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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wl9KZ5GeK9a",
        "colab_type": "text"
      },
      "source": [
        "# Бейзлайн для датасета:\n",
        "[Brazilian E-Commerce](https://www.kaggle.com/olistbr/brazilian-ecommerce)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnEMBP6UeAwJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install catboost\n",
        "!pip install ipywidgets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CqKUd8WekvD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from catboost import CatBoost\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "SEED = 2007\n",
        "prefix = 'drive/My Drive/automl/data/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trnVqqedhmiq",
        "colab_type": "text"
      },
      "source": [
        "# Загрузим предобработанный датасет:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieE3a2kEh0Bc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv(prefix + 'categorical_less_train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5l8nOniznJwh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "35b9ef93-624f-4750-eebb-5fd983820068"
      },
      "source": [
        "# Уберем текстовые признаки\n",
        "text_columns = []\n",
        "for col in train.columns:\n",
        "    if type(train.loc[0, col]) == str:\n",
        "        text_columns.append(col)\n",
        "print(text_columns)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['review_comment_message']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hD_Y5a7dncv1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.drop(text_columns, axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIv8SvC_ipsd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = train.drop(['price'], axis=1)\n",
        "y = train['price']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.70, random_state=SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gm_xaxZUhp44",
        "colab_type": "text"
      },
      "source": [
        "# Обучим модель с перебором гиперпараметров:\n",
        "**Замечание.** В последующей работе часть подбора гиперпараметров автоматизируется."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaiErzMkfOs1",
        "colab_type": "text"
      },
      "source": [
        "Определим гиперпараметры для перебора:\n",
        "\n",
        "\n",
        "*   `learning_rate`\n",
        "*   `max_depth`\n",
        "*   `n_estimators`\n",
        "*   `num_leaves`\n",
        "*   `l2_leaf_reg`\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NAkOfrtfLQ8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = CatBoost()\n",
        "grid = {\n",
        "    'learning_rate': [0.001, 0.03, 0.1],\n",
        "    'max_depth': [3, 7, 10, 20],\n",
        "    'n_estimators': [5, 10, 17, 25],\n",
        "    'l2_leaf_reg': [0.5, 2, 10]\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51B7B8ouhcZ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "8ebafc5bdcf64709abe1a908e8e9d999",
            "1797c6488ffd4b0ca8de8585f8a12a45"
          ]
        },
        "outputId": "efb5b532-6a97-4f81-abac-5dffa017be08"
      },
      "source": [
        "grid_search_result = model.grid_search(grid, X_train, y_train, plot=True, partition_random_seed=SEED)\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
