ISOVIS
/
StackGenVis
9
0
Fork 0
Code Issues Pull Requests Releases 2 Wiki Activity

updates

Browse Source
Angelos Chatzimparmpas 5 years ago
parent c6393c0e6f
commit f902727048
19 changed files with 346 additions and 274 deletions
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Show Stats Download Patch File Download Diff File
  1. BIN
      __pycache__/run.cpython-37.pyc
  2. 28
      frontend/package-lock.json
  3. 2
      frontend/package.json
  4. 4
      frontend/src/components/Active.vue
  5. 56
      frontend/src/components/AlgorithmHyperParam.vue
  6. 8
      frontend/src/components/BalancePredictions.vue
  7. 14
      frontend/src/components/BarChart.vue
  8. 61
      frontend/src/components/DataSpace.vue
  9. 64
      frontend/src/components/FinalResultsLinePlot.vue
  10. 1
      frontend/src/components/Heatmap.vue
  11. 13
      frontend/src/components/Main.vue
  12. 7
      frontend/src/components/PCPData.vue
  13. 6
      frontend/src/components/Parameters.vue
  14. 10
      frontend/src/components/PerMetricBarChart.vue
  15. 37
      frontend/src/components/PredictionsSpace.vue
  16. 81
      frontend/src/components/Provenance.vue
  17. 18
      frontend/src/components/ScatterPlot.vue
  18. 6
      frontend/src/components/ToggleSelection.vue
  19. 32
      run.py

BIN
__pycache__/run.cpython-37.pyc
View File

Binary file not shown.

28
frontend/package-lock.json generated
Unescape View File

@ -12346,6 +12346,13 @@
"pipe": "0.0.2",
"underscore": "1.1.6",
"underscore.string": "1.1.4"
},
"dependencies": {
"underscore": {
"version": "1.1.6",
"resolved": "https://registry.npmjs.org/underscore/-/underscore-1.1.6.tgz",
"integrity": "sha1-aGjaG91y11KFvgtOUPIo5w0AGiw="
}
}
},
"flat-cache": {
@ -24788,9 +24795,9 @@
"integrity": "sha1-5z3T17DXxe2G+6xrCufYxqadUPo="
},
"underscore": {
"version": "1.1.6",
"resolved": "https://registry.npmjs.org/underscore/-/underscore-1.1.6.tgz",
"integrity": "sha1-aGjaG91y11KFvgtOUPIo5w0AGiw="
"version": "1.9.2",
"resolved": "https://registry.npmjs.org/underscore/-/underscore-1.9.2.tgz",
"integrity": "sha512-D39qtimx0c1fI3ya1Lnhk3E9nONswSKhnffBI0gME9C99fYOkNi04xs8K6pePLhvl1frbDemkaBQ5ikWllR2HQ=="
},
"underscore.string": {
"version": "1.1.4",
@ -24798,6 +24805,13 @@
"integrity": "sha1-m+BrI7jj2ZbqICD5mEICBp497hI=",
"requires": {
"underscore": "1.1.6"
},
"dependencies": {
"underscore": {
"version": "1.1.6",
"resolved": "https://registry.npmjs.org/underscore/-/underscore-1.1.6.tgz",
"integrity": "sha1-aGjaG91y11KFvgtOUPIo5w0AGiw="
}
}
},
"unicode-canonical-property-names-ecmascript": {
@ -26429,6 +26443,14 @@
"integrity": "sha512-4gDntzrifFnCEvyoO8PqyJDmguXgVPxKiIxrBKjIowvL9l+N66196+72XVYR8BBf1Uv1Fgt3bGevJ+sEmxfZzw==",
"dev": true
},
"vue-underscore": {
"version": "0.1.4",
"resolved": "https://registry.npmjs.org/vue-underscore/-/vue-underscore-0.1.4.tgz",
"integrity": "sha1-w9zuJqbgJ53ylEimBBp+ZMIJgao=",
"requires": {
"underscore": "^1.8.3"
}
},
"vue2-perfect-scrollbar": {
"version": "1.3.0",
"resolved": "https://registry.npmjs.org/vue2-perfect-scrollbar/-/vue2-perfect-scrollbar-1.3.0.tgz",

2
frontend/package.json
Unescape View File

@ -69,6 +69,7 @@
"strip-loader": "^0.1.2",
"toposort": "^2.0.2",
"transform-loader": "^0.2.4",
"underscore": "^1.9.2",
"update": "^0.7.4",
"vega": "^5.9.1",
"vue": "^2.6.11",
@ -77,6 +78,7 @@
"vue-plotly": "^1.1.0",
"vue-router": "^3.1.5",
"vue-slider-component": "^3.1.1",
"vue-underscore": "^0.1.4",
"vue2-simplert-plugin": "^0.5.3",
"webpack-cli": "^3.3.11",
"webpack-require": "0.0.16"

4
frontend/src/components/Active.vue
Unescape View File

@ -15,12 +15,12 @@ export default {
name: 'Active',
data () {
return {
valueActive: 'Compute Active Metamodel\'s Performance'
valueActive: 'Compute Metamodel\'s Performance'
}
},
methods: {
sendUpdateActive () {
EventBus.$emit('OpenModal')
EventBus.$emit('sendToServerFeatures')
}
}
}

