fix only provenance

Former-commit-id: 3ae1d26727

frontend/src/components/DataSpace.vue

@@ -74,6 +74,7 @@ export default {
       responsiveWidthHeight: [],
       RetrieveDataSet: 'HeartC',
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
+      onlyOnce: true,
     }
   },
   methods: {
@@ -398,8 +399,10 @@ 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('OverviewDataPlotly', traces, layout, config)
-
-      this.selectedDataPoints()
+      if (this.onlyOnce) {
+        this.selectedDataPoints()
+      }
+      this.onlyOnce = false
     },
     selectedDataPoints () {
       const OverviewDataPlotly = document.getElementById('OverviewDataPlotly')
@@ -419,9 +422,10 @@ export default {
             }
           }
           if (ClassifierIDsList != '') {
+            EventBus.$emit('ChangeKey', 1)
             EventBus.$emit('SendSelectedPointsToServerEventfromData', ClassifierIDsListCleared)
           } else {
-            EventBus.$emit('SendSelectedPointsToServerEventfromData', '')
+            EventBus.$emit('ChangeKey', 0)
           }
         }
       })

frontend/src/components/FinalResultsLinePlot.vue

@@ -64,41 +64,41 @@ export default {
       this.scoresPositive.push(parseFloat(this.scoresMean[this.scoresMean.length - 1]) + parseFloat(this.scoresSTD[this.scoresSTD.length - 1]))
       this.scoresNegative.push(parseFloat(this.scoresMean[this.scoresMean.length - 1]) - parseFloat(this.scoresSTD[this.scoresSTD.length - 1]))
 
-      this.scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[2])*100).toFixed(2))
-      this.scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[3])*100).toFixed(2))
+      this.scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[4])*100).toFixed(2))
+      this.scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[5])*100).toFixed(2))
 
       this.scoresPositive2.push(parseFloat(this.scoresMean2[this.scoresMean2.length - 1]) + parseFloat(this.scoresSTD2[this.scoresSTD2.length - 1]))
       this.scoresNegative2.push(parseFloat(this.scoresMean2[this.scoresMean2.length - 1]) - parseFloat(this.scoresSTD2[this.scoresSTD2.length - 1]))
 
-      this.scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[4])*100).toFixed(2))
-      this.scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[5])*100).toFixed(2))
+      this.scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[8])*100).toFixed(2))
+      this.scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[9])*100).toFixed(2))
 
       this.scoresPositive3.push(parseFloat(this.scoresMean3[this.scoresMean3.length - 1]) + parseFloat(this.scoresSTD3[this.scoresSTD3.length - 1]))
       this.scoresNegative3.push(parseFloat(this.scoresMean3[this.scoresMean3.length - 1]) - parseFloat(this.scoresSTD3[this.scoresSTD3.length - 1]))
 
-      this.scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[6])*100).toFixed(2))
-      this.scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[7])*100).toFixed(2))
+      this.scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[12])*100).toFixed(2))
+      this.scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[13])*100).toFixed(2))
 
       this.scoresPositive4.push(parseFloat(this.scoresMean4[this.scoresMean4.length - 1]) + parseFloat(this.scoresSTD4[this.scoresSTD4.length - 1]))
       this.scoresNegative4.push(parseFloat(this.scoresMean4[this.scoresMean4.length - 1]) - parseFloat(this.scoresSTD4[this.scoresSTD4.length - 1]))
 
       // Stack
 
-      this.Stack_scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[8])*100).toFixed(2))
-      this.Stack_scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[9])*100).toFixed(2))
+      this.Stack_scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[2])*100).toFixed(2))
+      this.Stack_scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[3])*100).toFixed(2))
 
       this.Stack_scoresPositive.push(parseFloat(this.Stack_scoresMean[this.Stack_scoresMean.length - 1]) + parseFloat(this.Stack_scoresSTD[this.Stack_scoresSTD.length - 1]))
       this.Stack_scoresNegative.push(parseFloat(this.Stack_scoresMean[this.Stack_scoresMean.length - 1]) - parseFloat(this.Stack_scoresSTD[this.Stack_scoresSTD.length - 1]))
 
