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Angelos Chatzimparmpas 4 years ago
parent fd57d5dc1c
commit b48723a7fe
6 changed files with 715 additions and 124 deletions
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  1. BIN
      __pycache__/run.cpython-37.pyc
  2. 15
      frontend/src/components/History.vue
  3. 24
      frontend/src/components/HyperParameterSpace.vue
  4. 8
      frontend/src/components/Main.vue
  5. 107
      frontend/src/components/Predictions.vue
  6. 641
      run.py

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__pycache__/run.cpython-37.pyc
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15
frontend/src/components/History.vue
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@ -38,7 +38,7 @@ export default {
},
computePerformanceDiffS () {
var colorsforScatterPlot = this.PerF
console.log(this.storedEnsem)
console.log(this.storedCM)
var mergedStoreEnsembleLoc = [].concat.apply([], this.storedEnsem)
var mergedStoreEnsembleLocFormatted = []
for (let i = 0; i < mergedStoreEnsembleLoc.length; i++) {
@ -46,19 +46,15 @@ export default {
}
colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
console.log(colorsforScatterPlot)
var max = Math.max.apply(Math, colorsforScatterPlot)
var min = Math.min.apply(Math, colorsforScatterPlot)
console.log(max)
console.log(min)
var countMax = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
var countMin = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
console.log(this.PerFCM)
for (let i = 0; i < this.storedCM.length; i++) {
let tempSplit = this.storedCM[i].split(/([0-9]+)/)
console.log(tempSplit)
if (tempSplit[0] == 'KNNCC') {
if (this.PerFCM[i] > max) {
countMax[0] = countMax[0] + 1
@ -1138,7 +1134,7 @@ export default {
$("input[type='number']").change( function(d) {
valuesLoc[d.target.id] = parseInt(d.target.value)
EventBus.$emit('changeValues', valuesLoc)
EventBus.$emit('changeValues2', valuesLoc)
});
}
// .append("title")
@ -1532,6 +1528,9 @@ export default {
EventBus.$on('changeValues', data => { this.values = data })
EventBus.$on('changeValues', this.SankeyView )
EventBus.$on('changeValues2', data => { this.valuesStage2 = data})
EventBus.$on('changeValues2', this.SankeyViewStage2)
EventBus.$on('SendtheChangeinRangePos', data => { this.RandomSearLoc = data })
EventBus.$on('emittedEventCallingSankey', this.SankeyView)

24
frontend/src/components/HyperParameterSpace.vue
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@ -44,6 +44,8 @@ export default {
WH: [],
ScatterPlotResults: '',
representationDef: 'mds',
FlagFinalStage: 0,
RemainingPointsIndices: []
}
},
methods: {
@ -68,6 +70,7 @@ export default {
ScatterPlotView () {
Plotly.purge('OverviewPlotly')
if (this.FlagFinalStage == 0) {
var modelId = JSON.parse(this.ScatterPlotResults[0])
var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[1])
var parametersLoc = JSON.parse(this.ScatterPlotResults[2])
@ -75,6 +78,18 @@ export default {
var MDSData= JSON.parse(this.ScatterPlotResults[9])
var TSNEData = JSON.parse(this.ScatterPlotResults[10])
var UMAPData = JSON.parse(this.ScatterPlotResults[11])
} else {
console.log('mpike')
var modelId = this.RemainingPointsIndices.map((item) => JSON.parse(this.ScatterPlotResults[0])[item])
console.log(modelId)
var colorsforScatterPlot = this.RemainingPointsIndices.map((item) => JSON.parse(this.ScatterPlotResults[1])[item])
var parametersLoc = this.RemainingPointsIndices.map((item) => JSON.parse(this.ScatterPlotResults[2])[item])
var parameters = JSON.parse(parametersLoc)
var MDSData = this.RemainingPointsIndices.map((item) => JSON.parse(this.ScatterPlotResults[9])[item])
var TSNEData = this.RemainingPointsIndices.map((item) => JSON.parse(this.ScatterPlotResults[10])[item])
var UMAPData = this.RemainingPointsIndices.map((item) => JSON.parse(this.ScatterPlotResults[11])[item])
console.log(modelId)
}
EventBus.$emit('sendPointsNumber', modelId.length)
@ -292,6 +307,7 @@ export default {
OverviewPlotly.on('plotly_selected', function (evt) {
if (typeof evt !== 'undefined') {
var pushModelsRemainingTemp = []
var indices = []
const ClassifierIDsList = []
for (let i = 0; evt.points.length; i++) {
if (evt.points[i] === undefined) {
@ -305,8 +321,10 @@ export default {
for (let i = 0; i < allModels.length; i++) {
if (ClassifierIDsList.indexOf((allModels[i])) < 0) {
pushModelsRemainingTemp.push(allModels[i])
indices.push(i)
}
}
EventBus.$on('RemainingPointsIndices', indices)
EventBus.$emit('RemainingPoints', pushModelsRemainingTemp)
EventBus.$emit('SendSelectedPointsUpdateIndicator', ClassifierIDsList)
EventBus.$emit('SendSelectedPointsToServerEvent', ClassifierIDsList)
@ -317,7 +335,8 @@ export default {
EventBus.$emit('InitializeCrossoverMutation')
},
Add () {
EventBus.$emit('ChangeKey', 3)
EventBus.$emit('InitializeCrossoverMutation')
},
swapButtons () {
var add = document.getElementById('AddEnsemble');
@ -327,6 +346,9 @@ export default {
}
},
mounted() {
EventBus.$on('RemainingPointsIndices', data => { this.RemainingPointsIndices = data })
EventBus.$on('ChangeKey', data => { this.FlagFinalStage = data })
EventBus.$on('emittedEventCallingScatterPlot', data => {
this.ScatterPlotResults = data})
EventBus.$on('emittedEventCallingScatterPlot', this.ScatterPlotView)

8
frontend/src/components/Main.vue
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@ -348,6 +348,7 @@ export default Vue.extend({
EventBus.$emit('callValidation', this.OverviewResults)
EventBus.$emit('emittedEventCallingGrid', this.OverviewResults)
EventBus.$emit('emittedEventCallingGridSelection', this.OverviewResults)
EventBus.$emit('LegendPredict')
this.storeBothEnsCM[0] = this.OverviewResults
this.firstTimeExec = false
} else {
@ -398,7 +399,7 @@ export default Vue.extend({
EventBus.$emit('SendStoredCMHist', ModelsLocal)
var PerformanceCM = JSON.parse(this.OverviewResultsCM[1])
EventBus.$emit('SendPerformanceCM', PerformanceCM)
console.log('Server successfully sent all the data related to visualizations!')
console.log('Server successfully sent all the data related to visualizations for CM!')
EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResultsCM)
this.storeBothEnsCM[0] = this.OverviewResultsCM
//EventBus.$emit('emittedEventCallingSankey', this.OverviewResultsCM)
@ -960,7 +961,7 @@ export default Vue.extend({
axios.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the unselected points for crossover and mutation.')
this.CurrentStage = this.CurrentStage + 1
this.CurrentStage = 2
this.getDatafromtheBackEnd()
this.getCMComputedData()
this.changeActiveTo2()
@ -1023,6 +1024,7 @@ export default Vue.extend({
EventBus.$on('RemainingPoints', this.changeActiveTo1)
EventBus.$on('RemainingPoints', data => { this.unselectedRemainingPoints = data })
EventBus.$on('ChangeKey', data => { this.CurrentStage = data })
EventBus.$on('InitializeCrossoverMutation', this.sendPointsCrossMutat)
EventBus.$on('RemainingPointsCM', this.changeActiveTo2)
@ -1091,7 +1093,7 @@ export default Vue.extend({
EventBus.$on('factorsChanged', data => { this.basicValuesFact = data })
EventBus.$on('changeValues', data => { this.CMNumberofModelsOFFICIAL = data })
EventBus.$on('changeValuesS2', data => { this.CMNumberofModelsOFFICIALS2 = data })
EventBus.$on('changeValues2', data => { this.CMNumberofModelsOFFICIALS2 = data })
//Prevent double click to search for a word.
document.addEventListener('mousedown', function (event) {

107
frontend/src/components/Predictions.vue
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@ -2,6 +2,7 @@
<div>
<div id="containerAll"></div>
<div id="containerSelection"></div>
<div id="LegendHeat"></div>
</div>
</template>
@ -24,6 +25,7 @@ export default {
responsiveWidthHeight: [],
predictSelection: [],
StoreIndices: [],
classesNumber: 9,
}
},
methods: {
@ -32,6 +34,8 @@ export default {
svg.selectAll("*").remove();
var svg = d3.select("#containerSelection");
svg.selectAll("*").remove();
var svgLeg = d3.select("#LegendHeat");
svgLeg.selectAll("*").remove();
this.GetResultsAll = []
this.GetResultsSelection = []
this.predictSelection = []
@ -102,12 +106,12 @@ export default {
dataGradB.push({ id: element, value: GradBPred[element][targetNames[i]] })
});
for (let j = 0; j < size - getIndices[targetNames[i]].length; j++) {
dataAver.push({ id: null, value: 1.0 })
dataKNN.push({ id: null, value: 1.0 })
dataLR.push({ id: null, value: 1.0 })
dataMLP.push({ id: null, value: 1.0 })
dataRF.push({ id: null, value: 1.0 })
dataGradB.push({ id: null, value: 1.0 })
dataAver.push({ id: -1, value: 1.0 })
dataKNN.push({ id: -1, value: 1.0 })
dataLR.push({ id: -1, value: 1.0 })
dataMLP.push({ id: -1, value: 1.0 })
dataRF.push({ id: -1, value: 1.0 })
dataGradB.push({ id: -1, value: 1.0 })
}
dataAverGetResults.push(dataAver)
dataKNNResults.push(dataKNN)
@ -196,7 +200,7 @@ export default {
function databind(data, size, sqrtSize) {
colourScale = d3.scaleSequential(d3.interpolateReds).domain([1, 0])
colourScale = d3.scaleSequential(d3.interpolateGreens).domain([1, 0])
var join = custom.selectAll('custom.rect')
.data(data);
@ -220,9 +224,9 @@ export default {
.transition()
.attr('width', cellSize)
.attr('height', cellSize)
.attr('fillStyle', function(d) { return colourScale(d.value); })
.attr('fillStyle', function(d) { if(d.id == -1) { return "#ffffff" } else { return colourScale(d.value);}})
.attr('fill-opacity', function(d) {
if (d.id == null) {
if (d.id == -1) {
return "0.0";
} else {
return "1.0";
@ -348,12 +352,12 @@ export default {
dataGradB.push({ id: element, value: GradBPred[element][targetNames[i]] - GradBPredAll[element][targetNames[i]] })
});
for (let j = 0; j < size - getIndices[targetNames[i]].length; j++) {
dataAver.push({ id: null, value: 0 })
dataKNN.push({ id: null, value: 0 })
dataLR.push({ id: null, value: 0 })
dataMLP.push({ id: null, value: 0 })
dataRF.push({ id: null, value: 0 })
dataGradB.push({ id: null, value: 0 })
dataAver.push({ id: -1, value: 0 })
dataKNN.push({ id: -1, value: 0 })
dataLR.push({ id: -1, value: 0 })
dataMLP.push({ id: -1, value: 0 })
dataRF.push({ id: -1, value: 0 })
dataGradB.push({ id: -1, value: 0 })
}
dataAverGetResults.push(dataAver)
dataKNNResults.push(dataKNN)
@ -465,9 +469,9 @@ export default {
.transition()
.attr('width', cellSize)
.attr('height', cellSize)
.attr('fillStyle', function(d) { return colourScale(d.value); })
.attr('fillStyle', function(d) { if(d.id == -1) { return "#ffffff" } else { return colourScale(d.value);} })
.attr('fill-opacity', function(d) {
if (d.id == null) {
if (d.id == -1) {
return "0.0";
} else {
return "1.0";
@ -510,8 +514,77 @@ export default {
} // draw()
},
legendCol () {
//==================================================
var viewerWidth = this.responsiveWidthHeight[0]*7
var viewerHeight = this.responsiveWidthHeight[1]*1.6
var viewerPosTop = viewerHeight * 0.06;
var cellSizeHeat = 20
var legendElementWidth = cellSizeHeat * 3;
// http://bl.ocks.org/mbostock/5577023
var colors = colorbrewer.PRGn[this.classesNumber];
var svgLeg = d3.select("#LegendHeat");
svgLeg.selectAll("*").remove();
var svgLeg = d3.select("#LegendHeat").append("svg")
.attr("width", viewerWidth/2)
.attr("height", viewerHeight*0.10)
.style("margin-top", "12px")
var legend = svgLeg.append('g')
.attr("class", "legend")
