new iris

Former-commit-id: f473e54ae1

cachedir/joblib/run/GridSearchForModels/2dab796a5b751dd1136192a4c722feba/metadata.json

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+{"duration": 832.7775752544403, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 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, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n                       criterion='entropy', max_depth=None, max_features='auto',\n                       max_leaf_nodes=None, max_samples=None,\n                       min_impurity_decrease=0.0, min_impurity_split=None,\n                       min_samples_leaf=1, min_samples_split=2,\n                       min_weight_fraction_leaf=0.0, n_estimators=139,\n                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n                       warm_start=False)", "params": "{'n_estimators': [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, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "2446", "toggle": "1"}}

cachedir/joblib/run/GridSearchForModels/4d17e73710c1d8a53afd2430b1b370d4/metadata.json

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cachedir/joblib/run/GridSearchForModels/5621e8f8a676043bb4ef1ee18e128791/metadata.json

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cachedir/joblib/run/GridSearchForModels/655a6eaba76ddf35cbc9b77dd123f6d1/metadata.json

@@ -1 +0,0 @@
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cachedir/joblib/run/GridSearchForModels/66421e97b055342e7e63aae83a6e9b49/metadata.json

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cachedir/joblib/run/GridSearchForModels/6657b0bec5553e17d786be017af28c41/metadata.json

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cachedir/joblib/run/GridSearchForModels/6888817ccf76fe67584971034437f233/metadata.json

@@ -1 +0,0 @@
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cachedir/joblib/run/GridSearchForModels/7bc3e3feea131b3402478bc041615a34/metadata.json

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cachedir/joblib/run/GridSearchForModels/84ebbb538966f81baf960c8d388fc93c/metadata.json

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cachedir/joblib/run/GridSearchForModels/9bd661dd6799c27866025cbe33d490ee/metadata.json

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cachedir/joblib/run/GridSearchForModels/a6e1c86dc9d3d2dbb9ca6197c70a02a9/metadata.json

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cachedir/joblib/run/GridSearchForModels/a81e8eef0c9ffe1e9e5b4abce8734e6a/metadata.json

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cachedir/joblib/run/GridSearchForModels/bf24ce01a584b3438fbe38db27ba4cb3/metadata.json

@@ -1 +0,0 @@
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cachedir/joblib/run/GridSearchForModels/cf0ae38b4bb89b5de87ad55391a2c7cc/metadata.json

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cachedir/joblib/run/GridSearchForModels/cf6f6e78139c8ececc1cf7153490b24e/metadata.json

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cachedir/joblib/run/GridSearchForModels/d9cde41ca9be0fa46db6fe8a2c9b1d95/metadata.json

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cachedir/joblib/run/GridSearchForModels/dd8a1a124ca8df93a97ca4907bb5f007/metadata.json

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cachedir/joblib/run/GridSearchForModels/e0084a6c27d4b1128d4c84c200c7eefc/metadata.json

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cachedir/joblib/run/GridSearchForModels/e5db528caeaab8ad160eeaacffdd005c/metadata.json

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cachedir/joblib/run/GridSearchForModels/f1e1aa42dd34b354614621ec4f062fdf/metadata.json

@@ -0,0 +1 @@
+{"duration": 667.0271377563477, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 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, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n                     criterion='entropy', max_depth=None, max_features='auto',\n                     max_leaf_nodes=None, max_samples=None,\n                     min_impurity_decrease=0.0, min_impurity_split=None,\n                     min_samples_leaf=1, min_samples_split=2,\n                     min_weight_fraction_leaf=0.0, n_estimators=139,\n                     n_jobs=None, oob_score=False, random_state=42, verbose=0,\n                     warm_start=False)", "params": "{'n_estimators': [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, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'ExtraT'", "AlgorithmsIDsEnd": "2606", "toggle": "1"}}

cachedir/joblib/run/GridSearchForModels/func_code.py

@@ -1,7 +1,8 @@
-# first line: 632
+# first line: 642
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle):
-    print('inside models')
+    print('toggle:',toggle)
+    #print('inside')
     # instantiate spark session
     spark = (   
         SparkSession    

frontend/src/components/Algorithms.vue

@@ -275,7 +275,7 @@ export default {
           this.algorithmGradB.push({'# Performance (%) #': McGradB[j],Algorithm:'GradB',Model:'Model ID: ' + AlgorGradBIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorGradBIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorGradB['params'])[j]))
         }
-      } else {
+       } else {
         for (var j = 0; j < Object.keys(PerformAlgorKNN['params']).length; j++) {
           this.algorithmKNN.push({'# Performance (%) #': this.listClassPerf[0][j],Algorithm:'KNN',Model:'Model ID: ' + AlgorKNNIDs[j] + '<br> Parameters: '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[j])+'<br> # Performance (%) #',ModelID:AlgorKNNIDs[j]})
           this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[j]))
@@ -324,6 +324,7 @@ export default {
       EventBus.$emit('ParametersAll', this.parameters)
 
