fixed prediction space data space, etc.

frontend/src/components/DataSpace.vue

@@ -17,7 +17,12 @@ export default {
   },
   methods: {
     ScatterPlotDataView () {
+        
+        var target_names = JSON.parse(this.DataSpace[4])
         const XandYCoordinates = JSON.parse(this.DataSpace[7])
+        const DataSet = JSON.parse(this.DataSpace[14])
+        const DataSetY = JSON.parse(this.DataSpace[15])
+        var DataSetParse = JSON.parse(DataSet)
 
         var result = XandYCoordinates.reduce(function(r, a) {
             a.forEach(function(s, i) {
@@ -32,33 +37,59 @@ export default {
 
         var dataPointInfo = []
         for (let i = 0; i < XandYCoordinates.length; i++) {
-          dataPointInfo[i] = 'Data Point ID: ' + i
+          dataPointInfo[i] = 'Data Point ID: ' + i + '; Details: ' + JSON.stringify(DataSetParse[i])
+        }
+        
+        var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']
+
+         var traces = []
+         var countPrev = 0
+         var count = 0
+         for (let i = 0; i < target_names.length; i++) {
+          count = 0
+          for (let j = 0; j < DataSetY.length; j++) {
+            if (i == DataSetY[j]) {
+              count = count + 1
+            }
+          }
+
+          traces.push({
+            x: result.Xax.slice(countPrev,count+countPrev),
+            y: result.Yax.slice(countPrev,count+countPrev),
+            mode: 'markers',
+            name: target_names[i],
+            marker: {
+              color: colors[i]
+            },
+            hovertemplate: 
+                    "<b>%{text}</b><br><br>" +
+                    "<extra></extra>",
+            text: dataPointInfo.slice(countPrev,count+countPrev),
+          })
+          countPrev = count + countPrev
         }
 
         var width = this.WH[0]*3 // interactive visualization
-        var height = this.WH[1]*1.5 // interactive visualization
-        const Data = [{
-        x: result.Xax,
-        y: result.Yax,
-        mode: 'markers',
-        hovertemplate: 
-                "<b>%{text}</b><br><br>" +
-                "<extra></extra>",
-        text: dataPointInfo,
-        }]
+        var height = this.WH[1]*2.1 // interactive visualization
+
         const layout = {
-        title: 'Data Space Projection (tSNE)',
+        title: 'Data Space Projection (t-SNE)',
         xaxis: {
             visible: false
         },
         yaxis: {
             visible: false
         },
+        dragmode: 'lasso',
+        hovermode: "closest",
         autosize: true,
         width: width,
         height: height,
         }
-        Plotly.newPlot('OverviewDataPlotly', Data, layout, {responsive: true})
+
+        var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage', 'toggleSpikelines', 'autoScale2d', 'hoverClosestGl2d','hoverCompareCartesian','select2d','hoverClosestCartesian','zoomIn2d','zoomOut2d','zoom2d'], responsive: true}
+
+        Plotly.newPlot('OverviewDataPlotly', traces, layout, config)
     }
   },
   mounted() {

frontend/src/components/FinalResultsLinePlot.vue

@@ -46,7 +46,6 @@ export default {
       this.xaxis.push(this.NumberofExecutions)
 
       // Under Exploration = Current
-      console.log(this.FinalResultsforLinePlot)
       this.scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[0])*100).toFixed(1))
       this.scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[1])*100).toFixed(1))
 
@@ -106,7 +105,9 @@ export default {
         text: text,
         line: {color: "rgb(127,201,127)"}, 
         mode: "lines+markers", 
-        name: "Current Accuracy", 
+        marker : {
+          symbol: 'pentagon' },
+        name: "Current Accuracy",
         type: "scatter"
       }
 