-      this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[10])*100).toFixed(2))
-      this.Stack_scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[11])*100).toFixed(2))
+      this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[6])*100).toFixed(2))
+      this.Stack_scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[7])*100).toFixed(2))
 
       this.Stack_scoresPositive2.push(parseFloat(this.Stack_scoresMean2[this.Stack_scoresMean2.length - 1]) + parseFloat(this.Stack_scoresSTD2[this.Stack_scoresSTD2.length - 1]))
       this.Stack_scoresNegative2.push(parseFloat(this.Stack_scoresMean2[this.Stack_scoresMean2.length - 1]) - parseFloat(this.Stack_scoresSTD2[this.Stack_scoresSTD2.length - 1]))
 
 
-      this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[12])*100).toFixed(2))
-      this.Stack_scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[13])*100).toFixed(2))
+      this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[10])*100).toFixed(2))
+      this.Stack_scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[11])*100).toFixed(2))
 
       this.Stack_scoresPositive3.push(parseFloat(this.Stack_scoresMean3[this.Stack_scoresMean3.length - 1]) + parseFloat(this.Stack_scoresSTD3[this.Stack_scoresSTD3.length - 1]))
       this.Stack_scoresNegative3.push(parseFloat(this.Stack_scoresMean3[this.Stack_scoresMean3.length - 1]) - parseFloat(this.Stack_scoresSTD3[this.Stack_scoresSTD3.length - 1]))

frontend/src/components/Main.vue

@@ -275,6 +275,8 @@ export default Vue.extend({
       provenanceData: '',
       localFile: '',
       toggleDeepMain: 1,
+      keyLoc: 0,
+      keyData: true,
     }
   },
   methods: {
@@ -324,13 +326,13 @@ export default Vue.extend({
           this.OverviewResults = response.data.OverviewResults
           console.log('Server successfully sent all the data related to visualizations!')
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults)
-          if (this.firstTimeFlag == 1) {
-            this.selectedModels_Stack.push(0)
-            this.selectedModels_Stack.push(JSON.stringify(this.modelsUpdate))
-            EventBus.$emit('ParametersProvenance', this.OverviewResults)
-            EventBus.$emit('InitializeProvenance', this.selectedModels_Stack)
-          }
-          this.firstTimeFlag = 0
+          // if (this.firstTimeFlag == 1) {
+          //   this.selectedModels_Stack.push(0)
+          //   this.selectedModels_Stack.push(JSON.stringify(this.modelsUpdate))
+          //   EventBus.$emit('ParametersProvenance', this.OverviewResults)
+          //   EventBus.$emit('InitializeProvenance', this.selectedModels_Stack)
+          // }
+          // this.firstTimeFlag = 0
           EventBus.$emit('InitializeMetricsBarChart', this.OverviewResults)
           this.valueSel = 0
           this.valueAll = 0
@@ -350,6 +352,13 @@ export default Vue.extend({
           EventBus.$emit('emittedEventCallingPredictionsSpacePlotView', this.OverviewResults)
           EventBus.$emit('emittedEventCallingBalanceView', this.OverviewResults)
           this.DataSpaceCall()
+          if (this.keyData) {
+            this.selectedModels_Stack.push(0)
+            this.selectedModels_Stack.push(JSON.stringify(this.modelsUpdate))
+            EventBus.$emit('ParametersProvenance', this.OverviewResults)
+            EventBus.$emit('InitializeProvenance', this.selectedModels_Stack)
+          }
+          this.getFinalResults()
         })
         .catch(error => {
           console.log(error)
@@ -407,7 +416,6 @@ export default Vue.extend({
         this.OverSelLength = 0
         EventBus.$emit('resetViews')
       } else {
-        console.log(this.keyNow)
         this.OverSelLength = this.ClassifierIDsList.length
         const path = `http://127.0.0.1:5000/data/ServerRequestSelPoin`
         const postData = {
@@ -428,7 +436,7 @@ export default Vue.extend({
             if (this.keyNow == 0) {
               this.OverAllLength = this.ClassifierIDsList.length
               EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
-            }
+            } 
             this.getSelectedModelsMetrics()
             this.getFinalResults()
           })
@@ -455,7 +463,6 @@ export default Vue.extend({
       console.log('Sent the selected points to the server (scatterplot)!')
       EventBus.$emit('updateFlagForFinalResults', 0)
       this.updatePredictionsSpace()
-      this.getFinalResults()
       })
       .catch(error => {
       console.log(error)
@@ -826,9 +833,10 @@ export default Vue.extend({
       EventBus.$emit('emittedEventCallingTogglesUpdate', toggles)
     },
     DataSpaceFun () {
+      this.keyData = false
       const path = `http://127.0.0.1:5000/data/SendDataSpacPoints`
       const postData = {
-        points: this.dataPointsSelfromDataSpace
+        points: this.dataPointsSelfromDataSpace,
       }
       const axiosConfig = {
         headers: {
@@ -961,6 +969,9 @@ export default Vue.extend({
     $(window).on("unload", function(e) {
       alert('Handler for .unload() called.');
     })
+
+    EventBus.$on('sendKeyNow', data => { this.keyLoc = data })
+
     EventBus.$on('ReturningBrushedPointsIDs',  data => { this.modelsUpdate = data })
     //EventBus.$on('ReturningBrushedPointsIDs',  this.UpdateBarChartFeatures )
     EventBus.$on('ConfirmDataSet', this.fileNameSend)

