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vykhand/021.LOGREG-Gregor.ipynb

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021.LOGREG-Gregor.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from sklearn.preprocessing import Normalizer\n",
"\n",
"from sklearn.metrics import roc_auc_score, roc_curve\n",
"\n",
"import shap\n",
"\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"from sklearn.model_selection import KFold"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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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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"def cnt_plots(cols):\n",
" plt.subplots(figsize = (10, 4*len(cols)))\n",
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" plt.subplot(len(cols),1, i + 1)\n",
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"df.set_index(\"Unnamed: 0\", inplace=True)\n",
"df[\"country\"] = df[\"country\"].astype(\"category\")\n",
"df.index.name = \"ID\""
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>22.047791</td>\n",
" <td>44</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>-4</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" country gender age_int adl adl2 bmi casp chronic2w1 \\\n",
"ID \n",
"1 Austria 0 71 0 0 24.341759 37 0 \n",
"2 Austria 1 68 0 0 35.671818 37 1 \n",
"3 Austria 0 62 0 0 28.344671 42 0 \n",
"4 Austria 1 58 0 0 24.622961 38 0 \n",
"5 Austria 0 58 0 0 22.047791 44 1 \n",
"\n",
" chronicw1 cusmoke ... symptomsw1 isced1997_r Recall_Immi \\\n",
"ID ... \n",
"1 1 0 ... 1 1 7 \n",
"2 5 0 ... 4 1 4 \n",
"3 0 0 ... 0 3 7 \n",
"4 1 0 ... 1 3 1 \n",
"5 3 1 ... 4 5 7 \n",
"\n",
" Recall_delay Numeracy Orientation recall_immediate_w6_w1 \\\n",
"ID \n",
"1 4 2 4 -2 \n",
"2 4 4 3 1 \n",
"3 5 3 4 2 \n",
"4 1 4 4 5 \n",
"5 10 4 4 0 \n",
"\n",
" recall_delay_w6_w1 numeracy_w6_w1 output \n",
"ID \n",
"1 -1 3 -4 \n",
"2 1 1 4 \n",
"3 2 0 2 \n",
"4 7 1 16 \n",
"5 -4 1 -1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"<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",
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" <tr>\n",
" <th>gender</th>\n",
" <td>10453.0</td>\n",
" <td>0.438152</td>\n",
" <td>0.496184</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>1.00000</td>\n",
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" <tr>\n",
" <th>age_int</th>\n",
" <td>10453.0</td>\n",
" <td>61.473357</td>\n",
" <td>7.668304</td>\n",
" <td>50.00000</td>\n",
" <td>55.000000</td>\n",
" <td>60.000000</td>\n",
" <td>67.000000</td>\n",
" <td>80.00000</td>\n",
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" <tr>\n",
" <th>adl</th>\n",
" <td>10453.0</td>\n",
" <td>0.097962</td>\n",
" <td>0.465997</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>6.00000</td>\n",
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" <th>adl2</th>\n",
" <td>10453.0</td>\n",
" <td>0.061418</td>\n",
" <td>0.240107</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
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" <tr>\n",
" <th>bmi</th>\n",
" <td>10453.0</td>\n",
" <td>26.623436</td>\n",
" <td>4.220934</td>\n",
" <td>13.01541</td>\n",
" <td>23.765432</td>\n",
" <td>26.093275</td>\n",
" <td>28.964735</td>\n",
" <td>58.59375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>casp</th>\n",
" <td>10453.0</td>\n",
" <td>37.273127</td>\n",
" <td>5.947638</td>\n",
" <td>12.00000</td>\n",
" <td>33.000000</td>\n",
" <td>38.000000</td>\n",
" <td>42.000000</td>\n",
" <td>48.00000</td>\n",
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" <tr>\n",
" <th>chronic2w1</th>\n",
" <td>10453.0</td>\n",
" <td>0.376925</td>\n",
" <td>0.484639</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
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" <tr>\n",
" <th>chronicw1</th>\n",
" <td>10453.0</td>\n",
" <td>1.394911</td>\n",
" <td>1.334068</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>10.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cusmoke</th>\n",
" <td>10453.0</td>\n",
" <td>0.183584</td>\n",
" <td>0.387163</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro1</th>\n",
" <td>10453.0</td>\n",
" <td>0.376925</td>\n",
" <td>0.484639</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro2</th>\n",
" <td>10453.0</td>\n",
" <td>0.116809</td>\n",
" <td>0.321207</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
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" <tr>\n",
" <th>euro3</th>\n",
" <td>10453.0</td>\n",
" <td>0.057017</td>\n",
" <td>0.231886</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
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" <tr>\n",
" <th>euro4</th>\n",
" <td>10453.0</td>\n",
" <td>0.083612</td>\n",
" <td>0.276819</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro5</th>\n",
" <td>10453.0</td>\n",
" <td>0.303071</td>\n",
" <td>0.459608</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
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" <tr>\n",
" <th>euro6</th>\n",
" <td>10453.0</td>\n",
" <td>0.076820</td>\n",
" <td>0.266318</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro7</th>\n",
" <td>10453.0</td>\n",
" <td>0.254855</td>\n",
" <td>0.435801</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro8</th>\n",
" <td>10453.0</td>\n",
" <td>0.062566</td>\n",
" <td>0.242192</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro9</th>\n",
" <td>10453.0</td>\n",
" <td>0.298096</td>\n",
" <td>0.457444</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro10</th>\n",
" <td>10453.0</td>\n",
" <td>0.185497</td>\n",
" <td>0.388719</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro11</th>\n",
" <td>10453.0</td>\n",
" <td>0.113747</td>\n",
" <td>0.317519</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>euro12</th>\n",
" <td>10453.0</td>\n",
" <td>0.271788</td>\n",
" <td>0.444902</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gali</th>\n",
" <td>10453.0</td>\n",
" <td>0.334832</td>\n",
" <td>0.471954</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iadl</th>\n",
" <td>10453.0</td>\n",
" <td>0.145891</td>\n",
" <td>0.530771</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iadl2</th>\n",
" <td>10453.0</td>\n",
" <td>0.099876</td>\n",
" <td>0.299848</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>maxgrip</th>\n",
" <td>10453.0</td>\n",
" <td>35.089448</td>\n",
" <td>12.190161</td>\n",
" <td>2.00000</td>\n",
" <td>26.000000</td>\n",
" <td>33.000000</td>\n",
" <td>44.000000</td>\n",
" <td>80.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mobilit2</th>\n",
" <td>10453.0</td>\n",
" <td>0.428489</td>\n",
" <td>0.494883</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mobilit3</th>\n",
" <td>10453.0</td>\n",
" <td>0.167607</td>\n",
" <td>0.373535</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mobility</th>\n",
" <td>10453.0</td>\n",
" <td>1.100450</td>\n",
" <td>1.741485</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>10.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phactiv</th>\n",
" <td>10453.0</td>\n",
" <td>0.941835</td>\n",
" <td>0.234067</td>\n",
" <td>0.00000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sphus</th>\n",
" <td>10453.0</td>\n",
" <td>3.227399</td>\n",
" <td>1.027300</td>\n",
" <td>1.00000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>5.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sphus2</th>\n",
" <td>10453.0</td>\n",
" <td>0.373960</td>\n",
" <td>0.483876</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>symptoms2w1</th>\n",
" <td>10453.0</td>\n",
" <td>0.337702</td>\n",
" <td>0.472949</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>symptomsw1</th>\n",
" <td>10453.0</td>\n",
" <td>1.342772</td>\n",
" <td>1.466191</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>11.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>isced1997_r</th>\n",
" <td>10453.0</td>\n",
" <td>2.609681</td>\n",
" <td>1.578762</td>\n",
" <td>0.00000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>6.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall_Immi</th>\n",
" <td>10453.0</td>\n",
" <td>5.102172</td>\n",
" <td>1.679577</td>\n",
" <td>0.00000</td>\n",
" <td>4.000000</td>\n",
" <td>5.000000</td>\n",
" <td>6.000000</td>\n",
" <td>10.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall_delay</th>\n",
" <td>10453.0</td>\n",
" <td>3.642495</td>\n",
" <td>1.892060</td>\n",
" <td>0.00000</td>\n",
" <td>2.000000</td>\n",
" <td>4.000000</td>\n",
" <td>5.000000</td>\n",
" <td>10.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Numeracy</th>\n",
" <td>10453.0</td>\n",
" <td>3.447144</td>\n",
" <td>1.070784</td>\n",
" <td>1.00000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>5.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Orientation</th>\n",
" <td>10453.0</td>\n",
" <td>3.869033</td>\n",
" <td>0.394871</td>\n",
" <td>0.00000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>recall_immediate_w6_w1</th>\n",
" <td>10453.0</td>\n",
" <td>-0.095762</td>\n",
" <td>1.876781</td>\n",
" <td>-9.00000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>9.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>recall_delay_w6_w1</th>\n",
" <td>10453.0</td>\n",
" <td>0.035492</td>\n",
" <td>2.203697</td>\n",
" <td>-10.00000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>9.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>numeracy_w6_w1</th>\n",
" <td>10453.0</td>\n",
" <td>0.475940</td>\n",
" <td>1.545248</td>\n",
" <td>-5.00000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>4.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>output</th>\n",
" <td>10453.0</td>\n",
" <td>-0.699129</td>\n",
" <td>5.651649</td>\n",
" <td>-30.00000</td>\n",
" <td>-4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>24.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% \\\n",
"gender 10453.0 0.438152 0.496184 0.00000 0.000000 \n",
"age_int 10453.0 61.473357 7.668304 50.00000 55.000000 \n",
"adl 10453.0 0.097962 0.465997 0.00000 0.000000 \n",
"adl2 10453.0 0.061418 0.240107 0.00000 0.000000 \n",
"bmi 10453.0 26.623436 4.220934 13.01541 23.765432 \n",
"casp 10453.0 37.273127 5.947638 12.00000 33.000000 \n",
"chronic2w1 10453.0 0.376925 0.484639 0.00000 0.000000 \n",
"chronicw1 10453.0 1.394911 1.334068 0.00000 0.000000 \n",
"cusmoke 10453.0 0.183584 0.387163 0.00000 0.000000 \n",
"euro1 10453.0 0.376925 0.484639 0.00000 0.000000 \n",
"euro2 10453.0 0.116809 0.321207 0.00000 0.000000 \n",
"euro3 10453.0 0.057017 0.231886 0.00000 0.000000 \n",
"euro4 10453.0 0.083612 0.276819 0.00000 0.000000 \n",
"euro5 10453.0 0.303071 0.459608 0.00000 0.000000 \n",
"euro6 10453.0 0.076820 0.266318 0.00000 0.000000 \n",
"euro7 10453.0 0.254855 0.435801 0.00000 0.000000 \n",
"euro8 10453.0 0.062566 0.242192 0.00000 0.000000 \n",
"euro9 10453.0 0.298096 0.457444 0.00000 0.000000 \n",
"euro10 10453.0 0.185497 0.388719 0.00000 0.000000 \n",
"euro11 10453.0 0.113747 0.317519 0.00000 0.000000 \n",
"euro12 10453.0 0.271788 0.444902 0.00000 0.000000 \n",
"gali 10453.0 0.334832 0.471954 0.00000 0.000000 \n",
"iadl 10453.0 0.145891 0.530771 0.00000 0.000000 \n",
"iadl2 10453.0 0.099876 0.299848 0.00000 0.000000 \n",
"maxgrip 10453.0 35.089448 12.190161 2.00000 26.000000 \n",
"mobilit2 10453.0 0.428489 0.494883 0.00000 0.000000 \n",
"mobilit3 10453.0 0.167607 0.373535 0.00000 0.000000 \n",
"mobility 10453.0 1.100450 1.741485 0.00000 0.000000 \n",
"phactiv 10453.0 0.941835 0.234067 0.00000 1.000000 \n",
"sphus 10453.0 3.227399 1.027300 1.00000 3.000000 \n",
"sphus2 10453.0 0.373960 0.483876 0.00000 0.000000 \n",
"symptoms2w1 10453.0 0.337702 0.472949 0.00000 0.000000 \n",
"symptomsw1 10453.0 1.342772 1.466191 0.00000 0.000000 \n",
"isced1997_r 10453.0 2.609681 1.578762 0.00000 1.000000 \n",
"Recall_Immi 10453.0 5.102172 1.679577 0.00000 4.000000 \n",
"Recall_delay 10453.0 3.642495 1.892060 0.00000 2.000000 \n",
"Numeracy 10453.0 3.447144 1.070784 1.00000 3.000000 \n",
"Orientation 10453.0 3.869033 0.394871 0.00000 4.000000 \n",
"recall_immediate_w6_w1 10453.0 -0.095762 1.876781 -9.00000 -1.000000 \n",
"recall_delay_w6_w1 10453.0 0.035492 2.203697 -10.00000 -1.000000 \n",
"numeracy_w6_w1 10453.0 0.475940 1.545248 -5.00000 0.000000 \n",
"output 10453.0 -0.699129 5.651649 -30.00000 -4.000000 \n",
"\n",
" 50% 75% max \n",
"gender 0.000000 1.000000 1.00000 \n",
"age_int 60.000000 67.000000 80.00000 \n",
"adl 0.000000 0.000000 6.00000 \n",
"adl2 0.000000 0.000000 1.00000 \n",
"bmi 26.093275 28.964735 58.59375 \n",
"casp 38.000000 42.000000 48.00000 \n",
"chronic2w1 0.000000 1.000000 1.00000 \n",
"chronicw1 1.000000 2.000000 10.00000 \n",
"cusmoke 0.000000 0.000000 1.00000 \n",
"euro1 0.000000 1.000000 1.00000 \n",
"euro2 0.000000 0.000000 1.00000 \n",
"euro3 0.000000 0.000000 1.00000 \n",
"euro4 0.000000 0.000000 1.00000 \n",
"euro5 0.000000 1.000000 1.00000 \n",
"euro6 0.000000 0.000000 1.00000 \n",
"euro7 0.000000 1.000000 1.00000 \n",
"euro8 0.000000 0.000000 1.00000 \n",
"euro9 0.000000 1.000000 1.00000 \n",
"euro10 0.000000 0.000000 1.00000 \n",
"euro11 0.000000 0.000000 1.00000 \n",
"euro12 0.000000 1.000000 1.00000 \n",
"gali 0.000000 1.000000 1.00000 \n",
"iadl 0.000000 0.000000 7.00000 \n",
"iadl2 0.000000 0.000000 1.00000 \n",
"maxgrip 33.000000 44.000000 80.00000 \n",
"mobilit2 0.000000 1.000000 1.00000 \n",
"mobilit3 0.000000 0.000000 1.00000 \n",
"mobility 0.000000 2.000000 10.00000 \n",
"phactiv 1.000000 1.000000 1.00000 \n",
"sphus 3.000000 4.000000 5.00000 \n",
"sphus2 0.000000 1.000000 1.00000 \n",
"symptoms2w1 0.000000 1.000000 1.00000 \n",
"symptomsw1 1.000000 2.000000 11.00000 \n",
"isced1997_r 3.000000 3.000000 6.00000 \n",
"Recall_Immi 5.000000 6.000000 10.00000 \n",
"Recall_delay 4.000000 5.000000 10.00000 \n",
"Numeracy 3.000000 4.000000 5.00000 \n",
"Orientation 4.000000 4.000000 4.00000 \n",
"recall_immediate_w6_w1 0.000000 1.000000 9.00000 \n",
"recall_delay_w6_w1 0.000000 1.000000 9.00000 \n",
"numeracy_w6_w1 1.000000 2.000000 4.00000 \n",
"output 0.000000 3.000000 24.00000 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().T"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['country', 'gender', 'age_int', 'adl', 'adl2', 'bmi', 'casp',\n",
" 'chronic2w1', 'chronicw1', 'cusmoke', 'euro1', 'euro2', 'euro3',\n",
" 'euro4', 'euro5', 'euro6', 'euro7', 'euro8', 'euro9', 'euro10',\n",
" 'euro11', 'euro12', 'gali', 'iadl', 'iadl2', 'maxgrip', 'mobilit2',\n",
" 'mobilit3', 'mobility', 'phactiv', 'sphus', 'sphus2', 'symptoms2w1',\n",
" 'symptomsw1', 'isced1997_r', 'Recall_Immi', 'Recall_delay', 'Numeracy',\n",
" 'Orientation', 'recall_immediate_w6_w1', 'recall_delay_w6_w1',\n",
" 'numeracy_w6_w1', 'output'],\n",
" dtype='object')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"df[\"tgt_recall_immediate_w6_w1\"] = (df[\"recall_immediate_w6_w1\"] < 0).astype(\"int\") \n",