@@ -182,7 +183,7 @@ export default {
         line: {color: "rgb(190,174,212)"}, 
         mode: "lines+markers", 
         marker : {
-          symbol: 'square' },
+          symbol: 'circle' },
         name: "Stack Accuracy", 
         type: "scatter"
       }
@@ -207,7 +208,7 @@ export default {
         line: {color: "rgb(56,108,176)"}, 
         mode: "lines+markers", 
         marker : {
-          symbol: 'star-triangle-up' },
+          symbol: 'square' },
         name: "Stack Precision", 
         type: "scatter"
       }
@@ -230,9 +231,9 @@ export default {
         y: this.Stack_scoresMean3, 
         text: text,
         line: {color: "rgb(240,2,127)"}, 
-        mode: "lines+markers", 
+        mode: "lines+markers",
         marker : {
-          symbol: 'pentagon' },
+          symbol: 'star-triangle-up' },
         name: "Stack Recall", 
         type: "scatter"
       }

frontend/src/components/Heatmap.vue

@@ -21,7 +21,8 @@ export default {
       limitation: 0,
       flag: false,
       classesNumber: 10,
-      cellSize: 20
+      cellSize: 20,
+      ModelsIDHeatStack: []
     }
   },
   methods: {
@@ -30,16 +31,15 @@ export default {
     },
     Heatmap () {
 
-      // Clear Heatmap first
-      var svg = d3.select("#Heatmap");
-      svg.selectAll("*").remove();
+        // Clear Heatmap first
+        var svg = d3.select("#Heatmap");
+        svg.selectAll("*").remove();
       
-      var FeaturesAccuracy = JSON.parse(this.GetResultsAll[5])
-      var Features= JSON.parse(this.GetResultsAll[6])
-      var keepingArrayIndices = this.indicestoRem
-      var PermImpEli = JSON.parse(this.GetResultsAll[10])
-      var featureUni = JSON.parse(this.GetResultsAll[11])
-      var modelIds = JSON.parse(this.GetResultsAll[13])
+        var FeaturesAccuracy = JSON.parse(this.GetResultsAll[5])
+        var Features = JSON.parse(this.GetResultsAll[6])
+        var PermImpEli = JSON.parse(this.GetResultsAll[10])
+        var featureUni = JSON.parse(this.GetResultsAll[11])
+        var modelIds = JSON.parse(this.GetResultsAll[13])
 
         var len2 = modelIds.length
 
@@ -59,6 +59,30 @@ export default {
           indicesXAxis[i] = temp
         }
 
+        if (this.ModelsIDHeatStack.length != 0) {
+
+            var FeaturesAccuracyNew = []
+            var PermImpEliNew = []
+            indicesXAxis = new Array(len)
+
+            for (let i = 0; i < modelIds.length; i++) {
+                if (this.ModelsIDHeatStack.includes(modelIds[i])) {
+                } else {
+                    FeaturesAccuracyNew.push(FeaturesAccuracy[i])
+                    PermImpEliNew.push(PermImpEli[i])
+                }
+            }
+            FeaturesAccuracy = FeaturesAccuracyNew
+            PermImpEli = PermImpEliNew
+            len2 = this.ModelsIDHeatStack.length
+            for (let i = 0; i < len2; i++) {
+                temp = []
+                temp.push("R")
+                temp.push("Model "+this.ModelsIDHeatStack[i].toString())
+                indicesXAxis[i] = temp
+            }
+        }
+
         temp = []
         temp.push("R")
         temp.push("Average")
@@ -525,6 +549,8 @@ export default {
     }
   },
   mounted () {
+      EventBus.$on('NewHeatmapAccordingtoNewStack', data => { this.ModelsIDHeatStack = data })
+      EventBus.$on('NewHeatmapAccordingtoNewStack', this.Heatmap)
       EventBus.$on('emittedEventCallingToggles', data => { this.Toggles = data })
       EventBus.$on('emittedEventCallingHeatmapView', data => { this.GetResultsAll = data; this.flag = false })
       EventBus.$on('emittedEventCallingHeatmapView', this.Heatmap)

