fixed data

Former-commit-id: c6393c0e6f

cachedir/joblib/run/GridSearchForModels/0c5f8a1f4bea8be6ae4682e53eda9c9a/metadata.json

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cachedir/joblib/run/GridSearchForModels/3f6d2dbb8c264332ed46e83725cce2c3/output.pkl.REMOVED.git-id

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cachedir/joblib/run/GridSearchForModels/655a6eaba76ddf35cbc9b77dd123f6d1/metadata.json

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cachedir/joblib/run/GridSearchForModels/7987784bb00482e6d734e7418e8e7825/metadata.json

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cachedir/joblib/run/GridSearchForModels/857a30e45af06f6a78691a05559ad386/output.pkl.REMOVED.git-id

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cachedir/joblib/run/GridSearchForModels/90003ee6cd1c752ef075aa0a52f23492/metadata.json

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cachedir/joblib/run/GridSearchForModels/90f4ec7056539ba8f1ae91d2ce378613/metadata.json

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cachedir/joblib/run/GridSearchForModels/9147dc03a27d188e71203f91522c7d50/metadata.json

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cachedir/joblib/run/GridSearchForModels/95a22676f7ad60c99edc19898693b276/output.pkl.REMOVED.git-id

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cachedir/joblib/run/GridSearchForModels/c9f7ad12fbeadc6012d9b5d28fd5d889/metadata.json

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cachedir/joblib/run/GridSearchForModels/d47cb434b4b20b0c82fcf3a60162c740/metadata.json

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cachedir/joblib/run/GridSearchForModels/da0b6483b461b61b7f4703390b9d52a4/output.pkl.REMOVED.git-id

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cachedir/joblib/run/GridSearchForModels/f4d33884dc001f572bf3782e5bcfc337/metadata.json

@@ -0,0 +1 @@
+{"duration": 1597.3868968486786, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n298   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n299   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n300   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n301   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n302   57    0   1       130   236    0        0      174      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": "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n                     criterion='entropy', max_depth=None, max_features='auto',\n                     max_leaf_nodes=None, max_samples=None,\n                     min_impurity_decrease=0.0, min_impurity_split=None,\n                     min_samples_leaf=1, min_samples_split=2,\n                     min_weight_fraction_leaf=0.0, n_estimators=139,\n                     n_jobs=None, oob_score=False, random_state=42, verbose=0,\n                     warm_start=False)", "params": "{'n_estimators': [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, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'ExtraT'", "AlgorithmsIDsEnd": "2606", "toggle": "1"}}

cachedir/joblib/run/GridSearchForModels/fae571cb8275aa179fbc88d44e722bdf/output.pkl.REMOVED.git-id

@@ -1 +0,0 @@
-1fba0809198f795ea21629b439adfcfd5fd54733

cachedir/joblib/run/GridSearchForModels/fee66581911cfa2324cc40a9d0b5d65a/output.pkl.REMOVED.git-id

@@ -1 +0,0 @@
-70ce14a45756362ae1513bb2cb267b085e6bbbf1

cachedir/joblib/run/GridSearchForModels/func_code.py

@@ -1,4 +1,4 @@
-# first line: 559
+# first line: 552
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle):
     # instantiate spark session
@@ -63,6 +63,8 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
     PerFeatureAccuracyAll = []
     PerClassMetric = []
     perModelProb = []
+    
+    perModelPrediction = []
     resultsMicro = []
     resultsMacro = []
     resultsWeighted = []
@@ -85,7 +87,8 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
     inputs = range(len(XData))
     num_cores = multiprocessing.cpu_count()
     
-    impDataInst = Parallel(n_jobs=num_cores)(delayed(processInput)(i,XData,yData,crossValidation,clf) for i in inputs)
+    #impDataInst = Parallel(n_jobs=num_cores)(delayed(processInput)(i,XData,yData,crossValidation,clf) for i in inputs)
+    
     for eachModelParameters in parametersLocalNew:
         clf.set_params(**eachModelParameters)
         if (toggle == 1):
@@ -103,6 +106,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
         clf.fit(XData, yData) 
         yPredict = clf.predict(XData)
         yPredict = np.nan_to_num(yPredict)
+        perModelPrediction.append(yPredict)
         # retrieve target names (class names)
         PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
         yPredictProb = clf.predict_proba(XData)
@@ -128,7 +132,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
         resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2))
   
         resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True))
-    print('perase')
+
     maxLog = max(resultsLogLoss)
     minLog = min(resultsLogLoss)
     for each in resultsLogLoss:
@@ -154,6 +158,10 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
 
     metrics.insert(loop+13,'log_loss',resultsLogLossFinal)
 
+    perModelPredPandas = pd.DataFrame(perModelPrediction)
+    print(perModelPredPandas)
+    perModelPredPandas = perModelPredPandas.to_json()
+
     perModelProbPandas = pd.DataFrame(perModelProb)
     perModelProbPandas = perModelProbPandas.to_json()
 
@@ -189,6 +197,6 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
     metrics = metrics.to_json()
     results.append(metrics) # Position: 6 and so on
     results.append(perModelProbPandas) # Position: 7 and so on
-    results.append(json.dumps(impDataInst)) # Position: 8 and so on
+    results.append(json.dumps(perModelPredPandas)) # Position: 8 and so on
 
     return results

frontend/src/components/Active.vue

@@ -0,0 +1,27 @@
+<template>
+  <button style="float: right;"
+  id="updateActive"
+  v-on:click="sendUpdateActive">
+  <font-awesome-icon icon="calculator" />
+  {{ valueActive }}
+  </button>
+</template>
+
+<script>
+
+import { EventBus } from '../main.js'
+
+export default {
+  name: 'Active',
+  data () {
+    return {
+      valueActive: 'Compute Active Metamodel\'s Performance'
+    }
+  },
+  methods: {
+      sendUpdateActive () {
+        EventBus.$emit('OpenModal')
+      }
+  }
+}
+</script>

frontend/src/components/AlgorithmHyperParam.vue

@@ -1,5 +1,5 @@
 <template>
-  <div id="PCP" class="parcoords" style="min-height: 307px;"></div>
+  <div id="PCP" class="parcoords" style="min-height: 285px;"></div>
 </template>
 
 <script>

frontend/src/components/Algorithms.vue

@@ -1,6 +1,6 @@
 <template>
   <div>
-    <div id="exploding_boxplot" class="exploding_boxplot" style="min-height: 430px;"></div>
+    <div id="exploding_boxplot" class="exploding_boxplot" style="min-height: 450px;"></div>
   </div>
 </template>
 
@@ -22,7 +22,7 @@ export default {
       brushedBoxPl: [],
       previousColor: 0,
       selectedAlgorithm: 0,
-      AllAlgorithms: ['KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
+      AllAlgorithms: ['KNN','SVC','GauNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
       SVCModels: 576,
       GausNBModels: 736, 
       MLPModels: 1236,
@@ -232,92 +232,92 @@ export default {
       this.parameters = []
       if (this.keyAllOrClass) {
         for (var j = 0; j < Object.keys(PerformAlgorKNN['params']).length; j++) {
-          this.algorithmKNN.push({'# Performance (%) #': McKNN[j],Algorithm:'K-Nearest Neighbors',Model:'Model ID: ' + AlgorKNNIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorKNNIDs[j]})
+          this.algorithmKNN.push({'# Performance (%) #': McKNN[j],Algorithm:'KNN',Model:'Model ID: ' + AlgorKNNIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorKNNIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorSVC['params']).length; j++) {
-          this.algorithmSVC.push({'# Performance (%) #': McSVC[j],Algorithm:'C-Support Vector Classif',Model:'Model ID: ' + AlgorSVCIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorSVCIDs[j]})
+          this.algorithmSVC.push({'# Performance (%) #': McSVC[j],Algorithm:'SVC',Model:'Model ID: ' + AlgorSVCIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorSVCIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorSVC['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorGausNB['params']).length; j++) {
-          this.algorithmGausNB.push({'# Performance (%) #': McGausNB[j],Algorithm:'Gaussian Naive Bayes',Model:'Model ID: ' + AlgorGausNBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGausNBIDs[j]})
+          this.algorithmGausNB.push({'# Performance (%) #': McGausNB[j],Algorithm:'GauNB',Model:'Model ID: ' + AlgorGausNBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGausNBIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorMLP['params']).length; j++) {
-          this.algorithmMLP.push({'# Performance (%) #': McMLP[j],Algorithm:'Multilayer Perceptron',Model:'Model ID: ' + AlgorMLPIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorMLPIDs[j]})
+          this.algorithmMLP.push({'# Performance (%) #': McMLP[j],Algorithm:'MLP',Model:'Model ID: ' + AlgorMLPIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorMLPIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorMLP['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorLR['params']).length; j++) {
-          this.algorithmLR.push({'# Performance (%) #': McLR[j],Algorithm:'Logistic Regression',Model:'Model ID: ' + AlgorLRIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLRIDs[j]})
+          this.algorithmLR.push({'# Performance (%) #': McLR[j],Algorithm:'LR',Model:'Model ID: ' + AlgorLRIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLRIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorLR['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorLDA['params']).length; j++) {
-          this.algorithmLDA.push({'# Performance (%) #': McLDA[j],Algorithm:'Linear Discrim Analysis',Model:'Model ID: ' + AlgorLDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLDAIDs[j]})
+          this.algorithmLDA.push({'# Performance (%) #': McLDA[j],Algorithm:'LDA',Model:'Model ID: ' + AlgorLDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLDAIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorLDA['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorQDA['params']).length; j++) {
-          this.algorithmQDA.push({'# Performance (%) #': McQDA[j],Algorithm:'Quadratic Discrim Analysis',Model:'Model ID: ' + AlgorQDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorQDAIDs[j]})
+          this.algorithmQDA.push({'# Performance (%) #': McQDA[j],Algorithm:'QDA',Model:'Model ID: ' + AlgorQDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorQDAIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorQDA['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorRF['params']).length; j++) {
-          this.algorithmRF.push({'# Performance (%) #': McRF[j],Algorithm:'Random Forest',Model:'Model ID: ' + AlgorRFIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorRFIDs[j]})
+          this.algorithmRF.push({'# Performance (%) #': McRF[j],Algorithm:'RF',Model:'Model ID: ' + AlgorRFIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorRFIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorRF['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorExtraT['params']).length; j++) {
-          this.algorithmExtraT.push({'# Performance (%) #': McExtraT[j],Algorithm:'Extra Trees',Model:'Model ID: ' + AlgorExtraTIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorExtraTIDs[j]})
+          this.algorithmExtraT.push({'# Performance (%) #': McExtraT[j],Algorithm:'ExtraT',Model:'Model ID: ' + AlgorExtraTIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorExtraTIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorAdaB['params']).length; j++) {
-          this.algorithmAdaB.push({'# Performance (%) #': McAdaB[j],Algorithm:'AdaBoost',Model:'Model ID: ' + AlgorAdaBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorAdaBIDs[j]})
+          this.algorithmAdaB.push({'# Performance (%) #': McAdaB[j],Algorithm:'AdaB',Model:'Model ID: ' + AlgorAdaBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorAdaBIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorGradB['params']).length; j++) {
-          this.algorithmGradB.push({'# Performance (%) #': McGradB[j],Algorithm:'Gradient Boosting',Model:'Model ID: ' + AlgorGradBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGradBIDs[j]})
+          this.algorithmGradB.push({'# Performance (%) #': McGradB[j],Algorithm:'GradB',Model:'Model ID: ' + AlgorGradBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGradBIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorGradB['params'])[j]))
         }
       } else {
         for (var j = 0; j < Object.keys(PerformAlgorKNN['params']).length; j++) {
-          this.algorithmKNN.push({'# Performance (%) #': this.listClassPerf[0][j],Algorithm:'K-Nearest Neighbors',Model:'Model ID: ' + AlgorKNNIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorKNNIDs[j]})
+          this.algorithmKNN.push({'# Performance (%) #': this.listClassPerf[0][j],Algorithm:'KNN',Model:'Model ID: ' + AlgorKNNIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorKNNIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorSVC['params']).length; j++) {
-          this.algorithmSVC.push({'# Performance (%) #': this.listClassPerf[1][j],Algorithm:'C-Support Vector Classif',Model:'Model ID: ' + AlgorSVCIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorSVCIDs[j]})
+          this.algorithmSVC.push({'# Performance (%) #': this.listClassPerf[1][j],Algorithm:'SVC',Model:'Model ID: ' + AlgorSVCIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorSVCIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorSVC['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorGausNB['params']).length; j++) {
-          this.algorithmGausNB.push({'# Performance (%) #': this.listClassPerf[2][j],Algorithm:'Gaussian Naive Bayes',Model:'Model ID: ' + AlgorGausNBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGausNBIDs[j]})
+          this.algorithmGausNB.push({'# Performance (%) #': this.listClassPerf[2][j],Algorithm:'GauNB',Model:'Model ID: ' + AlgorGausNBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGausNBIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorMLP['params']).length; j++) {
-          this.algorithmMLP.push({'# Performance (%) #': this.listClassPerf[3][j],Algorithm:'Multilayer Perceptron',Model:'Model ID: ' + AlgorMLPIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorMLPIDs[j]})
+          this.algorithmMLP.push({'# Performance (%) #': this.listClassPerf[3][j],Algorithm:'MLP',Model:'Model ID: ' + AlgorMLPIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorMLPIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorMLP['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorLR['params']).length; j++) {
-          this.algorithmLR.push({'# Performance (%) #': this.listClassPerf[4][j],Algorithm:'Logistic Regression',Model:'Model ID: ' + AlgorLRIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLRIDs[j]})
+          this.algorithmLR.push({'# Performance (%) #': this.listClassPerf[4][j],Algorithm:'LR',Model:'Model ID: ' + AlgorLRIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLRIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorLR['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorLDA['params']).length; j++) {
-          this.algorithmLDA.push({'# Performance (%) #': this.listClassPerf[5][j],Algorithm:'Linear Discrim Analysis',Model:'Model ID: ' + AlgorLDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLDAIDs[j]})
+          this.algorithmLDA.push({'# Performance (%) #': this.listClassPerf[5][j],Algorithm:'LDA',Model:'Model ID: ' + AlgorLDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorLDAIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorLDA['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorQDA['params']).length; j++) {
-          this.algorithmQDA.push({'# Performance (%) #': this.listClassPerf[6][j],Algorithm:'Quadratic Discrim Analysis',Model:'Model ID: ' + AlgorQDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorQDAIDs[j]})
+          this.algorithmQDA.push({'# Performance (%) #': this.listClassPerf[6][j],Algorithm:'QDA',Model:'Model ID: ' + AlgorQDAIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorQDAIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorQDA['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorRF['params']).length; j++) {
-          this.algorithmRF.push({'# Performance (%) #': this.listClassPerf[7][j],Algorithm:'Random Forest',Model:'Model ID: ' + AlgorRFIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorRFIDs[j]})
+          this.algorithmRF.push({'# Performance (%) #': this.listClassPerf[7][j],Algorithm:'RF',Model:'Model ID: ' + AlgorRFIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorRFIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorRF['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorExtraT['params']).length; j++) {
-          this.algorithmExtraT.push({'# Performance (%) #': this.listClassPerf[8][j],Algorithm:'Extra Trees',Model:'Model ID: ' + AlgorExtraTIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorExtraTIDs[j]})
+          this.algorithmExtraT.push({'# Performance (%) #': this.listClassPerf[8][j],Algorithm:'ExtraT',Model:'Model ID: ' + AlgorExtraTIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorExtraTIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorAdaB['params']).length; j++) {
-          this.algorithmAdaB.push({'# Performance (%) #': this.listClassPerf[9][j],Algorithm:'AdaBoost',Model:'Model ID: ' + AlgorAdaBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorAdaBIDs[j]})
+          this.algorithmAdaB.push({'# Performance (%) #': this.listClassPerf[9][j],Algorithm:'AdaB',Model:'Model ID: ' + AlgorAdaBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorAdaBIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j]))
         }
         for (let j = 0; j < Object.keys(PerformAlgorGradB['params']).length; j++) {
-          this.algorithmGradB.push({'# Performance (%) #': this.listClassPerf[10][j],Algorithm:'Gradient Boosting',Model:'Model ID: ' + AlgorGradBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGradBIDs[j]})
+          this.algorithmGradB.push({'# Performance (%) #': this.listClassPerf[10][j],Algorithm:'GradB',Model:'Model ID: ' + AlgorGradBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGradBIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorGradB['params'])[j]))
         }
       }
@@ -340,8 +340,8 @@ export default {
       // color : color of the point / boxplot
       // label : displayed text in toolbox
       this.chart = exploding_boxplot(data, {y:'# Performance (%) #',group:'Algorithm',color:'Algorithm',label:'Model'})
-      this.chart.width(this.WH[0]*10) // interactive visualization
-      this.chart.height(this.WH[1]*0.9) // interactive visualization
+      this.chart.width(this.WH[0]*10.225) // interactive visualization
+      this.chart.height(this.WH[1]*0.95) // interactive visualization
       //call chart on a div
       this.chart('#exploding_boxplot')       
 
