fixed data wrangling

cachedir/joblib/run/GridSearchForModels/0c5f8a1f4bea8be6ae4682e53eda9c9a/metadata.json

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cachedir/joblib/run/GridSearchForModels/7987784bb00482e6d734e7418e8e7825/metadata.json

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cachedir/joblib/run/GridSearchForModels/84c9e65e096b72223ec9394ef6c77677/metadata.json

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cachedir/joblib/run/GridSearchForModels/8eb6d31b2f7dfdeafa1c9fbf54862bb3/metadata.json

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cachedir/joblib/run/GridSearchForModels/90003ee6cd1c752ef075aa0a52f23492/metadata.json

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cachedir/joblib/run/GridSearchForModels/90f4ec7056539ba8f1ae91d2ce378613/metadata.json

@@ -0,0 +1 @@
+{"duration": 1911.6582510471344, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n298   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n299   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n300   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n301   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n302   57    0   1       130   236    0        0      174      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='ball_tree', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=24, p=2,\n                     weights='distance')", "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], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0", "toggle": "1"}}

cachedir/joblib/run/GridSearchForModels/9147dc03a27d188e71203f91522c7d50/metadata.json

@@ -0,0 +1 @@
+{"duration": 110.97186303138733, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n298   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n299   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n300   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n301   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n302   57    0   1       130   236    0        0      174      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": "LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=0.99,\n                           solver='eigen', store_covariance=False, tol=0.0001)", "params": "{'shrinkage': [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35000000000000003, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41000000000000003, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47000000000000003, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.5700000000000001, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.6900000000000001, 0.7000000000000001, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.8200000000000001, 0.8300000000000001, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.9400000000000001, 0.9500000000000001, 0.96, 0.97, 0.98, 0.99], 'solver': ['lsqr', 'eigen']}", "eachAlgor": "'LDA'", "AlgorithmsIDsEnd": "1996", "toggle": "1"}}

cachedir/joblib/run/GridSearchForModels/c9f7ad12fbeadc6012d9b5d28fd5d889/metadata.json

@@ -0,0 +1 @@
+{"duration": 10927.442919969559, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n298   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n299   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n300   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n301   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n302   57    0   1       130   236    0        0      174      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": "SVC(C=4.39, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n    decision_function_shape='ovr', degree=3, gamma='scale', kernel='sigmoid',\n    max_iter=-1, probability=True, random_state=42, shrinking=True, tol=0.001,\n    verbose=False)", "params": "{'C': [0.1, 0.21000000000000002, 0.32000000000000006, 0.43000000000000005, 0.54, 0.65, 0.7600000000000001, 0.8700000000000001, 0.9800000000000001, 1.09, 1.2000000000000002, 1.3100000000000003, 1.4200000000000004, 1.5300000000000002, 1.6400000000000003, 1.7500000000000002, 1.8600000000000003, 1.9700000000000004, 2.08, 2.1900000000000004, 2.3000000000000003, 2.4100000000000006, 2.5200000000000005, 2.6300000000000003, 2.7400000000000007, 2.8500000000000005, 2.9600000000000004, 3.0700000000000003, 3.1800000000000006, 3.2900000000000005, 3.4000000000000004, 3.5100000000000007, 3.6200000000000006, 3.7300000000000004, 3.8400000000000007, 3.9500000000000006, 4.0600000000000005, 4.17, 4.28, 4.390000000000001], 'kernel': ['rbf', 'linear', 'poly', 'sigmoid']}", "eachAlgor": "'SVC'", "AlgorithmsIDsEnd": "576", "toggle": "1"}}

cachedir/joblib/run/GridSearchForModels/d47cb434b4b20b0c82fcf3a60162c740/metadata.json

@@ -0,0 +1 @@
+{"duration": 949.3681681156158, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n298   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n299   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n300   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n301   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n302   57    0   1       130   236    0        0      174      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": "MLPClassifier(activation='tanh', alpha=0.00081, batch_size='auto', beta_1=0.9,\n              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n              hidden_layer_sizes=(100,), learning_rate='constant',\n              learning_rate_init=0.001, max_fun=15000, max_iter=100,\n              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n              power_t=0.5, random_state=42, shuffle=True, solver='sgd',\n              tol=0.00081, validation_fraction=0.1, verbose=False,\n              warm_start=False)", "params": "{'alpha': [1e-05, 0.00021, 0.00041000000000000005, 0.0006100000000000001, 0.0008100000000000001], 'tol': [1e-05, 0.00041000000000000005, 0.0008100000000000001], 'max_iter': [100], 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver': ['adam', 'sgd']}", "eachAlgor": "'MLP'", "AlgorithmsIDsEnd": "1236", "toggle": "1"}}

cachedir/joblib/run/GridSearchForModels/f4d33884dc001f572bf3782e5bcfc337/metadata.json