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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 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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 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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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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return e%(f.minorTicks+1)==0}).classed(\"minor\",function(t,e){return!(e%(f.minorTicks+1)==0)}).style(F),$.selectAll(\".minor\").style({stroke:f.minorTickColor}),W.select(\"line.grid-line\").attr({x1:f.tickLength?x-f.tickLength:0,x2:x}).style({display:f.angularAxis.gridLinesVisible?\"block\":\"none\"}),$.append(\"text\").classed(\"axis-text\",!0).style(j);var Q=W.select(\"text.axis-text\").attr({x:x+f.labelOffset,dy:\".35em\",transform:function(t,e){var r=s(t,e),n=x+f.labelOffset,a=f.angularAxis.tickOrientation;return\"horizontal\"==a?\"rotate(\"+-r+\" \"+n+\" 0)\":\"radial\"==a?r<270&&r>90?\"rotate(180 \"+n+\" 0)\":null:\"rotate(\"+(r<=180&&r>0?-90:90)+\" \"+n+\" 0)\"}}).style({\"text-anchor\":\"middle\",display:f.angularAxis.labelsVisible?\"block\":\"none\"}).text(function(t,e){return e%(f.minorTicks+1)!=0?\"\":w?w[t]+f.angularAxis.ticksSuffix:t+f.angularAxis.ticksSuffix}).style(j);f.angularAxis.rewriteTicks&&Q.text(function(t,e){return e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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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",
              "l.scalegroup&&h.indexOf(l.scalegroup)===-1&&h.push(l.scalegroup)}for(o=0;o<h.length;o++){for(d=1/0,f=h[o],a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(d=Math.min(d,i.r*i.r/i.vTotal));for(a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(i.r=Math.sqrt(d*i.vTotal))}}function s(t){function e(t){var e=f.r*Math.sin(t),r=-f.r*Math.cos(t);return h?[e*(1-l*n*n)+r*i*l,e*i*l+r*(1-l*a*a),Math.sin(o)*(r*a-e*n)]:[e,r]}var r,n,a,o,i,l,s,c,u,f=t[0],d=f.trace,h=d.tilt,p=d.rotation*Math.PI/180,g=2*Math.PI/f.vTotal,v=\"px0\",m=\"px1\";if(\"counterclockwise\"===d.direction){for(s=0;s<t.length&&t[s].hidden;s++);if(s===t.length)return;p+=g*t[s].v,g*=-1,v=\"px1\",m=\"px0\"}for(h&&(o=h*Math.PI/180,r=d.tiltaxis*Math.PI/180,i=Math.sin(r)*Math.cos(r),l=1-Math.cos(o),n=Math.sin(r),a=Math.cos(r)),u=e(p),s=0;s<t.length;s++)c=t[s],c.hidden||(c[v]=u,p+=g*c.v/2,c.pxmid=e(p),c.midangle=p,p+=g*c.v/2,u=e(p),c[m]=u,c.largeArc=c.v>f.vTotal/2?1:0)}function c(t,e,r){if(!t)return 1;var 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 n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}: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": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ebafc5bdcf64709abe1a908e8e9d999",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "bestTest = 229.3661551\n",
            "bestIteration = 4\n",
            "\n",
            "0:\tloss: 229.3661551\tbest: 229.3661551 (0)\ttotal: 323ms\tremaining: 46.2s\n",
            "\n",
            "bestTest = 210.1466725\n",
            "bestIteration = 4\n",
            "\n",
            "1:\tloss: 210.1466725\tbest: 210.1466725 (1)\ttotal: 392ms\tremaining: 27.9s\n",
            "\n",
            "bestTest = 175.0813251\n",
            "bestIteration = 4\n",
            "\n",
            "2:\tloss: 175.0813251\tbest: 175.0813251 (2)\ttotal: 460ms\tremaining: 21.6s\n",
            "\n",
            "bestTest = 229.3674932\n",
            "bestIteration = 4\n",
            "\n",
            "3:\tloss: 229.3674932\tbest: 175.0813251 (2)\ttotal: 535ms\tremaining: 18.7s\n",
            "\n",
            "bestTest = 210.1798457\n",
            "bestIteration = 4\n",
            "\n",
            "4:\tloss: 210.1798457\tbest: 175.0813251 (2)\ttotal: 603ms\tremaining: 16.8s\n",
            "\n",
            "bestTest = 175.1518621\n",
            "bestIteration = 4\n",
            "\n",
            "5:\tloss: 175.1518621\tbest: 175.0813251 (2)\ttotal: 670ms\tremaining: 15.4s\n",
            "\n",
            "bestTest = 229.3744701\n",
            "bestIteration = 4\n",
            "\n",
            "6:\tloss: 229.3744701\tbest: 175.0813251 (2)\ttotal: 748ms\tremaining: 14.6s\n",