.attr("transform", "translate(0,0)")
.selectAll(".legendElement")
.data([1.00, 0.75, 0.50, 0.25, 0.00, 0.25, 0.50, 0.75, 1.00])
.enter().append("g")
.attr("class", "legendElement");
legend.append("svg:rect")
.attr("x", function(d, i) {
return (legendElementWidth * i) + 35;
})
.attr("y", viewerPosTop)
.attr("class", "cellLegend bordered")
.attr("width", legendElementWidth)
.attr("height", cellSizeHeat / 2)
.style("fill", function(d, i) {
console.log(colors[i])
return colors[i];
});
legend.append("text")
.attr("class", "mono legendElement")
.text(function(d, i) {
console.log(i)
if (i < 4) {
return "-" + (d * 100) + "%";
} else if (i > 4) {
return "+" + (d * 100) + "%";
} else {
return "" + (d * 100) + "%";
}
})
.attr("x", function(d, i) {
if (i > 4) {
return (legendElementWidth * i) + 45;
} else if (i == 4) {
return (legendElementWidth * i) + 55;
} else {
return (legendElementWidth * i) + 40;
}
})
.attr("y", (viewerPosTop + cellSizeHeat) + 5);
svgLeg.append("text").attr("x", 220).attr("y", 32).text("Difference in predictive power").style("font-size", "16px").attr("alignment-baseline","top")
},
},
mounted () {
EventBus.$on('LegendPredict', this.legendCol)
EventBus.$on('emittedEventCallingGrid', data => { this.GetResultsAll = data; })
EventBus.$on('emittedEventCallingGrid', this.Grid)

641
run.py
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@ -806,17 +806,19 @@ def PreprocessingPredEnsemble():
numberIDRF = []
numberIDGradB = []
print(EnsembleActive)
for el in EnsembleActive:
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
if match:
items = match.groups()
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM")):
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM") | (items[0] == "KNNCC") | (items[0] == "KNNCM") | (items[0] == "KNNMC") | (items[0] == "KNNMM")):
numberIDKNN.append(int(items[1]))
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM")):
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM") | (items[0] == "LRCC") | (items[0] == "LRCM") | (items[0] == "LRMC") | (items[0] == "LRMM")):
numberIDLR.append(int(items[1]))
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM")):
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM") | (items[0] == "MLPCC") | (items[0] == "MLPCM") | (items[0] == "MLPMC") | (items[0] == "MLPMM")):
numberIDMLP.append(int(items[1]))
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM")):
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM") | (items[0] == "RFCC") | (items[0] == "RFCM") | (items[0] == "RFMC") | (items[0] == "RFMM")):
numberIDRF.append(int(items[1]))
else:
numberIDGradB.append(int(items[1]))
@ -1366,6 +1368,7 @@ def CrossoverMutateFun():
RemainingIds = RemainingIds['RemainingPoints']
global EnsembleActive
global CurStage
EnsembleActive = request.get_data().decode('utf8').replace("'", '"')
EnsembleActive = json.loads(EnsembleActive)
@ -1384,8 +1387,10 @@ def CrossoverMutateFun():
if (CurStage == 1):
InitializeFirstStageCM(RemainingIds, setMaxLoopValue)
else:
elif (CurStage == 2):
InitializeSecondStageCM(RemainingIds, setMaxLoopValue)
else:
pass
return 'Okay'
def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
@ -1911,7 +1916,6 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
countMLP = 0
countRF = 0
countGradB = 0
paramAllAlgs = PreprocessingParam()
KNNIntIndex = []
LRIntIndex = []
@ -1919,7 +1923,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
RFIntIndex = []
GradBIntIndex = []
localCrossMutr = []
localCrossMutr.clear()
allParametersPerfCrossMutrKNNMC = []
for dr in KNNIDsM:
if (int(re.findall('\d+', dr)[0]) >= greater):
@ -2397,6 +2401,89 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
localCrossMutr.clear()
global allParametersPerformancePerModelEnsem
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrKNNCC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrKNNCM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrKNNCC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrKNNCM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrKNNCC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrKNNCM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrLRCC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrLRCM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrLRCC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrLRCM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrLRCC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrLRCM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrMLPCC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrMLPCM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrMLPCC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrMLPCM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrMLPCC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrMLPCM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrRFCC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrRFCM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrRFCC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrRFCM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrRFCC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrRFCM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrGradBCC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrGradBCM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrGradBCC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrGradBCM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrGradBCC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrGradBCM[3]], ignore_index=True)
