multinomial_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "agreed-beatles",
   "metadata": {},
   "source": [
    "# Quickstart with CatBoost 🚀🐈 - Multinomial Classifier\n",
    "## Install all the things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "progressive-variable",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 21.0.1 is available.\n",
      "You should consider upgrading via the '/Users/Shared/Dev/learning-stuff/ml/env/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install graphviz\n",
    "!pip3 install ipywidgets\n",
    "!pip3 install shap\n",
    "!pip3 install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fewer-floating",
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import sys\n",
    "import json\n",
    "from time import time\n",
    "from datetime import datetime, timedelta\n",
    "import seaborn as sns\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, auc, accuracy_score, roc_auc_score, confusion_matrix, roc_curve\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "vietnamese-convertible",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 21.0.1 is available.\n",
      "You should consider upgrading via the '/Users/Shared/Dev/learning-stuff/ml/env/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install catboost\n",
    "from catboost import CatBoostClassifier, Pool\n",
    "import random\n",
    "\n",
    "def seed_everything(seed=None):\n",
    "    if seed is None:\n",
    "        seed = np.random.randint(100)\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    return seed\n",
    "seed = seed_everything(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "determined-mayor",
   "metadata": {},
   "source": [
    "# Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "superb-ancient",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "0                5.1               3.5                1.4               0.2   \n",
      "1                4.9               3.0                1.4               0.2   \n",
      "2                4.7               3.2                1.3               0.2   \n",
      "3                4.6               3.1                1.5               0.2   \n",
      "4                5.0               3.6                1.4               0.2   \n",
      "\n",
      "   target species  \n",
      "0     0.0  setosa  \n",
      "1     0.0  setosa  \n",
      "2     0.0  setosa  \n",
      "3     0.0  setosa  \n",
      "4     0.0  setosa  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "\n",
    "def load_new_data():\n",
    "    iris = load_iris()\n",
    "    df= pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                     columns= iris['feature_names'] + ['target'])\n",
    "\n",
    "    df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
    "    return df\n",
    "\n",
    "df = load_new_data()\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moral-logic",
   "metadata": {},
   "source": [
    "## Look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "artificial-clause",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x135ce7f10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "guilty-probe",
   "metadata": {},
   "source": [
    "## Splitting data and filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artificial-tiger",
   "metadata": {},
   "source": [
    "## MultiNomial Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "proper-regulation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shared/Dev/learning-stuff/ml/env/lib/python3.8/site-packages/pandas/core/frame.py:4305: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    },
    {