@@ -366,7 +366,7 @@ export default {
       var flagEmptyGradB = 0
 
       el[0].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[0]
           allPoints[i].style.opacity = '1.0'
@@ -386,7 +386,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[1].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classif')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[1]
           allPoints[i].style.opacity = '1.0'
@@ -406,7 +406,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[2].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GauNB')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[2]
           allPoints[i].style.opacity = '1.0'
@@ -419,14 +419,14 @@ export default {
         }
 
         EventBus.$emit('clearPCP')
-        EventBus.$emit('updateFlagGausNB', flagEmptyGausNB)
+        EventBus.$emit('updateFlagGauNB', flagEmptyGausNB)
         if (flagEmptyGausNB == 1) {
-          EventBus.$emit('PCPCall', 'GausNB')
+          EventBus.$emit('PCPCall', 'GauNB')
         }
         EventBus.$emit('updateBarChart', [])
       }
       el[3].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Multilayer Perceptron')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[3]
           allPoints[i].style.opacity = '1.0'
@@ -446,7 +446,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[4].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Logistic Regression')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[4]
           allPoints[i].style.opacity = '1.0'
@@ -466,7 +466,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[5].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Linear Discrim Analysis')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[5]
           allPoints[i].style.opacity = '1.0'
@@ -486,7 +486,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[6].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Quadratic Discrim Analysis')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[6]
           allPoints[i].style.opacity = '1.0'
@@ -506,7 +506,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[7].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Random Forest')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[7]
           allPoints[i].style.opacity = '1.0'
@@ -526,7 +526,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[8].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Extra Trees')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[8]
           allPoints[i].style.opacity = '1.0'
@@ -546,7 +546,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[9].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaBoost')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[9]
           allPoints[i].style.opacity = '1.0'
@@ -566,7 +566,7 @@ export default {
         EventBus.$emit('updateBarChart', [])
       }
       el[10].onclick = function() {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gradient Boosting')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
         for (let i = 0; i < allPoints.length; i++) {
           allPoints[i].style.fill = previousColor[10]
           allPoints[i].style.opacity = '1.0'
@@ -620,37 +620,37 @@ export default {
       for (var j = 0; j < this.AllAlgorithms.length; j++) {
         algorithm = []
         if (this.AllAlgorithms[j] === 'KNN') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
           algorithm = this.algorithmKNN
         } else if (this.AllAlgorithms[j] === 'SVC') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classif')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
           algorithm = this.algorithmSVC
-        } else if (this.AllAlgorithms[j] === 'GausNB') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
+        } else if (this.AllAlgorithms[j] === 'GauNB') {
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GauNB')
           algorithm = this.algorithmGausNB
         } else if (this.AllAlgorithms[j] === 'MLP') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Multilayer Perceptron')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
           algorithm = this.algorithmMLP
         } else if (this.AllAlgorithms[j] === 'LR') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Logistic Regression')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
           algorithm = this.algorithmLR
         } else if (this.AllAlgorithms[j] === 'LDA') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Linear Discrim Analysis')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
           algorithm = this.algorithmLDA
         } else if (this.AllAlgorithms[j] === 'QDA') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Quadratic Discrim Analysis')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
           algorithm = this.algorithmQDA
         } else if (this.AllAlgorithms[j] === 'RF') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Random Forest')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
           algorithm = this.algorithmRF
         } else if (this.AllAlgorithms[j] === 'ExtraT') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Extra Trees')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
           algorithm = this.algorithmExtraT
         } else if (this.AllAlgorithms[j] === 'AdaB') {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaBoost')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
           algorithm = this.algorithmAdaB
         } else {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gradient Boosting')
+          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
           algorithm = this.algorithmGradB
         }
         for (let k = 0; k < allPoints.length; k++) {
@@ -663,7 +663,7 @@ export default {
             allPoints[i].style.fill = previousColor[0]
           } else if (this.AllAlgorithms[j] === 'SVC') {
             allPoints[i].style.fill = previousColor[1]
-          } else if (this.AllAlgorithms[j] === 'GausNB') {
+          } else if (this.AllAlgorithms[j] === 'GauNB') {
             allPoints[i].style.fill = previousColor[2]
           } else if (this.AllAlgorithms[j] === 'MLP') {
             allPoints[i].style.fill = previousColor[3]
@@ -698,7 +698,7 @@ export default {
             } else if (this.AllAlgorithms[j] === 'SVC') {
               allPoints[i].style.fill = previousColor[1]
               allPoints[i].style.opacity = '1.0'
-            } else if (this.AllAlgorithms[j] === 'GausNB') {
+            } else if (this.AllAlgorithms[j] === 'GauNB') {
               allPoints[i].style.fill = previousColor[2]
               allPoints[i].style.opacity = '1.0'
             } else if (this.AllAlgorithms[j] === 'MLP') {
@@ -740,7 +740,7 @@ export default {
                 allPoints[i].style.fill = "#d3d3d3"
                 allPoints[i].style.opacity = '0.4'
               }
-            } else if (this.AllAlgorithms[j] === 'GausNB') {
+            } else if (this.AllAlgorithms[j] === 'GauNB') {
               if (modelsActive.indexOf(i+this.GausNBModels) == -1) {
                 allPoints[i].style.fill = "#d3d3d3"
                 allPoints[i].style.opacity = '0.4'
@@ -796,27 +796,27 @@ export default {
     },
     brushed () {
       if (this.selectedAlgorithm === 'KNN') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point K-Nearest Neighbors')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
       } else if (this.selectedAlgorithm === 'SVC') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point C-Support Vector Classif')
-      } else if (this.selectedAlgorithm === 'GausNB') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gaussian Naive Bayes')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
+      } else if (this.selectedAlgorithm === 'GauNB') {
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GauNB')
       } else if (this.selectedAlgorithm === 'MLP') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Multilayer Perceptron')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
       } else if (this.selectedAlgorithm === 'LR') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Logistic Regression')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
       } else if (this.selectedAlgorithm === 'LDA') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Linear Discrim Analysis')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
       } else if (this.selectedAlgorithm === 'QDA') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Quadratic Discrim Analysis')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
       } else if (this.selectedAlgorithm === 'RF') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Random Forest')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
       } else if (this.selectedAlgorithm === 'ExtraT') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Extra Trees')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
       } else if (this.selectedAlgorithm === 'AdaB') {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaBoost')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
       } else {
-        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point Gradient Boosting')
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
       }
       const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
       var modelsActive = []
@@ -828,7 +828,7 @@ export default {
           allPoints[i].style.fill = previousColor[0]
         } else if (this.selectedAlgorithm === 'SVC') {
           allPoints[i].style.fill = previousColor[1]
-        } else if (this.selectedAlgorithm === 'GausNB') {
+        } else if (this.selectedAlgorithm === 'GauNB') {
           allPoints[i].style.fill = previousColor[2]
         } else if (this.selectedAlgorithm === 'MLP') {
           allPoints[i].style.fill = previousColor[3]
@@ -863,7 +863,7 @@ export default {
           } else if (this.selectedAlgorithm === 'SVC') {
             allPoints[i].style.fill = previousColor[1]
             allPoints[i].style.opacity = '1.0'
-          } else if (this.selectedAlgorithm === 'GausNB') {
+          } else if (this.selectedAlgorithm === 'GauNB') {
             allPoints[i].style.fill = previousColor[2]
             allPoints[i].style.opacity = '1.0'
           } else if (this.selectedAlgorithm === 'MLP') {
@@ -905,7 +905,7 @@ export default {
               allPoints[i].style.fill = "#d3d3d3"
               allPoints[i].style.opacity = '0.4'
             }
-          } else if (this.selectedAlgorithm === 'GausNB') {
+          } else if (this.selectedAlgorithm === 'GauNB') {
             if (modelsActive.indexOf(i+this.GausNBModels) == -1) {
               allPoints[i].style.fill = "#d3d3d3"
               allPoints[i].style.opacity = '0.4'
@@ -966,25 +966,25 @@ export default {
       for (let i = 0; i < allPoints.length; i++) {
         if (allPoints[i].style.fill != "rgb(211, 211, 211)") {
           activeModels.push(allPoints[i].__data__.Model)
-          if (allPoints[i].__data__.Algorithm === 'K-Nearest Neighbors') {
+          if (allPoints[i].__data__.Algorithm === 'KNN') {
             algorithmsSelected.push('KNN')
-          } else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classif') {
+          } else if (allPoints[i].__data__.Algorithm === 'SVC') {
             algorithmsSelected.push('SVC')
-          } else if (allPoints[i].__data__.Algorithm === 'Gaussian Naive Bayes') {
-            algorithmsSelected.push('GausNB')
-          } else if (allPoints[i].__data__.Algorithm === 'Multilayer Perceptron') {
+          } else if (allPoints[i].__data__.Algorithm === 'GauNB') {
+            algorithmsSelected.push('GauNB')
+          } else if (allPoints[i].__data__.Algorithm === 'MLP') {
             algorithmsSelected.push('MLP')
-          } else if (allPoints[i].__data__.Algorithm === 'Logistic Regression') {
+          } else if (allPoints[i].__data__.Algorithm === 'LR') {
             algorithmsSelected.push('LR')
-          } else if (allPoints[i].__data__.Algorithm === 'Linear Discrim Analysis') {
+          } else if (allPoints[i].__data__.Algorithm === 'LDA') {
             algorithmsSelected.push('LDA')
-          } else if (allPoints[i].__data__.Algorithm === 'Quadratic Discrim Analysis') {
+          } else if (allPoints[i].__data__.Algorithm === 'QDA') {
             algorithmsSelected.push('QDA')
-          } else if (allPoints[i].__data__.Algorithm === 'Random Forest') {
+          } else if (allPoints[i].__data__.Algorithm === 'RF') {
             algorithmsSelected.push('RF')
-          } else if (allPoints[i].__data__.Algorithm === 'Extra Trees') {
+          } else if (allPoints[i].__data__.Algorithm === 'ExtraT') {
             algorithmsSelected.push('ExtraT')
-          } else if (allPoints[i].__data__.Algorithm === 'AdaBoost') {
+          } else if (allPoints[i].__data__.Algorithm === 'AdaB') {
             algorithmsSelected.push('AdaB')
           } else {
             algorithmsSelected.push('GradB')
@@ -1010,25 +1010,25 @@ export default {
       for (let i = 0; i < allPoints.length; i++) {
         if (allPoints[i].style.fill != "rgb(211, 211, 211)") {
           activeModels.push(allPoints[i].__data__.Model)
-          if (allPoints[i].__data__.Algorithm === 'K-Nearest Neighbors') {
+          if (allPoints[i].__data__.Algorithm === 'KNN') {
             algorithmsSelected.push('KNN')
-          } else if (allPoints[i].__data__.Algorithm === 'C-Support Vector Classif') {
+          } else if (allPoints[i].__data__.Algorithm === 'SVC') {
             algorithmsSelected.push('SVC')
-          } else if (allPoints[i].__data__.Algorithm === 'Gaussian Naive Bayes') {
-            algorithmsSelected.push('GausNB')
-          } else if (allPoints[i].__data__.Algorithm === 'Multilayer Perceptron') {
+          } else if (allPoints[i].__data__.Algorithm === 'GauNB') {
+            algorithmsSelected.push('GauNB')
+          } else if (allPoints[i].__data__.Algorithm === 'MLP') {
             algorithmsSelected.push('MLP')
-          } else if (allPoints[i].__data__.Algorithm === 'Logistic Regression') {
+          } else if (allPoints[i].__data__.Algorithm === 'LR') {
             algorithmsSelected.push('LR')
-          } else if (allPoints[i].__data__.Algorithm === 'Linear Discrim Analysis') {
+          } else if (allPoints[i].__data__.Algorithm === 'LDA') {
             algorithmsSelected.push('LDA')
-          } else if (allPoints[i].__data__.Algorithm === 'Quadratic Discrim Analysis') {
+          } else if (allPoints[i].__data__.Algorithm === 'QDA') {
             algorithmsSelected.push('QDA')
-          } else if (allPoints[i].__data__.Algorithm === 'Random Forest') {
+          } else if (allPoints[i].__data__.Algorithm === 'RF') {
             algorithmsSelected.push('RF')
-          } else if (allPoints[i].__data__.Algorithm === 'Extra Trees') {
+          } else if (allPoints[i].__data__.Algorithm === 'ExtraT') {
             algorithmsSelected.push('ExtraT')
-          } else if (allPoints[i].__data__.Algorithm === 'AdaBoost') {
+          } else if (allPoints[i].__data__.Algorithm === 'AdaB') {
             algorithmsSelected.push('AdaB')
           } else {
             algorithmsSelected.push('GradB')
@@ -1056,7 +1056,7 @@ export default {
           $(el)[0].dispatchEvent(new Event('click'))
         } else if (this.selectedAlgorithm == 'SVC') {
           $(el)[1].dispatchEvent(new Event('click'))
-        } else if (this.selectedAlgorithm == 'GausNB') {
+        } else if (this.selectedAlgorithm == 'GauNB') {
           $(el)[2].dispatchEvent(new Event('click'))
         } else if (this.selectedAlgorithm == 'MLP') {
           $(el)[3].dispatchEvent(new Event('click'))

