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cachedir/joblib/run/randomSearch/02358b134734520a2f9f2e6a6700b6b2/metadata.json

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cachedir/joblib/run/randomSearch/2109a774f7daa782008f2c574d569857/metadata.json

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cachedir/joblib/run/randomSearch/247917f35f4bbdddd8c07a7be659536b/metadata.json

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cachedir/joblib/run/randomSearch/4584d1bca3d591becac6146adcd056c2/metadata.json

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cachedir/joblib/run/randomSearch/80458ad60b9e9e8bf5a0a61326cda85d/metadata.json

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cachedir/joblib/run/randomSearch/8b19b12ceaa836334e1b1bcfc3607276/metadata.json

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cachedir/joblib/run/randomSearch/9276de92067ebdc8888483eb71a98c9e/metadata.json

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cachedir/joblib/run/randomSearch/9890f28b9827e1f8d19a81df86005b7e/metadata.json

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cachedir/joblib/run/randomSearch/d0f8957952910338224ee58be0ed17df/metadata.json

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cachedir/joblib/run/randomSearch/e67a6548a421acc0fed271d796bf02b3/metadata.json

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cachedir/joblib/run/randomSearch/e7791db4cd562a31e481f5b0e2e3189a/metadata.json

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cachedir/joblib/run/randomSearch/ec7b79af178a4597718aa6998e3ffb77/metadata.json

@@ -1 +0,0 @@
-{"duration": 20.462234020233154, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='kd_tree', metric='euclidean', n_neighbors=57,\n                     weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}}

cachedir/joblib/run/randomSearch/eefd6d5dcd41fd5256e909602279d3a2/metadata.json

@@ -0,0 +1 @@
+{"duration": 24.68097996711731, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='brute', n_neighbors=23)", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}}

cachedir/joblib/run/randomSearch/func_code.py

@@ -1,7 +1,7 @@
-# first line: 595
+# first line: 585
 @memory.cache
 def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
-
+    print(clf)
     search = RandomizedSearchCV(    
         estimator=clf, param_distributions=params, n_iter=100,
         cv=crossValidation, refit='accuracy', scoring=scoring,

frontend/src/components/AlgorithmsController.vue

@@ -26,15 +26,15 @@ export default {
       var data = []
         for (let i=0; i<500; i++){
             if (i<100) {
-                data.push({Algorithm:"KNN",value:randomIntFromInterval(40,100)})
+                data.push({Algorithm:"KNN",value:randomIntFromInterval(40,100), size:(Math.floor(Math.random() * 4) + 4)})
             } else if (i<200){
-                data.push({Algorithm:"LR",value:randomIntFromInterval(50,100)})
+                data.push({Algorithm:"LR",value:randomIntFromInterval(50,100), size:(Math.floor(Math.random() * 4) + 4)})
             } else if (i<300){
-                data.push({Algorithm:"MLP",value:randomIntFromInterval(30,100)})
+                data.push({Algorithm:"MLP",value:randomIntFromInterval(30,100), size:(Math.floor(Math.random() * 4) + 4)})
             } else if (i<400){
-                data.push({Algorithm:"RF",value:randomIntFromInterval(10,100)})
+                data.push({Algorithm:"RF",value:randomIntFromInterval(10,100), size:(Math.floor(Math.random() * 4) + 4)})
             } else {
-                data.push({Algorithm:"GradB",value:randomIntFromInterval(0,100)})
+                data.push({Algorithm:"GradB",value:randomIntFromInterval(0,100), size:(Math.floor(Math.random() * 4) + 4)})
             }
         }
         function randomIntFromInterval(min, max) { // min and max included 
@@ -1346,7 +1346,7 @@ export default {
                   show: true,
                   showPlot: false,
                   plotType: 'none',
-                  pointSize: 6,
+                  pointSize: 8,
                   showLines: false,//['median'],
                   showBeanLines: false,
                   beanWidth: 20,
@@ -1444,6 +1444,7 @@ export default {
                             .interpolate(d3.interpolateRound)
                             .domain(chart.yScale.domain());
                         var maxWidth = Math.floor(chart.xScale.rangeBand() / dOpts.pointSize);
+
                         var ptsObj = {};
                         var cYBucket = null;
                         //  Bucket points
@@ -1559,7 +1560,7 @@ export default {
                           for (var pt = 0; pt < chart.groupObjs[cName].values.length; pt++) {
                               cPlot.objs.points.pts.push(cPlot.objs.points.g.append("circle")
                                   .attr("class", "point")
-                                  .attr('r', dOpts.pointSize / 2)// Options is diameter, r takes radius so divide by 2
+                                  .attr('r', function () { return chart.data[pt].size / 2; })// Options is diameter, r takes radius so divide by 2
                                   .style("fill", chart.dataPlots.colorFunct(cName)));
                           }
                       }