            "\n",
            "bestTest = 210.3534673\n",
            "bestIteration = 4\n",
            "\n",
            "7:\tloss: 210.3534673\tbest: 175.0813251 (2)\ttotal: 816ms\tremaining: 13.9s\n",
            "\n",
            "bestTest = 175.5234542\n",
            "bestIteration = 4\n",
            "\n",
            "8:\tloss: 175.5234542\tbest: 175.0813251 (2)\ttotal: 891ms\tremaining: 13.4s\n",
            "\n",
            "bestTest = 228.6461767\n",
            "bestIteration = 9\n",
            "\n",
            "9:\tloss: 228.6461767\tbest: 175.0813251 (2)\ttotal: 1.03s\tremaining: 13.9s\n",
            "\n",
            "bestTest = 193.7101254\n",
            "bestIteration = 9\n",
            "\n",
            "10:\tloss: 193.7101254\tbest: 175.0813251 (2)\ttotal: 1.19s\tremaining: 14.3s\n",
            "\n",
            "bestTest = 146.0400204\n",
            "bestIteration = 9\n",
            "\n",
            "11:\tloss: 146.0400204\tbest: 146.0400204 (11)\ttotal: 1.33s\tremaining: 14.6s\n",
            "\n",
            "bestTest = 228.6485542\n",
            "bestIteration = 9\n",
            "\n",
            "12:\tloss: 228.6485542\tbest: 146.0400204 (11)\ttotal: 1.47s\tremaining: 14.8s\n",
            "\n",
            "bestTest = 193.755109\n",
            "bestIteration = 9\n",
            "\n",
            "13:\tloss: 193.7551090\tbest: 146.0400204 (11)\ttotal: 1.62s\tremaining: 15.1s\n",
            "\n",
            "bestTest = 146.4995027\n",
            "bestIteration = 9\n",
            "\n",
            "14:\tloss: 146.4995027\tbest: 146.0400204 (11)\ttotal: 1.77s\tremaining: 15.2s\n",
            "\n",
            "bestTest = 228.6609797\n",
            "bestIteration = 9\n",
            "\n",
            "15:\tloss: 228.6609797\tbest: 146.0400204 (11)\ttotal: 1.92s\tremaining: 15.4s\n",
            "\n",
            "bestTest = 193.9606741\n",
            "bestIteration = 9\n",
            "\n",
            "16:\tloss: 193.9606741\tbest: 146.0400204 (11)\ttotal: 2.07s\tremaining: 15.5s\n",
            "\n",
            "bestTest = 147.2916202\n",
            "bestIteration = 9\n",
            "\n",
            "17:\tloss: 147.2916202\tbest: 146.0400204 (11)\ttotal: 2.22s\tremaining: 15.5s\n",
            "\n",
            "bestTest = 227.6559132\n",
            "bestIteration = 16\n",
            "\n",
            "18:\tloss: 227.6559132\tbest: 146.0400204 (11)\ttotal: 2.46s\tremaining: 16.2s\n",
            "\n",
            "bestTest = 174.9707093\n",
            "bestIteration = 16\n",
            "\n",
            "19:\tloss: 174.9707093\tbest: 146.0400204 (11)\ttotal: 2.71s\tremaining: 16.8s\n",
            "\n",
            "bestTest = 128.444206\n",
            "bestIteration = 16\n",
            "\n",
            "20:\tloss: 128.4442060\tbest: 128.4442060 (20)\ttotal: 2.96s\tremaining: 17.4s\n",
            "\n",
            "bestTest = 227.6599135\n",
            "bestIteration = 16\n",
            "\n",
            "21:\tloss: 227.6599135\tbest: 128.4442060 (20)\ttotal: 3.23s\tremaining: 17.9s\n",
            "\n",
            "bestTest = 175.0129152\n",
            "bestIteration = 16\n",
            "\n",
            "22:\tloss: 175.0129152\tbest: 128.4442060 (20)\ttotal: 3.46s\tremaining: 18.2s\n",
            "\n",
            "bestTest = 126.5562172\n",
            "bestIteration = 16\n",
            "\n",
            "23:\tloss: 126.5562172\tbest: 126.5562172 (23)\ttotal: 3.71s\tremaining: 18.5s\n",
            "\n",
            "bestTest = 227.6807754\n",
            "bestIteration = 16\n",
            "\n",
            "24:\tloss: 227.6807754\tbest: 126.5562172 (23)\ttotal: 3.97s\tremaining: 18.9s\n",
            "\n",
            "bestTest = 175.5471923\n",
            "bestIteration = 16\n",
