dataset_prep_heartdisease.ipynb

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    "## INSTALL REQUIREMENTS"
   ]
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   "execution_count": 1,
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    "# IMPORT Generals \n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import six\n",
    "import sys\n",
    "import math\n",
    "import argparse\n",
    "import collections\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import special\n",
    "from scipy.special import expit"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "f7d83ced",
   "metadata": {},
   "outputs": [
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     "text": [
      "Requirement already satisfied: shap in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (0.40.0)\n",
      "Requirement already satisfied: scipy in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (1.7.1)\n",
      "Requirement already satisfied: cloudpickle in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (2.0.0)\n",
      "Requirement already satisfied: slicer==0.0.7 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (0.0.7)\n",
      "Requirement already satisfied: packaging>20.9 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (21.0)\n",
      "Requirement already satisfied: tqdm>4.25.0 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (4.62.3)\n",
      "Requirement already satisfied: scikit-learn in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (0.24.2)\n",
      "Requirement already satisfied: pandas in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (1.3.4)\n",
      "Requirement already satisfied: numpy in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (1.20.3)\n",
      "Requirement already satisfied: numba in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from shap) (0.54.1)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from packaging>20.9->shap) (3.0.4)\n",
      "Requirement already satisfied: setuptools in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from numba->shap) (58.0.4)\n",
      "Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from numba->shap) (0.37.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from pandas->shap) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from pandas->shap) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas->shap) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn->shap) (2.2.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/stefan_rmmr/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn->shap) (1.1.0)\n"
     ]
    },
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p=this.xlabel.selectAll(\"option\").data(f);p.enter().append(\"option\").merge(p).attr(\"value\",function(t){return t}).text(function(t){return t}),p.exit().remove();var h=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";f=(0,y.map)(this.usedFeatures,function(e){return[t.props.featureNames[e],t.props.featureNames[e]+\" effects\"]}),f.unshift([\"model output value\",h]);var d=this.ylabel.selectAll(\"option\").data(f);d.enter().append(\"option\").merge(d).attr(\"value\",function(t){return t[0]}).text(function(t){return t[1]}),d.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var t=this,e=!0,n=!1,r=void 0;try{for(var i,o=this.props.explanations[Symbol.iterator]();!(e=(i=o.next()).done);e=!0){var a=i.value,c=!0,s=!1,l=void 0;try{for(var 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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!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 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IMPORT SHAPLEY Feature Analytics\n",
    "!{sys.executable} -m pip install shap\n",
    "import shap\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13659c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ScikitLearn Imports\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mutual_info_score, accuracy_score\n",
    "from sklearn.metrics import roc_auc_score, mean_squared_error\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e516e80",
   "metadata": {},
   "source": [
    "## Fundamental Data Analytics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52a3aa4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def traindata_analytics(df_source, data_cols):\n",
    "    \n",
    "    list_min = df_source.min().to_list()\n",
    "    list_max = df_source.max().to_list()\n",
    "    list_mean = df_source.mean().to_list()\n",
    "    list_var = df_source.var().to_list()\n",
    "    \n",
    "    list_mean = [round(num, 4) for num in list_mean]\n",
    "\n",
    "    # aggregate information within one single dataframe \n",