frontend/src/components/BarChart.vue

@@ -1,7 +1,7 @@
 <template>
 <b-row>
     <b-col cols="12">
-        <div id="barChart" class="barChart" style="min-height: 307px;"></div>
+        <div id="barChart" class="barChart" style="min-height: 285px;"></div>
     </b-col>
 </b-row>
 </template>
@@ -36,7 +36,8 @@ export default {
       AdaBModels: 2766,
       GradBModels: 2926,
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
-      WH: []
+      WH: [],
+      RetrieveDataSet: 'HeartC'
     }
   },
   methods: {
@@ -104,7 +105,7 @@ export default {
                   KNNModels.push(JSON.parse(this.modelsSelectedinBar[i]))
               } else if (this.algorithmsinBar[i] === "SVC") {
                   SVCModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.SVCModels)
-              } else if (this.algorithmsinBar[i] === "GausNB") {
+              } else if (this.algorithmsinBar[i] === "GauNB") {
                   GausNBModels.push(JSON.parse(this.modelsSelectedinBar[i] - this.GausNBModels))
               } else if (this.algorithmsinBar[i] === "MLP") {
                   MLPModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.MLPModels)
@@ -404,11 +405,11 @@ export default {
       }
       Plotly.purge('barChart')
       
-      var layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+      var layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
         autosize: true,
         barmode: 'group',
-        width: this.WH[0]*10,
-        height: this.WH[1]*0.635,
+        width: this.WH[0]*10.21,
+        height: this.WH[1]*0.59,
             xaxis: {
                 title: 'Algorithm',
                 type:"category",
@@ -433,9 +434,9 @@ export default {
         bargap:0.1,
         bargroupgap: 0.2,
         margin: {
-            l: 50,
+            l: 40,
             r: 0,
-            b: 30,
+            b: 0,
             t: 40,
             pad: 0
             },
@@ -460,13 +461,27 @@ export default {
           sumList[i] = keepSum
           sumLineList[i] = keepSumLine
         }
-
+        
+        var beautifyLabels = []
+        if (this.RetrieveDataSet == 'StanceC') {
+          beautifyLabels.push('Absence of Hypotheticality')
+          beautifyLabels.push('Presence of Hypotheticality')
+        }
+        else if (this.RetrieveDataSet == 'HeartC') {
+          beautifyLabels.push('< 50% diameter narrowing / Healthy')
+          beautifyLabels.push('> 50% diameter narrowing / Diseased')
+        } else {
+          target_names.forEach(element => {
+            beautifyLabels.push(element)
+          });
+        }
+        
         for (var i = 0; i < target_names.length; i++) {
           if (this.tNameAll == target_names[i]) {
             traces[i] = {
-            x:  ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+            x:  ['KNN','SVC','GauNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
             y: sumList[i],
-            name: '<b>'+target_names[i]+'</b>',
+            name: '<b>'+beautifyLabels[i]+'</b>',
             opacity: 0.5,
             marker: {
                 opacity: 0.5,
@@ -476,9 +491,9 @@ export default {
             };
           tracesSel[i] = {
               type: 'bar',
-              x: ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+              x: ['KNN','SVC','GauNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
               y: sumLineList[i],
-              name: '<b>'+target_names[i]+' (Sel)</b>',
+              name: '<b>'+beautifyLabels[i]+' (Sel)</b>',
               xaxis: 'x2',
               mode: 'markers',
               marker: {
@@ -491,9 +506,9 @@ export default {
               data.push(tracesSel[i])
           } else {
             traces[i] = {
-            x:  ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+            x:  ['KNN','SVC','GauNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
             y: sumList[i],
-            name: target_names[i],
+            name: beautifyLabels[i],
             opacity: 0.5,
             marker: {
                 opacity: 0.5,
@@ -503,9 +518,9 @@ export default {
             };
           tracesSel[i] = {
               type: 'bar',
-              x: ['K-Nearest Neighbors','C-Support Vector Classif','Gaussian Naive Bayes','Multilayer Perceptron','Logistic Regression','Linear Discrim Analysis','Quadratic Discrim Analysis','Random Forest','Extra Trees','AdaBoost','Gradient Boosting'],
+              x: ['KNN','SVC','GauNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
               y: sumLineList[i],
-              name: target_names[i]+' (Sel)',
+              name: beautifyLabels[i]+' (Sel)',
               xaxis: 'x2',
               mode: 'markers',
               marker: {
@@ -564,6 +579,8 @@ export default {
 
       // reset view
       EventBus.$on('resetViews', this.reset)
+
+      EventBus.$on('SendToServerDataSetConfirmation', data => { this.RetrieveDataSet = data })
     }
 }
 </script>

frontend/src/components/Controller.vue

@@ -1,5 +1,5 @@
 <template>
-  <button style="float: right;"
+  <button class="btn-outline-success" style="float: right;"
   id="Execute"
   v-on:click="execute">
   <font-awesome-icon icon="play" />

frontend/src/components/DataSetExecController.vue

@@ -9,13 +9,13 @@
       <option value="local">Upload New File</option>
   </select>
 
-  <button
+  <button class="btn-outline-primary"
   id="Confirm"
   v-on:click="confirm">
   <font-awesome-icon icon="check" />
   {{ value }}
   </button>
-  <button
+  <button class="btn-outline-danger"
   id="Reset"
   v-on:click="reset">
   <font-awesome-icon icon="trash" />

frontend/src/components/DataSpace.vue

@@ -72,6 +72,7 @@ export default {
       restoreData: 'Restore Step',
       userSelectedFilter: 'mean',
       responsiveWidthHeight: [],
+      RetrieveDataSet: 'HeartC',
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
     }
   },
@@ -134,17 +135,19 @@ export default {
       const XandYCoordinatesTSNE = JSON.parse(this.dataPoints[5])
       const XandYCoordinatesUMAP = JSON.parse(this.dataPoints[6])
       const impSizeArray = JSON.parse(this.dataPoints[7])
-      const KNNSize = JSON.parse(impSizeArray[8])
-      const SVCSize = JSON.parse(impSizeArray[17])
-      const GausNBSize = JSON.parse(impSizeArray[26])
-      const MLPSize = JSON.parse(impSizeArray[35])
-      const LRSize = JSON.parse(impSizeArray[44])
-      const LDASize = JSON.parse(impSizeArray[53])
-      const QDASize = JSON.parse(impSizeArray[62])
-      const RFSize = JSON.parse(impSizeArray[71])
-      const ExtraTSize = JSON.parse(impSizeArray[80])
-      const AdaBSize = JSON.parse(impSizeArray[89])
-      const GradBSize = JSON.parse(impSizeArray[98])
+      const KNNSize = JSON.parse(impSizeArray[7])
+      const SVCSize = JSON.parse(impSizeArray[16])
+      const GausNBSize = JSON.parse(impSizeArray[25])
+      const MLPSize = JSON.parse(impSizeArray[34])
+      const LRSize = JSON.parse(impSizeArray[43])
+      const LDASize = JSON.parse(impSizeArray[52])
+      const QDASize = JSON.parse(impSizeArray[61])
+      const RFSize = JSON.parse(impSizeArray[70])
+      const ExtraTSize = JSON.parse(impSizeArray[79])
+      const AdaBSize = JSON.parse(impSizeArray[88])
+      const GradBSize = JSON.parse(impSizeArray[97])
+
+      console.log(KNNSize)
       var sizeScatterplot = []
 
       var scale = d3.scaleLinear()
@@ -182,6 +185,20 @@ export default {
         var traces = []
         var layout = []
 