            "\n",
            "25:\tloss: 175.5471923\tbest: 126.5562172 (23)\ttotal: 4.22s\tremaining: 19.2s\n",
            "\n",
            "bestTest = 128.1404455\n",
            "bestIteration = 16\n",
            "\n",
            "26:\tloss: 128.1404455\tbest: 126.5562172 (23)\ttotal: 4.46s\tremaining: 19.3s\n",
            "\n",
            "bestTest = 226.5154029\n",
            "bestIteration = 24\n",
            "\n",
            "27:\tloss: 226.5154029\tbest: 126.5562172 (23)\ttotal: 4.81s\tremaining: 19.9s\n",
            "\n",
            "bestTest = 159.1495984\n",
            "bestIteration = 24\n",
            "\n",
            "28:\tloss: 159.1495984\tbest: 126.5562172 (23)\ttotal: 5.2s\tremaining: 20.6s\n",
            "\n",
            "bestTest = 119.0765011\n",
            "bestIteration = 24\n",
            "\n",
            "29:\tloss: 119.0765011\tbest: 119.0765011 (29)\ttotal: 5.55s\tremaining: 21.1s\n",
            "\n",
            "bestTest = 226.5180775\n",
            "bestIteration = 24\n",
            "\n",
            "30:\tloss: 226.5180775\tbest: 119.0765011 (29)\ttotal: 5.93s\tremaining: 21.6s\n",
            "\n",
            "bestTest = 159.1861347\n",
            "bestIteration = 24\n",
            "\n",
            "31:\tloss: 159.1861347\tbest: 119.0765011 (29)\ttotal: 6.33s\tremaining: 22.2s\n",
            "\n",
            "bestTest = 117.5988122\n",
            "bestIteration = 24\n",
            "\n",
            "32:\tloss: 117.5988122\tbest: 117.5988122 (32)\ttotal: 6.71s\tremaining: 22.6s\n",
            "\n",
            "bestTest = 226.5502954\n",
            "bestIteration = 24\n",
            "\n",
            "33:\tloss: 226.5502954\tbest: 117.5988122 (32)\ttotal: 7.07s\tremaining: 22.9s\n",
            "\n",
            "bestTest = 159.8805631\n",
            "bestIteration = 24\n",
            "\n",
            "34:\tloss: 159.8805631\tbest: 117.5988122 (32)\ttotal: 7.41s\tremaining: 23.1s\n",
            "\n",
            "bestTest = 117.8828062\n",
            "bestIteration = 24\n",
            "\n",
            "35:\tloss: 117.8828062\tbest: 117.5988122 (32)\ttotal: 7.76s\tremaining: 23.3s\n",
            "\n",
            "bestTest = 229.278562\n",
            "bestIteration = 4\n",
            "\n",
            "36:\tloss: 229.2785620\tbest: 117.5988122 (32)\ttotal: 7.9s\tremaining: 22.8s\n",
            "\n",
            "bestTest = 207.6725305\n",
            "bestIteration = 4\n",
            "\n",
            "37:\tloss: 207.6725305\tbest: 117.5988122 (32)\ttotal: 8.06s\tremaining: 22.5s\n",
            "\n",
            "bestTest = 167.263114\n",
            "bestIteration = 4\n",
            "\n",
            "38:\tloss: 167.2631140\tbest: 117.5988122 (32)\ttotal: 8.19s\tremaining: 22.1s\n",
            "\n",
            "bestTest = 229.2881319\n",
            "bestIteration = 4\n",
            "\n",
            "39:\tloss: 229.2881319\tbest: 117.5988122 (32)\ttotal: 8.34s\tremaining: 21.7s\n",
            "\n",
            "bestTest = 207.906259\n",
            "bestIteration = 4\n",
            "\n",
            "40:\tloss: 207.9062590\tbest: 117.5988122 (32)\ttotal: 8.47s\tremaining: 21.3s\n",
            "\n",
            "bestTest = 168.1290142\n",
            "bestIteration = 4\n",
            "\n",
            "41:\tloss: 168.1290142\tbest: 117.5988122 (32)\ttotal: 8.62s\tremaining: 20.9s\n",
            "\n",
            "bestTest = 229.3244066\n",
            "bestIteration = 4\n",
            "\n",
            "42:\tloss: 229.3244066\tbest: 117.5988122 (32)\ttotal: 8.75s\tremaining: 20.6s\n",
            "\n",
            "bestTest = 208.6282242\n",
            "bestIteration = 4\n",
            "\n",
            "43:\tloss: 208.6282242\tbest: 117.5988122 (32)\ttotal: 8.89s\tremaining: 20.2s\n",
