new

cachedir/joblib/run/randomSearch/12ed81ff43e60d39acfe8c23196f8092/metadata.json

@@ -0,0 +1 @@
+{"duration": 77.61082220077515, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 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, 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, 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, 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, 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, 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]", "clf": "LogisticRegression(C=76, max_iter=50, random_state=42, solver='saga')", "params": "{'C': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 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], 'max_iter': [50, 100, 150, 200, 250, 300, 350, 400, 450], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "200", "crossValidation": "10", "randomSear": "200"}}

cachedir/joblib/run/randomSearch/4a4d3498f45d1d7ad153ae029decbdcf/metadata.json

@@ -0,0 +1 @@
+{"duration": 22.581008911132812, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 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, 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, 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, 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, 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, 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]", "clf": "KNeighborsClassifier(algorithm='kd_tree', metric='manhattan', n_neighbors=24)", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 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], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0", "crossValidation": "10", "randomSear": "200"}}

cachedir/joblib/run/randomSearch/func_code.py

@@ -1,9 +1,9 @@
-# first line: 718
+# first line: 728
 @memory.cache
-def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation):
+def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation,randomSear):
     print(clf)
     search = RandomizedSearchCV(    
-        estimator=clf, param_distributions=params, n_iter=100,
+        estimator=clf, param_distributions=params, n_iter=randomSear,
         cv=crossValidation, refit='accuracy', scoring=scoring,
         verbose=0, n_jobs=-1)
     

frontend/src/components/DataSetExecController.vue

@@ -3,7 +3,6 @@
     <label id="data" for="param-dataset" data-toggle="tooltip" data-placement="right" title="Tip: use one of the data sets already provided or upload a new file.">{{ dataset }}</label>
     <select id="selectFile" @change="selectDataSet()">
         <option value="biodegC.csv" selected>Biodegradation</option>
-        <option value="seismicC.csv">Seismic bumps</option>
         <option value="heartC.csv">Heart disease</option>
         <option value="local">Upload file</option>
     </select>

frontend/src/components/Main.vue

@@ -706,6 +706,8 @@ export default Vue.extend({
       axios.post(path, postData, axiosConfig)
       .then(response => {
         console.log('File name was sent successfully!')
+        this.CMNumberofModelsOFFICIAL = [0,0,0,0,0,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0],
+        this.CMNumberofModelsOFFICIALS2 = [0,0,0,0,0,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,this.RandomSear/2,0],
         this.SendAlgorithmsToServer()
       })
       .catch(error => {

frontend/src/components/Predictions.vue

@@ -26,7 +26,8 @@ export default {
       StoreIndices: [],
       classesNumber: 9,
       InfoPred: [],
-      flag: false
+      flag: false,
+      RetrieveValueFi: 'biodegC' // default file name
     }
   },
   methods: {
@@ -67,18 +68,22 @@ export default {
       }
       else {
         var tempFirst = []
-        for (let i = 0; i < 169; i++) {
+        for (let i = 0; i < 100; i++) {
           tempFirst.push(i)
         }
         var tempLast = []
-        for (let i = 169; i < 338; i++) {
+        for (let i = 100; i < 200; i++) {
           tempLast.push(i)
         }
         getIndices.push(tempFirst)
         getIndices.push(tempLast)
       }
-      getIndices.reverse()
-      console.log(getIndices)
+
+      if (this.RetrieveValueFi == "heartC") {
+        getIndices.reverse()
+      }
+
+
       var predictions = JSON.parse(this.GetResultsAll[12])
       var KNNPred = predictions[0]
       var LRPred = predictions[1]
@@ -156,25 +161,24 @@ export default {
         dataRFResults.push(dataRF)
         dataGradBResults.push(dataGradB)
       }
-    dataAverGetResults.reverse()
-    dataKNNResults.reverse()
-    dataLRResults.reverse()
-    dataMLPResults.reverse()
-    dataRFResults.reverse()
-    dataGradBResults.reverse()
-    console.log(dataAverGetResults)
-    console.log(dataKNNResults)
+    // dataAverGetResults.reverse()
+    // dataKNNResults.reverse()
+    // dataLRResults.reverse()
+    // dataMLPResults.reverse()
+    // dataRFResults.reverse()
+    // dataGradBResults.reverse()
+
     var classArray = []
     this.StoreIndices = []
     for (let i = 0; i < dataAverGetResults.length; i++) {
-      dataAverGetResults[i].sort((a, b) => (a.value > b.value) ? 1 : -1)
+      dataAverGetResults[i].sort((a, b) => (a.value > b.value) ? -1 : 1)
       var len = dataAverGetResults[i].length
       var indices = new Array(len)
       for (let j = 0; j < len; j++) {
         indices[j] = dataAverGetResults[i][j].id;
       }
       this.StoreIndices.push(indices)
-      console.log(indices)
+
       dataKNNResults[i].sort(function(a, b){
         return indices.indexOf(a.id) - indices.indexOf(b.id)
       });
@@ -197,7 +201,7 @@ export default {
 
       classArray.push(dataAverGetResults[i].concat(dataKNNResults[i], dataLRResults[i],dataMLPResults[i],dataRFResults[i],dataGradBResults[i]));
     }
-    console.log(classArray)
+
     var classStore = [].concat.apply([], classArray);
 		// === Set up canvas === //
 
@@ -236,7 +240,7 @@ export default {
 
 		function databind(data, size, sqrtSize) {
 
-			colourScale = d3.scaleSequential(d3.interpolateGreens).domain([100, 0])
+			colourScale = d3.scaleSequential(d3.interpolateGreens).domain([0, 100])
 
 			var join = custom.selectAll('custom.rect')
         .data(data);
@@ -352,17 +356,20 @@ export default {
       }
       else {
         var tempFirst = []
-        for (let i = 0; i < 169; i++) {
+        for (let i = 0; i < 100; i++) {
           tempFirst.push(i)
         }
         var tempLast = []
-        for (let i = 169; i < 338; i++) {
+        for (let i = 100; i < 200; i++) {
           tempLast.push(i)
         }
         getIndices.push(tempFirst)
         getIndices.push(tempLast)
       }
-      getIndices.reverse()
+      
+      if (this.RetrieveValueFi == "heartC") {
+        getIndices.reverse()
+      }
 
       var dataAver = []
       var dataAverGetResults = []
@@ -387,6 +394,7 @@ export default {
         dataMLP = []
         dataRF = []
         dataGradB = []
+
         getIndices[targetNames[i]].forEach(element => {
           if (PredAver.length == 0) {
             dataAver.push({ id: -1, value: 0 })
@@ -401,6 +409,7 @@ export default {
           if (LRPred.length == 0) {
             dataLR.push({ id: -1, value: 0 })
           } else {
+ 
             dataLR.push({ id: element, value: LRPred[element] - LRPredAll[element] })
           }
           if (MLPPred.length == 0) {
@@ -435,12 +444,6 @@ export default {
         dataRFResults.push(dataRF)
         dataGradBResults.push(dataGradB)
       }
-    dataAverGetResults.reverse()
-    dataKNNResults.reverse()
-    dataLRResults.reverse()
-    dataMLPResults.reverse()
-    dataRFResults.reverse()
-    dataGradBResults.reverse()
 
     var classArray = []
 
@@ -473,7 +476,7 @@ export default {
 
       classArray.push(dataAverGetResults[i].concat(dataKNNResults[i], dataLRResults[i], dataMLPResults[i], dataRFResults[i], dataGradBResults[i]));
     }
-    
+
     var classStore = [].concat.apply([], classArray);
 
 		// === Set up canvas === //
@@ -693,6 +696,7 @@ export default {
   },
   },
   mounted () {
+      EventBus.$on('SendToServerDataSetConfirmation', data => { this.RetrieveValueFi = data })
       EventBus.$on('ON', data => {this.flag = data})
 
       EventBus.$on('emittedEventCallingInfo', data => { this.InfoPred = data })

insertMongo.py

@@ -10,7 +10,7 @@ def import_content(filepath):
     mng_client = pymongo.MongoClient('localhost', 27017)
     mng_db = mng_client['mydb']
     #collection_name = 'StanceCTest' 
-    collection_name = 'biodegCExt' 
+    collection_name = 'biodegC' 
     db_cm = mng_db[collection_name]
     cdir = os.path.dirname(__file__)
     file_res = os.path.join(cdir, filepath)
@@ -21,5 +21,5 @@ def import_content(filepath):
     db_cm.insert(data_json)
 
 if __name__ == "__main__":
-  filepath = '/Users/anchaa/Documents/Research/HyperSearVis_code/new_data_sets/biodegext.csv'
+  filepath = '/Users/anchaa/Documents/Research/HyperSearVis_code/new_data_sets/biodeg.csv'
   import_content(filepath)

