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minjk-bl
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Add ML > DataSets
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‎data/m_ml/mlLibrary.js

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@@ -20,6 +20,110 @@ define([
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* ]
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*/
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var ML_LIBRARIES = {
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/** Data Sets */
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'load_boston': {
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name: 'load_boston',
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import: 'from sklearn.datasets import load_boston',
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code: 'load_boston()',
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options: [
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]
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},
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'load_iris': {
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name: 'load_iris',
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import: 'from sklearn.datasets import load_iris',
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code: 'load_iris()',
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options: [
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]
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},
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'load_diabetes': {
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name: 'load_diabetes',
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import: 'from sklearn.datasets import load_diabetes',
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code: 'load_diabetes()',
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options: [
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]
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},
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'load_digits': {
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name: 'load_digits',
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import: 'from sklearn.datasets import load_digits',
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code: 'load_digits(${n_class})',
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options: [
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{ name: 'n_class', component: ['input_number'], default: 10, usePair: true },
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]
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},
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'load_linnerud': {
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name: 'load_linnerud',
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import: 'from sklearn.datasets import load_linnerud',
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code: 'load_linnerud()',
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options: [
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]
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},
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'load_wine': {
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name: 'load_wine',
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import: 'from sklearn.datasets import load_wine',
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code: 'load_wine()',
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options: [
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]
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},
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'load_breast_cancer': {
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name: 'load_breast_cancer',
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import: 'from sklearn.datasets import load_breast_cancer',
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code: 'load_breast_cancer()',
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options: [
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]
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},
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'make_classification': {
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name: 'make_classification',
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import: 'from sklearn.datasets import make_classification',
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code: 'make_classification(${n_samples}${n_features}${n_repeated}${n_classes}${shuffle}${random_state}${etc})',
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options: [
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{ name: 'n_samples', component: ['input_number'], default: 100, usePair: true },
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{ name: 'n_features', component: ['input_number'], default: 20, usePair: true },
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{ name: 'n_repeated', component: ['input_number'], default: 0, usePair: true },
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{ name: 'n_classes', component: ['input_number'], default: 2, usePair: true },
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{ name: 'shuffle', component: ['bool_select'], default: 'True', usePair: true },
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{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true }
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]
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},
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'make_blobs': {
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name: 'make_blobs',
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import: 'from sklearn.datasets import make_blobs',
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code: 'make_blobs(${n_samples}${n_features}${shuffle}${random_state}${etc})',
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options: [
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{ name: 'n_samples', component: ['input_number'], default: 100, usePair: true },
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{ name: 'n_features', component: ['input_number'], default: 20, usePair: true },
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{ name: 'shuffle', component: ['bool_select'], default: 'True', usePair: true },
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{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true }
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]
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},
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'make_circles': {
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name: 'make_circles',
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import: 'from sklearn.datasets import make_circles',
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code: 'make_circles(${n_samples}${shuffle}${noise}${random_state}${factor}${etc})',
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options: [
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{ name: 'n_samples', component: ['input_number'], default: 100, usePair: true },
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{ name: 'shuffle', component: ['bool_select'], default: 'True', usePair: true },
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{ name: 'noise', component: ['input_number'], usePair: true },
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{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true },
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{ name: 'factor', component: ['input_number'], default: 0.8, usePair: true }
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]
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},
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'make_moons': {
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name: 'make_moons',
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import: 'from sklearn.datasets import make_moons',
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code: 'make_moons(${n_samples}${shuffle}${noise}${random_state}${etc})',
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options: [
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{ name: 'n_samples', component: ['input_number'], default: 100, usePair: true },
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{ name: 'shuffle', component: ['bool_select'], default: 'True', usePair: true },
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{ name: 'noise', component: ['input_number'], usePair: true },
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{ name: 'random_state', component: ['input_number'], placeholder: '123', usePair: true }
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]
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},
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/** Data Preparation - Encoding */
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'prep-onehot': {
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name: 'OneHotEncoder',

‎html/m_ml/dataSets.html

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<body>
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<div class="vp-grid-box">
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<div class="vp-grid-border-box">
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<div class="vp-grid-col-110">
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<label for="loadType" class="vp-orange-text">Load type</label>
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<select id="loadType" class="vp-select vp-state">
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<!-- Auto Create -->
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</select>
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</div>
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<div class="vp-grid-col-110 vp-data-option-box">
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</div>
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</div>
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<div class="vp-grid-border-box vp-grid-col-110">
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<label for="allocateTo" class="vp-orange-text">Allocate to</label>
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<input type="text" class="vp-input vp-state" id="allocateTo" placeholder="New variable name" value="ldata"/>
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</div>
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</div>
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</body>

