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cachedir/joblib/run/randomSearch/06d95882b621744b1f98a9e4a74c2c74/metadata.json

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cachedir/joblib/run/randomSearch/394795b7c0f42fea4a08559c442933ec/metadata.json

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cachedir/joblib/run/randomSearch/3d0c7e83f7046123bbd56793b15c3834/metadata.json

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cachedir/joblib/run/randomSearch/41eb9e04ff245b2484996e93dbd27cc5/metadata.json

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cachedir/joblib/run/randomSearch/450e1077322e56cfc3517d46fc567126/metadata.json

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cachedir/joblib/run/randomSearch/47f8386eab2730da77a11b2a4f698ad7/metadata.json

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cachedir/joblib/run/randomSearch/5701f474535efb82abf5085f37a3d5f5/metadata.json

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cachedir/joblib/run/randomSearch/9c9dc8dad506f66ed9749d93d63f3644/metadata.json

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cachedir/joblib/run/randomSearch/c0b6ae93ae3358140cb42192f5140723/metadata.json

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cachedir/joblib/run/randomSearch/c4575447d0f5391b949d4431042dc0d4/metadata.json

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cachedir/joblib/run/randomSearch/d22e03918cbc8389dcdfdb56753a304a/metadata.json

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cachedir/joblib/run/randomSearch/d9441100431531e5a3447a697adb8ea6/metadata.json

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cachedir/joblib/run/randomSearch/e08b33eb170c53ac03d119cab9b2100f/metadata.json

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cachedir/joblib/run/randomSearch/f1525731493793574c4bab01f5bc906f/metadata.json

@@ -0,0 +1 @@
+{"duration": 20.35997724533081, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='brute', metric='euclidean', n_neighbors=72,\n                     weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0", "crossValidation": "10", "randomSear": "200"}}

cachedir/joblib/run/randomSearch/ff5710335de9f50465330e3ca239e4af/metadata.json

@@ -0,0 +1 @@
+{"duration": 174.78210496902466, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "GradientBoostingClassifier(learning_rate=0.34, loss='exponential',\n                           n_estimators=63, random_state=42,\n                           subsample=0.7000000000000001)", "params": "{'n_estimators': [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'loss': ['deviance', 'exponential'], 'learning_rate': [0.01, 0.12, 0.23, 0.34, 0.45], 'subsample': [0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6, 0.7000000000000001, 0.8, 0.9], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "600", "crossValidation": "10", "randomSear": "150"}}

cachedir/joblib/run/randomSearch/func_code.py

@@ -1,6 +1,7 @@
 # first line: 728
 @memory.cache
 def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation,randomSear):
+    print('search')
     print(clf)
     search = RandomizedSearchCV(    
         estimator=clf, param_distributions=params, n_iter=randomSear,

frontend/src/components/Ensemble.vue

@@ -328,6 +328,7 @@ export default {
                 pushModelsRemainingTempCM.push(allModels[i])
               }
             }
+            console.log(ClassifierIDsListCM)
               EventBus.$emit('RemainingPointsCM', pushModelsRemainingTempCM)
               EventBus.$emit('callValidationData', ResultsAll)
               EventBus.$emit('SendSelectedPointsUpdateIndicatorCM', ClassifierIDsListCM)