new_data_sets/breast.csv

@@ -1,700 +0,0 @@
-clump_thic,size_un,shape_un,marg_adh,epith_size,bare_nuc,bland_chr,nor_nuc,mitoses,class*
-5,1,1,1,2,1,3,1,1,Benign
-5,4,4,5,7,10,3,2,1,Benign
-3,1,1,1,2,2,3,1,1,Benign
-6,8,8,1,3,4,3,7,1,Benign
-4,1,1,3,2,1,3,1,1,Benign
-8,10,10,8,7,10,9,7,1,Malignant
-1,1,1,1,2,10,3,1,1,Benign
-2,1,2,1,2,1,3,1,1,Benign
-2,1,1,1,2,1,1,1,5,Benign
-4,2,1,1,2,1,2,1,1,Benign
-1,1,1,1,1,1,3,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-5,3,3,3,2,3,4,4,1,Malignant
-1,1,1,1,2,3,3,1,1,Benign
-8,7,5,10,7,9,5,5,4,Malignant
-7,4,6,4,6,1,4,3,1,Malignant
-4,1,1,1,2,1,2,1,1,Benign
-4,1,1,1,2,1,3,1,1,Benign
-10,7,7,6,4,10,4,1,2,Malignant
-6,1,1,1,2,1,3,1,1,Benign
-7,3,2,10,5,10,5,4,4,Malignant
-10,5,5,3,6,7,7,10,1,Malignant
-3,1,1,1,2,1,2,1,1,Benign
-8,4,5,1,2,4,7,3,1,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-5,2,3,4,2,7,3,6,1,Malignant
-3,2,1,1,1,1,2,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-1,1,3,1,2,1,1,1,1,Benign
-3,1,1,1,1,1,2,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-10,7,7,3,8,5,7,4,3,Malignant
-2,1,1,2,2,1,3,1,1,Benign
-3,1,2,1,2,1,2,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-10,10,10,8,6,1,8,9,1,Malignant
-6,2,1,1,1,1,7,1,1,Benign
-5,4,4,9,2,10,5,6,1,Malignant
-2,5,3,3,6,7,7,5,1,Malignant
-6,6,6,9,6,4,7,8,1,Benign
-10,4,3,1,3,3,6,5,2,Malignant
-6,10,10,2,8,10,7,3,3,Malignant
-5,6,5,6,10,1,3,1,1,Malignant
-10,10,10,4,8,1,8,10,1,Malignant
-1,1,1,1,2,1,2,1,2,Benign
-3,7,7,4,4,9,4,8,1,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-4,1,1,3,2,1,3,1,1,Benign
-7,8,7,2,4,8,3,8,2,Malignant
-9,5,8,1,2,3,2,1,5,Malignant
-5,3,3,4,2,4,3,4,1,Malignant
-10,3,6,2,3,5,4,10,2,Malignant
-5,5,5,8,10,8,7,3,7,Malignant
-10,5,5,6,8,8,7,1,1,Malignant
-10,6,6,3,4,5,3,6,1,Malignant
-8,10,10,1,3,6,3,9,1,Malignant
-8,2,4,1,5,1,5,4,4,Malignant
-5,2,3,1,6,10,5,1,1,Malignant
-9,5,5,2,2,2,5,1,1,Malignant
-5,3,5,5,3,3,4,10,1,Malignant
-1,1,1,1,2,2,2,1,1,Benign
-9,10,10,1,10,8,3,3,1,Malignant
-6,3,4,1,5,2,3,9,1,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-10,4,2,1,3,2,4,3,10,Malignant
-4,1,1,1,2,1,3,1,1,Benign
-5,3,4,1,8,10,4,9,1,Malignant
-8,3,8,3,4,9,8,9,8,Malignant
-1,1,1,1,2,1,3,2,1,Benign
-5,1,3,1,2,1,2,1,1,Benign
-6,10,2,8,10,2,7,8,10,Malignant
-1,3,3,2,2,1,7,2,1,Benign
-9,4,5,10,6,10,4,8,1,Malignant
-10,6,4,1,3,4,3,2,3,Malignant
-1,1,2,1,2,2,4,2,1,Benign
-1,1,4,1,2,1,2,1,1,Benign
-5,3,1,2,2,1,2,1,1,Benign
-3,1,1,1,2,3,3,1,1,Benign
-2,1,1,1,3,1,2,1,1,Benign
-2,2,2,1,1,1,7,1,1,Benign
-4,1,1,2,2,1,2,1,1,Benign
-5,2,1,1,2,1,3,1,1,Benign
-3,1,1,1,2,2,7,1,1,Benign
-3,5,7,8,8,9,7,10,7,Malignant
-5,10,6,1,10,4,4,10,10,Malignant
-3,3,6,4,5,8,4,4,1,Malignant
-3,6,6,6,5,10,6,8,3,Malignant
-4,1,1,1,2,1,3,1,1,Benign
-2,1,1,2,3,1,2,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-3,1,1,2,2,1,1,1,1,Benign
-4,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-2,1,1,2,2,1,1,1,1,Benign
-5,1,1,1,2,1,3,1,1,Benign
-9,6,9,2,10,6,2,9,10,Malignant
-7,5,6,10,5,10,7,9,4,Malignant
-10,3,5,1,10,5,3,10,2,Malignant
-2,3,4,4,2,5,2,5,1,Malignant
-4,1,2,1,2,1,3,1,1,Benign
-8,2,3,1,6,3,7,1,1,Malignant
-10,10,10,10,10,1,8,8,8,Malignant
-7,3,4,4,3,3,3,2,7,Malignant
-10,10,10,8,2,10,4,1,1,Malignant
-1,6,8,10,8,10,5,7,1,Malignant
-1,1,1,1,2,1,2,3,1,Benign
-6,5,4,4,3,9,7,8,3,Malignant
-1,3,1,2,2,2,5,3,2,Benign
-8,6,4,3,5,9,3,1,1,Malignant
-10,3,3,10,2,10,7,3,3,Malignant
-10,10,10,3,10,8,8,1,1,Malignant
-3,3,2,1,2,3,3,1,1,Benign
-1,1,1,1,2,5,1,1,1,Benign
-8,3,3,1,2,2,3,2,1,Benign
-4,5,5,10,4,10,7,5,8,Malignant
-1,1,1,1,4,3,1,1,1,Benign
-3,2,1,1,2,2,3,1,1,Benign
-1,1,2,2,2,1,3,1,1,Benign
-4,2,1,1,2,2,3,1,1,Benign
-10,10,10,2,10,10,5,3,3,Malignant
-5,3,5,1,8,10,5,3,1,Malignant
-5,4,6,7,9,7,8,10,1,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-7,5,3,7,4,10,7,5,5,Malignant
-3,1,1,1,2,1,3,1,1,Benign
-8,3,5,4,5,10,1,6,2,Malignant
-1,1,1,1,10,1,1,1,1,Benign
-5,1,3,1,2,1,2,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-5,10,8,10,8,10,3,6,3,Malignant
-3,1,1,1,2,1,2,2,1,Benign
-3,1,1,1,3,1,2,1,1,Benign
-5,1,1,1,2,2,3,3,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,1,1,1,Benign
-4,1,2,1,2,1,2,1,1,Benign
-1,1,1,1,1,4,2,1,1,Benign
-3,1,1,1,2,1,1,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-9,5,5,4,4,5,4,3,3,Malignant
-1,1,1,1,2,5,1,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-1,1,3,1,2,4,2,1,1,Benign
-3,4,5,2,6,8,4,1,1,Malignant
-1,1,1,1,3,2,2,1,1,Benign
-3,1,1,3,8,1,5,8,1,Benign
-8,8,7,4,10,10,7,8,7,Malignant
-1,1,1,1,1,1,3,1,1,Benign
-7,2,4,1,6,10,5,4,3,Malignant
-10,10,8,6,4,5,8,10,1,Malignant
-4,1,1,1,2,3,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-5,5,5,6,3,10,3,1,1,Malignant
-1,2,2,1,2,1,2,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-1,1,2,1,3,4,1,1,1,Benign
-9,9,10,3,6,10,7,10,6,Malignant
-10,7,7,4,5,10,5,7,2,Malignant
-4,1,1,1,2,1,3,2,1,Benign
-3,1,1,1,2,1,3,1,1,Benign
-1,1,1,2,1,3,1,1,7,Benign
-5,1,1,1,2,4,3,1,1,Benign
-4,1,1,1,2,2,3,2,1,Benign
-5,6,7,8,8,10,3,10,3,Malignant
-10,8,10,10,6,1,3,1,10,Malignant
-3,1,1,1,2,1,3,1,1,Benign
-1,1,1,2,1,1,1,1,1,Benign
-3,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-6,10,10,10,8,10,10,10,7,Malignant
-8,6,5,4,3,10,6,1,1,Malignant
-5,8,7,7,10,10,5,7,1,Malignant
-2,1,1,1,2,1,3,1,1,Benign
-5,10,10,3,8,1,5,10,3,Malignant
-4,1,1,1,2,1,3,1,1,Benign
-5,3,3,3,6,10,3,1,1,Malignant
-1,1,1,1,1,1,3,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-6,1,1,1,2,1,3,1,1,Benign
-5,8,8,8,5,10,7,8,1,Malignant
-8,7,6,4,4,10,5,1,1,Malignant
-2,1,1,1,1,1,3,1,1,Benign
-1,5,8,6,5,8,7,10,1,Malignant
-10,5,6,10,6,10,7,7,10,Malignant
-5,8,4,10,5,8,9,10,1,Malignant
-1,2,3,1,2,1,3,1,1,Benign
-10,10,10,8,6,8,7,10,1,Malignant
-7,5,10,10,10,10,4,10,3,Malignant
-5,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-3,1,1,1,2,1,3,1,1,Benign
-4,1,1,1,2,1,3,1,1,Benign
-8,4,4,5,4,7,7,8,2,Benign
-5,1,1,4,2,1,3,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-9,7,7,5,5,10,7,8,3,Malignant
-10,8,8,4,10,10,8,1,1,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-5,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-5,10,10,9,6,10,7,10,5,Malignant
-10,10,9,3,7,5,3,5,1,Malignant
-1,1,1,1,1,1,3,1,1,Benign
-1,1,1,1,1,1,3,1,1,Benign
-5,1,1,1,1,1,3,1,1,Benign
-8,10,10,10,5,10,8,10,6,Malignant
-8,10,8,8,4,8,7,7,1,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-10,10,10,10,7,10,7,10,4,Malignant
-10,10,10,10,3,10,10,6,1,Malignant
-8,7,8,7,5,5,5,10,2,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-6,10,7,7,6,4,8,10,2,Malignant
-6,1,3,1,2,1,3,1,1,Benign
-1,1,1,2,2,1,3,1,1,Benign
-10,6,4,3,10,10,9,10,1,Malignant
-4,1,1,3,1,5,2,1,1,Malignant
-7,5,6,3,3,8,7,4,1,Malignant
-10,5,5,6,3,10,7,9,2,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-10,5,7,4,4,10,8,9,1,Malignant
-8,9,9,5,3,5,7,7,1,Malignant
-1,1,1,1,1,1,3,1,1,Benign
-10,10,10,3,10,10,9,10,1,Malignant
-7,4,7,4,3,7,7,6,1,Malignant
-6,8,7,5,6,8,8,9,2,Malignant
-8,4,6,3,3,1,4,3,1,Benign
-10,4,5,5,5,10,4,1,1,Malignant
-3,3,2,1,3,1,3,6,1,Benign
-3,1,4,1,2,4,3,1,1,Benign
-10,8,8,2,8,10,4,8,10,Malignant
-9,8,8,5,6,2,4,10,4,Malignant
-8,10,10,8,6,9,3,10,10,Malignant
-10,4,3,2,3,10,5,3,2,Malignant
-5,1,3,3,2,2,2,3,1,Benign
-3,1,1,3,1,1,3,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,5,5,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-5,1,1,2,2,2,3,1,1,Benign
-8,10,10,8,5,10,7,8,1,Malignant
-8,4,4,1,2,9,3,3,1,Malignant
-4,1,1,1,2,1,3,6,1,Benign
-3,1,1,1,2,4,3,1,1,Benign
-1,2,2,1,2,1,1,1,1,Benign
-10,4,4,10,2,10,5,3,3,Malignant
-6,3,3,5,3,10,3,5,3,Benign
-6,10,10,2,8,10,7,3,3,Malignant
-9,10,10,1,10,8,3,3,1,Malignant
-5,6,6,2,4,10,3,6,1,Malignant
-3,1,1,1,2,1,1,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,3,1,1,Benign
-5,7,7,1,5,8,3,4,1,Benign
-10,5,8,10,3,10,5,1,3,Malignant
-5,10,10,6,10,10,10,6,5,Malignant
-8,8,9,4,5,10,7,8,1,Malignant
-10,4,4,10,6,10,5,5,1,Malignant
-7,9,4,10,10,3,5,3,3,Malignant
-5,1,4,1,2,1,3,2,1,Benign
-10,10,6,3,3,10,4,3,2,Malignant
-3,3,5,2,3,10,7,1,1,Malignant
-10,8,8,2,3,4,8,7,8,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-8,4,7,1,3,10,3,9,2,Malignant
-5,1,1,1,2,1,3,1,1,Benign
-3,3,5,2,3,10,7,1,1,Malignant
-7,2,4,1,3,4,3,3,1,Malignant
-3,1,1,1,2,1,3,2,1,Benign
-3,1,3,1,2,4,2,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-10,5,7,3,3,7,3,3,8,Malignant
-3,1,1,1,2,1,3,1,1,Benign
-2,1,1,2,2,1,3,1,1,Benign
-1,4,3,10,4,10,5,6,1,Malignant
-10,4,6,1,2,10,5,3,1,Malignant
-7,4,5,10,2,10,3,8,2,Malignant
-8,10,10,10,8,10,10,7,3,Malignant
-10,10,10,10,10,10,4,10,10,Malignant
-3,1,1,1,3,1,2,1,1,Benign
-6,1,3,1,4,5,5,10,1,Malignant
-5,6,6,8,6,10,4,10,4,Malignant
-1,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-8,8,8,1,2,4,6,10,1,Malignant
-10,4,4,6,2,10,2,3,1,Malignant
-1,1,1,1,2,4,2,1,1,Benign
-5,5,7,8,6,10,7,4,1,Malignant
-5,3,4,3,4,5,4,7,1,Benign
-5,4,3,1,2,4,2,3,1,Benign
-8,2,1,1,5,1,1,1,1,Benign
-9,1,2,6,4,10,7,7,2,Malignant
-8,4,10,5,4,4,7,10,1,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-10,10,10,7,9,10,7,10,10,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-8,3,4,9,3,10,3,3,1,Malignant
-10,8,4,4,4,10,3,10,4,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-7,8,7,6,4,3,8,8,4,Malignant
-3,1,1,1,2,5,5,1,1,Benign
-2,1,1,1,3,1,2,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-8,6,4,10,10,1,3,5,1,Malignant
-1,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,1,1,2,1,1,Benign
-4,6,5,6,7,4,4,9,1,Benign
-5,5,5,2,5,10,4,3,1,Malignant
-6,8,7,8,6,8,8,9,1,Malignant
-1,1,1,1,5,1,3,1,1,Benign
-4,4,4,4,6,5,7,3,1,Benign
-7,6,3,2,5,10,7,4,6,Malignant
-3,1,1,1,2,4,3,1,1,Benign
-3,1,1,1,2,1,3,1,1,Benign
-5,4,6,10,2,10,4,1,1,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-3,2,2,1,2,1,2,3,1,Benign
-10,1,1,1,2,10,5,4,1,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-8,10,3,2,6,4,3,10,1,Malignant
-10,4,6,4,5,10,7,1,1,Malignant
-10,4,7,2,2,8,6,1,1,Malignant
-5,1,1,1,2,1,3,1,2,Benign
-5,2,2,2,2,1,2,2,1,Benign
-5,4,6,6,4,10,4,3,1,Malignant
-8,6,7,3,3,10,3,4,2,Malignant
-1,1,1,1,2,1,1,1,1,Benign
-6,5,5,8,4,10,3,4,1,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,1,1,2,1,1,Benign