56
frontend/src/components/AlgorithmHyperParam.vue
Unescape View File

@ -185,7 +185,7 @@ export default {
} else if (this.selAlgorithm == 'SVC') {
Combined = JSON.parse(this.ModelsPerformance[10])
colorGiv = colors[1]
} else if (this.selAlgorithm == 'GausNB') {
} else if (this.selAlgorithm == 'GauNB') {
Combined = JSON.parse(this.ModelsPerformance[19])
colorGiv = colors[2]
} else if (this.selAlgorithm == 'MLP') {
@ -232,72 +232,72 @@ export default {
for (var i = 0; i < valuesPerf.length; i++) {
if (this.keyAllOrClass) {
if (this.selAlgorithm === 'KNN') {
newObjectsParamsΚΝΝ.push({model: i,'# Performance (%) #': McKNN[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
newObjectsParamsΚΝΝ.push({model: i,'# Perf (%) #': McKNN[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
ArrayCombined[i] = newObjectsParamsΚΝΝ[i]
} else if (this.selAlgorithm === 'SVC') {
newObjectsParamsSVC.push({model: this.SVCModels + i,'# Performance (%) #': McSVC[i],'C':ObjectsParams[i].C,'kernel':ObjectsParams[i].kernel})
newObjectsParamsSVC.push({model: this.SVCModels + i,'# Perf (%) #': McSVC[i],'C':ObjectsParams[i].C,'kernel':ObjectsParams[i].kernel})
ArrayCombined[i] = newObjectsParamsSVC[i]
} else if (this.selAlgorithm === 'GausNB') {
newObjectsParamsGausNB.push({model: this.GausNBModels + i,'# Performance (%) #': McGausNB[i],'var_smoothing':ObjectsParams[i].var_smoothing})
} else if (this.selAlgorithm === 'GauNB') {
newObjectsParamsGausNB.push({model: this.GausNBModels + i,'# Perf (%) #': McGausNB[i],'var_smoothing':ObjectsParams[i].var_smoothing})
ArrayCombined[i] = newObjectsParamsGausNB[i]
} else if (this.selAlgorithm === 'MLP') {
newObjectsParamsMLP.push({model: this.MLPModels + i,'# Performance (%) #': McMLP[i],'alpha':ObjectsParams[i].alpha,'tol':ObjectsParams[i].tol,'activation':ObjectsParams[i].activation,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver})
newObjectsParamsMLP.push({model: this.MLPModels + i,'# Perf (%) #': McMLP[i],'alpha':ObjectsParams[i].alpha,'tol':ObjectsParams[i].tol,'activation':ObjectsParams[i].activation,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver})
ArrayCombined[i] = newObjectsParamsMLP[i]
} else if (this.selAlgorithm === 'LR') {
newObjectsParamsLR.push({model: this.LRModels + i,'# Performance (%) #': McLR[i],'C':ObjectsParams[i].C,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver,'penalty':ObjectsParams[i].penalty})
newObjectsParamsLR.push({model: this.LRModels + i,'# Perf (%) #': McLR[i],'C':ObjectsParams[i].C,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver,'penalty':ObjectsParams[i].penalty})
ArrayCombined[i] = newObjectsParamsLR[i]
} else if (this.selAlgorithm === 'LDA') {
newObjectsParamsLDA.push({model: this.LDAModels + i,'# Performance (%) #': McLDA[i],'shrinkage':ObjectsParams[i].shrinkage,'solver':ObjectsParams[i].solver})
newObjectsParamsLDA.push({model: this.LDAModels + i,'# Perf (%) #': McLDA[i],'shrinkage':ObjectsParams[i].shrinkage,'solver':ObjectsParams[i].solver})
ArrayCombined[i] = newObjectsParamsLDA[i]
} else if (this.selAlgorithm === 'QDA') {
newObjectsParamsQDA.push({model: this.QDAModels + i,'# Performance (%) #': McQDA[i],'reg_param':ObjectsParams[i].reg_param,'tol':ObjectsParams[i].tol})
newObjectsParamsQDA.push({model: this.QDAModels + i,'# Perf (%) #': McQDA[i],'reg_param':ObjectsParams[i].reg_param,'tol':ObjectsParams[i].tol})
ArrayCombined[i] = newObjectsParamsQDA[i]
} else if (this.selAlgorithm === 'RF') {
newObjectsParamsRF.push({model: this.RFModels + i,'# Performance (%) #': McRF[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
newObjectsParamsRF.push({model: this.RFModels + i,'# Perf (%) #': McRF[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
ArrayCombined[i] = newObjectsParamsRF[i]
} else if (this.selAlgorithm === 'ExtraT') {
newObjectsParamsExtraT.push({model: this.ExtraTModels + i,'# Performance (%) #': McExtraT[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
newObjectsParamsExtraT.push({model: this.ExtraTModels + i,'# Perf (%) #': McExtraT[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
ArrayCombined[i] = newObjectsParamsExtraT[i]
} else if (this.selAlgorithm === 'AdaB') {
newObjectsParamsAdaB.push({model: this.AdaBModels + i,'# Performance (%) #': McAdaB[i],'n_estimators':ObjectsParams[i].n_estimators,'learning_rate':ObjectsParams[i].learning_rate,'algorithm':ObjectsParams[i].algorithm})
newObjectsParamsAdaB.push({model: this.AdaBModels + i,'# Perf (%) #': McAdaB[i],'n_estimators':ObjectsParams[i].n_estimators,'learning_rate':ObjectsParams[i].learning_rate,'algorithm':ObjectsParams[i].algorithm})
ArrayCombined[i] = newObjectsParamsAdaB[i]
} else {
newObjectsParamsGradB.push({model: this.GradBModels + i,'# Performance (%) #': McGradB[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion,'learning_rate':ObjectsParams[i].learning_rate})
newObjectsParamsGradB.push({model: this.GradBModels + i,'# Perf (%) #': McGradB[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion,'learning_rate':ObjectsParams[i].learning_rate})
ArrayCombined[i] = newObjectsParamsGradB[i]
}
} else {
if (this.selAlgorithm === 'KNN') {
newObjectsParamsΚΝΝ.push({model: i,'# Performance (%) #': this.listClassPerf[0][i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
newObjectsParamsΚΝΝ.push({model: i,'# Perf (%) #': this.listClassPerf[0][i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
ArrayCombined[i] = newObjectsParamsΚΝΝ[i]
} else if (this.selAlgorithm === 'SVC') {
newObjectsParamsSVC.push({model: this.SVCModels + i,'# Performance (%) #': this.listClassPerf[1][i],'C':ObjectsParams[i].C,'kernel':ObjectsParams[i].kernel})
newObjectsParamsSVC.push({model: this.SVCModels + i,'# Perf (%) #': this.listClassPerf[1][i],'C':ObjectsParams[i].C,'kernel':ObjectsParams[i].kernel})
ArrayCombined[i] = newObjectsParamsSVC[i]
} else if (this.selAlgorithm === 'GausNB') {
newObjectsParamsGausNB.push({model: this.GausNBModels + i,'# Performance (%) #': this.listClassPerf[2][i],'var_smoothing':ObjectsParams[i].var_smoothing})
} else if (this.selAlgorithm === 'GauNB') {
newObjectsParamsGausNB.push({model: this.GausNBModels + i,'# Perf (%) #': this.listClassPerf[2][i],'var_smoothing':ObjectsParams[i].var_smoothing})
ArrayCombined[i] = newObjectsParamsGausNB[i]
} else if (this.selAlgorithm === 'MLP') {
newObjectsParamsMLP.push({model: this.MLPModels + i,'# Performance (%) #': this.listClassPerf[3][i],'alpha':ObjectsParams[i].alpha,'tol':ObjectsParams[i].tol,'activation':ObjectsParams[i].activation,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver})
newObjectsParamsMLP.push({model: this.MLPModels + i,'# Perf (%) #': this.listClassPerf[3][i],'alpha':ObjectsParams[i].alpha,'tol':ObjectsParams[i].tol,'activation':ObjectsParams[i].activation,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver})
ArrayCombined[i] = newObjectsParamsMLP[i]
} else if (this.selAlgorithm === 'LR') {
newObjectsParamsLR.push({model: this.LRModels + i,'# Performance (%) #': this.listClassPerf[4][i],'C':ObjectsParams[i].C,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver,'penalty':ObjectsParams[i].penalty})
newObjectsParamsLR.push({model: this.LRModels + i,'# Perf (%) #': this.listClassPerf[4][i],'C':ObjectsParams[i].C,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver,'penalty':ObjectsParams[i].penalty})
ArrayCombined[i] = newObjectsParamsLR[i]
} else if (this.selAlgorithm === 'LDA') {
newObjectsParamsLDA.push({model: this.LDAModels + i,'# Performance (%) #': this.listClassPerf[5][i],'shrinkage':ObjectsParams[i].shrinkage,'solver':ObjectsParams[i].solver})
newObjectsParamsLDA.push({model: this.LDAModels + i,'# Perf (%) #': this.listClassPerf[5][i],'shrinkage':ObjectsParams[i].shrinkage,'solver':ObjectsParams[i].solver})
ArrayCombined[i] = newObjectsParamsLDA[i]
} else if (this.selAlgorithm === 'QDA') {
newObjectsParamsQDA.push({model: this.QDAModels + i,'# Performance (%) #': this.listClassPerf[6][i],'reg_param':ObjectsParams[i].reg_param,'tol':ObjectsParams[i].tol})
newObjectsParamsQDA.push({model: this.QDAModels + i,'# Perf (%) #': this.listClassPerf[6][i],'reg_param':ObjectsParams[i].reg_param,'tol':ObjectsParams[i].tol})
ArrayCombined[i] = newObjectsParamsQDA[i]
} else if (this.selAlgorithm === 'RF') {
newObjectsParamsRF.push({model: this.RFModels + i,'# Performance (%) #': this.listClassPerf[7][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
newObjectsParamsRF.push({model: this.RFModels + i,'# Perf (%) #': this.listClassPerf[7][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
ArrayCombined[i] = newObjectsParamsRF[i]
} else if (this.selAlgorithm === 'ExtraT') {
newObjectsParamsExtraT.push({model: this.ExtraTModels + i,'# Performance (%) #': this.listClassPerf[8][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
newObjectsParamsExtraT.push({model: this.ExtraTModels + i,'# Perf (%) #': this.listClassPerf[8][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
ArrayCombined[i] = newObjectsParamsExtraT[i]
} else if (this.selAlgorithm === 'AdaB') {
newObjectsParamsAdaB.push({model: this.AdaBModels + i,'# Performance (%) #': this.listClassPerf[9][i],'n_estimators':ObjectsParams[i].n_estimators,'learning_rate':ObjectsParams[i].learning_rate,'algorithm':ObjectsParams[i].algorithm})
newObjectsParamsAdaB.push({model: this.AdaBModels + i,'# Perf (%) #': this.listClassPerf[9][i],'n_estimators':ObjectsParams[i].n_estimators,'learning_rate':ObjectsParams[i].learning_rate,'algorithm':ObjectsParams[i].algorithm})
ArrayCombined[i] = newObjectsParamsAdaB[i]
} else {
newObjectsParamsGradB.push({model: this.GradBModels + i,'# Performance (%) #': this.listClassPerf[10][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion,'learning_rate':ObjectsParams[i].learning_rate})
newObjectsParamsGradB.push({model: this.GradBModels + i,'# Perf (%) #': this.listClassPerf[10][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion,'learning_rate':ObjectsParams[i].learning_rate})
ArrayCombined[i] = newObjectsParamsGradB[i]
}
}
@ -306,12 +306,6 @@ export default {
this.pc = ParCoords()("#PCP")
.data(ArrayCombined)
.color(colorGiv)
.margin({
top: 25,
left: 25,
right: 25,
bottom: 25
})
.hideAxis(['model'])
.bundlingStrength(0) // set bundling strength
.smoothness(0)