frontend/src/components/History.vue

@@ -23,6 +23,7 @@ export default {
       PerFCM: [],
       storedEnsem: [],
       storedCM: [],
+      previouslyIDs: [],
       percentageOverall: [],
       values: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0],
       valuesStage2: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0],
@@ -39,16 +40,85 @@ export default {
       svgLeg.selectAll("*").remove();
     },
     computePerformanceDiffS () {
-      var colorsforScatterPlot = this.PerF
+
-      var mergedStoreEnsembleLoc = [].concat.apply([], this.storedEnsem)
+      var max = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-      var mergedStoreEnsembleLocFormatted = []
+      var min = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-      for (let i = 0; i < mergedStoreEnsembleLoc.length; i++) {
+      var tempDataKNNC = []
-        mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[i].replace(/\D/g,'')))
+      var tempDataLRC = []
+      var tempDataMLPC = []
+      var tempDataRFC = []
+      var tempDataGradBC = []
+      var tempDataKNNM = []
+      var tempDataLRM = []
+      var tempDataMLPM = []
+      var tempDataRFM = []
+      var tempDataGradBM = []
+      var splitData = []
+      console.log(this.previouslyIDs)
+      for (let i = 0; i < this.previouslyIDs.length; i++) {
+        let tempSplit = this.previouslyIDs[i].split(/([0-9]+)/)
+        if (tempSplit[0] == 'KNNC') {
+          tempDataKNNC.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'LRC') {
+          tempDataLRC.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'MLPC') {
+          tempDataMLPC.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'RFC') {
+          tempDataRFC.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'GradBC') {
+          tempDataGradBC.push(this.previouslyIDs[i])
+        } else if (tempSplit[0] == 'KNNM') {
+          tempDataKNNM.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'LRM') {
+          tempDataLRM.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'MLPM') {
+          tempDataMLPM.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'RFM') {
+          tempDataRFM.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'GradBM') {
+          tempDataGradBM.push(this.previouslyIDs[i])
+        }
+        else {
+        }
       }
-      
+      splitData.push(tempDataKNNC)
-      colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
+      splitData.push(tempDataLRC)
-      var max = Math.max.apply(Math, colorsforScatterPlot)
+      splitData.push(tempDataMLPC)
-      var min = Math.min.apply(Math, colorsforScatterPlot)
+      splitData.push(tempDataRFC)
+      splitData.push(tempDataGradBC)
+      splitData.push(tempDataKNNM)
+      splitData.push(tempDataLRM)
+      splitData.push(tempDataMLPM)
+      splitData.push(tempDataRFM)
+      splitData.push(tempDataGradBM)
+
+      for (let i = 0; i < splitData.length; i++) {
+        var colorsforScatterPlot = this.PerF
+        if (splitData[i].length != 0) {
+          var mergedStoreEnsembleLoc = [].concat.apply([], splitData[i])
+          var mergedStoreEnsembleLocFormatted = []
+          for (let j = 0; j < mergedStoreEnsembleLoc.length; j++) {
+            mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[j].replace(/\D/g,'')))
+          }
+
+        colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
+
+        max[i] = Math.max.apply(Math, colorsforScatterPlot)
+        min[i] = Math.min.apply(Math, colorsforScatterPlot)
+        }
+        
+      } 
+
+      console.log(max)
+      console.log(min)
 
       var countMax = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
       var countMin = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
@@ -57,199 +127,200 @@ export default {
         let tempSplit = this.storedCM[i].split(/([0-9]+)/)
 
