new

frontend/src/components/DataSpace.vue

@@ -24,6 +24,11 @@ export default {
   },
   methods: {
     initializeBeeSwarm () {   
+      var svg = d3.select("#BeeSwarm");
+      svg.selectAll("*").remove();
+      var svg = d3.select("#tooltip");
+      svg.selectAll("*").remove();
+      
       var tooltip = d3.select("#tooltip")
 
       var width = 2500

frontend/src/components/FeatureSpaceDetail.vue

@@ -7,9 +7,9 @@
               <table class="table table-borderless centerTable" >
                 <tbody>
                   <tr>
-                    <td scope="row"><button class="btn btn-info active" onclick="setLayerProj();" style="margin-left: -1px !important" ><font-awesome-icon icon="search" style="margin-right: 5px"/>Feature exploration</button></td>
-                    <td><button class="btn btn-info" onclick="setLayerComp();" style="margin-left: -1.4px"><font-awesome-icon icon="balance-scale-left" style="margin-right: 5px" />Feature comparison</button></td>
-                    <td scope="row"><button class="btn btn-info" onclick="setLayerSche();" style="margin-left: -2px !important"><font-awesome-icon icon="wrench" style="margin-right: 5px" />Feature engineering</button></td>
+                    <td scope="row"><button class="btn btn-primary active" onclick="setLayerProj();" style="margin-left: -1px !important" ><font-awesome-icon icon="search" style="margin-right: 5px"/>Feature exploration</button></td>
+                    <td><button class="btn btn-primary" onclick="setLayerComp();" style="margin-left: -1.4px"><font-awesome-icon icon="balance-scale-left" style="margin-right: 5px" />Feature comparison</button></td>
+                    <td scope="row"><button class="btn btn-primary" onclick="setLayerSche();" style="margin-left: -2px !important"><font-awesome-icon icon="wrench" style="margin-right: 5px" />Feature engineering</button></td>
                   </tr>
                 </tbody>
               </table>
@@ -39,7 +39,8 @@ export default {
       corrMatrixCombTotal: [],
       VIFRemaining: [],
       MIRemaining: [],
-      computeNodes: 0
+      computeNodes: 0,
+      featureAddRemCount: [],
     }
   },
   methods: {
@@ -285,7 +286,6 @@ export default {
         })
 
         var links = []
-
         Object.entries(dataLoc).forEach(
             function ([feature, value]) {
               Object.entries(value).forEach( function ([featureInside, value]) {
@@ -308,14 +308,16 @@ export default {
           )
         })
 
+        var lengthFeatureAddRem = this.featureAddRemCount.length
+
         Object.entries(pushEachFinal).forEach(
             function ([index, feature]) {
               feature.value.forEach(function (element, indexIns) {
                 if (element.valueIns > 0) {
-                  links.push({"source": index, "target": index*feature.value.length+feature.value.length+indexIns, "value": Math.abs(element.valueIns) * 30, "lin_color": "#33a02c"})
+                  links.push({"source": index, "target": (index*feature.value.length+feature.value.length+indexIns)-lengthFeatureAddRem, "value": Math.abs(element.valueIns) * 30, "lin_color": "#33a02c"})
                 }
                 else {
-                  links.push({"source": index, "target": index*feature.value.length+feature.value.length+indexIns, "value": Math.abs(element.valueIns) * 30, "lin_color": "#e31a1c"})
+                  links.push({"source": index, "target": (index*feature.value.length+feature.value.length+indexIns)-lengthFeatureAddRem, "value": Math.abs(element.valueIns) * 30, "lin_color": "#e31a1c"})
                 }
               })
         })
@@ -329,6 +331,7 @@ export default {
         
         var computeNodesVar = this.computeNodes
         var listofNodes = this.dataFS[0]
+
         var corrTarget = JSON.parse(this.dataFS[8+this.quadrantNumber])
         var corrGlob = JSON.parse(this.dataFS[13+this.quadrantNumber])
         var uniqueTarget = JSON.parse(this.dataFS[18+this.quadrantNumber])
@@ -684,7 +687,7 @@ export default {
                     it.style.visibility = "hidden";
                   })
                 }
-                console.log(d3.zoom())
+
                 //Zoom functions 
                 function zoom_actions(){
                   svg.attr("transform", d3.event.transform)
@@ -819,6 +822,8 @@ export default {
     EventBus.$on('quad', this.initializeNetwork)
     EventBus.$on('countNodes1', data => { this.computeNodes = data })
 
