Prediction - Deep Learning.ipynb

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   "source": [
    "# Install required packages\n",
    "# !pip install tensorflow\n",
    "# !pip install keras\n",
    "# !pip install keras-tuner\n",
    "# !pip install shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31f332eb",
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#import tensorflow.compat.v1.keras.backend as K\n",
    "import tensorflow as tf\n",
    "#tf.compat.v1.disable_eager_execution()\n",
    "from tensorflow import keras\n",
    "\n",
    "import keras_tuner as kt\n",
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1eb45b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "npca_X_train_data = pd.read_csv('data/impv/without_pca/X_train.csv')\n",
    "npca_X_test = pd.read_csv('data/impv/without_pca/X_test.csv').values\n",
    "npca_X_train = npca_X_train_data.values\n",
    "npca_y_test = pd.read_csv('data/impv/without_pca/y_test.csv').values\n",
    "npca_y_train = pd.read_csv('data/impv/without_pca/y_train.csv').values\n",
    "\n",
    "\n",
    "X_test = pd.read_csv('data/impv/X_test.csv').values\n",
    "X_train = pd.read_csv('data/impv/X_train.csv').values\n",
    "y_test = pd.read_csv('data/impv/y_test.csv').values\n",
    "y_train = pd.read_csv('data/impv/y_train.csv').values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db3043d",
   "metadata": {},
   "source": [
    "### Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab45cd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(X_train, y_train):\n",
    "    count = 1\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Dense(64, input_shape=(X_train.shape[1],), activation='relu', name=\"input_layer\"))\n",
    "    model.add(tf.keras.layers.Dense(64, activation='relu', name=\"hidden_1\"))\n",
    "    model.add(tf.keras.layers.Dense(64, activation='relu', name=\"hidden_2\"))\n",
    "    model.add(tf.keras.layers.Dense(1, name=\"output_layer\")) \n",
    "    \n",
    "    # Compile the model\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(), loss='mse')\n",
    "    \n",
    "    # Train the model\n",
    "    history = model.fit(X_train, y_train, epochs = 200, verbose = 0, validation_split=0.2)\n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c9e0a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss(history):\n",
    "    plt.plot(history.history['loss'], label='loss')\n",
    "    plt.plot(history.history['val_loss'], label='val_loss')\n",
    "    plt.ylim([0, 10])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Error Life Expectancy')\n",
    "    plt.legend()\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12413ede",
   "metadata": {},
   "source": [
    "### First model with compressed dataset after PCA\n",
    "\n",
    "Following network architecture:\n",
    "- One input layer with 64 output units and relu as activation function.\n",
    "- Two hidden layers with 64 output units each and relue activation funciton.\n",
    "- Output layers with 1 output units and linear (the default activation function in keras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6eb0d941",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model1, history1 = build_model(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba30cf5",
   "metadata": {},
   "source": [
    "### Visaulise the network of first model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "533bf673",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 64)                640       \n",
      "                                                                 \n",
      " hidden_1 (Dense)            (None, 64)                4160      \n",
      "                                                                 \n",
      " hidden_2 (Dense)            (None, 64)                4160      \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 9,025\n",
      "Trainable params: 9,025\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea1477ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf1b16d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.857572078704834"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7cb9d1",
   "metadata": {},
   "source": [
    "### Second model without reducing dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62d50e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 41s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model2, history2 = build_model(npca_X_train, npca_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "637cbf24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (Dense)         (None, 64)                1216      \n",
      "                                                                 \n",
      " hidden_1 (Dense)            (None, 64)                4160      \n",
      "                                                                 \n",
      " hidden_2 (Dense)            (None, 64)                4160      \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 9,601\n",
      "Trainable params: 9,601\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd35b9b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38989805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9988572597503662"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.evaluate(npca_X_test, npca_y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d862202a",
   "metadata": {},
   "source": [
    "### Tuning some of the hyperparameters\n",
    "\n",
    "Here we are trying to find the number of hidden layres and number of nueron on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29f4156b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_tune_model(hp):\n",
    "    model = keras.Sequential()\n",
    "    \n",
    "    for i in range(hp.Int('num_layers', 1, 5)):\n",
    "        model.add(keras.layers.Dense(units=hp.Int('units_' + str(i),\n",
    "                                                  min_value=32,\n",
    "                                                  max_value=512,\n",
    "                                                  step=8),\n",
    "                                     activation='relu'))\n",
    "    \n",
    "    model.add(keras.layers.Dense(1))\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=tf.keras.optimizers.Adam(\n",
    "            hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),\n",
    "        loss='mse')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f07b159",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Random_Search(project_name):\n",
