new

frontend/src/components/FeatureSpaceDetail.vue

@@ -509,6 +509,7 @@ export default {
         
         var modeLoc = this.mode
         var selectionCounter = 0
+        var IDsGather = []
 
         var node = svg.append('g')
           .attr('class', 'nodes')
@@ -521,7 +522,6 @@ export default {
                 var numb = graph.nodes[i]['group'].match(/\d/g)
                 numb = parseInt(numb.join(""))
                 var items = document.getElementsByClassName(numb)
-                console.log(items)
                 items.forEach( function (it) {
                   it.childNodes[0].style.visibility = "hidden";
                   it.childNodes[1].setAttribute("transform", "translate(50,50) scale(0.3) rotate(180)");
@@ -547,15 +547,32 @@ export default {
               EventBus.$emit('brushLink', groupLoc-1)
             } else {
               var groupsColor = d3.select('#svg'+index)._groups['0'][0].childNodes[0].childNodes[1]
+              var regex = /\d+/g
+              var idLocal = parseInt(d3.select('#svg'+index)._groups['0'][0].getAttribute("id").match(regex))
               if (groupsColor.getAttribute('fill') == "black") {
                 if (selectionCounter < 3) {
                   // add here the different states of comparison! (=2 and =3)
                   groupsColor.setAttribute('fill', 'yellow')
                   selectionCounter = selectionCounter + 1
+                  IDsGather.push(idLocal);
+                  if (selectionCounter == 2) {
+                    EventBus.$emit('CompareTwo', IDsGather)
+                  } else if (selectionCounter == 3) {
+                    EventBus.$emit('CompareThree', IDsGather)
+                  } else {
+
+                  }
                 }
               } else {
                 groupsColor.setAttribute('fill', 'black')
                 selectionCounter = selectionCounter - 1
+                var index = IDsGather.indexOf(idLocal);
+                if (index > -1) {
+                  IDsGather.splice(index, 1);
+                }
+                if (selectionCounter == 1) {
+                  EventBus.$emit('Default')
+                }
               }
             }
           })
@@ -770,7 +787,6 @@ export default {
                             .attr("fill", function(d) { return d.color})
                             .on("mouseover", function(d) {
                               document.getElementsByClassName("bar"+d.class).forEach (function (element) {
-                                console.log(element)
                                 element.setAttribute("fill", "yellow")
                               })
                               d3.select(this)

frontend/src/components/FeatureSpaceOverview.vue

@@ -83,7 +83,7 @@ export default {
 
         var features = this.colorsReceive
         var activeLeafLoc = this.activeLeaf
-        var listofNodes = this.overallData[0]
+        var listofNodes = this.overallData[34]
 
         var featuresQuad1 = []
         var featuresQuad2 = []
@@ -182,7 +182,7 @@ export default {
         var DURATION = 700; // d3 animation duration
         var STAGGERN = 4; // delay for each node
         var STAGGERD = 200; // delay for each depth
-        var NODE_DIAMETER = 4; // diameter of circular nodes
+        var NODE_DIAMETER = 6; // diameter of circular nodes
         var MIN_ZOOM = 0.5; // minimum zoom allowed
         var MAX_ZOOM = 10;  // maximum zoom allowed
         var HAS_CHILDREN_COLOR = 'lightsteelblue';

frontend/src/components/Heatmap.vue

@@ -39,6 +39,7 @@ export default {
       smallScreenMode: '0px',
       dataFI: [],
       featureData: [],
+      generKey: [],
     }
   },
   methods: {
@@ -67,6 +68,7 @@ export default {
       // Clear Heatmap first
       var svg = d3.select("#Heatmap");
       svg.selectAll("*").remove();
+
       var featureUni = JSON.parse(this.dataFI[0])
 
