new

frontend/src/components/Heatmap.vue

@@ -88,6 +88,7 @@ export default {
       } else {
         var Features = this.generKey
       }
+      console.log(Features)
       let arr = Object.values(featureUni.Score);
       let minUni = Math.min(...arr);
       let maxUni = Math.max(...arr);
@@ -333,7 +334,7 @@ export default {
           })
           .attr("class", "row");
           svg.append("text").attr("x", 10).attr("y", -65).text("Technique").style("font-size", "14px").attr("alignment-baseline","top")
-          svg.append("text").attr("transform", "rotate(-90)").attr("x", (-1)*(cellSize*(len/2))).attr("y", -75).style("text-anchor", "middle").style("font-size", "14px").text("Feature"); // -130 before for HeartC
+          svg.append("text").attr("transform", "rotate(-90)").attr("x", (-1)*(cellSize*(len/2))).attr("y", -90).style("text-anchor", "middle").style("font-size", "14px").text("Feature"); // -130 before for HeartC
           var heatMap = row.selectAll(".cell")
           .data(function(d) {
               return d;

frontend/src/components/Main.vue

@@ -104,6 +104,7 @@ export default Vue.extend({
   },
   data () {
     return {
+      transformNodesFlag: false,
       storeDataTransf: [],
       compareNumber: 0,
       IDToCompare: [],
@@ -485,6 +486,11 @@ export default Vue.extend({
         console.log('Server successfully send the predictive results!')
         this.ValidResults = response.data.ValidResults
         EventBus.$emit('finalResults', this.ValidResults)
+        if (this.transformNodesFlag) {
+          console.log('mpikeMesa')
+          EventBus.$emit('Default')
+          this.transformNodesFlag = false
+        }
       })
       .catch(error => {
         console.log(error)
@@ -620,6 +626,7 @@ export default Vue.extend({
       .then(response => {
         console.log('Features transformation active!')
         this.threshold()
+        this.transformNodesFlag = true
       })
       .catch(error => {
         console.log(error)

run.py

@@ -476,7 +476,6 @@ def executeModel(exeCall, flagEx, nodeTransfName):
         else:    
             XData = XDataStored.copy()
     columnsNewGen = keepOriginalFeatures.columns.values.tolist()
-
     # Bayesian Optimization for 150 iterations
     if (keyFirstTime):
         create_global_function()
@@ -504,6 +503,10 @@ def executeModel(exeCall, flagEx, nodeTransfName):
         elif (flagEx == 4):
             splittedCol = nodeTransfName.split('_')
             XData.rename(columns={ XData.columns[exeCall[0]]: nodeTransfName }, inplace = True)
+            currentColumn = columnsNewGen[exeCall[0]]
+            subString = currentColumn[currentColumn.find("(")+1:currentColumn.find(")")]
+            replacement = currentColumn.replace(subString, nodeTransfName)
+            columnsNewGen[exeCall[0]] = replacement
             if (len(splittedCol) == 1):
                 XData[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
             else:
@@ -1216,7 +1219,6 @@ def Seperation():
 
     global packCorr
     packCorr = []
-
     packCorr.append(json.dumps(columnsNewGen))
     packCorr.append(json.dumps(target_names))
     packCorr.append(json.dumps(probabilityPredictions))