@@ -0,0 +1 @@
+{"duration": 1727.4443399906158, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n298   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n299   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n300   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n301   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n302   57    0   1       130   236    0        0      174      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": "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n                     criterion='entropy', max_depth=None, max_features='auto',\n                     max_leaf_nodes=None, max_samples=None,\n                     min_impurity_decrease=0.0, min_impurity_split=None,\n                     min_samples_leaf=1, min_samples_split=2,\n                     min_weight_fraction_leaf=0.0, n_estimators=139,\n                     n_jobs=None, oob_score=False, random_state=42, verbose=0,\n                     warm_start=False)", "params": "{'n_estimators': [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'ExtraT'", "AlgorithmsIDsEnd": "2606", "toggle": "1"}}

frontend/src/components/BalancePredictions.vue

@@ -37,7 +37,6 @@
 
             var performancePerModel = JSON.parse(this.resultsfromOverview[0])
             var performancePerModelSelection = []
-            console.log(this.newResultsFromSelection)
             if (this.newResultsFromSelection.length != 0) {
               var performancePerModelSelection = JSON.parse(this.newResultsFromSelection[0]) 
             }

frontend/src/components/BarChart.vue

@@ -468,8 +468,8 @@ export default {
           beautifyLabels.push('Presence of Hypotheticality')
         }
         else if (this.RetrieveDataSet == 'HeartC') {
-          beautifyLabels.push('< 50% diameter narrowing / Healthy')
-          beautifyLabels.push('> 50% diameter narrowing / Diseased')
+          beautifyLabels.push('< 50% Diameter Narrowing / Healthy')
+          beautifyLabels.push('> 50% Diameter Narrowing / Diseased')
         } else {
           target_names.forEach(element => {
             beautifyLabels.push(element)
@@ -544,10 +544,10 @@ export default {
             eventData.points.forEach((e) => {
               tName = e.data.name.replace(/ *\([^)]*\) */g, "")
             });
-            if (tName == "< 50% diameter narrowing / Healthy") {
+            if (tName == "< 50% Diameter Narrowing / Healthy") {
               tName = 0
               this.tNameAll = 0
-            } else if (tName == "> 50% diameter narrowing / Diseased"){
+            } else if (tName == "> 50% Diameter Narrowing / Diseased"){
               tName = 1
               this.tNameAll = 1
             } else {

frontend/src/components/DataSpace.vue

@@ -94,18 +94,23 @@ export default {
     },
     merge() {
       EventBus.$emit('SendAction', 'merge')
+      EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     remove () {
       EventBus.$emit('SendAction', 'remove')
+      EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     compose () {
       EventBus.$emit('SendAction', 'compose')
+      EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     save () {
       EventBus.$emit('SendProvenance', 'save')
+      EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     restore () {
       EventBus.$emit('SendProvenance', 'restore')
+      EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
     },
     clean(obj) {
       var propNames = Object.getOwnPropertyNames(obj);
@@ -125,10 +130,14 @@ export default {
 