+        var beautifyLabels = []
+        if (this.RetrieveDataSet == 'StanceC') {
+          beautifyLabels.push('Absence of Hypotheticality')
+          beautifyLabels.push('Presence of Hypotheticality')
+        }
+        else if (this.RetrieveDataSet == 'HeartC') {
+          beautifyLabels.push('< 50% diameter narrowing / Healthy')
+          beautifyLabels.push('> 50% diameter narrowing / Diseased')
+        } else {
+          target_names.forEach(element => {
+            beautifyLabels.push(element)
+          });
+        }
+
         for (let i = 0; i < target_names.length; i++) {
 
           const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
@@ -210,7 +227,7 @@ export default {
               x: aux_X,
               y: aux_Y,
               mode: 'markers',
-              name: target_names[i],
+              name: beautifyLabels[i],
               marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: sizeScatterplot },
               hovertemplate: 
                       "<b>%{text}</b><br><br>" +
@@ -219,7 +236,7 @@ export default {
             })
         }
 
-        layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+        layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
         title: 'MDS Projection',
         xaxis: {
             visible: false
@@ -289,7 +306,7 @@ export default {
             x: aux_X,
             y: aux_Y,
             mode: 'markers',
-            name: target_names[i],
+            name: beautifyLabels[i],
             marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: sizeScatterplot },
             hovertemplate: 
                     "<b>%{text}</b><br><br>" +
@@ -298,7 +315,7 @@ export default {
           })
         }
 
-        layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+        layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
         title: 't-SNE Projection',
         xaxis: {
             visible: false
@@ -358,7 +375,7 @@ export default {
               x: aux_X,
               y: aux_Y,
               mode: 'markers',
-              name: target_names[i],
+              name: beautifyLabels[i],
               marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: Opacity, size: sizeScatterplot },
               hovertemplate: 
                       "<b>%{text}</b><br><br>" +
@@ -367,7 +384,7 @@ export default {
             })
         }
 
-        layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+        layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
         title: 'UMAP Projection',
         xaxis: {
             visible: false
@@ -445,6 +462,8 @@ export default {
     
     // reset view
     EventBus.$on('resetViews', this.reset)
+
+    EventBus.$on('SendToServerDataSetConfirmation', data => { this.RetrieveDataSet = data })
   }
 }
 </script>

frontend/src/components/FinalResultsLinePlot.vue

@@ -1,5 +1,5 @@
 <template>
-  <div id="LinePlot" style="min-height: 306px;"></div>
+  <div id="LinePlot" style="min-height: 365px;"></div>
 </template>
 
 <script>
@@ -24,6 +24,10 @@ export default {
       scoresSTD3: [],
       scoresPositive3: [], 
       scoresNegative3: [],
+      scoresMean4: [],
+      scoresSTD4: [],
+      scoresPositive4: [], 
+      scoresNegative4: [],
       Stack_scoresMean: [],
       Stack_scoresSTD: [],
       Stack_scoresPositive: [], 
@@ -36,6 +40,10 @@ export default {
       Stack_scoresSTD3: [],
       Stack_scoresPositive3: [], 
       Stack_scoresNegative3: [],
+      Stack_scoresMean4: [],
+      Stack_scoresSTD4: [],
+      Stack_scoresPositive4: [], 
+      Stack_scoresNegative4: [],
       xaxis: [],
       WH: []
     }
@@ -61,34 +69,44 @@ export default {
       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(1))
       this.scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[5])*100).toFixed(1))
 
       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(1))
+      this.scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[7])*100).toFixed(1))
+
+      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[6])*100).toFixed(1))
-      this.Stack_scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[7])*100).toFixed(1))
+      this.Stack_scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[8])*100).toFixed(1))
+      this.Stack_scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[9])*100).toFixed(1))
 
       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[8])*100).toFixed(1))
-      this.Stack_scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[9])*100).toFixed(1))
+      this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[10])*100).toFixed(1))
+      this.Stack_scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[11])*100).toFixed(1))
 
       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[10])*100).toFixed(1))
-      this.Stack_scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[11])*100).toFixed(1))
+      this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[12])*100).toFixed(1))
+      this.Stack_scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[13])*100).toFixed(1))
 
       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]))
 
+      this.Stack_scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[14])*100).toFixed(1))
+      this.Stack_scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[15])*100).toFixed(1))
+
+      this.Stack_scoresPositive4.push(parseFloat(this.Stack_scoresMean4[this.Stack_scoresMean4.length - 1]) + parseFloat(this.Stack_scoresSTD4[this.Stack_scoresSTD4.length - 1]))
+      this.Stack_scoresNegative4.push(parseFloat(this.Stack_scoresMean4[this.Stack_scoresMean4.length - 1]) - parseFloat(this.Stack_scoresSTD4[this.Stack_scoresSTD4.length - 1]))
       
       var xaxisReversed = []
       xaxisReversed = this.xaxis.slice().reverse()
@@ -108,7 +126,7 @@ export default {
         line: {color: "rgb(55,126,184)"}, 
         mode: "lines+markers", 
         marker : {
-          symbol: 'pentagon' },
+          symbol: 'circle' },
         name: "Active Accuracy",
         type: "scatter"
       }
@@ -133,7 +151,7 @@ export default {
         line: {color: "rgb(55,126,184)"}, 
         mode: "lines+markers", 
         marker : {
-            symbol: 'x' },
+            symbol: 'square' },
         name: "Active Precision", 
         type: "scatter"
       }
@@ -158,7 +176,7 @@ export default {
         line: {color: "rgb(55,126,184)"}, 
         mode: "lines+markers", 
         marker : {
-            symbol: 'diamond' },
+            symbol: 'triangle' },
         name: "Active Recall", 
         type: "scatter"
       }
@@ -176,21 +194,46 @@ export default {
         type: "scatter"
       }
 
+      var trace7 = {
+        x: this.xaxis, 
+        y: this.scoresMean4, 
+        text: text,
+        line: {color: "rgb(55,126,184)"}, 
+        mode: "lines+markers", 
+        marker : {
+            symbol: 'diamond' },
+        name: "Active F1 Score", 
+        type: "scatter"
+      }
+
+      var trace8 = {
+        x: xaxisReversed, 
+        y: this.scoresPositive4.concat(this.scoresNegative4), 
+        text: '',
+        hoverinfo: 'text',
+        fill: "tozerox", 
+        fillcolor: "rgba(55,126,184,0)", 
+        line: {color: "transparent"}, 
+        name: "Active F1 Score", 
+        showlegend: false, 
+        type: "scatter"
+      }
+
       // Stack
 
-        var trace7 = {
+        var trace9 = {
         x: this.xaxis, 
         y: this.Stack_scoresMean, 
         text: text,
         line: {color: "rgb(228,26,28)"}, 
         mode: "lines+markers", 
         marker : {
-          symbol: 'circle' },
+          symbol: 'x' },
         name: "Stack Accuracy", 
         type: "scatter"
       }
 
-      var trace8 = {
+      var trace10 = {
         x: xaxisReversed, 
         y: this.Stack_scoresPositive.concat(this.Stack_scoresNegative), 
         text: '',
@@ -203,19 +246,19 @@ export default {
         type: "scatter"
       }
 
-      var trace9 = {
+      var trace11 = {
         x: this.xaxis, 
         y: this.Stack_scoresMean2, 
         text: text,
         line: {color: "rgb(228,26,28)"}, 
         mode: "lines+markers", 
         marker : {
-          symbol: 'square' },
+          symbol: 'cross' },
         name: "Stack Precision", 
         type: "scatter"
       }
 
-      var trace10 = {
+      var trace12 = {
         x: xaxisReversed, 
         y: this.Stack_scoresPositive2.concat(this.Stack_scoresNegative2), 
         text: '',
@@ -228,19 +271,19 @@ export default {
         type: "scatter"
       }
 
-      var trace11 = {
+      var trace13 = {
         x: this.xaxis, 
         y: this.Stack_scoresMean3, 
         text: text,
         line: {color: "rgb(228,26,28)"}, 
         mode: "lines+markers",
         marker : {
-          symbol: 'star-triangle-up' },
+          symbol: 'pentagon' },
         name: "Stack Recall", 
         type: "scatter"
       }
 
-      var trace12 = {
+      var trace14 = {
         x: xaxisReversed, 
         y: this.Stack_scoresPositive3.concat(this.Stack_scoresNegative3), 
         text: '',
@@ -253,7 +296,32 @@ export default {
         type: "scatter"
       }
 
-      const DataforLinePlot = [trace1, trace2, trace7, trace8, trace3, trace4, trace9, trace10, trace5, trace6, trace11, trace12]
+      var trace15 = {
+        x: this.xaxis, 
+        y: this.Stack_scoresMean4, 
+        text: text,
+        line: {color: "rgb(228,26,28)"}, 
+        mode: "lines+markers",
+        marker : {
+          symbol: 'hexagram' },
+        name: "Stack F1 Score", 
+        type: "scatter"
+      }
+
+      var trace16 = {
+        x: xaxisReversed, 
+        y: this.Stack_scoresPositive4.concat(this.Stack_scoresNegative4), 
+        text: '',
+        hoverinfo: 'text',
+        fill: "tozerox", 
+        fillcolor: "rgba(228,26,28,0)", 
+        line: {color: "transparent"}, 
+        name: "Stack F1 Score", 
+        showlegend: false, 
+        type: "scatter"
+      }
+
+      const DataforLinePlot = [trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8, trace9, trace10, trace11, trace12, trace13, trace14, trace15, trace16]
 
       var width = this.WH[0]*3 // interactive visualization
       var height = this.WH[1]*0.6 // interactive visualization

frontend/src/components/Heatmap.vue

@@ -434,6 +434,7 @@ export default {
                 }
                 finalresults.push(results)
             }
+            console.log(finalresults)
             EventBus.$emit('flagLocal', true)
             EventBus.$emit('sendSelectedFeaturestoPickle', finalresults)
             EventBus.$emit('SendSelectedFeaturesEvent', finalresults)