            "\n",
            "bestTest = 170.4137477\n",
            "bestIteration = 4\n",
            "\n",
            "44:\tloss: 170.4137477\tbest: 117.5988122 (32)\ttotal: 9.03s\tremaining: 19.9s\n",
            "\n",
            "bestTest = 228.4704691\n",
            "bestIteration = 9\n",
            "\n",
            "45:\tloss: 228.4704691\tbest: 117.5988122 (32)\ttotal: 9.31s\tremaining: 19.8s\n",
            "\n",
            "bestTest = 188.6465887\n",
            "bestIteration = 9\n",
            "\n",
            "46:\tloss: 188.6465887\tbest: 117.5988122 (32)\ttotal: 9.59s\tremaining: 19.8s\n",
            "\n",
            "bestTest = 132.2691232\n",
            "bestIteration = 9\n",
            "\n",
            "47:\tloss: 132.2691232\tbest: 117.5988122 (32)\ttotal: 9.85s\tremaining: 19.7s\n",
            "\n",
            "bestTest = 228.4959341\n",
            "bestIteration = 9\n",
            "\n",
            "48:\tloss: 228.4959341\tbest: 117.5988122 (32)\ttotal: 10.2s\tremaining: 19.7s\n",
            "\n",
            "bestTest = 189.5287687\n",
            "bestIteration = 9\n",
            "\n",
            "49:\tloss: 189.5287687\tbest: 117.5988122 (32)\ttotal: 10.4s\tremaining: 19.6s\n",
            "\n",
            "bestTest = 134.6816167\n",
            "bestIteration = 9\n",
            "\n",
            "50:\tloss: 134.6816167\tbest: 117.5988122 (32)\ttotal: 10.7s\tremaining: 19.5s\n",
            "\n",
            "bestTest = 228.580008\n",
            "bestIteration = 9\n",
            "\n",
            "51:\tloss: 228.5800080\tbest: 117.5988122 (32)\ttotal: 10.9s\tremaining: 19.3s\n",
            "\n",
            "bestTest = 190.7805258\n",
            "bestIteration = 9\n",
            "\n",
            "52:\tloss: 190.7805258\tbest: 117.5988122 (32)\ttotal: 11.2s\tremaining: 19.3s\n",
            "\n",
            "bestTest = 136.6576642\n",
            "bestIteration = 9\n",
            "\n",
            "53:\tloss: 136.6576642\tbest: 117.5988122 (32)\ttotal: 11.5s\tremaining: 19.1s\n",
            "\n",
            "bestTest = 227.3628702\n",
            "bestIteration = 16\n",
            "\n",
            "54:\tloss: 227.3628702\tbest: 117.5988122 (32)\ttotal: 11.9s\tremaining: 19.3s\n",
            "\n",
            "bestTest = 166.9042707\n",
            "bestIteration = 16\n",
            "\n",
            "55:\tloss: 166.9042707\tbest: 117.5988122 (32)\ttotal: 12.4s\tremaining: 19.5s\n",
            "\n",
            "bestTest = 109.779375\n",
            "bestIteration = 16\n",
            "\n",
            "56:\tloss: 109.7793750\tbest: 109.7793750 (56)\ttotal: 12.8s\tremaining: 19.6s\n",
            "\n",
            "bestTest = 227.3949686\n",
            "bestIteration = 16\n",
            "\n",
            "57:\tloss: 227.3949686\tbest: 109.7793750 (56)\ttotal: 13.3s\tremaining: 19.7s\n",
            "\n",
            "bestTest = 167.675207\n",
            "bestIteration = 16\n",
            "\n",
            "58:\tloss: 167.6752070\tbest: 109.7793750 (56)\ttotal: 13.7s\tremaining: 19.8s\n",
            "\n",
            "bestTest = 112.4817128\n",
            "bestIteration = 16\n",
            "\n",
            "59:\tloss: 112.4817128\tbest: 109.7793750 (56)\ttotal: 14.2s\tremaining: 19.9s\n",
            "\n",
            "bestTest = 227.5293193\n",
            "bestIteration = 16\n",
            "\n",
            "60:\tloss: 227.5293193\tbest: 109.7793750 (56)\ttotal: 14.6s\tremaining: 19.9s\n",
            "\n",
            "bestTest = 170.7693858\n",
            "bestIteration = 16\n",
            "\n",
            "61:\tloss: 170.7693858\tbest: 109.7793750 (56)\ttotal: 15.1s\tremaining: 19.9s\n",
            "\n",
            "bestTest = 113.0621609\n",
