ensemble

cachedir/joblib/run/randomSearch/40507c65ce68390237e1932f4fdedf5b/metadata.json

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cachedir/joblib/run/randomSearch/4d139f4fa0148dfbbd33e88c44d1076c/metadata.json

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cachedir/joblib/run/randomSearch/7b7c4f46bd8bbecee35ec82369081165/metadata.json

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cachedir/joblib/run/randomSearch/9048f73c37aedd04922355de19ec1bde/metadata.json

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cachedir/joblib/run/randomSearch/ad5ab98d963e85601767dd199e2b41b3/metadata.json

@@ -0,0 +1 @@
+{"duration": 126.85907101631165, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "RandomForestClassifier(criterion='entropy', n_estimators=66, random_state=42)", "params": "{'n_estimators': [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "300"}}

cachedir/joblib/run/randomSearch/ae13d107e8e776110b99fc08716278bc/metadata.json

@@ -1 +0,0 @@
-{"duration": 18.56320309638977, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='brute', metric='euclidean', n_neighbors=96)", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "400"}}

cachedir/joblib/run/randomSearch/f19c35077031eadb3ad003833af1cb49/metadata.json

@@ -1 +1 @@
-{"duration": 224.38773703575134, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "MLPClassifier(alpha=0.00021, hidden_layer_sizes=(90, 2), max_iter=100,\n              random_state=42, tol=0.0008100000000000001)", "params": "{'hidden_layer_sizes': [(60, 3), (61, 1), (62, 1), (63, 3), (64, 2), (65, 1), (66, 1), (67, 1), (68, 3), (69, 1), (70, 3), (71, 3), (72, 3), (73, 1), (74, 3), (75, 2), (76, 1), (77, 1), (78, 1), (79, 1), (80, 1), (81, 3), (82, 3), (83, 1), (84, 3), (85, 1), (86, 3), (87, 3), (88, 3), (89, 3), (90, 2), (91, 1), (92, 2), (93, 3), (94, 2), (95, 1), (96, 1), (97, 3), (98, 2), (99, 2), (100, 2), (101, 1), (102, 1), (103, 2), (104, 1), (105, 1), (106, 2), (107, 1), (108, 2), (109, 2), (110, 3), (111, 2), (112, 1), (113, 3), (114, 2), (115, 3), (116, 1), (117, 2), (118, 1), (119, 3)], '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": "200"}}
+{"duration": 140.90401196479797, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "MLPClassifier(activation='identity', alpha=0.0008100000000000001,\n              hidden_layer_sizes=(98, 2), max_iter=100, random_state=42,\n              solver='sgd', tol=0.0008100000000000001)", "params": "{'hidden_layer_sizes': [(60, 3), (61, 1), (62, 1), (63, 3), (64, 2), (65, 1), (66, 1), (67, 1), (68, 3), (69, 1), (70, 3), (71, 3), (72, 3), (73, 1), (74, 3), (75, 2), (76, 1), (77, 1), (78, 1), (79, 1), (80, 1), (81, 3), (82, 3), (83, 1), (84, 3), (85, 1), (86, 3), (87, 3), (88, 3), (89, 3), (90, 2), (91, 1), (92, 2), (93, 3), (94, 2), (95, 1), (96, 1), (97, 3), (98, 2), (99, 2), (100, 2), (101, 1), (102, 1), (103, 2), (104, 1), (105, 1), (106, 2), (107, 1), (108, 2), (109, 2), (110, 3), (111, 2), (112, 1), (113, 3), (114, 2), (115, 3), (116, 1), (117, 2), (118, 1), (119, 3)], '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": "200"}}

cachedir/joblib/run/randomSearch/f2dc1dfd6f43b37cdcbd71a6de0ceb80/metadata.json

@@ -1 +0,0 @@
-{"duration": 72.83721280097961, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "LogisticRegression(C=28, max_iter=350, penalty='none', random_state=42,\n                   solver='saga')", "params": "{'C': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'max_iter': [50, 100, 150, 200, 250, 300, 350, 400, 450], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "100"}}

cachedir/joblib/run/randomSearch/func_code.py

@@ -1,4 +1,4 @@
-# first line: 548
+# first line: 581
 @memory.cache
 def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 
@@ -37,8 +37,7 @@ def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 
     # copy and filter in order to get only the metrics
     metrics = df_cv_results_classifiers.copy()
-    metrics = metrics.filter(['mean_test_accuracy','mean_test_precision_weighted','mean_test_recall_weighted','mean_test_f1_weighted','mean_test_roc_auc_ovo_weighted']) 
-
+    metrics = metrics.filter(['mean_test_accuracy','mean_test_precision_macro','mean_test_recall_macro','mean_test_f1_macro','mean_test_roc_auc_ovo']) 
     # concat parameters and performance
     parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
     parametersLocal = parametersPerformancePerModel['params'].copy()
@@ -68,7 +67,7 @@ def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
         yPredictProb = np.nan_to_num(yPredictProb)
         perModelProb.append(yPredictProb.tolist())
 
-        resultsWeighted.append(geometric_mean_score(yData, yPredict, average='weighted'))
+        resultsWeighted.append(geometric_mean_score(yData, yPredict, average='macro'))
         resultsCorrCoef.append(matthews_corrcoef(yData, yPredict))
         resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True))
 
@@ -77,7 +76,7 @@ def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     for each in resultsLogLoss:
         resultsLogLossFinal.append((each-minLog)/(maxLog-minLog))
 