"\n",
"df[\"tgt_recall_delay_w6_w1\"] = (df[\"recall_delay_w6_w1\"] < 0).astype(\"int\")\n",
"\n",
"df[\"tgt_numeracy_w6_w1\"] = (df[\"numeracy_w6_w1\"] < 0).astype(\"int\")\n",
"\n",
"df[\"tgt_output\"] = (df[\"output\"] < 0).astype(\"int\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"df.drop(['recall_immediate_w6_w1', 'recall_delay_w6_w1','numeracy_w6_w1', 'output'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"['gender', 'age_int', 'adl', 'adl2', 'casp', 'chronic2w1', 'chronicw1', 'cusmoke', 'euro1', 'euro2', 'euro3', 'euro4', 'euro5', 'euro6', 'euro7', 'euro8', 'euro9', 'euro10', 'euro11', 'euro12', 'gali', 'iadl', 'iadl2', 'maxgrip', 'mobilit2', 'mobilit3', 'mobility', 'phactiv', 'sphus', 'sphus2', 'symptoms2w1', 'symptomsw1', 'isced1997_r', 'Recall_Immi', 'Recall_delay', 'Numeracy', 'Orientation']"
],
"text/plain": [
"['gender',\n",
" 'age_int',\n",
" 'adl',\n",
" 'adl2',\n",
" 'casp',\n",
" 'chronic2w1',\n",
" 'chronicw1',\n",
" 'cusmoke',\n",
" 'euro1',\n",
" 'euro2',\n",
" 'euro3',\n",
" 'euro4',\n",
" 'euro5',\n",
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" 'gali',\n",
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" 'mobilit2',\n",
" 'mobilit3',\n",
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" 'sphus',\n",
" 'sphus2',\n",
" 'symptoms2w1',\n",
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" 'isced1997_r',\n",
" 'Recall_Immi',\n",
" 'Recall_delay',\n",
" 'Numeracy',\n",
" 'Orientation']"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int_cols = list(df.columns[df.dtypes == \"int64\" ])\n",
"int_cols"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['age_int', 'adl', 'casp', 'chronicw1', 'iadl', 'maxgrip', 'mobility',\n",
" 'sphus', 'symptomsw1', 'isced1997_r', 'Recall_Immi', 'Recall_delay',\n",
" 'Numeracy', 'Orientation'],\n",
" dtype='object')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[int_cols].columns[df[int_cols].nunique() > 2]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"['adl', 'chronicw1', 'iadl', 'mobility', 'sphus', 'symptomsw1', 'isced1997_r', 'Orientation']"
],
"text/plain": [
"['adl',\n",
" 'chronicw1',\n",
" 'iadl',\n",
" 'mobility',\n",
" 'sphus',\n",
" 'symptomsw1',\n",
" 'isced1997_r',\n",
" 'Orientation']"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"norm_cols = ['age_int', 'casp', 'maxgrip', 'Recall_Immi', 'Recall_delay', 'bmi']\n",
"dummy_cols = [i for i in df[int_cols].columns[df[int_cols].nunique() > 2] if (i not in norm_cols and i !=\"Numeracy\")]\n",
"norm_cols.append(\"Numeracy\")\n",
"dummy_cols"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"['age_int', 'casp', 'maxgrip', 'Recall_Immi', 'Recall_delay', 'bmi', 'Numeracy']"
],
"text/plain": [
"['age_int',\n",
" 'casp',\n",
" 'maxgrip',\n",
" 'Recall_Immi',\n",
" 'Recall_delay',\n",
" 'bmi',\n",
" 'Numeracy']"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"norm_cols"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cnt_plots(norm_cols)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"y_all = df[['tgt_recall_immediate_w6_w1', 'tgt_recall_delay_w6_w1',\n",
" 'tgt_numeracy_w6_w1', 'tgt_output']]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"df.drop(['tgt_recall_immediate_w6_w1', 'tgt_recall_delay_w6_w1',\n",
" 'tgt_numeracy_w6_w1', 'tgt_output'], axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"X_all = pd.get_dummies(df[dummy_cols].astype(\"category\"))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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"</div>"
],
"text/plain": [
" adl_0 adl_1 adl_2 adl_3 adl_4 adl_5 adl_6 chronicw1_0 chronicw1_1 \\\n",
"ID \n",
"1 1 0 0 0 0 0 0 0 1 \n",
"2 1 0 0 0 0 0 0 0 0 \n",
"3 1 0 0 0 0 0 0 1 0 \n",
"4 1 0 0 0 0 0 0 0 1 \n",
"5 1 0 0 0 0 0 0 0 0 \n",
"\n",
" chronicw1_2 ... isced1997_r_2 isced1997_r_3 isced1997_r_4 \\\n",
"ID ... \n",
"1 0 ... 0 0 0 \n",
"2 0 ... 0 0 0 \n",
"3 0 ... 0 1 0 \n",
"4 0 ... 0 1 0 \n",
"5 0 ... 0 0 0 \n",
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"4 0 0 0 0 0 \n",
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"2 1 0 \n",
"3 0 1 \n",
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"[5 rows x 66 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_all.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"X_all = df[norm_cols].join( X_all)\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
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" <td>5</td>\n",
" <td>28.344671</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>58</td>\n",
" <td>38</td>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>24.622961</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>58</td>\n",
" <td>44</td>\n",
" <td>37</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>22.047791</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 73 columns</p>\n",
"</div>"
],
"text/plain": [
" age_int casp maxgrip Recall_Immi Recall_delay bmi Numeracy \\\n",
"ID \n",
"1 71 37 32 7 4 24.341759 2 \n",
"2 68 37 32 4 4 35.671818 4 \n",
"3 62 42 22 7 5 28.344671 3 \n",
"4 58 38 45 1 1 24.622961 4 \n",
"5 58 44 37 7 10 22.047791 4 \n",
"\n",
" adl_0 adl_1 adl_2 ... isced1997_r_2 isced1997_r_3 isced1997_r_4 \\\n",
"ID ... \n",
"1 1 0 0 ... 0 0 0 \n",
"2 1 0 0 ... 0 0 0 \n",
"3 1 0 0 ... 0 1 0 \n",
"4 1 0 0 ... 0 1 0 \n",
"5 1 0 0 ... 0 0 0 \n",
"\n",
" isced1997_r_5 isced1997_r_6 Orientation_0 Orientation_1 Orientation_2 \\\n",
"ID \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"5 1 0 0 0 0 \n",
"\n",
" Orientation_3 Orientation_4 \n",
"ID \n",
"1 0 1 \n",
"2 1 0 \n",
"3 0 1 \n",
"4 0 1 \n",
"5 0 1 \n",
"\n",
"[5 rows x 73 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_all.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\proglang\\Miniconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c33783fa48>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(X_all.age_int)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x24f1e369848>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"sns.distplot(X_all.casp)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x24f1e445e08>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"sns.distplot(X_all.maxgrip)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"other_cols = [c for c in df.columns if (c not in norm_cols and c not in dummy_cols and c != \"country\") ]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"X_all = df[other_cols].join(X_all)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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",
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"\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>gender</th>\n",
" <th>adl2</th>\n",
" <th>chronic2w1</th>\n",
" <th>cusmoke</th>\n",
" <th>euro1</th>\n",
" <th>euro2</th>\n",
" <th>euro3</th>\n",
" <th>euro4</th>\n",
" <th>euro5</th>\n",
" <th>euro6</th>\n",
" <th>euro7</th>\n",
" <th>euro8</th>\n",
" <th>euro9</th>\n",
" <th>euro10</th>\n",
" <th>euro11</th>\n",
" <th>euro12</th>\n",
" <th>gali</th>\n",
" <th>iadl2</th>\n",
" <th>mobilit2</th>\n",
" <th>mobilit3</th>\n",
" <th>phactiv</th>\n",
" <th>sphus2</th>\n",
" <th>symptoms2w1</th>\n",
" <th>age_int</th>\n",
" <th>casp</th>\n",
" <th>maxgrip</th>\n",
" <th>Recall_Immi</th>\n",
" <th>Recall_delay</th>\n",
" <th>bmi</th>\n",
" <th>Numeracy</th>\n",
" <th>adl_0</th>\n",
" <th>adl_1</th>\n",
" <th>adl_2</th>\n",
" <th>adl_3</th>\n",
" <th>adl_4</th>\n",
" <th>adl_5</th>\n",
" <th>adl_6</th>\n",
" <th>chronicw1_0</th>\n",
" <th>chronicw1_1</th>\n",
" <th>chronicw1_2</th>\n",
" <th>chronicw1_3</th>\n",
" <th>chronicw1_4</th>\n",
" <th>chronicw1_5</th>\n",
" <th>chronicw1_6</th>\n",
" <th>chronicw1_7</th>\n",
" <th>chronicw1_8</th>\n",
" <th>chronicw1_9</th>\n",
" <th>chronicw1_10</th>\n",
" <th>iadl_0</th>\n",
" <th>iadl_1</th>\n",
" <th>iadl_2</th>\n",
" <th>iadl_3</th>\n",
" <th>iadl_4</th>\n",
" <th>iadl_5</th>\n",
" <th>iadl_6</th>\n",
" <th>iadl_7</th>\n",
" <th>mobility_0</th>\n",
" <th>mobility_1</th>\n",
" <th>mobility_2</th>\n",
" <th>mobility_3</th>\n",
" <th>mobility_4</th>\n",
" <th>mobility_5</th>\n",
" <th>mobility_6</th>\n",
" <th>mobility_7</th>\n",
" <th>mobility_8</th>\n",
" <th>mobility_9</th>\n",
" <th>mobility_10</th>\n",
" <th>sphus_1</th>\n",
" <th>sphus_2</th>\n",
" <th>sphus_3</th>\n",
" <th>sphus_4</th>\n",
" <th>sphus_5</th>\n",
" <th>symptomsw1_0</th>\n",
" <th>symptomsw1_1</th>\n",
" <th>symptomsw1_2</th>\n",
" <th>symptomsw1_3</th>\n",
" <th>symptomsw1_4</th>\n",
" <th>symptomsw1_5</th>\n",
" <th>symptomsw1_6</th>\n",
" <th>symptomsw1_7</th>\n",
" <th>symptomsw1_8</th>\n",
" <th>symptomsw1_9</th>\n",
" <th>symptomsw1_10</th>\n",
" <th>symptomsw1_11</th>\n",
" <th>isced1997_r_0</th>\n",
" <th>isced1997_r_1</th>\n",
" <th>isced1997_r_2</th>\n",
" <th>isced1997_r_3</th>\n",
" <th>isced1997_r_4</th>\n",
" <th>isced1997_r_5</th>\n",
" <th>isced1997_r_6</th>\n",
" <th>Orientation_0</th>\n",
" <th>Orientation_1</th>\n",
" <th>Orientation_2</th>\n",
" <th>Orientation_3</th>\n",
" <th>Orientation_4</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71</td>\n",
" <td>37</td>\n",
" <td>32</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>24.341759</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>68</td>\n",
" <td>37</td>\n",
" <td>32</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>35.671818</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>62</td>\n",
" <td>42</td>\n",
" <td>22</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>28.344671</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>38</td>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>24.622961</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>58</td>\n",
" <td>44</td>\n",
" <td>37</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>22.047791</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender adl2 chronic2w1 cusmoke euro1 euro2 euro3 euro4 euro5 \\\n",
"ID \n",
"1 0 0 0 0 1 0 0 1 0 \n",
"2 1 0 1 0 1 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 0 0 0 1 \n",
"5 0 0 1 1 0 0 0 0 0 \n",
"\n",
" euro6 euro7 euro8 euro9 euro10 euro11 euro12 gali iadl2 mobilit2 \\\n",
"ID \n",
"1 0 0 0 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 1 1 0 1 \n",
"3 0 0 0 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 0 0 0 \n",
"5 0 0 0 0 0 0 0 0 0 1 \n",
"\n",
" mobilit3 phactiv sphus2 symptoms2w1 age_int casp maxgrip \\\n",
"ID \n",
"1 0 1 1 0 71 37 32 \n",
"2 1 1 1 1 68 37 32 \n",
"3 0 1 1 0 62 42 22 \n",
"4 0 1 1 0 58 38 45 \n",
"5 0 1 1 1 58 44 37 \n",
"\n",
" Recall_Immi Recall_delay bmi Numeracy adl_0 adl_1 adl_2 \\\n",
"ID \n",
"1 7 4 24.341759 2 1 0 0 \n",
"2 4 4 35.671818 4 1 0 0 \n",
"3 7 5 28.344671 3 1 0 0 \n",
"4 1 1 24.622961 4 1 0 0 \n",
"5 7 10 22.047791 4 1 0 0 \n",
"\n",
" adl_3 adl_4 adl_5 adl_6 chronicw1_0 chronicw1_1 chronicw1_2 \\\n",
"ID \n",
"1 0 0 0 0 0 1 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 1 0 0 \n",
"4 0 0 0 0 0 1 0 \n",
"5 0 0 0 0 0 0 0 \n",
"\n",
" chronicw1_3 chronicw1_4 chronicw1_5 chronicw1_6 chronicw1_7 \\\n",
"ID \n",
"1 0 0 0 0 0 \n",
"2 0 0 1 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"5 1 0 0 0 0 \n",
"\n",
" chronicw1_8 chronicw1_9 chronicw1_10 iadl_0 iadl_1 iadl_2 iadl_3 \\\n",
"ID \n",
"1 0 0 0 1 0 0 0 \n",
"2 0 0 0 1 0 0 0 \n",
"3 0 0 0 1 0 0 0 \n",
"4 0 0 0 1 0 0 0 \n",
"5 0 0 0 1 0 0 0 \n",
"\n",
" iadl_4 iadl_5 iadl_6 iadl_7 mobility_0 mobility_1 mobility_2 \\\n",
"ID \n",
"1 0 0 0 0 1 0 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 1 0 0 \n",
"4 0 0 0 0 1 0 0 \n",
"5 0 0 0 0 0 0 1 \n",
"\n",
" mobility_3 mobility_4 mobility_5 mobility_6 mobility_7 mobility_8 \\\n",
"ID \n",
"1 0 0 0 0 0 0 \n",
"2 0 1 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"5 0 0 0 0 0 0 \n",
"\n",
" mobility_9 mobility_10 sphus_1 sphus_2 sphus_3 sphus_4 sphus_5 \\\n",
"ID \n",
"1 0 0 0 0 0 1 0 \n",
"2 0 0 0 0 0 0 1 \n",
"3 0 0 0 0 0 0 1 \n",
"4 0 0 0 0 0 0 1 \n",
"5 0 0 0 0 0 1 0 \n",
"\n",
" symptomsw1_0 symptomsw1_1 symptomsw1_2 symptomsw1_3 symptomsw1_4 \\\n",
"ID \n",
"1 0 1 0 0 0 \n",
"2 0 0 0 0 1 \n",
"3 1 0 0 0 0 \n",
"4 0 1 0 0 0 \n",
"5 0 0 0 0 1 \n",
"\n",
" symptomsw1_5 symptomsw1_6 symptomsw1_7 symptomsw1_8 symptomsw1_9 \\\n",
"ID \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"5 0 0 0 0 0 \n",
"\n",
" symptomsw1_10 symptomsw1_11 isced1997_r_0 isced1997_r_1 isced1997_r_2 \\\n",
"ID \n",
"1 0 0 0 1 0 \n",
"2 0 0 0 1 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"5 0 0 0 0 0 \n",
"\n",
" isced1997_r_3 isced1997_r_4 isced1997_r_5 isced1997_r_6 Orientation_0 \\\n",
"ID \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 1 0 0 0 0 \n",
"4 1 0 0 0 0 \n",
"5 0 0 1 0 0 \n",
"\n",
" Orientation_1 Orientation_2 Orientation_3 Orientation_4 \n",
"ID \n",
"1 0 0 0 1 \n",
"2 0 0 1 0 \n",
"3 0 0 0 1 \n",
"4 0 0 0 1 \n",
"5 0 0 0 1 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.options.display.max_columns = 200\n",
"X_all.head()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"X_all.join(y_all).to_csv(\"../data/share_cog_logreg.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"df.join(y_all).to_csv(\"../data/share_cog_binary_tgt.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"kf = KFold(n_splits = 5, random_state=777, shuffle = False)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"pd.to_pickle(list(kf.split(X_all)), \"../folds_5_seed_777.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"folds = pd.read_pickle(\"../folds_5_seed_777.pkl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## normalizing"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"for i, (i_tr, i_te) in enumerate(folds):\n",
"\n",
" nzr = Normalizer()\n",
" nzr.fit(X_all.iloc[i_tr])\n",
" ind_te = X_all.index.values[i_te]\n",
" X_all.loc[ind_te, norm_cols] = nzr.transform(X_all.loc[ind_te, norm_cols])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"['age_int', 'casp', 'maxgrip', 'Recall_Immi', 'Recall_delay', 'bmi', 'Numeracy']"
],
"text/plain": [
"['age_int',\n",
" 'casp',\n",
" 'maxgrip',\n",
" 'Recall_Immi',\n",
" 'Recall_delay',\n",
" 'bmi',\n",
" 'Numeracy']"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"norm_cols"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\proglang\\Miniconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c338a1ba08>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(X_all.Numeracy)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x24f1ecf7a48>"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"sns.distplot(X_all.maxgrip)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"colors = [\"r\",\"g\", \"b\", \"y\", \"c\"]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x2160 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"oof_preds = np.zeros((X_all.shape[0], y_all.shape[1]))\n",
"scores = {}\n",
