tensorflow-shap-college-debt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpret a Keras Model that Predicts College Debt with SHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the notebook directly in your browser, use Colab [here](https://colab.research.google.com/github/kweinmeister/notebooks/blob/master/tensorflow-shap-college-debt.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow\n",
    "!pip install shap\n",
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_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=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}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))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[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===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);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&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(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 g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={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\"},K={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},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,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(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+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\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_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(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.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=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[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&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.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=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,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(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},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(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[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 j(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\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},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(97),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+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({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})},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:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);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=l.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=p},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(162),c=(n(2),!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){a.getNodeFromInstance(t).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;return{start:o,end:o+r}}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(405),l=n(172),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(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),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+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},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),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(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},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\"),Array.isArray(c)&&(c.length<=1||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||c(\"33\"),\"_hostNode\"in e||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 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>UNITID</th>\n",
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       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>100200</td>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>35762</td>\n",
       "      <td>Southern Association of Colleges and Schools C...</td>\n",
       "      <td>www.aamu.edu/</td>\n",
       "      <td>www2.aamu.edu/scripts/netpricecalc/npcalc.htm</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.5136</td>\n",
       "      <td>0.0034</td>\n",
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       "      <th>1</th>\n",
       "      <td>100663</td>\n",
       "      <td>105200</td>\n",
       "      <td>1052</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>35294-0110</td>\n",
       "      <td>Southern Association of Colleges and Schools C...</td>\n",
       "      <td>www.uab.edu</td>\n",
       "      <td>uab.studentaidcalculator.com/survey.aspx</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6117</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.2166</td>\n",
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       "      <td>0.3642</td>\n",
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       "      <td>0.2755</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100690</td>\n",
       "      <td>2503400</td>\n",
       "      <td>25034</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>36117-3553</td>\n",
       "      <td>Southern Association of Colleges and Schools C...</td>\n",
       "      <td>www.amridgeuniversity.edu</td>\n",
       "      <td>www2.amridgeuniversity.edu:9091/</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5503</td>\n",
       "      <td>0.5562</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.4379</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>100706</td>\n",
       "      <td>105500</td>\n",
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       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>35899</td>\n",
       "      <td>Southern Association of Colleges and Schools C...</td>\n",
       "      <td>www.uah.edu</td>\n",
       "      <td>finaid.uah.edu/</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>0.5651</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.2841</td>\n",
       "      <td>0.1385</td>\n",
       "      <td>0.3378</td>\n",
       "      <td>0.3547</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>0.4257</td>\n",
       "      <td>0.1959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100724</td>\n",
       "      <td>100500</td>\n",
       "      <td>1005</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>36104-0271</td>\n",
       "      <td>Southern Association of Colleges and Schools C...</td>\n",
       "      <td>www.alasu.edu</td>\n",
       "      <td>www.alasu.edu/cost-aid/forms/calculator/index....</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4105</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.3488</td>\n",
       "      <td>0.2931</td>\n",
       "      <td>0.3103</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>0.3966</td>\n",
       "      <td>0.2586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1899 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID    OPEID  OPEID6                               INSTNM        CITY  \\\n",
       "0  100654   100200    1002             Alabama A & M University      Normal   \n",
       "1  100663   105200    1052  University of Alabama at Birmingham  Birmingham   \n",
       "2  100690  2503400   25034                   Amridge University  Montgomery   \n",
       "3  100706   105500    1055  University of Alabama in Huntsville  Huntsville   \n",
       "4  100724   100500    1005             Alabama State University  Montgomery   \n",
       "\n",
       "  STABBR         ZIP                                       ACCREDAGENCY  \\\n",
       "0     AL       35762  Southern Association of Colleges and Schools C...   \n",
       "1     AL  35294-0110  Southern Association of Colleges and Schools C...   \n",
       "2     AL  36117-3553  Southern Association of Colleges and Schools C...   \n",
       "3     AL       35899  Southern Association of Colleges and Schools C...   \n",
       "4     AL  36104-0271  Southern Association of Colleges and Schools C...   \n",
       "\n",
       "                     INSTURL  \\\n",
       "0              www.aamu.edu/   \n",
       "1                www.uab.edu   \n",
       "2  www.amridgeuniversity.edu   \n",
       "3                www.uah.edu   \n",
       "4              www.alasu.edu   \n",
       "\n",
       "                                              NPCURL  ...  \\\n",
       "0      www2.aamu.edu/scripts/netpricecalc/npcalc.htm  ...   \n",
       "1           uab.studentaidcalculator.com/survey.aspx  ...   \n",
       "2                   www2.amridgeuniversity.edu:9091/  ...   \n",
       "3                                    finaid.uah.edu/  ...   \n",
       "4  www.alasu.edu/cost-aid/forms/calculator/index....  ...   \n",
       "\n",
       "   OMAWDP6_FTNFT_POOLED_SUPP  OMAWDP8_FTNFT_POOLED_SUPP  \\\n",
       "0                     0.4694                     0.5136   \n",
       "1                     0.6117                      0.624   \n",
       "2                     0.5503                     0.5562   \n",
       "3                     0.5456                     0.5651   \n",
       "4                     0.4105                      0.429   \n",
       "\n",
       "   OMENRYP8_FTNFT_POOLED_SUPP  OMENRAP8_FTNFT_POOLED_SUPP  \\\n",
       "0                      0.0034                      0.1429   \n",
       "1                      0.0075                      0.2166   \n",
       "2                      0.0059                      0.4379   \n",
       "3                      0.0123                      0.2841   \n",
       "4                      0.0062                       0.216   \n",
       "\n",
       "   OMENRUP8_FTNFT_POOLED_SUPP  OMAWDP6_PTNFT_POOLED_SUPP  \\\n",
       "0                      0.3401                     0.3333   \n",
       "1                      0.1519                     0.3642   \n",
       "2                           0                     0.4429   \n",
       "3                      0.1385                     0.3378   \n",
       "4                      0.3488                     0.2931   \n",
       "\n",
       "   OMAWDP8_PTNFT_POOLED_SUPP  OMENRYP8_PTNFT_POOLED_SUPP  \\\n",
       "0                     0.3333                           0   \n",
       "1                        0.4                      0.0434   \n",
       "2                     0.4714                      0.0429   \n",
       "3                     0.3547                      0.0236   \n",
       "4                     0.3103                      0.0345   \n",
       "\n",
       "   OMENRAP8_PTNFT_POOLED_SUPP  OMENRUP8_PTNFT_POOLED_SUPP  \n",
       "0                           0                      0.6667  \n",
       "1                      0.2755                      0.2811  \n",
       "2                      0.4857                           0  \n",
       "3                      0.4257                      0.1959  \n",
       "4                      0.3966                      0.2586  \n",
       "\n",
       "[5 rows x 1899 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider downloading the dataset and using a local reference\n",
    "CSV_FILE = 'https://ed-public-download.app.cloud.gov/downloads/Most-Recent-Cohorts-All-Data-Elements.csv'\n",
    "df_raw = pd.read_csv(CSV_FILE, low_memory=False)\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the ratio between student loan debt and future earnings\n",
    "# Any missing or invalid values will return NaN\n",
    "def debt_earnings_ratio(row):\n",
    "    try:\n",
    "        d = float(row['MD_INC_DEBT_MDN'])\n",
    "    except:\n",
    "        return np.nan\n",
    "    \n",
    "    try:\n",
    "        e = float(row['MN_EARN_WNE_INC2_P6'])\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "    return d / e\n",
    "\n",
    "# Net price in raw data is provided in separate columns.\n",
    "# This function returns the value found in either public and private institutions.\n",
    "# It also returns an average across two family household income levels.\n",
    "\n",
    "def net_price(row):\n",
    "\n",
    "    # First, check public institutions in the 30-48k and 48k-75k columns\n",
    "    if not math.isnan(row['NPT42_PUB']) and not math.isnan(row['NPT43_PUB']):\n",
    "\n",
    "        # Return mean of both middle income levels\n",
    "        return (row['NPT42_PUB'] + row['NPT43_PUB']) / 2\n",
    "    \n",
    "    # Repeat for private institutions\n",
    "    if not math.isnan(row['NPT42_PRIV']) and not math.isnan(row['NPT43_PRIV']):\n",
    "        return (row['NPT42_PRIV'] + row['NPT43_PRIV']) / 2\n",
    "    \n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy of the dataframe to start over from this cell as needed, without downloading CSV again\n",
    "df = df_raw.copy()\n",
    "\n",
    "FEATURES = ['ADM_RATE', 'SAT_AVG', 'TRANS_4', 'NPT4','INC_N', 'PUBLIC', 'UGDS','PPTUG_EF','FIRST_GEN','MD_INC_COMP_ORIG_YR4_RT']\n",
    "LABEL = 'MD_INC_DEBT_EARNINGS_RATIO'\n",
    "SELECTED_COLUMNS = FEATURES + [LABEL]\n",
    "\n",
    "# Create new feature derived from debt and future earnings data \n",
    "df['MD_INC_DEBT_EARNINGS_RATIO'] = df.apply(lambda row: debt_earnings_ratio(row), axis=1)\n",
    "\n",
    "# One-hot encode if the institution is public or not\n",
    "control = df.pop('CONTROL')\n",
    "df['PUBLIC'] = (control == 1)*1.0\n",
    "\n",
    "# Create a new feature for the net price of the university, combining this data from multiple columns\n",
    "df['NPT4'] = df.apply(lambda row: net_price(row),axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trim columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7175, 11)\n",
      "(7175, 11)\n",
      "(1117, 11)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kweinmeister/Projects/jupyterlab/lib/python3.6/site-packages/pandas/core/ops.py:1649: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  result = method(y)\n"
     ]
    },
    {
     "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>ADM_RATE</th>\n",
       "      <th>SAT_AVG</th>\n",
       "      <th>TRANS_4</th>\n",
       "      <th>NPT4</th>\n",
       "      <th>INC_N</th>\n",
       "      <th>PUBLIC</th>\n",
       "      <th>UGDS</th>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <th>FIRST_GEN</th>\n",
       "      <th>MD_INC_COMP_ORIG_YR4_RT</th>\n",
       "      <th>MD_INC_DEBT_EARNINGS_RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8738</td>\n",
       "      <td>849.0</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>16413.0</td>\n",
       "      <td>3186</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>0.0877</td>\n",
       "      <td>0.365828</td>\n",
       "      <td>0.239382</td>\n",
       "      <td>0.437096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5814</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>0.2433</td>\n",
       "      <td>16703.5</td>\n",
       "      <td>5367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12047.0</td>\n",
       "      <td>0.2578</td>\n",
       "      <td>0.341224</td>\n",
       "      <td>0.472991</td>\n",
       "      <td>0.369543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.7628</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>0.2983</td>\n",
       "      <td>19158.0</td>\n",
       "      <td>2389</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6346.0</td>\n",
       "      <td>0.1746</td>\n",
       "      <td>0.310132</td>\n",
       "      <td>0.447531</td>\n",
       "      <td>0.338982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4590</td>\n",
       "      <td>825.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>16316.0</td>\n",
       "      <td>2504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4704.0</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.343434</td>\n",
       "      <td>0.231552</td>\n",
       "      <td>0.632638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.5259</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>0.2104</td>\n",
       "      <td>21219.0</td>\n",
       "      <td>9310</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31663.0</td>\n",
       "      <td>0.0819</td>\n",
       "      <td>0.225713</td>\n",
       "      <td>0.476113</td>\n",
       "      <td>0.422101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ADM_RATE  SAT_AVG  TRANS_4     NPT4  INC_N  PUBLIC     UGDS  PPTUG_EF  \\\n",
       "0    0.8738    849.0   0.3765  16413.0   3186     1.0   4616.0    0.0877   \n",
       "1    0.5814   1125.0   0.2433  16703.5   5367     1.0  12047.0    0.2578   \n",
       "3    0.7628   1257.0   0.2983  19158.0   2389     1.0   6346.0    0.1746   \n",
       "4    0.4590    825.0   0.0000  16316.0   2504     1.0   4704.0    0.0727   \n",
       "5    0.5259   1202.0   0.2104  21219.0   9310     1.0  31663.0    0.0819   \n",
       "\n",
       "   FIRST_GEN  MD_INC_COMP_ORIG_YR4_RT  MD_INC_DEBT_EARNINGS_RATIO  \n",
       "0   0.365828                 0.239382                    0.437096  \n",
       "1   0.341224                 0.472991                    0.369543  \n",
       "3   0.310132                 0.447531                    0.338982  \n",
       "4   0.343434                 0.231552                    0.632638  \n",
       "5   0.225713                 0.476113                    0.422101  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove all columns except for features and label columns\n",
    "df = df[SELECTED_COLUMNS]\n",
    "print(df.shape)\n",
    "\n",
    "# Remove rows that have missing labels due to fewer than 30 students polled\n",
    "PRIVACY_SUPPRESSED = 'PrivacySuppressed'\n",
    "df = df[df!=(PRIVACY_SUPPRESSED)]\n",
    "print(df.shape)\n",
    "\n",
    "# Drop rows with missing data\n",
    "df = df.dropna()\n",
    "print(df.shape)\n",
    "\n",
    "# Convert types to numeric\n",
    "df[SELECTED_COLUMNS] = df[SELECTED_COLUMNS].apply(pd.to_numeric)\n",
    "                              \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(df, x_vars=FEATURES[0:5], y_vars=LABEL)\n",
    "g.map(plt.scatter);\n",
    "g = sns.PairGrid(df, x_vars=FEATURES[5:10], y_vars=LABEL)\n",
    "g.map(plt.scatter);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describe data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADM_RATE</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.664153</td>\n",
       "      <td>0.181681</td>\n",
       "      <td>0.048100</td>\n",
       "      <td>0.555500</td>\n",
       "      <td>0.682500</td>\n",
       "      <td>0.790800</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAT_AVG</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>1058.559534</td>\n",
       "      <td>124.330852</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>975.000000</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>1519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANS_4</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.123557</td>\n",
       "      <td>0.148810</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.255700</td>\n",
       "      <td>0.542900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPT4</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>17331.498209</td>\n",
       "      <td>5870.112300</td>\n",
       "      <td>3600.500000</td>\n",
       "      <td>13189.500000</td>\n",
       "      <td>16879.500000</td>\n",
       "      <td>20680.500000</td>\n",
       "      <td>44120.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INC_N</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>3334.373321</td>\n",
       "      <td>5074.552429</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>1413.000000</td>\n",
       "      <td>3817.000000</td>\n",
       "      <td>36424.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUBLIC</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.434199</td>\n",
       "      <td>0.495873</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UGDS</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>6272.341987</td>\n",
       "      <td>7904.776039</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>2943.000000</td>\n",
       "      <td>7447.000000</td>\n",
       "      <td>55113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.123639</td>\n",
       "      <td>0.110857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042200</td>\n",
       "      <td>0.094200</td>\n",
       "      <td>0.170900</td>\n",
       "      <td>0.755800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIRST_GEN</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.315067</td>\n",
       "      <td>0.092428</td>\n",
       "      <td>0.088670</td>\n",
       "      <td>0.251003</td>\n",
       "      <td>0.320899</td>\n",
       "      <td>0.377976</td>\n",
       "      <td>0.615339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD_INC_COMP_ORIG_YR4_RT</th>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.495971</td>\n",
       "      <td>0.144794</td>\n",
       "      <td>0.088608</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.489796</td>\n",
       "      <td>0.587500</td>\n",
       "      <td>0.963746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count          mean          std          min  \\\n",
       "ADM_RATE                 1117.0      0.664153     0.181681     0.048100   \n",
       "SAT_AVG                  1117.0   1058.559534   124.330852   712.000000   \n",
       "TRANS_4                  1117.0      0.123557     0.148810     0.000000   \n",
       "NPT4                     1117.0  17331.498209  5870.112300  3600.500000   \n",
       "INC_N                    1117.0   3334.373321  5074.552429   155.000000   \n",
       "PUBLIC                   1117.0      0.434199     0.495873     0.000000   \n",
       "UGDS                     1117.0   6272.341987  7904.776039   181.000000   \n",
       "PPTUG_EF                 1117.0      0.123639     0.110857     0.000000   \n",
       "FIRST_GEN                1117.0      0.315067     0.092428     0.088670   \n",
       "MD_INC_COMP_ORIG_YR4_RT  1117.0      0.495971     0.144794     0.088608   \n",
       "\n",
       "                                  25%           50%           75%  \\\n",
       "ADM_RATE                     0.555500      0.682500      0.790800   \n",
       "SAT_AVG                    975.000000   1040.000000   1115.000000   \n",
       "TRANS_4                      0.000000      0.004000      0.255700   \n",
       "NPT4                     13189.500000  16879.500000  20680.500000   \n",
       "INC_N                      773.000000   1413.000000   3817.000000   \n",
       "PUBLIC                       0.000000      0.000000      1.000000   \n",
       "UGDS                      1452.000000   2943.000000   7447.000000   \n",
       "PPTUG_EF                     0.042200      0.094200      0.170900   \n",
       "FIRST_GEN                    0.251003      0.320899      0.377976   \n",
       "MD_INC_COMP_ORIG_YR4_RT      0.400000      0.489796      0.587500   \n",
       "\n",
       "                                  max  \n",
       "ADM_RATE                     1.000000  \n",
       "SAT_AVG                   1519.000000  \n",
       "TRANS_4                      0.542900  \n",
       "NPT4                     44120.500000  \n",
       "INC_N                    36424.000000  \n",
       "PUBLIC                       1.000000  \n",
       "UGDS                     55113.000000  \n",
       "PPTUG_EF                     0.755800  \n",
       "FIRST_GEN                    0.615339  \n",
       "MD_INC_COMP_ORIG_YR4_RT      0.963746  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract labels\n",
    "df_train = df\n",
    "train_labels = df_train.pop(LABEL)\n",
    "\n",
    "# Describe the dataset\n",
    "train_stats = df_train.describe()\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADM_RATE</th>\n",
       "      <th>SAT_AVG</th>\n",
       "      <th>TRANS_4</th>\n",
       "      <th>NPT4</th>\n",
       "      <th>INC_N</th>\n",
       "      <th>PUBLIC</th>\n",
       "      <th>UGDS</th>\n",
       "      <th>PPTUG_EF</th>\n",
       "      <th>FIRST_GEN</th>\n",
       "      <th>MD_INC_COMP_ORIG_YR4_RT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.153925</td>\n",
       "      <td>-1.685499</td>\n",
       "      <td>1.699775</td>\n",
       "      <td>-0.156470</td>\n",
       "      <td>-0.029239</td>\n",
       "      <td>1.14102</td>\n",
       "      <td>-0.209537</td>\n",
       "      <td>-0.324192</td>\n",
       "      <td>0.549199</td>\n",
       "      <td>-1.772098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.455487</td>\n",
       "      <td>0.534384</td>\n",
       "      <td>0.804673</td>\n",
       "      <td>-0.106982</td>\n",
       "      <td>0.400553</td>\n",
       "      <td>1.14102</td>\n",
       "      <td>0.730528</td>\n",
       "      <td>1.210217</td>\n",
       "      <td>0.282999</td>\n",
       "      <td>-0.158710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.542965</td>\n",
       "      <td>1.596068</td>\n",
       "      <td>1.174272</td>\n",
       "      <td>0.311153</td>\n",
       "      <td>-0.186297</td>\n",
       "      <td>1.14102</td>\n",
       "      <td>0.009318</td>\n",
       "      <td>0.459701</td>\n",
       "      <td>-0.053387</td>\n",
       "      <td>-0.334545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.129194</td>\n",
       "      <td>-1.878532</td>\n",
       "      <td>-0.830301</td>\n",
       "      <td>-0.172995</td>\n",
       "      <td>-0.163635</td>\n",
       "      <td>1.14102</td>\n",
       "      <td>-0.198404</td>\n",
       "      <td>-0.459501</td>\n",
       "      <td>0.306916</td>\n",
       "      <td>-1.826176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.760967</td>\n",
       "      <td>1.153700</td>\n",
       "      <td>0.583585</td>\n",
       "      <td>0.662253</td>\n",
       "      <td>1.177567</td>\n",
       "      <td>1.14102</td>\n",
       "      <td>3.212065</td>\n",
       "      <td>-0.376511</td>\n",
       "      <td>-0.966737</td>\n",
       "      <td>-0.137144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ADM_RATE   SAT_AVG   TRANS_4      NPT4     INC_N   PUBLIC      UGDS  \\\n",
       "0  1.153925 -1.685499  1.699775 -0.156470 -0.029239  1.14102 -0.209537   \n",
       "1 -0.455487  0.534384  0.804673 -0.106982  0.400553  1.14102  0.730528   \n",
       "3  0.542965  1.596068  1.174272  0.311153 -0.186297  1.14102  0.009318   \n",
       "4 -1.129194 -1.878532 -0.830301 -0.172995 -0.163635  1.14102 -0.198404   \n",
       "5 -0.760967  1.153700  0.583585  0.662253  1.177567  1.14102  3.212065   \n",
       "\n",
       "   PPTUG_EF  FIRST_GEN  MD_INC_COMP_ORIG_YR4_RT  \n",
       "0 -0.324192   0.549199                -1.772098  \n",
       "1  1.210217   0.282999                -0.158710  \n",
       "3  0.459701  -0.053387                -0.334545  \n",
       "4 -0.459501   0.306916                -1.826176  \n",
       "5 -0.376511  -0.966737                -0.137144  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "\n",
    "df_train_normed = norm(df_train)\n",
    "\n",
    "df_train_normed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12bae2b70>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                176       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 465\n",
      "Trainable params: 465\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def build_model(df):\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(16, activation=tf.nn.relu, input_shape=[len(df.keys())]),\n",
    "    layers.Dense(16, activation=tf.nn.relu),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  # TF 2.0: optimizer = tf.keras.optimizers.RMSprop()\n",
    "  optimizer = tf.keras.optimizers.RMSprop()\n",
    "  # optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001)\n",
    "    \n",
    "  model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae'])\n",
    "  return model\n",
    "\n",
    "model = build_model(df_train_normed)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "................................................................................."