frontend/src/components/History.vue

@@ -19,6 +19,12 @@ export default {
     return {
       WH: [],
       RandomSearLoc : 100,
+      SendIDLocs: [],
+      PerF: [],
+      PerFCM: [],
+      storedEnsem: [],
+      storedCM: [],
+      percentageOverall: [],
       values: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0],
       valuesStage2: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0],
       loop: 0
@@ -29,6 +35,159 @@ export default {
       var svg = d3.select("#SankeyInter");
       svg.selectAll("*").remove();
     },
+    computePerformanceDiff () {
+      Array.prototype.max = function() {
+        return Math.max.apply(null, this);
+      };
+
+      Array.prototype.min = function() {
+        return Math.min.apply(null, this);
+      };
+
+      var colorsforScatterPlot = this.PerF
+
+      var mergedStoreEnsembleLoc = [].concat.apply([], this.storedEnsem)
+      var mergedStoreEnsembleLocFormatted = []
+      for (let i = 0; i < mergedStoreEnsembleLoc.length; i++) {
+        mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[i].replace(/\D/g,'')))
+      }
+
+      colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
+      console.log(colorsforScatterPlot)
+      var min = Math.min.apply(null, colorsforScatterPlot),
+        max = Math.max.apply(null, colorsforScatterPlot);
+      console.log(max)
+      var countMax = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+      var countMin = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+      console.log(this.storedEnsem)
+      console.log(this.storedCM)
+      console.log(this.PerFCM)
+      for (let i = 0; i < this.storedCM.length; i++) {
+        let tempSplit = this.storedCM[i].split(/([0-9]+)/)
+        console.log(tempSplit[0])
+        if (tempSplit[0] == 'KNN_C') {
+          console.log(this.PerFCM[i])
+          console.log(max)
+          if (this.PerFCM[i] > max) {
+            countMax[0]++
+          } else if (this.PerFCM[i] < min) {
+            countMin[0]++
+          } else {
+            continue
+          }
+        }
+        else if (tempSplit[0] == 'KNN_M') {
+          if (this.PerFCM[i] > max) {
+            countMax[1]++
+          } else if (this.PerFCM[i] < min) {
+            countMin[1]++
+          } else {
+            continue
+          }
+        }
+        else if (tempSplit[0] == 'LR_C') {
+          if (this.PerFCM[i] > max) {
+            countMax[2]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[2]++
+          } else {
+            continue
+          }
+        }
+        else if (tempSplit[0] == 'LR_M') {
+          if (this.PerFCM[i] > max) {
+            countMax[3]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[3]++
+          } else {
+            continue
+          }
+        }
+        else if (tempSplit[0] == 'MLP_C') {
+          if (this.PerFCM[i] > max) {
+            countMax[4]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[4]++
+          } else {
+            continue
+          }
+        }
+        else if (tempSplit[0] == 'MLP_M') {
+          if (this.PerFCM[i] > max) {
+            countMax[5]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[5]++
+          } else {
+            continue
+          }
+        }
+        else if (tempSplit[0] == 'RF_C') {
+          if (this.PerFCM[i] > max) {
+            countMax[6]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[6]++
+          }
+        }
+        else if (tempSplit[0] == 'RF_M') {
+          if (this.PerFCM[i] > max) {
+            countMax[7]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[7]++
+          } else {
+            continue
+          }
+        }
+        else if (tempSplit[0] == 'GradB_C') {
+          if (this.PerFCM[i] > max) {
+            countMax[8]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[8]++
+          } else {
+            continue
+          }
+        }
+        else {
+          if (this.PerFCM[i] > max) {
+            countMax[9]++
+          }
+          else if (this.PerFCM[i] < min) {
+            countMin[9]++
+          } else {
+            continue
+          }
+        }
+      }
+
+      console.log(countMax)
+      console.log(countMin)
+
+      var percentage = []
+      for (let j = 0; j < countMax.length; j++) {
+        if (j >= 5) {
+          if (countMax[j] == 0) {
+            percentage.push((countMin[j]/this.values[15-j])*(-1)*100)
+          } else {
+            percentage.push(countMax[j]/this.values[15-j] * 100)
+          }  
+        } else {
+          if (countMax[j] == 0) {
+            percentage.push((countMin[j]/this.values[16-j])*(-1) * 100)
+          } else {
