Former-commit-id: 04032910a7
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commit 209dee469c
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@ -1 +1 @@
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@ -1 +0,0 @@
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@ -1,7 +1,7 @@
# first line: 565
# first line: 632
@memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle):
print('inside')
print('inside models')
# instantiate spark session
spark = (
SparkSession

@ -0,0 +1,27 @@
<template>
<button style="float: right;"
id="updateActiveScatter"
v-on:click="sendUpdateActiveScatter">
<font-awesome-icon icon="calculator" />
{{ valueActive }}
</button>
</template>
<script>
import { EventBus } from '../main.js'
export default {
name: 'ActiveScatter',
data () {
return {
valueActive: 'Compute Metamodel\'s Performance'
}
},
methods: {
sendUpdateActiveScatter () {
EventBus.$emit('sendToServerSelectedScatter')
}
}
}
</script>

@ -130,14 +130,11 @@ export default {
var target_names = JSON.parse(this.dataPoints[0])
const XandYCoordinatesMDS = JSON.parse(this.dataPoints[1])
console.log(XandYCoordinatesMDS)
const DataSet = JSON.parse(this.dataPoints[2])
const originalDataLabels = JSON.parse(this.dataPoints[3])
//console.log(DataSetY)
//const originalDataLabels = JSON.parse(this.dataPoints[4])
console.log(originalDataLabels)
var DataSetParse = JSON.parse(DataSet)
console.log(DataSetParse)
var stringParameters = []
for (let i = 0; i < DataSetParse.length; i++) {
this.clean(DataSetParse[i])
@ -210,7 +207,6 @@ export default {
result.Yax = Yaxs
result.ID = IDs
result.colorUpdates = colorUpdate
console.log(result)
var traces = []
var layout = []
@ -482,7 +478,6 @@ export default {
ClassifierIDsListCleared.push(numberNumb)
}
}
console.log(ClassifierIDsListCleared)
if (ClassifierIDsList != '') {
EventBus.$emit('ChangeKey', 1)
EventBus.$emit('SendSelectedPointsToServerEventfromData', ClassifierIDsListCleared)

@ -115,7 +115,7 @@
<b-col cols="6">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Models' Space
[Sel: {{OverSelLength}} / All: {{OverAllLength}}]
[Sel: {{OverSelLength}} / All: {{OverAllLength}}]<small class="float-right"><active-scatter/></small>
</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 845px">
@ -171,6 +171,7 @@ import Controller from './Controller.vue'
import ResetClass from './ResetClass.vue'
import Knowledge from './Knowledge.vue'
import Active from './Active.vue'
import ActiveScatter from './ActiveScatter.vue'
import Export from './Export.vue'
import SlidersController from './SlidersController.vue'
import ScatterPlot from './ScatterPlot.vue'
@ -210,6 +211,7 @@ export default Vue.extend({
ResetClass,
Knowledge,
Active,
ActiveScatter,
SlidersController,
ScatterPlot,
PerMetricBarChart,
@ -277,6 +279,7 @@ export default Vue.extend({
toggleDeepMain: 1,
keyLoc: 0,
keyData: true,
ClassifierIDsListRemaining: []
}
},
methods: {
@ -412,6 +415,33 @@ export default Vue.extend({
console.log(error)
})
},
updateBarChartLocally () {
this.OverSelLength = this.ClassifierIDsList.length
const path = `http://127.0.0.1:5000/data/ServerRequestSelPoinLocally`
const postData = {
ClassifiersList: this.ClassifierIDsList,
}
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.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the selected points to the server (scatterplot)!')
if (this.keyNow == 0) {
this.OverAllLength = this.ClassifierIDsList.length
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
}
this.getSelectedModelsMetrics()
})
.catch(error => {
console.log(error)
})
},
SendSelectedPointsToServer () {
if (this.ClassifierIDsList === ''){
this.OverSelLength = 0
@ -438,7 +468,7 @@ export default Vue.extend({
this.OverAllLength = this.ClassifierIDsList.length
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
}
this.getSelectedModelsMetrics()
//this.getSelectedModelsMetrics()
this.getFinalResults()
})
.catch(error => {
@ -449,7 +479,7 @@ export default Vue.extend({
RemoveFromStackModels () {
const path = `http://127.0.0.1:5000/data/ServerRemoveFromStack`
const postData = {
ClassifiersList: this.ClassifierIDsList,
ClassifiersList: this.ClassifierIDsListRemaining,
}
const axiosConfig = {
headers: {
@ -462,7 +492,6 @@ export default Vue.extend({
axios.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the selected points to the server (scatterplot)!')
