Kaggle/Titanic Machine Learning from Disaster/Titanic machine learning insights from disaster.ipynb

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    "<h1><center>Titanic: machine learning insights from disaster</center></h1>\n\n### This notebook is based on the awesome course series brought by [Dan](https://www.kaggle.com/dansbecker), some of the code and explanations were taken from there, if you like what you see here I highly recommend to checkout these links:\n* [Permutation Importance](https://www.kaggle.com/dansbecker/permutation-importance)\n* [Partial Dependence Plots](https://www.kaggle.com/dansbecker/partial-plots)\n* [SHAP Values](https://www.kaggle.com/dansbecker/shap-values)\n* [Advanced Uses of SHAP Values](https://www.kaggle.com/dansbecker/advanced-uses-of-shap-values?utm_medium=email&utm_source=mailchimp&utm_campaign=ml4insights)\n\n### Here I'll try to put in practice some of what I learned from the course in another data set, and hopefully get more people attention to these very useful techniques of how to get more from your machine learning models.\n\n### Machine learning insights are meant to give you more information about how and what your model is doing, this way you have a better interpretability of what's is going on, explain the model and it's predictions to other people, validate and improve it.\n\n#### What we'll do:\n* Load data and do some basic data engineering.\n* Use [XGBoost](https://xgboost.readthedocs.io/en/latest/index.html) to model our data.\n* Take insights from our model.\n    * Permutation importance.\n    * Partial plots.\n    * SHAP values.\n    * Advanced use of SHAP values."
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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!y(s,l)||!y(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(m(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(12),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;P.postMountWrapper(t)}function s(){var t=this;N.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(){A.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),y=n(332),m=n(334),_=n(20),b=n(81),x=n(21),w=n(156),C=n(22),M=n(82),k=n(51),E=n(157),T=n(4),S=n(351),N=n(352),A=n(158),P=n(355),O=(n(10),n(364)),I=n(369),D=(n(9),n(54)),R=(n(0),n(93),n(79),n(95),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[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\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o);break;case\"select\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":P.mountWrapper(this,o,e),o=P.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 m=_(h);this._createInitialChildren(t,o,r,m),p=m}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(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.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=m.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=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"select\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"textarea\":o=P.getHostProps(this,o),a=P.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\":P.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&&m.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(95),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(80),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(84),s=n(4),l=n(12),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(80),a=n(20),u=n(4),c=n(54),s=(n(0),n(95),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),\nthis._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(84),c=n(4),s=n(12),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===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-- >0;)n(c[s],\"captured\",o)}var c=n(2);n(0);t.exports={isAncestor:i,getLowestCommonAncestor:r,getParentInstance:o,traverseTwoPhase:a,traverseEnterLeave:u}},function(t,e,n){\"use strict\";function r(){this.reinitializeTransaction()}var i=n(3),o=n(12),a=n(53),u=n(9),c={initialize:u,close:function(){p.isBatchingUpdates=!1}},s={initialize:u,close:o.flushBatchedUpdates.bind(o)},l=[s,c];i(r.prototype,a,{getTransactionWrappers:function(){return l}});var f=new r,p={isBatchingUpdates:!1,batchedUpdates:function(t,e,n,r,i,o){var a=p.isBatchingUpdates;return p.isBatchingUpdates=!0,a?t(e,n,r,i,o):f.perform(t,null,e,n,r,i,o)}};t.exports=p},function(t,e,n){\"use strict\";function r(){C||(C=!0,m.EventEmitter.injectReactEventListener(y),m.EventPluginHub.injectEventPluginOrder(u),m.EventPluginUtils.injectComponentTree(p),m.EventPluginUtils.injectTreeTraversal(d),m.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),m.HostComponent.injectGenericComponentClass(f),m.HostComponent.injectTextComponentClass(v),m.DOMProperty.injectDOMPropertyConfig(i),m.DOMProperty.injectDOMPropertyConfig(s),m.DOMProperty.injectDOMPropertyConfig(b),m.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),m.Updates.injectReconcileTransaction(_),m.Updates.injectBatchingStrategy(g),m.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),y=n(361),m=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(85),u=n(159),c=n(51),s=n(161),l=n(12),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(85),p=(n(40),n(10),n(15),n(24)),h=n(342),d=(n(9),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],y=a[u];g===y?(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(y,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(y)}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(10),n(53)),l=n(87),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": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score\n",
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "from pdpbox import pdp\n",
    "import shap\n",
    "\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "797a3cae8dc9affe06071c9b11f833888d5f8617"
   },
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "trusted": true,
    "_uuid": "528779f56fc1832206eee24d20001d415d13eff0",
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('../input/train.csv')\n",
    "test = pd.read_csv('../input/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4ac09f65a295b590a81290b425a654145fedbca1"
   },
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "trusted": true,
    "_uuid": "88a1c85e6a6e608383e81f6f12e33f3e9d79a057",
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "def pre_process_data(df):\n",
    "    # Add \"title\" feature\n",
    "    df['title'] = df.apply(lambda row: re.split('[,.]+', row['Name'])[1], axis=1)\n",
    "    \n",
    "    # Add \"family\" feature\n",
    "    df['family'] = df['SibSp'] + df['Parch'] + 1\n",
    "    \n",
    "    # One-hot encode categorical values.\n",
    "    df = pd.get_dummies(df, columns=['Sex', 'Embarked', 'title'])\n",
    "    \n",
    "    # Drop columns unwanted columns\n",
    "    # I'm dropping \"Cabin\" because it has too much missing data.\n",
    "    df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "# Get labels\n",
    "# labels = train['Survived'].values\n",
    "labels = train['Survived']\n",
    "train.drop(['Survived'], axis=1, inplace=True)\n",
    "# Get test ids\n",
    "test_ids = test['PassengerId'].values\n",
    "\n",
    "train = pre_process_data(train)\n",
    "test = pre_process_data(test)\n",
    "\n",
    "# align both data sets (by outer join), to make they have the same amount of features,\n",
    "# this is required because of the mismatched categorical values in train and test sets.\n",
    "train, test = train.align(test, join='outer', axis=1)\n",
    "\n",
    "# replace the nan values added by align for 0\n",
    "train.replace(to_replace=np.nan, value=0, inplace=True)\n",
    "test.replace(to_replace=np.nan, value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8dc26c2da2147a8eb1034fb5ddc591d87b68816d"
   },
   "source": [
    "### In case you wanna take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_kg_hide-output": true,
    "_kg_hide-input": true,
    "trusted": true,
    "_uuid": "ac7b662cd277a8a33b0e4cdb08dde76550f707dc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Embarked_C</th>\n      <th>Embarked_Q</th>\n      <th>Embarked_S</th>\n      <th>Fare</th>\n      <th>Parch</th>\n      <th>Pclass</th>\n      <th>Sex_female</th>\n      <th>Sex_male</th>\n      <th>SibSp</th>\n      <th>family</th>\n      <th>title_ Capt</th>\n      <th>title_ Col</th>\n      <th>title_ Don</th>\n      <th>title_ Dona</th>\n      <th>title_ Dr</th>\n      <th>title_ Jonkheer</th>\n      <th>title_ Lady</th>\n      <th>title_ Major</th>\n      <th>title_ Master</th>\n      <th>title_ Miss</th>\n      <th>title_ Mlle</th>\n      <th>title_ Mme</th>\n      <th>title_ Mr</th>\n      <th>title_ Mrs</th>\n      <th>title_ Ms</th>\n      <th>title_ Rev</th>\n      <th>title_ Sir</th>\n      <th>title_ the Countess</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>22.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7.2500</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7.9250</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>53.1000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>8.0500</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      ],
      "text/plain": [
       "    Age  Embarked_C         ...           title_ Sir  title_ the Countess\n0  22.0           0         ...                    0                    0\n1  38.0           1         ...                    0                    0\n2  26.0           0         ...                    0                    0\n3  35.0           0         ...                    0                    0\n4  35.0           0         ...                    0                    0\n\n[5 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "trusted": true,