+            percentage.push(countMax[j]/this.values[16-j] * 100)
+          }
+        }
+      }
+
+      this.percentageOverall = percentage
+      
+    },
     SankeyViewStage2 () {
       var valuesLoc = this.valuesStage2
       var localStep = 2
@@ -120,7 +279,7 @@ export default {
       //   .attr("y",20)
       //   .attr("transform", 
       //           "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + margin.bottom + ")");
-// FIX ORDER HERE!
+
       // load the data
       var graph = {
         "nodes":[
@@ -135,37 +294,37 @@ export default {
         {"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
         {"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
         {"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
-        {"name":"Mutate (M) stage 1","node":11,"month":"Crossover_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
+        {"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
         {"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
         {"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
         {"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
         {"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
         {"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
-        {"name":"Crossover (C) stage 1","node":17,"month":"Mutate_S1","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
+        {"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
         {"name":"GradB","node":18,"month":"Crossover_S2","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
         {"name":"RF","node":19,"month":"Crossover_S2","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
         {"name":"MLP","node":20,"month":"Crossover_S2","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
         {"name":"LR","node":21,"month":"Crossover_S2","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
         {"name":"KNN","node":22,"month":"Crossover_S2","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
-        {"name":"Mutate stage 2 (C)","node":23,"month":"Crossover_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
+        {"name":"Mutate S2 (M)","node":23,"month":"Crossover_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
         {"name":"GradB","node":24,"month":"Mutate_S2","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
         {"name":"RF","node":25,"month":"Mutate_S2","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
         {"name":"MLP","node":26,"month":"Mutate_S2","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
         {"name":"LR","node":27,"month":"Mutate_S2","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
         {"name":"KNN","node":28,"month":"Mutate_S2","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
-        {"name":"Crossover stage 2 (C)","node":29,"month":"Mutate_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
+        {"name":"Crossover S2 (M)","node":29,"month":"Mutate_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
         {"name":"GradB","node":30,"month":"Crossover_S2_Prime","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
         {"name":"RF","node":31,"month":"Crossover_S2_Prime","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
         {"name":"MLP","node":32,"month":"Crossover_S2_Prime","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
         {"name":"LR","node":33,"month":"Crossover_S2_Prime","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
         {"name":"KNN","node":34,"month":"Crossover_S2_Prime","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
-        {"name":"Mutate stage 2 (M)","node":35,"month":"Crossover_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
+        {"name":"Mutate S2 (C)","node":35,"month":"Crossover_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
         {"name":"GradB","node":36,"month":"Mutate_S2_Prime","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
         {"name":"RF","node":37,"month":"Mutate_S2_Prime","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
         {"name":"MLP","node":38,"month":"Mutate_S2_Prime","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
         {"name":"LR","node":39,"month":"Mutate_S2_Prime","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
         {"name":"KNN","node":40,"month":"Mutate_S2_Prime","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
-        {"name":"Crossover stage 2 (M)","node":41,"month":"Mutate_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
+        {"name":"Crossover S2 (C)","node":41,"month":"Mutate_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
         ],
 
