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nishanth6/wns-analytics.ipynb

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wns-analytics.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Librerías**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import functions as f\n",
"import importlib\n",
"from pathlib import Path\n",
"import pandas as pd\n",
"import numpy as np\n",
"import scipy\n",
"import ast\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import math\n",
"\n",
"color = sns.color_palette()\n",
"%matplotlib inline\n",
"\n",
"import plotly.offline as py\n",
"py.init_notebook_mode(connected=True)\n",
"import plotly.graph_objs as go\n",
"import plotly.tools as tls\n",
"\n",
"\n",
"from math import sqrt\n",
"from sklearn import preprocessing, linear_model, naive_bayes, metrics, svm\n",
"from sklearn import ensemble as ske\n",
"#from sklearn.linear_model import SGDClassifier\n",
"#from sklearn.tree import DecisionTreeClassifier\n",
"#from sklearn.linear_model import LogisticRegression\n",
"#from sklearn.decomposition import PCA\n",
"#from sklearn.preprocessing import StandardScaler\n",
"from sklearn.svm import SVC\n",
"\n",
"from sklearn.model_selection import KFold,cross_val_score,train_test_split,RandomizedSearchCV,GridSearchCV\n",
"\n",
"\n",
"from scipy.stats import randint as sp_randint\n",
"from scipy.stats import uniform as sp_uniform\n",
"\n",
"import xgboost as xgb\n",
"import catboost as cgb\n",
"import lightgbm as lgb\n",
"from sklearn import decomposition, ensemble\n",
"\n",
"\n",
"f = importlib.reload(f)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
},
"source": [
"Los tres archivos otorgados son:\n",
"\n",
"* train.csv - Operaciones con el dato de si son apócrifas o no\n",
"* test.csv - Operaciones a predecir su tipo.\n",
"* ejemploRespuesta.csv - Formato de entrega de solución\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "02081c71-977c-4f5a-b990-b1d0dc8bb858",
"_uuid": "67e924346e31d55b07a38a1038da815d76e28356",
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filas y columnas de train : (54808, 14)\n",
"Filas y columnas de test: (23490, 13)\n"
]
}
],
"source": [
"# Lectura data\n",
"train_o = pd.read_csv('./train.csv')\n",
"test_o = pd.read_csv('./test.csv')\n",
"\n",
"print(\"Filas y columnas de train : \", train_o.shape)\n",
"print(\"Filas y columnas de test: \", test_o.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Funciones a utilizar en el análisis**"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def featureEngineeringBTree(dat, removeCorrelated=False, withLabelEncoder=False,fillNA=False):\n",
" \"\"\"Función principal de manejo de variables.\n",
" dat - corresponde al set de datos total \n",
" \n",
" Devuelve un set de datos modificado\n",
" \n",
" \"\"\"\n",
" \n",
" label = preprocessing.LabelEncoder()\n",
" #convertColumns = ['level_type','tags','country']\n",
" convertColumns = dat.columns\n",
" convertColumns = convertColumns.tolist()\n",
" nums = dat.describe().columns.tolist() \n",
" [convertColumns.remove(x) for x in nums]\n",
" dat[convertColumns] = dat[convertColumns].fillna('NN')\n",
" if(withLabelEncoder):\n",
" dat[convertColumns] = dat[convertColumns].apply(label.fit_transform) \n",
" else: \n",
" for i in convertColumns: \n",
" dat[i] = pd.Categorical(dat[i])\n",
" \n",
" \n",
" # Borrar columnas sin variación\n",
" #--------------------------- \n",
" for i in dat.columns:\n",
" if (len(dat[i].value_counts(dropna=False))==1):\n",
" print(\"Drop by no variation:\",i)\n",
" dat.drop(i,axis=1, inplace=True)\n",
" \n",
" # Borrar otras variables\n",
" #--------------------------- \n",
" dat.drop(['employee_id','is_promoted'],axis=1, inplace=True)\n",
" if(fillNA):\n",
" dat.previous_year_rating = dat.previous_year_rating.fillna(0)\n",
" \n",
" return dat\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Armado modelo y predicciones**"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LGB auc: 0.91303 auc-stdv: 0.005382 best It: 995\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 2 out of 5 | elapsed: 8.5s remaining: 12.9s\n",
"[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 11.1s remaining: 0.0s\n",
"[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 11.1s finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5003019330710279\n"