       // concat the data
+      var data = this.algorithmKNN
       var data = this.algorithmKNN.concat(this.algorithmSVC)
       var data = data.concat(this.algorithmGausNB)
       var data = data.concat(this.algorithmMLP)

frontend/src/components/BarChart.vue

@@ -550,6 +550,12 @@ export default {
             } else if (tName == "> 50% Diameter Narrowing / Diseased"){
               tName = 1
               this.tNameAll = 1
+            } else if  (tName == "Absence of Hypotheticality") {
+              tName = 0
+              this.tNameAll = 0
+            } else if (tName == "Presence of Hypotheticality"){
+              tName = 1
+              this.tNameAll = 1
             } else {
               this.tNameAll = tName
             }

frontend/src/components/DataSetExecController.vue

@@ -4,7 +4,7 @@
   <select id="selectFile" @change="selectDataSet()">
       <option value="HeartC.csv" selected>Heart Disease</option>
       <option value="StanceC.csv">Stance in Texts</option>
-      <option value="DiabetesC.csv">Pima Indian Diabetes</option>
+      <!--<option value="DiabetesC.csv">Pima Indian Diabetes</option>-->
       <option value="IrisC.csv">Iris</option>
       <option value="local">Upload New File</option>
   </select>

frontend/src/components/DataSpace.vue

@@ -75,7 +75,7 @@ export default {
       RetrieveDataSet: 'HeartC',
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
       onlyOnce: true,
-      restylePoints: []
+      restylePoints: [],
     }
   },
   methods: {
@@ -93,22 +93,27 @@ export default {
       EventBus.$emit('SendFilter', this.userSelectedFilter)
     },
     merge() {
+      this.restylePoints = []
       EventBus.$emit('SendAction', 'merge')
       EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     remove () {
+      this.restylePoints = []
       EventBus.$emit('SendAction', 'remove')
       EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     compose () {
+      this.restylePoints = []
       EventBus.$emit('SendAction', 'compose')
       EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     save () {
+      this.restylePoints = []
       EventBus.$emit('SendProvenance', 'save')
       EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     restore () {
+      this.restylePoints = []
       EventBus.$emit('SendProvenance', 'restore')
       EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
@@ -132,6 +137,7 @@ export default {
       const XandYCoordinatesMDS = JSON.parse(this.dataPoints[1])
       const DataSet = JSON.parse(this.dataPoints[2])
       const originalDataLabels = JSON.parse(this.dataPoints[3])
+
       //console.log(DataSetY)
       //const originalDataLabels = JSON.parse(this.dataPoints[4])
       var DataSetParse = JSON.parse(DataSet)
@@ -182,6 +188,10 @@ export default {
         target_names.forEach(element => {
           beautifyLabels.push(element)
         });
+        target_names = []
+        target_names.push(0)
+        target_names.push(1)
+        target_names.push(2)
       }
 
       if (this.representationDef == 'mds') {
@@ -210,7 +220,6 @@ export default {
 
         var traces = []
         var layout = []
-
         for (let i = 0; i < target_names.length; i++) {
 
           const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
@@ -287,9 +296,12 @@ export default {
         for (let i = 0; i < result.Xax.length; i++) {
           IDs.push(i)
           if (this.restylePoints.length != 0) {
+            console.log('test1')
             if (XandYCoordinatesMDS[0].length == this.restylePoints.length) {
+              console.log('test2')
               colorUpdate.push('rgb(0, 0, 0)')
             } else {
+              console.log('test3')
               if (this.restylePoints.includes(i)) {
                   colorUpdate.push('rgb(175, 68, 39)')
                 } else {
@@ -297,7 +309,8 @@ export default {
                 }
               }
             } else {
-                  colorUpdate.push('rgb(0, 0, 0)')
+              console.log('test')
+              colorUpdate.push('rgb(0, 0, 0)')
           }
         }
         result.ID = IDs
@@ -379,7 +392,7 @@ export default {
                 }
               }
             } else {
-                  colorUpdate.push('rgb(0, 0, 0)')
+              colorUpdate.push('rgb(0, 0, 0)')
             }
         }
         result.Xax = Xaxs

frontend/src/components/Heatmap.vue

@@ -229,7 +229,7 @@ export default {
 
     //==================================================
     var viewerWidth = this.responsiveWidthHeight[0]*6.5
-    var viewerHeight = this.responsiveWidthHeight[1]*1.46
+    var viewerHeight = this.responsiveWidthHeight[1]*1.415
     var viewerPosTop = viewerHeight * 0.1;
     var viewerPosLeft = viewerWidth*0.1;
 
@@ -351,7 +351,7 @@ export default {
           })
           .attr("class", "row");
       svg.append("text").attr("x", 220).attr("y", -50).text("Models").style("font-size", "16px").attr("alignment-baseline","top")
-      svg.append("text").attr("transform", "rotate(-90)").attr("x", -130).attr("y", -45).style("text-anchor", "middle").style("font-size", "16px").text("Data Features"); 
+      svg.append("text").attr("transform", "rotate(-90)").attr("x", -40).attr("y", -45).style("text-anchor", "middle").style("font-size", "16px").text("Data Features"); // -130 before for HeartC
       var heatMap = row.selectAll(".cell")
           .data(function(d) {
               return d;