frontend/src/components/Provenance.vue

@@ -1,6 +1,6 @@
 <template>
     <div>
-        <div class="squares-container" style="min-height: 374px;">
+        <div class="squares-container" style="min-height: 374px; margin-left: 35px">
         <div id="tooltip"></div>	<!-- new  -->
           <div id="performanceCapture" style="min-height: 150px; margin-top: -10px !important;"></div>	<!-- new  -->
           <canvas id="main-canvas" style="overflow-y: auto; overflow-x: auto; height:190px;"></canvas>
@@ -53,7 +53,7 @@ export default {
       Stack_scoresMean2: [],
       Stack_scoresMean3: [],
       Stack_scoresMean4: [],
-      firstInside: 0
+      firstInside: 0,
     }
   },
   methods: {
@@ -346,7 +346,7 @@ export default {
         btnlocal.style.fontWeight = 'normal';
       });
 
-       $(document).on('click','.dynamic_buttons', function() {
+      $(document).on('click','.dynamic_buttons', function() {
         var btns = document.getElementsByClassName('dynamic_buttons')
 
         btns.forEach(btnlocal => {
@@ -356,9 +356,8 @@ export default {
         var btn = document.getElementById($(this).attr('id'));
         btn.style.fontWeight = 'bold';
 
-
-        EventBus.$emit('requestProven',parseInt($(this).attr('id').replace(/\D/g,''))-1)
         EventBus.$emit('ChangeKey', 0)
+        EventBus.$emit('requestProven',parseInt($(this).attr('id').replace(/\D/g,''))-1)
         }
       );
   },
@@ -376,17 +375,15 @@ export default {
     this.Stack_scoresMean3 = []
     this.Stack_scoresMean4 = []
 
-    this.Stack_scoresMean.push((JSON.parse(this.FinalResultsProv[0])*100).toFixed(0))
-    this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsProv[2])*100).toFixed(0))
-    this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsProv[4])*100).toFixed(0))
-    this.Stack_scoresMean4.push((JSON.parse(this.FinalResultsProv[6])*100).toFixed(0))
+    this.Stack_scoresMean.push((JSON.parse(this.FinalResultsProv[0])*100).toFixed(1))
+    this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsProv[4])*100).toFixed(1))
+    this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsProv[8])*100).toFixed(1))
+    this.Stack_scoresMean4.push((JSON.parse(this.FinalResultsProv[12])*100).toFixed(1))
 
-    const colorsSingle = ['#fc9272','#fb6a4a','#ef3b2c','#cb181d','#a50f15','#67000d']
+    const colorsSingle = ['#fdd0a2','#f16913','#7f2704']
 
-    var scaleColor = d3v5.scaleLinear()
-      .domain([0,100,5])
-      .range(colorsSingle)
-      .interpolate(d3v5.interpolateRgb); //interpolateHsl interpolateHcl interpolateRgb;
+    var scaleColor = d3v5.scaleSequential(d3v5.interpolateReds)
+    .domain([0, 100])
 
     var data = [
       {value: this.Stack_scoresMean, label: "Accuracy", color: scaleColor(this.Stack_scoresMean)},
@@ -395,7 +392,7 @@ export default {
       {value: this.Stack_scoresMean4, label: "F1 Score", color: scaleColor(this.Stack_scoresMean4)}
     ];
 
