fixed heatmap

master
parent 7ad5f7be6a
commit b4cc0f94e0
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      cachedir/joblib/run/GridSearchForModels/func_code.py
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  45. 4
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  46. 1234
      run.py

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@ -1 +1 @@
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@ -1,6 +1,7 @@
# first line: 504
# first line: 542
@memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
print('test')
# instantiate spark session
spark = (
SparkSession

@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1.0">
<title>StackedGenVis</title>
<title>StackGenVis</title>
</head>
<body>
<div id="app"></div>

@ -0,0 +1,229 @@
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@media (max-width:1205px){.w3-auto{max-width:95%}}
@media (max-width:600px){.w3-modal-content{margin:0 10px;width:auto!important}.w3-modal{padding-top:30px}
.w3-dropdown-hover.w3-mobile .w3-dropdown-content,.w3-dropdown-click.w3-mobile .w3-dropdown-content{position:relative}
.w3-hide-small{display:none!important}.w3-mobile{display:block;width:100%!important}.w3-bar-item.w3-mobile,.w3-dropdown-hover.w3-mobile,.w3-dropdown-click.w3-mobile{text-align:center}
.w3-dropdown-hover.w3-mobile,.w3-dropdown-hover.w3-mobile .w3-btn,.w3-dropdown-hover.w3-mobile .w3-button,.w3-dropdown-click.w3-mobile,.w3-dropdown-click.w3-mobile .w3-btn,.w3-dropdown-click.w3-mobile .w3-button{width:100%}}
@media (max-width:768px){.w3-modal-content{width:500px}.w3-modal{padding-top:50px}}
@media (min-width:993px){.w3-modal-content{width:1050px}.w3-hide-large{display:none!important}.w3-sidebar.w3-collapse{display:block!important}}
@media (max-width:992px) and (min-width:601px){.w3-hide-medium{display:none!important}}
@media (max-width:992px){.w3-sidebar.w3-collapse{display:none}.w3-main{margin-left:0!important;margin-right:0!important}.w3-auto{max-width:100%}}
.w3-top,.w3-bottom{position:fixed;width:100%;z-index:1}.w3-top{top:0}.w3-bottom{bottom:0}
.w3-overlay{position:fixed;display:none;width:100%;height:100%;top:0;left:0;right:0;bottom:0;background-color:rgba(0,0,0,0.5);z-index:2}
.w3-display-topleft{position:absolute;left:0;top:0}.w3-display-topright{position:absolute;right:0;top:0}
.w3-display-bottomleft{position:absolute;left:0;bottom:0}.w3-display-bottomright{position:absolute;right:0;bottom:0}
.w3-display-middle{position:absolute;top:50%;left:50%;transform:translate(-50%,-50%);-ms-transform:translate(-50%,-50%)}
.w3-display-left{position:absolute;top:50%;left:0%;transform:translate(0%,-50%);-ms-transform:translate(-0%,-50%)}
.w3-display-right{position:absolute;top:50%;right:0%;transform:translate(0%,-50%);-ms-transform:translate(0%,-50%)}
.w3-display-topmiddle{position:absolute;left:50%;top:0;transform:translate(-50%,0%);-ms-transform:translate(-50%,0%)}
.w3-display-bottommiddle{position:absolute;left:50%;bottom:0;transform:translate(-50%,0%);-ms-transform:translate(-50%,0%)}
.w3-display-container:hover .w3-display-hover{display:block}.w3-display-container:hover span.w3-display-hover{display:inline-block}.w3-display-hover{display:none}
.w3-display-position{position:absolute}
.w3-circle{border-radius:50%}
.w3-round-small{border-radius:2px}.w3-round,.w3-round-medium{border-radius:4px}.w3-round-large{border-radius:8px}.w3-round-xlarge{border-radius:16px}.w3-round-xxlarge{border-radius:32px}
.w3-row-padding,.w3-row-padding>.w3-half,.w3-row-padding>.w3-third,.w3-row-padding>.w3-twothird,.w3-row-padding>.w3-threequarter,.w3-row-padding>.w3-quarter,.w3-row-padding>.w3-col{padding:0 8px}
.w3-container,.w3-panel{padding:0.01em 16px}.w3-panel{margin-top:16px;margin-bottom:16px}
.w3-code,.w3-codespan{font-family:Consolas,"courier new";font-size:16px}
.w3-code{width:auto;background-color:#fff;padding:8px 12px;border-left:4px solid #4CAF50;word-wrap:break-word}
.w3-codespan{color:crimson;background-color:#f1f1f1;padding-left:4px;padding-right:4px;font-size:110%}
.w3-card,.w3-card-2{box-shadow:0 2px 5px 0 rgba(0,0,0,0.16),0 2px 10px 0 rgba(0,0,0,0.12)}
.w3-card-4,.w3-hover-shadow:hover{box-shadow:0 4px 10px 0 rgba(0,0,0,0.2),0 4px 20px 0 rgba(0,0,0,0.19)}
.w3-spin{animation:w3-spin 2s infinite linear}@keyframes w3-spin{0%{transform:rotate(0deg)}100%{transform:rotate(359deg)}}
.w3-animate-fading{animation:fading 10s infinite}@keyframes fading{0%{opacity:0}50%{opacity:1}100%{opacity:0}}
.w3-animate-opacity{animation:opac 0.8s}@keyframes opac{from{opacity:0} to{opacity:1}}
.w3-animate-top{position:relative;animation:animatetop 0.4s}@keyframes animatetop{from{top:-300px;opacity:0} to{top:0;opacity:1}}
.w3-animate-left{position:relative;animation:animateleft 0.4s}@keyframes animateleft{from{left:-300px;opacity:0} to{left:0;opacity:1}}
.w3-animate-right{position:relative;animation:animateright 0.4s}@keyframes animateright{from{right:-300px;opacity:0} to{right:0;opacity:1}}
.w3-animate-bottom{position:relative;animation:animatebottom 0.4s}@keyframes animatebottom{from{bottom:-300px;opacity:0} to{bottom:0;opacity:1}}
.w3-animate-zoom {animation:animatezoom 0.6s}@keyframes animatezoom{from{transform:scale(0)} to{transform:scale(1)}}
.w3-animate-input{transition:width 0.4s ease-in-out}.w3-animate-input:focus{width:100%!important}
.w3-opacity,.w3-hover-opacity:hover{opacity:0.60}.w3-opacity-off,.w3-hover-opacity-off:hover{opacity:1}
.w3-opacity-max{opacity:0.25}.w3-opacity-min{opacity:0.75}
.w3-greyscale-max,.w3-grayscale-max,.w3-hover-greyscale:hover,.w3-hover-grayscale:hover{filter:grayscale(100%)}
.w3-greyscale,.w3-grayscale{filter:grayscale(75%)}.w3-greyscale-min,.w3-grayscale-min{filter:grayscale(50%)}
.w3-sepia{filter:sepia(75%)}.w3-sepia-max,.w3-hover-sepia:hover{filter:sepia(100%)}.w3-sepia-min{filter:sepia(50%)}
.w3-tiny{font-size:10px!important}.w3-small{font-size:12px!important}.w3-medium{font-size:15px!important}.w3-large{font-size:18px!important}
.w3-xlarge{font-size:24px!important}.w3-xxlarge{font-size:36px!important}.w3-xxxlarge{font-size:48px!important}.w3-jumbo{font-size:64px!important}
.w3-left-align{text-align:left!important}.w3-right-align{text-align:right!important}.w3-justify{text-align:justify!important}.w3-center{text-align:center!important}
.w3-border-0{border:0!important}.w3-border{border:1px solid #ccc!important}
.w3-border-top{border-top:1px solid #ccc!important}.w3-border-bottom{border-bottom:1px solid #ccc!important}
.w3-border-left{border-left:1px solid #ccc!important}.w3-border-right{border-right:1px solid #ccc!important}
.w3-topbar{border-top:6px solid #ccc!important}.w3-bottombar{border-bottom:6px solid #ccc!important}
.w3-leftbar{border-left:6px solid #ccc!important}.w3-rightbar{border-right:6px solid #ccc!important}
.w3-section,.w3-code{margin-top:16px!important;margin-bottom:16px!important}
.w3-margin{margin:16px!important}.w3-margin-top{margin-top:16px!important}.w3-margin-bottom{margin-bottom:16px!important}
.w3-margin-left{margin-left:16px!important}.w3-margin-right{margin-right:16px!important}
.w3-padding-small{padding:4px 8px!important}.w3-padding{padding:8px 16px!important}.w3-padding-large{padding:12px 24px!important}
.w3-padding-16{padding-top:16px!important;padding-bottom:16px!important}.w3-padding-24{padding-top:24px!important;padding-bottom:24px!important}
.w3-padding-32{padding-top:32px!important;padding-bottom:32px!important}.w3-padding-48{padding-top:48px!important;padding-bottom:48px!important}
.w3-padding-64{padding-top:64px!important;padding-bottom:64px!important}
.w3-left{float:left!important}.w3-right{float:right!important}
.w3-button:hover{color:#000!important;background-color:#ccc!important}