-8,5,5,5,2,10,4,3,1,Malignant
-10,3,3,1,2,10,7,6,1,Malignant
-1,1,1,1,2,1,3,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-7,6,4,8,10,10,9,5,3,Malignant
-1,1,1,1,2,1,1,1,1,Benign
-5,2,2,2,3,1,1,3,1,Benign
-1,1,1,1,1,1,1,3,1,Benign
-3,4,4,10,5,1,3,3,1,Malignant
-4,2,3,5,3,8,7,6,1,Malignant
-5,1,1,3,2,1,1,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-3,4,5,3,7,3,4,6,1,Benign
-2,7,10,10,7,10,4,9,4,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-4,1,1,1,3,1,2,2,1,Benign
-5,3,3,1,3,3,3,3,3,Malignant
-8,10,10,7,10,10,7,3,8,Malignant
-8,10,5,3,8,4,4,10,3,Malignant
-10,3,5,4,3,7,3,5,3,Malignant
-6,10,10,10,10,10,8,10,10,Malignant
-3,10,3,10,6,10,5,1,4,Malignant
-3,2,2,1,4,3,2,1,1,Benign
-4,4,4,2,2,3,2,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-6,10,10,10,8,10,7,10,7,Malignant
-5,8,8,10,5,10,8,10,3,Malignant
-1,1,3,1,2,1,1,1,1,Benign
-1,1,3,1,1,1,2,1,1,Benign
-4,3,2,1,3,1,2,1,1,Benign
-1,1,3,1,2,1,1,1,1,Benign
-4,1,2,1,2,1,2,1,1,Benign
-5,1,1,2,2,1,2,1,1,Benign
-3,1,2,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,1,1,2,1,1,Benign
-3,1,1,4,3,1,2,2,1,Benign
-5,3,4,1,4,1,3,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-10,6,3,6,4,10,7,8,4,Malignant
-3,2,2,2,2,1,3,2,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-3,3,2,2,3,1,1,2,3,Benign
-7,6,6,3,2,10,7,1,1,Malignant
-5,3,3,2,3,1,3,1,1,Benign
-2,1,1,1,2,1,2,2,1,Benign
-5,1,1,1,3,2,2,2,1,Benign
-1,1,1,2,2,1,2,1,1,Benign
-10,8,7,4,3,10,7,9,1,Malignant
-3,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,1,1,1,1,1,Benign
-1,2,3,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,3,1,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-3,2,1,1,2,1,2,2,1,Benign
-1,2,3,1,2,1,1,1,1,Benign
-3,10,8,7,6,9,9,3,8,Malignant
-3,1,1,1,2,1,1,1,1,Benign
-5,3,3,1,2,1,2,1,1,Benign
-3,1,1,1,2,4,1,1,1,Benign
-1,2,1,3,2,1,1,2,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-4,2,2,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-2,3,2,2,2,2,3,1,1,Benign
-3,1,2,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,1,4,2,1,1,Benign
-10,10,10,6,8,4,8,5,1,Malignant
-5,1,2,1,2,1,3,1,1,Benign
-8,5,6,2,3,10,6,6,1,Malignant
-3,3,2,6,3,3,3,5,1,Benign
-8,7,8,5,10,10,7,2,1,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-5,2,2,2,2,2,3,2,2,Benign
-2,3,1,1,5,1,1,1,1,Benign
-3,2,2,3,2,3,3,1,1,Benign
-10,10,10,7,10,10,8,2,1,Malignant
-4,3,3,1,2,1,3,3,1,Benign
-5,1,3,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,1,1,1,Benign
-9,10,10,10,10,10,10,10,1,Malignant
-5,3,6,1,2,1,1,1,1,Benign
-8,7,8,2,4,2,5,10,1,Malignant
-1,1,1,1,2,1,2,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-1,3,1,1,2,1,2,2,1,Benign
-5,1,1,3,4,1,3,2,1,Benign
-5,1,1,1,2,1,2,2,1,Benign
-3,2,2,3,2,1,1,1,1,Benign
-6,9,7,5,5,8,4,2,1,Benign
-10,8,10,1,3,10,5,1,1,Malignant
-10,10,10,1,6,1,2,8,1,Malignant
-4,1,1,1,2,1,1,1,1,Benign
-4,1,3,3,2,1,1,1,1,Benign
-5,1,1,1,2,1,1,1,1,Benign
-10,4,3,10,4,10,10,1,1,Malignant
-5,2,2,4,2,4,1,1,1,Benign
-1,1,1,3,2,3,1,1,1,Benign
-1,1,1,1,2,2,1,1,1,Benign
-5,1,1,6,3,1,2,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-5,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,1,1,1,1,1,Benign
-5,7,9,8,6,10,8,10,1,Malignant
-4,1,1,3,1,1,2,1,1,Benign
-5,1,1,1,2,1,1,1,1,Benign
-3,1,1,3,2,1,1,1,1,Benign
-4,5,5,8,6,10,10,7,1,Malignant
-2,3,1,1,3,1,1,1,1,Benign
-10,2,2,1,2,6,1,1,2,Malignant
-10,6,5,8,5,10,8,6,1,Malignant
-8,8,9,6,6,3,10,10,1,Malignant
-5,1,2,1,2,1,1,1,1,Benign
-5,1,3,1,2,1,1,1,1,Benign
-5,1,1,3,2,1,1,1,1,Benign
-3,1,1,1,2,5,1,1,1,Benign
-6,1,1,3,2,1,1,1,1,Benign
-4,1,1,1,2,1,1,2,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-10,9,8,7,6,4,7,10,3,Malignant
-10,6,6,2,4,10,9,7,1,Malignant
-6,6,6,5,4,10,7,6,2,Malignant
-4,1,1,1,2,1,1,1,1,Benign
-1,1,2,1,2,1,2,1,1,Benign
-3,1,1,1,1,1,2,1,1,Benign
-6,1,1,3,2,1,1,1,1,Benign
-6,1,1,1,1,1,1,1,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-5,1,1,1,2,1,1,1,1,Benign
-3,1,1,1,2,1,1,1,1,Benign
-4,1,2,1,2,1,1,1,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-5,2,1,1,2,1,1,1,1,Benign
-4,8,7,10,4,10,7,5,1,Malignant
-5,1,1,1,1,1,1,1,1,Benign
-5,3,2,4,2,1,1,1,1,Benign
-9,10,10,10,10,5,10,10,10,Malignant
-8,7,8,5,5,10,9,10,1,Malignant
-5,1,2,1,2,1,1,1,1,Benign
-1,1,1,3,1,3,1,1,1,Benign
-3,1,1,1,1,1,2,1,1,Benign
-10,10,10,10,6,10,8,1,5,Malignant
-3,6,4,10,3,3,3,4,1,Malignant
-6,3,2,1,3,4,4,1,1,Malignant
-1,1,1,1,2,1,1,1,1,Benign
-5,8,9,4,3,10,7,1,1,Malignant
-4,1,1,1,1,1,2,1,1,Benign
-5,10,10,10,6,10,6,5,2,Malignant
-5,1,2,10,4,5,2,1,1,Benign
-3,1,1,1,1,1,2,1,1,Benign
-1,1,1,1,1,1,1,1,1,Benign
-4,2,1,1,2,1,1,1,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-6,1,1,1,2,1,3,1,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-4,1,1,2,2,1,2,1,1,Benign
-4,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-3,3,1,1,2,1,1,1,1,Benign
-8,10,10,10,7,5,4,8,7,Malignant
-1,1,1,1,2,4,1,1,1,Benign
-5,1,1,1,2,1,1,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-5,1,1,1,2,1,1,1,1,Benign
-3,1,1,1,1,1,2,1,1,Benign
-6,6,7,10,3,10,8,10,2,Malignant
-4,10,4,7,3,10,9,10,1,Malignant
-1,1,1,1,1,1,1,1,1,Benign
-1,1,1,1,1,1,2,1,1,Benign
-3,1,2,2,2,1,1,1,1,Benign
-4,7,8,3,4,10,9,1,1,Malignant
-1,1,1,1,3,1,1,1,1,Benign
-4,1,1,1,3,1,1,1,1,Benign
-10,4,5,4,3,5,7,3,1,Malignant
-7,5,6,10,4,10,5,3,1,Malignant
-3,1,1,1,2,1,2,1,1,Benign
-3,1,1,2,2,1,1,1,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-4,1,1,1,2,1,3,1,1,Benign
-6,1,3,2,2,1,1,1,1,Benign
-4,1,1,1,1,1,2,1,1,Benign
-7,4,4,3,4,10,6,9,1,Malignant
-4,2,2,1,2,1,2,1,1,Benign
-1,1,1,1,1,1,3,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-1,1,3,2,2,1,3,1,1,Benign
-5,1,1,1,2,1,3,1,1,Benign
-5,1,2,1,2,1,3,1,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-6,1,1,1,2,1,2,1,1,Benign
-5,1,1,1,2,2,2,1,1,Benign
-3,1,1,1,2,1,1,1,1,Benign
-5,3,1,1,2,1,1,1,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-2,1,3,2,2,1,2,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-6,10,10,10,4,10,7,10,1,Malignant
-2,1,1,1,1,1,1,1,1,Benign
-3,1,1,1,1,1,1,1,1,Benign
-7,8,3,7,4,5,7,8,2,Malignant
-3,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-3,2,2,2,2,1,4,2,1,Benign
-4,4,2,1,2,5,2,1,2,Benign
-3,1,1,1,2,1,1,1,1,Benign
-4,3,1,1,2,1,4,8,1,Benign
-5,2,2,2,1,1,2,1,1,Benign
-5,1,1,3,2,1,1,1,1,Benign
-2,1,1,1,2,1,2,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-5,1,1,1,2,1,3,1,1,Benign
-5,1,1,1,2,1,3,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-4,1,1,1,2,1,3,2,1,Benign
-5,7,10,10,5,10,10,10,1,Malignant
-3,1,2,1,2,1,3,1,1,Benign
-4,1,1,1,2,3,2,1,1,Benign
-8,4,4,1,6,10,2,5,2,Malignant
-10,10,8,10,6,5,10,3,1,Malignant
-8,10,4,4,8,10,8,2,1,Malignant
-7,6,10,5,3,10,9,10,2,Malignant
-3,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-10,9,7,3,4,2,7,7,1,Malignant
-5,1,2,1,2,1,3,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,3,1,1,Benign
-5,1,2,1,2,1,2,1,1,Benign
-5,7,10,6,5,10,7,5,1,Malignant
-6,10,5,5,4,10,6,10,1,Malignant
-3,1,1,1,2,1,1,1,1,Benign
-5,1,1,6,3,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-8,10,10,10,6,10,10,10,1,Malignant
-5,1,1,1,2,1,2,2,1,Benign
-9,8,8,9,6,3,4,1,1,Malignant
-5,1,1,1,2,1,1,1,1,Benign
-4,10,8,5,4,1,10,1,1,Malignant
-2,5,7,6,4,10,7,6,1,Malignant
-10,3,4,5,3,10,4,1,1,Malignant
-5,1,2,1,2,1,1,1,1,Benign
-4,8,6,3,4,10,7,1,1,Malignant
-5,1,1,1,2,1,2,1,1,Benign
-4,1,2,1,2,1,2,1,1,Benign
-5,1,3,1,2,1,3,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-5,2,4,1,1,1,1,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,1,1,2,1,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-5,4,6,8,4,1,8,10,1,Malignant
-5,3,2,8,5,10,8,1,2,Malignant
-10,5,10,3,5,8,7,8,3,Malignant
-4,1,1,2,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-5,10,10,10,10,10,10,1,1,Malignant
-5,1,1,1,2,1,1,1,1,Benign
-10,4,3,10,3,10,7,1,2,Malignant
-5,10,10,10,5,2,8,5,1,Malignant
-8,10,10,10,6,10,10,10,10,Malignant
-2,3,1,1,2,1,2,1,1,Benign
-2,1,1,1,1,1,2,1,1,Benign
-4,1,3,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,1,4,1,1,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-6,3,3,3,3,2,6,1,1,Benign
-7,1,2,3,2,1,2,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-5,1,1,2,1,1,2,1,1,Benign
-3,1,3,1,3,4,1,1,1,Benign
-4,6,6,5,7,6,7,7,3,Malignant
-2,1,1,1,2,5,1,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-6,2,3,1,2,1,1,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-8,7,4,4,5,3,5,10,1,Malignant
-3,1,1,1,2,1,1,1,1,Benign
-3,1,4,1,2,1,1,1,1,Benign
-10,10,7,8,7,1,10,10,3,Malignant
-4,2,4,3,2,2,2,1,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-5,1,1,3,2,1,1,1,1,Benign
-4,1,1,3,2,1,1,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-1,2,2,1,2,1,1,1,1,Benign
-1,1,1,3,2,1,1,1,1,Benign
-5,10,10,10,10,2,10,10,10,Malignant
-3,1,1,1,2,1,2,1,1,Benign
-3,1,1,2,3,4,1,1,1,Benign
-1,2,1,3,2,1,2,1,1,Benign
-5,1,1,1,2,1,2,2,1,Benign
-4,1,1,1,2,1,2,1,1,Benign
-3,1,1,1,2,1,3,1,1,Benign
-3,1,1,1,2,1,2,1,1,Benign
-5,1,1,1,2,1,2,1,1,Benign
-5,4,5,1,8,1,3,6,1,Benign
-7,8,8,7,3,10,7,2,3,Malignant
-1,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-4,1,1,1,2,1,3,1,1,Benign
-1,1,3,1,2,1,2,1,1,Benign
-1,1,3,1,2,1,2,1,1,Benign
-3,1,1,3,2,1,2,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-5,2,2,2,2,1,1,1,2,Benign
-3,1,1,1,2,1,3,1,1,Benign
-5,7,4,1,6,1,7,10,3,Malignant
-5,10,10,8,5,5,7,10,1,Malignant
-3,10,7,8,5,8,7,4,1,Malignant
-3,2,1,2,2,1,3,1,1,Benign
-2,1,1,1,2,1,3,1,1,Benign
-5,3,2,1,3,1,1,1,1,Benign
-1,1,1,1,2,1,2,1,1,Benign
-4,1,4,1,2,1,1,1,1,Benign
-1,1,2,1,2,1,2,1,1,Benign
-5,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-10,10,10,10,5,10,10,10,7,Malignant
-5,10,10,10,4,10,5,6,3,Malignant
-5,1,1,1,2,1,3,2,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,1,Benign
-3,1,1,1,2,1,2,3,1,Benign
-4,1,1,1,2,1,1,1,1,Benign
-1,1,1,1,2,1,1,1,8,Benign
-1,1,1,3,2,1,1,1,1,Benign
-5,10,10,5,4,5,4,4,1,Malignant
-3,1,1,1,2,1,1,1,1,Benign
-3,1,1,1,2,1,2,1,2,Benign
-3,1,1,1,3,2,1,1,1,Benign
-2,1,1,1,2,1,1,1,1,Benign
-5,10,10,3,7,3,8,10,2,Malignant
-4,8,6,4,3,4,10,6,1,Malignant
-4,8,8,5,4,5,10,4,1,Malignant

