new

cachedir/joblib/run/estimatorFeatureSelection/0458420fcaa67bddad7fbac56a5ddd15/metadata.json

@@ -0,0 +1 @@
+{"duration": 7.263703107833862, "input_args": {"Data": "     F1  F2  F3  F4  F5  F6  F7  F8  F9\n0     7   8   7   8   9  10  10   1  10\n1     4   5   2   3   4   3   3   1   3\n2     5   8   7  10   5   7   5   4   9\n3     3   7   6   4   4   4   6   1   1\n4     1  10   4   6   4   7   7   2  10\n..   ..  ..  ..  ..  ..  ..  ..  ..  ..\n694   1   1   2   3   1   1   1   1   1\n695   1   3   2   1   1   1   1   1   1\n696   1   3   2   1   2   1   1   2   1\n697   1   3   3   1   1   1   1   1   2\n698   1   2   2   1   1   1   1   1   1\n\n[699 rows x 9 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.21830844624751583,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.218308449,\n              max_delta_step=0, max_depth=7, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=35, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/8fb5e7eee5656f738012f2c6142b8a56/metadata.json

@@ -0,0 +1 @@
+{"duration": 1.832244873046875, "input_args": {"Data": "     F1  F2  F3  F4  F5  F6  F7  F9\n0     7   8   7   8   9  10  10  10\n1     4   5   2   3   4   3   3   3\n2     5   8   7  10   5   7   5   9\n3     3   7   6   4   4   4   6   1\n4     1  10   4   6   4   7   7  10\n..   ..  ..  ..  ..  ..  ..  ..  ..\n694   1   1   2   3   1   1   1   1\n695   1   3   2   1   1   1   1   1\n696   1   3   2   1   2   1   1   1\n697   1   3   3   1   1   1   1   2\n698   1   2   2   1   1   1   1   1\n\n[699 rows x 8 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.21830844624751583,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.218308449,\n              max_delta_step=0, max_depth=7, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=35, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/ba2cbf4c0fc95bedd05a0cdcd2264654/metadata.json

@@ -0,0 +1 @@
+{"duration": 2.1526169776916504, "input_args": {"Data": "     F1  F2  F3  F4  F5  F6  F7  F8  F9  F1+F6\n0     7   8   7   8   9  10  10   1  10     17\n1     4   5   2   3   4   3   3   1   3      7\n2     5   8   7  10   5   7   5   4   9     12\n3     3   7   6   4   4   4   6   1   1      7\n4     1  10   4   6   4   7   7   2  10      8\n..   ..  ..  ..  ..  ..  ..  ..  ..  ..    ...\n694   1   1   2   3   1   1   1   1   1      2\n695   1   3   2   1   1   1   1   1   1      2\n696   1   3   2   1   2   1   1   2   1      2\n697   1   3   3   1   1   1   1   1   2      2\n698   1   2   2   1   1   1   1   1   1      2\n\n[699 rows x 10 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.21830844624751583,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.218308449,\n              max_delta_step=0, max_depth=7, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=35, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/d02d75616f80e0afe0d40624003cf44f/metadata.json

@@ -0,0 +1 @@
+{"duration": 6.626190185546875, "input_args": {"Data": "     F1  F6  F1+F6  |F1-F6|  F1xF6     F1/F6     F6/F1\n0     7  10     17        3     70  0.700000  1.428571\n1     4   3      7        1     12  1.333333  0.750000\n2     5   7     12        2     35  0.714286  1.400000\n3     3   4      7        1     12  0.750000  1.333333\n4     1   7      8        6      7  0.142857  7.000000\n..   ..  ..    ...      ...    ...       ...       ...\n694   1   1      2        0      1  1.000000  1.000000\n695   1   1      2        0      1  1.000000  1.000000\n696   1   1      2        0      1  1.000000  1.000000\n697   1   1      2        0      1  1.000000  1.000000\n698   1   1      2        0      1  1.000000  1.000000\n\n[699 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.21830844624751583,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.218308449,\n              max_delta_step=0, max_depth=7, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=35, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

frontend/src/components/FeatureSpaceDetail.vue

@@ -63,7 +63,8 @@ export default {
       KeepIDs: [],
       KeepIDTransform: [],
       keepNumberOfCompareNodes: 0,
-      activeLeafNode: -1
+      activeLeafNode: -1,
+      spaceChangeDetail: false
     }
   },
   methods: {
@@ -95,13 +96,15 @@ export default {
 
     },
     setLayerExplore() {
-      console.log('mpike')
       this.mode = 0
       this.KeepIDs = []
       this.KeepIDTransform = []
       this.keepNumberOfCompareNodes = 0
       //this.graphVizualization()
-      EventBus.$emit('storeGeneration')
+      if(!this.spaceChangeDetail) {
+        EventBus.$emit('storeGeneration')
+      }
+      this.spaceChangeDetail = false
     },
     setLayerCompare() {
       this.mode = 1 
@@ -1920,9 +1923,7 @@ export default {
     EventBus.$on('CorrThres', this.initializeNetwork)
 