-    var svg = d3.select('#svg'+this.firstInside).attr('width', width).attr('height', width).style('margin-right', '25px');
+    var svg = d3.select('#svg'+this.firstInside).attr('width', width).attr('height', width).style('margin-right', '38px');
 
     var arcs = data.map(function (obj, i) {
         return d3.svg.arc().innerRadius(i * arcSize + innerRadius).outerRadius((i + 1) * arcSize - (width / 100) + innerRadius);
@@ -477,7 +474,10 @@ export default {
     });
   },
   updateExtraction () {
-    EventBus.$emit('SendSelectedPointsToServerEvent', this.storeData[this.flagUpdated])
+    if (this.flag) {
+      EventBus.$emit('SendSelectedPointsToServerEvent', this.storeData[this.flagUpdated])
+    }
+    this.flag = true
     
     var stringParameters = []
     var temp = 0

frontend/src/components/ScatterPlot.vue

@@ -62,7 +62,8 @@ export default {
       UMAPStore: [],
       keyLocal: 0,
       activeModels: 0,
-      pushModelsRemaining: []
+      pushModelsRemaining: [],
+      onlyOnce: true
     }
   },
   methods: {
@@ -382,10 +383,13 @@ export default {
       var scat = document.getElementById('OverviewPlotly')
       
       Plotly.newPlot(scat, DataGeneral, layout, config)
-
-      this.selectedPointsOverview()
+      if (this.onlyOnce) {
+        this.selectedPointsOverview()
+      }
+      this.onlyOnce = false
     },
     selectedPointsOverview () {
+      console.log('mpike')
       const OverviewPlotly = document.getElementById('OverviewPlotly')
       var allModels = JSON.parse(this.ScatterPlotResults[13])
       OverviewPlotly.on('plotly_selected', function (evt) {
@@ -416,7 +420,6 @@ export default {
             EventBus.$emit('SendSelectedPointsToBrushHeatmap', ClassifierIDsListCleared)
           } else {
             EventBus.$emit('ChangeKey', 1)
-            EventBus.$emit('SendSelectedPointsToServerEvent', '')
           }
         }
       })

run.py

@@ -67,6 +67,14 @@ cors = CORS(app, resources={r"/data/*": {"origins": "*"}})
 def Reset():
     global DataRawLength
     global DataResultsRaw
+    global previousState
+    previousState = []
+
+    global keySpecInternal
+    keySpecInternal = 1
+
+    global previousStateActive
+    previousStateActive = []
 
     global RANDOM_SEED
     RANDOM_SEED = 42
@@ -86,6 +94,9 @@ def Reset():
     global AdaBModelsCount
     global GradBModelsCount
 
+    global keyData
+    keyData = 0
+
     KNNModelsCount = 0
     SVCModelsCount = 576
     GausNBModelsCount = 736
@@ -168,12 +179,24 @@ def RetrieveFileName():
 
     fileName = request.get_data().decode('utf8').replace("'", '"')
 
+    global keySpecInternal
+    keySpecInternal = 1
+
     global RANDOM_SEED
     RANDOM_SEED = 42
 
+    global keyData
+    keyData = 0
+
     global XData
     XData = []
 
+    global previousState
+    previousState = []
+
+    global previousStateActive
+    previousStateActive = []
+
     global yData
     yData = []
 
@@ -1685,10 +1708,11 @@ def RetrieveSelClassifiersID():
     ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
     ComputeMetricsForSel(ClassifierIDsList)
     ClassifierIDCleaned = json.loads(ClassifierIDsList)
+    
+    global keySpecInternal
+    keySpecInternal = 1
+    keySpecInternal = ClassifierIDCleaned['keyNow']
 
-    global keySpec
-    keySpec = ClassifierIDCleaned['keyNow']
-    print(keySpec)
     EnsembleModel(ClassifierIDsList, 1)
     return 'Everything Okay'
 
