new

frontend/src/components/FeatureSpaceDetail.vue

@@ -683,7 +683,7 @@ export default {
                 }
                 EventBus.$emit('UpdateIDs', IDsGather)
                 if (selectionCounter == 1) {
-                  EventBus.$emit('storeGeneration')
+                  //EventBus.$emit('storeGeneration')
                 } else if (selectionCounter == 2) {
                   EventBus.$emit('UpdateIDs', IDsGather)
                   EventBus.$emit('CompareTwo', IDsGather)

frontend/src/components/Heatmap.vue

@@ -75,13 +75,17 @@ export default {
 
       var algorithms = []
       algorithms.push("Univariate FS")
+      algorithms.push("Impurity-based FI")
       algorithms.push("Permutation FI")
       algorithms.push("Accuracy-based FI")
+      algorithms.push("Ranking-based FS")
       algorithms.push("Average")
       algorithms.push("# Action #")
 
-      var PermImpEli = JSON.parse(this.dataFI[1])
-      var FeaturesAccuracy = JSON.parse(this.dataFI[2])
+      var ImpurityFS = JSON.parse(this.dataFI[1])
+      var PermImpEli = JSON.parse(this.dataFI[2])
+      var FeaturesAccuracy = JSON.parse(this.dataFI[3])
+      var RankingFS = JSON.parse(this.dataFI[4])
 
       if (Object.entries(this.generKey).length == 0) {
         var Features = JSON.parse(this.featureData[0])
@@ -115,12 +119,16 @@ export default {
           for (let i = 0; i < len; i++) {
               if (algorithms[j] == "Univariate FS") {
                 values[j] = ((Object.values(featureUni.Score)[i]-minUni)/(maxUni-minUni))
+              } else if (algorithms[j] == "Impurity-based FI") {
+                values[j] = ImpurityFS[0][i]
               } else if (algorithms[j] == "Permutation FI") {
                 values[j] = PermImpEli[i][0]
               } else if (algorithms[j] == "Accuracy-based FI") {
                 values[j] = FeaturesAccuracy[i][0]
+              } else if (algorithms[j] == "Ranking-based FS") {
+                values[j] = RankingFS[0][i]
               } else if (algorithms[j] == "Average") {
-                values[j] = ((((Object.values(featureUni.Score)[i]-minUni)/(maxUni-minUni)))+(PermImpEli[i][0])+(FeaturesAccuracy[i][0]))/(len2-2)
+                values[j] = ((((Object.values(featureUni.Score)[i]-minUni)/(maxUni-minUni)))+(ImpurityFS[0][i])+(PermImpEli[i][0])+(FeaturesAccuracy[i][0])+(RankingFS[0][i]))/(len2-2)
               } else {
                 if (Object.entries(this.generKey).length == 0) {
                   values[j] = -2
@@ -332,7 +340,7 @@ export default {
             return d.idx;
           })
           .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("x", 28).attr("y", -80).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", -90).style("text-anchor", "middle").style("font-size", "14px").text("Feature"); // -130 before for HeartC
           var heatMap = row.selectAll(".cell")
           .data(function(d) {
@@ -417,7 +425,7 @@ export default {
                     if (index > -1) {
                       featuresGen.splice(index, 1);
                     }
-                    //EventBus.$emit('updateHistoryKey', 4)
+                    EventBus.$emit('updateHistoryKey', 4)
                     EventBus.$emit('removeFeaturesGen', featuresGen)
                     return "url(#diagonalHatch)"
                   } else {
@@ -550,7 +558,7 @@ export default {
             });
 
         //==================================================
-      d3.select('#colLabel_3').dispatch('click');
+      d3.select('#colLabel_5').dispatch('click');
     },
     changeOrder(newOrder, heatmapId) {
   var svg = d3.select(heatmapId);

frontend/src/components/Main.vue

@@ -562,6 +562,7 @@ export default Vue.extend({
     axios.post(path, postData, axiosConfig)
       .then(response => {
         console.log('Store newly generated features!')
+        this.transformNodesFlag = true
         EventBus.$emit('updateHistoryKey', 5)
         this.threshold()
         if (!this.spaceChange) {

