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  "metadata": {
    "colab": {
      "name": "cat_boost.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
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      "name": "python2",
      "display_name": "Python 2"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gmihaila/machine_learning_toolbox/blob/master/cat_boost.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "XWorsZZgWvko",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### CatBoost is a machine learning method based on gradient boosting over decision trees.\n",
        "\n",
        "#### Main advantages of CatBoost:\n",
        "\n",
        "* **Superior quality when compared with other GBDT libraries.**\n",
        "* **Best in class inference speed.**\n",
        "* **Support for both numerical and categorical features.**\n",
        "* **Fast GPU and multi-GPU support for training (compiled binaries and python package for learning on one host, build cmd-line MPI version from source to learn on several GPU machines).**\n",
        "* **Data visualization tools included.**\n",
        "\n",
        "\n",
        "### [Tutorial](https://medium.com/bigdatarepublic/feature-importance-whats-in-a-name-79532e59eea3)\n",
        " \n",
        "### [Guide](https://github.com/catboost/catboost/blob/master/catboost/tutorials/catboost_python_tutorial.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "OGKHkNADhq5V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "aa94ac50-db63-4938-efef-2e2b0017cfbb"
      },
      "cell_type": "code",
      "source": [
        "# INSTALLS\n",
        "\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Install pydot\n",
        "print(\"Installing graphiz and pydot\")\n",
        "!apt-get -qq install -y graphviz && pip install -q pydot\n",
        "clear_output()\n",
        "\n",
        "#Install non-standard packages (assuming jupyter notebook)\n",
        "print(\"Installing shap\")\n",
        "!pip install shap\n",
        "clear_output()\n",
        "\n",
        "print(\"Installing lime\")\n",
        "!pip install lime\n",
        "clear_output()\n",
        "\n",
        "#eli5 package (https://eli5.readthedocs.io/en/latest)\n",
        "print(\"Installing eli5\")\n",
        "!pip install eli5\n",
        "clear_output()\n",
        "\n",
        "print(\"Installing catboost\")\n",
        "!pip install catboost\n",
        "clear_output()\n",
        "\n",
        "print(\"Installing ipywidgets\")\n",
        "!pip install ipywidgets\n",
        "clear_output()\n",
        "\n",
        "print(\"Downloading pima-indians-diabetes\")\n",
        "!wget https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\n",
        "clear_output()\n",
        "\n",
        "!ls"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model.png\t\t\t  pima-indians-diabetes.data.csv.2\n",
            "pima-indians-diabetes.data.csv\t  pima-indians-diabetes.data.csv.3\n",
            "pima-indians-diabetes.data.csv.1  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U_nb5Lu7h0us",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "39d0bf06-ff68-4b72-8f6a-bbe12ba5a8b6"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from catboost import CatBoostClassifier, Pool, cv\n",
        "\n",
        "#scikit-learn package (https://pypi.org/project/scikit-learn)\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, auc, roc_auc_score\n",
        "from sklearn.model_selection import train_test_split #TAKES NUMPY OR DATA FRAME!!\n",
        "from sklearn.metrics.scorer import make_scorer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import eli5\n",
        "from eli5.sklearn import PermutationImportance\n",
        "\n",
        "import itertools\n",
        "\n",
        "\n",
        "#eli5 package (https://eli5.readthedocs.io/en/latest)\n",
        "import eli5\n",
        "from eli5.sklearn import PermutationImportance\n",
        "\n",
        "#lime package (https://github.com/marcotcr/lime)\n",
        "import lime\n",
        "import lime.lime_tabular\n",
        "\n",
        "#shap package (https://github.com/slundberg/shap)\n",
        "import shap\n",
        "\n",
        "#Split train and test set.\n",
        "RANDOM_STATE = 123\n",
        "\n",
        "# Parse data\n",
        "path_file = 'pima-indians-diabetes.data.csv'\n",
        "\n",
        "df = pd.read_csv(path_file, header=None)\n",
        "\n",
        "x_df = df.drop(df.columns[8],axis=1)\n",
        "y_df = df[df.columns[8]]\n",
        "\n",
        "df.head()\n",
        "\n",
        "\n",
        "\n",
        "# Parse data\n",
        "# path_file = 'BayesianNetworkGenerator_breast-cancer_small.csv'\n",
        "\n",
        "# df = pd.read_csv(path_file, header=None)\n",
        "\n",
        "# x_df = df.drop(df.columns[9],axis=1)\n",
        "# y_df = df[df.columns[9]]\n",
        "\n",
        "\n",
        "# df.head()\n",
        "\n",
        "\n",
        "# X, X_test, y, y_test = train_test_split(x_df, y_df, train_size=0.90, random_state=42)\n",
        "# X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.85, random_state=RANDOM_STATE)\n",
        "\n",
        "\n",
        "# print(\"Train: \", X_train.shape[0])\n",
        "# print(\"Validation: \",X_validation.shape[0])\n",
        "# print(\"Test: \",X_test.shape[0])\n",
        "\n",
        "\n",
        "# x_tr = pd.get_dummies(X_train)\n",
        "# y_tr = [1 if a == 'no-recurrence-events' else 0 for a in y_train]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {
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      ]
    },
    {
      "metadata": {
