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fix stack provenance

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master
Angelos Chatzimparmpas 5 years ago
parent ca46cb0be6
commit e3104a8e91
8 changed files with 129 additions and 50 deletions
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  1. BIN
      __pycache__/run.cpython-37.pyc
  2. 5
      frontend/src/components/BalancePredictions.vue
  3. 9
      frontend/src/components/Heatmap.vue
  4. 22
      frontend/src/components/Main.vue
  5. 2
      frontend/src/components/PerMetricBarChart.vue
  6. 72
      frontend/src/components/Provenance.vue
  7. 46
      frontend/src/components/ScatterPlot.vue
  8. 23
      run.py

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__pycache__/run.cpython-37.pyc
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5
frontend/src/components/BalancePredictions.vue
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@ -92,7 +92,7 @@
var histogram = d3.histogram() var histogram = d3.histogram()
.value(function(d) { return +d.value; }) // I need to give the vector of value .value(function(d) { return +d.value; }) // I need to give the vector of value
.domain(x.domain()) // then the domain of the graphic .domain(x.domain()) // then the domain of the graphic
.thresholds(x.ticks(10)); // then the numbers of bins .thresholds(x.ticks(20)); // then the numbers of bins
// And apply twice this function to data to get the bins. // And apply twice this function to data to get the bins.
var bins1 = histogram(data.filter( function(d){return d.type === "variable 1"} )); var bins1 = histogram(data.filter( function(d){return d.type === "variable 1"} ));
@ -190,8 +190,9 @@
svg.append("circle").attr("cx", 215).attr("cy", heightforText-1.5).attr("r", 6).style("fill", "#000") 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("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("Entire Distribution").style("font-size", "15px").attr("alignment-baseline","middle") svg.append("text").attr("x", 230).attr("y", heightforText).text("Entire Distribution").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", 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("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");
// Function to compute density // Function to compute density
function kernelDensityEstimator(kernel, X) { function kernelDensityEstimator(kernel, X) {

9
frontend/src/components/Heatmap.vue
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@ -54,11 +54,11 @@ export default {
var maxUni = Math.max.apply(Math, featureUni.map(function(o) { return o.Score; })) var maxUni = Math.max.apply(Math, featureUni.map(function(o) { return o.Score; }))
var minUni = Math.min.apply(Math, featureUni.map(function(o) { return o.Score; })) var minUni = Math.min.apply(Math, featureUni.map(function(o) { return o.Score; }))
let len = Features.length let len = Features.length
let indicesYAxis = new Array(len) let indicesYAxis = []
for (let i = 0; i < len; i++) { for (let i = 0; i < len; i++) {
indicesYAxis[i] = [Features[i]] indicesYAxis[i] = [Features[i]]
} }
let indicesXAxis = new Array(len) let indicesXAxis = []
var temp = [] var temp = []
for (let i = 0; i < len2; i++) { for (let i = 0; i < len2; i++) {
temp = [] temp = []
@ -68,10 +68,9 @@ export default {
} }
if (this.ModelsIDHeatStack.length != 0) { if (this.ModelsIDHeatStack.length != 0) {
var FeaturesAccuracyNew = [] var FeaturesAccuracyNew = []
var PermImpEliNew = [] var PermImpEliNew = []
indicesXAxis = new Array(len) indicesXAxis = []
for (let i = 0; i < modelIds.length; i++) { for (let i = 0; i < modelIds.length; i++) {
if (this.ModelsIDHeatStack.includes(modelIds[i])) { if (this.ModelsIDHeatStack.includes(modelIds[i])) {
@ -90,7 +89,7 @@ export default {
indicesXAxis[i] = temp indicesXAxis[i] = temp
} }
} }
temp = [] temp = []
temp.push("R") temp.push("R")
temp.push("Average") temp.push("Average")

22
frontend/src/components/Main.vue
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@ -232,6 +232,7 @@ export default Vue.extend({
OverviewResults: 0, OverviewResults: 0,
preDataResults: '', preDataResults: '',
DataResults: '', DataResults: '',
keyNow: 1,
instancesImportance: '', instancesImportance: '',
RetrieveValueFile: 'DiabetesC', // this is for the default data set RetrieveValueFile: 'DiabetesC', // this is for the default data set
ClassifierIDsList: '', ClassifierIDsList: '',
@ -398,9 +399,9 @@ export default Vue.extend({
EventBus.$emit('resetViews') EventBus.$emit('resetViews')
} else { } else {
const path = `http://127.0.0.1:5000/data/ServerRequestSelPoin` const path = `http://127.0.0.1:5000/data/ServerRequestSelPoin`
const postData = { const postData = {
ClassifiersList: this.ClassifierIDsList ClassifiersList: this.ClassifierIDsList,
keyNow: this.keyNow,
} }
const axiosConfig = { const axiosConfig = {
headers: { headers: {
@ -414,9 +415,13 @@ export default Vue.extend({
.then(response => { .then(response => {
console.log('Sent the selected points to the server (scatterplot)!') console.log('Sent the selected points to the server (scatterplot)!')