         "links":[
@@ -184,26 +343,26 @@ export default {
         {"source":11,"target":23,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[18]+valuesLoc[19]+valuesLoc[20]+valuesLoc[21]+valuesLoc[22]))},
         {"source":11,"target":35,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[30]+valuesLoc[31]+valuesLoc[32]+valuesLoc[33]+valuesLoc[34]))},
         {"source":6,"target":18,"value":valuesLoc[18],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[18]/25)},
-        {"source":6,"target":30,"value":valuesLoc[30],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[30]/25)},
+        {"source":6,"target":24,"value":valuesLoc[24],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[24]/25)},
         {"source":7,"target":19,"value":valuesLoc[19],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[19]/25)},
-        {"source":7,"target":31,"value":valuesLoc[31],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[31]/25)},
+        {"source":7,"target":25,"value":valuesLoc[25],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[25]/25)},
         {"source":8,"target":20,"value":valuesLoc[20],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[20]/25)},
-        {"source":8,"target":32,"value":valuesLoc[32],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[32]/25)},
+        {"source":8,"target":26,"value":valuesLoc[26],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[26]/25)},
         {"source":9,"target":21,"value":valuesLoc[21],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[21]/25)},
-        {"source":9,"target":33,"value":valuesLoc[33],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[33]/25)},
+        {"source":9,"target":27,"value":valuesLoc[27],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[27]/25)},
         {"source":10,"target":22,"value":valuesLoc[22],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[22]/25)},
-        {"source":10,"target":34,"value":valuesLoc[34],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[34]/25)},
+        {"source":10,"target":28,"value":valuesLoc[28],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[28]/25)},
         {"source":17,"target":29,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[24]+valuesLoc[25]+valuesLoc[26]+valuesLoc[27]+valuesLoc[28]))},
         {"source":17,"target":41,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[36]+valuesLoc[37]+valuesLoc[38]+valuesLoc[39]+valuesLoc[40]))},
-        {"source":12,"target":24,"value":valuesLoc[24],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[24]/25)},
+        {"source":12,"target":30,"value":valuesLoc[30],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[30]/25)},
         {"source":12,"target":36,"value":valuesLoc[36],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[36]/25)},
-        {"source":13,"target":25,"value":valuesLoc[25],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[25]/25)},
+        {"source":13,"target":31,"value":valuesLoc[31],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[31]/25)},
         {"source":13,"target":37,"value":valuesLoc[37],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[37]/25)},
-        {"source":14,"target":26,"value":valuesLoc[26],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[26]/25)},
+        {"source":14,"target":32,"value":valuesLoc[32],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[32]/25)},
         {"source":14,"target":38,"value":valuesLoc[38],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[38]/25)},
-        {"source":15,"target":27,"value":valuesLoc[27],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[27]/25)},
+        {"source":15,"target":33,"value":valuesLoc[33],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[33]/25)},
         {"source":15,"target":39,"value":valuesLoc[39],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[39]/25)},
-        {"source":16,"target":28,"value":valuesLoc[28],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[28]/25)},
+        {"source":16,"target":34,"value":valuesLoc[34],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[34]/25)},
         {"source":16,"target":40,"value":valuesLoc[40],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[40]/25)},
       ]}
 
@@ -211,6 +370,9 @@ export default {
           .links(graph.links)
           .layout(0);
 