"models = {}\n",
"plt.subplots(figsize = (20, 30))\n",
"\n",
"for c, tgt in enumerate(y_all.columns):\n",
" scores[tgt] = []\n",
" models[tgt] = []\n",
" #print(tgt, \":\")\n",
" plt.subplot(4, 1, c + 1)\n",
" for i, (i_tr, i_te) in enumerate(folds):\n",
" Xtr, Xte = X_all.iloc[i_tr], X_all.iloc[i_te]\n",
" ytr, yte = y_all.iloc[i_tr, c], y_all.iloc[i_te, c]\n",
"\n",
" mdl = LogisticRegression(penalty = \"l1\", solver = \"liblinear\")\n",
" mdl.fit(Xtr, ytr)\n",
" models[tgt].append(mdl)\n",
" preds = mdl.predict_proba(Xte)[:, 1]\n",
" oof_preds[i_te, c] = preds\n",
" score = roc_auc_score(yte, preds)\n",
" scores[tgt].append(score)\n",
" fpr, tpr, threshold = roc_curve(yte, preds)\n",
" plt.plot(fpr, tpr, color = colors[i] , label = f\"fold:{i}, AUC:{score:.3f}\")\n",
" plt.title(f\"{tgt}\")\n",
" plt.legend(loc=4)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.71801047, 0.41490647, 0.18243645, 0.63933466],\n",
" [0.28925016, 0.59507963, 0.26474263, 0.62645482],\n",
" [0.60699725, 0.46162741, 0.17829845, 0.56809834],\n",
" ...,\n",
" [0.44871784, 0.33782538, 0.11651652, 0.42526615],\n",
" [0.22791555, 0.45115992, 0.22126094, 0.45706117],\n",
" [0.11524985, 0.31690398, 0.25481758, 0.38825015]])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oof_preds"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'tgt_recall_immediate_w6_w1': [0.7665772215847516,\n",
" 0.7449301782368718,\n",
" 0.755721139025894,\n",
" 0.7376383517864972,\n",
" 0.7358969623789569],\n",
" 'tgt_recall_delay_w6_w1': [0.7713010551825112,\n",
" 0.7483987155091305,\n",
" 0.762479923893744,\n",
" 0.7533201218330909,\n",
" 0.7380617744590637],\n",
" 'tgt_numeracy_w6_w1': [0.6744632044472918,\n",
" 0.6979758374236879,\n",
" 0.6843529635982466,\n",
" 0.613433188389843,\n",
" 0.6455250481695568],\n",
" 'tgt_output': [0.6740019048012639,\n",
" 0.6457212248202295,\n",
" 0.6716276528760325,\n",
" 0.6487477349922605,\n",
" 0.6790615292186858]}"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scores"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def display_summary(tgt, fold):\n",
" expl = shap.LinearExplainer(models[tgt][fold], X_all.iloc[folds[fold][0]], \n",
" feature_dependence= \"independent\")\n",
" shap_vals = expl.shap_values(X_all.iloc[folds[fold][1]])\n",
" shap.summary_plot(shap_vals, X_all.iloc[folds[fold][1]])"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5490ef6ba5b949ab8e663035f18c6795",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='Target', options=('tgt_recall_immediate_w6_w1', 'tgt_recall_delay_…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.display_summary(tgt, fold)>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(display_summary, tgt = widgets.Dropdown(options=list(y_all.columns), \n",
" value=\"tgt_recall_immediate_w6_w1\", description=\"Target\"),\n",
" fold = widgets.IntSlider(min=0,max = 4, value=0)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"[2091, 2091, 2091, 2090, 2090]"
],
"text/plain": [
"[2091, 2091, 2091, 2090, 2090]"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[len(i[1]) for i in folds]"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
"def display_local(ind, fold):\n",
" i = folds[fold][1][ind]\n",
" ret = shap.force_plot(\n",
" expl1.expected_value, shap_vals1[i,:], X_all.iloc[i].values,\n",
" feature_names=X_all.columns\n",
" )\n",
" return ret"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "95371d897e2b428ab9eb53216f130ac6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(BoundedIntText(value=0, description='ind', max=2089), IntSlider(value=0, description='fo…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.display_local(ind, fold)>"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(display_local, ind = widgets.BoundedIntText(value=0, min = 0, max = 2089), fold = widgets.IntSlider(min=0,max = 4, value=0) )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lgb, \n"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"display_local(0, 0)"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.28265923, 0.71734077]])"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"models[\"tgt_recall_immediate_w6_w1\"][0].predict_proba(X_all.iloc[i].values.reshape(1, -1))"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.71734077, 0.39115908, 0.1855288 , 0.58615041])"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oof_preds[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shap.force_plot(expl1.expected_value, shap_vals1, X_all.iloc[folds[0][1]])"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x684 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"shap.summary_plot(shap_vals1, X_all.iloc[folds[0][1]], plot_type=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Belgium 1549\n",
"Greece 1414\n",
"Sweden 1284\n",
"Italy 1119\n",
"Israel 948\n",
"Spain 933\n",
"France 932\n",
"Denmark 695\n",
"Germany 651\n",
"Austria 526\n",
"Switzerland 402\n",
"Name: country, dtype: int64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.country.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"df_new = pd.read_csv(\"../data/cogdec_share_w7_w6_3tests_all_pred.zip\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
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" <th>isced1997_r_W5</th>\n",
" <th>Cog_func_Fluency_W5</th>\n",
" <th>age_int_W6</th>\n",
" <th>adl_W6</th>\n",
" <th>adl2_W6</th>\n",
" <th>bmi_W6</th>\n",
" <th>casp_W6</th>\n",
" <th>Cog_func_Recall_Immi_W6</th>\n",
" <th>Cog_func_Recall_delay_W6</th>\n",
" <th>euro1_W6</th>\n",
" <th>euro2_W6</th>\n",
" <th>euro3_W6</th>\n",
" <th>euro4_W6</th>\n",
" <th>euro5_W6</th>\n",
" <th>euro6_W6</th>\n",
" <th>euro7_W6</th>\n",
" <th>euro8_W6</th>\n",
" <th>euro9_W6</th>\n",
" <th>euro10_W6</th>\n",
" <th>euro11_W6</th>\n",
" <th>euro12_W6</th>\n",
" <th>gali_W6</th>\n",
" <th>iadl_W6</th>\n",
" <th>iadl2_W6</th>\n",
" <th>maxgrip_W6</th>\n",
" <th>mobilit2_W6</th>\n",
" <th>mobilit3_W6</th>\n",
" <th>mobility_W6</th>\n",
" <th>Cog_func_Numeracy_W6</th>\n",
" <th>Cog_func_Orient_W6</th>\n",
" <th>phactiv_W6</th>\n",
" <th>sphus_W6</th>\n",
" <th>sphus2_W6</th>\n",
" <th>isced1997_r_W6</th>\n",
" <th>Cog_func_Fluency_W6</th>\n",
" <th>OUT_cog_func_W7_W6</th>\n",
" <th>OUT_Recall_Immi_w7_w6</th>\n",
" <th>OUT_Recall_delay_w7_w6</th>\n",
" <th>OUT_Numeracy_w7_w6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Austria</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>72.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>29.065928</td>\n",
" <td>35.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>24.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>74.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>29.065928</td>\n",
" <td>37.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>24.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>76</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>28.075043</td>\n",
" <td>34.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>24.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>-1.0</td>\n",
" <td>-3.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Austria</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>59.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>21.410946</td>\n",
" <td>43.0</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>23.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>10.0</td>\n",
" <td>61.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>21.048048</td>\n",
" <td>43.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>22.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>10.0</td>\n",
" <td>63</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>21.048048</td>\n",
" <td>38.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>21.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>-1.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Austria</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>59.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>23.198315</td>\n",
" <td>47.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>52.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>61.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>24.151672</td>\n",
" <td>48.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>50.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>63</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>23.589836</td>\n",
" <td>48.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>57.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Austria</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>30.477219</td>\n",
" <td>36.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>22.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>83.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>30.443840</td>\n",
" <td>33.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>17.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>85</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>25.536703</td>\n",
" <td>31.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Austria</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>87.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>42.240589</td>\n",
" <td>39.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>37.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>88.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>44.077133</td>\n",
" <td>39.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>34.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>8.0</td>\n",
" <td>90</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>36.763420</td>\n",
" <td>32.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 country gender age_int_W1 Cog_func_Recall_Immi_W1 \\\n",
"0 1 Austria 0 NaN NaN \n",
"1 2 Austria 0 NaN NaN \n",
"2 3 Austria 1 NaN NaN \n",
"3 4 Austria 0 NaN NaN \n",
"4 5 Austria 1 NaN NaN \n",
"\n",
" Cog_func_Recall_delay_W1 adl_W1 adl2_W1 bmi_W1 casp_W1 cusmoke_W1 \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN \n",
"\n",
" euro1_W1 euro2_W1 euro3_W1 euro4_W1 euro5_W1 euro6_W1 euro7_W1 \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" euro8_W1 euro9_W1 euro10_W1 euro11_W1 euro12_W1 gali_W1 iadl_W1 \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" iadl2_W1 maxgrip_W1 mobilit2_W1 mobilit3_W1 mobility_W1 \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN \n",
"\n",
" Cog_func_Numeracy_W1 Cog_func_Orient_W1 phactiv_W1 sphus_W1 sphus2_W1 \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN \n",
"\n",
" isced1997_r_W1 Cog_func_Fluency_W1 age_int_W2 Cog_func_Recall_Immi_W2 \\\n",
"0 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN \n",
"\n",
" Cog_func_Recall_delay_W2 adl_W2 adl2_W2 bmi_W2 casp_W2 cusmoke_W2 \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN \n",
"\n",
" euro1_W2 euro2_W2 euro3_W2 euro4_W2 euro5_W2 euro6_W2 euro7_W2 \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" euro8_W2 euro9_W2 euro10_W2 euro11_W2 euro12_W2 gali_W2 iadl_W2 \\\n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" iadl2_W2 maxgrip_W2 mobilit2_W2 mobilit3_W2 mobility_W2 \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN \n",
"\n",
" Cog_func_Numeracy_W2 Cog_func_Orient_W2 phactiv_W2 sphus_W2 sphus2_W2 \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN \n",
"\n",
" isced1997_r_W2 Cog_func_Fluency_W2 age_int_W4 adl_W4 adl2_W4 \\\n",
"0 NaN NaN 72.0 0.0 0.0 \n",
"1 NaN NaN 59.0 0.0 0.0 \n",
"2 NaN NaN 59.0 0.0 0.0 \n",
"3 NaN NaN 81.0 0.0 0.0 \n",
"4 NaN NaN 87.0 1.0 1.0 \n",
"\n",
" bmi_W4 casp_W4 Cog_func_Recall_Immi_W4 Cog_func_Recall_delay_W4 \\\n",
"0 29.065928 35.0 6.0 4.0 \n",
"1 21.410946 43.0 7.0 7.0 \n",
"2 23.198315 47.0 10.0 9.0 \n",
"3 30.477219 36.0 6.0 4.0 \n",
"4 42.240589 39.0 6.0 4.0 \n",
"\n",
" cusmoke_W4 euro1_W4 euro2_W4 euro3_W4 euro4_W4 euro5_W4 euro6_W4 \\\n",
"0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 5.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 2.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"4 2.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" euro7_W4 euro8_W4 euro9_W4 euro10_W4 euro11_W4 euro12_W4 gali_W4 \\\n",
"0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 1.0 1.0 \n",
"2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n",
"\n",
" iadl_W4 iadl2_W4 maxgrip_W4 mobilit2_W4 mobilit3_W4 mobility_W4 \\\n",
"0 0.0 0.0 24.0 1.0 1.0 3.0 \n",
"1 0.0 0.0 23.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 52.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 22.0 1.0 0.0 1.0 \n",
"4 0.0 0.0 37.0 1.0 1.0 3.0 \n",
"\n",
" Cog_func_Numeracy_W4 Cog_func_Orient_W4 phactiv_W4 sphus_W4 sphus2_W4 \\\n",
"0 5.0 4.0 0.0 3.0 1.0 \n",
"1 5.0 4.0 0.0 3.0 1.0 \n",
"2 5.0 4.0 0.0 2.0 0.0 \n",
"3 5.0 4.0 0.0 2.0 0.0 \n",
"4 5.0 4.0 0.0 4.0 1.0 \n",
"\n",
" isced1997_r_W4 Cog_func_Fluency_W4 age_int_W5 adl_W5 adl2_W5 \\\n",
"0 5.0 8.0 74.0 0.0 0.0 \n",
"1 3.0 10.0 61.0 0.0 0.0 \n",
"2 4.0 10.0 61.0 0.0 0.0 \n",
"3 1.0 7.0 83.0 0.0 0.0 \n",
"4 1.0 6.0 88.0 1.0 1.0 \n",
"\n",
" bmi_W5 casp_W5 Cog_func_Recall_Immi_W5 Cog_func_Recall_delay_W5 \\\n",
"0 29.065928 37.0 7.0 6.0 \n",
"1 21.048048 43.0 6.0 6.0 \n",
"2 24.151672 48.0 10.0 10.0 \n",
"3 30.443840 33.0 7.0 4.0 \n",
"4 44.077133 39.0 5.0 4.0 \n",
"\n",
" euro1_W5 euro2_W5 euro3_W5 euro4_W5 euro5_W5 euro6_W5 euro7_W5 \\\n",
"0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" euro8_W5 euro9_W5 euro10_W5 euro11_W5 euro12_W5 gali_W5 iadl_W5 \\\n",
"0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 1.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 1.0 0.0 0.0 0.0 1.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 1.0 1.0 \n",
"\n",
" iadl2_W5 maxgrip_W5 mobilit2_W5 mobilit3_W5 mobility_W5 \\\n",
"0 0.0 24.0 1.0 0.0 1.0 \n",
"1 0.0 22.0 0.0 0.0 0.0 \n",
"2 0.0 50.0 0.0 0.0 0.0 \n",
"3 0.0 17.0 1.0 1.0 5.0 \n",
"4 1.0 34.0 1.0 1.0 5.0 \n",
"\n",
" Cog_func_Numeracy_W5 Cog_func_Orient_W5 phactiv_W5 sphus_W5 sphus2_W5 \\\n",
"0 5.0 NaN 0.0 4.0 1.0 \n",
"1 5.0 NaN 0.0 3.0 1.0 \n",
"2 5.0 NaN 0.0 3.0 1.0 \n",
"3 5.0 NaN 0.0 5.0 1.0 \n",
"4 5.0 NaN 1.0 4.0 1.0 \n",
"\n",
" isced1997_r_W5 Cog_func_Fluency_W5 age_int_W6 adl_W6 adl2_W6 \\\n",
"0 5.0 6.0 76 0.0 0.0 \n",
"1 3.0 10.0 63 0.0 0.0 \n",
"2 4.0 10.0 63 0.0 0.0 \n",
"3 1.0 9.0 85 0.0 0.0 \n",
"4 1.0 8.0 90 2.0 1.0 \n",
"\n",
" bmi_W6 casp_W6 Cog_func_Recall_Immi_W6 Cog_func_Recall_delay_W6 \\\n",
"0 28.075043 34.0 7.0 6.0 \n",
"1 21.048048 38.0 7.0 4.0 \n",
"2 23.589836 48.0 4.0 4.0 \n",
"3 25.536703 31.0 4.0 3.0 \n",
"4 36.763420 32.0 4.0 3.0 \n",
"\n",
" euro1_W6 euro2_W6 euro3_W6 euro4_W6 euro5_W6 euro6_W6 euro7_W6 \\\n",
"0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 1.0 0.0 0.0 0.0 0.0 0.0 1.0 \n",
"3 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" euro8_W6 euro9_W6 euro10_W6 euro11_W6 euro12_W6 gali_W6 iadl_W6 \\\n",
