new

cachedir/joblib/run/estimatorFeatureSelection/1ed1e3543e5b8c0b8ed8273e2b6d737b/metadata.json

@@ -0,0 +1 @@
+{"duration": 7.470085859298706, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=9.044474534604408, gamma=0.1, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/2ea39cd41bf1eea844679842787364a7/metadata.json

@@ -0,0 +1 @@
+{"duration": 2.0144307613372803, "input_args": {"Data": "      F1    F2_mms   F3   F4  F1xF3\n0    2.5  0.541667  6.3  6.0  15.75\n1    1.9  0.291667  5.8  5.1  11.02\n2    2.1  0.416667  7.1  5.9  14.91\n3    1.8  0.375000  6.3  5.6  11.34\n4    2.2  0.416667  6.5  5.8  14.30\n..   ...       ...  ...  ...    ...\n145  0.3  0.416667  4.8  1.4   1.44\n146  0.2  0.750000  5.1  1.6   1.02\n147  0.2  0.500000  4.6  1.4   0.92\n148  0.2  0.708333  5.3  1.5   1.06\n149  0.2  0.541667  5.0  1.4   1.00\n\n[150 rows x 5 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/6c4a3c6ccc38028fd8eb8e3cafea6fb8/metadata.json

@@ -0,0 +1 @@
+{"duration": 2.988314151763916, "input_args": {"Data": "      F1   F2    F3_l10   F4\n0    2.5  3.3  0.298389  6.0\n1    1.9  2.7  0.282605  5.1\n2    2.1  3.0  0.320533  5.9\n3    1.8  2.9  0.298389  5.6\n4    2.2  3.0  0.304254  5.8\n..   ...  ...       ...  ...\n145  0.3  3.0  0.244984  1.4\n146  0.2  3.8  0.257268  1.6\n147  0.2  3.2  0.236226  1.4\n148  0.2  3.7  0.264948  1.5\n149  0.2  3.3  0.253280  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/77fea9bd3b261749a4919132a55d4c8b/metadata.json

@@ -0,0 +1 @@
+{"duration": 7.335001707077026, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=46.46493418419044, gamma=0.1, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/8acd1de1317c1ff3977e18a58b7d3733/metadata.json

@@ -0,0 +1 @@
+{"duration": 3.0411131381988525, "input_args": {"Data": "      F1   F2    F3_l1p   F4\n0    2.5  3.3  1.987874  6.0\n1    1.9  2.7  1.916923  5.1\n2    2.1  3.0  2.091864  5.9\n3    1.8  2.9  1.987874  5.6\n4    2.2  3.0  2.014903  5.8\n..   ...  ...       ...  ...\n145  0.3  3.0  1.757858  1.4\n146  0.2  3.8  1.808289  1.6\n147  0.2  3.2  1.722767  1.4\n148  0.2  3.7  1.840550  1.5\n149  0.2  3.3  1.791759  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/94da8e9a31bea28d8ff8ea53a406d37b/metadata.json

@@ -0,0 +1 @@
+{"duration": 8.3280029296875, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=4.407539756085735, gamma=0.1935853267468035, probability=True,\n    random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/9902cbfccf20447a3c3f958bc3ba28f0/metadata.json

@@ -0,0 +1 @@
+{"duration": 5.4526307582855225, "input_args": {"Data": "      F1    F2_mms   F3   F4\n0    2.5  0.541667  6.3  6.0\n1    1.9  0.291667  5.8  5.1\n2    2.1  0.416667  7.1  5.9\n3    1.8  0.375000  6.3  5.6\n4    2.2  0.416667  6.5  5.8\n..   ...       ...  ...  ...\n145  0.3  0.416667  4.8  1.4\n146  0.2  0.750000  5.1  1.6\n147  0.2  0.500000  4.6  1.4\n148  0.2  0.708333  5.3  1.5\n149  0.2  0.541667  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/9a08c17b423b35a0c58bb1d4053f6ab9/metadata.json

@@ -0,0 +1 @@
+{"duration": 8.652288913726807, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/a42f327147499bdd5ab86868e8668849/metadata.json

@@ -0,0 +1 @@
+{"duration": 3.5112879276275635, "input_args": {"Data": "      F1   F3  F1+F3  |F1-F3|  F1xF3     F1/F3      F3/F1\n0    2.5  6.3    8.8      3.8  15.75  0.396825   2.520000\n1    1.9  5.8    7.7      3.9  11.02  0.327586   3.052632\n2    2.1  7.1    9.2      5.0  14.91  0.295775   3.380952\n3    1.8  6.3    8.1      4.5  11.34  0.285714   3.500000\n4    2.2  6.5    8.7      4.3  14.30  0.338462   2.954545\n..   ...  ...    ...      ...    ...       ...        ...\n145  0.3  4.8    5.1      4.5   1.44  0.062500  16.000000\n146  0.2  5.1    5.3      4.9   1.02  0.039216  25.500000\n147  0.2  4.6    4.8      4.4   0.92  0.043478  23.000000\n148  0.2  5.3    5.5      5.1   1.06  0.037736  26.500000\n149  0.2  5.0    5.2      4.8   1.00  0.040000  25.000000\n\n[150 rows x 7 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/c5a7d28fb08d1dd352f1a14ab72c2f82/metadata.json