-    metrics.insert(5,'geometric_mean_score_weighted',resultsWeighted)
+    metrics.insert(5,'geometric_mean_score_macro',resultsWeighted)
     metrics.insert(6,'matthews_corrcoef',resultsCorrCoef)
     metrics.insert(7,'log_loss',resultsLogLossFinal)
 

frontend/index.html

@@ -3,7 +3,7 @@
   <head>
     <meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1.0">
-    <title>HyperSearVis</title>
+    <title>VisEvol</title>
   </head>
   <body>
     <div id="app"></div>

frontend/src/components/About.vue

@@ -3,6 +3,6 @@
 <template>
   <div>
     <img src="@/assets/isovis.jpg">
-    <p>HyperSearVis is a visual analytics tool created by Angelos Chatzimparmpas, member of the ISOVIS Group, Linnaeus University, Sweden.</p>
+    <p>VisEvol is a visual analytics tool created by Angelos Chatzimparmpas, member of the ISOVIS Group, Linnaeus University, Sweden.</p>
   </div>
 </template>

frontend/src/components/History.vue

@@ -215,7 +215,6 @@ export default {
       node.append("text")
           .attr("x", -6)
           .attr("y", function(d) {
-            console.log(d)
             if (d.node <= 5) {
               return d.dy - 70; 
             } else {

frontend/src/components/HyperParameterSpace.vue

@@ -311,4 +311,5 @@ export default {
     EventBus.$on('resetViews', this.reset)
   }
 }
-</script>
+</script>
+

frontend/src/components/Main.vue

@@ -6,7 +6,7 @@
       <b-row class="md-3">
         <b-col cols="3" >
           <mdb-card>
-            <mdb-card-header color="primary-color" tag="h5" class="text-center">Data Sets and Performance Metrics Manager</mdb-card-header>
+            <mdb-card-header color="primary-color" tag="h5" class="text-center">Data Sets and Validation Metrics Manager</mdb-card-header>
               <mdb-card-body>
                 <mdb-card-text class="text-left" style="font-size: 18.5px;">
                   <PerformanceMetrics/>
@@ -28,9 +28,10 @@
         </b-col>
         <b-col cols="3">
             <mdb-card >
-              <mdb-card-header color="primary-color" tag="h5" class="text-center">Final Results of Majority-Voting Ensemble </mdb-card-header>
+              <mdb-card-header color="primary-color" tag="h5" class="text-center">Results of Majority-Voting Ensemble </mdb-card-header>
               <mdb-card-body>
                 <mdb-card-text class="text-left" style="font-size: 18.5px; min-height: 359px">
+                  <VotingResults/>
                 </mdb-card-text>
               </mdb-card-body>
             </mdb-card>

frontend/src/components/Parameters.vue

@@ -81,7 +81,7 @@ export default {
   methods: {
     reset () {
       setTimeout(() => {
-          var svg = d3.select("#overview");
+        var svg = d3.select("#overview");
         svg.selectAll("*").remove();
       }, 50);
     },

frontend/src/components/VotingResults.vue

@@ -1,9 +1,8 @@
 <template>
-  <div id="LinePlot" style="min-height: 363px;"></div>
+  <div id="doubleBarChart" style="min-height: 363px;"></div>
 </template>
 
 <script>
-import * as Plotly from 'plotly.js'
 import { EventBus } from '../main.js'
 