.w3-transparent,.w3-hover-none:hover{background-color:transparent!important}
.w3-hover-none:hover{box-shadow:none!important}
/* Colors */
.w3-amber,.w3-hover-amber:hover{color:#000!important;background-color:#ffc107!important}
.w3-aqua,.w3-hover-aqua:hover{color:#000!important;background-color:#00ffff!important}
.w3-blue,.w3-hover-blue:hover{color:#fff!important;background-color:#2196F3!important}
.w3-light-blue,.w3-hover-light-blue:hover{color:#000!important;background-color:#87CEEB!important}
.w3-brown,.w3-hover-brown:hover{color:#fff!important;background-color:#795548!important}
.w3-cyan,.w3-hover-cyan:hover{color:#000!important;background-color:#00bcd4!important}
.w3-blue-grey,.w3-hover-blue-grey:hover,.w3-blue-gray,.w3-hover-blue-gray:hover{color:#fff!important;background-color:#607d8b!important}
.w3-green,.w3-hover-green:hover{color:#fff!important;background-color:#4CAF50!important}
.w3-light-green,.w3-hover-light-green:hover{color:#000!important;background-color:#8bc34a!important}
.w3-indigo,.w3-hover-indigo:hover{color:#fff!important;background-color:#3f51b5!important}
.w3-khaki,.w3-hover-khaki:hover{color:#000!important;background-color:#f0e68c!important}
.w3-lime,.w3-hover-lime:hover{color:#000!important;background-color:#cddc39!important}
.w3-orange,.w3-hover-orange:hover{color:#000!important;background-color:#ff9800!important}
.w3-deep-orange,.w3-hover-deep-orange:hover{color:#fff!important;background-color:#ff5722!important}
.w3-pink,.w3-hover-pink:hover{color:#fff!important;background-color:#e91e63!important}
.w3-purple,.w3-hover-purple:hover{color:#fff!important;background-color:#9c27b0!important}
.w3-deep-purple,.w3-hover-deep-purple:hover{color:#fff!important;background-color:#673ab7!important}
.w3-red,.w3-hover-red:hover{color:#fff!important;background-color:#f44336!important}
.w3-sand,.w3-hover-sand:hover{color:#000!important;background-color:#fdf5e6!important}
.w3-teal,.w3-hover-teal:hover{color:#fff!important;background-color:#009688!important}
.w3-yellow,.w3-hover-yellow:hover{color:#000!important;background-color:#ffeb3b!important}
.w3-white,.w3-hover-white:hover{color:#000!important;background-color:#fff!important}
.w3-black,.w3-hover-black:hover{color:#fff!important;background-color:#000!important}
.w3-grey,.w3-hover-grey:hover,.w3-gray,.w3-hover-gray:hover{color:#000!important;background-color:#9e9e9e!important}
.w3-light-grey,.w3-hover-light-grey:hover,.w3-light-gray,.w3-hover-light-gray:hover{color:#000!important;background-color:#f1f1f1!important}
.w3-dark-grey,.w3-hover-dark-grey:hover,.w3-dark-gray,.w3-hover-dark-gray:hover{color:#fff!important;background-color:#616161!important}
.w3-pale-red,.w3-hover-pale-red:hover{color:#000!important;background-color:#ffdddd!important}
.w3-pale-green,.w3-hover-pale-green:hover{color:#000!important;background-color:#ddffdd!important}
.w3-pale-yellow,.w3-hover-pale-yellow:hover{color:#000!important;background-color:#ffffcc!important}
.w3-pale-blue,.w3-hover-pale-blue:hover{color:#000!important;background-color:#ddffff!important}
.w3-text-amber,.w3-hover-text-amber:hover{color:#ffc107!important}
.w3-text-aqua,.w3-hover-text-aqua:hover{color:#00ffff!important}
.w3-text-blue,.w3-hover-text-blue:hover{color:#2196F3!important}
.w3-text-light-blue,.w3-hover-text-light-blue:hover{color:#87CEEB!important}
.w3-text-brown,.w3-hover-text-brown:hover{color:#795548!important}
.w3-text-cyan,.w3-hover-text-cyan:hover{color:#00bcd4!important}
.w3-text-blue-grey,.w3-hover-text-blue-grey:hover,.w3-text-blue-gray,.w3-hover-text-blue-gray:hover{color:#607d8b!important}
.w3-text-green,.w3-hover-text-green:hover{color:#4CAF50!important}
.w3-text-light-green,.w3-hover-text-light-green:hover{color:#8bc34a!important}
.w3-text-indigo,.w3-hover-text-indigo:hover{color:#3f51b5!important}
.w3-text-khaki,.w3-hover-text-khaki:hover{color:#b4aa50!important}
.w3-text-lime,.w3-hover-text-lime:hover{color:#cddc39!important}
.w3-text-orange,.w3-hover-text-orange:hover{color:#ff9800!important}
.w3-text-deep-orange,.w3-hover-text-deep-orange:hover{color:#ff5722!important}
.w3-text-pink,.w3-hover-text-pink:hover{color:#e91e63!important}
.w3-text-purple,.w3-hover-text-purple:hover{color:#9c27b0!important}
.w3-text-deep-purple,.w3-hover-text-deep-purple:hover{color:#673ab7!important}
.w3-text-red,.w3-hover-text-red:hover{color:#f44336!important}
.w3-text-sand,.w3-hover-text-sand:hover{color:#fdf5e6!important}
.w3-text-teal,.w3-hover-text-teal:hover{color:#009688!important}
.w3-text-yellow,.w3-hover-text-yellow:hover{color:#d2be0e!important}
.w3-text-white,.w3-hover-text-white:hover{color:#fff!important}
.w3-text-black,.w3-hover-text-black:hover{color:#000!important}
.w3-text-grey,.w3-hover-text-grey:hover,.w3-text-gray,.w3-hover-text-gray:hover{color:#757575!important}
.w3-text-light-grey,.w3-hover-text-light-grey:hover,.w3-text-light-gray,.w3-hover-text-light-gray:hover{color:#f1f1f1!important}
.w3-text-dark-grey,.w3-hover-text-dark-grey:hover,.w3-text-dark-gray,.w3-hover-text-dark-gray:hover{color:#3a3a3a!important}
.w3-border-amber,.w3-hover-border-amber:hover{border-color:#ffc107!important}
.w3-border-aqua,.w3-hover-border-aqua:hover{border-color:#00ffff!important}
.w3-border-blue,.w3-hover-border-blue:hover{border-color:#2196F3!important}
.w3-border-light-blue,.w3-hover-border-light-blue:hover{border-color:#87CEEB!important}
.w3-border-brown,.w3-hover-border-brown:hover{border-color:#795548!important}
.w3-border-cyan,.w3-hover-border-cyan:hover{border-color:#00bcd4!important}
.w3-border-blue-grey,.w3-hover-border-blue-grey:hover,.w3-border-blue-gray,.w3-hover-border-blue-gray:hover{border-color:#607d8b!important}
.w3-border-green,.w3-hover-border-green:hover{border-color:#4CAF50!important}
.w3-border-light-green,.w3-hover-border-light-green:hover{border-color:#8bc34a!important}
.w3-border-indigo,.w3-hover-border-indigo:hover{border-color:#3f51b5!important}
.w3-border-khaki,.w3-hover-border-khaki:hover{border-color:#f0e68c!important}
.w3-border-lime,.w3-hover-border-lime:hover{border-color:#cddc39!important}
.w3-border-orange,.w3-hover-border-orange:hover{border-color:#ff9800!important}
.w3-border-deep-orange,.w3-hover-border-deep-orange:hover{border-color:#ff5722!important}
.w3-border-pink,.w3-hover-border-pink:hover{border-color:#e91e63!important}
.w3-border-purple,.w3-hover-border-purple:hover{border-color:#9c27b0!important}
.w3-border-deep-purple,.w3-hover-border-deep-purple:hover{border-color:#673ab7!important}
.w3-border-red,.w3-hover-border-red:hover{border-color:#f44336!important}
.w3-border-sand,.w3-hover-border-sand:hover{border-color:#fdf5e6!important}
.w3-border-teal,.w3-hover-border-teal:hover{border-color:#009688!important}
.w3-border-yellow,.w3-hover-border-yellow:hover{border-color:#ffeb3b!important}
.w3-border-white,.w3-hover-border-white:hover{border-color:#fff!important}
.w3-border-black,.w3-hover-border-black:hover{border-color:#000!important}
.w3-border-grey,.w3-hover-border-grey:hover,.w3-border-gray,.w3-hover-border-gray:hover{border-color:#9e9e9e!important}
.w3-border-light-grey,.w3-hover-border-light-grey:hover,.w3-border-light-gray,.w3-hover-border-light-gray:hover{border-color:#f1f1f1!important}
.w3-border-dark-grey,.w3-hover-border-dark-grey:hover,.w3-border-dark-gray,.w3-hover-border-dark-gray:hover{border-color:#616161!important}
.w3-border-pale-red,.w3-hover-border-pale-red:hover{border-color:#ffe7e7!important}.w3-border-pale-green,.w3-hover-border-pale-green:hover{border-color:#e7ffe7!important}
.w3-border-pale-yellow,.w3-hover-border-pale-yellow:hover{border-color:#ffffcc!important}.w3-border-pale-blue,.w3-hover-border-pale-blue:hover{border-color:#e7ffff!important}