"0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 1.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 1.0 1.0 0.0 0.0 1.0 1.0 1.0 \n",
"4 0.0 1.0 0.0 0.0 0.0 1.0 2.0 \n",
"\n",
" iadl2_W6 maxgrip_W6 mobilit2_W6 mobilit3_W6 mobility_W6 \\\n",
"0 0.0 24.0 1 1 3 \n",
"1 0.0 21.0 0 0 0 \n",
"2 0.0 57.0 0 0 0 \n",
"3 1.0 14.0 1 1 7 \n",
"4 1.0 28.0 1 1 6 \n",
"\n",
" Cog_func_Numeracy_W6 Cog_func_Orient_W6 phactiv_W6 sphus_W6 sphus2_W6 \\\n",
"0 4.0 3.0 0.0 4.0 1.0 \n",
"1 5.0 4.0 0.0 3.0 1.0 \n",
"2 4.0 4.0 0.0 2.0 0.0 \n",
"3 1.0 4.0 1.0 4.0 1.0 \n",
"4 5.0 4.0 1.0 4.0 1.0 \n",
"\n",
" isced1997_r_W6 Cog_func_Fluency_W6 OUT_cog_func_W7_W6 \\\n",
"0 5.0 6.0 NaN \n",
"1 3.0 7.0 NaN \n",
"2 4.0 10.0 NaN \n",
"3 1.0 5.0 NaN \n",
"4 1.0 7.0 NaN \n",
"\n",
" OUT_Recall_Immi_w7_w6 OUT_Recall_delay_w7_w6 OUT_Numeracy_w7_w6 \n",
"0 -1.0 -3.0 NaN \n",
"1 -1.0 0.0 NaN \n",
"2 2.0 2.0 NaN \n",
"3 1.0 1.0 NaN \n",
"4 0.0 0.0 NaN "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_new.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 25211\n",
"1 17242\n",
"Name: OUT_Recall_Immi_w7_w6, dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df_new[\"OUT_Recall_Immi_w7_w6\"] < 0).astype(\"int\").value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True 29547\n",
"False 12906\n",
"Name: OUT_Numeracy_w7_w6, dtype: int64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_new.OUT_Numeracy_w7_w6.isnull().value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test = train_test_split(X_all, test_size = .2)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3340, 5084, 2669, ..., 6010, 5205, 6035], dtype=int64)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.index.values"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ID\n",
"3340 0\n",
"5084 0\n",
"2669 0\n",
"3519 0\n",
"3375 1\n",
" ..\n",
"1939 0\n",
"9598 0\n",
"6010 0\n",
"5205 0\n",
"6035 1\n",
"Name: tgt_recall_immediate_w6_w1, Length: 8362, dtype: int32"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_all.loc[X_train.index.values, \"tgt_recall_immediate_w6_w1\"]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"del X_train, X_test"
]
},
{
"cell_type": "code",
"execution_count": 90,
"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>gender</th>\n",
" <th>adl2</th>\n",
" <th>chronic2w1</th>\n",
" <th>cusmoke</th>\n",
" <th>euro1</th>\n",
" <th>euro2</th>\n",
" <th>euro3</th>\n",
" <th>euro4</th>\n",
" <th>euro5</th>\n",
" <th>euro6</th>\n",
" <th>euro7</th>\n",
" <th>euro8</th>\n",
" <th>euro9</th>\n",
" <th>euro10</th>\n",
" <th>euro11</th>\n",
" <th>euro12</th>\n",
" <th>gali</th>\n",
" <th>iadl2</th>\n",
" <th>mobilit2</th>\n",
" <th>mobilit3</th>\n",
" <th>phactiv</th>\n",
" <th>sphus2</th>\n",
" <th>symptoms2w1</th>\n",
" <th>age_int</th>\n",
" <th>casp</th>\n",
" <th>maxgrip</th>\n",
" <th>Recall_Immi</th>\n",
" <th>Recall_delay</th>\n",
" <th>bmi</th>\n",
" <th>Numeracy</th>\n",
" <th>adl_0</th>\n",
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" <th>adl_3</th>\n",
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" <th>adl_5</th>\n",
" <th>adl_6</th>\n",
" <th>chronicw1_0</th>\n",
" <th>chronicw1_1</th>\n",
" <th>chronicw1_2</th>\n",
" <th>chronicw1_3</th>\n",
" <th>chronicw1_4</th>\n",
" <th>chronicw1_5</th>\n",
" <th>chronicw1_6</th>\n",
" <th>chronicw1_7</th>\n",
" <th>chronicw1_8</th>\n",
" <th>chronicw1_9</th>\n",
" <th>chronicw1_10</th>\n",
" <th>iadl_0</th>\n",
" <th>iadl_1</th>\n",
" <th>iadl_2</th>\n",
" <th>iadl_3</th>\n",
" <th>iadl_4</th>\n",
" <th>iadl_5</th>\n",
" <th>iadl_6</th>\n",
" <th>iadl_7</th>\n",
" <th>mobility_0</th>\n",
" <th>mobility_1</th>\n",
" <th>mobility_2</th>\n",
" <th>mobility_3</th>\n",
" <th>mobility_4</th>\n",
" <th>mobility_5</th>\n",
" <th>mobility_6</th>\n",
" <th>mobility_7</th>\n",
" <th>mobility_8</th>\n",
" <th>mobility_9</th>\n",
" <th>mobility_10</th>\n",
" <th>sphus_1</th>\n",
" <th>sphus_2</th>\n",
" <th>sphus_3</th>\n",
" <th>sphus_4</th>\n",
" <th>sphus_5</th>\n",
" <th>symptomsw1_0</th>\n",
" <th>symptomsw1_1</th>\n",
" <th>symptomsw1_2</th>\n",
" <th>symptomsw1_3</th>\n",
" <th>symptomsw1_4</th>\n",
" <th>symptomsw1_5</th>\n",
" <th>symptomsw1_6</th>\n",
" <th>symptomsw1_7</th>\n",
" <th>symptomsw1_8</th>\n",
" <th>symptomsw1_9</th>\n",
" <th>symptomsw1_10</th>\n",
" <th>symptomsw1_11</th>\n",
" <th>isced1997_r_0</th>\n",
" <th>isced1997_r_1</th>\n",
" <th>isced1997_r_2</th>\n",
" <th>isced1997_r_3</th>\n",
" <th>isced1997_r_4</th>\n",
" <th>isced1997_r_5</th>\n",
" <th>isced1997_r_6</th>\n",
" <th>Orientation_0</th>\n",
" <th>Orientation_1</th>\n",
" <th>Orientation_2</th>\n",
" <th>Orientation_3</th>\n",
" <th>Orientation_4</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ID</th>\n",
" <th></th>\n",
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" <td>0.355654</td>\n",
" <td>0.077799</td>\n",
" <td>0.044457</td>\n",
" <td>0.270539</td>\n",
" <td>0.022228</td>\n",
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],
"text/plain": [
" gender adl2 chronic2w1 cusmoke euro1 euro2 euro3 euro4 euro5 \\\n",
"ID \n",
"1 0 0 0 0 1 0 0 1 0 \n",
"2 1 0 1 0 1 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 0 0 0 1 \n",
"5 0 0 1 1 0 0 0 0 0 \n",
"\n",
" euro6 euro7 euro8 euro9 euro10 euro11 euro12 gali iadl2 mobilit2 \\\n",
"ID \n",
"1 0 0 0 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 1 1 0 1 \n",
"3 0 0 0 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 0 0 0 \n",
"5 0 0 0 0 0 0 0 0 0 1 \n",
"\n",
" mobilit3 phactiv sphus2 symptoms2w1 age_int casp maxgrip \\\n",
"ID \n",
"1 0 1 1 0 0.789107 0.411225 0.355654 \n",
"2 1 1 1 1 0.744717 0.405214 0.350455 \n",
"3 0 1 1 0 0.742187 0.502772 0.263357 \n",
"4 0 1 1 0 0.671641 0.440041 0.521101 \n",
"5 0 1 1 1 0.677906 0.514273 0.432457 \n",
"\n",
" Recall_Immi Recall_delay bmi Numeracy adl_0 adl_1 adl_2 adl_3 \\\n",
"ID \n",
"1 0.077799 0.044457 0.270539 0.022228 1 0 0 0 \n",
"2 0.043807 0.043807 0.390668 0.043807 1 0 0 0 \n",
"3 0.083795 0.059854 0.339307 0.035912 1 0 0 0 \n",
"4 0.011580 0.011580 0.285134 0.046320 1 0 0 0 \n",
"5 0.081816 0.116880 0.257695 0.046752 1 0 0 0 \n",
"\n",
" adl_4 adl_5 adl_6 chronicw1_0 chronicw1_1 chronicw1_2 chronicw1_3 \\\n",
"ID \n",
"1 0 0 0 0 1 0 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 1 0 0 0 \n",
"4 0 0 0 0 1 0 0 \n",
"5 0 0 0 0 0 0 1 \n",
"\n",
" chronicw1_4 chronicw1_5 chronicw1_6 chronicw1_7 chronicw1_8 \\\n",
"ID \n",
"1 0 0 0 0 0 \n",
"2 0 1 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"5 0 0 0 0 0 \n",
"\n",
" chronicw1_9 chronicw1_10 iadl_0 iadl_1 iadl_2 iadl_3 iadl_4 iadl_5 \\\n",
"ID \n",
"1 0 0 1 0 0 0 0 0 \n",
"2 0 0 1 0 0 0 0 0 \n",
"3 0 0 1 0 0 0 0 0 \n",
"4 0 0 1 0 0 0 0 0 \n",
"5 0 0 1 0 0 0 0 0 \n",
"\n",
" iadl_6 iadl_7 mobility_0 mobility_1 mobility_2 mobility_3 \\\n",
"ID \n",
"1 0 0 1 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 1 0 0 0 \n",
"4 0 0 1 0 0 0 \n",
"5 0 0 0 0 1 0 \n",
"\n",
" mobility_4 mobility_5 mobility_6 mobility_7 mobility_8 mobility_9 \\\n",
"ID \n",
"1 0 0 0 0 0 0 \n",
"2 1 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"5 0 0 0 0 0 0 \n",
"\n",
" mobility_10 sphus_1 sphus_2 sphus_3 sphus_4 sphus_5 symptomsw1_0 \\\n",
"ID \n",
"1 0 0 0 0 1 0 0 \n",
"2 0 0 0 0 0 1 0 \n",
"3 0 0 0 0 0 1 1 \n",
"4 0 0 0 0 0 1 0 \n",
"5 0 0 0 0 1 0 0 \n",
"\n",
" symptomsw1_1 symptomsw1_2 symptomsw1_3 symptomsw1_4 symptomsw1_5 \\\n",
"ID \n",
"1 1 0 0 0 0 \n",
"2 0 0 0 1 0 \n",
"3 0 0 0 0 0 \n",
"4 1 0 0 0 0 \n",
"5 0 0 0 1 0 \n",
"\n",
" symptomsw1_6 symptomsw1_7 symptomsw1_8 symptomsw1_9 symptomsw1_10 \\\n",
"ID \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"5 0 0 0 0 0 \n",
"\n",
" symptomsw1_11 isced1997_r_0 isced1997_r_1 isced1997_r_2 isced1997_r_3 \\\n",
"ID \n",
"1 0 0 1 0 0 \n",
"2 0 0 1 0 0 \n",
"3 0 0 0 0 1 \n",
"4 0 0 0 0 1 \n",
"5 0 0 0 0 0 \n",
"\n",
" isced1997_r_4 isced1997_r_5 isced1997_r_6 Orientation_0 Orientation_1 \\\n",
"ID \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"5 0 1 0 0 0 \n",
"\n",
" Orientation_2 Orientation_3 Orientation_4 \n",
"ID \n",
"1 0 0 1 \n",
"2 0 1 0 \n",
"3 0 0 1 \n",
"4 0 0 1 \n",
"5 0 0 1 "
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_all.head()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"def predict_by_cntry(country, tgt):\n",
" Xal = X_all.loc[df.country == country].copy()\n",
" Xtr, Xte = train_test_split(Xal, test_size = .4, stratify = y_all.loc[Xal.index.values, tgt].values)\n",
" ytr = y_all.loc[Xtr.index.values, tgt] \n",
" yte = y_all.loc[Xte.index.values, tgt]\n",
" \n",
" mdl = LogisticRegression(penalty = 'l2', solver= 'liblinear')\n",
" mdl.fit(Xtr, ytr)\n",
" preds = mdl.predict_proba(Xte)[:, 1]\n",
" \n",
" score = roc_auc_score(yte, preds)\n",
" print(\"AUC=\", score)\n",
" expl = shap.LinearExplainer(mdl, Xtr, \n",
" feature_dependence= \"independent\")\n",
" shap_vals = expl.shap_values(Xte)\n",
" return shap.summary_plot(shap_vals, Xte)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"model_id": "45f39961e975449b8055f5e72cd5becd",
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"interactive(children=(Dropdown(description='country', options=('Austria', 'Belgium', 'Switzerland', 'Germany',…"
]
},
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},
{
"data": {
"text/plain": [
"<function __main__.predict_by_cntry(country, tgt)>"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(predict_by_cntry, country = widgets.Dropdown(options = df.country.unique()),\n",
" tgt = widgets.Dropdown(options =list(y_all.columns)))"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"def predict_train_test(tgt, tsize = .4): \n",
" Xtr, Xte = train_test_split(X_all, test_size = tsize, stratify = y_all[tgt].values)\n",
" ytr = y_all.loc[Xtr.index.values, tgt] \n",
" yte = y_all.loc[Xte.index.values, tgt]\n",
" \n",
" mdl = LogisticRegression(penalty = 'l1', solver= 'liblinear')\n",
" mdl.fit(Xtr, ytr)\n",
" preds = mdl.predict_proba(Xte)[:, 1]\n",
" \n",
" score = roc_auc_score(yte, preds)\n",
" print(\"AUC=\", score)\n",
" expl = shap.LinearExplainer(mdl, Xtr, \n",
" feature_dependence= \"independent\")\n",
" shap_vals = expl.shap_values(Xte)\n",
" return shap.summary_plot(shap_vals, Xte)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
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"interactive(children=(Dropdown(description='tgt', options=('tgt_recall_immediate_w6_w1', 'tgt_recall_delay_w6_…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.predict_train_test(tgt, tsize=0.4)>"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(predict_train_test, tgt = widgets.Dropdown(options =list(y_all.columns)))"
]
},
{
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}
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"layout": "IPY_MODEL_f7e42f0f33eb4d51be50e2cfed419c74",
"outputs": [
{
"ename": "TypeError",
"evalue": "Singleton array array(True) cannot be considered a valid collection.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mC:\\proglang\\Miniconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 250\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 251\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 252\u001b[0m \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 253\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-96-305c99d8cb5a>\u001b[0m in \u001b[0;36mpredict_by_cntry\u001b[1;34m(country, tgt)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict_by_cntry\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcountry\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtgt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mXal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX_all\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountry\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcountry\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mXtr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXte\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXal\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m.4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstratify\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mytr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_all\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXtr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtgt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0myte\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_all\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXte\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtgt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\proglang\\Miniconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[1;34m(*arrays, **options)\u001b[0m\n\u001b[0;32m 2207\u001b[0m random_state=random_state)\n\u001b[0;32m 2208\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2209\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstratify\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2210\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2211\u001b[0m return list(chain.from_iterable((safe_indexing(a, train),\n",
"\u001b[1;32mC:\\proglang\\Miniconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py\u001b[0m in \u001b[0;36msplit\u001b[1;34m(self, X, y, groups)\u001b[0m\n\u001b[0;32m 1771\u001b[0m \u001b[0mto\u001b[0m \u001b[0man\u001b[0m \u001b[0minteger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1772\u001b[0m \"\"\"\n\u001b[1;32m-> 1773\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1774\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStratifiedShuffleSplit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1775\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\proglang\\Miniconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m 575\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 576\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 577\u001b[1;33m \u001b[0mn_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 578\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 579\u001b[0m raise ValueError(\"Found array with %d sample(s) (shape=%s) while a\"\n",
"\u001b[1;32mC:\\proglang\\Miniconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_num_samples\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 141\u001b[0m raise TypeError(\"Singleton array %r cannot be considered\"\n\u001b[1;32m--> 142\u001b[1;33m \" a valid collection.\" % x)\n\u001b[0m\u001b[0;32m 143\u001b[0m \u001b[1;31m# Check that shape is returning an integer or default to len\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 144\u001b[0m \u001b[1;31m# Dask dataframes may not return numeric shape[0] value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: Singleton array array(True) cannot be considered a valid collection."