EventBus.$emit('updateFlagForFinalResults', 0)
this.updatePredictionsSpace()
})
.catch(error => {
@ -490,6 +519,8 @@ export default Vue.extend({
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
}
EventBus.$emit('updatePredictionsSpace', this.UpdatePredictions)
EventBus.$emit('updateFlagForFinalResults', 0)
this.getFinalResults()
})
.catch(error => {
console.log(error)
@ -982,7 +1013,8 @@ export default Vue.extend({
EventBus.$on('ChangeKey', data => { this.keyNow = data })
EventBus.$on('SendSelectedPointsToServerEvent', data => { this.ClassifierIDsList = data })
EventBus.$on('SendSelectedPointsToServerEvent', this.SendSelectedPointsToServer)
EventBus.$on('SendSelectedPointsToServerEvent', this.updateBarChartLocally)
EventBus.$on('sendToServerSelectedScatter', this.SendSelectedPointsToServer)
EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data })
EventBus.$on('SendSelectedDataPointsToServerEvent', this.SendSelectedDataPointsToServer)
@ -1012,7 +1044,7 @@ export default Vue.extend({
EventBus.$on('sendPointsNumber', data => {this.OverAllLength = data})
EventBus.$on('AllSelModels', data => {this.valueSel = data})
EventBus.$on('RemoveFromStack', data => { this.ClassifierIDsList = data })
EventBus.$on('RemoveFromStack', data => { this.ClassifierIDsListRemaining = data })
EventBus.$on('RemoveFromStack', this.RemoveFromStackModels)
EventBus.$on('OpenModal', this.openModalFun)

@ -14,6 +14,7 @@ export default {
WH: [],
barchartmetricsprediction: [],
SelBarChartMetrics: [],
boldXAxis: '',
factors: [1,0,0
,1,0,0,1,0
,0,1,0,0,0
@ -40,7 +41,33 @@ export default {
var perModelAllClear = []
var perModelSelectedClear = []
var resultsColors = []
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']
if (this.boldXAxis == 'Accuracy') {
var chooseFrom = ['<b>Accuracy</b>','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']
}
else if (this.boldXAxis == 'G-Mean') {
var chooseFrom = ['Accuracy','<b>G-Mean</b>','<b>G-Mean</b>','<b>G-Mean</b>','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']
}
else if (this.boldXAxis == 'Precision') {
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','<b>Precision</b>','<b>Precision</b>','<b>Precision</b>','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']
}
else if (this.boldXAxis == 'Recall') {
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','<b>Recall</b>','<b>Recall</b>','<b>Recall</b>','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']
}
else if (this.boldXAxis == 'F-Beta Score') {
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','MCC','ROC AUC','Log Loss']
}
else if (this.boldXAxis == 'MCC') {
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','<b>MCC</b>','ROC AUC','Log Loss']
}
else if (this.boldXAxis == 'ROC AUC') {
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','<b>ROC AUC</b>','Log Loss']
}
else if (this.boldXAxis == 'Log Loss') {
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','<b>Log Loss</b>']
}
else {
var chooseFrom = ['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']
}
for (let i = 0; i < metricsPerModel.length; i++) {
if (factorsLocal[i] != 0) {
resultsColors.push(metricsPerModel[i])
@ -57,7 +84,6 @@ export default {
}
}
}
console.log(resultsColors)
var width = this.WH[0]*6.5 // interactive visualization
var height = this.WH[1]*0.5 // interactive visualization
var trace1 = {
@ -116,37 +142,30 @@ export default {
xAxisHovered = eventData.points[0].x
var index
if (xAxisHovered == 'Accuracy') {
Plotly.restyle(boxPlot, 'x', [['<b>Accuracy</b>','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
index = 0