this.OverSelLength = this.ClassifierIDsList.length this.OverSelLength = this.ClassifierIDsList.length
EventBus.$emit('emittedEventCallingHeatmapView', this.OverviewResults) this.OverAllLength = this.ClassifierIDsList.length
this.getSelectedModelsMetrics() this.getSelectedModelsMetrics()
this.getFinalResults() this.getFinalResults()
if (this.keyNow == 0) {
EventBus.$emit('GrayOutPoints', '')
EventBus.$emit('newPointsStack', this.OverviewResults)
}
}) })
.catch(error => { .catch(error => {
console.log(error) console.log(error)
@ -440,8 +445,8 @@ export default Vue.extend({
axios.post(path, postData, axiosConfig) axios.post(path, postData, axiosConfig)
.then(response => { .then(response => {
console.log('Sent the selected points to the server (scatterplot)!') console.log('Sent the selected points to the server (scatterplot)!')
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
this.updatePredictionsSpace() this.updatePredictionsSpace()
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
this.getFinalResults() this.getFinalResults()
}) })
.catch(error => { .catch(error => {
@ -463,8 +468,11 @@ export default Vue.extend({
.then(response => { .then(response => {
this.UpdatePredictions = response.data.UpdatePredictions this.UpdatePredictions = response.data.UpdatePredictions
console.log('Updating Predictions Space!') console.log('Updating Predictions Space!')
if (this.keyNow == 1) {
console.log('mpike')
EventBus.$emit('InitializeProvenance', this.UpdatePredictions)
}
EventBus.$emit('updatePredictionsSpace', this.UpdatePredictions) EventBus.$emit('updatePredictionsSpace', this.UpdatePredictions)
EventBus.$emit('InitializeProvenance', this.UpdatePredictions)
}) })
.catch(error => { .catch(error => {
console.log(error) console.log(error)
@ -558,6 +566,7 @@ export default Vue.extend({
axios.get(path, axiosConfig) axios.get(path, axiosConfig)
.then(response => { .then(response => {
this.FinalResults = response.data.FinalResults this.FinalResults = response.data.FinalResults
console.log(this.FinalResults)
EventBus.$emit('emittedEventCallingLinePlot', this.FinalResults) EventBus.$emit('emittedEventCallingLinePlot', this.FinalResults)
}) })
.catch(error => { .catch(error => {
@ -966,8 +975,11 @@ export default Vue.extend({
EventBus.$on('reset', this.Reset) EventBus.$on('reset', this.Reset)
EventBus.$on('ReturningAlgorithms', data => { this.selectedAlgorithms = data }) EventBus.$on('ReturningAlgorithms', data => { this.selectedAlgorithms = data })
EventBus.$on('ReturningBrushedPointsParams', data => { this.parametersofModels = data; }) EventBus.$on('ReturningBrushedPointsParams', data => { this.parametersofModels = data; })
EventBus.$on('ChangeKey', data => { this.keyNow = data })
EventBus.$on('SendSelectedPointsToServerEvent', data => { this.ClassifierIDsList = data }) EventBus.$on('SendSelectedPointsToServerEvent', data => { this.ClassifierIDsList = data })
EventBus.$on('SendSelectedPointsToServerEvent', this.SendSelectedPointsToServer) EventBus.$on('SendSelectedPointsToServerEvent', this.SendSelectedPointsToServer)
EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data }) EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data })
EventBus.$on('SendSelectedDataPointsToServerEvent', this.SendSelectedDataPointsToServer) EventBus.$on('SendSelectedDataPointsToServerEvent', this.SendSelectedDataPointsToServer)
EventBus.$on('SendSelectedFeaturesEvent', data => { this.SelectedFeaturesPerClassifier = data }) EventBus.$on('SendSelectedFeaturesEvent', data => { this.SelectedFeaturesPerClassifier = data })

2
frontend/src/components/PerMetricBarChart.vue
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@ -153,7 +153,7 @@ export default {
Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','<b>Log Loss</b>']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','MAE','RMSE','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','F-Beta Sc','MCC','ROC AUC','<b>Log Loss</b>']]);