@@ -0,0 +1 @@
+{"duration": 2.5880067348480225, "input_args": {"Data": "      F1   F2    F3_l1p   F4\n0    2.5  3.3  1.987874  6.0\n1    1.9  2.7  1.916923  5.1\n2    2.1  3.0  2.091864  5.9\n3    1.8  2.9  1.987874  5.6\n4    2.2  3.0  2.014903  5.8\n..   ...  ...       ...  ...\n145  0.3  3.0  1.757858  1.4\n146  0.2  3.8  1.808289  1.6\n147  0.2  3.2  1.722767  1.4\n148  0.2  3.7  1.840550  1.5\n149  0.2  3.3  1.791759  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=46.46493418419044, gamma=0.1, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/func_code.py

@@ -0,0 +1,99 @@
+# first line: 687
+@memory.cache
+def estimatorFeatureSelection(Data, clf):
+
+    resultsFS = []
+    permList = []
+    PerFeatureAccuracy = []
+    PerFeatureAccuracyAll = []
+    ImpurityFS = []
+    RankingFS = []
+    
+    rf = RandomForestClassifier(n_estimators = 100,
+                            n_jobs = -1,
+                            random_state = RANDOM_SEED)
+    rf.fit(Data, yData)
+
+    importances = rf.feature_importances_
+
+    std = np.std([tree.feature_importances_ for tree in rf.estimators_],
+                axis=0)
+
+    maxList = max(importances)
+    minList = min(importances)
+
+    for f in range(Data.shape[1]):
+        ImpurityFS.append((importances[f] - minList) / (maxList - minList))
+
+    estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED)
+
+    selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation)
+    selector = selector.fit(Data, yData)
+    RFEImp = selector.ranking_
+
+    for f in range(Data.shape[1]):
+        if (RFEImp[f] == 1):
+            RankingFS.append(0.95)
+        elif (RFEImp[f] == 2):
+            RankingFS.append(0.85)
+        elif (RFEImp[f] == 3):
+            RankingFS.append(0.75)
+        elif (RFEImp[f] == 4):
+            RankingFS.append(0.65)
+        elif (RFEImp[f] == 5):
+            RankingFS.append(0.55)
+        elif (RFEImp[f] == 6):
+            RankingFS.append(0.45)
+        elif (RFEImp[f] == 7):
+            RankingFS.append(0.35)
+        elif (RFEImp[f] == 8):
+            RankingFS.append(0.25)
+        elif (RFEImp[f] == 9):
+            RankingFS.append(0.15)
+        else: 
+            RankingFS.append(0.05)
+
+    perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData)
+    permList.append(perm.feature_importances_)
+    n_feats = Data.shape[1]
+
+    num_cores = multiprocessing.cpu_count()
+    print("Parallelization Initialized")
+    flat_results = Parallel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats))
+    PerFeatureAccuracy = [item for sublist in flat_results for item in sublist]
+    # for i in range(n_feats):
+    #     scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1)
+    #     PerFeatureAccuracy.append(scoresHere.mean())
+    PerFeatureAccuracyAll.append(PerFeatureAccuracy)
+
+    clf.fit(Data, yData) 
+    yPredict = clf.predict(Data)
+    yPredict = np.nan_to_num(yPredict)
+
+    RankingFSDF = pd.DataFrame(RankingFS)
+    RankingFSDF = RankingFSDF.to_json()
+
+    ImpurityFSDF = pd.DataFrame(ImpurityFS)
+    ImpurityFSDF = ImpurityFSDF.to_json()
+
+    perm_imp_eli5PD = pd.DataFrame(permList)
+    perm_imp_eli5PD = perm_imp_eli5PD.to_json()
+
+    PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracyAll)
+    PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
+
+    bestfeatures = SelectKBest(score_func=f_classif, k='all')
+    fit = bestfeatures.fit(Data,yData)
+    dfscores = pd.DataFrame(fit.scores_)
+    dfcolumns = pd.DataFrame(Data.columns)
+    featureScores = pd.concat([dfcolumns,dfscores],axis=1)
+    featureScores.columns = ['Specs','Score']  #naming the dataframe columns
+    featureScores = featureScores.to_json()
+
+    resultsFS.append(featureScores) 
+    resultsFS.append(ImpurityFSDF)
+    resultsFS.append(perm_imp_eli5PD) 
+    resultsFS.append(PerFeatureAccuracyPandas)
+    resultsFS.append(RankingFSDF) 
+
+    return resultsFS

cachedir/joblib/run/executeModel/12cb5fa7abfa03ab029a92fa8c40616f/metadata.json

@@ -0,0 +1 @@
+{"duration": 13.781832933425903, "input_args": {"exeCall": "[]", "flagEx": "0", "nodeTransfName": "''"}}

cachedir/joblib/run/executeModel/72a755383fba437e4dead6ff3e3d81e3/metadata.json

@@ -1 +0,0 @@
-{"duration": 7.80327582359314, "input_args": {}}

cachedir/joblib/run/executeModel/func_code.py

@@ -1,15 +1,175 @@
-# first line: 457
+# first line: 473
 @memory.cache
-def executeModel():
+# check this issue later because we are not getting the same results
+def executeModel(exeCall, flagEx, nodeTransfName):
 
-    create_global_function()
+    global keyFirstTime
     global estimator
+    global yPredictProb
+    global scores
+    global featureImportanceData
+    global XData
+    global XDataStored
+    global previousState
+    global columnsNewGen
+    global columnsNames
+    global listofTransformations
+    global XDataStoredOriginal
+    global finalResultsData
+    columnsNames = []
+    scores = []
 