]
}
]
}
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{
"data": {
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t7lyHUrS0t3XN08N0b1EdadbdL7I/0pui5Wyt9SqgHSdCvmKMmaG1HoW7QZ9sjFmcWBp+BIwD9tZaK+DrwHeNMTOMMdYY87YxZgaAMeYxY8zTxpjQGLMIuBj44MY0VGvtAccAPzDGLDLGNAHf65TtNOBSY8x/jDGxMeYp4CbguO7KXFs7jTExTrgdX3XI8cm2XqexsXGD07X43ebZ2PQndvQrdxBrIa6663pqzZ0ZlnoFuYzCC2DUUI+vHFLH4e8dxh7bZzjywDoa6hS7TE2xzajKM3UI2Lh8y0xuR0kwapTcyZV1+yJcPApKgedhlep48nfuInew+9PV7RMBRBEqcet0nGa1KEnO2SZCyYkhVUlb6544E+NT3I0/PU6sQRGWWFXdxJL2ebELOq49qKuYKHd1tiZbOaIlRtUIFShHk8SdnDflvRWHUYVqV1C5vLLEiGvEypooC5vaLWtaK7ezVixVZMy6SrBd6ussgNbz5pDuNM+QAr58IIxc0+8uoTEHw+u73aVO/jCMG17ZMKIB3r8jbJPEogSe+x29c2vYd3sYWufcK9U39t2nkDnzKDjnKNh3B6jPuGPTARy7H5x9VOVUh9evPWZFKThyHzh4t66//atOhiljXVzM9d+AwHfbLz2+cg3KP5b6jMuTTpE7/hA46UMuPiebcnE8w+pdPNC1pzhrzLB6+OgecMpH8e78vmun78ER+8CHd6+04efHOffViAa4+Dj47Qmw+xS3/rNjaNhju9o2A1zyJXjXNq6Nl36Zhu0mds3Tw7TQc3rTPfTTJKZlOM5CcWDyd0qyf4bWujp/CtgGZ5mop2KVqUFr/W6c22VXnBdAARs3QYWrMwPMrdo2p1OeKcABWutvV23zgW4tIz1o5zXAD7TWE3HWpN2AQzf8FAYO1x6V4+UlrcxYEK/7kbE8PMZaMj48e+Zw3jGq+8nmTv3MUE79jJvTZdmKkDseaOH1OQXemFvAFsGLY5R1VfqA8jystS4QVwHWEgDKS26oscXrLBjKysVTeIFi30+OZurOjSx+o41pew5j4cwWHrpiDvlVJboLwgUqbiqlOqwfKorxyuInEUyq7ILyFLbsjUoCacvXxYuBwCMqxQQdh7t4mqCzd0HZDtVSdoaU40ziqniRsgWq1uZQfo6LO/ZU20s6W1iqrR8Wm8SPuHTFYqI6xIxNKVSpI8KooxSXP+4orfP+inyy1LqJKvYjqyxqSNYFaDZk4FsHoz65K+qzf4BFTbDrBBhdh33wJWjMor77EdTsxU5svHsK3Po/eH0hTB0LM+ZCWxEO2tndTHMBjBnmgkmfmgWjG+GUQ+GTe8P85c71sv1458J46nU46cMwIrmZXQO8+jb88i73Xfn8B9yNfdxwF1syPOlOSqGLB4ktvLEItp/g6r78RBd42pB1wbY9paXduWfKAmZIHfznp93n/cnnXMBvY1JHuT6Aljxk0659DWsZ3r7vDvDGlV23TxgFS/4Iq9ud+OiMUnDFV93SHTeeVru+x7aw4sbu807bGp7/Ve22Z3+55jaDcxe9uCmM/ML6sDliWlZqrb8CzNZafwL4X7JrmjFmaef8iaWlFeeqeb3zfuDPwF+BTxljVmutPwbcvZHNXAoUgcnA7GTblE553gSuM8b8vIdlrrWdxpiFWuu/A18ChgN3GmPW4BjeMuktY2MupZh+WgMH/b6Vh98oR48kxImQ6bjZW377yRzLWuHwnVNrFCydGTUi4ITPuhEP/32qlUuuXNZxu/PKI4zKo3eSY6otI0Ay0sji+XR4SQ79wlYccPiYLvVtu7sTS6Mn5tjloNFc8dmnaFpU6LDCqDBc63N6+favarZQ00bVKb+KY7xkZJBSEFuLj4LYxYXEgUKFFj+xMqlYEXsWL+rsbikLF8uarq7LURYCtsqmoqrKqN5ednsp0ruPJbfbaLLvHU/9x6dBFBPOW03+8XnY+c3UH787wcQhFB6ZS+mVpRQfeJ34+QUQxqTfN5H6Sz9G9ODrhG+txB+aI17cDItWExy4Ld6YBpgyktKFD6KG15H+1v74U0Zi24uwsg3GDV3zBHmvruFG3Zmj39ezfJ2ZNMYtAAfv5pbObD/BWSLWRipwC7gbczVDu7fErJW1CYzuqBYUQ7tJd3bLrA+e171gEQYtm2VOEWPMCq31r3CWh52BW4ArtNbfNMbM11oPAw4AHjTGtGitrwQu1lrPA17CxYOMMMa8gLNKNAHNiZXi+5ugfbHW+hbgR1rrF3EurQs6Zfs18Eet9RPAf3EP4zsDyhhjuim2J+38A/B7nGXpsxt7Hpub3vbg7zzO4+HZUe3dWHnUmuYVX9orQ11mwz2d792rnokT0ixZWmLZkhI33FgZAeHbSl1dhIOF9x48lKNPHM/yxUUyWY+GoT37SR3yrancfvYrxKVkmK7nudFEcRLrUq6nLEo8hY0rQsoLo44PQFXNXaMCjyCjGDZ1CAde9T48XzH75tm8edtcyMekGgLi1SXahreh9k8xtW0yK59aSri4QG6rHE2PLMJWBU+WU155hJCn8Bp8VCmC9nI8iyU1pYHRR09l9b1vEq8oULf7KOKmPMXXVpAa18C4X+xLdo8xFGcsI15dILvXOPyhaVSqexnkj20gs+fWNduyh25H9tDt4PSuIiH11b3Xer1TN36+Zl3l0m7oryAMaAZGHEs1m3MitEuBb+FiQE4AzgIe1VpvBazCuVkeSPKejRvBcycuUHcRcAbwAnAi8EvgHOBV4EZgAx91ajgNuDwpczVu9NDh5Z3GmAe01icCPwfeievPX6KbUUYJPWnnA0k5TbjRRUIVu49PhEiNOrLON5HcTRvSbJRgKTNh6xQTtnZPhKNGBjz40GpKhZA3XosJqQxVDrGkfNjhXXV8/OjRTHyHeyodOXb9boBT9x7BKX/di+ZlBUZNriPMx0RhzMv3Lebpa2ZTaA5dTIxSbkSLtaiUz/Btcux96jRev+Mthm/bwB4nbEeqLmDuA/Npmd/GtCMnkRlS25YdT9mRHU/ZsWbb3Xc7o9+Oh+3RpW2lpiKrn11Os1nG2E9PITMuR9urTfg5n9y0yisTbGyJmosEQzOVg3/6nrWed7Dv+PW6ToIgCNUMuBlx+xvJRHwPGGN+thmr3aAPfXPPiAsw/ierWbAaOpqcWBU8BduOVNz+hQZ2Gtd7L0yMopgXp7dRLFqefaKZcePTfPxTvTussXlJnru++zzLZ7XgKRg6NsPeJ05luw+N22R1lEXLYYcdtsnKFARhg+kVk0isvlDT13v2+n5veukPU84PWJLh0nvihnb3OzbHt3/O9xuZfMFqFpZHB3qK3bZWPPfNIZuhdvB9j12TFyLuuc/mifZvHJPlmOv32Sx1CYIg9CcGjWjRWp+Fc0l1x2af0E1r/TQwFTi1u4Dk/sDwzLrzbCzpQDHre0P464wiD8wMed9kn6/usxkqFgRB6Pf0e8NKF8Q9NDjZoA99t+tCpleNb3r4U3DgpEGjewcU4h4ShC2KXnIPfbGTe+i6fq9ien1GXGHg8PwXA762K+w8Cm7/uAgWQRCELRvVaen/yF1HWC+uPFi+MoIgCELfIHcgQRAEQRiADJT3DVUj7iFB6Oc8NT9k/BUh5/17LW/9FQRBGACIaBGEfszvngnZ+0+woA1+9FT3c+kIgjBYGXgxLSJaBKEfc9IjfVTxC2+AOsItJ1/RR40QBGGwIaJFEIT1Z5fTK+krH4JQLDyCsKVhOy0DAREtgiBsPLlP93ULBEEYBMjoIUEQNh4xtAjCFsjAiGOpRiwtgiAIgiD0C0S09AJa63OStzcLgiAIQp9gUTXLQEBEiyAIgiAI/QIRLVsgWutUX7dBEARB6O8MvHlaBkUgrtZ6K+AqYD9gMXARcDUwxRgzV2t9AnAasA3wBvA9Y8wDybHnAe8HngS+khR5pTHmh1XlHwr8HJgIPArM6lT/SOBi4BAgCzwCnGqMWZzsnwtcCxwA7AUcD/x5E14CQRAEQej3DBZLy81AESdK9gU+X96htT4R+B5wDDAcOBu4XWs9ter4/YB5wNbAYcBZWuv3Jce/A7gd+BkwDLgMOKGqfAXciRsmvxMwCWgGbunUxhOAbwMNwN82wTkLgiAIgxiJaemHaK0nAAcC3zXGrDbGLAF+XJXlG8D5xpjpxpjYGPMPnCXks1V5ZhpjfmeMCY0xTwLPAzrZdzTwlDHmpmT/AziRUubdyfJ1Y0yTMaYNOAM4MGlbmauMMc8ZY6wxpn0TXoIuNDc3S3qApavpSf50Ot0n9Upa0pLumhZ6zmBwD41P/s6r2vZmVXoKcLnW+rKqbQHwdtX6wk5ltgKNSXoCMLfT/jlV9U4BMsBirXV1njzOnVSup3MZvUZjY6OkB0y66wQpPTm2WCxukjasb72SlrSku6Z7j4FhXalmMIiW+cnfibh4lXK6zJvAD40xt25E+R/qtG1Kp/JbgRHGmHgt5axtnyAIgiAMega8aDHGvJ3MmXKh1vp4IAecU5XlEuA8rfXrwHRcoOy7gWXGmFd7UMWfgHO11kcDtwL7A58ATLkJOHfSpVrr84wxy7XWo4EPGmMk2FYQBEHoFQZKHEs1Az6mJeFzQB3OFfMYTlwAFIwxV+FG9vwRWIlzI/0A6NGwY2PMbOAo4FxgFfAt3Mik8v4YOBx3rZ/RWjfjRiLtv7EnJQiCIAiDCWXtQHn3Y8/RWn8IN0InZ4wZfBdg4Lzwc9CjftE1psWevm4D6t133w3AYYcdtoEVH9F1m719w8oSBKFXTCIFdVJNX5+xV/Z708uAdw8BaK13xd2oX8DFm/wE+MsgFSyCIAiC0C8ZLO6hEbi5VFpw7qEZuMnkBEHYFKy8tq9bIAhCJwbiPC2DwtJijHkEmLrOjIIg9IyfHQNn3ezS246CYcP6tj2CIAwKBoulRRAGJJ+Zsu48vcKZR7oYFns7zPpDHzVCEIS1M/DePSSiRRD6MX8+MuB7e7j0tKE9C8IVBEHor0gPJwj9nAsPDLjwwL5uhSAIWxoDJY6lGrG0CIIgCILQLxBLiyAMIlpfXMGqh+bjpUrEE3s0f6IgCP2WgWdpEdEiCIOElffO45WP3gfAMGD1j4bDYfDStTN5/revEjSk2P/SvRi7+8i+baggCMIaGJQz4goyI+5govW/C5hz8B0U22IsiiIBFo98naJp1FDy+YjmYVlQCqxl55N3YI/jp5JpCGhZVSKT81m5KmT6c61sOy3HuPFpShEMqRfvsiBsInrFJNKmvlHT19fZy/q96UVEy+BEPvRBQmH6EubsfiPtNoXFJ0ZRwiPEo7kuSzEd0DQkRex7lFIexWyGQn0WFJTqs6zK1pEPAtrTKUBRUDB3aAMhijHDPW784WjqsyJeBGEjEdHSQ8Q9JAgDgNJljxJd9wRqnylwyE7kb3uZ1sfn0zanmPzI08RAER9Q+EBDW5FFuRSBBcIYP7Y0j0wsLkDQkscL0ijfJ10KKQUBaRREMWEqxYJVlpN/tZzrzxrdV6ctCMIgQ0SLIPRTbHM7PD+P4tX/JbrhGUBRem4x4ZXPAYo0inYaKZHGw1LqNMGUhyXM+h3rsV9rMVFAMZ3GtxYVx7QTs6SuniGxxS+FLE75PPd2ROqMVYwf7nPPl3LstJV0KYIg9B7SwwhCf2LGHLh/OvaFeXDT/xJHXw7IAh5e8pYRNz+DxcPiBExIjCJMLC0WCFF4kSX0FIWUj/UU6bYCxbqMq8talLWEvk+soCWd6bDC1Mcx7QQsDzyCYsSby2HnX7dDXQCeEz91WcU/Pu3zgYl+57MQBGEzMBDnaRHRIgj9hRsfheMuS1YCVNIhOfuJEwoWRVz1s87QRoEMcSJospRoJU1EQKwg11pk1fAc1lPEnsIGHkEUYYHWTJrI81CAb2F4vkBbOpXUA8t9j3bPA2tdI9Jeh2ABaCtY9v9zjO9bdhkNDxzlM6pu4HWigiBsPkS0CMKWQEs7tBbgqZlOmKxqc9s9BdkA2ko12VXVE5SzrECMjwJS5CmRARQBEFAgTyNlaZMlZDUpSiknMvzIUgJK2TQ2cREpgMDvsKwUfJ9S4JMJI9oCn0gpppZCpvtpwrqyBaaTIPEUKIgsPLcERl8R1ez+5LZw46EebzVDxrj4VIMAACAASURBVLcMz3oMy9aWsSrv4giHZRVLWi0NaahLifARhJ4x8H4rIloEoS95fg4ccj4sbep+f2y7CBaHBZzbxadAkXogsYoQExMSESRiJg2dRI4C0sUYZUNKgUch46PiuKYGL4a6fIGmuiz5xMIyJAyJPUXB92mMLVlP0aKUEy2RhSgG33PVBapD9HTHHbOh4bLqOiPeMRSePtbjzP9Ybn3NsrLg9gxNQ1MRsh58bKri9ZWWQyYrLtrPQ62lDkEQBhYiWnqA1roBOA84AhgNzAO+CkwAzgSmAK3AXcC3jTGtyXHfAL4FjAJWA9cbY87SWk8G5gAnAGcAY4B/AScYY5ZsthMT+p6PX7BmwbJWvKqUxSMiojLDrYtasYn1xZIipJSImHZSlEe9q9jS2hDgWYtVChXFWKUoplPEgU+mWMTPpGtq9mMLPihrycYxLa5C8JUrNooh669VsKyJN5rg43fEPL6gdntT0f3Nx/DXma7t05dadhtj+dwOIloEoTsGYkyLTLDQM64B9gY+CAwBDgcWAU3A53ATjL4/Wc4B0FpvB1wIfMwY0wi8CydqqjkO2A+YCMTATb19IgDNzc2S3kLSdtmGCJaueISAi0WJ8CgREBGg8PCJSBOSI08rASEBHk5nFNIeKEWU8p2rKIwJShGpYgmvFNKWzZCOYoLIuXYiIO97bvi0glWeD8UI4tjFs5TdRMXYWYk2YB6opW3xujMlLGvfMj5HSUt6Y9JCz5HJ5daB1noMsBjYyRjz0jryngIcZ4zZS2v9DuAl4AvAP4wxLVX5JuMsLQcZYx5Otk0FXgfGG2MWdCl80yIf+pbCz26Ds29ez4OcE4hkbFBMihKNLrgWnzxZIpx1xAJ50oSkaSNNE/VEeMTJE1h7zmf5mBxhKiBfn+2ooZQOWDFsCPm6XEc5y7Np5jc0dFhQXsqmmJ/JVJpVn6oE4vrKBeaWm9tDq4sH3HekxwkPxry5uuv+reqgPg2zV8G7RsK/P+szIjfwniaFQUevfIlb1Ldq+voGe0m//7GIe2jdTE7+zuy8Q2t9MHAusD2Qwd1JlgAYY97QWh8DnARcrbWeAZxvjHmgqoi53aQnAL0tWoQthbOOhA/vDnc8Ae/ZDl5+C866GUrrsjbEkEiPEsOwyeggj5AUxQ5XUTlgN0YlYsUmR7nt6UJMEFn8uIQFCnVpYs9DWUshWxEkCvBiWxM/MqEYMb9KsxDbiu02WHff+PXd4JCJcMcseGkZ7LsN/HRfn1xK8dqXFQtaLPe8EZPxFUdMUxQixfhGRSmyLGyFcfWQ8vt9HywIvcZAdA+JaFk3c5O/04CXyxu11mngTlxMyrXGmPbE0nJ6OY8x5nbg9iTv14C/aa2r30Y3GZhdlQZ4e9OfgrBFs8c73ALwUQ2nf7JrnvnL4W4DJ/0eknlY3EwsfvK3dtI4Hyd6ygKlvK2OEs2df/bWlVbKJNs99w4iFcdOwOAsLUFca6DLWYtvLVFZyMS2yghU1R4FU4bCo5/1mNDY1SP98e26nm4mUEwZpjh1j675U75i4pCuxwiCMPAR91AP0FrfigvA/SLwJrAtbjav6cDnjTG3aK13BG4HssaYyVrrd+ICdP8NtOPiV64ERgBb4dxD/wI+k+z/IzDUGHPQZjgl+dD7Kyub4Rd3YZ+cBQ/PBDwioMQQyu6iduo75mqJUDTTmLiI3LY8KdpIY1G0NaYI0x6FjEdLfZq2IXWgFM1D6imk07RmMijlwnoLQcC8xnqKvo8FWgKfvFI8V+eGVxMoSPuQCiCAVKB48DMyuZwg9IBeMYk0q2/X9PWN9lf93vQilpae8WXgxziRMRInXL6Kc/1crLX+A/A0cEuSFyAN/BDYMVmfBRxpjMlrrcvl3gT8BxiLEzfH9vqZCP2b4Y3w02Oc9aOtAE/OJvzCjfDWKsAS4RNTEQlFMlhUh2AByFIiUh5LRzQAyejk0IKFXHM7xWxAlBpGYC2NhQKFIGB1NkNbOsXwYolZDSliLxFIyll8gkDxuyMyfHmvan+RIAjCpkUsLX1AVSDuNsaYvnAHyYc+gIgLJQo7nA9zVsA2w4m//WHaL3yMcEWBllJAkQyFZLK5MivJ0TQi1xEgWwo8Vje64N0w5bF0/Bhi34mf9lTAioYGIiD0PRZmMixPp7AKDnl3mguPH7G5T1kQBhq9YgFZrb5T09cPsb8US4sgCH2Ll0mRe+PHNdvqv7kvAK33zGLOYffU7Cv3Ypn2kHwuIAw8WuoqXUHs+eRa2ynUZ2lLZ2jKuFFFPmCtJfYUQ6OY7aakRLAIgrBZEdEiCAOY+o9NZdIDn2TuoXcRltzIoRIBWSJKEYRBmub6AJsE3wL447IcdPJ27PqJ8cyc3szCOXm8xhT5gmXfA4a6GXGLlpFDJVZFELZs+r1hpQviHhqcyIc+CAmbijw58no33T7Q/pE63nftkTx2xtO89fhSALbefys+fM37+rKZgjAY6SX30Omd3EO/6PcqRiwtgjBICIam2XP+MSy9cRYzml4j3CtLbqscB9+wH/kVBVCQHS6BtIIwUBiI87TINP6CMIhIj61j/Om7EO6VrdmeHZERwSIIwhaPWFoEQRAEYQAilhZBEARBEIQ+QiwtgjDImPe3ebSd2U6cgkXDF7PVvmP7ukmCIPQKYmkRBKEfs/KFFfzva/8jbrKwzPLPz/2L1Qva+dNJz3LDl57mredXduRdsqTECSfN5XPHvcEPf7yAYnFdL3EUBEHoXWTI8+BEPvRByoyLXuDF37zcMROutbB4ymjiRI9YoHl4I+2ZNKuDNG3pNDFQUorMtHpSjQFHH1zHwe/KrrEOQRDWm14xiaxU36vp64fbi/q96UXcQ4IwCMi/2UzYVCI7Ol27Q0EcV3rMWCmKVlFQPoUgwCbvFkoBr86PiD3LkzObmLxDO1ceO5SJw8VYKwjC5kNEiyD0Y+LlrbR95f8Ipy8knjCK/OzVRC0xcUOOzAenMPrSg3jzwhm8dfELWCCf8snWp2jP+uTrUlilyDa3U2jMAeBZSz6TAeVhVeWhzCb/lZRipVK8Otsy6RdtgOKYXT1u+nSuL05fEIS1MBBHD4l7aHAiH3o/Jj7gQuxTcygWIIwyxASEpLEoimQISQGWEI8CaVZTTztpQJFP+7Q2OrHSng0oZN1zS8vwBqJUQOh5LBgzCut55H2PtlSK1iCgJfCxSrHC93gjHdAU+BB4ELjj/++zaT61c6rvLoog9G96RV2sUN+v6etH2Av7vYoRS4sgbOm8NA+OuQReX4JtA5X8S+NRoo6YFOChgDRFInwsHqBoJ0ecpAHa6wNIXD65fEgh41PMZbBKESuwgU+uUGB1XR2R75OKY1oCv+OYhjimKREqxCS+JcWZDxZ5O6/43n+gFMPXdoErD5HuRRD6ln6vUbogvcpmQGt9L/CIMebivm6L0A8ohfDq23DcZfDaAmgvAmBJozp+spaywaz6UUoBCkuEokCARaE68ipUJxtbMR1QrM+4uJUwwqLI5Qu05HLkiiVCz8PD6RNw7qEO4qQwa5nd7PHth2JnfVHwuxnw+xkh+0+Au4/wqE9L7IsgCBuPiJbNgDHmI33dBmEL5B8G7ngS5i0DMwtKEbQWKmKgE4oQiLD4hDQAHmnaiPGIEmtKiE+JgBJpPBQ58kRkSRMS4uEVY0opJyCiwMOmarsAP4poyWU75kJIxzFj29pZnMsRKngpk6luUAWrwE82WAvKSaVH3oaGy2IgpvxOaE/B2Hr43Pbwye18dhhhuWu25YrnLasLMHEIfGdPj4MmidARhI1hIMa0iGjpRbTWKWNMqa/bIWyB/O1JOPyi9TzIiRlFiCLEkk4cQSVKZIjwKJIhxk/iWwIiPMJk3QOy+RKtw9IdQ569qBuBpGo7utDzSFlLylrGRxFzPUXe9yoiRSnIJAIjESzdEZX/Wni7BS42cLGJyHhQqJoC5uUVcN/cmLsOh8OminARBKFCvxctWuu5wNXAB4E9gTnAMcC7gB8Do4Fbga8ZY0Kt9R+Bg4BhwFvAT4wxtyRlfQX4EbC7MWaJ1noM8DxwjjHmWq21As4ETgbqgOuBXYD/GGPO01rvDzwEfCkpZzTQqLV+FHjIGPMTrfXkpI0nAGcAY4B/AScYY5b01nUStjCue3QDDlKU54NUFIhwc6V4xIDCBwJCQnxiFDEeYRLr4mEpoSD2GLq0QNPoDHE5jiXRGRaIAp9MsUg+41xGJaVYnkkTe67eIWHEUN8nVpaiBVLWuYRQVX6qNQuX7iisYc66K6fHIloEYSMYiJaWgdIjfAEnJIYD04E7gAOAXYGdgY8Dn07yPgbshhMt5wPXaa13BDDGXI0THTdrrVPAzcCDxphrk2M/D5wGHAaMBRYC+3Vqiw98BNg9ybMmjkuOnYgLGbhpA857g2hubpZ0X6cP2pn1x+9YXKSKIibAA7zEjhHhJzLFxbHEyRpAihgPS1Cy+AVL5CtybQXS7XkoRcRKEaZS5LPZjmMCawmqRhjOzKZZnPIpeqrKPaScz6fMJhqbtvc4teV8XpKWdC+mhZ7T74c8J5aWy40xP0/WPwr8HRhjjFmabPs/YL4x5lvdHG+Aa40xVyTrdcDTQBE3p9Zexpi2ZN9DwH+NMecm6wqYC/yxytLyCDDJGDOvqo5H6WppOcgY83CyfyrwOjDeGLNg012dNdK/P/SBwlk3wW1PwIIV0JJfR+byFG8VcdDGSMrPHRZoo4F26ogJiIF20rSS7YgmsUALaSwey7fKUsq47YVsQCkd0J7LUKjLsXzIEErpyvDlufU52tNuUrpHGnNE1VaUlA++D7lkCHS5qd66n/DqAth5NLx3HPzpVVjU5rbnAvjyTnDZB3289bDYCEI/ple+6MvU2TV9/Sj7037/g+r37qGEhVXpNiAqC5aqbY1aaw84D/gMsBWuH6/HuXEAMMa0aa2vBn4FfLksWBLGA29W5bVa67c6tSXGuZ3Wxdxu0hOAzSFahC2Bnx3rFnA+mlIIVz8Idz4FMxfAm8uqMldGAJXXqlG4EN04GSvkATlKtJADLBZoL9taFDWjiNwII4jTKTygPt/OqpQbGl3wPBqjGK9UIu/7jC6FLKoSNCRuIwqRq9T3arrfjAd//hi8a7THa8stobW8a6Ri2gifan51YPkyWJQIFUEQ1sBAES095WjgK8AhwMvGmDixtHT0klrr7XHC5krgQq31fcaYsiiaD0yqyquAbTrVYY0xPbFkTAZmV6UB3l6fkxEGEEpBOgUnf9QtAC3tUAzhwz8GMwtrQ8CN3rH4+BQIyXYManZuIksJkrlZIEOJImmK+BT9itjItIUUsj5h4BGlPEqpgMh3QiIdRgRxxLJcjtj3UUB95OJmpuSLhErR5inasklAr7WkFcSBG98EMCoLv/mgx2e2Vx0iZNrwnlwGESyCsKkYiDEtg020DAFCYCngaa2/iIt7uQc6XEO3Ar82xvxQa50BbtFaH2SMiYAbgYu01rcBLwPfALbewLb8QGv9ItAOXAQ8vJlcQ0J/oSGZGv8pN72PAuKP/hzufQFw9pNy9EqBNKVE0JSDcUukyBLSSpoYVTOyJ/YUXhRjA49SENDWUNexLwycUOksIJRSLEyliFC0o6AYOY+V7/G99wccv2eKS5+NmTQETnt3rSVFEARhUzDYRMv1wIHALJzL6EbgP1X7L8cJmh8l66cAT+IsLz8AbsAFzt4LZJPyngAKG9CWm5K6xwL/Bo7dgDKEQYb3j+9in5oNC5tI77Mt+S/9ifDB2dhQ4asYa2MsbkI4pcCOqSNakiK2llQckU/55OtTlHI+qcjit5doH1pHulAiTPmEQcDquhwKaMznWVlXRwwUfZ8SsCDwKPqJSyjwwFNMGKo494A0ga/41QEiVgRhS2EgWlr6fSBuX5LEyMwDzigPm+7BMZNxgbjbGGP6yh0kH/oAJW4pEi9txZ80DJUEw+bnt/LKUQ8TrS7R/I4hLH5mRUd+CyyeMAKbCJF8Os3SEcOIPI+V2TSlIHBxMgoWBj4rsxmK1jJhWoYj9spx8LSAaSM9vB4E3gqCsEZ65Qe0RP2gpq8fY3/c73+og83SstForT8D/A0XQnAmLpD33j5tlCAkeA1pvIZ0zbbs+Hp2/9/HAXjym09in1lRNSga9/4g33NhvnFMulSikEqRii0h4GMhhrqhPs0hHLJ7hl99cdjmPTFBEDaAfq9RuiCiZf05FfhDkn4R+KgxZmUftkcQesykIycx5y9zoWqalQO/vz3/vGIOCvjQKVPZ/pCxKE9x+10ruecfbi6JyRNT3PKTDQ3fEgRB2DSIe2hwIh/6IOaBjz3IimdXYIGGHYfysQc/tMZRO8uWhZRKlnHjUt3uFwRhk9ArJpHF6tyavn6sPb/fm14Gyoy4giD0kEPuOZjM2Rkyp2U49L5D1jrMeNSoQASLIAhbDOIeEoRBSDDRjfLxAnluEYSBykAcPSQ9liAIgiAI/QIRLYIg0LykwOKZLdhYwp0EYaBgkxerlpeBgLiHBGEQM+/J5dz+zWfJpzLuJYee4uQ/78mQMZm+bpogCEIXxNIiCIOYO77xDESQLRQgiigpj58f+zwv/G9VXzdNEISNRnVa+j8iWgRhgGGtZeUX72TxpF+z6qt3rzFfuAqI3XT/hUyaUi4LvofyFDddMJcX/lsRLstbYw74XSvTLmrmthdKvX4OgiAI3SHztAxO5EMfwCzf/48U/jWPCA+LIsxkyR63C4ufaWH19FWEKY/mSSmaW1Ms32oYceDTVp8j9jzy6TSR52bHHTU+zRlX7khLPmLYOc1E5W+Np5h7ViOThsszjyBsInrFDLJAnV/T129tz+335hbpdQRhAGBnvEX+qGtoHXUWpX/NIUJh3WsTCQoFmq+agXr2LYgsfj4m+0aJoBTjl8KOpRQEhEGA9TzwPFYuKTFrYZGtvr+qIlgSfvvf2neEvrgs5sT7Iy57Ntp8Jy0IwqBDAnEFob8yfzn86XHC254jfuItYtLEpFE00PnBTQEBljqKtJCiaViWlsY0QRxDMSZVLFFMpyikK+8tiiL4yIWrnf+oGgu//F/EuQdZGjOKOasidr6uomr+Pivk/k9L1yIIfc1ANKmLpUUQ+httBdjlmzDhq/DdG/GfeBFFIREmRRQxPhHlLivET6wuECpFa32WbLvFjyzp1gJBvoQC/FIIZXexteRRRGGEKm9TqkPAWAtn3O+sLV+8t7ZrfGAe/PXVsLevgiAIg5AtTrRorR/VWlut9X6dts/SWn+xj5olCH3DG4vcAnDlfTDhBKg/Gl6YR9maogCPGA+Lh6WeZjxiAoqUUOTJEOIRo4g8hVWK5iGpjpcm+lGMV4ooZDKkwhAvckKllEoxqVAkrhIrHVjL9c85V9C/53dt9uflveeC0OfIPC2bj+XAL7TWextjtjgLl9baB6wxJu7rtggDjCiCn/8NXp0Pr8+H/85cS2YLKCxg8fEoERMQEhAnsiVNREhIATfvSqTcc0rk13ZgMZCyMaoU055K0VRfB8CIUsiEYshM368SLhYstIeKUx4oJc2oLS8fwfK2iJF1/kZfEkEQhDJbqmi5CvgCcDRwS/UOrfX+wEPGmKBq23nAvsaYg5J1C5wKfBHYAZgOfBr4FPBtoA74nTHm7KoydgJ+CbwbaANuBs41xpS01pOBOcBXgO8A2wKTkracCUwBWoG7gG8bY1qTMhuA84AjgNHAPOCrQCNwAzDeGFNM8jYCC4GPGGP+s8FXTujfXHA7/OBPPcxcbWkJsQRYIopkifGTfYocBYqkyJMiDj0iDzLtIW1B2h1sYdXIYR3PYblSiWWeR+R5+MCEMGKOjSklggdPQRyD53P5MxaC7p/gjvqb5ZGjN/RCCIKw8QwM60o1W5x7KKEVOBf4mdZ6Q6fmPBY4HCcW8sA/geE4wXEgcLrW+r0AWusxwL+A24GtgfcAB+MESTWfS45tBJYCTcm2YcD7k+WcqvzXAHsDHwSGJO1ZBNyfnOMnqvIeDby1OQRLc3OzpLfU9Etv0XOqjZCVzilDe00uF9OiyBDiE6OsJWUtqWJIDBRTHmG2EoAbA7HngVI0hG4c0rvbC86Y0mGhURB4yZxV3XeMryxfz3OXtKQHaVroOVvcPC1a60eBh4ALgOeBG40xF2utZwE/AebSM0vLp40xtybrJwMXAsPKLh2t9VPAzcaYS7XWpwMfNcYcWFXmkcBFxpipVZaWDxhj/r2Wtp8CHGeM2SsRQouBnYwxL3WT95ykzR9O1p8E/s8Y88v1vmjrz5b1oQsV/m7g8Isg7MnQ4bKvOsCSw+IRkaJAjjYaifApkKaZespupCayFEgTedAyLEUx47Fk6+G0NdQTpd1PanlDAy25LOAEzLz6HCjFY0PrKPp+EqyroC6AtO+ETDfC5cSd4fcf2lKNuYKwRdErJpG31E9r+vpt7Nn93vSyxfYoxphIa30G8