fixed first views

frontend/src/components/Ensemble.vue

@@ -287,25 +287,25 @@ export default {
         var allModels = JSON.parse(this.ScatterPlotResults[0])
         OverviewPlotly.on('plotly_selected', function (evt) {
           if (typeof evt !== 'undefined') {
-            var pushModelsRemainingTemp = []
-            const ClassifierIDsList = []
+            var pushModelsRemainingTempCM = []
+            const ClassifierIDsListCM = []
             for (let i = 0; evt.points.length; i++) {
               if (evt.points[i] === undefined) {
                 break
               } else {
                 const OnlyId = evt.points[i].text.split(' ')[2]
                 const OnlyIdCleared = OnlyId.split('<br>')
-                ClassifierIDsList.push(OnlyIdCleared[0])
+                ClassifierIDsListCM.push(OnlyIdCleared[0])
               }
             }
             for (let i = 0; i < allModels.length; i++) {
-              if (ClassifierIDsList.indexOf((allModels[i])) < 0) {
-                pushModelsRemainingTemp.push(allModels[i])
+              if (ClassifierIDsListCM.indexOf((allModels[i])) < 0) {
+                pushModelsRemainingTempCM.push(allModels[i])
               }
             }
-              EventBus.$emit('RemainingPointsCM', pushModelsRemainingTemp)
-              EventBus.$emit('SendSelectedPointsUpdateIndicatorCM', ClassifierIDsList)
-              EventBus.$emit('SendSelectedPointsToServerEventCM', ClassifierIDsList)
+              EventBus.$emit('RemainingPointsCM', pushModelsRemainingTempCM)
+              EventBus.$emit('SendSelectedPointsUpdateIndicatorCM', ClassifierIDsListCM)
+              EventBus.$emit('SendSelectedPointsToServerEventCM', ClassifierIDsListCM)
           }
         })
       },

frontend/src/components/Main.vue

@@ -144,7 +144,7 @@
             <b-row class="md-3">
               <b-col cols="6">
                 <mdb-card style="margin-top: 15px;">
-                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Hyper-Parameters Predictions
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Hyper-Parameters' Predictions
                     </mdb-card-header>
                     <mdb-card-body>
                       <mdb-card-text class="text-center"  style="min-height: 270px">
@@ -155,7 +155,7 @@
               </b-col>
               <b-col cols="6">
                 <mdb-card style="margin-top: 15px;">
-                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Majority-Voting Ensemble Predictions
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Majority-Voting Ensemble's Predictions
                     </mdb-card-header>
                     <mdb-card-body>
                       <mdb-card-text class="text-center"  style="min-height: 270px">   
@@ -217,8 +217,10 @@ export default Vue.extend({
   data () {
     return {
       storeEnsemble: [],
+      PredictSelEnsem: [],
       firstTimeExec: true,
       unselectedRemainingPoints: [],
+      unselectedRemainingPointsEnsem: [],
       Collection: 0,
       OverviewResults: 0,
       preDataResults: '',
@@ -321,6 +323,8 @@ export default Vue.extend({
             EventBus.$emit('emittedEventCallingGridSelection', this.OverviewResults)
             this.firstTimeExec = false
           } else {
+            this.PredictSelEnsem = []
+            EventBus.$emit('SendSelectedPointsToServerEventCM', this.PredictSelEnsem)
             EventBus.$emit('emittedEventCallingCrossoverMutation', this.OverviewResults)
             EventBus.$emit('emittedEventCallingGridCrossoverMutation', this.OverviewResults)
             EventBus.$emit('emittedEventCallingGridSelectionCrossoverMutation', this.OverviewResults)
@@ -346,6 +350,8 @@ export default Vue.extend({
         .then(response => {
           this.OverviewResultsCM = response.data.OverviewResultsCM
           console.log('Server successfully sent all the data related to visualizations!')
+          this.PredictSel = []
+          EventBus.$emit('SendSelectedPointsToServerEvent', this.PredictSel)
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResultsCM)
           EventBus.$emit('emittedEventCallingGrid', this.OverviewResultsCM)
           EventBus.$emit('emittedEventCallingGridSelection', this.OverviewResultsCM)
@@ -423,8 +429,52 @@ export default Vue.extend({
           console.log(error)
         })
     },
+    SendSelectedIDsEnsemble () {
+      const path = `http://127.0.0.1:5000/data/SendtoSeverSelIDsEnsem`
+      const postData = {
+        predictSelectionIDsCM: this.ClassifierIDsListCM
+      }
+      const axiosConfig = {
+      headers: {
+      'Content-Type': 'application/json',
+      'Access-Control-Allow-Origin': '*',
+      'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
+      'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
+      }
+      }
+      axios.post(path, postData, axiosConfig)
+      .then(response => {
+        console.log('Sent the selected IDs to compute predictions!')
+        this.retrievePredictionsSelEnsemble()
+      })
+      .catch(error => {
+      console.log(error)
+      }) 
+    },
+    retrievePredictionsSelEnsemble () {
+      const path = `http://localhost:5000/data/RetrievePredictionsEnsem`
+
+      const axiosConfig = {
+        headers: {
+          'Content-Type': 'application/json',
+          'Access-Control-Allow-Origin': '*',
+          'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
+          'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
+        }
+      }
+      axios.get(path, axiosConfig)
+        .then(response => {
+          this.PredictSelEnsem = response.data.PredictSelEnsem
+          console.log('Server successfully sent the predictions!')
+          EventBus.$emit('SendSelectedPointsToServerEventCM', this.PredictSelEnsem)
+        })
+        .catch(error => {
+          console.log(error)
+        })
+    },
     SelectedPointsCM () {
       this.OverSelLengthCM = this.ClassifierIDsListCM.length
+      this.SendSelectedIDsEnsemble()
     },
     SendSelectedPointsToServer () {
       if (this.ClassifierIDsList === ''){
@@ -819,8 +869,11 @@ export default Vue.extend({
       