-    params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
-    svc_bayesopt = BayesianOptimization(estimator, params)
-    svc_bayesopt.maximize(init_points=10, n_iter=25, acq='ucb')
-    bestParams = svc_bayesopt.max['params']
-    estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True)
+    if (len(exeCall) == 0):
+        if (flagEx == 3):
+            XDataStored = XData.copy()
+        else:
+            XData = XDataStored.copy()
+            XDataStoredOriginal = XDataStored.copy()
+    else:
+        if (flagEx == 4):
+            XDataStored = XData.copy()
+        else:    
+            XData = XDataStored.copy()
+            XDataStoredOriginal = XDataStored.copy()
+    columnsNewGen = keepOriginalFeatures.columns.values.tolist()
+    # Bayesian Optimization for 150 iterations
+    if (keyFirstTime):
+        create_global_function()
+        params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
+        svc_bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
+        svc_bayesopt.maximize(init_points=130, n_iter=20, acq='ucb')
+        bestParams = svc_bayesopt.max['params']
+        estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True, random_state=RANDOM_SEED)
+
+    if (len(exeCall) != 0):
+        if (flagEx == 1):
+            XData = XData.drop(XData.columns[exeCall], axis=1)
+            XDataStoredOriginal = XDataStoredOriginal.drop(XDataStoredOriginal.columns[exeCall], axis=1)
+        elif (flagEx == 2):
+            columnsKeepNew = []
+            columns = XDataGen.columns.values.tolist()
+            for indx, col in enumerate(columns):
+                if indx in exeCall:
+                    columnsKeepNew.append(col)
+                    columnsNewGen.append(col)
+
+            XDataTemp = XDataGen[columnsKeepNew]
+            XData[columnsKeepNew] = XDataTemp.values
+            XDataStoredOriginal[columnsKeepNew] = XDataTemp.values
+        elif (flagEx == 4):
+            splittedCol = nodeTransfName.split('_')
+            XData.rename(columns={ XData.columns[exeCall[0]]: nodeTransfName }, inplace = True)
+            currentColumn = columnsNewGen[exeCall[0]]
+            subString = currentColumn[currentColumn.find("(")+1:currentColumn.find(")")]
+            replacement = currentColumn.replace(subString, nodeTransfName)
+            storePositions.append(exeCall[0])
+            storeReplacements.append(replacement)
+            pos = 0
+            for repl in storeReplacements:
+                columnsNewGen[storePositions[pos]] = repl
+                pos += 1
+            if (len(splittedCol) == 1):
+                XData[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
+            else:
+                if (splittedCol[1] == 'r'):
+                    XData[nodeTransfName] = XData[nodeTransfName].round()
+                elif (splittedCol[1] == 'b'):
+                    number_of_bins = np.histogram_bin_edges(XData[nodeTransfName], bins='auto')
+                    emptyLabels = []
+                    for index, number in enumerate(number_of_bins):
+                        if (index == 0):
+                            pass
+                        else:
+                            emptyLabels.append(index)
+                    XData[nodeTransfName] = pd.cut(XData[nodeTransfName], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
+                    XData[nodeTransfName] = pd.to_numeric(XData[nodeTransfName], downcast='signed')
+                elif (splittedCol[1] == 'zs'):
+                    zScore = (XData[nodeTransfName]-XData[nodeTransfName].mean())/XData[nodeTransfName].std()
+                    XData[nodeTransfName] = abs(zScore.min()) + zScore
+                elif (splittedCol[1] == 'mms'):
+                    XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].min())/(XData[nodeTransfName].max()-XData[nodeTransfName].min())
+                elif (splittedCol[1] == 'l2'):
+                    dfTemp = np.log10(XData[nodeTransfName])
+                    if (dfTemp < 0).values.any():
+                        XData[nodeTransfName] = abs(dfTemp.min()) + dfTemp
+                    else:
+                        XData[nodeTransfName] = dfTemp
+                elif (splittedCol[1] == 'l1p'):
+                    XData[nodeTransfName] = np.log1p(XData[nodeTransfName])
+                elif (splittedCol[1] == 'l10'):
+                    dfTemp = np.log10(XData[nodeTransfName])
+                    if (dfTemp < 0).values.any():
+                        XData[nodeTransfName] = abs(dfTemp.min()) + dfTemp
+                    else:
+                        XData[nodeTransfName] = dfTemp
+                elif (splittedCol[1] == 'e2'):
+                    XData[nodeTransfName] = np.exp2(XData[nodeTransfName])
+                elif (splittedCol[1] == 'em1'):
+                    XData[nodeTransfName] = np.expm1(XData[nodeTransfName])
+                elif (splittedCol[1] == 'p2'):
+                    XData[nodeTransfName] = np.power(XData[nodeTransfName], 2)
+                elif (splittedCol[1] == 'p3'):
+                    XData[nodeTransfName] = np.power(XData[nodeTransfName], 3)
+                else:
+                    XData[nodeTransfName] = np.power(XData[nodeTransfName], 4)
+            XDataStored = XData.copy()     
+            
+    columnsNamesLoc = XData.columns.values.tolist()
+
+    for col in columnsNamesLoc:
+        splittedCol = col.split('_')
+        if (len(splittedCol) == 1):
+            for tran in listofTransformations:
+                columnsNames.append(splittedCol[0]+'_'+tran)
+        else:
+            for tran in listofTransformations:
+                if (splittedCol[1] == tran):
+                    columnsNames.append(splittedCol[0])
+                else:
+                    columnsNames.append(splittedCol[0]+'_'+tran)
+    featureImportanceData = estimatorFeatureSelection(XData, estimator)
     estimator.fit(XData, yData)
     yPredict = estimator.predict(XData)
     yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
+    print(XData)
+    num_cores = multiprocessing.cpu_count()
+    inputsSc = ['accuracy','precision_macro','recall_macro']
+
+    flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
+    scoresAct = [item for sublist in flat_results for item in sublist]
+
+    howMany = 0
+
+    if (keyFirstTime):
+        previousState = scoresAct
+        keyFirstTime = False
+        howMany = 3
+    
+    if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
+        finalResultsData = XData.copy()
+        print('improved')
+
+    if (keyFirstTime == False):
+        if ((scoresAct[0]-scoresAct[1]) > (previousState[0]-previousState[1])):
+            previousState[0] = scoresAct[0]
+            previousState[1] = scoresAct[1]
+            howMany = howMany + 1
+        elif ((scoresAct[2]-scoresAct[3]) > (previousState[2]-previousState[3])):
+            previousState[2] = scoresAct[2]
+            previousState[3] = scoresAct[3]
+            howMany = howMany + 1
+        elif ((scoresAct[4]-scoresAct[5]) > (previousState[4]-previousState[5])):
+            previousState[4] = scoresAct[4]
+            previousState[5] = scoresAct[5]
+            howMany = howMany + 1
+        else:
+            pass
+
+    scores = scoresAct + previousState
+
+    if (howMany == 3):
+        scores.append(1)
+    else:
+        scores.append(0)
+
+    return 'Everything Okay'