new_data_sets/cancer.csv

@@ -1,836 +0,0 @@
-Age,Number of sexual partners,First sexual intercourse,Num of pregnancies,Smokes,Smokes (years),Smokes (packs/year),Hormonal Contraceptives,Hormonal Contraceptives (years),IUD,IUD (years),STDs,STDs (number),STDs:condylomatosis,STDs:cervical condylomatosis,STDs:vaginal condylomatosis,STDs:vulvo-perineal condylomatosis,STDs:syphilis,STDs:pelvic inflammatory disease,STDs:genital herpes,STDs:molluscum contagiosum,STDs:AIDS,STDs:HIV,STDs:Hepatitis B,STDs:HPV,STDs: Number of diagnosis,STDs: Time since first diagnosis,STDs: Time since last diagnosis,Dx:Cancer,Dx:CIN,Dx:HPV,Dx,Hinselmann,Schiller,Citology,Biopsy*
-18,4.0,15.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-15,1.0,14.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-34,1.0,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-52,5.0,16.0,4.0,1.0,37.0,37.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,1,0,0,0,0,0
-46,3.0,21.0,4.0,0.0,0.0,0.0,1.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-42,3.0,23.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-51,3.0,17.0,6.0,1.0,34.0,3.4,0.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,1,1,0,1
-26,1.0,26.0,3.0,0.0,0.0,0.0,1.0,2.0,1.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-45,1.0,20.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,1,1,0,0,0,0
-44,3.0,15.0,0,1.0,1.266972909,2.8,0.0,0.0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-44,3.0,26.0,4.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-27,1.0,17.0,3.0,0.0,0.0,0.0,1.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-45,4.0,14.0,6.0,0.0,0.0,0.0,1.0,10.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
-44,2.0,25.0,2.0,0.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0,0,0
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new_data_sets/diabetes.csv