    "_uuid": "689ca911ff044fa3561aa78fd202831bbbb91ee3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>Embarked_C</th>\n      <th>Embarked_Q</th>\n      <th>Embarked_S</th>\n      <th>Fare</th>\n      <th>Parch</th>\n      <th>Pclass</th>\n      <th>Sex_female</th>\n      <th>Sex_male</th>\n      <th>SibSp</th>\n      <th>family</th>\n      <th>title_ Capt</th>\n      <th>title_ Col</th>\n      <th>title_ Don</th>\n      <th>title_ Dona</th>\n      <th>title_ Dr</th>\n      <th>title_ Jonkheer</th>\n      <th>title_ Lady</th>\n      <th>title_ Major</th>\n      <th>title_ Master</th>\n      <th>title_ Miss</th>\n      <th>title_ Mlle</th>\n      <th>title_ Mme</th>\n      <th>title_ Mr</th>\n      <th>title_ Mrs</th>\n      <th>title_ Ms</th>\n      <th>title_ Rev</th>\n      <th>title_ Sir</th>\n      <th>title_ the Countess</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.0</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>23.799293</td>\n      <td>0.188552</td>\n      <td>0.086420</td>\n      <td>0.722783</td>\n      <td>32.204208</td>\n      <td>0.381594</td>\n      <td>2.308642</td>\n      <td>0.352413</td>\n      <td>0.647587</td>\n      <td>0.523008</td>\n      <td>1.904602</td>\n      <td>0.001122</td>\n      <td>0.002245</td>\n      <td>0.001122</td>\n      <td>0.0</td>\n      <td>0.007856</td>\n      <td>0.001122</td>\n      <td>0.001122</td>\n      <td>0.002245</td>\n      <td>0.044893</td>\n      <td>0.204265</td>\n      <td>0.002245</td>\n      <td>0.001122</td>\n      <td>0.580247</td>\n      <td>0.140292</td>\n      <td>0.001122</td>\n      <td>0.006734</td>\n      <td>0.001122</td>\n      <td>0.001122</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>17.596074</td>\n      <td>0.391372</td>\n      <td>0.281141</td>\n      <td>0.447876</td>\n      <td>49.693429</td>\n      <td>0.806057</td>\n      <td>0.836071</td>\n      <td>0.477990</td>\n      <td>0.477990</td>\n      <td>1.102743</td>\n      <td>1.613459</td>\n      <td>0.033501</td>\n      <td>0.047351</td>\n      <td>0.033501</td>\n      <td>0.0</td>\n      <td>0.088337</td>\n      <td>0.033501</td>\n      <td>0.033501</td>\n      <td>0.047351</td>\n      <td>0.207186</td>\n      <td>0.403390</td>\n      <td>0.047351</td>\n      <td>0.033501</td>\n      <td>0.493796</td>\n      <td>0.347485</td>\n      <td>0.033501</td>\n      <td>0.081830</td>\n      <td>0.033501</td>\n      <td>0.033501</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>6.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.910400</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>24.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>14.454200</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>35.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>31.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>80.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>512.329200</td>\n      <td>6.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>11.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      ],
      "text/plain": [
       "              Age         ...           title_ the Countess\ncount  891.000000         ...                    891.000000\nmean    23.799293         ...                      0.001122\nstd     17.596074         ...                      0.033501\nmin      0.000000         ...                      0.000000\n25%      6.000000         ...                      0.000000\n50%     24.000000         ...                      0.000000\n75%     35.000000         ...                      0.000000\nmax     80.000000         ...                      1.000000\n\n[8 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "467acd53b8f96256b8e2f634387ecc8609479226"
   },
   "source": [
    "### Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "trusted": true,
    "_uuid": "5bd2f6256812d537f5317c6a62587581e9b5e3c3"
   },
   "outputs": [],
   "source": [
    "X_train, X_val, Y_train, Y_val = train_test_split(train, labels, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6b35a24c53847c6fcd51ff464649d352e7f4c533"
   },
   "source": [
    "### Model\n* Model really isn't the focus here so I won't try to build something super good, but I think it's good to leave a clear and organized code anyway."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "23e0e375eb806a05979f104a2395f9239911f597"
   },
   "source": [
    "#### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "trusted": true,
    "_uuid": "d01316b1fae2da81f09599133ca7c6203e7da729",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bytree=0.3, gamma=0, learning_rate=0.1, max_delta_step=0,\n       max_depth=8, min_child_weight=1, missing=None, n_estimators=50,\n       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n       silent=True, subsample=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = xgb.XGBClassifier(objective ='binary:logistic', colsample_bytree=0.3, learning_rate=0.1,max_depth=8, n_estimators=50)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3dd2f2907dabd235d1d8acc7f33b61a792f3d04f"
   },
   "source": [
    "#### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "trusted": true,
    "_uuid": "03b50697178b24aa59b4a0b3d559614eed30d2dd",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model metrics\nAccuracy: 0.79\nRecall: 0.85\nPrecision: 0.60\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_val)\n",
    "# Basic metrics from the model\n",
    "accuracy = accuracy_score(predictions, Y_val)\n",
    "recall = recall_score(predictions, Y_val)\n",
    "precision = precision_score(predictions, Y_val)\n",
    "\n",
    "print('Model metrics')\n",
    "print('Accuracy: %.2f' % accuracy)\n",
    "print('Recall: %.2f' % recall)\n",
    "print('Precision: %.2f' % precision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "753357f7ad5988014e59532fa686b11c89f8b625"
   },
   "source": [
    "### Now we can finally take some insights from our model.\n\n#### Let's begin with permutation importance, XGBoost already give us a nice way to visualize feature importance base on F score, let's take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true,
    "trusted": true,
    "_uuid": "a327e099ec212c96ce231801213d26d887d824ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (15, 6)\n",
    "xgb.plot_importance(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bb31039cc83aa9f51cba1831d4f8fed3dc17daa3"
   },
   "source": [
    "Based on this graph we can see that age and fare values were the most important features (older and people who paid more had a higher chance to survive), also females had more chance than males to survive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a504e3ce4bc346c289f2165e02e123403dd6b316"
   },
   "source": [
    "### Now we can try permutation importance.\n* Permutation importance is a cool technique that give us another angle evaluation feature importance.\n* It's calculated after a model has been fitted.\n* Basically we ask the following question: If I randomly shuffle a single column of the validation data, leaving the target and all other columns in place, how would that affect the accuracy of predictions in that now-shuffled data?\n* This way if a feature has high importance shuffling it should lower our model metrics, on the other hand if shuffling don't impact the metrics too much that feature has low importance (it doesn't really matter to the model).\n\n\n###### Important note here for anyone trying to use eli5's PermutationImportance on XGBoost estimators, currently you need to train your models using \".as_matrix()\" with you input data (X and Y), otherwise PermutationImportance won't work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "trusted": true,
    "_uuid": "051a80599d97b58415324568a50e8858c31fe795"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bytree=0.3, gamma=0, learning_rate=0.1, max_delta_step=0,\n       max_depth=8, min_child_weight=1, missing=None, n_estimators=50,\n       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n       silent=True, subsample=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model again so it can work with PermutationImportance.\n",
    "model_PI = xgb.XGBClassifier(objective ='binary:logistic', colsample_bytree=0.3, learning_rate=0.1,max_depth=8, n_estimators=50)\n",
    "model_PI.fit(X_train.values, Y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "trusted": true,
    "_uuid": "1a4b0e903b8e341abf36fa1764e39584f6e91a30"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n    <style>\n    table.eli5-weights tr:hover {\n        filter: brightness(85%);\n    }\n</style>\n\n\n\n    \n\n    \n\n    \n\n    \n\n    \n\n    \n\n\n    \n\n    \n\n    \n\n    \n\n    \n\n    \n\n\n    \n\n    \n\n    \n\n    \n\n    \n        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n    <thead>\n    <tr style=\"border: none;\">\n        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n    </tr>\n    </thead>\n    <tbody>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0489\n                \n                    &plusmn; 0.0054\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Fare\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 83.60%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0368\n                \n                    &plusmn; 0.0065\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Pclass\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 86.33%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0284\n                \n                    &plusmn; 0.0184\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Sex_male\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 88.09%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0233\n                \n                    &plusmn; 0.0072\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Age\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 88.39%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0225\n                \n                    &plusmn; 0.0131\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Sex_female\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 94.02%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0087\n                \n                    &plusmn; 0.0028\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Master\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 94.43%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0079\n                \n                    &plusmn; 0.0068\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                family\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 95.15%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0065\n                \n                    &plusmn; 0.0063\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Embarked_S\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 96.57%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0039\n                \n                    &plusmn; 0.0021\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                SibSp\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 96.92%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0034\n                \n                    &plusmn; 0.0029\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Parch\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 