 export default {
@@ -51,350 +50,132 @@ export default {
   },
   methods: {
     reset () {
-      Plotly.purge('LinePlot')
+        var svg = d3.select("#doubleBarChart");
+        svg.selectAll("*").remove();
     },
-    LinePlotView () {
-      this.NumberofExecutions ++ 
-      this.xaxis.push(this.NumberofExecutions)
-
-      // Under Exploration = Current
-      this.scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[0])*100).toFixed(2))
-      this.scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[1])*100).toFixed(2))
-
-      this.scoresPositive.push(parseFloat(this.scoresMean[this.scoresMean.length - 1]) + parseFloat(this.scoresSTD[this.scoresSTD.length - 1]))
-      this.scoresNegative.push(parseFloat(this.scoresMean[this.scoresMean.length - 1]) - parseFloat(this.scoresSTD[this.scoresSTD.length - 1]))
-
-      this.scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[4])*100).toFixed(2))
-      this.scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[5])*100).toFixed(2))
-
-      this.scoresPositive2.push(parseFloat(this.scoresMean2[this.scoresMean2.length - 1]) + parseFloat(this.scoresSTD2[this.scoresSTD2.length - 1]))
-      this.scoresNegative2.push(parseFloat(this.scoresMean2[this.scoresMean2.length - 1]) - parseFloat(this.scoresSTD2[this.scoresSTD2.length - 1]))
-
-      this.scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[8])*100).toFixed(2))
-      this.scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[9])*100).toFixed(2))
-
-      this.scoresPositive3.push(parseFloat(this.scoresMean3[this.scoresMean3.length - 1]) + parseFloat(this.scoresSTD3[this.scoresSTD3.length - 1]))
-      this.scoresNegative3.push(parseFloat(this.scoresMean3[this.scoresMean3.length - 1]) - parseFloat(this.scoresSTD3[this.scoresSTD3.length - 1]))
-
-      this.scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[12])*100).toFixed(2))
-      this.scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[13])*100).toFixed(2))
-
-      this.scoresPositive4.push(parseFloat(this.scoresMean4[this.scoresMean4.length - 1]) + parseFloat(this.scoresSTD4[this.scoresSTD4.length - 1]))
-      this.scoresNegative4.push(parseFloat(this.scoresMean4[this.scoresMean4.length - 1]) - parseFloat(this.scoresSTD4[this.scoresSTD4.length - 1]))
-
-      // Stack
-
-      this.Stack_scoresMean.push((JSON.parse(this.FinalResultsforLinePlot[2])*100).toFixed(2))
-      this.Stack_scoresSTD.push((JSON.parse(this.FinalResultsforLinePlot[3])*100).toFixed(2))
-
-      this.Stack_scoresPositive.push(parseFloat(this.Stack_scoresMean[this.Stack_scoresMean.length - 1]) + parseFloat(this.Stack_scoresSTD[this.Stack_scoresSTD.length - 1]))
-      this.Stack_scoresNegative.push(parseFloat(this.Stack_scoresMean[this.Stack_scoresMean.length - 1]) - parseFloat(this.Stack_scoresSTD[this.Stack_scoresSTD.length - 1]))
-
-      this.Stack_scoresMean2.push((JSON.parse(this.FinalResultsforLinePlot[6])*100).toFixed(2))
-      this.Stack_scoresSTD2.push((JSON.parse(this.FinalResultsforLinePlot[7])*100).toFixed(2))
-
-      this.Stack_scoresPositive2.push(parseFloat(this.Stack_scoresMean2[this.Stack_scoresMean2.length - 1]) + parseFloat(this.Stack_scoresSTD2[this.Stack_scoresSTD2.length - 1]))
-      this.Stack_scoresNegative2.push(parseFloat(this.Stack_scoresMean2[this.Stack_scoresMean2.length - 1]) - parseFloat(this.Stack_scoresSTD2[this.Stack_scoresSTD2.length - 1]))
-
-
-      this.Stack_scoresMean3.push((JSON.parse(this.FinalResultsforLinePlot[10])*100).toFixed(2))
-      this.Stack_scoresSTD3.push((JSON.parse(this.FinalResultsforLinePlot[11])*100).toFixed(2))
-
-      this.Stack_scoresPositive3.push(parseFloat(this.Stack_scoresMean3[this.Stack_scoresMean3.length - 1]) + parseFloat(this.Stack_scoresSTD3[this.Stack_scoresSTD3.length - 1]))
-      this.Stack_scoresNegative3.push(parseFloat(this.Stack_scoresMean3[this.Stack_scoresMean3.length - 1]) - parseFloat(this.Stack_scoresSTD3[this.Stack_scoresSTD3.length - 1]))
-
-      this.Stack_scoresMean4.push((JSON.parse(this.FinalResultsforLinePlot[14])*100).toFixed(2))
-      this.Stack_scoresSTD4.push((JSON.parse(this.FinalResultsforLinePlot[15])*100).toFixed(2))
-
-      this.Stack_scoresPositive4.push(parseFloat(this.Stack_scoresMean4[this.Stack_scoresMean4.length - 1]) + parseFloat(this.Stack_scoresSTD4[this.Stack_scoresSTD4.length - 1]))
-      this.Stack_scoresNegative4.push(parseFloat(this.Stack_scoresMean4[this.Stack_scoresMean4.length - 1]) - parseFloat(this.Stack_scoresSTD4[this.Stack_scoresSTD4.length - 1]))
-      
-      var xaxisReversed = []
-      xaxisReversed = this.xaxis.slice().reverse()
-      xaxisReversed = this.xaxis.concat(xaxisReversed)
-
-      // fill in 'text' array for hover
-      var text = this.scoresSTD.map (function(value, i) {
-          return `STD: +/-${value}`
-        })
-
-      // Current
-
-      var trace1 = {
-        x: this.xaxis, 
-        y: this.scoresMean, 
-        text: text,
-        line: {color: "rgb(55,126,184)"}, 
-        mode: "lines+markers", 
-        marker : {
-          symbol: 'circle' },
-        name: "Active Accuracy",
-        type: "scatter"
-      }
-
-      var trace2 = {
-        x: xaxisReversed, 
-        y: this.scoresPositive.concat(this.scoresNegative), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(55,126,184,0)", 
-        line: {color: "transparent"}, 
-        name: "Active Accuracy", 
-        showlegend: false, 
-        type: "scatter"
-      }
-
-      var text = this.scoresSTD2.map (function(value, i) {
-          return `STD: +/-${value}`
-      })
-
-      var trace3 = {
-        x: this.xaxis, 
-        y: this.scoresMean2, 
-        text: text,
-        line: {color: "rgb(55,126,184)"}, 
-        mode: "lines+markers", 
-        marker : {
-            symbol: 'square' },
-        name: "Active Precision", 