# MUTATION
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrKNNMC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrKNNMM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrKNNMC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrKNNMM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrKNNMC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrKNNMM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrLRMC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrLRMM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrLRMC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrLRMM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrLRMC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrLRMM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrMLPMC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrMLPMM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrMLPMC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrMLPMM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrMLPMC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrMLPMM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrRFMC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrRFMM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrRFMC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrRFMM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrRFMC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrRFMM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrGradBMC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrGradBMM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrGradBMC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrGradBMM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrGradBMC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrGradBMM[3]], ignore_index=True)
allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNCC + allParametersPerfCrossMutrKNNCM + allParametersPerfCrossMutrLRCC + allParametersPerfCrossMutrLRCM + allParametersPerfCrossMutrMLPCC + allParametersPerfCrossMutrMLPCM + allParametersPerfCrossMutrRFCC + allParametersPerfCrossMutrRFCM + allParametersPerfCrossMutrGradBCC + allParametersPerfCrossMutrGradBCM + allParametersPerfCrossMutrKNNMC + allParametersPerfCrossMutrKNNMM + allParametersPerfCrossMutrLRMC + allParametersPerfCrossMutrLRMM + allParametersPerfCrossMutrMLPMC + allParametersPerfCrossMutrMLPMM + allParametersPerfCrossMutrRFMC + allParametersPerfCrossMutrRFMM + allParametersPerfCrossMutrGradBMC + allParametersPerfCrossMutrGradBMM
allParametersPerformancePerModel[0] = allParametersPerformancePerModel[0] + allParametersPerfCrossMutrKNNCC[0] + allParametersPerfCrossMutrKNNCM[0]
@ -3228,10 +3315,34 @@ def PreprocessingIDsCM():
dicGradBC = allParametersPerfCrossMutr[32]
dicGradBM = allParametersPerfCrossMutr[36]
df_concatIDs = dicKNNC + dicKNNM + dicLRC + dicLRM + dicMLPC + dicMLPM + dicRFC + dicRFM + dicGradBC + dicGradBM
return df_concatIDs
def PreprocessingIDsCMSecond():
dicKNNCC = allParametersPerfCrossMutr[0]
dicKNNCM = allParametersPerfCrossMutr[4]
dicLRCC = allParametersPerfCrossMutr[8]
dicLRCM = allParametersPerfCrossMutr[12]
dicMLPCC = allParametersPerfCrossMutr[16]
dicMLPCM = allParametersPerfCrossMutr[20]
dicRFCC = allParametersPerfCrossMutr[24]
dicRFCM = allParametersPerfCrossMutr[28]
dicGradBCC = allParametersPerfCrossMutr[32]
dicGradBCM = allParametersPerfCrossMutr[36]
dicKNNMC = allParametersPerfCrossMutr[40]
dicKNNMM = allParametersPerfCrossMutr[44]
dicLRMC = allParametersPerfCrossMutr[48]
dicLRMM = allParametersPerfCrossMutr[52]
dicMLPMC = allParametersPerfCrossMutr[56]
dicMLPMM = allParametersPerfCrossMutr[60]
dicRFMC = allParametersPerfCrossMutr[64]
dicRFMM = allParametersPerfCrossMutr[68]
dicGradBMC = allParametersPerfCrossMutr[72]
dicGradBMM = allParametersPerfCrossMutr[76]
df_concatIDs = dicKNNCC + dicKNNCM + dicLRCC + dicLRCM + dicMLPCC + dicMLPCM + dicRFCC + dicRFCM + dicGradBCC + dicGradBCM + dicKNNMC + dicKNNMM + dicLRMC + dicLRMM + dicMLPMC + dicMLPMM + dicRFMC + dicRFMM + dicGradBMC + dicGradBMM
return df_concatIDs
def PreprocessingMetricsCM():
dicKNNC = allParametersPerfCrossMutr[2]
dicKNNM = allParametersPerfCrossMutr[6]
@ -3259,6 +3370,53 @@ def PreprocessingMetricsCM():
df_concatMetrics = df_concatMetrics.reset_index(drop=True)
return df_concatMetrics
def PreprocessingMetricsCMSecond():
dicKNNCC = allParametersPerfCrossMutr[2]
dicKNNCM = allParametersPerfCrossMutr[6]
dicLRCC = allParametersPerfCrossMutr[10]
dicLRCM = allParametersPerfCrossMutr[14]
dicMLPCC = allParametersPerfCrossMutr[18]
dicMLPCM = allParametersPerfCrossMutr[22]
dicRFCC = allParametersPerfCrossMutr[26]
dicRFCM = allParametersPerfCrossMutr[30]
dicGradBCC = allParametersPerfCrossMutr[34]
dicGradBCM = allParametersPerfCrossMutr[38]
dicKNNMC = allParametersPerfCrossMutr[42]