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n=t(\"../../lib\");e.exports=function(t,e,r,a){a=a||{},r(\"hoverlabel.bgcolor\",a.bgcolor),r(\"hoverlabel.bordercolor\",a.bordercolor),n.coerceFont(r,\"hoverlabel.font\",a.font)}},{\"../../lib\":136}],66:[function(t,e,r){\"use strict\";function n(t){var e=i.isD3Selection(t)?t:o.select(t);e.selectAll(\"g.hovertext\").remove(),e.selectAll(\".spikeline\").remove()}function a(t,e,r){var n=t.hoverlabel||{},a=i.nestedProperty(n,r).get();return Array.isArray(a)?Array.isArray(e)&&Array.isArray(a[e[0]])?a[e[0]][e[1]]:a[e]:a}var o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var n=t(\"../../lib/extend\").extendFlat,a=t(\"../../plots/font_attributes\"),o=t(\"./constants\");e.exports={dragmode:{valType:\"enumerated\",values:[\"zoom\",\"pan\",\"select\",\"lasso\",\"orbit\",\"turntable\"],dflt:\"zoom\"},hovermode:{valType:\"enumerated\",values:[\"x\",\"y\",\"closest\",!1]},hoverlabel:{bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\"},font:{family:n({},a.family,{dflt:o.HOVERFONT}),size:n({},a.size,{dflt:o.HOVERFONTSIZE}),color:n({},a.color)}}}},{\"../../lib/extend\":132,\"../../plots/font_attributes\":195,\"./constants\":61}],68:[function(t,e,r){\"use strict\";function n(t){for(var e=!0,r=0;r<t.length;r++){if(\"h\"!==t[r].orientation){e=!1;break}}return e}var a=t(\"../../lib\"),o=t(\"./layout_attributes\");e.exports=function(t,e,r){function i(r,n){return a.coerce(t,e,o,r,n)}i(\"dragmode\");var 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n=t(\"../../plots/cartesian/constants\");e.exports={_isLinkedToArray:\"image\",visible:{valType:\"boolean\",dflt:!0},source:{valType:\"string\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\"],dflt:\"above\"},sizex:{valType:\"number\",dflt:0},sizey:{valType:\"number\",dflt:0},sizing:{valType:\"enumerated\",values:[\"fill\",\"contain\",\"stretch\"],dflt:\"contain\"},opacity:{valType:\"number\",min:0,max:1,dflt:1},x:{valType:\"any\",dflt:0},y:{valType:\"any\",dflt:0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},yanchor:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"top\"},xref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.x.toString()],dflt:\"paper\"},yref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.y.toString()],dflt:\"paper\"}}},{\"../../plots/cartesian/constants\":176}],71:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,o){e=e||{};var i=\"log\"===r&&\"linear\"===e.type,l=\"linear\"===r&&\"log\"===e.type;if(i||l)for(var s,c,u=t._fullLayout.images,f=e._id.charAt(0),d=0;d<u.length;d++)if(s=u[d],c=\"images[\"+d+\"].\",s[f+\"ref\"]===e._id){var h=s[f],p=s[\"size\"+f],g=null,v=null;if(i){g=a(h,e.range);var m=p/Math.pow(10,g)/2;v=2*Math.log(m+Math.sqrt(1+m*m))/Math.LN10}else g=Math.pow(10,h),v=g*(Math.pow(10,p/2)-Math.pow(10,-p/2));n(g)?n(v)||(v=null):(g=null,v=null),o(c+f,g),o(c+\"size\"+f,v)}}},{\"../../lib/to_log_range\":154,\"fast-isnumeric\":10}],72:[function(t,e,r){\"use strict\";function n(t,e,r){function n(r,n){return a.coerce(t,e,l,r,n)}if(!n(\"visible\",!!n(\"source\")))return e;n(\"layer\"),n(\"xanchor\"),n(\"yanchor\"),n(\"sizex\"),n(\"sizey\"),n(\"sizing\"),n(\"opacity\");for(var i={_fullLayout:r},s=[\"x\",\"y\"],c=0;c<2;c++){var u=s[c],f=o.coerceRef(t,e,i,u,\"paper\");o.coercePosition(e,i,n,f,u,0)}return e}var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var V=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault()}).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var U=a.behavior.drag().on(\"dragstart\",function(){a.event.sourceEvent.preventDefault(),a.event.sourceEvent.stopPropagation()}).on(\"drag\",this._onBarDrag.bind(this));A&&this.hbar.on(\".drag\",null).call(U),O&&this.vbar.on(\".drag\",null).call(U)}this.setTranslate(e,r)},n.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},n.prototype._onBoxDrag=function(){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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i=n.hsl(a.color).l,l=i>=.5?\"#aaa\":\"white\",c=i>=.5?\"black\":\"white\",u=o||\"\";e.style({fill:c,\"font-size\":a.fontSize+\"px\"}).text(u);var f=a.padding,d=e.node().getBBox(),h={fill:a.color,stroke:l,\"stroke-width\":\"2px\"},p=d.width+2*f+10,g=d.height+2*f;return r.attr({d:\"M\"+[[10,-g/2],[10,-g/4],[a.hasTick?0:10,0],[10,g/4],[10,g/2],[p,g/2],[p,-g/2]].join(\"L\")+\"Z\"}).style(h),t.attr({transform:\"translate(\"+[10,-g/2+2*f]+\")\"}),t.style({display:\"block\"}),s},s.