         if (tempSplit[0] == 'KNNCC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[0]) {
             countMax[0] = countMax[0] + 1
-          } else if (this.PerFCM[i] < min) {
+          } else if (this.PerFCM[i] < min[0]) {
             countMin[0] = countMin[0] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'KNNCM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[0]) {
             countMax[1] = countMax[1] + 1
-          } else if (this.PerFCM[i] < min) {
+          } else if (this.PerFCM[i] < min[0]) {
             countMin[1] = countMin[1] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'LRCC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[1]) {
             countMax[2] = countMax[2] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[1]) {
             countMin[2] = countMin[2] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'LRCM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[1]) {
             countMax[3] = countMax[3] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[1]) {
             countMin[3] = countMin[3] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'MLPCC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[2]) {
             countMax[4] = countMax[4] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[2]) {
             countMin[4] = countMin[4] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'MLPCM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[2]) {
             countMax[5] = countMax[5] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[2]) {
             countMin[5] = countMin[5] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'RFCC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[3]) {
             countMax[6] = countMax[6] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[3]) {
             countMin[6] = countMin[6] + 1
           }
         }
         else if (tempSplit[0] == 'RFCM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[3]) {
             countMax[7] = countMax[7] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[3]) {
             countMin[7] = countMin[7] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'GradBCC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[4]) {
             countMax[8] = countMax[8] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[4]) {
             countMin[8] = countMin[8] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'GradBCM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[4]) {
             countMax[9] = countMax[9] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[4]) {
             countMin[9] = countMin[9] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'KNNMC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[5]) {
             countMax[10] = countMax[10] + 1
-          } else if (this.PerFCM[i] < min) {
+          } else if (this.PerFCM[i] < min[5]) {
             countMin[10] = countMin[10] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'KNNMM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[5]) {
             countMax[11] = countMax[11] + 1
-          } else if (this.PerFCM[i] < min) {
+          } else if (this.PerFCM[i] < min[5]) {
             countMin[11] = countMin[11] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'LRMC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[6]) {
             countMax[12] = countMax[12] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[6]) {
             countMin[12] = countMin[12] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'LRMM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[6]) {
             countMax[13] = countMax[13] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[6]) {
             countMin[13] = countMin[13] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'MLPMC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[7]) {
             countMax[14] = countMax[14] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[7]) {
             countMin[14] = countMin[14] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'MLPMM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[7]) {
             countMax[15] = countMax[15] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[7]) {
             countMin[15] = countMin[15] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'RFMC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[8]) {
             countMax[16] = countMax[16] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[8]) {
             countMin[16] = countMin[16] + 1
           }
         }
         else if (tempSplit[0] == 'RFMM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[8]) {
             countMax[17] = countMax[17] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[8]) {
             countMin[17] = countMin[17] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'GradBMC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[9]) {
             countMax[18] = countMax[18] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[9]) {
             countMin[18] = countMin[18] + 1
           } else {
             continue
           }
         }
         else {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[9]) {
             countMax[19] = countMax[19] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[9]) {
             countMin[19] = countMin[19] + 1
           } else {
             continue
           }
         }
       }
-
+      console.log(countMax)
+      console.log(countMin)
       // var percentage = []
       // for (let j = 0; j < countMax.length; j++) {
       //   if (j >= 15) {
@@ -279,8 +350,7 @@ export default {
       //   }
       // }
 //CORRECT
-console.log(countMax)
+
-console.log(countMin)
       var percentage = []
       for (let j = 0; j < countMax.length; j++) {
         if (j >= 15) {
@@ -314,7 +384,7 @@ console.log(countMin)
     },
     SankeyViewStage3 () {
       var valuesLoc = this.valuesStage2
-      console.log(valuesLoc)
+
       var localStep = 2
       var numberofModels = 6
       var units = "Models";
@@ -440,9 +510,9 @@ console.log(countMin)
       var colorDiff
       colorDiff = d3v5.scaleSequential(d3v5.interpolatePRGn).domain([-100, 100])
       var percentage = this.percentageOverall
-      console.log(percentage)
+
       var previousPercentage = this.storePreviousPercentage
-      console.log(previousPercentage)
+
       // add in the links
         var link = svg.append("g").selectAll(".link")
             .data(graph.links)
@@ -630,19 +700,60 @@ console.log(countMin)
 
     },
     computePerformanceDiff () {
-      var colorsforScatterPlot = this.PerF
 
-      var mergedStoreEnsembleLoc = [].concat.apply([], this.storedEnsem)
+      var max = [0, 0, 0, 0, 0]
-      var mergedStoreEnsembleLocFormatted = []
+      var min = [0, 0, 0, 0, 0]
-      for (let i = 0; i < mergedStoreEnsembleLoc.length; i++) {
+      var tempDataKNN = []
-        mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[i].replace(/\D/g,'')))
+      var tempDataLR = []
+      var tempDataMLP = []
+      var tempDataRF = []
+      var tempDataGradB = []
+      var splitData = []
+
+      for (let i = 0; i < this.previouslyIDs.length; i++) {
+        let tempSplit = this.previouslyIDs[i].split(/([0-9]+)/)
+        if (tempSplit[0] == 'KNN') {
+          tempDataKNN.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'LR') {
+          tempDataLR.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'MLP') {
+          tempDataMLP.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'RF') {
+          tempDataRF.push(this.previouslyIDs[i])
+        }
+        else if (tempSplit[0] == 'GradB') {
+          tempDataGradB.push(this.previouslyIDs[i])
+        }
+        else {
+        }
       }
-      
+      splitData.push(tempDataKNN)
-      colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
+      splitData.push(tempDataLR)
+      splitData.push(tempDataMLP)
+      splitData.push(tempDataRF)
+      splitData.push(tempDataGradB)
+
+      for (let i = 0; i < splitData.length; i++) {
+        var colorsforScatterPlot = this.PerF
+        if (splitData[i].length != 0) {
+          var mergedStoreEnsembleLoc = [].concat.apply([], splitData[i])
+          var mergedStoreEnsembleLocFormatted = []
+          for (let j = 0; j < mergedStoreEnsembleLoc.length; j++) {
+            mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[j].replace(/\D/g,'')))
+          }
 