       var algorithms = []
@@ -78,7 +80,12 @@ export default {
 
       var PermImpEli = JSON.parse(this.dataFI[1])
       var FeaturesAccuracy = JSON.parse(this.dataFI[2])
+
+      if (Object.entries(this.generKey).length == 0) {
         var Features = this.featureData[0]
+      } else {
+        var Features = this.generKey
+      }
 
       let arr = Object.values(featureUni.Score);
       let minUni = Math.min(...arr);
@@ -114,7 +121,16 @@ export default {
               } else if (algorithms[j] == "Average") {
                 values[j] = ((((Object.values(featureUni.Score)[i]-minUni)/(maxUni-minUni)))+(PermImpEli[i][0])+(FeaturesAccuracy[i][0]))/(len-2)
               } else {
+                if (Object.entries(this.generKey).length == 0) {
                   values[j] = -2
+                } else {
+                  if (i == 0 || i == 1) {
+                    values[j] = -3
+                  }
+                  else {
+                    values[j] = -4
+                  }
+                }
               }
               data.push(values[j])
           }
@@ -288,7 +304,7 @@ export default {
           })
           .attr("class", "row");
           svg.append("text").attr("x", 10).attr("y", -65).text("Technique").style("font-size", "16px").attr("alignment-baseline","top")
-          svg.append("text").attr("transform", "rotate(-90)").attr("x", -33).attr("y", -50).style("text-anchor", "middle").style("font-size", "16px").text("Feature"); // -130 before for HeartC
+          svg.append("text").attr("transform", "rotate(-90)").attr("x", -33).attr("y", -75).style("text-anchor", "middle").style("font-size", "16px").text("Feature"); // -130 before for HeartC
           var heatMap = row.selectAll(".cell")
           .data(function(d) {
               return d;
@@ -323,6 +339,8 @@ export default {
           .style("fill", function(d) {
               if (d == -1) return "url(#diagonalHatch)"
               else if (d == -2) return "yellow"
+              else if (d == -3) return "white"
+              else if (d == -4) return "url(#diagonalHatch)"
               else return colorScale(d)
           })
           .on('mouseover', function(d, i, j) {
@@ -346,6 +364,16 @@ export default {
                     }
                     EventBus.$emit('addFeature', featuresAddRem)
                     return 'yellow'
+                  } else if (d == -3) {
+                    return 'white'
+                  } else if (d == -4) {
+                    svg.selectAll("rect").each(function(d){
+                      if (d == -4) {
+                        d3.select(this).style("fill", "url(#diagonalHatch)")
+                      }
+                    })
+                    EventBus.$emit('addFeatureGen', [k])
+                    return 'yellow'
                   } else {
                     return colorScale(d)
                   }
@@ -354,9 +382,13 @@ export default {
                   EventBus.$emit('removeFeatures', featuresAddRem)
                   return "url(#diagonalHatch)"
                 }
+              } else {
+                if (d == -3) {
+                  return 'white'
                 } else {
                   return colorScale(d)
                 }
+              }
             })
           });
 
@@ -552,6 +584,8 @@ export default {
     }
   },
   mounted () {
+    EventBus.$on('Generation', data => { this.generKey = data })
+
     EventBus.$on('HeatmapCall', data => { this.dataFI = data })
     EventBus.$on('HeatmapCall', this.Heatmap)
 