index = 9 index = 9
} }
EventBus.$emit('updateMetricsScatter', resultsColors[index]) //EventBus.$emit('updateMetricsScatter', resultsColors[index])
}); });
}, },
reset () { reset () {

72
frontend/src/components/Provenance.vue
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@ -42,7 +42,11 @@ export default {
AdaBModels: 2766, AdaBModels: 2766,
GradBModels: 2926, GradBModels: 2926,
AllDetails: '', AllDetails: '',
platform: '' platform: '',
count: 0,
storeData: [],
storePerformance: [],
storeParameters: []
} }
}, },
methods: { methods: {
@ -64,7 +68,7 @@ export default {
}, },
provenance () { provenance () {
var canvas = document.getElementById("main-canvas"); var canvas = document.getElementById("main-canvas");
var width = this.WH[0]*9 // interactive visualization var width = this.WH[0]*7 // interactive visualization
var height = this.WH[1]*0.58 // interactive visualization var height = this.WH[1]*0.58 // interactive visualization
var flagKNN = 0 var flagKNN = 0
@ -79,10 +83,22 @@ export default {
var flagAdaB = 0 var flagAdaB = 0
var flagGradB = 0 var flagGradB = 0
var localStackStore = []
var StackInfo = JSON.parse(this.stackInformation[1]) var StackInfo = JSON.parse(this.stackInformation[1])
var arrayOfNumbers = StackInfo.map(Number)
this.storeData.push(arrayOfNumbers)
localStackStore = this.storeData.slice()
var localPerfStore = []
var performanceLoc = JSON.parse(this.AllDetails[0]) var performanceLoc = JSON.parse(this.AllDetails[0])
this.storePerformance.push(performanceLoc)
localPerfStore = this.storePerformance.slice()
var localParamsStore = []
var parameters = JSON.parse(this.AllDetails[2]) var parameters = JSON.parse(this.AllDetails[2])
var parameters = JSON.parse(parameters) var parameters = JSON.parse(parameters)
this.storeParameters.push(parameters)
localParamsStore = this.storeParameters.slice()
var stringParameters = [] var stringParameters = []
var temp = 0 var temp = 0
@ -286,15 +302,18 @@ export default {
var y = e.clientY - canvas.getBoundingClientRect().top; var y = e.clientY - canvas.getBoundingClientRect().top;
var p = plat.getPickingPixel(x * plat.pixelRatio, y * plat.pixelRatio); var p = plat.getPickingPixel(x * plat.pixelRatio, y * plat.pixelRatio);
var mergedIDs = [].concat.apply([], localStackStore)
var mergedPerf = [].concat.apply([], localPerfStore)
var mergedParams = [].concat.apply([], localParamsStore)
if (p) { if (p) {
// Show the tooltip only when there is nodeData found by the mouse // Show the tooltip only when there is nodeData found by the mouse
d3.select('#tooltip')
d3.select('#tooltip')
.style('opacity', 0.8) .style('opacity', 0.8)
.style('top', x + 5 + 'px') .style('top', x + 5 + 'px')
.style('left', y + 5 + 'px') .style('left', y + 5 + 'px')
.html('Model ID: '+StackInfo[p[1]]+'<br>'+'Parameters: '+JSON.stringify(parameters[p[1]])+'<br> # Performance (%) #: '+performanceLoc[p[1]]); .html('Model ID: '+mergedIDs[p[1]]+'<br>'+'Parameters: '+JSON.stringify(mergedParams[p[1]])+'<br> # Performance (%) #: '+mergedPerf[p[1]]);
} else { } else {
@ -306,21 +325,46 @@ export default {
} }
} }
const stringStep = "Stack " const stringStep = "Stack "
var myButton = '<button id="HistoryReturnButtons'+this.counter+'" class="dynamic_buttons">'+stringStep+this.counter+'</button>&nbsp;&nbsp;&nbsp;' var myButton = '<button id="HistoryReturnButtons'+this.counter+'" class="dynamic_buttons">'+stringStep+this.counter+'</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'
$("#dynamic-buttons").append(myButton); $("#dynamic-buttons").append(myButton);
function checkhistory () { $(document).on('click','.dynamic_buttons', function() {
console.log(event) var btns = document.getElementsByClassName('dynamic_buttons')
//call the previous stack depending on button clicked.