-      var max = Math.max.apply(Math, colorsforScatterPlot)
+        colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
-      var min = Math.min.apply(Math, colorsforScatterPlot)
 
+        max[i] = Math.max.apply(Math, colorsforScatterPlot)
+        min[i] = Math.min.apply(Math, colorsforScatterPlot)
+        }
+        
+      } 
+      console.log(max)
+      console.log(min)
       var countMax = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
       var countMin = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
 
@@ -650,104 +761,104 @@ console.log(countMin)
         let tempSplit = this.storedCM[i].split(/([0-9]+)/)
 
         if (tempSplit[0] == 'KNNC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[0]) {
             countMax[0] = countMax[0] + 1
-          } else if (this.PerFCM[i] < min) {
+          } else if (this.PerFCM[i] < min[0]) {
             countMin[0] = countMin[0] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'KNNM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[0]) {
             countMax[1] = countMax[1] + 1
-          } else if (this.PerFCM[i] < min) {
+          } else if (this.PerFCM[i] < min[0]) {
             countMin[1] = countMin[1] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'LRC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[1]) {
             countMax[2] = countMax[2] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[1]) {
             countMin[2] = countMin[2] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'LRM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[1]) {
             countMax[3] = countMax[3] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[1]) {
             countMin[3] = countMin[3] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'MLPC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[2]) {
             countMax[4] = countMax[4] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[2]) {
             countMin[4] = countMin[4] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'MLPM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[2]) {
             countMax[5] = countMax[5] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[2]) {
             countMin[5] = countMin[5] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'RFC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[3]) {
             countMax[6] = countMax[6] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[3]) {
             countMin[6] = countMin[6] + 1
           }
         }
         else if (tempSplit[0] == 'RFM') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[3]) {
             countMax[7] = countMax[7] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[3]) {
             countMin[7] = countMin[7] + 1
           } else {
             continue
           }
         }
         else if (tempSplit[0] == 'GradBC') {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[4]) {
             countMax[8] = countMax[8] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[4]) {
             countMin[8] = countMin[8] + 1
           } else {
             continue
           }
         }
         else {
-          if (this.PerFCM[i] > max) {
+          if (this.PerFCM[i] > max[4]) {
             countMax[9] = countMax[9] + 1
           }
-          else if (this.PerFCM[i] < min) {
+          else if (this.PerFCM[i] < min[4]) {
             countMin[9] = countMin[9] + 1
           } else {
             continue
           }
         }
       }
-console.log(countMax)
+      console.log(countMax)
-console.log(countMin)
+      console.log(countMin)
       // var percentage = []
       // for (let j = 0; j < countMax.length; j++) {
       //   if (j >= 5) {
@@ -1357,6 +1468,10 @@ console.log(countMin)
   mounted() {
     //EventBus.$on('emittedEventCallingSankeyLegend', this.LegendStable)
 
+    EventBus.$on('updateRandomS', data => { this.RandomSearLoc = data })
+    EventBus.$on('updateStage1', data => { this.values = data })
+    EventBus.$on('updateStage2', data => { this.valuesStage2 = data })
+
     EventBus.$on('emittedEventCallingSankeyStage2', this.SankeyViewStage2)
     EventBus.$on('emittedEventCallingSankeyStage3', this.SankeyViewStage3)
 