        "id": "h5C1j4ahquKY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "26370833-df3e-4271-cfe5-676b2f290063"
      },
      "cell_type": "code",
      "source": [
        "categorical_features_indices = np.where(x_df.dtypes != np.float)[0];\n",
        "categorical_features_indices"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "UhZZ-qwVi0TX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "426dcf00-d40d-472f-8dce-a84772df518b"
      },
      "cell_type": "code",
      "source": [
        "X, X_test, y, y_test = train_test_split(x_df, y_df, train_size=0.85, random_state=42)\n",
        "X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.85, random_state=RANDOM_STATE)\n",
        "\n",
        "\n",
        "print(\"Train: \", X_train.shape[0])\n",
        "print(\"Validation: \",X_validation.shape[0])\n",
        "print(\"Test: \",X_test.shape[0])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Train: ', 554)\n",
            "('Validation: ', 98)\n",
            "('Test: ', 116)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "V3xNLV2-iIn9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17901
        },
        "outputId": "b4ff3621-d263-4b46-fcf8-45049898317b"
      },
      "cell_type": "code",
      "source": [
        "# CatBoost\n",
        "\n",
        "model = None\n",
        "del model\n",
        "\n",
        "\n",
        "model = CatBoostClassifier(\n",
        "    custom_loss=['Accuracy'],\n",
        "    random_seed=42,\n",
        "    logging_level='Silent'\n",
        ")\n",
        "\n",
        "# https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_catboostregressor_fit-docpage/\n",
        "\n",
        "model.fit(X, y,\n",
        "          cat_features=categorical_features_indices,\n",
        "          eval_set=(X_validation, y_validation),\n",
        "          logging_level='Verbose',  # you can uncomment this for text output)\n",
        "          plot=True)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
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            "999:\tlearn: 0.0641624\ttest: 0.2837032\tbest: 0.2815733 (940)\ttotal: 31.5s\tremaining: 0us\n",
            "\n",
            "bestTest = 0.2815733046\n",
            "bestIteration = 940\n",
            "\n",
            "Shrink model to first 941 iterations.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f439838e850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "ojJ8zM30y6q4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17903
        },
        "outputId": "13a10705-801f-4913-b4e5-2127bf2609d6"
      },
      "cell_type": "code",
      "source": [
        "# Model Evaluation using Cross validation\n",
        "\n",
        "cv_params = model.get_params()\n",
        "cv_params.update({\n",
        "    'loss_function': 'Logloss'\n",
        "})\n",
        "cv_data = cv(\n",
        "    Pool(X, y, cat_features=categorical_features_indices),\n",
        "    cv_params,\n",
        "    logging_level='Verbose', \n",
        "    plot=True\n",
        ")\n",
        "\n",
        "print('Best validation accuracy score: {:.2f}±{:.2f} on step {}'.format(\n",
        "    np.max(cv_data['test-Accuracy-mean']),\n",
        "    cv_data['test-Accuracy-std'][np.argmax(cv_data['test-Accuracy-mean'])],\n",
        "    np.argmax(cv_data['test-Accuracy-mean'])\n",
        "))\n",
        "\n",
        "print('Precise validation accuracy score: {}'.format(np.max(cv_data['test-Accuracy-mean'])))"
      ],
      "execution_count": 6,
      "outputs": [
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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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              "/* 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.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",
              "                }\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",
              "\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",
              "\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",
              "\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",
              "    }\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",
              "\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",
              "\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",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9cf8973cce724f5c82c8d1d4fc18ebb1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "MetricVisualizer(layout=Layout(align_self=u'stretch', height=u'500px'))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
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            "994:\tlearn: 0.2478595\ttest: 0.5389029\tbest: 0.5255444 (480)\ttotal: 1m 46s\tremaining: 537ms\n",
            "995:\tlearn: 0.2476490\ttest: 0.5390686\tbest: 0.5255444 (480)\ttotal: 1m 46s\tremaining: 430ms\n",
            "996:\tlearn: 0.2474474\ttest: 0.5391715\tbest: 0.5255444 (480)\ttotal: 1m 47s\tremaining: 322ms\n",
            "997:\tlearn: 0.2473014\ttest: 0.5392234\tbest: 0.5255444 (480)\ttotal: 1m 47s\tremaining: 215ms\n",
            "998:\tlearn: 0.2471712\ttest: 0.5392965\tbest: 0.5255444 (480)\ttotal: 1m 47s\tremaining: 108ms\n",
            "999:\tlearn: 0.2468339\ttest: 0.5393644\tbest: 0.5255444 (480)\ttotal: 1m 47s\tremaining: 0us\n",
            "Best validation accuracy score: 0.73±0.03 on step 612\n",
            "Precise validation accuracy score: 0.725482320777\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:52: FutureWarning: 'argmax' is deprecated. Use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. Use 'series.values.argmax' to get the position of the maximum now.\n",