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),s},s.hide=function(){if(t)return t.style({display:\"none\"}),s},s.show=function(){if(t)return t.style({display:\"block\"}),s},s.config=function(t){return o(a,t),s},s},i.tooltipPanel.uid=1,i.adapter={},i.adapter.plotly=function(){var t={};return t.convert=function(t,e){var r={};if(t.data&&(r.data=t.data.map(function(t,r){var n=o({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach(function(t,r){i.util.translator.apply(null,t.concat(e))}),e||delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",n.dotVisible===!0?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var a=i.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=a.indexOf(t.geometry);n!=-1&&(r.data[e].groupId=n)})}if(t.layout){var l=o({},t.layout);if([[l,[\"plot_bgcolor\"],[\"backgroundColor\"]],[l,[\"showlegend\"],[\"showLegend\"]],[l,[\"radialaxis\"],[\"radialAxis\"]],[l,[\"angularaxis\"],[\"angularAxis\"]],[l.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[l.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[l.angularaxis,[\"nticks\"],[\"ticksCount\"]],[l.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.angularaxis,[\"range\"],[\"domain\"]],[l.angularaxis,[\"endpadding\"],[\"endPadding\"]],[l.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[l.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.radialaxis,[\"range\"],[\"domain\"]],[l.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[l.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[l.angularAxis,[\"nticks\"],[\"ticksCount\"]],[l.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.angularAxis,[\"range\"],[\"domain\"]],[l.angularAxis,[\"endpadding\"],[\"endPadding\"]],[l.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[l.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.radialAxis,[\"range\"],[\"domain\"]],[l.font,[\"outlinecolor\"],[\"outlineColor\"]],[l.legend,[\"traceorder\"],[\"reverseOrder\"]],[l,[\"labeloffset\"],[\"labelOffset\"]],[l,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach(function(t,r){i.util.translator.apply(null,t.concat(e))}),e?(void 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l.angularaxis.nticks,delete l.radialaxis.ticksCount,delete l.radialaxis.ticksCount,delete l.radialaxis.ticksStep,delete l.radialaxis.rewriteTicks,delete l.radialaxis.nticks),r.layout=l}return r},t}},{\"../../lib\":136,d3:7}],204:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../lib\"),o=t(\"../../components/color\"),i=t(\"./micropolar\"),l=t(\"./undo_manager\"),s=a.extendDeepAll,c=e.exports={};c.framework=function(t){function e(e,a){return a&&(f=a),n.select(n.select(f).node().parentNode).selectAll(\".svg-container>*:not(.chart-root)\").remove(),r=r?s(r,e):e,o||(o=i.Axis()),u=i.adapter.plotly().convert(r),o.config(u).render(f),t.data=r.data,t.layout=r.layout,c.fillLayout(t),r}var r,a,o,u,f,d=new l;return e.isPolar=!0,e.svg=function(){return o.svg()},e.getConfig=function(){return r},e.getLiveConfig=function(){return i.adapter.plotly().convert(o.getLiveConfig(),!0)},e.getLiveScales=function(){return{t:o.angularScale(),r:o.radialScale()}},e.setUndoPoint=function(){var t=this,e=i.util.cloneJson(r);!function(e,r){d.add({undo:function(){r&&t(r)},redo:function(){t(e)}})}(e,a),a=i.util.cloneJson(e)},e.undo=function(){d.undo()},e.redo=function(){d.redo()},e},c.fillLayout=function(t){var e=n.select(t).selectAll(\".plot-container\"),r=e.selectAll(\".svg-container\"),a=t.framework&&t.framework.svg&&t.framework.svg(),i={width:800,height:600,paper_bgcolor:o.background,_container:e,_paperdiv:r,_paper:a};t._fullLayout=s(i,t.layout)}},{\"../../components/color\":25,\"../../lib\":136,\"./micropolar\":203,\"./undo_manager\":205,d3:7}],205:[function(t,e,r){\"use strict\";e.exports=function(){function t(t,e){return t?(a=!0,t[e](),a=!1,this):this}var e,r=[],n=-1,a=!1;return{add:function(t){return a?this:(r.splice(n+1,r.length-n),r.push(t),n=r.length-1,this)},setCallback:function(t){e=t},undo:function(){var a=r[n];return a?