btns.forEach(btnlocal => {
btnlocal.style.fontWeight = 'normal';
});
function cleanLoc(obj) {
var propNames = Object.getOwnPropertyNames(obj);
for (var i = 0; i < propNames.length; i++) {
var propName = propNames[i];
if (obj[propName] === null || obj[propName] === undefined) {
delete obj[propName];
}
}
}
var btn = document.getElementById($(this).attr('id'));
btn.style.fontWeight = 'bold';
EventBus.$emit('ChangeKey', 0)
EventBus.$emit('SendSelectedPointsToServerEvent', localStackStore[parseInt($(this).attr('id').replace(/\D/g,''))-1])
stringParameters = []
temp = 0
for (let i = 0; i < localStackStore[parseInt($(this).attr('id').replace(/\D/g,''))-1].length; i++) {
cleanLoc(localPerfStore[parseInt($(this).attr('id').replace(/\D/g,''))-1][i])
temp = JSON.stringify(Object.assign({ID: localStackStore[parseInt($(this).attr('id').replace(/\D/g,''))-1][i]}, localPerfStore[parseInt($(this).attr('id').replace(/\D/g,''))-1][i]))
stringParameters.push(temp)
} }
EventBus.$emit('ExtractResults', stringParameters)
}
);
}, },
}, },
mounted () { mounted () {
// fix that
$(document).on('click','.dynamic_buttons', function() {
console.log($(this).attr('id'))
}
);
EventBus.$on('ParametersProvenance', data => {this.AllDetails = data}) EventBus.$on('ParametersProvenance', data => {this.AllDetails = data})
EventBus.$on('InitializeProvenance', data => {this.stackInformation = data}) EventBus.$on('InitializeProvenance', data => {this.stackInformation = data})
EventBus.$on('InitializeProvenance', this.provenance) EventBus.$on('InitializeProvenance', this.provenance)

46
frontend/src/components/ScatterPlot.vue
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@ -52,7 +52,14 @@ export default {
valueStackRemove: 'Remove Unselected from Stack', valueStackRemove: 'Remove Unselected from Stack',
DataPointsSelUpdate: [], DataPointsSelUpdate: [],
ModelsIDGray: [], ModelsIDGray: [],
valueResetSel: 'Reset Metric Selection' valueResetSel: 'Reset Metric Selection',
newStackPoints: '',
colorsStore: [],
MDSStore: [],
parametersStore: [],
TSNEStore: [],
modelIDStore: [],
UMAPStore: []
} }
}, },
methods: { methods: {
@ -86,17 +93,21 @@ export default {
var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0]) var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
if (this.newColorsUpdate.length != 0) { /*if (this.newColorsUpdate.length != 0) {
let resultsClear = JSON.parse(this.newColorsUpdate) let resultsClear = JSON.parse(this.newColorsUpdate)
for (let j = 0; j < Object.values(resultsClear).length; j++) { for (let j = 0; j < Object.values(resultsClear).length; j++) {
colorsforScatterPlot.push(Object.values(resultsClear)[j]) colorsforScatterPlot.push(Object.values(resultsClear)[j])
} }
} }*/
var MDSData = JSON.parse(this.ScatterPlotResults[1]) var MDSData = JSON.parse(this.ScatterPlotResults[1])
var parameters = JSON.parse(this.ScatterPlotResults[2]) var parameters = JSON.parse(this.ScatterPlotResults[2])
var TSNEData = JSON.parse(this.ScatterPlotResults[12]) var TSNEData = JSON.parse(this.ScatterPlotResults[12])
var modelId = JSON.parse(this.ScatterPlotResults[13]) if (this.newStackPoints.length != 0) {
var modelId = this.newStackPoints
} else {
var modelId = JSON.parse(this.ScatterPlotResults[13])
}
var UMAPData = JSON.parse(this.ScatterPlotResults[17]) var UMAPData = JSON.parse(this.ScatterPlotResults[17])
EventBus.$emit('sendPointsNumber', modelId.length) EventBus.$emit('sendPointsNumber', modelId.length)
@ -136,11 +147,6 @@ export default {
var colorsforScatterPlotNew = [] var colorsforScatterPlotNew = []
for (let i = 0; i < modelId.length; i++) { for (let i = 0; i < modelId.length; i++) {
if (listofNumbersModelsIDs.includes(modelId[i])) { if (listofNumbersModelsIDs.includes(modelId[i])) {