    "    tuner = kt.RandomSearch(\n",
    "        build_tune_model,\n",
    "        objective='val_loss',\n",
    "        directory='project',\n",
    "        project_name=project_name\n",
    "    )\n",
    "    return tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0c24b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c709cca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 10 Complete [00h 00m 14s]\n",
      "val_loss: 0.9309148192405701\n",
      "\n",
      "Best val_loss So Far: 0.9309136867523193\n",
      "Total elapsed time: 00h 03m 13s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "npca_tuner = Random_Search(\"life expectancy npca\")\n",
    "npca_tuner.search(npca_X_train, npca_X_train, epochs=200, validation_split=0.2, callbacks=[stop_early])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4caa510",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model3 = npca_tuner.get_best_hyperparameters(num_trials=1)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fb5892",
   "metadata": {},
   "source": [
    "### Third model without reducing dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "51e5e457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 58s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model3 = npca_tuner.hypermodel.build(best_model3)\n",
    "history3 = model3.fit(npca_X_train, npca_y_train, epochs=200, validation_split=0.2, verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37877b6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best epoch: 191\n"
     ]
    }
   ],
   "source": [
    "val_loss_per_epoch1 = history3.history['val_loss']\n",
    "best_epoch = val_loss_per_epoch1.index(min(val_loss_per_epoch1)) + 1\n",
    "print('Best epoch: %d' % (best_epoch,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3568bfc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 44s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "hypermodel1 = npca_tuner.hypermodel.build(best_model3)\n",
    "# Retrain the model\n",
    "history3 = hypermodel1.fit(npca_X_train, npca_y_train, epochs=best_epoch, validation_split=0.2, verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "57fbdcfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_10 (Dense)            (None, 352)               6688      \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 320)               112960    \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 400)               128400    \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 64)                25664     \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 273,777\n",
      "Trainable params: 273,777\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "hypermodel1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9d8e7329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "606da166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9281240701675415"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypermodel1.evaluate(npca_X_test, npca_y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beae4a89",
   "metadata": {},
   "source": [
    "### Fourth model without reducing dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7e3545d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 10 Complete [00h 00m 07s]\n",
      "val_loss: 8.139677047729492\n",
      "\n",
      "Best val_loss So Far: 4.257599830627441\n",
      "Total elapsed time: 00h 02m 29s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "tuner = Random_Search(\"life expectancy\")\n",
    "tuner.search(X_train, y_train, epochs=50, validation_split=0.2, callbacks=[stop_early])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "767cf2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = tuner.get_best_hyperparameters(num_trials=1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24fcea09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model4 = tuner.hypermodel.build(best_model)\n",
    "history4 = model4.fit(X_train, y_train, epochs=200, validation_split=0.2, verbose = 0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8895ed3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best epoch: 192\n"
     ]
    }
   ],
   "source": [
    "val_loss_per_epoch2 = history4.history['val_loss']\n",
    "best_epoch = val_loss_per_epoch2.index(min(val_loss_per_epoch2)) + 1\n",
    "print('Best epoch: %d' % (best_epoch,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2da0427a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "hypermodel2 = tuner.hypermodel.build(best_model)\n",
    "# Retrain the model\n",
    "history4 = hypermodel2.fit(X_train, y_train, epochs=best_epoch, validation_split=0.2, verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a818828c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_9 (Dense)             (None, 400)               4000      \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 32)                12832     \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 18,977\n",
      "Trainable params: 18,977\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "hypermodel2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aa0f0a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "24b7d9bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7040529251098633"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypermodel2.evaluate(X_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1302dc91",
   "metadata": {},
   "source": [
    "### Best performing model interpertation\n",
    "\n",
    "Let's try to identify the impact of features on the output of model using <a href=\"https://shap.readthedocs.io/en/latest/index.html\">SHAP</a> package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f489b3dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b61f3af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bd2f11a5f8b43a19aac4cc635a1db20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5913 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5h 8min 34s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_summary = shap.kmeans(npca_X_train, 10)\n",
    "explainer = shap.KernelExplainer(hypermodel1.predict, train_summary)\n",
    "shap_values = explainer.shap_values(npca_X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "17fd94fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x626.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values[0], npca_X_train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65da8f21",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}