(t(a,\"undo\"),n-=1,e&&e(a.undo),this):this},redo:function(){var a=r[n+1];return 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s},{\"../../components/color\":25,\"./helpers\":232,\"fast-isnumeric\":10,tinycolor2:13}],231:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./attributes\");e.exports=function(t,e,r,o){function i(r,o){return n.coerce(t,e,a,r,o)}var l=n.coerceFont,s=i(\"values\");if(!Array.isArray(s)||!s.length)return void(e.visible=!1);var c=i(\"labels\");Array.isArray(c)||(i(\"label0\"),i(\"dlabel\")),i(\"marker.line.width\")&&i(\"marker.line.color\");var u=i(\"marker.colors\");Array.isArray(u)||(e.marker.colors=[]),i(\"scalegroup\");var f=i(\"text\"),d=i(\"textinfo\",Array.isArray(f)?\"text+percent\":\"percent\");if(i(\"hovertext\"),i(\"hoverinfo\",1===o._dataLength?\"label+text+value+percent\":void 0),d&&\"none\"!==d){var h=i(\"textposition\"),p=Array.isArray(h)||\"auto\"===h,g=p||\"inside\"===h,v=p||\"outside\"===h;if(g||v){var m=l(i,\"textfont\",o.font);g&&l(i,\"insidetextfont\",m),v&&l(i,\"outsidetextfont\",m)}}i(\"domain.x\"),i(\"domain.y\"),i(\"hole\"),i(\"sort\"),i(\"direction\"),i(\"rotation\"),i(\"pull\")}},{\"../../lib\":136,\"./attributes\":228}],232:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");r.formatPiePercent=function(t,e){var r=(100*t).toPrecision(3);return r.lastIndexOf(\".\")!==-1&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return r.lastIndexOf(\".\")!==-1&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)}},{\"../../lib\":136}],233:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 0,idealAlign:e.pxmid[0]<0?\"left\":\"right\",color:e.hbg||y.bgcolor||e.color,borderColor:e.hbc||y.bordercolor,fontFamily:e.htf||y.font.family,fontSize:e.hts||y.font.size,fontColor:e.htc||y.font.color},{container:o._hoverlayer.node(),outerContainer:o._paper.node()}),f.hover(t,n,\"pie\"),T=!0}function l(e){e.originalEvent=u.event,t.emit(\"plotly_unhover\",{event:u.event,points:[e]}),T&&(f.loneUnhover(r._hoverlayer.node()),T=!1)}function d(){t._hoverdata=[e],t._hoverdata.trace=c.trace,f.click(t,u.event)}function x(t,r,n,a){return\"a\"+a*c.r+\",\"+a*_+\" \"+y+\" \"+e.largeArc+(n?\" 1 \":\" 0 \")+a*(r[0]-t[0])+\",\"+a*(r[1]-t[1])}if(e.hidden)return void u.select(this).selectAll(\"path,g\").remove();s[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var 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W=C.selectAll(\".js-line\").data(Y);g(W.exit()).style(\"opacity\",0).remove(),W.each(Z(!1)),W.enter().append(\"path\").classed(\"js-line\",!0).style(\"vector-effect\",\"non-scaling-stroke\").call(s.lineGroupStyle).each(Z(!0)),G.length&&(S?B&&H&&(O?(\"y\"===O?B[1]=H[1]=A.c2p(0,!0):\"x\"===O&&(B[0]=H[0]=M.c2p(0,!0)),g(S).attr(\"d\",\"M\"+H+\"L\"+B+\"L\"+U.substr(1))):g(S).attr(\"d\",U+\"Z\")):\"tonext\"===T.fill.substr(0,6)&&U&&D&&(\"tonext\"===T.fill?g(z).attr(\"d\",U+\"Z\"+D+\"Z\"):g(z).attr(\"d\",U+\"L\"+D.substr(1)+\"Z\"),T._polygons=T._polygons.concat(P)),T._prevRevpath=X,T._prevPolygons=V);var $=C.selectAll(\".points\");_=$.data([n]),$.each(b),_.enter().append(\"g\").classed(\"points\",!0).each(b),_.exit().remove()}}function o(t,e,r,n,a){var o=r.xaxis,s=r.yaxis,c=i.extent(l.simpleMap(o.range,o.r2c)),f=i.extent(l.simpleMap(s.range,s.r2c)),d=n[0].trace;if(u.hasMarkers(d)){var h=d.marker.maxdisplayed;if(0!==h){var p=n.filter(function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6833799\ttotal: 6.25ms\tremaining: 56.3ms\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ff25e41ef70441eb2d10a17f7b3fbf0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:\tlearn: 0.4549939\ttotal: 10.1ms\tremaining: 40.4ms\n",
      "2:\tlearn: 0.3392548\ttotal: 11.6ms\tremaining: 27.1ms\n",
      "3:\tlearn: 0.2602614\ttotal: 13.2ms\tremaining: 19.8ms\n",
      "4:\tlearn: 0.2093591\ttotal: 14.7ms\tremaining: 14.7ms\n",
      "5:\tlearn: 0.1805643\ttotal: 16.2ms\tremaining: 10.8ms\n",
      "6:\tlearn: 0.1576708\ttotal: 17.5ms\tremaining: 7.52ms\n",
      "7:\tlearn: 0.1407669\ttotal: 19.1ms\tremaining: 4.77ms\n",
      "8:\tlearn: 0.1247294\ttotal: 20.4ms\tremaining: 2.26ms\n",
      "9:\tlearn: 0.1126088\ttotal: 21.8ms\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x1365045e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from catboost import CatBoostClassifier, Pool\n",
    "\n",
    "df = load_new_data()\n",
    "X_tr, X_eval = train_test_split(df)\n",