       EventBus.$emit('SendStoredEnsemble', this.storeEnsemble)
 
+      var mergedStoreEnsembleLoc = [].concat.apply([], this.storeEnsemble)
+      
       const postData = {
-        RemainingPoints: this.unselectedRemainingPoints
+        RemainingPoints: this.unselectedRemainingPoints,
+        StoreEnsemble: mergedStoreEnsembleLoc
       }
       const axiosConfig = {
         headers: {
@@ -885,6 +938,8 @@ export default Vue.extend({
     EventBus.$on('RemainingPoints', data => { this.unselectedRemainingPoints = data })
     EventBus.$on('InitializeCrossoverMutation', this.sendPointsCrossMutat)
 
+    EventBus.$on('RemainingPointsCM', data => { this.unselectedRemainingPointsEnsem = data })
+
     EventBus.$on('ChangeKey', data => { this.keyNow = data })
     EventBus.$on('SendSelectedPointsUpdateIndicator', data => { this.ClassifierIDsList = data; this.storeEnsemble.push(this.ClassifierIDsList)})
     EventBus.$on('SendSelectedPointsUpdateIndicator', this.SelectedPoints)

frontend/src/components/Predictions.vue

@@ -61,8 +61,6 @@ export default {
       var KNNPred = predictions[0]
       var LRPred = predictions[1]
       var PredAver = predictions[2]
-      console.log(PredAver)
-
       var dataAver = []
       var dataAverGetResults = []
       var dataKNN = []
@@ -125,7 +123,7 @@ export default {
     }
     
     var classStore = [].concat.apply([], classArray);
-    console.log(classStore)
+
 		// === Set up canvas === //
 
 		var width = 1200,

run.py

@@ -76,6 +76,9 @@ def reset():
     global yData
     yData = []
 
+    global EnsembleActive
+    EnsembleActive = []
+
     global addKNN
     addKNN = 0
 
@@ -206,6 +209,9 @@ def retrieveFileName():
     global detailsParams
     detailsParams = []
     
+    global EnsembleActive
+    EnsembleActive = []
+
     global addKNN
     addKNN = 0
 
@@ -652,6 +658,53 @@ def PreprocessingPred():
 
     return [predictionsKNN, predictionsLR, predictions]
 
+def PreprocessingPredEnsemble():
+
+    global EnsembleActive
+
+    numberIDKNN = []
+    numberIDLR = []
+    for el in EnsembleActive:
+        match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
+        if match:
+            items = match.groups()
+            if (items[0] == 'KNN'):
+                numberIDKNN.append(int(items[1]))
+            else:
+                numberIDLR.append(int(items[1]))
+
+    dicKNN = allParametersPerformancePerModel[3]
+    dicLR = allParametersPerformancePerModel[7]
+
+    dfKNN = pd.DataFrame.from_dict(dicKNN)
+    dfLR = pd.DataFrame.from_dict(dicLR)
+    df_concatProbs = pd.concat([dfKNN, dfLR])
+    df_concatProbs = df_concatProbs.reset_index(drop=True)
+
+    dfKNN = df_concatProbs.loc[numberIDKNN]
+    dfLR = df_concatProbs.loc[numberIDLR]
+
+    df_concatProbs = pd.DataFrame()
+    df_concatProbs = df_concatProbs.iloc[0:0]
+    df_concatProbs = pd.concat([dfKNN, dfLR])
+
+    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)
+
+    predictions = []
+    for column, content in df_concatProbs.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictions.append(el)
+
+    return [predictionsKNN, predictionsLR, predictions]
+
 def PreprocessingParam():
     dicKNN = allParametersPerformancePerModel[1]
     dicLR = allParametersPerformancePerModel[5]
@@ -765,6 +818,7 @@ def InitializeEnsemble():
     global ModelSpaceMDS
     global ModelSpaceTSNE
     global allParametersPerformancePerModel
+    global EnsembleActive
 