frontend/src/components/DataSetSlider.vue

@@ -3,7 +3,7 @@
       <b-col cols="2" style="margin-left: -35px">
         <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">Biodegradation</option>
+            <option value="HeartC.csv">Heart disease</option>
             <option value="IrisC.csv" selected>Iris flower</option>
             <option value="local">Upload file</option>
         </select>
@@ -54,7 +54,7 @@ export default {
       this.defaultDataSet = fileName.options[fileName.selectedIndex].value
       this.defaultDataSet = this.defaultDataSet.split('.')[0]
 
-      if (this.defaultDataSet == "BreastC" || this.defaultDataSet == "HeartC" || this.defaultDataSet == "IrisC" || this.defaultDataSet == "BiodegC") { // This is a function that handles a new file, which users can upload.
+      if (this.defaultDataSet == "BiodegC" || this.defaultDataSet == "HeartC" || this.defaultDataSet == "IrisC") { // This is a function that handles a new file, which users can upload.
         this.dataset = "Data set"
         d3.select("#data").select("input").remove(); // Remove the selection field.
         EventBus.$emit('SendToServerDataSetConfirmation', this.defaultDataSet)

frontend/src/components/DataSpace.vue

@@ -566,7 +566,7 @@ text {
 #BeeSwarm { position: absolute; top: 0; left: 0; z-index: 1}
 #Sliders { position: absolute; top: 0; left: 0; z-index: 2}
 #NoAction { position: absolute; top: 0; left: 0; z-index: -1}
-#TextLabels {position: absolute; top: 0; left: 0; z-index: 3}
+#TextLabels {position: absolute; top: 0; left: 0; z-index: 1}
 
 .active-d3-item {
     cursor: pointer;

frontend/src/components/Export.vue

@@ -0,0 +1,40 @@
+<template>
+  <div id="ExportResults">
+  =======================================================
+  <br>
+  New features: {{ FeaturesPickled }}
+  <br>
+  =======================================================
+  </div>
+</template>
+
+<script>
+
+import { EventBus } from '../main.js'
+import * as Cryo from 'cryo'
+export default {
+  name: 'Export',
+  data () {
+    return {
+      FeaturesPickled: '',
+      Features: [],
+    }
+  },
+  methods: {
+    Pickle () {
+      this.FeaturesPickled = Cryo.stringify(this.Features)
+    }
+  },
+  mounted () {
+    EventBus.$on('sendSelectedFeaturestoPickle', data => {
+    this.Features = data})
+    EventBus.$on('sendSelectedFeaturestoPickle', this.Pickle)
+  }
+}
+</script>
+
+<style scoped>
+#ExportResults {
+  word-break: break-all !important;
+}
+</style>

frontend/src/components/FeatureSpaceDetail.vue

@@ -95,6 +95,7 @@ export default {
 
     },
     setLayerExplore() {
+      console.log('mpike')
       this.mode = 0
       this.KeepIDs = []
       this.KeepIDTransform = []
@@ -1090,6 +1091,7 @@ export default {
                 }
                 EventBus.$emit('UpdateIDs', IDsGather)
                 if (selectionCounter == 1) {
+                  EventBus.$emit('diactiveTransform')
                   EventBus.$emit('Default')
                 } else if (selectionCounter == 2) {
                   EventBus.$emit('UpdateIDs', IDsGather)