       var target_names = JSON.parse(this.dataPoints[0])
       const XandYCoordinatesMDS = JSON.parse(this.dataPoints[1])
+      console.log(XandYCoordinatesMDS)
       const DataSet = JSON.parse(this.dataPoints[2])
-      const DataSetY = JSON.parse(this.dataPoints[3])
-      const originalDataLabels = JSON.parse(this.dataPoints[4])
+      const originalDataLabels = JSON.parse(this.dataPoints[3])
+      //console.log(DataSetY)
+      //const originalDataLabels = JSON.parse(this.dataPoints[4])
+      console.log(originalDataLabels)
       var DataSetParse = JSON.parse(DataSet)
+      console.log(DataSetParse)
       var stringParameters = []
       for (let i = 0; i < DataSetParse.length; i++) {
         this.clean(DataSetParse[i])
@@ -170,8 +179,8 @@ export default {
         beautifyLabels.push('Presence of Hypotheticality')
       }
       else if (this.RetrieveDataSet == 'HeartC') {
-        beautifyLabels.push('< 50% diameter narrowing / Healthy')
-        beautifyLabels.push('> 50% diameter narrowing / Diseased')
+        beautifyLabels.push('< 50% Diameter Narrowing / Healthy')
+        beautifyLabels.push('> 50% Diameter Narrowing / Diseased')
       } else {
         target_names.forEach(element => {
           beautifyLabels.push(element)
@@ -201,6 +210,7 @@ export default {
         result.Yax = Yaxs
         result.ID = IDs
         result.colorUpdates = colorUpdate
+        console.log(result)
 
         var traces = []
         var layout = []
@@ -453,6 +463,7 @@ export default {
         this.selectedDataPoints()
       }
       this.onlyOnce = false
+
     },
     selectedDataPoints () {
       const OverviewDataPlotly = document.getElementById('OverviewDataPlotly')
@@ -471,6 +482,7 @@ export default {
               ClassifierIDsListCleared.push(numberNumb)
             }
           }
+          console.log(ClassifierIDsListCleared)
           if (ClassifierIDsList != '') {
             EventBus.$emit('ChangeKey', 1)
             EventBus.$emit('SendSelectedPointsToServerEventfromData', ClassifierIDsListCleared)
@@ -482,6 +494,7 @@ export default {
     }
   },
   mounted() {
+    EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
     // initialize the first data space projection based on the data set 
     EventBus.$on('emittedEventCallingDataSpacePlotView', data => {
       this.dataPoints = data})

frontend/src/components/Heatmap.vue

@@ -350,7 +350,7 @@ export default {
             return d.idx;
           })
           .attr("class", "row");
-          svg.append("text").attr("x", 220).attr("y", -50).text("Models").style("font-size", "16px").attr("alignment-baseline","top")
+      svg.append("text").attr("x", 220).attr("y", -50).text("Models").style("font-size", "16px").attr("alignment-baseline","top")
       svg.append("text").attr("transform", "rotate(-90)").attr("x", -130).attr("y", -45).style("text-anchor", "middle").style("font-size", "16px").text("Data Features"); 
       var heatMap = row.selectAll(".cell")
           .data(function(d) {
@@ -393,7 +393,7 @@ export default {
               d3.select('#rowLabel_' + k).classed("hover", true);
               if (d != null) {
                   tooltip.style("visibility", "visible");
-                  tooltip.html('<div class="heatmap_tooltip">' + d.toFixed(3) + '</div>');
+                  tooltip.html('<div class="heatmap_tooltip">' + d.toFixed(2) + '</div>');
               } else
                   tooltip.style("visibility", "hidden");
           })
@@ -478,6 +478,8 @@ export default {
           })
           .attr("y", viewerPosTop + cellSize);
 
+      svgLeg.append("text").attr("x", 220).attr("y", 50).text("Importance (Normalized)").style("font-size", "16px").attr("alignment-baseline","top")
+
       //==================================================
       // Change ordering of cells
       function sortByValues(rORc, i, sortOrder) {

frontend/src/components/Main.vue

@@ -325,6 +325,7 @@ export default Vue.extend({
         .then(response => {
           this.OverviewResults = response.data.OverviewResults
           console.log('Server successfully sent all the data related to visualizations!')
+          EventBus.$emit('onlyOnce', true)
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults)
           // if (this.firstTimeFlag == 1) {
           //   this.selectedModels_Stack.push(0)
@@ -874,8 +875,10 @@ export default Vue.extend({
         })
     },
     ActionFun () {
+      console.log(this.actionData)
       const path = `http://127.0.0.1:5000/data/UpdateAction`
       const postData = {
+        points: this.dataPointsSelfromDataSpace,
         action: this.actionData
       }
       const axiosConfig = {

frontend/src/components/PCPData.vue

@@ -46,7 +46,7 @@ export default {
         }
       }
 
-      if (DataSetParse.length == this.ClassifierIDsListClearedData.length || this.ClassifierIDsListClearedData.length == 0)
+      if (DataSetParse.length == this.ClassifierIDsListClearedData.length || this.ClassifierIDsListClearedData.length == 0) {
         var pc = ParCoords()("#PCPDataView")
             .data(DataSetParse)
             .width(1200)
@@ -60,6 +60,7 @@ export default {
             .brushMode('1D-axes')
             .reorderable()
             .interactive();
+      }
       else {
         var pc = ParCoords()("#PCPDataView")
           .data(DataSetParse)

frontend/src/components/PerMetricBarChart.vue

@@ -57,6 +57,7 @@ export default {
           }
         }
       }
+      console.log(resultsColors)
       var width = this.WH[0]*6.5 // interactive visualization
       var height = this.WH[1]*0.5 // interactive visualization
           var trace1 = {
@@ -120,33 +121,33 @@ export default {
             }
             else if (xAxisHovered == 'G-Mean') {
               Plotly.restyle(boxPlot, 'x', [['Accuracy','<b>G-Mean</b>','<b>G-Mean</b>','<b>G-Mean</b>','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
-              index = 3
+              index = 1
             }
             else if (xAxisHovered == 'Precision') {
               Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','<b>Precision</b>','<b>Precision</b>','<b>Precision</b>','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
-              index = 4
+              index = 2
             }
             else if (xAxisHovered == 'Recall') {
               Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','<b>Recall</b>','<b>Recall</b>','<b>Recall</b>','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
-              index = 5
+              index = 3
             }
             else if (xAxisHovered == 'F-Beta Score') {
               Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','MCC','ROC AUC','Log Loss']]);
-              index = 6
+              index = 4
             }
             else if (xAxisHovered == 'MCC') {
               Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','<b>MCC</b>','ROC AUC','Log Loss']]);
-              index = 7
+              index = 5
             }
             else if (xAxisHovered == 'ROC AUC') {
               Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','<b>ROC AUC</b>','Log Loss']]);
-              index = 8
+              index = 6
             }
             else {
               Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','<b>Log Loss</b>']]);
-              index = 9
+              index = 7
             }
-            //EventBus.$emit('updateMetricsScatter', resultsColors[index])
+            EventBus.$emit('updateMetricsScatter', resultsColors[index])
           });
       },
       reset () {

frontend/src/components/PredictionsSpace.vue

@@ -28,7 +28,8 @@ export default {
       representationSelection: 'mds',
       RetrieveDataSet: 'HeartC',
       colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
-      WH: []
+      WH: [],
+      onlyOnce: true,
     }
   },
   methods: {
@@ -59,8 +60,8 @@ export default {
       var target_names = JSON.parse(this.PredictionsData[4])
       const XandYCoordinatesMDS = JSON.parse(this.PredictionsData[8])
       const DataSet = JSON.parse(this.PredictionsData[14])
-      const DataSetY = JSON.parse(this.PredictionsData[15])
-      const originalDataLabels = JSON.parse(this.PredictionsData[16])
+      const originalDataLabels = JSON.parse(this.PredictionsData[15])
+      //const originalDataLabels = JSON.parse(this.PredictionsData[16])
       var DataSetParse = JSON.parse(DataSet)
       var stringParameters = []
       for (let i = 0; i < DataSetParse.length; i++) {
@@ -81,8 +82,8 @@ export default {
         beautifyLabels.push('Presence of Hypotheticality')
       }
       else if (this.RetrieveDataSet == 'HeartC') {
-        beautifyLabels.push('< 50% diameter narrowing / Healthy')
-        beautifyLabels.push('> 50% diameter narrowing / Diseased')
+        beautifyLabels.push('< 50% Diameter Narrowing / Healthy')
+        beautifyLabels.push('> 50% Diameter Narrowing / Diseased')
       } else {
         target_names.forEach(element => {
           beautifyLabels.push(element)
@@ -107,7 +108,7 @@ export default {
           const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
           const aux_Y = result.Yax.filter((item, index) => originalDataLabels[index] == target_names[i]);
           const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
-          console.log(aux_X)
+
           var Text = aux_ID.map((item, index) => {
             let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
             return popup;
@@ -279,11 +280,14 @@ export default {
       var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage', 'toggleSpikelines', 'autoScale2d', 'hoverClosestGl2d','hoverCompareCartesian','select2d','hoverClosestCartesian','zoomIn2d','zoomOut2d','zoom2d'], responsive: true}
 
