9FZTZ3+oMzDff8GDn2Qbt/LntyWuyaJJldx9rI38FAbPthIeDHSUK3919MBnJx+mw2tAHIUUolaq2rI8sGALWRsrZrz3aNhv2f10X+DtZaFzbcuDm01muAywCUUqNxms7XAM80svkO56a1zldKfYCTrGYA72itd35zP9F2Dh3qPLa78rh2OYwrPYHkuyc3vtLnhZvObJfj7q64ZC8Tfj+i3crfnmg6htdNTb68N/W/58GAyT0ZMLln/QKmjHceON8MD7+4T6PH6TMmlT5jGtwMfepskgbbHDNleL3FcSlexv9hVLPPIT7exSlnZ++wfMDAeAYMjG92OQ0dPdbH0WMbHzDUK83FP6fsuC4n0c0jP09ossxfjPfwi0aWXzbWxWVjG1mxm7onWTwwace+kMOznP6Z7al7ssUDR0s/TEfXqL2MJc3lXYTWugB4E3hUKdVPKWUppQbhJIjPA3copXpG+ijejzPwZWYjRc3ESUbvV0olKqV6AncAz2qt99Tgty1Em7m3n9vLOOfWC0Apla6U+nkkYd4lpdTFkXMBKAVCOAOAdnn8GE/gJKoX4PQRFUIIIdpEV2wulySza7kM+An4GqgA3sUZJPN7QAOzcAa19ABOadBkDUCk+fhkIC+y7UycQTZ/aP/w6/wVuF0pVaKUejyy7BfAMmCaUqoCWIAzYKi5ie9RwGylVBVOF4GXgRea2PZO4ILI8T+KWf4pTi1nGU4/UiGEEEI0wTKmK96ZSYj2EblV0qda67v20CHlBSr2aVOnTgVgypQpHRyJEHXapZpxk3V7vff7XuaWvb46U/pkCtFMSqnDid5ySQghhGgzXaWJPJYkmWK3KaUW4dz7sqF1WuuRezqe9qCUmgUMAn7b2sFHQgghRNMkyRRiB10lkdwZrfUBHR2DEEIIsTeRJFMIIYQQooNJc7kQQgghhGhzXXGUp9zCSAghhBBCtDmpyRRCCCGE6GDSXC6EEEIIIdpcV0wypblcCCGEEEK0OanJFEIIIYTocF2vJlOSTCGEEEKIDtYVR5dLkimEEEII0cGkT6YQQohmsU1XrJdoX7ZtMA2um7F3fh13tb65jG23STlCiCipyRRCiDZUWG048a0wcwrg9MEWr5zswu3qejUUbWnVpiA3PFpCYYnNsG3b2K+kiONuGUH59HwqHvdjZViUDikjfWha3T52IMy6Mz6i/IO1JE7oRv8PpuDJiG/xsYPztlByyiuEN1eQ/JfDSLltUluemhDNJjWZQgghduoBbaO3gm3gjeWGd1ZKjeauPPZOJVuKbcIGFmVlsy3o4svbF7L4P8vABlNkmHfPwnr7lL62gvKpa8A2VP+whaJ/z2/VsSv+9Dnh9WUQsqm8/WtCq4rb4pSEaDGDVe/RFUiSKbo8pdRapdQFHR2H2Dd4XTufFzvyuOvPW8bg9tS/cJan/oeu1VYXOnY/C/DIP0yItiKvJiGEaEPXH+Bicj+LzHj41ViLKQO7Ro1Ee7rqtBSG9PaQ4gNVto2eWW6OvXM0+/15NFYyuPpY7P+XMfX2ST9zEBkXD8OdGU/KSf3I/u3YVh079f7j8OzXHVd2IqkPHIenb3obnJEQLWcaPLoC6ZMphBCtsK3S5tvVIUZ0c9M708Vn6wy9UyzGdbP45Aynau7L5UE+XRrm2KFeXHugX2ZFwPDlOkO/NIuxuXtPcpuX6+HZv2Tz9aoQpZUZ5PkDWNlxjLhyKEuql4GB5D5JFC8vp3xdJT0n5hCX4qXPc8dStrqC0hXl1BT5qfpiCwn9kkkYnMKGGUWk9IgnZ5jTj9M/dyvB9eWE/IbaeUWkXTiUhGGZeAZnkfDQKZTrbVT3S4WfCgmurSA5vgZ3KACTR2PF7fhRafwhqj9dizs3kfgJPXd6fiYYgk8WQmYyVk4yLNxAuG93gusq8R7QE3detK/pti/zMSFD9rA4rNmrYfwA6JO9k8INfDYP3C44ekzT2zWDWbUV5m+AMXlYC9dDvxwY299ZOWMFFJbD5DHg8+7WcURT9p7XbHNJkin2FX2UUl8AE4C1wC+11t8rpZ4DQlrrK7ZvqJRaC9yktX4xMn8YcDcwAigBHgUe0Fp3lS+booUKKmzU/eVsKLWJ80D/YSksK7WwgGePd3HxKBfXv1PNA9NqAbhAxfHCBcntGlNN0HDoy2HmF4LLgpdPdnH2sL2nseovH9Zw9xd+AAZU1zK5NJ/JqaWUfumM+v548fds/r4QEzakDUjm1LeOpGhBCZ9e+h12wMZjQeamaizLouSIXpRuqQULjrljDD3Lyth64QcEjQsb5wtA4d9n0f/bM6n4bAP5t84CIIgbP176s4Y01juBHTsK88kfsaxoAmDCNpsmv0HtNxsByH7oaNJ/O67R8zLGYE95yEkyActjcIWCYHkoM3mY9GSyf7wCz5BsFl8/i3UPLyXJVHKwawaegB9SE2DGnTC8V+MX7vJH4NkvnenfnggPXdH4drtgZq6CI++GGj94/BAKgcsFL/8OtpTD7553Npw0Ej7/q7NOiF2QZ4nYV1wGXAOkAZ8B/23OTkqpEcCHwL1ADnAScDVwYfuEKfYG01YG2VDqJD8By8WyUicBMcALi53vHs/P8tdt//LsAOE2utVOU34qgPmFzrRt4MXFe9d3oOd1oG56dWI8QQPbvsmvW7b5uwJM2DmnstWVFM4rYc37G7EDzv8hZCDgcxPwupwEE8DAsg82U/HiYjBgx3zkuWyb4heWUfT8srplXsKEschkWzSwzxZCfmm9WEPryusSTICKFxY1fWIF5XUJJoAJOefgNiF8VGNKa6mdutw5xxdXA5Brb3USTIDyGnhXN162MfDC19H556c1HceuvDkLagKAjRUKOctsG178Bp7/NrrdV4tgQ1HrjyOaJAN/hNh7Pa61XqS1DgNPAYOUUmm72gn4DfCG1vpdrXVYa70U+DdwUXsGu11FRYVMd8LpPsn+6PiQsCHBHU3oxuQ4f0fk1i1iWA51tzFqr9j6p0FyTCvmmJzOca2aOz26R3T0T1oohAfwdEuoWxaf7aubdse7SO2XTEbMLY0wBnfQxhO08cQMEsoanAJDUwGwYnq6GcAalUrC6Ky6ZTYWFoZaose1e6RBVnK9mN3dEnHlRLexhqU3fY6ZSZCXEd02EoMBgsQB4B3tPFkShqc4+1mpxKoelNN4+ZWVMKpPdMMx/RqPoTnTo3s3iHB7mX1j1gHd06lIcNXfdx+dbmtdMcm0Gt74VoiuppHm737AGqA3cCc7aS5XSn0AHAX4Y4p0ARu01iP3QPjyAu2kPlkS4I15Qfbv5Wbi4Dgen+/0ybzxQIs4t0VRlc1dn9USCBtuPDqBvPT2/07/42bDUwtsBqRZ/OEAC6977/mgKqm2uesLP1sKg4yrqWZ4XhyHTkzgvT9/ggF+dvdxrJq6kbI1FQz+WR+6H5CNMYYlL6yieHEZOQOSCcwpJnFACknH92LJO5tI6RHP/pcMwIWh5N5ZBJYVU7OxhsDmKlLPG0aPmw8kXOZn8+2zKPt6C65BmST1TcDaUk638Cbiki34/fFYQ3vsEG9g8TZK/zUbd7ckMv40AVdi0/0UzYqtmPs/gcwkrNwkrIUb8KdnU1PiwnfsABLOGe2Uua2WVf9YiAnaDBpaRZxeBkeNgvMPbfrCbS6Gv7/t9Mn8y+mQ05zvzk3E+eRXMGMVDOuGtXwDDOgGfzgVgmG4+x2nVvZ3JzbddL/vaJcX1lLrgXrv98PMdXvPC7gJkmSKLm8XSeaNQLbW+tzIOg9QjtNn80Wl1KOA0Vpf1RGxI0nmXqnGb/PSh5WUlIc57ahkBvaWgRK7suiTraydVUKf/dMYfVI0qZs6dSoAU6ZM6ajQhGhIksxmkoE/Yl83G7hHKdUf2AzcDsRmBI8CXyulPgY+xkn6hgA5WuuvGxYmBMC/Xy3j4+9rAPh+np+X78olIV56JzVlzcxiPrxzKQCLP9lKfIqXwYfvZES1EF1QV2kijyXvemJf9xLwHjAHWAWsBzZtX6m1XgicDPwOyAcKgOdwBgEJ0ai1m0N10+VVNiUV8rvYO1O0prre/La1VR0UiRAdpyv2yZSaTNHlaa37NZhfS/3mjisij+0eabD9D8DR7RSe6IKOPziRpWvLANhvaBzdsty72GPfNuiwLGa8sI6ashC+ZDdDpBZTiC5BkkwhhGhjU45IYlj/OErKw+w/zFc3slw0Lr1nApc8p9i6rJLcIcmk5Ph2vZMQXUxX7IAvSaYQQrSDwX281O/eK3YmOdtHcrYkl2Lf1VWayGNJkimEEEII0cG6YpIpA3+EEEIIIUSbk5pMIYQQQogOJn0yhRBCCCFEm5PmciGEEEIIIZpBajKFEEIIITpYV6zJlCRTCCGEEKKDdcU+mdJcLoQQQggh2pzUZAohhBBCdDBpLhdCCCGEEO1AkkwhhBBCCNHGpCZTCCGEaEPGNiy8/HvyX16N121IC1XR/Q/70fOug/Z4LGuKbU5+tpoVRTa/mRjHP0+Jb9H++q75LH1uJSm9k5j09CGk9ktup0iF2DvIwB8hhBAdZtvHm9j8/CpMyBDwQ1XIQ8Hds6lZWLTHY/nbZ34WF9gEw/Cv7wLM3BBu9r5FC0tY/MRy7IBN2aoKfrp/UTtGKroi0+DRFUhNpmg1pVQm8AowEQgBmUBvrfXGDg2sGZRSjwEhrfXVTaw/FPhWa9312i+E6Excjb/ELPeef+k1DKUlIVgNdu6I+MXeTZrLhajvSiAZyALygDUdG07zaa2v7OgYhBCQfVxPel85hM0vrsZjwqRg6P7HCcQPz9zjsdwx2ceirWFWbLP5zUFxjM9zN3vfzBHpjLlmOEueXUlK3yT2/8PIdoxUiL2DJJlidwwAlmitQ0qpjo5FiHYXsg1frDOkx1tM6NG+tQ5h2/D5aps0n8XE3i3v2WSM4cv1Bq/L4pBe8MU6Q3IcHNxr93pJhW3D69/XUhG2uOgQH/GenV+HUKmfqun5+IakEz8kvZECDf1PyaXfydkQMnjH5OLtmwaAf+ZmkmdXUDk22rfRrgxQ/fVGvH1T8Y3Kxl64GbOuhODI3pQuKSd1RDqJfaPbhysCFD2xEK87THp/gzU8D4b0rFtfNa8I/8Yq0o7qSZ4nzLvWckIDE8k5Ygg/zq8lJ8NNkstmW36AAcMTKc+vYcuGWvzJ8fTpE0dwQyWWyyJvXAZjfjGIYUO8uPun481LAqBwRQWVBX5S+yWzdZOfvIGJpGZ5nYP/uAK7oIIyMvHmJZO8f3Yr/ytNs4urCX63HvfwHDyDslpXyJxVsKUUjh4DPm+bxieiukoTeSxJMkWrKKWmAsdHps8B3mqwfizwEDAScAMzgKu11quUUiOBuUAvrXVhZHsLWAXcprX+r1LqWuDXQC+gBHgJuElrHY5sb4CrgEuBYcAi4BKt9dLI+kTgbuA0IAGYDlyjtV4fWf8cTnP5FZH5wcCTwHhgNfBsg/M5B/gbTo1tNfCx1vri3buKYm9z2rs2U1c5HwV/P8zFjRPar1v7aa8GeG+ZDcBdR3v48+Et+3C//BObZxc6sQ5OhxXFzvSthxj+dkjza+gauuieYtavDQDw/PQEpv05DU8TTd6hUj/LDngT/8oyLK+LgR+cROqxvettU3DaW1RPXRmZs7CSvPT45nxqv1lPye+/oD9QsX8y5mcGUxtiw2Gv4v+pENwWPa4dhvufn4BtqPQlo+OGYyXHccgHx5C+fxbhqiDLB/8X79ZSAGoooWfcSnj/L3DsfhS+uIIVF30FBpJVFrUVZSzxpwIQ/ucKXh8/DgvoWVNNSk2AtDQ31oYSVuXmEPR4GFRQQHZJOQDjTuvJ8Be/JrSgANwWGW+cyWpfGp/esZiQy01xTgZhY5GY4uaqeweT/cLHmBtfYhHjqSADLBj4xOF0u2JYq/83DdlF1RSrx7DXloLPQ/onFxJ3RP+WFfLkZ/Crx8AYOGwEfHkbeFr//BFNs7tgc7kM/BGtorWegpP4/VdrnYyTgMUywK04SWI/oBJ4MbLvIuAn4PyY7Y8EsoE3IvMbgROAVOBU4DLgigbHuAQ4PbLfBuDhmHUP4vQVnQj0BbYBU5VSO7w7KqU8wPs4iWoucAZOV4Dt6xOBF4CrtNYpODW4TzVyWUQXtq3a1CWYAM8ustvtWMXVpi7BBHj2p+YPQAGwjeG/i6KxriiJiXtB6+OuqLbrEkyA2s1+lhc1Xf9SNT0f/8oyAEzQpvil5fXWh7dVUxOTYAKYqiBVbyyl8tkFddulzK0kvLkS/9wCJ8EECBvCL80C2zl+sr+SlHAV4aoQm99Z7xx/VgGureV15VSTQSgAvPQtAAX/XV5XfRTSm1hfGVe3rbvET1wwiAFKPE6CX1YWpiwxkaDHA8aQVRIte8