    "\n",
    "y_tr = X_tr.species\n",
    "y_tr_clean = y_tr\n",
    "X_tr.drop(columns=['species', 'target'], inplace=True)\n",
    "y_eval =  df_eval.species\n",
    "y_eval_clean = y_eval\n",
    "X_eval = df_eval.drop(columns=['species', 'target'])\n",
    "\n",
    "\n",
    "train_dataset = Pool(X_tr, y_tr_clean, feature_names=list(X_tr.columns))\n",
    "\n",
    "model_params = {\n",
    "    'iterations': 10, \n",
    "    'loss_function': 'MultiClass',\n",
    "    'train_dir': 'crossentropy',\n",
    "    'allow_writing_files': False,\n",
    "    'random_seed': 42,\n",
    "}\n",
    "\n",
    "model = CatBoostClassifier(**model_params)\n",
    "model.fit(train_dataset, verbose=True, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governing-addiction",
   "metadata": {},
   "source": [
    "## MultiNomial Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "settled-spoke",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "accuracy": 1,
       "confusion": [
        [
         15,
         0,
         0
        ],
        [
         0,
         11,
         0
        ],
        [
         0,
         0,
         12
        ]
       ],
       "feature_importance": {
        "petal length (cm)": 44.59195747324745,
        "petal width (cm)": 35.39161539300946,
        "sepal length (cm)": 6.727274259951441,
        "sepal width (cm)": 13.289152873791648
       },
       "macro avg": {
        "f1-score": 1,
        "precision": 1,
        "recall": 1,
        "support": 38
       },
       "model_features": [
        "sepal length (cm)",
        "sepal width (cm)",
        "petal length (cm)",
        "petal width (cm)"
       ],
       "setosa": {
        "f1-score": 1,
        "precision": 1,
        "recall": 1,
        "support": 15
       },
       "versicolor": {
        "f1-score": 1,
        "precision": 1,
        "recall": 1,
        "support": 11
       },
       "virginica": {
        "f1-score": 1,
        "precision": 1,
        "recall": 1,
        "support": 12
       },
       "weighted avg": {
        "f1-score": 1,
        "precision": 1,
        "recall": 1,
        "support": 38
       }
      },
      "text/plain": [
       "<IPython.core.display.JSON object>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "application/json": {
       "expanded": false,
       "root": "root"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_eval)\n",
    "\n",
    "report_dict = {}\n",
    "report_dict = classification_report(y_eval_clean, predictions, output_dict=True)\n",
    "# print(predictions)\n",
    "report_dict['accuracy'] = accuracy_score(y_eval_clean, predictions)\n",
    "report_dict['confusion'] = confusion_matrix(y_eval_clean, predictions).tolist()\n",
    "report_dict['model_features'] = X_tr.columns.to_list()\n",
    "\n",
    "importances = model.get_feature_importance()\n",
    "# std = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0).tolist()\n",
    "indices = np.argsort(importances)[::-1]\n",
    "report_dict['feature_importance'] = {X_tr.columns[idx]: float(importances[idx]) for idx in indices}\n",
    "\n",
    "from IPython.display import JSON\n",
    "JSON(report_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "billion-forge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(model, X_eval, y_eval)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "corrected-livestock",
   "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": [
    "feat_imp_index = report_dict['feature_importance'].keys()\n",
    "feat_imp_vals = report_dict['feature_importance'].values()\n",
    "\n",
    "plt.title(\"Features' Importance\")\n",
    "plt.bar(feat_imp_index, feat_imp_vals)\n",
    "plt.ylabel('Importance')\n",
    "plt.xlabel('Features')\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "stupid-blank",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "apparent-struggle",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "failing-warren",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "agreed-saudi",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}