several measurements

frontend/src/components/DataSetExecController.vue

@@ -2,8 +2,8 @@
   <div>
     <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="HeartC.csv" selected>Heart Disease</option>
-        <option value="IrisC.csv">Iris</option>
+        <option value="HeartC.csv">Heart Disease</option>
+        <option value="IrisC.csv" selected>Iris</option>
         <option value="local">Upload New File</option>
     </select>
     <button class="btn-outline-success"
@@ -34,7 +34,7 @@ export default {
   name: 'DataSetExecController',
   data () {
     return {
-      defaultDataSet: 'HeartC', // default value for the first data set
+      defaultDataSet: 'IrisC', // default value for the first data set
       searchText: 'Exploration of Features',
       resetText: 'Reset',
       dataset: 'Data set'

frontend/src/components/FeatureSpace2.vue

@@ -23,8 +23,8 @@ export default {
   },
   methods: {
       initializeNetwork () {   
-        var dataLoc = JSON.parse(this.dataFS2[1])
-        var listofNodes = this.dataFS2[4]
+        var dataLoc = JSON.parse(this.dataFS2[3])
+        var listofNodes = this.dataFS2[0]
 
         var nodes = []
         listofNodes.forEach(element => nodes.push({"name": element}))

frontend/src/components/Main.vue

@@ -215,7 +215,7 @@ export default Vue.extend({
       DataResults: '',
       keyNow: 1,
       instancesImportance: '',
-      RetrieveValueFile: 'HeartC', // this is for the default data set
+      RetrieveValueFile: 'IrisC', // this is for the default data set
       ClassifierIDsList: [],
       ClassifierIDsListCM: [],
       SelectedFeaturesPerClassifier: '',

run.py

@@ -18,6 +18,10 @@ from sklearn.svm import SVC
 from bayes_opt import BayesianOptimization
 from sklearn.model_selection import cross_validate
 from sklearn.model_selection import cross_val_predict
+from sklearn.preprocessing import OneHotEncoder
+
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+from statsmodels.tools.tools import add_constant
 
 # this block of code is for the connection between the server, the database, and the client (plus routing)
 
@@ -498,26 +502,133 @@ def Seperation():
         else:
             quadrant4.append(index)
 