       Plotly.newPlot('OverviewPredPlotly', traces, layout, config)
-      this.selectedPointsOverview()
+      if (this.onlyOnce) {
+        this.selectedPointsOverview()
+      }
+      this.onlyOnce = false
+
     },
     UpdateScatterPlot () {
       const XandYCoordinates = JSON.parse(this.UpdatedData[0])
-      console.log(XandYCoordinates)
 
       Plotly.animate('OverviewPredPlotly', {
         data: [
@@ -326,6 +330,7 @@ export default {
     },
   },
   mounted() {
+    EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
     EventBus.$on('updatePredictionsSpace', data => { this.UpdatedData = data })
     EventBus.$on('updatePredictionsSpace', this.UpdateScatterPlot)
     EventBus.$on('emittedEventCallingPredictionsSpacePlotView', data => {

frontend/src/components/ScatterPlot.vue

@@ -96,13 +96,22 @@ export default {
       Plotly.purge('OverviewPlotly')
 
       var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
-
-      /*if (this.newColorsUpdate.length != 0) {
+      console.log(this.newColorsUpdate)
+      if (this.newColorsUpdate.length != 0) {
+        console.log('den mpike')
+        colorsforScatterPlot = []
         let resultsClear = JSON.parse(this.newColorsUpdate)
         for (let j = 0; j < Object.values(resultsClear).length; j++) {
           colorsforScatterPlot.push(Object.values(resultsClear)[j])
         }
-      }*/
+        this.colorsStore = []
+        this.MDSStore = []
+        this.parametersStore = []
+        this.TSNEStore = []
+        this.modelIDStore = []
+        this.UMAPStore = []
+      }
+      console.log(colorsforScatterPlot)
 
       var MDSData = JSON.parse(this.ScatterPlotResults[1])
       var parametersLoc = JSON.parse(this.ScatterPlotResults[2])
@@ -124,6 +133,8 @@ export default {
         TSNEData = this.TSNEStore.slice(this.activeModels,this.activeModels+1)[0]
         modelId = this.modelIDStore.slice(this.activeModels,this.activeModels+1)[0]
         UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0]
+
+      console.log(colorsforScatterPlot)
       }
 
       EventBus.$emit('sendPointsNumber', modelId.length)
@@ -197,7 +208,6 @@ export default {
         EventBus.$emit('NewHeatmapAccordingtoNewStack', modelId)
 
       }
-      
       var DataGeneral
 
       var maxX
@@ -470,8 +480,11 @@ export default {
     }
   },
   mounted() {
-    /*EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data })
-    EventBus.$on('updateMetricsScatter', this.ScatterPlotView)*/
+    EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
+
+    EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data })
+    EventBus.$on('updateMetricsScatter', this.ScatterPlotView)
+
     EventBus.$on('updateRemaining', data => { this.pushModelsRemaining = data })
 
     EventBus.$on('requestProven', data => { this.activeModels = data })

frontend/src/components/ToggleSelection.vue

@@ -47,7 +47,7 @@ export default {
     $('#toggle-uni').change(function() {
         var toggleIDUni = document.getElementById('toggle-uni')
         var toggleIDPer = document.getElementById('toggle-per')
-        var toggleIDFi = document.getElementById('toggle-per')
+        var toggleIDFi = document.getElementById('toggle-fi')
         if (toggleIDUni.checked === false) {
             EventBus.$emit('toggle1', 0)
         } else {
@@ -62,7 +62,7 @@ export default {
     $('#toggle-per').change(function() {
         var toggleIDUni = document.getElementById('toggle-uni')
         var toggleIDPer = document.getElementById('toggle-per')
-        var toggleIDFi = document.getElementById('toggle-per')
+        var toggleIDFi = document.getElementById('toggle-fi')
         if (toggleIDPer.checked === false) {
             EventBus.$emit('toggle2', 0)
         } else {
@@ -77,7 +77,7 @@ export default {
     $('#toggle-fi').change(function() {
         var toggleIDUni = document.getElementById('toggle-uni')
         var toggleIDPer = document.getElementById('toggle-per')
-        var toggleIDFi = document.getElementById('toggle-per')
+        var toggleIDFi = document.getElementById('toggle-fi')
         if (toggleIDFi.checked === false) {
             EventBus.$emit('toggle3', 0)
         } else {