@@ -1,769 +0,0 @@
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-0,188,82,14,185,32,0.682,22,1
-0,67,76,0,0,45.3,0.194,46,0
-1,89,24,19,25,27.8,0.559,21,0
-1,173,74,0,0,36.8,0.088,38,1
-1,109,38,18,120,23.1,0.407,26,0
-1,108,88,19,0,27.1,0.4,24,0
-6,96,0,0,0,23.7,0.19,28,0
-1,124,74,36,0,27.8,0.1,30,0
-7,150,78,29,126,35.2,0.692,54,1
-4,183,0,0,0,28.4,0.212,36,1
-1,124,60,32,0,35.8,0.514,21,0
-1,181,78,42,293,40,1.258,22,1
-1,92,62,25,41,19.5,0.482,25,0
-0,152,82,39,272,41.5,0.27,27,0
-1,111,62,13,182,24,0.138,23,0
-3,106,54,21,158,30.9,0.292,24,0
-3,174,58,22,194,32.9,0.593,36,1
-7,168,88,42,321,38.2,0.787,40,1
-6,105,80,28,0,32.5,0.878,26,0
-11,138,74,26,144,36.1,0.557,50,1
-3,106,72,0,0,25.8,0.207,27,0
-6,117,96,0,0,28.7,0.157,30,0
-2,68,62,13,15,20.1,0.257,23,0
-9,112,82,24,0,28.2,1.282,50,1
-0,119,0,0,0,32.4,0.141,24,1
-2,112,86,42,160,38.4,0.246,28,0
-2,92,76,20,0,24.2,1.698,28,0
-6,183,94,0,0,40.8,1.461,45,0
-0,94,70,27,115,43.5,0.347,21,0
-2,108,64,0,0,30.8,0.158,21,0
-4,90,88,47,54,37.7,0.362,29,0
-0,125,68,0,0,24.7,0.206,21,0
-0,132,78,0,0,32.4,0.393,21,0
-5,128,80,0,0,34.6,0.144,45,0
-4,94,65,22,0,24.7,0.148,21,0
-7,114,64,0,0,27.4,0.732,34,1
-0,102,78,40,90,34.5,0.238,24,0
-2,111,60,0,0,26.2,0.343,23,0
-1,128,82,17,183,27.5,0.115,22,0
-10,92,62,0,0,25.9,0.167,31,0
-13,104,72,0,0,31.2,0.465,38,1
-5,104,74,0,0,28.8,0.153,48,0
-2,94,76,18,66,31.6,0.649,23,0
-7,97,76,32,91,40.9,0.871,32,1
-1,100,74,12,46,19.5,0.149,28,0
-0,102,86,17,105,29.3,0.695,27,0
-4,128,70,0,0,34.3,0.303,24,0
-6,147,80,0,0,29.5,0.178,50,1
-4,90,0,0,0,28,0.61,31,0
-3,103,72,30,152,27.6,0.73,27,0
-2,157,74,35,440,39.4,0.134,30,0
-1,167,74,17,144,23.4,0.447,33,1
-0,179,50,36,159,37.8,0.455,22,1
-11,136,84,35,130,28.3,0.26,42,1
-0,107,60,25,0,26.4,0.133,23,0
-1,91,54,25,100,25.2,0.234,23,0
-1,117,60,23,106,33.8,0.466,27,0
-5,123,74,40,77,34.1,0.269,28,0
-2,120,54,0,0,26.8,0.455,27,0
-1,106,70,28,135,34.2,0.142,22,0
-2,155,52,27,540,38.7,0.24,25,1
-2,101,58,35,90,21.8,0.155,22,0
-1,120,80,48,200,38.9,1.162,41,0
-11,127,106,0,0,39,0.19,51,0
-3,80,82,31,70,34.2,1.292,27,1
-10,162,84,0,0,27.7,0.182,54,0
-1,199,76,43,0,42.9,1.394,22,1
-8,167,106,46,231,37.6,0.165,43,1
-9,145,80,46,130,37.9,0.637,40,1
-6,115,60,39,0,33.7,0.245,40,1
-1,112,80,45,132,34.8,0.217,24,0
-4,145,82,18,0,32.5,0.235,70,1
-10,111,70,27,0,27.5,0.141,40,1
-6,98,58,33,190,34,0.43,43,0
-9,154,78,30,100,30.9,0.164,45,0
-6,165,68,26,168,33.6,0.631,49,0
-1,99,58,10,0,25.4,0.551,21,0
-10,68,106,23,49,35.5,0.285,47,0
-3,123,100,35,240,57.3,0.88,22,0
-8,91,82,0,0,35.6,0.587,68,0
-6,195,70,0,0,30.9,0.328,31,1
-9,156,86,0,0,24.8,0.23,53,1
-0,93,60,0,0,35.3,0.263,25,0
-3,121,52,0,0,36,0.127,25,1
-2,101,58,17,265,24.2,0.614,23,0
-2,56,56,28,45,24.2,0.332,22,0
-0,162,76,36,0,49.6,0.364,26,1
-0,95,64,39,105,44.6,0.366,22,0
-4,125,80,0,0,32.3,0.536,27,1
-5,136,82,0,0,0,0.64,69,0
-2,129,74,26,205,33.2,0.591,25,0
-3,130,64,0,0,23.1,0.314,22,0
-1,107,50,19,0,28.3,0.181,29,0
-1,140,74,26,180,24.1,0.828,23,0
-1,144,82,46,180,46.1,0.335,46,1
-8,107,80,0,0,24.6,0.856,34,0
-13,158,114,0,0,42.3,0.257,44,1
-2,121,70,32,95,39.1,0.886,23,0
-7,129,68,49,125,38.5,0.439,43,1
-2,90,60,0,0,23.5,0.191,25,0
-7,142,90,24,480,30.4,0.128,43,1
-3,169,74,19,125,29.9,0.268,31,1
-0,99,0,0,0,25,0.253,22,0
-4,127,88,11,155,34.5,0.598,28,0
-4,118,70,0,0,44.5,0.904,26,0
-2,122,76,27,200,35.9,0.483,26,0
-6,125,78,31,0,27.6,0.565,49,1
-1,168,88,29,0,35,0.905,52,1
-2,129,0,0,0,38.5,0.304,41,0
-4,110,76,20,100,28.4,0.118,27,0
-6,80,80,36,0,39.8,0.177,28,0
-10,115,0,0,0,0,0.261,30,1
-2,127,46,21,335,34.4,0.176,22,0
-9,164,78,0,0,32.8,0.148,45,1
-2,93,64,32,160,38,0.674,23,1
-3,158,64,13,387,31.2,0.295,24,0
-5,126,78,27,22,29.6,0.439,40,0
-10,129,62,36,0,41.2,0.441,38,1
-0,134,58,20,291,26.4,0.352,21,0
-3,102,74,0,0,29.5,0.121,32,0
-7,187,50,33,392,33.9,0.826,34,1
-3,173,78,39,185,33.8,0.97,31,1
-10,94,72,18,0,23.1,0.595,56,0
-1,108,60,46,178,35.5,0.415,24,0
-5,97,76,27,0,35.6,0.378,52,1
-4,83,86,19,0,29.3,0.317,34,0
-1,114,66,36,200,38.1,0.289,21,0
-1,149,68,29,127,29.3,0.349,42,1
-5,117,86,30,105,39.1,0.251,42,0
-1,111,94,0,0,32.8,0.265,45,0
-4,112,78,40,0,39.4,0.236,38,0
-1,116,78,29,180,36.1,0.496,25,0
-0,141,84,26,0,32.4,0.433,22,0
-2,175,88,0,0,22.9,0.326,22,0
-2,92,52,0,0,30.1,0.141,22,0
-3,130,78,23,79,28.4,0.323,34,1
-8,120,86,0,0,28.4,0.259,22,1
-2,174,88,37,120,44.5,0.646,24,1
-2,106,56,27,165,29,0.426,22,0
-2,105,75,0,0,23.3,0.56,53,0
-4,95,60,32,0,35.4,0.284,28,0
-0,126,86,27,120,27.4,0.515,21,0
-8,65,72,23,0,32,0.6,42,0
-2,99,60,17,160,36.6,0.453,21,0
-1,102,74,0,0,39.5,0.293,42,1
-11,120,80,37,150,42.3,0.785,48,1
-3,102,44,20,94,30.8,0.4,26,0
-1,109,58,18,116,28.5,0.219,22,0
-9,140,94,0,0,32.7,0.734,45,1
-13,153,88,37,140,40.6,1.174,39,0
-12,100,84,33,105,30,0.488,46,0
-1,147,94,41,0,49.3,0.358,27,1
-1,81,74,41,57,46.3,1.096,32,0
-3,187,70,22,200,36.4,0.408,36,1
-6,162,62,0,0,24.3,0.178,50,1
-4,136,70,0,0,31.2,1.182,22,1
-1,121,78,39,74,39,0.261,28,0
-3,108,62,24,0,26,0.223,25,0
-0,181,88,44,510,43.3,0.222,26,1
-8,154,78,32,0,32.4,0.443,45,1
-1,128,88,39,110,36.5,1.057,37,1
-7,137,90,41,0,32,0.391,39,0
-0,123,72,0,0,36.3,0.258,52,1
-1,106,76,0,0,37.5,0.197,26,0
-6,190,92,0,0,35.5,0.278,66,1
-2,88,58,26,16,28.4,0.766,22,0
-9,170,74,31,0,44,0.403,43,1
-9,89,62,0,0,22.5,0.142,33,0
-10,101,76,48,180,32.9,0.171,63,0
-2,122,70,27,0,36.8,0.34,27,0
-5,121,72,23,112,26.2,0.245,30,0
-1,126,60,0,0,30.1,0.349,47,1
-1,93,70,31,0,30.4,0.315,23,0

run.py

@@ -52,6 +52,11 @@ cors = CORS(app, resources={r"/data/*": {"origins": "*"}})
 @app.route('/data/Reset', methods=["GET", "POST"])
 def reset():
 