frontend/src/components/Main.vue

@@ -4,9 +4,9 @@
 <body>
     <b-container fluid class="bv-example-row">
       <b-row class="md-3">
-        <b-col cols="3">
+        <b-col cols="3" >
           <mdb-card>
-            <mdb-card-header color="primary-color" tag="h5" class="text-center">Data Sets and Performance Metrics Manager</mdb-card-header>
+            <mdb-card-header color="primary-color" tag="h5" class="text-center" style="background-color: #C0C0C0;">Data Sets and Performance Metrics Manager</mdb-card-header>
               <mdb-card-body>
                 <mdb-card-text class="text-left" style="font-size: 18.5px;">
                   <DataSetExecController/>
@@ -17,7 +17,7 @@
         </b-col>
         <b-col cols="6">
           <mdb-card>
-            <mdb-card-header color="primary-color" tag="h5" class="text-center">Provenance of the Stacking Ensemble<small class="float-right"><knowledge/></small></mdb-card-header>
+            <mdb-card-header color="primary-color" tag="h5" class="text-center" style="background-color: #C0C0C0;"><small class="float-left"><knowledge/></small>History of the Stacking Ensemble<small class="float-right"><active/></small></mdb-card-header>
             <mdb-card-body>
                 <Provenance/>
             </mdb-card-body>
@@ -25,22 +25,23 @@
         </b-col>
         <b-col cols="3">
             <mdb-card>
-              <mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [1, 3 (W), 4 (W)]</small>Performance of the Metamodel</mdb-card-header>
+              <mdb-card-header color="primary-color" tag="h5" class="text-center" style="background-color: #C0C0C0;"><small class="float-left" style="padding-top: 3px;"><font-awesome-icon class="fa-lg" icon="dice-four"/>&nbsp;Metrics Support: [M1, M3-M5 (W)]</small>Metamodel's Performance</mdb-card-header>
               <mdb-card-body>
                 <FinalResultsLinePlot/>
               </mdb-card-body>
             </mdb-card>
         </b-col>
       </b-row>
+      <hr/>
       <ul class="nav nav-tabs" id="myTab" role="tablist" style="margin-top: 15px">
       <li class="nav-item">
-        <a class="nav-link active" id="home-tab" data-toggle="tab" href="#home" role="tab" aria-controls="home" aria-selected="true">Algorithms</a>
+        <a class="nav-link active" id="home-tab" data-toggle="tab" href="#home" role="tab" aria-controls="home" aria-selected="true">Algorithms' Exploration</a>
       </li>
       <li class="nav-item">
-        <a class="nav-link" id="profile-tab" data-toggle="tab" href="#profile" role="tab" aria-controls="profile" aria-selected="false">Data</a>
+        <a class="nav-link" id="profile-tab" data-toggle="tab" href="#profile" role="tab" aria-controls="profile" aria-selected="false">Data Wrangling</a>
       </li>
       <li class="nav-item">
-        <a class="nav-link" id="contact-tab" data-toggle="tab" href="#contact" role="tab" aria-controls="contact" aria-selected="false">Models</a>
+        <a class="nav-link" id="contact-tab" data-toggle="tab" href="#contact" role="tab" aria-controls="contact" aria-selected="false">Models' Exploration</a>
       </li>
       </ul>
       <div class="tab-content" id="myTabContent">
@@ -49,14 +50,14 @@
             <b-col cols="3">
               <mdb-card style="margin-top: 15px">
                 <mdb-card-header color="primary-color" tag="h5" class="text-center">Overview of the Algorithms Space</mdb-card-header>
-                <mdb-card-body class="text-left">
+                <mdb-card-body class="text-center">
                   <Parameters/>
                 </mdb-card-body>
               </mdb-card>
             </b-col>
             <b-col cols="9">
               <mdb-card style="margin-top: 15px;">
-                <mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Performance of the Algorithms [Sel: {{valueSel}} / All: {{valueAll}}]<small class="float-right"><Controller/></small></mdb-card-header>
+                <mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Performance of the Algorithms [Sel: {{valueSel}} / All: {{valueAll}}]<small class="float-right" style="font-size:85% !important"><Controller/></small></mdb-card-header>
                   <mdb-card-body>
                       <Algorithms :width="width" :height="height"/>
                   </mdb-card-body>
@@ -74,7 +75,7 @@
             </b-col>
             <b-col cols="9">
               <mdb-card style="margin-top: 15px;">
-                <mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [3*, 4*, 5* (F1 Score)]</small>Performance of the Algorithms for Each Class<small class="float-right"><ResetClass/></small></mdb-card-header>
+                <mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;"><font-awesome-icon class="fa-lg" icon="dice-three"/>&nbsp;Metrics Support: [M3*, M4*, M5* (F1 Score)]</small>Performance of the Algorithms for Each Class<small class="float-right"><ResetClass/></small></mdb-card-header>
                 <mdb-card-body>
                     <BarChart/>
                 </mdb-card-body>
@@ -113,7 +114,7 @@
           <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"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Models Space
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Models' Space
                     [Sel: {{OverSelLength}} / All: {{OverAllLength}}]
                     </mdb-card-header>
                     <mdb-card-body>
@@ -126,7 +127,7 @@
               </b-col>
               <b-col cols="6">
                 <mdb-card style="margin-top: 15px;">
-                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Predictions Space</mdb-card-header>
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Predictions' Space</mdb-card-header>
                   <mdb-card-body>
                     <mdb-card-text class="text-center"  style="min-height: 845px">
                       <PredictionsSpace/>
@@ -169,6 +170,7 @@ import AlgorithmHyperParam from './AlgorithmHyperParam.vue'
 import Controller from './Controller.vue'
 import ResetClass from './ResetClass.vue'
 import Knowledge from './Knowledge.vue'
+import Active from './Active.vue'
 import Export from './Export.vue'
 import SlidersController from './SlidersController.vue'
 import ScatterPlot from './ScatterPlot.vue'
@@ -207,6 +209,7 @@ export default Vue.extend({
     Controller,
     ResetClass,
     Knowledge,
+    Active,
     SlidersController,
     ScatterPlot,
     PerMetricBarChart,
@@ -320,7 +323,6 @@ export default Vue.extend({
         .then(response => {
           this.OverviewResults = response.data.OverviewResults
           console.log('Server successfully sent all the data related to visualizations!')
-          this.DataSpaceCallAfterDataManipulation()
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults)
           if (this.firstTimeFlag == 1) {
             this.selectedModels_Stack.push(0)
@@ -347,6 +349,7 @@ export default Vue.extend({
           EventBus.$emit('emittedEventCallingHeatmapView', this.OverviewResults)
           EventBus.$emit('emittedEventCallingPredictionsSpacePlotView', this.OverviewResults)
           EventBus.$emit('emittedEventCallingBalanceView', this.OverviewResults)
+          this.DataSpaceCall()
         })
         .catch(error => {
           console.log(error)
@@ -404,7 +407,6 @@ export default Vue.extend({
         this.OverSelLength = 0
         EventBus.$emit('resetViews')
       } else {
-        console.log(this.ClassifierIDsList)
         const path = `http://127.0.0.1:5000/data/ServerRequestSelPoin`
         const postData = {
           ClassifiersList: this.ClassifierIDsList,
@@ -626,26 +628,6 @@ export default Vue.extend({
           console.log(error)
         })
     },
-    DataSpaceCallAfterDataManipulation () {
-      const path = `http://localhost:5000/data/requestDataSpaceResultsAfterDataManipulation`
-
-      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 => {
-          console.log('Calling Data Space!')
-          this.DataSpaceCall()
-        })
-        .catch(error => {
-          console.log(error)
-        })
-    },
     SendAlgorithmsToServer () {
       const path = `http://127.0.0.1:5000/data/ServerRequestSelParameters`
       const postData = {
@@ -1068,12 +1050,23 @@ body {
   right: 0px;
   top: 0px;
   bottom: 0px;
-  margin-top: 18px !important;
+  margin-top: -4px !important;
 }
 
 .modal-backdrop {
   z-index: -1 !important;
 }
 
+.card-body {
+   padding: 0.60rem !important;
+}
+
+hr {
+  margin-top: 1rem;
+  margin-bottom: 1rem;
+  border: 0;
+  border-top: 1px solid rgba(0, 0, 0, 0.1);
+}
+
 @import './../assets/w3.css';
 </style>

frontend/src/components/Parameters.vue

@@ -75,7 +75,7 @@ export default {
       countAdaBRelated: [],
       countAdaB: 0,
       countGradBRelated: [],
-      countGradB: 0
+      countGradB: 0,
     }
   },
   methods: {
@@ -89,7 +89,7 @@ export default {
       var cfg = {
       w: 600,				//Width of the circle
       h: 600,				//Height of the circle
-      margin: {top: 20, right: 20, bottom: 20, left: 20}, //The margins around the circle
+      margin: {top: 60, right: 20, bottom: 20, left: 120}, //The margins around the circle
       legendPosition: {x: 20, y: 20}, // the position of the legend, from the top-left corner of the svg
       levels: 3,				//How many levels or inner circles should there be drawn
       maxValue: 0, 				//What is the value that the biggest circle will represent
@@ -105,6 +105,7 @@ export default {
       areaName:"areaName",
       value: "value",
       sortAreas: true,
+      colorsDiff: ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
       };
       
       //Put all of the options into a variable called cfg
@@ -174,6 +175,7 @@ export default {
         .enter()
         .append("circle")
         .attr("class", "gridCircle")
+        .attr("transform", "translate(5,5)")
         .attr("r", function(d, i){return radius/cfg.levels*d;})
         .style("fill", "#CDCDCD")
         .style("stroke", "#CDCDCD")
@@ -185,10 +187,10 @@ export default {
         .data(d3.range(1,(cfg.levels+1)).reverse())
         .enter().append("text")
         .attr("class", "axisLabel")
-        .attr("x", 4)
+        .attr("x", 10)
         .attr("y", function(d){return -d*radius/cfg.levels;})
         .attr("dy", "0.4em")
-        .style("font-size", "10px")
+        .style("font-size", "16px")
         .attr("fill", "#737373")
         .text(function(d,i) { return Format(maxValue * d/cfg.levels); });
 
@@ -206,19 +208,31 @@ export default {
       axis.append("line")
         .attr("x1", 0)
         .attr("y1", 0)
-        .attr("x2", function(d, i){ return rScale(maxValue*1.1) * Math.cos(angleSlice*i - Math.PI/2); })
-        .attr("y2", function(d, i){ return rScale(maxValue*1.1) * Math.sin(angleSlice*i - Math.PI/2); })
+        .attr("transform", "translate(5,5)")
+        .attr("x2", function(d, i){ return rScale(maxValue*1.2) * Math.cos(angleSlice*i - Math.PI/2); })
+        .attr("y2", function(d, i){ return rScale(maxValue*1.2) * Math.sin(angleSlice*i - Math.PI/2); })
         .attr("class", "line")
         .style("stroke", "white")
         .style("stroke-width", "2px");
 
+      axis.append("rect")
+        .attr("text-anchor", "left")
+        .attr("dy", "0.35em")
+        .attr("x", function(d, i){ return (rScale(maxValue * cfg.labelFactor) * Math.cos(angleSlice*i - Math.PI/2)) - 25; })
+        .attr("y", function(d, i){ return rScale(maxValue * cfg.labelFactor) * Math.sin(angleSlice*i - Math.PI/2); })
+        .text(function(d){return d})
+        .attr("width", 15)
+        .attr("height", 15)
+        .style("fill", function(d,i) { return cfg.colorsDiff[i]; })
+
       //Append the labels at each axis
       axis.append("text")
         .attr("class", "legend")
-        .style("font-size", "11px")
+        .style("font-size", "16px")
         .attr("text-anchor", "middle")
-        .attr("dy", "0.35em")
-        .attr("x", function(d, i){ return rScale(maxValue * cfg.labelFactor) * Math.cos(angleSlice*i - Math.PI/2); })
+        .attr("dy", "0em")
+        .style("font-size", "16px")
+        .attr("x", function(d, i){ return (rScale(maxValue * cfg.labelFactor) * Math.cos(angleSlice*i - Math.PI/2)) + 15; })
         .attr("y", function(d, i){ return rScale(maxValue * cfg.labelFactor) * Math.sin(angleSlice*i - Math.PI/2); })
         .text(function(d){return d})
         .call(wrap, cfg.wrapWidth);
@@ -241,6 +255,7 @@ export default {
       var blobWrapper = g.selectAll(".radarWrapper")
         .data(data)
         .enter().append("g")
+        .attr("transform", "translate(5,5)")
         .attr("class", "radarWrapper");
           
       //Append the backgrounds	
@@ -316,8 +331,8 @@ export default {
           newY =  parseFloat(d3.select(this).attr('cy')) - 10;
               
           tooltip
-            .attr('x', newX)
-            .attr('y', newY)
+            .attr('x', newX+5)
+            .attr('y', newY+5)
             .text(Format(d[value]))
             .transition().duration(200)
             .style('opacity', 1);
@@ -619,39 +634,39 @@ export default {
     ////////////////////////////////////////////////////////////// 
 
       var margin = {top: 50, right: 120, bottom: 55, left: 65},
-        legendPosition = {x: 425, y: 185},
-				width = Math.min(510, window.innerWidth - 10) - margin.left - margin.right,
-				height = Math.min(width, window.innerHeight - margin.top - margin.bottom - 20);
-					
+        legendPosition = {x: 425, y: 25},
+				width = Math.min(520, window.innerWidth - 10) - margin.left - margin.right,
+        height = Math.min(width + 12, window.innerHeight + 12 - margin.top - margin.bottom);
+        
 			////////////////////////////////////////////////////////////// 
 			////////////////////////// Data ////////////////////////////// 
 			////////////////////////////////////////////////////////////// 
 