run.py

@@ -418,7 +418,7 @@ def callPreResults():
     global yData
     global target_names
     global impDataInst
-
+    print(XData)
     DataSpaceResMDS = FunMDS(XData)
     DataSpaceResTSNE = FunTsne(XData)
     DataSpaceResTSNE = DataSpaceResTSNE.tolist()
@@ -442,7 +442,7 @@ def callPreResults():
 @app.route('/data/requestDataSpaceResults', methods=["GET", "POST"])
 def SendDataSpaceResults():
     global preResults
-
+    callPreResults()
     response = {    
         'preDataResults': preResults,
     }
@@ -1689,8 +1689,6 @@ def RetrieveSelClassifiersIDandRemoveFromStack():
 
     PredictionProbSelUpdate = PreprocessingPredUpdate(ClassifierIDsList)
 
-    print(PredictionProbSelUpdate)
-
     global resultsUpdatePredictionSpace
     resultsUpdatePredictionSpace = []
     resultsUpdatePredictionSpace.append(json.dumps(PredictionProbSelUpdate[0])) # Position: 0
@@ -1932,7 +1930,7 @@ def RetrieveSelDataPoints():
 
     if (len(paramsListSeptoDicGradB['n_estimators']) is 0):
         RetrieveParamsClearedListGradB = []
-    print(algorithms)
+
     for eachAlgor in algorithms:
         if (eachAlgor) == 'KNN':
             clf = KNeighborsClassifier()
@@ -2093,6 +2091,17 @@ def RetrieveSelDataPoints():
 