]
}
],
"source": [
"NFOLDS = 5\n",
"SEED = 0\n",
"\n",
"train_objs_num = len(train_o)\n",
"Ytrain = train_o.is_promoted.values\n",
"test_id = test_o[\"employee_id\"].values\n",
"if 'df_all' in globals():\n",
" del df_all\n",
"df_all = pd.concat(objs=[train_o, test_o], axis=0)\n",
"\n",
"# Feature Enginering\n",
"df_all = featureEngineeringBTree(df_all)\n",
"\n",
"train = df_all[:train_objs_num]\n",
"test = df_all[train_objs_num:]\n",
"\n",
"# LGB -------------------------------------------------------------------------\n",
"lgb_opt_params = {\n",
" 'learning_rate':0.01, \n",
" 'boosting_type': 'gbdt',\n",
" \"objective\" : \"binary\",\n",
" \"metric\" : \"auc\",\n",
" \"num_leaves\" : 40,\n",
" \"bagging_fraction\" : 0.8,\n",
" \"feature_fraction\" : 0.6,\n",
" \"reg_alpha\": 2, \n",
" \"reg_lambda\":0.1,\n",
" \"bagging_seed\" : 2018,\n",
" \"verbosity\" : -1\n",
" }\n",
"\n",
"lgbh = f.SklearnHelper(clf=lgb.LGBMClassifier, seed=SEED, params=lgb_opt_params )\n",
"\n",
"\n",
"# Optimizo parametro n_estimators\n",
"cvlgm = lgb.cv(lgb_opt_params,lgb.Dataset(train,label=Ytrain),stratified=True,\n",
" verbose_eval=False,early_stopping_rounds=70,num_boost_round=1000,seed=SEED)\n",
"lgbParams = lgb_opt_params.copy()\n",
"lgbParams['n_estimators']=len(cvlgm['auc-mean'])\n",
"categs = lgbParams.pop('categorical_column', None)\n",
"lgbh.clf.set_params(**lgbParams)\n",
"print(\"LGB auc:\",round(cvlgm['auc-mean'][-1],5),\" auc-stdv:\",round(cvlgm['auc-stdv'][-1],6), \" best It:\",len(cvlgm['auc-mean']))\n",
"\n",
"kf = KFold(n_splits=5, shuffle=True, random_state=SEED)\n",
"cvlgm = lgbh.get_cv_score(train, Ytrain, kf)\n",
"print(cvlgm.mean())\n",
"cvPred = lgbh.cross_val_predict(train, Ytrain, kf,method='predict_proba')\n",
"cvPred= cvPred[:,1]\n",
"average_precision = metrics.average_precision_score(Ytrain, cvPred)\n",
"precision, recall, thresholds = metrics.precision_recall_curve(Ytrain, cvPred)\n",
"#for i in np.arange(0.20, 0.40, 0.01):\n",
"# print(i,' - ',metrics.f1_score(Ytrain, np.where(cvPred>i,1,0)))\n",
" #max 0.29\n",
" \n",
"# Entreno modelo\n",
"lgbh.clf.fit(train, Ytrain)\n",
"# Realizo predicciones\n",
"pModelLGB = lgbh.clf.predict_proba(test)[:,1]\n",
"\n",
"thresholdTrain=0.29\n",
"posTest=int(len(pModelLGB)*cvPred[cvPred>thresholdTrain].shape[0]/cvPred.shape[0])\n",
"thresholdTest = pd.Series(pModelLGB).sort_values(ascending=False).iloc[posTest]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"XGB auc: 0.90879 auc-stdv: 0.004279 best It: 115\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 2 out of 5 | elapsed: 2.7s remaining: 4.2s\n",
"[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 4.7s remaining: 0.0s\n",
"[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 4.7s finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.4986770031730707\n"
]
}
],
"source": [
"NFOLDS = 5\n",
"SEED = 0\n",
"\n",
"train_objs_num = len(train_o)\n",
"Ytrain = train_o.is_promoted.values\n",
"test_id = test_o[\"employee_id\"].values\n",
"if 'df_all' in globals():\n",
" del df_all\n",
"df_all = pd.concat(objs=[train_o, test_o], axis=0)\n",
"\n",
"# Feature Enginering\n",
"df_all = featureEngineeringBTree(df_all,withLabelEncoder=True,fillNA=True)\n",
"\n",
"train = df_all[:train_objs_num]\n",
"test = df_all[train_objs_num:]\n",
"\n",
"# XGB -------------------------------------------------------------------------\n",
"xgb_params = {\n",
" #'learn-rate' \n",
" 'booster': \"gbtree\",\n",
" 'eta':0.1,\n",
" 'eval_metric':'auc',\n",
" 'max_depth': 6,\n",
" 'min_child_weight': 4,\n",
" 'subsample':0.7,\n",
" 'colsample_bytree':0.7,\n",
" 'objective': 'binary:logistic',\n",
" 'n_jobs': 4\n",
" }\n",
"\n",
"xgbh = f.SklearnHelper(clf=xgb.XGBClassifier, seed=SEED, params=xgb_params )\n",
"cvxgb = xgb.cv(xgb_params,xgb.DMatrix(train,label=Ytrain),stratified=True,\n",
" verbose_eval=False,early_stopping_rounds=50,num_boost_round=1000,seed=SEED )#0.9895\n",
"xgbParams = xgb_params.copy()\n",
"xgbParams['n_estimators']=len(cvxgb)\n",
"xgbh.clf.set_params(**xgbParams)\n",
"\n",
"print(\"XGB auc:\",round(float(cvxgb.loc[:,'test-auc-mean'].tail(1)),5),\" auc-stdv:\",round(float(cvxgb.loc[:,'test-auc-std'].tail(1)),6),\" best It:\",len(cvxgb))\n",