97.29%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0028\n                \n                    &plusmn; 0.0167\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Mr\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 97.89%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0020\n                \n                    &plusmn; 0.0072\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                Embarked_C\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(120, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0.0000\n                \n                    &plusmn; 0.0031\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Miss\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0\n                \n                    &plusmn; 0.0000\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Col\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0\n                \n                    &plusmn; 0.0000\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Don\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0\n                \n                    &plusmn; 0.0000\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ the Countess\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0\n                \n                    &plusmn; 0.0000\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Jonkheer\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0\n                \n                    &plusmn; 0.0000\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Sir\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0\n                \n                    &plusmn; 0.0000\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Capt\n            </td>\n        </tr>\n    \n        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n                0\n                \n                    &plusmn; 0.0000\n                \n            </td>\n            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n                title_ Dona\n            </td>\n        </tr>\n    \n    \n        \n            <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n                    <i>&hellip; 9 more &hellip;</i>\n                </td>\n            </tr>\n        \n    \n    </tbody>\n</table>\n    \n\n    \n\n\n    \n\n    \n\n    \n\n    \n\n    \n\n    \n\n\n\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm = PermutationImportance(model_PI, random_state=1).fit(X_train, Y_train)\n",
    "eli5.show_weights(perm, feature_names=X_val.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "trusted": true,
    "_uuid": "5c5cbec00d233ebc7bad358d4e2cb9bae6c35dd1"
   },
   "source": [
    "So, the values can go from green to red, being the greener the more important the feature is (top features) and the whiter, less important the feature is (bottom features).\n\nThe first number in each row shows how much model performance  (in this case, using \"accuracy\" as the performance metric) decreased with a random shuffling with a confidence interval (variance from the multiple shuffles).\n\nNegative values (red) means that the shuffling actually improved the model performance, so those features don't really matter but the shuffling (fake data) improved them.\n\nIn this case the top 2 features (the 2 most important) were the same that we got from XGBoost feature importance method, but here \"Pclass\" seems to be as important as \"Age\", another interesting thing here is that \"Sex_female\" and \"Sex_female\" have the same importance, this may seem strange at first but remember that they are true/false features, and the opposite of the other, so actually this make all sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "trusted": true,
    "_uuid": "7ba470eaf8f5d0d0f894ed7144e6763ed2210cb3"
   },
   "source": [
    "### Next let's do some partial plots.\n* The aim of partial plots is to show how a feature affects predictions.\n* It's calculated after a model has been fitted.\n* To calculate the data for our plot, we could take one row of data make a prediction on that row, and repeatedly alter the value for one variable to make a series of predictions. Then we trace out predicted outcomes (on the vertical axis). To have a more robust representation, we make this process to multiple rows of data, and plot the average predicted outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "trusted": true,
    "_uuid": "9b93c083fa501e77a106a06755bc1fbc4dd9c603"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = train.columns.tolist()\n",
    "pdp_fare = pdp.pdp_isolate(model=model, dataset=X_val, model_features=feature_names, feature='Fare')\n",
    "pdp.pdp_plot(pdp_fare, 'Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "75abbde465869f414d94c4e119ba70b22f93504b"
   },
   "source": [
    "About the plot\n* The y axis is interpreted as change in the prediction from what it would be predicted at the baseline or leftmost value.\n* A blue shaded area indicates level of confidence\n\nHere we have a strange behavior, on the first few points, this may be because of the small dataset and some noisy data, but if we smooth that line, we can see that the higher the fare is more it contributes to higher predictions, this is true to around fare = 60, from this point it seems that increasing fare has no impact in predictions. Anyway the impact seems to be small.\n\n### Let's try on \"Pclass\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "trusted": true,
    "_uuid": "869f61c2b3045a40e55b025542acfe3fc0e68679"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WyIQ2AMCF2WP2fchEotW41neUcYwPQD5H+HzI6xiFTFLPo2naSyGTtBdBxvv6Wc55xpgZ238EsqS1w9jPfN4O4N0A2gDkIZMwaJrWCeAHAD4IwA/gNshkzTXLfl4F+fNqBPAzAP8KAKFQ6G2Qid8rjZ/VlyHj1wBgPeR99j7IBBaapn1C07Sfz3XCmqZ9TdO0JIBeAIPGuRERERERLVo1jOgBciToPk3TvrKIbY+GQqGbAUDTtB8B+BSAz4VCoQyAX2ualoVMQh4z1v9FKBS6x1j/UwDGNU1bD+BayNLKm431Dmqa9j8A3gBAM5b9NBQK3Wf8O118EsY+ngvg5aFQKA3gMU3TvgmZmPxmEdc1k5tDoVDYON6PIZOVcxgTs1wF4EVGDO7RNO3WMo7xPgDvD4VCA8b+PgvghKZpbzNHVYvcYJzT00Xr/lHJOufETNO04u+Z2z9VtP2b5zm/7xWt/2nIOL8DwBshf7Z3GN/7e8iS4GshP0godW8oFLrNWPd7kAnibHKQCd62UCj0BIpG40Kh0E3znC9CodCfapr2AQC/B5kMZ+bbhoiIiIhoLtUwogfjjfvPAXxiEZsXl8+ljP2VLise0TtZdNw4ZInlWsjywms0TRszX5BJy5qZtp3BWgAjoVBosmjZccgywko5U/TvJGaegGUtgNGSZwGPl3GMjQD+tygGzwIoAAjOcqzimMwUn/liVvz9hZxn6fpOyJHLtcXbh0KhKWPd2eJfGkuPpmmzfTDyPQC3A/ihpmmnNU37sqZpzgWc61mhUKgQCoXuBdAO4E/K2ZaIiIiIqFS1jOgBQAjymbl/KFpmJis1ACaMfxcnXoux3vyHUdLZDOA0ZFLw21Ao9OI5ttXn+N5pAM2aptUVJXsbAJxa4HklIK/TtNjrHATQpGlabVGytwHT537OcYwJUvxF258E8O6iUbj5jtVe9PX6GdaZK2aDJdtsWMAxS9fPARiCjP/F5jeMdhbrsfD4FzvnnEOhUA5yVFcznoe8DfK5x28tYt8OyGf0iIiIiIgWrWoSPWPyjh8B+HMATxrLYpqmnQLwVk3T9kE+K7XUN8l/oGna8yAncPk8gAOhUOik8ZzVTZqmvQ3y2S0AuAxA3JzQY57zP6lp2v0AvqRp2kcgJ4R5D84vZZzNYwA+bpRejgP4q7Kuavo8jmua9jBkUvJJAFcDeCXkc2gAEIYcvXo5gF8D+CTkpDWmrwP4oqZp7zD25QdwbSgU+ukMh/sxgG8bpY/HAXy6zNP9MYCbNU27BcAxyGR/Pm8tWv9zAH4SCoUKRinrJzRN6wZwD2TZZgbA/WWeEyBHibeYX2ia9gLIZPIZyA8ccgCm5tuJpmkByMlzfg45svwiyNLU+cpTiYiIiIjmVBWlm0U+B9lvrNh7AXwUwDCAXVjcG/di34dMKEYgZ9d8KwAYo3AvgZyE5TRkad/f4twkaD5vBrDJ2P5/5W4X1ovNeLbsR5ATyzwCmRws1lsgJ6kZgbzWW4qOMw7gTyFnkTwFOcI3ULTtVyCTwl9rmjYJOWvnNbOc8y8hJ0O5C0C/sS6wwGfQjO3/GfIZxn4s7FnG70HOKHoGgAfygwGEQqFDkD/Lr0ImZa+EnFAlu5BzKfElAH9tlK9+BHJ09SeQSd6zAH5rnAc0Tfukpmm/nGU/OmSZ5gDkpDZ/D+CDoVDoZ7OsT0RERES0IELX56qco9WgtHXDMh5nB4CnALhnmLilEvu/G0ZrjUrvm4iIiIiomlRN6SZVJ03TXgv5zFoN5AjorcuR5BERERER0bRqK92k6rMXQBTAYcjZOTmjJBERERHRMmPpJhERERERkcVwRI+IiIiIiMhimOgRERERERFZDBM9IiIiIiIii2GiR0REREREZDFM9IiIiIiIiCyGiR4REREREZHFMNEjIiIiIiKyGCZ6REREREREFsNEj4iIiIiIyGKY6BEREREREVkMEz0iIiIiIiKLYaJHRERERERkMUz0iIiIiIiILIaJHhERERERkcUw0SMiIiIiIrIYJnpEREREREQWw0SPiIiIiIjIYpjoERERERERWQwTPSIiIiIiIothokdERERERGQxTPSIiIiIiIgshokeERERERGRxTDRIyIiIiIishgmekRERERERBbDRI+IiIiIiMhimOgRERERERFZDBM9IiIiIiIii2GiR0REREREZDFM9IiIiIiIiCyGiR4REREREZHFMNEjIiIiIiKyGCZ6REREREREFsNEj4iIiIiIyGKY6BEREREREVkMEz0iIiIiIiKLYaJHRERERERkMUz0iIiIiIiILIaJHhERERERkcUw0SMiIiIiIrIYJnpEREREREQWw0SPiIiIiIjIYpjoERERERERWQwTPSIiIiIiIothokdERERERGQxTPSIiIiIiIgshokeERERERGRxTDRIyIiIiIishgmekRERERERBbDRI+IiIiIiMhimOgRERERERFZDBM9IiIiIiIii2GiR0REREREZDFM9IiIiIiIiCyGiR4REREREZHFMNEjIiIiIiKyGCZ6REREREREFsNEj4iIiIiIyGKY6BEREREREVmMoxI76e7ufimArwCwA/hmT0/PTSXfdwO4BcAVAIYBvLGnp+dYJY5NRERERERE51ryiF53d7cdwL8BeBmAnQDe3N3dvbNktfcAGO3p6dkG4J8A/O1Sj0tEREREREQzq0Tp5tUA+nt6eo709PRkAfwQwKtL1nk1gO8a//4JgO7u7m5RgWMTERERERFRiUokeusAnCz6esBYNuM6PT09eQDjAFoqcGwiIiIiIiIqUZFn9Crl5ptvvhHAjQAwNjb2Xx/+8IdvAzAImRS6ABwHsBHABIA8gGYApwAEIZPWUwDWAxgzdtkImWCuAzAFIGL8ewTy2uuL9pmFfH6wDcAQAA8An/n9tpe/+QfC7th9o30Ye+0jZ8/5TxydvelstuVm2zG/uWxfoRnfKLTgV84j8IsCAOAw3Om3O3ce+Jv84S3P1yc2mOv+7eWvf3/H4Ue3v278yAfMZY+3dX3tpvquh35w6P9uNpcNuese/cSVb/7y53/3n3/Vlk9eai7/i9d85tWvu/Obr3t+/PQ7zGUPbLvmc79IiPEvDB74B3PZQG3LnX+94+U3f+XRH/5Nw1R2IwBkhH3sL1/2kXf/8V3feudlqaFXmeveetnLPnhqYKDlfUNPftpc1t/U/iOt/bm/+I+nfvJVj15oAIBJh/voR67b+9GP3fudP9+anbjOXPcb17/77f6Dd1/52vEjf77Ia3rN6+781uueHz/1dnPZ/duu+fxtCdvYFwYfOPeaul7+7a8c/OGXSq7pXX9817fetdhr+uh1ez/20Xu/+4Gt2fHia3qH/+Bvr3zt+OF5fk6+Rz5+xZu//IUH/+uTi76mJf6cPtt+7c+/+dT/fNWjFxorc03y5xS6/7ufXq9nds11TRfy3pvlms67955o2/5vX6rf8XDF772Zr+m8e+/nl73sgwMDJ5vfN/TUZxZ5Te/w3ffL574lN3jjQn5OC/kdsVz3nnFN/1J8783yO6Kse28Zfkcs+Jo+4th09w+HHrjp7M/J7j764Wvf9YFP3X/LBzcVki8y1/2fzb/3nPreh697sTv3ZXPZ8JT40FV3hW870t1xyFyWyhd+veX/Hnp776uu/EGDy/ECc3nbTw7U3fGiiz90UWPt58xlY9n8H/7d0ydjX9y9+R5zWSJfuGXb/z30ySOvuep2r8O+CwDyU3p0/f/7XeeBl172hY0+z/vNdU8nM9feEx1ve9OmwP+YyyZz+Zs6f/rw10++7poHHTYRAIBsYerJjf/74Asef8XlXwt4XDeY6z4yPLklWZj6/T2Bhn8vOqc/3/Gzh28bfP1z+i/ANV1z4KWX/aXFrqkqfk5Pjibe+cNj0SNWuqZq+TmNZ/N3d/3s4TdZ6Zqq5ef07Hjymy+844kPQFGuASBt7HcNgBiAWgA1uq6f/RtSLqHr+mK3BQB0d3f/HoDP9vT0/L7x9V8BQE9Pz5eK1rndWOeB7u5uB4AzAPw9PT1LO/gFtO7Vb/8bAH8BoAZAEsA/v+IjnwwZX9cCaEnHJ9uGB06snYhGW1PjY83JiYnGbDLRlM9l/VP5fLCQL/j1qYIfctKaYnmb3R612R1Ru8MRtbtcUafbHXXV1ES9dQ3RhuCa6JptHUPuWl/+gl70CnTkrl81b3nBS0fmX5MqjbFXh7FXZ4bYC8jf4Q7jv3bIP/5zfXCqQ/6Bzxgv899pADnINxMF479nX/v2dFXN38jlIIQI6roeUX0eqxFjrw5jr44VY1+JEb2HAHR0d3dvhsxy3wTgLSXr/AzAOwA8AOD1AH5TTUme4TMAdH2q8FJhs/8KQGjfnq48ZMY/ATny+NTe/XXudV27agHUAWiF/CTATOxy+Wwmc6Y/XDt2ZjCQHBsLZJPJQC6TDuSz2UAhnwvkMukdmWTiOsgs/6wn7/ilLmy24bPJoNMZdbjdUZfXG/X66qO+ltZoYPOWaH0gmL5QASEiWoV0TCdj5TCTwhrIvw8OyASx9BEKYRwDe/f35nF+cmi+zHM4J0Hct6erUPYVERGRJS050evp6cl3d3e/H8DtkH/Evt3T0/N0d3f35wA83NPT8zMA3wLwve7u7n7Iocw3LfW4F9qpn96SB/ApIcQtcw2h7tvTZf4RHgFwfO/+XoHpUb8mh8vd0r7zYrTvvDgGIAr5iW4aRW8apgoFDJ88Xj88cCKQGBkJpBPxQC6dDuSzmUAhlwsUctn2XDp1uT6h1xcfu3f/XRBCTNrs9ojN4YjZnc6Iw+WOOj2eqKfWF61tao62rN8QbVm/ccJmLx1UrA7jAyfqIGNLFxhjrw5jr04FY18wXuWwQf5ddQHwYjpZnPUX+N79vaWjh8Uvc/