8
frontend/src/components/BalancePredictions.vue
Unescape View File

@ -189,10 +189,10 @@
var heightforText = 215
svg.append("circle").attr("cx", 215).attr("cy", heightforText-1.5).attr("r", 6).style("fill", "#000")
svg.append("circle").attr("cx", 785).attr("cy", heightforText-1.5).attr("r", 6).style("fill", "#D3D3D3")
svg.append("text").attr("x", 230).attr("y", heightforText).text("All Points").style("font-size", "15px").attr("alignment-baseline","middle")
svg.append("text").attr("x", 515).attr("y", heightforText-10).text("# Performance (%) #").style("font-size", "15px").attr("alignment-baseline","top")
svg.append("text").attr("x", 800).attr("y", heightforText).text("Selected Points").style("font-size", "15px").attr("alignment-baseline","middle")
svg.append("text").attr("transform", "rotate(-90)").attr("x", -89).attr("y", -45).style("text-anchor", "middle").text("Number of Models");
svg.append("text").attr("x", 230).attr("y", heightforText).text("All Points").style("font-size", "16px").attr("alignment-baseline","middle")
svg.append("text").attr("x", 515).attr("y", heightforText-10).text("# Performance (%) #").style("font-size", "16px").attr("alignment-baseline","top")
svg.append("text").attr("x", 800).attr("y", heightforText).text("Selected Points").style("font-size", "16px").attr("alignment-baseline","middle")
svg.append("text").attr("transform", "rotate(-90)").attr("x", -89).attr("y", -45).style("text-anchor", "middle").style("font-size", "16px").text("Number of Models");
// Function to compute density
function kernelDensityEstimator(kernel, X) {

14
frontend/src/components/BarChart.vue
Unescape View File

@ -408,8 +408,8 @@ export default {
var layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
autosize: true,
barmode: 'group',
width: this.WH[0]*10.21,
height: this.WH[1]*0.59,
width: this.WH[0]*10.205,
height: this.WH[1]*0.6,
xaxis: {
title: 'Algorithm',
type:"category",
@ -535,7 +535,7 @@ export default {
}
var barc = document.getElementById('barChart');
var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
var config = {displayModeBar: false, scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
Plotly.newPlot(barc, data, layout, config)
@ -544,7 +544,15 @@ export default {
eventData.points.forEach((e) => {
tName = e.data.name.replace(/ *\([^)]*\) */g, "")
});
if (tName == "< 50% diameter narrowing / Healthy") {
tName = 0
this.tNameAll = 0
} else if (tName == "> 50% diameter narrowing / Diseased"){
tName = 1
this.tNameAll = 1
} else {
this.tNameAll = tName
}
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]])