            "bestIteration = 16\n",
            "\n",
            "62:\tloss: 113.0621609\tbest: 109.7793750 (56)\ttotal: 15.5s\tremaining: 19.9s\n",
            "\n",
            "bestTest = 226.1085373\n",
            "bestIteration = 24\n",
            "\n",
            "63:\tloss: 226.1085373\tbest: 109.7793750 (56)\ttotal: 16.2s\tremaining: 20.2s\n",
            "\n",
            "bestTest = 148.7291189\n",
            "bestIteration = 24\n",
            "\n",
            "64:\tloss: 148.7291189\tbest: 109.7793750 (56)\ttotal: 16.8s\tremaining: 20.5s\n",
            "\n",
            "bestTest = 99.61170525\n",
            "bestIteration = 24\n",
            "\n",
            "65:\tloss: 99.6117053\tbest: 99.6117053 (65)\ttotal: 17.5s\tremaining: 20.7s\n",
            "\n",
            "bestTest = 226.1333469\n",
            "bestIteration = 24\n",
            "\n",
            "66:\tloss: 226.1333469\tbest: 99.6117053 (65)\ttotal: 18.1s\tremaining: 20.8s\n",
            "\n",
            "bestTest = 149.6482298\n",
            "bestIteration = 24\n",
            "\n",
            "67:\tloss: 149.6482298\tbest: 99.6117053 (65)\ttotal: 18.8s\tremaining: 21s\n",
            "\n",
            "bestTest = 101.7311878\n",
            "bestIteration = 24\n",
            "\n",
            "68:\tloss: 101.7311878\tbest: 99.6117053 (65)\ttotal: 19.5s\tremaining: 21.2s\n",
            "\n",
            "bestTest = 226.3210129\n",
            "bestIteration = 24\n",
            "\n",
            "69:\tloss: 226.3210129\tbest: 99.6117053 (65)\ttotal: 20.1s\tremaining: 21.3s\n",
            "\n",
            "bestTest = 152.7405351\n",
            "bestIteration = 24\n",
            "\n",
            "70:\tloss: 152.7405351\tbest: 99.6117053 (65)\ttotal: 20.8s\tremaining: 21.4s\n",
            "\n",
            "bestTest = 102.5182719\n",
            "bestIteration = 24\n",
            "\n",
            "71:\tloss: 102.5182719\tbest: 99.6117053 (65)\ttotal: 21.5s\tremaining: 21.5s\n",
            "\n",
            "bestTest = 229.2564233\n",
            "bestIteration = 4\n",
            "\n",
            "72:\tloss: 229.2564233\tbest: 99.6117053 (65)\ttotal: 21.9s\tremaining: 21.3s\n",
            "\n",
            "bestTest = 206.7967262\n",
            "bestIteration = 4\n",
            "\n",
            "73:\tloss: 206.7967262\tbest: 99.6117053 (65)\ttotal: 22.4s\tremaining: 21.2s\n",
            "\n",
            "bestTest = 165.7394278\n",
            "bestIteration = 4\n",
            "\n",
            "74:\tloss: 165.7394278\tbest: 99.6117053 (65)\ttotal: 22.9s\tremaining: 21.1s\n",
            "\n",
            "bestTest = 229.2826762\n",
            "bestIteration = 4\n",
            "\n",
            "75:\tloss: 229.2826762\tbest: 99.6117053 (65)\ttotal: 23.4s\tremaining: 20.9s\n",
            "\n",
            "bestTest = 207.4106711\n",
            "bestIteration = 4\n",
            "\n",
            "76:\tloss: 207.4106711\tbest: 99.6117053 (65)\ttotal: 23.9s\tremaining: 20.8s\n",
            "\n",
            "bestTest = 167.4046923\n",
            "bestIteration = 4\n",
            "\n",
            "77:\tloss: 167.4046923\tbest: 99.6117053 (65)\ttotal: 24.4s\tremaining: 20.6s\n",
            "\n",
            "bestTest = 229.3204438\n",
            "bestIteration = 4\n",
            "\n",
            "78:\tloss: 229.3204438\tbest: 99.6117053 (65)\ttotal: 24.8s\tremaining: 20.4s\n",
            "\n",
            "bestTest = 208.920641\n",
            "bestIteration = 4\n",
            "\n",
            "79:\tloss: 208.9206410\tbest: 99.6117053 (65)\ttotal: 25.2s\tremaining: 20.1s\n",
            "\n",
            "bestTest = 171.9227975\n",
            "bestIteration = 4\n",
            "\n",
            "80:\tloss: 171.9227975\tbest: 99.6117053 (65)\ttotal: 25.6s\tremaining: 19.9s\n",