         df_concatMetrics = pd.concat([dfKNNCleared, dfSVCCleared, dfGausNBCleared, dfMLPCleared, dfLRCleared, dfLDACleared, dfQDACleared, dfRFCleared, dfExtraTCleared, dfAdaBCleared, dfGradBCleared])
     else:
+        dfSVCCleared = pd.DataFrame()
+        dfKNNCleared = pd.DataFrame()
+        dfGausNBCleared = pd.DataFrame()
+        dfMLPCleared = pd.DataFrame()
+        dfLRCleared = pd.DataFrame()
+        dfLDACleared = pd.DataFrame()
+        dfQDACleared = pd.DataFrame()
+        dfRFCleared = pd.DataFrame()
+        dfExtraTCleared = pd.DataFrame()
+        dfAdaBCleared = pd.DataFrame()
+        dfGradBCleared = pd.DataFrame()
         if (len(metricsSelList[0]) != 0):
             dicKNN = json.loads(metricsSelList[0])
             dfKNN = pd.DataFrame.from_dict(dicKNN)
@@ -2103,8 +2112,7 @@ def RetrieveSelDataPoints():
                 dfKNNCleared = dfKNN
             else:
                 dfKNNCleared = dfKNN.drop(dfKNN.index[set_diff_df])
-            df_concatMetrics = dfKNNCleared
-        elif (len(metricsSelList[1]) != 0):
+        if (len(metricsSelList[1]) != 0):
             dicSVC = json.loads(metricsSelList[1])
             dfSVC = pd.DataFrame.from_dict(dicSVC)
             parametersSelDataPD = parametersSelData[1].apply(pd.Series)
@@ -2114,8 +2122,7 @@ def RetrieveSelDataPoints():
                 dfSVCCleared = dfSVC
             else:
                 dfSVCCleared = dfSVC.drop(dfSVC.index[set_diff_df])
-            df_concatMetrics = dfSVCCleared
-        elif (len(metricsSelList[2]) != 0):
+        if (len(metricsSelList[2]) != 0):
             dicGausNB = json.loads(metricsSelList[2])
             dfGausNB = pd.DataFrame.from_dict(dicGausNB)
             parametersSelDataPD = parametersSelData[2].apply(pd.Series)
@@ -2125,8 +2132,7 @@ def RetrieveSelDataPoints():
                 dfGausNBCleared = dfGausNB
             else:
                 dfGausNBCleared = dfGausNB.drop(dfGausNB.index[set_diff_df])
-            df_concatMetrics = dfGausNBCleared
-        elif (len(metricsSelList[3]) != 0):
+        if (len(metricsSelList[3]) != 0):
             dicMLP = json.loads(metricsSelList[3])
             dfMLP = pd.DataFrame.from_dict(dicMLP)
             parametersSelDataPD = parametersSelData[3].apply(pd.Series)
@@ -2136,8 +2142,7 @@ def RetrieveSelDataPoints():
                 dfMLPCleared = dfMLP
             else:
                 dfMLPCleared = dfMLP.drop(dfMLP.index[set_diff_df])
-            df_concatMetrics = dfMLPCleared
-        elif (len(metricsSelList[4]) != 0):
+        if (len(metricsSelList[4]) != 0):
             dicLR = json.loads(metricsSelList[4])
             dfLR = pd.DataFrame.from_dict(dicLR)
             parametersSelDataPD = parametersSelData[4].apply(pd.Series)
@@ -2147,8 +2152,7 @@ def RetrieveSelDataPoints():
                 dfLRCleared = dfLR
             else:
                 dfLRCleared = dfLR.drop(dfLR.index[set_diff_df])
-            df_concatMetrics = dfLRCleared
-        elif (len(metricsSelList[5]) != 0):
+        if (len(metricsSelList[5]) != 0):
             dicLDA = json.loads(metricsSelList[5])
             dfLDA = pd.DataFrame.from_dict(dicLDA)
             parametersSelDataPD = parametersSelData[5].apply(pd.Series)
@@ -2158,8 +2162,7 @@ def RetrieveSelDataPoints():
                 dfLDACleared = dfLDA
             else:
                 dfLDACleared = dfLDA.drop(dfLDA.index[set_diff_df])
-            df_concatMetrics = dfLDACleared
-        elif (len(metricsSelList[6]) != 0):
+        if (len(metricsSelList[6]) != 0):
             dicQDA = json.loads(metricsSelList[6])
             dfQDA = pd.DataFrame.from_dict(dicQDA)
             parametersSelDataPD = parametersSelData[6].apply(pd.Series)
@@ -2169,8 +2172,7 @@ def RetrieveSelDataPoints():
                 dfQDACleared = dfQDA
             else:
                 dfQDACleared = dfQDA.drop(dfQDA.index[set_diff_df])
-            df_concatMetrics = dfQDACleared
-        elif (len(metricsSelList[7]) != 0):
+        if (len(metricsSelList[7]) != 0):
             dicRF = json.loads(metricsSelList[7])
             dfRF = pd.DataFrame.from_dict(dicRF)
             parametersSelDataPD = parametersSelData[7].apply(pd.Series)
@@ -2180,8 +2182,7 @@ def RetrieveSelDataPoints():
                 dfRFCleared = dfRF
             else:
                 dfRFCleared = dfRF.drop(dfRF.index[set_diff_df])
-            df_concatMetrics = dfRFCleared
-        elif (len(metricsSelList[8]) != 0):
+        if (len(metricsSelList[8]) != 0):
             dicExtraT = json.loads(metricsSelList[8])
             dfExtraT = pd.DataFrame.from_dict(dicExtraT)
             parametersSelDataPD = parametersSelData[8].apply(pd.Series)
@@ -2191,8 +2192,7 @@ def RetrieveSelDataPoints():
                 dfExtraTCleared = dfExtraT
             else:
                 dfExtraTCleared = dfExtraT.drop(dfExtraT.index[set_diff_df])
-            df_concatMetrics = dfExtraTCleared
-        elif (len(metricsSelList[9]) != 0):
+        if (len(metricsSelList[9]) != 0):
             dicAdaB = json.loads(metricsSelList[9])
             dfAdaB = pd.DataFrame.from_dict(dicAdaB)
             parametersSelDataPD = parametersSelData[9].apply(pd.Series)
@@ -2202,8 +2202,7 @@ def RetrieveSelDataPoints():
                 dfAdaBCleared = dfAdaB
             else:
                 dfAdaBCleared = dfAdaB.drop(dfAdaB.index[set_diff_df])
-            df_concatMetrics = dfAdaBCleared
-        else:
+        if (len(metricsSelList[10]) != 0):
             dicGradB = json.loads(metricsSelList[10])
             dfGradB = pd.DataFrame.from_dict(dicGradB)
             parametersSelDataPD = parametersSelData[10].apply(pd.Series)
@@ -2213,14 +2212,15 @@ def RetrieveSelDataPoints():
                 dfGradBCleared = dfGradB
             else:
                 dfGradBCleared = dfGradB.drop(dfGradB.index[set_diff_df])
-            df_concatMetrics = dfGradBCleared
-    
+        df_concatMetrics = pd.concat([dfKNNCleared, dfSVCCleared, dfGausNBCleared, dfMLPCleared, dfLRCleared, dfLDACleared, dfQDACleared, dfRFCleared, dfExtraTCleared, dfAdaBCleared, dfGradBCleared])
+    df_concatMetrics = df_concatMetrics.reset_index(drop=True)
+    print(df_concatMetrics)
     global foreachMetricResults
     foreachMetricResults = []
     foreachMetricResults = preProcSumForEachMetric(factors, df_concatMetrics)
 