frontend/src/components/Main.vue

@@ -89,7 +89,7 @@
               <mdb-card style="margin-top: 15px;">
                   <mdb-card-header color="primary-color" tag="h5" class="text-center">Data Space</mdb-card-header>
                   <mdb-card-body>
-                    <mdb-card-text class="text-center" style="min-height: 845px">
+                    <mdb-card-text class="text-center" style="min-height: 822px">
                       <DataSpace/>
                       <PCPData/>
                     </mdb-card-text>
@@ -100,7 +100,7 @@
                 <mdb-card style="margin-top: 15px;">
                   <mdb-card-header color="primary-color" tag="h5" class="text-center">Features Selection for Each Model<small class="float-right"><active/></small></mdb-card-header>
                     <mdb-card-body>
-                      <mdb-card-text class="text-center" style="min-height: 845px">
+                      <mdb-card-text class="text-center" style="min-height: 822px">
                         <ToggleSelection/>
                         <br/>
                         <Heatmap/>
@@ -118,7 +118,7 @@
                     [Sel: {{OverSelLength}} / All: {{OverAllLength}}]<small class="float-right"><active-scatter/></small>
                     </mdb-card-header>
                     <mdb-card-body>
-                      <mdb-card-text class="text-center"  style="min-height: 845px">
+                      <mdb-card-text class="text-center"  style="min-height: 822px">
                         <ScatterPlot/>
                         <PerMetricBarChart/>
                       </mdb-card-text>
@@ -129,7 +129,7 @@
                 <mdb-card style="margin-top: 15px;">
                   <mdb-card-header color="primary-color" tag="h5" class="text-center">Predictions' Space</mdb-card-header>
                   <mdb-card-body>
-                    <mdb-card-text class="text-center"  style="min-height: 845px">
+                    <mdb-card-text class="text-center"  style="min-height: 822px">
                       <PredictionsSpace/>
                       <BalancePredictions/>
                     </mdb-card-text>
@@ -362,6 +362,7 @@ export default Vue.extend({
             EventBus.$emit('ParametersProvenance', this.OverviewResults)
             EventBus.$emit('InitializeProvenance', this.selectedModels_Stack)
           }
+          this.keyData = false
           this.getFinalResults()
         })
         .catch(error => {
@@ -863,7 +864,6 @@ export default Vue.extend({
       EventBus.$emit('emittedEventCallingTogglesUpdate', toggles)
     },
     DataSpaceFun () {
-      this.keyData = false
       const path = `http://127.0.0.1:5000/data/SendDataSpacPoints`
       const postData = {
         points: this.dataPointsSelfromDataSpace,
@@ -1098,6 +1098,7 @@ body {
   top: 0px;
   bottom: 0px;
   margin-top: -4px !important;
+  overflow-x: hidden !important;
 }
 
 .modal-backdrop {

frontend/src/components/PCPData.vue

@@ -18,7 +18,8 @@ export default {
     return {
       PCPDataReceived: '',
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
-      ClassifierIDsListClearedData: []
+      ClassifierIDsListClearedData: [],
+      RetrieveDataSet: 'HeartC',
     }
   },
   methods: {
@@ -34,7 +35,12 @@ export default {
 
       var extraction = []
       for (let i = 0; i < DataSetParse.length; i++) {
-        extraction.push(Object.assign(DataSetParse[i], {Outcome: target_names_original[i]}, {ID: i}))
+        if (this.RetrieveDataSet == 'IrisC') {
+          extraction.push(Object.assign(DataSetParse[i], {Outcome: target_names[i]}, {ID: i}))
+        } else {
+          extraction.push(Object.assign(DataSetParse[i], {Outcome: target_names_original[i]}, {ID: i}))
+        }
+
       }
       var colors = this.colorsValues
       EventBus.$emit('sendDatatoPickle', extraction)
@@ -50,9 +56,9 @@ export default {
         var pc = ParCoords()("#PCPDataView")
             .data(DataSetParse)
             .width(1200)
-            .height(280)
+            .height(272)
             .hideAxis(["Outcome","ID"])
-            .color(function(d, i) { return colors[target_names[i]] })
+            .color(function(d, i) { return colors[d.Outcome] })
             .bundlingStrength(0) // set bundling strength
             .smoothness(0)
             .showControlPoints(false)
@@ -65,9 +71,9 @@ export default {
         var pc = ParCoords()("#PCPDataView")
           .data(DataSetParse)
           .width(1200)
-          .height(280)
+          .height(272)
           .hideAxis(["Outcome","ID"])
-          .color(function(d, i) { return colors[target_names[i]] })
+          .color(function(d, i) { return colors[d.Outcome] })
           .bundlingStrength(0) // set bundling strength
           .smoothness(0)
           .showControlPoints(false)
@@ -77,6 +83,7 @@ export default {
           .reorderable()
           .interactive();
       }
+      pc.alphaOnBrushed(0.2);
       pc.on('brushend', function(brushed, args){
         var brushedCleared = []
         for (let i = 0; i < brushed.length; i++) {
@@ -98,6 +105,8 @@ export default {
 