frontend/src/components/Results.vue

@@ -162,9 +162,16 @@ export default {
       }
 
       var previously = this.previousState
+      if (toWhichTrans == 5) {
+        previously.push("black")
+        previously.push("black")
+        previously.push("black")
+        previously.push("black")
+      } 
+
       var lengthFeatures = this.valuesGlobal.length
       var testLoc = this.flag
-      console.log('Better results found: ', testLoc)
+
       dots.transition()
           .duration(duration)
           .attr('r', function(d){ return d*2 })
@@ -178,6 +185,18 @@ export default {
               if (d == state) {
                 previously[counterSet] = '#B15928'
                 return previously[counterSet]
+              } else if (toWhichTrans == 3 || toWhichTrans == 4) {
+                var found = false
+                for (let i = 0; i < toWhichPosition.length; i++) {
+                  if (counterSet == (4*toWhichPosition[i] + 0) || counterSet == (4*toWhichPosition[i] + 1) || counterSet == (4*toWhichPosition[i] + 2) || counterSet == (4*toWhichPosition[i] + 3)) {
+                    previously[counterSet] = 'black'
+                    found = true
+                    return previously[counterSet]
+                  }
+                }
+                if (!found) {
+                  return previously[counterSet]
+                }
               } else if (counterSet == (4*toWhichPosition + 0) || counterSet == (4*toWhichPosition + 1) || counterSet == (4*toWhichPosition + 2) || counterSet == (4*toWhichPosition + 3)) {
                 previously[counterSet] = 'black'
                 return previously[counterSet]
@@ -196,7 +215,6 @@ export default {
 
       this.flag = false
       this.previousState = previously
-
       var dotLabels = rows.selectAll('.dot-label')
           .data(function(d){ return d.values })
 

run.py

@@ -24,7 +24,9 @@ from sklearn.metrics import classification_report
 from sklearn.feature_selection import mutual_info_classif
 from sklearn.feature_selection import SelectKBest
 from sklearn.feature_selection import chi2
-from sklearn.feature_selection import RFE
+from sklearn.feature_selection import RFECV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
 
 import eli5
 from eli5.sklearn import PermutationImportance
@@ -486,6 +488,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
             XDataStored = XData.copy()
         else:    
             XData = XDataStored.copy()
+            XDataStoredOriginal = XDataStored.copy()
     columnsNewGen = keepOriginalFeatures.columns.values.tolist()
     # Bayesian Optimization for 150 iterations
     if (keyFirstTime):
@@ -521,8 +524,6 @@ def executeModel(exeCall, flagEx, nodeTransfName):
             storeReplacements.append(replacement)
             pos = 0
             for repl in storeReplacements:
-                print(pos)
-                print(repl)
                 columnsNewGen[storePositions[pos]] = repl
                 pos += 1
             if (len(splittedCol) == 1):
@@ -602,7 +603,53 @@ def estimatorFeatureSelection(Data, clf):
     permList = []
     PerFeatureAccuracy = []
     PerFeatureAccuracyAll = []
+    ImpurityFS = []
+    RankingFS = []
     
+    rf = RandomForestClassifier(n_estimators = 100,
+                            n_jobs = -1,
+                            random_state = RANDOM_SEED)
+    rf.fit(Data, yData)
+
+    importances = rf.feature_importances_
+
+    std = np.std([tree.feature_importances_ for tree in rf.estimators_],
+                axis=0)
+
+    maxList = max(importances)
+    minList = min(importances)
+
+    for f in range(Data.shape[1]):
+        ImpurityFS.append((importances[f] - minList) / (maxList - minList))
+
+    estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED)
+
+    selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation)
+    selector = selector.fit(Data, yData)
+    RFEImp = selector.ranking_
+
+    for f in range(Data.shape[1]):
+        if (RFEImp[f] == 1):
+            RankingFS.append(0.95)
+        elif (RFEImp[f] == 2):
+            RankingFS.append(0.85)
+        elif (RFEImp[f] == 3):
+            RankingFS.append(0.75)
+        elif (RFEImp[f] == 4):
+            RankingFS.append(0.65)
+        elif (RFEImp[f] == 5):
+            RankingFS.append(0.55)
+        elif (RFEImp[f] == 6):
+            RankingFS.append(0.45)
+        elif (RFEImp[f] == 7):
+            RankingFS.append(0.35)
+        elif (RFEImp[f] == 8):
+            RankingFS.append(0.25)
+        elif (RFEImp[f] == 9):
+            RankingFS.append(0.15)
+        else: 
+            RankingFS.append(0.05)
+
     perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(Data, yData)
     permList.append(perm.feature_importances_)
     n_feats = Data.shape[1]
@@ -616,6 +663,12 @@ def estimatorFeatureSelection(Data, clf):
     yPredict = clf.predict(Data)
     yPredict = np.nan_to_num(yPredict)
 
+    RankingFSDF = pd.DataFrame(RankingFS)
+    RankingFSDF = RankingFSDF.to_json()
+
+    ImpurityFSDF = pd.DataFrame(ImpurityFS)
+    ImpurityFSDF = ImpurityFSDF.to_json()
+
     perm_imp_eli5PD = pd.DataFrame(permList)
     perm_imp_eli5PD = perm_imp_eli5PD.to_json()
 
@@ -631,8 +684,10 @@ def estimatorFeatureSelection(Data, clf):
     featureScores = featureScores.to_json()
 
     resultsFS.append(featureScores) 
+    resultsFS.append(ImpurityFSDF)
     resultsFS.append(perm_imp_eli5PD) 
-    resultsFS.append(PerFeatureAccuracyPandas) 
+    resultsFS.append(PerFeatureAccuracyPandas)
+    resultsFS.append(RankingFSDF) 
 
     return resultsFS