+    EventBus.$on('removeFeatures', data => { this.featureAddRemCount = data })
+
     EventBus.$on('reset', this.reset)
   }
 }

frontend/src/components/FeatureSpaceOverview.vue

@@ -259,7 +259,6 @@ export default {
         var root = treeData;
         root.x0 = curY;
         root.y0 = 0;
-        console.log(treeData.children)
         selectNode(treeData.children[this.keepRoot]); // current selected node
 
         // Collapse all children of root's children before rendering

frontend/src/components/Heatmap.vue

@@ -36,7 +36,9 @@ export default {
       keyLocal: 0,
       activeModels: 0,
       flagLocal: false,
-      smallScreenMode: '0px'
+      smallScreenMode: '0px',
+      dataFI: [],
+      featureData: [],
     }
   },
   methods: {
@@ -65,12 +67,69 @@ export default {
       // Clear Heatmap first
       var svg = d3.select("#Heatmap");
       svg.selectAll("*").remove();
+      var featureUni = JSON.parse(this.dataFI[0])
 
-      var dataAll = {"columns":[["R","F1"],["R","F2"],["R","F2"],["R","F1&F2"],["R","F2&F3"],["R","F1&F3"],["R","F1&F2&F3"],["R","F1+F2"],["R","F2+F3"],["R","F1+F3"],["R","F1+F2+F3"],["R","F1-F2"],["R","F2-F3"],["R","F1-F3"],["R","F1-F2-F3"],["R","F1*F2"],["R","F2*F3"],["R","F1*F3"],["R","F1*F2*F3"],["R","F1/F2"],["R","F2/F3"],["R","F1/F3"],["R","F1/F2/F3"]],"index":[["Information Gain"],["Fisher-Score"],["Relative Risk"],["Odds Ratio"]],"data":[[0.1178,0.4834,0.1647,0.1633,0.3355,0.1353,0.0556,0.9388,0.9354,0.8548,0.7037,0.6092,0.3835,0.7285,0.6405,0.424,0.6935,0.5766,0.2275,0.8309,0.972,0.9439,0.9366],[0.0372,-0.09,0.5015,0.5663,0.5126,-0.0274,0.4304,0.9518,0.9499,0.9216,-0.0278,0.6516,0.3019,-0.0936,0.6597,0.2323,-0.0833,0.4921,0.122,0.9275,0.96095,0.955,0.9501],[0.378,0.5514,0.378,0.3929,0.3271,0.3816,0.3647,0.9303,0.9372,0.8356,0.5314,0.4069,0.4288,0.5496,0.4156,0.427,0.5299,0.3454,0.4511,0.88235,0.9705,0.9468,0.9405],[0.4988,0.4149,0.5952,0.0628,-0.1015,0.497,0.58,0.9531,0.9572,0.8494,0.6262,0.3854,0.6904,0.5711,0.346,0.5132,0.583,0.3917,0.5305,0.7074,0.9763,0.9632,0.9582]]}
+      var algorithms = []
+      algorithms.push("Univariate FS")
+      algorithms.push("Permutation FI")
+      algorithms.push("Accuracy-based FI")
+      algorithms.push("Average")
+      algorithms.push("# Action #")
+
+      var PermImpEli = JSON.parse(this.dataFI[1])
+      var FeaturesAccuracy = JSON.parse(this.dataFI[2])
+      var Features = this.featureData[0]
+
+      var len2 = Features.length
+
+      let arr = Object.values(featureUni.Score);
+      let minUni = Math.min(...arr);
+      let maxUni = Math.max(...arr);
+
+      let len = algorithms.length
+      let indicesYAxis = []
+      for (let i = 0; i < len+1; i++) {
+          indicesYAxis[i] = [algorithms[i]]
+      } 
+      let indicesXAxis = []
+      var temp = []
+      for (let i = 0; i < len2; i++) {
+        temp = []
+        temp.push("R")
+        var loop = i+1
+        temp.push(Features[i].toString()+" (F"+loop+")")
+        indicesXAxis[i] = temp
+      }
+
+        var values = []
+        var modelData = []
+        for (let j = 0; j < len2; j++) {
+          var data = []
+          for (let i = 0; i <len; i++) {
+              if (algorithms[i] == "Univariate FS") {
+                values[i] = ((Object.values(featureUni.Score)[j]-minUni)/(maxUni-minUni))
+              } else if (algorithms[i] == "Permutation FI") {
+                values[i] = PermImpEli[j][0]
+              } else if (algorithms[i] == "Accuracy-based FI") {
+                values[i] = FeaturesAccuracy[j][0]
+              } else if (algorithms[i] == "Average") {
+                values[i] = ((((Object.values(featureUni.Score)[j]-minUni)/(maxUni-minUni)))+(PermImpEli[j][0])+(FeaturesAccuracy[j][0]))/(len-2)
+              } else {
+                values[i] = -2
+              }
+              data.push(values[i])
+          }
+          modelData.push(data)
+        }
+      var transposedArray = []
+      transposedArray = modelData[0].map((col, i) => modelData.map(row => row[i]))
+
+      var dataAll = {"columns":indicesXAxis,"index":indicesYAxis,"data":transposedArray}
       this.heatmap_display(dataAll, "#Heatmap");
 
