test

cachedir/joblib/run/GridSearchForModels/01391d5b7d809b9da8216d82630ec74d/metadata.json

@@ -0,0 +1 @@
+{"duration": 225.68257093429565, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "LogisticRegression(C=1.925, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=200,\n                   multi_class='auto', n_jobs=None, penalty='none',\n                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n                   warm_start=False)", "params": "{'C': [0.5, 0.575, 0.6499999999999999, 0.7249999999999999, 0.7999999999999998, 0.8749999999999998, 0.9499999999999997, 1.0249999999999997, 1.0999999999999996, 1.1749999999999996, 1.2499999999999996, 1.3249999999999995, 1.3999999999999995, 1.4749999999999994, 1.5499999999999994, 1.6249999999999993, 1.6999999999999993, 1.7749999999999992, 1.8499999999999992, 1.9249999999999992], 'max_iter': [50, 100, 150, 200], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "1816"}}

cachedir/joblib/run/GridSearchForModels/1289ed21cfae05e620dbdf09a1ffb437/metadata.json

@@ -0,0 +1 @@
+{"duration": 23.182815074920654, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "QuadraticDiscriminantAnalysis(priors=None, reg_param=49, store_covariance=False,\n                              tol=0.00051)", "params": "{'reg_param': [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], 'tol': [1e-05, 0.00051]}", "eachAlgor": "'QDA'", "AlgorithmsIDsEnd": "2716"}}

cachedir/joblib/run/GridSearchForModels/231801fa9c8325943fbbb9638021cf43/metadata.json

@@ -0,0 +1 @@
+{"duration": 608.9179818630219, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "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=None, 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": "3036"}}

cachedir/joblib/run/GridSearchForModels/27fb24df3dff8a334de9acfb20665e0e/metadata.json

@@ -0,0 +1 @@
+{"duration": 829.405121088028, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "GradientBoostingClassifier(ccp_alpha=0.0, criterion='mae', init=None,\n                           learning_rate=0.23, loss='deviance', max_depth=3,\n                           max_features=None, max_leaf_nodes=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=109,\n                           n_iter_no_change=None, presort='deprecated',\n                           random_state=None, subsample=1.0, tol=0.0001,\n                           validation_fraction=0.1, verbose=0,\n                           warm_start=False)", "params": "{'n_estimators': [90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109], 'learning_rate': [0.01, 0.12, 0.23], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "3356"}}

cachedir/joblib/run/GridSearchForModels/3fe88bc8502752b8df3f40320d27a183/metadata.json

@@ -0,0 +1 @@
+{"duration": 112.00387978553772, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "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=None, shuffle=True, solver='sgd',\n              tol=0.00051, 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, 0.00051], 'max_iter': [100], 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver': ['adam', 'sgd']}", "eachAlgor": "'MLP'", "AlgorithmsIDsEnd": "1736"}}

cachedir/joblib/run/GridSearchForModels/44564a790ced36689a726d58cdcc81eb/metadata.json

@@ -0,0 +1 @@
+{"duration": 457.51465487480164, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "AdaBoostClassifier(algorithm='SAMME', base_estimator=None, learning_rate=1.2,\n                   n_estimators=79, random_state=None)", "params": "{'n_estimators': [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], 'learning_rate': [0.1, 1.2000000000000002], 'algorithm': ['SAMME.R', 'SAMME']}", "eachAlgor": "'AdaB'", "AlgorithmsIDsEnd": "3196"}}

cachedir/joblib/run/GridSearchForModels/60b98008196c95d4a5009597dd562ea5/metadata.json

@@ -0,0 +1 @@
+{"duration": 476.14403200149536, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "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=None, 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"}}

cachedir/joblib/run/GridSearchForModels/7542a9fb28e131960e44852d33049c20/metadata.json

@@ -1 +0,0 @@
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cachedir/joblib/run/GridSearchForModels/76519e903e81fb48c4d44abbc75bbc3c/metadata.json

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cachedir/joblib/run/GridSearchForModels/7aa4b0bfdf5ad1ec09afbdf111d870b6/metadata.json

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cachedir/joblib/run/GridSearchForModels/98bb8d718b1c0023eaafddc6746211e8/metadata.json

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cachedir/joblib/run/GridSearchForModels/9bdfbbb781609486c8697f91d6960c0f/metadata.json

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cachedir/joblib/run/GridSearchForModels/ab217966ac94ecfcb1f16130f136aadd/metadata.json

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cachedir/joblib/run/GridSearchForModels/ad0a720d75812a4bedd57fc40900baac/metadata.json

@@ -0,0 +1 @@
+{"duration": 703.3166279792786, "input_args": {"XData": "     sepal_l  sepal_w  petal_l  petal_w\n0        6.3      3.3      6.0      2.5\n1        7.1      3.0      5.9      2.1\n2        5.8      2.7      5.1      1.9\n3        6.3      2.9      5.6      1.8\n4        7.6      3.0      6.6      2.1\n..       ...      ...      ...      ...\n145      5.1      3.8      1.6      0.2\n146      5.0      3.5      1.6      0.6\n147      5.1      3.4      1.5      0.2\n148      4.6      3.2      1.4      0.2\n149      4.8      3.0      1.4      0.3\n\n[150 rows x 4 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "RandomForestClassifier(bootstrap=True, 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=None,\n                       verbose=0, 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": "'RF'", "AlgorithmsIDsEnd": "2876"}}

cachedir/joblib/run/GridSearchForModels/func_code.py

@@ -1,4 +1,4 @@
-# first line: 466
+# first line: 510
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 
@@ -78,6 +78,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     resultsMacroBeta2 = []
     resultsWeightedBeta2 = []
     resultsLogLoss = []
+    resultsLogLossFinal = []
 
     loop = 10
 
@@ -118,8 +119,12 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
         resultsMacroBeta2.append(fbeta_score(yData, yPredict, average='macro', beta=2))
         resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2))
   