@ -261,92 +261,92 @@ export default {
this.parameters = []
if (this.keyAllOrClass) {
for (var j = 0; j < Object.keys(PerformAlgorKNN['params']).length; j++) {
this.algorithmKNN.push({'# Performance (%) #': McKNN[j],Algorithm:'KNN',Model:'Model ' + AlgorKNNIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'; # Performance (%) # ',ModelID:AlgorKNNIDs[j]})
this.algorithmKNN.push({'# Performance (%) #': McKNN[j],Algorithm:'K-Nearest Neighbors',Model:'Model ' + AlgorKNNIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'; # Performance (%) # ',ModelID:AlgorKNNIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorSVC['params']).length; j++) {
this.algorithmSVC.push({'# Performance (%) #': McSVC[j],Algorithm:'SVC',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
this.algorithmSVC.push({'# Performance (%) #': McSVC[j],Algorithm:'C-Support Vector Classification',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorSVC['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorGausNB['params']).length; j++) {
this.algorithmGausNB.push({'# Performance (%) #': McGausNB[j],Algorithm:'GausNB',Model:'Model ' + AlgorGausNBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGausNBIDs[j]})
this.algorithmGausNB.push({'# Performance (%) #': McGausNB[j],Algorithm:'Gaussian Naive Bayes',Model:'Model ' + AlgorGausNBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGausNBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorMLP['params']).length; j++) {
this.algorithmMLP.push({'# Performance (%) #': McMLP[j],Algorithm:'MLP',Model:'Model ' + AlgorMLPIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'; # Performance (%) # ',ModelID:AlgorMLPIDs[j]})
this.algorithmMLP.push({'# Performance (%) #': McMLP[j],Algorithm:'Multilayer Perceptron',Model:'Model ' + AlgorMLPIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'; # Performance (%) # ',ModelID:AlgorMLPIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorMLP['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorLR['params']).length; j++) {
this.algorithmLR.push({'# Performance (%) #': McLR[j],Algorithm:'LR',Model:'Model ' + AlgorLRIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLRIDs[j]})
this.algorithmLR.push({'# Performance (%) #': McLR[j],Algorithm:'Logistic Regression',Model:'Model ' + AlgorLRIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLRIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorLR['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorLDA['params']).length; j++) {
this.algorithmLDA.push({'# Performance (%) #': McLDA[j],Algorithm:'LDA',Model:'Model ' + AlgorLDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLDAIDs[j]})
this.algorithmLDA.push({'# Performance (%) #': McLDA[j],Algorithm:'Linear Discrim Analysis',Model:'Model ' + AlgorLDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLDAIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorLDA['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorQDA['params']).length; j++) {
this.algorithmQDA.push({'# Performance (%) #': McQDA[j],Algorithm:'QDA',Model:'Model ' + AlgorQDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorQDAIDs[j]})
this.algorithmQDA.push({'# Performance (%) #': McQDA[j],Algorithm:'Quadratic Discrim Analysis',Model:'Model ' + AlgorQDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorQDAIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorQDA['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorRF['params']).length; j++) {
this.algorithmRF.push({'# Performance (%) #': McRF[j],Algorithm:'RF',Model:'Model ' + AlgorRFIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'; # Performance (%) # ',ModelID:AlgorRFIDs[j]})
this.algorithmRF.push({'# Performance (%) #': McRF[j],Algorithm:'Random Forest',Model:'Model ' + AlgorRFIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'; # Performance (%) # ',ModelID:AlgorRFIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorRF['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorExtraT['params']).length; j++) {
this.algorithmExtraT.push({'# Performance (%) #': McExtraT[j],Algorithm:'ExtraT',Model:'Model ' + AlgorExtraTIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'; # Performance (%) # ',ModelID:AlgorExtraTIDs[j]})
this.algorithmExtraT.push({'# Performance (%) #': McExtraT[j],Algorithm:'Extra Trees',Model:'Model ' + AlgorExtraTIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'; # Performance (%) # ',ModelID:AlgorExtraTIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorAdaB['params']).length; j++) {
this.algorithmAdaB.push({'# Performance (%) #': McAdaB[j],Algorithm:'AdaB',Model:'Model ' + AlgorAdaBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorAdaBIDs[j]})
this.algorithmAdaB.push({'# Performance (%) #': McAdaB[j],Algorithm:'AdaBoost',Model:'Model ' + AlgorAdaBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorAdaBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorGradB['params']).length; j++) {
this.algorithmGradB.push({'# Performance (%) #': McGradB[j],Algorithm:'GradB',Model:'Model ' + AlgorGradBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGradBIDs[j]})
this.algorithmGradB.push({'# Performance (%) #': McGradB[j],Algorithm:'Gradient Boosting',Model:'Model ' + AlgorGradBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGradBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorGradB['params'])[j]))
}
} else {
for (var j = 0; j < Object.keys(PerformAlgorKNN['params']).length; j++) {
this.algorithmKNN.push({'# Performance (%) #': this.listClassPerf[0][j],Algorithm:'KNN',Model:'Model ' + AlgorKNNIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'; # Performance (%) # ',ModelID:AlgorKNNIDs[j]})
this.algorithmKNN.push({'# Performance (%) #': this.listClassPerf[0][j],Algorithm:'K-Nearest Neighbors',Model:'Model ' + AlgorKNNIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'; # Performance (%) # ',ModelID:AlgorKNNIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorSVC['params']).length; j++) {
this.algorithmSVC.push({'# Performance (%) #': this.listClassPerf[1][j],Algorithm:'SVC',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
this.algorithmSVC.push({'# Performance (%) #': this.listClassPerf[1][j],Algorithm:'C-Support Vector Classification',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorSVC['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorGausNB['params']).length; j++) {
this.algorithmGausNB.push({'# Performance (%) #': this.listClassPerf[2][j],Algorithm:'GausNB',Model:'Model ' + AlgorGausNBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGausNBIDs[j]})
this.algorithmGausNB.push({'# Performance (%) #': this.listClassPerf[2][j],Algorithm:'Gaussian Naive Bayes',Model:'Model ' + AlgorGausNBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGausNBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorMLP['params']).length; j++) {
this.algorithmMLP.push({'# Performance (%) #': this.listClassPerf[3][j],Algorithm:'MLP',Model:'Model ' + AlgorMLPIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'; # Performance (%) # ',ModelID:AlgorMLPIDs[j]})
this.algorithmMLP.push({'# Performance (%) #': this.listClassPerf[3][j],Algorithm:'Multilayer Perceptron',Model:'Model ' + AlgorMLPIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'; # Performance (%) # ',ModelID:AlgorMLPIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorMLP['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorLR['params']).length; j++) {
this.algorithmLR.push({'# Performance (%) #': this.listClassPerf[4][j],Algorithm:'LR',Model:'Model ' + AlgorLRIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLRIDs[j]})
this.algorithmLR.push({'# Performance (%) #': this.listClassPerf[4][j],Algorithm:'Logistic Regression',Model:'Model ' + AlgorLRIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLRIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorLR['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorLDA['params']).length; j++) {
this.algorithmLDA.push({'# Performance (%) #': this.listClassPerf[5][j],Algorithm:'LDA',Model:'Model ' + AlgorLDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLDAIDs[j]})
this.algorithmLDA.push({'# Performance (%) #': this.listClassPerf[5][j],Algorithm:'Linear Discrim Analysis',Model:'Model ' + AlgorLDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLDAIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorLDA['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorQDA['params']).length; j++) {
this.algorithmQDA.push({'# Performance (%) #': this.listClassPerf[6][j],Algorithm:'QDA',Model:'Model ' + AlgorQDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorQDAIDs[j]})
this.algorithmQDA.push({'# Performance (%) #': this.listClassPerf[6][j],Algorithm:'Quadratic Discrim Analysis',Model:'Model ' + AlgorQDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorQDAIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorQDA['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorRF['params']).length; j++) {
this.algorithmRF.push({'# Performance (%) #': this.listClassPerf[7][j],Algorithm:'RF',Model:'Model ' + AlgorRFIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'; # Performance (%) # ',ModelID:AlgorRFIDs[j]})
this.algorithmRF.push({'# Performance (%) #': this.listClassPerf[7][j],Algorithm:'Random Forest',Model:'Model ' + AlgorRFIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'; # Performance (%) # ',ModelID:AlgorRFIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorRF['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorExtraT['params']).length; j++) {
this.algorithmExtraT.push({'# Performance (%) #': this.listClassPerf[8][j],Algorithm:'ExtraT',Model:'Model ' + AlgorExtraTIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'; # Performance (%) # ',ModelID:AlgorExtraTIDs[j]})
this.algorithmExtraT.push({'# Performance (%) #': this.listClassPerf[8][j],Algorithm:'Extra Trees',Model:'Model ' + AlgorExtraTIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'; # Performance (%) # ',ModelID:AlgorExtraTIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorAdaB['params']).length; j++) {