    "    joint_df = pd.DataFrame(list(zip(data_cols, list_mean, list_var, list_min, list_max)),\n",
    "                            columns =['attribute name', 'mean', 'var', 'min', 'max'])\n",
    "\n",
    "    print('\\n', df_source.hist(bins=100, figsize=(15, 10), color='#86BC25'))\n",
    "    display(joint_df)\n",
    "    \n",
    "    # plot CORRELATION MATRIX\n",
    "    f, ax = plt.subplots(figsize=(6, 6))\n",
    "    corr = df_source.corr()\n",
    "    sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool),\n",
    "                cmap=sns.light_palette(\"darkslategrey\", as_cmap=True),\n",
    "                square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b7a0ad",
   "metadata": {},
   "source": [
    "## LOAD &  PREPROCESS DATASET - Kaggle Health Care Heart Disease\n",
    " \n",
    "Learn more about this dataset on the respective Kaggle page. \n",
    "[KAGGLE - Health Care: Heart-Disease](https://www.kaggle.com/datasets/nareshbhat/health-care-data-set-on-heart-attack-possibility?select=heart.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41c5f925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'# CLEAN Dataset and REMOVE OUTLIARS\\ndf_source_cleaned = df_source.copy()\\nfor col in cols_attr:\\n    # q_low = df_source_cleaned[col].quantile(0.0002)\\n    q_hi  = df_source_cleaned[col].quantile(0.998)\\n    # df_source_cleaned = df_source_cleaned[(df_source_cleaned[col] > q_low)]\\n    df_source_cleaned = df_source_cleaned[(df_source_cleaned[col] < q_hi)]\\ndf_source = df_source_cleaned.copy()'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOAD the GiveSomeCredit Datasets \n",
    "df_source = pd.read_csv('source_data/heart.csv')\n",
    "\n",
    "# PREPROCESS the dataset before training a generative model\n",
    "df_source = df_source.dropna()\n",
    "# df_source.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "\n",
    "# DEFINE TARGET VARIABLE\n",
    "target_variable = 'target'\n",
    "\n",
    "# MOVE TARGET column to the end --> 'target'\n",
    "cols_attr = list(df_source.columns.values) #Make a list of all of the columns in the df\n",
    "cols_attr.pop(cols_attr.index(target_variable)) #Remove 'SeriousDlqin2yrs' from list\n",
    "df_source = df_source[cols_attr+[target_variable]] #Create new dataframe with columns in the order you want\n",
    "data_cols_orig = list(df_source.columns.values) # store a list of column names as listed in the original dataset \n",
    "\n",
    "# ANALYZE AND REMOVE nan values\n",
    "df_source = df_source.replace(' ', np.nan)\n",
    "df_source = df_source.replace('', np.nan)\n",
    "df_source = df_source.replace('NaN', np.nan)\n",
    "df_source = df_source.replace('NULL', np.nan)\n",
    "df_source = df_source.dropna()\n",
    "\n",
    "\"\"\"# CLEAN Dataset and REMOVE OUTLIARS\n",
    "df_source_cleaned = df_source.copy()\n",
    "for col in cols_attr:\n",
    "    # q_low = df_source_cleaned[col].quantile(0.0002)\n",
    "    q_hi  = df_source_cleaned[col].quantile(0.998)\n",
    "    # df_source_cleaned = df_source_cleaned[(df_source_cleaned[col] > q_low)]\n",
    "    df_source_cleaned = df_source_cleaned[(df_source_cleaned[col] < q_hi)]\n",
    "df_source = df_source_cleaned.copy()\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b980d89a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NORMALIZATION of Dataset before Train/Test Split\n",
    "scaler = MinMaxScaler()\n",
    "source_scaled = scaler.fit_transform(df_source)\n",
    "df_source_scaled = pd.DataFrame(data=source_scaled, columns=data_cols_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b22193b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'target'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# REMOVAL of all Categorical attributes \n",
    "# Prepare for synthetic data generation using DPWGAN which can only handle continuous attributes\n",
    "\n",
    "df_source_contattr = df_source.copy()\n",
    "\n",
    "for attr in df_source_contattr.columns:\n",
    "    if (df_source_contattr[attr].nunique() < 20) and attr != target_variable:\n",
    "        df_source_contattr = df_source_contattr.drop([attr], axis=1)\n",
    "        \n",
    "# store a list of column names as listed in the continuous\n",
    "data_cols_cont = list(df_source_contattr.columns.values) \n",
    "\n",
    "cols_attr_cont = list(df_source_contattr.columns.values) #Make a list of all of the columns in the df\n",
    "cols_attr_cont.pop(cols_attr_cont.index(target_variable)) #Remove 'SeriousDlqin2yrs' from list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6dee73b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " [[<AxesSubplot:title={'center':'age'}>\n",