 			var data = [
 					  [
-						{axis:"KNN [M 576]",legend:"Entire",value:KNNAll/max},
-            {axis:"SVC [M 160]",legend:"Entire",value:SVCAll/max},
-            {axis:"GausNB [M 500]",legend:"Entire",value:GausNBAll/max},
-            {axis:"MLP [M 120]",legend:"Entire",value:MLPAll/max},
-            {axis:"LR [M 640]",legend:"Entire",value:LRAll/max},
-            {axis:"LDA [M 200]",legend:"Entire",value:LDAAll/max},
-            {axis:"QDA [M 250]",legend:"Entire",value:QDAAll/max},
-            {axis:"RF [M 160]",legend:"Entire",value:RFAll/max},
-            {axis:"ExtraT [M 160]",legend:"Entire",value:ExtraTAll/max},
-            {axis:"AdaB [M 160]",legend:"Entire",value:AdaBAll/max},
-            {axis:"GradB [M 180]",legend:"Entire",value:GradBAll/max},
+						{axis:"KNN [576]",legend:"Entire",value:KNNAll/max},
+            {axis:"SVC [160]",legend:"Entire",value:SVCAll/max},
+            {axis:"GauNB [500]",legend:"Entire",value:GausNBAll/max},
+            {axis:"MLP [120]",legend:"Entire",value:MLPAll/max},
+            {axis:"LR [640]",legend:"Entire",value:LRAll/max},
+            {axis:"LDA [200]",legend:"Entire",value:LDAAll/max},
+            {axis:"QDA [250]",legend:"Entire",value:QDAAll/max},
+            {axis:"RF [160]",legend:"Entire",value:RFAll/max},
+            {axis:"ExtraT [160]",legend:"Entire",value:ExtraTAll/max},
+            {axis:"AdaB [160]",legend:"Entire",value:AdaBAll/max},
+            {axis:"GradB [180]",legend:"Entire",value:GradBAll/max},
             ],[
-						{axis:"KNN [M 576]",legend:"Selection",value:KNNSelection/max},
-            {axis:"SVC [M 160]",legend:"Selection",value:SVCSelection/max},
-            {axis:"GausNB [M 500]",legend:"Selection",value:GausNBSelection/max},
-            {axis:"MLP [M 120]",legend:"Selection",value:MLPSelection/max},
-            {axis:"LR [M 640]",legend:"Selection",value:LRSelection/max},
-            {axis:"LDA [M 200]",legend:"Selection",value:LDASelection/max},
-            {axis:"QDA [M 250]",legend:"Selection",value:QDASelection/max},
-            {axis:"RF [M 160]",legend:"Selectionn",value:RFSelection/max},
-            {axis:"ExtraT [M 160]",legend:"Selection",value:ExtraTSelection/max},
-            {axis:"AdaB [M 160]",legend:"Selection",value:AdaBSelection/max},
-            {axis:"GradB [M 180]",legend:"Selection",value:GradBSelection/max},
+						{axis:"KNN [576]",legend:"Selection",value:KNNSelection/max},
+            {axis:"SVC [160]",legend:"Selection",value:SVCSelection/max},
+            {axis:"GauNB [500]",legend:"Selection",value:GausNBSelection/max},
+            {axis:"MLP [120]",legend:"Selection",value:MLPSelection/max},
+            {axis:"LR [640]",legend:"Selection",value:LRSelection/max},
+            {axis:"LDA [200]",legend:"Selection",value:LDASelection/max},
+            {axis:"QDA [250]",legend:"Selection",value:QDASelection/max},
+            {axis:"RF [160]",legend:"Selectionn",value:RFSelection/max},
+            {axis:"ExtraT [160]",legend:"Selection",value:ExtraTSelection/max},
+            {axis:"AdaB [160]",legend:"Selection",value:AdaBSelection/max},
+            {axis:"GradB [180]",legend:"Selection",value:GradBSelection/max},
 					  ],
 					];
 			////////////////////////////////////////////////////////////// 
@@ -765,10 +780,12 @@ export default {
   stroke: #fff;
   stroke-width: 3px;
   fill: url(#gradient-chart-area);
+  transform: translate(5px, 5px);
 }
 
 .center-circle {
   fill: #fff;
+  transform: translate(5px, 5px);
 }
 
 .bars {
@@ -778,6 +795,7 @@ export default {
 .gridlines {
   fill: none;
   stroke: #fff;
+  transform: translate(5px, 5px);
 }
 
 .minor {
@@ -796,12 +814,12 @@ export default {
 
 .category-label-text {
   font-weight: bold;
-  font-size: 14px;
+  font-size: 16px;
   fill: #fff;
 }
 
 .question-label-text {
-  font-size: 7px;
+  font-size: 16px;
   font-weight: bold;
   fill: gray;
 }
@@ -814,7 +832,7 @@ export default {
 .category-labels {
   text-anchor: middle;
   font-weight: bold;
-  font-size: 14px;
+  font-size: 16px;
   fill: #fff;
 }
 
@@ -823,6 +841,6 @@ export default {
 }
 
 #overview {
-  min-height: 430px;
+  min-height: 450px;
 }
 </style>

frontend/src/components/PerMetricBarChart.vue

@@ -80,12 +80,13 @@ export default {
           }
           };
           var data = [trace1, trace2];
-          var layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+          var layout = {
           boxmode: 'group',
           autosize: true,
           width:  width,
           height: height,
           hovermode: 'x',
+          font: { family: 'Helvetica', size: 14, color: '#000000' },
           margin: {
             l: 50,
             r: 0,

frontend/src/components/PredictionsSpace.vue

@@ -26,6 +26,7 @@ export default {
       UpdatedData: '',
       representationDef: 'mds',
       representationSelection: 'mds',
+      RetrieveDataSet: 'HeartC',
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
       WH: []
     }
@@ -102,7 +103,7 @@ export default {
               x: aux_X,
               y: aux_Y,
               mode: 'markers',
-              name: target_names[i],
+              name: beautifyLabels[i],
               marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: 1, size: 12 },
               hovertemplate: 
                       "<b>%{text}</b><br><br>" +
@@ -111,7 +112,7 @@ export default {
             })
         }
 
-        layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+        layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
         title: 'MDS Projection',
         xaxis: {
             visible: false
@@ -169,7 +170,7 @@ export default {
             x: aux_X,
             y: aux_Y,
             mode: 'markers',
-            name: target_names[i],
+            name: beautifyLabels[i],
             marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: 1, size: 12 },
             hovertemplate: 
                     "<b>%{text}</b><br><br>" +
@@ -178,7 +179,7 @@ export default {
           })
         }
 
-        layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+        layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
         title: 't-SNE Projection',
         xaxis: {
             visible: false
@@ -211,6 +212,20 @@ export default {
 
         var traces = []
 
+        var beautifyLabels = []
+        if (this.RetrieveDataSet == 'StanceC') {
+          beautifyLabels.push('Absence of Hypotheticality')
+          beautifyLabels.push('Presence of Hypotheticality')
+        }
+        else if (this.RetrieveDataSet == 'HeartC') {
+          beautifyLabels.push('< 50% diameter narrowing / Healthy')
+          beautifyLabels.push('> 50% diameter narrowing / Diseased')
+        } else {
+          target_names.forEach(element => {
+            beautifyLabels.push(element)
+          });
+        }
+
         for (let i = 0; i < target_names.length; i++) {
 
           const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
@@ -226,7 +241,7 @@ export default {
               x: aux_X,
               y: aux_Y,
               mode: 'markers',
-              name: target_names[i],
+              name: beautifyLabels[i],
               marker: { color: this.colorsValues[i], line: { color: 'rgb(0, 0, 0)', width: 2 }, opacity: 1, size: 12 },
               hovertemplate: 
                       "<b>%{text}</b><br><br>" +
@@ -235,7 +250,7 @@ export default {
             })
         }
 
-        layout = {font: { family: 'Helvetica', size: 16, color: '#000000' },
+        layout = {font: { family: 'Helvetica', size: 14, color: '#000000' },
         title: 'UMAP Projection',
         xaxis: {
             visible: false
@@ -321,6 +336,8 @@ export default {
 
     // reset the views
     EventBus.$on('resetViews', this.reset)
+
+    EventBus.$on('SendToServerDataSetConfirmation', data => { this.RetrieveDataSet = data })
   }
 }
 </script>

frontend/src/components/Provenance.vue

@@ -1,23 +1,19 @@
 <template>
     <div>
-        <div class="squares-container" style="min-height: 306px;">
+        <div class="squares-container" style="min-height: 364px;">
         <div id="tooltip"></div>	<!-- new  -->
-            <canvas id="main-canvas" style="overflow-y: auto; height:290px;"></canvas>
-            <br>
-            <div id="dynamic-buttons"></div>
+          <div id="performanceCapture" style="min-height: 150px;"></div>	<!-- new  -->
+          <canvas id="main-canvas" style="overflow-y: auto; height:190px;"></canvas>
+          <br>
+          <div id="dynamic-buttons"></div>
         </div>
     </div>
 </template>
 
 <script>
-import * as d3Base from 'd3'
-
 import { EventBus } from '../main.js'
 import $ from 'jquery'
 
-// attach all d3 plugins to the d3 library
-const d3 = Object.assign(d3Base)
-
 import * as Stardust from 'stardust-core'
 import * as StardustGL from 'stardust-webgl'
 
@@ -72,7 +68,6 @@ export default {
       var canvas = document.getElementById("main-canvas");
       var width = this.WH[0]*4 // interactive visualization
       var height = this.WH[1]*0.58 // interactive visualization
-      console.log(width)
 
       var flagKNN = 0
       var flagSVC = 0
@@ -88,6 +83,7 @@ export default {
 
       var localStackStore = []
       var StackInfo = JSON.parse(this.stackInformation[1])
+      console.log(StackInfo)
       var arrayOfNumbers = StackInfo.map(Number)
       this.storeData.push(arrayOfNumbers)
       localStackStore = this.storeData.slice()
@@ -213,7 +209,9 @@ export default {
       this.data.forEach(d => {
         if (d.column == this.counter) {
           d.typeIndex = this.typeCounter[d.type]++;
+          console.log(d.typeIndex)
           d.typeColumnIndex = this.typeColumnCounter[d.column]++;
+          console.log(d.typeColumnIndex)
         }
       });
 
@@ -226,7 +224,7 @@ export default {
       let isotypeHeight = 18;
       let colors = [[166,206,227], [31,120,180], [178,223,138], [51,160,44], [251,154,153], [227,26,28], [253,191,111], [255,127,0], [202,178,214], [106,61,154], [177,89,40]];
       colors = colors.map(x => [x[0] / 255, x[1] / 255, x[2] / 255, 1]);
-
+      // here 10 was 5!
       let pScale = Stardust.scale.custom(`
               Vector2(
                   20 + column * 100 + typeColumnIndex % 5 * 8,
@@ -327,7 +325,7 @@ export default {
 
 			}
       }
-      const stringStep = "Stack "
+      const stringStep = "Stack Ensemble"
       var myButton = '<button id="HistoryReturnButtons'+this.counter+'" class="dynamic_buttons">'+stringStep+this.counter+'</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'
       $("#dynamic-buttons").append(myButton);
 
@@ -354,6 +352,103 @@ export default {
         EventBus.$emit('ChangeKey', 0)
         }
       );
+
+      var width = 150;
+      var arcSize = (6 * width / 100);
+      var innerRadius = arcSize * 3;            
+
+      var data = [
+          {value: 45, label: "Accuracy", color: '#ff0000'},
+          {value: 33, label: "Precision", color: '#00ff00'},
+          {value: 66, label: "Recall", color: '#0000ff'},
+          {value: 50, label: "F1 Score", color: '#ffff00'}
+      ];
+
+      function render() {
+          var svg = d3.select('#performanceCapture').append('svg').attr('width', width).attr('height', width);
+
+          var arcs = data.map(function (obj, i) {
+              return d3.svg.arc().innerRadius(i * arcSize + innerRadius).outerRadius((i + 1) * arcSize - (width / 100) + innerRadius);
+          });
+          var arcsGrey = data.map(function (obj, i) {
+              return d3.svg.arc().innerRadius(i * arcSize + (innerRadius + ((arcSize / 2) - 2))).outerRadius((i + 1) * arcSize - ((arcSize / 2)) + (innerRadius));
+          });
+
+          var pieData = data.map(function (obj, i) {
+              return [
+                  {value: obj.value * 0.75, arc: arcs[i], object: obj},
+                  {value: (100 - obj.value) * 0.75, arc: arcsGrey[i], object: obj},
+                  {value: 100 * 0.25, arc: arcs[i], object: obj}];
+          });
+
+          var pie = d3.layout.pie().sort(null).value(function (d) {
+              return d.value;
+          });
+
+          var g = svg.selectAll('g').data(pieData).enter()
+              .append('g')
+              .attr('transform', 'translate(' + width / 2 + ',' + width / 2 + ') rotate(180)');
+          var gText = svg.selectAll('g.textClass').data([{}]).enter()
+              .append('g')
+              .classed('textClass', true)
+              .attr('transform', 'translate(' + width / 2 + ',' + width / 2 + ') rotate(180)');
+
+
+          g.selectAll('path').data(function (d) {
+              return pie(d);
+          }).enter().append('path')
+              .attr('id', function (d, i) {
+                  if (i == 1) {
+                      return "Text" + d.data.object.label
+                  }
+              })
+              .attr('d', function (d) {
+                  return d.data.arc(d);
+              }).attr('fill', function (d, i) {
+              return i == 0 ? d.data.object.color : i == 1 ? '#D3D3D3' : 'none';
+          });
+
+          svg.selectAll('g').each(function (d, index) {
+              var el = d3.select(this);
+              var path = el.selectAll('path').each(function (r, i) {
+                  if (i === 1) {
+                      var centroid = r.data.arc.centroid({
+                          startAngle: r.startAngle + 0.05,
+                          endAngle: r.startAngle + 0.001 + 0.05
+                      });
+                      var lableObj = r.data.object;
+                      g.append('text')
+                          .attr('font-size', ((5 * width) / 100))
+                          .attr('dominant-baseline', 'central')
+                          /*.attr('transform', "translate(" + centroid[0] + "," + (centroid[1] + 10) + ") rotate(" + (180 / Math.PI * r.startAngle + 7) + ")")
+                            .attr('alignment-baseline', 'middle')*/
+                          .append("textPath")
+                          .attr("textLength", function (d, i) {
+                              return 0;
+                          })
+                          .attr("xlink:href", "#Text" + r.data.object.label)
+                          .attr("startOffset", '5')
+                          .attr("dy", '-3em')
+                          .text(lableObj.value + '%');
+                  }
+                  if (i === 0) {
+                      var centroidText = r.data.arc.centroid({
+                          startAngle: r.startAngle,
+                          endAngle: r.startAngle
+                      });
+                      var lableObj = r.data.object;
+                      gText.append('text')
+                          .attr('font-size', ((5 * width) / 100))
+                          .text(lableObj.label)
+                          .attr('transform', "translate(" + (centroidText[0] - ((1.5 * width) / 100)) + "," + (centroidText[1] + ") rotate(" + (180) + ")"))
+                          .attr('dominant-baseline', 'central');
+                  }
+              });
+          });
+      }
+
+
+      render()
   },
   updateExtraction () {
     EventBus.$emit('SendSelectedPointsToServerEvent', this.storeData[this.flagUpdated])