     // reset the views
     EventBus.$on('resetViews', this.reset)
+
+    EventBus.$on('SendToServerDataSetConfirmation', data => { this.RetrieveDataSet = data })
   }
 }
 </script>

frontend/src/components/PerMetricBarChart.vue

@@ -85,7 +85,7 @@ export default {
         }
       }
       var width = this.WH[0]*6.5 // interactive visualization
-      var height = this.WH[1]*0.5 // interactive visualization
+      var height = this.WH[1]*0.482 // interactive visualization
           var trace1 = {
           x: x,
           y: perModelAllClear, 
@@ -180,6 +180,7 @@ export default {
       EventBus.$on('InitializeMetricsBarChart', data => {this.barchartmetrics = data;})
       EventBus.$on('InitializeMetricsBarChart', this.LineBar)
       
+      EventBus.$on('InitializeMetricsBarChartPrediction', data => {this.SelBarChartMetrics.length = []})
       EventBus.$on('InitializeMetricsBarChartPrediction', data => {this.barchartmetrics[9] = data;})
       EventBus.$on('InitializeMetricsBarChartPrediction', this.LineBar)
 

frontend/src/components/PredictionsSpace.vue

@@ -55,7 +55,7 @@ export default {
 
       // responsive visualization
       var width = this.WH[0]*6.5 // interactive visualization
-      var height = this.WH[1]*1.22 // interactive visualization
+      var height = this.WH[1]*1.185 // interactive visualization
       var XandYCoordinatesMDS
       var target_names = JSON.parse(this.PredictionsData[4])
       if (this.UpdatedData.length != 0) {
@@ -74,6 +74,7 @@ export default {
         stringParameters.push(JSON.stringify(DataSetParse[i]).replace(/,/gi, '<br>'))
       }
       const XandYCoordinatesTSNE = JSON.parse(this.PredictionsData[18])
+      console.log(XandYCoordinatesTSNE)
       const XandYCoordinatesUMAP= JSON.parse(this.PredictionsData[19])
 
       var result = [];
@@ -93,6 +94,10 @@ export default {
         target_names.forEach(element => {
           beautifyLabels.push(element)
         });
+        target_names = []
+        target_names.push(0)
+        target_names.push(1)
+        target_names.push(2)
       }
 
       if (this.representationDef == 'mds') {

frontend/src/components/Provenance.vue

@@ -1,11 +1,11 @@
 <template>
     <div>
-        <div class="squares-container" style="min-height: 374px; margin-left: 35px">
+        <div class="squares-container" style="overflow: auto; width: 6000px; min-height: 374px; margin-left: 10px; margin-top:-10px">
         <div id="tooltip"></div>	<!-- new  -->
-          <div id="performanceCapture" style="min-height: 150px; margin-top: -10px !important;"></div>	<!-- new  -->
+          <div id="performanceCapture" style="overflow: auto; width: 6000px; min-height: 150px;"></div>	<!-- new  -->
           <canvas id="main-canvas" style="overflow-y: auto; overflow-x: auto; height:190px;"></canvas>
           <br>
-          <div id="dynamic-buttons"></div>
+          <div id="dynamic-buttons" style="overflow: auto; width: 6000px;"></div>
         </div>
     </div>
 </template>
@@ -236,7 +236,7 @@ export default {
       // here 10 was 5!
       let pScale = Stardust.scale.custom(`
               Vector2(
-                  20 + column * 195 + typeColumnIndex % 12 * 11.7,
+                  20 + column * 207 + typeColumnIndex % 12 * 11.7,
                   height - 10 - floor(typeColumnIndex / 12) * 10
               )
           `);
@@ -335,7 +335,13 @@ export default {
 			}
       }
       const stringStep = "Stacking Ensemble "
-      var myButton = '<button id="HistoryReturnButtons'+this.counter+'" class="dynamic_buttons">'+stringStep+this.counter+'</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'
+      if (this.counter == 1) {
+        var text = "Parent Stack ID: None"
+      } else {
+        var text1 = "Parent Stack ID: "
+        var text = text1.concat(this.flagUpdated+1)
+      }
+      var myButton = '<button id="HistoryReturnButtons'+this.counter+'" class="dynamic_buttons" data-placement="bottom" title="'+text+'">'+stringStep+this.counter+'</button>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;'
       $("#dynamic-buttons").append(myButton);
 
       EventBus.$emit('requestProven',this.counter-1)
@@ -351,15 +357,25 @@ export default {
 
         btns.forEach(btnlocal => {
           btnlocal.style.fontWeight = 'normal';
-        });
+        })
 
         var btn = document.getElementById($(this).attr('id'));
-        btn.style.fontWeight = 'bold';
+        btn.style.fontWeight = 'bold'
 
         EventBus.$emit('ChangeKey', 0)
         EventBus.$emit('requestProven',parseInt($(this).attr('id').replace(/\D/g,''))-1)
-        }
-      );
+        })
+
+      var btns = document.getElementsByClassName('dynamic_buttons')
+
+      btns.forEach(btnlocal => {
+        btnlocal.style.fontWeight = 'normal';
+      })
+      
+      var idConcat = "HistoryReturnButtons".concat(this.counter)
+      var btn = document.getElementById(idConcat);
+      btn.style.fontWeight = 'bold';
+
   },
   RadialPerf () {
     this.firstInside++
@@ -391,8 +407,8 @@ export default {
       {value: this.Stack_scoresMean3, label: "Recall", color: scaleColor(this.Stack_scoresMean3)},
       {value: this.Stack_scoresMean4, label: "F1 Score", color: scaleColor(this.Stack_scoresMean4)}
     ];
-
-    var svg = d3.select('#svg'+this.firstInside).attr('width', width).attr('height', width).style('margin-right', '38px');
+    console.log(data)
+    var svg = d3.select('#svg'+this.firstInside).attr('width', width).attr('height', width).style('margin-right', '48px');
 