vry50EMxJb9QvzWfJhPhio9cURNs75VVeEWfxjGTz7FTUkOAkmgIGC55Y1eS1bIzBtjZNgAvhD1L68YKfbN+q5L50EE+DbxbBqS5vFJ7o+STJFu9Baz9daf6W19muty4DbgImRhA2cmsJLY3a5FHhNa10d2f8trfUarbXRWs/FSfKObnCYe7XW67XWfuA5QAEopVzAxTg1n5u01lXA74DhwIGNhDsBJxH+o9a6Rmu9Ari/wTZBYJhSKlNrXaW1/rbFF6UVKioqZLqTTLuDleSl1M0yKstqt2NZwUryUqO1GqNyW3Ysl2UR26XRF9NmNSrbanVsifEWnvhoXEGfm54pTcfmG5IO3ujHjGtwUv040+Nx113UaLIaNyqHuJHRpuNghofquBDefmlYSdEaXat/dJsQLmpcPgBShqdRUVGBb0AqYVf0e6UbP27CMMqpTfXExBNOTiDZjibQuHCSScAXdhJzlwUJgYCTdFkW1XHRpDRuaBYkRC+0d1QuKXnOek+4/peE1BwDI/OII4CH6DG9Q6NPsLZ4LnmGZoMnev3Dg9JaXs7IPtHAM5OhR0aneD12hum2ZrDqPboCy5imv4UKsTOxTc5KqX44A396a603KqUGAvfiJHApOJ8gqUA/rfU6pVQ6kA8cAqwAtgDHaK1/iJR9LnAdTq2hB4gDZmitJ0XWG+AwrfX0yPyRwOdaa49SqlukvMFa6+3VJCilNgLXaa1fbxD72cB9WuveMdseA3y2fXR5pPzrgMNwmtPv11q/3GYXs2nyAu1Elhcb7p1lk+6Dmw5ykeZrvw+CFUU290wPkRZvcfMRHtLiW3asDeWGu3608brgghEWT82zSY6zuPlgFxktLCvWys1B/vJiJTUGbjwrhUP777zXVflnGyh+aTkJozLJvW6/HUZhB5cXUXbvj4QLqrESvcQf2YfUX+2PXeGn9M7vWTt3GdtOzWLyVWcCUPPdJsqeWoB3YDoZ140jfP+X2KuLKB02gE1rDJkHZNPv8iF15Zd+tI5tN3xNXKCKbsP8+A7rB9edAi4Xtj/MxrvnElhfSbcrR+Aqq2De7XMJJfpI/dNBfLnOTXa6i27hAEX5AfY/OJXCecUsXhWkKiOJIX29JK3ehuWCCZcNwLMon+pnfsIzOJOkGw4hbMOs/66hYmstnn7pFG4LM3i/FMYdmQHl1XDHm1SurGaLqw9xw3Po9df9cSe0bS82/0fL8b+2EM/Y7iT87iAsq4X/+xo//N9bsLkYrjkJ9mthc3vX1C4v/O+tJ+q93x9sfrnXZ5rSJ1O0l8eAzcAYrXWRUmoUsIDIi1NrXaqUegenyXsesD4mweyN07R+GvCR1jqglLqPSE1lMxQCfpzayZWRMpNxmsI3NLL9JiBXKZW4vSY1sm8drfU0YFqkuf0U4C2l1I9a61XNjEl0AUMyLZ48bs/0Rxuc5eLJU+N2vWETeqda/OfYaKwH9mibhqtBPb28fkNGs7dPPbb3Dv0wY3mHZJH95InU/rCJyqfnYRfXYkI2rhQfmf+YxHdTK+ttn3BILxIO6VU377r5eAC6RR4NpZ/Ql/QTLmr02C6fmz63xr6t5HLwsQPr5vZrZJ/h41I4vN6SmME6h/Ul7rC+dbMe4KBfDKRRqYlw70UkA4Ma36JN+E4Ygu+EIbvesCkJPrjzvLYLSDSpq9RexpIkU7SXVJwaylKlVDZweyPbPAu8jFPbGTvQJhmnK0chEFRKTQQuBJY058Baa1sp9Txwh1JqMVCK0/y9FJjZyC4zgHXAP5RSNwA9cWotAYjUjB6KU1NappQqjaxqWUc5IUSjQpsr2Hrsq5iqIOD0y8y48/Bd7CWE6OykT6ZoL7/HaVouB77FGVjT0Oc4I7XHA89vX6i1XoIzkOhdnATxTzg3fW/p8TUwC1gP9ABO2T46PZbWOoRTOzkGKADeBp6I2cSFM5J9rVKqAngEuFhrvbaFMQkhGhFaU1aXYAIEtg+gEWIfIn0yhRB7mrxARZdnVwfJn/g8wQWF4LLIefVUks50buUzdepUAKZMmdKRIQoRq10ywG+tp+q93x9mrtjrM01pLhdCCNGhXIleenx3AbVfrcfTL424MbkdHZIQog1IkimEEKLDuVJ8JJ4yuKPDEKLDdJUm8liSZAohhBBCdLCumGTKwB8hhBBCCNHmpCZTCCGEEKKDdcVRnpJkCiGEEEJ0MGkuF0IIIYQQbc40eHQ0y7KOtSzracuypkbmlWVZR7WkDEkyhRBCCCFEHcuyfgv8B+eX+7b//FYNcGdLypEkUwghhBCig3WyX/z5HXCMMebvgB1ZthQY2pJCpE+mEEIIIUQH6wSJZawUYENkenvrvRcItKQQqckUQgghhBCxvgH+1GDZNcBXLSlEajKFEEIIITqYvetN9qTfAlMty/oFkGJZ1jKgAji5JYVIkimEEEII0cGMq/M0lxtj8i3LOgA4EOiD03Q+0xjTolxYkkwhhNjHhTeUUXzqK4SWbCPxl+NJ+9cJu9yntrCW7y76htIlZfQ9vS/D/ziKby7/nrLlZQw4uz99zhvE1L8uoLYixGG/Hsh+p+c1O57P3ijgi7cKSM/20m+Cl6T0YJPbzv+ulLcf3QhAKMFDtR+mnJPLpJMym308IToD03lyTACMMQb4MfJoFUkyhRBiH1dx+9eE5m4BoPqhH0k4ayRxh/TZ6T5LH15M8dxiANa8tJrKIj/FC0oAWPH8Kpau8lOx1Q/AVw8uZ+ix3UhI9e4ylm35fj5+ZSsAhZsD2DqD0ccUNLqtMYY3HtpAoNapXLErwwR8cbz9/FbGH5JKarp8xAnRGpZlbaCJ23UaY3b+5hBDXoFC7IJS6kjgc621vF5E19SwBqU5NSqW1aL5TlZJI0Sn05may4ELGsz3AK4FXm1JITK6XAgh9nEptxyBd3wPrEQvSb+bSNzBu66oGHb1cLJUFu4ENwMuHMiBd48ja2wG7gQ3Qy4ZxOSbR5DWM564JDdHXT+U+GbUYgJk9/Bxwnnd8MW76JbnY4AqaXJby7I485reJKa4SUp1E58TT3yCi9Mv7ia1mGKvY1z1Hx0aizFfN3i8CvwcuLQl5cirUAghmqvGD98sgd5ZMKJ3h4ZigmGC09Zg5STh3a/HbpXlzksj5aWzCKwpI+mwXgBUzt1GzZwC4nskkDSpN66E+h8XVqUfddVgEvv48KzPp6awjLG/G0HGmEzic+IBuPzNgynQ27DD0Va38kI/BWuq6Tk0mcS0xhPPw07KIm9AAlnd45gxe1nd8ppam6Ur/FBcTU6qi5rkBLyZPm55YSTWvLVsWVJKxaB+GJ+XVYurGDgiqW7fuWuD1AQMBw32YlkWP20MUVRlc8QgLx63U4O0pTTMks1hRvf2kJ3iYs7GMFsrbbDA67YIhgzjermpsS2WFBkm9rTIiN917VNRtWHmhjCjurnonb772cOMzYbakOGI3hZWwxrkNrJkm2F9ueGw3haJ3j1Tw1Zaa/hhs2FYpkX/9E5VqyccfqB/S3aQJFPsU5RS1wK/BnoBJcBLwE04d4+4E+dbWgpQBNyvtX44Zt+zgbuAbOAT4HKtdcUePQHRcfxBOPJWmLkS3C546Ro4+5AOCcXYNmUnv0jg01UApPxnCglXHtDq8srfW8360z+AkE38fjkkXDSajddNJ4VaLCD+gG70/fYsXD7nI6Pq+3xWHfMOpiZEnFXLYKOx3G5mZxyN3S2Toz+aTFLvJGb93zwWP7UCgCHnD2DAZUN5/neLCFSHSc7ycum/R5Oa46sXi7/W5uG/rCZ/XS1uj8WIIxPI6l1Drd/mL3dtZf3GIJYxdC8tpzzZSSIPyy1j/Ovv8faYKdR6NhDyuAE4/MRMTru8Bw9+WMU/P64B4OfKx9D+Xn73VhUAk4d5ee9XKazYEub0f5ZRXmPISbE49YhEbvk8ct9pywK3BTak+6Am2YcfF/3TYOYFbrITm06I8sttDvhPLZvKDclx8PUV8Yzr5W71/+rm6WHunOEk7RePtHjuhNaX1ZS3ltqc/W6YsAHV3eLbC9zEe9o36SuuMRz4UphVpRDvgU/PcHNY3r6VaBp35zlfy7Jub7AoETgR+Kgl5UhzudjXbAROAFKBU4HLgCuAY4GLgQla6xSc2zZMj9nPDUwGxgJDgP1xbkwr9hUL1jsJJkDYhuemdVgo9vqyugQToObp2btVXulziyHkDJ6p/amQgkcXEk+wrh9l7ayt+Odvq9u+5KVlmJoQAAETTyXpxIdr6enPx7/NT/6nmwBY8draun1Wvr6GxdOKCFSHAagsCrLyx9IdYlm/opr8dbUAhEOGLSuTnbJWB1i/0RllbiyL8qTEun2mb01F543FWBYhd/RjbcYXTlP7qz/465b9T/t5+ofauvlPlwbZVGbzwVw/5TVO8lZYYXjyx5gR7cYAFrgsSmsN/hrnHNaUwZfrGx0bUeej5WE2lTvbVAbgtQXhnW6/K08tiB7v+UWGQHjnx2+NZxfYbC9WbzHMK2j7YzT01QbDqlJnujYELy3pZHeN3ANsl1Xv0cF6N3jEAw/gfE42mySZYp+itX5La71Ga2201nOBF4CjcX4qKx4YqZSK11oXRNbH+pPWulJrvRV4B1DtHW9FRYVMd5LpygwfJMfXzTO8V4fF48pJgqyEuuVmUPpulekbEb3dj5XgIX5kJqGYjwcr0YO3d0rd9vHDM4iy8eHUEpZ7UgHw5DnN4OmDU+q2ShuQSnJu/Q/O7D4JO8TjSwrgiWmeTYzcvigpoRZvTNubJxxN1rpZVeRWOkmwy0QTom55Ti1p/5zosrxMGNkjWvuXm2yRlegir8FtkobkNPLxuL3sSCLrtmBIhrXTazssx1VvDNTw3J1vv6vpoWnR8+6fahMXqf1qy+fYiOxowEle6JO6ezE3Z7pXXDWxFXnDM9v+vNp6uiszxlza4HG1MeYpY4x/13tHWca0/zcUIToLpdS5wHXAAJzuInHADK31JKXUL4GLgDHADOAvWmvd2OhypdStwKFa62PaOWR5gXYm3y+Dxz6Fvjlw0+nga95glvYQnLeFmge+x5WbROLfjsSV7Nv1Tk0wwTCFd2v8K0rJuHwk8eNzWXfzbAJfrSWxTyI5f1IkHtIrur0xFD4wl5q520hPqSCtfD1bU3qx1tWb7kf1oO8Z/QCoLqhh3kNLMGHD2N8OJ6lnIvrdLWxaXMGgiRmMnJTdaDwrFlQy68sScnrEUe2bicsFU6ZMYcGSWj79opzg+gqGpwQpzE7H8ro55dh40h5+h6/WplE4cADBrFSS0jxMPiOH9CwvxZU2//y4mpqA4epjE8lMcXHnJ9UUVRp+NymeUT2dl/ZL39Xw48oQk0Z4OXq0j9s+q2X5NhvL7SIQhrQ4mxOHeikyHuZsNZw51OLUwbuuq3ljQYh3loSY2NvNbw/avedMYbXhtu9tasPw1wmudum7GAgb/u97m9Wlhl/t5+LQ3numPur9VTavLjWMzbG4/gALVzv1N20D7RLYe2kv1Xu/P6Xs/D16ASzLOqo52xljvmx2mZJkin2FUqo3sBY4DfhIax1QSt0HKK31kTHbJQK3AudorftIkinEjrbOKWLF2+tI65/CyEsG4Wqn/mRTp04FnCRTiE6iXZ7s72a8XO/9/tSS8/Z0krmmGZsZY8yA5pYpA3/EviQZp4tIIRBUSk0ELgSWKKUOBHzATJwRdBXA7nWeEqKLqtxczccXTycU6ZsY9ofZ7zfDOjgqIfZuHf2LP8aYFo0cbw7pkyn2GVrrJcDfgHeBUuBPwCuR1cnAv4BtOCPLJwNn7/kohej8ytdV1iWYAMVLyjowGiFEZyU1mWKforW+HWh4a4btxjWxzzQavFa01re2aWBC7EWyR2eQ1j+ZsjWVWG6LAVOa/7vkQojGdaZf/LEsKxWn29gROLftqwtOflZSCCFEu4lL9jLlrSPJ/6GQlD5JZA1P7+iQhNjr2Z0nxwR4FMjDqZR5EednJv8IvNWSQiTJFEII0WK+1Dj6Hddr1xsKIfZGk4Hhxpgiy7LCxph3LcvSwFTgweYWIkmmEEIIIUQH60zN5ThjdrZ3tq60LCsNyAcGtaQQSTKFEEIIITpYR48ub2AeTn/ML4BvcZrPK4HlLSlERpcLIYQQQohYv8C5rzTAtUANkI7zgyXNJjWZQgghhBAdzHSuXzhaZ4wJAxhjCoArWlOI1GQKIYQQQnQw26r/6GBbLMt61LKsQ3enEEkyhRBCCCE6mHFZ9R4dbDJOH8yXLctaY1nW3ZZljW5pIZJkCiGEEEKIOsaYucaYGyI3Xr8EyAC+tCxrfkvKkT6ZQgghhBAdrJONLo+1FFgCrAcGt2RHqckUQgghhOhgxrLqPTqSZVnplmVdblnWF8Bq4EjgH0BuS8qRmkwhhBBCCBFrM/A98DJwujGmtDWFSJIphBBCCNHBOsGI8lgDjTH5u1uIJJlCCLEPmK6refTFUrxei+suy2Ds8PiODmmX7BWFqN/OJL6gFv9f4vHddCwA/r9/QeCuz3H1TmfpiYfy09dlpPdP5riHDqAmOZ6fv+znpy02F4z18J8pXqzOdf9BIRrV0U3ksdoiwQTpkymEEF2ebRsefKaE8kqbopIw/36+pKNDahb/n94neV0VnpowgZs/wl5WgL1qG4E/fwAVfuzFW0l4/GuC1WEKF5Ux+z/L+fs3Qb5bb1MVgMdnhfhsld3RpyHEPkuSzL2UUmqtUuqCNiinn1LKKKXyIvPnK6XmtWD/Fm3f1pRSxyulViqlKpRS17XTMW5VSn3eHmUL0RFs09ERNFODOI0xYOovtGLmjb3DLnvPuYp9nrHqP7oCaS4X9WitXwJeau32SqnngJDWulU/QdUKDwEPaK0f3UPHE2Kv43JZXHNxBo+8WILXY3HVhRkdHVKzxN11IuUzV+ErrCXhxqNxD+vmLL/tOAJ3f4HVO4PKEw/F/U056f2TGf/rwQxP8TJ9nc28LTYXjHVz3CCpSxF7B7sTNZe3FUkyxd5uANCim8MK0VmEwob5KwNkpLjo39Pb5HZFNYY5Ww2jsi16JEc/iDaW2szPDxO0YUi2i+Hd3PX2qwoYflhv0z/DoueQBH55fTyH9LLwuWH1okp8CS6SMuLYtNFP3/7xJCe7Gx4aYwyb55TgTXCTOyJtp+dTEzR8v9nQJ9WirxWkeF4xqYNTSeyZGI1peRn+DdWkHZKLO75BvLqA6hXluLITSD+kG64huSy4ajR2govJvzuhbjvfLcdRcs4h+Ev8jCgvZ+zV6firDcFl5YRTqnncW8rAX/YktacPgHlrg4TDMG5g09cYILC8mMovN+AZmEFipgWVfoLlIezVpSScPxpr0zYoqYbDh2K52jh51SuhNgiHDIPOkmz8sAwsYOLQjo5kn9CZai8tpyPzFcC5QLYxZoxlWYcD3Y0xrze3HEkyOzGl1LXAr4FeQAlOjeFNWutwI9uOAe4BxgNuYI7W+pjIumeBY4B0YANwp9b65SaOeUnkGIMi89OA2UA/nJ+ZKgCu01q/23B7pdQNwPmR5edEiuwLbAQO1lrPjTnON8BnWus7dnENjoic1zAgH3hQa/24UqonsDxyrp8qpWxgnNZ6+U7KavI6KKUygCeAo3BeFxuBK7XW30Z2t5RSd+G86AD+o7X+285iF2Jnwrbhhn8XM2tJAMuCGy9M46SDE3fYbmOF4cAXw+RXQboPvjvXzYhsi9kbwxz5n0oqA852lgtePCeR8/Z3EqmqgOGQx2uZt8XG4wI70YvtcaG6w1VbNrFkeikhy6I6NYFgCDIyPfz1jt6kZ9T/WPjspvks/8gZAzDx6sGoywY2ej7+kOHI18LM3AKZNbU88vZXsLUaT5KHo96aROaYTAr+t44FZ32NCRlSJ2Sjvj4el89JNPPvnM3amzVV+ACLlHGZ5PWz6fv2egBKwrPIuP4AAH58ZQPTHl8LQN7WbQxfvpliO5nSDB/beiYBMPOhpZz+xmG8sNTNE5/WAHDOYfH87eyURuOvfGcF+ae/G2lft+nGViwghAc/CYT++AapwQJn458rePvanf+DW+L21+FvrzrTlx4Fz1zddmW31u+fgX++70xfNwXuv7Rj4xF72u3AscA/gcciyzYCDwLNTjKlHaFz2wicAKQCpwKXEU1y6iilegBfRx79gO7A32M2mQ7sh5Nc3Q48p5Qa0YI4LgbuB9KAfwP/VUrt8Gmotb4HJxH+r9Y6OfIoAt6IjVspNQQ4CHhmZwdVSvUHPgb+A2Th/LTV3UqpM7XWm7XWyZFNJ0eO1WSCGbGz6/BHIBEnKU4Hfo5z/bc7HOfXDnoCpwB/UUodsovjCdGkzYVhZi1xMkRj4P3p1Y1u995KQ36VM13qh9eWOQNZXpoTqEswt5fx1KzoghkbnCZjgJANtt/5bqq3wGfz/AD4PR6CIWf7kuIQ83+qqnfsQHWoLsEEWPT2RpoypwBmbnGmR67ZClud8wlVhVj3v3UAbHpqBSbkdJIs/3EbFfOK6/YvfGwRATw4VWdQMaeY4rfX1K0vezza9Xvue1vqpjd2y6bUckbKl2VFR8ybsGHxq+t4bXpt3bI3vqt1+nU2ovzpBTEdOF0EcZJ1DyHAkBiMGSz1P40pKGvyWrTYY59Ep5/9EgLBtiu7tR77NDr9+GcdF8c+pDPdjB3n8/ZkY8yrRLs6r8FpPWw2STI7Ma31W1rrNVprE6kFfAE4upFNLwRWaq3v1lpXaa0DWuvPY8p5WmtdpLUOa61fxWlePrIFobymtf5ea23j1Pal0bKflnoCOE8ptf0T4HLgY631pl3sdy5OjexzWuuQ1noG8DiNJNrNsYvrEMBJZIcCltZ6udZ6Tczuy7XWj8XE8ROgWhNHS1RUVMh0F53OTHWRnFC3mO6ZptHte8fXEGtopvPh0y91x0RkaI6rbt/+GRZx7piEyuXsF++GnvFO8uk29Udep6aG6sXgjXeT3D2auKX3SWzyvDJdlSREKkHz05PrNf2lDEwFwDsgesKuRDfxvZPqyokflo47ZtiOK95NXG5cdD5SBkBaTNeChFo/vrATt9dfv5Ene3ga/WO6EPTLdVNZWdlo/GZAcsyeBjdOWXYk6Q0R09TeLY1Kd/Ta7fbzYVivaNkDukGct8Ofn/ViGtqz4+PphNNtrZMlmW5g+4tl+wszOWZZs0hzeSemlDoXuA7nm4MHiANmNLJpP5ym48bKcAG3Amfj1HAaIAnIaUEodVUZWusqpRRA421OjdBaT1dKbQbOUEq9ilMz+stm7Nob55tTrFU4tbot0ozrcC/gBf4L9FBKvQ/coLXeGlnf8J5hVbTgGrRWSkqKTHfR6aQEFw/+LovXP68iM83NZScnN7r9lOFJ/Ne2+WC14dBeFucNd+oGfntECkErwEfLgtSG4JB+Hv52jI/EOOfDaUCmi6kXxvPsnBADMy1sn5s1ZRa/GGMx8sx+THuzAF+CC2+3BDZsCLD/+CT2G5+6QwynPqrQT6/Gm+hmwpWDSUiJazTOod1T+PA0m8fnGwZMyObAQyey9dNNZO6XycDzncqP4fdOJD4lgZo1FeRdORRfj8S6cuJfPoa4m2ZS9GMx7t6p9Pn9SJJ7xjH/N29iJ7oY//yUumOdevNIpj+3jupVZQzbUk7m4cMoqYknp8jPOstF+dZaBkzuwYgz+/Kv0jD//rCaUNhw1YlJpKS4G42/xz1Hsc3tpWLqGqy8FDw983DVBgiuqsRdHiR86ZFQuBGKq+DGk0nJymi0nFZNv3Kd01xeG4SbzmibMnd3+r0/w62vORXLfzu74+PphNNd3EfAA5Zl/R7q+mjeAUxtSSGSZHZSSqnewIvAacBHWuuAUuo+Gq89Wwuc0URR5+LU/E0GFmutbaWUZnubVNtr6qZ0j+PUYFYCYeCDZpS1ATixwbIBkeUttdProLWuAv4K/FUp1R3n2t8LXNSKYwnRLMP6xnHL5XG73O6ikS4uGll/mWVZXH+Ej+uP8DW53+TBHiYPdt7mv10d4pVCP0s3upl0sI+zf9+nWTFm9Evm2DvGNGvbI/u4OLKu2L4MPK1vvfXueDeD7hrX6L7e3ET6PXEk/Rosz7+up7NvVrQWNDHdS/chyeTbhrgLB5B7YEbdDyqPbbB/brqb28/bdWLg8nnIfWASuQ9Mqrd8x16y7aBbOjx25Z44UvP1zoanr+roKPYpnWngD/B74DmgDKcCphL4lBZ+JkqS2Xkl43RnKASCSqmJOM3iSxrZ9kWc5OhG4GEgBBweaTJPjcwXAq7IQJ2xwPvtFPcWYKJSyhVpXt/uBeBu4G/As40NXmrEK8DNSqmLcH4/dRzwK5zBUC210+uglJoCrMSpEa4EaoHmxChEp7dyW5hjH6/EH2kND9lwzWFNJ6ed3cJPt/LRPSsAmPf+Fi5+fH+6DU7exV5CdG7G1TmyTMuy3DgVV+fhfHb2BTYYY7bsdMdGSJ/MTkprvQQnIXsXKAX+hJN0NbbtZpy+hcfiDFbZgjOQBZzm3x9xEqhNwAjg2x1LaTNP4TRDFymlSpVS7kiMJcCbOInd080pKNIn8kTgaqAIJ1G9WWvd7JFtMXZ1HQbiNAOU49QM1wA3tuI4QnQ6i7fYdQkmwNxNe/f3p63Lo93CjA2Fq6t2srUQe4fO0ifTGBMGHjDG1BpjCowxs1qTYAJYTY20E6KtKaVuxbmV0eSOjmUvIi9QsduKqmzGPVjB+hKDxwXvXpbEicN3fs/IzmLqVKcL2JQp0T6ZG+eX8er1CwgHDYkZXi55Yn9Scvbemlmx12mXDPDx4e/We7//1ZJTOyzTtCzrBeB1Y0yL+mA2JM3lYo9QSnUDfkHzBvwIIdpQVpKL2b9L4cuVIYbluhnTc8ebru9N8sakcelT4yhYXUXvMWkkZ+26X6sQnV1naS6PiAfetCzrB5xxEHUJsDGm2f0yJckU7U4p9QBOX8oXtNYfNFjX1O0QvtVan9DEuqaOswin70hD67TWIxtZLsQ+IzvZxVn7dZ1kLKtvIll998iwHCH2jI6/bVGshZHHbpEkU7Q7rfV1OLdiamxdm/XWl0RSCCGE2H3GmNvaohxJMoUQQgghOlhnai63LOuoptYZY75sbjmSZAohhBBCdLBO8Cs/sRreBSYH5wdhNtKCn5aUJFMIIYQQQtQxxvSPnY/cO/MmoEW/qyn3yRRCCCGE6GDGctV7dCaRe2f+H3BDS/aTmkwhhBBCiA7WmfpkNuFYmv7p6EZJkimEEEII0cE6U59My7Lq3RsTSMS5d2aLftBekkwhhBBCCBHrggbzVcByY0x5SwqRJFMIIYQQoqN1nopMgAOMMfc1XGhZ1nXGmAeaW0jn6lkqhBBCCLEPMpZV79HBbmli+U0tKURqMoUQQgghROxN2N2WZU2ifv3qAFp4CyNJMoUQQuwVqov8fHKdpnhlJcN/3puD/zCixWXkl4Q57z/lzC+wiffCecrH5z/5qfS42eZykxZv4Q+EsSx49LQEfjaq6/zeu+jcOsno8u03YY8HnolZboAtwG9bUpgkmUIIIfYK+rHlbJlbAsC851fTb1I3eo7PalEZ939YzbwtYWzLoioAT35fS5KBzfEusKC01oCxwLa56JVqyu70YnV806XYB3SCJvK6m7BblvW8Meai3S1P+mQKIYTYKxjb7HS+OZrcpZEP+LANpuWHEGKv1xYJJkiSKTqYUqqfUsoopfIi8+crpeZ1cEyVSqmDdrL+KaXUc3swJCEEMP4Xg8kcnILLYzH89D70VC2rxQT43fGJDMxwYWHwuOCccT6SvNDLFcLrguxESPEakn3w5JmJuDpHE6bYB3SmgT+WZaValvWAZVmzLctaZ1nW+u2PlpQjzeWiU9FavwS81MExJHfk8YVolh+Xg9cN4wbudDNTFSA4ayOuvulYG0uwspNwDe++43b+IMxYCXmZWAO71S0PVIXYurQctzFYtiFtYApFa6vI6p9MYmb9/opb55fg8rrIGZ7WaCxlm6qp2FJL91FpeHzu+itr/PDjCuibA/277bBvUVGIgmIY/38HkL/FT1GVoaA0RLcML8YYfloTItFnMbTXzj/W4n0W91yQQobbprjMJi7BxYWH+Ji/IczIbhYhY2HiXPRJt9hYavh6bRi3x8XBvWBjBawuNRzQ3SIpzkkC5mw1BG2Y0MOisNqwcJthUBqsKgzTP9NF30z3TuNpyqJthpJaw8G9LFzNTDgq/Aa92WZIloteqZIc7206OrFs4FEgD7gdeBHnvpl/BN5qSSGSZAohxN7mqifg0Y+d6VvOgtvOaXQzU+mn9ODHCS/Ygs/lx20HwWUR9+Q5eC6LVtabQAiO/jt8txw8bswbV2P9TFFbEeTVKzQVK8qIr6jBAkyil6p4H/HpcZz1hCKzbxIAX//fAha97lRyqCsHc+Cvh9SLZc13hXxw4zzCQUO3kamc8R8VTTRr/HDYTTB7FcR54L0/w3H71+27rTCBP/9xPaYmRJzt/KpdYm0t0540XPv4KP7zcQ1TZ/kBuPHnSVxydGKj12N5QZiDH67AXRZiYDCEBVRZFvPiPOByEW/bWC4XNR43rlDIaVq3gJwkDu3nZtYmgz8Mo3Pg+ws83DPT5o4ZTnv6+cMtPllj2FZl8JRUEwrYxHvgg18kc9Rgb3P/swA8OjfM1Z/ZGOD0IRZv/mzXH9UlNYYJT/pZUWRIjoMvLvZxYJ40Vu5NOlmSORkYbowpsiwrbIx517IsDUwFHmxuIZJkihZTSq0FngKOBg4A1gDnAyOBO4Ac4A3gSq11SCn