this.algorithmAdaB.push({'# Performance (%) #': this.listClassPerf[9][j],Algorithm:'AdaB',Model:'Model ' + AlgorAdaBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorAdaBIDs[j]})
this.algorithmAdaB.push({'# Performance (%) #': this.listClassPerf[9][j],Algorithm:'AdaBoost',Model:'Model ' + AlgorAdaBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorAdaBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorGradB['params']).length; j++) {
this.algorithmGradB.push({'# Performance (%) #': this.listClassPerf[10][j],Algorithm:'GradB',Model:'Model ' + AlgorGradBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGradBIDs[j]})
this.algorithmGradB.push({'# Performance (%) #': this.listClassPerf[10][j],Algorithm:'Gradient Boosting',Model:'Model ' + AlgorGradBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGradBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorGradB['params'])[j]))
}
}
@ -395,7 +395,7 @@ export default {
var flagEmptyGradB = 0
el[0].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[0]
allPoints[i].style.opacity = '1.0'
@ -413,7 +413,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[1].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classification')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[1]
allPoints[i].style.opacity = '1.0'
@ -431,7 +431,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[2].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GausNB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[2]
allPoints[i].style.opacity = '1.0'
@ -449,7 +449,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[3].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Multilayer Perceptron')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[3]
allPoints[i].style.opacity = '1.0'
@ -467,7 +467,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[4].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Logistic Regression')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[4]
allPoints[i].style.opacity = '1.0'
@ -485,7 +485,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[5].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Linear Discrim Analysis')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[5]
allPoints[i].style.opacity = '1.0'
@ -503,7 +503,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[6].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Quadratic Discrim Analysis')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[6]
allPoints[i].style.opacity = '1.0'
@ -521,7 +521,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[7].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Random Forest')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[7]
allPoints[i].style.opacity = '1.0'
@ -539,7 +539,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[8].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Extra Trees')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[8]
allPoints[i].style.opacity = '1.0'
@ -557,7 +557,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[9].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaBoost')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[9]
allPoints[i].style.opacity = '1.0'
@ -575,7 +575,7 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[10].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gradient Boosting')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[10]
allPoints[i].style.opacity = '1.0'
@ -627,37 +627,37 @@ export default {
for (var j = 0; j < this.AllAlgorithms.length; j++) {
algorithm = []
if (this.AllAlgorithms[j] === 'KNN') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
algorithm = this.algorithmKNN
} else if (this.AllAlgorithms[j] === 'SVC') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classification')
algorithm = this.algorithmSVC
} else if (this.AllAlgorithms[j] === 'GausNB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GausNB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
algorithm = this.algorithmGausNB
} else if (this.AllAlgorithms[j] === 'MLP') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Multilayer Perceptron')
algorithm = this.algorithmMLP
} else if (this.AllAlgorithms[j] === 'LR') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Logistic Regression')
algorithm = this.algorithmLR
} else if (this.AllAlgorithms[j] === 'LDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Linear Discrim Analysis')
algorithm = this.algorithmLDA
} else if (this.AllAlgorithms[j] === 'QDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Quadratic Discrim Analysis')
algorithm = this.algorithmQDA
} else if (this.AllAlgorithms[j] === 'RF') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Random Forest')
algorithm = this.algorithmRF
} else if (this.AllAlgorithms[j] === 'ExtraT') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Extra Trees')
algorithm = this.algorithmExtraT
} else if (this.AllAlgorithms[j] === 'AdaB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaBoost')
algorithm = this.algorithmAdaB
} else {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gradient Boosting')
algorithm = this.algorithmGradB
}
for (let k = 0; k < allPoints.length; k++) {
@ -803,27 +803,27 @@ export default {
},
brushed () {
if (this.selectedAlgorithm === 'KNN') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
} else if (this.selectedAlgorithm === 'SVC') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classification')
} else if (this.selectedAlgorithm === 'GausNB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GausNB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
} else if (this.selectedAlgorithm === 'MLP') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Multilayer Perceptron')
} else if (this.selectedAlgorithm === 'LR') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Logistic Regression')
} else if (this.selectedAlgorithm === 'LDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Linear Discrim Analysis')
} else if (this.selectedAlgorithm === 'QDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Quadratic Discrim Analysis')
} else if (this.selectedAlgorithm === 'RF') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Random Forest')
} else if (this.selectedAlgorithm === 'ExtraT') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
} else if (this.selectedAlgorithm === 'AdaB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Extra Trees')
} else {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gradient Boosting')
}
const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
var modelsActive = []
@ -973,25 +973,25 @@ export default {
for (let i = 0; i < allPoints.length; i++) {
if (allPoints[i].style.fill != "rgb(211, 211, 211)") {
activeModels.push(allPoints[i].__data__.Model)
if (allPoints[i].__data__.Algorithm === 'KNN') {
if (allPoints[i].__data__.Algorithm === 'K-Nearest Neighbors') {
algorithmsSelected.push('KNN')
} else if (allPoints[i].__data__.Algorithm === 'SVC') {
} else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classification') {
algorithmsSelected.push('SVC')
} else if (allPoints[i].__data__.Algorithm === 'GausNB') {
} else if (allPoints[i].__data__.Algorithm === 'Gaussian Naive Bayes') {
algorithmsSelected.push('GausNB')
} else if (allPoints[i].__data__.Algorithm === 'MLP') {
} else if (allPoints[i].__data__.Algorithm === 'Multilayer Perceptron') {
algorithmsSelected.push('MLP')
} else if (allPoints[i].__data__.Algorithm === 'LR') {
} else if (allPoints[i].__data__.Algorithm === 'Logistic Regression') {
algorithmsSelected.push('LR')
} else if (allPoints[i].__data__.Algorithm === 'LDA') {
} else if (allPoints[i].__data__.Algorithm === 'Linear Discrim Analysis') {
algorithmsSelected.push('LDA')
} else if (allPoints[i].__data__.Algorithm === 'QDA') {
} else if (allPoints[i].__data__.Algorithm === 'Quadratic Discrim Analysis') {
algorithmsSelected.push('QDA')
} else if (allPoints[i].__data__.Algorithm === 'RF') {
} else if (allPoints[i].__data__.Algorithm === 'Random Forest') {
algorithmsSelected.push('RF')
} else if (allPoints[i].__data__.Algorithm === 'ExtraT') {
} else if (allPoints[i].__data__.Algorithm === 'Extra Trees') {
algorithmsSelected.push('ExtraT')
} else if (allPoints[i].__data__.Algorithm === 'AdaB') {
} else if (allPoints[i].__data__.Algorithm === 'AdaBoost') {
algorithmsSelected.push('AdaB')
} else {
algorithmsSelected.push('GradB')
@ -1017,25 +1017,25 @@ export default {
for (let i = 0; i < allPoints.length; i++) {
if (allPoints[i].style.fill != "rgb(211, 211, 211)") {
activeModels.push(allPoints[i].__data__.Model)
if (allPoints[i].__data__.Algorithm === 'KNN') {
if (allPoints[i].__data__.Algorithm === 'K-Nearest Neighbors') {
algorithmsSelected.push('KNN')
} else if (allPoints[i].__data__.Algorithm === 'SVC') {
} else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classification') {
algorithmsSelected.push('SVC')
} else if (allPoints[i].__data__.Algorithm === 'GausNB') {
} else if (allPoints[i].__data__.Algorithm === 'Gaussian Naive Bayes') {
algorithmsSelected.push('GausNB')
} else if (allPoints[i].__data__.Algorithm === 'MLP') {
} else if (allPoints[i].__data__.Algorithm === 'Multilayer Perceptron') {
algorithmsSelected.push('MLP')
} else if (allPoints[i].__data__.Algorithm === 'LR') {
} else if (allPoints[i].__data__.Algorithm === 'Logistic Regression') {
algorithmsSelected.push('LR')
} else if (allPoints[i].__data__.Algorithm === 'LDA') {
} else if (allPoints[i].__data__.Algorithm === 'Linear Discrim Analysis') {
algorithmsSelected.push('LDA')
} else if (allPoints[i].__data__.Algorithm === 'QDA') {
} else if (allPoints[i].__data__.Algorithm === 'Quadratic Discrim Analysis') {
algorithmsSelected.push('QDA')
} else if (allPoints[i].__data__.Algorithm === 'RF') {
} else if (allPoints[i].__data__.Algorithm === 'Random Forest') {
algorithmsSelected.push('RF')
} else if (allPoints[i].__data__.Algorithm === 'ExtraT') {
} else if (allPoints[i].__data__.Algorithm === 'Extra Trees') {
algorithmsSelected.push('ExtraT')
} else if (allPoints[i].__data__.Algorithm === 'AdaB') {
} else if (allPoints[i].__data__.Algorithm === 'AdaBoost') {
algorithmsSelected.push('AdaB')
} else {
algorithmsSelected.push('GradB')