frontend/src/components/Main.vue

@@ -186,7 +186,7 @@ export default Vue.extend({
       PerformancePerModel: '',
       PerformanceCheck: '',
       firstTimeFlag: 1,
-      selectedAlgorithms_Stack: [],
+      selectedModels_Stack: [],
       selectedAlgorithms: [],
       parametersofModels: [],
       reset: false,
@@ -252,8 +252,9 @@ export default Vue.extend({
           console.log('Server successfully sent all the data related to visualizations!')
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults)
           if (this.firstTimeFlag == 1) {
-            this.selectedAlgorithms_Stack = this.selectedAlgorithms
-            EventBus.$emit('InitializeProvenance', this.selectedAlgorithms_Stack)
+            this.selectedModels_Stack.push(0)
+            this.selectedModels_Stack.push(JSON.stringify(this.modelsUpdate))
+            EventBus.$emit('InitializeProvenance', this.selectedModels_Stack)
           }
           this.firstTimeFlag = 0
           EventBus.$emit('InitializeMetricsBarChart', this.OverviewResults)
@@ -332,6 +333,53 @@ export default Vue.extend({
           })
       }
     },
+    RemoveFromStackModels () {
+      const path = `http://127.0.0.1:5000/data/ServerRemoveFromStack`
+
+      const postData = {
+        ClassifiersList: this.ClassifierIDsList
+      }
+      const axiosConfig = {
+      headers: {
+      'Content-Type': 'application/json',
+      'Access-Control-Allow-Origin': '*',
+      'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
+      'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
+      }
+      }
+      axios.post(path, postData, axiosConfig)
+      .then(response => {
+      console.log('Sent the selected points to the server (scatterplot)!')
+      EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
+      this.updatePredictionsSpace()
+      this.getFinalResults()
+      })
+      .catch(error => {
+      console.log(error)
+      })
+    },
+    updatePredictionsSpace () {
+      const path = `http://localhost:5000/data/UpdatePredictionsSpace`
+
+      const axiosConfig = {
+        headers: {
+          'Content-Type': 'application/json',
+          'Access-Control-Allow-Origin': '*',
+          'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
+          'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
+        }
+      }
+      axios.get(path, axiosConfig)
+        .then(response => {
+          this.UpdatePredictions = response.data.UpdatePredictions
+          console.log('Updating Predictions Space!')
+          EventBus.$emit('updatePredictionsSpace', this.UpdatePredictions)
+          EventBus.$emit('InitializeProvenance', this.UpdatePredictions)
+        })
+        .catch(error => {
+          console.log(error)
+        })
+    },
     SendSelectedDataPointsToServer () {
 
       const path = `http://127.0.0.1:5000/data/ServerRequestDataPoint`
@@ -691,6 +739,7 @@ export default Vue.extend({
     EventBus.$on('sendPointsNumber', data => {this.OverSelLength = data})
     EventBus.$on('sendPointsNumber', data => {this.OverAllLength = data})
     EventBus.$on('AllSelModels', data => {this.valueSel = data})
+    EventBus.$on('RemoveFromStack', this.RemoveFromStackModels)
     //Prevent double click to search for a word. 
     document.addEventListener('mousedown', function (event) {
       if (event.detail > 1) {

frontend/src/components/PredictionsSpace.vue

@@ -11,12 +11,17 @@ export default {
   data () {
     return {
       PredictionsData: '',
+      UpdatedData: '',
       WH: []
     }
   },
   methods: {
     ScatterPlotDataView () {
+        var target_names = JSON.parse(this.PredictionsData[4])
         const XandYCoordinates = JSON.parse(this.PredictionsData[8])
+        const DataSet = JSON.parse(this.PredictionsData[14])
+        const DataSetY = JSON.parse(this.PredictionsData[15])
+        var DataSetParse = JSON.parse(DataSet)
 
         var result = XandYCoordinates.reduce(function(r, a) {
             a.forEach(function(s, i) {
@@ -31,22 +36,43 @@ export default {
 
         var dataPointInfo = []
         for (let i = 0; i < XandYCoordinates.length; i++) {
-          dataPointInfo[i] = 'Data Point ID: ' + i
+          dataPointInfo[i] = 'Data Point ID: ' + i + '; Details: ' + JSON.stringify(DataSetParse[i])
         }
+        
+        var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']
 