            "  return getattr(obj, method)(*args, **kwds)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "T5wtUR-oS-ku",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Get predictions\n",
        "y_predict = model.predict(X_test)\n",
        "\n",
        "# Probability of malignant tissue produced by the model\n",
        "y_prob = [probs[1] for probs in model.predict_proba(X_test)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_ksKHh4WS7hH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "e46fb00f-7f77-43da-ec4c-656be7d7f9a8"
      },
      "cell_type": "code",
      "source": [
        "#Accuracy on test set\n",
        "print(\"Test accuracy: %s\"%(accuracy_score(y_test, y_predict).round(2)))\n",
        "\n",
        "# Confusion matrix test set\n",
        "pd.DataFrame(\n",
        "    confusion_matrix(y_test, y_predict),\n",
        "    columns=['Predicted NO', 'Predicted YES'],\n",
        "    index=['Actual NO', 'Actual YES']\n",
        ")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted NO</th>\n",
              "      <th>Predicted YES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual NO</th>\n",
              "      <td>52</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual YES</th>\n",
              "      <td>11</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Predicted NO  Predicted YES\n",
              "Actual NO             52             24\n",
              "Actual YES            11             29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "_8S56V8DUrF7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "a65b855b-495b-4aa4-b276-a43c65a867ef"
      },
      "cell_type": "code",
      "source": [
        "# Compute area under the curve\n",
        "fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "#Set default figure size\n",
        "plt.rcParams['figure.figsize'] = (8,8)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title(\"Title\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hpzwyHc5H/GGbeEjqGESSEAQBr7yyAw8fZqNjx5oICxsGFxfO+24sRCv2lJQU+Pj46B67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\nQ4MGDRg6dChJSUl69zd8+GaOHr2KrW1T/vUvLxw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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f439838eb50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vnGCtq2YVjz1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "d9ed1eaf-5fd5-4f5b-e47c-6a7ce4f207ac"
      },
      "cell_type": "code",
      "source": [
        "# Feature importance dataframe\n",
        "imp_df = pd.DataFrame({'feature': X_train.columns.values,\n",
        "                       'importance': model.feature_importances_})\n",
        " \n",
        "# Reorder by importance\n",
        "ordered_df = imp_df.sort_values(by='importance')\n",
        "imp_range=range(1,len(imp_df.index)+1)\n",
        " \n",
        "## Barplot with confidence intervals\n",
        "height = ordered_df['importance']\n",
        "bars = ordered_df['feature']\n",
        "y_pos = np.arange(len(bars))\n",
        "\n",
        "# Create horizontal bars\n",
        "plt.barh(y_pos, height)\n",
        " \n",
        "# Create names on the y-axis\n",
        "plt.yticks(y_pos, bars)\n",
        "\n",
        "plt.xlabel(\"Mean reduction in tree impurity in random forest\")\n",
        "\n",
        "plt.tight_layout()\n",
        "# Show graphic\n",
        "plt.show()\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y2EMDACRP0AAAyRM0AEDyBA0AkDxBAwAkT9AAAMkTNABA8gQNAJA8QQMAJE/QAADJEzQA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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4386fe6f50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zPPUsEA5Tlij",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "6ce821c5-ee92-45e2-d831-b1ff284509d6"
      },
      "cell_type": "code",
      "source": [
        "# Feature importance based on TRAINING set\n",
        "\n",
        "perm_test = PermutationImportance(model, scoring=make_scorer(roc_auc_score),\n",
        "                                   n_iter=50, random_state=RANDOM_STATE, cv=\"prefit\")\n",
        "\n",
        "# fit and see the permuation importances\n",
        "perm_test.fit(X_train, y_train)\n",
        "\n",
        "imp_df = eli5.explain_weights_df(perm_test)\n",
        "label_df = pd.DataFrame({'feature': [ \"x\" + str(i) for i in range(len(X_test.columns))], 'feature_name': X_test.columns.values})\n",
        "imp_df = pd.merge(label_df, imp_df, on='feature', how='inner', validate=\"one_to_one\")\n",
        " \n",
        "# Reorder by importance\n",
        "ordered_df = imp_df.sort_values(by='weight')\n",
        "imp_range=range(1,len(imp_df.index)+1)\n",
        " \n",
        "\n",
        "## Barplot with confidence intervals\n",
        "\n",
        "height = ordered_df['weight']\n",
        "bars = ordered_df['feature_name']\n",
        "ci = 1.96 * ordered_df['std']\n",
        "y_pos = np.arange(len(bars))\n",
        "\n",
        "# Create horizontal bars\n",
        "plt.barh(y_pos, height, xerr=ci)\n",
        " \n",
        "# Create names on the y-axis\n",
        "plt.yticks(y_pos, bars)\n",
        "\n",
        "plt.xlabel(\"Permutation feature importance training set (decrease in AUC)\")\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show graphic\n",
        "plt.show()\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ssccecfrpp8fFF18cS5cujRUrVsS3vvWtOPvss2PatGn5dR80aFD07ds3Xnjh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\nQPL+HzkRg9lN6dgpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f438717c8d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}