@ -435,7 +435,7 @@ export default {
l: 50,
r: 0,
b: 30,
t: 30,
t: 40,
pad: 0
},
legend: {orientation: 'h', xanchor: 'center', x: 0.5},
@ -462,7 +462,7 @@ export default {
for (var i = 0; i < target_names.length; i++) {
traces[i] = {
x: ['KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
x: ['K-Nearest Neighbors','C-Support Vector Classifier','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
y: sumList[i],
name: target_names[i],
opacity: 0.5,
@ -474,7 +474,7 @@ export default {
};
tracesSel[i] = {
type: 'bar',
x: ['KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
x: ['K-Nearest Neighbors','C-Support Vector Classifier','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
y: sumLineList[i],
name: target_names[i]+' (Sel)',
xaxis: 'x2',
@ -491,14 +491,12 @@ export default {
var barc = document.getElementById('barChart');
Plotly.newPlot(barc, data, layout)
var X, Y;
barc.on('plotly_click', (eventData) => {
var tName
eventData.points.forEach((e) => {
tName = e.data.name.replace(/ *\([^)]*\) */g, "")
});
EventBus.$emit('clearPCP')
EventBus.$emit('alternateFlagLock')
EventBus.$emit('boxplotSet', [storeKNN[tName],storeSVC[tName],storeGausNB[tName],storeMLP[tName],storeLR[tName],storeLDA[tName],storeQDA[tName],storeRF[tName],storeExtraT[tName],storeAdaB[tName],storeGradB[tName]])