     },
     heatmap_display(data, heatmapId) {
+    var featuresAddRem = []
     var cellSize = this.cellSize
     //##########################################################################
     // Patrick.Brockmann@lsce.ipsl.fr
@@ -226,8 +285,8 @@ export default {
             return d.idx;
           })
           .attr("class", "row");
-          svg.append("text").attr("x", 150).attr("y", -40).text("Feature combination").style("font-size", "16px").attr("alignment-baseline","top")
-          svg.append("text").attr("transform", "rotate(-90)").attr("x", -42).attr("y", -90).style("text-anchor", "middle").style("font-size", "16px").text("Algorithm"); // -130 before for HeartC
+          svg.append("text").attr("x", 15).attr("y", -60).text("Feature").style("font-size", "16px").attr("alignment-baseline","top")
+          svg.append("text").attr("transform", "rotate(-90)").attr("x", -52).attr("y", -90).style("text-anchor", "middle").style("font-size", "16px").text("Technique"); // -130 before for HeartC
           var heatMap = row.selectAll(".cell")
           .data(function(d) {
               return d;
@@ -260,61 +319,43 @@ export default {
           .attr("width", cellSize)
           .attr("height", cellSize)
           .style("fill", function(d) {
-              if (d != null) return colorScale(d);
-              else return "url(#diagonalHatch)";
+              if (d == -1) return "url(#diagonalHatch)"
+              else if (d == -2) return "yellow"
+              else return colorScale(d)
           })
           .on('mouseover', function(d, i, j) {
-              var k = Array.prototype.indexOf.call(j[i].parentNode.parentNode.childNodes,j[i].parentNode) - 3;
-              d3.select('#colLabel_' + i).classed("hover", true);
-              d3.select('#rowLabel_' + k).classed("hover", true);
-              if (d != null) {
-                  tooltip.style("visibility", "visible");
-                  tooltip.html('<div class="heatmap_tooltip">' + d.toFixed(2) + '</div>');
-              } else
-                  tooltip.style("visibility", "hidden");
+
           })
           .on('mouseout', function(d, i, j) {
-              var k = Array.prototype.indexOf.call(j[i].parentNode.parentNode.childNodes,j[i].parentNode) - 3;
-              d3.select('#colLabel_' + i).classed("hover", false);
-              d3.select('#rowLabel_' + k).classed("hover", false);
-              tooltip.style("visibility", "hidden");
+
           })
           .on("mousemove", function(d, i) {
-              tooltip.style("top", (d3.mouse(this)[1] + 55) + "px").style("left", (d3.mouse(this)[0]) + "px");
           })
           .on('click', function(d, i, j) {
             var rowsExtracted = svg.selectAll(".row")._groups[0]
             var k = Array.prototype.indexOf.call(j[i].parentNode.parentNode.childNodes,j[i].parentNode) - 3;
             d3.select(this).style("fill", function(d) {
-            if (d3.select(this).style("fill") === "url(\"#diagonalHatch\")"){
-                return colorScale(d)
-            } else {
-                return "url(#diagonalHatch)"
-            }
-            })
-            if (i+1 === j.length) {
-                if(d3.select(this).style("fill") === "url(\"#diagonalHatch\")") {
-                    row.selectAll(".cr"+k).style("fill", "url(#diagonalHatch)")
-                } else {
-                    row.selectAll(".cr"+k).style("fill", function(d) {
-                        return colorScale(d)
-                    })
-                }
-            }
-            var finalresults = []
-            for (let i = 0; i < rowsExtracted[0].childNodes.length - 1; i++) {
-                var results = []
-                for (let j = 0; j < rowsExtracted.length; j++) {
-                    if (rowsExtracted[j].childNodes[i].style.fill === "url(\"#diagonalHatch\")") {
-                    } else {
-                        results.push(j)
+              if (d3.select(this).style("fill") === "yellow" || d3.select(this).style("fill") === "url(\"#diagonalHatch\")") {
+                if (d3.select(this).style("fill") === "url(\"#diagonalHatch\")"){
+                  if (d == -2) {
+                    const index = featuresAddRem.indexOf(i);
+                    if (index > -1) {
+                      featuresAddRem.splice(index, 1);
                     }
+                    EventBus.$emit('addFeature', featuresAddRem)
+                    return 'yellow'
+                  } else {
+                    return colorScale(d)
+                  }
+                } else {
+                  featuresAddRem.push(i)
+                  EventBus.$emit('removeFeatures', featuresAddRem)
+                  return "url(#diagonalHatch)"
                 }
-                finalresults.push(results)
-            }
-            EventBus.$emit('flagLocal', true)
-            EventBus.$emit('sendSelectedFeaturestoPickle', finalresults)
-            EventBus.$emit('SendSelectedFeaturesEvent', finalresults)
+              } else {
+                return colorScale(d)
+              }
+            })
           });
 