61
frontend/src/components/DataSpace.vue
Unescape View File

@ -135,27 +135,15 @@ export default {
const XandYCoordinatesTSNE = JSON.parse(this.dataPoints[5])
const XandYCoordinatesUMAP = JSON.parse(this.dataPoints[6])
const impSizeArray = JSON.parse(this.dataPoints[7])
const KNNSize = JSON.parse(impSizeArray[7])
const SVCSize = JSON.parse(impSizeArray[16])
const GausNBSize = JSON.parse(impSizeArray[25])
const MLPSize = JSON.parse(impSizeArray[34])
const LRSize = JSON.parse(impSizeArray[43])
const LDASize = JSON.parse(impSizeArray[52])
const QDASize = JSON.parse(impSizeArray[61])
const RFSize = JSON.parse(impSizeArray[70])
const ExtraTSize = JSON.parse(impSizeArray[79])
const AdaBSize = JSON.parse(impSizeArray[88])
const GradBSize = JSON.parse(impSizeArray[97])
console.log(KNNSize)
var sizeScatterplot = []
var scale = d3.scaleLinear()
.domain([0,1])
.range([2,30]);
.range([10,20]);
for (let i = 0; i < KNNSize.length; i++) {
sizeScatterplot.push(scale((KNNSize[i] + SVCSize[i] + GausNBSize[i] + MLPSize[i] + LRSize[i] + LDASize[i] + QDASize[i] + RFSize[i] + ExtraTSize[i] + AdaBSize[i] + GradBSize[i]) / 11))
for (let i = 0; i < impSizeArray.length; i++) {
sizeScatterplot.push(scale(impSizeArray[i]))
}
let intData = []
@ -172,19 +160,6 @@ export default {
var Yaxs = []
var Opacity
if (this.representationDef == 'mds') {
for (let i = 0; i < XandYCoordinatesMDS[0].length; i++) {
Xaxs.push(XandYCoordinatesMDS[0][i])
Yaxs.push(XandYCoordinatesMDS[1][i])
IDs.push(i)
}
result.Xax = Xaxs
result.Yax = Yaxs
result.ID = IDs
var traces = []
var layout = []
var beautifyLabels = []
if (this.RetrieveDataSet == 'StanceC') {
beautifyLabels.push('Absence of Hypotheticality')
@ -199,6 +174,19 @@ export default {
});
}
if (this.representationDef == 'mds') {
for (let i = 0; i < XandYCoordinatesMDS[0].length; i++) {
Xaxs.push(XandYCoordinatesMDS[0][i])
Yaxs.push(XandYCoordinatesMDS[1][i])
IDs.push(i)
}
result.Xax = Xaxs
result.Yax = Yaxs
result.ID = IDs
var traces = []
var layout = []
for (let i = 0; i < target_names.length; i++) {
const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
@ -237,7 +225,7 @@ export default {
}
layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
title: 'MDS Projection',
legend: {orientation: 'h', xanchor: 'center', x: 0.5},
xaxis: {
visible: false
},
@ -316,7 +304,7 @@ export default {
}
layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
title: 't-SNE Projection',
legend: {orientation: 'h', xanchor: 'center', x: 0.5},
xaxis: {
visible: false
},
@ -385,7 +373,7 @@ export default {
}
layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
title: 'UMAP Projection',
legend: {orientation: 'h', xanchor: 'center', x: 0.5},
xaxis: {
visible: false
},
@ -423,11 +411,10 @@ export default {
if (evt.points[i] === undefined) {
break
} else {
const OnlyId = evt.points[i].text.split(';')
ClassifierIDsList.push(OnlyId[0])
let numb = OnlyId[0].match(/\d/g);
numb = numb.join("");
let numberNumb = Number(numb)
const OnlyId = evt.points[i].text.split('<br>')
const ImpID = OnlyId[0].split(' ')
ClassifierIDsList.push(ImpID[3])
let numberNumb = parseInt(ImpID[3])
ClassifierIDsListCleared.push(numberNumb)
}
}

64
frontend/src/components/FinalResultsLinePlot.vue
Unescape View File

@ -1,5 +1,5 @@
<template>
<div id="LinePlot" style="min-height: 365px;"></div>
<div id="LinePlot" style="min-height: 363px;"></div>
</template>
<script>
@ -45,7 +45,8 @@ export default {
Stack_scoresPositive4: [],
Stack_scoresNegative4: [],
xaxis: [],
WH: []
WH: [],
firstTime: 0
}
},
methods: {
@ -57,53 +58,53 @@ export default {
this.xaxis.push(this.NumberofExecutions)
// Under Exploration = Current
this.scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[0])*100).toFixed(1))
this.scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[1])*100).toFixed(1))
this.scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[0])*100).toFixed(2))
this.scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[1])*100).toFixed(2))
this.scoresPositive.push(parseFloat(this.scoresMean[this.scoresMean.length - 1]) + parseFloat(this.scoresSTD[this.scoresSTD.length - 1]))
this.scoresNegative.push(parseFloat(this.scoresMean[this.scoresMean.length - 1]) - parseFloat(this.scoresSTD[this.scoresSTD.length - 1]))
this.scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[2])*100).toFixed(1))
this.scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[3])*100).toFixed(1))
this.scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[2])*100).toFixed(2))
this.scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[3])*100).toFixed(2))
this.scoresPositive2.push(parseFloat(this.scoresMean2[this.scoresMean2.length - 1]) + parseFloat(this.scoresSTD2[this.scoresSTD2.length - 1]))
this.scoresNegative2.push(parseFloat(this.scoresMean2[this.scoresMean2.length - 1]) - parseFloat(this.scoresSTD2[this.scoresSTD2.length - 1]))
this.scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[4])*100).toFixed(1))
this.scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[5])*100).toFixed(1))
this.scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[4])*100).toFixed(2))
this.scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[5])*100).toFixed(2))
this.scoresPositive3.push(parseFloat(this.scoresMean3[this.scoresMean3.length - 1]) + parseFloat(this.scoresSTD3[this.scoresSTD3.length - 1]))
this.scoresNegative3.push(parseFloat(this.scoresMean3[this.scoresMean3.length - 1]) - parseFloat(this.scoresSTD3[this.scoresSTD3.length - 1]))
this.scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[6])*100).toFixed(1))
this.scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[7])*100).toFixed(1))
this.scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[6])*100).toFixed(2))
this.scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[7])*100).toFixed(2))
this.scoresPositive4.push(parseFloat(this.scoresMean4[this.scoresMean4.length - 1]) + parseFloat(this.scoresSTD4[this.scoresSTD4.length - 1]))
this.scoresNegative4.push(parseFloat(this.scoresMean4[this.scoresMean4.length - 1]) - parseFloat(this.scoresSTD4[this.scoresSTD4.length - 1]))
// Stack
this.Stack_scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[8])*100).toFixed(1))
this.Stack_scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[9])*100).toFixed(1))
this.Stack_scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[8])*100).toFixed(2))
this.Stack_scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[9])*100).toFixed(2))
this.Stack_scoresPositive.push(parseFloat(this.Stack_scoresMean[this.Stack_scoresMean.length - 1]) + parseFloat(this.Stack_scoresSTD[this.Stack_scoresSTD.length - 1]))
this.Stack_scoresNegative.push(parseFloat(this.Stack_scoresMean[this.Stack_scoresMean.length - 1]) - parseFloat(this.Stack_scoresSTD[this.Stack_scoresSTD.length - 1]))
this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[10])*100).toFixed(1))
this.Stack_scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[11])*100).toFixed(1))
this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[10])*100).toFixed(2))
this.Stack_scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[11])*100).toFixed(2))
this.Stack_scoresPositive2.push(parseFloat(this.Stack_scoresMean2[this.Stack_scoresMean2.length - 1]) + parseFloat(this.Stack_scoresSTD2[this.Stack_scoresSTD2.length - 1]))
this.Stack_scoresNegative2.push(parseFloat(this.Stack_scoresMean2[this.Stack_scoresMean2.length - 1]) - parseFloat(this.Stack_scoresSTD2[this.Stack_scoresSTD2.length - 1]))
this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[12])*100).toFixed(1))
this.Stack_scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[13])*100).toFixed(1))
this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[12])*100).toFixed(2))
this.Stack_scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[13])*100).toFixed(2))
this.Stack_scoresPositive3.push(parseFloat(this.Stack_scoresMean3[this.Stack_scoresMean3.length - 1]) + parseFloat(this.Stack_scoresSTD3[this.Stack_scoresSTD3.length - 1]))
this.Stack_scoresNegative3.push(parseFloat(this.Stack_scoresMean3[this.Stack_scoresMean3.length - 1]) - parseFloat(this.Stack_scoresSTD3[this.Stack_scoresSTD3.length - 1]))
this.Stack_scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[14])*100).toFixed(1))
this.Stack_scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[15])*100).toFixed(1))
this.Stack_scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[14])*100).toFixed(2))
this.Stack_scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[15])*100).toFixed(2))
this.Stack_scoresPositive4.push(parseFloat(this.Stack_scoresMean4[this.Stack_scoresMean4.length - 1]) + parseFloat(this.Stack_scoresSTD4[this.Stack_scoresSTD4.length - 1]))
this.Stack_scoresNegative4.push(parseFloat(this.Stack_scoresMean4[this.Stack_scoresMean4.length - 1]) - parseFloat(this.Stack_scoresSTD4[this.Stack_scoresSTD4.length - 1]))
@ -176,7 +177,7 @@ export default {
line: {color: "rgb(55,126,184)"},
mode: "lines+markers",
marker : {
symbol: 'triangle' },
symbol: 'triangle-up' },
name: "Active Recall",
type: "scatter"
}
@ -278,7 +279,7 @@ export default {
line: {color: "rgb(228,26,28)"},
mode: "lines+markers",
marker : {
symbol: 'pentagon' },
symbol: 'triangle-down' },
name: "Stack Recall",
type: "scatter"
}
@ -323,10 +324,11 @@ export default {
const DataforLinePlot = [trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8, trace9, trace10, trace11, trace12, trace13, trace14, trace15, trace16]
var width = this.WH[0]*3 // interactive visualization
var height = this.WH[1]*0.6 // interactive visualization
var width = this.WH[0]*3.18 // interactive visualization
var height = this.WH[1]*0.765 // interactive visualization
var layout = {
font: { family: 'Helvetica', size: 14, color: '#000000' },
paper_bgcolor: "rgb(255,255,255)",
plot_bgcolor: "rgb(229,229,229)",
xaxis: {
@ -355,17 +357,25 @@ export default {
width: width,
height: height,
margin: {
l: 60,
r: 40,
b: 40,
t: 40,
l: 50,
r: 0,
b: 50,
t: 5,
pad: 0
},
}
Plotly.newPlot('LinePlot', DataforLinePlot, layout, {showSendToCloud: true, responsive: true})
var config = {displayModeBar: false, scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
Plotly.newPlot('LinePlot', DataforLinePlot, layout, config)
if (this.firstTime == 0) {
EventBus.$emit('emittedEventCallingReally')
this.firstTime = 1
}
}
},
mounted() {
EventBus.$on('updateFlagForFinalResults', data => { this.firstTime = data})
EventBus.$on('emittedEventCallingLinePlot', data => {
this.FinalResultsforLinePlot = data})
EventBus.$on('emittedEventCallingLinePlot', this.LinePlotView)