dicKNNMM = allParametersPerfCrossMutr[46]
dicLRMC = allParametersPerfCrossMutr[50]
dicLRMM = allParametersPerfCrossMutr[54]
dicMLPMC = allParametersPerfCrossMutr[58]
dicMLPMM = allParametersPerfCrossMutr[62]
dicRFMC = allParametersPerfCrossMutr[66]
dicRFMM = allParametersPerfCrossMutr[70]
dicGradBMC = allParametersPerfCrossMutr[74]
dicGradBMM = allParametersPerfCrossMutr[78]
dfKNNCC = pd.DataFrame.from_dict(dicKNNCC)
dfKNNCM = pd.DataFrame.from_dict(dicKNNCM)
dfLRCC = pd.DataFrame.from_dict(dicLRCC)
dfLRCM = pd.DataFrame.from_dict(dicLRCM)
dfMLPCC = pd.DataFrame.from_dict(dicMLPCC)
dfMLPCM = pd.DataFrame.from_dict(dicMLPCM)
dfRFCC = pd.DataFrame.from_dict(dicRFCC)
dfRFCM = pd.DataFrame.from_dict(dicRFCM)
dfGradBCC = pd.DataFrame.from_dict(dicGradBCC)
dfGradBCM = pd.DataFrame.from_dict(dicGradBCM)
dfKNNMC = pd.DataFrame.from_dict(dicKNNMC)
dfKNNMM = pd.DataFrame.from_dict(dicKNNMM)
dfLRMC = pd.DataFrame.from_dict(dicLRMC)
dfLRMM = pd.DataFrame.from_dict(dicLRMM)
dfMLPMC = pd.DataFrame.from_dict(dicMLPMC)
dfMLPMM = pd.DataFrame.from_dict(dicMLPMM)
dfRFMC = pd.DataFrame.from_dict(dicRFMC)
dfRFMM = pd.DataFrame.from_dict(dicRFMM)
dfGradBMC = pd.DataFrame.from_dict(dicGradBMC)
dfGradBMM = pd.DataFrame.from_dict(dicGradBMM)
df_concatMetrics = pd.concat([dfKNNCC, dfKNNCM, dfLRCC, dfLRCM, dfMLPCC, dfMLPCM, dfRFCC, dfRFCM, dfGradBCC, dfGradBCM, dfKNNMC, dfKNNMM, dfLRMC, dfLRMM, dfMLPMC, dfMLPMM, dfRFMC, dfRFMM, dfGradBMC, dfGradBMM])
df_concatMetrics = df_concatMetrics.reset_index(drop=True)
return df_concatMetrics
def PreprocessingPredCM():
dicKNNC = allParametersPerfCrossMutr[3]
dicKNNM = allParametersPerfCrossMutr[7]
@ -3326,68 +3484,94 @@ def PreprocessingPredCM():
return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
def PreprocessingParamCM():
dicKNNC = allParametersPerfCrossMutr[1]
dicKNNM = allParametersPerfCrossMutr[5]
dicLRC = allParametersPerfCrossMutr[9]
dicLRM = allParametersPerfCrossMutr[13]
dicMLPC = allParametersPerfCrossMutr[17]
dicMLPM = allParametersPerfCrossMutr[21]
dicRFC = allParametersPerfCrossMutr[25]
dicRFM = allParametersPerfCrossMutr[29]
dicGradBC = allParametersPerfCrossMutr[33]
dicGradBM = allParametersPerfCrossMutr[37]
def PreprocessingPredCMSecond():
dicKNNCC = allParametersPerfCrossMutr[3]
dicKNNCM = allParametersPerfCrossMutr[7]
dicLRCC = allParametersPerfCrossMutr[11]
dicLRCM = allParametersPerfCrossMutr[15]
dicMLPCC = allParametersPerfCrossMutr[19]
dicMLPCM = allParametersPerfCrossMutr[23]
dicRFCC = allParametersPerfCrossMutr[27]
dicRFCM = allParametersPerfCrossMutr[31]
dicGradBCC = allParametersPerfCrossMutr[35]
dicGradBCM = allParametersPerfCrossMutr[39]
dicKNNMC = allParametersPerfCrossMutr[43]
dicKNNMM = allParametersPerfCrossMutr[47]
dicLRMC = allParametersPerfCrossMutr[51]
dicLRMM = allParametersPerfCrossMutr[55]
dicMLPMC = allParametersPerfCrossMutr[59]
dicMLPMM = allParametersPerfCrossMutr[63]
dicRFMC = allParametersPerfCrossMutr[67]
dicRFMM = allParametersPerfCrossMutr[71]
dicGradBMC = allParametersPerfCrossMutr[75]
dicGradBMM = allParametersPerfCrossMutr[79]
dfKNNCC = pd.DataFrame.from_dict(dicKNNCC)
dfKNNCM = pd.DataFrame.from_dict(dicKNNCM)
dfLRCC = pd.DataFrame.from_dict(dicLRCC)
dfLRCM = pd.DataFrame.from_dict(dicLRCM)
dfMLPCC = pd.DataFrame.from_dict(dicMLPCC)
dfMLPCM = pd.DataFrame.from_dict(dicMLPCM)
dfRFCC = pd.DataFrame.from_dict(dicRFCC)
dfRFCM = pd.DataFrame.from_dict(dicRFCM)
dfGradBCC = pd.DataFrame.from_dict(dicGradBCC)
dfGradBCM = pd.DataFrame.from_dict(dicGradBCM)
dfKNNMC = pd.DataFrame.from_dict(dicKNNMC)
dfKNNMM = pd.DataFrame.from_dict(dicKNNMM)
dfLRMC = pd.DataFrame.from_dict(dicLRMC)
dfLRMM = pd.DataFrame.from_dict(dicLRMM)
dfMLPMC = pd.DataFrame.from_dict(dicMLPMC)
dfMLPMM = pd.DataFrame.from_dict(dicMLPMM)
dfRFMC = pd.DataFrame.from_dict(dicRFMC)
dfRFMM = pd.DataFrame.from_dict(dicRFMM)
dfGradBMC = pd.DataFrame.from_dict(dicGradBMC)
dfGradBMM = pd.DataFrame.from_dict(dicGradBMM)
dfKNN = pd.concat([dfKNNCC, dfKNNCM, dfKNNMC, dfKNNMM])
dfLR = pd.concat([dfLRCC, dfLRCM, dfLRMC, dfLRMM])
dfMLP = pd.concat([dfMLPCC, dfMLPCM, dfMLPMC, dfMLPMM])
dfRF = pd.concat([dfRFCC, dfRFCM, dfRFMC, dfRFMM])
dfGradB = pd.concat([dfGradBCC, dfGradBCM, dfGradBMC, dfGradBMM])
df_concatProbs = pd.concat([dfKNNCC, dfKNNCM, dfLRCC, dfLRCM, dfMLPCC, dfMLPCM, dfRFCC, dfRFCM, dfGradBCC, dfGradBCM, dfKNNMC, dfKNNMM, dfLRMC, dfLRMM, dfMLPMC, dfMLPMM, dfRFMC, dfRFMM, dfGradBMC, dfGradBMM])
dicKNNC = dicKNNC['params']
dicKNNM = dicKNNM['params']
dicLRC = dicLRC['params']
dicLRM = dicLRM['params']
dicMLPC = dicMLPC['params']
dicMLPM = dicMLPM['params']
dicRFC = dicRFC['params']
dicRFM = dicRFM['params']
dicGradBC = dicGradBC['params']
dicGradBM = dicGradBM['params']
predictionsKNN = []
for column, content in dfKNN.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsKNN.append(el)
predictionsLR = []
for column, content in dfLR.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsLR.append(el)
dicKNNC = {int(k):v for k,v in dicKNNC.items()}
dicKNNM = {int(k):v for k,v in dicKNNM.items()}
dicLRC = {int(k):v for k,v in dicLRC.items()}
dicLRM = {int(k):v for k,v in dicLRM.items()}
dicMLPC = {int(k):v for k,v in dicMLPC.items()}