-        type: "scatter"
-      }
-
-      var trace4 = {
-        x: xaxisReversed, 
-        y: this.scoresPositive2.concat(this.scoresNegative2), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(55,126,184,0)", 
-        line: {color: "transparent"}, 
-        name: "Active Precision", 
-        showlegend: false, 
-        type: "scatter"
-      }
-      var text = this.scoresSTD3.map (function(value, i) {
-          return `STD: +/-${value}`
-      })
-      var trace5 = {
-        x: this.xaxis, 
-        y: this.scoresMean3, 
-        text: text,
-        line: {color: "rgb(55,126,184)"}, 
-        mode: "lines+markers", 
-        marker : {
-            symbol: 'triangle-up' },
-        name: "Active Recall", 
-        type: "scatter"
-      }
-
-      var trace6 = {
-        x: xaxisReversed, 
-        y: this.scoresPositive3.concat(this.scoresNegative3), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(55,126,184,0)", 
-        line: {color: "transparent"}, 
-        name: "Active Recall", 
-        showlegend: false, 
-        type: "scatter"
-      }
-      var text = this.scoresSTD4.map (function(value, i) {
-          return `STD: +/-${value}`
-      })
-      var trace7 = {
-        x: this.xaxis, 
-        y: this.scoresMean4, 
-        text: text,
-        line: {color: "rgb(55,126,184)"}, 
-        mode: "lines+markers", 
-        marker : {
-            symbol: 'diamond' },
-        name: "Active F1 Score", 
-        type: "scatter"
-      }
-
-      var trace8 = {
-        x: xaxisReversed, 
-        y: this.scoresPositive4.concat(this.scoresNegative4), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(55,126,184,0)", 
-        line: {color: "transparent"}, 
-        name: "Active F1 Score", 
-        showlegend: false, 
-        type: "scatter"
-      }
-
-      // Stack
-      var text = this.Stack_scoresSTD.map (function(value, i) {
-          return `STD: +/-${value}`
-      })
-        var trace9 = {
-        x: this.xaxis, 
-        y: this.Stack_scoresMean, 
-        text: text,
-        line: {color: "rgb(228,26,28)"}, 
-        mode: "lines+markers", 
-        marker : {
-          symbol: 'x' },
-        name: "Stack Accuracy", 
-        type: "scatter"
-      }
-
-      var trace10 = {
-        x: xaxisReversed, 
-        y: this.Stack_scoresPositive.concat(this.Stack_scoresNegative), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(228,26,28,0)", 
-        line: {color: "transparent"}, 
-        name: "Stack Accuracy", 
-        showlegend: false, 
-        type: "scatter"
-      }
-      var text = this.Stack_scoresSTD2.map (function(value, i) {
-          return `STD: +/-${value}`
-      })
-      var trace11 = {
-        x: this.xaxis, 
-        y: this.Stack_scoresMean2, 
-        text: text,
-        line: {color: "rgb(228,26,28)"}, 
-        mode: "lines+markers", 
-        marker : {
-          symbol: 'cross' },
-        name: "Stack Precision", 
-        type: "scatter"
-      }
-
-      var trace12 = {
-        x: xaxisReversed, 
-        y: this.Stack_scoresPositive2.concat(this.Stack_scoresNegative2), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(228,26,28,0)", 
-        line: {color: "transparent"}, 
-        name: "Stack Precision", 
-        showlegend: false, 
-        type: "scatter"
-      }
-      var text = this.Stack_scoresSTD3.map (function(value, i) {
-          return `STD: +/-${value}`
-      })
-      var trace13 = {
-        x: this.xaxis, 
-        y: this.Stack_scoresMean3, 
-        text: text,
-        line: {color: "rgb(228,26,28)"}, 
-        mode: "lines+markers",
-        marker : {
-          symbol: 'triangle-down' },
-        name: "Stack Recall", 
-        type: "scatter"
-      }
-
-      var trace14 = {
-        x: xaxisReversed, 
-        y: this.Stack_scoresPositive3.concat(this.Stack_scoresNegative3), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(228,26,28,0)", 
-        line: {color: "transparent"}, 
-        name: "Stack Recall", 
-        showlegend: false, 
-        type: "scatter"
-      }
-      var text = this.Stack_scoresSTD4.map (function(value, i) {
-          return `STD: +/-${value}`
-      })
-      var trace15 = {
-        x: this.xaxis, 
-        y: this.Stack_scoresMean4, 
-        text: text,
-        line: {color: "rgb(228,26,28)"}, 
-        mode: "lines+markers",
-        marker : {
-          symbol: 'hexagram' },
-        name: "Stack F1 Score", 
-        type: "scatter"
-      }
-
-      var trace16 = {
-        x: xaxisReversed, 
-        y: this.Stack_scoresPositive4.concat(this.Stack_scoresNegative4), 
-        text: '',
-        hoverinfo: 'text',
-        fill: "tozerox", 
-        fillcolor: "rgba(228,26,28,0)", 
-        line: {color: "transparent"}, 
-        name: "Stack F1 Score", 
-        showlegend: false, 
-        type: "scatter"
-      }
-
-      const DataforLinePlot = [trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8, trace9, trace10, trace11, trace12, trace13, trace14, trace15, trace16]
-
-      var width = this.WH[0]*3.18 // interactive visualization
-      var height = this.WH[1]*0.765 // interactive visualization
-
-      var layout = {
-        font: { family: 'Helvetica', size: 14, color: '#000000' },
-        paper_bgcolor: "rgb(255,255,255)", 
-        plot_bgcolor: "rgb(229,229,229)", 
-        xaxis: {
-            gridcolor: "rgb(255,255,255)",
-            title: 'Step of the Execution',
-            tickformat: '.0f',
-            range: [0, this.scoresMean.length + 2], 
-            showgrid: true, 
-            showline: false, 
-            showticklabels: true, 
-            tickcolor: "rgb(127,127,127)", 
-            ticks: "outside", 
-            zeroline: false
-        }, 
-        yaxis: {
-            gridcolor: "rgb(255,255,255)", 
-            title: '# Performance (%) #',