frontend/src/components/Main.vue

@@ -104,9 +104,14 @@ export default Vue.extend({
   },
   data () {
     return {
+      compareNumber: 0,
+      IDToCompare: [],
+      ImportanceCompare: [],
+      featureNames: [],
       initAuto: true,
       keySlider: true,
       featureAddRem: [],
+      featureAddRemGen: [],
       ValidResults: [],
       correlResulTranformed: [],
       PositiveValue: 75,
@@ -114,13 +119,10 @@ export default Vue.extend({
       unselectedRemainingPoints: [],
       Collection: 0,
       OverviewResults: 0,
-      preDataResults: '',
       DataResults: '',
       keyNow: 1,
       instancesImportance: '',
       RetrieveValueFile: 'IrisC', // this is for the default data set
-      ClassifierIDsList: [],
-      ClassifierIDsListCM: [],
       SelectedFeaturesPerClassifier: '',
       FinalResults: 0,
       selectedAlgorithm: '',
@@ -426,7 +428,7 @@ export default Vue.extend({
             EventBus.$emit('SlidersCall')
             this.keySlider = false
           }
-          //EventBus.$emit('ConfirmDataSet') // REMOVE THAT!
+          EventBus.$emit('ConfirmDataSet') // REMOVE THAT!
         } else {
           EventBus.$emit('dataSpace', this.correlResul)
           EventBus.$emit('quad', this.correlResul)
@@ -455,8 +457,10 @@ export default Vue.extend({
     }
     axios.get(path, axiosConfig)
       .then(response => {
-        console.log('Server successfully send the predictive results!')
+        console.log('Server successfully send the importances!')
         this.Importance = response.data.Importance
+        this.featureNames = []
+        EventBus.$emit('Generation', this.featureNames)
         EventBus.$emit('HeatmapCall', this.Importance)
         this.returnResults()
       })
@@ -507,6 +511,74 @@ export default Vue.extend({
         console.log(error)
       })
   },
+  ManipulFeatureGen () {
+    const path = `http://127.0.0.1:5000/data/AddRemGenFun`
+    const postData = {
+      featureAddRemGen: this.featureAddRemGen,
+    }
+    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.post(path, postData, axiosConfig)
+      .then(response => {
+        console.log('Sent an order to add or remove features!')
+        this.threshold()
+      })
+      .catch(error => {
+        console.log(error)
+      })
+  },
+  Compare () {
+    const path = `http://127.0.0.1:5000/data/compareFun`
+    const postData = {
+      getIDs: this.IDToCompare,
+      compareNumber: this.compareNumber
+    }
+    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.post(path, postData, axiosConfig)
+      .then(response => {
+        console.log('Sent the features to compare!')
+        this.returnImportanceComp()
+      })
+      .catch(error => {
+        console.log(error)
+    })
+  },
+  returnImportanceComp () {
+    const path = `http://127.0.0.1:5000/data/sendFeatImpComp`
+    
+    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('Server successfully send the importances for comparison!')
+        this.ImportanceCompare = response.data.ImportanceCompare
+        this.featureNames = response.data.FeatureNames
+        EventBus.$emit('Generation', this.featureNames)
+        EventBus.$emit('HeatmapCall', this.ImportanceCompare)
+      })
+      .catch(error => {
+        console.log(error)
+      })
+  },
   },
   created () {
     // does the browser support the Navigation Timing API?
@@ -538,6 +610,15 @@ export default Vue.extend({
       alert('Handler for .unload() called.');
     })
 
+    EventBus.$on('CompareTwo', data => { this.IDToCompare = data })
+    EventBus.$on('CompareTwo', data => { this.compareNumber = 1 })
+    EventBus.$on('CompareTwo', this.Compare)
+
+    EventBus.$on('CompareThree', data => { this.IDToCompare = data })
+    EventBus.$on('CompareThree', data => { this.compareNumber = 2 })
+    EventBus.$on('CompareThree', this.Compare)
+
+    EventBus.$on('Default', this.returnImportance)
 
     EventBus.$on('ReturningBrushedPointsIDs',  data => { this.modelsUpdate = data })
     //EventBus.$on('ReturningBrushedPointsIDs',  this.UpdateBarChartFeatures )
@@ -553,13 +634,8 @@ export default Vue.extend({
     EventBus.$on('InitializeCrossoverMutation', this.sendPointsCrossMutat)
 
     EventBus.$on('ChangeKey', data => { this.keyNow = data })
-    EventBus.$on('SendSelectedPointsUpdateIndicator', data => { this.ClassifierIDsList = data })
-    EventBus.$on('SendSelectedPointsUpdateIndicator', this.SelectedPoints)
     EventBus.$on('sendToServerSelectedScatter', this.SendSelectedPointsToServer)
 