     var arcs = data.map(function (obj, i) {
         return d3.svg.arc().innerRadius(i * arcSize + innerRadius).outerRadius((i + 1) * arcSize - (width / 100) + innerRadius);
@@ -454,7 +470,7 @@ export default {
                         return 0;
                     })
                     .attr("xlink:href", "#Text" + r.data.object.label)
-                    .attr("startOffset", '5')
+                    .attr("startOffset", '0')
                     .attr("dy", '-3em')
                     .text(lableObj.value + '%');
             }

frontend/src/components/ScatterPlot.vue

@@ -71,18 +71,22 @@ export default {
       this.newColorsUpdate = []
       EventBus.$emit('updateBold', '')
       EventBus.$emit('updateBoxPlots')
-      this.colorsStore = []
-      this.MDSStore = []
-      this.parametersStore = []
-      this.TSNEStore = []
-      this.modelIDStore = []
-      this.UMAPStore = []
+      this.colorsStore.pop()
+      this.MDSStore.pop()
+      this.parametersStore.pop()
+      this.modelIDStore.pop()
+      this.UMAPStore.pop()
       this.ScatterPlotView()
     },
     reset () {
       Plotly.purge('OverviewPlotly')
     },
     selectVisualRepresentation () {
+      this.colorsStore.pop()
+      this.MDSStore.pop()
+      this.parametersStore.pop()
+      this.modelIDStore.pop()
+      this.UMAPStore.pop()
       const representationSelectionDocum = document.getElementById('selectBarChart')
       this.representationSelection = representationSelectionDocum.options[representationSelectionDocum.selectedIndex].value
       EventBus.$emit('RepresentationSelection', this.representationSelection)
@@ -106,6 +110,11 @@ export default {
 
       colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
       if (this.newColorsUpdate.length != 0) {
+        this.colorsStore.pop()
+        this.MDSStore.pop()
+        this.parametersStore.pop()
+        this.modelIDStore.pop()
+        this.UMAPStore.pop()
         colorsforScatterPlot = []
         let resultsClear = JSON.parse(this.newColorsUpdate)
         for (let j = 0; j < Object.values(resultsClear).length; j++) {
@@ -120,7 +129,28 @@ export default {
       var TSNEData = JSON.parse(this.ScatterPlotResults[12])
       var modelId = JSON.parse(this.ScatterPlotResults[13])
       var UMAPData = JSON.parse(this.ScatterPlotResults[17])
+
+      if (this.DataPointsSelUpdate.length != 0) {
+        this.colorsStore.pop()
+        this.MDSStore.pop()
+        this.parametersStore.pop()
+        this.modelIDStore.pop()
+        this.UMAPStore.pop()
+      }
+
+
       if (this.keyLocal == 0) {
+        if (this.DataPointsSelUpdate.length != 0) {
+          colorsforScatterPlot = JSON.parse(this.DataPointsSelUpdate[0])
+        }
+        if (this.DataPointsSelUpdate.length != 0) {
+          MDSData = JSON.parse(this.DataPointsSelUpdate[1])
+        }
+        this.colorsStore.pop()
+        this.MDSStore.pop()
+        this.parametersStore.pop()
+        this.modelIDStore.pop()
+        this.UMAPStore.pop()
         this.colorsStore.push(colorsforScatterPlot)
         this.MDSStore.push(MDSData)
         this.parametersStore.push(parameters)
@@ -160,7 +190,6 @@ export default {
 
       var listofNumbersModelsIDs = []
       var StackModelsIDs = []
-      console.log(this.ModelsIDGray.length)
       if (this.ModelsIDGray.length != 0) {
         for (let j = 0; j < this.ModelsIDGray.length; j++){
           listofNumbersModelsIDs.push(parseInt(this.ModelsIDGray[j]))
@@ -171,14 +200,6 @@ export default {
         var MDSDataNewY = []
         var colorsforScatterPlotNew = []
 
-        if (this.DataPointsSelUpdate.length != 0) {
-          this.colorsStore.pop()
-          this.MDSStore.pop()
-          this.parametersStore.pop()
-          this.modelIDStore.pop()
-          this.UMAPStore.pop()
-        }
-
         for (let i = 0; i < modelId.length; i++) {
           if (listofNumbersModelsIDs.includes(modelId[i])) {
             StackModelsIDs.push(modelId[i])
@@ -199,10 +220,10 @@ export default {
             }
           }
         }
+
         this.DataPointsSelUpdate = []
         MDSData[0] = MDSDataNewX
         MDSData[1] = MDSDataNewY
-        console.log(StackModelsIDs)
         modelId = StackModelsIDs
         parameters = parametersNew
         colorsforScatterPlot = colorsforScatterPlotNew
@@ -213,19 +234,15 @@ export default {
           //this.TSNEStore.push(TSNEData)
           this.modelIDStore.push(modelId)
           this.UMAPStore.push(UMAPData)
-          console.log(this.activeModels)
+          console.log('mpike5')
           colorsforScatterPlot = this.colorsStore.slice(this.activeModels,this.activeModels+1)[0]
           MDSData = this.MDSStore.slice(this.activeModels,this.activeModels+1)[0]
           parameters = this.parametersStore.slice(this.activeModels,this.activeModels+1)[0]
           //TSNEData = this.TSNEStore.slice(this.activeModels,this.activeModels+1)[0]
           modelId = this.modelIDStore.slice(this.activeModels,this.activeModels+1)[0]
-          console.log(modelId)
           //UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0]
         }
 