@ -1,5 +1,5 @@
<template>
<button style="floatfloat: right;"
<button style="float: right;"
id="Execute"
v-on:click="execute">
<font-awesome-icon icon="play" />

@ -28,7 +28,7 @@
<font-awesome-icon icon="eraser" />
{{ removeData }}
</button>
History Controller: <button
History Manager: <button
id="saveID"
v-on:click="save">
<font-awesome-icon icon="save" />
@ -62,6 +62,7 @@ export default {
composeData: 'Compose',
saveData: 'Save Step',
restoreData: 'Restore Step',
instanceImpSize: '',
userSelectedFilter: 'mean',
responsiveWidthHeight: [],
colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
@ -98,7 +99,7 @@ export default {
},
scatterPlotDataView () {
Plotly.purge('OverviewDataPlotly')
// responsive visualization
let width = this.responsiveWidthHeight[0]*6.5
let height = this.responsiveWidthHeight[1]*1.1
@ -125,6 +126,13 @@ export default {
var Xaxs = []
var Yaxs = []
var Opacity
var impSizeArray
if (this.instanceImpSize.length != 0) {
impSizeArray = JSON.parse(this.instanceImpSize)
}
console.log(impSizeArray)
if (this.representationDef == 'mds') {
for (let i = 0; i < XandYCoordinatesMDS[0].length; i++) {
@ -168,7 +176,7 @@ export default {
y: aux_Y,
mode: 'markers',
name: target_names[i],
marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: 12 },
marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: impSizeArray },
hovertemplate:
"<b>%{text}</b><br><br>" +
"<extra></extra>",
@ -240,7 +248,7 @@ export default {
y: aux_Y,
mode: 'markers',
name: target_names[i],
marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: 12 },
marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: impSizeArray },
hovertemplate:
"<b>%{text}</b><br><br>" +
"<extra></extra>",
@ -302,7 +310,7 @@ export default {
y: aux_Y,
mode: 'markers',
name: target_names[i],
marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: 12 },
marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: impSizeArray },
hovertemplate:
"<b>%{text}</b><br><br>" +
"<extra></extra>",
@ -360,6 +368,8 @@ export default {
}
},
mounted() {
EventBus.$on('emittedEventCallingDataSpaceImportance', data => { this.instanceImpSize = data })
// initialize the first data space projection based on the data set
EventBus.$on('emittedEventCallingDataSpacePlotView', data => {
this.dataPoints = data})

@ -0,0 +1,18 @@
<template>
<div id="ExportResults">Results go here</div>
</template>
<script>
import { EventBus } from '../main.js'
export default {
name: 'Export',
data () {
return {
}
},
methods: {
}
}
</script>

@ -263,7 +263,7 @@ export default {
plot_bgcolor: "rgb(229,229,229)",
xaxis: {
gridcolor: "rgb(255,255,255)",
title: 'Step of Execution',
title: 'Step of the Execution',
tickformat: '.0f',
range: [0, this.scoresMean.length + 2],
showgrid: true,
@ -275,7 +275,7 @@ export default {
},
yaxis: {
gridcolor: "rgb(255,255,255)",
title: 'Performance (%)',
title: '# Performance (%) #',
showgrid: true,
showline: false,
showticklabels: true,

@ -1,5 +1,8 @@
<template>
<div id="Heatmap"></div>
<div>
<div id="Heatmap"></div>
<div id="LegendHeat"></div>
</div>
</template>
<script>
@ -120,8 +123,11 @@ export default {
else if (this.Toggles[0] == 0 && this.Toggles[1] == 0 && this.Toggles[2] == 1) {
values[j] = FeaturesAccuracy[j][i]*100
} else {
alert('Please, keep at least one metric active') // Fix this!
values[j] = ((((featureUni[i].Score-minUni)/(maxUni-minUni))*100)+(PermImpEli[j][i]*100+(FeaturesAccuracy[j][i]*100)))/3
alert('Please, keep at least one toggle active! The states of the toggles are being reset.') // Fix this!
this.Toggles[0] = 1
this.Toggles[1] = 1
this.Toggles[2] = 1
EventBus.$emit('resetToggles')
}
data.push(values[j]/100)
}
@ -162,28 +168,20 @@ export default {
.append("div")
.style("position", "absolute")
.style("visibility", "hidden");
//==================================================
// http://bl.ocks.org/mbostock/3680958
/* function zoom() {
console.log(d3.event.translate)
console.log(d3.event.scale)
svg.attr("transform", "translate(" + d3.event.translate + ")scale(" + d3.event.scale + ")");
}*/
// define the zoomListener which calls the zoom function on the "zoom" event constrained within the scaleExtents
const zoom = d3.zoom()
.scaleExtent([0.1, 3]) //zoom limit
.on('zoom', () => {
svg.attr('transform', d3.event.transform) // updated for d3 v4
})
//==================================================
var viewerWidth = this.responsiveWidthHeight[0]*6.5
var viewerHeight = this.responsiveWidthHeight[1]*1.1
var viewerPosTop = 125;
var viewerPosLeft = 100;
var viewerHeight = this.responsiveWidthHeight[1]*1.46
var viewerPosTop = viewerHeight * 0.1;
var viewerPosLeft = viewerWidth*0.1;
var legendElementWidth = cellSize * 2;
var legendElementWidth = cellSize * 3;
// http://bl.ocks.org/mbostock/5577023
var colors = colorbrewer.RdYlGn[this.classesNumber];
@ -386,8 +384,11 @@ export default {
}
EventBus.$emit('SendSelectedFeaturesEvent', finalresults)
});
var svgLeg = d3.select("#LegendHeat").append("svg")
.attr("width", viewerWidth/2)
.attr("height", viewerHeight*0.13)
var legend = svg.append("g")
var legend = svgLeg.append('g')
.attr("class", "legend")
.attr("transform", "translate(0,0)")
.selectAll(".legendElement")

@ -0,0 +1,27 @@
<template>
<button style="float: right;"
id="know"
v-on:click="knowClass">
<font-awesome-icon icon="file-export" />
{{ valueKnowE }}
</button>
</template>
<script>
import { EventBus } from '../main.js'
export default {
name: 'Knowledge',
data () {
return {
valueKnowE: 'Knowledge Extraction'
}
},
methods: {
knowClass () {
EventBus.$emit('OpenModal')
}
}
}
</script>