-    EventBus.$on('SendSelectedPointsUpdateIndicatorCM', data => { this.ClassifierIDsListCM = data })
-    EventBus.$on('SendSelectedPointsUpdateIndicatorCM', this.SelectedPointsCM)
-
     EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data })
     EventBus.$on('SendSelectedDataPointsToServerEvent', this.SendSelectedDataPointsToServer)
     EventBus.$on('SendSelectedFeaturesEvent', data => { this.SelectedFeaturesPerClassifier = data })
@@ -598,6 +674,9 @@ export default Vue.extend({
     EventBus.$on('addFeature', this.ManipulFeature)
     EventBus.$on('removeFeatures', this.ManipulFeature)
 
+    EventBus.$on('addFeatureGen', data => { this.featureAddRemGen = data })
+    EventBus.$on('addFeatureGen', this.ManipulFeatureGen)
+
     //Prevent double click to search for a word. 
     document.addEventListener('mousedown', function (event) {
       if (event.detail > 1) {

frontend/src/components/Results.vue

@@ -60,7 +60,7 @@ export default {
       var color = JSON.parse(this.ValidResultsVar[12])
 
       var data = []
-      var features = this.featuresReceived[33]
+      var features = this.featuresReceived[35]
       var labelsX = ['Add', 'Remove', 'Combine', 'Round']
       for (let i=0; i< features.length; i++) {
         data.push({
@@ -82,9 +82,9 @@ export default {
       //   return f(d)
       // }
 
-      var c = d3.scale.ordinal()
+      var c = d3.scale.linear()
-          .domain([0, 1])
+        .domain([d3.min(allValues), d3.max(allValues)])
-          .range(["#D3D3D3", "#b15928"]);
+        .rangeRound([255 * 0.8, 0])
 
       var rows = chart.selectAll('.row')
           .data(data, function(d){ return d.label })
@@ -129,13 +129,13 @@ export default {
             if (testLoc) {
               if (d == state) {
                 previously.push(c(color)) 
-                return c(color); 
+                return '#B15928'
               } else {
                 previously.push(c(0)) 
-                return c(0)
+                return 'rgb(' + c(d) + ',' + c(d) + ',' + c(d) + ')' 
               }
             } else {
-              return c(0)
+              return 'rgb(' + c(d) + ',' + c(d) + ',' + c(d) + ')' 
             }
           })
 

run.py

@@ -110,6 +110,12 @@ def reset():
 
     global target_namesLoc
     target_namesLoc = []
+
+    global featureCompareData
+    featureCompareData = []
+
+    global columnsKeep
+    columnsKeep = []
     return 'The reset was done!'
 
 # retrieve data from client and select the correct data set
@@ -199,9 +205,14 @@ def retrieveFileName():
     keyFirstTime = True
 
     global target_namesLoc
-    
     target_namesLoc = []
 
+    global featureCompareData
+    featureCompareData = []
+
+    global columnsKeep
+    columnsKeep = []
+
     DataRawLength = -1
     DataRawLengthTest = -1
     data = json.loads(fileName)  
@@ -379,13 +390,15 @@ def dataSetSelection():
     global XData, yData, RANDOM_SEED
     XData, yData = ArrayDataResults, AllTargetsFloatValues
 
+    XData.columns = [str(col) + ' (F'+str(idx+1)+')' for idx, col in enumerate(XData.columns)]
+
     global XDataStored, yDataStored
     XDataStored = XData.copy()
     yDataStored = yData.copy()
 
     warnings.simplefilter('ignore')
 
-    executeModel([])
+    executeModel([], 0)
     
     return 'Everything is okay'
 
@@ -400,7 +413,7 @@ def create_global_function():
         return np.mean(result['test_score'])
 