+      var colorDiff = d3v5.scaleLinear().domain([-100,100]).range(['#40004b','#762a83','#9970ab','#c2a5cf','#e7d4e8','#d9f0d3','#a6dba0','#5aae61','#1b7837','#00441b'])
+      var percentage = this.percentageOverall
+      console.log(percentage)
       // add in the links
         var link = svg.append("g").selectAll(".link")
             .data(graph.links)
@@ -226,7 +388,31 @@ export default {
             }
             if(d.source.node == 17){
               return "transparent";
-            }})
+            }
+            if(d.source.target == 16){
+                return colorDiff(percentage[0]);
+              } else if(d.source.target == 15){
+                return colorDiff(percentage[1]);
+              } else if(d.source.target == 14){
+                return colorDiff(percentage[2]);
+              } else if(d.source.target == 13){
+                return colorDiff(percentage[3]);
+              } else if(d.source.target == 12){
+                return colorDiff(percentage[4]);
+              } else if(d.source.target == 10){
+                return colorDiff(percentage[5]);
+              } else if(d.source.target == 9){
+                return colorDiff(percentage[6]);
+              } else if(d.source.target == 8){
+                return colorDiff(percentage[7]);
+              } else if(d.source.target == 7){
+                return colorDiff(percentage[8]);
+              } else if(d.source.target == 6){
+                return colorDiff(percentage[9]);
+              } else {
+                return "#000000"
+              }
+            }) 
             .style("stroke-width", function(d) { return Math.max(.5, d.dh); })   //setting the stroke length by the data . d.dh is defined in sankey.js
             .sort(function(a, b) { return b.dh - a.dh; })
             .on("mouseover",linkmouseover)
@@ -301,10 +487,15 @@ export default {
             .attr("transform", "scale(1,-1)")
             .text(function(d) { return d.name.replace(/-.*/, ""); })
             .style("font-weight", function(d) {
-              if (d.node == 5 || d.node == 11 || d.node == 17 || d.node == 23 || d.node == 29 || d.node == 35 || d.node == 51) {
+              if (d.node == 5 || d.node == 11 || d.node == 17 || d.node == 23 || d.node == 29 || d.node == 35 || d.node == 41) {
                 return "bold";
               }
             })
+            .style("font-size", function(d) {
+              if (d.node > 17) {
+                return "14.5px";
+              }
+            })
           .filter(function(d) { return d.x < width / 2; })//positioning left or right of node
             .attr("x", 6 + sankey.nodeWidth())
             .attr("text-anchor", "start");
@@ -473,13 +664,13 @@ export default {
         {"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
         {"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
         {"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
-        {"name":"Mutate (M) stage 1","node":11,"month":"Crossover_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
+        {"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
         {"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
         {"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
         {"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
         {"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
         {"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
-        {"name":"Crossover (C) stage 1","node":17,"month":"Mutate_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
+        {"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
         ],
 
         "links":[
@@ -510,7 +701,10 @@ export default {
             .style("stroke",function(d){
             if(d.source.node == 5){
               return "transparent";
-            }})
+            } else {
+              return "#000000 !important"
+            }
+            })
             .style("stroke-width", function(d) { return Math.max(.5, d.dh); })   //setting the stroke length by the data . d.dh is defined in sankey.js
             .sort(function(a, b) { return b.dh - a.dh; })
             .on("mouseover",linkmouseover)
@@ -672,6 +866,16 @@ export default {
     EventBus.$on('ResponsiveandChange', data => {
     this.WH = data})
 
+    EventBus.$on('SendIDs', data => {
+    this.SendIDLocs = data})
+    EventBus.$on('SendPerformance', data => {
+    this.PerF = data})
+    EventBus.$on('SendPerformanceCM', data => {
+    this.PerFCM = data})
+    EventBus.$on('SendPerformance', this.computePerformanceDiff)
+    EventBus.$on('SendStoredEnsembleHist', data => { this.storedEnsem = data })
+    EventBus.$on('SendStoredCMHist', data => { this.storedCM = data })
+
     // reset the views
     EventBus.$on('resetViews', this.reset)
   }
@@ -692,22 +896,6 @@ export default {
 
 .link {
 	fill: none;
-	stroke: #000;
-	stroke-opacity: .05;
-	}
-
-.link:hover {
-	stroke-opacity: .5;
-	}
-
-.node text {
-	pointer-events: none;
-	text-shadow: 0 1px 0 #fff;
-	}
-
-.link {
-	fill: none;
-	stroke: #000;
 	stroke-opacity: .05;
 	}
 