1
frontend/src/components/Heatmap.vue
Unescape View File

@ -434,7 +434,6 @@ export default {
}
finalresults.push(results)
}
console.log(finalresults)
EventBus.$emit('flagLocal', true)
EventBus.$emit('sendSelectedFeaturestoPickle', finalresults)
EventBus.$emit('SendSelectedFeaturesEvent', finalresults)

13
frontend/src/components/Main.vue
Unescape View File

@ -17,7 +17,7 @@
</b-col>
<b-col cols="6">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center" style="background-color: #C0C0C0;"><small class="float-left"><knowledge/></small>History of the Stacking Ensemble<small class="float-right"><active/></small></mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center" style="background-color: #C0C0C0;">History of the Stacking Ensemble<small class="float-right"><knowledge/></small></mdb-card-header>
<mdb-card-body>
<Provenance/>
</mdb-card-body>
@ -98,7 +98,7 @@
</b-col>
<b-col cols="6">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Features Selection for Each Model</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Features Selection for Each Model<small class="float-right"><active/></small></mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 845px">
<ToggleSelection/>
@ -407,6 +407,7 @@ export default Vue.extend({
this.OverSelLength = 0
EventBus.$emit('resetViews')
} else {
this.OverSelLength = this.ClassifierIDsList.length
const path = `http://127.0.0.1:5000/data/ServerRequestSelPoin`
const postData = {
ClassifiersList: this.ClassifierIDsList,
@ -423,7 +424,6 @@ export default Vue.extend({
axios.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the selected points to the server (scatterplot)!')
this.OverSelLength = this.ClassifierIDsList.length
if (this.keyNow == 0) {
this.OverAllLength = this.ClassifierIDsList.length
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
@ -438,7 +438,7 @@ export default Vue.extend({
},
RemoveFromStackModels () {
const path = `http://127.0.0.1:5000/data/ServerRemoveFromStack`
console.log(this.ClassifierIDsList)
const postData = {
ClassifiersList: this.ClassifierIDsList
}
@ -453,6 +453,7 @@ export default Vue.extend({
axios.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the selected points to the server (scatterplot)!')
EventBus.$emit('updateFlagForFinalResults', 0)
this.getFinalResults()
this.updatePredictionsSpace()
})
@ -700,6 +701,7 @@ export default Vue.extend({
},
UpdateBasedonFeatures () {
const path = `http://127.0.0.1:5000/data/FeaturesSelection`
console.log(this.SelectedFeaturesPerClassifier)
const postData = {
featureSelection: this.SelectedFeaturesPerClassifier
}
@ -942,6 +944,7 @@ export default Vue.extend({
window.addEventListener('resize', this.change)
},
mounted() {
this.Alg()
var modal = document.getElementById('myModal')
window.onclick = function(event) {
@ -972,7 +975,7 @@ export default Vue.extend({
EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data })
EventBus.$on('SendSelectedDataPointsToServerEvent', this.SendSelectedDataPointsToServer)
EventBus.$on('SendSelectedFeaturesEvent', data => { this.SelectedFeaturesPerClassifier = data })
EventBus.$on('SendSelectedFeaturesEvent', this.UpdateBasedonFeatures )
EventBus.$on('sendToServerFeatures', this.UpdateBasedonFeatures)
EventBus.$on('SendToServerDataSetConfirmation', data => { this.RetrieveValueFile = data })
EventBus.$on('SendToServerLocalFile', data => { this.localFile = data })
EventBus.$on('SendToServerLocalFile', this.SendToServerData)