1LHAMkA5sAO7UWr/cRNmXADdprQdF5r04354uBnoCBcCNWus3dxHjTo+plBoD3AOMB9zAHK31MZF1BjhMaz09Mn8Z8NfIeb2L87ETas61EqLNhcPw2KfR+Uc/bjLJDH67lvCCrVgYJ8EEsA2hx76rl2Ty0zonwQQIheHJafAzxfpZxZSsryahNlB3HxOrOojL66W2LMjyz7cy8fIB2GHD4jejrWiLXlu3Q5K56N1NhINOQrZ1UTlbl5TTa78MZ+WPK5wEEyAQgqc+r5dkrluTTiBgSLSjP5tc7fPRo7iU198vZerc6HFe+bamySTz1bkBiqoMo0PhuvNJMgYfFn6g1uUi3RhqbDvad9MANUGmb3JD2Fm0oBC+3WB45Kdoh81Xlhhnn2CYUMCJszYEz/wYaEWSadf9nt9byw1bqwzdknaegHy6KsyKImevygA8Py/EgXkyMl60mgsoi0xXWpaVBuQDg1paiBCtcTHwGyADmAf8D5gEjAVGA6cAZ0e2nQ7sh5Pw3Q48p5Rq7r1H7sSppj8TSAWOAJY3Y78mj6mU6gF8HXn0A7oDf2+sEKXUYcAjwJVAJvBZzHm1u4qKCpmW6frTbjfENGczuEeT29fk+sDjwmDV+xHiUL+M+tvnZUJCTEIyuDsVFRWk5yViucB2Rz8qjAW2y5mPz3YSH5fbIrlnfN02yXnR6e3xpPdJqlvmjnPhSg7XzVdmJTo1mDHnFLtvckoAADumpsdt24QtyMuDnNRofH1z3Ttch+3TQyLramP6WYaBYGTWMoawZe04CMjjItFTb5ZcbzVDopeRbttPz13/Y3VwjqvJeJqMMyN6/JxEcAcrd7lvD18Nsd1HB2e2/Lgy3bLpttaZ+mTifK4fEZn+Fqf5/D807/O3jmVk6JxooUhN5iNa63sj8ycCHwC5WuvCyLLXgU1a6983sr8GntFaP6qU6odTE9pba70xtiZTKWXh3Pj1bK31B7sZc+wxbwDO1Fof0MS2dTWZSqkngXit9YUx678DVmitL9mdmJpJXqBiRyvz4bbXncTs9nOgV9MDYPxTl+B//ifcaR48ZRVY3VPx3nkSVlpCve3MV4vh0c+hfw7cdjpWJOlc/uVWln6cT3hTJSmZXhIHplG8xU+v/TPY/+w+dfuXra9i1mMrcHktJlw1lKTc+HrlhwI2Pz65itKN1Yw6NY++ExvE/MlcpwZzcA/429ngc2r/pk6dim2DCR/CmpW1UBMkf5OfUDDEgEk5XH1RDivyQzz+cTVJ8RbXnpxEZkrT9ScPfVPLtOVBMquCVFfYuJLdrAlabK0w9I0Hd7wLkj3kJkN5jU1R2E3vHj6uUxYfrDQsLjJcONLFKYNdbKow3PydTdCGWw928eFqw7T1hh6eEFu2BRnezc0tk+PxuluWMBTXGG761qa41vDniW7G5jZv/9cXhnh9UZhxPVz86VCPDFpqP+1yYe897Ot67/d//PaIDvsHWpY1ACdHXGVZVi5wF5AC3GaMWdzccqS5XLRWfsx0NRDenmDGLEtRSrmAW3Fq/7rjJE1JOE3Pu5IT2bZF35yaccx+LSgzD9ANlq1pSTxCtLlBPeCFa5u1qW/KcHxThu9yO2vSCJi0YwPDkKO6MeSoHQfiNJTWJ4lj7tqvyfWeOBeHXDW46QKO279eE3kslwumnJrZ5K6De3i479LUXcYIcM3h8VxzePyuN2zEgT3qz/dKsXjm+OjAnt+Os/jtOHB64PhadQyAzASLRye3fMDQWaM8nDVKPtbF7jPGrI6ZLgCuaE050lwu2tu5OE/O04EMrXU6TjV8c76hFeIkqzv5ZGrVMde2oMxNOElprIbzQgghxG4xLqveoyNZjl9YlvWlZVnzI8sOtyzrrJaUI195RHtLxRkkUwi4Is3hY4H3d7Wj1toopR4F7lFKrQcWAb2ATK31/N045ovAX5VSNwIPR7Y9XGv9eSNlvQB8HLkv5tfAOcAEYOWu4hdCCCGaqxP0w4x1O3As8E/gsciyjTgjy19vbiFSkyna23+BH3GSsk3ACJxOxM31V5wn9Ds4/TOnsevRbTs9ptZ6M3AkzgtoI87vsf6xsYK01l/j/FbrU0AxcDzwWgviF0IIIXapkw38uQQ42RjzKtGxAWuAAS0pRAb+CNG5yQtU7NOmTp0KwJQpUzo4EiHqtEsG+PdJ0+u93//pq0M7cuDPZmCAMabWsqxiY0ymZVkpwGJjTO/mliM1mUIIIYQQHayT1WR+CDxgWZYPnD6aOPfBntqSQqRPptgrKaUWAX0bWbVOaz1yT8cjhBBC7I5OkFjGug6n61kZ4AUqgU+Bi1pSiCSZYq8kiaQQQgjRtizL6m6M2WKMKQd+HrlHZl9ggzFmS0vLk+ZyIYQQQogO1kmayxveQ/oxY8ys1iSYIDWZQgghhBAdrpM0lzcM4sjdKUxqMoUQQgghBLTxHU2kJlMIIYQQooOZTlGRiceyrElEazQbzmOM+bLZhbVxcEIIIYQQooU6SXN5AfBMzHxRg3lDC27ILkmmEEIIIUQH6wxJpjGmX1uWJ30yhRBCCCFEm5OaTCGEEEKIDmZ3gprMtiZJphBCCCFEBzPt85PoHUqay4UQQnQqZQ/PZm23f7Nh3HPEbQ7s8ePP+bqEOy5dzH1XL2PjquoW71/78Ury+z5Efv+Hqf1yTTtEKMTeQZJMIYQQnUZoQznbrv2CcEE1gbkFdHt66x49fqDW5o2HN1JREqJgo5//PbapxWUUX/AO4fVlhNeWUnLxe+0QpeiKOskv/rQpaS4XQgjRaZigXe920FaoTe8NvUu2Mdh29JihYMuPb4J2dDoQbpO4RNfXVRLLWG1ek6mUylNKGaVUv7Yuuz0ppT5XSt3a0XE0h1IqUyn1iVKqTCk1uxX7h5RSR0amz1dKzYtZ95xS6qk2DLe5MT2llHqujctcpJQ6uy3LFEK0L++AdDJuORjcFu5eyRRclLtHjx+f4OaUy3ri9lgkprg55fKeLS4j4/ETsRK9WMlxZPznhHaIUoi9Q7vXZCqlLgKuBIYDYWAWcIPWekGDbW4E+gDFwLPAbVprE1mfCzwGHAvU4twY9M9aazvmUCilVgLna61/3M2YM4F3gGFAPFAYien/YmK6BjgfGA1s1loPalDGJZE4Yzv0TNVan7s7sUVcCSQDWVrr0O4UpLV+CXiptftHEsOQ1vqK3YmjPWitR3Z0DEK0paIaw6JtMCobMhOcWg9jDD/mw9oym1HZLkbl7Lw2ZGO5YU2pYXwPi0Rvy2tOFm4JUxWECb3dO6yzq4LUztmKd0Aa/gI/xobqBC8uj0XOkFTM1nJqPl5BuHcOyZN6E1hfSc3yUtwu8A3NIC4vGYDU0weSckJv3DmJbHjzQzyrA4R/XIt7Qj/nnGuDhGZtxNU3g/KQG39FiG5j0rEsC9YWwMYiOGAQ+Lx1sQVnbsBK8OIZ3R2AyoBhzlZDz20V5NT6STkgm6pZBXjSfRx8TDqDE6pZlJpOcv8kAMIhw/rlVaRmeMnq4SNcG6Lgi3xqseh1ZHfikjyEK4NUztlG/Mhccl45iUDAojQlBYpqSYgzMHc1DOkJVX7YUgIThoBnx+so9k1dsSZzTzSXpwB/A74HQsAtwKdKqYFa62ql1FicZOw0YCowFPgK2AQ8GSnjJaACyAOygI9xktF/bD+IUmo0kADMbIOYq4BfA8u11kGlVH/gQ5w74T8R2WYzcA9OInppE+Wsbph8tpEBwJLdTTCFEHuPNaWGg14Os7UauifBjPPc9E2zuOBDm5eXGDAGjM19R7q4/sDGE5fvNthMfilIdRBG51p8f6mX5Ljmf7Dd/22AP3zkDMS5bLyHp0+Pr1tnVwRYf9DLBBYVUelNpCroZWPvdLblpgBw4M9yGPt/z+GpDRDCx9LDj6DshyJ8QT8uDFaihwEfn4L722XU/vUTXASJc/k5wDYYLPzMwH3pROIePYuyw58kPGsjxuvmm7yhbE1OZ+BxPZh8pIHT7oFACA4eCl/dDnFeKn75P/xPagAS/3EcgWsO46AXQiwthsRaL/c89TUTqSC8sQIsiO8fxymnnsi6TB/pn9fy1SVxzHp0LSvnV+JywzlX51F64/csqQHb7SI5ewmnPTOR5ZM/pHZ5GXEE6M0WLCyCbg+fDB7MlNpvca/dCglx4A+CbeCYMfDxzeCWRFN0mp+VbFPNSjKVUlnAg8DkyKJPgN9rrYuVUt1xEq8jgK04iVcdrfUjDcq6A/gLTnI2BxgIFGitt/eOXqqUmgaMjWzfHzgGGKS1LgPKlFL/AG4iJskEfga8G1PT+Gvgd0B3YAnwR631t5F1FvAn4CogEfgvMb/LqbX2A4saXAYbJwHevs2bkbIuaeq6tdYurvdU4PjIducA92ut/7aTslKAfwNTcBL1WxqsvwS4qalkWCllgMO01tMj80cCn2utPUqpG3Bqc7fHApCmtQ4rpX4G3Izz/80H7ozUmm4v9zLgr0AO8C7O9d9p0hy5LgVAb631ZqXUUcAXwOVa62eUUh6cn8A6Vms9Uym1NnJuL26POxLvXUA2znW9XGtdEVP+PTjXPR7ny85vtdZbI+uvAX4f2bcc+K/W+i87i1mItvLaMsPWSLvIlip4c7nhklE4CSaAZQGGh+fYTSaZT80NUx10phcUGKattTl5SPMTnId+CNZNPzM7xMNTDImRJLX6i3UEFhU500EPBtiWk1y3/fwPtrJ/rZOgxuPH9c0yLNJxRTpgmuoQxU8vJvXjGQB4CGJF+kZaGAwQfvZHgmeMJzxro7M8GGZA8Va2Jqez6pN8wvN/wh2IvI18vwzmrMbsN6AuwQSofXgGn085lKXFznx1vJdPxw/ggHenOwsMFG0Osy4zA4DSsIvHvw+SOr8SADsM859di2dTLXZOIgCV2wKseWQptcvLAEikFivykRIfDjFg4xrclZEBTDUxo+U/nw9LNsKovs3+H4iuqyveJ7O5fTJfAjJwmryH43zIvhCzLozT1H04cMkuyjoapwl5RWT+E2CzUurnSimXUmpUpJztSedYoExrvSqmjDlAP6VUasyyn+M0caOUOhe4A7gIp+bzSeBjpdT2V/IFOMnCqThJ6LbIMetRSr2vlKoBVuPUyD6+i3NrqLdSaotSaoNS6tVIwtwcTV5vrfWUyPr/aq2Td5ZgRvwTGAyMAMbgnHObfG3WWt/TIJbkSIJ5LPA0TpKfCVwM/FspdTiAUuow4BGcZv9M4DNgl30ntdZFwDycLx3gdJ9YGTM/AefLgN5xb8A578k4z6khwP7ANZGYLJznjwFGAX1xkvKXI+uHAH8HTtZapwAjiT5H201FRYVMyzQAgzOop1d8DalxkJtYf3n/lOhAk4blDM6Mfoi5LeifbrUshqzoR0avVItQbWXdfKCbF1xO+W7CWECcP/q9MS2j/ttOMD4RgxU7xgffoHTMAOdETczH0/ZtrJ5puIZkgze6rjLOqU2Nz4gjOCA7uo/PC72ywOeBvOhHhemfxsAMq94dCXsWVdRrqnS56g/2Gd7dRVx89JjJg5OJC9frrUXKfpl1VRXBBvU3ZXFJjTeFJsdTmRxt0u/o55hMt2xa7NouazKVUj2B44AhWuuSyLLrcGocewFHUb+W8Tbg0ybKGoLTt/H67bVHWusqpdSzwPM4zd1u4B9a6+1lpABlDYoqjfxNBcqVUn2A/jg1T+A0Xz8e0zfzaaXUFcB5wN04yefjWuvZkbjuxkl46tFan6yUcgMH4NQEbtvV9YrxDU5/zZVALk6C8plSaqzWuqqpnXZxvXtorfObG4BSyoVTc3eS1npLZNmNOAl5e7oW+Nf2mmNgplLqRZzr/k3k75ta688i659XSv2qmWV/jpNUPh/5ezPwr0iSeAzwVcO+ug38SWtdCVQqpd4BVGT5+MjjmEhNNpGa2m1KqTycWlYLGKmUWqe1LgVmNDPmVktJSZFpmQbg9CEu/nUUfLnecGxfi3NGO30FPz3DzY3f2KwoNkzoYfHAJF+T5dxwsKEmCAsLDReOdjEy14XzFtu8GF48y8dfPw1QFYCbj/KSmhpNHDMP6of3tZMpf2kJKX3TKNtmUH7D1p6peDN8TLxyEDzsp/qleVR160G/R0+h6LVVVH2zCY/LkHJUL3JvHIe5dAg1N32CqfDjSjJsXbwKbIvcQX3x3nI8rkHZpLxzIf6nZmH6ZGCn9mRAlc24KwYR3+8ISE6AVVuwrjwOemdjAemfXkr1bV9iJXpJ+r9jyepu8fIUN6/MD9Hnp81c3r2Wni8eTdn/VuPJ9JFzZDaPfDGHj/L6cNCkHH57sJc1af357v1tpGV7Oe787pSPS2Dm/Uuo8bgYddUw+v2sN0mWYdvrq0isKsOXH6ai2k1Bj26kXTQeEveHt36AMX2hpAo2FcO1J5Hcr2ezr79Md67ptrav9snsHfkbe0fZ7bWKeZG/62LWNXrnWaXUCJwaq/u01o/FLL8UuBUnQZiFU4P0ilLqDq31zTi1SWkNikuP/N3+leJnwIda6+1tOb2B1xvssyrmXPKAtdtXaK1tpdQ6GqG1DgMzIrVwjwDNGrijtV4dM7tFKfULnGR5Ik4Tb1N2dr174zQ9N1cO4CPmXGni/9PG+gOTIsnxdm5ge9KZx461jc2N63Pg2cjgrCHAWziJ5lic59ArO9k3rLUujJmvIvoJ2x/nWm1VSsXuUwv00Vp/r5Q6H6ev7lNKqfnA7TFfhoRod9eMc3HNuPrLxuZafHxG8xonPC6LOya1vit+9xRXvX6YDaWcMYSUM4YA0KOxDf5vCnH/N6XuDTx5fM4Om1i90kh69qy6+Z+mTgVgypQpdcviThxK3IlO76UdmqDuvXiHMj3Dc0l99Zx6y84Z4eKcEXFwTj+gHwC550V7Df3mXPhNzPYDRiUzYFS0+T/n5D6cdHKfemXmnDuInHOjZaQR/ZCEIXDBETvEJsR2+2qSuSHytx9OrRw4A0/AGfwCTmK4Kma7epRS43AG69yhtX64werxwJcxtY5rlFIvAZfjJA/zgDSl1ICYxG1/YG2k9hScmrlHG8TcMI4BOAOLwBlUVLc+Ugu2q04xHpxm59YykceunkU7u94bdth657YBgUhZTf5/dqESSIqZb3g/j8ZqDdcBz2mt722izHrXPyaulTtsuaNvcbpAXAV8GxmY9TnOc2ACzvOmNdbhJJ2ZTdWEaq3fBt5WSsXh1Hy/q5TK0lq3/CdBhBBCiC5ul0lmZIDFp8D9SqmLcZKk+4GPtNYbIoN07onUSCaw48CSQ4D3cW5b9CQ7+g6nuXO81nq2Uqo3Tp/J2ZHjr4kkEfdEBotk4dzu6PFI+Vk4zdkfxZT5XKTM93D6b14I7Ee0FvKFSHn/AxYAf8Dpm7k95ok4idX3OEnaIThNwE/EbOOJXD8vYCml4iPx1kbWn4STIG/C6V95N07St9Mm1l1c75bUYhLpH/kycJtSaiFQg9Ns3xKzgYuVUl/hJJjXNVi/BZiolHLFJGf/BJ5TSs3AuYZunK4DltZa41z/jyO3P/oaOAcnQdxlkqm1rlFKfY/zP9v+XPsiUuYWrfXyFp5fXdE4/6+HlFJ/01oXKaVygKO11q8qpYbi1HZ+g3Mdy3C+NOysaV4IIYRoln154M8FOE3Ty4ClOH0iL4qsOw+nmXEDTi3T8w32vROn1eBBpVRlzOMwAK31K8C9wKtKqQqcWxAtpH4yc34k1k04TervEh3FPgWYFulnR6TMl4HbgBdxRhv/GjhRa729Sfx54GGcms2tOH0mv4k5Xlyk/AKgBCehfQinWX+7m3CSjSdwahprIo/tjoycSyXOSPUsnFHPlezazq53S12L0xS9FCehnoozUKu5rgYG4dwy6nWcBD7WUzgJeZFSqlQp5Y40If8C5/+6DaeJ/0Gce3uitf4a+G1k32Kc0fKvtSCmz3H6427v0zkN5y4Bn7egjHoiCfKpOEn97MhzcQbO/xGc58QtkXMpxRkwdPr2LxVCCCHE7jBW/UdXYBmzZ3+yq61FBm+8r7Xe479SI8QesHe/QIXYTVMb6ZMpRAdrlxTw+tMW1Hu/v//t0Xt9qtkVfrv8eyK3LhJCCCGE2BuZ9sldO9Ren2RG7tW4V1FKPYbTJN6YEVrr9S0o6y84N7dvzAkxtxHaK0RGcDd1P9Jfxd7QXQghhOgqumKfzL2+uVyILk5eoGKfJs3lohNql2zw2jMW13u//9ebI/b6rLO5A3+EEEIIIYRotr2+uVwIIYQQYm+3r96MXQghhBBCtCO76+WY0lwuhBBCCCHantRkCiGEEEJ0MGkuF0IIIYQQbc6W+2QKIYQQQoi21hVrMqVPphBCCCGEaHNSkymEEEII0cG64uhySTKFEEK0Su26Cpaf8yW16yrp9YfR9LpuDACB6hAf3LqYLUsqGHxENkdfPwSriabAFW+uZfaDi4nPiOOIBw4kY0jqbselVwX588sVBEOGw/f38ey8EBkJFi9dkMTonh3zsTd9o+GyT8IEw/DIMS5OHLBjQ+INX4d5fpFhTI7Fqye7yEzoglmHaFJX/FlJaS4XQgjRKutunEnFjAKC+dWsvf5HalaUATD7tY2s/q6I6uIA8/63mVXTtzW6v780wPS/zqF6Sw3FS8r44da5bRLXjS9VsKYgzMZimxe/qGFjqc2C/DC/ebO6TcpvjUs+DrOiBNaWw/kf2Dusn7be5t5Zhq3V8Nk6w10/7riNEHsbSTJFl6aU+kgpdUNHxyFEV2TXhuvP+535cKB+ghTyN54w2SEbE4r+XHO4ie1ayh809RcYZ742ZBrZes+oDUWn/WEwpn4sDS5lve3FvsFYVr1HVyBJpujStNYnaK3v6eg4hOiK+tyhiMtLAgt6/n4USaMyARh3Vh45g5MBGHBIFoOPzGl0/4TseMb/YSSW28KXEccBfx7dJnH97YxkEuMgzgOTxvlwuy0yEy3uPzWxTcpvjX8f7SLJCz43PHqMa4fuA5P7WZwzzFk2LBNuPFA+nvc1tlX/0RVYDb9NCSE6FXmBij1jTQEUlIMaAO5dJzj+BVup/WEzCQf3Iri4EKtbCgmH5GF5nH3LNlZTua6cbNuPd3g2RbVuaqtD9CotxPIHsLaVQ04KgcF9qFheTsqYDOLSfFSsqSA8czVpw9OxPC6+/vQLygdmMeX/27vv8LiK6+Hj39ld9WLJcq9yAWxjwMBQQi8GAqYl/hFiQowDKYSShBBSCCGUvKGF0EICCcW0ACGBgEM3vcMxGAzYxr13q/fdnfePuZJXsmRLsqS15PN5Hj2+u3fu3DN3V96jKXdPOaXZODYXx/h0XjWD+qXQv1+Er1bUURl3VGHYc1CEzLBh8fooew6JkJXuY1tVEmd5cZx9B4VZU+ZYX+6wQ0KEQ4aiascXGxw1NTEK80OMKgg3nCvuHLIW+mTAyLy2ZQGVdY5P1jsKc2FwTgvXd81mahdvIHW/QshIa1P9qkt1Sgr4vbMXNvr//v6HR3f7VFMX/qitWGuXAvcAxwIHAEuA7wB7AtcCfYEngPNFJGqtvR+YCOQBK4A/iMg/E+qbBNwEDANeBxYA+4rIUcF+B1wIfA8YA3wBTBORecH+CPBLYBrQL9j/UxGRYP/EoP5RQC0wW0QmBvteB2aKyB+stYVBW4aKyMpg/zTgChEZ3Z62t/caK7VTefw9+M6dEIvDpH3hmUsh1HKiWXbfbMrO+y8h4tQSwwB1hNl4zF4MfWkyC2au443fzGLiF59QVltNLDedB+xRbMrOZd/lc5k8eyZhaqkkjfczDqUuGiZjRDaDfrEnsUsfZGzZfAAccY7EseSkMdBMkjlvUQ2/vnED0WCoOZ5iWBROIWYMMWBDRoT+Lo6pjjNqQJgnLsnjkzVxTryvgqo6GFUQYmkJxJzhpD3C3H5qGoc9EmNtBRCPY0qrmH5GOlP3T8U5xzefjvP0QkfYwAMnhvjOuNb1NpbWOA59NMbnGyEnFWaeYThwYJP84b35cNzVpFZUw4QR8NYfIDujVfWrnsH1wJuxa3+8ask5wAVAPvAp8BRwNLAPsBdwKnBmUPZtYAI+ybwGmG6tHQdgrR0FPIlP0PKAW4DzmjnfNGAy0AefqN6RsO9q4DTg60ABcB/wgrU2P9j/IHA70AsYDPyh3a322tJ2pbq/v7zkE0yAZz+BBWu3Wbz8pvcwQCjhO0pSiFH36iJqvtjEZ48vZ9DmjeTUVgMQLq1m3PKlAHwybCxVKenEibA6NIi6qO8prFpSzry/z2f3sgUJZ/IfUYXPz4O6rf+me+mtioYEE8DUOeofhoG0ujjrYj7CRWtjvDW3lrs/qKWqzpdZtCne0Ozn5se4c1bcJ5gAoRAuJcztb9cCsKQEng46mmIO7vik9fNHX1nu+DxY+1RWC/fOaebYf7wMFf56MXsJvP5Fq+tXPUPcmEY/PYEmmaolfxeRuSJSB/wTGAn8VkQqRGQ5vkfSAojIvSKySURiIvIY8BlwVFDPFOADEXlURKIi8grwdDPnu0lElotIDTC9vm5rrQF+AlwmIouDc9wLrAEmBcfW4nsx+4tIjYi83lVt72xlZWW6rdudvz2yX8NjstOhb+42y4cL84DGPS8OiKenEhmQRUb/FMrTGvfClWRlAZBVU0lqtA5wZLiqRmWyhmRREcmiqeqCLMqqt5Stj2dA38aDcTGAhA/nqDGkBAOQxsCQgjCDs+sa9id+jGenOvbs2+SDPe4YluvT1j4ZkJu6ZTRzaNaWpHd717kw1xAyW44dnFG7dfmR/becNxSiok/m1mV0e6fa7mg9McnU4XLVkjUJ25VATEQ2NHkux1obAq7C9+wNwH/WZOGHlcH3LC5rUvcyYOg2zlcB5ATbfYBsYEYwrF4vBRgSbJ8GXA7MsdZuwCeJt26/iS1qVdt3oP5Wy8nJ0W3d7vzt28+BzFRYXQyXngS9sxu9wZuWz/rPZNad8Bh1X23EDMiA0iqiffIZdMOxRPplMvHyvXg3J43Fr0UorCkm5YhCUvqMZs9VZRw1by6RMX0Il5cxZFA6tXsNpGhDiH4nD6H/5OHMu6yOQa++Qc7wTNLzUli9fh3zz9qXo5uJ/5sn5LCxKMb7s6vo2zvMyNHpvLmgjjW1hrq0MKeMT2WQifPV6hgn7ZvGPoUpjBmSiwlXM3d9nG9PSOHtZXFWlzouPTyVI0aEKa6BJ+fHKS2P8rXRIa4/ySd7uWmG5yZHuOmjOP0z4YYj0lq8Pk239+1vePikMP+c69inH1x+SPrW5X/1Daiogc+Xw9SjyDp4XNe+B3S7zdtq+zTJVDtqCvB94HjgSxGJW2uFLZ0Eq4J9iYa1of6N+KRzooh81FwBEfkUODPo9TwMeMla+5mIvNqkaP2foIldJYPaEItSPVOvTPhbc7NYmhfKTGXgW1Nb3J+Wk8LRl+8Jl+/Z8Ny3GrbGN2zVVMVZ+OwmYjHH2JN6k5YTYZ97TgBOaCgza8aMFs8TiRguODufC87Ob3juwu3EnhYx3DhpSy/rd/ZtvP/Sg8JcelAY/3dsY4cONhw6OLzV860xZWyIKWO3USAlAted3a66Vc/QU1aUJ9IkU+2oXCAKbABCwUKafYD/BfsfA35nrf0W8B/gCOB04OPWVC4izlp7G/Ana+33RWSBtTYbOBSYg09CpwDPishGa20REIeGqVmJdW2y1i4DzrXWXg6MA37QXFmlVOebfstKvvy4HIC5n5Rz6fUjkxyRUskT14U/Sm3lAeADYCG+13Ic8Fb9ThFZCJyBX7xTAlwKPATUtOEcv8fP43zaWluKX51+Plvev2cC86y15cAzwO9F5I0W6joHODmI5c/AvW2IQynVgZYt2DLPcsWiauIxvWOXUj2J3idTdTlr7aNAmYj8MNmxdAP6C6p6rMf+tpr3XikGYLzN5ge/3nomzYxguLyl+2QqlQSd0uV45rRljf6/f3z68G7ftanD5arTWWtPxd/mqBS/InwyiZOulFK7pG/9aCBj9s0mFnVMODg32eEolVQ6J1Op9jkCf2/LdGA5/kbmryU3JKVUsoVCRpNLpXowTTJVpxORXwC/SHYcSiml1M6qp9wbM5EmmUoppZRSSdYTV5drkqmUUkoplWSxnpdj6i2MlFJKKaVUx9OeTKWUUkqpJNM5mUoppZRSqsP1xFsY6XC5UkoppZTqcNqTqZRSSimVZLq6XCmllFJKdbhYD5yTqcPlSimlOkT1v+ZQecs7xNeVJzsUpdROQHsylVJK7bCKa1+j8spXAKj6y/v0nnMxJjM1yVEp1X3owh+llFKqGXUvL2zYji8uIra4KInRKNX9xDCNfnoC7clUSinVbhVfFFE5t5jUA4bg3loGQLgwj/DI/HbXWfvaYlxpNZW9e2PeqyW+Z4Tlz68kc0AGffYt6KjQldqp9MRv/NEkU3UYa+004AoRGd0BdTngcBF5e4cDa9/5jwJmioj+jijVgs0vreLzSS/honGGp20kMxgcSz1z73YPlVdc9QpVV7/KevJYEepPBCgemsZb8fcAOPC6/dntOyM7qglKqU6kw+W7CGvt69baK9pQ/ipr7czOjEkp1b1teHwxLuoIEyOzpqLh+doZ89tdZ83DswHYbHIBiEUMsfiWj6qlTy9vd91K7czixjT66Qm0l0YppVSLohsqiS4pwQ3OoXJ9Db3G9CKSESFeEyOcl0qEKLWhELXxCKlEAYiM79eojuqSWkqWV9J7dDbhWJy6LzcSGZVHeNk6KMjGRSKwpgQmDCUyYSCli8pJo5YKMgjFHCbuqE1LASBnt9yGelcvrSacAqtCqRRkGoal1lEsq1iW05vdxuaSmQqfrorRK8MwoiAMwIriOMuLHSHj2H3tCgrWrod9RkDhlphjccfstY5+WTC0l09wl5XEeWslHDIYRuZ1fP/MpirHomIY3wcyU3pGgqHapifewkiTzF2AtfYvwOHA16y1vwZWAXsClwPTgHzgY+CnIvK5tfbMYF/IWlt/L5K9gVrgHmB/IBX4DPiZiMxqYzxHATOB84BrgBzgGeAiEUm898ne1tpbgDHAF8A0EZkX1BEBfhnE3y/Y/1MRkWD/dCAMVANnABXANSJyd0Ick4ErgUJgKXCViDzVQswTgZuAUcF1mC0iE9vSbqW6m+qP17HqmH9RXhFnYd/+xJyh15heHPnYkXw56SUis5bSjzpcHBZkD2C32hXk1W4i64O3Yd0J0D+PoiXlPHnu+1QX1dGnfwqHfLWA2NISQmkwsGY+kXAUZ1JwUQMTx7F2yP6so5LPxw3FRB3DVm0glpnK2sF9AFj2pWP0+lrefHojbz+/GYDXh/RlzsA8bv7gRR4u2IvNmbUMy1lPv5EZPPV5HeEQ/GNKNplZYb71WA3R2jg4x1NPPszpCz+BkIGHfgpnHUE87jjt0VqeXRAnJQT/OiMVE4FvPO1wgAH+Ocnx7bHhDrvOn29wHPl4jM3VMKY3vHtWmPz0npdwqF2PDpfvAkTkIuAt4FoRyRaRPYDLgKnAScCAYP/L1tpcEXkc+CPwelA+W0QW498vfwWGB8d8DDxprU1pR1hh4BR88joW2B34c5My04DJQB9gBXBHwr6rgdOArwMFwH3AC9baxNUG/wfMAHoDFwN/sdYOB7DWHgI8Avw6OP5y4FFr7UEtxPsgcDvQCxgM/KGtDVaquyn9x2fES2rYlJFFzPmkp2ReCcv+OpfKWevJoA7wiVdeTTWf5wwlm02YZevhyQ8BmDdjFdVFvlzmF2uILS0BIF4D5eRhYnFMtBaA2Mx5rJu+kGg4xIoR/dg4JI+h5UWsHti7IabU4iref624IcEE2H9dEdG44Z5B+7A5MxuA5WUhXvosqDcOt79RxW3vRolGHQDDSjf5BBMg7uCWGQDM3eh4dkEcgLo43PZBlBs+8AkmgAOu/9DRke77PM7mar89bzM8u7hj61fdQ7TJT0+gSeau63vADSIyT0Rq8D2KMWBSSweIyHIReUZEKkWkCrgCGAbs1s4YfiUiJSKyDt+jONVam/ievCk4Zw0wHbAA1loD/AS4TEQWi0hMRO4F1jSJ/9Ug3riIPAkUAxOCfdOA/4jI8yISFZFngaeAc1uItRbfi9lfRGpE5PV2trlNysrKdFu3k7YdKfRD06mxWMPzGMgen0/chIhveZbacISsWOWWJ0b44efU3lt65KpSGy8GigRJqgtu1xJKDREpSCUci5NaU0dtSoTaSJjUui0fudFwmJy8GLm9twzElQRD6f0qykiUlrbl3IW9wxTmGwiGJDenZ1GSmtGwv26oT2T7ZRkyI1uSvCFZUUblAwl536g806HXubBX417LwtyOrV+3O2e7o8WMafTTE+hw+a5rKLCk/oGIxK21S4Pnm2Wt7YPvbTwKyIOGz5i+7YxhWcL2UiAN32u5PnhuTcL+CvywOkGZbGBGsAq9XgowJOFx4vFN6xgKNB3mXwTs10Ksp+F7O+dYazcAfxeRW1so22FycnJ0W7eTtu0uPYB4cQ0Zn6wjvXcBlYQZetowBp8xgpT40Wz482xCyzZQVR2ndkhv9i6dQ21xBtH8AtL2GEYY2O+s3YiVG9Z9VsyIo/ek9/rRVP1vEWmDUsleG8L1GQ+RNFhTQvjiY9lrwECWXfUxx2VWs3DUAGbnjmKfRSuYmzeayqx0xp44kCNPGMhue+TzwmPrKIoaFg/uw3l9Q1w/uJJHXvqM2f2GcOzpgxk1PI0bZ1bRJyvEdadmkhIxpIUN7y2Lkl0V4omJX+e82W9gDh9Lyp0/BKBvluGZs9L483tRhuQabjwuHWOgpCbG+2tg//5wzwkhctI77jpfOMGxodIga2Hy7obDhhi2/Fe187wfdLvxdkeL9oy8shFNMncd8SaPV+DnIgIQ9CAWBs83Vx7gOmAgcJCIrLHW5gCl0O67xg7HJ3YE564BNrbiuI34hHGiiHzUznM3an9gJFva34iIfAqcGfSiHga8ZK39TERebef5ldrpmUiIPtcdATT+6w2g35kj6XfmllsJ1T75OeWTP6GcAVAE7rZ3yLr1FEzIcOD5iYMdw8i9cP/G50nYzgb2fPp4AA4FZsyYTwW9ufCUQxsdM6gwnXN/PRyASxuePZCfTmsc51M/aDyb5+7TU/FTyjPh198FvrtVu48dGebYkY3nXD7zzc77uAyHDNce1nFzPJXaWWiSuetYCyTev3I68Etr7Zv4XsRf4d8PzyaUH2atTRWR2uC5XKASKLLWZgM37GBM11lrvw+kA1cBD4lIc8ltIyLirLW3AX+y1n5fRBYE8RwKzBGR1a049wPATGvtQ/hFSMcD38T30jZirU0FpgDPishGa20RPgmPNS2r1K7K5KU3ehzKz2ihpFKqOdEe8i0/iXRO5q7jFsBaa4uttV/gV0o/CrwErAOOAY4XkdKg/BP4Xr21wTEj8PMm+wGb8CvL36X9iVYMn9DOAeYDi4Gft+H43wNPA09ba0uBBcD5tPI9LSLvAOcAfwKKgBuBs0Xk/RYOOROYF6y2fwb4vYi80YZ4lerRUo4ZTcbVEwmN7UfqdyaQftkRyQ5JqW6lzjT+6QmMc7qKTXUt/TadNtFfULVLmzHDr/o+5ZRTkhyJUg06JQUcffGGRv/fL7yjb7dPNfVDXimllFIqyep6yIryRJpkqk6RcBP3pt5ix+dyKqWUUj1KXbID6ASaZKpOISLZ2ymi7z2llFKqB9MPeqWUUkqpJKvU4XKllFJKKdXRqnpejqlJplJKKaVUstXqfTKVUkoppZTaPu3JVEoppZRKtp7XkalJplJKKaVU0vXAhT86XK6UUkoppTqcJplKKaWUUqrD6XC5UkoppVSy6XC5UkoppZRS26c9mUoppVq0YFWU3z9cSlVZlGPKNhEvqmbC8X058uwhHXqeWHWML859h+J31tP3lCGMuf0gTMj37GyuTeXY+2tYVOTonRJnU2mUU8alcPtp6YRCPa/3R+2ieuBbWXsylVJKtejaf5Yyd3mUnK82UTSvlJJ1tbzx0CpWfFHWoedZcec81j66hOrlFay4cz7r/r2sYd8DK0fz6pI4y4odn2wwLC9x3PluLf+eU9ehMSiVXKbJT/enPZldzFp7DvD/gDzgHOAUICoi309mXK1hrR0GfAnsLiKrWygzE3hbRK7awXNdBRwmIhN3pJ4djOF1YKaI/CFZMSiVbFW1DoCwc42er62Odeh5YhXRxo/LtySQNfFwk9IGcJTXdGgISiVXz8grG9GezC5krY0AfwV+KCLZIvKfZMfUFiKyPIi72QRTKdXz/Oz0bDLTDAvzexHOTQFg7OG9Gblvrw49z9AL9iB773wA8o/sz4ApIxr2fXvQYgbm+O3eqXFwjiNHhpmyb0qHxqCU6ljak9m1BgCZwGfJDkQppbZn/YYoQ3LhvotzWbe8mt69clngUghVxagoiZKdvyXJW7DZ4RzsXrB1d0zl6kpqi2vJHZPL55sMvdNhSM6WcqXziimZW8KEmcdRuqyC3mPzWFUdYt2aOjYVZdA/o5rnJsZJTYXdR6fx6QdVxNJh49wSlm6MUrhpE3lZEbKOLSSU1rTXcwu3rgRWF+P2GICbv4HQ0DxMn+yty8XjMGcF9MnBDO7dtov2xXLITIMR/ZvfX1wBi9fCmCG+nFL1emBPpiaZTVhrlwJ/B44FDgKW4nse3w16Ii8HpgH5wMfAT0Xk8+DY6UAYqAbOACqAa0Tkbmvt14CZwWnmW2sdUNDM+f8IfBvoB6wD7hCRW4N9TwCrRORnCeWnAb8DRgODgXuA/YFUfDL7MxGZFZS9Cjgc+ACoH57/m4j8PqG+I4EbgTHAGuAWEbk72FcILAGGishKa60Bfg1ciE+eH6CVvybBUPTsIO6jgGXAL0Tk+YRiJrgeLcU6HrgZ2A+oAh4BrhSRuoRYpwK/AYYC7wHniMia4PgC4Bbg+KDKF4FLRGRzM/GmAXcApwPp+NfmchF5ojXtVaq7eeX1Mv7xwGacg0gsRkFVNQaYX5DDi6MHst+/N/LEdzMYuW8v/vhujN++4YfPf39YiKsO3/LRsnzGCt678D1c1LFy/yFcdsSBpIQNj0wKccYeIRb8dR5f/vZjYiHDpv6ZxEIhTO90rhs/nn2q6kh3u5EerePt6mUYIL+mij1mLcQZ+PmJB/DOmGH0rkjh2bvvoiA1j5Gfn0s4e+sezvjr84hPug0qa4nnZFJXFoLcDNJnXkD4gGEN5Vw8DpNvh//OgtQI7l8XYU7bv3UX7aJ/wJ3PQygEd/4Azj+h8f6vVsPhv4X1JbDHYHj3j9A7p60vjeqxel6WqcPlzTsX+AnQC3gZnzwBXIZPWk7C90q+Bbxsrc1NOPb/gBlAb+Bi4C/W2uEi8h6wZ1Bmj2DYubkZRV8ChwE5wA+A66y19f9T3Q+cZa1N/B/0e8B0EXH41/OvwPAgvo+BJ5uUPwJYDgwCTgUut9YeCmCtHQG8APwNnwBPC85/RgvX6WzgEuC04Hwbg/pb6zzgNvz81D8CTwXJYWti7Qe8ATyJT66/BhyHTygTnRnUMxjIAq5J2PcI/o+FscFPH+ChFmI9BzgAGCsiucAxwBdtaKtS3cozz5dSPw0zGg5TPyNzj01lZNfU8fHAPrzy9AYArn9vy/zMG96PN6pn/t3zcFF/9JBZK+lbWkldHP4svtzC274EoCozQizkP5Lc5mqOWr6W9CCAzNpow8dvUVoGNWmphBzss2QdAJuzsph+4EH0WraEopdWNdsed8crUFkLQKisEkMMSquJ3vVO44Lz1/gEE6A2Cre80LoLVl7lE0yAeBxu+u/WZe5/1SeYAPNXwX8/bF3dSnVTmmQ2724R+UJEYviewdHW2l74hO4GEZkXJIjXADFgUsKxr4rIMyISF5EngWJgQmtPLCIPi8hqEXEi8irwLL5XFXxPWxQ4GcBaOwo4FJgeHLs8OHeliFQBVwDDgN0STvGViNwlIlEReR/fm2iDfVOAj0VkesL+u9nSk9jU1OBazRKRWuA6YG1r2wr8V0ReDs71CCDAWa2MdSrwqYjcLSK1IrIqOP/UJue4WkQ2ikgp8M/64621g4ATgJ+LSJGIFAE/B06y1g5sJtZaIBsYZ62NiMgKEfmyDW1tl7KyMt3W7aRs5yVMuTTONSR5taEQNZEwGXVR8oMyw3K39MAMy21cT2q/1Ibt6tQIFWn+b95BGX6hT1r/DADC0cYLizZlbBlKjiXcpigUixGJ+mM35mQ0PD+0uJhaUkkbmtV8u4ZtGThygAs+/syw/Mbl++biMrb8XV43MLdxPS1tZ6RC34T+hmF9m4mhT6M2MqzPTvFa63b7tjtcz1tcrsPlLViTsF0R/JuDH3JdUr9DROLB8PrQFo6tP77V4yHW2p/gezCH4N9mGfjkCBGJWWsfxCe7T+F7Gl8RkRXBsX2AP+OHn/OA+i6Fvq2Mr1H7AovwPZXNGYKfTkAQX9xau6yFss1Z2szjxJvvbSvWEcCh1trihP0GP10hUdPXMrGt0Li9ixL2NT33w0B//PD6btbaV4BfishCOlFOTo5u63ZStn/64/48+GgR6zZGKSuNYuJhSAnxQf98hlZV8qNeFZx6vl+c89TkCL96LUrcwfVHR8jJ2ZJYHvyng/kkdzbV66upOWMPDo+l0T8T/ny0TyIPeeIo3jnjdSKrK8kZk0M8L41e+/Tmzd5DqJxbQZ/KzWRHaomUZpGVBgeNMiwoKiBeHaVXfoQDV6/hwCVLmDrvK1Ju/SY5B/Rttl3uD98gXhuFBetwY4YQmruJ0PiBpPx6YqPypk8OPH0J/Ol5GJxPys1nNapnm9vP/w6ufBRyMuBP52xd5kfHw+rN8N5XcPqBMHGfRh8OO8Prrtut3+54PSSzTKBJZtusAArrH1hrQ8HjFR1ReTAUfAO+5/KDIKn8N43fedOBz4LetqnArxL2XQcMBA4SkTXW2hyglNa/c1fgpwIkGknL7VtF4+th8EP1rVXYzOPnWnnsMvzthSZtt2Tz6ttUCNQniiOb7GsgIlH8a3ODtTYP+AtwH22bHqBUt9E7P8LPLujLijV1vPZhFYP6FTDxa5kJJfIatnbrbXhycvMrvdN6p3HwrQc1PG4yS5H0/hkc++aJWx3nf7GymDHjA4g5jl41kPimSrLO2I+v/2Rkk9JD2d6voslKI3zn2YD/S3Rb69LNcXvBcXtts75m