@ -1,14 +1,14 @@
<!-- Main Visualization View -->
<template>
<div>
<body>
<b-container fluid class="bv-example-row">
<b-row class="md-3">
<b-col cols="3">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Data Sets and Performance Metrics Manager</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-left">
<mdb-card-text class="text-left" style="font-size: 18px;">
<DataSetExecController/>
<SlidersController/>
</mdb-card-text>
@ -17,7 +17,7 @@
</b-col>
<b-col cols="6">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Stacking Ensemble Provenance</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Provenance of the Stacking Ensemble<small class="float-right"><knowledge/></small></mdb-card-header>
<mdb-card-body>
<Provenance/>
</mdb-card-body>
@ -25,7 +25,7 @@
</b-col>
<b-col cols="3">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [1, 5, 6]</small>Meta-Model Performance</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [1, 5, 6]</small>Performance of the Metamodel</mdb-card-header>
<mdb-card-body>
<FinalResultsLinePlot/>
</mdb-card-body>
@ -90,6 +90,7 @@
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 845px">
<ToggleSelection/>
<br/>
<Heatmap/>
</mdb-card-text>
</mdb-card-body>
@ -138,7 +139,25 @@
</div>
</div>
</b-container>
</div>
<div class="w3-container">
<div id="myModal" class="w3-modal" style="position: fixed;">
<div class="w3-modal-content w3-card-4 w3-animate-zoom">
<header class="w3-container w3-blue">
<h3 style="display:inline-block; font-size: 16px; margin-top: 15px; margin-bottom:15px">Serialized Ensemble Learning Models Using Pickling</h3>
</header>
<Export/>
<div class="w3-container w3-light-grey w3-padding">
<button style="float: right; margin-top: -3px; margin-bottom: -3px"
id="closeModal" class="w3-button w3-right w3-white w3-border"
v-on:click="closeModalFun">
<font-awesome-icon icon="window-close" />
{{ valuePickled }}
</button>
</div>
</div>
</div>
</div>
</body>
</template>
<script>
@ -149,6 +168,8 @@ import Algorithms from './Algorithms.vue'
import AlgorithmHyperParam from './AlgorithmHyperParam.vue'
import Controller from './Controller.vue'
import ResetClass from './ResetClass.vue'
import Knowledge from './Knowledge.vue'
import Export from './Export.vue'
import SlidersController from './SlidersController.vue'
import ScatterPlot from './ScatterPlot.vue'
import PerMetricBarChart from './PerMetricBarChart.vue'
@ -181,9 +202,11 @@ export default Vue.extend({
components: {
DataSetExecController,
Algorithms,
Export,
AlgorithmHyperParam,
Controller,
ResetClass,
Knowledge,
SlidersController,
ScatterPlot,
PerMetricBarChart,
@ -204,10 +227,12 @@ export default Vue.extend({
},
data () {
return {
valuePickled: 'Close',
Collection: 0,
OverviewResults: 0,
preDataResults: '',
DataResults: '',
instancesImportance: '',
RetrieveValueFile: 'DiabetesC', // this is for the default data set
ClassifierIDsList: '',
SelectedFeaturesPerClassifier: '',
@ -248,6 +273,12 @@ export default Vue.extend({
}
},
methods: {
openModalFun () {
$('#myModal').modal('show')
},
closeModalFun () {
$('#myModal').modal('hide')
},
getCollection () {
this.Collection = this.getCollectionFromBackend()
},
@ -339,8 +370,6 @@ export default Vue.extend({
})
},
SendToServerData () {
// fix that for the upload!
console.log(this.localFile)
const path = `http://127.0.0.1:5000/data/SendtoSeverDataSet`
const postData = {
@ -526,6 +555,8 @@ export default Vue.extend({
axios.get(path, axiosConfig)
.then(response => {
this.FinalResults = response.data.FinalResults
this.DataSpaceImportance()
EventBus.$emit('emittedEventCallingLinePlot', this.FinalResults)
})
.catch(error => {
@ -580,8 +611,8 @@ export default Vue.extend({
console.log(error)
})
},
DataSpaceCallAfterDataManipulation () {
const path = `http://localhost:5000/data/requestDataSpaceResultsAfterDataManipulation`
DataSpaceImportance () {
const path = `http://localhost:5000/data/SendInstancesImportance`
const axiosConfig = {
headers: {
@ -593,15 +624,33 @@ export default Vue.extend({
}
axios.get(path, axiosConfig)
.then(response => {
this.DataResults = response.data.DataResults
EventBus.$emit('emittedEventCallingDataSpacePlotView', this.DataResults)
EventBus.$emit('emittedEventCallingDataPCP', this.DataResults)
this.instancesImportance = response.data.instancesImportance
EventBus.$emit('emittedEventCallingDataSpaceImportance', this.instancesImportance)
this.DataSpaceCall()
})
.catch(error => {
console.log(error)
})
},
DataSpaceCallAfterDataManipulation () {
const path = `http://localhost:5000/data/requestDataSpaceResultsAfterDataManipulation`
const axiosConfig = {
headers: {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
}
}
axios.get(path, axiosConfig)
.then(response => {
this.DataSpaceImportance()
})
.catch(error => {
console.log(error)
})
},
SendAlgorithmsToServer () {
const path = `http://127.0.0.1:5000/data/ServerRequestSelParameters`
const postData = {
@ -897,6 +946,13 @@ export default Vue.extend({
window.addEventListener('resize', this.change)
},
mounted() {
var modal = document.getElementById('myModal')
window.onclick = function(event) {
//alert(event.target)
if (event.target == modal) {
modal.style.display = "none";
}
}
this.render(true)
loadProgressBar()
window.onbeforeunload = function(e) {
@ -942,6 +998,8 @@ export default Vue.extend({
EventBus.$on('AllSelModels', data => {this.valueSel = data})
EventBus.$on('RemoveFromStack', this.RemoveFromStackModels)
EventBus.$on('OpenModal', this.openModalFun)
EventBus.$on('SendSelectedPointsToServerEventfromData', data => {this.dataPointsSelfromDataSpace = data})
EventBus.$on('SendSelectedPointsToServerEventfromData', this.DataSpaceFun)
@ -967,7 +1025,7 @@ export default Vue.extend({
})
</script>
<style>
<style lang="scss">
#nprogress .bar {
background: red !important;
@ -988,6 +1046,12 @@ body {
right: 0px;
top: 0px;
bottom: 0px;
margin: 0px;
margin-top: 18px !important;
}
.modal-backdrop {
z-index: -1 !important;
}
@import './../assets/w3.css';
</style>

@ -26,10 +26,12 @@ export default {
} else {
metricsPerModelSel = this.SelBarChartMetrics
}
console.log(metricsPerModel)
console.log(metricsPerModelSel)
var width = this.WH[0]*6.5 // interactive visualization
var height = this.WH[1]*0.5 // interactive visualization
var trace1 = {
x: ['Acc','F1s','Pre','Rec','Jac'],
x: ['Accuracy','MAE','RMSE','G-Mean','Precision','Recall','F-Beta Sc','MCC','ROC AUC','Log Loss'],
y: metricsPerModel,
name: 'Projection average',
type: 'bar',
@ -38,7 +40,7 @@ export default {
}
};
var trace2 = {
x: ['Acc','F1s','Pre','Rec','Jac'],
x: ['Accuracy','MAE','RMSE','G-Mean','Precision','Recall','F-Beta Sc','MCC','ROC AUC','Log Loss'],
y: metricsPerModelSel,
name: 'Selected points',
type: 'bar',
@ -54,7 +56,7 @@ export default {
margin: {
l: 50,
r: 30,
b: 30,
b: 35,
t: 5,
pad: 4
},