     EventBus.$on('updateSlice', data => { this.quadrantNumber = data })
-    EventBus.$on('updateSlice', data => { 
-      document.getElementById("initButton").click()
-    })
+    EventBus.$on('updateSlice', this.initializeNetwork)
     //EventBus.$on('updateSlice', this.setLayerExplore)
     //EventBus.$on('updateSlice', this.initializeNetwork)
 
@@ -1935,6 +1936,8 @@ export default {
 
     EventBus.$on('reset', this.reset)
 
+    EventBus.$on('flagSpace', data => { this.spaceChangeDetail = data })
+
     // Get the container element
     var btnContainer2 = document.getElementById("resetAllFilters"); //resetAllFilters button
     // Get all buttons with class="btn" inside the container

frontend/src/components/Main.vue

@@ -149,7 +149,7 @@ export default Vue.extend({
       DataResults: '',
       keyNow: 1,
       instancesImportance: '',
-      RetrieveValueFile: 'IrisC', // this is for the default data set
+      RetrieveValueFile: 'BreastC', // this is for the default data set
       SelectedFeaturesPerClassifier: '',
       FinalResults: 0,
       selectedAlgorithm: '',
@@ -476,7 +476,7 @@ 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)

frontend/src/components/Results.vue

@@ -43,31 +43,6 @@ export default {
       var svg = d3.select("#HistoryPlot");
       svg.selectAll("*").remove();
 
-      var margin = {top: 0, right: 0, bottom: 0, left: 0}
-      var width = 390 - margin.left - margin.right
-      var height = 578 - margin.top - margin.bottom
-      var padding = 3
-      var xLabelHeight = 30
-      var yLabelWidth = 80
-      var borderWidth = 0
-      var duration = 50
-
-      var chart = d3.select('#HistoryPlot').append('svg')
-          .attr('width', width + margin.left + margin.right)
-          .attr('height', height + margin.top + margin.bottom)
-          .append('g')
-          .attr('transform', 'translate(' + 0 + ',' + 0 + ')')
-
-      var border = chart.append('rect')
-          .attr('x', yLabelWidth)
-          .attr('y', xLabelHeight)
-          .style('fill-opacity', 0)
-          .style('stroke', '#000')
-          .style('stroke-width', borderWidth)
-          .style('shape-rendering', 'crispEdges')
-
-      var color = JSON.parse(this.ValidResultsVar[12])
-
       var data = []
       
       var features = this.featuresReceived[33]
@@ -107,10 +82,43 @@ export default {
           values: this.valuesGlobal[i]
         })
       }
+      
+      if (data.length <= 7) {
+        var heightFinal = 578
+      } else {
+        var heightFinal = data.length * 80
+      }
+
+
+      var margin = {top: 0, right: 0, bottom: 0, left: 0}
+      var width = 390 - margin.left - margin.right
+      var height = heightFinal - margin.top - margin.bottom
+      var padding = 3
+      var xLabelHeight = 30
+      var yLabelWidth = 80
+      var borderWidth = 0
+      var duration = 50
+
+      var chart = d3.select('#HistoryPlot').append('svg')
+          .attr('width', width + margin.left + margin.right)
+          .attr('height', height + margin.top + margin.bottom)
+          .append('g')
+          .attr('transform', 'translate(' + 0 + ',' + 0 + ')')
+
+      var border = chart.append('rect')
+          .attr('x', yLabelWidth)
+          .attr('y', xLabelHeight)
+          .style('fill-opacity', 0)
+          .style('stroke', '#000')
+          .style('stroke-width', borderWidth)
+          .style('shape-rendering', 'crispEdges')
+
+      var color = JSON.parse(this.ValidResultsVar[12])
+
 
       var allValues = Array.prototype.concat.apply([], data.map(function(d) { return d.values }))
       var maxWidth = d3.max(data.map(function(d) { return d.values.length }))
-      var maxR = d3.min([(width - yLabelWidth) / maxWidth, (height - xLabelHeight) / data.length]) / 2
+      var maxR = d3.min([(width - yLabelWidth) / maxWidth, (height - xLabelHeight) / 5]) / 2 // Changed to 4 from data.length
 
       // var r = function(d) {
 
@@ -432,7 +440,7 @@ export default {
         },
         barmode: 'group',
         autosize: false,
-        width: '400',
+        width: '395',
         height: '300',
         margin: {
           l: 55,
@@ -504,4 +512,9 @@ body {
   font-size: 12px;
 }
 
+div#HistoryPlot {
+  height: 578px;
+  overflow: scroll;
+}
+
 </style>

run.py

@@ -80,6 +80,12 @@ def reset():
     global yData
     yData = []
 