@@ -2429,6 +2453,11 @@ def FeatureSelPerModel():
 
 def EnsembleModel(Models, keyRetrieved): 
     global scores
+    global previousState
+    global previousStateActive
+    global keySpec
+    global keySpecInternal
+    global keyData
     scores = []
 
     global all_classifiersSelection  
@@ -2581,9 +2610,6 @@ def EnsembleModel(Models, keyRetrieved):
             arg = dfParamGradBFilt[eachelem-GradBModelsCount]
             all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GradientBoostingClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
-        global sclfStack
-        sclfStack = 0
-
         global sclf 
         sclf = 0
         sclf = StackingCVClassifier(classifiers=all_classifiers,
@@ -2591,11 +2617,10 @@ def EnsembleModel(Models, keyRetrieved):
                             meta_classifier=lr,
                             random_state=RANDOM_SEED,
                             n_jobs = -1)
-        sclfStack = sclf
+        keySpec = 0
     elif (keyRetrieved == 1):     
         Models = json.loads(Models)
         ModelsAll = preProceModels()
-        global keySpec
         for index, modHere in enumerate(ModelsAll):
             flag = 0
             for loop in Models['ClassifiersList']:
@@ -2609,11 +2634,8 @@ def EnsembleModel(Models, keyRetrieved):
                             meta_classifier=lr,
                             random_state=RANDOM_SEED,
                             n_jobs = -1)
-        print('mpike')
-        if (keySpec == 0):
-            sclfStack = sclf
+        keySpec = 1
     elif (keyRetrieved == 2):
-        # fix this part!
         if (len(all_classifiersSelection) == 0):
             all_classifiers = []
             columnsInit = []
@@ -2769,22 +2791,25 @@ def EnsembleModel(Models, keyRetrieved):
                                 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']:
-                if (int(loop) == int(modHere)):
-                    flag = 1
-            if (flag is 1):
-                all_classifiersSelection.append(all_classifiers[index])
-
-        sclfStack = StackingCVClassifier(classifiers=all_classifiersSelection,
-                            use_probas=True,
-                            meta_classifier=lr,
-                            random_state=RANDOM_SEED,
-                            n_jobs = -1)
+            keySpec = 1
+    else:
+        keySpec = 2
+
+        # Models = json.loads(Models)
+        # ModelsAll = preProceModels()
+        # for index, modHere in enumerate(ModelsAll):
+        #     flag = 0
+        #     for loop in Models['ClassifiersList']:
+        #         if (int(loop) == int(modHere)):
+        #             flag = 1
+        #     if (flag is 1):
+        #         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:
@@ -2803,24 +2828,134 @@ def EnsembleModel(Models, keyRetrieved):
         #                        random_state=RANDOM_SEED,
         #                        n_jobs = -1)
 
-    if (keyRetrieved == 0):
-        pass
-    else:
+    # if (keyRetrieved == 0):
+    #     pass
+    # else:
+    print(keySpec)
+    print(keySpecInternal)
+    if (keySpec == 0 or keySpec == 1):
         num_cores = multiprocessing.cpu_count()
-        inputsSc = ['accuracy','precision_weighted','recall_weighted','accuracy','precision_weighted','recall_weighted','f1_weighted','f1_weighted']
-        flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,sclfStack,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
+        inputsSc = ['accuracy','precision_weighted','recall_weighted','f1_weighted']
+
+        flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
         scores = [item for sublist in flat_results for item in sublist]
 