@ -21,14 +21,23 @@ export default {
name: 'Provenance',
data () {
return {
stackInformation: '',
WH: [],
data: [],
counter: 0,
typeCounter: [],
typeColumnCounter: [],
KNNModels: 576, //KNN models
platform: ''
stackInformation: '',
WH: [],
data: [],
counter: 0,
typeCounter: [],
typeColumnCounter: [],
SVCModels: 576,
GausNBModels: 736,
MLPModels: 1236,
LRModels: 1356,
LDAModels: 1996,
QDAModels: 2196,
RFModels: 2446,
ExtraTModels: 2606,
AdaBModels: 2766,
GradBModels: 2926,
platform: ''
}
},
methods: {
@ -45,30 +54,113 @@ export default {
var height = this.WH[1]*0.5 // interactive visualization
var flagKNN = 0
var flagSVC = 0
var flagGausNB = 0
var flagMLP = 0
var flagLR = 0
var flagLDA = 0
var flagQDA = 0
var flagRF = 0
var flagExtraT = 0
var flagAdaB = 0
var flagGradB = 0
var StackInfo = JSON.parse(this.stackInformation[1])
// Create a WebGL 2D platform on the canvas:
this.platform = Stardust.platform("webgl-2d", canvas, width, height);
for (let i = 0; i < StackInfo.length; i++) {
if (StackInfo[i] < this.KNNModels){
if (StackInfo[i] < this.SVCModels){
this.data.push({
type:0, column:this.counter, height:height
})
flagKNN = 1
} else {
} else if (StackInfo[i] < this.GausNBModels){
this.data.push({
type:1, column:this.counter, height:height
})
flagSVC = 1
} else if (StackInfo[i] < this.MLPModels){
this.data.push({
type:2, column:this.counter, height:height
})
flagGausNB = 1
} else if (StackInfo[i] < this.LRModels){
this.data.push({
type:3, column:this.counter, height:height
})
flagMLP = 1
} else if (StackInfo[i] < this.LDAModels){
this.data.push({
type:4, column:this.counter, height:height
})
flagLR = 1
} else if (StackInfo[i] < this.QDAModels){
this.data.push({
type:5, column:this.counter, height:height
})
flagLDA = 1
} else if (StackInfo[i] < this.RFModels){
this.data.push({
type:6, column:this.counter, height:height
})
flagQDA = 1
} else if (StackInfo[i] < this.ExtraTModels){
this.data.push({
type:7, column:this.counter, height:height
})
flagRF = 1
} else if (StackInfo[i] < this.AdaBModels){
this.data.push({
type:8, column:this.counter, height:height
})
flagExtraT = 1
} else if (StackInfo[i] < this.GradBModels){
this.data.push({
type:9, column:this.counter, height:height
})
flagAdaB = 1
} else {
this.data.push({
type:10, column:this.counter, height:height
})
flagGradB = 1
}
}
if (flagKNN == 1) {
this.typeCounter.push(0)
}
if (flagSVC == 1) {
this.typeCounter.push(0)
}
if (flagGausNB == 1) {
this.typeCounter.push(0)
}
if (flagMLP == 1) {
this.typeCounter.push(0)
}
if (flagLR == 1) {
this.typeCounter.push(0)
}
if (flagLDA == 1) {
this.typeCounter.push(0)
}
if (flagQDA == 1) {
this.typeCounter.push(0)
}
if (flagRF == 1) {
this.typeCounter.push(0)
}
if (flagExtraT == 1) {
this.typeCounter.push(0)
}
if (flagAdaB == 1) {
this.typeCounter.push(0)
}
if (flagGradB == 1) {
this.typeCounter.push(0)
}
this.typeColumnCounter.push(0)
this.data.forEach(d => {

@ -1,5 +1,5 @@
<template>
<button style="floatfloat: right;"
<button style="float: right;"
id="ResetClass"
v-on:click="resetClass">
<font-awesome-icon icon="sync-alt" />

@ -60,7 +60,7 @@ export default {
ScatterPlotView () {
Plotly.purge('OverviewPlotly')
var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
console.log(colorsforScatterPlot)
var MDSData = JSON.parse(this.ScatterPlotResults[1])
var parameters = JSON.parse(this.ScatterPlotResults[2])
var TSNEData = JSON.parse(this.ScatterPlotResults[12])
@ -218,6 +218,9 @@ export default {
visible: false,
range: [minY, maxY]
},
autosize: true,
width: width,
height: height,
dragmode: 'lasso',
hovermode: "closest",
hoverlabel: { bgcolor: "#FFF" },

@ -8,7 +8,7 @@
<p>(4*) G-Mean:<b-form-slider ref="basic4" v-model="basicValue4" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue4 }}%</p>
<p>(5*) Precision:<b-form-slider ref="basic5" v-model="basicValue5" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue5 }}%</p>
<p>(6*) Recall:<b-form-slider ref="basic6" v-model="basicValue6" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue6 }}%</p>
<p>(7*) F-Beta Score:<b-form-slider ref="basic7" v-model="basicValue7" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider >{{ basicValue7 }}%</p>
<p>(7*) F-Beta Sc:<b-form-slider ref="basic7" v-model="basicValue7" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider >{{ basicValue7 }}%</p>
<p>(8) MCC:<b-form-slider ref="basic8" v-model="basicValue8" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue8 }}%</p>
<p>(9) ROC AUC:<b-form-slider ref="basic9" v-model="basicValue9" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue9 }}%</p>
<p>(10) Log Loss:<b-form-slider ref="basic10" v-model="basicValue10" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue10 }}%</p>

@ -1,8 +1,8 @@
<template>
<div id="toggles" style="visibility:hidden">
Univariate Selection:<input type="checkbox" id="toggle-uni" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small">
Permutation Importance:<input type="checkbox" id="toggle-per" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small">
Feature Accuracy Importance:<input type="checkbox" id="toggle-fi" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small">
Univariate Feature Selection:&nbsp;<input type="checkbox" id="toggle-uni" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small">&nbsp;&nbsp;&nbsp;
Permutation Feature Importance:&nbsp;<input type="checkbox" id="toggle-per" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small">&nbsp;&nbsp;&nbsp;
Accuracy Feature Importance:&nbsp;<input type="checkbox" id="toggle-fi" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small">
</div>
</template>
@ -19,6 +19,11 @@ export default {
}
},
methods: {
ResetPosition () {
$('input[type=checkbox]').each(function() {
$(this).bootstrapToggle('on'); //you can set "on" or "off"
});
},
ToggleSelection () {
},
ToggleShow () {
@ -26,13 +31,17 @@ export default {
}
},
mounted () {
EventBus.$on('resetToggles', this.ResetPosition)
$('#toggle-uni').bootstrapToggle({
on: 'Enabled',
off: 'Disabled'
off: 'Disabled',
width: '8%',
});
$('#toggle-uni').change(function() {
var toggleID = document.getElementById('toggle-uni')
if (toggleID.checked === false) {
var toggleIDUni = document.getElementById('toggle-uni')
var toggleIDPer = document.getElementById('toggle-per')
var toggleIDFi = document.getElementById('toggle-per')
if (toggleIDUni.checked === false) {
EventBus.$emit('toggle1', 0)
} else {
EventBus.$emit('toggle1', 1)
@ -40,11 +49,14 @@ export default {
})
$('#toggle-per').bootstrapToggle({
on: 'Enabled',
off: 'Disabled'
off: 'Disabled',
width: '8%',
});
$('#toggle-per').change(function() {
var toggleID = document.getElementById('toggle-per')
if (toggleID.checked === false) {
var toggleIDUni = document.getElementById('toggle-uni')
var toggleIDPer = document.getElementById('toggle-per')
var toggleIDFi = document.getElementById('toggle-per')
if (toggleIDPer.checked === false) {
EventBus.$emit('toggle2', 0)
} else {
EventBus.$emit('toggle2', 1)
@ -52,11 +64,14 @@ export default {
})
$('#toggle-fi').bootstrapToggle({
on: 'Enabled',
off: 'Disabled'
off: 'Disabled',
width: '8%',
});
$('#toggle-fi').change(function() {
var toggleID = document.getElementById('toggle-fi')
if (toggleID.checked === false) {
var toggleIDUni = document.getElementById('toggle-uni')
var toggleIDPer = document.getElementById('toggle-per')
var toggleIDFi = document.getElementById('toggle-per')
if (toggleIDFi.checked === false) {
EventBus.$emit('toggle3', 0)
} else {
EventBus.$emit('toggle3', 1)

@ -6,10 +6,10 @@ import 'bootstrap-vue/dist/bootstrap-vue.css'
import router from './router'
import { library } from '@fortawesome/fontawesome-svg-core'
import { FontAwesomeIcon } from '@fortawesome/vue-fontawesome'
import { faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt } from '@fortawesome/free-solid-svg-icons'
import { faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt, faFileExport, faWindowClose } from '@fortawesome/free-solid-svg-icons'
import bFormSlider from 'vue-bootstrap-slider'
library.add(faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt)
library.add(faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt, faFileExport, faWindowClose)
Vue.component('font-awesome-icon', FontAwesomeIcon)

1234
run.py

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