+    global XDataNoRemoval
+    XDataNoRemoval = []
+
+    global XDataNoRemovalOrig
+    XDataNoRemovalOrig = []
+
     global XDataStored
     XDataStored = []
     global yDataStored
@@ -183,6 +189,12 @@ def retrieveFileName():
     global XData
     XData = []
 
+    global XDataNoRemoval
+    XDataNoRemoval = []
+
+    global XDataNoRemovalOrig
+    XDataNoRemovalOrig = []
+
     global previousState
     previousState = []
 
@@ -365,6 +377,12 @@ def sendToServerData():
     global finalResultsData
     finalResultsData = XData.copy()
 
+    global XDataNoRemoval 
+    XDataNoRemoval = XData.copy()
+
+    global XDataNoRemovalOrig
+    XDataNoRemovalOrig = XData.copy()
+
     return 'Processed uploaded data set'
 
 def dataSetSelection():
@@ -478,6 +496,12 @@ def dataSetSelection():
     global finalResultsData
     finalResultsData = XData.copy()
 
+    global XDataNoRemoval 
+    XDataNoRemoval = XData.copy()
+
+    global XDataNoRemovalOrig
+    XDataNoRemovalOrig = XData.copy()
+
     warnings.simplefilter('ignore')
 
     executeModel([], 0, '')
@@ -512,18 +536,27 @@ def executeModel(exeCall, flagEx, nodeTransfName):
     global listofTransformations
     global XDataStoredOriginal
     global finalResultsData
+
+    global XDataNoRemoval
+    global XDataNoRemovalOrig
+
     columnsNames = []
     scores = []
-
+    print(exeCall)
+    print(flagEx)
     if (len(exeCall) == 0):
         if (flagEx == 3):
             XDataStored = XData.copy()
+            XDataNoRemovalOrig = XDataNoRemoval.copy()
         else:
             XData = XDataStored.copy()
+            XDataNoRemoval = XDataNoRemovalOrig.copy()
             XDataStoredOriginal = XDataStored.copy()
     else:
         if (flagEx == 4):
             XDataStored = XData.copy()
+        elif (flagEx == 2):
+            pass
         else:    
             XData = XDataStored.copy()
             XDataStoredOriginal = XDataStored.copy()
@@ -533,7 +566,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
         create_global_function()
         params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "max_depth": (6,12)}
         bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
-        bayesopt.maximize(init_points=35, n_iter=15, acq='ucb')
+        bayesopt.maximize(init_points=10, n_iter=5, acq='ucb') # 35 and 15
         bestParams = bayesopt.max['params']
         estimator = XGBClassifier(n_estimators=int(bestParams.get('n_estimators')), eta=bestParams.get('eta'), max_depth=int(bestParams.get('max_depth')), probability=True, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
 
@@ -552,6 +585,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
             XDataTemp = XDataGen[columnsKeepNew]
             XData[columnsKeepNew] = XDataTemp.values
             XDataStoredOriginal[columnsKeepNew] = XDataTemp.values
+            XDataNoRemoval[columnsKeepNew] = XDataTemp.values
         elif (flagEx == 4):
             splittedCol = nodeTransfName.split('_')
             XData.rename(columns={ XData.columns[exeCall[0]]: nodeTransfName }, inplace = True)
@@ -634,11 +668,11 @@ def executeModel(exeCall, flagEx, nodeTransfName):
                     columnsNames.append(splittedCol[0])
                 else:
                     columnsNames.append(splittedCol[0]+'_'+tran)
-    featureImportanceData = estimatorFeatureSelection(XData, estimator)
+    featureImportanceData = estimatorFeatureSelection(XDataNoRemoval, estimator)
     estimator.fit(XData, yData)
     yPredict = estimator.predict(XData)
     yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
-    print('Data set:',XData)
+    print(XData)
     num_cores = multiprocessing.cpu_count()
     inputsSc = ['accuracy','precision_macro','recall_macro']
 
@@ -704,7 +738,7 @@ def featFun (clfLocalPar,DataLocalPar,yDataLocalPar):
 # # calculating for all algorithms and models the performance and other results
 # @memory.cache
 def estimatorFeatureSelection(Data, clf):
-
+    print(Data)
     resultsFS = []
     permList = []
     PerFeatureAccuracy = []
@@ -1756,8 +1790,9 @@ def CompareFunPy():
     for indx, col in enumerate(columns):
         if indx in IDsToCompare:
             columnsKeepNonOrig.append(col)
-            columnsKeep.append(columnsOriganl[indx])
-            columnsKeepID.append(str(indx+1))
+            columnExtracted = re.findall('\d+', col)
+            columnsKeep.append(columnsOriganl[int(columnExtracted[0]) - 1])
+            columnsKeepID.append(str(int(columnExtracted[0])))
     if (compareMode == 1):
         XDataGen = XData[columnsKeepNonOrig]
         feat1 = XDataGen.iloc[:,0]