      "  <AxesSubplot:title={'center':'trestbps'}>]\n",
      " [<AxesSubplot:title={'center':'chol'}>\n",
      "  <AxesSubplot:title={'center':'thalach'}>]\n",
      " [<AxesSubplot:title={'center':'oldpeak'}>\n",
      "  <AxesSubplot:title={'center':'target'}>]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute name</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>54.3663</td>\n",
       "      <td>82.484558</td>\n",
       "      <td>29.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trestbps</td>\n",
       "      <td>131.6238</td>\n",
       "      <td>307.586453</td>\n",
       "      <td>94.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chol</td>\n",
       "      <td>246.2640</td>\n",
       "      <td>2686.426748</td>\n",
       "      <td>126.0</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thalach</td>\n",
       "      <td>149.6469</td>\n",
       "      <td>524.646406</td>\n",
       "      <td>71.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oldpeak</td>\n",
       "      <td>1.0396</td>\n",
       "      <td>1.348095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>target</td>\n",
       "      <td>0.5446</td>\n",
       "      <td>0.248836</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  attribute name      mean          var    min    max\n",
       "0            age   54.3663    82.484558   29.0   77.0\n",
       "1       trestbps  131.6238   307.586453   94.0  200.0\n",
       "2           chol  246.2640  2686.426748  126.0  564.0\n",
       "3        thalach  149.6469   524.646406   71.0  202.0\n",
       "4        oldpeak    1.0396     1.348095    0.0    6.2\n",
       "5         target    0.5446     0.248836    0.0    1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "traindata_analytics(df_source_contattr, data_cols_cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16e6b367",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPLIT DATASET - Original & Normalized Original\n",
    "data_train_orig, data_test_orig = train_test_split(df_source, test_size=0.25, random_state=42)\n",
    "data_train_cont, data_test_cont = train_test_split(df_source_contattr, test_size=0.25, random_state=42)\n",
    "data_train_norm, data_test_norm = train_test_split(df_source_scaled, test_size=0.25, random_state=42)\n",
    "data_train_cont_norm, data_test_cont_norm = train_test_split(df_source_scaled[df_source_contattr.columns],\n",
    "                                                             test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f67ebbd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save original train & test datasets\n",
    "data_train_orig.to_csv('source_data/heart_orig_train.csv', index=False)\n",
    "data_test_orig.to_csv('source_data/heart_orig_test.csv', index=False)\n",
    "\n",
    "# Save original continuous train & test datasets\n",
    "data_train_cont.to_csv('source_data/heart_cont_train.csv', index=False)\n",
    "data_test_cont.to_csv('source_data/heart_cont_test.csv', index=False)\n",
    "\n",
    "# Save normalized train & test datasets\n",
    "data_train_norm.to_csv('source_data/heart_norm_train.csv', index=False)\n",
    "data_test_norm.to_csv('source_data/heart_norm_test.csv', index=False)\n",
    "\n",
    "# Save normalized continuous train & test datasets\n",
    "data_train_cont_norm.to_csv('source_data/heart_cont_norm_train.csv', index=False)\n",
    "data_test_cont_norm.to_csv('source_data/heart_cont_norm_test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e9d45b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         227\n",
       "trestbps    227\n",
       "chol        227\n",
       "thalach     227\n",
       "oldpeak     227\n",
       "target      227\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b6448f4c",
   "metadata": {},
   "source": [
    "## Analyze Feature Importance for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39ae865c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODIFIED VERSION of Model training function for FEATURE IMPORTANCE extraction\n",
    "def supervised_model_training(x_train, y_train, x_test, y_test, data_name, log_result, feature_list):\n",
    "    \n",
    "    # Train LogsticRegression model and report the results.\n",
    "    model = LogisticRegression()\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # get importance\n",
    "    importance = model.coef_[0]\n",
    "    # create dataframe from joined lists\n",
    "    df_feature_importance = pd.DataFrame(list(zip(feature_list,importance)), columns =['feature_name', 'importance'])\n",