}
else if (xAxisHovered == 'G-Mean') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','<b>G-Mean</b>','<b>G-Mean</b>','<b>G-Mean</b>','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
index = 1
}
else if (xAxisHovered == 'Precision') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','<b>Precision</b>','<b>Precision</b>','<b>Precision</b>','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
index = 2
}
else if (xAxisHovered == 'Recall') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','<b>Recall</b>','<b>Recall</b>','<b>Recall</b>','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
index = 3
}
else if (xAxisHovered == 'F-Beta Score') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','MCC','ROC AUC','Log Loss']]);
index = 4
}
else if (xAxisHovered == 'MCC') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','<b>MCC</b>','ROC AUC','Log Loss']]);
index = 5
}
else if (xAxisHovered == 'ROC AUC') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','<b>ROC AUC</b>','Log Loss']]);
index = 6
}
else {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','<b>Log Loss</b>']]);
index = 7
}
EventBus.$emit('updateBold', xAxisHovered)
EventBus.$emit('updateMetricsScatter', resultsColors[index])
});
},
@ -155,6 +174,9 @@ export default {
}
},
mounted () {
EventBus.$on('updateBold', data => {this.boldXAxis = data;})
EventBus.$on('updateBold', this.LineBar)
EventBus.$on('InitializeMetricsBarChart', data => {this.barchartmetrics = data;})
EventBus.$on('InitializeMetricsBarChart', this.LineBar)

@ -51,14 +51,18 @@ export default {
}
},
ScatterPlotPredView () {
Plotly.purge('OverviewPredPlotly')
Plotly.purge('OverviewPredPlotly')
// responsive visualization
var width = this.WH[0]*6.5 // interactive visualization
var height = this.WH[1]*1.22 // interactive visualization
var XandYCoordinatesMDS
var target_names = JSON.parse(this.PredictionsData[4])
const XandYCoordinatesMDS = JSON.parse(this.PredictionsData[8])
if (this.UpdatedData.length != 0) {
XandYCoordinatesMDS = JSON.parse(this.UpdatedData[0])
} else {
XandYCoordinatesMDS = JSON.parse(this.PredictionsData[8])
}
const DataSet = JSON.parse(this.PredictionsData[14])
const originalDataLabels = JSON.parse(this.PredictionsData[15])
//const originalDataLabels = JSON.parse(this.PredictionsData[16])
@ -66,6 +70,7 @@ export default {
var stringParameters = []
for (let i = 0; i < DataSetParse.length; i++) {
this.clean(DataSetParse[i])
stringParameters.push(JSON.stringify(DataSetParse[i]).replace(/,/gi, '<br>'))
}
const XandYCoordinatesTSNE = JSON.parse(this.PredictionsData[18])
@ -280,37 +285,47 @@ export default {
var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage', 'toggleSpikelines', 'autoScale2d', 'hoverClosestGl2d','hoverCompareCartesian','select2d','hoverClosestCartesian','zoomIn2d','zoomOut2d','zoom2d'], responsive: true}
Plotly.newPlot('OverviewPredPlotly', traces, layout, config)
if (this.onlyOnce) {
this.selectedPointsOverview()
}
this.onlyOnce = false
//if (this.onlyOnce) {
this.selectedPointsOverview()
// }
// this.onlyOnce = false
},
UpdateScatterPlot () {
const XandYCoordinates = JSON.parse(this.UpdatedData[0])
Plotly.animate('OverviewPredPlotly', {
data: [
{x: XandYCoordinates[0], y: XandYCoordinates[1]}
],
traces: [0],
layout: {}
}, {
transition: {
duration: 1000,
easing: 'cubic-in-out'
},
frame: {
duration: 1000
}
})
//this.selectedPointsOverview()
},
// UpdateScatterPlot () {
// const XandYCoordinates = JSON.parse(this.UpdatedData[0])
// const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