StackModelsIDs.push(modelId[i])
parametersNew.push(parameters[i])
colorsforScatterPlotNew.push('rgb(211,211,211)')
MDSDataNewX.push(MDSData[0][i])
MDSDataNewY.push(MDSData[1][i])
} else { } else {
StackModelsIDs.push(modelId[i]) StackModelsIDs.push(modelId[i])
parametersNew.push(parameters[i]) parametersNew.push(parameters[i])
@ -157,7 +163,7 @@ export default {
MDSData[0] = MDSDataNewX MDSData[0] = MDSDataNewX
MDSData[1] = MDSDataNewY MDSData[1] = MDSDataNewY
colorsforScatterPlot = colorsforScatterPlotNew colorsforScatterPlot = colorsforScatterPlotNew
//EventBus.$emit('NewHeatmapAccordingtoNewStack', StackModelsIDs) EventBus.$emit('NewHeatmapAccordingtoNewStack', StackModelsIDs)
} }
var DataGeneral var DataGeneral
@ -192,7 +198,7 @@ export default {
colorscale: 'Viridis', colorscale: 'Viridis',
colorbar: { colorbar: {
title: '# Performance (%) #', title: '# Performance (%) #',
titleside: 'Top' titleside:'right',
}, },
} }
@ -358,9 +364,10 @@ export default {
if (evt.points[i] === undefined) { if (evt.points[i] === undefined) {
break break
} else { } else {
const OnlyId = evt.points[i].text.split(';') const OnlyId = evt.points[i].text.split(' ')[2]
ClassifierIDsList.push(OnlyId[0]) const OnlyIdCleared = OnlyId.split('<br>')
let numb = OnlyId[0].match(/\d/g); ClassifierIDsList.push(OnlyIdCleared[0])
let numb = OnlyIdCleared[0].match(/\d/g);
numb = numb.join(""); numb = numb.join("");
let numberNumb = Number(numb) let numberNumb = Number(numb)
ClassifierIDsListCleared.push(numberNumb) ClassifierIDsListCleared.push(numberNumb)
@ -372,9 +379,11 @@ export default {
} }
} }
if (allModels != '') { if (allModels != '') {
EventBus.$emit('ChangeKey', 1)
EventBus.$emit('SendSelectedPointsToServerEvent', pushModelsRemaining) EventBus.$emit('SendSelectedPointsToServerEvent', pushModelsRemaining)
EventBus.$emit('SendSelectedPointsToBrushHeatmap', pushModelsRemaining) EventBus.$emit('SendSelectedPointsToBrushHeatmap', ClassifierIDsListCleared)
} else { } else {
EventBus.$emit('ChangeKey', 1)
EventBus.$emit('SendSelectedPointsToServerEvent', '') EventBus.$emit('SendSelectedPointsToServerEvent', '')
} }
} }
@ -426,8 +435,11 @@ export default {
} }
}, },
mounted() { mounted() {
EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data }) /*EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data })
EventBus.$on('updateMetricsScatter', this.ScatterPlotView) EventBus.$on('updateMetricsScatter', this.ScatterPlotView)*/
EventBus.$on('newPointsStack', data => { this.newStackPoints = data })
EventBus.$on('newPointsStack', this.ScatterPlotView)
EventBus.$on('GrayOutPoints', data => { this.ModelsIDGray = data }) EventBus.$on('GrayOutPoints', data => { this.ModelsIDGray = data })
EventBus.$on('GrayOutPoints', this.ScatterPlotView) EventBus.$on('GrayOutPoints', this.ScatterPlotView)

23
run.py
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@ -1049,7 +1049,6 @@ def PreprocessingPredUpdate(Models):
dfGradBFiltered = dfGradB.loc[GradBModels, :] dfGradBFiltered = dfGradB.loc[GradBModels, :]
df_concatProbs = pd.concat([dfKNNFiltered, dfSVCFiltered, dfGausNBFiltered, dfMLPFiltered, dfLRFiltered, dfLDAFiltered, dfQDAFiltered, dfRFFiltered, dfExtraTFiltered, dfAdaBFiltered, dfGradBFiltered]) df_concatProbs = pd.concat([dfKNNFiltered, dfSVCFiltered, dfGausNBFiltered, dfMLPFiltered, dfLRFiltered, dfLDAFiltered, dfQDAFiltered, dfRFFiltered, dfExtraTFiltered, dfAdaBFiltered, dfGradBFiltered])
listProbs = df_concatProbs.index.values.tolist() listProbs = df_concatProbs.index.values.tolist()
deletedElements = 0 deletedElements = 0
for index, element in enumerate(listProbs): for index, element in enumerate(listProbs):
@ -1474,7 +1473,9 @@ def InitializeEnsemble():