-            showgrid: true, 
-            showline: false, 
-            showticklabels: true, 
-            tickcolor: "rgb(127,127,127)", 
-            ticks: "outside", 
-            zeroline: false
-        },
-        autosize: false,
-        width: width,
-        height: height,
-        margin: {
-          l: 50,
-          r: 0,
-          b: 50,
-          t: 5,
-          pad: 0
-        },
-      }
-      var config = {displayModeBar: false, scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage'], responsive: true}
-      Plotly.newPlot('LinePlot', DataforLinePlot, layout, config)
-
-      if (this.firstTime == 0) {
-        EventBus.$emit('emittedEventCallingReally')
-        this.firstTime = 1
-      }
+    VotingResultsFun () {
+
+      var data = [
+        {'countries': 'Active Accuracy', 'infant.mortality': 60, 'gdp': 100, 'group': 1, 'color': 'LIGHTSKYBLUE'},
+        {'countries': 'Best Accuracy', 'infant.mortality': 60, 'gdp': 100, 'group': 1, 'color': 'LIGHTCORAL'},
+        {'countries': 'Active Precision', 'infant.mortality': 0, 'gdp': 33, 'group': 2, 'color': 'LIGHTSKYBLUE'},
+        {'countries': 'Best Precision', 'infant.mortality': 60, 'gdp': 100, 'group': 2, 'color': 'LIGHTCORAL'},
+        {'countries': 'Active Recall', 'infant.mortality': 34, 'gdp': 53, 'group': 3, 'color': 'LIGHTSKYBLUE'},
+        {'countries': 'Best Recall', 'infant.mortality': 60, 'gdp': 100, 'group': 3, 'color': 'LIGHTCORAL'},
+        {'countries': 'Active F1-score', 'infant.mortality': 50, 'gdp': 43, 'group': 4, 'color': 'LIGHTSKYBLUE'},
+        {'countries': 'Best F1-score', 'infant.mortality': 60, 'gdp': 100, 'group': 4, 'color': 'LIGHTCORAL'}
+      ]
+
+      var labelArea = 160;
+      var chart,
+              width = 214,
+              bar_height = 15,
+              height = bar_height * 22;
+      var rightOffset = width + labelArea;
+
+      var lCol = "infant.mortality";
+      var rCol = "gdp";
+      var xFrom = d3.scale.linear()
+        .domain([0,100])
+        .range([0, width-60]);
+      var xTo = d3.scale.linear()
+        .domain([0,100])
+        .range([0, width-40]);
+      var y = d3.scale.ordinal()
+        .rangeBands([30, height-58]);
+
+      var chart = d3.select("#doubleBarChart")
+        .append('svg')
+        .attr('class', 'chart')
+        .attr('width', labelArea + width + width)
+        .attr('height', height);
+
+      y.domain(data.map(function (d) {
+        return d.countries;
+      }));
+
+      var yPosByIndex = function (d) {
+        return (y(d.countries) + d.group*15);
+      };
+
+      chart.selectAll("rect.left")
+              .data(data)
+              .enter().append("rect")
+              .attr("x", function (d) {
+                  return width - xFrom(d[lCol]);
+              })
+              .attr("y", yPosByIndex)
+              .attr("class", "left")
+              .attr("width", function (d) {
+                  return xFrom(d[lCol]);
+              })
+              .attr('fill', function (d) {
+                return d.color;
+              })
+              .attr("height", y.rangeBand());
+      chart.selectAll("text.leftscore")
+              .data(data)
+              .enter().append("text")
+              .attr("x", function (d) {
+                  return width - xFrom(d[lCol])-40;
+              })
+              .attr("y", function (d) {
+                  return (y(d.countries) + y.rangeBand() / 2) + d.group*15;
+              })
+              .attr("dx", "20")
+              .attr("dy", ".36em")
+              .attr("text-anchor", "end")
+              .attr('class', 'leftscore')
+              .text(function(d){return d[lCol];});
+
+      chart.selectAll("text.name")
+              .data(data)
+              .enter().append("text")
+              .attr("x", (labelArea / 2) + width)
+              .attr("y", function (d) {
+                  return (y(d.countries) + y.rangeBand() / 2) + d.group*15;
+              })
+              .attr("dy", ".20em")
+              .attr("text-anchor", "middle")
+              .attr('class', 'name')
+              .text(function(d){return d.countries;});
+
+      chart.selectAll("rect.right")
+              .data(data)
+              .enter().append("rect")
+              .attr("x", rightOffset)
+              .attr("y", yPosByIndex)
+              .attr("class", "right")
+              .attr("width", function (d) {
+                  return xTo(d[rCol]);
+              })
+              .attr('fill', function (d) {
+                return d.color;
+              })
+              .attr("height", y.rangeBand());
+
+      chart.selectAll("text.score")
+              .data(data)
+              .enter().append("text")
+              .attr("x", function (d) {
+                  return xTo(d[rCol]) + rightOffset+40;
+              })
+              .attr("y", function (d) {
+                  return (y(d.countries) + y.rangeBand() / 2) + d.group*15;
+              })
+              .attr("dx", -5)
+              .attr("dy", ".36em")
+              .attr("text-anchor", "end")
+              .attr('class', 'score')
+              .text(function(d){return d[rCol];});
+
+      chart.append("text").attr("x",width/3).attr("y", 15).attr("class","title").text("Healthy");
+      chart.append("text").attr("x",width/3+rightOffset).attr("y", 15).attr("class","title").text("Diseased");
+      chart.append("text").attr("x",width+labelArea/3).attr("y", 15).attr("class","title").text("Metrics");
     }
   },
   mounted() {
-    EventBus.$on('updateFlagForFinalResults', data => { this.firstTime = data})
-
-    EventBus.$on('emittedEventCallingLinePlot', data => {
-      this.FinalResultsforLinePlot = data})
-    EventBus.$on('emittedEventCallingLinePlot', this.LinePlotView)
+    this.VotingResultsFun()
 