dicMLPM = {int(k):v for k,v in dicMLPM.items()}
dicRFC = {int(k):v for k,v in dicRFC.items()}
dicRFM = {int(k):v for k,v in dicRFM.items()}
dicGradBC = {int(k):v for k,v in dicGradBC.items()}
dicGradBM = {int(k):v for k,v in dicGradBM.items()}
predictionsMLP = []
for column, content in dfMLP.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsMLP.append(el)
dfKNNC = pd.DataFrame.from_dict(dicKNNC)
dfKNNM = pd.DataFrame.from_dict(dicKNNM)
dfLRC = pd.DataFrame.from_dict(dicLRC)
dfLRM = pd.DataFrame.from_dict(dicLRM)
dfMLPC = pd.DataFrame.from_dict(dicMLPC)
dfMLPM = pd.DataFrame.from_dict(dicMLPM)
dfRFC = pd.DataFrame.from_dict(dicRFC)
dfRFM = pd.DataFrame.from_dict(dicRFM)
dfGradBC = pd.DataFrame.from_dict(dicGradBC)
dfGradBM = pd.DataFrame.from_dict(dicGradBM)
predictionsRF = []
for column, content in dfRF.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsRF.append(el)
dfKNNC = dfKNNC.T
dfKNNM = dfKNNM.T
dfLRC = dfLRC.T
dfLRM = dfLRM.T
dfMLPC = dfMLPC.T
dfMLPM = dfMLPM.T
dfRFC = dfRFC.T
dfRFM = dfRFM.T
dfGradBC = dfGradBC.T
dfGradBM = dfGradBM.T
predictionsGradB = []
for column, content in dfGradB.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictionsGradB.append(el)
df_params = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM, dfMLPC, dfMLPM, dfRFC, dfRFM, dfGradBC, dfGradBM])
df_params = df_params.reset_index(drop=True)
return df_params
predictions = []
for column, content in df_concatProbs.items():
el = [sum(x)/len(x) for x in zip(*content)]
predictions.append(el)
def PreprocessingParamSepCM():
return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
def PreprocessingParamCM():
dicKNNC = allParametersPerfCrossMutr[1]
dicKNNM = allParametersPerfCrossMutr[5]
dicLRC = allParametersPerfCrossMutr[9]
@ -3410,6 +3594,7 @@ def PreprocessingParamSepCM():
dicGradBC = dicGradBC['params']
dicGradBM = dicGradBM['params']
dicKNNC = {int(k):v for k,v in dicKNNC.items()}
dicKNNM = {int(k):v for k,v in dicKNNM.items()}
dicLRC = {int(k):v for k,v in dicLRC.items()}
@ -3443,9 +3628,228 @@ def PreprocessingParamSepCM():
dfGradBC = dfGradBC.T
dfGradBM = dfGradBM.T
return [dfKNNC, dfKNNM, dfLRC, dfLRM, dfMLPC, dfMLPM, dfRFC, dfRFM, dfGradBC, dfGradBM]
df_params = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM, dfMLPC, dfMLPM, dfRFC, dfRFM, dfGradBC, dfGradBM])
df_params = df_params.reset_index(drop=True)
return df_params
def PreprocessingParamCMSecond():
dicKNNCC = allParametersPerfCrossMutr[1]
dicKNNCM = allParametersPerfCrossMutr[5]
dicLRCC = allParametersPerfCrossMutr[9]
dicLRCM = allParametersPerfCrossMutr[13]
dicMLPCC = allParametersPerfCrossMutr[17]
dicMLPCM = allParametersPerfCrossMutr[21]
dicRFCC = allParametersPerfCrossMutr[25]
dicRFCM = allParametersPerfCrossMutr[29]
dicGradBCC = allParametersPerfCrossMutr[33]
dicGradBCM = allParametersPerfCrossMutr[37]
dicKNNMC = allParametersPerfCrossMutr[41]
dicKNNMM = allParametersPerfCrossMutr[45]
dicLRMC = allParametersPerfCrossMutr[49]
dicLRMM = allParametersPerfCrossMutr[53]
dicMLPMC = allParametersPerfCrossMutr[57]
dicMLPMM = allParametersPerfCrossMutr[61]
dicRFMC = allParametersPerfCrossMutr[65]
dicRFMM = allParametersPerfCrossMutr[69]
dicGradBMC = allParametersPerfCrossMutr[73]
dicGradBMM = allParametersPerfCrossMutr[77]
dicKNNCC = dicKNNCC['params']
dicKNNCM = dicKNNCM['params']
dicLRCC = dicLRCC['params']
dicLRCM = dicLRCM['params']
dicMLPCC = dicMLPCC['params']
dicMLPCM = dicMLPCM['params']
dicRFCC = dicRFCC['params']
dicRFCM = dicRFCM['params']
dicGradBCC = dicGradBCC['params']
dicGradBCM = dicGradBCM['params']
dicKNNMC = dicKNNMC['params']
dicKNNMM = dicKNNMM['params']
dicLRMC = dicLRMC['params']
dicLRMM = dicLRMM['params']
dicMLPMC = dicMLPMC['params']
dicMLPMM = dicMLPMM['params']
dicRFMC = dicRFMC['params']
dicRFMM = dicRFMM['params']
dicGradBMC = dicGradBMC['params']
dicGradBMM = dicGradBMM['params']
dicKNNCC = {int(k):v for k,v in dicKNNCC.items()}
dicKNNCM = {int(k):v for k,v in dicKNNCM.items()}
dicLRCC = {int(k):v for k,v in dicLRCC.items()}
dicLRCM = {int(k):v for k,v in dicLRCM.items()}
dicMLPCC = {int(k):v for k,v in dicMLPCC.items()}
dicMLPCM = {int(k):v for k,v in dicMLPCM.items()}
dicRFCC = {int(k):v for k,v in dicRFCC.items()}
dicRFCM = {int(k):v for k,v in dicRFCM.items()}
dicGradBCC = {int(k):v for k,v in dicGradBCC.items()}
dicGradBCM = {int(k):v for k,v in dicGradBCM.items()}
dicKNNMC = {int(k):v for k,v in dicKNNMC.items()}
dicKNNMM = {int(k):v for k,v in dicKNNMM.items()}
dicLRMC = {int(k):v for k,v in dicLRMC.items()}
dicLRMM = {int(k):v for k,v in dicLRMM.items()}
dicMLPMC = {int(k):v for k,v in dicMLPMC.items()}
dicMLPMM = {int(k):v for k,v in dicMLPMM.items()}
dicRFMC = {int(k):v for k,v in dicRFMC.items()}
dicRFMM = {int(k):v for k,v in dicRFMM.items()}
dicGradBMC = {int(k):v for k,v in dicGradBMC.items()}
dicGradBMM = {int(k):v for k,v in dicGradBMM.items()}
dfKNNCC = pd.DataFrame.from_dict(dicKNNCC)
dfKNNCM = pd.DataFrame.from_dict(dicKNNCM)
dfLRCC = pd.DataFrame.from_dict(dicLRCC)
dfLRCM = pd.DataFrame.from_dict(dicLRCM)
dfMLPCC = pd.DataFrame.from_dict(dicMLPCC)
dfMLPCM = pd.DataFrame.from_dict(dicMLPCM)
dfRFCC = pd.DataFrame.from_dict(dicRFCC)