frontend/src/components/Knowledge.vue

@@ -0,0 +1,44 @@
+<template>
+  <h5><button class="btn-outline-custom"
+  style="float: center;"
+  id="know"
+  v-on:click="knowClass">
+  <font-awesome-icon icon="file-export" />
+  {{ valueKnowE }}
+  </button></h5>
+</template>
+
+<script>
+
+import { EventBus } from '../main.js'
+
+export default {
+  name: 'Knowledge',
+  data () {
+    return {
+      valueKnowE: 'Feature extraction'
+    }
+  },
+  methods: {
+      knowClass () {
+        EventBus.$emit('OpenModal')
+      }
+  }
+}
+</script>
+
+<style>
+
+.btn-outline-custom {
+    color: #B15928;
+    background-color: #FFFFFF;
+    border-color: #B15928;
+}
+
+.btn-outline-custom:hover {
+    color: #FFFFFF;
+    background-color: #B15928;
+    border-color: #B15928;
+}
+
+</style>

frontend/src/components/Main.vue

@@ -55,6 +55,7 @@
               </mdb-card-header>
               <mdb-card-body>
                 <mdb-card-text class="text-center"  style="min-height: 920px">
+                  <Knowledge/>
                   <Results/>
                 </mdb-card-text>
               </mdb-card-body>
@@ -62,6 +63,24 @@
           </b-col>
         </b-row>
     </b-container>
+  <div class="w3-container">
+  <div id="myModal" class="w3-modal" style="position: fixed;">
+    <div class="w3-modal-content w3-card-4 w3-animate-zoom">
+      <header class="w3-container w3-blue"> 
+      <h3 style="display:inline-block; font-size: 16px; margin-top: 15px; margin-bottom:15px">Serialized features using Cryo</h3>
+      </header>
+      <Export/>
+      <div class="w3-container w3-light-grey w3-padding">
+      <button style="float: right; margin-top: -3px; margin-bottom: -3px"
+        id="closeModal" class="w3-button w3-right w3-white w3-border" 
+        v-on:click="closeModalFun">
+        <font-awesome-icon icon="window-close" />
+        {{ valuePickled }}
+        </button>
+      </div>
+      </div>
+    </div>
+  </div>
   </body>
 </template>
 
@@ -73,6 +92,8 @@ import DataSpace from './DataSpace.vue'
 import FeatureSpaceOverview from './FeatureSpaceOverview.vue'
 import FeatureSpaceDetail from './FeatureSpaceDetail.vue'
 import Heatmap from './Heatmap.vue'
+import Export from './Export.vue'
+import Knowledge from './Knowledge.vue'
 import Results from './Results.vue'
 import axios from 'axios'
 import { loadProgressBar } from 'axios-progress-bar'
@@ -80,8 +101,8 @@ import 'axios-progress-bar/dist/nprogress.css'
 import 'bootstrap-css-only/css/bootstrap.min.css'
 import { mdbCard, mdbCardBody, mdbCardText, mdbCardHeader } from 'mdbvue'
 import { EventBus } from '../main.js'
-import * as jQuery from 'jquery' 
-import $ from 'jquery'
+import $ from 'jquery'; // <-to import jquery
+import 'bootstrap';
 import * as d3Base from 'd3'
 import Papa from 'papaparse'
 
@@ -96,6 +117,8 @@ export default Vue.extend({
     FeatureSpaceOverview,
     FeatureSpaceDetail,
     Heatmap,
+    Export,
+    Knowledge,
     Results,
     mdbCard,
     mdbCardBody,
@@ -104,6 +127,7 @@ export default Vue.extend({
   },
   data () {
     return {
+      valuePickled: 'Close',
       transformNodesFlag: false,
       storeDataTransf: [],
       compareNumber: 0,
@@ -170,6 +194,27 @@ export default Vue.extend({
     closeModalFun () {
       $('#myModal').modal('hide')
     },
+    openModalCalculate () {
+      const path = `http://127.0.0.1:5000/data/RequestBestFeatures`
+
+      const axiosConfig = {
+        headers: {
+          'Content-Type': 'application/json',
+          'Access-Control-Allow-Origin': '*',
+          'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
+          'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
+        }
+      }
+      axios.get(path, axiosConfig)
+        .then(response => {
+          var finalResultsData = JSON.parse(response.data.finalResultsData)
+          EventBus.$emit('sendSelectedFeaturestoPickle', finalResultsData)
+          console.log('Pickle data successful!')
+        })
+        .catch(error => {
+          console.log(error)
+        })
+    },
     getCollection () {
       this.Collection = this.getCollectionFromBackend()
     },
@@ -431,7 +476,7 @@ export default Vue.extend({
             EventBus.$emit('SlidersCall')
             this.keySlider = false
           }
-          EventBus.$emit('ConfirmDataSet') // REMOVE THAT!
+          //EventBus.$emit('ConfirmDataSet') // REMOVE THAT!
         } else {
           EventBus.$emit('dataSpace', this.correlResul)
           EventBus.$emit('quad', this.correlResul)
@@ -657,8 +702,35 @@ export default Vue.extend({
     window.addEventListener('resize', this.change)
   },
   mounted() {
-
-    EventBus.$on('storeGeneration', this.storeGenFun)
+ var coll = document.getElementsByClassName("collapsible");
+    var i;
+    var flagLocalMounted = true
+    var flagLocalSkip = true
+    EventBus.$on('OpenModal', data =>{ flagLocalSkip = false }) 
+    for (i = 0; i < coll.length; i++) {
+      coll[i].addEventListener("click", function() {
+        console.log(flagLocalSkip)
+        if (flagLocalSkip) {
+          var content = document.getElementsByClassName("content")
+          var value = "370px"
+          for (let j = 0; j < content.length; j++) {
+            if (content[j].style.display === "block") {
+              content[j].style.display = "none";
+            } else {
+              content[j].style.display = "block";
+              if (flagLocalMounted) {
+                content[j].style.display = "none";
+              }
+            }
+          }
+          flagLocalMounted = false
+          var combineWH = []
+          combineWH.push(this.width)
+          combineWH.push(this.height - 100)
+        }
+      flagLocalSkip = true
+      });
+    }
 
     var modal = document.getElementById('myModal')
     window.onclick = function(event) {
@@ -684,6 +756,8 @@ export default Vue.extend({
     EventBus.$on('CompareThree', data => { this.compareNumber = 2 })
     EventBus.$on('CompareThree', this.Compare)
 