            "\n",
            "bestTest = 228.4294583\n",
            "bestIteration = 9\n",
            "\n",
            "81:\tloss: 228.4294583\tbest: 99.6117053 (65)\ttotal: 26.5s\tremaining: 20.1s\n",
            "\n",
            "bestTest = 187.6035222\n",
            "bestIteration = 9\n",
            "\n",
            "82:\tloss: 187.6035222\tbest: 99.6117053 (65)\ttotal: 27.5s\tremaining: 20.2s\n",
            "\n",
            "bestTest = 129.4635236\n",
            "bestIteration = 9\n",
            "\n",
            "83:\tloss: 129.4635236\tbest: 99.6117053 (65)\ttotal: 28.5s\tremaining: 20.3s\n",
            "\n",
            "bestTest = 228.4841649\n",
            "bestIteration = 9\n",
            "\n",
            "84:\tloss: 228.4841649\tbest: 99.6117053 (65)\ttotal: 29.4s\tremaining: 20.4s\n",
            "\n",
            "bestTest = 189.4781924\n",
            "bestIteration = 9\n",
            "\n",
            "85:\tloss: 189.4781924\tbest: 99.6117053 (65)\ttotal: 30.4s\tremaining: 20.5s\n",
            "\n",
            "bestTest = 134.6411749\n",
            "bestIteration = 9\n",
            "\n",
            "86:\tloss: 134.6411749\tbest: 99.6117053 (65)\ttotal: 31.4s\tremaining: 20.5s\n",
            "\n",
            "bestTest = 228.5702494\n",
            "bestIteration = 9\n",
            "\n",
            "87:\tloss: 228.5702494\tbest: 99.6117053 (65)\ttotal: 32.2s\tremaining: 20.5s\n",
            "\n",
            "bestTest = 190.6798181\n",
            "bestIteration = 9\n",
            "\n",
            "88:\tloss: 190.6798181\tbest: 99.6117053 (65)\ttotal: 33s\tremaining: 20.4s\n",
            "\n",
            "bestTest = 138.9578951\n",
            "bestIteration = 9\n",
            "\n",
            "89:\tloss: 138.9578951\tbest: 99.6117053 (65)\ttotal: 33.9s\tremaining: 20.3s\n",
            "\n",
            "bestTest = 227.3066731\n",
            "bestIteration = 16\n",
            "\n",
            "90:\tloss: 227.3066731\tbest: 99.6117053 (65)\ttotal: 35.5s\tremaining: 20.7s\n",
            "\n",
            "bestTest = 165.1109334\n",
            "bestIteration = 16\n",
            "\n",
            "91:\tloss: 165.1109334\tbest: 99.6117053 (65)\ttotal: 37.1s\tremaining: 21s\n",
            "\n",
            "bestTest = 105.8227996\n",
            "bestIteration = 16\n",
            "\n",
            "92:\tloss: 105.8227996\tbest: 99.6117053 (65)\ttotal: 38.7s\tremaining: 21.2s\n",
            "\n",
            "bestTest = 227.3793042\n",
            "bestIteration = 16\n",
            "\n",
            "93:\tloss: 227.3793042\tbest: 99.6117053 (65)\ttotal: 40.2s\tremaining: 21.4s\n",
            "\n",
            "bestTest = 168.3195271\n",
            "bestIteration = 16\n",
            "\n",
            "94:\tloss: 168.3195271\tbest: 99.6117053 (65)\ttotal: 41.8s\tremaining: 21.5s\n",
            "\n",
            "bestTest = 111.4230156\n",
            "bestIteration = 16\n",
            "\n",
            "95:\tloss: 111.4230156\tbest: 99.6117053 (65)\ttotal: 43.4s\tremaining: 21.7s\n",
            "\n",
            "bestTest = 227.5517388\n",
            "bestIteration = 16\n",
            "\n",
            "96:\tloss: 227.5517388\tbest: 99.6117053 (65)\ttotal: 44.9s\tremaining: 21.7s\n",
            "\n",
            "bestTest = 169.7782778\n",
            "bestIteration = 16\n",
            "\n",
            "97:\tloss: 169.7782778\tbest: 99.6117053 (65)\ttotal: 46.3s\tremaining: 21.7s\n",
            "\n",
            "bestTest = 117.6587223\n",
            "bestIteration = 16\n",
            "\n",
            "98:\tloss: 117.6587223\tbest: 99.6117053 (65)\ttotal: 47.8s\tremaining: 21.7s\n",
            "\n",
            "bestTest = 226.0286268\n",