+    global labelsClass0
+    global labelsClass1
+    labelsClass0 = []
+    labelsClass1 = []
+
     global yDataSorted
     yDataSorted = []
 
@@ -231,6 +236,11 @@ def retrieveFileName():
     global DataResultsRawExternal
     global DataRawLengthExternal
 
+    global labelsClass0
+    global labelsClass1
+    labelsClass0 = []
+    labelsClass1 = []
+
     global yDataSorted
     yDataSorted = []
 
@@ -257,7 +267,7 @@ def retrieveFileName():
     print(crossValidation)
     global randomSearchVar
     randomSearchVar = int(data['RandomSearch'])
-
+    print(randomSearchVar)
     global stage1addKNN
     global stage1addLR
     global stage1addMLP
@@ -384,6 +394,9 @@ def retrieveFileName():
 
     global keySend
     keySend=0
+    
+    global fileInput
+    fileInput = data['fileName']
 
     DataRawLength = -1
     DataRawLengthTest = -1
@@ -401,12 +414,8 @@ def retrieveFileName():
         CollectionDB = mongo.db.biodegC.find()
         CollectionDBTest = mongo.db.biodegCTest.find()
         CollectionDBExternal = mongo.db.biodegCExt.find()
-        names_labels.append('Biodegradable')
         names_labels.append('Non-biodegradable')
-    elif data['fileName'] == 'seismicC':
-        CollectionDB = mongo.db.seismicC.find()
-        names_labels.append('Hazardous')
-        names_labels.append('Non-hazardous')
+        names_labels.append('Biodegradable')
     elif data['fileName'] == 'breastC':
         CollectionDB = mongo.db.diabetesC.find()
         names_labels.append('Malignant')
@@ -479,7 +488,7 @@ def sendToServerData():
             target_names.append(value)
             AllTargetsFloatValues.append(Class)
             previous = value
-
+ 
     ArrayDataResults = pd.DataFrame.from_dict(DataResults)
 
     global XData, yData, RANDOM_SEED
@@ -493,7 +502,7 @@ def sendToServerData():
     global storeClass1
 
     for item in yData:
-        if (item == 0):
+        if (item == 1):
             storeClass0 = storeClass0 + 1
         else:
             storeClass1 = storeClass1 + 1
@@ -629,7 +638,7 @@ def dataSetSelection():
     global storeClass1
 
     for item in yData:
-        if (item == 0):
+        if (item == 1):
             storeClass0 = storeClass0 + 1
         else:
             storeClass1 = storeClass1 + 1
@@ -673,6 +682,7 @@ def retrieveModel():
     global allParametersPerformancePerModel
     global HistoryPreservation
     global crossValidation
+    global randomSearchVar
 
     for eachAlgor in algorithms:
         if (eachAlgor) == 'KNN':
@@ -706,7 +716,7 @@ def retrieveModel():
             countAllModels = countAllModels + randomSearchVar
             AlgorithmsIDsEnd = countAllModels
             countAllModels = countAllModels + randomSearchVar
-        allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation)
+        allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation,randomSearchVar)
     HistoryPreservation = allParametersPerformancePerModel.copy()
     # call the function that sends the results to the frontend
 
@@ -716,10 +726,11 @@ location = './cachedir'
 memory = Memory(location, verbose=0)
 
 @memory.cache
-def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation):
+def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation,randomSear):
+    print('search')
     print(clf)
     search = RandomizedSearchCV(    
-        estimator=clf, param_distributions=params, n_iter=100,
+        estimator=clf, param_distributions=params, n_iter=randomSear,
         cv=crossValidation, refit='accuracy', scoring=scoring,
         verbose=0, n_jobs=-1)
     
@@ -900,7 +911,7 @@ def PreprocessingPred():
 
         el = [sum(x)/len(x) for x in zip(*content)]
         predictions.append(el)
-    
+
     global storeClass0
     global storeClass1
     global yDataSorted
@@ -921,65 +932,84 @@ def PreprocessingPred():
     yDataSortedLast = []
 
     for index, item in enumerate(yData):
-        if (item == 0):
-            if (len(predictionsKNN[index]) != 0):
-                firstElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                firstElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                firstElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                firstElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                firstElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                firstElPredAv.append(predictions[index][item]*100)
-            yDataSortedFirst.append(item)
-        else:
-            if (len(predictionsKNN[index]) != 0):
-                lastElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                lastElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                lastElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                lastElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                lastElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                lastElPredAv.append(predictions[index][item]*100)
-            yDataSortedLast.append(item)
-
+            if (item == 1):
+                if (len(predictionsKNN[index]) != 0):
+                    firstElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    firstElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    firstElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    firstElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    firstElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    firstElPredAv.append(predictions[index][item]*100)
+                yDataSortedFirst.append(item)
+            else:
+                if (len(predictionsKNN[index]) != 0):
+                    lastElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    lastElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    lastElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    lastElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    lastElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    lastElPredAv.append(predictions[index][item]*100)
+                yDataSortedLast.append(item)      
+    predictions = firstElPredAv + lastElPredAv
+    predictionsKNN = firstElKNN + lastElKNN
+    predictionsLR = firstElLR + lastElLR   
+    predictionsMLP = firstElMLP + lastElMLP
+    predictionsRF = firstElRF + lastElRF
+    predictionsGradB = firstElGradB + lastElGradB
+    yDataSorted = yDataSortedFirst + yDataSortedLast
     if (storeClass0 > 169 and storeClass1 > 169):
         yDataSortedFirst = []
         yDataSortedLast = []    
-        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
-        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB,1)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB,2)
         for item in lastElPredAv:
             yDataSortedFirst.append(0)
             yDataSortedLast.append(0)
 
-    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
-    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
-    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
-    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
-    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
-    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
-    yDataSorted = yDataSortedFirst + yDataSortedLast
-    
+        predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+        predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+        predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+        predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+        predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+        predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
+        yDataSorted = yDataSortedFirst + yDataSortedLast
+
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
-def computeClusters(dataLocal,one,two,three,four,five):
+def computeClusters(dataLocal,one,two,three,four,five,flagLocal):
     if (len(dataLocal) != 0):
+        global labelsClass0
+        global labelsClass1
         XKNN = np.array(list(zip(one,np.zeros(len(one)))), dtype=np.int)
         XLR = np.array(list(zip(two,np.zeros(len(two)))), dtype=np.int)
         XMLP = np.array(list(zip(three,np.zeros(len(three)))), dtype=np.int)
         XRF = np.array(list(zip(four,np.zeros(len(four)))), dtype=np.int)
         XGradB = np.array(list(zip(five,np.zeros(len(five)))), dtype=np.int)
         X = np.array(list(zip(dataLocal,np.zeros(len(dataLocal)))), dtype=np.int)
-        ms = cluster.KMeans(n_clusters=169,random_state=RANDOM_SEED, n_jobs=-1)
-        ms.fit(X)
-        labels = ms.labels_
+        if (flagLocal == 1):
+            ms = cluster.KMeans(n_clusters=100,random_state=RANDOM_SEED, n_jobs=-1)
+            ms.fit(X)
+            labelsClass0 = ms.labels_
+            labels = labelsClass0
+        if (flagLocal == 2):
+            ms = cluster.KMeans(n_clusters=100,random_state=RANDOM_SEED, n_jobs=-1)
+            ms.fit(X)
+            labelsClass1 = ms.labels_
+            labels = labelsClass1
+        if (flagLocal == 3):
+            labels = labelsClass0
+        if (flagLocal == 4):
+            labels = labelsClass1
         #labels_unique = np.unique(labels)
         #n_clusters_ = len(labels)
 
@@ -989,7 +1019,8 @@ def computeClusters(dataLocal,one,two,three,four,five):
         gatherPointsMLP = []
         gatherPointsRF = []
         gatherPointsGradB = []
-        for k in range(169):
+
+        for k in range(100):
             my_members = labels == k
             if (len(X[my_members, 0]) == 0):
                 gatherPointsAv.append(0)
@@ -1162,51 +1193,58 @@ def PreprocessingPredEnsemble():
     yDataSortedLast = []
 
     for index, item in enumerate(yData):
-        if (item == 0):
-            if (len(predictionsKNN[index]) != 0):
-                firstElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                firstElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                firstElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                firstElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                firstElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                firstElPredAv.append(predictions[index][item]*100)
-            yDataSortedFirst.append(item)
-        else:
-            if (len(predictionsKNN[index]) != 0):
-                lastElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                lastElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                lastElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                lastElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                lastElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                lastElPredAv.append(predictions[index][item]*100)
-            yDataSortedLast.append(item)
+            if (item == 1):
+                if (len(predictionsKNN[index]) != 0):
+                    firstElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    firstElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    firstElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    firstElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    firstElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    firstElPredAv.append(predictions[index][item]*100)
+                yDataSortedFirst.append(item)
+            else:
+                if (len(predictionsKNN[index]) != 0):
+                    lastElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    lastElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    lastElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    lastElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    lastElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    lastElPredAv.append(predictions[index][item]*100)
+                yDataSortedLast.append(item)  
+    predictions = firstElPredAv + lastElPredAv
+    predictionsKNN = firstElKNN + lastElKNN
+    predictionsLR = firstElLR + lastElLR   
+    predictionsMLP = firstElMLP + lastElMLP
+    predictionsRF = firstElRF + lastElRF
+    predictionsGradB = firstElGradB + lastElGradB
+    yDataSorted = yDataSortedFirst + yDataSortedLast
 
     if (storeClass0 > 169 and storeClass1 > 169):
         yDataSortedFirst = []
         yDataSortedLast = []    
-        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
-        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB,3)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB,4)
         for item in lastElPredAv:
             yDataSortedFirst.append(0)
             yDataSortedLast.append(0)
 