     EventBus.$on('Responsive', data => {
     this.WH = data})
@@ -405,4 +186,28 @@ export default {
     EventBus.$on('resetViews', this.reset)
   }
 }
-</script>
+</script>
+
+<style>
+
+.chart .right {
+    stroke: white;
+}
+
+.chart .left {
+    stroke: white;
+}
+
+.chart text {
+    fill: black;
+}
+
+.chart text.name {
+    fill: black;
+}
+
+.chart text.title {
+    fill: black;
+    font-weight: bold;
+}
+</style>

run.py

@@ -12,16 +12,20 @@ import pandas as pd
 import numpy as np
 import multiprocessing
 
-from joblib import Memory
+from joblib import Parallel, delayed, Memory
 
+from sklearn.pipeline import make_pipeline
 from sklearn.model_selection import RandomizedSearchCV
 from sklearn.model_selection import GridSearchCV
+from sklearn import model_selection
+from sklearn.model_selection import cross_val_predict
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.svm import SVC
 from sklearn.neural_network import MLPClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
-from sklearn.model_selection import cross_val_predict
+from mlxtend.classifier import EnsembleVoteClassifier
+from mlxtend.feature_selection import ColumnSelector
 from sklearn.metrics import matthews_corrcoef
 from sklearn.metrics import log_loss
 from imblearn.metrics import geometric_mean_score
@@ -29,6 +33,7 @@ from sklearn.manifold import MDS
 from sklearn.manifold import TSNE
 import umap
 
+
 # this block of code is for the connection between the server, the database, and the client (plus routing)
 
 # access MongoDB 
@@ -156,10 +161,7 @@ def reset():
     RFModels = []
 
     global scoring
-    scoring = {'accuracy': 'accuracy', 'precision_micro': 'precision_micro', 'precision_macro': 'precision_macro', 'precision_weighted': 'precision_weighted', 'recall_micro': 'recall_micro', 'recall_macro': 'recall_macro', 'recall_weighted': 'recall_weighted', 'roc_auc_ovo_weighted': 'roc_auc_ovo_weighted'}
-
-    global loopFeatures
-    loopFeatures = 2
+    scoring = {'accuracy': 'accuracy', 'precision_macro': 'precision_macro', 'recall_macro': 'recall_macro', 'f1_macro': 'f1_macro', 'roc_auc_ovo': 'roc_auc_ovo'}
 
     global results
     results = []
@@ -294,10 +296,7 @@ def retrieveFileName():
     all_classifiers = []
 
     global scoring
-    scoring = {'accuracy': 'accuracy', 'precision_weighted': 'precision_weighted', 'recall_weighted': 'recall_weighted', 'f1_weighted': 'f1_weighted', 'roc_auc_ovo_weighted': 'roc_auc_ovo_weighted'}
-
-    global loopFeatures
-    loopFeatures = 2
+    scoring = {'accuracy': 'accuracy', 'precision_macro': 'precision_macro', 'recall_macro': 'recall_macro', 'f1_macro': 'f1_macro', 'roc_auc_ovo': 'roc_auc_ovo'}
 
     # models
     global KNNModels
@@ -620,8 +619,7 @@ def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 
     # copy and filter in order to get only the metrics
     metrics = df_cv_results_classifiers.copy()
-    metrics = metrics.filter(['mean_test_accuracy','mean_test_precision_weighted','mean_test_recall_weighted','mean_test_f1_weighted','mean_test_roc_auc_ovo_weighted']) 
-
+    metrics = metrics.filter(['mean_test_accuracy','mean_test_precision_macro','mean_test_recall_macro','mean_test_f1_macro','mean_test_roc_auc_ovo']) 
     # concat parameters and performance
     parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
     parametersLocal = parametersPerformancePerModel['params'].copy()
@@ -651,7 +649,7 @@ def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
         yPredictProb = np.nan_to_num(yPredictProb)
         perModelProb.append(yPredictProb.tolist())
 
-        resultsWeighted.append(geometric_mean_score(yData, yPredict, average='weighted'))
+        resultsWeighted.append(geometric_mean_score(yData, yPredict, average='macro'))
         resultsCorrCoef.append(matthews_corrcoef(yData, yPredict))
         resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True))
 
@@ -660,7 +658,7 @@ def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     for each in resultsLogLoss:
         resultsLogLossFinal.append((each-minLog)/(maxLog-minLog))
 
-    metrics.insert(5,'geometric_mean_score_weighted',resultsWeighted)
+    metrics.insert(5,'geometric_mean_score_macro',resultsWeighted)
     metrics.insert(6,'matthews_corrcoef',resultsCorrCoef)
     metrics.insert(7,'log_loss',resultsLogLossFinal)
 
@@ -749,6 +747,38 @@ def PreprocessingPred():
 