‎js/m_ml/DataSets.js

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@@ -13,11 +13,14 @@
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// [CLASS] DataSets
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//============================================================================
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define([
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'text!vp_base/html/m_ml/dataSets.html!strip',
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'vp_base/js/com/com_util',
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'vp_base/js/com/com_Const',
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'vp_base/js/com/com_String',
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'vp_base/js/com/component/PopupComponent',
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], function(com_util, com_Const, com_String, PopupComponent) {
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'vp_base/js/com/com_generatorV2',
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'vp_base/data/m_ml/mlLibrary',
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], function(dsHTML, com_util, com_Const, com_String, PopupComponent, com_generator, ML_LIBRARIES) {
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/**
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* DataSets
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this.config.dataview = false;
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this.state = {
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loadType: 'load_boston',
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userOption: '',
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allocateTo: 'ldata',
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...this.state
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}
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this.mlConfig = ML_LIBRARIES;
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this.loadTypeList = {
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'Load Data': [
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'load_boston', 'load_iris', 'load_diabetes', 'load_digits', 'load_linnerud', 'load_wine', 'load_breast_cancer'
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],
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'Create Data': [
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'make_classification', 'make_blobs', 'make_circles', 'make_moons'
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]
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}
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}
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_bindEvent() {
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super._bindEvent();
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let that = this;
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// select model
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$(this.wrapSelector('#loadType')).on('change', function() {
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let loadType = $(this).val();
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that.state.loadType = loadType;
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$(that.wrapSelector('.vp-data-option-box')).html(that.templateForOption(loadType));
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// change allocateTo default variable name
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if (that.loadTypeList['Load Data'].includes(loadType)) {
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$(that.wrapSelector('#allocateTo')).val('ldata');
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that.state.allocateTo = 'ldata';
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} else {
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$(that.wrapSelector('#allocateTo')).val('df');
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that.state.allocateTo = 'df';
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}
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});
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}
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templateForBody() {
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return 'Data Set test';
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let page = $(dsHTML);
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let that = this;
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// load types
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let loadTypeTag = new com_String();
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Object.keys(this.loadTypeList).forEach(category => {
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let optionTag = new com_String();
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that.loadTypeList[category].forEach(opt => {
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let optConfig = that.mlConfig[opt];
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let selectedFlag = '';
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if (opt == that.state.modelType) {
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selectedFlag = 'selected';
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}
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optionTag.appendFormatLine('<option value="{0}" {1}>{2}</option>',
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opt, selectedFlag, optConfig.name);
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})
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loadTypeTag.appendFormatLine('<optgroup label="{0}">{1}</optgroup>',
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category, optionTag.toString());
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});
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$(page).find('#loadType').html(loadTypeTag.toString());
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// render option page
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$(page).find('.vp-data-option-box').html(this.templateForOption(this.state.loadType));
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return page;
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}
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templateForOption(loadType) {
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let config = this.mlConfig[loadType];
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let state = this.state;
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let optBox = new com_String();
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// render tag
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config.options.forEach(opt => {
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optBox.appendFormatLine('<label for="{0}" title="{1}">{2}</label>'
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, opt.name, opt.name, opt.name);
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let content = com_generator.renderContent(this, opt.component[0], opt, state);
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optBox.appendLine(content[0].outerHTML);
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});
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// show user option
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if (config.code.includes('${etc}')) {
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// render user option
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optBox.appendFormatLine('<label for="{0}">{1}</label>', 'userOption', 'User option');
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optBox.appendFormatLine('<input type="text" class="vp-input vp-state" id="{0}" placeholder="{1}" value="{2}"/>',
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'userOption', 'key=value, ...', this.state.userOption);
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}
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return optBox.toString();
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}
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generateCode() {
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let { loadType, userOption, allocateTo } = this.state;
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let code = new com_String();
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let config = this.mlConfig[loadType];
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code.appendLine(config.import);
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code.appendLine();
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// model code
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let modelCode = config.code;
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modelCode = com_generator.vp_codeGenerator(this, config, this.state, userOption);
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if (this.loadTypeList['Load Data'].includes(loadType)) {
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code.appendFormatLine('{0} = {1}', allocateTo, modelCode);
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// FIXME: decide between 2 codes
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// code.appendFormat("df_{0} = pd.concat([pd.DataFrame({1}.data, columns={2}.feature_names), pd.DataFrame({3}.target, columns=['target'])], axis=1)", allocateTo, allocateTo, allocateTo, allocateTo);
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code.appendFormat("df_{0} = pd.DataFrame(np.hstack(({1}.data, {2}.target.reshape(-1,1))), columns=np.hstack(({3}.feature_names, ['target'])))", allocateTo, allocateTo, allocateTo, allocateTo);
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} else {
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code.appendFormatLine("_X, _y = {0}", modelCode);
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code.appendLine("_columns = np.hstack((['X{}'.format(i+1) for i in range(len(_X[0]))],['target']))");
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code.appendFormat("{0} = pd.DataFrame(np.hstack((_X, _y.reshape(-1,1))), columns=_columns)", allocateTo);
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}
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if (allocateTo != '') {
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code.appendLine();
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code.append(allocateTo);
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}
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return code.toString();
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}
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}
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return DataSets;

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