frontend/src/components/Main.vue

@@ -232,6 +232,9 @@ export default Vue.extend({
     return {
       CMNumberofModelsOFFICIAL: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0],
       CMNumberofModels: [0,0,0,0,0,0,5,5,5,5,5,0,5,5,5,5,5,0], // Remove that!
+      CMNumberofModelsOFFICIALS2: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0],
+      CMNumberofModelsS2: [0,0,0,0,0,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0], // Remove that!
+      CurrentStage: 1,
       projectionID_A: 1,
       projectionID_B: 1,
       storeEnsemble: [],
@@ -347,6 +350,11 @@ export default Vue.extend({
             this.storeBothEnsCM[0] = this.OverviewResults
             this.firstTimeExec = false
           } else {   
+            var IDsLocal = JSON.parse(this.OverviewResults[0])
+            var Performance = JSON.parse(this.OverviewResults[1])
+            EventBus.$emit('SendIDs', IDsLocal)
+            EventBus.$emit('SendPerformance', Performance)
+            EventBus.$emit('SendStoredEnsembleHist', this.ModelsLocal)
             EventBus.$emit('SendStoredEnsemble', this.storeEnsemble)
             EventBus.$emit('emittedEventCallingCrossoverMutation', this.OverviewResults)
             this.PredictSelEnsem = []
@@ -377,6 +385,10 @@ export default Vue.extend({
       axios.get(path, axiosConfig)
         .then(response => {
           this.OverviewResultsCM = response.data.OverviewResultsCM
+          var ModelsLocal = JSON.parse(this.OverviewResultsCM[0])
+          EventBus.$emit('SendStoredCMHist', ModelsLocal)
+          var PerformanceCM = JSON.parse(this.OverviewResultsCM[1])
+          EventBus.$emit('SendPerformanceCM', PerformanceCM)
           console.log('Server successfully sent all the data related to visualizations!')
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResultsCM)
           this.storeBothEnsCM[0] = this.OverviewResultsCM
@@ -908,12 +920,23 @@ export default Vue.extend({
       const path = `http://127.0.0.1:5000/data/CrossoverMutation`
 
       var mergedStoreEnsembleLoc = [].concat.apply([], this.storeEnsemble)
-      
-      const postData = {
-        RemainingPoints: this.unselectedRemainingPoints,
-        StoreEnsemble: mergedStoreEnsembleLoc,
-        loopNumber: this.CMNumberofModels
+
+      if (this.CurrentStage == 1) {
+        var postData = {
+          RemainingPoints: this.unselectedRemainingPoints,
+          StoreEnsemble: mergedStoreEnsembleLoc,
+          loopNumber: this.CMNumberofModels,
+          Stage: this.CurrentStage
+        }
+      } else {
+        var postData = {
+          RemainingPoints: this.unselectedRemainingPoints,
+          StoreEnsemble: mergedStoreEnsembleLoc,
+          loopNumber: this.CMNumberofModelsS2,
+          Stage: this.CurrentStage
+        }
       }
+
       const axiosConfig = {
         headers: {
           'Content-Type': 'application/json',
@@ -925,6 +948,7 @@ export default Vue.extend({
       axios.post(path, postData, axiosConfig)
         .then(response => {
           console.log('Sent the unselected points for crossover and mutation.')
+          this.CurrentStage = this.CurrentStage + 1
           this.getDatafromtheBackEnd()
           this.getCMComputedData()
           this.changeActiveTo2()
@@ -1054,7 +1078,8 @@ export default Vue.extend({
     EventBus.$on('SendtheChangeinRangeNeg', data => { this.crossVal = data })
     EventBus.$on('factorsChanged', data => { this.basicValuesFact = data })
 
-    EventBus.$on('changeValues', data => { this.CMNumberofModels = data })
+    EventBus.$on('changeValues', data => { this.CMNumberofModelsOFFICIAL = data })
+    EventBus.$on('changeValuesS2', data => { this.CMNumberofModelsOFFICIALS2 = data })
 
     //Prevent double click to search for a word. 
     document.addEventListener('mousedown', function (event) {

frontend/src/components/Predictions.vue

@@ -280,7 +280,6 @@ export default {
         var RFPred = predictions[3]
         var GradBPred = predictions[4]
         var PredAver = predictions[5]
-        console.log(predictions)
       } else {
         var KNNPred = predictionsAll[0]
         var LRPred = predictionsAll[1]

frontend/src/components/ValidationController.vue

@@ -44,7 +44,7 @@ export default {
             selector:"#violin",
             constrainExtremes:true});
         chart2.renderBoxPlot({showBox:false});
-        chart2.renderDataPlots({showBeanLines:true,beanWidth:15,showPlot:false,colors:['#000000'],showLines:['median']});
+        chart2.renderDataPlots({showBeanLines:true,beanWidth:15,showPlot:false,colors:['#ff7f00','#fdbf6f','#fb9a99','#b15928','#a6cee3'],showLines:['median']});
         chart2.renderNotchBoxes({showNotchBox:false});
         chart2.renderViolinPlot({reset:true, width:75, clamp:0, resolution:30, bandwidth:50});