"cvxgb = xgbh.get_cv_score(train, Ytrain, kf)\n",
"print(cvxgb.mean())\n",
"\n",
"\n",
"# Entreno modelo\n",
"xgbh.clf.fit(train, Ytrain)\n",
"# Realizo predicciones\n",
"pModelXGB = xgbh.clf.predict_proba(test)[:,1]\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learning rate set to 0.115269\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\sdonikian\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning:\n",
"\n",
"From version 0.21, test_size will always complement train_size unless both are specified.\n",
"\n"
]
},
{
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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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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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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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0===e[n].data.visible||e[n].data.visible?\"block\":\"none\"}},m=n.select(this).selectAll(\"g.layer\").data(s);m.enter().append(\"g\").attr({class:\"layer\"});var y=m.selectAll(\"path.mark\").data(function(t,e){return t});y.enter().append(\"path\").attr({class:\"mark\"}),y.style(v).each(f[t.geometryType]),y.exit().remove(),m.exit().remove()})}var e=[i.PolyChart.defaultConfig()],r=n.dispatch(\"hover\"),a={solid:\"none\",dash:[5,2],dot:[2,5]};return t.config=function(t){return 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i.PolyChart()},i.DotPlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"dot\",dotType:\"circle\"}}},i.LinePlot=function(){return i.PolyChart()},i.LinePlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"line\"}}},i.Legend=function(){function t(){var r=e.legendConfig,a=e.data.map(function(t,e){return[].concat(t).map(function(t,n){var a=o({},r.elements[e]);return a.name=t,a.color=[].concat(r.elements[e].color)[n],a})}),i=n.merge(a);i=i.filter(function(t,e){return r.elements[e]&&(r.elements[e].visibleInLegend||void 0===r.elements[e].visibleInLegend)}),r.reverseOrder&&(i=i.reverse());var l=r.container;(\"string\"==typeof l||l.nodeName)&&(l=n.select(l));var s=i.map(function(t,e){return t.color}),c=r.fontSize,u=null==r.isContinuous?\"number\"==typeof 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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 Math.max((t||0)/b,3)},v.ppad=m.ppad=Array.isArray(c)?c.map(x):x(c)}l(e),!(\"tozerox\"===e.fill||\"tonextx\"===e.fill&&t.firstscatter)||h[0]===h[g-1]&&p[0]===p[g-1]?e.error_y.visible||[\"tonexty\",\"tozeroy\"].indexOf(e.fill)===-1&&(i.hasMarkers(e)||i.hasText(e))||(v.padded=!1,v.ppad=0):v.tozero=!0,!(\"tozeroy\"===e.fill||\"tonexty\"===e.fill&&t.firstscatter)||h[0]===h[g-1]&&p[0]===p[g-1]?[\"tonextx\",\"tozerox\"].indexOf(e.fill)!==-1&&(m.padded=!1):m.tozero=!0,a.expand(f,h,v),a.expand(d,p,m);var _=new Array(g);for(u=0;u<g;u++)_[u]=n(h[u])&&n(p[u])?{x:h[u],y:p[u]}:{x:o,y:o},e.ids&&(_[u].id=String(e.ids[u]));return s(_,e),t.firstscatter=!1,_}},{\"../../constants/numerical\":122,\"../../plots/cartesian/axes\":171,\"./arrays_to_calcdata\":239,\"./colorscale_calc\":244,\"./subtypes\":260,\"fast-isnumeric\":10}],242:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=0;e<t.length;e++){var r=t[e];if(\"scatter\"===r.type){var n=r.fill;if(\"none\"!==n&&\"toself\"!==n&&(r.opacity=void 0,\"tonexty\"===n||\"tonextx\"===n))for(var a=e-1;a>=0;a--){var o=t[a];if(\"scatter\"===o.type&&o.xaxis===r.xaxis&&o.yaxis===r.yaxis){o.opacity=void 0;break}}}}}},{}],243:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../lib\"),o=t(\"../../plots/plots\"),i=t(\"../../components/colorscale\"),l=t(\"../../components/colorbar/draw\");e.exports=function(t,e){var r=e[0].trace,s=r.marker,c=\"cb\"+r.uid;if(t._fullLayout._infolayer.selectAll(\".\"+c).remove(),void 0===s||!s.showscale)return void o.autoMargin(t,c);var u=s.color,f=s.cmin,d=s.cmax;n(f)||(f=a.aggNums(Math.min,null,u)),n(d)||(d=a.aggNums(Math.max,null,u));var 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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",
" "
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"text/html": [
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"<p>\n",
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"data": {
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