@@ -1375,6 +1490,8 @@ console.log(countMin)
     EventBus.$on('ResponsiveandChange', data => {
     this.WH = data})
 
+    EventBus.$on('SendModelsAll', data => { this.previouslyIDs = data })
+
     EventBus.$on('SendPerformance', data => {
     this.PerF = data})
     EventBus.$on('SendPerformanceCM', data => {

frontend/src/components/Main.vue

@@ -40,7 +40,7 @@
     <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">Solution Space of Hyper-Parameters
+            <mdb-card-header color="primary-color" tag="h5" class="text-center">Hyper-Parameters' Space
               [Sel: {{OverSelLength}} / All: {{OverAllLength}}]<small class="float-right"></small><span class="badge badge-info badge-pill float-right">Projection<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">1</span></span>
             </mdb-card-header>
             <mdb-card-body>
@@ -88,7 +88,7 @@
         </b-col>
         <b-col cols="3">
           <mdb-card style="margin-top: 15px;">
-            <mdb-card-header color="primary-color" tag="h5" class="text-center"><span class="float-left"><font-awesome-icon icon="calculator" /></span>Predictive Results for Majority-Voting Ensemble<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">2</span></span>
+            <mdb-card-header color="primary-color" tag="h5" class="text-center"><span class="float-left"><font-awesome-icon icon="calculator" /></span>Performance for Majority-Voting Ensemble<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">2</span></span>
               </mdb-card-header>
               <mdb-card-body>
                 <mdb-card-text class="text-center"  style="min-height: 270px">   
@@ -311,12 +311,13 @@ export default Vue.extend({
             this.firstTimeExec = false
             EventBus.$emit('callAlgorithhms')
             this.Status = " (S) Stage 1"
-          } else {   
+          } else {
+            var IDsPreviously = JSON.parse(this.OverviewResults[16])   
             var Performance = JSON.parse(this.OverviewResults[1])
-            console.log(this.storeEnsemblePermanently)
+            EventBus.$emit('SendModelsAll', IDsPreviously)
+            EventBus.$emit('SendPerformance', Performance)
             EventBus.$emit('SendStoredEnsembleHist', this.storeEnsemblePermanently)
             EventBus.$emit('SendStoredEnsemble', this.storeEnsemblePermanently)
-            EventBus.$emit('SendPerformance', Performance)
             EventBus.$emit('emittedEventCallingCrossoverMutation', this.OverviewResults)
             this.PredictSelEnsem = []
             this.storeBothEnsCM[1] = this.OverviewResults
@@ -706,8 +707,11 @@ export default Vue.extend({
       axios.post(path, postData, axiosConfig)
       .then(response => {
         console.log('File name was sent successfully!')
-        this.CMNumberofModelsOFFICIAL = [0,0,0,0,0,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0],
+        this.CMNumberofModelsOFFICIAL = [0,0,0,0,0,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0]
-        this.CMNumberofModelsOFFICIALS2 = [0,0,0,0,0,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0],
+        this.CMNumberofModelsOFFICIALS2 = [0,0,0,0,0,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,Math.floor(this.RandomSear/4),this.RandomSear/4,Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),0,Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),0,Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),0,Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),Math.floor(this.RandomSear/4),0]
+        EventBus.$emit('updateRandomS', this.RandomSear)
+        EventBus.$emit('updateStage1', this.CMNumberofModelsOFFICIAL)
+        EventBus.$emit('updateStage2', this.CMNumberofModelsOFFICIALS2)
         this.SendAlgorithmsToServer()
       })
       .catch(error => {
@@ -923,7 +927,7 @@ export default Vue.extend({
         this.storeEnsemblePermanently.push(this.storeEnsemble[i])
       }
       var mergedStoreEnsembleLoc = [].concat.apply([], this.storeEnsemblePermanently)
-      console.log(mergedStoreEnsembleLoc)
+
       if (this.CurrentStage == 1) {
         var postData = {
           RemainingPoints: this.unselectedRemainingPoints,

frontend/src/components/Predictions.vue

@@ -64,7 +64,6 @@ export default {
           }
           getIndices.push(clTemp)
         }
-
       }
       else {
         var tempFirst = []
@@ -78,12 +77,10 @@ export default {
         getIndices.push(tempFirst)
         getIndices.push(tempLast)
       }
-
       if (this.RetrieveValueFi == "heartC") {
         getIndices.reverse()
       }
 