 # check this issue later because we are not getting the same results
-def executeModel(exeCall):
+def executeModel(exeCall, flagEx):
 
     global keyFirstTime
     global estimator
@@ -412,6 +425,8 @@ def executeModel(exeCall):
     global previousState
     scores = []
 
+    XData = XDataStored.copy()
+
     if (keyFirstTime):
         create_global_function()
         params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
@@ -419,12 +434,20 @@ def executeModel(exeCall):
         svc_bayesopt.maximize(init_points=130, n_iter=20, acq='ucb')
         bestParams = svc_bayesopt.max['params']
         estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True, random_state=RANDOM_SEED)
-        featureImportanceData = estimatorFeatureSelection(estimator)
+        featureImportanceData = estimatorFeatureSelection(XData, estimator)
 
-    XData = XDataStored.copy()
     if (len(exeCall) != 0):
+        if (flagEx == 1):
             XData = XData.drop(XData.columns[exeCall], axis=1)
-
+        else:
+            columnsKeepNew = []
+            columns = XDataGen.columns.values.tolist()
+            for indx, col in enumerate(columns):
+                if indx in exeCall:
+                    columnsKeepNew.append(col)
+            XDataTemp = XDataGen[columnsKeepNew]
+            XData[columnsKeepNew] = XDataTemp.values
+    print(XData)
     estimator.fit(XData, yData)
     yPredict = estimator.predict(XData)
     yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
@@ -465,24 +488,24 @@ def executeModel(exeCall):
 
     return 'Everything Okay'
 
-def estimatorFeatureSelection(clf):
+def estimatorFeatureSelection(Data, clf):
 
     resultsFS = []
     permList = []
     PerFeatureAccuracy = []
     PerFeatureAccuracyAll = []
     
-    perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData)
+    perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(Data, yData)
     permList.append(perm.feature_importances_)
-    n_feats = XData.shape[1]
+    n_feats = Data.shape[1]
     PerFeatureAccuracy = []
     for i in range(n_feats):
-        scores = model_selection.cross_val_score(clf, XData.values[:, i].reshape(-1, 1), yData, cv=crossValidation)
+        scores = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=crossValidation)
         PerFeatureAccuracy.append(scores.mean())
     PerFeatureAccuracyAll.append(PerFeatureAccuracy)
 
-    clf.fit(XData, yData) 
+    clf.fit(Data, yData) 
-    yPredict = clf.predict(XData)
+    yPredict = clf.predict(Data)
     yPredict = np.nan_to_num(yPredict)
 
     perm_imp_eli5PD = pd.DataFrame(permList)
@@ -492,9 +515,9 @@ def estimatorFeatureSelection(clf):
     PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
 
     bestfeatures = SelectKBest(score_func=chi2, k='all')
-    fit = bestfeatures.fit(XData,yData)
+    fit = bestfeatures.fit(Data,yData)
     dfscores = pd.DataFrame(fit.scores_)
-    dfcolumns = pd.DataFrame(XData.columns)
+    dfcolumns = pd.DataFrame(Data.columns)
     featureScores = pd.concat([dfcolumns,dfscores],axis=1)
     featureScores.columns = ['Specs','Score']  #naming the dataframe columns
     featureScores = featureScores.to_json()
@@ -514,6 +537,17 @@ def sendFeatureImportance():
     }
     return jsonify(response)
 
+@app.route('/data/sendFeatImpComp', methods=["GET", "POST"])
+def sendFeatureImportanceComp():
+    global featureCompareData
+    global columnsKeep
+
+    response = {    
+        'ImportanceCompare': featureCompareData,
+        'FeatureNames': columnsKeep
+    }
+    return jsonify(response)
+
 def solve(sclf,XData,yData,crossValidation,scoringIn,loop):
     scoresLoc = []
     temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
@@ -1060,6 +1094,16 @@ def Seperation():
     global packCorr
     packCorr = []
 