// const aux_Y = result.Yax.filter((item, index) => originalDataLabels[index] == target_names[i]);
// const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
// var Text = aux_ID.map((item, index) => {
// let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
// return popup;
// });
// Plotly.animate('OverviewPredPlotly', {
// data: [
// {x: XandYCoordinates[0], y: XandYCoordinates[1]}
// ],
// traces: [0],
// layout: {}
// }, {
// transition: {
// duration: 1000,
// easing: 'cubic-in-out'
// },
// frame: {
// duration: 1000
// }
// })
// //this.selectedPointsOverview()
// },
selectedPointsOverview () {
const OverviewPlotly = document.getElementById('OverviewPredPlotly')
OverviewPlotly.on('plotly_selected', function (evt) {
if (typeof evt !== 'undefined') {
const DataPoints = []
console.log(evt)
for (let i = 0; evt.points.length; i++) {
if (evt.points[i] === undefined) {
break
@ -332,7 +347,7 @@ export default {
mounted() {
EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
EventBus.$on('updatePredictionsSpace', data => { this.UpdatedData = data })
EventBus.$on('updatePredictionsSpace', this.UpdateScatterPlot)
EventBus.$on('updatePredictionsSpace', this.ScatterPlotPredView)
EventBus.$on('emittedEventCallingPredictionsSpacePlotView', data => {
this.PredictionsData = data})
EventBus.$on('emittedEventCallingPredictionsSpacePlotView', this.ScatterPlotPredView)

@ -69,8 +69,15 @@ export default {
methods: {
resetSelection () {
this.newColorsUpdate = []
this.ScatterPlotView()
EventBus.$emit('updateBold', '')
EventBus.$emit('updateBoxPlots')
this.colorsStore = []
this.MDSStore = []
this.parametersStore = []
this.TSNEStore = []
this.modelIDStore = []
this.UMAPStore = []
this.ScatterPlotView()
},
reset () {
Plotly.purge('OverviewPlotly')
@ -94,32 +101,25 @@ export default {
},
ScatterPlotView () {
Plotly.purge('OverviewPlotly')
var colorsforScatterPlot
var MDSData
var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
console.log(this.newColorsUpdate)
colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
if (this.newColorsUpdate.length != 0) {
console.log('den mpike')
colorsforScatterPlot = []
let resultsClear = JSON.parse(this.newColorsUpdate)
for (let j = 0; j < Object.values(resultsClear).length; j++) {
colorsforScatterPlot.push(Object.values(resultsClear)[j])
}
this.colorsStore = []
this.MDSStore = []
this.parametersStore = []
this.TSNEStore = []
this.modelIDStore = []
this.UMAPStore = []
}
console.log(colorsforScatterPlot)
var MDSData = JSON.parse(this.ScatterPlotResults[1])
MDSData= JSON.parse(this.ScatterPlotResults[1])
var parametersLoc = JSON.parse(this.ScatterPlotResults[2])
var parameters = JSON.parse(parametersLoc)
var TSNEData = JSON.parse(this.ScatterPlotResults[12])
var modelId = JSON.parse(this.ScatterPlotResults[13])
var UMAPData = JSON.parse(this.ScatterPlotResults[17])
if (this.keyLocal == 0) {
this.colorsStore.push(colorsforScatterPlot)
this.MDSStore.push(MDSData)
@ -133,8 +133,6 @@ export default {
TSNEData = this.TSNEStore.slice(this.activeModels,this.activeModels+1)[0]
modelId = this.modelIDStore.slice(this.activeModels,this.activeModels+1)[0]
UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0]
console.log(colorsforScatterPlot)
}
EventBus.$emit('sendPointsNumber', modelId.length)
@ -162,6 +160,7 @@ export default {
var listofNumbersModelsIDs = []
var StackModelsIDs = []
console.log(this.ModelsIDGray.length)
if (this.ModelsIDGray.length != 0) {
for (let j = 0; j < this.ModelsIDGray.length; j++){
listofNumbersModelsIDs.push(parseInt(this.ModelsIDGray[j]))
@ -171,18 +170,39 @@ export default {