7
frontend/src/components/PCPData.vue
Unescape View File

@ -29,7 +29,6 @@ export default {
d3.selectAll("#PCPDataView > *").remove();
const DataSetNew = JSON.parse(this.PCPDataReceived[2])
var DataSetParse = JSON.parse(DataSetNew)
this.ClassifierIDsListClearedData = DataSetParse.slice();
const target_names = JSON.parse(this.PCPDataReceived[3])
const target_names_original = JSON.parse(this.PCPDataReceived[4])
@ -41,8 +40,10 @@ export default {
EventBus.$emit('sendDatatoPickle', extraction)
var highlighted = []
for (let i = 0; i < this.ClassifierIDsListClearedData.length; i++) {
highlighted.push(DataSetParse[i])
for (let i = 0; i < DataSetParse.length; i++) {
if (this.ClassifierIDsListClearedData.includes(i)) {
highlighted[i] = DataSetParse[i]
}
}
if (DataSetParse.length == this.ClassifierIDsListClearedData.length || this.ClassifierIDsListClearedData.length == 0)

6
frontend/src/components/Parameters.vue
Unescape View File

@ -674,7 +674,7 @@ export default {
//////////////////////////////////////////////////////////////
var color = d3.scale.ordinal()
.range(["#ffed6f","#008080"]);
.range(["#ffed6f","#000000"]);
var radarChartOptions = {
w: width,
@ -722,7 +722,7 @@ export default {
mounted () {
EventBus.$on('updateFlagKNN', data => { this.FlagKNN = data })
EventBus.$on('updateFlagSVC', data => { this.FlagSVC = data })
EventBus.$on('updateFlagGausNB', data => { this.FlagGausNB = data })
EventBus.$on('updateFlagGauNB', data => { this.FlagGausNB = data })
EventBus.$on('updateFlagMLP', data => { this.FlagMLP = data })
EventBus.$on('updateFlagLR', data => { this.FlagLR = data })
EventBus.$on('updateFlagLDA', data => { this.FlagLDA = data })
@ -736,7 +736,7 @@ export default {
EventBus.$on('updateFlagKNN', this.overview)
EventBus.$on('updateFlagSVC', this.overview)
EventBus.$on('updateFlagGausNB', this.overview)
EventBus.$on('updateFlagGauNB', this.overview)
EventBus.$on('updateFlagMLP', this.overview)
EventBus.$on('updateFlagLR', this.overview)
EventBus.$on('updateFlagLDA', this.overview)

10
frontend/src/components/PerMetricBarChart.vue
Unescape View File

@ -81,33 +81,33 @@ export default {
};
var data = [trace1, trace2];
var layout = {
font: { family: 'Helvetica', size: 14, color: '#000000' },
boxmode: 'group',
autosize: true,
width: width,
height: height,
hovermode: 'x',
font: { family: 'Helvetica', size: 14, color: '#000000' },
margin: {
l: 50,
r: 0,
b: 30,
b: 35,
t: 40,
pad: 0
},
xaxis: {
title: 'Performance Metrics',
titlefont: {
size: 12,
size: 16,
color: 'black'
}},
yaxis: {
title: '# Performance (%) #',
titlefont: {
size: 12,
size: 16,
color: 'black'
}}};
var boxPlot = document.getElementById('PerMetricBar');
var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
var config = {displayModeBar: false, scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
Plotly.newPlot(boxPlot, data, layout, config);
boxPlot.on('plotly_click', (eventData) => {

37
frontend/src/components/PredictionsSpace.vue
Unescape View File

@ -75,6 +75,20 @@ export default {
var Xaxs = [];
var Yaxs = [];
var beautifyLabels = []
if (this.RetrieveDataSet == 'StanceC') {
beautifyLabels.push('Absence of Hypotheticality')
beautifyLabels.push('Presence of Hypotheticality')
}
else if (this.RetrieveDataSet == 'HeartC') {
beautifyLabels.push('< 50% diameter narrowing / Healthy')
beautifyLabels.push('> 50% diameter narrowing / Diseased')
} else {
target_names.forEach(element => {
beautifyLabels.push(element)
});
}
if (this.representationDef == 'mds') {
for (let i = 0; i < XandYCoordinatesMDS[0].length; i++) {
Xaxs.push(XandYCoordinatesMDS[0][i])
@ -113,7 +127,7 @@ export default {
}
layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
title: 'MDS Projection',
xaxis: {
visible: false
},
@ -125,6 +139,7 @@ export default {
autosize: true,
width: width,
height: height,
legend: {orientation: 'h', xanchor: 'center', x: 0.5},
margin: {
l: 50,
r: 0,
@ -180,7 +195,7 @@ export default {
}
layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
title: 't-SNE Projection',
xaxis: {
visible: false
},
@ -192,6 +207,7 @@ export default {
autosize: true,
width: width,
height: height,
legend: {orientation: 'h', xanchor: 'center', x: 0.5},
margin: {
l: 50,
r: 0,
@ -212,20 +228,6 @@ export default {
var traces = []
var beautifyLabels = []
if (this.RetrieveDataSet == 'StanceC') {
beautifyLabels.push('Absence of Hypotheticality')
beautifyLabels.push('Presence of Hypotheticality')
}
else if (this.RetrieveDataSet == 'HeartC') {
beautifyLabels.push('< 50% diameter narrowing / Healthy')
beautifyLabels.push('> 50% diameter narrowing / Diseased')
} else {
target_names.forEach(element => {
beautifyLabels.push(element)
});
}
for (let i = 0; i < target_names.length; i++) {
const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
@ -251,13 +253,14 @@ export default {
}
layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
title: 'UMAP Projection',
xaxis: {
visible: false
},
yaxis: {
visible: false
},
legend: {orientation: 'h', xanchor: 'center', x: 0.5},
dragmode: 'lasso',
hovermode: "closest",
autosize: true,