-
       var predictions = JSON.parse(this.GetResultsAll[12])
       var KNNPred = predictions[0]
       var LRPred = predictions[1]
@@ -227,8 +224,13 @@ export default {
 		var cellSpacing = 2;
     var cellSize = 4
 
+    if (!this.flag) {
+      var lengthOverall = classStore.length
+    } else {
+      var lengthOverall = 2028
+    }
 		// === First call === //
-		databind(classStore, size, sqrtSize); // ...then update the databind function
+		databind(classStore, size, sqrtSize, lengthOverall); // ...then update the databind function
 
 		var t = d3.timer(function(elapsed) {
 			draw();
@@ -238,7 +240,7 @@ export default {
 
 		// === Bind and draw functions === //
 
-		function databind(data, size, sqrtSize) {
+		function databind(data, size, sqrtSize, lengthOverallLocal) {
 
 			colourScale = d3.scaleSequential(d3.interpolateGreens).domain([0, 100])
 
@@ -253,7 +255,7 @@ export default {
 	        return groupSpacing * x0 + (cellSpacing + cellSize) * (x1 + x0 * 10);
 	      })
 	      .attr('y', function(d, i) {
-	        var y0 = Math.floor(i / 2028), y1 = Math.floor(i % size / sqrtSize);
+	        var y0 = Math.floor(i / lengthOverallLocal), y1 = Math.floor(i % size / sqrtSize);
 	        return groupSpacing * y0 + (cellSpacing + cellSize) * (y1 + y0 * 10);
 	      })
 				.attr('width', 0)
@@ -503,8 +505,14 @@ export default {
 		var cellSpacing = 2;
     var cellSize = 4
 
+    if (!this.flag) {
+      var lengthOverall = classStore.length
+    } else {
+      var lengthOverall = 2028
+    }
+
 		// === First call === //
-		databind(classStore, size, sqrtSize); // ...then update the databind function
+		databind(classStore, size, sqrtSize, lengthOverall); // ...then update the databind function
 		
 		var t = d3.timer(function(elapsed) {
 			draw();
@@ -514,7 +522,7 @@ export default {
 
 		// === Bind and draw functions === //
 
-		function databind(data, size, sqrtSize) {
+		function databind(data, size, sqrtSize, lengthOverallLocal) {
 
 			colourScale = d3.scaleSequential(d3.interpolatePRGn).domain([-100, 100])
 
@@ -529,7 +537,7 @@ export default {
 	        return groupSpacing * x0 + (cellSpacing + cellSize) * (x1 + x0 * 10);
 	      })
 	      .attr('y', function(d, i) {
-	        var y0 = Math.floor(i / 2028), y1 = Math.floor(i % size / sqrtSize);
+	        var y0 = Math.floor(i / lengthOverallLocal), y1 = Math.floor(i % size / sqrtSize);
 	        return groupSpacing * y0 + (cellSpacing + cellSize) * (y1 + y0 * 10);
 	      })
 				.attr('width', 0)

frontend/src/components/ValidationController.vue

@@ -104,6 +104,7 @@ export default {
                 activeLines.push('meanSelection')
             }
         } else {
+
             var valid = JSON.parse(this.ResultsValid[3])
 
             var mergedStoreEnsembleLoc = [].concat.apply([], this.storedEnsemble)
@@ -139,8 +140,8 @@ export default {
                         }
                         if (this.selectedEnsem.length != 0) {
                             if (this.selectedEnsem.includes(mergedStoreEnsembleLoc[i])) {
-                                sumGlobalSel[j] = sumGlobalSel[j] + tempValid[i]
+                                sumGlobalSel[j-measure] = sumGlobalSel[j-measure] + tempValid[i]
-                                countValuesSel[j] = countValuesSel[j] + 1
+                                countValuesSel[j-measure] = countValuesSel[j-measure] + 1
                             }
                         }
                     }

frontend/src/components/VotingResults.vue

@@ -172,8 +172,8 @@ export default {
               .attr('class', 'score')
               .text(function(d){return d[rCol];});
 