     ]
    }
   ],
   "source": [
    "# Display training progress by printing a single dot for each completed epoch\n",
    "class PrintDot(keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs):\n",
    "    if epoch % 100 == 0: print('')\n",
    "    print('.', end='')\n",
    "\n",
    "EPOCHS = 1000\n",
    "\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)\n",
    "\n",
    "history = model.fit(\n",
    "  df_train_normed, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[early_stop, PrintDot()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "\n",
    "def plot_history(history):\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Absolute Error')\n",
    "  plt.plot(hist['epoch'], hist['mean_absolute_error'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mean_absolute_error'],\n",
    "           label = 'Val Error')\n",
    "  plt.legend()\n",
    "  \n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explain model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KernelExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1117 [00:00<?, ?it/s]/Users/kweinmeister/Projects/jupyterlab/lib/python3.6/site-packages/shap/explainers/kernel.py:535: UserWarning: l1_reg=\"auto\" is deprecated and in the next version (v0.29) the behavior will change from a conditional use of AIC to simply \"num_features(10)\"!\n",
      "  \"l1_reg=\\\"auto\\\" is deprecated and in the next version (v0.29) the behavior will change from a \" \\\n",
      "100%|██████████| 1117/1117 [04:18<00:00,  4.43it/s]\n"
     ]
    }
   ],
   "source": [
    "# KernelExplainer is a general approach that can work with any ML framework\n",
    "# Its inputs are the predictions and training data\n",
    "\n",
    "# Summarize the training set to accelerate analysis\n",
    "df_train_normed_summary = shap.kmeans(df_train_normed.values, 25)\n",
    "\n",
    "# Instantiate an explainer with the model predictions and training data summary\n",
    "explainer = shap.KernelExplainer(model.predict, df_train_normed_summary)\n",
    "\n",
    "# Extract Shapley values from the explainer\n",
    "shap_values = explainer.shap_values(df_train_normed.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize the Shapley values in a plot\n",
    "shap.summary_plot(shap_values[0], df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<div id='iXBX9C6AKOTXYQOZDCD2X'>\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\": 0.4542235998084957, \"outValue\": 0.5261498093605042, \"link\": \"identity\", \"featureNames\": [\"ADM_RATE\", \"SAT_AVG\", \"TRANS_4\", \"NPT4\", \"INC_N\", \"PUBLIC\", \"UGDS\", \"PPTUG_EF\", \"FIRST_GEN\", \"MD_INC_COMP_ORIG_YR4_RT\"], \"features\": {\"0\": {\"effect\": -0.024387747099971907, \"value\": 1.1539249948871313}, \"1\": {\"effect\": 0.12135202638114871, \"value\": -1.6854990680246418}, \"2\": {\"effect\": -0.011045876636329627, \"value\": 1.6997750096686979}, \"3\": {\"effect\": 0.0038947592492796466, \"value\": -0.15647029606301632}, \"4\": {\"effect\": -0.0022650381965780876, \"value\": -0.02923870104387817}, \"5\": {\"effect\": 0.037191181556169406, \"value\": 1.1410197995577327}, \"6\": {\"effect\": 0.002430416043526524, \"value\": -0.2095368647150239}, \"7\": {\"effect\": 0.014333217064882003, \"value\": -0.32419172629496584}, \"8\": {\"effect\": -0.008389884455273597, \"value\": 0.5491985532650931}, \"9\": {\"effect\": -0.061186844354844616, \"value\": -1.7720981160688063}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iXBX9C6AKOTXYQOZDCD2X')\n",
       "  );\n",
       "</script>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Plot the SHAP values for one instance\n",
    "INSTANCE_NUM = 0\n",
    "shap.force_plot(explainer.expected_value[0], shap_values[0][INSTANCE_NUM], df_train.iloc[INSTANCE_NUM,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "<div id='iW4CA1Z7POWJ792SE4VDH'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "# Plot the SHAP values for multiple instances\n",
    "NUM_ROWS = 10\n",
    "shap.force_plot(explainer.expected_value[0], shap_values[0][0:NUM_ROWS], df_train.iloc[0:NUM_ROWS])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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A/vYyl9JYrrqtpV6f+rX9pXlcCHF4q6mqoZQaB4xL2PSc1vq5+l5DKTUceIB4jR3gz8AmrbWrlLoc+FQp1bXioeBAkQB+mDMMxbgh+9ZM/Y9ZLo/PcemRrZh4lrHbJvEKPbMTrq/gyAPQZL+mwOXv30VJ8yvuHu4jWxZbEULss13/jsSC9Z4C9nqgnVLK1Fo7Sqnd5hFRSg0DJgDnaK2XJlxjY8LrV5RS/8IbMH1Ac4lIAG9gtpdp/t8PDoaC3w2teeDY6kLNgz+4BE34VU/FHd94yV/WF2n6vuhw11CD2wbX3HuyoUhz//exkeUBePwUxTFtd73GjpDmm/WaHtmqSna3utBaM/KVMCu3e9dZss3l48uCe3UOIYSorj594FrrrUqpijwiE2Jf5yT2fwMopQYDbwCjtdY/Vfteu4ogrpQ6DW+k+kYOMAngDczZb0SYvsELfN+td/nisl0Tnp/+jsOyWIr2innjFbaXw+3TXIa28VYnq+75+S5r8x1AsUMrCkPxQL+qUPPTFk3fHDj3XYdl28FvwP/ON+mWrbj3Wwe/AQ8ON+nQZPcfpLIIlcEbYMFW6WMXQuy7fRiFfj3wslKqSh4RpdRkYLzW2gaeAlKBZ5WqvM5YrfWC2LGtABfYCZytta5p0az9SgJ4AzMrV9f4ukLU1SxPWF9l9Q7NwJaKudVy0m0r09TU3PT2vAiEYud1FG0zvEF0c7dqjp/kUBKBzAAUxSZORFyYuMhl5kZdmVd9WYHDzMt3f2ulBRSj+xi8vchrGbhyYP2TyQghRIX6VgW01jXmEdFan5nwevAejh9Zz0vvExmF3sCcn5Cc5fwaErX4DMUFPRICswafhrmXG7SOJVoZ3h7O6LJr8HZdzeKE2nAqmgt6etd4f7lLiTcdnaIw+BJi7oKt8eANsLKg9o/RG78K8OnYAN9eHeD+k2UqmhBi3zW25USlBt7ATDjXx/lHuJgKzttNprVXzlRMf9ZlUywv+okdFANaGqwep9haCu0zwVC73thbSqFTS5M1hRoMRftsKItoUv2KAS0VFc+3CvjHCIP5WzXbSuHDlVUD9i1W7c+FhqE4bQ9LpwohxN5K9oBdnQTwBsZnKC7ss+fAl+o3mH2l4j/zXFqlK64Z4N3UKT5FxyZV9y2Pah6f7bK9TPPGMlhTakCsW315Edz1ncu/TjI5r4fBUyM1by7VnNZZcavlleGh7x0vgMfi+/jjDO45TgKzEOLga2yjaSSAJ6k2GYq7Y4HUcTWTFjqEHRhzpEHQF39KHfepw6s/x257c9en14r+9B0hzf/9qFm1A6at13Rq4nJxb4PrBxq8s9Tlpy0wvKPizmOkV0YIcWhIDVwknXEfRvnvXC+fwJuLDD6+JD5yfWbiQDitIaFp3acgWu7wxA+aPq0NVu2I7QY8MssL4M1SFbOv9FMa0aQdhCQzQgixO40tgEt1qRH4ICGf+ifLXRw3HrTP7ZFwC7iA1vTMholnKIzSCJ8tcbj54whfrXS8AB+zdkfVxioJ3kKIQ03X8C+ZSQBvBI7rEP81D2mnMI14sH3kJJNXzjIwTby7QSm6ZykyTAhH4ufYUKjJDuIFcVfTLyE725ZizcdLHTbsSPaPixDicCaj0EXSee0CP0/M8vrAbxxssmRFiC15UQb1SyEzw2TskSYR7aVcbZ2heHKkQRO/t3jK2kKNz4CLjjS55ViD8d85BH1waheDmbma1qmaIc+FyCuBJkGYcV2Qvi3luVAIIQ40CeCNQJpfccdx3q966vRiHv/PdgDatPLxt7tbMeZjl8krNT2y4ZUzjMosavavg0xd43JEjuLIVl5Qfudck2MnOVz/uZeERYUddLl3nZ0h+H8zHZ4/u24BPOpofDUMnBNCiPpI9hp3dbX+pbUsy2dZ1gLLslIORoHE/hdxNAvzNAXlmu9nl1Vu37QlyiszQkxe6kDEZXm+5p+z4v3lOemK0X1N0gPwy0lhTno5zOuLvBHnFbRR9QPzws+aCbEMa3tyy8dhgveX0fVfZSzdVvv+QghRG+kDr8a27SiQRfL/XySlsojmhNcc+r0YpduzUZzMeKNLepri4/UKohocDWGXVN+uv+ax70X4aJnL1DUut34aoVnFo1zFoLaAAr+CFBP8Bm8v2/OtMjvX5fEforgaVhdoxn8V2eP+QghRF42tD7yunZWPAQ9aliVN7g3MF2s1P2zyAmpBCO7JTWVLx0xGnpzB/Xe0IjdU9QYf23fXJCwVGd0ACstg8vkG53YHozwKpVEIu966owHvdrJa7flDE6h2iWDsvdaaqetcvt8oNXIhRH2oGv4lr7oG8F8DvwV2WJa1wrKsZRX/DmDZxH7QNkNVu4UV041Uoj2b0LljgNEJWd2GdTDoWcPSoPecaFbmePnDsSZD2xr8tMHFjSTUtMMOaDi5I9x1zJ4/NP1aGTx4ip+W6d41Hxrp5UK/5hOHkyY5HDvB4c/fOHs8hxBCVNfYauB1rVH/9YCWQhwwR7dWvHC6yd9/cFiW53pN5UDLdO/Gvnu4nyHtDOZucjmuo1FlilmFKwf6OL27SVlE0yXbIBTV5JZTmT4V8GrgwCkdVY151qu7a7ifu4bHFzGJupqXFsQfCF6Y5/LgiZKSVQhRd42tn7dOAdy27ZcPdEHEgXN1f4Mr+ynu+DzKRytddmiDR+doujbTDG6jmJPr8scpXj/0DUN8PHX2rmuMt86IN0d9uU4TxYA0f2XNm6BBmk9z55CqQff7jS6TFntriI8bYKB2E9x9hqJnM1jqDZCndw0tAUIIsSfJXuOurs592pZlDQauBjoA64H/2rb944EqmNg3S7c6LNrqckIXk5x0A0Mp/u9UP5OeibK5BDaXwsUfO6y41se/v4+vO//Mj1EeGOnjqzWaDk0Ux7Sv2svy0jyHP3wZAW16udNTfeBq0NAqqKvU4FcVak55w6EsdvpNWyKk54do09xkzMj0XaaQfXqhj7/OcAiainuPk7nkQoi9IzXwGliWdS4wCXgPmAN0BaZZlnWpbdvvHcDyiXr4ekWU0/9TStiBjlmK2b9NJyfdC4iF5fH9CmKvuzVT5BZ5t35OhmL4KxF+jq0LftUAg22lMLitwWk9DK761AFMrzLuuFVyp5e7VQPyom26Mnj7HJcvP9qBG/XOW1SmueHczCr7d26qeOEMGScphKgfV2rgNboXuMC27ckVGyzLOgN4GC+oi8PIG/MihGNjwNYVaqaudBjd3wvg/zfC4KYvXQwF/xjubZt0UYDfTY4weT3kRRV522PPsRpenOuNCP9wmcvSQpPKUZ0aKI/StplJbpl3niNbVP3wDGun6JAJ64sgLepUBm+AFev339SxL1a7vLHYpX9LxU1H776ZXgiR3KQJvWadgU+rbfsMr1YuDjP92piAFyB9BvROSG16w0CDS3orDAWZAcWK7ZrXF7o0b2pQsjoWYJXy5ic4uspAtVQDsoPedDS05sIjDO4f5efXn0QxDcV/z6ja7N08VXF9H/j391E65yg6uT7Wbo6iFIwcnLpLuV2t+edMh5/zNJceaTKya+3N6Iu3ac580yHiUlnQmy0Z/CZEYyRN6DVbC4wEpiRsOwVYt99LJPbZb4b50Rrm5jpcOMBP39ZVA1rToPeUuq1Uc+x/w+SVAgqUT1V+APq0UCzebnjvXU1T0+WmwSb3n6SYts6lR5bi6LYG4z6OMG2Nl/No3Mcw+eJ40P10pcOfp7ugDbbkOvQ8MoO//hJaNzPp3dlPdf+c6fCHL7ymg9cWuiz4tZ+ezfccxJfk61jw9szf2tg+wkKIClIDr9kDwP8sy3obWI1XI78AuOIAlUvsA6UUNx2360jy6hblaS94A4bj0qw8TLHpI107pGUF0MQCv6H46NIAA1p7wXRMwtzxtxfHo+cnK11ynohweheDl84wuGaKjmdtMRWlruakQcHdlmdhQvANu/D5arfWAH5iB0WnprB2B/gNuKi3DH4TorGSAF4D27bfsSxrE17AtvBGoY+ybXvGgSycOLD6tVS0zYTcIgiGHfzFEbIrmt6zFMQCeEYA+uTs+sHIK9XsTOzKVpBfrpi4WJNuRMndob3meFNhmIp7RlS93X7YpNlUrDmtsyLVr7jkSJPXFrpEDO+Ym77ShLXD7wbvvkm8eZpi9pU+vlmv6Z2jOEKmnwnRaDW29rfdBnDLst6xbfuC2OurbNt+EZCA3YCVRjRp/niAy05VzLw6wLtLHFQI/v5OeWV686uP9mGFDTYWaX43xKBZqnfc+8tc5m3VnNfTwFDg+AxQsYMqpoW5mufnVHyUvK/jTzAY0CZeO356