7T8Knr2i5f2hEFx7Vsv7lephNMlsm+nAL621b+J73X6Fv4bPdlD9ufjh9w2As9ZOAk4EGhaXiMg8a60A9+J75Z5qcnwlUGStzcYnRW3xKPA7a+1UfO/pfsCPgB+3UP4h4EZr7VPAHOAX+LmZrXW6tfZY4HXgW/ih7O+28tgHgUuttecGsdbiE8bdRWS7k6hEZLW19iXg5uDepQa/iOj5+oVBiay1xwAl+MVUVfhe0VjTckr1JMWlMX5+wwbKKvxQdklZjMnHd2ZPTvOG/mMlJc/NBqDyoTn0//LHmJSmgxZKdXM9ryNT52S20U34ROwl/OriY4Djg/l+HeFFfPL0IX4Rzf/ROImsdz8++fxnk8VDV+JXpW/CJ0Pv0oZESESW4HsyLwrqeAj4nYj8q4VDHsSvuJ6Bvx79gDdbez58ovxzfPJ2JTA5iKE1sa4Fjsav9l4KFOGvVdMujm05GygD5gPz8PNnm87prNcffz2K8EPpw4EftuFcSnU7y9ZEGxJMgC8W1iYljqwvyxu2Yws3E1tbvo3SSnVTPXBOpnFNvsVBqa6g36bTavoLqpKmrCLOj69ez8Yi/7fqz6bm8fXDs7o0hhkzZjDowdUM+LdfSZ6y30D6fngeJqx9JCppOiUFNL8pb/T/vbsuu9unmjpcrpRSqlk5WSFuu7wv782uYlC/CPuNS09KHKunDmLclCOIb6wk49vjNcFUqpvQJFN1Cmvt5fgb1zdn61n+SqmdUkFemJOP2vpbcbpaxjfHJjsEpTpXt++33JommapTiMgf8TdYb8lRXRSKUkoptfMzPS/L1DEHpZRSSinV4TTJVEoppZRSHU6Hy5VSSimlkq3njZZrkqmUUkoplXw9L8vU4XKllFJKKdXhtCdTKaWUUirZel5HpiaZSimllFJJ1wOTTB0uV0oppZRSHU57MpVSSimlkq7ndWVqkqmUUkoplWw9L8fUJFMppVTnim+qpPycJ4jN20D6jw7ETd6XL6e9TcWScooK0omOzsH1z6V0fQ3jTx7IQecUNlvPklfW8MGtc0lJD5MZclStq2KP74xi+NJlVD82h5T9BpHTr5zQC7PgkDFwzwWQltK1jVVKNdAkUymlVKeq/P1M6p6d77d/+QLLni6l+J1NAGStrGRBLET16jgA79y9mKH75zNofK9GddRVRZl52SxiNXFSaqOUR315+eNnpK2aT25dNTWLiwiznmw2wOJ1MKEQLj2t6xqqlGpEk0zVLtbaQmAJMFREViY5nDaz1j4PvCYiNyY7FqV6Olda0+hxtLSuYdsAuMbl6ypjW9URr3PEauJBhY33RUPhLedKXM9aVtWecJVKjh44XK6ry9V2WWuPstZGkx1HRxKREzXBVKprZPzmSEKDcwFI/dZejL79YCK9/DB2ce9UUlIdGb18n8foI/sydP/8repIy03hgIvHAOCyU0jNTwWg8OQh9D9yIADhkflkHlDgDxg3FC74eqe2S6kOZUzjnx5AezK7OWttiojUbb+kUkq1zdyNjtw0GJzT+AOvrCLOxqIYQwdEiEQM65dVkWbiZBRXEBmdTyhzyzxIF49DzJH70QWsLAGq4vTtm8KYpWeyvijG3tWVhFYWEc1Kx/TPJV5eS/mbK8jIMUT2HNBQTzzmGHbcQPL270t633Rqy6JkL1hHKBwifsIA0i88lKy9ehFfXUJVPI3MgwaxORpizQbHuAIIh5r/0HbVdcS/2khoeB6mV0ajfbUxx7zNMCwH8tINzF0JuRkwuKDjLrJSPZgmmTsha+1S4D7geGACMA/4sYh8ZK2dDqQAdcCpwOPAj621PwZ+BgwA5gKXichbQX37ALcDewJh4H3gIhFZFOyfHjxfDZwBVADXiMjd1tpBwPNA2FpbHoR4IfBGsH20tfY3wFDgPeAcEVkT1FsA3BK0A+BF4BIR2RzszwauAr4J9AVWAD8CsoEHgcEiUhuUzQHWACeKyFvW2j8C3wb6AeuAO0Tk1qBsIX4ofyrQUmyvAzNF5A+tLP8T4BKgD1AKPCAil7f4IirVzZ37bJT75zgiIXjw5DBTxvmBrwXLavnNnzdSXukYMzKFr5kylr64mkkfzCK7qprIyDwGvHM2kQHZuHic0tMepu5/8/nbMRN5c9QehOJxRq5Yy/3H7U0NIfZZWs6PX/yEccUr+Xi38Rz30SekR9dSRRwzqoCUq/agNieVp34+m2UfFREHoikRTnvvQ0xdHTWEiRAnjKOYWlbQn1rSYP++TP72cRQR5phhhhcmh0gJN040XUkVZYf/jfictZh+2WS/eT7hPfoBUFHrOPLxGLPWQUEGvPb5v9jrrn9DJAwP/gSmHN7VL4lS3Y4Ol++8zgd+CvQG/g08Z63NDfadgU/8+gKXWmunANfik6QC4B/AC9ba4UF5h0/mBgOFQDnwcJPz/R8wIzjfxcBfrLXDRWQ1cCIQE5Hs4OeBhOPOBI4I6s4CrknY9wiQD4wNfvoADyXsvxc4CDgWyMUnzWvwyWgFkDhjfwqwoj5xBr4EDgNygB8A11lrT2jSpm3F1pxmy1trdweuB04WkRx8sv7MdupSqttaU+64f46f+BiNww3vb5kj+b/XKyiv9PvmLa7jnQ8qGLV6LdlV1b784mIqHv0SgNina6n733w2Z2bx5qg9AIiHQswdPpCa4OPn08IBrCroxVd5gylcvZ68aClh/NxLt2gTfd/ZQPXqEMs+KgL8h1Z+WTkZdX4AJ40Y4WCSZpxU0ghinbWB0fPWAvDqcsf7a7ZuZ92MucTn+DJufTm1//iwYd8LSx2z1vntTVVw17pgIVI0Bjf+t+0XVantMU1+egBNMnde94rIrKAn7wagCjg52Pe2iDwuIjERqQS+B9wtIh+ISFRE7gU+A84CEJHPROQ1EakRkRLgauBga21mwvleFZFnRCQuIk8Cxfhe1O25WkQ2ikgp8E/AAgQ9oCcAPxeRIhEpAn4OnGStHWit7Qd8CzhfRJaIiBORhcFPHLgHOC/hPOcFzxG06WERWR0c9yrwLD5Z3W5sbW0LEMX/yu9prc0WkWIReb8V12aHlZWV6bZud/l2birkpm5ZXTMgY0uSmZu5ZXq2MZAVdlSkp5OorsAPl5u+WZAaJrO2loza2ob9kdiW+iLRGDnVNaTGo1Sm+5QxUU1BGpEsh0l42m1jvlo0+FhzIdiU6/+LCxvHwKyt21vdu/HtjcyQXg1lBmc3PseQ6tKEBwVJf410O/nbavt0uHzntbR+Q0SctXY5MKTpvsBQ4F9NnlsUPI+1dhRwE77XMIctazP7AsuC7aZ/51cEZbcn8bjEY4YG/y5pElPiPoCvWqj3XuB31tphQC98wjupfmcwfP0D/DUxQAY+MWxNbC1ptryILLbWfgf4MXCPtfYz/HSCl7ZT3w7LycnRbd3u8u2sVMOM/4tw7btxCjLg1mPTGsp89/Q+1ERLWLqqjolfy2R0Vg5vPZbGqn4wvKqE7JNGkjd1XwDCQ3qR88QUUm57l8tDC/jXgD2JLy/lyEgJAwv7s6wcjn77S8bUFTMopZRZhx7Igqw4o5cvole+I/37B7BpbAUpOCZdPZ73H1xGcZljY9+BfB6uZtTcFVTXRUgNxUjNhoLBtdRuqKUivzf5l+zHxCG9WVAE5+8TYnS+2aq9uSftRc0dp1H3nzmEDxhK2kWHNJQ5OAfuPi7Eo/McE/rBpXZvqJsPBTlw67lJf410O/nbHa+HdF8m0CRz51VYv2GtNcAwYCUwDoKxpC1WJJYPjMQPfwPcBawG9haRTdba8cAcWv+Obnq+1lgR/FsILEyIqX5ffVfGbvih70ZEZI219ll8L20+8F8R2QhgrT0U37t7LPCBiMSstf+mE39Dg97dJ621qfipDE9bawuCnmSlepwjhoV4edjWg12pKYYLpuQ1em7khFxgTLP1pJ06lrRTx3IwcDAAgwD4Rn2BafsCPindH4D9Glcww/83tvsx/dj9mH4JO8Y2e77eCdt3N1uiSXwXHUraRYc2u++H+4T44T71j/aEo/ZsRY1KtVPPyzE1ydyJnWutfQqfDF4CZOKHhI9vpux04DZr7TPAx8B38T1/U4L9ucACoNha24ftz01sai1+4c8IEVmy3dKAiKy21r4E3GytPQf/63Mz8HzCYpp/A3+11k7D96iOCo6tT0r/jv+cyMIv8qmXi09SNwDOWjsJP2/0iTa2q1WstXsAI4A38dMWSvC9we1JvpVSSqldgs7J3Hn9Hb8ivAi/IGVSMJ9yKyLyT/w8y4eBTfhh3ZNEpH4o/BLgcPyq6LeA/7UlEBH5Cvgb8KG1ttha+91WHno2UAbMx6+QL8YvTqp3LjAbv1K9DHgavzq+3kv4RK4EeCXh+Rfxq88/BDbiFy091ZY2tVEqcCV+OL0Y+AkwWUSqO/GcSimlVLdmnHPbL6W6VHALoytEpOkK8F1OcKuhl0Tkj8mOJUn0F1Tt0mYEw+WnnHJKkiNRqkGnDGybP9Q0+v/eXZHW7QfQdbhc7bSstUcAB+Bv2aSUUkqpbkSTTLVTstZ+BIwGLhaRDcmORymllFJto0nmTkhECpMdQ7KJyAHJjkEppZTqMj3k+8oTaZKplFJKKZVsPS/H1NXlSimllFKq42mSqZRSSimlOpwOlyullFJKJVsPHC7XJFMppZRSKul6Xpapw+VKKaWUUqrDaU+mUkoppVSy9byOTO3JVEoppZRSHU+TTKWUUkop1eF0uFwppZRSKtl0uFwppZRSSqnt0yRTKaWUUkp1OB0uV0oppZRKNh0uV0oppZRSyWCMWWqMGZ/sOFpLezKVUkoppZLN9LyuTO3JVEoppZTqpowxU40xc4wxnxljnjLG9Auef88Yc0Cw/VdjzBfBdsQYs9EYk9XZsWmSqZRSSimVbKbJT2sO8UPn1wPHO+f2Bj4H7gh2vwIcG2wfBlQZYwYCBwBznXMVHRZ7C3S4XKmdmDHmRaBPZ58nEon0iUajGzv7PMmi7evetH3dWw9s3wvOua93dKXuF5H2jJcfDTznnFsTPL4b+DTYfgX4rTHmEWAT8AY+6RwBvLqD4baKJplK7cQ64z+y5lhrRURsV5wrGbR93Zu2r3vr6e3bib0L7AdMwiecbwDn4pPMK7siAB0uV0oppZTqnl4DTjLGDAge/wB4GcA5VwN8DPwamAm8DxwK7B1sdzrtyVRKKaWU6j5mGmOiCY9/A7xsjHHAYuBHCftewc/B/Mg5FzPGLASWOOdquyJQTTKVUgB/T3YAnUzb171p+7q3nt6+LuOcK2xh1wMtlL8OuC7h8UmdEFaLjHOuK8+nlFJKKaV2ATonUymllFJKdTgdLldqF2StzQTuB/YHosAvROR/zZSbANyH/4M0BXgHuFhEarou2rZrQ/tOw6+yTMPfme4+Ebm5K2Ntjza0bzDwMH6F6YKdeYWvtXZ3/JBfAf52K1NFZEGTMmHgduDrgAOuF5F7ujrW9mhl+44H/gjsBdwhIr/o8kDbqZXt+x3wbSAG1AGXi8iLXR2r6jrak6nUrukXQKmIjAZOAe6x1mY3U24+cLCITMB/8BXQeFL5zqq17VsLnCIi44FDgB9baw/vwjjbq7XtK8cn0Wd1ZXDtdBdwp4jsDtyJv99fU98BRgO7AV8DrrLWFnZZhDumNe1bDHwfuKkrA+sgrWnfh8ABIrI3/lY6j1trM7owRtXFNMlUatd0JsGHQNDbIMCJTQuJSJWI1K9CTAEygHhXBbkDWtu+D0RkdbBdAswFhndhnO3V2vaViMhbQKd/s8eOsNb2w/e2Pho89Siwn7W2b5OiZwL/EJG4iGwA/guc0WWBtlNr2yciC0VkNr53uttoQ/teFJHK4OFn+NGDgi4LVHU5TTKV2jUNA5YlPF4ODG2uoLV2kLV2NrARKKN7rBRtdfvqWWvHAAfTRd+EsYPa3L6d3FBglYjEAIJ/V7N1m7pru1vbvu6qPe2bCiwSkZVdEJ9KEp2TqVQPZK39GP+B3Jz+bakr6OmbYK3Nws/v+ybw2I5FuGM6sn1BfQOBp4EL6ns2k6mj26fUzsRaeyRwLXBcsmNRnUuTTKV6IBHZb1v7rbXL8cPCG4KnhuG/OWJbdVZYax/Hz4tLapLZke0LhvpmAjeKyBMdGWd7dcbrt5NbAQy21oZFJBYs8BkUPJ+ovt0fBY+b9mzurFrbvu6q1e2z1n4N/8fqaSIyv4vjVF1Mh8uV2jU9QbCAx1q7G/4bIV5oWshaO9JamxZspwKnAXO6MM72am37CvBfwfYXEbm3SyPcMa1qX3chIuuB2cCU4KkpwCfBvMtETwA/sNaGgvl+pwP/7qo426sN7euWWts+a+0BwOPA/4nIx10apEoKvRm7UrugYOh7OrAv/nYivxSRp4N91wCrReQua+3ZwK/wi33CwBv42+VUJSXwVmpD+24CLsKvoq93m4jc38Uht0kb2hfG9/SlAb2A9cA9InJVMuLelmBO7ANAPlCEvwXOfGvtc8CVIiJBe/4CHB8cdoOIdIc5wq1t32H4UYJc/KKYEuC87nCbn1a27yOgEFiVcOh3RaQ7/OGq2kGTTKWUUkop1eF0uFwppZRSSnU4TTKVUkoppVSH0yRTKaWUUkp1OE0ylVJKKaVUh9MkUymllFJKdThNMpVSqhMYYwqNMc4YM6STz3O+MeahhMfPG2N+2ZnnVM0zxiw0xkxrZdkueX90BWNMWtD2McmORe1cNMlUSiWVMWakMeYJY8xaY0y5MWaFMeYpY0xqsH+aMWZhM8e19Px3gg/v3zez73VjTE1wnhJjzCfGmMmd07LOZ4zJAq4Brqp/zjl3onPuxqQFtR3Ba3NYsuPYFXTGtTbGHGWMiSY+55yrAW4KfpRqoEmmUirZngPWAHsAOcDXgBfxN6Nujx8Bm4HzjDHhZvZf65zLBgqAR4HHjTG7t/NcyXY2MMc5tyjZgahd3qPAMcaY0ckORO08NMlUSiWNMaYAn1ze5Zwrcd5K59xdQe9IW+sbCxwOnAMMBE5sqaxzLgr8Ff9NRns1U9eFxpjZTZ4bYYyJGWMKg8f3Bz2vZcaYL40xZ20jtquMMTObPPe6MeaKhMfjjTEvGmM2GGOWG2OuM8akbKPJp+O/FrPZOhOGZM8J4qswxjxnjMk3xlxvjFkf9CBfmHD8tGDo81fGmDVBmZsT49heu40xextjXgjasbm+3caYT4MiLwW9yfe0cK0yjTG3BefYaIz5rzFmWJM23myM+U8QwyJjzGktXaSENl1ijFkZHPMnY0xBUEepMWZeYq+fMSZijLnSGLPYGFNkjHnFGDM+YX+KMebPCdfwV82c93BjzNvBNVhkjLnUGNPqP56MMZONMZ8Gve6fGmO+0bRNTcpPr7+mLV1rY8zSoF1vB8+LMeaA5upIeG6pMeZsY8wg4HkgHBxbbow5B8A5V4r/TvlTW9s+1fNpkqmUShrn3CbgC+AeY8xUY8y4tnwIN+OHwGfOuf/he0h/1FJB44fjLwTqgE+bKfJPYIwxZkLCc9OA151zS4PHbwMTgDz8sPV0Y8y49gRujOmH/9rOJ4HB+B7d44DfbOOw/YAvW1H9ZOAwYBj+a/0+ABYBg4DvAbcmJnHA8KDsyCCOU4DLEva32G5jzMCgHW8E5xoAXA/gnNsnOP5451y2c+77LcR7C3Bw8DMc2AjMMI17ps8BbsZ/XeZfgAeMMZnbuAbDg3hHBtfiYnzCdBP+qxCfBBK/TvQyYCpwUtCGt4CXjTG5wf5fAycDhwAjgrYOrz84uB7PBfX3BSbhv8L0u9uIsYEx5hDgkeA8BcDlwKPGmINac/x2rvX5wE+B3vjvfn8uoV3bqnM1/g+3WFBntnPugYQic/DvSaUATTKVUsl3FPA68DNgNrDOGPO7JsnmCGNMceIPvheygTEmHZ8U1CcK9wInmq0XVvw2OH4lcBow2Tm31dxO51wR8DQ+CSOI5xzgvoQy9zrnNjnnYs65x4DPgva0x1TgU+fc3c65WufcKuC64PmW5AOlraj7Wufc5iCp/x9Q55z7h3Mu6px7Hv9d0/smlI8DlznnqoKh+BvxCTaw3XZ/F1jonLvOOVcRtKVRD+62GGNC+Ot8hXNulXOuAv/eGAscmFD0cefcu865OPB3fLK52zaqrgKuDuL5FP+HxUfOufedczHgYWC0MaZXUP57wA3OuXlBr/o1+O+JnxTsnxrsX+icqwJ+ASR+T/MFwBPOuaeD6zQPnwxv6/VMNA34j3Pu+eB1ehZ4Cji3lcdvy73OuVnOuVrgBvy1ObkD6i3FJ65KAZpkKqWSzDm30Tl3uXNuP3xP0y+BKwmSu8AS51xe4g/+QzzRGUA2PlkA34u0AWjaW/b/gjr6OecOcc7N2EZ49wNnBUPFxwTxPQk+GTLGXGOMmR8MZxYD++B7rdpjBHBok0T6PnwvWkuKgO32QOHnvNarbPK4/rmchMfrnXOVCY+XAkOgVe0uBL5qRUwt6QukAUvqn3DOlQPrgaEJ5dYk7K8INhPb0NT6ICGt1/Q61Le3vo6hTWKI469DfQxDgseJMaxPqG8EMKXJ6/l7/DSO1mh0/sAiGl+D9lpav+Gcc8Bygtd3B+Xi50MrBWiSqZTaiTjnKp1z0/E9YxPaePgP8fMrPzfGrMX3VObT8gKg1ngZqMEPF08DHgt6rQCm4BPYyUB+kPh+SssLlsqArCbPDUrYXgbMbJJM9woWKbXkE6Bdw/Pb0a/J0HMh/nrC9tu9lG33KLpt7AP/h0FNcE4AjDHZQD9gRWuC7yArmsQQCh7Xx7Cqyf4sGv+BsQy4r8nrmeuc27M95w+MTDj/9t5P0PK1Tozb4KdG1L++jeo1xkTw175eYqLe1Hj8e1IpQJNMpVQSGb8A5TrjF7ykBIstJuM/rN5qQz3j8PPsvoFPTut/DsT3BJ7UnviCYdQHgZ8A3yRhqBzfaxPFJ0UhY8y5+B69lswC9jPG7B+08yJ8b1e9BwFrjDnXGJMe9BiONMZ8fRt1/heY2OaGbV8IuMEYk2GMGYkfCq6fe7e9dj8M7GH8wqFMY0yqMSYxxrVsIwkNegwfBK41xgwKkt2bgXnAhx3UvtaYDvzSGLN7MH/3t0AEeDbY/xBwmTFmlDEmAz+lIPEz9a/At40xpyS8t8cZY45s5fkfACYbY04wxoSNMSfi34P100Fm4/8YODl4r3wDOKJJHS1d63ONMfsFPfSXAZkJ7ZoFHGv8Irc04P8BiYvP1uIX/iS+dzHG5OB/355pZfvULkCTTKVUMtXie0mexA+zbQCuAH7inHuiDfX8CPjYOTfDObc24ecz4Am2sQCoFe4HjsQP2ScmOQ/gF9AsxPdqjWMbibFz7nXgz8AL+GHa/sA7CfvXAkfjV4wvxQ+FP4XvvWrJQ8A+QSLYkZbhe7aW4Nv4Aj6Jgu20O1gcchR+0dJKfFKSuGjot8A1xq/YvruF818CCH618nL8EPOpQdLfVW7C35bnJWAdfrrE8cEqavDzZV8E3sdfp+X46waAc+5z/DzHn+Ff7/X4xLVV0ymcc+/g56b+Cf9euBE42zn3frB/EX7xzt/xvztfB/7TpJqWrvXfgduDes8EJjnnSoJ9j+ATxY/xw/PL8a9zfVxfAX8DPgymAdQvZJoCvOacW9Ca9qldg/HTMZRSSnVHxpjzgUOdc61atdyK+qbhF93o/Q57IGPMUvzr+/D2yrahzjTgc/wfAnM7ql7V/UWSHYBSSqn2c87dBdyV7DjUritYfb+tebhqF6XD5UoppZRSqsPpcLlSSimllOpw2pOplFJKKaU6nCaZSimllFKqw2mSqZRSSimlOpwmmUoppZRSqsNpkqmUUkoppTqcJplKKaWUUqrD/X+mFmkhRZZSvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(svm_explanation.shap_values[1], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, indeed, a higher value of the `nonflavonoid_phenols` feature contributes to a sample being classified as class `1`, but that this effect is rather limited compared to features such as `proline` or `alcohol`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise the impact of the feature across all classes, that is, the importance of a particular feature for the model, we simply pass all the shap values to the `summary_plot` functions. We see, that, for example, the `color_intensity` feature is much more important for deciding whether an instance should be classified as `class_2` then in `class_0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(svm_explanation.shap_values, X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dependence plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to visualise the model dependence on a particular feature is through a dependence plot. This plot shows the impact of the feature value on its importance for classification with respect to class `0`."
   ]
  },
  {
   "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 = 'flavanoids'\n",
    "shap.dependence_plot(\n",
    "    feature, \n",
    "    svm_explanation.shap_values[0], \n",
    "    X_test_norm, \n",
    "    feature_names=feature_names,\n",
    "    interaction_index='auto',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colour of the individual instances is represented by the value of the feature `nonflavanoid_phenols`. By specifying `interaction_index=auto`, the `nonflavanoid_phenols` was estimated as a the feature with the strongest interaction with the `flavanoids_feature`; this interaction is approximate, and is estimate by computing the Pearson Correlation Coefficient between the shap values of the reference feature (`flavanoids` in this case) and the value of each feature in turn on bins along the feature value. \n",
    "\n",
    "We see that, for class `0` wines, a higher value for `nonfavanoid_phenols` is generally associated with a low value in `flavanoids` and that they have a negative impact on the score for class `0` classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Footnotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Footnotes'></a>\n",
    "\n",
    "[(a)](#f_1) The weights are applied to each point in a copy, so the number of weights should be the same as the number of samples in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#source_1) Lundberg, S.M. and Lee, S.I., 2017. A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774).\n",
    "\n",
    "[[2]](#source_2) Wikipedia entry on the Least-angle regression: <https://en.wikipedia.org/wiki/Least-angle_regression>."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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