-      chart.append("text").attr("x",width/3).attr("y", 20).attr("class","title").text(info[0]);
+      chart.append("text").attr("x",width/3).attr("y", 20).attr("class","title").text(info[0]+' (%)');
-      chart.append("text").attr("x",width/3+rightOffset).attr("y", 20).attr("class","title").text(info[1]);
+      chart.append("text").attr("x",width/3+rightOffset).attr("y", 20).attr("class","title").text(info[1]+' (%)');
       chart.append("text").attr("x",width+labelArea/3).attr("y", 20).attr("class","title").text("Metrics");
     },
     legendColFinal () {        

insertMongo.py

@@ -10,7 +10,7 @@ def import_content(filepath):
     mng_client = pymongo.MongoClient('localhost', 27017)
     mng_db = mng_client['mydb']
     #collection_name = 'StanceCTest' 
-    collection_name = 'biodegC' 
+    collection_name = 'biodegCTest' 
     db_cm = mng_db[collection_name]
     cdir = os.path.dirname(__file__)
     file_res = os.path.join(cdir, filepath)
@@ -21,5 +21,5 @@ def import_content(filepath):
     db_cm.insert(data_json)
 
 if __name__ == "__main__":
-  filepath = '/Users/anchaa/Documents/Research/HyperSearVis_code/new_data_sets/biodeg.csv'
+  filepath = '/Users/anchaa/Documents/Research/HyperSearVis_code/new_data_sets/biodegtest.csv'
   import_content(filepath)

run.py

@@ -333,6 +333,18 @@ def retrieveFileName():
     global addGradB
     addGradB = addRF+randomSearchVar
 
+    global KNNModelsCount
+    global LRModelsCount
+    global MLPModelsCount
+    global RFModelsCount
+    global GradBModelsCount
+
+    KNNModelsCount = 0
+    LRModelsCount = KNNModelsCount+randomSearchVar
+    MLPModelsCount = LRModelsCount+randomSearchVar
+    RFModelsCount = MLPModelsCount+randomSearchVar
+    GradBModelsCount = RFModelsCount+randomSearchVar
+
     # Initializing models
 
     global RetrieveModelsList
@@ -398,6 +410,7 @@ def retrieveFileName():
     global fileInput
     fileInput = data['fileName']
 
+
     DataRawLength = -1
     DataRawLengthTest = -1
     print(data['fileName'])
@@ -983,7 +996,6 @@ def PreprocessingPred():
         predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
         predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
         yDataSorted = yDataSortedFirst + yDataSortedLast
-
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
 def computeClusters(dataLocal,one,two,three,four,five,flagLocal):
@@ -1813,12 +1825,10 @@ def EnsembleModel (Models, keyRetrieved):
         sclf.fit(XData, yData)
         y_pred = sclf.predict(XDataTest)
         print('Test data set')
-        print(accuracy_score(yDataTest, y_pred))
         print(classification_report(yDataTest, y_pred))
 
         y_pred = sclf.predict(XDataExternal)
         print('External data set')
-        print(accuracy_score(yDataExternal, y_pred))
         print(classification_report(yDataExternal, y_pred))
 
     return 'Okay'
@@ -1867,6 +1877,8 @@ def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,parametersGen,sumP
     XDataJSONEntireSet = XData.to_json(orient='records')
     XDataColumns = XData.columns.tolist()
 
+    ModelsIDsPreviously = PreprocessingIDs()
+
     Results.append(json.dumps(ModelsIDs))
     Results.append(json.dumps(sumPerClassifier))
     Results.append(json.dumps(parametersGenPD))
@@ -1883,6 +1895,7 @@ def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,parametersGen,sumP
     Results.append(json.dumps(names_labels))
     Results.append(json.dumps(yDataSorted))
     Results.append(json.dumps(mode))
+    Results.append(json.dumps(ModelsIDsPreviously))
 
     return Results