+         var traces = []
+         var countPrev = 0
+         var count = 0
+         for (let i = 0; i < target_names.length; i++) {
+          count = 0
+          for (let j = 0; j < DataSetY.length; j++) {
+            if (i == DataSetY[j]) {
+              count = count + 1
+            }
+          }
+
+          traces.push({
+            x: result.Xax.slice(countPrev,count+countPrev),
+            y: result.Yax.slice(countPrev,count+countPrev),
+            mode: 'markers',
+            name: target_names[i],
+            marker: {
+              color: colors[i]
+            },
+            hovertemplate: 
+                    "<b>%{text}</b><br><br>" +
+                    "<extra></extra>",
+            text: dataPointInfo.slice(countPrev,count+countPrev),
+          })
+          countPrev = count + countPrev
+        }
+     
         var width = this.WH[0]*3 // interactive visualization
         var height = this.WH[1]*1.48 // interactive visualization
-        const Data = [{
-        x: result.Xax,
-        y: result.Yax,
-        mode: 'markers',
-        hovertemplate: 
-                "<b>%{text}</b><br><br>" +
-                "<extra></extra>",
-        text: dataPointInfo,
-        }]
+
         const layout = {
-        title: 'Predictions Space Projection (tSNE)',
+        title: 'Predictions Space Projection (t-SNE)',
         xaxis: {
             visible: false
         },
@@ -62,9 +88,29 @@ export default {
 
         var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage', 'toggleSpikelines', 'autoScale2d', 'hoverClosestGl2d','hoverCompareCartesian','select2d','hoverClosestCartesian','zoomIn2d','zoomOut2d','zoom2d'], responsive: true}
 
-        Plotly.newPlot('OverviewPredPlotly', Data, layout, config)
+        Plotly.newPlot('OverviewPredPlotly', traces, layout, config)
         this.selectedPointsOverview()
     },
+    UpdateScatterPlot () {
+      const XandYCoordinates = JSON.parse(this.UpdatedData[0])
+
+      Plotly.animate('OverviewPredPlotly', {
+        data: [
+          {x: XandYCoordinates[0], y: XandYCoordinates[1]}
+        ],
+        traces: [0],
+        layout: {}
+      }, {
+        transition: {
+          duration: 1000,
+          easing: 'cubic-in-out'
+        },
+        frame: {
+          duration: 1000
+        }
+      })
+      this.selectedPointsOverview()
+    },
     selectedPointsOverview () {
       const OverviewPlotly = document.getElementById('OverviewPredPlotly')
       OverviewPlotly.on('plotly_selected', function (evt) {
@@ -88,6 +134,8 @@ export default {
     },
   },
   mounted() {
+    EventBus.$on('updatePredictionsSpace', data => { this.UpdatedData = data })
+    EventBus.$on('updatePredictionsSpace', this.UpdateScatterPlot)
     EventBus.$on('emittedEventCallingPredictionsSpacePlotView', data => {
       this.PredictionsData = data})
     EventBus.$on('emittedEventCallingPredictionsSpacePlotView', this.ScatterPlotDataView)

frontend/src/components/Provenance.vue

@@ -26,7 +26,8 @@ export default {
         data: [],
         counter: 0,
         typeCounter: [],
-        typeColumnCounter: []
+        typeColumnCounter: [],
+        KNNModels: 576, //KNN models
 
     }
   },
@@ -38,12 +39,12 @@ export default {
 
       var flagKNN = 0
       var flagRF = 0
-
+      var StackInfo = JSON.parse(this.stackInformation[1])
       // Create a WebGL 2D platform on the canvas:
       var platform = Stardust.platform("webgl-2d", canvas, width, height);
-
-      for (let i = 0; i < this.stackInformation.length; i++) {
-        if (this.stackInformation[i] == 'KNN'){
+    
+      for (let i = 0; i < StackInfo.length; i++) {
+        if (StackInfo[i] < this.KNNModels){
           this.data.push({
             type:0, column:this.counter, height:height
           })
@@ -62,10 +63,12 @@ export default {
         this.typeCounter.push(0)
       }
       this.typeColumnCounter.push(0)
-
+      
   this.data.forEach(d => {
-    d.typeIndex = this.typeCounter[d.type]++;
-    d.typeColumnIndex = this.typeColumnCounter[d.column]++;
+    if (d.column == this.counter) {
+      d.typeIndex = this.typeCounter[d.type]++;
+      d.typeColumnIndex = this.typeColumnCounter[d.column]++;
+    }
   });
 