81
frontend/src/components/Provenance.vue
Unescape View File

@ -1,8 +1,8 @@
<template>
<div>
<div class="squares-container" style="min-height: 364px;">
<div class="squares-container" style="min-height: 374px;">
<div id="tooltip"></div> <!-- new -->
<div id="performanceCapture" style="min-height: 150px;"></div> <!-- new -->
<div id="performanceCapture" style="min-height: 150px; margin-top: -10px !important;"></div> <!-- new -->
<canvas id="main-canvas" style="overflow-y: auto; height:190px;"></canvas>
<br>
<div id="dynamic-buttons"></div>
@ -13,6 +13,10 @@
<script>
import { EventBus } from '../main.js'
import $ from 'jquery'
import * as d3Base from 'd3'
// attach all d3 plugins to the d3 library
const d3v5 = Object.assign(d3Base)
import * as Stardust from 'stardust-core'
import * as StardustGL from 'stardust-webgl'
@ -43,7 +47,13 @@ export default {
storeData: [],
storePerformance: [],
storeParameters: [],
flagUpdated: 0
flagUpdated: 0,
FinalResultsProv: [],
Stack_scoresMean: [],
Stack_scoresMean2: [],
Stack_scoresMean3: [],
Stack_scoresMean4: [],
firstInside: 0
}
},
methods: {
@ -54,6 +64,8 @@ export default {
$('.dynamic_buttons').remove();
this.platform.clear();
}
var svg = d3.select("#performanceCapture");
svg.selectAll("*").remove();
},
clean(obj) {
var propNames = Object.getOwnPropertyNames(obj);
@ -66,8 +78,8 @@ export default {
},
provenance () {
var canvas = document.getElementById("main-canvas");
var width = this.WH[0]*4 // interactive visualization
var height = this.WH[1]*0.58 // interactive visualization
var width = this.WH[0]*3.5 // interactive visualization
var height = this.WH[1]*0.375 // interactive visualization
var flagKNN = 0
var flagSVC = 0
@ -83,7 +95,6 @@ export default {
var localStackStore = []
var StackInfo = JSON.parse(this.stackInformation[1])
console.log(StackInfo)
var arrayOfNumbers = StackInfo.map(Number)
this.storeData.push(arrayOfNumbers)
localStackStore = this.storeData.slice()
@ -209,9 +220,7 @@ export default {
this.data.forEach(d => {
if (d.column == this.counter) {
d.typeIndex = this.typeCounter[d.type]++;
console.log(d.typeIndex)
d.typeColumnIndex = this.typeColumnCounter[d.column]++;
console.log(d.typeColumnIndex)
}
});
@ -227,8 +236,8 @@ export default {
// here 10 was 5!
let pScale = Stardust.scale.custom(`
Vector2(
20 + column * 100 + typeColumnIndex % 5 * 8,
height - 10 - floor(typeColumnIndex / 5) * 10
20 + column * 195 + typeColumnIndex % 12 * 11.7,
height - 10 - floor(typeColumnIndex / 12) * 10
)
`);
pScale.attr("typeColumnIndex", d => d.typeColumnIndex);
@ -239,8 +248,8 @@ export default {
let qScale = Stardust.scale.custom(`
Vector2(
65 + typeIndex % 30 * 8,
height - 10 - floor(typeIndex / 15) * 18
60 + typeIndex % 30 * 8,
height - 10 - floor(typeIndex / 15) * 40
)
`);
qScale.attr("typeIndex", d => d.typeIndex);
@ -314,7 +323,7 @@ export default {
.style('opacity', 0.8)
.style('top', x + 5 + 'px')
.style('left', y + 5 + 'px')
.html('Model ID: '+mergedIDs[p[1]]+'<br>'+'Parameters: '+JSON.stringify(mergedParams[p[1]])+'<br> # Performance (%) #: '+mergedPerf[p[1]]);
.html('Model ID: '+mergedIDs[p[1]]+'<br>'+'Parameters: '+JSON.stringify(mergedParams[p[1]])+'<br> # Performance (%) #: '+mergedPerf[p[1]].toFixed(2));
} else {
@ -325,8 +334,11 @@ export default {
}
}
const stringStep = "Stack Ensemble"
const stringStep = "Stacking Ensemble "
var myButton = '<button id="HistoryReturnButtons'+this.counter+'" class="dynamic_buttons">'+stringStep+this.counter+'</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'
var svgs = '<svg id=svg'+this.counter+'></svg>'
this.firstInside = this.counter
$("#performanceCapture").append(svgs);
$("#dynamic-buttons").append(myButton);
EventBus.$emit('requestProven',this.counter-1)
@ -352,20 +364,32 @@ export default {
EventBus.$emit('ChangeKey', 0)
}
);
var width = 150;
},
RadialPerf () {
var width = 160;
var arcSize = (6 * width / 100);
var innerRadius = arcSize * 3;
this.Stack_scoresMean.push((JSON.parse(this.FinalResultsProv[8])*100).toFixed(0))
this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsProv[10])*100).toFixed(0))
this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsProv[12])*100).toFixed(0))
this.Stack_scoresMean4.push((JSON.parse(this.FinalResultsProv[14])*100).toFixed(0))
const colorsSingle = ['#fc9272','#fb6a4a','#ef3b2c','#cb181d','#a50f15','#67000d']
var scaleColor = d3v5.scaleLinear()
.domain([0,100,5])
.range(colorsSingle)
.interpolate(d3v5.interpolateRgb); //interpolateHsl interpolateHcl interpolateRgb;
var data = [
{value: 45, label: "Accuracy", color: '#ff0000'},
{value: 33, label: "Precision", color: '#00ff00'},
{value: 66, label: "Recall", color: '#0000ff'},
{value: 50, label: "F1 Score", color: '#ffff00'}
{value: this.Stack_scoresMean, label: "Accuracy", color: scaleColor(this.Stack_scoresMean)},
{value: this.Stack_scoresMean2, label: "Precision", color: scaleColor(this.Stack_scoresMean2)},
{value: this.Stack_scoresMean3, label: "Recall", color: scaleColor(this.Stack_scoresMean3)},
{value: this.Stack_scoresMean4, label: "F1 Score", color: scaleColor(this.Stack_scoresMean4)}
];
function render() {
var svg = d3.select('#performanceCapture').append('svg').attr('width', width).attr('height', width);
var svg = d3.select('#svg'+this.firstInside).attr('width', width).attr('height', width).style('margin-right', '20px');
var arcs = data.map(function (obj, i) {
return d3.svg.arc().innerRadius(i * arcSize + innerRadius).outerRadius((i + 1) * arcSize - (width / 100) + innerRadius);
@ -418,7 +442,7 @@ export default {
});
var lableObj = r.data.object;
g.append('text')
.attr('font-size', ((5 * width) / 100))
.attr('font-size', ((5 * width) / 100) + 2)
.attr('dominant-baseline', 'central')
/*.attr('transform', "translate(" + centroid[0] + "," + (centroid[1] + 10) + ") rotate(" + (180 / Math.PI * r.startAngle + 7) + ")")
.attr('alignment-baseline', 'middle')*/
@ -438,17 +462,13 @@ export default {
});
var lableObj = r.data.object;
gText.append('text')
.attr('font-size', ((5 * width) / 100))
.attr('font-size', ((5 * width) / 100) + 2)
.text(lableObj.label)
.attr('transform', "translate(" + (centroidText[0] - ((1.5 * width) / 100)) + "," + (centroidText[1] + ") rotate(" + (180) + ")"))
.attr('dominant-baseline', 'central');
}
});
});
}
render()
},
updateExtraction () {
EventBus.$emit('SendSelectedPointsToServerEvent', this.storeData[this.flagUpdated])
@ -464,6 +484,10 @@ export default {
}
},
mounted () {
EventBus.$on('emittedEventCallingLinePlot', data => {
this.FinalResultsProv = data})
EventBus.$on('emittedEventCallingReally', this.RadialPerf)
EventBus.$on('requestProven', data => {
this.flagUpdated = data})
EventBus.$on('requestProven', this.updateExtraction)
@ -471,6 +495,7 @@ export default {
EventBus.$on('ParametersProvenance', data => {this.AllDetails = data})
EventBus.$on('InitializeProvenance', data => {this.stackInformation = data})
EventBus.$on('InitializeProvenance', this.provenance)
EventBus.$on('Responsive', data => {
this.WH = data})
EventBus.$on('ResponsiveandChange', data => {