-    Datarows1 = XData.iloc[quadrant1, :]
-    Datarows2 = XData.iloc[quadrant2, :]
-    Datarows3 = XData.iloc[quadrant3, :]
-    Datarows4 = XData.iloc[quadrant4, :]
+    DataRows1 = XData.iloc[quadrant1, :]
+    DataRows2 = XData.iloc[quadrant2, :]
+    DataRows3 = XData.iloc[quadrant3, :]
+    DataRows4 = XData.iloc[quadrant4, :]
+    
+    corrMatrix1 = DataRows1.corr()
+    corrMatrix2 = DataRows2.corr()
+    corrMatrix3 = DataRows3.corr()
+    corrMatrix4 = DataRows4.corr()
+
+    DataRows1 = DataRows1.reset_index(drop=True)
+    DataRows2 = DataRows2.reset_index(drop=True)
+    DataRows3 = DataRows3.reset_index(drop=True)
+    DataRows4 = DataRows4.reset_index(drop=True)
+
+    targetRows1 = [yData[i] for i in quadrant1] 
+    targetRows2 = [yData[i] for i in quadrant2] 
+    targetRows3 = [yData[i] for i in quadrant3] 
+    targetRows4 = [yData[i] for i in quadrant4] 
+
+    targetRows1Arr = np.array(targetRows1)
+    targetRows2Arr = np.array(targetRows2)
+    targetRows3Arr = np.array(targetRows3)
+    targetRows4Arr = np.array(targetRows4)
+
+    uniqueTarget1 = unique(targetRows1)
+    uniqueTarget2 = unique(targetRows2)
+    uniqueTarget3 = unique(targetRows3)
+    uniqueTarget4 = unique(targetRows4)
+    
+    if (len(targetRows1Arr) > 0):
+        onehotEncoder1 = OneHotEncoder(sparse=False)
+        targetRows1Arr = targetRows1Arr.reshape(len(targetRows1Arr), 1)
+        onehotEncoder1 = onehotEncoder1.fit_transform(targetRows1Arr)
+        hotEncoderDF1 = pd.DataFrame(onehotEncoder1)
+        concatDF1 = pd.concat([DataRows1, hotEncoderDF1], axis=1)
+        corrMatrixComb1 = concatDF1.corr()
+        corrMatrixComb1 = corrMatrixComb1.iloc[:,-len(uniqueTarget1):]
+    else:
+        corrMatrixComb1 = pd.DataFrame()
+
+    if (len(targetRows2Arr) > 0):
+        onehotEncoder2 = OneHotEncoder(sparse=False)
+        targetRows2Arr = targetRows2Arr.reshape(len(targetRows2Arr), 1)
+        onehotEncoder2 = onehotEncoder2.fit_transform(targetRows2Arr)
+        hotEncoderDF2 = pd.DataFrame(onehotEncoder2)
+        concatDF2 = pd.concat([DataRows2, hotEncoderDF2], axis=1)
+        corrMatrixComb2 = concatDF2.corr()
+        corrMatrixComb2 = corrMatrixComb2.iloc[:,-len(uniqueTarget2):]
+    else:
+        corrMatrixComb2 = pd.DataFrame()
+
+    if (len(targetRows3Arr) > 0):
+        onehotEncoder3 = OneHotEncoder(sparse=False)
+        targetRows3Arr = targetRows3Arr.reshape(len(targetRows3Arr), 1)
+        onehotEncoder3 = onehotEncoder3.fit_transform(targetRows3Arr)
+        hotEncoderDF3 = pd.DataFrame(onehotEncoder3)
+        concatDF3 = pd.concat([DataRows3, hotEncoderDF3], axis=1)
+        corrMatrixComb3 = concatDF3.corr()
+        corrMatrixComb3 = corrMatrixComb3.iloc[:,-len(uniqueTarget3):]
+    else:
+        corrMatrixComb3 = pd.DataFrame()
+
+    if (len(targetRows4Arr) > 0):
+        onehotEncoder4 = OneHotEncoder(sparse=False)
+        targetRows4Arr = targetRows4Arr.reshape(len(targetRows4Arr), 1)
+        onehotEncoder4 = onehotEncoder4.fit_transform(targetRows4Arr)
+        hotEncoderDF4 = pd.DataFrame(onehotEncoder4)
+        concatDF4 = pd.concat([DataRows4, hotEncoderDF4], axis=1)
+        corrMatrixComb4 = concatDF4.corr()
+        corrMatrixComb4 = corrMatrixComb4.iloc[:,-len(uniqueTarget4):]
+    else:
+        corrMatrixComb4 = pd.DataFrame()
+
+    X1 = add_constant(DataRows1.dropna())
+
+    VIF1 = pd.Series([variance_inflation_factor(X1.values, i) 
+                for i in range(X1.shape[1])], 
+                index=X1.columns)
+    
+    print(VIF1)
+
+    targetRows1ArrDF = pd.DataFrame(targetRows1Arr)
+    targetRows2ArrDF = pd.DataFrame(targetRows2Arr)
+    targetRows3ArrDF = pd.DataFrame(targetRows3Arr)
+    targetRows4ArrDF = pd.DataFrame(targetRows4Arr)
+
+    concatAllDF1 = pd.concat([DataRows1, targetRows1ArrDF], axis=1)
+    concatAllDF2 = pd.concat([DataRows2, targetRows2ArrDF], axis=1)
+    concatAllDF3 = pd.concat([DataRows3, targetRows3ArrDF], axis=1)
+    concatAllDF4 = pd.concat([DataRows4, targetRows4ArrDF], axis=1)
+
+    corrMatrixCombTotal1 = concatAllDF1.corr()
+    corrMatrixCombTotal2 = concatAllDF2.corr()
+    corrMatrixCombTotal3 = concatAllDF3.corr()
+    corrMatrixCombTotal4 = concatAllDF4.corr()
+
+    corrMatrixCombTotal1 = pd.concat([corrMatrixCombTotal1.tail(1)])
+    corrMatrixCombTotal2 = pd.concat([corrMatrixCombTotal2.tail(1)])
+    corrMatrixCombTotal3 = pd.concat([corrMatrixCombTotal3.tail(1)])
+    corrMatrixCombTotal4 = pd.concat([corrMatrixCombTotal4.tail(1)])
 
     global packCorr
     packCorr = []
 
-    corrMatrix1 = Datarows1.corr()
-    corrMatrix2 = Datarows2.corr()
-    corrMatrix3 = Datarows3.corr()
-    corrMatrix4 = Datarows4.corr()
+    packCorr.append(list(XData.columns.values.tolist()))
+    packCorr.append(json.dumps(target_names))
 
     packCorr.append(corrMatrix1.to_json())
     packCorr.append(corrMatrix2.to_json())
     packCorr.append(corrMatrix3.to_json())
     packCorr.append(corrMatrix4.to_json())
 
-    packCorr.append(list(XData.columns.values.tolist()))
+    packCorr.append(corrMatrixComb1.to_json())
+    packCorr.append(corrMatrixComb2.to_json())
+    packCorr.append(corrMatrixComb3.to_json())
+    packCorr.append(corrMatrixComb4.to_json())
 
+    packCorr.append(corrMatrixCombTotal1.to_json())
+    packCorr.append(corrMatrixCombTotal2.to_json())
+    packCorr.append(corrMatrixCombTotal3.to_json())
+    packCorr.append(corrMatrixCombTotal4.to_json())
+
+    packCorr.append(json.dumps(uniqueTarget1))
+    packCorr.append(json.dumps(uniqueTarget2))
+    packCorr.append(json.dumps(uniqueTarget3))
+    packCorr.append(json.dumps(uniqueTarget4))
 
     return 'Everything Okay'
 
@@ -529,3 +640,15 @@ def SendCorrel():
         'correlResul': packCorr
     }
     return jsonify(response)
+
+def unique(list1): 
+  
+    # intilize a null list 
+    unique_list = [] 
+      
+    # traverse for all elements 
+    for x in list1: 
+        # check if exists in unique_list or not 
+        if x not in unique_list: 
+            unique_list.append(x) 
+    return unique_list