+    EventBus.$on('diactiveTransform', data => { this.transformNodesFlag = true })
+    EventBus.$on('activeTransform', data => { this.transformNodesFlag = false })
     EventBus.$on('Default', this.returnImportance)
     EventBus.$on('updateFeatureImp', this.returnCorrel)
 
@@ -751,6 +825,11 @@ export default Vue.extend({
 
     EventBus.$on('flagSpace', data => { this.spaceChange = data })
 
+    EventBus.$on('finalResults', this.openModalCalculate)
+    EventBus.$on('OpenModal', this.openModalFun)
+
+    EventBus.$on('storeGeneration', this.storeGenFun)
+
     //Prevent double click to search for a word. 
     document.addEventListener('mousedown', function (event) {
       if (event.detail > 1) {

frontend/src/components/Results.vue

@@ -37,7 +37,7 @@ export default {
       var state = this.globalStep
       //console.log('Current Step: ', state)
       if (state == 1) {
-        this.storeBestSoFarAV = ((parseFloat(this.scoresMean[0]) + parseFloat(this.scoresMean[1]) + parseFloat(this.scoresMean[2])) / 3)
+        this.storeBestSoFarAV = parseFloat(this.scoresMean[0]) + parseFloat(this.scoresMean[1]) + parseFloat(this.scoresMean[2]) - parseFloat(this.scoresSTD[0]) - parseFloat(this.scoresSTD[1]) - parseFloat(this.scoresSTD[2])
       }
 
       var svg = d3.select("#HistoryPlot");
@@ -45,7 +45,7 @@ export default {
 
       var margin = {top: 0, right: 0, bottom: 0, left: 0}
       var width = 390 - margin.left - margin.right
-      var height = 620 - margin.top - margin.bottom
+      var height = 578 - margin.top - margin.bottom
       var padding = 3
       var xLabelHeight = 30
       var yLabelWidth = 80
@@ -155,10 +155,9 @@ export default {
           .duration(duration)
           .attr('r', 0)
           .remove()
-
-      if (this.storeBestSoFarAV <= ((parseFloat(this.scoresMean[0]) + parseFloat(this.scoresMean[1]) + parseFloat(this.scoresMean[2])) / 3)) {
+      if (this.storeBestSoFarAV <= (parseFloat(this.scoresMean[0]) + parseFloat(this.scoresMean[1]) + parseFloat(this.scoresMean[2]) - parseFloat(this.scoresSTD[0]) - parseFloat(this.scoresSTD[1]) - parseFloat(this.scoresSTD[2]))) {
         this.flag = true
-        this.storeBestSoFarAV = ((parseFloat(this.scoresMean[0]) + parseFloat(this.scoresMean[1]) + parseFloat(this.scoresMean[2])) / 3)
+        this.storeBestSoFarAV = parseFloat(this.scoresMean[0]) + parseFloat(this.scoresMean[1]) + parseFloat(this.scoresMean[2]) - parseFloat(this.scoresSTD[0]) - parseFloat(this.scoresSTD[1]) - parseFloat(this.scoresSTD[2])
       }
 
       var previously = this.previousState

frontend/src/main.js

@@ -6,10 +6,10 @@ import 'bootstrap-vue/dist/bootstrap-vue.css'
 import router from './router'
 import { library } from '@fortawesome/fontawesome-svg-core'
 import { FontAwesomeIcon } from '@fortawesome/vue-fontawesome'
-import { faDna, faSearch, faTrash, faBalanceScale , faWrench } from '@fortawesome/free-solid-svg-icons'
+import { faDna, faSearch, faTrash, faBalanceScale , faWrench, faFileExport, faWindowClose} from '@fortawesome/free-solid-svg-icons'
 import bFormSlider from 'vue-bootstrap-slider'
 
-library.add(faDna, faSearch, faTrash, faBalanceScale, faWrench)
+library.add(faDna, faSearch, faTrash, faBalanceScale, faWrench, faFileExport, faWindowClose)
 
 Vue.component('font-awesome-icon', FontAwesomeIcon)
 

run.py

@@ -23,7 +23,7 @@ from sklearn.preprocessing import OneHotEncoder
 from sklearn.metrics import classification_report
 from sklearn.feature_selection import mutual_info_classif
 from sklearn.feature_selection import SelectKBest
-from sklearn.feature_selection import chi2
+from sklearn.feature_selection import f_classif
 from sklearn.feature_selection import RFECV
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.linear_model import LogisticRegression
@@ -53,7 +53,7 @@ def reset():
     global DataRawLength
     global DataResultsRaw
     global previousState
-    previousState = []
+    previousState = []\
 
     global filterActionFinal
     filterActionFinal = ''
@@ -85,6 +85,9 @@ def reset():
     global yDataStored
     yDataStored = []
     