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
+def EnsembleIDs():
+    global EnsembleActive
+    global numberIDKNNGlob
+    global numberIDLRGlob
+    global numberIDMLPGlob
+    global numberIDRFGlob
+    global numberIDGradBGlob
+    
+    numberIDKNNGlob = []
+    numberIDLRGlob = []
+    numberIDMLPGlob = []
+    numberIDRFGlob = []
+    numberIDGradBGlob = []
+
+    for el in EnsembleActive:
+        match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
+        if match:
+            items = match.groups()
+            if (items[0] == 'KNN'):
+                numberIDKNNGlob.append(int(items[1]))
+            elif (items[0] == 'LR'):
+                numberIDLRGlob.append(int(items[1]))
+            elif (items[0] == 'MLP'):
+                numberIDMLPGlob.append(int(items[1]))
+            elif (items[0] == 'RF'):
+                numberIDRFGlob.append(int(items[1]))
+            else:
+                numberIDGradBGlob.append(int(items[1]))
+    EnsembleIdsAll = numberIDKNNGlob + numberIDLRGlob + numberIDMLPGlob + numberIDRFGlob + numberIDGradBGlob
+
+    return EnsembleIdsAll
+
 def PreprocessingPredEnsemble():
 
     global EnsembleActive
@@ -922,12 +952,12 @@ def preProcMetricsAllAndSel():
     global factors
     metricsPerModelColl = []
     metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'])
-    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_f1_weighted'])
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_macro'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_macro'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_f1_macro'])
     metricsPerModelColl.append(loopThroughMetrics['matthews_corrcoef'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo_weighted'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo'])
     metricsPerModelColl.append(loopThroughMetrics['log_loss'])
 
     f=lambda a: (abs(a)+a)/2
@@ -987,9 +1017,197 @@ def InitializeEnsemble():
         PredictionProbSel = PreprocessingPred()
     else:
         PredictionProbSel = PreprocessingPredEnsemble()
+        ModelsIds = EnsembleIDs()
+        key = 0
+        EnsembleModel(ModelsIds, key)
 
     returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel)
 
+def EnsembleModel (Models, keyRetrieved):
+    global XDataTest, yDataTest
+    global scores
+    global previousState
+    global previousStateActive
+    global keySpec
+    global keySpecInternal
+    global crossValidation
+    global keyData
+    scores = []
+
+    global all_classifiersSelection  
+    all_classifiersSelection = []
+
+    global all_classifiers
+
+    global XData
+    global yData
+    global sclf
+
+    global numberIDKNNGlob
+    global numberIDLRGlob
+    global numberIDMLPGlob
+    global numberIDRFGlob
+    global numberIDGradBGlob
+
+    if (keyRetrieved == 0):
+        all_classifiers = []
+        columnsInit = []
+        columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData]
+
+        temp = allParametersPerformancePerModel[1]
+
+        temp = temp['params']
+        temp = {int(k):v for k,v in temp.items()}
+        tempDic = {    
+            'params': temp
+        }
+        dfParamKNN = pd.DataFrame.from_dict(tempDic)
+        dfParamKNNFilt = dfParamKNN.iloc[:,0]
+        for eachelem in numberIDKNNGlob:
+            arg = dfParamKNNFilt[eachelem]
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), KNeighborsClassifier().set_params(**arg)))
+        temp = allParametersPerformancePerModel[5]
+        temp = temp['params']
+        temp = {int(k):v for k,v in temp.items()}
+        tempDic = {    
+            'params': temp
+        }
+        dfParamLR = pd.DataFrame.from_dict(tempDic)
+        dfParamLRFilt = dfParamLR.iloc[:,0]
+        for eachelem in numberIDLRGlob:
+            arg = dfParamLRFilt[eachelem-LRModelsCount]
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LogisticRegression(random_state=RANDOM_SEED).set_params(**arg)))
+
+        temp = allParametersPerformancePerModel[9]
+        temp = temp['params']
+        temp = {int(k):v for k,v in temp.items()}
+        tempDic = {    
+            'params': temp
+        }
+        dfParamMLP = pd.DataFrame.from_dict(tempDic)
+        dfParamMLPFilt = dfParamMLP.iloc[:,0]
+        for eachelem in numberIDMLPGlob:
+            arg = dfParamMLPFilt[eachelem-MLPModelsCount]
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), MLPClassifier(random_state=RANDOM_SEED).set_params(**arg)))
+
+        temp = allParametersPerformancePerModel[13]
+        temp = temp['params']
+        temp = {int(k):v for k,v in temp.items()}
+        tempDic = {    
+            'params': temp
+        } 
+        dfParamRF = pd.DataFrame.from_dict(tempDic)
+        dfParamRFFilt = dfParamRF.iloc[:,0]
+        for eachelem in numberIDRFGlob:
+            arg = dfParamRFFilt[eachelem-RFModelsCount]
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier(random_state=RANDOM_SEED).set_params(**arg)))
+
+        temp = allParametersPerformancePerModel[17]
+        temp = temp['params']
+        temp = {int(k):v for k,v in temp.items()}
+        tempDic = {    
+            'params': temp
+        }
+        dfParamGradB = pd.DataFrame.from_dict(tempDic)
+        dfParamGradBFilt = dfParamGradB.iloc[:,0]
+        for eachelem in numberIDGradBGlob:
+            arg = dfParamGradBFilt[eachelem-GradBModelsCount]
+            all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GradientBoostingClassifier(random_state=RANDOM_SEED).set_params(**arg)))
+
+        global sclf 
+        sclf = 0
+        sclf = EnsembleVoteClassifier(clfs=all_classifiers,
+                            voting='soft')
+        keySpec = 0
+
+    if (keySpec == 0 or keySpec == 1):
+        num_cores = multiprocessing.cpu_count()
+        inputsSc = ['accuracy','precision','recall','f1']
+        flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
+        scores = [item for sublist in flat_results for item in sublist]
+    
+    if (keySpec == 0):
+        previousState = []
+        previousState.append(scores[1])
+        previousState.append(scores[3])
+        previousState.append(scores[5])
+        previousState.append(scores[7])
+        previousStateActive = []
+        previousStateActive.append(scores[0])
+        previousStateActive.append(scores[2])
+        previousStateActive.append(scores[4])
+        previousStateActive.append(scores[6])
+    elif (keySpec == 1):
+        if (keySpecInternal == 1):
+            previousStateActive = []
+            previousStateActive.append(scores[0])
+            previousStateActive.append(scores[2])
+            previousStateActive.append(scores[4])
+            previousStateActive.append(scores[6])
+        else:
+            previousStateActive = []
+            previousStateActive.append(scores[0])
+            previousStateActive.append(scores[2])
+            previousStateActive.append(scores[4])
+            previousStateActive.append(scores[6])
+            previousState = []
+            previousState.append(scores[1])
+            previousState.append(scores[3])
+            previousState.append(scores[5])
+            previousState.append(scores[7])
+    else:
+        scores = []
+        previousState = []
+        scores.append(previousStateActive[0])
+        scores.append(previousStateActive[0])
+        previousState.append(previousStateActive[0])
+        scores.append(previousStateActive[2])
+        scores.append(previousStateActive[2])
+        previousState.append(previousStateActive[2])
+        scores.append(previousStateActive[4])
+        scores.append(previousStateActive[4])
+        previousState.append(previousStateActive[4])
+        scores.append(previousStateActive[6])
+        scores.append(previousStateActive[6])
+        previousState.append(previousStateActive[6])
+    print(scores)
+
+    return 'Okay'
+
+def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop):
+    scoresLoc = []
+    if (keySpec == 0):
+        temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+        scoresLoc.append(temp.mean())
+        if (keyData == 1):
+            if (loop == 0):
+                scoresLoc.append(previousStateLoc[0])
+            elif (loop == 1):
+                scoresLoc.append(previousStateLoc[1])
+            elif (loop == 2):
+                scoresLoc.append(previousStateLoc[2])
+            else:
+                scoresLoc.append(previousStateLoc[3])
+        else:
+            scoresLoc.append(temp.mean())
+    else:
+        if (keySpecInternal == 1):
+            temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+            scoresLoc.append(temp.mean())
+            if (loop == 0):
+                scoresLoc.append(previousStateLoc[0])
+            elif (loop == 1):
+                scoresLoc.append(previousStateLoc[1])
+            elif (loop == 2):
+                scoresLoc.append(previousStateLoc[2])
+            else:
+                scoresLoc.append(previousStateLoc[3])
+        else:
+            temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
+            scoresLoc.append(temp.mean())
+            scoresLoc.append(temp.mean())
+    return scoresLoc
+
 def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel):
 