       var svgLeg = d3.select("#LegendHeat");
@@ -354,7 +395,7 @@ export default {
           })
           .attr("y", viewerPosTop + cellSize);
 
-      svgLeg.append("text").attr("x", 200).attr("y", 50).text("Normalized feature importance").style("font-size", "16px").attr("alignment-baseline","top")
+      svgLeg.append("text").attr("x", 200).attr("y", 50).text("Normalized feature importance (FI)").style("font-size", "16px").attr("alignment-baseline","top")
 
       //==================================================
       // Change ordering of cells
@@ -509,7 +550,11 @@ export default {
     }
   },
   mounted () {
-    this.Heatmap()
+    EventBus.$on('HeatmapCall', data => { this.dataFI = data })
+    EventBus.$on('HeatmapCall', this.Heatmap)
+
+    EventBus.$on('quad', data => { this.featureData = data })
+
     EventBus.$on('reset', this.reset)
   }
 }
@@ -537,59 +582,5 @@ text.hover {
   fill: #66F;
   font-background: #000;
 }
-.heatmap_tooltip {
-  text-align: center;
-  font-family: monospace;
-  font-size: 14pt;
-  color: #000;
-  position: relative;
-  background: rgba(255, 255, 255, 0.8);
-  border: 4px solid #66F;
-  padding: 5px;
-  border-radius: 8px ;
-  -webkit-border-top-left-radius: 8px;
-  -webkit-border-top-right-radius: 8px;
-  -webkit-border-bottom-right-radius: 8px;
-  -webkit-border-bottom-left-radius: 8px;
-  -khtml-border-top-left-radius: 8px;
-  -khtml-border-top-right-radius: 8px;
-  -khtml-border-bottom-right-radius: 8px;
-  -khtml-border-bottom-left-radius: 8px;
-  -moz-border-radius-topleft: 8px;
-  -moz-border-radius-topright: 8px;
-  -moz-border-radius-bottomright: 8px;
-  -moz-border-radius-bottomleft: 8px;
-  border-top-left-radius: 8px;
-  border-top-right-radius: 8px;
-  border-bottom-right-radius: 8px;
-  border-bottom-left-radius: 8px;
-  width: 100px;
-  z-index:10000;
-  -webkit-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.8);
-  -moz-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.8);
-  box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.8);
-}
-.heatmap_tooltip:after, .heatmap_tooltip:before {
-  top: 100%;
-  border: solid transparent;
-  content: " ";
-  height: 0;
-  width: 0;
-  position: absolute;
-  pointer-events: none;
-}
-.heatmap_tooltip:after {
-  border-color: rgba(236, 240, 241, 0);
-  border-top-color: #FFFFF;
-  border-width: 10px;
-  left: 50%;
-  margin-left: -10px;
-}
-.heatmap_tooltip:before {
-  border-color: rgba(44, 62, 80, 0);
-  border-top-color: #66F;
-  border-width: 16px;
-  left: 50%;
-  margin-left: -16px;
-}
+
 </style>

