StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
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156 lines
8.5 KiB
156 lines
8.5 KiB
<template>
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<div id="PCP" class="parcoords" style="min-height: 307px;"></div>
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</template>
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<script>
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import 'parcoord-es/dist/parcoords.css';
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import ParCoords from 'parcoord-es';
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import * as d3Base from 'd3'
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// attach all d3 plugins to the d3 library
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const d3 = Object.assign(d3Base)
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import { EventBus } from '../main.js'
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export default {
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name: 'AlgorithmHyperParam',
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data () {
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return {
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ModelsPerformance: 0,
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selAlgorithm: 0,
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pc: 0,
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factors: [1,1,1,0,0
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,1,0,0,1,0
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,0,1,0,0,0
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,0,0,1,0,0
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,0,1,1,1
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],
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KNNModels: 576 //KNN models
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}
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},
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methods: {
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reset () {
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d3.selectAll("#PCP > *").remove();
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},
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PCPView () {
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d3.selectAll("#PCP > *").remove();
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if (this.selAlgorithm != '') {
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var colors = ['#8dd3c7','#8da0cb']
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var colorGiv = 0
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var factorsLocal = this.factors
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var divide = 0
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factorsLocal.forEach(element => {
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divide = element + divide
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});
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var Mc1 = []
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const performanceAlg1 = JSON.parse(this.ModelsPerformance[6])
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var max
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var min
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for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
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if (j == 0) {
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max = Object.values(performanceAlg1['log_loss'])[j]
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min = Object.values(performanceAlg1['log_loss'])[j]
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}
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if (Object.values(performanceAlg1['log_loss'])[j] > max) {
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max = Object.values(performanceAlg1['log_loss'])[j]
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}
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if (Object.values(performanceAlg1['log_loss'])[j] < min) {
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min = Object.values(performanceAlg1['log_loss'])[j]
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}
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}
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for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
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let sum
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sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg1['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg1['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg1['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['geometric_mean_score_macro'])[j])
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+ (factorsLocal[5] * Object.values(performanceAlg1['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg1['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg1['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg1['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg1['mean_test_recall_micro'])[j])
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+ (factorsLocal[10] * Object.values(performanceAlg1['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg1['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg1['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg1['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg1['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg1['f1_micro'])[j])
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+ (factorsLocal[16] * Object.values(performanceAlg1['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg1['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg1['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg1['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg1['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg1['matthews_corrcoef'])[j])
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+ (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg1['log_loss'])[j])/(max - min))))
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Mc1.push((sum/divide)*100)
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}
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var Mc2 = []
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const performanceAlg2 = JSON.parse(this.ModelsPerformance[14])
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for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) {
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let sum2
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sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg2['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg2['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg2['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['geometric_mean_score_macro'])[j])
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+ (factorsLocal[5] * Object.values(performanceAlg2['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg2['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg2['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg2['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg2['mean_test_recall_micro'])[j])
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+ (factorsLocal[10] * Object.values(performanceAlg2['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg2['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg2['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg2['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg2['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg2['f1_micro'])[j])
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+ (factorsLocal[16] * Object.values(performanceAlg2['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg2['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg2['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg2['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg2['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg2['matthews_corrcoef'])[j])
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+ (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg2['log_loss'])[j])/(max - min))))
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Mc2.push((sum2/divide)*100)
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}
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var Combined = 0
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if (this.selAlgorithm == 'KNN') {
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Combined = JSON.parse(this.ModelsPerformance[1])
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colorGiv = colors[0]
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} else {
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Combined = JSON.parse(this.ModelsPerformance[9])
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colorGiv = colors[1]
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}
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var valuesPerf = Object.values(Combined['params'])
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var ObjectsParams = Combined['params']
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var newObjectsParams = []
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var newObjectsParams2 = []
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var ArrayCombined = []
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var temp
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for (var i = 0; i < valuesPerf.length; i++) {
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if (this.selAlgorithm === 'KNN') {
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// There is a problem here!
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newObjectsParams.push({model: i,'perf_metrics': Mc1[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
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ArrayCombined[i] = newObjectsParams[i]
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} else {
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newObjectsParams2.push({model: this.KNNModels + i,'perf_metrics': Mc2[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
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ArrayCombined[i] = newObjectsParams2[i]
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}
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}
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EventBus.$emit('AllAlModels', ArrayCombined.length)
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this.pc = ParCoords()("#PCP")
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.data(ArrayCombined)
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.color(colorGiv)
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.hideAxis(['model'])
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.bundlingStrength(0) // set bundling strength
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.smoothness(0)
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.showControlPoints(false)
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.render()
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.brushMode('1D-axes')
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.reorderable()
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.interactive();
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this.pc.on("brushend", function(d) {
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EventBus.$emit('AllSelModels', d.length)
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EventBus.$emit('UpdateBoxPlot', d)
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});
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}
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},
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sliders () {
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},
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clear () {
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d3.selectAll("#PCP > *").remove();
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},
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},
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mounted() {
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EventBus.$on('ReturningBrushedPointsModels', this.brushed)
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EventBus.$on('emittedEventCallingModelSelect', data => { this.selAlgorithm = data })
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EventBus.$on('emittedEventCallingModel', data => { this.ModelsPerformance = data })
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EventBus.$on('emittedEventCallingModel', this.PCPView)
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EventBus.$on('ResponsiveandChange', this.PCPView)
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EventBus.$on('emittedEventCallingModelClear', this.clear)
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EventBus.$on('CallFactorsView', data => { this.factors = data })
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EventBus.$on('CallFactorsView', this.PCPView)
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// reset view
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EventBus.$on('resetViews', this.reset)
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EventBus.$on('clearPCP', this.reset)
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}
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}
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</script> |