     XModels = XModels.fillna(0)
 
@@ -773,7 +827,10 @@ def InitializeEnsemble():
     ModelSpaceTSNE = ModelSpaceTSNE.tolist()
     ModelSpaceUMAP = FunUMAP(XModels)
 
-    PredictionProbSel = PreprocessingPred()
+    if (len(EnsembleActive) == 0):
+        PredictionProbSel = PreprocessingPred()
+    else:
+        PredictionProbSel = PreprocessingPredEnsemble()
 
     returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel)
 
@@ -820,6 +877,13 @@ def CrossoverMutateFun():
 
     RemainingIds = RemainingIds['RemainingPoints']
     
+    global EnsembleActive
+
+    EnsembleActive = request.get_data().decode('utf8').replace("'", '"')
+    EnsembleActive = json.loads(EnsembleActive)
+
+    EnsembleActive = EnsembleActive['StoreEnsemble']
+    
     global XData
     global yData
     global LRModelsCount
@@ -1377,28 +1441,25 @@ def PreprocessingPredSel(SelectedIDs):
 
     numberIDKNN = []
     numberIDLR = []
-    print(SelectedIDs)
     for el in SelectedIDs:
         match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
         if match:
             items = match.groups()
             if (items[0] == 'KNN'):
-                numberIDKNN.append(int(items[1]) - addKNN)
+                numberIDKNN.append(int(items[1]) -addKNN)
             else:
                 numberIDLR.append(int(items[1]) - addLR)
-    print(numberIDKNN)
+
     dicKNN = allParametersPerformancePerModel[3]
     dicLR = allParametersPerformancePerModel[7]
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
-    print(dfKNN)
+
     dfKNN = dfKNN.loc[numberIDKNN]
     dfLR = pd.DataFrame.from_dict(dicLR)
     dfLR = dfLR.loc[numberIDLR]
-    print(dfLR)
     dfLR.index += addKNN
     df_concatProbs = pd.concat([dfKNN, dfLR])
-    print(df_concatProbs)
 
     predictionsKNN = []
     for column, content in dfKNN.items():
@@ -1437,3 +1498,70 @@ def SendPredictSel():
         'PredictSel': ResultsSelPred
     }
     return jsonify(response)
+
+def PreprocessingPredSelEnsem(SelectedIDsEnsem):
+
+    numberIDKNN = []
+    numberIDLR = []
+    for el in SelectedIDsEnsem:
+        match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
+        if match:
+            items = match.groups()
+            if (items[0] == 'KNN'):
+                numberIDKNN.append(int(items[1]))
+            else:
+                numberIDLR.append(int(items[1]))
+
+    dicKNN = allParametersPerformancePerModel[3]
+    dicLR = allParametersPerformancePerModel[7]
+
+    dfKNN = pd.DataFrame.from_dict(dicKNN)
+    dfLR = pd.DataFrame.from_dict(dicLR)
+    df_concatProbs = pd.concat([dfKNN, dfLR])
+    df_concatProbs = df_concatProbs.reset_index(drop=True)
+
+    dfKNN = df_concatProbs.loc[numberIDKNN]
+
+    dfLR = df_concatProbs.loc[numberIDLR]
+
+    df_concatProbs = pd.DataFrame()
+    df_concatProbs = df_concatProbs.iloc[0:0]
+    df_concatProbs = pd.concat([dfKNN, dfLR])
+
+    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)
+
+    predictions = []
+    for column, content in df_concatProbs.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictions.append(el)
+
+    return [predictionsKNN, predictionsLR, predictions]
+
+@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
+@app.route('/data/SendtoSeverSelIDsEnsem', methods=["GET", "POST"])
+def RetrieveSelIDsPredictEnsem():
+    global ResultsSelPredEnsem
+    ResultsSelPredEnsem = []
+    RetrieveIDsSelectionEnsem = request.get_data().decode('utf8').replace("'", '"')
+    RetrieveIDsSelectionEnsem = json.loads(RetrieveIDsSelectionEnsem)
+    RetrieveIDsSelectionEnsem = RetrieveIDsSelectionEnsem['predictSelectionIDsCM']
+    
+    ResultsSelPredEnsem = PreprocessingPredSelEnsem(RetrieveIDsSelectionEnsem)
+
+    return 'Everything Okay'
+
+@app.route('/data/RetrievePredictionsEnsem', methods=["GET", "POST"])
+def SendPredictSelEnsem():
+    global ResultsSelPredEnsem
+    response = {    
+        'PredictSelEnsem': ResultsSelPredEnsem
+    }
+    return jsonify(response)