-    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
-    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
-    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
-    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
-    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
-    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
-    yDataSorted = yDataSortedFirst + yDataSortedLast
+        predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+        predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+        predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+        predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+        predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+        predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
+        yDataSorted = yDataSortedFirst + yDataSortedLast
 
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
@@ -1607,6 +1645,7 @@ def EnsembleModel (Models, keyRetrieved):
     PerClassResultsClass0 = []
     global PerClassResultsClass1
     PerClassResultsClass1 = []
+    global fileInput
 
     nested_score = model_selection.cross_val_score(sclf, X=XData, y=yData, cv=crossValidation, scoring=make_scorer(classification_report_with_accuracy_score))
     PerClassResultsClass0Con = pd.concat(PerClassResultsClass0, axis=1, sort=False)
@@ -1617,81 +1656,157 @@ def EnsembleModel (Models, keyRetrieved):
     conf_mat = confusion_matrix(yData, y_pred)
     cm = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis]
     cm.diagonal()
-
-    if (keyRetrieved == 0):
-        scores.append(cm[0][0])
-        scores.append(cm[1][1])
-        scores.append(cm[0][0])
-        scores.append(cm[1][1])
-        scores.append(averageClass0.precision)
-        scores.append(averageClass1.precision)
-        scores.append(averageClass0.precision)
-        scores.append(averageClass1.precision)
-        scores.append(averageClass0.recall)
-        scores.append(averageClass1.recall)
-        scores.append(averageClass0.recall)
-        scores.append(averageClass1.recall)
-        scores.append(averageClass0['f1-score'])
-        scores.append(averageClass1['f1-score'])
-        scores.append(averageClass0['f1-score'])
-        scores.append(averageClass1['f1-score'])
-        previousState.append(scores[0])
-        previousState.append(scores[1])
-        previousState.append(scores[4])
-        previousState.append(scores[5])
-        previousState.append(scores[8])
-        previousState.append(scores[9])
-        previousState.append(scores[12])
-        previousState.append(scores[13])
-    else:
-        scores.append(cm[0][0])
-        scores.append(cm[1][1])
-        if (cm[0][0] > previousState[0]):
+    print(cm)
+    if (fileInput == 'heartC'):
+        if (keyRetrieved == 0):
+            scores.append(cm[1][1])
             scores.append(cm[0][0])
-            previousState[0] = cm[0][0]
-        else:
-            scores.append(previousState[0])
-        if (cm[1][1] > previousState[1]):
             scores.append(cm[1][1])
-            previousState[1] = cm[1][1]
-        else:
-            scores.append(previousState[1])
-        scores.append(averageClass0.precision)
-        scores.append(averageClass1.precision)
-        if (averageClass0.precision > previousState[2]):
+            scores.append(cm[0][0])
+            scores.append(averageClass1.precision)
             scores.append(averageClass0.precision)
-            previousState[2] = averageClass0.precision
-        else:
-            scores.append(previousState[2])
-        if (averageClass1.precision > previousState[3]):
             scores.append(averageClass1.precision)
-            previousState[3] = averageClass1.precision
-        else:
-            scores.append(previousState[3])
-        scores.append(averageClass0.recall)
-        scores.append(averageClass1.recall)
-        if (averageClass0.recall > previousState[4]):
+            scores.append(averageClass0.precision)
+            scores.append(averageClass1.recall)
             scores.append(averageClass0.recall)
-            previousState[4] = averageClass0.recall
-        else:
-            scores.append(previousState[4])
-        if (averageClass1.recall > previousState[5]):
             scores.append(averageClass1.recall)
-            previousState[5] = averageClass1.recall
-        else:
-            scores.append(previousState[5])
-        scores.append(averageClass0['f1-score'])
-        scores.append(averageClass1['f1-score'])
-        if (averageClass0['f1-score'] > previousState[6]):
+            scores.append(averageClass0.recall)
+            scores.append(averageClass1['f1-score'])
             scores.append(averageClass0['f1-score'])
-            previousState[6] = averageClass0['f1-score']
+            scores.append(averageClass1['f1-score'])
+            scores.append(averageClass0['f1-score'])
+            previousState.append(scores[0])
+            previousState.append(scores[1])
+            previousState.append(scores[4])
+            previousState.append(scores[5])
+            previousState.append(scores[8])
+            previousState.append(scores[9])
+            previousState.append(scores[12])
+            previousState.append(scores[13])
         else:
-            scores.append(previousState[6])
-        if (averageClass1['f1-score'] > previousState[7]):
+            scores.append(cm[1][1])
+            scores.append(cm[0][0])
+            if (cm[1][1] > previousState[0]):
+                scores.append(cm[1][1])
+                previousState[0] = cm[1][1]
+            else:
+                scores.append(previousState[0])
+            if (cm[0][0] > previousState[1]):
+                scores.append(cm[0][0])
+                previousState[1] = cm[0][0]
+            else:
+                scores.append(previousState[1])
+            scores.append(averageClass1.precision)
+            scores.append(averageClass0.precision)
+            if (averageClass1.precision > previousState[2]):
+                scores.append(averageClass1.precision)
+                previousState[2] = averageClass1.precision
+            else:
+                scores.append(previousState[2])
+            if (averageClass0.precision > previousState[3]):
+                scores.append(averageClass0.precision)
+                previousState[3] = averageClass0.precision
+            else:
+                scores.append(previousState[3])
+            scores.append(averageClass1.recall)
+            scores.append(averageClass0.recall)
+            if (averageClass1.recall > previousState[4]):
+                scores.append(averageClass1.recall)
+                previousState[4] = averageClass1.recall
+            else:
+                scores.append(previousState[4])
+            if (averageClass0.recall > previousState[5]):
+                scores.append(averageClass0.recall)
+                previousState[5] = averageClass0.recall
+            else:
+                scores.append(previousState[5])
+            scores.append(averageClass1['f1-score'])
+            scores.append(averageClass0['f1-score'])
+            if (averageClass1['f1-score'] > previousState[6]):
+                scores.append(averageClass1['f1-score'])
+                previousState[6] = averageClass1['f1-score']
+            else:
+                scores.append(previousState[6])
+            if (averageClass0['f1-score'] > previousState[7]):
+                scores.append(averageClass0['f1-score'])
+                previousState[7] = averageClass0['f1-score']
+            else:
+                scores.append(previousState[7])
+    else:
+        if (keyRetrieved == 0):
+            scores.append(cm[0][0])
+            scores.append(cm[1][1])
+            scores.append(cm[0][0])
+            scores.append(cm[1][1])
+            scores.append(averageClass0.precision)
+            scores.append(averageClass1.precision)
+            scores.append(averageClass0.precision)
+            scores.append(averageClass1.precision)
+            scores.append(averageClass0.recall)
+            scores.append(averageClass1.recall)
+            scores.append(averageClass0.recall)
+            scores.append(averageClass1.recall)
+            scores.append(averageClass0['f1-score'])
+            scores.append(averageClass1['f1-score'])
+            scores.append(averageClass0['f1-score'])
             scores.append(averageClass1['f1-score'])
-            previousState[7] = averageClass1['f1-score']
+            previousState.append(scores[0])
+            previousState.append(scores[1])
+            previousState.append(scores[4])
+            previousState.append(scores[5])
+            previousState.append(scores[8])
+            previousState.append(scores[9])
+            previousState.append(scores[12])
+            previousState.append(scores[13])
         else:
-            scores.append(previousState[7])
+            scores.append(cm[0][0])
+            scores.append(cm[1][1])
+            if (cm[0][0] > previousState[0]):
+                scores.append(cm[0][0])
+                previousState[0] = cm[0][0]
+            else:
+                scores.append(previousState[0])
+            if (cm[1][1] > previousState[1]):
+                scores.append(cm[1][1])
+                previousState[1] = cm[1][1]
+            else:
+                scores.append(previousState[1])
+            scores.append(averageClass0.precision)
+            scores.append(averageClass1.precision)
+            if (averageClass0.precision > previousState[2]):
+                scores.append(averageClass0.precision)
+                previousState[2] = averageClass0.precision
+            else:
+                scores.append(previousState[2])
+            if (averageClass1.precision > previousState[3]):
+                scores.append(averageClass1.precision)
+                previousState[3] = averageClass1.precision
+            else:
+                scores.append(previousState[3])
+            scores.append(averageClass0.recall)
+            scores.append(averageClass1.recall)
+            if (averageClass0.recall > previousState[4]):
+                scores.append(averageClass0.recall)
+                previousState[4] = averageClass0.recall
+            else:
+                scores.append(previousState[4])
+            if (averageClass1.recall > previousState[5]):
+                scores.append(averageClass1.recall)
+                previousState[5] = averageClass1.recall
+            else:
+                scores.append(previousState[5])
+            scores.append(averageClass0['f1-score'])
+            scores.append(averageClass1['f1-score'])
+            if (averageClass0['f1-score'] > previousState[6]):
+                scores.append(averageClass0['f1-score'])
+                previousState[6] = averageClass0['f1-score']
+            else:
+                scores.append(previousState[6])
+            if (averageClass1['f1-score'] > previousState[7]):
+                scores.append(averageClass1['f1-score'])
+                previousState[7] = averageClass1['f1-score']
+            else:
+                scores.append(previousState[7])
 