            "bestIteration = 24\n",
            "\n",
            "99:\tloss: 226.0286268\tbest: 99.6117053 (65)\ttotal: 50.1s\tremaining: 22s\n",
            "\n",
            "bestTest = 145.6546565\n",
            "bestIteration = 24\n",
            "\n",
            "100:\tloss: 145.6546565\tbest: 99.6117053 (65)\ttotal: 52.5s\tremaining: 22.3s\n",
            "\n",
            "bestTest = 93.93459986\n",
            "bestIteration = 24\n",
            "\n",
            "101:\tloss: 93.9345999\tbest: 93.9345999 (101)\ttotal: 54.8s\tremaining: 22.6s\n",
            "\n",
            "bestTest = 226.1047273\n",
            "bestIteration = 24\n",
            "\n",
            "102:\tloss: 226.1047273\tbest: 93.9345999 (101)\ttotal: 57s\tremaining: 22.7s\n",
            "\n",
            "bestTest = 150.3476524\n",
            "bestIteration = 24\n",
            "\n",
            "103:\tloss: 150.3476524\tbest: 93.9345999 (101)\ttotal: 59.4s\tremaining: 22.8s\n",
            "\n",
            "bestTest = 98.8928511\n",
            "bestIteration = 24\n",
            "\n",
            "104:\tloss: 98.8928511\tbest: 93.9345999 (101)\ttotal: 1m 1s\tremaining: 22.9s\n",
            "\n",
            "bestTest = 226.3984646\n",
            "bestIteration = 24\n",
            "\n",
            "105:\tloss: 226.3984646\tbest: 93.9345999 (101)\ttotal: 1m 3s\tremaining: 22.9s\n",
            "\n",
            "bestTest = 151.7734651\n",
            "bestIteration = 24\n",
            "\n",
            "106:\tloss: 151.7734651\tbest: 93.9345999 (101)\ttotal: 1m 6s\tremaining: 22.8s\n",
            "\n",
            "bestTest = 105.6418218\n",
            "bestIteration = 24\n",
            "\n",
            "107:\tloss: 105.6418218\tbest: 93.9345999 (101)\ttotal: 1m 8s\tremaining: 22.8s\n",
            "Estimating final quality...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJMmdMWJqQGS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "07ea35f1-b7b9-4b4c-c00a-8d41c1f119c5"
      },
      "source": [
        "# Посчитаем RMSE, MAE, R^2 на тестовой выборке\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "print(\"RMSE:\", np.sqrt(mean_squared_error(y_test, y_pred)))\n",
        "print(\"MAE:\", mean_absolute_error(y_test, y_pred))\n",
        "print(\"R^2:\", r2_score(y_test, y_pred))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 102.63750863419821\n",
            "MAE: 45.85677803106738\n",
            "R^2: 0.7490074661405318\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_wT7uNssRaQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "754d4a5e-e202-4fa8-e3c0-f213e112369b"
      },
      "source": [
        "# Рассмотрим кривые обучения для разных статистик\n",
        "from matplotlib import pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "cv = grid_search_result['cv_results']\n",
        "x = cv['iterations']\n",
        "plt.plot(x, cv['test-RMSE-mean'], label='test RMSE mean')\n",
        "plt.plot(x, cv['test-RMSE-std'], label='test RMSE std')\n",
        "plt.plot(x, cv['train-RMSE-mean'], label='train RMSE mean')\n",
        "plt.plot(x, cv['train-RMSE-std'], label='train RMSE std')\n",
        "\n",
        "\n",
        "plt.xlabel('номер итерации')\n",
        "plt.ylabel('значение статистики')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0i31lNYOn6xD",
        "colab_type": "text"
      },
      "source": [
        "# Выводы по бейзлайну:\n",
        "\n",
        "\n",
        "\n",
        "1.   Далее сравниваем качество по R^2. Для CatBoost $R^2=0.749$ -- бейзлайн.\n",
        "2.   Получили значения гиперпараметров:\n",
        "`{'depth': 10, 'iterations': 25, 'learning_rate': 0.1, 'l2_leaf_reg': 0.5}`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jP1k3aY4nEbL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}