frontend/src/components/Main.vue

@@ -54,7 +54,7 @@
           </b-col>
           <b-col cols="6">
             <mdb-card style="margin-top: 15px;">
-              <mdb-card-header color="primary-color" tag="h5" class="text-center">Feature Selection Algorithms
+              <mdb-card-header color="primary-color" tag="h5" class="text-center">Feature Selection (FS) Techniques
                 </mdb-card-header>
                 <mdb-card-body>
                   <mdb-card-text class="text-center"  style="min-height: 234px">
@@ -107,6 +107,7 @@ export default Vue.extend({
   },
   data () {
     return {
+      featureAddRem: [],
       ValidResults: [],
       correlResulTranformed: [],
       PositiveValue: 75,
@@ -153,8 +154,7 @@ export default Vue.extend({
       provenanceData: '',
       localFile: '',
       toggleDeepMain: 1,
-      keyLoc: 0,
-      keyData: true,
+      keyImp: true,
       ClassifierIDsListRemaining: [],
       PredictSel: []
     }
@@ -383,28 +383,6 @@ export default Vue.extend({
           console.log(error)
         })
     },
-    returnCorrel () {
-      const path = `http://127.0.0.1:5000/data/returnCorrelations`
-
-      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 correlation matrices!')
-          this.correlResul = response.data.correlResul
-          EventBus.$emit('quad', this.correlResul)
-          this.returnResults()
-        })
-        .catch(error => {
-          console.log(error)
-        })
-    },
     returnCorrelTranformed () {
     const path = `http://127.0.0.1:5000/data/returnCorrelationsTransformed`
 
@@ -427,6 +405,55 @@ export default Vue.extend({
         console.log(error)
       })
   },
+  returnCorrel () {
+    const path = `http://127.0.0.1:5000/data/returnCorrelations`
+
+    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 correlation matrices!')
+        this.correlResul = response.data.correlResul
+        EventBus.$emit('quad', this.correlResul)
+        if (this.keyImp) {
+          this.returnImportance()
+          this.keyImp = false
+        } else {
+          this.returnResults()
+        }
+      })
+      .catch(error => {
+        console.log(error)
+      })
+  },
+  returnImportance () {
+    const path = `http://127.0.0.1:5000/data/sendFeatImp`
+
+    const axiosConfig = {
+      headers: {
+        'Content-Type': 'application/json',
+        'Access-Control-Allow-Origin': '*',
+        'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
+        'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
+      }
+    }
+    axios.get(path, axiosConfig)
+      .then(response => {
+        console.log('Server successfully send the predictive results!')
+        this.Importance = response.data.Importance
+        EventBus.$emit('HeatmapCall', this.Importance)
+        this.returnResults()
+      })
+      .catch(error => {
+        console.log(error)
+      })
+  },
   returnResults () {
     const path = `http://127.0.0.1:5000/data/sendResults`
 
@@ -447,7 +474,29 @@ export default Vue.extend({
       .catch(error => {
         console.log(error)
       })
-  }
+  },
+    ManipulFeature () {
+    const path = `http://127.0.0.1:5000/data/AddRemFun`
+    const postData = {
+      featureAddRem: this.featureAddRem,
+    }
+    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)
+      })
+  },
   },
   created () {
     // does the browser support the Navigation Timing API?
@@ -480,9 +529,6 @@ export default Vue.extend({
     })
 
 
-
-    EventBus.$on('sendKeyNow', data => { this.keyLoc = data })
-
     EventBus.$on('ReturningBrushedPointsIDs',  data => { this.modelsUpdate = data })
     //EventBus.$on('ReturningBrushedPointsIDs',  this.UpdateBarChartFeatures )
     EventBus.$on('ConfirmDataSet', this.fileNameSend)
@@ -531,6 +577,10 @@ export default Vue.extend({
     EventBus.$on('SendtheChangeinRangePos', this.threshold)
     EventBus.$on('SendtheChangeinRangeNeg', this.threshold)
 