rstvvvBq78PawjdjTE7tZvD91T6sV+O1+sd/cvcYwMEL4uf1ksAtRGMnNfC4UxJePwa8eIDLIg6QkrDmjLcdvt2gsVorPr/QJCvFu9E7NFX8dqgP8NE5AybPDzGwo49rh6dUGc3tuJqJi1yu+NgLrv+Y5TL3SpOTOii+3qDwKc1ZK9aRlVfMvOE9mFsUv7Uu662499iqQThxxbLvc2FlIfRqBke1NmiT7rKpxPte9+zG9YEUQtSfBPC4ny3LmgQsAAKWZd1V0062bT90QEom9ptXf3b5doNXE7Y3a56b53LH0F1rtWcPCnJ2Qh91WUQzcaHL9NVRXpsbRaeYYHrHlURg2moX/0/b6RsxOWPRctru9KJum+kO2447kg1FkOEHq7nLz5ui9G0Tv936t1DMyPXKZKLJK3Lo1cyHoRRTLvTxwAyHzACSTlUIIXZjTwH8MuCPwEl4naGjathHA3sM4JZl/beWMmjbtq+pZR+xDxKbzQG2FGs+XOpwWncDrWFJPnRqSmWtvDismb5Rc9/UKDPXuxCKTSoPuZBmAAoTzZ/eKqVrsaaJG6ZNLHgDBLeUcG4vxamdDe5+r5g73opiKJhwRQYXDPQeEO4bpnjuhwiuUjjlDnd8qpjxa+92PLKF4o1zJKGLEGLvSB94jG3bq/FWIcOyrLm2bZ9Uz2vsblkpA/gl0ByQAH4AXdpH8c16xWdrNC1S4J/fRfkncGIng8KoYn4e5KTCtMt8dGoCx77msGAboFXlIiUARDXdgg5Z6Qaz10UpVAabg35ahiIsaNOC/pvycIGvurenVTFkKoclm7xUbK6GiT+GKgN4wFRQ7lR+4iJO42r6EkLsf9KEXgPbtgfW9wK2bV9XfZtlWScD/wDKgavqe25RN6YRT1F61LOhyu3frNfgi80JL4Pn57r8oofygjd4I8h9CvwGqbh0ylK8MjrAYzPCzNYQMU3Wpptktgpw9qU9+O+cTszYrChpEiT0Yzkf/+gS9EEolk7VUfFBbNmpin+eGeCOT8NkpcD/O6v2aW+JXFeTWwQt0yHga1wfWiFEzRpSDVwp9ZzWelzC+yFa61l7c46D2k5pWVZvvMB9PPB34F+2bZfv+SixP/VvpZi72bvNm6do8uPrmNChCXRtqgia8VbzkzsZ/LKrwfVHm6TEAuWdJwaYOD9UmVV1mzI577hUzhgSZPyXUf4xNQSupgxF01Q/TshFo/hwjbfy2VFtvUD+22P9nNHTIGAqOmfHg/v2Mo2joUVazYG5NKwZ9UqYGeu9h4ppVwXolCXzv4Vo7BpYDXwMMC7h/adAs705wUEJ4JZltcRLBnM53mj2HrZt5x2Mazd2n61y+Wqd5pROilO7GDx9lp+OTaJsK4VbjzH5ap3mrSXe6PRbLAOfofjofIOXFmp6NVP8cYjCX23VsH6tTbo1U6zc4b3fUgqzNri8NDvC07OiYBpgeA8JO9ICkK68NvSyKBt3ao5q6x1379cR7p/mPUFc2N9H75YGKSbc/a2Lq+GREQa31zDY7v0lDjPWe6PY1xZqnpzl8MipEsCFaOwaWACvXti9LvwBD+CWZd0N/AH4Ghhg2/ayA31N4fl6rcsZbzlo4B8/wNcXw/COBg+cHE9j2isHbhhU9biRnQxGdtrzuX/R2+SxmV41XQMLt7pMW5Mw3EEp0tMNSiqmohmK7AyDoR0UjqvZUa7527cVS5UZvLkcWO4tsuLG2sHu+sbl6DaKlYXwy26KVuneuZpXq5k33zWtuhCiEXJr3+VwUr3Ff697AA5GDfx+IB9IA56wLGuXHWzbPnVfL2JvqLgEAAAgAElEQVRZVk/gZbxBcfnA5bZtL6+2jwn8Gzgd7z/rYdu2X4h97z7gN0BubPfptm3fuK/lOpR+2KRJTKfywybN8I7759wX9/PxrO1QHoW0DIP31yj6tDVZtNULyoPaGqzakXCA1hSENGPeibJsu8uGnZDmU0TC2hvOGOMm3MKpATj5Te8j2TET5l3hzV8/rbvJX07y8c4ih8HtDG4dJiPWhRCgjQZVAw8opRKnZ6dUe4/Weo+zvOq6HrgJ/AkvlWpL27abWpZ1GtDFtu1najn8fg7O2IJngCdt255gWdZlwLPAydX2uRToDvTAC/RzLMv6wrbtNbHvv2Lb9u0HoawHxWldDP4y3aU8Cqk+7319LFobYfxLOygNaX53QSajjk5haHuDBb8Jcuc3Lu+uhA9XaQIYPHaGD5/P4LKBPs6fUM6X6yJek3rUAb+X8jQae0wujUKvFooVJQondod0zPT64h2tSPHD1A3e9nVF8NkaTSjiMrCVYvwIP+NH7LogihCi8dL1jN9KqV0qgFrr5dX2uQev39rBW+7xLq31Z7HvpeF1Dx8NRIHbtdYf1XLZmVSdnv1Dtfe1TtPem8VMRgJ3AhXzupfhrQe+xwBu2/Z9dbxGvcX62AcR/+En4dX2W1Tra78IeN62bRfIsyzrfeBXeAPrks5RrRSzr/AxfYPmuPaqxnzmu7O6UPPYTy7NUhSLp+5kzRavefzel3cwvH+QgF/RvbmBMryVyADCKKKTV3PLQ0cAMGlMCvd+EeLDZS4bSk1QENV4o9vR4GqKtYkT+9SZCmaMNWmXGVu7/EeXqRvijWJXfOQQCmt8BkwZ4+OkztLvLYSI24ca+DPAk1rrCUqp3VUAZwGPaq1LlVIDgGlKqTZa6zLgdmCn1rq7UqoH8K1SqrvWuni3ZdV6RH0LW6GufwEvAc6xbftd4t0Ma/BWJdsjy7LOrPa+fbX319exDHvSAdho27YDEPuaG9ueqCPe0qgV1lXbZ4xlWfMty5piWdaw3V3MsqxxlmXZlmXZBQUF+6H4B06fHMV1A429Ct4RRzPiDYfHftLcO8NlbWG8AcVxqzZzj2rjYjreLdFh2w6yZ3lV5oIyzZ2fh1m1A54/N8CTZ/poYjqo8giqJOTNLw/62FhMZfuMz4CWCf3bt1mKvs3j1wrFxnhEXXhvmcvWEo2OJW/PL9E8+X2YdxYmrq4ihGhMtLHrv9oopSoqgJNimyYBg5RSLaqcW+vPtNax9RuZjzforOIv1EV4QZ9Yzd0Gzqjlupm1fL9vbWWvaw08DdhabVsAbx53bV4HmiS8n0/VofKPUEst/iB5BnjQtu2IZVmj8JZP7W3bdn71HW3bfg54DmDs2LENaephneSXe03WFYrapdNifRFlYc3vL8gkJeAF0r/OcLjnW7h62lyalIfpurWA7hd2ZMpql/PejVIaVhBy+WZNiBZpip2lGlBejnWz4pOlSDE0Kabm8VFmlRHvSinaZcLPFb8Bx4WIAwqeW6h4fL7DCe3hg3MMTniujMVbvQeJ+05xuXfk7pctFUIkJ23uWlFRSo2j6nSt57TWzyW87wBs1Fo7AFprRylVUQHc3Wypy4GVWutYJ1+tlcOabCQhNiql5mqtE3OufE/V2LmLugbwn/ASrryQsO0SvCaF2tQ2VH5/jDpYD7SzLMu0bduJ9dm3jW1PtA7oBPwYe1/5n27b9uaKnWzb/tyyrPXAkcC0/VC+w8qq7S4rCzTD2htkBHf972+VBiM6KKau955NLhwS5O5bUnC1lxSmwkPfewHz1eP6c+K6XG68rTPdzmhHu6ejlEaV174TNCkrcVm3I/6cowF0bKlRrWm3sYChG7bi3wxL7+zLbZ+GcVz4wwl+ftgUL5cKu2gXCBiEXK8c326AF+a5lcEb4NNlUQngQjRCbg1N6LFg/dyue9ePUmo4XrdyTenF9+pU1d5Xn/tTa2ysawC/HZhqWdYYIM2yrA/x1gWvS3rV2obK73MN1rbtrZZlzQUuBibEvs6pYa75W8B1lmW9i9f0cS5wAoBlWe1s294Yez0Qr3tg6b6W7XDz5SqHsyaGCTnQp4Vi5rVBMqsFcaUUn15g8OFKTbNUOLmjV1uu/nDbPhOWF0DEZzK/Q2uembiW4YVbiTgJDSwKzultsqlIMyvWn21EXTLLyujSKZWFa8KszMxgZe8M3lOa9hPLWR6rcX+3LkRxSjxDm4k3OqS6rtmKLtmK1QXerXRiFxmVLkRjVJcm8xqsB9oppcxY7Xt3FUCUUsPwYsw5WuvE+FBROayIOR3xpk7vsbh7+X4XdU2lujCWRe1yYAlerfVa27a31OX4g+R64GXLssYDBXhlxbKsycB427Zt4FVgKFAxuvD+WM53gIcsyzoab4RhGBibWCtPFi/NdSqzrC3K03y3zuWMHrsmSwn6FKNrWWP7/fN93DHVYcOqcvot2UQkrPnivXz+dmtTbvvRxNUw/liT24b4KYvCOz9H2bzDJctvckYfP8e/5hBNGDZapgzWlJtUpM8vDmnS0jWlsTzpVw4yedGO4oRd2mUpMlMNzu+hOL+XyTHXp/Hi7AitMxRXHS2j04VojOoziE1rvVUptUsFUGtdpQKolBoMvAGM1lr/VO00b+GtHWLHBrENjp3ngKpzVSVWm320HtdIsSwrsfkirdr7/dLWadv2ErzgXH37mQmvHeCG3Rx/xf4ox+HuiITBbH4Duu3Dett9chQfjfbx8r8K+ak8PnhsaI4m/7c+fAkfpvQAXH5UPLBqrckt9vqzK58zfYoezWBRcax5PeLQ1xfisuFp9Mr2psE9eoqPwnJNx4TUqeO/dXh7qcvgNia3nWBiNKy5oEKI/aS+08iIVQCVUlUqgEqpycB4rbUNPAWkAs8qVXmhsVrrBXgzmV5SSq3Aq4GM01oXsWd+pdTFxJvKq7+vNT7XdR54jWuBQ53WA58IJFaJJlV7/1pdyiD2jzuP86G1V/seO8CkZ86+T8U665KWbN4QYtuWCBu6ZNH/fz46NYny1i8Nlmx1aZOpGNmj6q2mlOK87oq3lvq9gWmGokUTyCtwwTC8oe6Oy7WDg4wbFC9jkxRFkxTFp6tcXlzg4lPw2mLvCWBxvuaIZi5/GiZriAvRGNV3GpnWusYKoNb6zITXg/dwfAnelOS9sYWq87y3VXtfawu3qpiGsyeWZVVvy28LdAG+s227+ly5RmXs2LH61VdfPdTFOCy8u9Tlgvdj7fPlUSiLVtau7x0Z4L5RVRtbJi+OcNbLZd4bVzO8Z4BpCb1Ovx1icNMQHze9VcLOMpe/nJXGqCMCrCzQ9PlPlHANC9UaCjIC8OpZBmfX0DUghDhs7PemsvdyJu0S0M7bdnHSNsnVtQ98l8FqlmXdBLSoYXfRiGzf6fD+t2VkpimyOqV4G7X2ljNL+Ci9MS/KvSMD/GaKy4SfXQa2VLx7nsm4wX4mzQ1zVFuTI9v6mLY+PkxtRFcfv55UzHervG1jXiwi98FmrN6hqwTvFqmQV/EcAOyMKEZ/4LL+eqMyf7oQIvm5SfpxV0rt1FrvMqVsX4brPo2XLOXePe1kWdZJwLm2bf829r4Mbw55hZNt2066qVqNQdTR3PjP7ayLZWk7+/gofzk+g9cXu6wspUqQHdTOYMpqzTNzvJHo323Q/P0HzbMXpPLsBd5qJJ2ejoDfoP2OMjqVlrP4ax87CuOfyOIQ9H+okPbNDPo0T2NRPgRMePUXBss2RbllKt4GIOIqvl6nGdNbsa1U89ZSl/aZil92l+xtQiSrBpYLfW/U+IPtSwAfsLuTVvMbqvZzh/DSsgIcA9xMEs61bgx2FLuVwRtg/oowEy81GX+cybTVBr/5X4jtJS7n9vHx6FlBpq2v2roVS+BGSVhz+xdRthW5ZDgO/QuKUcD0n6L0DIdx0wL8nOIF+TXbXdZsd+nhL+ABK5VLTsvgtR/LueeTckj1VwZwU0FHv8MTj2/li2UOM7Kbkpce5JERmj/UsESpEKLh24dBbIe7Gvu66zqI7fNqJ0jHSz33zzocfjRwZWJBbNueHjvvT3gBXjRA2ZkGvTv5WbzWG4E+7Mh4H/fwLiY/35pWZf/Tu8KV/RQTf9YMaKn44zCvNjx+msMzP7mgNdlhp8pTYVQpRkVK+X+3t2LUEzsrt+8s0SycnE/JkT6+WBb1HiXTfIAmJ1Xx3KkGM9/bwvx5pWQBp+wI8Wbv9ny8UvOHXYaqCCGSgVbJG8FrUtca+HfV3hcBd9Wx6TvHtu2ShPeXV7ywbbssthCJaIAMQ/HYrdl8aZeTmWZwQv8AX761hW25YQaPzKZr34wq+yulePEsHy+eVfU863dqr9/c0RSgWJYWpGdpCENrmobC7PSZZGWYXHR0kDdmh9AKVNAHJbB8bZhTegSYtsnLrQ6wrRzWFkF+fnxqW9DV+F2XE9pL7VuIZJWsfeC7U9dBbH/Zh2uUWZbV3rbtDbFzfVjxjdjCJmX7cG5xiKWnGJx9fBpfzirlhutXk73Ne1ab/W0hdz3di6wW8eEOq7e7nDexnFXbXW491s/9sVHptww2eW9xlGhJGFzNcr+f5r2D9F26jfygn29ymvPGKyGGdQgQzvZu2XVK0aW0nKc+K+OlP6ezLmTyQkJepIABZ56VzX+e34rrQsYRGTz5iwBX929kn3AhGhHpA4+xLKttXc5q23ZuLbt8C9yIt554dTfGvi8OMzvLNT/luvRqoWiTueeBX8VlLo+8VEizMpfsio1RzeQPtnHJNfHb6M9Twszb5HV8P/B1hNFH+ujfxiQnDaKurrLMWV4UJrZrTbmrIGiAYTJ5I6Acb01Sn0mh30962GXh6gh/Oj7I8iKHuXmaEzsYXNNPsbk4nfa/aktOQHPzqamoGprXSsKa9EDSfuiFaFSSuA+8xji5pxr4Bvaci7Uih1ZtbZJ/A76zLKsZ3spkG4H2eAujjwWOr+V4cZBtK9EMfaaMVQWaJkGYek0KR7Xd/a/ZcTRRB1Y2TaddSRkGkJcSYMGbqyEhgNeU6HfzTpcZ62PJWwxVGcRTgwblhQ4EfJUD0wAImlBcRo4Ok+64pAYVOtPHgGfDFIe9s35Y5DI4zyG/0CG3xLtQnhnhgZHx1oCIoznvfy4fr9Ic0Qy+vNCkbUbyfvqFaAwaWh+4UkrpWDIWpdTxVF3ie3rCCmln1nT8ngJ4l/1RQNu2Z1uWdTZeGrrr8P5uK2AV3vSy2fvjOmL/+WRZlFWxhUF2huDVudE9BvCmGSbXnJvJEx+V8E7nNhiGIruwmBFztqK1rqz5PjAqwPzNLqu2u/z2WD9vz4vw1y9D3sqiKX7ITvX6wiMOVltYmBuB8gikmfHlRx2vXOce5WNETgbH9A3y7CIqgzc+E5RiQb7GLNO0Ly4nYhpMWa54YCSURTTzch0W7VB8vMo715LtcNcXUV46V3KoC9GQNaQ+cKXU1XhrhldkcJtC1SylV+JlMt2t3QZw27bX7u57e8u27c+BHpZl9cBL/rLNtu1l1fdL7CsXh0735kbFSp8A9Ghe+9zpS87I5FejMlj5wTq+uncBmaUhhtw3sEqzdffmRuXI9OKQJvNub1S5g/ICOHg50JXm+M4GWsM3q6MM7uSwDYNpK6M4RWH8Jlx1bArHdvZu39550YRjVeVrbSiCUZewUqzcFOWVnyL839flLNjskpphQnpKZdmmzirl6/YBTrJS9+W/TghxCDWwGvhlwD0J70Na6zSorI0/QH0DeHWWZR0BjMALwJX/S7Zt31/Xc9i2vZz4SmA1WUQtC5iLA29YR5MJo4O883MUq53BrwfX7Tbx+xRHnN+J7me2R7saf9rujwv6IDMIRSHiteuYlAD86eso7ZsoPrsuo/IBYvEWxdRVimM6+jiqXbxF4KqBPnaG4JEZUXLL4uuM+yIOJT6DrWlBcOGKt8oh6vXBlxU79M6KsKrUICMUpcP2UqbPczFbpbC8UPOLrkqyuAnRwDSwPvCewMzdfO97oEdtJ6jrPPCLgZeA+UD/2NcBwDd1OX4vNKz//iR2yQAflwyoX54fX0rtU7X8puL9K9P57QflLNxhQDgKfhNTQXm5S7mGvBLN7z8J8+FlXk25dyuT3q1qPvdvh/p4b7FD7k7Xu4tcjWEowokPB0pVWfzsom6aHz4voLTc21KS6mf4Gw7p4Sh/y1LYVwfISpFbUoiGwm1YNfCsau+7JrxWEB8TvDt1/Qv9Z7z1sd+0LKvAtu3BlmVdDRxRx+PrqvaVVUTSOLm7j8+vS6frY+WURYGww+i+Bm8siO9TkY41r8DhnxN3UFjkMnRIKo/9GEUVRBnV2899F2eSGlRc1Ndg2pooFc+BFx/t56oBKYx6qZyQqzCB83sYlJgGA9ua/PnUIKsH+PnaLqNTax/PbQ0yfPVmBm4uJGIoPu3WnjGnSYOQEA1FA6uBbwV64bU8o7UuSPjeEUBeTQclqmsA74i3YHmiV4D1wB11PIdo4Irzw0x5cjVlO6Mcf1l7Og1sCsCrM8t5ZEoZrZsYvDA2g07N654spXWm4v0xAf76VRiz3OG6ztA63eTxmQ5NU+Bvo7y+8X+9toPvF4QAWLQ2gjIUARRfz4pSHtE8ek0TbhjsZ9V2zesLHQa1UTx2ZoBUH/QqKKXYMEh3XApXKqb8Pb4GT48Ofnp08K7x4+flbNtcCIChNd9P3cFJx2dKU7oQDUQD6wP/BHhYKXVexWhzAKWUCTwIfFzbCeoawAuBprGvWyzL6g3k46VUFY3E5EdXsuIH7yFx09JibnnTYlsIfvNBhOLMdBaWwWWvl/HtjRk1Hh8NOZRvD5PeKgWVkHChkxklb94O8g2Tmxc7FLROxw1BQQjeXRBlUFuTgiK3cn+lIaAhZCoWN8vgx1yD6c+H+c8vTU7trLh3RAoZQcXOIofnJhTQLRpmszYJGwZtmpk88E4xb86LUpbm43fdolxxeiYZmSbbil2iSqHQvNezPRubpPHCM1E+Hm0yopMsgiLE4a6BBfD7ARuYr5R6m/gU6wvw4u2vaztBXQP4F8B5wIvAm7H3EbwniP2pQf3vNzZF+eHK1+FSh3Cpw6TPyijOTK0c/T2nuOapWDvWFPPBFdMp3VpO0z5ZdLmhHwOPyyKYavDMt+UsCXhztPP8JpTGj/tmVZSV2/2MHpXBIy8WEIpAnz5Bpq1wKEzxE4r1cc/erBnyWDHhiKZtlkFG61R6rSggmFeOCbQxXXoObUJ6ED75opgmgM80uGdHkLxZ67nn4U78XGiwsl0LupSWsS0lwG8+tGmXX8RHW7oz4pGuu/5QQojDSkNqQtdab1FKDcUbbX4tsRlaeHF1vNZ6c23nqGsq1asT3t4LLMEbLf5ybcdalvVH27Yfrst1gAl13E8cAsPGtOODvy3HdTQDzmhJapafz6flYXZKwYkF8I7ZVWuquUWaL9a4RN/fSGRrOauzm/Bui+5EP4hywRcbePjBDuhUE4i1IGlolgLby72320KK7o+FyAzCOzfnMKCFQctmJsXlLlfdn8vbxKeCtdtexJqMdDYXOLhE6bgzSsXyKoajuez0DP7+ciHbfSbrUwL4Y/PkcjeEKSyIMnagjytXpbO6STrXfjWfX/y4AgD331spuS6H9B7SHy6E2H+01rnANfU9vq6j0Dvatr0OwLZtTdXlQWtzF1CnAG7b9g17cV5xkPU5KYeO/ZsQKnVo3sGbL52dYTBw806WNk8nw69467x4QN1crDn6xQibS8D0deaGttt4ZVBfdqZ6YfWl8gB/zI9ww/BUXpoTYYcRQJmKC4826ZujKIvCHV96gb0oBH+f4TC6U4Qj2vgY0TvA7S13sm1WCStbZHHU+i1817JFZVB2i0Isz07H2rwDBazLTCU106RfryATiuNNbS1CYdq0C9A0y8flOYp+rQzmzSki9OqWyp/DcDVvflPCZwvTGNlF8dEazUcrvQRxk35hcI6sMS7EYaEh5UJXStWUrjyita518FqFujahr7Is62vgP8B7tm2H6noBpFm8Qft8eZS/fBWmeZriybODtG8eIKN5/Pv33dSc/7yzg9N8pVw/pikdWsR/3d9t0GyOrUPnoFh9fDdKnHgTe5nfR5MsH80DBqcPTOONn1008Mxsl4mj/XTKUpiGU7lu+OwVYRbN8VKoXn9CkGvG9uCBbQvYvmQTxUe1Zsq2+LmNiEthmwx+aBaktNTl1yOCTJ5TzqPzoujUFG8+eFSTk+Pj9ps64PN55R7YWvHNC0twWmTibN6J6WpWdG7O7zdmEd3i8sZiwKfA8B4wLvsgQtHv48uoCiEOnQbWB15junKlVAnetO0/aK33GGvrGsB74KV1+xvwlGVZrwP/tW3brsOxyrKsDuwhkFfU7sXhpTikOW9iOSWxru+IE2LylVUzlfXqEuCR21vUcDT0a6EImhCKtY6fc14Lhm53GT81ioHmL6f48Ae82qvPrHp7XPqBg9+nuGWYj+/XOqQozeIFYZpFo3QJRZj2eYgVC0r55z0DSU0x+GJuOdkv7CTf542Az0lTrLjZT6rfIOJoZq1zOP2JcopbZXg517UmUBrhX6NTyGxikl+qmbHWoXu6yxqdSv4RLVjUuTWLm6Yzs3NLXCOhlp3wkSsuiLB2u59OzaQWLsSh1pBq4NScrtyPF2/vA+6maqa2XdS1D3w1Xt/3vZZlnQJcAXxtWdYq27YH1HJ4GrBmN9+r64Io4hAoDuvK4A1ek3hdFIU08/I0PbIVX17i4+0lLgNaKq7sbwImV1s+fAa0Slg85C8n+Zid67JiuyYaS4kaceCr9Zq516WwscDh5GWlNCtzKp8EN26Osnp9hD49gmxfG2JIaTmrA36iBrxyYzNS/V5Q9ZuK6z6KEg2aXvAGUIoWGYphnUzGfhTlrUUOoRKHdNfh/ExvFH04JUiOBtdnQKwVwHDdWDBXEHGh3GHhZkcCuBCHgwZUA9da7y5d+Qql1DLgU/ZHAK9mKt4Atg7AiXXYvxRvsrpoYFpnGtww1MfTP0QJmHD3iECtx+SXaYZNiLK8AJoG4YVRiot7KYZ0iD+jtWuy64esWzODxTenkFfi0vGpKOURb/u8LfDpSpfTu5l89Pum/Ptlh41LygCFP03x9DLFl1+GaVJk0AboHvYODETjDxvfrY6yJDfi5XbXuvJDrsIRLnvf5MM1AArCUUrLIsxu3oT16Sk0C0XoWVwCStEsVdM9VM72zWG2G4rtQT84Gh+aoZ2qPn9+vzbKkzMidMxS3DMySKq/4fxREaIha2A18N3SWq9USrWsbb+9yYXeH7gKuAQI4SVyua4Oh7q2bW+s63XE4eWpc1K440SXjIAipw4JTT5ZpVkeyye0IwS/ejsCZVHGDfHzbMIAt91pkW5w4yCDR3+Iz/v+Yo3L6d0MurU0MbdH8GsAjdExgyfmVATqAEdlpdKlsIyO7fz06Bp/2LhoYhk6toqZsa0UFTAxwlE2uooNO0LgNwhol3BpBO03WZTplbMo4GNTkyCURtm+0yU/BW4ZlcLqfJd3F0YIaXjwnDRy0uO1720lLqe9UOrleMdrjXj8XFkgRYiDoYH1ge+WUqor+ysTm2VZc/Bq0R8AlwOf27bt7vmoeFnquJ84THXOrnvzcNfq2X1j63s/NyvCv38ZJOir/Xa4ZoDJk7NdymOLjI3o6F2/tMxl05Zo5X5bdrqQEBtPHtmUS9pn0LtnkJSgweRFEZ6fGWJLUSzIp/pjzeEuRsRBmwYKzTkbttCjoJjCgI8J7VtTllCWsGlU5nNdVWbQsqCMi89pxr0XaJqkmxiGYumCEj56N59gcRGWuZYiN77E/eKtdf2YiMONUxxh3S3fUb6kgBbj+pBz5f7OHC32N60aTlfWbkah+4HueEleqmc/3UVda+DPA6/Ztl1Y9+JVeqgex4gG6th2Bi+fCe8sc5m/IcqaAi/4dcxSdQreAL1zFNMv9/HpSs2QtoqRXbwPZUa6yZG9U1i42Jskfkk/k+JSxY+5miOaK87qbZKd7Sc1RbEq3+G8F4u92KsUBHzgi324DYNIMAChKJ2iUXoUFAOQFY5y7PZCvmyaSsVzZ1Y0ytgvf6JLfiEze3XiayeH56dvwTTgzquyOKZPkCf+th4d1YDBXNWGY/0rmNG2O4aCawbX3u0gDk+59/5I/otLACiZuYX0wS1J7dvsEJdK7EkDa0KvaRS6wmvhfhUviO9RXQexPbXXRYv7r2VZLW3b3gpgWVYAb274QOAb27b/uQ/nFoeJ4pDm6R+92vENg31cfqSPjfnw9L0r8W8t5aRL2gM1p1ityaDWBoNa77r9j79vyazZpaSlGhw1IJWrtWZrCfzxqygnv+ZgKIfnzzTpmuFWLoSC1rQOh9mc6qvs/06JRLhx6mwW9aj6ENyvU4AvTaNyMfQbps9n9MyFABy/YiOPpp0MWZk4Lrz8YRF92hix4O3ZnpbFy+89zIr7rqHjxUfTZzerp4nDX2RTSfyNhsjWMlL7HrryiNo1sCb0mkahR4BNWus6jRg+GO0N/wVOTXj/d+BWIAr82bKsOw9CGcQBdv7rYe6YEuWOKVEueN0bur7hrdW0nrGe5ivyWfjQfApWF+/zdfx+xXHHpHPUAK/tfOICl1v+P3vnHR5HdfXhd2a2F7VV78WyLPeybtiAjcGA6b2FElogwBcgJIF89IQkENIIhHwhkBB6b8EUF3DDbXFvsmVbsqzed6XtM/f7Y9YqtrHcKE72fR492ml37s7szLn33HN/5+MIz6+PCbgI+N1yjUkFBibk68ZTkaDIHyarK4QpqpISCvPE5rWMqm/hzBVb2JTsoMNsZJfLwY9uSidBVnVDL0mMj/p6zq0IQWY42LOcnKBQ1xKh1WykxWqhzWxit8PK62Xns+uFZkxrGolz7JJxxyiUJN2D4jw5F8fU/bQo43y3kPbz993lL0KI6r3+6g7WeMM3Y8DHoIfD43a7ZfTZpKMAACAASURBVPT55N/zeDwXoou2X/kN1CHO18yi6t6x3oXVGmvqVLZX9PZgtKjAWx/Y57hH5oco+o2PM/7hp81/aNlkP67UuOq9KG9sFj09ZoCCRLAYJRbc4mTRrU7m32hHNkoU+QK4m7xclx0hJ6pHmUVNRkZUN7IgJYm8czJ5ZrWKtyWkS795gyiXDiFs0hsCGzNdVE/NZXiJidRMA3P8Bu5+009UMaDKMhGDgUEd3SQHIqgRwRe/3XRI3+eYoakDpt0HadfA3S9827X52rCPT2dE1fcYvvUyBn98BrIx7k35riMkaZ+/7zDHH2kBhzON7FBxeDyeltjnkYCJmEEHFgA530Ad4nzNzBos8/Ym3YgXJwjGPOFnUHcqtxmakKMaaUMSyB7Tf/xwRY3KvZ/ohrSqPcpDc0P86ez9R6rXNURYtKybzAwjkUwrP5kXpb5Do6eJLaDMBe5smcdn6C9ai1FiarH+E//0sTTeWejHYpI4c4qN5rKhfHjnapaMKkdTZEYGw1RURnAPM8Geed7A5+lpvPyDc8n3+SmOaKStaCGSbqYrYKBMCxFVVXZaTGy2mDBrgqneLrpMRtqtZnISDWiaQO4zLhcJqOxe00FCtgVXwbeYzE/T4G9zYFczfP8kKN1fPM1eRKLwyJvw8iLYVq+ve/QdOMsNU8q/8rCGpU2sfnwjRruBib8Yg7Pg4IdSvm0MiWYMiXGlvWOFY2wM/IgZ0IC73e5BwAhgrcfj2XEY5/C63W6Xx+NpBcYDazwez55QYhPfdSdHnIPilQtNvLRORZLg7g91N3NlSiIPThnL2itlsoYmYrD078F0hfr3uH2h/ffAvT6Vnz/SgK9boAEfl2YQFBK6A0kDAQlmePciI0NS9+NUaurAZjJywhAjO7cH6WiNkDsjm8F/tDP3X71xme0VXXxhdIIi44yqyAieXQmZJU4ca0MkR/ShAdEUwm6BiKIgCcEqqxkhSfhlWJFo46PzTiSqKKBq/OXpEPOya9n5mIcvhZ3OlBQsnSFkReKsR4Yx6Pj9q9h9bXR2QzACT86GX76pr3t2Hmx9EhIHaFA89LpuwPcmHCW8tY32x1eiJJlJvncySoJu9NSwxmc3fkHEpz/yS+5ayWlvTD+a3yhOnB6+4z3uvTFKknQlB7CBQoh/HaiAAxpwt9t9PvAaulJa2O12n+/xeGYfYiU/Bp5yu93/BP6H/hnHRgBxGdX/AEwGie+P1X9Ozy6XaezSI8iSsizkjd1/j2tascIlIw28ti5KfpLEPdP3H7G9uz6Cr1s37kKCcFSQHIkQkGSCMYnTJCMUJkk01IXZsS1AcamVzGwTPPAqPPw6lVnFPHbSD1CFhMUicd8v8hg5yo4wepEiugZ7pcVCQw1kRiKkRnSD0x1R2SoM7G1m9zQ1DOFIv5dGc5IdTYk1VBSZul1ddN7xLukRldOBHZnprCsuQFMFG2c37NeAP7Miwtsbo0zMk7n/JFO/HvwR8c4yuOwPEIpAQZ/zNnXC9gYYW3Lg4zfv7r8sS3DVNMSUcmqLnyFaq8c4RKq8ZL1+NgBaWO0x3gDB1kNJoxAnzqFxjBlwE3D/AbYLdL2Vr2SgHvi96BHjfwFujX0+VAN+N/ASeh7xBcCf+2y7DJh3iOXF+Y7z2uUW7vs0TDAqeGDGV7sfZVni1StsPHOhwGEC6SsevoJcE2ahEpIUZAGn7GrBIECVYEVqAm0WE7t8sHxDkNf/sJtIWKAocM/PM8h/+HUA1qWVosaSBQeDgk0bA5x0SiJ/fDiThx+p502Dg3aj/jg4o2rPue2ahqIJViXYSA9HSIqqbLZbkcxGLKpGniaYVVnDRyW5OMIRfM4+31cIUru6kaIanRYzicEQ1lCvAXMV7dvjXbBD5cZ39H0+3qqSpqjcsGU1UooNw8Wjv/IaHRT3v6obb4DqPhoRZTkwJHfg46+ZDu+vhKgKo4tg4S/BaUVt9vcYb4DQer1soQlab59PTsRPrdGGbJAYdXs8jDvO18fhGnBJkgajp8d2Aa3AVUKIbXvtMxN9WvQI4M9CiLv6bHsQ+CFQF1u1RAhxywCn7RZClB5WhWMMZMCLgN95PB7N7Xb/HrjjUE8QG/8+9Su2/fhQy4vz3Sc7QebZCwdWXduD03zgh85uk7luppn33mghIsnUJOtqMYqA/O4QbRYT+YlQtaSNSFjvG6sqvPtSC//jtIIvQElLr6NHlqGoWDe0uZlGbr0mhSXPdukGXJEIGBXMYb3XGJIlVE0jIMm8k5asR6ebDUgmhUEOlbHrvbjCEu7VFWhGhT+bBtEpSxATgNmVkMBlN1xGs9lCZmcnJlnj+swIZaVWJlxVsM93rWrvL/yy7W+rCb36FgDGlTVYHj/7oK/rPricfS6qBV69Axo74dyJYDuIcd6zxsPGP0FVE0wZopfR4sUwbx22qZn4FzcAkHDNcAB8r1fgfXYDeUC6opD2wCQyz847/PrHiTMAR9AD/yvwlBDiRUmSvgf8H3DSXvvsAK4HLgT294L7V1+j/k0wkAFX9iiueTyeSGwO91HD7XbbgR97PJ4BJ6zH+e9mxmXZjD4+mV21ER58zosa6yTPGGrg/HIDQyobWftWPSQ4dSMrBNHOCLzzM7jnRUY5Ddx6iYnKbhsjRtooKul9/kaOc/CU2ci5f++i02Kk1mYmqMgomsDk83PF2u14ctOoyEwBiwkMMgKoDyhUqQq5ikJnaiIBWebiuma2eC2sTU6ABCOTtBBSRCNkVFlZkAWAvUTmk6v338A5q9zA4NQwW1sEKRbB5XMX9mxTZ2+GIzHgz94CN/0VOrrhkStg5ugD7q61dENERc5K6F05OFv/A72cCT+DnY1kSzL+e69FPmM01kn6dhHq9WSYVRXjvpkT48T51olpjo8FTomtegV4UpKktL65uYUQlbH9zz1apz7SAgYy4Ca32/3zPsuWvZbxeDwDKq253e4TADdQ4fF4PoxNJ7sFPdNKKwehOBMnjivXiivXyj0mI3OWdJOXaeSKcxMwGiSe+FsjlkiUNG8XQZMRRVUZf34mzMiCFY8B+nzGMX3KW1MTZdG2CJOLDYhulTFShKoulaDZgCkUZWJFDZet3grA2ZuruPPsqdTJMsawYFR3EKMQrEpLpmzVRko6mjAFwzQWF5IccjC5tRPVqLDbZuPTTBcdRgOOcJQuo4FPt2u8euECptxRTpIBfJ4Wkk/JwTo4kRSbxKrbbGxs1ChOAutrMlpsDodyQvGRXcCSTLS37yG6tRXDYNcB55CG/7mSwPVvgqphfmgmlvtP2Xen5Vthpz7XXRIa9u0VMGlWz2bnpWX4Xt2C/+MqzOMzSbrlwA2GbwLR7geHGSk+Jew/kv31wCVJuhG4sc+qvwkh/tZnOQ+oFUKoAEIIVZKkutj6AfXI+3BpzM3eADwghFg6wP6nS5LkRJdO3SqE6B5g/30YyIAvo7dVArB8r2XBAFKpbrf7ZuBJdEPtcrvd9wMzgFzgTuDlQ6xznP9yJo2xMmlM/wQh6YU2vE1hrJEIKTbBmfeUUDYxuWd7Y2UX/3imno/NSYwe4+DSMQbOfsJLWNW93cOCIRxRjetWVTK8sp4kbwC/3UjIoj8iRk1Q3O6lNsXJcW0+8mJjySUdXaTXtFEa+pLjGir5qPAimtA1s4VZodJkoN1kBKDLYGBScztpgRDNld0svnYxeZXtoAkUp5HRq8/DWpKA3dSbvU37/IdUP7iQtTsEm3JHU7YiwLpOmbwkiWvHGQ8pwE3d7aVpwrOIeh9agoXU5ddhGeLa777B+z8FVXfnhx6ag/nu6UimvV4XQ3J017s/Nq4/tr+wlGw2kPvRBYiI2mMwty9poX2Xn9IT00jM/pqSvATDYDbuk1oycP1rRJ5dgeSyYfv4RhR33J3/n8b+DHjMWP9t372PKn8FHhFCRCRJOgV4T5KkciFE6wGOUYBadInKDkmSZgkhlh3KSQ9owD0ez7RDKewruBW4yOPxvO12uy9BD2j7C3Cqx+OJHIXy48ThnLsHsfilWiJBlcmXZJOc1d9F/eL9FTw8aAgBo4EFG8DTovZIraoatCPjQKOyIJ2JG/Xxcs1mRBW6AlvQbmJTpj6PPaFPkBsGhTH1bbw39lSen3gO5aFmckttaFGNjiGJpMxthczUnt1TwlHSIyq7sjPIW1HRk+xF9UXwLmzAWtLHXQ34bTZ+4x9Ca5rEknojjW9Feua6rqvXuGi4gbE5Cv6I4LYPQtR5BT870cQZQ/Z9tP0vrkfU68pysjfIzis/oXzl5f32qX9xK5X3ejB0uCimCwtRpGQr7K/HWpAO8x6EFxdCeS7iplOJtIYwppj6BdvtMd7r3q9jzqMVAKx8aRdXvzABW/JR1oq/8x/whw8gIwlm/29PZL26to7IsysAEK1+Qg99iu2D647uueN86xzmPPAaIEeSJCXW+1aA7Nj6gzuvEA19Ps+RJKkGGI4euP1VPAL8A71xcRPwC/p3kAfksIRc3G63BMwCfuDxeAYalMvxeDxvxz6/gS7S/tOjbbzdbvc+UYQej2fbXvsowBPAaejeg994PJ6/D7QtzrfH7EqNZ1arDHZJPHyC0i8hSnO34IaPolR1wB0TZK6+ad+gMAAhBA3dEDD2/ty7NQmDDFFNnw117gQLa+e1sCXJysv3n8J9YwWjPrWQ7vVT2OalsSyNi0ZZ+XheO+n1TTgVAy2uJEqqG7GGIrQ5HXyRX4i/OZWxTd1MnL+Kv4wbyVkVdexKTmB3khN3bRNZsWGviMHA7LJ8rvpCT5axLTeFpYkZnFKnMjpbN3h1jVEaG8Mo/jD50Si2SJgXEmz6F9AETy0K8eeFIcrTZYZkK7yzUW9YrNwdoOZue780pwAitX/Uu69D7bccbguy9oalaEIG7FQ5chk2UmB57Iyvjn6fVAaTygi3hlg+aTa+9e043C7Cl5fja48w7qxM8kfqjZLqlW09h/nbIzRXdlEw/igmB9myWzfeAI0dcPavYcmvoCAdyWnWb3SswSQlHnyQZZxjh8MJYhNCNEmStAZ9VtSLsf+r+45/D4QkSTlCiNrY59FAIVAxwGHlwAwhREiSpLuBykOt+yEZcLfbnY0ehXcdkIU+NWwget4isWj2Lo/HEzzQAYfJX4GnPB7Pi263+6uiCK9AH28oRTf0q91u91yPx1M1wLY4XyOrP22mZnM3ZROTKJvUm490R7vgvDejsZ6yQJbg19N7f7I/ma/y3lb9hXzdhyon5MsUJe13DIwzL0jjrTVediYnICG4baKR4TMTWLgtwqRiI2ONQUpWO2lw2FneBbWrg5RKGi0OK6udVi7e1YSvSuGudRUYNN21nFXbxNT11dSkJ1HY2sFgzyY6XIm0APOGDeOKFet5a+xw7pm7HK/FxLbCbPzJiSAE/85MxZ6TgcFhJyIE88rzaV4u8XOPn2fy2sn4qIruz+uoTU/CMioHCUgJR8jydlOfYAch9tgiNjdpGCNRjKpERFEwdoWov3Yp3vZu0n48FuepesPGed0oap5Yj7y5Aa/BRsqt4/pdp2iDL2a8Y8soOJbcdFD3sOYf2/Ct1xPBb2mVaH5b75BsW9rOzc+PwekykT/cScMHVXRbLJhMkLq+AsZPPqjyDwqLqSeAEYDaNjj3UVj9O+RiF5anLyD8+wVIBcmYf3vW0TvvofDImzBnLZw0Au6/+Nupw38wRxCFfhPwvCRJ9wPt6GmzkSRpNnC/EMIjSdJU4FUgQd8kXQpcJ4T4BPiVJEnjABUIA1f27ZV/BUYhRAhACNEtSdIhS/4djBKbBJwO/CD2vwVIAsZ5PJ71B3EOi9vt7jv+YNtrGY/HcyNHgNvt3m8UodvtTvN4PH1bUZcAz8Qi65vdbve7wEXAbwfYFudrYv3nrbz7hyoAVn3azPW/Kyd3iC78sssrejOKAZVt/aOYWwO9y6qAylaN694Is61V45ZJRn4ySWHTzzx417aTf0khy36czuJ6KMk1MSpTBhQmFOnj0zterqbB0ZusYmm7kUm+blxAVtSPFgZHONhjvAGaXQk8ffnJpLZ1MmrzDnaW9M6lDprNZPr81CbbufnaWYQkmRs+W4V5dzPrM1KpTktkQouPHfnp2KNRxnm7ifq6mZ+axOJ/t1DSLpGW5qCsqoldw7JAkRmys54XP1rOp+WFbMpI4YNhuntYFoIzP1rLZVGVf40s5dGPlyBqmukCuhfXU159DYY0G5IkUb7qCpo/ayAjyUTK+F7XPoD82TbyRC3Vcg5dyWYspVb8bSFsKQO/VwyJva7wkNXY8zkS1PA1h7FJKmkPfcypm1tQZXAamrEu6kCkW5HOPErBbYXp8OT1cMszvev2SL4CphsnY7rxKDYYDpU3voB7YyE/CzZCSQZcceK3V5//QA7XgAshtgAT97N+Vp/Pi9Fjt/Z3/NWHcVqjJEmX0RuNvvcyQogDxogNpMR2H3pvOxtdwOUC4CP0sYGDTbX0EnqS8j28stfy0SAPqPV4PCqAx+NR3W73/qII84HqPsu7YvsMtK0fbre7J6oxMzOeoehIqN/u7/ksNGisCvQY8Ek5EhOyJVbUCcwK3DCm/zjsz49TWLw7SkcQ3JkS/1gZ5rMdusW/55MwU+Zux/t3fRSlY2ULk8e4OH/S/qVLcyemcsIHNSws0m95Zqh3hEcxKdjCfhqcdkIGA+ZoFE2ChpREkCTUmPJaYoePoE13zXoNCrdcdDorhuX3lKMhcfmiTVwCPHf8cKwSZIQibCzXz2kQMLTDh0hwUJGcyLbCTCaLrSR0+EkanURWsyApGObi1VtRJYkSa5iNmpmztlQyrXo3C8qGcGtVNQ5fb9IYEYgSbQliSNNd77JBJuOUfXXPtZoOAre9T65QqSpIpyUljRYfNPxgJZe9PgVJklDbArRc8BbhL+uxXTKUlL/N6nGt531/EN5VrbQubGTIaCeeWoGmCnLKHWSU2Ag8v5boZj2cXtFACcfu5epqOEIDrgWjNP/PfELrWnB+r5zku86Bx9/TN95y2iGXt/Kv26j8pI608kSm3T9iHwngw2Z364GX4xwxx5gSWyP9g8Bb9loWDBDkPVAP/CH08eRz+0qout3ug66hx+P5/kHvfIzg8Xh6ohqvvPLK+OTWI2Do1GRWfNBEJKRhTzRQMrY3iMtikFhwpQFPvSA/QSI/sf/DOTlX5r4pMj+ZozLt+S/4Ii8b8nqNU6ix/0hNqHHfbGh7MJUk8e8fh/jw3Z3M63Kyu9tAVNbzgk+dYKflnWYkn6BjRhmTSuBPvkRW+C1M391Ke5KThvRkXG1eVhfl4lJVjJKEwWbBFo4ypq6FP737Z8bVbqPGlsEu/yimVNby63Mmc8OSjT11EECoT4MgKsssnlCKQVWxLt1NsNCO6jCiBjXCisy1s7/A2UfZLX9HK00BwY68TJI6t6MIgXF6Puay5L2/7j6IiBpzPcu02Hv3b9veRdOaNjLGuPA9vozQ53obt/vva7CeXYrtrMGIDj9bn1zLyhUtWPKdnHp3OWNtRry1fvLGJKMYZZQcZ7/zKUTBboazx3CktD+6gs5ndGdgcHk9lmWXY734ON2d7h50SGXVLGth5dP61MH2HV0kFdhx/+CIxLJ6uWwq/Hm2PvWuIA2+F+99H22OJQMuhCiUJKkUPdhtrRDikHONDGTAr0TvaX7gdrvXoef2fgm+c4oMNUCO2+1WYr3vr4oi3AUUACtjy3173QfaFudrIBgRrI6YOP7eIaQGQuQNdZDg6h+VbDFITM3b96GsqgmzYXOQ+VUGrv5iPTcs/JKTXNu57uJZ1Cc6udZtYOKwISybU0O4OUjSxFTSZx4445bTncGl7gyaf1dPZFU3QUXBLAkuvCAD+/dSiYQ0rE79kTkuqPHak1VoXh85cxZx4valLMubgIcytJg++4gWLykhlXM3LGRcre4JyPM3snSwisGncdG2XWwaUYQhFGFbgo06o5ET2jpA0o8PKzIGNG6ZN4eiliba19tZUFRO2GRmsyuZ3LZmLl2zAQCv1YqqybhqO6gtTWPRxGGMOTWVqfePQNpPZK6mCf62LMyudo1rJ5gYVOzC/L/T8T2ymFSfl26zPsVLUlWWX/MZZ6+9sCcArAdVIFbvInLS7yjp8OO0JjPbP5kVv1rN6Tvm41q0GU4oh9n3YD19EEm/nUHgg234k20sbS/Bl5/D8fUC5aE3kO1GEh+dgZLt3KeuAxFt8PdbVhu64ZzDM7ohb//Y2qD3KMbaZibDhj9CZT2UZOpKdnGOKseSAZck6Tz0XCMGICxJ0vlCiEOSKh9oGtlLwEtut7sc3ZA/ADyGPn/NzUHoorvd7m0MYPA9Hs/gg63wVxzf5Ha794ki3Gv8G/Qo+Bvcbvfb6IFq59Kbk/VA2+IcZaKqYMZffHxRpUuW/u1iGze4Dm5KUfXuMPf8spFwRGCWoLhVzyhW0trBZ0+/TNJLZ5BxwRDAwvQN5xBqCGAtdCAbDiRd0svlV6cSDGl0tkc589wUEhP1xyTkjfD509tAAmuCEf8r2wHYaSlmsvwl42s8fFx+PGGL/mJWIlFsUY2w0n/EqCMlmarMLGqz0/X9gNFNjfxq5UI6k9KZXz4GVZKICoWJ27dR1NIEQHKwm6L2Zioycilvbeefo4ZTmepiWIeXTVlZNKUkkuYPMn3RZobPcDHqwZG0Lqih6YmVKCOzGHTvJGRFf8Hd/3GQR+bqHornVoSouDsRJSeNMFZK6xuptSeBJCFHVdTuAGqLH+ePJxL8vJrwqgZsFw/FevZg1B+8gNShG9D0QDuFTU0kvNiIR0ujlAYSF26G5z6D204n4a5JWG4Zzz/OX4lqFFAnGHPhG5i79XqotT7S5l95UPeoL0m3jqbrra2ozQEsU7KxnVp4yGXsoWhaOtnuFOo8bTizrYy8fD9lra/WE8CcMBSMhziRx2aGkYdfvzgHRhw79ht0IbP/5QhyjRzUr8/j8WwG7nC73XcDFwM3AP92u90ej8czYYDDf3koFToCbgKejwnF9EQRut3u2cD9Ho/Hgz6FbSKwZ3rZwx6PZ2fs84G2xTnKbGvReow3wL9Whrlh8sH1SDZWhAhHYm1CAebTSglVVGLuDGIekUrqmb2CIgaHEcOgA4dctP5rC12L6kmcVUDSecW4Uo385Of7pqn/4M7VNG32AmBN7H10NFmhw5JEUWc1hY01tCamEzYoeB0OLNEoC0vcDG2sZGRdBRszS/msdCKJ3t5eY0CR2Zidyo6TT+Znn81jdPV2/MJEh91G2ND/EY3G3OsCSIhEaHOlssXsYEeWHpDWZTKSP60Yc7LK5umzmb5oETlqBO1tmLu4CfvNExg/M5XGD6sZ1aawNiuVRp+goi5CwQo94MsejVDW1MCOlFQEEvNGD+GpX/kYNcjELx8ah/XdpTDWBrKElJPU0zrXAGcHSBpoGNhiGMnE6AKiuzrRXlmD8cxyNElBjd07RdN6jDdAdEdvatcDoQWiBNa1YipOwJhmxTwslaId1xNt6MZYlIikHFxDbX8oJoVz/j6JQFsYc4IRxbhXWc/OhRue1ocbTh4Jn9wfyx8f57vAsdQDJ5ZrRAihSZJ0WLlGDqn56PF4QuiG7gW32z2U/vJ0X8WUvlHmbrd7gsfjWXFo1Tyouu03itDj8czq81kFbv6K479yW5yjT3aCRIpNos2vv8xHZB98oNCQQWYMip4US5Jg5sXZDL39OiJVnZjKXciW/f+sw1HB7bMjrNitccEwhXtONNL+1naqr54PQMuzm/noguNoTHSSV2jm+pszSHH1Gv/WHb0Zt4K+KAI9XNQUirJTzeO9oSOZU1TMuMYOlFAUnE4SQmHCssxLo8/hz1PtPUFfXWYTZl8XYbuNFVlJ7HbaGFelct8pZzOhppaoI4mAyUR6WwebM3Ioam4iJEy0GhMwhaPsTEtkS0oifoNCoT+EUZZRJQlrNEriljpag2FKWuqxqroLWAbSl28he+5K3isZznGakeOA98sKWT+pmCW3rWBzjco0RUZWNQYLL9UJOaxzpeEpyIcgLNkQ5s3nlnLKxlWsez+IbX6Y8U+cjKGlC7G+lg4pAbGo93qHDGbUUWX4Hl8FrEYZk41z2S2c+P08FvyzBsVhRD1vGMo7ehyA447+j29bpY+dCxpJLUugYKrurVC9YbZMeYfghjaURBODPzsb25g0ZIcJ06CjIwojSRI211dE3j8zp3ea2tx1sKMRBmUdlfPGOXKOMQOuCCE0gJiC2yH/gA9LyAXA4/FsAm4/iF0vpb+h/xg4iuoNcY5FEq0y837o5KnFIbITJO45+eBlNYsLTDz8swzWbgpSXmpmRHnMZZ184B78E8uiPL1C7/V/WacxLltm+Do9ElhCQ7NAlcGGCAgqNgd55YUWbrm99+U89Mxs1r+l58QOKkY0owKSYOiWnRj8iYzdpCEl1BI2WPmkMJvciCApHCHN202iz8vWlCTW5WeTGorQkeAASSK7q5uALPHsc/9mzM56NAkWjC7Fk6GP19dmpPLRiLGYwxpJchTRFaEp1clLgwqos+pGZo3RwNBAGJMAYziCJRhG0gT2NhUNCTnWR16cl8uZW7fTarKjqCqGiMqpzY2cscxLqNZPu9XGxyNHceb3s0k+qxjfBUv3fSFGBHOyTySrNUDNiiArrljFxAfOZMqTCXTbf0dlySCKqprQZJmO88vx74oAVQCoq+vQKluZekUuEzK64Jp/gD8Mvzwd6cJxrNwl8/GNW0lIMnD+ZS7m3bKMSLd+v055dAylp2Xj/aSG4AZdEEbtDNPy983kP7X/mQVfC+W5sFx30olkB1JG0gAHxPkm0Y4tA26SJKlfrpG9lhFCHFCqfKBpZEdj/HrvK3pMXeE4Xx+jcww8c8mhtyFbZu/GUefnvAsKMCYfvPZBU1f/n3JTtyDpvGKafrcGe3cHPoMF0ccdGgz0T+05/e6hDJqR9K9o4AAAIABJREFUQZ2nnfqfrsQSiBA2S/htMqXhnaRFWyjYvI6/TbiMk6vrWJnp4uq5y3H5umlz2vjj9ecSMZtIaO3Tk5dlTl5TyZiduvtaFjC2cjdfDhvUU5ei6jbswRCW7hALTxxO0GTge8vWccb6bVSnJHLHOdNI6Q6DQaHLYiaiSJhVDaEZqSKHBLqotSeyLj0Tp9FBe0oKCEFiSydSQEWt8mIADOEIwmHCevZgbAU2hp6Tg/p+HbtSkqhLTuDhz9/gpM1riEoKy1NHM2/iSAA2PNXIY8uiPDEijZHrq9hUkofFZea0p8cjHmglurgKACnDgZwXM3j/8yrUxlzmD7+Hv7uT1yvKEZJMZ1uUd/7ViLW7d4ildkUrpadlYypw6G+Q2K00FfWXnv26iV5+CuqLa5CiYaKOfKxq/IX2XUIcW3fjiHONDPT27Dt+LQFPoSctPxT2bgB81yLY4xxDVD+2nsqffQlAzR83MeHLs5DNB+d+v3mCgVfXq9R0CsbnyJw3VMFmSqV83SU0TnkeU0MXEyq2saKsFJtV4pwL+ziK6trgl2+QBwQdo2gPRAhaFTpT9QbEl9ZyzqubjctdSsmjbm74ROOKxetw+fQEQyk+P99fsZHfnjSekCxh1gQIwYiKXWxOsBFRZIx7kocYDTi6g/icNoZU1lEzSHcf27pDuFq8qGaZCz2bABhR38Izr88ht62bqCwxZ2weH+fncnrlLupS7GS3gVe28/IZEzh301rWpuniL0gSvmQnts4uTGGtZ517xxYe/HEWlwTrGXNiGiNfmMj3nUYc/14OT68BwCBUBnX3hofIwJbKEF/+9lxOfGM56apgqSuPF29ex9gRxQx5NAF/TRfJP3TrkqbQ64YG0DTUP85GzBrWs0qJRjE5DIS7dCOeO0kf47dPyKDwnyfR9moltjGpZNw+8qDu/R4+r4zw7oYI4/MMXDHu0F3ukTfXE4nGGiE1AaLzt2E8/9DqEOfr41hyoQshph1pGQNFoT/fd9ntdv9+73UHwVFJSRrn2MUb1FheFaUkVaE49chEMZrf750Z2L2xg8AOH/byg3NjFqXIbL3dQr1PkJcoYYhFZJuLE0m+9zjabvuU075czayhGll/nYWhj+465/waPHrUubmgDcghZO39LlHFQMsjN5J/5ySukRXGZivM29r/u1qCfgq7uyhtacZnNLMiNYkrGlvJbqvjodMnc/3yjYSNCm02I1e/swRVlthRkE5loi7+5LebaTKZmWD29Su3oM2LioJBE0zdsJvMrBAy4Eu0sNlppOSxKeT9uZIKaxaGUJio2QSaiBnviG5MJYlUv5f89lZufncesirT/OEWXP4oiQ9MRkuxIXpG/cGwR7JUkghKEnUmEylZVlxPnMKbj+5Ae3URM2rW0LTIxZNjZ9KpJlD6qo+r7ktHMUgYnr6c6BXPQXcIZUYRKW8v4LRN8/mkfBr2kJ/z3nqNhJdvoapakDo4gbzJvW5y11VluK4qQ4tooBz8C3tdncop/9dNVANd7ZJ9jHj0kwpCv1uAnJOA5fdnIyXb+m2Xy9J7PmuyQsfCZpRdq0i6aeRXxl7E+eY4lgz40eCb+MUdsZsgzrFLZ0Bj6h862dqkYTHC+zcmcGLp4QvxJR6XTucSfVqVOceGJd8+wBH9sRglilL2fcidt7ixzChE84Uxj9/PfPGNvQ2H3MZ1dP3wJLa/VUVPDLUiscOWybx7trFro4+MkYm8aMnGMqSQE7bsQEHj7HUVSAkJseQAPiIIfjNzIj9fMI8nPn+NJbmFFLe3sjmrnGtOOgNHIESiBFObdY1xVcDKZCeT1lXRbjeT3B1EQhBULD299/rEBIJGA7aI3nOVJYl5L9aSFggiAYZQGNPwZBw72+gKx+Y4SxKDW2oZ3lJLp8lCZVIaCImCtnZCa/WZmBWLISgPo0SrwpIik7rwR/y0CR6cp7HeaOeuSTamxaL9Vc9OLlrzHjJQ3FpNwGxlzpCT2LbKx7anVjLky6XIEwdjqn1UP39VEyxbz+mb5zNzy+fIQtObCQ11JF89db/3cfvjG9j6wBoMCUbGvjYN1wkZA977NXVqzHjreHarXNFHDl5r7sJ/3j8hEEEF0ATW5y/rV4bpR8dDKIq6to6G1RFCf1oHgH9JHblvnDlgHeJ8vcQN+FHmKKUkjXOMsnB7lK1N+lszGIGXVoaOyIAP+vVYrIUOQnV+sq8vRbEfPVVe45DUr9547Qx46iMA5OtOYugTExn6xESW/3EzK/9ZRdBuof7tBiIGBRSF2nVezKXJbMtOZeYWPSnRh0MG8XZRLjndAY5raMa9s5Y8s4nZpx9H+zIjPz1+FmGDAYcaJk0TtBsNrLZZyPYHKe4OoEjg9nlRvRFaEm0UdzeyurAQLSBoNtnptph47NSJWKIqP/9kKV1mE0l+FRGRaU5z4ejqxhYIMuK0dLSP/Wys6e3JOyMhJASflIzEHNHvV7fFxBmXD6FjRQtVT1egGdNpjKSRPauIL+d38cwaI1m+Nt7d8i4fJVzKry1l3DjewNhRMvKbvZcuMdh7HuvDL0FbDbywQFdiu+YkXcN88xPw3FzkO2IOviQ7HFfWc1z3Th8tS5pIGuvCmmGl4r7VICDSHmbLPV8yZcksBmJ6iQGXTaLVLzAqcM6w/q8/0dwFgV7hFm3XvtPaJEXGfM8MREQlZHqiZ71/Ye2A548T52gT9/nE+VoZlCr3pO0EKMs8Mhe6pMjk/nDIUajZIfLkDXDhZH3e2om9Y7Wm4iS6knvVw6TY2K4sBO6QF4GM12KhKsnJ3aediCbLrEqDcdW1ZPi6cfm6KV7g5e4zziKsSiBLdFmsaJEoBpNAEhLpoXBP+Rn+MJZcO201Ap9VZnztWj7NG89Nl52OukdxTYIfXDULVZa4b9G63qlrDjtdisxra6L87y/cbDpjPmp3GIMsaE9OJOPX0zH/uVd8MJhgZfsft9Be46czVc/0pYQ16g0mHqtIASu0W52c5bqOze2ZMC/KKxtU1t41hdBbH2FeU4lmt9J87nTSVAtjEtoo+HevJ0P9y3z4zafIo3PhL9fg3yUTYjCSw4jzufMx5Otu867tXj4/6ROivgiySWbiyyciGWRErKFxsDEQeckyq+908vn2KKOyFUbuNXVRHpKO4dzhRN/dACYF6dJx7Jr4ImpdFyn3TSbxxlG999moYJuRh3+e/n0cswoPqg5xvl6OMSGXI+ZQo9AT3G731r77HKmKWpz/bMozDbxxnZNXvwwxPMvA7dOOrnxkV52frlo/qSOTMRzki/ywmTZ8n1WlU10sfXk33sYQFoeB1OGJ1G0P4LBLnPr8PGyhCH6zieVnj+uRWAUwRHrTrFmiKjYZggYFHEaQJQKakV+88SkjaxvYWlhIR5I+zu/qamFoxVwaps+k6O0aHp92ObtcWVy1vZZ38zOICI0umwVrVOW0yjr8VhPWqH4uVZb4zZSxCFXi45dVnrh9JMte0KfFtdpymXTxUIa1yGz8lz7W71A1/Eua8SbbwBJzj5tkEk/LgT7zvWudvTEI6xsF7ZqRlBW/IvT+BqJmKzNnDeZUWYL6NnghBeraUCUFT6UNTTYy7vUvkaMSobd2syU7j6rUDEr/voPx5+lJTpoXNBL16T1jLazRurSJksk2qhd0oMiC0mt6E8YMRF6yzJXu/QevSbKM9a2r0DY0IKU5qL9hDqGYuE3TzXOwn1mCIdvRW9YH59L54mZki0LC5d9Co3IvGroEb20VFCXBrOL/TnGZY2wa2RFzKFHoceIcFrOGmZg17OiIbPSl7osmPr3hC9SQRuqIJM545cSjlznqIHGkmLj272No2t6NK9+KPdlEpKKVpVPfwxbLaGYLRZgY8JFt6KDOmYRRVQkZDahBCUUIVhRm0eaw6so0sV60kCVak+yM8DQxuLmNF06exsj6TZy2ZR4JoS42LVnM58Vj2OXS56kbgBvWVDDDs5qq1GQmtW9nUEsNO1xZvDpuJiGjmQ0piT1jhKHtXqq3NGFLUljjcpE/2E5FvcagW4ZSfGYOYb/Klpn6kIGhz8CxbJLJK7Rw/T83sRMr25MTGLG7gzdG6pHYk3Ilkq3Q+sR6Gm9fAIDzgm1k/2U6cnIC0vv34L/jFT6tzCBksOBXrKiSzORmHzvTMtiSl0WrPZEvzWkkre+idISDpNEpSIqEUPW+REKGCW3OJvY42NXnV/G5OZmmXQFGT09hsDvxsO+nJMsoI/UYCBHoncaGJhChaL99ZauB5BtGHPa5jia+sGDyyypVnfryEzPgtrH/fUY8Pgbeh8OIOI8T52tH1QSvvN3Jik86SUpxUVDfTMv6DppWt5I9OX3gAo4yFoeB/FG9RsP/QSWZLa346RWnmXZ+Jquqd7D02fVUOEqxhqEmL4P3BuexJdNFWleAZoelJ7IbYGhtLFgvGsUQDnHe+n9j1PTetJJiZqezv9yrJyeDZYkncNfyJZQ17wKgtKWOsqZKXh57AmFVY0i7l51OG+Oa21kSVihv6SSnQ2WxmsfyjW04jfDLH7ngrrlY/GEEkOgL4s9wkDk1nZIrS/j40QqU9jCDCDNp1y5O3fQlZ42S8Z85hitGKkiSRPtf1/XUy/dWJbVvrcGYYiRN3kKkQyXLKVNvzSQ51IFXtiJdPorBP/+AMcvXoCHx4oQzqd2RRekIB8ljXRz31nQa59aTMiGV9LFJ1PUZl2la08rcf9UBsHFxBz98vBRHOIKlyIlii73iFmyEH/8TrCZ4+kYYXgBAaHc3Wy6bT7Cqi9wfjyDn9l4vS+qvT6DurLdRm/0k3zMJY9F3V7RlUws9xhtg9g7BbWO/vfp8W8QNeB/cbrcBkDweT6TPumuA0cBCj8fz9tdbvThx9uWT+V28M9sLmKktLcDhD5Lm78KRbRvw2G8C08g0TEQRBImgoJxUTNAryL71eM790Yl07Ormy+d3krA7grliF51V9czLy4oZcADBmKpajt9aBcC2USXc8mAJHU+diOuNz+nOyqDglWtxLdGwftxGwGik0mFlkSuRaEYKp+1IYOau3vq42rxk+rpJC4UZ3Q5zstPZmqrPce82GRnWrCubCQRZje1sv24hZSuqCJrSCJsM2P0RJj01ibyZ2WiqwPe/vSlQfRYr1kyJ7zl2I8pHIckS4W1epNYuNIOg22QhwR9AQiC3dWKgkw5rLhUJ+sib15jAKHkH8k1/wQEIrMgYcdduJnXsGT3nSTshk7QTMnuWkx48no57P0cCNmX0zhpQo4KlFywgcX0TliIHY5ecgTnDAuc9Cu0xAZ3v/QnW/B6AnT9dgXdxIwA77lhG8qw8bIP1xphlQhZFDT+EiIZk+mY9O4fK4BTIsEFjTGL/hNz/LkO2h7gLvT+vAZ8Qy33tdrvvBe4H1gE/cLvdt3k8nr9/vVWME6c/zW39XZl2dzozLhlOQoHjK474ZrHOLCbt1XMIzKli2xdBvJ93wudLqF3YxMLifBrqI0w/OZtr7kuhsynET5/3MnRpLcN31/LqcaNAwOqCHKbcfSOpXQH+cmMK921WCZ53Lff++SYGpemP7cVDoTMk8eHKAJ+l9ebwXu/KocqWSXaghXaTk2pDOmmxQLigovRTm2uxWdmWloIkBDk+P7ctfpvyRj0tscneyQdl0whbLVS92cBlU9Mx2wyMbN/B2qRiAEpbtrLAlIP1jzspvOeXdCVlYZU1pGY/FgApxLaiDMbu3EEEM9VJaYQj/YdTRGs3TRTRhQsz3WRQT9HFZdjzvjpewnpWKV33zkcAhU2NbCosRJVkkmxg39yif9edXTS+uJ3828shljym0ZLKF/6RcMlCptw7Ai3Q/7e097IkSfAdN94AyRaJJZcrvLhJUJQIVw3/73Ofw39fENtAd9kN/LvP8m3A9R6Pxw18j3jyjzjfAjOOd5Dg1H+6udlGrvnDCPJP+m4llPBJNjrysvFW9Mqmzt8hsXljgPa2KG+/3kZ1VYjEdDO/uDaZ0pEuLt9ayZ1LVpDUFQANwoqRoGLm+8/6mbegi+c9YWY+2z/3dUdxAssTnKT79PXOQJCZW2tZkjmcNwtOZLlzOIN2+rAEdANuVVWSA70ZwEo7Ovj9g+n82FjHxIrtzCmezEdlU1ElmQ67k4DTjmo0UF8Z4Nnr11B/9suM3bGd89fP5+L171NtzaPGkcuW5FKeOeVMnjxlBs9MOA6fVTe+5kiEzZOHYRiWhuWsMja//ADPjBuPWejXJTEZ8oOteMlEw0iAJDpnnIDtsfMPeH2NIzNwPHACQlHIaW3n4gULOd/RxCVnJfYbszfn2PSUn49cDpLE3JxpNCnJNK1rZ+6dHgoeHocp5rkpvNiF48L7wXWVnrTkQAgBFbXQ6jvwft8gJckSD0yR/2uNN+hSqnv//SczUA882ePx1AHEcoInAq/Htr1LrGceJ843SW6WkSd/nU1TS5TsTANm03frhVX9581suX0lAJJBQkRj6TOz+rv4wyHd0KS7DPzywTx48FKmhwWfPBemo10gR1Sqnfo4ui1ixBqMsNOvUP5slKJEiedOk+kKCjJ8fv7wyqe0OG2YtSgtybonQsgSTtWLkKyMXl5JbUEarS4npzZvp8luwxqJMqm5gsiYZ2jOO5mW4myQJFYkJmGNhuiSrD3j8QCBGh/mxetRkXFENEImmUgs13lHooPWWKR8h9OOZ3Ax09du4suyEtJGJJP50k0AnAZMPPEivEFBBlHMSSbaL/TT/Y4+gGsmTOfaLvzTXifj2VMxlX113iPHjyfjfWgxAIl+P8qyrWS9dhrRnV7a59aTPCOL9Mt0TwE/Ox+uP4XgKQsgoMcRhDrD2EekMGH3ZYiQinzSfVCpR51z8//p0waT9+PV0TS48LfwznI9v/f798CMuJzqd4H/Nhf6QG++brfbvecX7AY2eDyePc13ifg88jjfEnabTFG+6TtnvAHaPmvo+SyigtwbSxnyxASu+FUpKS79kZlygpNBg/u7iNfUqbyyOsJb5xv48EIZr9WkR6VL4DcqhA0yktPEljb4aKfgJws0bphm5XhvK85wlKJWL/aQ2q/MLwsK2Dw2m11DUknq8KIqMs0ZaSRqglV56Vx44aW8OeQUokZDP2PdPGUkXeePRdJivVkhGFu9qU/JEq9MPIsum65trkT7nzeEkS+umo7xzkkkjU7m1oca+MVt6+i8+q8kf7GeghQFS4oZaflWmt6pQ0VBRSGAmWhLgOCSWhqv/+SA11l2mjFN7h3/tswaBEDRA2MYu2gWRQ+OQZIkOt6upPEXKwg2hJl01zAkGSRFYuJd+nx+SZJ0GVStj0yboL9ee1/WVevGG8Afgt++e8B6xvnmEJK0z99/MgMZ4EXAL9xu9/8BP0BPBbqHMqD+66pYnDjHKq5Tsml6Txf4MGdZKX1kLKYU3dA99qcCIhGBaa+Gx+KdUU56JkBEBYMmSEowIFB6Ul0V2jXuO9vOdfN6j+kIQW6KwqO/LGTdhx4MYZUEf4iOrERMbX62JSdiMRuw+wMAtGY5EWoYnymB5yYPw2fWx6K/KB3Eyeu3Yw6ECFnNIMOvikdgaAtwpjlESXMVp22dS0p3Fz7yEChUpmeyMy0DBOTVNZIeaSSpup3dyVkUttVyzq2DSb16BNvrNW77hR5Nv4MUmjvG88i5f8C56lcwOBs+30CY3oZMX5en1hk68IWes4bUNXPx40A+vgzrU6frU/FavJCeCLJM63Ob2B27aE2Pr6Zs4xUMOuN0kCTMCXup+P3xWr1n3dYFj14JKc79nBRIdepu+ZhcLdnx7MjfFf7TDfbeDGTAfwbMBn4EbAB+32fbFcDir6leceIcs+TfXIYlz45/u5eM8/J7jDfovT2Tad+XzOwKlT3aLmah0REB+tiXBdebyU9SWNSo8s8NghQLXDoYRjwVpNlvwXXZTE7YtpvNWSlsL0znmeUr+J9Jo7lpfQVZMQOOJija1UDZCS7eDvf2Nj8vzuG4xkaMkQjdJiOzSwtZZ7ZTYBEIWaaws4b0bj1vehI7eG/oqSwaPAGjEHQaDWwsKUDIRQA8OPsPFLfV6hkQblbofPwuoHe6m89s4w+Tb+DaL+rJHZwN04bjYCMduAAJMxEkQLIZSP318f2ukfeTXdTctgjJrFDw9+nYH3kLORDERgh50RJYegZc+yRsrYMJpTDvQboX9Eqcat4wwTXNJJxZtP8bN6kMdh9ETG5uKrx6J/zx35CfCr+7ZuBj4nwjaP9d9nvAeeA7gXK3253i8Xja9tr8GHtS+sSJE6cf6WfmHtL+k/P1SOeh3QHyQhEWaIJogkV3awfCmBW9t/yP0xXcGSqfVgvunRuhql2AgMbsVDZl61ruI3Y3E9jUwUXWbbxaXsRFW3dS0NFFRocXY1Sj2dPC1ZEOusxGOq1mPhmcz4dDSnEEI/jMZiRJ9w5UJztYWJBJsjSOcQ3rsPu7aHSm4CkcCZKMBCSYoDU2EicJjfyO3uEDoioj73+S0pt/xbZWCWNUJa+9k6DZyt92p7Hy9SgOYwm/vXMyzt+vBiSMGTayv7gGQ7YD2WJgU6NKZV2EzA8rUH6xDCmst3Kqrp5HyeBkVhhn0CUnkCnqGPP2MqSt+nxwVmyj4pb3CY8YBmwBQHFZsI47SjoB50/S/+J8p4j3wPfDfow3Ho9nX6X/OHHiHBZnDTXw4vkmXvi7F4DMcIQq/SPj8xTS7BLtnVE+WBHk1rUxl3M3sSgWCYTEieka2rpmrv5sDZKAi1dv46RtNWgGsMe69wIwNHRRLEkIQDUopPuD+BITMZhkIlGVMaYwP5gmWNgkM3VmNjePzYPOCXj+tI6PFmoETL0CNTlpBrq8EqGQYPLWCpS+48iAwWrkwdZVfLooSLfFSn5rC4uGDuMPhnR8O/Qx5uax0/nw38VEKlqxT3BhzLD8f3v3HV9VeT9w/POcO7IX2YQ9giCbg4JbEBTFOkBxobaKWlv112rVOlt3HW211rrqqlUrakWcaN2i4iMjsiGsBMgke957z/n9cS4kgQQuIYSM7/v1uq/ce896HhLO9zwbwt28s8LH2S9VM3vRSqau3cLg+sCuCnaryseGXkdQaTjrkuepnlRUQGyjay9fXMOareWc9ezJJFVWE3t6fzzp+7d6XVupXlfOut/9gO2zGfTAOKJHJOz7ILHfrC7e63x30glNiA5i5hgvb0UoqmpsMut8jO1pcPKJMcwc6eaU52r4ONsiPBCARAtcBijbWT3LAi82X5eGYfXLIGV0Bcll1RTFhDMhdxsn2NXk7qjDNqA0IXpXZ7VdwTA8HBdg11ukUUfyqgp2PFrIXx4YTEpfJ+BZ0ZEs+BoMDFKLSqmIiSQ62sV5v0hFJYSzfHkNR81agAounWCjqIxPoPzSaUQ89BXHWQ3tyQXTR1LRqP9sToVN1Hn94OnX4fofnJ7fC+7g5VU9CViQUVYJSlEQH01qaSVGuItejx5D/uJGU48BdWefAHYF1R8uZ5nRmzUpmWBDSWQkQy87tEs2/HTOZ1Quc5aFrVpZytEbZx7S9HRVUgIXQhwSYV7FH3+dyL/frSA60uCq8+JIiDWY+FQti7YEl2R1uTBKaiHCjV3rx/bbeGybRDdsdzlB8c2JQ6GsBgyDr8cNYPYJVahZC/CX17N+xgg8i/NRdsOkF64Y767GMMsGd8CiptTiySuWc/pv+jPmlBQMlyIqwUNtfg2Tvl2OJ+CU6K3wQvq8NJX0dA+UN2pvxmD6OX/k7rnPcoRVSB1RgIFnZBLnPzqG776Av/1oYSi4aYILe+Fq1DvO0DtKKuHBtxk+59fMzYIvBvRiaEEJZVHheI7LYNJrx+Ny2UQfVUf5shLKs3bQc1Z/kk/pDadcTcX6Kr665ieot/Aqi74pBnbAQrkO3YiF2pyG8ft1W6uxbXvXKnGi7UgbuBDikBmeGcb9v23o9La+2GLR1qbV0gRs3CU14DGoxyC1to6w8Eb/lW3bicSBAHmGm4rRaYwtvJQH/1fDTe/VkjYmmTS7nt8dF45yGQyO8vDm3GKUDTE1dbvKxn5lMP+RDdT5YMLpKcy4PZMv712xK3gD1K13SsHzV/nJTEhj6A6nDXpzQip+l4tt0YmEs4wUVhNwheN5fQ7K6+KxKfDLMQaRHugbp/Cd9xVuGmoFSInjlklheF2wtqgvqVekcLjXR8rEZFzzv4fZj+Hx+Tni8Tkw/5wm/zypg6K49IkR5L6XQ8SNH1Nx0iLWJEUxcO0leBLadjW8UPW7ZQTrb9AA9L1puATvg6S7jQOXAC5EB5YSpYiPUJRW4wRl28YVHL40Jt3F99ttdnjcXLBxOzVuFzvCvdTWB3YtApwaBX3inJuaznUCb15UBH6/l7987SOzqhIXUORyE1vvI0IpLJymdcOysJRi+TeljD85EX33EiqX7qA0JoL4ihpwKZJ+5UxgopTi1HN+yx+/fgsDxZujpwHw0qhpjMnOZdAR4XivOxWGOOO2rcp6+q4qwDsoHuKiCby/FojDRTU2LlxHDMb96TJunjI6+C/RaBKc3zwPtfVkJ/bh83+VkJRcxGlnJOJ2K4qu/5TyJ5biyUwgIT6auprgMg5FVWy95kv6vTz14Pyi9qHv9cNJObsvtt8mcnDsvg8I1Wc/QWUtTBsL7o4/5atoWxLAhejAYsMVCy4JY+brPraUWbjLa1FATJjiufMj2V4J24r9vPhMHCdtzKPU6+H7Pj3IR9EzzuDLKyPwuJwAPmOklzeyfNg29FA2aXX1uAALiLVs8HjY4fHg9vtJrq4moAz8Hg8ZgyIp/KmUgmUloBRr0hMZPDWGox4wCRsUT01+DWO37GDa2HiuTrmScRkuLv1wKTkr8qmPCuffo37G5X8eRp/RzkxtgdJatkx8jfrVOzBivKS8eAr1/jBiiCRAJN7YarjkMecf4LaZcPcFzvvcItDZEOamLDyahyfPodYTBv8tZcUHP8VoAAAgAElEQVQ3pfSurqTfG9n0rPWzugCK/B7GNPq3bDwH/KEQ0b+FceWtdfsrcM8bzvvTTXjnlrY9fyckbeBCHCJ+y+apHwPsqIE5Y12kRXev/4wtGd/LhdnXYssaA3+PSJTf4uULvQxLdzMM2BQDfw/zsiY4McvlAxTXnBtLUpRC2eD3Wbg9BrPGeOmbYLAyz8+S5XWs+7YWCFaHN7rxlYaHsT45EW+Y4vC+Lv60yUNUjs3xMWGMz11JcXgMzw0ex9P/qWfsqmUENpaRvrWIKV64f/5k4obEsGbgQF54cAupFTvYmJ7OPQ8WcMY5AU49PZ7y//uA+tU7CKMSq8LF1lu+5ZXjz+TojT/h9dcyObvRbDWvfeME8LXb4MiboLQKosIoHjbMCd5BW3PqsUpryRl5OKctXMRrxx9NwDCgPkBG4Q4Swl0MePAo6jeVUb+ymIgJ6bh6NPSm36mmqJaSteX0GBpHeELYHts7lJe/bHg/XzsLtsR2jBX5DhVpAxeijdiWjTJC/x917Yd+/qGdgPLyTwFW/NKLez+O78ruPs7NwlwfeZUK22XwwDcBJg1w4XUr+qW5ufqMaF75XxU9E1386swYwm2bO+/JY1N2LbE1tZx7biJHn5XCA29XsWSDj5gYg3OnxFH0UyWxhkVZlU1hcQAjYJEfE41lGPh8sDDbIt/jtMFH9EvmRv0NN51wId8We/jDE1+QUl6NpeD7IwcydNNWNry5mYVTD+fa7+P5aMU8VvTrT02Ys0zqvNeLGfHlIox//0isq5L0wBZsYFOti5LY8bw76miUbTF+x1JiS5yJYxiQ6vx8Z5ETvAGq6ug9Yzh9q8PYvKkOsIkOLtBiuQxq46Oo97ixleK9iWNJSXbxs6vS+b+/bOaqR9/HW+fH3TuGfj9cgDu1YVhZ2cYK3pv5OXUl9UQkhzP9rROJzujAAXF0P9jkzHLHgFSI2fOBpLvp6ouX7K7jTSQtuoSvn9/EIyd9zRMzvyN/beW+DwC+2tLQWWttsU1+aId1C8OSFUel2lBvQcDmi01NO7ddfHIUHz6YwnM3JZKe6OLfb5SwZqOPNTFRvN2vJ7d+4uOVr6pZssFpE66osPhfnuKxe3tyz929+POfenHmseFsifLia9xbu9F04FbwYWpbTA8G5ZWQElyi07AhJb8cG/jwgzI+eG4rlg2D8/P36FS0ckERH/cew0e9j2ZNzEAU0DuwCa8VHKeuDNbf+2uICpZ+/5cFHy6GEX2bnMczqje/vy2D665P5/IL4om3fERW1HJYbzdHbLucs2c446zdbjjtjARmPFtOjwXr8NY5/Qf8ORVUfby5yTk3vpdLXYnTHb+msJbNC7bSob14Lfz+bPj1NPj0j01qUborS6k9XqFQSmUqpb5VSq0N/hzczD5TlVJaKVWnlHp4t20updTflVLZSqn1SqnL2yhLeyUlcNHmyrbXsvCFLQBUFtbzxVMbOPeRfa/WdNogg+UFzo18bLoirWMs791h9I1vuBm5DegRDle+XsXyvAA94t1UKjcnDzC4+Wg3VVUWRWEedHIcANujwhmY03TBkbIqm8o6m+gwhcejmHVZKpfk1ZFQUcdhpdXYCjKKdlCYnozXH+C6b98BYM7ST7jhxMuodxl4A85DRCDSw5bUNMoSYhieu4MvknrwSeZAJmdlURAXz47oaKoNg+01TonWVgZZccMZUpGNe0gav3tkEEu+LyetVxhjt6+EquA86AEL3vwOnrkaXvkNfLwMJo2A00zCgNFjomBMFEkLc8m9dxV8D2vCfZz1xEQmT4rF7VbkVdtUvlXPhh7xDZk31B4rncXt1ka9++fGqmos/BbERR3CMlBsJNx30aG7fgd0AL3QnwT+btv2y0qpi4CngEm77bMBuByYCew+nOFCYBAwGEgEliilPrFte1NrExQKCeCizSkXznigYOnN8IR2k7t/shuzp0Fxjc35w124pPq8iXsmeagPwLpim6vGu3l1cT1Pf7dzNuMAJLv4cqvFMx+UMmJLBTVxTWcd255dw6DySnKjIoiwbArrvMTcXcORvQwWXBpGbLgiI0axMRBGfmQY6eVVnL1kG9Mq8hm+MhdXdD8KTk7gmMn9ef+yXuSeOZmK17LxDIgjwhNJ7lKnysTlVhw/0M3rN57FkOyfiPyygqzUFFLLSvEGfNQHlyCNUD648Dh46GLS07xsWu8hZ0MtvdJTSA3zsDEynicmTCX58KFc57cJO/9YOL/p/Og7FTy7Ztf7rU+uIfPRI4mNdXpl94+wmTnayxv2YCItP5dHlXH4ZUOIGJ/G2qxKSop8jDgilv6n9aKmuJaNn+VTHBtLVmUYaX4bt7vp3+GCH2q466VyAhb88oxoLp56aGZ3E3tqTRu4UioFGAtMCX71KvC4UirZtu3CnfvZtr0+uP+ZzZxmFvCMbdsWUKiUehs4B3ho/1MUOgngos3FpoQz+ZqBfPvSFqKTvEz61YCQjlNKMXOYDIVpSaRX8fhp3l2f38tqND48xgseFxawIS6KqdU5DN5RSnZcJEUeL9Eum94/FZJSU8f4HaUsS0pgmeUMZ/o+1+L5xX6uNl38Y0KAx5bZ5K4oY/pPGwAIX1VEILeKAOCbdTTG78aTBCSd1RPOcoaFDSmo563Hc6gs83HS+WncO2FnAeUISj6rZMf3NQxKi2LCmoWsLA7DZVkkndyHx8aMZNhyF+gd/Pdl5175daTBzXNv4/isXuS4o6EOsj+xeOqUlv82IgbHUp/vtIOH943G8Bgsnp/Hqi+KSc+M4qVf9OHmqREkRB5Fr3jnPF+9X8yb/3Tmbv90XjHXPziAvjP688RCF5XFFry2g/LyAOddkNTkWk/Mq2Tn6qlPzqvk/EmReNzysNkRNDeVqlLqCuCKRl89bdv2040+9wa22rYdALBtO6CU2hb8vpDQ9AEat8lsCR5/UEkAFwfFuBkZjJuRse8dRatdc0wYby/3UVxlY3gMdoVzpagI85BWWcPFS9dzxJ3DUCvKWLyoYXnOlN2aJ7yGzX13b2XTxjp6eRWHuy2Ux8O2pAgiKnz0x+lUZlX4mk1LQoqXy+4a2Oy2UyeEM3mIQUR6BPbZl5IxbxXryz08/G0E/FjF4h+rOLx/w62opirA4ohe5LgaSrY/5FlAywF8wLPHsPiuFcT6fRx+1yi2ZJXz4V+dB5DNS8qISvAydEoKL/1jO8UFPiadlkDWoopdx+fn1lGwtY6Ay6CysuHBaPOmPddrio0yyNvh7BMVrjiEE7yJ3TQ3jCwYrJ/ec+/OT/70hOiE/rMiwJ3f2Mw5IYoVN8bw8UVeooPLj6ZV19B/RwVr42P42/jDOO8DeM4fS3m4s4O7XxTxp2cwMMNNbASc0E8xMcLPpo1OgK+vtymsUmxPSqQoPo5tw52V1cIy40j9zZ59GSprnGBmBWzKC+v48MdaxtxYxNgbi/jgv/m8O24+7419h28u/goV5SV89hg292n6cBef4tQsuP1+Mrfksv76hfzh80V4/U6nsxmZLd+qyorrefq+LXxaHs4HdhzlXg8VhU3XEi8vrOOtfxeyYmkVedvqefXZfJLTG2ozouNcJKZ66dnTS+8+Dd8fOWHPjhh3XhLHqIEehvR288AV8RjS1NNhWGrPVwhygAyllAucDmlAz+D3odoCNO5p2Wc/j28VKYEL0cn8uN3i/P/6sYF5QHKUi99OcLHlSpttlZCZEMXqL/tzng7HV+MEvo+2GajD+hPlD1Ad68VaBmCAy83nK6v4T7SLekNR4nIRGwjQwwN1tU4nhpEzMxjzz8swoj1NpgAtq7K4/G+lrM71M6qfm2PX51K8rpKKMA/+Pr2o8bhZ9NAqhu1wgun2j7dR+F0hKUencsSR0Xz0YRnlZQHi4lyceV4SJ54UR9YL68lb75R6k7eX8WR8Ab1O7c2U/i0H8DU/lFO+w6kZqKuxyPqqlBNmpJDcP5LCjdVExLkZfWoq3z7c0KvctuGIyQmk9wkjZ20125aW8Picnzjlit7ccFM6Tz+8ldqN5ZRvqqTpGmcwsKebp65v2gFOdAyt6cRm23aBUmopcD7wcvDnksbt3yGYC8xRSr2F04ntTKD5DhttSAK4EJ3MhhK78egu1pc4nxLCFc5U34oRJybRZ1s9K7OD1cG2ja0UlcqA8nqnG3uM1/mp4LPFdbiiovAr+DY+gldmhlG3oYq4eBfHT4prtpQ59+saVuc6JeRlm/x4Cw0GATF1PoaUlrE0OZHaYKkfJ1mE9XCGhyWneLjvgd7k5tbTq5eX6BgXyckeqkbGkPdhwyHHDfEysFHwthdvJvC7N8DjwvXXWajD0knu1bRDcEqvcMKi3Pz8iZEUb6khNjWMgs011G8oQ0VFYRsGvVIMEuINtmcXU/JJIZX1bmzD4L+PbGTMBX2IeWM5fUsrqfzM4AfvkYy/qE9rf12iHR3AcqJXAS8qpe4ASoCLAZRS7wN32LatlVLHAK/hPNEppdR5wGW2bX8E/As4ElgXPN9dtm1vbH1OQiMBXIhOZsoAg8MSFauLbaK9cOnI5tuGnzzNwxXv+iissqnKr2dtwAXBcdDUBwO7xyAyYOGqdnpluW1Ir/Vzuw7j19mFLM0q56d/h3HhPUNIzHAC5fwldSz4qR7Lt9va343WAvdEuUmNM5j6wGgi/qmoyC5nwEUDiRvaMJQrOsbFYUObTj4y/Ny+FK8tJ29ZCf2OS2XA5LQm2/0/exy2ljrvZz2NZ9md9B8ezawb+rL6h3L6HBbFmElO6djtNUgd5LSj2wEbj2WRUlGBpRTZA3ry4IwfSK9wes7HedyU9ojDtmxq1pTQo7RyV54K3twAEsA7hUAr47dt26txAvDu35/a6P3XQK8Wjg8Av2zd1VtPArgQnUx8uOLuSW7+tMgiM0FxWFLzd62+8YqPLnLac6tqPTz9RS2//bhh+6h4ixmHG8R5Inj9DWeOdAC/yyAn38+GrErAoHh7PV+8vJWzbxrIog0+rnqhYte+g5Jd5BcGqHAZbIwIp0+YH3NiHNceE0ttViExqRmEPXXUPvNUl11G4d+zcKdEcsItoynb4SMsytWkyt62LCho6HjG9ob1wEcd34NRx7dcrd13ZAxjpyWzdEERdu8YlpR7GFHVsMSn2+/H7VWccmUf0tI8LHxaoYKZTBvWhouPiINKViMTQnRoWytsLnrfoi4AOt8m2mvx1NS9D7+LCjf49UkRvJ1t8eWGAG5sklaUUpJj88tb0+ntjeXeN6qochlsSoxi8vpG7cU4pVmA+bp2V/AGGNDby1d1CrwutsdFcsyxBke5i1kz4S1sv4WrRxjDlpxDWJ+WJ0WxavysO/4tfFudqVLXv5PLZ9GpuDyKGbcOZshRTmBWhoFxx3Ss2+eBoTDuPD3kfzOlFKdf15/p1/ZjVY6fd/5UwsbEeDILdwBw2LSenHT3iF0PDBP+MYF1L6wnflAMI28ZEfJ1AKjzgdctM6MdAjIXuhCiQ8uvhrpGk6ptKQ/tOI9L8cmVUbz0USUf/KeYSMuiqAgWLCjnggsSOXpMBG+vt+kbC2vur6IoeFxUvJuKI9J45Csfb35Ti7Kd4TpuA04fG8aLOcGqc5diVZnBgm8K+GJ0JkdsyWdoQQkVn28j7OIhLabLl1e9K3gD+JYXw4RUAj6br1/duiuAA7hum45xyVHgMlA945s73V4ppRjWx8OvTo/ijS/7461PZPakKEaektqktN/3jD70PaPlavOAZVNQHKBHnIswb6Oocf3z8Jd3ITUOPrgdRvff7zSK1juANvBOSQK4EJ3MqGSY1l/xwUabcDdcNy70m5bHpTiyt4svGrVXRwenA02JUlwxyjnXuLszee3VIpLiDH5MSeDmjyzAT1hUBH3Ka7Bsm5G9XAxOdwMNY6ULywLMUn2xju2Hxx/gxbmfMGxM04lQduftE03khFSqv8sHoCCjIWBH93CaADbl+ymusBjV34O7d9Oqctu2qa4IEBHtCnlI1+WnRHH5KVFAckj7N1ZbZ/H7vxSzKruexHiDB29IJiPVDaty4c/znZ3ySuHWf8N7t+33+UXrBbpZrUenD+CmaUYCzwPjAD9wg9b63Rb2nQPchDPR5wfAtVpryzTNE4D3gbXBXeu01nt0aBCiI3AZivlnGSwvgtQoSIvav5vW8BGRnHVWAosWVdK/fxinTItrst0fsLn5pUqWb3ShFNSk1QFOb/I6twtLKWI9Nr+ZHs3IVINbj3Xx1+8CDOyhGNbD4tNgKcjndrH1vhOJHJHYbDru+dLPQwv99ItXvPHGGfT7ahPulEg84VEseyKHElxEHZHBez/UcstL5Vg2TDzMw+2z40iLVnhcitrqAM/enk3uumrS+oYz595BRMWGdlvz1QbIySonLi2MxD6hrzq2KKuWVdnOQ0txqcX8zyq56rx4CPc41eY72xgiO/hypKLT6/QBHLgBKNdaDzJNczDwlWmag7TWTdayMk2zP3AnMAYoxgngFwEvBXdZqbU22zHdQrSay1CMSmn98WedncBZZyc0u21zfoDlG51x1bYNSXU+MJwAfliSYsF18cRHGsREOCX3eyZ5uGeSs/2zdT6e+LoOywavC6ZMbT54ry6yuP0zp0d8Vr7NLQsVc8/LBOCV92u5Py04J8ZnAc5KqscKxsRvV/sY9Gg9vZNdfH6hm5yvS8hd53RGy9tcy6IPi8j01mN4FGnTerW4nK2vLsDL12aRv64Kw6U464+HMfjo5tO6u7jYpv0N4mOCn/unwmOXwZ/+C72T4OFLQjqfaDvSBt75zAIuAdBarzNNUwPTcAbWNzYTeFtrXQhgmuYzwM9pCOBCdCvF1Tbx4eyxaExyvEFspKK82omaJw/3MGOAhx01NnPGuUmJbvkueeJgD5//KpqFGwOclOlmVEbztxjLbvo50GhEWlFV0+FpcdEN48ADCvwuRXYp/GOJxczIphO85L+1ifKPnClU+148kNGPNl+Rlremkvx1Tru7FbDJ+jA/5AA+akgYl8+M5YsfauiTbNC7vIw1X9Yx5Lgk+PWpzkscEgFpA+90Qp1Efl/7ZZqmuRjwAU9orV9s6YKmae6aHD8tLa2l3YTokOr8Nqe9WMv/sp1q788uj6B3fEMgjI00+Nt1CbzxRTUp8S4uPjmKolp4eqnF62ssrhpj4N5LW/OxAzwcO8DT4naAYclO1fvDCwP0jVfcP7nhVvR/x4Yxf6WfvAqbkwa7+ctF4Tz5QTXLcgPMKw3DMpy09giHUeMT2LKmmnVLKug3LAr3H7/ZdZ5t7+S0GMDj0sJxew38wfHwSftRhQ4w8+QYph8VwbM/X8xXxU51+uRf9Wf8TJn//1Bq7TjwzqrDB/BgUG2pO2hqG11mMdBba10WrGr/xDTNrVrrT5rbWWu9a3L82bNn283tI0RH9cgntWQtr6GHUmwsdvPoQh8Pn9q0vXZIbw+3XuS0jdf5bY79t5+NwWHXq3fA41MOfNW4Pxzn5trhkJDgxuNpuPMOS3Ox8ZYYCqtscjbUcttDRcRGG/zlkgQGrlS8vdZiQobiGtPAMBRnXNkwt8YX81ZSutgZGhY/tuVx4bEpYZzzwDCy3s8nvmc4R120/wtHFW2qprK4oQPfpsWlEsAPMRkH3sForcfubbtpmjsnkd85b20f4LNmdm1xsnmt9a6BOFrrjaZpvg0cDTQbwIXorMqqLV74uIqI4DA0hU1c+N47W+VVsSt4AyzcuveVwUJRVRXgnvu2k5PjIyXFze23ppOQ0HA7CvcoEsNsrntmB/XBBdAef6mEe65P5p7jW772hNdPIPuJNRgexcCrh+41DX3HxNN3zP4PRdspqV8k0YneXUG839jWn0u0DemF3vnMBa4EdLAT23icyeh39ybwpWmaf8TpxDYHeAXANM10IE9rbZum2QOYCsj4D9HllFTZ1DVaETQtAq4/Zu/V3RkxMCYVljijvJg+MLRFDPNK/Mz/vpaMHm6mjQ9rMs76u++ryMlxElJQ4OfzLyo468ymnerq6u1dwRugsrpp23hzwhLDGXb7qJDStzt/XYD/3fgj274rJH1AFLHnDsEV6WXcpATcnj3zHB7jZvbjI1n1WRHxPcM57Pg9h8tVvraSmg82EH5cb2Iua126ROj8hzoB7awrBPCHgBdM01wPBIArtNYVAKZp3gVs01o/qbXeYJrm3cB3weMW4Kw8AzAD+KVpmj6cf5MXtdbz2jUXQrSDvkkG00Z5+WBZPR4X/GlmFJHevZda3Ibi8/PdvL7aJjkSzhi87wA+76d6ZrxUTcCGnr46bi3wc/X0hqU5Y2OalqJjY/csVVdgEDs+nvU/VdHb8jP7rLg99mlLq+ZuZuMneQBsyiqn91cL+GrMSNYtq+CiG/s1e0xcWjgTzm92emxqPt1E0fn/JZ6t8JKf6tILiLz+hIOUegFSAu90tNZVwDktbLtjt89PAU81s9/jwOMHJYFCdCBKKf7xixhWbQuQEKlITwitKjw2THH5qIab4/Yyi+verKKgwuKmKRFMG+Ztsv8N79Y6PYIVbPO4+GaVj6unQ63P5rYPallbaDPx6Biqt9YxZEg4J57QdKrVomqbI/9ZT16lB1Li+cUpLsYNP7i3K8u/W+/3qmriqqtZt7R1161fUUQim4ih2Dn/Hc/ALyfK+PCDyN+94jeh1YUJIboMpRTDMtwhB+/mXPtGJfOy6vl2o5/zn6+gZLfq7diIhjupAo4c4gTBOz+q5ZEv6pi/0s+d2W5+fl0aF12YSGV9k8NZlmeT12gmh0+3NO0rOm+FjxOfquLS12sorWmbfqRDz+lHcixg2yRVVpBUXUlVWBgDR7Q8j/veRE4fhNdVt+uzUV0DRSHOe9tOLMumpNSP3981+uL6UXu8urJOXwIXQrS/goqGG36ND8pqbBIajcR6/txILnq1moJyizljPFz7M2dZzw3FDYHeF4BVBRaz3vSht9kckaH4eHYYseGK4SmKHhGwo8bZ97g+DWWN3FKLc/9dQ30AIIDHgGdmNl2WtCVfbw6wPN9iWqaLvvFNyy/eKDdnfTqN8nu+xper2DB2OqcOSmb8lJZ7s++Np388gbvPhluCU01MGQW9Qhtr3h7q6iz+9GAea9fVkZLi5tZb0kns0blDgq9rx+s9dO7flhDikLhxSgQXvFBBrQ8uPTKMfolNS/Mje7rIun7PkuuVE728u9JHrR8m9HWxvBj0NudhYNFWmxeWBvj1kS7+sTjA8DSDHmFwwXCDc4Y1nL+wyg4Gb0du2b47twG8tcLPzP/UO7PLRfrI+nUE6TFN7/hGhIf4e08EWjNL+p5cvz8TTh4BheUwaQQYHafS8wddzdp1Tg1BQYGfjz8u57xZrXtY6Sh80gYuhBB7d9rhXjb+IYGyWpv+iaFXxZ+U6WHd72PJKbUY28vFi0sDTbbHhME/frT449cNQfm3E5relEelG0wf6ubdVX4iPPDbY0NrU35vbWDXNOVF1fB9ToAzh7XDLXDswIN/jVaIjjb2+rkz8u17ly5FArgQ3cwbKwNsLrM573AXGbENwXFVkc03uRZH9TIYlrTvkkyPKIMeUXvfp9Znk73Dol+CQVSwt3uveINewerrX4xx8V2uxacbLaYMNLh4lIubPmsa1DeW2Rzb6LNhKOZdHMHqQovkKEVyiIHn6D4unlvsnDvK6zwIdGejR0Vy5hnxaF3FgAFhnDz14Pbybw/VUgIXQnRVDy30c+P/nCD22A8BLhiqeHOVxYAeBl9uhxo/hLsDLJztZkzagQW44mqbo5+sZk2RTe84xddXRtBnt3Znt0vx3JlNe7BfOsLg+SyLHTWQ2QOmD9ozHYahGJa6f53wfjHOTbQXsvItZgxz0z9h7/mzd1RhLcnFODwdlRa7X9fqLGbOSGDmjOYXtemMarpX/JYALkR38vHGhqrpLSU2DwSrqteVWhCcrKTWDx9usBlzgNP8v57lZ02RU2edU2bz/I9+7pzs3cdRMDzFYM2VHrJLbIYnq10l91AFAjblRfVEJ3jweJsG6XNHuDl3RMNnO2Cx+q8rKV9TRp+Z/Uif6kyFam8tpfaIh2FbGSREEvbNbzCGtu26BwXfF5I9dxOx/WMYemUmhrt71wi0hfou3ut8dxLAhehGJvUz+HiDUwJPjITinUO1Gi0PpoAjex74jbBnbNNzZMSGfs6kSEVS5P6noa4mwD9vXU/uumrikz3MuX8wCaktt5GvfXw1Kx/4CYCt83M46ctTiR0cS2BelhO8AUqqCbz2I8YfT9vv9LSkams1n87+ikCt87uwfBYj/m9Ym52/2+pe8VsCuBDdyc1Hu+kfr9hcZjPjMINfvuvj42yL/vGKOycZrCiCE/saTOp34KXBM4a5uf9kL++t9nNsPxe/GOfcbtYVW/zl+wDx4YpbjnERvZ8l7L1Z+W3ZrvXBSwt9fP9BEadc2vICIxXrGsZl236bqo0VxA6ORQ1puk6SyjyAxdeDLMvmq8/KKSv1MzSqblfwBihdXbaXIw8927bZ/EU+ls+m36Q0DFc3i5QdlARwIbqZWYc3tB0vuDisxXXB28LNx3u5+fiGanNfwGbSyz5yg3FzQ4nNazP2Phf7/oiKc+/18+6qp/TFemszhs8iZnAsSROdQO2aPATPcxcSeH8FrmMG4r5w/AGn7a3/FPPh/BIAFsYrjh4UQ/n6CpRL0e/slhZc7Bi+vCuLVXO3ADDw5J5MeWTcIU5RC6QTmxCiO0lsRVV1KDbk+dlaHGDcIC+RYc41SmrZFbwBsgradgawzHGxTLkonRXfltIrM4qJ01seza23W5y0LomES6fSs6yKx65NxhPT8DDh/vkE3D+f0GZpW7e6Ztf7olKb0U8ei9pcRnSfKOKHdOwe4Nkfbd/1fsPH27DtsU0WpxGHhgRwIUSb+zSrjuufLcNvQWaGm5evTyDCq0iOhJMHKj7KdgL37BFt33HrxMoO0K8AAA4sSURBVFlpnDir5Q5nm8ts5q2zWFFk47OgID6agvhovtxhNBmu1tZGjIkie10tAD0zvKT1j8CTuY9xeB1E8rA4tn5XBEDSsPiOG7w7aroOEgngQog29873texcG2TtVj8rt/gYN8iLUor5szx8lG2REKE4unf79rwurLY54kU/BU4zOS4XBGxwKTih98G9+U8/swcZvbyUlvgZPzEGTzNLlHZUU/9ssuzFbCy/zahLBhzq5LSse8VvCeBCiLbz+Hc+3lwZIK664btwD2Q0mq3N41JMz2z9QioHYnGevSt4A0zqA5P7GRzbS3FUxsEPqGPM6H3v1AGFxXo44prDDnUyQtC9IrgEcCFEm/hsQ4Br3gtOZmm7uWSkomc4nDUxnLQDWPmsLY1MUcSHQWlwkbCzMw2uGtMx0ibaQPeK3xLAhRBtI6esUYc0pUjuE859p+x74pb2lB6tWDjbzdzVFkOTFOcc1nmqsUUIJIALIcT+O2Ooi+EpiuUFNilRcPm4jnl7GZqkuOMYKXV3Td0rgnfM/2FCiA6psNrm8cUWkR64ZqxBpKfhhhkXrvjhqnDWFtv0jVfEhXevm6noALrZn5wEcCFEyE76j5+sQue9zrOZe0bTW0i4RzEyrZvdRUUH0r3+9qQBSAgRkmqfvSt4A3y7te0mYSmutnltpcXivLad2EV0M6qZVyiHKZWplPpWKbU2+HNwM/u4lFJ/V0plK6XWK6Uub7TtD0qpAqXU0uDr722Tob2TErgQIiSRHsWUfoqPNzlB9szBbfP8X1rrjM3eUAqGgrlnuTh7iJQtRCu0vgD+JPB327ZfVkpdBDwFTNptnwuBQcBgIBFYopT6xLbtTcHtL9m2fUOrU9AK8r9ECBGy+We7eOk0F2+c4eJvJ7XN7WPRNpsNpc57y4a5q6y9HyBEi/a/CK6USgHGAq8Gv3oVGKuU2n0e3lnAM7ZtW7ZtFwJvA+e0UcJbRQK4ECJkYW7F7MMNZgwx2mw6zcMSFZGN1jMZK23oorWaid9KqSuUUrrR64rdjuoNbLVtOwAQ/Lkt+H1jfYDNjT5v2W2f85RSWUqpBUqpiW2YqxZJFboQ4pDqE6f43/kuXl5uMzQJrh4r5QrRSs08VNq2/TTw9EG+8pPAvbZt+5RSU4B5Sqmhtm0XH8yLSgAXQhxyEzIMJrS8bLcQB1MOkKGUctm2HVBKuYCewe8b2wL0BX4Ift5VIrdtO2/nTrZtf6yUygGGA18czITLo64QQohuy7btAmApcH7wq/OBJcF27sbmAnOUUkawffxM4A0ApdSux0+l1GigH7DmICddSuBCCCG6iNZ3n7gKeFEpdQdQAlwMoJR6H7jDtm0N/As4ElgXPOYu27Y3Bt/fp5QaBwSAemB241L5wSIBXAghRBfRughu2/ZqnOC8+/enNnofAH7ZwvGXtOrCB0gCuBBCiK6hmw1gkAAuhBCia5AALoQQQnRG3SuCSwAXQgjRNXSv+C3DyIQQ3cN3Wy3mrrKorJcFU0TXICVwIUSX9+zSAHPeDwAwOlXx3SVuwtzdrLjWHXSzX6mUwIUQXd6rKxoWSFmab7OqWErhXZJSe766MAngQogur/ECKT0ioG9s176xi+5BqtCFEF3efSe4SIpU5JTbXDHGICFCAniX1M1+rRLAhRBdnseluGmi61AnQxx03SuCSwAXQgjRNXSv+N35A7hpmpHA88A4wA/coLV+t5n9MoCXgbHAOq21udv2OcBNOH8CHwDXaq2t3c8jhBBCdARdoRPbDUC51noQcDrwrGma0c3sVwncAVyw+wbTNPsDdwITgcHB10UHLcVCCCHanmrm1YV1hQA+C3gKQGu9DtDAtN130lqXaa2/AqqaOcdM4G2tdWGw1P1M8LxCCCFEh9Tpq9CBPsDmRp+3AL0P5jlM07wCuAIgLS1tPy8lhBDioOji47531+EDuGmai3ECbHNS2zMtO2mtnwaeBpg9e7bMCCGEEKLddfgArrUeu7ftpmluAfoChcGv+gCf7edldp5jpz5Azn6eQwghxKHUvQrgXaINfC5wJYBpmoOB8cCH+3mON4EzTdNMNk3TAOYAr7dpKoUQQog21BUC+ENAvGma64F3gSu01hUApmneZZrmVcH3LtM0c3EC/kjTNHNN0/wDgNZ6A3A38B2wDtiAM+RMCCFEZ9HNeqEr25Ym3AMxe/Zs+1//+tehToYQQnQ2bR5e1f2+PQKa/XtPlw3jHb4NXAghhAhJlw3VzesKVehCCCFEtyMlcCGEEF2DlMCFEEII0dFJCVwIIUTXICVwIYQQQnR0UgIXQgjRNchc6EIIIUQn1L3it0zkcqBM0yyk6UpmB0MSUHSQr3EodfX8QdfPY1fPH3T9PLZ3/oq01qe04/W6HAngnYBpmlprbR7qdBwsXT1/0PXz2NXzB10/j109f12RdGITQgghOiEJ4EIIIUQnJAG8c3j6UCfgIOvq+YOun8eunj/o+nns6vnrcqQNXAghhOiEpAQuhBBCdEIyDryDME0zE3gRSASKgYu11ut222cqcB8wAvib1vqGdk9oK4WYv9uB84AA4ANu0Vp/1N5pba0Q8/hz4DeABbiAZ7TWj7V3WlsjlPw12ncIsAR4ogv+nf4BuBrYFvzqG631r9ozna0V6u/QNM1zgdtxRlbbwEla6/z2TKvYNymBdxxPAn/XWmcCfweeamafDcDlwEPtmbA2Ekr+FgHjtdYjgV8A/zFNM6Id03igQsnjm8AorfVo4CjgetM0R7ZjGg9EKPnDNE1XcNvb7Zi2thJSHoGXtNajg69OEbyD9pk/0zRN4A/AFK31cOAYoKw9EylCIwG8AzBNMwUYC7wa/OpVYKxpmsmN99Nar9daLwX87ZzEA7If+ftIa10d/JiF8/Sf2G4JPQD7kcdyrfXOjieRgAenhNOhhZq/oJuBd4G17ZS8NrGfeex09iN/vwEe1lrnAWity7TWte2XUhEqCeAdQ29gq9Y6ABD8uS34fVfQmvxdDGRrrXPbIX1tIeQ8mqb5M9M0V+DM4PeQ1vqndk1p64SUP9M0RwEnA39p9xQeuP35Oz3PNM0s0zQXmKY5sT0TeQBCzd8wYIBpml+aprnYNM3bTNPsZpOUdg4SwEWHY5rm8cDdwPmHOi0Hg9b6Ha314UAmMDvYXtzpmabpwRmKdNXOINFFPQn0Dzb1PATMM02zU9QUhcgFjASmAMcD04DZhzRFolkSwDuGHCAj2Ha4sw2xZ/D7riDk/AVLMy8DZ2qt17RrKg/Mfv8OtdZbcNr9p7dLCg9MKPlLBwYC75umuQn4P2COaZqdZXxxSL9DrXWe1toXfP9xcPvwdk5ra4T6N7oFeENrXae1rgDmAUe0a0pFSCSAdwBa6wJgKQ0lzvOBJVrrwkOXqrYTav5M0xwP/AeYqbVe3L6pPDD7kcehjd4nAScCHb4KPZT8aa23aK2TtNb9tNb9gL/i9LK/ot0T3Ar78TvMaPR+NNAP6PAPm/txn3kFmGqapgrWqkwGlrVfSkWoZBhZx3EV8KJpmncAJThtwJim+T5wh9Zam6Z5DPAaEAso0zTPAy7rJEOt9pk/4AkgAnjK6QgLwOxO0kYMoeXxiuBwQB9OJ73HtdYLDlWC91Mo+evsQsnjfaZpjsMZ7liP8zead6gSvJ9Cyd9rgAmsxBnu+BHwz0OTXLE3MhObEEII0QlJFboQQgjRCUkAF0IIITohCeBCCCFEJyQBXAghhOiEJIALIYQQnZAEcCGEEKITkgAuhBBCdEIykYsQbcw0zc+BiTiTtez0GvA1cJvWelBwvxeAC4E6nAkztuKs8/5Eo3MlAw8ApwBxQAXObFq/AH4O3BLcVeGsblZNw+pm92mt79tHWscFz3Fs8Pgi4EecJSc/bSadjZ2ntX43uD72ncCdWuu7Gp37WcCttb50b2kQQrSOBHAhDo67tdb3NP7CNM1Lm9nvRa315aZpGsBZwFzTNFdqrT8Pbn8ZZy3mMVrrguCSkKcAdjA43xc8dy+cOa0P11pvCiWBpmlOAeYDj+EsIZkDRANTg2n5dPd07uV0xcDvTNN8uhPNSiZEpyYBXIgOQGttAW+aplmMM43l58FNRwGzgvNY75zP+qU2uuw/gJe11jc2+q4CeDP42h9LcUr/dwGdYu5zITo7CeBCdADBlaFmAkk0XRjjS+ChYAn7ByCrLZbqNE0zE2flsCsP9FyN3AgsNU3zUa31ijY8rxCiGRLAhTg4bjVN84ZGn09pYb/ZpmnOBKJw1mG+Q2s9v9H2WcA1OO3djwJ1wTbpm7XWtQeQvuTgz607vzBN82c4pXsFhGmtw5tJZ2Mjg0uiAqC1Xm2a5vM4a2SfegBpE0KEQAK4EAfHvc20gR/WzH7/CraBRwIPApNN07xfa+0H0FpXAvcD95um6cV5EPgXUA7ccQDpKwr+7AWsDl7rHSA+uOrdV82lM4Tz3gmsN01z8gGkTQgRAhlGJkQHoLWuBn4LZAC/amGf+mCQ/QQYfYCXXAtsAM47wPM0EWyj/xNOKVzuL0IcRFICF6KD0FrXm6Z5F/Bn0zSf01pXmKb5Z+BV4CectaePA07EKZUfyLVs0zR/BcwLdpx7HMjFWY/9yAM5N/BnnHWnTwfeO8BzCSFaIE/IQnQsrwA7gOuDnw3geaAAKAGeAB4GHjnQC2mtPwSOATKBxUAlsAI4Gpi02+6XmKZZudvr6hbOWwPchtMhTwhxkCjbtve9lxBCCCE6FCmBCyGEEJ2QtIEL0UWZpvkkcFELm4c1HgImhOh8pApdCCGE6ISkCl0IIYTohCSACyGEEJ2QBHAhhBCiE5IALoQQQnRCEsCFEEKITuj/AY5+EcFfOiSBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a SHAP dependence plot to show the effect of a single feature across the whole dataset\n",
    "shap.dependence_plot('FIRST_GEN', shap_values[0], df_train, interaction_index='PPTUG_EF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DeepExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DeepExplainer is optimized for neural network models\n",
    "# It currently does not support eager execution or TF 2.0\n",
    "\n",
    "explainer = shap.DeepExplainer(model, df_train_normed)\n",
    "shap_values = explainer.shap_values(df_train_normed.values)\n",
    "shap.summary_plot(shap_values[0], df_train)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}