-
-        console.log(this.colorsStore)
-        console.log(colorsforScatterPlot)
         EventBus.$emit('sendPointsNumber', modelId.length)
         var classifiersInfoProcessing = []
         for (let i = 0; i < modelId.length; i++) {
@@ -234,6 +251,8 @@ export default {
         EventBus.$emit('NewHeatmapAccordingtoNewStack', modelId)
 
       }
+      console.log(this.colorsStore)
+      console.log(this.MDSStore)
       var DataGeneral
 
       var maxX
@@ -242,7 +261,7 @@ export default {
       var minY
 
       var width = this.WH[0]*6.5 // interactive visualization
-      var height = this.WH[1]*1.22 // interactive visualization
+      var height = this.WH[1]*1.192 // interactive visualization
 
       var layout
       if (this.representationDef == 'mds') {
@@ -443,13 +462,11 @@ export default {
               ClassifierIDsListCleared.push(numberNumb)
             }
           }
-          console.log(ClassifierIDsListCleared)
           for (let i = 0; i < allModels.length; i++) {
             if (!ClassifierIDsListCleared.includes(allModels[i])) {
               pushModelsRemainingTemp.push(allModels[i])
             }
           }
-          console.log(pushModelsRemainingTemp)
             EventBus.$emit('updateRemaining', pushModelsRemainingTemp)
           if (allModels != '') {
             EventBus.$emit('ChangeKey', 1)

insertMongo.py

@@ -9,7 +9,8 @@ import os
 def import_content(filepath):
     mng_client = pymongo.MongoClient('localhost', 27017)
     mng_db = mng_client['mydb']
-    collection_name = 'StanceCTest' 
+    #collection_name = 'StanceCTest' 
+    collection_name = 'StanceC' 
     db_cm = mng_db[collection_name]
     cdir = os.path.dirname(__file__)
     file_res = os.path.join(cdir, filepath)
@@ -20,5 +21,5 @@ def import_content(filepath):
     db_cm.insert(data_json)
 
 if __name__ == "__main__":
-  filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stancetest.csv'
+  filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stance.csv'
   import_content(filepath)

run.py

@@ -74,9 +74,15 @@ def Reset():
     global previousState
     previousState = []
 
+    global filterActionFinal
+    filterActionFinal = ''
+
     global keySpecInternal
     keySpecInternal = 1
 
+    global dataSpacePointsIDs
+    dataSpacePointsIDs = []
+
     global previousStateActive
     previousStateActive = []
 
@@ -175,6 +181,9 @@ def Reset():
 
     global target_names
     target_names = []
+
+    global target_namesLoc
+    target_namesLoc = []
     return 'The reset was done!'
 
 # Retrieve data from client and select the correct data set
@@ -191,6 +200,12 @@ def RetrieveFileName():
     global keySpecInternal
     keySpecInternal = 1
 
+    global filterActionFinal
+    filterActionFinal = ''
+
+    global dataSpacePointsIDs
+    dataSpacePointsIDs = []
+
     global RANDOM_SEED
     RANDOM_SEED = 42
 
@@ -290,6 +305,11 @@ def RetrieveFileName():
     global target_names
     
     target_names = []
+
+    global target_namesLoc
+    
+    target_namesLoc = []
+
     DataRawLength = -1
     DataRawLengthTest = -1
 
@@ -352,6 +372,7 @@ def SendToServerData():
 
     global AllTargets
     global target_names
+    global target_namesLoc
     AllTargets = [o[target] for o in DataResultsRaw]
     AllTargetsFloatValues = []
 
@@ -392,8 +413,10 @@ def CollectionData():
 def DataSetSelection():
     global XDataTest, yDataTest
     XDataTest = pd.DataFrame()
-    yDataTest = []
     global StanceTest
+    global AllTargets
+    global target_names
+    target_namesLoc = []
     if (StanceTest):
         DataResultsTest = copy.deepcopy(DataResultsRawTest)
 
@@ -412,7 +435,7 @@ def DataSetSelection():
             del dictionary['InstanceID']
             del dictionary[target]
 
-        AllTargetsTest = [o[target] for o in DataResultsRaw]
+        AllTargetsTest = [o[target] for o in DataResultsRawTest]
         AllTargetsFloatValuesTest = []
 
         previous = None
@@ -450,8 +473,6 @@ def DataSetSelection():
         del dictionary['InstanceID']
         del dictionary[target]
 
-    global AllTargets
-    global target_names
     AllTargets = [o[target] for o in DataResultsRaw]
     AllTargetsFloatValues = []
 