+    AbbreviatedFeatures = []
+    for index, value in enumerate(XData.columns.values.tolist()):
+        realIndex = index + 1
+        AbbreviatedFeatures.append('F'+str(realIndex))
+
+    AbbreviatedFeaturesOriginal = []
+    for index, value in enumerate(XDataStored.columns.values.tolist()):
+        realIndex = index + 1
+        AbbreviatedFeaturesOriginal.append('F'+str(realIndex))
+
     packCorr.append(list(XData.columns.values.tolist()))
     packCorr.append(json.dumps(target_names))
     packCorr.append(json.dumps(probabilityPredictions))
@@ -1101,6 +1145,8 @@ def Seperation():
     packCorr.append(json.dumps(MI5List))
 
     packCorr.append(list(XDataStored.columns.values.tolist()))
+    packCorr.append(AbbreviatedFeatures)
+    packCorr.append(AbbreviatedFeaturesOriginal)
 
     return 'Everything Okay'
 
@@ -1140,5 +1186,55 @@ def ManipulFeat():
     featureProcess = request.get_data().decode('utf8').replace("'", '"')
     featureProcess = json.loads(featureProcess)
     featureProcessExtract = featureProcess['featureAddRem']
-    executeModel(featureProcessExtract)
+    executeModel(featureProcessExtract, 1)
+    return 'Okay'
+
+@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
+@app.route('/data/AddRemGenFun', methods=["GET", "POST"])
+def ManipulFeatGen():
+    featureProcess = request.get_data().decode('utf8').replace("'", '"')
+    featureProcess = json.loads(featureProcess)
+    featureProcessExtract = featureProcess['featureAddRemGen']
+    executeModel(featureProcessExtract, 2)
+    return 'Okay'
+
+@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
+@app.route('/data/compareFun', methods=["GET", "POST"])
+def CompareFunPy():
+    global featureCompareData
+    global columnsKeep
+    global XDataGen
+    global IDsToCompare
+
+    retrieveComparison = request.get_data().decode('utf8').replace("'", '"')
+    retrieveComparison = json.loads(retrieveComparison)
+    compareMode = retrieveComparison['compareNumber']
+    IDsToCompare = retrieveComparison['getIDs']
+    XDataGen = XDataStored.copy()
+    columns = XData.columns.values.tolist()
+    columnsKeep = []
+    columnsKeepID = []
+    for indx, col in enumerate(columns):
+        if indx in IDsToCompare:
+            columnsKeep.append(col)
+            columnsKeepID.append(str(indx+1))
+    if (compareMode == 1):
+        XDataGen = XData[columnsKeep]
+        feat1 = XDataGen.iloc[:,0]
+        feat2 = XDataGen.iloc[:,1]
+        XDataGen['F'+columnsKeepID[0]+'+F'+columnsKeepID[1]] = feat1 + feat2
+        XDataGen['|F'+columnsKeepID[0]+'-F'+columnsKeepID[1]+'|'] = abs(feat1 - feat2)
+        XDataGen['F'+columnsKeepID[0]+'xF'+columnsKeepID[1]] = feat1 + feat2
+        XDataGen['F'+columnsKeepID[0]+'/F'+columnsKeepID[1]] = feat1 / feat2
+        XDataGen['F'+columnsKeepID[1]+'/F'+columnsKeepID[0]] = feat2 / feat1
+        columnsKeep.append('F'+columnsKeepID[0]+'+F'+columnsKeepID[1])
+        columnsKeep.append('|F'+columnsKeepID[0]+'-F'+columnsKeepID[1]+'|')
+        columnsKeep.append('F'+columnsKeepID[0]+'xF'+columnsKeepID[1])
+        columnsKeep.append('F'+columnsKeepID[0]+'/F'+columnsKeepID[1])
+        columnsKeep.append('F'+columnsKeepID[1]+'/F'+columnsKeepID[0])
+    elif (compareMode == 2):
+        pass
+    else:
+        pass
+    featureCompareData = estimatorFeatureSelection(XDataGen, estimator)
     return 'Okay'