       // Convert the SVG file to Stardust mark spec.
@@ -80,7 +83,7 @@ export default {
 
       let pScale = Stardust.scale.custom(`
               Vector2(
-                  20 + column * 160 + typeColumnIndex % 5 * 8,
+                  20 + column * 100 + typeColumnIndex % 5 * 8,
                   height - 10 - floor(typeColumnIndex / 5) * 10
               )
           `);
@@ -110,7 +113,7 @@ export default {
       isotypes.data(this.data);
 
       isotypes.render();
-
+      this.counter = this.counter + 1
   }
   },
   mounted () {

frontend/src/components/ScatterPlot.vue

@@ -40,7 +40,8 @@ export default {
       parametersAll: [],
       length: 0,
       valueStackRemove: 'Remove from Stack',
-      DataPointsSelUpdate: []
+      DataPointsSelUpdate: [],
+      ModelsIDGray: []
     }
   },
   methods: {
@@ -50,36 +51,10 @@ export default {
       EventBus.$emit('RepresentationSelection', this.representationSelection)
     },
     RemoveStack () {
-      EventBus.$emit('PCPCallDB')
+      EventBus.$emit('RemoveFromStack')
     },
     ScatterPlotView () {
 
-      function isEquivalent(a, b) {
-        // Create arrays of property names
-        var aProps = Object.getOwnPropertyNames(a);
-        var bProps = Object.getOwnPropertyNames(b);
-
-        // If number of properties is different,
-        // objects are not equivalent
-        if (aProps.length != bProps.length) {
-            return false;
-        }
-
-        for (var i = 0; i < aProps.length; i++) {
-            var propName = aProps[i];
-
-            // If values of same property are not equal,
-            // objects are not equivalent
-            if (a[propName] !== b[propName]) {
-                return false;
-            }
-        }
-
-        // If we made it this far, objects
-        // are considered equivalent
-        return true;
-    }
-
       Plotly.purge('OverviewPlotly')
       var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
       var MDSData = JSON.parse(this.ScatterPlotResults[1])
@@ -106,6 +81,37 @@ export default {
         classifiersInfoProcessing[i] = 'Model ID: ' + modelId[i] + '; Details: ' + JSON.stringify(parameters[i])
       }
 
+      var listofNumbersModelsIDs = []
+      var StackModelsIDs = []
+      if (this.ModelsIDGray.length != 0) {
+        for (let j = 0; j < this.ModelsIDGray.length; j++){
+          listofNumbersModelsIDs.push(parseInt(this.ModelsIDGray[j].replace(/\D/g, "")))
+        }
+
+        var parametersNew = []
+        var MDSDataNewX = []
+        var MDSDataNewY = []
+        var colorsforScatterPlotNew = []
+        for (let i = 0; i < modelId.length; i++) {
+          if (listofNumbersModelsIDs.includes(modelId[i])) {
+          } else {
+            StackModelsIDs.push(modelId[i])
+            parametersNew.push(parameters[i])
+            colorsforScatterPlotNew.push(colorsforScatterPlot[i])
+            MDSDataNewX.push(MDSData[0][i])
+            MDSDataNewY.push(MDSData[1][i])
+          }
+        }
+        EventBus.$emit('sendPointsNumber', StackModelsIDs.length)
+        var classifiersInfoProcessing = []
+        for (let i = 0; i < StackModelsIDs.length; i++) {
+          classifiersInfoProcessing[i] = 'Model ID: ' + StackModelsIDs[i] + '; Details: ' + JSON.stringify(parametersNew[i])
+        }
+        MDSData[0] = MDSDataNewX
+        MDSData[1] = MDSDataNewY
+        colorsforScatterPlot = colorsforScatterPlotNew
+        EventBus.$emit('NewHeatmapAccordingtoNewStack', StackModelsIDs)
+      }
       var DataGeneral
       var layout
       if (this.representationDef == 'mds') {
@@ -249,6 +255,8 @@ export default {
     }
   },
   mounted() {
+    EventBus.$on('GrayOutPoints', data => { this.ModelsIDGray = data })
+    EventBus.$on('GrayOutPoints', this.ScatterPlotView)
     EventBus.$on('emittedEventCallingBrushedBoxPlot', data => {
       this.brushedBox = data})
     EventBus.$on('emittedEventCallingScatterPlot', data => {

run.py

@@ -575,6 +575,54 @@ def PreprocessingPred():
         predictions.append(el)
     return predictions
 