     df_concatMetrics.loc[:, 'log_loss'] = 1 - df_concatMetrics.loc[:, 'log_loss']
-
+    print(df_concatMetrics)
     global sumPerClassifierSelUpdate
     sumPerClassifierSelUpdate = []
     sumPerClassifierSelUpdate = preProcsumPerMetricAccordingtoData(factors, df_concatMetrics)
@@ -2230,7 +2230,8 @@ def RetrieveSelDataPoints():
     ModelSpaceMDSNewSel = FunMDS(df_concatMetrics)
 
     ModelSpaceMDSNewSelComb = [list(a) for a in  zip(ModelSpaceMDSNewSel[0], ModelSpaceMDSNewSel[1])]
-
+    print(len(ModelSpaceMDSNewComb))
+    print(len(ModelSpaceMDSNewSelComb))
     global mt2xFinal
     mt2xFinal = []
     mtx1, mtx2, disparity = procrustes(ModelSpaceMDSNewComb, ModelSpaceMDSNewSelComb)
@@ -2242,6 +2243,8 @@ def RetrieveSelDataPoints():
 
 
 def GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, DataPointsSel):
+    global XData
+    global yData
     if (len(params) == 0):
         resultsMetrics.append([]) # Position: 0 and so on 
         parametersSelData.append([])
@@ -2253,8 +2256,7 @@ def GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, DataPointsSel):
             .getOrCreate()    
             )
         sc = spark.sparkContext 
-
-        XDatasubset = XData.loc[DataPointsSel,:]
+        XDatasubset = XData.iloc[DataPointsSel,:]
 
         yDataSubset = [yData[i] for i in DataPointsSel]
 
@@ -3022,19 +3024,19 @@ def RetrieveAction():
     filterActionCleared = json.loads(filterAction)
 
     global filterActionFinal
-    global dataSpacePointsIDs
     global filterDataFinal
     global XData
     global yData
 
     filterActionFinal = filterActionCleared['action']
-
+    dataSpacePointsIDs = filterActionCleared['points']
+    print(dataSpacePointsIDs)
     if (filterActionFinal == 'merge'):
         if (filterDataFinal == 'mean' or filterDataFinal == ''):
-            mean = XData.iloc[dataSpacePointsIDs, :].mean()
+            mean = XData.loc[dataSpacePointsIDs, :].mean()
             XData.loc[len(XData)]= mean
         else:
-            median = XData.iloc[dataSpacePointsIDs, :].median()
+            median = XData.loc[dataSpacePointsIDs, :].median()
             XData.loc[len(XData)]= median
         yDataSelected = [yData[i] for i in dataSpacePointsIDs]
         storeMode = mode(yDataSelected)
@@ -3044,10 +3046,10 @@ def RetrieveAction():
         XData.reset_index(drop=True, inplace=True)
     elif (filterActionFinal == 'compose'):
         if (filterDataFinal == 'mean' or filterDataFinal == ''):
-            mean = XData.iloc[dataSpacePointsIDs, :].mean()
+            mean = XData.loc[dataSpacePointsIDs, :].mean()
             XData.loc[len(XData)]= mean
         else:
-            median = XData.iloc[dataSpacePointsIDs, :].median()
+            median = XData.loc[dataSpacePointsIDs, :].median()
             XData.loc[len(XData)]= median
         yDataSelected = [yData[i] for i in dataSpacePointsIDs]
         storeMode = mode(yDataSelected)