TwvCRxhY4eNkKWQdGFx9irw9irY7nYL7l0c7URQtToup5cyj727u91QCZ+tZCjfi0A3Ma3CwBSkMnfnD+ciWjEEz16JBAfHgqk4hOBbCoVyGeMZDCfD0wV8gF9aqoF8hPiYmmb3R6zORwRu8MZc7hcEafbHXPV1EZqGhujjWvaosGtnSNOt3tqKde5HEaO9nmaN3dw1FIBxl4dxl6dKo39TGWldsi/BWdHDIuYo5SzJYjnlZUCKCz36GEl/t7S4jD26jD26lgx9itqMpYqsQ5A31J2YJR8jhuv0wCwd3+vBzLxq4dM/JowXdKTg0z+ssX7qQ8E0/WB4AkAJ2Y7ViYRd5zp72udiEb8yYmxoCwVzQQK2WygkM8HcunUpZlEPIDz74WCsNmH7A571OZwRBxOV8zhdkdc3pqYt74+Utfqj63Z1hmtaWjMLSUW5Tr5wD3+5s0dJ+dfkyqNsVeHsVenSmO/mNLS4tHDmqKv5xo9nMJ0cpiF/IDSfPZwxucOIRPEhX7CvOS/t7RojL06jL06los9E73yLcso1749XWYJ5zCAo3v399ogS3VqIZO+FsjRP/PT2PNKPmfirvXlN166+wzkBDhPzrROIZcT0aOHG0dPDwSTY2P+dCIRzGXS/kI2GyzkcoF8Nrstm0pdC12vKd7umbvuhLDZRm12e9TmcEQdDvncoNPjjXh8vpivpTXSumFTrHlde2JJwSkiHHYOQSvC2KvD2KuzimI/ZbzK+fDOnJjGjukPKs2vZ+uVq+/d35vDuc8dmkliFkUJYWvnTtfe/b0uyNLSFVdhYnGMtzqMvTqWiz1LN8skhPDpuh5XcexKlXwu1sipgdqh40cD8ZHhQDoeD+TSqWA+k/Hn87ngVD7vnyoUgvrUVON5GwqRsNntMbvdEbE7nVG7yxV1eTxRd01ttKapKdrUti4a2Lx1zO50znvesUNPe/3bd6WW4/poboy9Ooy9Ooz9sjBLSmeavfSssRNHPY0bNmcg/6ZN4fySUjNBnPG5Q5Q3ekhFVL7XWe0Ye3WsGHuO6JVvHYBF97NYigWUfJqzfJp/LLOYoeRzsZrXtSea17UfBXB0tnUSoyOuyJF+/2QsGkhNTgayqWQgn8kE8rlsYCqfD2aSyav1+GRr4vxyoJzRYiJmdzoidqcr5nS7I+6a2qi3XraYCG7dNjzw4H0B//ZdxytxPVQexl4dxl4dxn5ZmBMKDWZ7AAAgAElEQVTTzDl6ePS3d2zc/bYbY8aXxW0tXJCTJphfz0bfu783i/NbWxS3tTgvSeToIQCF73WIsVfIcrFnole+FTXz3Swln2dn+YRM/sxm7uYnoimUP/vbgtQ2NWe3XHH1KchWGzPKZTK2yOFw89jgYDA5PubPJhNB2WIiJ1tMpNM7M4lEANOjlSZdCDFy5it/HzFaTEQcbnfM5fVGvL76WF2rPxLYsjVW1+qvtokTqkLTpq3jqs9htWLs1WHs1SmJ/VLbWnghGxPPOHpYzGhrUZwgms8emm0tZup5aLW2Fivqvc4qw9irY7nYM9Er34qOmfFJZNx4RQBg7/5eJ2Ti54Ms92yG/EQUmC75NMtjlp3T7Z5q33nxUPvOi4dmW2eqUMDQ8aMNI6cG/PHRkWAmEffn0ulgNj6xXhe2hkIuuz6XTl2hT+h1xds9e89vIISYsNkdUZvDHrU7XVGHWSrqq4vUNjXFWtdvjDS3b5is1hYTquTTqRV971sZY68OY69OhWK/mLYWxaOHbkxPjlZ6PsU9D0vbWhQniebo4UwJ4kotLeV9rw5jr47lYm+5C7oA6iGbo1eNfXu6cgDGjNcAMGvJp/nwvFnyeUFn1Cxms9sR2LJtPLBl2ziAfnP5we99Y+Put914tozKbDExORwLpCcng9l0yp/PZIKFXM5fyOeC+Wy8Mz15bouJ/gP3A0D6bDLocEYdLlfU6fZE3bW1UW9Dg3xucMu2FdliQpXJM6drAcyanNPyYezVYezVURj7xY4eOiATwhoAdTi3rcWMjNHD0rJSM0mcrefhhRg9rLr3OhbC2KtjudhzMpYyCSE8uq5brjRwlpLPWsg/UMte8rlQ4wMnXA3tG8p65jA1OeGIHO5vnYhGAqmJ8UA2dW6LialCPjhVKPhx/gcf+aIWE1GH0xV1uN1Rl7cm6q1viNb7A9Hg1m2xC91iQpXFxJ4qg7FXh7FXZ5XEvriNRelrpp6HMJYVjx4WJ4dzPXu44Dd8Vn2vUw0Ye3WsGHsmemUSQmzXdd1SD2rOpqTk0xz1M0s+85ie5fOCKR3Rq5RCLiciR/qbxgZPBRJjY4FMIh6Qzw1mA1O5fKBQyAemCoUgdN1buq3RYsJ8bjDqcLmjTo836qmri/qaW6L+TVuiTW1rq74B53LFnubH2KvD2KvD2M+pdLbS+UYPzVHK4rLS4tc5ZaX/9foXbPmjn9z1rAWfPVzxVtP7zJXGirFn6Wb5rP7p4lmlJZ979/cKAGbJZwOmWzzYMP0J47KWfDprapZl33anU1+7fcfI2u07RgD0zrTOVKGA0cFTvqHjxwKJ0ZFAOj4ZyKXTcnQwlwsU8vk1uXTmEl2fOKfFRPi+ewAh4ja7PWZ3OCJ2hzPmcLkiTo8n6q71RWsbm6JN69qjgc1bx1byc4PLFXuaH2OvDmOvDmM/p8WUlpqjhy7ICp7iNhfnuOZ9HwkCWLd3f69Z0WNOTmNOTFPc1qL02UO2tViaVfM+cwWyXOyZ6JVvWPUJqGL84k4ZryEAh42ST7O3n1ny2WBsYjZ2T6FCTSj923cpmwHPZrejpX1DvKV9QxzAkdnWi48Mu6NH+v2TQzF/anIimE2mAvns2RYTgUwysSUdn2xF8axvDwEAsja7PWazO6LTLSY8EXdNTczb0BBpCKyJrenoHHJ5a5R8wqoy9qsdY68OY68OY19xU8Zr3gS6bs3aFIAEpiemsWP6uf7i0tJiZqmpvnd/bw7nPntY/PzhjAki21qctWrfZ64Alos9E73ytQGYUH0SK4Xxi3nSeJ0BZiz5bMH0vZaH/GW/qJLP0wcfag1etDuxxNNeVr7mloyvuWUAxsQ3M8mmkvbI4f7m8TODweTEuD+TTATzmbQ/n80FjRYTFxktJlzF2z3x69umhM02bLM7omapqFM+Nxjx1NXF6lr9keDWjpivuSVT6euqhthbFWOvDmOvDmOvTlHsl9rWwoOFt7UwRw9nes343CGsOXrI95nqWC72TPTKx9nX5lFmyScwPdHLvJ8ytnR0jS3HOV9oLm9NYf1Fl8TWX3RJbLZ1zBYTwwMnA4mx0UAmHg/k0qlgPpv1F/K5YCGX3ZBNp65KTei+4u2e/e1vIIQYt9kdMZvDEZHPDbqiLo836vb5or6m5mjLhk2R5nXt8XJKRa0S+2rE2KvD2KvD2KtTgdhXoq2Frehr8/ulSZ2+d39vcc/D4tdME9OYyeFKHj3k+0x1LBd7Jnrl86g+gWozS8mnHdOzfDZDJn/ms23F65/zyzg9OnLOCJeVlbSY6JttvbHIoDd29GggPhwLpOPxQDaVDOSz2UAhlw0U8vlgPpvpMlpMnNV34D5AiLTNbo/Y7PaY3emMOJyumNPjibhraqM1DY3RprXrosGtHSN2p1MHVlfsVxrGXh3GXh3GXh1FsV/q6KEXCx89zGM6OSx+9tAcPZyp5+GFemyC7zPVsVzsmeiVzzf/KjQf4xdmacmnC9Mln37IBNC8R3MA0omhaM2FP9uVrTHYlmoMth0HMOvsdNMtJs4EUxMTssVEOm1MIpML5FKpyzPx+DktJo4++hAA5G12+5DNbo/ohXxiYOBrRx1ud8xdUxvx1NXHGgLBSHBbR8xbV1/uH2YqA+97dRh7dRh7daos9osZPbQZLyfkm/smTCeIM9q7v7e0rYX57zTOHT0sffaw3NJSvs9Ux3KxZ3uFMlmxx8ZKZZR8elFU8pmIRVpq/UEzqTBLPplkVEAhlxORw33No4On/cmx0WAmmfDn0ulgPpf1T+XywUI+F5yamgpA18/7xEvYbCM2u914btAlS0W93qjHVxfxtbTG/Ju3RBqDbSkV12UFq6Sf2IrE2KvD2KvD2M/KgXN7H5ptLczZx4uZpaZmW4vil5kknvfc4S8+9G77wMP3V31Lpmpkxff4TPTKZMUeG9XE7avf8c5fPjiAc0s+zd52ZslnGhWa5ZOmHfzeNzZe+pb3HB85NeAbPnk8EB8ZDmTi8WA2nfLns9lgIZfzT+Vl83ldn2o8bweyxYRMBh3yucHiFhPN7euj/k1bVnSLCVXYT0wdxl4dxl4dxr6iihPD0td5nr31v9t3vPINJ3Hu6KE5Y2lxW4vzkkQLTkxzQVnxPT5LN8tnqUy/2mQTk8l9e7pmK/msw3Rj93NKPsGf25K56+ozNrsdrRs2xls3bJy3xUTkcJ9/cmgokJ6cCGRTqUAumw4UsrlgIZ/357OJren4ZPGEPDj80AFAtpiI2hxyVlGH0xV1eDxRd01NtKa+MdoQXBMNbusYVtViQhV3XX3FZ1GlhWHs1WHs1WHsK2rBbS0AQC8UHJDT/JujhbO1tSidnMZsa1E6OY2ZJJ733CHY1qKU5d4rMtErH2cBU+u8+O/b02X+UhsFcKKk5LMRMvlrxfQvRZZ8LkLT5m3xha674BYT/X0t45EzgeTEWCCTSAbymXQgLyeRmbXFBG6H2WIiYncaLSZcHlkqWlcfrWttjS5XiwlVyok9VRZjrw5jrw5jr05R7BczMY05euiCnPDOXrSs2NkksaitRenMpcWjh6VJohXbWgAWfI/PRK98awCwias688bf+OWTNF4xAH3GLJ9mY/cWyMTPfNZsCiz5nNeZJx5tabv0yor98Xd5awrrL740uv7iS6OzrTNVKCB27EjjiNliIhEP5NJpOatoPhfIZ7ObsqnU1Sl9fIYWE7Yxm91utpiIOVyuiMvrjbprfVFfc0u0Zf3GaLktJlSpdOxp4Rh7dRh7dRh7dZYY+7JGDw1mWws75GQkDTi3rcVMzNHD4mcOzdLSLGbueVgNo4eWe4/PRK98s/Y9owtiUfE3ZvmcMF6DALB3f68b0yWfLcbLfNefg0z+LDMqtFSt23eOXuhj2ux2BLd2jAW3dowBCM+23ujg6Zqh40cD8eEhfzo+GcymUsUtJgL5bGZnenKquXibvgfuBYRI2ez2qN3uiNqcjuIWE7GaxqZI09p10eCWbaNmiwlVVMSeJMZeHcZeHcZeHQWxX2pbCw/Ob2sxU89DUdLWoristHRimuLnDi9k9ZXl3uMz0StfLYAR1SexilUs/vv2dJm/aEYAHJ+j5BOYLvk0SxlWnUR00AuZKK84TW1rk01ta48BODbbOsnxMWf0yOHW8WgkmJ6c8GeSiWA+k/Hnc7nglGwxcWUmHm9FcYuJRwDIFhMxOZGMU5aKejxRl7cm4q2vj9UH1kSCW7cNLWeLiZUce6tj7NVh7NVh7NWpotgvpq2FOXroAOCGfK815+hhSVuL0vLS2Z49LCxy9NBy7/GZ6JWvmnrLWNGyxX+Wkk8Hphu7z1bymcL5n1xZTmp0tKobidY0NOY27b5iEMaI7kzOtpg4fSqQHB8LZJKJgNliopDLBXPZTGcmldyD8bFzYvHUnYB8brC0xURN1OPzRX0t/qh/8+boYltMVHvsqxljrw5jrw5jr47FY7/U0UMv5Oih2ebCNtsGxuhhaVmpmSDO9NxhHhZ8j8/2CmWyYo+NarIS4l9S8mnO8mn5kk/2VZKmCgWMDJyoGz55IhAfHQlk4vFANm2WiuYCssVE3q/resN5GwsRt9vtEZvDEbM7nBGHyxVzerwRt0+2mGhpXx9t3bh5vPS5QcZeHcZeHcZeHcZeHca+YsxE0Jy9dK62FgKAHo+esfkCax7Yt6fLMpOycESvfBsBWKrHRpVRHv9ZSj7NUb8mTI/8mTXqZnuHqi75PHLXr9rYV0k+N9i6cfNk68bNkwAOz7be5FDMEz16uHVyKBZMTU4EcqlUIJfJBAq5nPHcYGJrOj5p3icAgMMPPgAAmbOTyDicMYfLGSkk4vn69o29NfUNsYY1bZE1HduHnW73Sn+o3RJ436vD2KvD2KvD2FeMOTHNgt97nfzd/st2vPINzuU7pQuPiV75kqpPYJVbcfE3Sj4TxisKAEbJpznLZytk8uc2NilgepbPqhlS9zY1cSS7DHWt/nRdq3/OFhOZRNwROdzfMh4940+NjwczyaQ/l0kHC7msv5DLB3Pp1MWZRLwbgDN9pP/sdo//6ucFYbMN2x2OqM3sN+hyR1013qi3rj5S1+qPBbd2RGubmvmp8BLxvleHsVeHsVeHsVfH09Boudgz0StfQvUJrHJVEX9jlqhx43UaOKfksx4y8WvGdH25WfK5Yt+Y1wYW93wZzc5d68tvuOSyCIAIgKdmWmeqUEDvnbetF55ar9FiIphLp/35bDZYyOf8+Wx2czaVuhq6fk6LiWfu7pEtJhxmA3pn1Ol2R50eT9Tjq4vWNjVHWzduija1rauKFhOq8L5Xh7FXh7FXh7FXp6YlwESP4IfFZuSpMlUb/5KSz2N79/faMD3L54ov+Rw69EzT+qufVw0zgVmKzW5HJjpo2/22G8OYo8XEyKmB2qETx/zxkeFAenIykEunAvlsUaloJrMrPTnRVLyN0WIiWdRiIupwuaJOtzfqrq2N1jY2RhvbVkaLCVV436vD2KvD2KvD2KszcuRQQ3DXpapPo6KY6JXvjOoTWOUsE39j6t+qKflcc8nlwxf6mCQtJPbN69oTzevaE5inxUTkcL9/IhYNpCbGA9lUMpDPZAL5XDYwlc8HssnUlZl43I/zH1Y3Wkw4InaHI2p3uaJOtzvq8tZEvfUN0YbgmuiabR1D7lrfivhQopJ436vD2KvD2KvD2KsT2HFJVQ4kzIWJXvkaIcvxSA1Lx3+Wkk8Ppks+zVk+zZLPLC5Qyefo0X5f26VXxpf7OHS+SsW+pqExt/nyK0/DuLdmkstkbNEj/c2jg6cCqfHxQCaRCOQyaTmraD4XyGUyXZlk4jpMtxkBADx5xy91o8VEzO5wRuxOZ8zhdkdcXm/U46uL1rX4o4HNW6L1gWBVlcbwvleHsVeHsVeHsVdn7ORRX8u2LtWnUVFM9Mpn5f4m1WDVxX/fni6zhHMYwFGj5LN4ls9WyJJWQM4wlYFM/sptZDqnzOSEe/61aDlcyNg73e6pdTt2Da3bsWsIwDMzrWO2mBg6eTyYGB31Z+KTwWzaSAZz2UAhl2vPpVOX6xN6ffF2vfvvghBi0ma2mHA6Iw6XO+b0eCLuWl/M19QcaVm/IdqyfuPESnlukPe9Ooy9Ooy9Ooy9Otn4pEv1OVQa++iVaSX0cVvNGP+Z7d3f64RM/HyQI34tAMxfWGbJZwZLKPlkbx91qjX2k0MxT/TIYf/kUCyYik/4s6lUMJ/J+Au5XLCQzwemCvmAPjXVgqIWE4aMzT49iYzD5Yo43e6Yq6Y2UtPQGGtsa4sEt3aOXIgWE9Uaeytg7NVh7NVh7NVJxCJrav3BB/ft6YqpPpdK4Yhe+ZT3cVvlGP8Z7NvTlQMwZrwGgFlLPs031GbJZ26hx2BvH3WqNfZGi4mTAE7Ots7ZFhORM4HkxFgwm0z6c5lMcYuJSzKJeADAdG+jgwCAgrDZh+zGrKIOpyvqOPvcoNFiYsu22FJbTFRr7K2AsVeHsVeHsVfnxIHfBne88gbVp1FRTPTKx7pptRj/BVpgyWejsfq8JZ+1rYEV18NwtbBy7EtaTDw50zqFXE5Ejx5uHB08FUiOjgYyyUQgm04HCtlsoJDLBfLZ7NZsKvV70PWa4u2euetOCJttzCwVdTicEYfbHXV6vFGPzxf1NbdEWzdujhqT2MzIyrFf6Rh7dRh7dRh7dWqa/ZZrbcFEr3wsG1SL8V8kY5bPuPGKAOeVfJqjfmbJZx7Ts3zCw+bbyqz22NudTr2ts2u0rbNrFHOM6J9tMTE8FEzH47LFRCYTyOdzgal8PpDOZHbpJS0mwvfvL24xEbE7nTG7yxVxuNxDqYmxK/KZzJZTp//9nue+5R3/7vLWVPS5V5rbar/vVWLs1WHs1XE3NFou9kz0ytcKOUJCajD+FVRa8rl3f6+AnPCmFkADpls82BKxyDoAR1BmySct3XBfb+OG51xn2dlmK2UhLSYSoyOuyJF+/+RQzJ+amAhmU0l/PpMJ5nNZ/1Q+H8wkk1fp8clWFLWYSIyObvn11/7l5R6f7z6Pz3eorjXQt/6iS/rmGgmkpeN9rw5jrw5jr87o0b6GNRftVn0aFcVEr3yDqk9glWP8l9G+PV06ZCKXAjAE4LBR8llrs9mCkM9JtUImgcB0Y/cUZPknLYO1u68aUn0OVlHb1JzdcsXVpwCcmm2dXCZju/Pr/3JLIZcrmmdbr8nE49enJydeMzZ4GieffAw2u33A4XL3uWpqwrWNTWH/pi3hDZdcNrhSZgytdrzv1WHs1WHs1Qnuuox99AgtACZUn8QqxvhfYEbJ56S47q9b777pU/3AjCWfLZj+fZKHTP5YZlshsUNPNwQv2s3RowvE6XZPeXx1DyRGRzZBjnCnaxubfvT8d773a6eeeSoQOdLfmRgd6cgkk9vz2UxHdjh5fXx4SEQO9+Gp3/w67nC5wk6PJ+z11fc1rGk7tOmyy48sdVKY1Yj3vTqMvTqMvTojh8N1zVs6VZ9GRTHRK5/lemxUGcZfnbOxL6fk09jEnOiFJZ+LkEsmnfOvRZX03Le8/ev3ff8WPTU2usfb2LT/uW95+z6b3Y71F18aXX/xpVEA95rrTkQjnuNPHNw2EY10puPxzlw61ZmamHh1anzcO3LqJI4+8mDB5nAcd7o9YXdNbbi2uTnc1tkVXrt9h+U+Pa4k3vfqMPbqMPbq5NLWiz376JWJfdzUYvzVKTf2e/f32jE9y2czZPJnzopYXCLKks95sK+SOouNfSGXE8cee2Td8Inj25PjYx3ZVLIzl8l26lOFNeY6wmYbdrhcfS6v95C3vqGvaW17ePPlVx7npC8S73t1GHt1GHt1rNhHj4lemYQQ23VdZx83RRh/dSoR+737e12YLvn0QyaAZmVBDiz5nNHB731jI/sqqVHp2MeOHak/9ezTHZNDsc5MItGZy6Q7CrncVkz3Cczanc7DTrcn7PH5wnWt/vC6nRf3tW7YuOpay/C+V4exV4exV+fZW3986Y5X3nCrlRI9lm6Wj8+HqcX4q7Pk2O/b05WFbNY+CuCkUfLpxfkln2Zjd7PkM7/UY1ezujVr+byGIpWOvX/Tlgn/pi2PAHjEXJZJxB1HDz68afT0qY7UxMT2XCrVkUkmrkvHJ189dmYQJ596Aja7/bTD5Q67amr6ahoaD7Vu3NS38ZLdp+1Op2U/reV9rw5jrw5jr47P32a5HoZM9Mq3qt9wrgCMvzoVj70xy2fSeMUA9M9S8mk2djdLPtNYRSWfDo+X970iFyL27lpfvut51/cD6AfwSwCYKhRw+tAzrZH+vs746EhnNpHozGUzndnh5HXx4SFb9Eg/nrm7J+FwuvqcHk/YU1cXbgis6dt46e7Dda1+S4yK875Xh7FXh7FXx+7xWK5snole+Zoh35CSGoy/Ohck9vv2dBUATBqvM8A5JZ91mG7svmpKPkePHW7YtKd7TPV5rEaqYm+z29G+8+Kh9p0XDwG431w+ORTzHH/84Nbx6JmO9ORkZy6d7kxNTrw8NTF+w+ipARw7+PCUzeE44XS7D7lqavt8Tc3hNR3bw22dXUPV1vaB9706jL06jL064yeP1q297CrVp1FRfEavTEIIn67rq+5ZiZWC8VdnJcW+pOSzETL5q4cs+dRhsZLP2KGnvf7tu1Kqz2M1qobYF3I5cfyJg2uHjh/rTI6PdWaTyY58NrN9qlBoM9cRNtuow+kKO73esLe+Pty0dl3f5t1XHnPX+lbs/yPVEHurYuzVYezVGTtxdF3jhs0P8Bm91S0IYEW82V2lGH91VkzsZyj57DNKPmuNVwtk8ucxNplCFZd8Dj7+UIt/+64B1eexGlVD7O1Op77liqtPGY3g7zKXD5047jv1zJMdE0Ox7Zl4vCOXSXcmx0bfmBwbdQ2fOI7+A/fnjIlf+ty1vkN1ra197TsvCrdu3Dyp8HLOqobYWxVjrw5jr070mSeaGjdsVn0aFcVEr3y2+VehZcT4q7OiY2+UfE4Yr0EA2Lu/143pks8WyJJPs34tD5n8ZS74yZZJzxfE/GvRcqjm2Ldu2Bhv3bDxIICD5rJsKmk/+ujDG0dPD3Smxsc7s+lUZyaZeG46PvnK8cggBp5+Eja7/YzD5Qq7vDXhmsbGcMv6jeFNl11x6kJP/FLNsa92jL06jL06eiFvudizdLNMQogaXdctNytPtWD81bFC7Oco+QSmSz7TWGElnyNH+zzNmzss+wziSrZaYn+695mWwb5DHYmRkc5MMt6Zy2Q6p/L5jTA/GBEi6XA6+5web5/H5ws3BNaEN1xyWX99