+    EventBus.$on('addFeature', data => { this.featureAddRem = data })
+    EventBus.$on('removeFeatures', data => { this.featureAddRem = data })
+    EventBus.$on('addFeature', this.ManipulFeature)
+    EventBus.$on('removeFeatures', this.ManipulFeature)
 
     //Prevent double click to search for a word. 
     document.addEventListener('mousedown', function (event) {

frontend/src/components/Results.vue

@@ -152,7 +152,7 @@ export default {
             gridcolor: "rgb(230,230,230)",
             title: 'Step of the feature engineering',
             tickformat: '.0f',
-            range: [0, this.scoresMean.length + 10], 
+            range: [0, this.scoresMean.length + 5], 
             showgrid: true, 
             showline: false, 
             showticklabels: true, 

run.py

@@ -20,7 +20,14 @@ from bayes_opt import BayesianOptimization
 from sklearn.model_selection import cross_validate
 from sklearn.model_selection import cross_val_predict
 from sklearn.preprocessing import OneHotEncoder
+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
+
+import eli5
+from eli5.sklearn import PermutationImportance
 
 from joblib import Parallel, delayed
 import multiprocessing
@@ -52,24 +59,12 @@ def reset():
     global keySpecInternal
     keySpecInternal = 1
 
-    global dataSpacePointsIDs
-    dataSpacePointsIDs = []
-
-    global previousStateActive
-    previousStateActive = []
-
     global RANDOM_SEED
     RANDOM_SEED = 42
 
-    global KNNModelsCount
-    global LRModelsCount
-
     global keyData
     keyData = 0
 
-    KNNModelsCount = 0
-    LRModelsCount = 100
-
     global XData
     XData = []
     global yData
@@ -89,38 +84,18 @@ def reset():
     global ClassifierIDsList
     ClassifierIDsList = ''
 
-    # Initializing models
-
-    global resultsList
-    resultsList = []
-
     global RetrieveModelsList
     RetrieveModelsList = []
 
-    global allParametersPerformancePerModel
-    allParametersPerformancePerModel = []
-
     global allParametersPerfCrossMutr
     allParametersPerfCrossMutr = []
 
-    global HistoryPreservation
-    HistoryPreservation = []
-
     global all_classifiers
     all_classifiers = []
 
     global crossValidation
     crossValidation = 5
     
-    # models
-    global KNNModels
-    KNNModels = []
-    global RFModels
-    RFModels = []
-
-    global results
-    results = []
-
     global resultsMetrics
     resultsMetrics = []
 
@@ -130,6 +105,9 @@ def reset():
     global target_names
     target_names = []
 
+    global keyFirstTime
+    keyFirstTime = True
+
     global target_namesLoc
     target_namesLoc = []
     return 'The reset was done!'
@@ -198,9 +176,6 @@ def retrieveFileName():
     global resultsList
     resultsList = []
 
-    global allParametersPerformancePerModel
-    allParametersPerformancePerModel = []
-
     global allParametersPerfCrossMutr
     allParametersPerfCrossMutr = []
 
@@ -213,43 +188,6 @@ def retrieveFileName():
     global crossValidation
     crossValidation = 5
 
-    global scoring
-    scoring = {'accuracy': 'accuracy', 'precision_weighted': 'precision_weighted', 'recall_weighted': 'recall_weighted', 'f1_weighted': 'f1_weighted', 'roc_auc_ovo_weighted': 'roc_auc_ovo_weighted'}
-
-    global loopFeatures
-    loopFeatures = 2
-
-    # models
-    global KNNModels
-    global SVCModels
-    global GausNBModels
-    global MLPModels
-    global LRModels
-    global LDAModels
-    global QDAModels 
-    global RFModels
-    global ExtraTModels
-    global AdaBModels
-    global GradBModels
-
-    KNNModels = []
-    SVCModels = []
-    GausNBModels = []
-    MLPModels = []
-    LRModels = []
-    LDAModels = []
-    QDAModels = []
-    RFModels = []
-    ExtraTModels = []
-    AdaBModels = []
-    GradBModels = []
-
-    global results
-    results = []
-
-    global resultsMetrics
-    resultsMetrics = []
-
     global parametersSelData
     parametersSelData = []
 