+def PreprocessingPredUpdate(Models):
+    Models = json.loads(Models)
+    ModelsList= []
+    for loop in Models['ClassifiersList']:
+        temp = [int(s) for s in re.findall(r'\b\d+\b', loop)]
+        ModelsList.append(temp[0])
+    dicKNN = json.loads(allParametersPerformancePerModel[7])
+    dicRF = json.loads(allParametersPerformancePerModel[15])
+    dfKNN = pd.DataFrame.from_dict(dicKNN)
+    dfKNN.index = dfKNN.index.astype(int)
+    dfKNNFiltered = dfKNN.loc[KNNModels, :]
+    dfRF = pd.DataFrame.from_dict(dicRF)
+    dfRF.index = dfRF.index.astype(int) + 576
+    dfRFFiltered = dfRF.loc[RFModels, :]
+    df_concatProbs = pd.concat([dfKNNFiltered, dfRFFiltered])
+    listProbs = df_concatProbs.index.values.tolist()
+    deletedElements = 0
+    for index, element in enumerate(listProbs):
+        if element in ModelsList:
+            index = index - deletedElements
+            df_concatProbs = df_concatProbs.drop(df_concatProbs.index[index])
+            deletedElements = deletedElements + 1
+    df_concatProbsCleared = df_concatProbs
+    listIDsRemaining = df_concatProbsCleared.index.values.tolist()
+
+    predictionsAll = PreprocessingPred()
+    PredictionSpaceAll = FunTsne(predictionsAll)
+
+    predictionsSel = []
+    for column, content in df_concatProbsCleared.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsSel.append(el)
+
+    PredictionSpaceSel = FunTsne(predictionsSel)
+
+    #ModelSpaceMDSNewComb = [list(a) for a in  zip(PredictionSpaceAll[0], ModelSpaceMDS[1])]
+
+    #ModelSpaceMDSNewSel = FunMDS(df_concatMetrics)
+    #ModelSpaceMDSNewSelComb = [list(a) for a in  zip(ModelSpaceMDSNewSel[0], ModelSpaceMDSNewSel[1])]
+
+    mtx2PredFinal = []
+    mtx1Pred, mtx2Pred, disparity2 = procrustes(PredictionSpaceAll, PredictionSpaceSel)
+
+    a1, b1 = zip(*mtx2Pred)
+    mtx2PredFinal.append(a1)
+    mtx2PredFinal.append(b1)
+    return [mtx2PredFinal,listIDsRemaining]
+
 def PreprocessingParam():
     dicKNN = json.loads(allParametersPerformancePerModel[1])
     dicRF = json.loads(allParametersPerformancePerModel[9])
@@ -733,6 +781,7 @@ def ReturnResults(ModelSpaceMDS,ModelSpaceTSNE,DataSpaceList,PredictionSpaceList
     FeatureAccuracy = FeatureAccuracy.to_json(orient='records')
     perm_imp_eli5PDCon = perm_imp_eli5PDCon.to_json(orient='records')
     featureScoresCon = featureScoresCon.to_json(orient='records')
+    XDataJSONEntireSet = XData.to_json(orient='records')
     XDataJSON = XData.columns.tolist()
 
     Results.append(json.dumps(sumPerClassifier)) # Position: 0 
@@ -749,6 +798,8 @@ def ReturnResults(ModelSpaceMDS,ModelSpaceTSNE,DataSpaceList,PredictionSpaceList
     Results.append(featureScoresCon) # Position: 11
     Results.append(json.dumps(ModelSpaceTSNE)) # Position: 12
     Results.append(json.dumps(ModelsIDs)) # Position: 13
+    Results.append(json.dumps(XDataJSONEntireSet)) # Position: 14
+    Results.append(json.dumps(yData)) # Position: 15
 
     return Results
 
@@ -764,12 +815,40 @@ def SendToPlot():
     }
     return jsonify(response)
 