    "    df_feature_importance = df_feature_importance.sort_values(by='importance', key=abs)\n",
    "    # summarize feature importance\n",
    "    display(df_feature_importance)\n",
    "    \n",
    "    y_predict = model.predict_proba(x_test)[:,1]\n",
    "    y_predict_binary = np.round(y_predict)\n",
    "    \n",
    "    # PLOT ROC curve\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, y_predict)\n",
    "\n",
    "    # AUC / AUPRC Computation\n",
    "    auc = round(metrics.roc_auc_score(y_test, y_predict), 4)\n",
    "    apr = round(metrics.average_precision_score(y_test, y_predict), 4)\n",
    "    acc = round(metrics.accuracy_score(y_test, y_predict_binary), 4) \n",
    "    if log_result:\n",
    "        print(f'\\nLogReg Performance for Dataset: \\\"{data_name.ljust(20)}\\\" - AUC: {auc}, Accuracy: {acc}')\n",
    "        \n",
    "    # return a list of feature names ranked by importance\n",
    "    return df_feature_importance['feature_name'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dae625ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data for analyzing feature importance \n",
    "train = pd.read_csv('source_data/heart_cont_train.csv')\n",
    "test = pd.read_csv('source_data/heart_cont_test.csv')\n",
    "\n",
    "X_train = (train.drop([target_variable], axis=1)).to_numpy()\n",
    "y_train = (train[target_variable]).to_numpy()\n",
    "X_test = (test.drop([target_variable], axis=1)).to_numpy()\n",
    "y_test = (test[target_variable]).to_numpy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b12769a",
   "metadata": {
    "scrolled": true
   },
   "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>feature_name</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chol</td>\n",
       "      <td>-0.000900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trestbps</td>\n",
       "      <td>-0.005381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.009117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thalach</td>\n",
       "      <td>0.028474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oldpeak</td>\n",
       "      <td>-0.781189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature_name  importance\n",
       "2         chol   -0.000900\n",
       "1     trestbps   -0.005381\n",
       "0          age   -0.009117\n",
       "3      thalach    0.028474\n",
       "4      oldpeak   -0.781189"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LogReg Performance for Dataset: \"cont orig data      \" - AUC: 0.8348, Accuracy: 0.7763\n"
     ]
    }
   ],
   "source": [
    "# store list of features ranked by importance\n",
    "importance_ranking = supervised_model_training(X_train, y_train, X_test, y_test, 'cont orig data', True, cols_attr_cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5b8dc27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data for analyzing feature importance \n",
    "train = pd.read_csv('source_data/heart_cont_norm_train.csv')\n",
    "test = pd.read_csv('source_data/heart_cont_norm_test.csv')\n",
    "\n",
    "X_train = (train.drop([target_variable], axis=1)).to_numpy()\n",
    "y_train = (train[target_variable]).to_numpy()\n",
    "X_test = (test.drop([target_variable], axis=1)).to_numpy()\n",
    "y_test = (test[target_variable]).to_numpy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db2364a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_name</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chol</td>\n",
       "      <td>-0.176583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trestbps</td>\n",
       "      <td>-0.299430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.726221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thalach</td>\n",
       "      <td>2.183506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oldpeak</td>\n",
       "      <td>-2.819875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature_name  importance\n",
       "2         chol   -0.176583\n",
       "1     trestbps   -0.299430\n",
       "0          age   -0.726221\n",
       "3      thalach    2.183506\n",
       "4      oldpeak   -2.819875"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LogReg Performance for Dataset: \"cont norm data      \" - AUC: 0.8404, Accuracy: 0.7895\n"
     ]
    }
   ],
   "source": [
    "# store list of features ranked by importance\n",
    "importance_ranking = supervised_model_training(X_train, y_train, X_test, y_test, 'cont norm data', True, cols_attr_cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92cf56d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e95ed94a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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