@@ -260,6 +198,9 @@ def retrieveFileName():
     
     target_names = []
 
+    global keyFirstTime
+    keyFirstTime = True
+
     global target_namesLoc
     
     target_namesLoc = []
@@ -440,7 +381,7 @@ def dataSetSelection():
 
     warnings.simplefilter('ignore')
 
-    executeModel()
+    executeModel([])
     
     return 'Everything is okay'
 
@@ -455,23 +396,34 @@ def create_global_function():
         return np.mean(result['test_score'])
 
 # check this issue later because we are not getting the same results
-def executeModel():
+def executeModel(exeCall):
 
-    create_global_function()
+    global keyFirstTime
     global estimator
     global yPredictProb
     global scores
+    global featureImportanceData
+    global XData
+    global XDataStored
+
+    if (keyFirstTime):
+        create_global_function()
+        params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
+        svc_bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
+        svc_bayesopt.maximize(init_points=120, 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)
+        keyFirstTime = False
+    XData = XDataStored.copy()
+    if (len(exeCall) != 0):
+        XData = XData.drop(XData.columns[exeCall], axis=1)
 
-    params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
-    svc_bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
-    svc_bayesopt.maximize(init_points=120, 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)
     estimator.fit(XData, yData)
     yPredict = estimator.predict(XData)
     yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
 
-    num_cores = 1
+    num_cores = multiprocessing.cpu_count()
     inputsSc = ['accuracy','precision_macro','recall_macro']
 
     flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
@@ -479,6 +431,55 @@ def executeModel():
     
     return 'Everything Okay'
 
+def estimatorFeatureSelection(clf):
+
+    resultsFS = []
+    permList = []
+    PerFeatureAccuracy = []
+    PerFeatureAccuracyAll = []
+    
+    perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData)
+    permList.append(perm.feature_importances_)
+    n_feats = XData.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)
+        PerFeatureAccuracy.append(scores.mean())
+    PerFeatureAccuracyAll.append(PerFeatureAccuracy)
+
+    clf.fit(XData, yData) 
+    yPredict = clf.predict(XData)
+    yPredict = np.nan_to_num(yPredict)
+
+    perm_imp_eli5PD = pd.DataFrame(permList)
+    perm_imp_eli5PD = perm_imp_eli5PD.to_json()
+
+    PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracyAll)
+    PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
+
+    bestfeatures = SelectKBest(score_func=chi2, k='all')
+    fit = bestfeatures.fit(XData,yData)
+    dfscores = pd.DataFrame(fit.scores_)
+    dfcolumns = pd.DataFrame(XData.columns)
+    featureScores = pd.concat([dfcolumns,dfscores],axis=1)
+    featureScores.columns = ['Specs','Score']  #naming the dataframe columns
+    featureScores = featureScores.to_json()
+
+    resultsFS.append(featureScores) 
+    resultsFS.append(perm_imp_eli5PD) 
+    resultsFS.append(PerFeatureAccuracyPandas) 
+
+    return resultsFS
+
+@app.route('/data/sendFeatImp', methods=["GET", "POST"])
+def sendFeatureImportance():
+    global featureImportanceData
+
+    response = {    
+        'Importance': featureImportanceData
+    }
+    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)
@@ -500,13 +501,13 @@ def sendFinalResults():
 def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
 
     XDataNumeric = XData.select_dtypes(include='number')
+
     columns = list(XDataNumeric)   
 
     global packCorrTransformed
     packCorrTransformed = []
 
     for count, i in enumerate(columns): 
-
         dicTransf = {}
         
         d={}
@@ -1096,3 +1097,12 @@ def unique(list1):
         if x not in unique_list: 
             unique_list.append(x) 
     return unique_list
+
+@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
+@app.route('/data/AddRemFun', methods=["GET", "POST"])
+def ManipulFeat():
+    featureProcess = request.get_data().decode('utf8').replace("'", '"')
+    featureProcess = json.loads(featureProcess)
+    featureProcessExtract = featureProcess['featureAddRem']
+    executeModel(featureProcessExtract)
+    return 'Okay'