Cet9TUbUMTCqnQbsKRTDEobzmICzr3zPq119dzlCujskJ9bvaK1PhhnEdoeyCT5lyS7Jyd/1xSUcA3wA631RJwVZjfg0LWekTDwOVTDC5fArEWw7Vg4/nIXdOsraGrvlNkmUqRITB0RASEpimQJiAiIaKaOajdSmogC4MegYkvLsDpy+SKZQomm4Y2Ushnq2ttpzaQp+j4rMmlQitWeR9FLDLOJQFHFmFe+nmH7P3Yf2nXlIRLPIgjCpmWLFS0Axph7E4vIuVWbWwBfa50xxpRnuNp6I6t6E2e9WZdo6OidtdZp4E7gDOBaY0x7Ymk5PckyN/k7DXi5c0HGmIVa678DX8K5re40xizbqLMQBgbbT3ALwH8vrGzPF+HpWXD5vfCXxwGSINwUYCmRpkSWskixQIoSebIdj3FFAsK0IvYVrUOzWM/t8aylvqWdpnSaKJtiZEsryzJpmupzrAh8XqtyHwGgFDuPhXeO9BiSjlldrN09JgfeGtxGgiBsHgaiSX2LFi0J3wWeAMrd4ms44fIVrfWVwHuBo4BnN6KOG4DvaK2/jAv8LeIsJdsZY+5bwzFpIAusTATLjsAp5Z3GmCVa678CVyRDtd/ExdNgjJmVZPsD8HugHvjsRrRfGAxk0/D+Hd3yp2/DZy6Be58lbkkRkk5MgzFQop16ADJENOM6rxhoTacIs0mQbifXcOwrPKCuUKQtl2VoGLHc94mVIlIKorjiBgp8bv2sEzJf3w0ueKq2qc98XgSLIAibni01ELcDY8x04M84FwrGmGacdeI7uEDY04DrN7KORcABuEDZucBK4A7gHWs5pgU4CbhYa90CXE6nkU7Al3FxOf8CmoG/AVtV7X8Ady9pAh7emHMQBhlKwf99G9V8E37parzDdklES5EC2Y45W3xChtCMRRHhoap+8UNWFyG2WCDyPTw3khkvdgbFdADHHJzjjcAHFFiVROnC+yap/2fvzuPkKMrHj3+qe2b23twJJISEIxwGOeQBVORGBTHKIYocgoggigcSwS+iAuIBogg/RRFEREAQRDFiBAEDgoAUchkgIJCLI+dms/fMdNfvj+7Nzg6bZCfZ2Zmded6v17A1fVRX77CdZ56q6maHCUkALtzX54CpkPRg21HQ/RXDVs3aNaRUqVXifVrKbiButYkHHt9rrf3eMB5WP/QK5FZ34N5cy7JdftHv8tSDTwujaKWRrO/RUZ8EYwgMvD59DC4RBRihMbSNbiYwhs7aGvw6j+/dtisHXrGGB5fmVGjAXT56WM9NqQpXlIhikflev2v9NHf+iI9cyj7TUsniu/7uRXRfGqU2ixnbgDdzS2qP22XdshBo+Pxe7O6+zLuXHc/kE7dn2oemwBk1dBxeB87hnCPre3Q2RjeU88JoWvTOMgqA+740ivoa4unNsP+2I6FXWSmlmRY1ZETkCWB74Bxr7fXDfHj90Ctc1x+fJ1zUSt1n3oXX9PZbHc2ZMwcXwsKLkqTrUnTX1eU8V8hRs10TX/vFTEy8rK3bcYNNM6nJ8PHdUm+rTym1WYoSUSw03+93rZ/u/m/ERy4atFQn/dCr3Jw5cwDI3j2JhU+10l1bQ5hIgHP4DT5fn/PuErdQqapSpKDlB3lBy9dHfNCi3UNKVbEjr96L9521PbW+I+my7HzAWM67a59SN0sppQakndNKVTHjGfY6aRv2OmmbUjdFKTXEKjGlrpkWpZRSSo0ImmlRSimlKlClzBjKpUGLUlVk0S9e5NWrXsDVdcMX6krdHKWUKojOHqpO+qFXoUW/XMD8sx7H9D5msQbaxtay58V7MHrvSSyd38aM946lx8GcP6xm0pYpPnzMODzP4Jzj989kaOlyfGbvFEm/8r7BKVVCRfmDesVc1u9av507d8T/4WrQUp30Q69C9zb8FnpCAmNwnqG7zqezKUFrYw2doxoIkwna6mpZOWYMmChQGTU+wWU/2459rmrj30ui2/uPrYdVF40q8dkoVVE0aBkk7R5SqoJlFq+lbe5C3rxrCXWd3QC01ybpbEwR+B6hgXRDHWFNKn4Gkb/uJnPGGFpWBbzVEkQBS7x8dafjXwszvHd6slSnpZQaBB3TopQqe2tOvJ2eO18gCAxd6QRrqaeD+nW38k5lQto8Q5Aw+A6CZALnRRMJ69MZWpxbF6CkfY9Lr3wLaOw7gDG8sCzkvdOH/9yUUtVNgxalKoQLQlZPu4zs6x2AwQdShGQYjQNCDOmkT3ddktrukCDjWD2mFuf1fRtLhiHNHR2saG4i9Dwyvsfrr6VhfAiety6Y+e+yDPD2xwMopcpHJY4D0Pu0KDXCZS++i8wu36Jj6gXw+qp4mG10ucrikyAbb2nIJP11+/mBI11fQzKbhTAar+KizehJJsj40eUhBExvlUEIYchPHoUVHSGPLA2YcV2W6ddkuevlYF3dj7zuuH1BQDasxMumUqpUNNOi1EixdCWc91t4bAFsOwm3NgP/XoiPpenNLgAAIABJREFUIcQnZAwJkvh0E5CgnQZ6qKeeDJ3U4AAvdPSGFm2NSbxMhlRnFFg4Y+ioq6U+nSGRDcgmfDo9j7dqati1O83yhM+bqSQ4B84x7WdpukzfGJgj73I8fnzAV+Y5Hn0jOkbCBCz/gs+Y2srrW1eq/FXe313FBi0icgOQtdaeFr9fCFxgrb2plO0aiIicAJxrrd2t1G1Rw+DVt+Dp1wAHjfVRvrMzA5NHw9ynoCcDt/8L1nZC6KC9CzwD7emcOpYDSQwG1k1iNhgCwBDg4+PwyZAmhU9ISEiyO8CF0Do2RWdDEi90eD0ZQt8jW5siGYS40OE5R7fxWJlM0eN7GGB8ELI6DOnxPEgYugIDyf4XxQ/83tGa7XufdfD+3wfYTyVoTzvO/kdAVxauONBjQoMmepVShSlq0CIi84D3ABkgAF4Dvmutvb2Yx90c+cHOcLDW3gzcPFzHUyX0oz/D7BuGpCpDuK6UpR4vHmob4BOQIMDHkSBJSBM9tOGRICTte/TU9v/TN/GtD0Ig7fsExpAEpqTTtIQ+K2pSGCBIeJBKREHUAF/icgOWXk8uj35u88uAldEEJu54KaT7bA1alCqmSpw9NBxXje9YaxuBccANwC0isv0wHFep8nPRbUNUUe6friOgbt3lySfAIyBD35TkFNl1QY7D4AcOE483CY0hU5Oks7aW9uYmgkSCTKIvqBmVDXDAyoRPNpUETBTd9PSNYRmM3oCFeNe27sL2V0qpYfuqY63NAtcSZXd2BxCRehG5XEReE5HVIvK33IBGRJIicr6ILBCRNhF5RUSOidcdIiKPi0iLiKwQkVtFZOJQt1tEnIicJSJWRDpE5F8ispWInC0iS0RklYh8N2f7A0UkKyLHx+3tEJEbRaRZRK6N27tIRI7O2ecUEfnfULd9fdra2rRcqnJDcWbcePSlOAI8wCNJX3eSA7pI0GFSeGlwBpLZkFQ6wAsd3fV1dDQ2AJBOJGjqSVOfyYBzdHgeKzzD6tq8+7LkZlU2eJPKgdd1dnSuK5f8c9GylktYLpbe2xz0virBsAUtIpICzozfvhT/vA7YCXg3sAXwOPAXEem9Ol4CnAgcCzQDBwAvx+t6gLOACcA7gcnAlUVq/onAkfGxuoEHgDHAdsDBwGwReW/O9j5wYNyunYHDgMeAPxFlnL4PXC8i9UVq7wY1NTVpuVTlP30dRud87Ek/6mopWJhT9kjSjiFLCHTTiAHqSJOkmyyGTmqpJRtNffYMmN7RMNFFwDiHcY6sMbTXpPCBmiCkPpPh2foassYQhCFkAwgCcGG0c+Ci1wZF5zdzXN+SiXUwaVwZfS5a1nIJy2rwhmMg7jdEZDbQRDS25TRr7bMiMh74JDDNWrsMQEQuAr4C7CMijwBfAD5hrX02rmtp/MJa+3DOMd4SkcuA64t0Dj+y1i6N23gH8APgQmttCDwjIs8AewH/ytnnG9baTmBxPLanwVp7d1zHjcDPgRnAM0VqsypH++wALesZC766DdZ0wLZbgP0f3PYITGiCx1+GpjpYtgb+9vS6zaOht4n4Hiw1JMjQwShyB5ukyLCWJsCRwlGbyNJVlyTVE5Cu8cE5OhuirqWGzk5aGhrWzQYCSIaOLdIZnmxuAM+nbza1g+S6RqzzyR3gdy/Rz0e3i34+e4rPr54L6UzDmXvoeBaliq0SbzgwHEHLd621l4jIGOBXRJmJXwHbxOufFZHc7ZPAVKKsRgN9WZl+RGRP4HvAbkA90aWzcaBth8CbOeVOYHkcsOQuyw2bA2vtirz1a3vfWGs743PWUFv1GdsUvQBk++iVb3UbPPYSvGcHzLOLCU6/Hl5agcMjQ4q+qMLgcHRTS4oACOgBOpINGKC+M0tNd5Y1o1IECQ8vG4AXUpNOY8K6dXfIDYEp6QxPmrxskINpox2Leoj+8oxhTA3c8pEEO/8ry8X/inqP3rsl/Omo6DLjGcNnd/VRSqlNNWxTnq21LSJyGvCKiHwUeDReNSPvH3gARMQAHUTZiJfz1wO3AncAx1pr14rIh4E5xWm9UmVibBN8aM+ofMBM/AU/wnWl8V5dgamroXO7y4lvB0eGJOl+d611uJxQ2zhI16YIapIYwA8dfhiuC1gg6jpKOBdNvfZy0ioGnvxsHeMaPB5eGlX6vq2i/b753gTfzO0sVUqVRKWMY8k1rDlaa+1q4MdEGZKVwC3A1SIyBUBERovIUSLSaK11RF0ol4nILiJiRGSKiLwzrq4ZaAXaRGRr4OvDeS5KlQtTl8LMnEJi2/HU/eDw+EZzvRmNvgRxOymS3SGJdEA6YVjbnOrXFQRvn8UcdT3RN34lDMGFfOfQBOPi+6y8bytvXcCilFLFVIorzZXAlsCngM8CC4B5ItIGPEc06Lb3SvsN4PdEA1jbgAeJMi8ApwOnxcvvBMr23i9KDZfG8w6g8buH4G07Gm/r0SRTUdjRQi1dpABDsjsacJtJephsFhNEU5qznkfg+9T19IBzmDDE4Zixax3rwpk4innv1hqkKFXuKnH2kHEbnKqoKpR+6FUmvaqbeRNuxZko2HAG3ppSTzbp0VNfQ7o2RU9tilWjmskkEzjj4TyPbs/jlHOmsO/vc+6pYuCBM+o5aPvkeo6mlCpQUSKK581P+l3r3+G+MuIjF/26pFQVSI2rpWb75rctN0BNZ5RZ8YOAyVOSBH6C0PPIGkPzxBR7v6uBs/ZN0jtHeuuxHvtOr9gngChVMVzeqxJUXaZFRM4Hzl/P6sOttf8czvaUSHV96AqA9gWtPDzzLnCQ9aF1Qgo3uZE9vrQzM0/abt12Pd0hzz/bgZc0zHxnA4lE9OVsyZqQpa0he07xSSVG/Bc2pcpJUf6g5udlWmZWQKal6oIWBWjQUrUya9O8fuurPLv6eczMJLNmzSp1k5RSRQpa/muu7Het38V9ecQHLdo9pFQVSTanmH76TpiZOh5FKTXyaMe0UkopVYEqZcZQLs20KFUFZl6Xpf7HWVa0ZTe+sVJKlSkNWpSqcObyLM+vga4QJl4DnT0auChVDSpx9pAGLUpVmYb/V+oWKKXUptGgRSm1cc++Cubo6PXBb5e6NUqpQajEO+Jq0KKU2rjdZveV730OXlpYsqYopaqXBi1KqcLt+NVSt0AptRGaaVFKKaWUKhG9T4tSSilVgSplxlCujWZaRGSuiJw7HI3JOeaJIrJwOI+plFJKqfK20UyLtfbw4WjIhojIccAXgN2Aemvt29otIp8DvgpMBl4GzrbWzstZfwBwKfAOYDVwubX2pznr2/OqTBL9fiZZa1cO8flcB7wH2BG4wVp72lDWr5RSSlXKOJZcI6V7qAW4GqgDfpm/UkSOBb4DvB94DvgscLeI7GytXSwi04G7gdOB24C9gL+LyFvW2jsArLWNeXXeDIwpNGARER9w1tpwA5s9C9wOnFFI3UoppVQ122jQIiLzgPuAy4CfAkcCtcBbwPm9/+jHmYxLgJlACMyx1n46XrcL8CNgT6ATuBn4lrU2E6/fmygo2Ql4Grg3tw3W2nvi7Q5cTzOPBW6y1j4dv/+FiJwHnAJcDHwIeNlae0u8/jERuQP4PHDHAOc8DjgG+Pggfj/TgdeA04BzgO2AafHvZ0DW2qvifT+5sfqVUkqpTVN5mZZCZg+dQpSh2Nla2wwcAjwPICK7AvcAvwK2BKYCN8brJgIPAncSdd28hygj8n/x+lHAXKLgYSxwNlEwUeh55H86Bth9A+u9nPX5Pg2sIMrODNbxwMFAU7xv2Wpra9NyFZZzpVKpgvZd34C+cjgvLWt5pJfV4BnnNjy+OCfTshT4BnAq8Ki1NpuzzdXABGvtsQPsPxv4kLX24JxlxwCXWmu3F5ETgO8D06y1Ll7/XeAEa+30vLoOBO7LH9MiIicTZXIOA54h6na5CnjAWnuoiOxA1G10GnArsA/wVwYYHyMiBlgA3GytvWiDvxz6ZVoOsNY+tLHt8/a9AciWYExLJQ4qV+thLn/7s4b+vONcAGbNmjXISo5++zJ35+Y0SynVpygpkSfNz/td6/d0Z4741EshY1puAiYBVwAzROR+4Fxr7f+A6cBT69lvG2BfEVmTs8wAflzeCljUG7DEXiugXRBldbYg6nYaD9wF3A+sArDWviQiRwMXAVcSBTC/Bj4xQF0HAdsC1xXYhoUFbq+UUkqpAgw6aIkzK5cCl4rIaKLxLdcD+xP9gz1jPbsuIsqOHLGe9a8D00TE5AQu2wy2XXHbXG/bAEQkBbxKNMamd5u7yenuEZHbgXkDVPc5ovE4rxfSBqJxPEoppVRZqMSU+qCDFhE5GGglmvnSBXQAvXnna4DHReQkotk5PrBPPOX4RuAcETkVuAVIE2VmdrDW/g34C1FXztdE5ArgnURdUD05x/aJpiCn4ve18aoea62Lx8VMBl4kyrT8AFgL/Canjr2IBvkmgZOJupL2yTvHiUQDjQeZM980cVDlEf2eXHw+obU2XczjKqWUUiNZIQNxJwG/JZp+/CbRDJkzAKy1zxDN0DkTWA4sBk6K171F1OVyJFFGpgX4I1EXDNbaNcARRF01LUQBzM/zjn0SUaB0D9E/9F3xa1q8vploCnEb0XiUFHCQtbYrp46LgJVx+z4Wr38+7zinEo3duZfiupeo/ScSDXDuGoZjKqWUqiKV+OyhjQ7EVRVJP/QqUpSBuE9+F9618+Y2TSkVKUpE8YT5Rb9r/V7ucyM+ctEHJiqlNu7eb/SVd9taAxalRgCX96oEI+WOuCUlIvPp64rKtchaO3OA7fMfCdDrn+XwWASlCvb+PXWKs1Kq5DRoGYSBApONbN+48a2UKo2VZ8C/5pW6FUqpYgsrZBxLLu0eUqrCudkJJtREnebPnwjjmvS7ilJqZNKrl1JVYPkX9U9dqWpTKTOGcmmmRSmllFIjgn79UkrhQseK3y4gbM8y8bSd8Wr8je+klCprlTJjKJcGLUpViZblPTxw05u89ugqXGcTE2d2sLjxTRon1LD4k/fR9d8WDPDGpU+z+wsfJ3ilBX+7MXgNqY3WrZRSw0FvLled9EOvMm0tGS791HOQCahNp6nt7KappQ0/dHhByA4LV5LI+b9iHGtooAtT4zPh5S+RmDqqdI1XqvIVZfDJI+baftf6fd1nR/wgFx3TolQVuPfWtwhc9Adfk85gfI/2sc0ECZ/Q91jR3EgHCXyyNNKJT0iAT9gTsHKva3CBPg9UKVV62j2kVIXpzjjebHOsas0y98luVv+vi5XPdjMF8IIArze76hl6alPUt3XipwNqyFBPD3WkSRJgMDh8gmXtdB53I3W7jsEctw9mxhYlPT+lVPXSoEWpCvLUGwHv/kU36QCSQcAeLR1MTmdo9Dyc55FN9P+Td75H05o0o7oz+BhCfDqpI0mGeroxGHwCknc8hLsjwH37TrybTscc/A7YYkyJzlIpNRiVOOVZgxalRrila0OeWg4pz/Gxm6OABSDj+4wJAuqzWRoyGQwQ+B5Z38MAfjbAC0M8Y/Cdo4bMuktchiQBaVJkSBDgEeATgAN3ws+Abvjhp2D2kaU5aaVUVdKgpQjiZw+931r7aKnboirXwtaQd90Y0tIDhCFkgdAD4vEnzvFWwmdit6PH98n4HqM6u8AYHBAkE7ieNF4mS9YY2l0tSQIa6cbgSNFNAx34BPQwCp8Woj3j0OZrN8JHBMaPgrFNJfkdKKXWrxJnXGjQUgSFPntIRBywn7X24SI1SVWIlZ2OO14KuPARWNaVsyI0YBz4HiSBdBY6M7yaTDA1lSQFBMCo9g56amswYUgqnaGppZOwxqetMUVzWw8ZfLpJMJ5WaukhQQCEJOgmS4oEaQwBUeDiYMcvRccfVQ/3fAvGN8N2OuZFKVUcGrQoVYZC5/ji/SF/+p+jKws1Pqztgc7senZImL6vVcaDjgAMBJ5Hu+8xNghpTKfBiyYMOs/DT2dp6kgD0FOXpDsIGNvZST1p0qTI4pPEo4YOPELAEWDwyOL1Bi29Wjvh3V+PyqkEnHgA/OIMSOolRqlS0TEtI4yIfBk4E5gCtAA3AxdYawMR2QG4FtgDeA24HviJtdbE+yaAc4FTgInAfOBL1tonB3HcdZkTETkFuAC4Kq6vAfg98Pm4Hc/Eu90rIiFwq7X2tKE4fzVy3fS84+qnC0zuGgM+USwROjCGTt/wRH0dH2zrIPD63+GgubUDgGRPQH1bBuMgRZYEIY10AAm6aKSGNsDh0YVHNg5VNtC2dBauvx/22AbO+lBh56CUUhtQ6fdpWQocDjQDHwVOBU6LA5I5wDPAJOAo4LN5+14c73MYMI4oqLlHRDZlysS0+DjbAXsBxwLHAVhrd4u3+YC1tnE4Apa2tjYtl3l52dpuNokDMkG/mKLHGEwYEmBI+z6BMRAEmO40IY6GtRm8MOpdaqGBGnpIEqzbv5MmIMQjSvOYnP9uUGvnBs9Ry1rWcnE5TL9XJaiqO+KKyOXA1kRZj/uB0dbarnjdZ4DrrLVGRAywFjjCWvtQzv7PAZdaa2/ayHHyMy1XAWOstUG8/nZgqbX27Pzth/aM16t6PvQRqrXHccjvA55cFr3P64wZWOggHUR9SNkAsiE4x+5d3ezTspbGdGbdpqPWtjFmZSujVrdR0xMyelU3fgiGkO15gxrSOZc4x1iW4MXjWwxpTNxdtF7v2AoevCQa46KU2piiRBQPmuv7/ZEe4E4d8ZFLpXcPfRL4KrAt0bmmgMeIuouW9wYssUU55fFAIzAnDih6JYGtNqEpy3sDllgHoNMt1HqNqjE8caLPqi5IeI4a39CZhbaeKJD51N3QmXuT2tDF3ULx+4QPvscurR3s0J0hbTxMzheUZHeaptYOnO/RXe/RGtYwZlUPBkMntXjRbeUwOJpYGadk/fgVxK8cu06DzxwC9TXwkb2iYMWr9ESuUuWtEr+dVmzQIiJTgZuAo4G51tp0nGkR4HVggojU5QQuW+fsvpIosDjUWvvEMDS3Ev/fUpvJGMP4euj9ElaXhHF1CaaPhmN2BOcc7/hVwItrAFw05dkz69IyBkjGgYox/b/KBclEv/eh7+HjSJGhjXqaacMAKTqpYQNdVe5OcNH4GaWUKrZK/irUSHR+K4CMiLwbOCle9xiwGPi+iNSKyDbAV3p3tNY64ErgchGZASAijSLyQRGZXIS2vgXMKEK9qoIZY3jhtARLzvB54lNJnvmsT72LMyA9WVxnmlWYKJYJ+8fF2YRPd1389GbnaOjsoYEeUoREnUQGj5CQBGHeZcL1Zlm+dWxvQ4p3kkqpTVaJY1oqNmix1r4AfBu4C1gDfB34XbwuC3wEeBdRUPMn4LdAOqeK3n3vEpG1wMvA5yjO7+wbwMUi0iIi1xShflXBtmoyyBaGXSf6PHZaTXRpSvpgDItrUrxQkyIRZKOMCIBz+EFAZ1MdXuCo6QloTOeOYTGsZhQhhixJ1jKeDEnCeCSLeeZyWPUbuOiTpThdpVQVq6qBuBsiImcA51hrdyh1W4aBfugVbHl7yN0LsmzbDI891cWTT7Qz5eVV1AQBzhhMHLRs9+IiGruiGUGj2rsY2x51Azmgnk7GsRYAnwxN76ghMSaJf8VxeHttU6pTU6pSFSUN8oD5db9r/cHu0yM+3VKxY1o2RkT2JeqWeRV4J9E9VDY4K0ipkWBio8en94y6fg6YkYKPj+KnX2vn9Rc71z3h2Q9DjOlLGrY21FLXnSWVje7D4hES4OETEo5pom7+10txKkop1U/VBi1EA29/RzRTaAVwO/D9wewoIvOJ7r2Sb5G1duaQtVCpIfLJs6dx+ekv4FzUs20ctI1uoL4zHb93EDpCPBJk8QCDw2EYfcsxJW69UmpTVMo4llzaPVSd9EOvQs8/uoZ7fvMGmTU9+J2tjNuti0P3fyftL7XR8j2Le6MTcHgGdntiFtn7F1Jz8HRSMqXUTVeq0hUlurjf3NDvWn+IO2XERzEatFQn/dCr3Jw5cwCYNWsWAEFHhtfOeIjMsk62/tF7adh1XCmbp1S1KUowcZ/5Tb9r/aHu5BEftFRz95BSKuY3JNn+pkNK3QyllNogDVqUUkqpChRufJMRp2Lv06KUGryeTJaLH87SGk+BVkqpcqSZFqWq3HcfzHJB/LCKbz8G02qzLDxLLw1KjXTOG/FDWN5GMy1KVbkL8p6utWgDjxpSSqlS0q9TSimlVAVylZdo0UyLUkoppUYGzbQopZRSFUjHtCillFJKlYhmWpRSSqkK5CowLTGoUxKRuSJybrEbk3fME0Vk4XAeU6kRxxzd99r6tFK3RimlimpQmRZr7eHFbsjGiMhxwBeA3YB6a+3b2i4inwO+CkwGXgbOttbOy1l/AHAp8A5gNXC5tfanOevb86pMEv2OJllrVw7x+Tigi/43LZxirW0dyuOoCmaO7v9+yerStEMpVZacX15jWowxOwMfA7Zwzn3BGLMTkHLOPTvYOkZS91ALcDVQB/wyf6WIHAt8B3g/8BzwWeBuEdnZWrtYRKYDdwOnA7cBewF/F5G3rLV3AFhrG/PqvBkYU2jAIiI+4Ky1G7uL8gestQ8XUrdSSik10hhjjgV+BtwJHE+UhGgEfgAcOth6BhW0iMg84D7gMuCnwJFALfAWcH7vP/pxJuMSYCZRBmGOtfbT8bpdgB8BewKdwM3At6y1mXj93kRByU7A08C9uW2w1t4Tb3fgepp5LHCTtfbp+P0vROQ84BTgYuBDwMvW2lvi9Y+JyB3A54E7BjjnccAxwMcH8fuZDrwGnAacA2wHTIt/P0oppdSwC8tr9tDFwAecc08bYz4RL3uGqPdk0AodpnMKUYZiZ2ttM3AI8DyAiOwK3AP8CtgSmArcGK+bCDxIFGFNBt5DlBH5v3j9KGAuUfAwFjibKJgo9FzyPyED7L6B9V7O+nyfBlYQZWcG63jgYKAp3ndjbheRlSLyuIgcvfHNh0ZbW5uWK6CcZmCD2TeVSuXsMXBCsBzOUctaroZylZhIFKQAuJyfbuDNB2ac2/j2OZmWpcA3gFOBR6212ZxtrgYmWGuPHWD/2cCHrLUH5yw7BrjUWru9iJwAfB+YZq118frvAidYa6fn1XUgcF/+mBYROZkok3MY0S/mDOAq4AFr7aEisgNRt9FpwK3APsBfGWB8jIgYYAFws7X2okH8fqYTZVoOsNY+tLHt430OAR6J334UuAE4ylr7t8Hsv5kK+p9Elan8MS0A7s5B7TpnzhwAZs2ahbn87Q9JdLNHUs+xUiNeUVIifx51c79r/UdaTyhZ6sUYcy9wk3PuRmPMaufcWGPMicBxzrkPD7aeQq9MNwGTgCuAGSJyP3CutfZ/wHTgqfXstw2wr4isyT0HwI/LWwGLegOW2GsFtu1GYAuibqfxwF3A/cAqAGvtS3E24yLgSqIA5tfAJwao6yBgW+C6AtuwcLAbWmvvz3l7m4gcCpwADEfQoipB+2+h8aRSt0IppQbjS8C9xpjPAA3GmHuAHYAPFFJJQUFLnFm5FLhUREYTjW+5Htif6B/sGevZdRFRduSI9ax/HZgmIiYncNmmwLa53rYBiEgKeJVojE3vNneT090jIrcD8wao7nNE43FeL6QNrC/PPvh9y6oDUpW5hgZovREmfwZ22wYe+UGpW6SUKiPldEdc59yL8WyhDwN/AZYAf3HO5c/a3aCCghYRORhoBZ4lmq7bAfTmlq8BHheRk4hm5/jAPvGU4xuBc0TkVOAWou746cAOcXfIX4i6cr4mIlcA7yTqgurJObZPNAU5Fb+vjVf1WGtdPC5mMvAiUablB8Ba4Dc5dexFNMg3CZxM1JW0T945TiQaaDyrkN9NIeJByfVxWxxwBHAScFyxjqkqVHMjtN9W6lYopdRGOec6gd9vTh2FDsSdBPyWaPrxm0QzZM4AsNY+QzRD50xgObCY6B9irLVvEXW5HEmUkWkB/kjUBYO1dg3RP9yfiNddBfw879gnEQVK9xAFRF3xa1q8vhm4HWgjGo+SAg6y1nbl1HERsDJu38fi9c/nHedUorE791I8E4i6plritlwAnGqt/XMRj6mUUqqKONP/VUrGmH8aYx4a6FVQPYMZiKsqjn7oVU4H4ipVVooSUvxp7C39rvVHrj6+lANxT85btAXwGaLBuRcPth69MimllFIVqMzGtPwmf5kx5g9EvQ4atAw1EZlPX1dUrkXW2pkDbL++wUX/LIfHIiillFIl9jqwayE7aNAySAMFJhvZvnHjWymllFLFEZZPogVjzKl5i+qBo4HHCqlHgxalqpybneg3rkXHsyiliiD/plIdwL+I7vs2aHp1UkppoKJUBSqzMS0HDUU9eqVSSiml1JAzxmw7mO2cc68Otk4NWpRSfPT3Wf68OCq3nwUNtXppUGqkK/W9WYD/Ed1iY0MtcfQ90mejCr25nFKqwtw1vy9gAWj8aenaopSqHM45zznnxz/X9xp0wAKaaVGq6h05t9QtUEoVgzOlT7UMNQ1alFJKKVVUxpgE8HngAKLnA66LqJxz+w+2Hu0eUqoSffMmMEf3vZRSVSc0/V8ldgXRswofAvYE/gBMBB4opBINWpSqRJfc2f+9Bi5KqdI6GjjcOXclkI1/Hkn0MOVB0+4hpZRSqgKV031aiO6AuyQudxlj6p1zLxpj9iikEg1alFJKKVVsLwB7Af8GLHChMWYt0fOHBk2DlhIRkUuAI4CZwEPW2kNL3CSllFIVpAzu05Lry0AQl78K/BxoAk4vpBINWoaYiCSttZlBbPoK8C3gg8BOxW2VUkopVTrOuSdyyi8Dm/RFXYMWQETqgYuBY4BRROmrs6y1/xORecB91tpLcrZ3wH7W2odF5EJgf+A/RA+E+g9wuIjsCvwE2ANoAa4Hvm+tDQCstb+O69pzWE5SKaVUVSmn+7QYY54BbgJudc4t2dj266OzhyLXEWU73g1sATwO/EVEkoPcf3/gTWAqcIw5ZBsRAAAgAElEQVSIjAL+Dvwjru8I4FSilFjJtbW1ablKyrlyl6dSqZw1PRvdXsta1nLxylXiQqIxLS8YYx40xpxhjBlbaCXGOTfkLRtJRGQ8sAKYZq1dHC/ziLIjRwCXsPFMy6estdvmrD8euBTY2lrr4mVnAF+11u6Yd/wLgfcN85iW6v7Qq8FAU5xd3zToOXPmADBr1izM5dm3b6pPfVZqOBUlJfLbabf3u9aftOjYkqdejDFNRNOfPwnsB9zvnPvIYPfXKxNsE/98VkRylyeJMieDsTDv/VRgYW/AEnulgPqUUkqpiuOcazPG3AKsIfp39kOF7K9BCyyKf86w1q7IXykiJwANOe8nD1BHmPd+CTBNRExO4LItfXPUlVJKqaIqszEtBjgYOB44iujf3luAUwqpp+qDFmvtchG5BbhaRL5irX1dREYT3aXv70TzyT8uIj8GuoHvDqLau4kG4Z4vIj8kyuacB1zTu0E8XsYn+gw8EakFnLV24AEGSiml1Mj1BtAO3Ars65x7YVMq0YG4kc8CC4B5ItIGPAccSzT24wrgRaLunaeJApINsta2Ah8gmtK1DLgHuBH4cc5m1wJdwDeIAqSuuA1KKaXUZnOm/6vEjnTOzXDOfXNTAxbQgbjVSj/0Spc/EHfaWFh43bq3OhBXqbJSlJDiN9vc0e9af/JrHyt96LKZNNOiVCXKmSnEtPH9AhalVHUIjen3qgT6dUqpSuXu3Pg2Sik1gmjQopRSSlWgMhjHMuS0e0ipKqfjV5RSw8EY835jzK+MMXPi92KMObiQOvRqpZTCzU4w5+Us75wA00frZUGpSlBm92n5ItGTnq8DPhYv7gKuAt472Hr06qSUAmDWDL0cKKWK5ivAIc65hcaY8+JlLwI7bmCft9GrlFJKKVWByinTAjTRd1f43qnYSSBdSCU6pkWpCvfXl7KYy/tenT1vvy+LUkoV2UPA1/OWfQn4RyGVaNCiVIU74s/93zf8v9K0Qyk1vMrsjrhfBI4yxiwEmowxC4juPP/VQirR7iGllFJKFdsyYK/4NY2oq+jfzrn8Bw5vkAYtSqk+f7Nw+Pf63usN6pQasZxX+vQKgDHGJ3pY4mjn3L+Bf29qXdo9pJTqkxuwAEw8sTTtUEpVDOdcALwEjNvcujTTopRavxWdpW6BUmoTldnsoZuBvxhjrgSWkvPgXufcA4OtRIMWpZRSShXbmfHPC/OWO2DbwVZS
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