frontend/src/components/SlidersController.vue

@@ -1,26 +1,57 @@
 <template>
 <div>
 <b-row>
-    <b-col cols="10">
-      <div id="WrapSliders">
-        <p>(1) Accuracy:<b-form-slider ref="basic1" v-model="basicValue1" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue1 }}%</p>
-        <p>(2*) G-Mean:<b-form-slider ref="basic2" v-model="basicValue2" :min="0" :max="100" trigger-change-event @slide-start="slideStart"  @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue2 }}%</p>
-        <p>(3*) Precision:<b-form-slider ref="basic3" v-model="basicValue3" :min="0" :max="100" trigger-change-event @slide-start="slideStart"  @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue3 }}%</p>
-        <p>(4*) Recall:<b-form-slider ref="basic4" v-model="basicValue4" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue4 }}%</p>
-        <p>(5*) F-Beta Score:<b-form-slider ref="basic5" v-model="basicValue5" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider >{{ basicValue5 }}%</p>
-        <p>(6) MCC:<b-form-slider ref="basic6" v-model="basicValue6" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue6 }}%</p>
-        <p>(7) ROC AUC:<b-form-slider ref="basic7" v-model="basicValue7" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue7 }}%</p>
-        <p>(8) Log Loss:<b-form-slider ref="basic8" v-model="basicValue8" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue8 }}%</p>
-      </div>
-    </b-col>
-    <b-col cols="2">
-      <br>
-      <br>
-      Deep Feature Search:
-      <input type="checkbox" id="toggleDeepID" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small">
+    <b-col cols="12">
+      <table class="table table-borderless table-sm">
+        <tbody>
+          <tr>
+            <td>(M1) Accuracy:</td>
+            <td><b-form-slider ref="basic1" v-model="basicValue1" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;"></b-form-slider>{{ basicValue1 }}%</td>
+            <td style="text-align: center; width: 70px"><font-awesome-icon class="fa-lg" icon="dice-four"/></td>
+          </tr>
+          <tr>
+            <td>(M2*) G-Mean:</td>
+            <td><b-form-slider ref="basic2" v-model="basicValue2" :min="0" :max="100" trigger-change-event @slide-start="slideStart"  @slide-stop="slideStop" style="padding-right: 15px;"></b-form-slider>{{ basicValue2 }}%</td>
+            <td style="text-align: center; width: 70px"></td>
+          </tr>
+          <tr>
+            <td>(M3*) Precision:</td>
+            <td><b-form-slider ref="basic3" v-model="basicValue3" :min="0" :max="100" trigger-change-event @slide-start="slideStart"  @slide-stop="slideStop" style="padding-right: 15px; "></b-form-slider>{{ basicValue3 }}%</td>
+            <td style="text-align: center; width: 70px"><font-awesome-icon class="fa-lg" icon="dice-three"/>&nbsp;<font-awesome-icon class="fa-lg" icon="dice-four"/></td>
+          </tr>
+          <tr>
+            <td>(M4*) Recall:</td>
+            <td><b-form-slider ref="basic4" v-model="basicValue4" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; "></b-form-slider>{{ basicValue4 }}%</td>
+            <td style="text-align: center; width: 70px"><font-awesome-icon class="fa-lg" icon="dice-three"/>&nbsp;<font-awesome-icon class="fa-lg" icon="dice-four"/></td>
+          </tr>
+          <tr>
+            <td width="185">(M5*) <select id="selectFilterBeta" @change="selectAppliedFilterBeta()" >
+        <option value="one" selected>F1 Score</option>
+        <option value="half">F0.5 Score</option>
+        <option value="two">F2 Score</option>
+      </select>:</td>
+            <td><b-form-slider ref="basic5" v-model="basicValue5" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; "></b-form-slider >{{ basicValue5 }}%</td>
+            <td style="text-align: center; width: 70px"><font-awesome-icon class="fa-lg" icon="dice-three"/>&nbsp;<font-awesome-icon class="fa-lg" icon="dice-four"/></td>
+          </tr>
+          <tr>
+            <td>(M6) MCC:</td>
+            <td><b-form-slider ref="basic6" v-model="basicValue6" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; "></b-form-slider>{{ basicValue6 }}%</td>
+            <td style="text-align: center; width: 70px"></td>
+          </tr>
+          <tr>
+            <td>(M7) ROC AUC:</td>
+            <td><b-form-slider ref="basic7" v-model="basicValue7" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;"></b-form-slider>{{ basicValue7 }}%</td>
+            <td style="text-align: center; width: 70px"></td>
+          </tr>
+          <tr>
+            <td>(M8) Log Loss:</td>
+            <td><b-form-slider ref="basic8" v-model="basicValue8" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;"></b-form-slider>{{ basicValue8 }}%</td>
+            <td style="text-align: center; width: 70px"></td>
+          </tr>
+        </tbody>
+      </table>
     </b-col>
 </b-row>
-<br>
 <b-row>
     <b-col cols="6">
         <p>(*) Average: <select id="selectFilterAverage" @change="selectAppliedFilterAverage()">
@@ -30,11 +61,7 @@
       </select></p>
     </b-col>
     <b-col cols="6">
-        <p>(5) Beta Value: <select id="selectFilterBeta" @change="selectAppliedFilterBeta()">
-        <option value="one" selected>F1 Score</option>
-        <option value="half">F0.5 Score</option>
-        <option value="two">F2 Score</option>
-      </select></p>
+        <p>Detailed Feature Search: <input type="checkbox" id="toggleDeepID" data-toggle="toggle" checked="checked" data-on="Enabled" data-off="Disabled" data-size="small"></p>
     </b-col>
 </b-row>
 </div>
@@ -217,9 +244,9 @@
         },
         mounted () {
           $('#toggleDeepID').bootstrapToggle({
-            on: 'I',
-            off: '0',
-            width: '50%',
+            on: 'On',
+            off: 'Off',
+            width: '20%',
          });
         $('#toggleDeepID').change(function() {
           var toggleDeepSlid = document.getElementById('toggleDeepID')
@@ -239,9 +266,9 @@ p {
     padding: 0 !important;
 }
 
-/*.slider.slider-horizontal {
-    width: 300px !important;
-}*/
+.slider.slider-horizontal{
+  width:260px; /* sample value - set it as you like*/
+}
 
 .slider-handle {
     background: black;

frontend/src/main.js

@@ -6,10 +6,10 @@ import 'bootstrap-vue/dist/bootstrap-vue.css'
 import router from './router'
 import { library } from '@fortawesome/fontawesome-svg-core'
 import { FontAwesomeIcon } from '@fortawesome/vue-fontawesome'
-import { faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt, faFileExport, faWindowClose, faHistory } from '@fortawesome/free-solid-svg-icons'
+import { faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt, faFileExport, faWindowClose, faHistory, faDiceThree, faDiceFour, faCalculator } from '@fortawesome/free-solid-svg-icons'
 import bFormSlider from 'vue-bootstrap-slider'
 
-library.add(faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt, faFileExport, faWindowClose, faHistory )
+library.add(faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus, faEraser, faClone, faObjectGroup, faUndo, faSyncAlt, faFileExport, faWindowClose, faHistory, faDiceThree, faDiceFour,faCalculator )
 
 Vue.component('font-awesome-icon', FontAwesomeIcon)
 

run.py

@@ -388,30 +388,15 @@ def DataSetSelection():
     XDataStored = XData.copy()
     yDataStored = yData.copy()
 
-    callPreResults()
-
     warnings.simplefilter('ignore')
     return 'Everything is okay'
 
-# Sending each model's results to frontend
-@app.route('/data/requestDataSpaceResultsAfterDataManipulation', methods=["GET", "POST"])
-def SendDataSpaceResultsAfterDataSpaceManipul():
-
-    callPreResults()
-
-    global preResults
-
-    response = {    
-        'DataResults': preResults,
-    }
-    return jsonify(response)
-
 def callPreResults():
 
     global XData
     global yData
     global target_names
-    global allParametersPerformancePerModel
+    global impDataInst
 
     DataSpaceResMDS = FunMDS(XData)
     DataSpaceResTSNE = FunTsne(XData)
@@ -430,7 +415,7 @@ def callPreResults():
     preResults.append(json.dumps(AllTargets)) # Position: 4
     preResults.append(json.dumps(DataSpaceResTSNE)) # Position: 5
     preResults.append(json.dumps(DataSpaceUMAP)) # Position: 6
-    preResults.append(json.dumps(allParametersPerformancePerModel)) # Position: 7
+    preResults.append(json.dumps(impDataInst)) # Position: 7
 
 # Sending each model's results to frontend
 @app.route('/data/requestDataSpaceResults', methods=["GET", "POST"])
@@ -507,7 +492,7 @@ def RetrieveModel():
             params = {'n_neighbors': list(range(1, 25)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}
             AlgorithmsIDsEnd = 0
         elif (eachAlgor) == 'SVC':
-            clf = SVC(probability=True)
+            clf = SVC(probability=True,random_state=RANDOM_SEED)
             params = {'C': list(np.arange(0.1,4.43,0.11)), 'kernel': ['rbf','linear', 'poly', 'sigmoid']}
             AlgorithmsIDsEnd = SVCModelsCount
         elif (eachAlgor) == 'GausNB':
@@ -515,11 +500,11 @@ def RetrieveModel():
             params = {'var_smoothing': list(np.arange(0.00000000001,0.0000001,0.0000000002))}
             AlgorithmsIDsEnd = GausNBModelsCount
         elif (eachAlgor) == 'MLP':
-            clf = MLPClassifier()
+            clf = MLPClassifier(random_state=RANDOM_SEED)
             params = {'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0004)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']}
             AlgorithmsIDsEnd = MLPModelsCount
         elif (eachAlgor) == 'LR':
-            clf = LogisticRegression()
+            clf = LogisticRegression(random_state=RANDOM_SEED)
             params = {'C': list(np.arange(0.5,2,0.075)), 'max_iter': list(np.arange(50,250,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}
             AlgorithmsIDsEnd = LRModelsCount
         elif (eachAlgor) == 'LDA':
@@ -531,19 +516,19 @@ def RetrieveModel():
             params = {'reg_param': list(np.arange(0,1,0.02)), 'tol': list(np.arange(0.00001,0.001,0.0002))}
             AlgorithmsIDsEnd = QDAModelsCount
         elif (eachAlgor) == 'RF':
-            clf = RandomForestClassifier()
+            clf = RandomForestClassifier(random_state=RANDOM_SEED)
             params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']}
             AlgorithmsIDsEnd = RFModelsCount
         elif (eachAlgor) == 'ExtraT':
-            clf = ExtraTreesClassifier()
+            clf = ExtraTreesClassifier(random_state=RANDOM_SEED)
             params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']}
             AlgorithmsIDsEnd = ExtraTModelsCount
         elif (eachAlgor) == 'AdaB':
-            clf = AdaBoostClassifier()
+            clf = AdaBoostClassifier(random_state=RANDOM_SEED)
             params = {'n_estimators': list(range(40, 80)), 'learning_rate': list(np.arange(0.1,2.3,1.1)), 'algorithm': ['SAMME.R', 'SAMME']}
             AlgorithmsIDsEnd = AdaBModelsCount
         else: 
-            clf = GradientBoostingClassifier()
+            clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
             params = {'n_estimators': list(range(85, 115)), 'learning_rate': list(np.arange(0.01,0.23,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']}
             AlgorithmsIDsEnd = GradBModelsCount
         allParametersPerformancePerModel = GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle)
@@ -620,6 +605,8 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
     PerFeatureAccuracyAll = []
     PerClassMetric = []
     perModelProb = []
+    
+    perModelPrediction = []
     resultsMicro = []
     resultsMacro = []
     resultsWeighted = []
@@ -642,7 +629,8 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
     inputs = range(len(XData))
     num_cores = multiprocessing.cpu_count()
     