     global StanceTest
     if (StanceTest):
@@ -3947,51 +4062,57 @@ def PreprocessingPredCM():
     yDataSortedLast = []
 
     for index, item in enumerate(yData):
-        if (item == 0):
-            if (len(predictionsKNN[index]) != 0):
-                firstElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                firstElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                firstElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                firstElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                firstElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                firstElPredAv.append(predictions[index][item]*100)
-            yDataSortedFirst.append(item)
-        else:
-            if (len(predictionsKNN[index]) != 0):
-                lastElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                lastElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                lastElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                lastElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                lastElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                lastElPredAv.append(predictions[index][item]*100)
-            yDataSortedLast.append(item)
-
+            if (item == 1):
+                if (len(predictionsKNN[index]) != 0):
+                    firstElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    firstElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    firstElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    firstElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    firstElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    firstElPredAv.append(predictions[index][item]*100)
+                yDataSortedFirst.append(item)
+            else:
+                if (len(predictionsKNN[index]) != 0):
+                    lastElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    lastElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    lastElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    lastElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    lastElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    lastElPredAv.append(predictions[index][item]*100)
+                yDataSortedLast.append(item)  
+    predictions = firstElPredAv + lastElPredAv
+    predictionsKNN = firstElKNN + lastElKNN
+    predictionsLR = firstElLR + lastElLR   
+    predictionsMLP = firstElMLP + lastElMLP
+    predictionsRF = firstElRF + lastElRF
+    predictionsGradB = firstElGradB + lastElGradB
+    yDataSorted = yDataSortedFirst + yDataSortedLast
     if (storeClass0 > 169 and storeClass1 > 169):
         yDataSortedFirst = []
         yDataSortedLast = []    
-        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
-        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB,3)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB,4)
         for item in lastElPredAv:
             yDataSortedFirst.append(0)
             yDataSortedLast.append(0)
 
-    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
-    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
-    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
-    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
-    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
-    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
-    yDataSorted = yDataSortedFirst + yDataSortedLast
+        predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+        predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+        predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+        predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+        predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+        predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
+        yDataSorted = yDataSortedFirst + yDataSortedLast
     
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
@@ -4100,51 +4221,57 @@ def PreprocessingPredCMSecond():
     yDataSortedLast = []
 
     for index, item in enumerate(yData):
-        if (item == 0):
-            if (len(predictionsKNN[index]) != 0):
-                firstElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                firstElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                firstElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                firstElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                firstElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                firstElPredAv.append(predictions[index][item]*100)
-            yDataSortedFirst.append(item)
-        else:
-            if (len(predictionsKNN[index]) != 0):
-                lastElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                lastElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                lastElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                lastElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                lastElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                lastElPredAv.append(predictions[index][item]*100)
-            yDataSortedLast.append(item)
-
+            if (item == 1):
+                if (len(predictionsKNN[index]) != 0):
+                    firstElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    firstElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    firstElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    firstElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    firstElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    firstElPredAv.append(predictions[index][item]*100)
+                yDataSortedFirst.append(item)
+            else:
+                if (len(predictionsKNN[index]) != 0):
+                    lastElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    lastElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    lastElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    lastElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    lastElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    lastElPredAv.append(predictions[index][item]*100)
+                yDataSortedLast.append(item)  
+    predictions = firstElPredAv + lastElPredAv
+    predictionsKNN = firstElKNN + lastElKNN
+    predictionsLR = firstElLR + lastElLR   
+    predictionsMLP = firstElMLP + lastElMLP
+    predictionsRF = firstElRF + lastElRF
+    predictionsGradB = firstElGradB + lastElGradB
+    yDataSorted = yDataSortedFirst + yDataSortedLast
     if (storeClass0 > 169 and storeClass1 > 169):
         yDataSortedFirst = []
         yDataSortedLast = []    
-        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
-        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB,3)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB,4)
         for item in lastElPredAv:
             yDataSortedFirst.append(0)
             yDataSortedLast.append(0)
 
-    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
-    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
-    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
-    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
-    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
-    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
-    yDataSorted = yDataSortedFirst + yDataSortedLast
+        predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+        predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+        predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+        predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+        predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+        predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
+        yDataSorted = yDataSortedFirst + yDataSortedLast
     
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
@@ -4723,7 +4850,6 @@ def PreprocessingPredSel(SelectedIDs):
 
     predictionsRF = []
     for column, content in dfRF.items():
-
         el = [sum(x)/len(x) for x in zip(*content)]
         predictionsRF.append(el)
 
@@ -4757,52 +4883,58 @@ def PreprocessingPredSel(SelectedIDs):
     yDataSortedLast = []
 
     for index, item in enumerate(yData):
-        if (item == 0):
-            if (len(predictionsKNN[index]) != 0):
-                firstElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                firstElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                firstElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                firstElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                firstElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                firstElPredAv.append(predictions[index][item]*100)
-            yDataSortedFirst.append(item)
-        else:
-            if (len(predictionsKNN[index]) != 0):
-                lastElKNN.append(predictionsKNN[index][item]*100)
-            if (len(predictionsLR[index]) != 0):
-                lastElLR.append(predictionsLR[index][item]*100)
-            if (len(predictionsMLP[index]) != 0):
-                lastElMLP.append(predictionsMLP[index][item]*100)
-            if (len(predictionsRF[index]) != 0):
-                lastElRF.append(predictionsRF[index][item]*100)
-            if (len(predictionsGradB[index]) != 0):
-                lastElGradB.append(predictionsGradB[index][item]*100)
-            if (len(predictions[index]) != 0):
-                lastElPredAv.append(predictions[index][item]*100)
-            yDataSortedLast.append(item)
-
+            if (item == 1):
+                if (len(predictionsKNN[index]) != 0):
+                    firstElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    firstElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    firstElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    firstElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    firstElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    firstElPredAv.append(predictions[index][item]*100)
+                yDataSortedFirst.append(item)
+            else:
+                if (len(predictionsKNN[index]) != 0):
+                    lastElKNN.append(predictionsKNN[index][item]*100)
+                if (len(predictionsLR[index]) != 0):
+                    lastElLR.append(predictionsLR[index][item]*100)
+                if (len(predictionsMLP[index]) != 0):
+                    lastElMLP.append(predictionsMLP[index][item]*100)
+                if (len(predictionsRF[index]) != 0):
+                    lastElRF.append(predictionsRF[index][item]*100)
+                if (len(predictionsGradB[index]) != 0):
+                    lastElGradB.append(predictionsGradB[index][item]*100)
+                if (len(predictions[index]) != 0):
+                    lastElPredAv.append(predictions[index][item]*100)
+                yDataSortedLast.append(item)  
+    predictions = firstElPredAv + lastElPredAv
+    predictionsKNN = firstElKNN + lastElKNN
+    predictionsLR = firstElLR + lastElLR   
+    predictionsMLP = firstElMLP + lastElMLP
+    predictionsRF = firstElRF + lastElRF
+    predictionsGradB = firstElGradB + lastElGradB
+    yDataSorted = yDataSortedFirst + yDataSortedLast
     if (storeClass0 > 169 and storeClass1 > 169):
         yDataSortedFirst = []
         yDataSortedLast = []    
-        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
-        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB,3)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB,4)
         for item in lastElPredAv:
             yDataSortedFirst.append(0)
             yDataSortedLast.append(0)
 
-    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
-    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
-    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
-    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
-    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
-    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
-    yDataSorted = yDataSortedFirst + yDataSortedLast
-    
+        predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+        predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+        predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+        predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+        predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+        predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
+        yDataSorted = yDataSortedFirst + yDataSortedLast
+
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@@ -4849,11 +4981,11 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
             else:
                 numberIDGradB.append(int(items[1]))
 
-    dicKNN = allParametersPerformancePerModel[3]
-    dicLR = allParametersPerformancePerModel[7]
-    dicMLP = allParametersPerformancePerModel[11]
-    dicRF = allParametersPerformancePerModel[15]
-    dicGradB = allParametersPerformancePerModel[19]
+    dicKNN = allParametersPerformancePerModelEnsem[3]
+    dicLR = allParametersPerformancePerModelEnsem[7]
+    dicMLP = allParametersPerformancePerModelEnsem[11]
+    dicRF = allParametersPerformancePerModelEnsem[15]
+    dicGradB = allParametersPerformancePerModelEnsem[19]
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
     dfLR = pd.DataFrame.from_dict(dicLR)
@@ -4924,7 +5056,7 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
     yDataSortedLast = []
 
     for index, item in enumerate(yData):
-        if (item == 0):
+        if (item == 1):
             if (len(predictionsKNN[index]) != 0):
                 firstElKNN.append(predictionsKNN[index][item]*100)
             if (len(predictionsLR[index]) != 0):
@@ -4951,24 +5083,31 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
                 lastElGradB.append(predictionsGradB[index][item]*100)
             if (len(predictions[index]) != 0):
                 lastElPredAv.append(predictions[index][item]*100)
-            yDataSortedLast.append(item)
+            yDataSortedLast.append(item)  
+    predictions = firstElPredAv + lastElPredAv
+    predictionsKNN = firstElKNN + lastElKNN
+    predictionsLR = firstElLR + lastElLR   
+    predictionsMLP = firstElMLP + lastElMLP
+    predictionsRF = firstElRF + lastElRF
+    predictionsGradB = firstElGradB + lastElGradB
+    yDataSorted = yDataSortedFirst + yDataSortedLast
 
     if (storeClass0 > 169 and storeClass1 > 169):
         yDataSortedFirst = []
         yDataSortedLast = []    
-        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
-        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB,3)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB,4)
         for item in lastElPredAv:
             yDataSortedFirst.append(0)
             yDataSortedLast.append(0)
 
-    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
-    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
-    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
-    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
-    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
-    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
-    yDataSorted = yDataSortedFirst + yDataSortedLast
+        predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+        predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+        predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+        predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+        predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+        predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
+        yDataSorted = yDataSortedFirst + yDataSortedLast
     
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]