dfRFCM = pd.DataFrame.from_dict(dicRFCM)
dfGradBCC = pd.DataFrame.from_dict(dicGradBCC)
dfGradBCM = pd.DataFrame.from_dict(dicGradBCM)
dfKNNMC = pd.DataFrame.from_dict(dicKNNMC)
dfKNNMM = pd.DataFrame.from_dict(dicKNNMM)
dfLRMC = pd.DataFrame.from_dict(dicLRMC)
dfLRMM = pd.DataFrame.from_dict(dicLRMM)
dfMLPMC = pd.DataFrame.from_dict(dicMLPMC)
dfMLPMM = pd.DataFrame.from_dict(dicMLPMM)
dfRFMC = pd.DataFrame.from_dict(dicRFMC)
dfRFMM = pd.DataFrame.from_dict(dicRFMM)
dfGradBMC = pd.DataFrame.from_dict(dicGradBMC)
dfGradBMM = pd.DataFrame.from_dict(dicGradBMM)
dfKNNCC = dfKNNCC.T
dfKNNCM = dfKNNCM.T
dfLRCC = dfLRCC.T
dfLRCM = dfLRCM.T
dfMLPCC = dfMLPCC.T
dfMLPCM = dfMLPCM.T
dfRFCC = dfRFCC.T
dfRFCM = dfRFCM.T
dfGradBCC = dfGradBCC.T
dfGradBCM = dfGradBCM.T
dfKNNMC = dfKNNMC.T
dfKNNMM = dfKNNMM.T
dfLRMC = dfLRMC.T
dfLRMM = dfLRMM.T
dfMLPMC = dfMLPMC.T
dfMLPMM = dfMLPMM.T
dfRFMC = dfRFMC.T
dfRFMM = dfRFMM.T
dfGradBMC = dfGradBMC.T
dfGradBMM = dfGradBMM.T
df_params = pd.concat([dfKNNCC, dfKNNCM, dfLRCC, dfLRCM, dfMLPCC, dfMLPCM, dfRFCC, dfRFCM, dfGradBCC, dfGradBCM, dfKNNMC, dfKNNMM, dfLRMC, dfLRMM, dfMLPMC, dfMLPMM, dfRFMC, dfRFMM, dfGradBMC, dfGradBMM])
df_params = df_params.reset_index(drop=True)
return df_params
def PreprocessingParamSepCM():
dicKNNCC = allParametersPerfCrossMutr[1]
dicKNNCM = allParametersPerfCrossMutr[5]
dicLRCC = allParametersPerfCrossMutr[9]
dicLRCM = allParametersPerfCrossMutr[13]
dicMLPCC = allParametersPerfCrossMutr[17]
dicMLPCM = allParametersPerfCrossMutr[21]
dicRFCC = allParametersPerfCrossMutr[25]
dicRFCM = allParametersPerfCrossMutr[29]
dicGradBCC = allParametersPerfCrossMutr[33]
dicGradBCM = allParametersPerfCrossMutr[37]
dicKNNMC = allParametersPerfCrossMutr[41]
dicKNNMM = allParametersPerfCrossMutr[45]
dicLRMC = allParametersPerfCrossMutr[49]
dicLRMM = allParametersPerfCrossMutr[53]
dicMLPMC = allParametersPerfCrossMutr[57]
dicMLPMM = allParametersPerfCrossMutr[61]
dicRFMC = allParametersPerfCrossMutr[65]
dicRFMM = allParametersPerfCrossMutr[69]
dicGradBMC = allParametersPerfCrossMutr[73]
dicGradBMM = allParametersPerfCrossMutr[77]
dicKNNCC = dicKNNCC['params']
dicKNNCM = dicKNNCM['params']
dicLRCC = dicLRCC['params']
dicLRCM = dicLRCM['params']
dicMLPCC = dicMLPCC['params']
dicMLPCM = dicMLPCM['params']
dicRFCC = dicRFCC['params']
dicRFCM = dicRFCM['params']
dicGradBCC = dicGradBCC['params']
dicGradBCM = dicGradBCM['params']
dicKNNMC = dicKNNMC['params']
dicKNNMM = dicKNNMM['params']
dicLRMC = dicLRMC['params']
dicLRMM = dicLRMM['params']
dicMLPMC = dicMLPMC['params']
dicMLPMM = dicMLPMM['params']
dicRFMC = dicRFMC['params']
dicRFMM = dicRFMM['params']
dicGradBMC = dicGradBMC['params']
dicGradBMM = dicGradBMM['params']
dicKNNCC = {int(k):v for k,v in dicKNNCC.items()}
dicKNNCM = {int(k):v for k,v in dicKNNCM.items()}
dicLRCC = {int(k):v for k,v in dicLRCC.items()}
dicLRCM = {int(k):v for k,v in dicLRCM.items()}
dicMLPCC = {int(k):v for k,v in dicMLPCC.items()}
dicMLPCM = {int(k):v for k,v in dicMLPCM.items()}
dicRFCC = {int(k):v for k,v in dicRFCC.items()}
dicRFCM = {int(k):v for k,v in dicRFCM.items()}
dicGradBCC = {int(k):v for k,v in dicGradBCC.items()}
dicGradBCM = {int(k):v for k,v in dicGradBCM.items()}
dicKNNMC = {int(k):v for k,v in dicKNNMC.items()}
dicKNNMM = {int(k):v for k,v in dicKNNMM.items()}
dicLRMC = {int(k):v for k,v in dicLRMC.items()}
dicLRMM = {int(k):v for k,v in dicLRMM.items()}
dicMLPMC = {int(k):v for k,v in dicMLPMC.items()}
dicMLPMM = {int(k):v for k,v in dicMLPMM.items()}
dicRFMC = {int(k):v for k,v in dicRFMC.items()}
dicRFMM = {int(k):v for k,v in dicRFMM.items()}
dicGradBMC = {int(k):v for k,v in dicGradBMC.items()}
dicGradBMM = {int(k):v for k,v in dicGradBMM.items()}
dfKNNCC = pd.DataFrame.from_dict(dicKNNCC)
dfKNNCM = pd.DataFrame.from_dict(dicKNNCM)
dfLRCC = pd.DataFrame.from_dict(dicLRCC)
dfLRCM = pd.DataFrame.from_dict(dicLRCM)
dfMLPCC = pd.DataFrame.from_dict(dicMLPCC)
dfMLPCM = pd.DataFrame.from_dict(dicMLPCM)
dfRFCC = pd.DataFrame.from_dict(dicRFCC)
dfRFCM = pd.DataFrame.from_dict(dicRFCM)
dfGradBCC = pd.DataFrame.from_dict(dicGradBCC)
dfGradBCM = pd.DataFrame.from_dict(dicGradBCM)
dfKNNMC = pd.DataFrame.from_dict(dicKNNMC)
dfKNNMM = pd.DataFrame.from_dict(dicKNNMM)
dfLRMC = pd.DataFrame.from_dict(dicLRMC)
dfLRMM = pd.DataFrame.from_dict(dicLRMM)
dfMLPMC = pd.DataFrame.from_dict(dicMLPMC)
dfMLPMM = pd.DataFrame.from_dict(dicMLPMM)
dfRFMC = pd.DataFrame.from_dict(dicRFMC)
dfRFMM = pd.DataFrame.from_dict(dicRFMM)
dfGradBMC = pd.DataFrame.from_dict(dicGradBMC)
dfGradBMM = pd.DataFrame.from_dict(dicGradBMM)
dfKNNCC = dfKNNCC.T
dfKNNCM = dfKNNCM.T
dfLRCC = dfLRCC.T
dfLRCM = dfLRCM.T
dfMLPCC = dfMLPCC.T
dfMLPCM = dfMLPCM.T
dfRFCC = dfRFCC.T
dfRFCM = dfRFCM.T
dfGradBCC = dfGradBCC.T
dfGradBCM = dfGradBCM.T
dfKNNMC = dfKNNMC.T
dfKNNMM = dfKNNMM.T
dfLRMC = dfLRMC.T
dfLRMM = dfLRMM.T
dfMLPMC = dfMLPMC.T
dfMLPMM = dfMLPMM.T
dfRFMC = dfRFMC.T
dfRFMM = dfRFMM.T
dfGradBMC = dfGradBMC.T
dfGradBMM = dfGradBMM.T
return [dfKNNCC, dfKNNCM, dfLRCC, dfLRCM, dfMLPCC, dfMLPCM, dfRFCC, dfRFCM, dfGradBCC, dfGradBCM, dfKNNMC, dfKNNMM, dfLRMC, dfLRMM, dfMLPMC, dfMLPMM, dfRFMC, dfRFMM, dfGradBMC, dfGradBMM]