+    global finalResultsData
+    finalResultsData = []
+
     global detailsParams
     detailsParams = []
 
@@ -133,6 +136,9 @@ def reset():
     global columnsNames
     columnsNames = []
 
+    global fileName
+    fileName = []
+
     global listofTransformations
     listofTransformations = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
 
@@ -152,6 +158,8 @@ def retrieveFileName():
     storePositions = []
     storeReplacements = []
 
+    global fileName
+    fileName = []
     fileName = request.get_data().decode('utf8').replace("'", '"')
 
     global keySpecInternal
@@ -187,8 +195,8 @@ def retrieveFileName():
     global yDataStored
     yDataStored = []
 
-    global filterDataFinal
-    filterDataFinal = 'mean'
+    global finalResultsData
+    finalResultsData = []
 
     global ClassifierIDsList
     ClassifierIDsList = ''
@@ -249,25 +257,25 @@ def retrieveFileName():
 
     global listofTransformations
     listofTransformations = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
-
+    print('data set:',fileName)
     DataRawLength = -1
     DataRawLengthTest = -1
     data = json.loads(fileName)  
     if data['fileName'] == 'HeartC':
         CollectionDB = mongo.db.HeartC.find()
-        names_labels.append('Healthy')
-        names_labels.append('Diseased')
+        target_names.append('Healthy')
+        target_names.append('Diseased')
     elif data['fileName'] == 'BiodegC':
         StanceTest = True
         CollectionDB = mongo.db.biodegC.find()
         CollectionDBTest = mongo.db.biodegCTest.find()
         CollectionDBExternal = mongo.db.biodegCExt.find()
-        names_labels.append('Non-biodegradable')
-        names_labels.append('Biodegradable')
+        target_names.append('Non-biodegradable')
+        target_names.append('Biodegradable')
     elif data['fileName'] == 'BreastC':
-        CollectionDB = mongo.db.diabetesC.find()
-        names_labels.append('Malignant')
-        names_labels.append('Benign')
+        CollectionDB = mongo.db.breastC.find()
+        target_names.append('Malignant')
+        target_names.append('Benign')
     else:
         CollectionDB = mongo.db.IrisC.find()
     DataResultsRaw = []
@@ -317,17 +325,26 @@ def sendToServerData():
     AllTargets = [o[target] for o in DataResultsRaw]
     AllTargetsFloatValues = []
 
+    global fileName
+    data = json.loads(fileName) 
+
     previous = None
     Class = 0
     for i, value in enumerate(AllTargets):
         if (i == 0):
             previous = value
-            target_names.append(value)
+            if (data['fileName'] == 'IrisC'):
+                target_names.append(value)
+            else:
+                pass
         if (value == previous):
             AllTargetsFloatValues.append(Class)
         else:
             Class = Class + 1
-            target_names.append(value)
+            if (data['fileName'] == 'IrisC'):
+                target_names.append(value)
+            else:
+                pass
             AllTargetsFloatValues.append(Class)
             previous = value
 
@@ -342,6 +359,10 @@ def sendToServerData():
 
     global XDataStoredOriginal
     XDataStoredOriginal = XData.copy()
+
+    global finalResultsData
+    finalResultsData = XData.copy()
+
     return 'Processed uploaded data set'
 
 def dataSetSelection():
@@ -410,17 +431,26 @@ def dataSetSelection():
     AllTargets = [o[target] for o in DataResultsRaw]
     AllTargetsFloatValues = []
 
+    global fileName
+    data = json.loads(fileName) 
+
     previous = None
     Class = 0
     for i, value in enumerate(AllTargets):
         if (i == 0):
             previous = value
-            target_names.append(value)
+            if (data['fileName'] == 'IrisC'):
+                target_names.append(value)
+            else:
+                pass
         if (value == previous):
             AllTargetsFloatValues.append(Class)
         else:
             Class = Class + 1
-            target_names.append(value)
+            if (data['fileName'] == 'IrisC'):
+                target_names.append(value)
+            else:
+                pass
             AllTargetsFloatValues.append(Class)
             previous = value
 
@@ -443,6 +473,9 @@ def dataSetSelection():
     global XDataStoredOriginal
     XDataStoredOriginal = XData.copy()
 
+    global finalResultsData
+    finalResultsData = XData.copy()
+
     warnings.simplefilter('ignore')
 
     executeModel([], 0, '')
@@ -474,6 +507,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
     global columnsNames
     global listofTransformations
     global XDataStoredOriginal
+    global finalResultsData
     columnsNames = []
     scores = []
 
@@ -490,12 +524,12 @@ def executeModel(exeCall, flagEx, nodeTransfName):
             XData = XDataStored.copy()
             XDataStoredOriginal = XDataStored.copy()
     columnsNewGen = keepOriginalFeatures.columns.values.tolist()
-    # Bayesian Optimization for 150 iterations
+    # Bayesian Optimization for 50 iterations
     if (keyFirstTime):
         create_global_function()
-        params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
+        params = {"C": (0.01, 100), "gamma": (0.01, 100)}
         svc_bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
-        svc_bayesopt.maximize(init_points=130, n_iter=20, acq='ucb')
+        svc_bayesopt.maximize(init_points=30, n_iter=20, acq='ucb')
         bestParams = svc_bayesopt.max['params']
         estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True, random_state=RANDOM_SEED)
 