+
+# Retrieve data from client 
+@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
+@app.route('/data/ServerRemoveFromStack', methods=["GET", "POST"])
+def RetrieveSelClassifiersIDandRemoveFromStack():
+    ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
+
+    PredictionProbSelUpdate = PreprocessingPredUpdate(ClassifierIDsList)
+
+    global resultsUpdatePredictionSpace
+    resultsUpdatePredictionSpace = []
+    resultsUpdatePredictionSpace.append(json.dumps(PredictionProbSelUpdate[0])) # Position: 0
+    resultsUpdatePredictionSpace.append(json.dumps(PredictionProbSelUpdate[1]))
+
+    key = 3
+    EnsembleModel(ClassifierIDsList, key)
+
+    return 'Everything Okay'
+
+# Sending the overview classifiers' results to be visualized as a scatterplot
+@app.route('/data/UpdatePredictionsSpace', methods=["GET", "POST"])
+def SendPredBacktobeUpdated():
+    response = {    
+        'UpdatePredictions': resultsUpdatePredictionSpace
+    }
+    return jsonify(response)
+
 # Retrieve data from client 
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
 @app.route('/data/ServerRequestSelPoin', methods=["GET", "POST"])
 def RetrieveSelClassifiersID():
     ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
     ComputeMetricsForSel(ClassifierIDsList)
+
     key = 1
     EnsembleModel(ClassifierIDsList, key)
     return 'Everything Okay'
@@ -909,6 +988,7 @@ def RetrieveSelDataPoints():
     ModelSpaceMDSNewComb = [list(a) for a in  zip(ModelSpaceMDS[0], ModelSpaceMDS[1])]
 
     ModelSpaceMDSNewSel = FunMDS(df_concatMetrics)
+
     ModelSpaceMDSNewSelComb = [list(a) for a in  zip(ModelSpaceMDSNewSel[0], ModelSpaceMDSNewSel[1])]
 
     global mt2xFinal
@@ -917,6 +997,7 @@ def RetrieveSelDataPoints():
     a, b = zip(*mtx2)
     mt2xFinal.append(a)
     mt2xFinal.append(b)
+
     return 'Everything Okay'
 
 
@@ -1029,7 +1110,6 @@ def EnsembleModel(Models, keyRetrieved):
     global all_classifiersSelection  
     all_classifiersSelection = []
 
-    sclf = 0
     lr = LogisticRegression()
 
     if (keyRetrieved == 0):
@@ -1055,6 +1135,9 @@ def EnsembleModel(Models, keyRetrieved):
         
         global sclfStack
         sclfStack = 0
+
+        global sclf 
+        sclf = 0
         sclf = StackingCVClassifier(classifiers=all_classifiers,
                             use_probas=True,
                             meta_classifier=lr,
@@ -1078,7 +1161,8 @@ def EnsembleModel(Models, keyRetrieved):
                             meta_classifier=lr,
                             random_state=RANDOM_SEED,
                             n_jobs = -1)
-    else: 
+    elif (keyRetrieved == 2):
+        # fix this part!
         if (len(all_classifiersSelection) == 0):
             all_classifiers = []
             columnsInit = []
@@ -1106,12 +1190,30 @@ def EnsembleModel(Models, keyRetrieved):
                 print(index)
                 print(featureSelection['featureSelection'][index+store])
                 all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][index+store]), RandomForestClassifier().set_params(**arg)))
-
+                
             sclf = StackingCVClassifier(classifiers=all_classifiers,
                                 use_probas=True,
                                 meta_classifier=lr,
                                 random_state=RANDOM_SEED,
                                 n_jobs = -1)
+    else: 
+        Models = json.loads(Models)
+        ModelsAll = preProceModels()
+        for index, modHere in enumerate(ModelsAll):
+            flag = 0
+            for loop in Models['ClassifiersList']:
+                temp = [int(s) for s in re.findall(r'\b\d+\b', loop)]
+                if (int(temp[0]) == int(modHere)):
+                    flag = 1
+            if (flag is 0):
+                all_classifiersSelection.append(all_classifiers[index])
+
+        sclfStack = StackingCVClassifier(classifiers=all_classifiersSelection,
+                            use_probas=True,
+                            meta_classifier=lr,
+                            random_state=RANDOM_SEED,
+                            n_jobs = -1)
+        
         #else:
         #    for index, eachelem in enumerate(algorithmsWithoutDuplicates):
         #        if (eachelem == 'KNN'):