XModels = PreprocessingMetrics() XModels = PreprocessingMetrics()
global ModelSpaceMDS global ModelSpaceMDS
global ModelSpaceTSNE global ModelSpaceTSNE
XModels = XModels.fillna(0)
ModelSpaceMDS = FunMDS(XModels) ModelSpaceMDS = FunMDS(XModels)
ModelSpaceTSNE = FunTsne(XModels) ModelSpaceTSNE = FunTsne(XModels)
ModelSpaceTSNE = ModelSpaceTSNE.tolist() ModelSpaceTSNE = ModelSpaceTSNE.tolist()
@ -1583,14 +1584,17 @@ def SendPredBacktobeUpdated():
def RetrieveSelClassifiersID(): def RetrieveSelClassifiersID():
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"') ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
ComputeMetricsForSel(ClassifierIDsList) ComputeMetricsForSel(ClassifierIDsList)
ClassifierIDCleaned = json.loads(ClassifierIDsList)
key = 1 global keySpec
EnsembleModel(ClassifierIDsList, key) keySpec = ClassifierIDCleaned['keyNow']
EnsembleModel(ClassifierIDsList, 1)
return 'Everything Okay' return 'Everything Okay'
def ComputeMetricsForSel(Models): def ComputeMetricsForSel(Models):
Models = json.loads(Models) Models = json.loads(Models)
MetricsAlltoSel = PreprocessingMetrics() MetricsAlltoSel = PreprocessingMetrics()
listofModels = [] listofModels = []
for loop in Models['ClassifiersList']: for loop in Models['ClassifiersList']:
listofModels.append(loop) listofModels.append(loop)
@ -2340,6 +2344,8 @@ def EnsembleModel(Models, keyRetrieved):
global all_classifiersSelection global all_classifiersSelection
all_classifiersSelection = [] all_classifiersSelection = []
global all_classifiers
global XData global XData
global yData global yData
global sclf global sclf
@ -2347,7 +2353,6 @@ def EnsembleModel(Models, keyRetrieved):
lr = LogisticRegression() lr = LogisticRegression()
if (keyRetrieved == 0): if (keyRetrieved == 0):
global all_classifiers
all_classifiers = [] all_classifiers = []
columnsInit = [] columnsInit = []
columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData] columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData]
@ -2443,8 +2448,11 @@ def EnsembleModel(Models, keyRetrieved):
elif (keyRetrieved == 1): elif (keyRetrieved == 1):
Models = json.loads(Models) Models = json.loads(Models)
ModelsAll = preProceModels() ModelsAll = preProceModels()
global keySpec
print(ModelsAll)
for index, modHere in enumerate(ModelsAll): for index, modHere in enumerate(ModelsAll):
flag = 0 flag = 0
print(Models['ClassifiersList'])
for loop in Models['ClassifiersList']: for loop in Models['ClassifiersList']:
if (int(loop) == int(modHere)): if (int(loop) == int(modHere)):
flag = 1 flag = 1
@ -2456,6 +2464,9 @@ def EnsembleModel(Models, keyRetrieved):
meta_classifier=lr, meta_classifier=lr,
random_state=RANDOM_SEED, random_state=RANDOM_SEED,
n_jobs = -1) n_jobs = -1)
print(keySpec)
if (keySpec == 0):
sclfStack = sclf
elif (keyRetrieved == 2): elif (keyRetrieved == 2):
# fix this part! # fix this part!
if (len(all_classifiersSelection) == 0): if (len(all_classifiersSelection) == 0):
@ -2640,7 +2651,7 @@ def EnsembleModel(Models, keyRetrieved):
# meta_classifier=lr, # meta_classifier=lr,
# random_state=RANDOM_SEED, # random_state=RANDOM_SEED,
# n_jobs = -1) # n_jobs = -1)
num_cores = multiprocessing.cpu_count() num_cores = multiprocessing.cpu_count()
inputsSc = ['accuracy','precision_weighted','recall_weighted','accuracy','precision_weighted','recall_weighted'] inputsSc = ['accuracy','precision_weighted','recall_weighted','accuracy','precision_weighted','recall_weighted']
flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,sclfStack,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc)) flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,sclfStack,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))

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