@@ -487,7 +508,6 @@ def callPreResults():
     global yData
     global target_names
     global impDataInst
-    print(XData)
     DataSpaceResMDS = FunMDS(XData)
     DataSpaceResTSNE = FunTsne(XData)
     DataSpaceResTSNE = DataSpaceResTSNE.tolist()
@@ -633,7 +653,8 @@ memory = Memory(location, verbose=0)
 # calculating for all algorithms and models the performance and other results
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle):
-    print('inside models')
+    print('toggle:',toggle)
+    #print('inside')
     # instantiate spark session
     spark = (   
         SparkSession    
@@ -1023,7 +1044,6 @@ def PreprocessingMetrics():
     dfExtraTFiltered = dfExtraT.loc[ExtraTModels, :]
     dfAdaBFiltered = dfAdaB.loc[AdaBModels, :]
     dfGradBFiltered = dfGradB.loc[GradBModels, :]
-
     df_concatMetrics = pd.concat([dfKNNFiltered, dfSVCFiltered, dfGausNBFiltered, dfMLPFiltered, dfLRFiltered, dfLDAFiltered, dfQDAFiltered, dfRFFiltered, dfExtraTFiltered, dfAdaBFiltered, dfGradBFiltered])
     return df_concatMetrics
 
@@ -1075,7 +1095,6 @@ def PreprocessingPred():
     dfExtraTFiltered = dfExtraT.loc[ExtraTModels, :]
     dfAdaBFiltered = dfAdaB.loc[AdaBModels, :]
     dfGradBFiltered = dfGradB.loc[GradBModels, :]
-
     df_concatProbs = pd.concat([dfKNNFiltered, dfSVCFiltered, dfGausNBFiltered, dfMLPFiltered, dfLRFiltered, dfLDAFiltered, dfQDAFiltered, dfRFFiltered, dfExtraTFiltered, dfAdaBFiltered, dfGradBFiltered])
     predictions = []
     for column, content in df_concatProbs.items():
@@ -1159,19 +1178,14 @@ def PreprocessingPredUpdate(Models):
         predictionsSel.append(el)
     PredictionSpaceSel = FunMDS(predictionsSel)
 
-    print(PredictionSpaceSel)
     PredictionSpaceSelComb = [list(a) for a in  zip(PredictionSpaceSel[0], PredictionSpaceSel[1])]
-    print(PredictionSpaceSelComb)
+
     mtx2PredFinal = []
     mtx2Pred, mtx2Pred, disparityPred = procrustes(PredictionSpaceAllComb, PredictionSpaceSelComb)
 
-    #a1 = [i[1] for i in mtx2Pred]
-    #b1 = [i[0] for i in mtx2Pred]
-    #print(a1)
     a1, b1 = zip(*mtx2Pred)
     mtx2PredFinal.append(a1)
     mtx2PredFinal.append(b1)
-    print(mtx2Pred)
     return [mtx2PredFinal,listIDsRemoved]
 
 def PreprocessingParam():
@@ -1679,7 +1693,22 @@ def processDataInstance(ModelsIDs, allParametersPerformancePerModel):
     dfGradBFiltered = dfGradB.loc[GradBModels, :]
 
     df_connect = pd.concat([dfKNNFiltered, dfSVCFiltered, dfGausNBFiltered, dfMLPFiltered, dfLRFiltered, dfLDAFiltered, dfQDAFiltered, dfRFFiltered, dfExtraTFiltered, dfAdaBFiltered, dfGradBFiltered])
+
     global yData
+    global filterActionFinal
+    global dataSpacePointsIDs
+    lengthDF = len(df_connect.columns)
+
+    if (filterActionFinal == 'compose'):
+        getList = []
+        for index, row in df_connect.iterrows():
+            yDataSelected = []
+            for column in row[dataSpacePointsIDs]:
+                yDataSelected.append(column)
+            storeMode = mode(yDataSelected)
+            getList.append(storeMode)
+        df_connect[str(lengthDF)] = getList
+
     countCorrect = []
     length = len(df_connect.index)
     for index, element in enumerate(yData):
@@ -2292,13 +2321,13 @@ def RetrieveSelDataPoints():
                 dfGradBCleared = dfGradB.drop(dfGradB.index[set_diff_df])
         df_concatMetrics = pd.concat([dfKNNCleared, dfSVCCleared, dfGausNBCleared, dfMLPCleared, dfLRCleared, dfLDACleared, dfQDACleared, dfRFCleared, dfExtraTCleared, dfAdaBCleared, dfGradBCleared])
     df_concatMetrics = df_concatMetrics.reset_index(drop=True)
-    print(df_concatMetrics)
+
     global foreachMetricResults
     foreachMetricResults = []
     foreachMetricResults = preProcSumForEachMetric(factors, df_concatMetrics)
 
     df_concatMetrics.loc[:, 'log_loss'] = 1 - df_concatMetrics.loc[:, 'log_loss']
-    print(df_concatMetrics)
+
     global sumPerClassifierSelUpdate
     sumPerClassifierSelUpdate = []
     sumPerClassifierSelUpdate = preProcsumPerMetricAccordingtoData(factors, df_concatMetrics)
@@ -2308,8 +2337,7 @@ def RetrieveSelDataPoints():
     ModelSpaceMDSNewSel = FunMDS(df_concatMetrics)
 