# remove that maybe!
def preProcsumPerMetricCM(factors):
sumPerClassifier = []
loopThroughMetrics = PreprocessingMetricsCM()
@ -3462,6 +3866,22 @@ def preProcsumPerMetricCM(factors):
sumPerClassifier.append(rowSum/sum(factors) * 100)
return sumPerClassifier
def preProcsumPerMetricCMSecond(factors):
sumPerClassifier = []
loopThroughMetrics = PreprocessingMetricsCMSecond()
loopThroughMetrics = loopThroughMetrics.fillna(0)
loopThroughMetrics.loc[:, 'log_loss'] = 1 - loopThroughMetrics.loc[:, 'log_loss']
for row in loopThroughMetrics.iterrows():
rowSum = 0
name, values = row
for loop, elements in enumerate(values):
rowSum = elements*factors[loop] + rowSum
if sum(factors) is 0:
sumPerClassifier = 0
else:
sumPerClassifier.append(rowSum/sum(factors) * 100)
return sumPerClassifier
def preProcMetricsAllAndSelCM():
loopThroughMetrics = PreprocessingMetricsCM()
loopThroughMetrics = loopThroughMetrics.fillna(0)
@ -3487,12 +3907,42 @@ def preProcMetricsAllAndSelCM():
metricsPerModelColl[index] = metricsPerModelColl[index].to_json()
return metricsPerModelColl
def preProcMetricsAllAndSelCMSecond():
loopThroughMetrics = PreprocessingMetricsCMSecond()
loopThroughMetrics = loopThroughMetrics.fillna(0)
global factors
metricsPerModelColl = []
metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'])
metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'])
metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_macro'])
metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_macro'])
metricsPerModelColl.append(loopThroughMetrics['mean_test_f1_macro'])
metricsPerModelColl.append(loopThroughMetrics['matthews_corrcoef'])
metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo'])
metricsPerModelColl.append(loopThroughMetrics['log_loss'])
f=lambda a: (abs(a)+a)/2
for index, metric in enumerate(metricsPerModelColl):
if (index == 5):
metricsPerModelColl[index] = ((f(metric))*factors[index]) * 100
elif (index == 7):
metricsPerModelColl[index] = ((1 - metric)*factors[index] ) * 100
else:
metricsPerModelColl[index] = (metric*factors[index]) * 100
metricsPerModelColl[index] = metricsPerModelColl[index].to_json()
return metricsPerModelColl
# Sending the overview classifiers' results to be visualized as a scatterplot
@app.route('/data/PlotCrossMutate', methods=["GET", "POST"])
def SendToPlotCM():
while (len(DataResultsRaw) != DataRawLength):
pass
global CurStage
if (CurStage == 1):
PreProcessingInitial()
else:
PreProcessingSecond()
response = {
'OverviewResultsCM': ResultsCM
}
@ -3513,6 +3963,20 @@ def PreProcessingInitial():
CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSelCM)
def PreProcessingSecond():
XModels = PreprocessingMetricsCMSecond()
XModels = XModels.fillna(0)
ModelSpaceMDSCM = FunMDS(XModels)
ModelSpaceTSNECM = FunTsne(XModels)
ModelSpaceTSNECM = ModelSpaceTSNECM.tolist()
ModelSpaceUMAPCM = FunUMAP(XModels)
PredictionProbSelCM = PreprocessingPredCMSecond()
CrossMutateResultsSecond(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSelCM)
def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSelCM):
global ResultsCM
@ -3544,6 +4008,37 @@ def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,Predict
return ResultsCM
def CrossMutateResultsSecond(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSelCM):
global ResultsCM
global AllTargets
ResultsCM = []
parametersGenCM = PreprocessingParamCMSecond()
metricsPerModelCM = preProcMetricsAllAndSelCMSecond()
sumPerClassifierCM = preProcsumPerMetricCMSecond(factors)
ModelsIDsCM = PreprocessingIDsCMSecond()
parametersGenPDGM = parametersGenCM.to_json(orient='records')
XDataJSONEntireSet = XData.to_json(orient='records')
XDataColumns = XData.columns.tolist()
ResultsCM.append(json.dumps(ModelsIDsCM))
ResultsCM.append(json.dumps(sumPerClassifierCM))
ResultsCM.append(json.dumps(parametersGenPDGM))
ResultsCM.append(json.dumps(metricsPerModelCM))
ResultsCM.append(json.dumps(XDataJSONEntireSet))
ResultsCM.append(json.dumps(XDataColumns))
ResultsCM.append(json.dumps(yData))
ResultsCM.append(json.dumps(target_names))
ResultsCM.append(json.dumps(AllTargets))
ResultsCM.append(json.dumps(ModelSpaceMDSCM))
ResultsCM.append(json.dumps(ModelSpaceTSNECM))
ResultsCM.append(json.dumps(ModelSpaceUMAPCM))
ResultsCM.append(json.dumps(PredictionProbSelCM))
ResultsCM.append(json.dumps(names_labels))
return ResultsCM
def PreprocessingPredSel(SelectedIDs):
global addKNN
@ -3561,13 +4056,13 @@ def PreprocessingPredSel(SelectedIDs):
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
if match:
items = match.groups()
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM")):
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM") | (items[0] == "KNNCC") | (items[0] == "KNNCM") | (items[0] == "KNNMC") | (items[0] == "KNNMM")):
numberIDKNN.append(int(items[1]) - addKNN)
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM")):
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM") | (items[0] == "LRCC") | (items[0] == "LRCM") | (items[0] == "LRMC") | (items[0] == "LRMM")):
numberIDLR.append(int(items[1]) - addLR)
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM")):
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM") | (items[0] == "MLPCC") | (items[0] == "MLPCM") | (items[0] == "MLPMC") | (items[0] == "MLPMM")):
numberIDMLP.append(int(items[1]) - addMLP)
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM")):
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM") | (items[0] == "RFCC") | (items[0] == "RFCM") | (items[0] == "RFMC") | (items[0] == "RFMM")):
numberIDRF.append(int(items[1]) - addRF)
else:
numberIDGradB.append(int(items[1]) - addGradB)
@ -3668,13 +4163,13 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
if match:
items = match.groups()
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM")):
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM") | (items[0] == "KNNCC") | (items[0] == "KNNCM") | (items[0] == "KNNMC") | (items[0] == "KNNMM")):
numberIDKNN.append(int(items[1]))
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM")):
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM") | (items[0] == "LRCC") | (items[0] == "LRCM") | (items[0] == "LRMC") | (items[0] == "LRMM")):
numberIDLR.append(int(items[1]))
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM")):
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM") | (items[0] == "MLPCC") | (items[0] == "MLPCM") | (items[0] == "MLPMC") | (items[0] == "MLPMM")):
numberIDMLP.append(int(items[1]))
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM")):
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM") | (items[0] == "RFCC") | (items[0] == "RFCM") | (items[0] == "RFMC") | (items[0] == "RFMM")):
numberIDRF.append(int(items[1]))
else:
numberIDGradB.append(int(items[1]))

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