ILhssVktsV+JGHt1GHt1Jk4PrK1f236ApZur23oA7OOmDuOvTtXHfgklnynIRFCJ4/feFWze3MG+Sgqsltiv7do5vLZr5zCAA+ay+Miw+/hjj24Zj5zpTMUnOnKp9PbU5MRLUxPjrx89fQrHHntEtzkcJ5yy7UO4tqm5L7i149C6HbtilZj4ZbXEfiVi7NVh7NU59cj9/vq1N6g+jYpiolc+zoSkFuOvjiVjv4CST3OWT/Odaw4XuOSzoX3DinhmajVazbH3Nbdkdr3wxc8CeNZcNlUo4MQTj7XFjh3Znhgb7cgmk525THpnJpl48eRQDGf6DuGJ228bc7hcfU6v95C3rr6vsW1teNPuK4566+rLGilfzbFXjbFXh7FXp66tfUXMQ1BJTPSIiErs29OVgUzkRgAcN0o+zd5+TZge+TNn+TTbO6yokk+iSrPZ7di0+4rBTbuvGARwt7l8eOCEb+Dpp7ZNxqKd6US8M5dOdyTHRt+QHBt1D588jsMPPpC3O51HHG532FPrC/taWsPrunb2BbZsG1d3NURE1sZEr3yNACKqT2IVY/zVWbWxN0o+E8YrCgB79/c6cG7JZwsAt7FJ8SyfSy75HB84UQeZdNIFxtgvTEv7hnhL+4bHADxmLsumkvZjjz26fmTgZGdqYrwzm0p2ZpPJ52Ti8VeMR87g1DNPQdjsEYfb1efyesM1DY3hlvaN4c2XX3nS7nTqjL06jL06jL06k4MDPtXnUGmcjKVMVpiQopox/uow9vMrKvmsx/Qsn+ZspWbJZ7bc/fLhfHUY+8obDPc2DYZ7O+Mjwx2ZRGJ7PpPuKOTzmzE98Uva4XT22Z3Ow976xt6GQPDQ+ksu7W8MtrG32AXC+14dxl4dTsZCALAOQJ/qk1jFGH91GPt5lJR8Htu7v9eG6Vk+F13yefKBe/zNmztOLuOp0ywY+8pr6+wabevs+h2A35nLEqMjruOPP7pl7MyZjvTkhGz7kEi8OJNIvGZs8BSOP/6obrPbBxxu9yF3TW1fbWNTOLBlW7h918WRSkz8Qufifa8OY6/O4MHftdSvbVd9GhXFRK98Vdds2WIYf3UY+zLt29M1hblLPltxbslnATOUfAqHnaUXijD2F0ZtU3N25/Uv6gXQay57/Ec3tzdsvzQfO3akMzk22plJJjrymUxXNpl80eRQDGf6w3jyjl9O2F2usMvjDXvq6sONbW19my674khNQ2NO4eVUPd736jD26gi7w3KxZ+lmmYQQPl3XLTcrT7Vg/NVh7JfP3v29HkyXfLZCjv6ZJZ/Z4f5DaNm2nZNWKBA79LTXv30XSwYVmC32o4Ona04++fi2iVikMx2Pd+bS6c5CLrsN021R8naH85jD7Q67a2vDvpbW8NrtO8JrtnVacubg5cD7Xh3GXp2xE0fXNW7Y/ABLN1e3dajyXmJVjvFXh7FfJvv2dJklnMMAjholn2dn+Tz96IHdLdu2O43VzcbuKcgRQFpGAw/eF/Bv38WeVgrMFvumtrXJpra1TwB4wlyWy2Rsxw4+sn5k4ERncnysM5tKdWRTySszifgfTEQjOP3s0xA2W8zhcoddXm9fTUPjoeb29X2bdl95wul2s1qhBO97dRh7dQYff6i1ccNm1adRUUz0yseZkNRi/NVh7C8Qo3vzEOUAACAASURBVOQzbrwi4rqbhi++4R1jkMmfD9OzfLqMTcySzwwUNna3oqZNWzmSqkg5sXe63VMdz7n2OHDtcQB3mMsjh/saT/c+0zE5PLQ9k0x05NOZzsToyDWJ0RFH7NgRHLr3t2m709Xv9Hj6PD5fuN4fCK+/+LK+pra1q3riKd736jD26jSs32y5HoZM9MrHmKnF+KvD2Kvj2LenKwdg3HidAmYs+Sye5TOLRc7ySdPy6RTve0UqEfvg1o6x4NaOhwA8ZC5Ljo85jz/26OaxM6c7UxPGxC/xyRemJydeOzZ4GieeeAzGxC9hl7cmXNvY1OffvPXQhosvPbNaJn7hfa8OY69OIZ223P/gvJnKVw9gUPVJrGKMvzqMvTozxn6+kk/I5M9vrD4FlnyWbfLM6VoAQ6rPYzVartjXNDTmdjz/hWEAYXPZVKGAU888FYgc6e9MjI50ZpKJznwm25lNJl8QHx4SkcN9eLrn9km7y9UnJ36pCzcE28KbLrv8SG1Ts+U+TOF9rw5jr048Nlij+hwqjZOxlEkI4dF1nf1NFGH81WHs1VlK7Pfu73VCJn4+TI/6mSWfeUzP8kkzGB844Wpo32C5N/LVYCXEfiwy6D35hDnxy2RHLp3ens/ltkHXvcYqBbvDcczh9vS5a2sP+Zpb+tZ0bA+v3b6jqkvdV0LsVyvGXp1ELLKm1h980EqTsTDRK5MQYruu65yQQhHGXx3GXp1Kxn7v/l4BOTthLYAGyOSvAbK3HzBd8snp6QEc/N43Nu5+242cGEGBlRr7Qi4njh18pH3o5LHtyfHxjmwq2ZnPZDv1qULQXEfYbEMOl6vP5fWGvfWN4eZ17eFNu6844fLWVMVo+kqN/WrA2Kvz7K0/vnTHK2+41UqJHks3y8dPWdRi/NVh7NWpWOz37enSIRO5FGR50OFZSj4bjE3Mxu4prMJeis6aGia8iqzU2NudTn3r1c85ufXq55wEcKe5PHqkv+F077Mdk8Oxzkwi0ZnLpDsSo6N/lBgddQwdP4rw/fszdqfrsNPjDntqfX11/sCh9l0X97e0b1hxbWtWauxXA8ZeHafHerFnole+YdUnsMox/uow9uosa+xLZ/kEZi35NFs85CGTP8uXfPq37+IMeIpUW+wDW7aNB7ZsexjAw+ay1OSE4/hjj24aHTzVmZqY6MylUp2ZROL69OTka8bODOLkk4/DZrefdrjcYVdNTbimoTHs37QlvPHS3adVTvxSbbG3EsZeneatnZx1k9AGYEL1SaxijL86jL06Fzz2xiyfY8ZrYJaSzxZMz/JpTvRiqU9ETx98qDV40e6E6vNYjawQe29dfb5rz/X9APoB3AYYE788+7TfmPilI5tIbM9lMx3Z4eTz48NDInqkH0/fdUfcIds+HPLW1fXVB9eEN112xWFfc0vmQpy3FWJfrRh7dSJPP9bcvKVT9WlUFBO98nEmJLUYf3UYe3WUx36Wkk87pks+myGTv0Zjk+L1q7bks6Wja0z1OaxWVo29zW7H+osuia2/6JIYgPvM5RPRiOfEE49tG49GOtLxyc5cOtWZmpx4ZWpivGbk1ACOPfrwlM3hOO50u8PumtpwbXNLuK1je3ht186Kj/hbNfbVgLFXp2lzh+VGU5nolc+j+gRWOcZfHcZenRUZ+317ugoAJo3XGQDYu7/XhemSTz9kAmj+rcmhyko+06MjrvnXouWw2mJfHwimL3rR7z8F4ClzWSGXE8cff3Td0Iljncmxsc5sKtmRS6cvzSQSvz8Ri2Lw0LM4eNvPRhwuV9jp8fbV1Dccalq7rm/T7iuOu2t9+cWey2qL/UrC2KuTGR+zXOyZ6JXPp/oEVjnGXx3GXp2qif2+PV1ZyMljRgGcnKPk05zl0yz5XPSb0uWUGIparq9StWDs5cQvW668ZmDLldcMAPiNuXzo+NG6gWee7pgcinVmEvHOXCbdmRwbfVNybNQ5dOIY+g7cl7M7nYedbk/YXesL17X6w+27Lg63bti4oIlfGHt1GHt1kiMx7/xrVRe2VygTe4mpxfirw9irY7XYz1Lyaf6BNUs+01gBJZ/saaUOY1+eTCLuOHbwkY2jp091JCfGt+fSqY58Nrtdn5pqMtex2e2DDpcr7PLW9NU0NoZbN2wKb7z08lN2p/OcN4OMvTqMvTrso1eiu7u7GcCPAGwCcAzADT09PaMzrPcrAM8BcG9PT88rFn3AFYC9xNRi/NVh7NVZDbEvKvmsw/Qsn8pLPtnTSh3GfummCgUMhntbz/SHOxIjw52ZZKIzl8l0TuXzG2FOpCRE0uF09jk93rDHVxduCK4Jp04ezl71zj/pU3v2qxPve3XYR+98nwDQ09PTc1N3d/cnjK8/PsN6fwf56e3eJR5vJbDMp+pVivFXh7FXx/KxLyn5PGGUfHohk79GyOSvFbLkU8cFKvl019VfkFkO6XyM/dLZ7Has27FraN2OXUMAHjCXx0eG3ccee2TrRORMZ2pysiOXTm9PTU78QWpi/A2jpwcAYOq2f/7yCafLHXbV1oZrm5r7glu2hdft2BVT2fZhNeB9r47LV2e5kdSlJnqvBnC98e/vArgbMyR6PT09Pd3d3deXLq9SnA1JLcZfHcZenVUXe2OWz6TxigHoM0o+a41XC2TiZ05UM4VlKPls2rxtxTWzXi0Y++Xja27JXPTClzyD/8/enQfHeR52nv+9OBqNg7hBALxJEUDrFiVZ8aFW5HnljLO7Hk82Lq2TTMbZdUo9U8lObWmTSrKe2lQlu1XezczuTKU2CTKeKHLKycTljWNr4/h6LFvtQ9ZFkRLFRoP3hYu40Y3Gxd4/nhcWSJMiXjaAH/p5f58qlMDuVuPpL8AmH77v+zzAO6u3XVtZwfljR3eNnTvTOzc2ct/y8sqBpYXCfQv53M/Njo1iOJvB8W9+baqqOpatrq3N1jY2Zlu6d2cPPvzouVIWfpHr6eeep3nvQefalzrR6zTGDAWfDwPoLOXJnnvuuWcAPAMAU1NTX3j22We/BmAI9g/0GIDzAPbD7ie1DHtqz+Xg61YEn+/Fu38pagZwEcBu2D/4R4LPJ2Bfe+Oa51yE3ZS4G3bp8DjsAgir9xeC5/2A53mvwP5Fo27N/XkAOdhV5oaDrx1fc/9c8Bzt2/A1dcH+RaocXtNuz/Nec+w1lcv36YjneS859prK5fv0Ac/zjjn2mkr9PjUCONF6qLe2trW989Ff/zdTV17/0X0Nnbvr2w735UZOHGtt2X9oIjc2sjJz5VLVoQ9/dOjMi1/vrtnRuNBy8PDc8PE32tr77pnMjQ7Vzk9Oxlfvr21pKdTv7J6/OvBOS9cDD49nXvhSYvj4G2Or99e378zHW1oXxwczzbuOvO/q2MCJpqV8vnr1/h1du3JV8drlyXOnm/Y89qHRoWOvthWXV7y9H3hi7Pz3X+xs2rNvFgCmL13Ysf/xD49c/NFLHV5VZbH7wfeNX3rlBztbDtw1vVyYr5odvlK/+pzVdXVLHX33Tl85+mp7W09iqjA5EctdHa2709c0efZUw8LsTM12f01TF862DB9/I+/Sa9ru36eVwvxidW7qbAOWL1U31i9V17Uv1XXtWb54/M2Hr1VVdy7Oz/euLC7ctbRQeDo/NRkbv3Aep378wyXP8y5V19RkKysqzld5xcsHHvvQyxPvvNmwHV5TuX2fZocvN81PXL3k0msql+/T5LlT9xx43H/Ne+LuvhL/fNrQP3NLuXTjttfo+b7/7eAL3ugzAJ43xjSveeykMablJo9FcETvtxy4Rq+1WCxOsMcRVerPo/Y8ar9+tzjlszG4e/WUzwLWecrnxVe+37j3sce3dLN6sdSe53btF+fzleeOvr5v4vKl3vmZqd7F+fne5cXFnuK1a+2rj/EqKoeramKDsdq6bH1Tc7Zt7/7sgSOPXLpx4Re5nn7ueUZOHDvQee+DL0XqGj1jzFO3us/3/RHf97uNMUO+73cDGN3Q0W1P9bCzdOFQfx6151H7dSrhlM952IngdXKjQ7Ww/7IrW0zteW7XPlZbt9L7weRZAGcBfGP19isDJ1uHBwd65ybGexZyub7lhUJPbn78g7mJ8crRs6dxMv3ivF34JZ6NN+wYbNzZObD/gSOnGnd2On8d8nrp554nPz66LfesLUWpp25+FcCnAHw2+O9XSh7R9qf9TbjUn0ftedS+BMHG7jPBxxAApNKZGvz0Kp+rq0wswU78FuYnJ537g79cqD3Pnbbf1Xf3xK6+u18G8PLqbbnJidi5N984ND0y1FuYne1dLMz3zs/OfnR+ZuYTk1cu4/ybbxQrKqsuVtXUZGvq6rL1La3ZzkOHs7vvuW80igu/6OeepzA9WcMew0YrdXuFNgBfBLAP9vzSp40xE77vPwrgXxljfj14XBpAAvY81HEAnzbGfOMWT7utubafVblRfx6151H7zRec8rm6t18L7JG/HbmrI7H69s4C7PWGzq3Itp1pPzGezW5/bWUFF9461jV29nRfbmqyZzGf71teXOi5trKyZ/UxnudNV8Zig7F47UBtY+Ngc9eu7IGHHz1Tu6PR6YVf9HPPo330JBL7WW1n6s+j9jxqz5FKZ6pe+B9/9cGP/fFfTcBeXN+Adzd0z1EHFwHaT4yH1X7i8qX6i28f75m9OtpTmJvrXSoUeleWFg8DWD3SslxZVX22Kl6Tjdc3ZBta27K7EvcMdt7V48zKxPq559E+egLY6z6ER/151J5H7Qn6k4nlPz/22mh/MnERwNlUOlMLoAnALtjTPStgT/Ocwybv5xdFtS0tOopNwmrfuntPrnX3njcBvLl629LCQsW5o6/tG794oXd+Zrp3cX6+ZzGf/5mFubn/enpkGJdPnoBXUTFaFavJxmprB+uamgda9+wbPHDkkYvVNTUbttXKVtHPPU+8qcW5PQw10QtP/4rLpf48as+j9jw/ad+fTKwu2jKcSmeqYCd9HbBLZdfALuySQwQ2uN8K9Tu759ljiKrt1L66puZaz/s/dK7n/R86B+Cbq7cPn8o2Xxk42Ts3frV3IZfrXV5Y6M1NTrw/NzlRNXbuDAZ+8L1CVXX1qaqaeDbe0JBt7Ng5uPf+hwZbundt6384207to6aubadz792a6IXXAa1+x6T+PGrPo/Y8N23fn0wsw15zPp5KZwZgT+tsgT3a1wF7imcB9mhs2R1V2A6uDrzTomXmOcqhfdfh3qmuw72vAHhl9bb89FT1uTdfPzQ1NNRTmJ3pWyzM9yzMzT5VmJ35b6eGruDC8TdRUVl5qaqmZqCmrn6wrrklu/PgXdm99z0wvF0WfimH9q6aODPQ1Hnvg+xhbChdoxeS53lNxWJxmj2OqFJ/HrXnUXueO2kfrOjZBLsHbRfsKZ7LsKd4Lm34IB01dOy1hu4HH51jjyOKXGp/bWUFF98+3jV29nRPbmqydyFvj/5dW1nZu/oYz/NmK2OxbCxem43vaMw2d3UNHjjy6Om6puYt//3qUvtyM34qs7ftcOIHukYv2poB6C9cPOrPo/Y8as8Tun1/MrEAu6/saCqdOQG7hUMb7IIuzbBH+1b3+pNbmDx7Sn/hJXGpfUVlJfY/eGR4/4NHhgGkV2+fHLpSd/GtY4dnxkZ7F+Zme5YWCn35melfyE9PxScuXcCZ115ZrqyqOldVEx+sqa8faGhtG+zuTWS7exOTmzlel9qXm6mLZxvaDifYw9hQmuiFp/1NuNSfR+151J6npPbBHn5TwcfpVDpTDzvZ64Zd0MWD3bZhDsBKaUN1y8LsjHN7WpWLKLRv6d6Vb+nedRzA8dXblhYWKs6/+cae8Yvne/MzU72L8/O9i/P5RxZycz8/MzqCK5l34FVUXK2KxbKx2rpsbWNTtnXP3uzBh993YaMWfolC++1qcW42xh7DRtOpmyFpPysu9edRex6159nM9ql0phrXL+gSg53s5QA4t/pbWNpPjEftrzdyerD5ysDJw3PjV/sKubme5YWF3pWlpUN494DJQmV17FR1PD4Yr2/I7ujYmd173wODrbv3hF5IS+15tI+eaD8rMvXnUXsetefZqvbBhu07YBd02Q2gEe8u6JILPo8U7SfGo/a3Nz87U3Xu6OsHp4au9M7PTPcuFQq9y4uLPcXitebVx1RUVl6uitVkY3V12frmlsGOA4cG9j3w0NB7Lfyi9jzaR08Ae3qN8Kg/j9rzqD3PlrTvTyaKAGaCj/OpdCYOe7SvG/aIXyUitmdffftOXcNIova3V7ujcfnuJz48CGAQwD8AduGXy++8vXPkzKne3OREz0I+37e8uNCzOJ5/cm78qjdyehBvf+ebc1XVscHq2vhAbUPjYFNnV3b/Qw+faWhtWwDUnqmutcO5rS000QtPp09xqT+P2vOoPQ+lfX8yUQi+9kgqnamEPcLXAbt9Qxx2y4Y87L5+Toq3tOr0NRK1vzMVlZXYe/+Do3vvf3AUwPdXb58ZHYmfP3708MzoSG9hbq53qTDfOz8z8/H56enaicsXcfaNV1cqqqrOV9fUZCsrKi7MvvDl4909fYO7EveME19O5NQ0NTv3c6+JXnjtsHsnCYf686g9j9rz0NsHC7pMAphMpTODAFYXdNkFOz7AXtOXg0N79o0PZpr3vf8JrTZLoPYbq3FnZ+H+pz76NoC3V29bWVryzr35+u7xC+f78tNTPYvz+d7F+cJDxWsrH80PnMTQwEkc/dpXx6tiscFYvDZb29SUbdm1J3vw4UfPx2rrtHDTJpg8O9jUdd8R9jA2lK7RC8nzvMZisaiNLEnUn0ftedSeZ7u3T6UzMdhTPHfCnuZZBbugyxzsap5la+Tto/Wd9x0JvZiFlE7teU69ZLrmcvnds1fHehdyud6lhULPytLSXQCqg4csVlZXn66uiWdrGhoGG9s7Bnbfc/9g+779OsW/RBNnsntbD/VqH72Ia4O9hkI41J9H7XnUnmdbt+9PJhYBjAEYS6UzJ2EXdGmFXdClA3YRl3nYo31lZWzgRJMmGxxqz1MYG6p46Bf/xesAXl+9bSE3V3X26GsHJq9c7pmfmelbmp/vWcjnfrYwN/vx6eEhXHz7OCoqK4eqYjUDsbq6wbqm5mz7/gPZ/Q8cuVJZXa0jOus0cTq7o/VQL3sYG0oTvfCc22OjzKg/j9rzqD1P2bTvTyauwW7uPg3gbCqdqYU92rd6imcFymhBl6V8vvr2j5LNoPY8N2tfU9+wnHj8yVMATgH4R8Au/HJl4J32kVODvXOTE72LuVzv0uJC7+J4/om58asVo2dO4Z3vmlxVdWywOh7PxnfsyDbt7Brc/+CR0zvaO3Td900sFdz7udepmyFpPysu9edRex6153GlfSqdqcL1e/bVwF7Pl8M2XexH+4nxqD1Pqe1nr47Fzx87etf06HBPYXbWbvuwtNiDYrE+eMi1iqqqC9Wxmmysvj7b0NKa7Tzck93Vd8/V99r2IQq0j55oPysy9edRex6153GxfbBnXwPsnn27YCeAgJ3w5bFNFnTRfmI8as+zGe1Xlpa888eP7rp6/lxvfnqqdzGf71leXOi7trLSvfoYr6Jisqo6lq2urR2sbWwcaNm1e/DgkUfP1dQ3bPuj/xtF++gJsI2v1YgI9edRex6153GufbBn32zwcSGVztTATva6go8K2FM752BP9aTY0bVL14iRqD3PZrSvrK4uHnrkscuHHnnsMoAXV2+/ev7sjkvvnOiZHR/rXZib61laKPTlpyafzk9NxsYvnMepl3+4FCz8MlhTX5/d0d6R3XPPfdn2/QdnN3qM20FDR7dzexhqohdeZP5lY5tSfx6151F7Hufb9ycTCwBGAYym0pkTsAu6tMEu6NIMu6BLPvjYMlXxWufbb1dqz7OV7dv3H5xt33/wDQBvrN62OJ+vPPvGa/snL1/qnZ+Z7l0szPcu5HMfKszNfmx6ZBiXTrwFr6JyuLomlo3V1mXrmpoH2/btHzjw0COXy33hl8p43LltKzTRC68VdoUz4VB/HrXnUXueSLUP9uybCj5Op9KZOly/Z58Hu23DHOw2Dptm8tzppgNJf2ozv4bcnNrzsNvHautW+j70xBkAZwB8ffX2K5l32oYGB3pyE+N9C/lcz9LCQu/i/PjjcxPjFaNnT+PkSy/mq6qrT1XHa7PxhoZs487O7P4Hjpxq3Nm5La8Bvpnpi2d37HrofexhbChdoxeS53kNxWJRe5WQqD+P2vOoPY/avyuVzlTj+gVdYrCTvRzshu0bamzgRG1H373zG/28cntqz1NO7ecmxmvOv/nGoemR4d752Zm1C780BA8pVlRVXaiK1QzW1NUN1Le0DnYeOpzdfc99o9tx4ZepC2d3N+87+CNdoxdtnbD/kikc6s+j9jxqz6P2gf5kYgnAVQBXU+lMBvYUzxZcv2dfAXbiV/K/Ig8de7Wto+/eS6U+j4Sn9jzl1L6htW3h3n/ykZMATq7edm1lBeePHd119fzZ3tzUZM9iPt+3vFC4ezGfe2r26hiGBwdw/Jv/OFUVs9s+1DY2ZZu7d2UPHHnkbO2ORuopw6PvHG9p3neQOYQNp4leeBXsAUSc+vOoPY/a86j9TQQLuswEH+dT6Uwc9mhfN+ykrxIl7tlXXF7xNma0Epba85R7+4rKShx8+NErBx9+9AqA767ePn7pQsOlE28dnh0b6y3k5nqXCoXe/PTUJ/LTUzXjF8/j9Cs/Wq6srj5TVVOTjdc3DDa0tQ/sTtwzuPPQ4emtGntxZbms29+MTt0MyfO8umKx6NyqPOVC/XnUnkftedQ+vFQ6UwmgEXbCtwtAHHbLhjyAdZ+SNnF2MN56sKdsru9xidrzRKn94ny+8tybb+yduHSxb35mumdxPt+7vLjYW7x2rX31MV5FxWhVTU02VlubrWtszrbt3Z/d/9DDl6prajZ8G5iZK5d2Ne7a87JO3Yy2vQCc2lOpzKg/j9rzqD2P2ocULOgyCWAylc4MAqjHTy/osnqK5y3/snb++y92th7s0V5uBGrPE6X2sdq6ld4PPH4OwDkA31i9fSibaRnKZnrnJsZ7F3K53uWFQk9ufv4DuYmJyrFzZ5D5/ncLVdXVp6pr4gM1DTsGGzt2Zvc9+NBgc2d3Sdc2Xn79hx2Nu54u7UVtM5rohadVqLjUn0ftedSeR+1LEJziORd8XEqlMzHYUzx3wu7ZVw27oMsc7GqeP9G0Z5+Te3WVA7XnUXuguzcx2d2b+DGAH6/elpuciJ0/9sahqeHhnsLsTN9iYb6nMDf7c/OzM784NXQZF44fLVZUVl6qqqnJ1tTVZ+ubW7I7Dx3O7rn3/pH1Lvyyo3uPc9dja6InIiIiW6I/mViE3a5iLJXOnIRd0KUV1y/oMg97tE9EBABQ39K6eM+TT2UAZAC8ANiFXy68daxr7NyZ3vzUZO9CPte7vLDQt5jP+7NXxzB8Kou3vvWPM5WxWDYWrx2M72gcaO7uHjzw0CNn6pqal7ivaGtoohdeM4AR9iAiTP151J5H7XnUfpP0JxPXAEwHH2dT6Uwt7NG+XQDaF3Nz+2BP7bzjBV3kzkxfurADwAR7HFGk9utXUVmJAw89PHzgoYeHAby0evvk0JW6i2+92TMzNtpTmPvJwi+/kJ+eik9cuoAzr/54ubKq+lxVTU22pr4+W9/Sempq+MoHF+fm3n/i5Zdf2P3vCr91+Sufd+I9R4uxhKQL87nUn0ftedSeR+05UulM1eC3Xujq+cjHGmBX8qyBnfTlYK/vk00UpQVBthu13xxLCwsV546+vnfi0oXe/PRU7+L8fM/y4kJf8dq1jhseugDg31/+yuc/wxjnRtNELyTP83qKxeIgexxRpf48as+j9jxqz7PaPpXOeAAaYPfs2wV71A+wE7483mNBF7kzx/76P+998Jc/fZE9jihS+601cnqw+fUXvvyfri0vr91A78eXv/L599MGtYF06mZ4+gOFS/151J5H7XnUnuca8JMFXWaDjwupdKYGdrLXCXu0rwL21M452L37pEReVaWOApCo/dbqvKtnqnZH43dzkxPdsFvBzAMw5GFtGB3RC8nzvIZisejcqjzlQv151J5H7XnUnmc97YM9+3YAaINd0KUOdkGXfPAhd2Bs4ERtR9+9JS1VL3dG7bfe4ny+8gd//fnUQm7uca+i4mtLhcJv6xq9iPI8r69YLGpPJRL151F7HrXnUXueO2mfSmfqYI/27Yad/Hmw2zbMwW7jIOtw9K/+fP+RX30mEnu5bTdqz3PyhS8+ePfHnn5BG6ZHm1ZC4lJ/HrXnUXsetecJ3b4/mVg9kjeUSmeqYSd9HbCneMZgJ3s52AUX5BZaDtw1zR5DVKk9T9Peg87tYaiJXnhqxqX+PGrPo/Y8as9TUvv+ZGIJwFUAV1PpTAb2FM8WXL9nXwF24qfTm9ZYLszr555E7XlWCoX17axeRvTDFF4jgCH2ICJM/XnUnkftedSeZ8PaBwu6zAQf51PpTBz2aF837KSvEnYhF+3ZB2B2+Eo97CRZtpja88yNDdWxx7DRdI1eSJ7nxYvFovY3IVF/HrXnUXsetefZqvbBgi6NANphj/bFYVf8zMOuwBc505cuxJr27FtkjyOK1J4nNzbSVd/R+YpL1+hVsAdQhvazBxBx6s+j9jxqz6P2PFvSvj+ZWOlPJib7k4lBAN8D8H0A78Au4tIOe8SvERH6O9OZF7/ezR5DVKk9z4WXv9fJHsNG06mb4elfWbjUn0ftedSeR+15trx9cIrnXPBxKZXOxGBP8dwJoAtANeyCLnOM8W2V6ro67UdIovY81XH32muiF944ewARp/48as+j9jxqz0Nv359MLAIYAzCWSmdOwi7o0orrF3SZh13QxRkdffdq5UcStedpvatXq24KumEv5hYO9edRex6151F7nm3Vvj+ZuAZgOvg4m0pnamGP9u2CPcWzAo4s6HLl6KvtnfcdcWryq2TDbAAAIABJREFUWi7UnmfkxJutrYd62cPYUJrohaeVkLjUn0ftedSeR+15tnX7/mRiHvZo3nAqnamCvY5vJ+wEtQZ2QZcc7BYOZaWtJzHFHkNUqT1Py8Ee546maqIXXpw9gIhTfx6151F7HrXnKZv2/cnEMuwG7xOpdGYAQAPsnn2rR/sAO+HLw04At7XC5ESMPYaoUnuehekp59prohdeA3sAEaf+PGrPo/Y8as9Tlu2DBV1mg48LqXSmBvYUz07Yo30VsKd2zsGe6rnt5K6OOrefWLlQe578xFgtewwbTfvohaQ9lbjUn0ftedSeR+15XGwf7Nm3A0Ab7IIudbALuuSDj21Be7nxqD2P9tETQHsqsak/j9rzqD2P2vM41z7Ys2+qP5k43Z9MvATgJQDHYa/1W92zrwlAJXGY2suNSO15tI+eAGV4UbVj1J9H7XnUnkfteZxv359MrB7JG0qlM9Wwk7wO2FM8Y7DX880BWNjKcdXsaNzSryfvUnueWMMO546kaqIXnlZD4lJ/HrXnUXseteeJVPv+ZGIJdqXRq6l0JgN7imcL7Cmeaxd0ycGe7rlpWg4entvM55dbU3ue5r0HnWuviV54XbB76AiH+vOoPY/a86g9T2TbBwu6zAQf51PpTBz2aF837BG/Smzinn3Dx99o637wUef+0lsO1J5n9OTx1rbDCfYwNpQmeuE5c4FmmVJ/HrXnUXsetedR+0B/MlGAPZo3Eizo0gh7lG837DYU12BPAZ3fiK/X3nfP5EY8j4Sn9jyth/qc+4clTfTCq4fdK0c41J9H7XnUnkftedT+JvqTiRUAkwAmU+nMKdiVO9fu2efBXtM3hzvcsy83OlQLezRRtpja8+THR8tm78710kQvPO1vwqX+PGrPo/Y8as+j9rcRnOKZCz4updKZGOwpnjthT32tBrACO+lb90IT85OTzv2Ft1yoPU9herKGPYaNpn30QnJxX59yov48as+j9jxqz6P2pUmlMxWwC7q0wh7t2wG7iMs87MTwlrSXG4/a82gfPQEc3NenzKg/j9rzqD2P2vOofQn6k4lr/cnEdH8ycbY/mfgBgO8BOAZ7dK8NdlGXZtzk7C7t5caj9jzaR08Ae7Gz8Kg/j9rzqD2P2vOo/QbqTybmYY/mDafSmSrYBV12wq7kWQN7PV8OQKG2pUVHUknUnife1OLcHoaa6IX3nqc7yKZTfx6151F7HrXnUftN0p9MLMMudDORSmcGADRgzYIurYf66oLb8rjDBV3kztTv7N6QlVMlvLq2nc5NsnXqZngd7AFEnPrzqD2P2vOoPY/ab4H+ZKLYn0zM9icTF/qTiZcBfPfNv/7cZdiJYDPs96EFdmEX2WRXB95pYY8hqibODDSxx7DRNNELb5g9gIhTfx6151F7HrXnUXuC/mRi4dxL33qnP5l4C4AB8DKAswBisJO+dmhF1E3T9cDD4+wxRNXOux9wbjsXnboZXjMA5zZULCPqz6P2PGrPo/Y8as/TDGC6P5m4BmAq+DidSmfqYLdvWLtn3yLsAi8rpLE6ZfLsqYbuBx+dY48jiqYunm1oO5xgD2NDaaIXnvY34VJ/HrXnUXsetedRe56btu9PJvKw1+0NpdKZathJXwfsgi4x2Ov55mA3bJc7sDA749xebuVicW42xh7DRtM+eiFpXx8u9edRex6151F7HrXnCds+lc54sPv0tQDYHXwOAAXYRXX0l8110j56PNpHTwDt68Om/jxqz6P2PGrPo/Y8odoHC7rM9CcT5/uTiR/C7tn3Juwpny14jz375HraR49H++gJYE9JEB7151F7HrXnUXsetecpqX1/MlGAPZo3kkpnKmH37GuHPdoXhz3FMw+7r5+sUd++U/tHktS1djj386iJXng6jYRL/XnUnkftedSeR+15Nqx9fzKxAmASwGQqnTkFu2LnT/bsg13QZQF2chn5PfviLa06bZOkpqnZufaa6IXXDkBL3/KoP4/a86g9j9rzqD3PprTvTyaKsNfs5QBcSqUzMdgFXXYC6ILdq28FdtLn3F+612N8MNO87/1PaLVZgsmzg01d9x1hD2NDaaIX3hB7ABGn/jxqz6P2PGrPo/Y8W9K+P5lYBDAGYCyVzpyEXcSlFfZoXwfsIi7zsKd5RmJBl11H3neVPYao6rz3Ie2jJ2gDMMMeRISpP4/a86g9j9rzqD3PlrcP9uybDj7OptKZWly/Z18FgCXYo33LWzm2rTQ2cKKp874jOfY4omjidHZH66Fe9jA2lCZ64Tm3x0aZUX8etedRex6151F7Hnr7/mRiHvZo3nAqnamCXdClA3biVwN7PV8Ojl3LuZTPV7PHEFVLBffaax+9kLSvD5f686g9j9rzqD2P2vNs5/bBnn0NsNs17IY96ufh3VM8y3pBF+2jx+PiPnolHdHzfb8VwN8COADgHICnjTGTNzzmIQB/CvsvMSsA/ndjzN+W8nXJ9gMYYA8iwtSfR+151J5H7XnUnmfbtg8WdJkNPi6m0pka2MleJ+yCLhV4d0GXJdY479SZF7/efeRXnznPHkcUXXj5e513f+xp9jA2VKkbpv8uAGOM6QFggl/fKA/gXxpj7gXwUQD/wff95hK/LpOuF+BSfx6151F7HrXnUXuesmnfn0ws9CcTo/3JxFuwfxf9MYCzsKefdsBe31dHHGIoO7p26fo8koaObuf2MCz1Gr2PA3gy+Px5AN8F8DtrH2CMya75/Irv+6Owv/GmSvzaLM5eAFwm1J9H7XnUnkftedSepyzbBwu6TAUfp1PpTB2uX9DFg922YQ72qN+2UxWvLcv2LqiMx7flz0QpSp3odRpjVpfgHYY9bH5Lvu8/BvsvLKdvdv9zzz33DIBnAGBqauoLzz777Ndgl/htC/6/87CnE8zAvgm1ArgcfN2K4PO9eHcS2QzgIuw53NcAjASfT8C+9sY1z7kIu2dMN4CrAOKw54Cv3l8InvcRz/NeAVAP+y9Eq/fnYS8K7ghaNAfPsXr/XPAc7dvwNXXBLm9cDq9pt+d5rzn2msrl+3TE87yXHHtN5fJ9esTzvGOOvaZy+T7d53neFcdeU7l8n9o9z2t17DWVy/cpFrR34TUBwFzvz//CwPzk+L1d9x3Zse+DT+4cy7zd1Xzgrsnc6PDyzOWLlYc+/NGhMy9+vbtmR+NCy8HDc8PH32hr77tnMjc6VDs/ORlfvb+2paVQv7N7/urAOy1dDzw8Pnn2VMPC7EzN6v317Tvz8ZbWxfHBTPOuI++7OjZwomkpn69evX9H165cVbx2efLc6aY9j31odOjYq23F5RVv7weeGDv//Rc7Z4cvN11bXqqYvnRhx/7HPzxy8UcvdXhVlcXuB983fumVH+xsOXDX9HJhvmp2+Er96nNW19UtdfTdO33l6KvtbT2JqcLkRCx3dbRuu7ympj37ZgFgu7+myXOnelYWCj/ynri7b4N/P5X0HlEsFu/4NOrbLsbi+/63gy94o88AeN4Y07zmsZPGmJZbPE837BG/TxljXr7TAbN5ntdQLBbn2OOIKvXnUXsetedRex6153G9fbCgyw4ALbB/Kd8R3FWAnZjSViocGzhR29F37zzr60fZ1IWzu5v3HfxRpBZjMcY8dav7fN8f8X2/2xgzFEzkRm/xuEYA/wDgM+U8yQt0wv4LlHCoP4/a86g9j9rzqD2P0+2DBV1mgo/zqXQmDnuKZxeAnQAqQdqzb+jYq20dffde2sqvKdboO8dbmvcdZA9jQ5V66uZXAXwKwGeD/37lxgf4vh8D8GUAnzfGfKnEr7cdlLqAjZRG/XnUnkftedSeR+15ItW+P5kowB7NG0mlM5Wwp9u1wx7ti8OekpeH3cJhUxWXV7zN/hpyc8WVZefalzrR+yyAL/q+/2nY80ufBgDf9x8F8K+MMb8e3PYEgDbf938t+P9+zRjzZolfm+UyewARp/48as+j9jxqz6P2PJFt359MrACYBDCZSmdOwV5zuLpn3+qCLguwR/s2fM++vR94wpnTBstN95GfGWePYaNpw/SQPM/rK+WiSCmN+vOoPY/a86g9j9rzqP3NpdKZGOwpnjthT/Oshp3szcIutFGyo3/15/u1jx7HyRe++ODdH3v6hUhdoyc/pVy3hXCF+vOoPY/a86g9j9rzqP1N9CcTi7CrIY6l0pmTsIu4tOLd7RsAe3pnHne4oMvqCpWy9XZ073HuulRN9EREREREQgj27JsOPs6m0pla2KN93bDbSVSAtKCLyCpN9MJrht0jQzjUn0ftedSeR+151J5H7UPqTybmYY/mDafSmdX90zpgj/bVwJ7imYNd9OWWpi9d2IF39/+TLTQ7dKmBPYaNpmv0QvI8r65YLObZ44gq9edRex6151F7HrXnUfuNE+zZV4939+xrgl3QZXXPvusWdJk4OxhvPdjznpNB2RwzVy7taty152VdoxdtuwEMsgcRYerPo/Y8as+j9jxqz6P2GyTYs28u+LiYSmdqYCd7nbALulTCnto5B2Dp4o9e6mg92HORNd4oGzr647bGXXvYw9hQmuiFt+FL6Uoo6s+j9jxqz6P2PGrPo/abpD+ZWAAwCmA0lc6cgD3Fsw32FM/m2rb2ZgDjsAu6yBbyKqucO81RE73wdM46l/rzqD2P2vOoPY/a86j9FggWdJkKPk6n0pm6ylisG/YaydU9+xZhj/at0AYaETvveWCSPYaNVsEeQBnazR5AxKk/j9rzqD2P2vOoPY/aE/QnE3nz+89W9ScTrwP4DoBXAQwBaIBd2KUNQJw4RKcNHXu1/faPKi86oheeVkLiUn8etedRex6151F7HrXnmQCA/mRiCfYUzvFUOpOB3bNvdUGXjuCx87ALujh3yiFD096Dzu1hqIleeGrGpf48as+j9jxqz6P2PGrP81PtgwVdZoKP86l0Jg67oEsX7KIu2rNvA6wUCpXsMWw0/UYOrxH2MLpwqD+P2vOoPY/a86g9j9rz3LZ9fzJRgN2eYSSVzlQG/0877NG+OOxiOnnYI36yTnNjQ3XsMWw07aMXkud58WKxqP1NSNSfR+151J5H7XnUnkfteUppH+zZVwe7mMtu2FM9PQALsKd4akGX95AbG+mq7+h8RfvoRdt+AAPsQUSY+vOoPY/a86g9j9rzqD3PHbcPTvHMBR+XU+lMDPYUz52wp3lWwx7tm4VdzVPWuPDy9zrv/tjT7GFsKE30wtNvDC7151F7HrXnUXsetedRe54Na9+fTCwCGAMwlkpnTsIu6NIKu2dfB+wiLvOwp3lG/hS/6njdEnsMG00TvfDG2QOIOPXnUXsetedRex6151F7nk1pH+zZNx18nE2lM7WwR/u6YSd9kV/QpfWuXq26KeiGXfVIONSfR+151J5H7XnUnkftebakfX8yMQ97NG84lc5UwS7o0gF7tK8G9hTPHOyiL5EwcuLN1tZDvexhbChN9MK7yh5AxKk/j9rzqD2P2vOoPY/a82x5+/5kYhl2/76JVDqTBVCPn96zrwA78bu21ePbKi0He6bZY9homuiFF2cPIOLUn0ftedSeR+151J5H7Xmo7YMFXeaCj4updKYG9hTPTtgFXSphT+2cgz3V0xkL01Mx9hg2miZ64TWwBxBx6s+j9jxqz6P2PGrPo/Y826p9fzKxAGAUwGgqnTkBe4pnG+wpnk3Bw/LBR1nLT4zVssew0bSPXkjaW4ZL/XnUnkftedSeR+151J6nnNqn0pk62MneLtgN2z3YVUPnUIZ79rm4j14FewBlaD97ABGn/jxqz6P2PGrPo/Y8as9TNu37k4l8fzIx1J9MvA7gOwBeBXAF9qhkB+yRv7I5DfjCy9/rZI9ho+nUzfDK4l9ZHKb+PGrPo/Y8as+j9jxqz1OW7fuTiSXYrSHGU+nMAOyefTcu6DIPu6DLtjydMNaww7n9IzXRC2+KPYCIU38etedRex6151F7HrXnKfv2wYIuM8HH+VQ6E4c9xbMLwE7YMwpXF3TZNnv2Ne89OMcew0bTRC+8LtjNJoVD/XnUnkftedSeR+151J7Hufb9yUQB9kjlSCqdqYRd0KUd9mhfHHbLhjzsET+a0ZPHW9sOJ5hD2HCa6IXnzAWaZUr9edSeR+151J5H7XnUnsfp9v3JxAqASQCTqXTmFIA6AM24fkGXBdhTPLd0QZfWQ31OTbABTfTuRD3sppLCof48as+j9jxqz6P2PGrPE5n2wSmeueDjciqdicGe4rkT9shmNezRvjnYyd+myo+Pls3CMeuliV54dewBRJz686g9j9rzqD2P2vOoPU9k2/cnE4uwRzTHUunMSdgFXVphj/Z1wC7iMg97mueGL+hSmJ6s2ejnZNM+eiGV0/4mLlJ/HrXnUXsetedRex6151H7m0ulM7WwR/u6YU/xrASwhA1c0EX76AlQRvubOEr9edSeR+151J5H7XnUnkftb6I/mZjvTyaG+5OJowBeBPAKgEuwR0A3ZM8+7aMngD1cLDzqz6P2PGrPo/Y8as+j9jxqfxv9ycQy7HWME6l0Jgt7XWMzgD14d8++Auy1f9fW+7zxppZNvw5wq2miF16OPYCIU38etedRex6151F7HrXnUfsQggVd5oKPS6l0pgb2FM9O2AVdKmFX75yFPdXzluradjp3yqwmeuF1ICKrIW1T6s+j9jxqz6P2PGrPo/Y8al+C/mRiAcAogNFUOnMCds++NtgFXZqCh+VxkyOnE2cGmjrvfXCrhrolNNELb5g9gIhTfx6151F7HrXnUXsetedR+w3Sn0xcAzAVfJxOpTN1eHdBlw7YPfsWYY8Gruy8+wHnJtia6IXXDMC5DRXLiPrzqD2P2vOoPY/a86g9j9pvkv5kYvVI3lAqnamGPdrXAXu0LzY7fLm97XCCOcQNp4leeM5tplhm1J9H7XnUnkftedSeR+151H4L9CcTSwDGAYyn0pkBAA3nf/Diwwce9526RlL76IWk/U241J9H7XnUnkftedSeR+151J7HxfbaRy887W/Cpf48as+j9jxqz6P2PGrPo/Y8zrXXRC+8OfYAIk79edSeR+151J5H7XnUnkfteZxrr4leeE4d0i1D6s+j9jxqz6P2PGrPo/Y8as/jXHtN9MJrZw8g4tSfR+151J5H7XnUnkftedSex7n2muiFN8QeQMSpP4/a86g9j9rzqD2P2vOoPY9z7TXRC6+NPYCIU38etedRex6151F7HrXnUXse59prohdejD2AiFN/HrXnUXsetedRex6151F7Hufaax+9kFzcY6OcqD+P2vOoPY/a86g9j9rzqD2Pi+11RC885/bYKDPqz6P2PGrPo/Y8as+j9jxqz+Nce030wpthDyDi1J9H7XnUnkftedSeR+151J7Hufaa6IW3zB5AxKk/j9rzqD2P2vOoPY/a86g9j3PtNdELr5U9gIhTfx6151F7HrXnUXsetedRex7n2muiF95l9gAiTv151J5H7XnUnkftedSeR+15nGuviV54newBRJz686g9j9rzqD2P2vOoPY/a8zjXXhO98NSMS/151J5H7XnUnkftedSeR+15nGuvffRC8jyvrlgs5tnjiCr151F7HrXnUXsetedRex6153GxvXMz1y2wlz2AiFN/HrXnUXsetedRex6151F7Hufaa6IX3hR7ABGn/jxqz6P2PGrPo/Y8as+j9jzOtddET0RERERExDGa6IXXzB5AxKk/j9rzqD2P2vOoPY/a86g9j3PttRhLSC5eqFlO1J9H7XnUnkftedSeR+151J7HxfY6ohfebvYAIk79edSeR+151J5H7XnUnkfteZxrr4leeNfYA4g49edRex6151F7HrXnUXsetedxrr0meuGNsAcQcerPo/Y8as+j9jxqz6P2PGrP41x7TfTCc+6wbplRfx6151F7HrXnUXsetedRex7n2muiF94EewARp/48as+j9jxqz6P2PGrPo/Y8zrXXRC+8KvYAIk79edSeR+151J5H7XnUnkfteZxrr4leeI3sAUSc+vOoPY/a86g9j9rzqD2P2vM411776IXkeV68WCwW2OOIKvXnUXsetedRex6151F7HrXncbG9juiFt589gIhTfx6151F7HrXnUXsetedRex7n2muiF94iewARp/48as+j9jxqz6P2PGrPo/Y8zrXXRC+8cfYAIk79edSeR+151J5H7XnUnkfteZxrX9LqMr7vtwL4WwAHAJwD8LQxZvKGx+wH8GXYSWU1gD82xvxZKV+XrBvADHsQEab+PGrPo/Y8as+j9jxqz6P2PM61L/WI3u8CMMaYHgAm+PWNhgB8wBjzEICfAfC7vu/vKvHrMl1lDyDi1J9H7XnUnkftedSeR+151J7Hufal7hfxcQBPBp8/D+C7AH5n7QOMMWvPd61B+Z8uGmcPIOLUn0ftedSeR+151J5H7XnUnse59qVO9DqNMUPB58MAOm/2IN/39wL4BwCHAfy2MebKzR733HPPPQPgGQCYmpr6wrPPPvs12COCbQBiAM7DrogzA2AZQCuAy8HXrQg+3wtgKnjKZgAXAewGcA3ASPD5BOxrb1zznIuw5+Z2w87o4wAa1txfCJ73fs/z5gHUA6hbc38eQA5AR9CiOXiO1fvngudo34avqQvAWJm8pt2e58069prK5ft0v+d5k469pnL5Pt3ved41x15TuXyfej3PW/v/u/CayuX71B60d+k1lcv3KRa0d+k1lcv3qc3zvGXHXlO5fJ/6PM8b326vqVgsDuAO3XYfPd/3vx18wRt9BsDzxpjmNY+dNMa0vMdz7QLw9wA+ZowZubMhc7m4x0Y5UX8etedRex6151F7HrXnUXseF9vf9oieMeapW93n+/6I7/vdxpgh3/e7AYze5rmu+L7/NoAkgC+FHu32sB/AHc+spWTqz6P2PGrPo/Y8as+j9jxqz+Nc+1Kvl/sqgE8Fn38KwFdufIDv+3t8368NPm8B8DjKO6JTM/0ypP48as+j9jxqz6P2PGrPo/Y8zrUvdaL3WQAf8X1/EMBTwa/h+/6jvu9/LnjM3QB+7Pv+MQDfA/DvjDFvlfh1maZu/xDZROrPo/Y8as+j9jxqz6P2PGrP41z7216jJ9fzPK+vlIsipTTqz6P2PGrPo/Y8as+j9jxqz+Ni+3Lf6oBhjD2AiFN/HrXnUXsetedRex6151F7Hufaa6IXXj17ABGn/jxqz6P2PGrPo/Y8as+j9jzOtddEL7w69gAiTv151J5H7XnUnkftedSeR+15nGuva/RCcnGPjXKi/jxqz6P2PGrPo/Y8as+j9jwuttcRvfD2swcQcerPo/Y8as+j9jxqz6P2PGrP41x7TfTCy7MHEHHqz6P2PGrPo/Y8as+j9jxqz+Nce030wsuxBxBx6s+j9jxqz6P2PGrPo/Y8as/jXHtN9MLrYA8g4tSfR+151J5H7XnUnkftedSex7n2muiFN8weQMSpP4/a86g9j9rzqD2P2vOoPY9z7TXRC6+ZPYCIU38etedRex6151F7HrXnUXse59prohdenD2AiFN/HrXnUXsetedRex6151F7Hufaax+9kFzcY6OcqD+P2vOoPY/a86g9j9rzqD2Pi+11RC885/bYKDPqz6P2PGrPo/Y8as+j9jxqz+Nce030wptjDyDi1J9H7XnUnkftedSeR+151J7Hufaa6IXn1CHdMqT+PGrPo/Y8as+j9jxqz6P2PM6110QvvHb2ACJO/XnUnkftedSeR+151J5H7Xmca6+JXnhD7AFEnPrzqD2P2vOoPY/a86g9j9rzONdeE73w2tgDiDj151F7HrXnUXsetedRex6153GuvSZ64cXYA4g49edRex6151F7HrXnUXsetedxrr320QvJxT02yon686g9j9rzqD2P2vOoPY/a87jYXkf0wnNuj40yo/48as+j9jxqz6P2PGrPo/Y8zrXXRC+8GfYAIk79edSeR+151J5H7XnUnkfteZxrr4leeMvsAUSc+vOoPY/a86g9j9rzqD2P2vM4114TvfBa2QOIOPXnUXsetedRex6151F7HrXnca69JnrhXWYPIOLUn0ftedSeR+151J5H7XnUnse59prohdfJHkDEqT+P2vOoPY/a86g9j9rzqD2Pc+010QtPzbjUn0ftedSeR+151J5H7XnUnse59tpHLyTP8+qKxWKePY6oUn8etedRex6151F7HrXnUXseF9s7N3PdAnvZA4g49edRex6151F7HrXnUXsetedxrr0meuFNsQcQcerPo/Y8as+j9jxqz6P2PGrP41x7TfREREREREQco4leeM3sAUSc+vOoPY/a86g9j9rzqD2P2vM4116LsYTk4oWa5UT9edSeR+151J5H7XnUnkfteVxsryN64e1mDyDi1J9H7XnUnkftedSeR+151J7Hufaa6IV3jT2AiFN/HrXnUXsetedRex6151F7Hufaa6IX3gh7ABGn/jxqz6P2PGrPo/Y8as+j9jzOtddELzznDuuWGfXnUXsetedRex6151F7HrXnca69JnrhTbAHEHHqz6P2PGrPo/Y8as+j9jxqz+Nce030wqtiDyDi1J9H7XnUnkftedSeR+151J7Hufaa6IXXyB5AxKk/j9rzqD2P2vOoPY/a86g9j3PttY9eSJ7nxYvFYoE9jqhSfx6151F7HrXnUXsetedRex4X2+uIXnj72QOIOPXnUXsetedRex6151F7HrXnca69JnrhLbIHEHHqz6P2PGrPo/Y8as+j9jxqz+Nce030whtnDyDi1J9H7XnUnkftedSeR+151J7Hufaa6IXXzR5AxKk/j9rzqD2P2vOoPY/a86g9j3PtNdEL7yp7ABGn/jxqz6P2PGrPo/Y8as+j9jzOtddEL7w4ewARp/48as+j9jxqz6P2PGrPo/Y8zrXXRC+8BvYAIk79edSeR+151J5H7XnUnkfteZxrr330QnK2f1+wAAAXnElEQVRxj41yov48as+j9jxqz6P2PGrPo/Y8LrbXEb3wnNtjo8yoP4/a86g9j9rzqD2P2vOoPY9z7TXRC8+pmX4ZUn8etedRex6151F7HrXnUXse59prohfeFHsAEaf+PGrPo/Y8as+j9jxqz6P2PM6110QvvC72ACJO/XnUnkftedSeR+151J5H7Xmca6+JXnhj7AFEnPrzqD2P2vOoPY/a86g9j9rzONdeE73w6tkDiDj151F7HrXnUXsetedRex6153GuvSZ64dWxBxBx6s+j9jxqz6P2PGrPo/Y8as/jXHvtoxeSi3tslBP151F7HrXnUXsetedRex6153GxvY7ohefcHhtlRv151J5H7XnUnkftedSeR+15nGuviV54efYAIk79edSeR+151J5H7XnUnkfteZxrr4leeDn2ACJO/XnUnkftedSeR+151J5H7Xmca6+JXngd7AFEnPrzqD2P2vOoPY/a86g9j9rzONdeE73whtkDiDj151F7HrXnUXsetedRex6153GuvSZ64TWzBxBx6s+j9jxqz6P2PGrPo/Y8as/jXHtN9MKLswcQcerPo/Y8as+j9jxqz6P2PGrP41x77aMXkot7bJQT9edRex6151F7HrXnUXsetedxsb2O6IXn3B4bZUb9edSeR+151J5H7XnUnkfteZxrr4leeHPsAUSc+vOoPY/a86g9j9rzqD2P2vM4114TvfCcOqRbhtSfR+151J5H7XnUnkftedSex7n2VaX8z77vtwL4WwAHAJwD8LQxZvIWj20E8A6AvzfG/GYpX5esHcA4exARpv48as+j9jxqz6P2PGrPo/Y8zrUv9Yje7wIwxpgeACb49a38IYCXSvx628EQewARp/48as+j9jxqz6P2PGrPo/Y8zrUvdaL3cQDPB58/D+Cf3+xBvu8/AqATwDdL/HrbQRt7ABGn/jxqz6P2PGrPo/Y8as+j9jzOtS/p1E0AncaY1dnvMOxk7jq+71cA+PcA/gWAp97ryZ577rlnADwDAFNTU1949tlnvwY7u24DEANwHnZFnBkAywBaAVwOvm5F8PleAFPBUzYDuAhgN4BrAEaCzydgX3vjmudchD1c2w3gKuxeGg1r7i8Ez9vned40gHoAdWvuzwPIAegIWjQHz7F6/1zwHO3b8DV1ARgrk9e02/M8115TuXyf+jzPW30drrymcvk+9XmeV3DsNZXL9+mA53lrx+zCayqX71Nr0N6l11Qu36eY53l9jr2mcvk+tXmel3fsNZXL96nP87yh7faaisXiAO7QbffR833/28EXvNFnADxvjGle89hJY0zLDf//bwKoM8b8n77v/xqAR8v5Gj0X99goJ+rPo/Y8as+j9jxqz6P2PGrP42L7kjZM931/AMCTxpgh3/e7AXzXGNN3w2O+ACAJO8ttgP0XgD8xxrzX9Xzblud5faXMrKU06s+j9jxqz6P2PGrPo/Y8as/jYvtST938KoBPAfhs8N+v3PgAY8yvrH6+5oheWU7yAjPsAUSc+vOoPY/a86g9j9rzqD2P2vM4177UxVg+C+Ajvu8Pwl5/91kA8H3/Ud/3P1fq4LapZfYAIk79edSeR+151J5H7XnUnkfteZxrX9Kpm1Hk4mHdcqL+PGrPo/Y8as+j9jxqz6P2PC62L/WIXhRdZg8g4tSfR+151J5H7XnUnkftedSex7n2muiF91NbSMiWUn8etedRex6151F7HrXnUXse59proheemnGpP4/a86g9j9rzqD2P2vOoPY9z7XWNXkie59UVi8U8exxRpf48as+j9jxqz6P2PGrPo/Y8LrZ3bua6BfayBxBx6s+j9jxqz6P2PGrPo/Y8as/jXHtN9MKbYg8g4tSfR+151J5H7XnUnkftedSex7n2muiJiIiIiIg4RhO98JrZA4g49edRex6151F7HrXnUXsetedxrr0WYwnJxQs1y4n686g9j9rzqD2P2vOoPY/a87jYXkf0wtvNHkDEqT+P2vOoPY/a86g9j9rzqD2Pc+010QvvGnsAEaf+PGrPo/Y8as+j9jxqz6P2PM6110QvvBH2ACJO/XnUnkftedSeR+151J5H7Xmca6+JXnjOHdYtM+rPo/Y8as+j9jxqz6P2PGrP41x7TfTCm2APIOLUn0ftedSeR+151J5H7XnUnse59prohVfFHkDEqT+P2vOoPY/a86g9j9rzqD2Pc+010QuvkT2AiFN/HrXnUXsetedRex6151F7Hufaax+9kDzPixeLxQJ7HFGl/jxqz6P2PGrPo/Y8as+j9jwuttcRvfD2swcQcerPo/Y8as+j9jxqz6P2PGrP41x7TfTCW2QPIOLUn0ftedSeR+151J5H7XnUnse59prohTfOHkDEqT+P2vOoPY/a86g9j9rzqD2Pc+010Quvmz2AiFN/HrXnUXsetedRex6151F7Hufaa6IX3lX2ACJO/XnUnkftedSeR+151J5H7Xmca6+JXnhx9gAiTv151J5H7XnUnkftedSeR+15nGuviV54DewBRJz686g9j9rzqD2P2vOoPY/a8zjXXvvoheTiHhvlRP151J5H7XnUnkftedSeR+15XGyvI3rhObfHRplRfx6151F7HrXnUXsetedRex7n2muiF55TM/0ypP48as+j9jxqz6P2PGrPo/Y8zrXXRC+8KfYAIk79edSeR+151J5H7XnUnkfteZxrr4leeF3sAUSc+vOoPY/a86g9j9rzqD2P2vM4114TvfDG2AOIOPXnUXsetedRex6151F7HrXnca69Jnrh1bMHEHHqz6P2PGrPo/Y8as+j9jxqz+Nce030wqtjDyDi1J9H7XnUnkftedSeR+151J7HufbaRy8kF/fYKCfqz6P2PGrPo/Y8as+j9jxqz+Niex3RC8+5PTbKjPrzqD2P2vOoPY/a86g9j9rzONdeE73w8uwBRJz686g9j9rzqD2P2vOoPY/a8zjXXhO98HLsAUSc+vOoPY/a86g9j9rzqD2P2vM4114TvfA62AOIOPXnUXsetedRex6151F7HrXnca69JnrhDbMHEHHqz6P2PGrPo/Y8as+j9jxqz+Nce030wmtmDyDi1J9H7XnUnkftedSeR+151J7Hufaa6IUXZw8g4tSfR+151J5H7XnUnkftedSex7n22kcvJBf32Cgn6s+j9jxqz6P2PGrPo/Y8as/jYnsd0QvPuT02yoz686g9j9rzqD2P2vOoPY/a8zjXXhO98ObYA4g49edRex6151F7HrXnUXsetedxrr0meuE5dUi3DKk/j9rzqD2P2vOoPY/a86g9j3PtNdELr509gIhTfx6151F7HrXnUXsetedRex7n2muiF94QewARp/48as+j9jxqz6P2PGrPo/Y8zrXXRC+8NvYAIk79edSeR+151J5H7XnUnkfteZxrr4leeDH2ACJO/XnUnkftedSeR+151J5H7Xmca6999EJycY+NcqL+PGrPo/Y8as+j9jxqz6P2PC621xG98JzbY6PMqD+P2vOoPY/a86g9j9rzqD2Pc+010Qtvhj2AiFN/HrXnUXsetedRex6151F7Hufaa6IX3jJ7ABGn/jxqz6P2PGrPo/Y8as+j9jzOtddEL7xW9gAiTv151J5H7XnUnkftedSeR+15nGuviV54l9kDiDj151F7HrXnUXsetedRex6153GuvSZ64XWyBxBx6s+j9jxqz6P2PGrPo/Y8as/jXHtN9MJTMy7151F7HrXnUXsetedRex6153GuvfbRC8nzvLpisZhnjyOq1J9H7XnUnkftedSeR+151J7HxfbOzVy3wF72ACJO/XnUnkftedSeR+151J5H7Xmca6+JXnhT7AFEnPrzqD2P2vOoPY/a86g9j9rzONdeEz0RERERERHHaKIXXjN7ABGn/jxqz6P2PGrPo/Y8as+j9jzOtddiLCG5eKFmOVF/HrXnUXsetedRex6151F7Hhfb64heeLvZA4g49edRex6151F7HrXnUXsetedxrr0meuFdYw8g4tSfR+151J5H7XnUnkftedSex7n2muiFN8IeQMSpP4/a86g9j9rzqD2P2vOoPY9z7TXRC8+5w7plRv151J5H7XnUnkftedSeR+15nGuviV54E+wBRJz686g9j9rzqD2P2vOoPY/a8zjXXhO98KrYA4g49edRex6151F7HrXnUXsetedxrr0meuE1sgcQcerPo/Y8as+j9jxqz6P2PGrP41z7kvbR832/FcDfAjgA4ByAp40xkzd53AqAt4JfXjDG/LM7/qJknufFi8VigT2OqFJ/HrXnUXsetedRex6151F7Hhfbl3pE73cBGGNMDwAT/Ppm5o0xDwUfZTvJC+xnDyDi1J9H7XnUnkftedSeR+151J7HufalTvQ+DuD54PPnAfzzEp+vHCyyBxBx6s+j9jxqz6P2PGrPo/Y8as/jXPtSLzrsNMYMBZ8PA+i8xePivu+/BmAZwGeNMX9/swc999xzzwB4BgCmpqa+8Oyzz34NwBCANgAxAOdhZ9szwXO1ArgcfN2K4PO9AKaCp2wGcBF2udRrsPtj7IZdVacK9lzc1edcBDAOoBvAVQBxAA1r7i8Ez9vied40gHoAdWvuzwPIAegIWjQHz7F6/1zwHO3b8DV1ARgrk9cU8zyv0bHXVC7fpxbP81ZfhyuvqVy+Ty2e5xUce03l8n2q8zyvz7HXVC7fp6WgvUuvqVy+TxNBe5deU7l8n5Y9z+t07DWVy/epxfO8oe32morF4gDu0G2v0fN9/9vBF7zRZwA8b4xpXvPYSWNMy02eY7cx5rLv+4cAfAeAb4w5faeDZvI8r6+U4FIa9edRex6151F7HrXnUXsetedxsf1tj+gZY5661X2+74/4vt9tjBnyfb8bwOgtnuNy8N8zvu9/F8ARAGU50fuLv/iLnwXg1A9BOVF/HrXnUXsetedRex6151F7Hhfbl3qN3lcBfCr4/FMAvnLjA3zfb/F9vyb4vB3AhwC8U+LXZXqGPYCIU38etedRex6151F7HrXnUXse59qXOtH7LICP+L4/COCp4Nfwff9R3/c/FzzmbgCv+b5/DMCLsNfolfNET0REREREZFsraTEWY8w4AP8mt78G4NeDz38I4P5Svo6IiIiIiIisX6lH9KLoz9kDiDj151F7HrXnUXsetedRex6153Gu/W1X3RQREREREZHyoiN6IiIiIiIijtFET0RERERExDElLcbiGt/3/wLAfwNg1Bhz303u9wD8RwD/FYA8gF8zxrwR3PcpAP82eOj/Zox5fmtG7YZ1tP8VAL8DwAMwC+BfG2OOBfedC25bAbBsjHl0q8btgnW0fxJ265SzwU1/Z4z5g+C+j8L+nqgE8DljzGe3ZNCOWEf73wbwK8Evq2BXMe4wxkzo5740vu/vBfB5AJ0AigD+3BjzH294jN7zN8E62+s9fxOss/2T0Hv+hltne73nbwLf9+MAXgJQA9v1S8aY37/hMTWw359HAIwD+O+MMeeC+34PwKdh2/8bY8w3tm70pdERvev9JYCPvsf9Pw+gJ/h4BsCfAoDv+60Afh/AzwB4DMDv+77fsqkjdc9f4r3bnwXws8aY+wH8IX76gtkPG2Me0hvfHflLvHd7AEgHfR9a8wd+JYD/B/b3xT0Afsn3/Xs2daTu+Uu8R3tjzB+tdgfwewC+Z4yZWPMQ/dzfuWUA/7Mx5h4A7wfwGzf5+dV7/uZYT3u952+O9bQH9J6/GW7bXu/5m2YBwD8xxjwI4CEAH/V9//03PObTACaNMYcB/N8A/g8ACL5HnwRwL+yf138S/F4oC5rorWGMeQnAxHs85OMAPm+MKRpjXgbQ7Pt+N4B/CuBbxpgJY8wkgG/h9n9xljVu194Y88OgLQC8DGDPlgwsAtbxc38rjwE4ZYw5Y4xZBPBfYH+PyDqFbP9LAP5mE4cTKcaYodWjc8aYWQAnAey+4WF6z98E62mv9/zNsc6f+1vRe34J7qC93vM3SPAePhf8sjr4uHE1yo8DWD0z40sA/OCsjo8D+C/GmAVjzFkAp2B/L5QFTfTC2Q3g4ppfXwpuu9Xtsjk+DeAf1/y6COCbvu+/7vv+M6Qxue4Dvu8f833/H33fvze4TT/3W8T3/TrYicT/u+Zm/dxvEN/3DwA4AuDHN9yl9/xN9h7t19J7/ia4TXu952+i2/3c6z1/4/m+X+n7/psARmH/oe6W7/fGmGUA0wDaUOY/95roSVnxff/DsH/o/86amx83xjwMezrJb/i+/wRlcO56A8D+4JSHPwbw9+TxRNHHAPzghlN49HO/AXzfb4D9y9T/ZIyZYY8nStbTXu/5m+M27fWev4nW+Z6j9/wNZoxZCU6J3QPgMd/3f+q6eBdpohfOZQB71/x6T3DbrW6XDeT7/gMAPgfg48aY8dXbjTGXg/+OAvgyyuiQejkwxsysnvJgjPkagGrf99uhn/ut9EnccAqPfu5L5/t+NexfuL5gjPm7mzxE7/mbZB3t9Z6/SW7XXu/5m2c9P/cBvedvEmPMFIAX8dOn2//k59v3/SoATbCLspT1z70meuF8FcC/9H3fCy7inDbGDAH4BoCf832/Jbgg/+eC22SD+L6/D8DfAfhVY0x2ze31vu/vWP0ctv3bnFG6yff9ruA8dfi+/xjs+8Y4gFcB9Pi+f9D3/RjsH0xf5Y3UTb7vNwH4WdhV8FZv0899iYKf6f8M4KQx5v+6xcP0nr8J1tNe7/mbY53t9Z6/Cdb5nqP3/E3g+36H7/vNwee1AD4CIHPDw74K4FPB558A8B1jTDG4/ZO+79f4vn8QdnGuV7Zm5KXT9gpr+L7/NwCeBNDu+/4l2FXVqgHAGPNnAL4Gu8z2Kdiltv/74L4J3/f/EPZNEAD+4IbD7XIb62j/v8KeK/0nvu8D7y4t3Angy8FtVQD+2hjz9S1/AWVsHe0/AeBf+76/DGAewCeDN79l3/d/E/YvuJUA/sIYc4LwEsrWOtoDwC8A+KYxJrfmf9XPfek+BOBXAbwVXLcBAP8LgH2A3vM32Xra6z1/c6ynvd7zN8d62gN6z98M3QCeD1bLrADwRWPM/+f7/h8AeM0Y81XYSfhf+b5/CnaRtE8CgDHmhO/7XwTwDuzKqb9hjFmhvIo74BWLNy46IyIiIiIiIuVMp26KiIiIiIg4RhM9ERERERERx2iiJyIiIiIi4hhN9ERERERERByjiZ6IiIiIiIhjNNETERFneZ53zvO8jOd5xzzPe9vzvE++x2Of9Dzvta0cn4iIyGbRPnoiIuK6TxSLxbc9zzsC4Iee5327WCxeZQ9KRERkM+mInoiIREKxWDwKYBbAQc/zfs/zvLeCI30/9Dzvuj8PPc+r8jzvG57nveZ53gnP857zPC8W3PdBz/Pe8DzvzeC+Xwpuf8bzvJPB7cc9z0ts/asUERGxdERPREQiwfO8DwOIA7gHwD8D8MFisTjreV5bsVi85nne2oevAPjlYrE47tk7ngfwPwD4MwC/A+CPisXi3wT3NQX/zx8BSBSLxSHP82oAVG7NKxMREflpmuiJiIjrvuR5XgHADIBfBPAMgD8tFouzAFAsFsdv8v9UAPgtz/N+HnbC1gIgH9z3IoB/63neXQC+VSwWfxzc/h0Az3ue9wKAfygWi2c27RWJiIjchk7dFBER132iWCw+VCwWnygWi99a5//zywAeB5AsFov3A/gT2KOBKBaL/+H/b98OVbQIowAMvwcsIuKC0WawW4zeguBdGDQbNhqsYrKJ12CybLCIV2Ax2dRoUWE/wz9xt7kszD5PnTMwX3z5znS4EfxRvZ6ZF9s7j6vj6kZ1skUiAFwKoQfAVfO+ejIzN6tm5vYZM0fVz22181aH8Gubv7fW+rrWelO9qh7MzLXq7lrr81rrZfWhun/hJwGAc1jdBOCqeVfdqT7NzN/q18w8PGPm0cx8qb5XH6vr27Nn2/9+f6rf1dMO651vZ+aoOq2+Vc8v/CQAcI5Za132NwAAAPAfWd0EAADYGaEHAACwM0IPAABgZ4QeAADAzgg9AACAnRF6AAAAOyP0AAAAduYfiEZYLPLT9mcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_fare = pdp.pdp_isolate(model=model, dataset=X_val, model_features=feature_names, feature='Pclass')\n",