     ModelSpaceMDSNewSelComb = [list(a) for a in  zip(ModelSpaceMDSNewSel[0], ModelSpaceMDSNewSel[1])]
-    print(len(ModelSpaceMDSNewComb))
-    print(len(ModelSpaceMDSNewSelComb))
+
     global mt2xFinal
     mt2xFinal = []
     mtx1, mtx2, disparity = procrustes(ModelSpaceMDSNewComb, ModelSpaceMDSNewSelComb)
@@ -2883,7 +2911,7 @@ def EnsembleModel(Models, keyRetrieved):
             dfParamGradBFilt = dfParamGradB.iloc[:,0]
             for index, eachelem in enumerate(GradBModels):
                 arg = dfParamGradBFilt[eachelem-GradBModelsCount]
-                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][countItems]), GradientBoostingClassifier().set_params(**arg)))
+                all_classifiers.append(make_pipeline(ColumnSelector(cols=featureSelection['featureSelection'][countItems]), GradientBoostingClassifier(random_state=RANDOM_SEED).set_params(**arg)))
                 store = index
                 flag = 1          
 
@@ -2932,12 +2960,11 @@ def EnsembleModel(Models, keyRetrieved):
     # if (keyRetrieved == 0):
     #     pass
     # else:
-    global StanceTest
     if (keySpec == 0 or keySpec == 1):
         num_cores = multiprocessing.cpu_count()
         inputsSc = ['accuracy','precision_weighted','recall_weighted','f1_weighted']
 
-        flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(StanceTest,XDataTest,yDataTest,sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
+        flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
         scores = [item for sublist in flat_results for item in sublist]
 
     if (keySpec == 0):
@@ -2950,6 +2977,15 @@ def EnsembleModel(Models, keyRetrieved):
         previousState.append(scores[11])
         previousState.append(scores[14])
         previousState.append(scores[15])
+        previousStateActive = []
+        previousStateActive.append(scores[0])
+        previousStateActive.append(scores[1])
+        previousStateActive.append(scores[4])
+        previousStateActive.append(scores[5])
+        previousStateActive.append(scores[8])
+        previousStateActive.append(scores[9])
+        previousStateActive.append(scores[12])
+        previousStateActive.append(scores[13])
     elif (keySpec == 1):
         if (keySpecInternal == 1):
             previousStateActive = []
@@ -3007,10 +3043,20 @@ def EnsembleModel(Models, keyRetrieved):
         scores.append(previousStateActive[7])
         previousState.append(previousStateActive[6])
         previousState.append(previousStateActive[7])
+    print(scores)
+    global StanceTest
+    if (StanceTest):
+        sclf.fit(XData, yData)
+        y_pred = sclf.predict(XDataTest)
+
+        print(accuracy_score(yDataTest, y_pred))
+        print(precision_score(yDataTest, y_pred, average='weighted'))
+        print(recall_score(yDataTest, y_pred, average='weighted'))
+        print(f1_score(yDataTest, y_pred, average='weighted'))
 
     return 'Okay'
 
-def solve(StanceTest,y_Test,y_true,sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop):
+def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop):
     scoresLoc = []
     if (keySpec == 0):
         temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
@@ -3055,22 +3101,13 @@ def solve(StanceTest,y_Test,y_true,sclf,keyData,keySpec,keySpecInternal,previous
             scoresLoc.append(temp.std())
             scoresLoc.append(temp.mean())
             scoresLoc.append(temp.std())
-    if (StanceTest):
-        y_pred = sclf.predict(y_Test)
-        if (loop == 0):
-            print(accuracy_score(y_true, y_pred))
-        elif (loop == 1):
-            print(precision_score(y_true, y_pred, average='weighted'))
-        elif (loop == 2):
-            print(recall_score(y_true, y_pred, average='weighted'))
-        else:
-            print(f1_score(y_true, y_pred, average='weighted'))
-
+    
     return scoresLoc
 
 # Sending the final results to be visualized as a line plot
 @app.route('/data/SendFinalResultsBacktoVisualize', methods=["GET", "POST"])
 def SendToPlotFinalResults():
+    global scores
     response = {    
         'FinalResults': scores
     }
@@ -3116,12 +3153,12 @@ def RetrieveAction():
 
     global filterActionFinal
     global filterDataFinal
+    global dataSpacePointsIDs
     global XData
     global yData
 
     filterActionFinal = filterActionCleared['action']
     dataSpacePointsIDs = filterActionCleared['points']
-    print(dataSpacePointsIDs)
     if (filterActionFinal == 'merge'):
         if (filterDataFinal == 'mean' or filterDataFinal == ''):
             mean = XData.loc[dataSpacePointsIDs, :].mean()
@@ -3164,6 +3201,7 @@ def RetrieveProvenance():
     global XData
     global yDataStored
     global yData
+    global filterActionFinal
 
     # save and restore
     if (filterProvenanceFinal == 'save'):
@@ -3172,5 +3210,6 @@ def RetrieveProvenance():
     else:
         XData = XDataStored.copy()
         yData = yDataStored.copy()
+        filterActionFinal = ''
 
     return 'Done'