     global Results
@@ -1053,7 +1271,6 @@ def CrossoverMutateFun():
     # loop through the algorithms
     global allParametersPerfCrossMutr
     global HistoryPreservation
-    global allParametersPerformancePerModel
 
     KNNIDs = list(filter(lambda k: 'KNN' in k, RemainingIds))
     LRIDs = list(filter(lambda k: 'LR' in k, RemainingIds))
@@ -1615,7 +1832,7 @@ def crossoverMutation(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 
     # copy and filter in order to get only the metrics
     metrics = df_cv_results_classifiers.copy()
-    metrics = metrics.filter(['mean_test_accuracy','mean_test_precision_weighted','mean_test_recall_weighted','mean_test_f1_weighted','mean_test_roc_auc_ovo_weighted']) 
+    metrics = metrics.filter(['mean_test_accuracy','mean_test_precision_macro','mean_test_recall_macro','mean_test_f1_macro','mean_test_roc_auc_ovo']) 
 
     # concat parameters and performance
     parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
@@ -1647,7 +1864,7 @@ def crossoverMutation(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
         yPredictProb = np.nan_to_num(yPredictProb)
         perModelProb.append(yPredictProb.tolist())
 
-        resultsWeighted.append(geometric_mean_score(yData, yPredict, average='weighted'))
+        resultsWeighted.append(geometric_mean_score(yData, yPredict, average='macro'))
         resultsCorrCoef.append(matthews_corrcoef(yData, yPredict))
         resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True))
 
@@ -1656,7 +1873,7 @@ def crossoverMutation(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     for each in resultsLogLoss:
         resultsLogLossFinal.append((each-minLog)/(maxLog-minLog))
 
-    metrics.insert(5,'geometric_mean_score_weighted',resultsWeighted)
+    metrics.insert(5,'geometric_mean_score_macro',resultsWeighted)
     metrics.insert(6,'matthews_corrcoef',resultsCorrCoef)
     metrics.insert(7,'log_loss',resultsLogLossFinal)
 
@@ -1921,12 +2138,12 @@ def preProcMetricsAllAndSelCM():
     global factors
     metricsPerModelColl = []
     metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'])
-    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_weighted'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_f1_weighted'])
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_macro'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_macro'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_f1_macro'])
     metricsPerModelColl.append(loopThroughMetrics['matthews_corrcoef'])
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo_weighted'])
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo'])
     metricsPerModelColl.append(loopThroughMetrics['log_loss'])
 
     f=lambda a: (abs(a)+a)/2