    "pdp.pdp_plot(pdp_fare, 'Pclass')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6decc7fe329522e6420a6421216726cd4cb4725f"
   },
   "source": [
    "Here we have a more clear plot of how this feature affects predictions, as we increase the \"Pclass\" we have a lower predicted value, this make all sense because higher classes had people with more money, and probably had priority leaving the ship, compared to people from lower classes.\n\n### 2D Partial Dependence Plots\n* We can also use partial plots to evaluate interactions between features and the label, with 2D dependence plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "trusted": true,
    "_uuid": "86c68e9e9c2b7dfc202a1527e8b964871042c404"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x684 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_to_plot1 = ['family', 'Pclass']\n",
    "pdp_inter = pdp.pdp_interact(model=model, dataset=X_val, model_features=feature_names, features=features_to_plot1)\n",
    "pdp.pdp_interact_plot(pdp_interact_out=pdp_inter, feature_names=features_to_plot1, plot_type='contour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "959870c0665c9230d13a1c108d92ef125a536906"
   },
   "source": [
    "Here we have a couple of plots of the interaction between some of our features, on the side of each plot we have a scale of feature importance.\n\nOn the first one we have how \"Pclas\" and \"family\" interact together on the prediction, as we can see people with smaller families (max of 5 people) and from higher class (1st class) had higher chance to survive, and the people who had more than 9 family member and were from 3rd class had the lower chance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "trusted": true,
    "_uuid": "cf9c94d41207842a361fbca2ec91263745ac4bdc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x684 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_to_plot2 = ['Sex_female', 'Pclass']\n",
    "pdp_inter = pdp.pdp_interact(model=model, dataset=X_val, model_features=feature_names, features=features_to_plot2)\n",
    "pdp.pdp_interact_plot(pdp_interact_out=pdp_inter, feature_names=features_to_plot2, plot_type='contour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "85360eb83e508339473629a76772202476e53ef7"
   },
   "source": [
    "The second plot show the interaction from \"Pclass\" and \"Sex_female\", this on is pretty clear as well, we can see that the people with the higher chance to survive were female (Sex_female = 1) and from the 1st class (Pclass = 1), and the ones with the lowest chances were the opposite, males from the 3rd class.\n\nThis kind of plot give us a good understanding of what our model learned, and how features behaves according to it, one good idea would be to validate this insights with the domain expert and see if everything is going as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5fe856d61848e34e0285e7c7fd89a12ded533fe2"
   },
   "source": [
    "### SHAP values\n* SHAP Values (an acronym from SHapley Additive exPlanations) break down a prediction to show the impact of each feature.\n* It can be used to help explain how the model got to one prediction, e.g. a model says a bank shouldn't loan someone money, and the bank is legally required to explain the basis for each loan rejection.\n* SHAP values interpret the impact of having a certain value for a given feature in comparison to the prediction we'd make if that feature took some baseline value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "trusted": true,
    "_uuid": "d1551028622392aacb5492be3737f0a6a8c461fd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n<div id='iM1K0VPAW9WH23271NN7W'>\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    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": -0.5721812844276428, \"outValue\": 0.86900395154953, \"link\": \"identity\", \"featureNames\": [\"Age\", \"Embarked_C\", \"Embarked_Q\", \"Embarked_S\", \"Fare\", \"Parch\", \"Pclass\", \"Sex_female\", \"Sex_male\", \"SibSp\", \"family\", \"title_ Capt\", \"title_ Col\", \"title_ Don\", \"title_ Dona\", \"title_ Dr\", \"title_ Jonkheer\", \"title_ Lady\", \"title_ Major\", \"title_ Master\", \"title_ Miss\", \"title_ Mlle\", \"title_ Mme\", \"title_ Mr\", \"title_ Mrs\", \"title_ Ms\", \"title_ Rev\", \"title_ Sir\", \"title_ the Countess\"], \"features\": {\"0\": {\"effect\": -0.024479500949382782, \"value\": 0.0}, \"1\": {\"effect\": -0.022952932864427567, \"value\": 0.0}, \"2\": {\"effect\": 0.007863118313252926, \"value\": 1.0}, \"3\": {\"effect\": 0.38703739643096924, \"value\": 0.0}, \"4\": {\"effect\": -0.3918435573577881, \"value\": 7.75}, \"5\": {\"effect\": 0.04654828831553459, \"value\": 0.0}, \"6\": {\"effect\": -0.4477503001689911, \"value\": 3.0}, \"7\": {\"effect\": 0.6664687395095825, \"value\": 1.0}, \"8\": {\"effect\": 0.6841824650764465, \"value\": 0.0}, \"9\": {\"effect\": 0.045427463948726654, \"value\": 0.0}, \"10\": {\"effect\": 0.10071838647127151, \"value\": 1.0}, \"19\": {\"effect\": -0.04818148910999298, \"value\": 0.0}, \"20\": {\"effect\": 0.0739632099866867, \"value\": 1.0}, \"23\": {\"effect\": 0.39839667081832886, \"value\": 0.0}, \"24\": {\"effect\": -0.03421267122030258, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n    document.getElementById('iM1K0VPAW9WH23271NN7W')\n  );\n</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_to_show = 14\n",
    "data_for_prediction = X_val.iloc[[row_to_show]]\n",
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values_single = explainer.shap_values(data_for_prediction)\n",
    "shap.force_plot(explainer.expected_value, shap_values_single, data_for_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a13e388e5ee79f4ed36bb90040fbf66ff54cf434"
   },
   "source": [
    "Feature values causing increased predictions are in pink, and their visual size shows the magnitude of the feature's effect. Feature values decreasing the prediction are in blue. The features that more increase prediction values were \"Sex_female\" and \"Sex_male\" (the person here was a female) here seems clear that having both \"Sex_female\" and \"Sex_male\" is redundant, as they mean the same. And the feature that decreased more the prediction was \"Pclass\" that in this case was 3.\n\nIf you subtract the length of the blue bars from the length of the pink bars, it equals the distance from the base value to the output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "785c689ca325ca0cd4376712ca73aed897032626"
   },
   "source": [
    "### Advanced Uses of SHAP Values\n\n### SHAP summary plots\n* These plots will give us a more detailed view of feature importance and what is driving it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "trusted": true,
    "_uuid": "a830566a15a915259b57187055bf5166aa51dc59"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values = explainer.shap_values(X_val)\n",
    "shap.summary_plot(shap_values, X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d197cca67611e3633d143cfc284735ddac3c4785"
   },
   "source": [
    "* Vertical location shows what feature it is depicting\n* Color shows whether that feature was high or low for that row of the dataset\n* Horizontal location shows whether the effect of that value caused a higher or lower prediction.\n\nFor example, pink dots in the \"Fare\" row, meas high fare values, blue values in the \"Sex_female\" row means low values, in this case 0 that means that point is a male. Those pink values in \"title_Master\" row means that the few people that had the title Master had increased chance to survive.\n\nSome insights we can get from this plot:\n* The model ignored the bottom 5 features.\n* Binary features only have 2 possible colors.\n* High values of \"Pclass\" caused lower predictions, and high values caused higher predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "474964307bc1a18c751467605f82a8616c2ba3ad"
   },
   "source": [
    "### SHAP Dependence Contribution Plots\n* This one reminds the partial dependence plots that we did before, but with a lot more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "trusted": true,
    "_uuid": "d720555ef312262f9d1558d0e9a33fa594c57f74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot('Fare', shap_values, X_val, interaction_index=\"Pclass\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c84400a4f24bf2ceeb528ace56161b08f5257f15"
   },
   "source": [
    "* Each dot represents a row of the data.\n* The horizontal location is the actual value from the dataset.\n* The vertical location shows what having that value did to the prediction.\n* Color show the values of another feature, to help we see how they interact.\n\nHere we can see how \"Pclass\" and \"Fare\" interact together affecting the predictions, it's clear that higher fares means lower class (1st class), and in general people who paid more had more chance to survive.\n\nLet's try another one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "trusted": true,
    "_uuid": "d0f96a9cfb257f926dac11509d85403f3a9f0dce"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot('Pclass', shap_values, X_val, interaction_index=\"Sex_female\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ba24d3bbe985fbee084cb65bba6559403371d7bc"
   },
   "source": [
    "Here we have something interesting, again people from higher classes had more chance to survive, but for people from the 3rd class females seems to had less chance to survive, what may be the cause?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7a856a8a45bb086f0d37112a5499e630db9b2136"
   },
   "source": [
    "#### And finally because this is a kernel from a competition, let's make our predictions and output results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_kg_hide-input": false,
    "trusted": true,
    "_uuid": "ef7fd2761ca7b1b0ef44ae25038b2a95f8b93dcc"
   },
   "outputs": [],
   "source": [
    "test_predictions = model.predict(test)\n",
    "submission = pd.DataFrame({\"PassengerId\":test_ids})\n",
    "submission[\"Survived\"] = test_predictions\n",
    "submission.to_csv(\"submission.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.6.6",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}