var MDSDataNewX = []
var MDSDataNewY = []
var colorsforScatterPlotNew = []
if (this.DataPointsSelUpdate.length != 0) {
this.colorsStore.pop()
this.MDSStore.pop()
this.parametersStore.pop()
this.modelIDStore.pop()
this.UMAPStore.pop()
}
for (let i = 0; i < modelId.length; i++) {
if (listofNumbersModelsIDs.includes(modelId[i])) {
} else {
StackModelsIDs.push(modelId[i])
parametersNew.push(parameters[i])
colorsforScatterPlotNew.push(colorsforScatterPlot[i])
MDSDataNewX.push(MDSData[0][i])
MDSDataNewY.push(MDSData[1][i])
if (this.DataPointsSelUpdate.length != 0) {
colorsforScatterPlot = JSON.parse(this.DataPointsSelUpdate[0])
colorsforScatterPlotNew.push(colorsforScatterPlot[i])
} else {
colorsforScatterPlotNew.push(colorsforScatterPlot[i])
}
if (this.DataPointsSelUpdate.length != 0) {
MDSData = JSON.parse(this.DataPointsSelUpdate[1])
MDSDataNewX.push(MDSData[0][i])
MDSDataNewY.push(MDSData[1][i])
} else {
MDSDataNewX.push(MDSData[0][i])
MDSDataNewY.push(MDSData[1][i])
}
}
}
this.DataPointsSelUpdate = []
MDSData[0] = MDSDataNewX
MDSData[1] = MDSDataNewY
console.log(StackModelsIDs)
modelId = StackModelsIDs
parameters = parametersNew
colorsforScatterPlot = colorsforScatterPlotNew
@ -193,13 +213,19 @@ export default {
//this.TSNEStore.push(TSNEData)
this.modelIDStore.push(modelId)
this.UMAPStore.push(UMAPData)
console.log(this.activeModels)
colorsforScatterPlot = this.colorsStore.slice(this.activeModels,this.activeModels+1)[0]
MDSData = this.MDSStore.slice(this.activeModels,this.activeModels+1)[0]
parameters = this.parametersStore.slice(this.activeModels,this.activeModels+1)[0]
//TSNEData = this.TSNEStore.slice(this.activeModels,this.activeModels+1)[0]
modelId = this.modelIDStore.slice(this.activeModels,this.activeModels+1)[0]
console.log(modelId)
//UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0]
}
console.log(this.colorsStore)
console.log(colorsforScatterPlot)
EventBus.$emit('sendPointsNumber', modelId.length)
var classifiersInfoProcessing = []
for (let i = 0; i < modelId.length; i++) {
@ -397,7 +423,6 @@ export default {
if (this.onlyOnce) {
this.selectedPointsOverview()
}
this.onlyOnce = false
},
selectedPointsOverview () {
const OverviewPlotly = document.getElementById('OverviewPlotly')
@ -418,11 +443,13 @@ export default {
ClassifierIDsListCleared.push(numberNumb)
}
}
console.log(ClassifierIDsListCleared)
for (let i = 0; i < allModels.length; i++) {
if (!ClassifierIDsListCleared.includes(allModels[i])) {
pushModelsRemainingTemp.push(allModels[i])
}
}
console.log(pushModelsRemainingTemp)
EventBus.$emit('updateRemaining', pushModelsRemainingTemp)
if (allModels != '') {
EventBus.$emit('ChangeKey', 1)
@ -437,47 +464,47 @@ export default {
UpdateScatter () {
this.ScatterPlotView()
},
animate() {
var maxX
var minX
var maxY
var minY
var colorsforScatterPlot = JSON.parse(this.DataPointsSelUpdate[0])
var MDSData = JSON.parse(this.DataPointsSelUpdate[1])
maxX = Math.max(MDSData[0])
minX = Math.min(MDSData[0])
maxY = Math.max(MDSData[1])
minY = Math.max(MDSData[1])
Plotly.animate('OverviewPlotly', {
data: [
{x: MDSData[0], y: MDSData[1],marker: {
color: colorsforScatterPlot,
}}
],
traces: [0],
layout: {
xaxis: {
visible: false,
range: [minX, maxX]
},
yaxis: {
visible: false,
range: [minY, maxY]
},
}
}, {
transition: {
duration: 3000,
easing: 'cubic-in-out'
},
frame: {
duration: 3000
}
})
}
// animate() {
// var maxX
// var minX
// var maxY
// var minY
// var colorsforScatterPlot = JSON.parse(this.DataPointsSelUpdate[0])
// var MDSData = JSON.parse(this.DataPointsSelUpdate[1])
// maxX = Math.max(MDSData[0])