@@ -546,11 +580,17 @@ def executeModel(exeCall, flagEx, nodeTransfName):
                 elif (splittedCol[1] == 'mms'):
                     XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].min())/(XData[nodeTransfName].max()-XData[nodeTransfName].min())
                 elif (splittedCol[1] == 'l2'):
-                    XData[nodeTransfName] = np.log2(XData[nodeTransfName])
+                    dfTemp = np.log2(XData[nodeTransfName])
+                    dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+                    dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+                    XData[nodeTransfName] = dfTemp
                 elif (splittedCol[1] == 'l1p'):
                     XData[nodeTransfName] = np.log1p(XData[nodeTransfName])
                 elif (splittedCol[1] == 'l10'):
-                    XData[nodeTransfName] = np.log10(XData[nodeTransfName])
+                    dfTemp = np.log10(XData[nodeTransfName])
+                    dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+                    dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+                    XData[nodeTransfName] = dfTemp
                 elif (splittedCol[1] == 'e2'):
                     XData[nodeTransfName] = np.exp2(XData[nodeTransfName])
                 elif (splittedCol[1] == 'em1'):
@@ -580,7 +620,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
     estimator.fit(XData, yData)
     yPredict = estimator.predict(XData)
     yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
-
+    print(XData)
     num_cores = multiprocessing.cpu_count()
     inputsSc = ['accuracy','precision_macro','recall_macro']
 
@@ -593,7 +633,12 @@ def executeModel(exeCall, flagEx, nodeTransfName):
         previousState = scoresAct
         keyFirstTime = False
         howMany = 3
-    else:
+    
+    if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
+        finalResultsData = XData.copy()
+        print('improved')
+
+    if (keyFirstTime == False):
         if ((scoresAct[0]-scoresAct[1]) > (previousState[0]-previousState[1])):
             previousState[0] = scoresAct[0]
             previousState[1] = scoresAct[1]
@@ -610,6 +655,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
             pass
 
     scores = scoresAct + previousState
+
     if (howMany == 3):
         scores.append(1)
     else:
@@ -617,6 +663,28 @@ def executeModel(exeCall, flagEx, nodeTransfName):
 
     return 'Everything Okay'
 
+@app.route('/data/RequestBestFeatures', methods=["GET", "POST"])
+def BestFeat():
+    global finalResultsData
+    finalResultsDataJSON = finalResultsData.to_json()
+
+    response = {    
+        'finalResultsData': finalResultsDataJSON
+    }
+    return jsonify(response)
+
+def featFun (clfLocalPar,DataLocalPar,yDataLocalPar):
+    PerFeatureAccuracyLocalPar = []
+    scores = model_selection.cross_val_score(clfLocalPar, DataLocalPar, yDataLocalPar, cv=None, n_jobs=-1)
+    PerFeatureAccuracyLocalPar.append(scores.mean())
+    return PerFeatureAccuracyLocalPar
+
+
+location = './cachedir'
+memory = Memory(location, verbose=0)
+
+# calculating for all algorithms and models the performance and other results
+@memory.cache
 def estimatorFeatureSelection(Data, clf):
 
     resultsFS = []
@@ -670,13 +738,17 @@ def estimatorFeatureSelection(Data, clf):
         else: 
             RankingFS.append(0.05)
 
-    perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(Data, yData)
+    perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData)
     permList.append(perm.feature_importances_)
     n_feats = Data.shape[1]
-    PerFeatureAccuracy = []
-    for i in range(n_feats):
-        scores = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=crossValidation)
-        PerFeatureAccuracy.append(scores.mean())
+
+    num_cores = multiprocessing.cpu_count()
+    print("Parallelization Initialized")
+    flat_results = Parallel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats))
+    PerFeatureAccuracy = [item for sublist in flat_results for item in sublist]
+    # for i in range(n_feats):
+    #     scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1)
+    #     PerFeatureAccuracy.append(scoresHere.mean())
     PerFeatureAccuracyAll.append(PerFeatureAccuracy)
 
     clf.fit(Data, yData) 
@@ -695,7 +767,7 @@ def estimatorFeatureSelection(Data, clf):
     PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracyAll)
     PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
 
-    bestfeatures = SelectKBest(score_func=chi2, k='all')
+    bestfeatures = SelectKBest(score_func=f_classif, k='all')
     fit = bestfeatures.fit(Data,yData)
     dfscores = pd.DataFrame(fit.scores_)
     dfcolumns = pd.DataFrame(Data.columns)
@@ -854,7 +926,10 @@ def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
         else:
             d={}
             XDataNumericCopy = XDataNumeric.copy()
-            XDataNumericCopy[i] = np.log2(XDataNumericCopy[i])
+            dfTemp = np.log2(XDataNumericCopy[i])
+            dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+            dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+            XDataNumericCopy[i] = dfTemp
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
@@ -890,7 +965,10 @@ def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
         else:
             d={}
             XDataNumericCopy = XDataNumeric.copy()
-            XDataNumericCopy[i] = np.log10(XDataNumericCopy[i])
+            dfTemp = np.log10(XDataNumericCopy[i])
+            dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+            dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+            XDataNumericCopy[i] = dfTemp
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
@@ -991,6 +1069,13 @@ def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
     return 'Everything Okay'
 
 def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count):
+    
+    print(DataRows1)
+    print(DataRows2)
+    print(DataRows3)
+    print(DataRows4)
+    print(DataRows5)
+
     corrMatrix1 = DataRows1.corr()
     corrMatrix1 = corrMatrix1.abs()
     corrMatrix2 = DataRows2.corr()