18
frontend/src/components/ScatterPlot.vue
Unescape View File

@ -236,7 +236,7 @@ export default {
}]
layout = {
title: 'MDS Projection',
xaxis: {
visible: false,
range: [minX, maxX]
@ -298,7 +298,7 @@ export default {
}
}]
layout = {
title: 't-SNE Projection',
xaxis: {
visible: false,
range: [minX, maxX]
@ -351,7 +351,7 @@ export default {
}]
layout = {
title: 'UMAP Projection',
xaxis: {
visible: false,
range: [minX, maxX]
@ -389,8 +389,8 @@ export default {
const OverviewPlotly = document.getElementById('OverviewPlotly')
var allModels = JSON.parse(this.ScatterPlotResults[13])
OverviewPlotly.on('plotly_selected', function (evt) {
this.pushModelsRemaining = []
if (typeof evt !== 'undefined') {
var pushModelsRemainingTemp = []
const ClassifierIDsList = []
const ClassifierIDsListCleared = []
for (let i = 0; evt.points.length; i++) {
@ -400,17 +400,16 @@ export default {
const OnlyId = evt.points[i].text.split(' ')[2]
const OnlyIdCleared = OnlyId.split('<br>')
ClassifierIDsList.push(OnlyIdCleared[0])
let numb = OnlyIdCleared[0].match(/\d/g);
numb = numb.join("");
let numberNumb = Number(numb)
let numberNumb = parseInt(OnlyIdCleared[0])
ClassifierIDsListCleared.push(numberNumb)
}
}
for (let i = 0; i < allModels.length; i++) {
if (!ClassifierIDsListCleared.includes(allModels[i])) {
this.pushModelsRemaining.push(allModels[i])
pushModelsRemainingTemp.push(allModels[i])
}
}
EventBus.$emit('updateRemaining', pushModelsRemainingTemp)
if (allModels != '') {
EventBus.$emit('ChangeKey', 1)
EventBus.$emit('SendSelectedPointsToServerEvent', ClassifierIDsListCleared)
@ -470,8 +469,9 @@ export default {
mounted() {
/*EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data })
EventBus.$on('updateMetricsScatter', this.ScatterPlotView)*/
EventBus.$on('updateRemaining', data => { this.pushModelsRemaining = data })
EventBus.$on('requestProven',data => { this.activeModels = data })
EventBus.$on('requestProven', data => { this.activeModels = data })
EventBus.$on('sendKeyScatt', data => { this.keyLocal = data })

6
frontend/src/components/ToggleSelection.vue
Unescape View File

@ -25,11 +25,6 @@ export default {
$(this).bootstrapToggle('on'); //you can set "on" or "off"
});
},
ToggleSelection () {
},
ToggleShow () {
document.getElementById('toggles').style.visibility = "visible"
},
ToggleManage () {
if(this.toggleDeepLocal == 0) {
document.getElementById('toggles').style.visibility = "hidden"
@ -39,6 +34,7 @@ export default {
}
},
mounted () {
this.ToggleManage()
EventBus.$on('toggleDeep', data => {this.toggleDeepLocal = data})
EventBus.$on('toggleDeep', this.ToggleManage)

32
run.py
Unescape View File

@ -329,8 +329,6 @@ def SendToServerData():
XDataStored = XData.copy()
yDataStored = yData.copy()
callPreResults()
return 'Processed uploaded data set'
# Sent data to client
@ -1513,6 +1511,8 @@ def InitializeEnsemble():
impDataInst = processDataInstance(ModelsIDs,allParametersPerformancePerModel)
callPreResults()
key = 0
EnsembleModel(ModelsIDs, key)
@ -1520,16 +1520,27 @@ def InitializeEnsemble():
def processDataInstance(ModelsIDs, allParametersPerformancePerModel):
dicKNN = json.loads(allParametersPerformancePerModel[8])
dicKNN = json.loads(dicKNN)
dicSVC = json.loads(allParametersPerformancePerModel[17])
dicSVC = json.loads(dicSVC)
dicGausNB = json.loads(allParametersPerformancePerModel[26])
dicGausNB = json.loads(dicGausNB)
dicMLP = json.loads(allParametersPerformancePerModel[35])
dicMLP = json.loads(dicMLP)
dicLR = json.loads(allParametersPerformancePerModel[44])
dicLR = json.loads(dicLR)
dicLDA = json.loads(allParametersPerformancePerModel[53])
dicLDA = json.loads(dicLDA)
dicQDA = json.loads(allParametersPerformancePerModel[62])
dicQDA = json.loads(dicQDA)
dicRF = json.loads(allParametersPerformancePerModel[71])
dicExtraT = json.loads(allParametersPerformancePerModel[70])
dicRF = json.loads(dicRF)
dicExtraT = json.loads(allParametersPerformancePerModel[80])
dicExtraT = json.loads(dicExtraT)
dicAdaB = json.loads(allParametersPerformancePerModel[89])
dicAdaB = json.loads(dicAdaB)
dicGradB = json.loads(allParametersPerformancePerModel[98])
dicGradB = json.loads(dicGradB)
dfKNN = pd.DataFrame.from_dict(dicKNN)
dfSVC = pd.DataFrame.from_dict(dicSVC)
@ -1568,8 +1579,17 @@ def processDataInstance(ModelsIDs, allParametersPerformancePerModel):
dfGradBFiltered = dfGradB.loc[GradBModels, :]
df_connect = pd.concat([dfKNNFiltered, dfSVCFiltered, dfGausNBFiltered, dfMLPFiltered, dfLRFiltered, dfLDAFiltered, dfQDAFiltered, dfRFFiltered, dfExtraTFiltered, dfAdaBFiltered, dfGradBFiltered])
print(df_connect.sum(axis=1))
return df_connect
global yData
countCorrect = []
length = len(df_connect.index)
for index, element in enumerate(yData):
countTemp = 0
dfPart = df_connect[[str(index)]]
for indexdf, row in dfPart.iterrows():
if (int(row.values[0]) == int(element)):
countTemp += 1
countCorrect.append(1 - (countTemp/length))
return countCorrect
def ReturnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionSpaceMDS,PredictionSpaceTSNE,PredictionSpaceUMAP):
@ -1637,6 +1657,8 @@ def RetrieveSelClassifiersIDandRemoveFromStack():
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
PredictionProbSelUpdate = PreprocessingPredUpdate(ClassifierIDsList)
print(ClassifierIDsList)
print(PredictionProbSelUpdate)
global resultsUpdatePredictionSpace
resultsUpdatePredictionSpace = []

Write Preview
Loading…
Cancel
Save