// minX = Math.min(MDSData[0])
// maxY = Math.max(MDSData[1])
// minY = Math.max(MDSData[1])
// Plotly.animate('OverviewPlotly', {
// data: [
// {x: MDSData[0], y: MDSData[1],marker: {
// color: colorsforScatterPlot,
// }}
// ],
// traces: [0],
// layout: {
// xaxis: {
// visible: false,
// range: [minX, maxX]
// },
// yaxis: {
// visible: false,
// range: [minY, maxY]
// },
// }
// }, {
// transition: {
// duration: 3000,
// easing: 'cubic-in-out'
// },
// frame: {
// duration: 3000
// }
// })
// }
},
mounted() {
EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
@ -513,7 +540,7 @@ export default {
EventBus.$on('RepresentationSelection', data => {this.representationDef = data})
EventBus.$on('RepresentationSelection', this.ScatterPlotView)
EventBus.$on('UpdateModelsScatterplot', data => {this.DataPointsSelUpdate = data})
EventBus.$on('UpdateModelsScatterplot', this.animate)
EventBus.$on('UpdateModelsScatterplot', this.ScatterPlotView)
// reset view
EventBus.$on('resetViews', this.reset)

@ -9,7 +9,7 @@ import os
def import_content(filepath):
mng_client = pymongo.MongoClient('localhost', 27017)
mng_db = mng_client['mydb']
collection_name = 'StanceC'
collection_name = 'StanceCTest'
db_cm = mng_db[collection_name]
cdir = os.path.dirname(__file__)
file_res = os.path.join(cdir, filepath)
@ -20,5 +20,5 @@ def import_content(filepath):
db_cm.insert(data_json)
if __name__ == "__main__":
filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stance.csv'
filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stancetest.csv'
import_content(filepath)

@ -32,6 +32,10 @@ from sklearn.manifold import TSNE
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import log_loss
from sklearn.metrics import fbeta_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from imblearn.metrics import geometric_mean_score
import umap
from sklearn.metrics import classification_report
@ -79,6 +83,9 @@ def Reset():
global RANDOM_SEED
RANDOM_SEED = 42
global StanceTest
StanceTest = False
global factors
factors = [1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1]
@ -176,6 +183,8 @@ def Reset():
def RetrieveFileName():
global DataRawLength
global DataResultsRaw
global DataResultsRawTest
global DataRawLengthTest
fileName = request.get_data().decode('utf8').replace("'", '"')
@ -275,14 +284,22 @@ def RetrieveFileName():
global parametersSelData
parametersSelData = []
global StanceTest
StanceTest = False
global target_names
target_names = []
DataRawLength = -1
DataRawLengthTest = -1
data = json.loads(fileName)
if data['fileName'] == 'HeartC':
CollectionDB = mongo.db.HeartC.find()
elif data['fileName'] == 'StanceC':
StanceTest = True
CollectionDB = mongo.db.StanceC.find()
CollectionDBTest = mongo.db.StanceCTest.find()
elif data['fileName'] == 'DiabetesC':
CollectionDB = mongo.db.DiabetesC.find()
else:
@ -293,6 +310,15 @@ def RetrieveFileName():
item['InstanceID'] = index
DataResultsRaw.append(item)
DataRawLength = len(DataResultsRaw)
DataResultsRawTest = []
if (StanceTest):
for index, item in enumerate(CollectionDBTest):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawTest.append(item)
DataRawLengthTest = len(DataResultsRawTest)
DataSetSelection()
return 'Everything is okay'
@ -364,6 +390,49 @@ def CollectionData():
return jsonify(response)
def DataSetSelection():
global XDataTest, yDataTest
XDataTest = pd.DataFrame()
yDataTest = []
global StanceTest
if (StanceTest):
DataResultsTest = copy.deepcopy(DataResultsRawTest)
for dictionary in DataResultsRawTest:
for key in dictionary.keys():
if (key.find('*') != -1):
target = key
continue
continue