-    impDataInst = Parallel(n_jobs=num_cores)(delayed(processInput)(i,XData,yData,crossValidation,clf) for i in inputs)
+    #impDataInst = Parallel(n_jobs=num_cores)(delayed(processInput)(i,XData,yData,crossValidation,clf) for i in inputs)
+    
     for eachModelParameters in parametersLocalNew:
         clf.set_params(**eachModelParameters)
         if (toggle == 1):
@@ -660,6 +648,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
         clf.fit(XData, yData) 
         yPredict = clf.predict(XData)
         yPredict = np.nan_to_num(yPredict)
+        perModelPrediction.append(yPredict)
         # retrieve target names (class names)
         PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
         yPredictProb = clf.predict_proba(XData)
@@ -685,7 +674,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
         resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2))
   
         resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True))
-    print('perase')
+
     maxLog = max(resultsLogLoss)
     minLog = min(resultsLogLoss)
     for each in resultsLogLoss:
@@ -711,6 +700,10 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
 
     metrics.insert(loop+13,'log_loss',resultsLogLossFinal)
 
+    perModelPredPandas = pd.DataFrame(perModelPrediction)
+    print(perModelPredPandas)
+    perModelPredPandas = perModelPredPandas.to_json()
+
     perModelProbPandas = pd.DataFrame(perModelProb)
     perModelProbPandas = perModelProbPandas.to_json()
 
@@ -746,7 +739,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,
     metrics = metrics.to_json()
     results.append(metrics) # Position: 6 and so on
     results.append(perModelProbPandas) # Position: 7 and so on
-    results.append(json.dumps(impDataInst)) # Position: 8 and so on
+    results.append(json.dumps(perModelPredPandas)) # Position: 8 and so on
 
     return results
 
@@ -1500,6 +1493,8 @@ def InitializeEnsemble():
     XModels = PreprocessingMetrics()
     global ModelSpaceMDS
     global ModelSpaceTSNE
+    global allParametersPerformancePerModel
+    global impDataInst
     
     XModels = XModels.fillna(0)
 
@@ -1515,11 +1510,67 @@ def InitializeEnsemble():
     PredictionSpaceUMAP = FunUMAP(PredictionProbSel)
 
     ModelsIDs = preProceModels()
+    
+    impDataInst = processDataInstance(ModelsIDs,allParametersPerformancePerModel)
+
     key = 0
     EnsembleModel(ModelsIDs, key)
 
     ReturnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionSpaceMDS,PredictionSpaceTSNE,PredictionSpaceUMAP)
 
+def processDataInstance(ModelsIDs, allParametersPerformancePerModel):
+    dicKNN = json.loads(allParametersPerformancePerModel[8])
+    dicSVC = json.loads(allParametersPerformancePerModel[17])
+    dicGausNB = json.loads(allParametersPerformancePerModel[26])
+    dicMLP = json.loads(allParametersPerformancePerModel[35])
+    dicLR = json.loads(allParametersPerformancePerModel[44])
+    dicLDA = json.loads(allParametersPerformancePerModel[53])
+    dicQDA = json.loads(allParametersPerformancePerModel[62])
+    dicRF = json.loads(allParametersPerformancePerModel[71])
+    dicExtraT = json.loads(allParametersPerformancePerModel[70])
+    dicAdaB = json.loads(allParametersPerformancePerModel[89])
+    dicGradB = json.loads(allParametersPerformancePerModel[98])
+
+    dfKNN = pd.DataFrame.from_dict(dicKNN)
+    dfSVC = pd.DataFrame.from_dict(dicSVC)
+    dfGausNB = pd.DataFrame.from_dict(dicGausNB)
+    dfMLP = pd.DataFrame.from_dict(dicMLP)
+    dfLR = pd.DataFrame.from_dict(dicLR)
+    dfLDA = pd.DataFrame.from_dict(dicLDA)
+    dfQDA = pd.DataFrame.from_dict(dicQDA)
+    dfRF = pd.DataFrame.from_dict(dicRF)
+    dfExtraT = pd.DataFrame.from_dict(dicExtraT)
+    dfAdaB = pd.DataFrame.from_dict(dicAdaB)
+    dfGradB = pd.DataFrame.from_dict(dicGradB)
+
+    dfKNN.index = dfKNN.index.astype(int)
+    dfSVC.index = dfSVC.index.astype(int) + SVCModelsCount
+    dfGausNB.index = dfGausNB.index.astype(int) + GausNBModelsCount
+    dfMLP.index = dfMLP.index.astype(int) + MLPModelsCount
+    dfLR.index = dfLR.index.astype(int) + LRModelsCount
+    dfLDA.index = dfLDA.index.astype(int) + LDAModelsCount
+    dfQDA.index = dfQDA.index.astype(int) + QDAModelsCount
+    dfRF.index = dfRF.index.astype(int) + RFModelsCount
+    dfExtraT.index = dfExtraT.index.astype(int) + ExtraTModelsCount
+    dfAdaB.index = dfAdaB.index.astype(int) + AdaBModelsCount
+    dfGradB.index = dfGradB.index.astype(int) + GradBModelsCount
+
+    dfKNNFiltered = dfKNN.loc[KNNModels, :]
+    dfSVCFiltered = dfSVC.loc[SVCModels, :]
+    dfGausNBFiltered = dfGausNB.loc[GausNBModels, :]
+    dfMLPFiltered = dfMLP.loc[MLPModels, :]
+    dfLRFiltered = dfLR.loc[LRModels, :]
+    dfLDAFiltered = dfLDA.loc[LDAModels, :]
+    dfQDAFiltered = dfQDA.loc[QDAModels, :]
+    dfRFFiltered = dfRF.loc[RFModels, :]
+    dfExtraTFiltered = dfExtraT.loc[ExtraTModels, :]
+    dfAdaBFiltered = dfAdaB.loc[AdaBModels, :]
+    dfGradBFiltered = dfGradB.loc[GradBModels, :]
+
+    df_connect = pd.concat([dfKNNFiltered, dfSVCFiltered, dfGausNBFiltered, dfMLPFiltered, dfLRFiltered, dfLDAFiltered, dfQDAFiltered, dfRFFiltered, dfExtraTFiltered, dfAdaBFiltered, dfGradBFiltered])
+    print(df_connect.sum(axis=1))
+    return df_connect
+
 def ReturnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionSpaceMDS,PredictionSpaceTSNE,PredictionSpaceUMAP):
 
     global Results
@@ -1589,7 +1640,6 @@ def RetrieveSelClassifiersIDandRemoveFromStack():
 
     global resultsUpdatePredictionSpace
     resultsUpdatePredictionSpace = []
-    print(PredictionProbSelUpdate)
     resultsUpdatePredictionSpace.append(json.dumps(PredictionProbSelUpdate[0])) # Position: 0
     resultsUpdatePredictionSpace.append(json.dumps(PredictionProbSelUpdate[1]))
 
@@ -1827,7 +1877,7 @@ def RetrieveSelDataPoints():
             params = RetrieveParamsClearedListKNN
             AlgorithmsIDsEnd = 0
         elif (eachAlgor) == 'SVC':
-            clf = SVC(probability=True)
+            clf = SVC(probability=True,random_state=RANDOM_SEED)
             params = RetrieveParamsClearedListSVC
             AlgorithmsIDsEnd = SVCModelsCount
         elif (eachAlgor) == 'GausNB':
@@ -1835,11 +1885,11 @@ def RetrieveSelDataPoints():
             params = RetrieveParamsClearedListGausNB
             AlgorithmsIDsEnd = GausNBModelsCount
         elif (eachAlgor) == 'MLP':
-            clf = MLPClassifier()
+            clf = MLPClassifier(random_state=RANDOM_SEED)
             params = RetrieveParamsClearedListMLP
             AlgorithmsIDsEnd = MLPModelsCount
         elif (eachAlgor) == 'LR':
-            clf = LogisticRegression()
+            clf = LogisticRegression(random_state=RANDOM_SEED)
             params = RetrieveParamsClearedListLR
             AlgorithmsIDsEnd = LRModelsCount
         elif (eachAlgor) == 'LDA':
@@ -1851,19 +1901,19 @@ def RetrieveSelDataPoints():
             params = RetrieveParamsClearedListQDA
             AlgorithmsIDsEnd = QDAModelsCount
         elif (eachAlgor) == 'RF':
-            clf = RandomForestClassifier()
+            clf = RandomForestClassifier(random_state=RANDOM_SEED)
             params = RetrieveParamsClearedListRF
             AlgorithmsIDsEnd = RFModelsCount
         elif (eachAlgor) == 'ExtraT':
-            clf = ExtraTreesClassifier()
+            clf = ExtraTreesClassifier(random_state=RANDOM_SEED)
             params = RetrieveParamsClearedListExtraT
             AlgorithmsIDsEnd = ExtraTModelsCount
         elif (eachAlgor) == 'AdaB':
-            clf = AdaBoostClassifier()
+            clf = AdaBoostClassifier(random_state=RANDOM_SEED)
             params = RetrieveParamsClearedListGradB
             AlgorithmsIDsEnd = AdaBModelsCount
         else: 
-            clf = GradientBoostingClassifier()
+            clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
             params = RetrieveParamsClearedListGradB
             AlgorithmsIDsEnd = GradBModelsCount
         metricsSelList = GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, listofDataPoints)
@@ -2367,7 +2417,7 @@ def EnsembleModel(Models, keyRetrieved):
     global yData
     global sclf
 
-    lr = LogisticRegression()
+    lr = LogisticRegression(random_state=RANDOM_SEED)
 
     if (keyRetrieved == 0):
         all_classifiers = []
@@ -2721,7 +2771,7 @@ def EnsembleModel(Models, keyRetrieved):
         #            del columnsReduce[0:len(resultsList[index][1])]
         #        else:
         #            for j, each in enumerate(resultsList[index][1]):
-        #                all_classifiersSelection.append(make_pipeline(ColumnSelector(cols=columnsReduce[j]), RandomForestClassifier().set_params(**each)))
+        #                all_classifiersSelection.append(make_pipeline(ColumnSelector(cols=columnsReduce[j]), RandomForestClassifier(random_state=RANDOM_SEED).set_params(**each)))
         #            del columnsReduce[0:len(resultsList[index][1])]
         #    sclf = StackingCVClassifier(classifiers=all_classifiersSelection,
         #                        use_probas=True,
@@ -2733,7 +2783,7 @@ def EnsembleModel(Models, keyRetrieved):
         pass
     else:
         num_cores = multiprocessing.cpu_count()
-        inputsSc = ['accuracy','precision_weighted','recall_weighted','accuracy','precision_weighted','recall_weighted']
+        inputsSc = ['accuracy','precision_weighted','recall_weighted','accuracy','precision_weighted','recall_weighted','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))
         scores = [item for sublist in flat_results for item in sublist]
 
@@ -2749,14 +2799,6 @@ def solve(sclf,sclfStack,XData,yData,crossValidation,scoringIn,loop):
     scoresLoc.append(temp.std())
     return scoresLoc
 
-def processInput(indexValue,XData,yData,crossValidation,sclf):
-    XDataRemove = XData.copy()
-    XDataRemove.drop(indexValue, inplace=True)
-    yDataRemove = yData.copy()
-    del yDataRemove[indexValue]
-    tempRemove = model_selection.cross_val_score(sclf, XDataRemove, yDataRemove, cv=crossValidation, scoring='accuracy', n_jobs=-1)
-    return tempRemove.mean()
-
 # Sending the final results to be visualized as a line plot
 @app.route('/data/SendFinalResultsBacktoVisualize', methods=["GET", "POST"])
 def SendToPlotFinalResults():