+    if (keySpec == 0):
+        previousState = []
+        previousState.append(scores[2])
+        previousState.append(scores[3])
+        previousState.append(scores[6])
+        previousState.append(scores[7])
+        previousState.append(scores[10])
+        previousState.append(scores[11])
+        previousState.append(scores[14])
+        previousState.append(scores[15])
+    elif (keySpec == 1):
+        if (keySpecInternal == 1):
+            previousStateActive = []
+            previousStateActive.append(scores[0])
+            previousStateActive.append(scores[1])
+            previousStateActive.append(scores[4])
+            previousStateActive.append(scores[5])
+            previousStateActive.append(scores[8])
+            previousStateActive.append(scores[9])
+            previousStateActive.append(scores[12])
+            previousStateActive.append(scores[13])
+        else:
+            previousStateActive = []
+            previousStateActive.append(scores[0])
+            previousStateActive.append(scores[1])
+            previousStateActive.append(scores[4])
+            previousStateActive.append(scores[5])
+            previousStateActive.append(scores[8])
+            previousStateActive.append(scores[9])
+            previousStateActive.append(scores[12])
+            previousStateActive.append(scores[13])
+            previousState = []
+            previousState.append(scores[2])
+            previousState.append(scores[3])
+            previousState.append(scores[6])
+            previousState.append(scores[7])
+            previousState.append(scores[10])
+            previousState.append(scores[11])
+            previousState.append(scores[14])
+            previousState.append(scores[15])
+    else:
+        scores = []
+        previousState = []
+        scores.append(previousStateActive[0])
+        scores.append(previousStateActive[1])
+        scores.append(previousStateActive[0])
+        scores.append(previousStateActive[1])
+        previousState.append(previousStateActive[0])
+        previousState.append(previousStateActive[1])
+        scores.append(previousStateActive[2])
+        scores.append(previousStateActive[3])
+        scores.append(previousStateActive[2])
+        scores.append(previousStateActive[3])
+        previousState.append(previousStateActive[2])
+        previousState.append(previousStateActive[3])
+        scores.append(previousStateActive[4])
+        scores.append(previousStateActive[5])
+        scores.append(previousStateActive[4])
+        scores.append(previousStateActive[5])
+        previousState.append(previousStateActive[4])
+        previousState.append(previousStateActive[5])
+        scores.append(previousStateActive[6])
+        scores.append(previousStateActive[7])
+        scores.append(previousStateActive[6])
+        scores.append(previousStateActive[7])
+        previousState.append(previousStateActive[6])
+        previousState.append(previousStateActive[7])
+
     return 'Okay'
 
-def solve(sclf,sclfStack,XData,yData,crossValidation,scoringIn,loop):
+def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop):
     scoresLoc = []
-    if (loop < 3):
+    if (keySpec == 0):
         temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+        scoresLoc.append(temp.mean())
+        scoresLoc.append(temp.std())
+        if (keyData == 1):
+            if (loop == 0):
+                scoresLoc.append(previousStateLoc[0])
+                scoresLoc.append(previousStateLoc[1])
+            elif (loop == 1):
+                scoresLoc.append(previousStateLoc[2])
+                scoresLoc.append(previousStateLoc[3])
+            elif (loop == 2):
+                scoresLoc.append(previousStateLoc[4])
+                scoresLoc.append(previousStateLoc[5])
+            else:
+                scoresLoc.append(previousStateLoc[6])
+                scoresLoc.append(previousStateLoc[7])
+        else:
+            scoresLoc.append(temp.mean())
+            scoresLoc.append(temp.std())
     else:
-        temp = model_selection.cross_val_score(sclfStack, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
-    scoresLoc.append(temp.mean())
-    scoresLoc.append(temp.std())
+        if (keySpecInternal == 1):
+            temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+            scoresLoc.append(temp.mean())
+            scoresLoc.append(temp.std())
+            if (loop == 0):
+                scoresLoc.append(previousStateLoc[0])
+                scoresLoc.append(previousStateLoc[1])
+            elif (loop == 1):
+                scoresLoc.append(previousStateLoc[2])
+                scoresLoc.append(previousStateLoc[3])
+            elif (loop == 2):
+                scoresLoc.append(previousStateLoc[4])
+                scoresLoc.append(previousStateLoc[5])
+            else:
+                scoresLoc.append(previousStateLoc[6])
+                scoresLoc.append(previousStateLoc[7])
+        else:
+            temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+            scoresLoc.append(temp.mean())
+            scoresLoc.append(temp.std())
+            scoresLoc.append(temp.mean())
+            scoresLoc.append(temp.std())
+
     return scoresLoc
 
 # Sending the final results to be visualized as a line plot
@@ -2856,6 +2991,8 @@ def RetrieveFilter():
 def RetrieveDataSpacePoints():
     dataSpacePoints = request.get_data().decode('utf8').replace("'", '"')
     dataSpacePointsCleared = json.loads(dataSpacePoints)
+    global keyData
+    keyData = 1
     global dataSpacePointsIDs
     dataSpacePointsIDs = dataSpacePointsCleared['points']
     return 'Done'