DataResultsRawTest.sort(key=lambda x: x[target], reverse=True)
DataResultsTest.sort(key=lambda x: x[target], reverse=True)
for dictionary in DataResultsTest:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[target]
AllTargetsTest = [o[target] for o in DataResultsRaw]
AllTargetsFloatValuesTest = []
previous = None
Class = 0
for i, value in enumerate(AllTargetsTest):
if (i == 0):
previous = value
target_namesLoc.append(value)
if (value == previous):
AllTargetsFloatValuesTest.append(Class)
else:
Class = Class + 1
target_namesLoc.append(value)
AllTargetsFloatValuesTest.append(Class)
previous = value
ArrayDataResultsTest = pd.DataFrame.from_dict(DataResultsTest)
XDataTest, yDataTest = ArrayDataResultsTest, AllTargetsFloatValuesTest
DataResults = copy.deepcopy(DataResultsRaw)
for dictionary in DataResultsRaw:
@ -564,7 +633,7 @@ memory = Memory(location, verbose=0)
# calculating for all algorithms and models the performance and other results
@memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle):
print('inside')
print('inside models')
# instantiate spark session
spark = (
SparkSession
@ -1712,7 +1781,7 @@ def SendPredBacktobeUpdated():
@app.route('/data/ServerRequestSelPoin', methods=["GET", "POST"])
def RetrieveSelClassifiersID():
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
ComputeMetricsForSel(ClassifierIDsList)
#ComputeMetricsForSel(ClassifierIDsList)
ClassifierIDCleaned = json.loads(ClassifierIDsList)
global keySpecInternal
@ -1722,6 +1791,15 @@ def RetrieveSelClassifiersID():
EnsembleModel(ClassifierIDsList, 1)
return 'Everything Okay'
# Retrieve data from client
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@app.route('/data/ServerRequestSelPoinLocally', methods=["GET", "POST"])
def RetrieveSelClassifiersIDLocally():
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
ComputeMetricsForSel(ClassifierIDsList)
return 'Everything Okay'
def ComputeMetricsForSel(Models):
Models = json.loads(Models)
MetricsAlltoSel = PreprocessingMetrics()
@ -2473,6 +2551,8 @@ def FeatureSelPerModel():
return 'Everything Okay'
def EnsembleModel(Models, keyRetrieved):
global XDataTest, yDataTest
global scores
global previousState
global previousStateActive
@ -2852,11 +2932,12 @@ def EnsembleModel(Models, keyRetrieved):
# if (keyRetrieved == 0):
# pass
# else:
global StanceTest
if (keySpec == 0 or keySpec == 1):
num_cores = multiprocessing.cpu_count()
inputsSc = ['accuracy','precision_weighted','recall_weighted','f1_weighted']
flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(StanceTest,XDataTest,yDataTest,sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
scores = [item for sublist in flat_results for item in sublist]
if (keySpec == 0):
@ -2929,7 +3010,7 @@ def EnsembleModel(Models, keyRetrieved):
return 'Okay'
def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop):
def solve(StanceTest,y_Test,y_true,sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop):
scoresLoc = []
if (keySpec == 0):
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
@ -2974,6 +3055,16 @@ def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateAct
scoresLoc.append(temp.std())
scoresLoc.append(temp.mean())
scoresLoc.append(temp.std())
if (StanceTest):
y_pred = sclf.predict(y_Test)
if (loop == 0):
print(accuracy_score(y_true, y_pred))
elif (loop == 1):
print(precision_score(y_true, y_pred, average='weighted'))
elif (loop == 2):
print(recall_score(y_true, y_pred, average='weighted'))
else:
print(f1_score(y_true, y_pred, average='weighted'))
return scoresLoc

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