-        resultsLogLoss.append(log_loss(yData, yPredict, normalize = True))
+        resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True))
 
+    maxLog = max(resultsLogLoss)
+    minLog = min(resultsLogLoss)
+    for each in resultsLogLoss:
+        resultsLogLossFinal.append((each-minLog)/(maxLog-minLog))
 
     metrics.insert(loop,'geometric_mean_score_micro',resultsMicro)
     metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro)
@@ -139,7 +144,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     metrics.insert(loop+11,'f2_macro',resultsMacroBeta2)
     metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2)
 
-    metrics.insert(loop+13,'log_loss',resultsLogLoss)
+    metrics.insert(loop+13,'log_loss',resultsLogLossFinal)
 
     perModelProbPandas = pd.DataFrame(perModelProb)
     perModelProbPandas = perModelProbPandas.to_json()

frontend/src/components/AlgorithmHyperParam.vue

@@ -35,7 +35,7 @@ export default {
     PCPView () {
       d3.selectAll("#PCP > *").remove(); 
       if (this.selAlgorithm != '') {
-        var colors = ['#8dd3c7','#8da0cb']
+        var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
         var colorGiv = 0
         
         var factorsLocal = this.factors
@@ -47,21 +47,6 @@ export default {
 
         var Mc1 = []
         const performanceAlg1 = JSON.parse(this.ModelsPerformance[6])
-        var max
-        var min
-
-        for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
-          if (j == 0) {
-            max = Object.values(performanceAlg1['log_loss'])[j]
-            min = Object.values(performanceAlg1['log_loss'])[j]
-          }
-          if (Object.values(performanceAlg1['log_loss'])[j] > max) {
-            max = Object.values(performanceAlg1['log_loss'])[j]
-          }
-          if (Object.values(performanceAlg1['log_loss'])[j] < min) {
-            min = Object.values(performanceAlg1['log_loss'])[j]
-          }
-        }
 
         for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
           let sum
@@ -69,7 +54,7 @@ export default {
             + (factorsLocal[5] * Object.values(performanceAlg1['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg1['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg1['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg1['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg1['mean_test_recall_micro'])[j])
             + (factorsLocal[10] * Object.values(performanceAlg1['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg1['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg1['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg1['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg1['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg1['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlg1['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg1['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg1['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg1['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg1['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg1['matthews_corrcoef'])[j])
-            + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg1['log_loss'])[j])/(max - min))))
+            + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg1['log_loss'])[j]))
           Mc1.push((sum/divide)*100)
         }
 
@@ -81,7 +66,7 @@ export default {
             + (factorsLocal[5] * Object.values(performanceAlg2['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg2['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg2['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg2['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg2['mean_test_recall_micro'])[j])
             + (factorsLocal[10] * Object.values(performanceAlg2['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg2['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg2['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg2['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg2['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg2['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlg2['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg2['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg2['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg2['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg2['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg2['matthews_corrcoef'])[j])
-            + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg2['log_loss'])[j])/(max - min))))
+            + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg2['log_loss'])[j]))
           Mc2.push((sum2/divide)*100)
         }
 
@@ -103,10 +88,10 @@ export default {
         for (var i = 0; i < valuesPerf.length; i++) {
           if (this.selAlgorithm === 'KNN') {
             // There is a problem here!
-            newObjectsParams.push({model: i,'perf_metrics': Mc1[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
+            newObjectsParams.push({model: i,'performance (%)': Mc1[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
             ArrayCombined[i] = newObjectsParams[i]
           } else {
-            newObjectsParams2.push({model: this.KNNModels + i,'perf_metrics': Mc2[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
+            newObjectsParams2.push({model: this.KNNModels + i,'performance (%)': Mc2[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
             ArrayCombined[i] = newObjectsParams2[i]
           }
         }

frontend/src/components/Algorithms.vue

@@ -58,24 +58,8 @@ export default {
         divide = element + divide
       });
 
-      var max
-      var min 
       var Mc1 = []
       const performanceAlg1 = JSON.parse(this.PerformanceAllModels[6])
-      console.log(performanceAlg1)
-
-      for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
-        if (j == 0) {
-          max = Object.values(performanceAlg1['log_loss'])[j]
-          min = Object.values(performanceAlg1['log_loss'])[j]
-        }
-        if (Object.values(performanceAlg1['log_loss'])[j] > max) {
-          max = Object.values(performanceAlg1['log_loss'])[j]
-        }
-        if (Object.values(performanceAlg1['log_loss'])[j] < min) {
-          min = Object.values(performanceAlg1['log_loss'])[j]
-        }
-      }
 
       for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
         let sum
@@ -83,7 +67,7 @@ export default {
           + (factorsLocal[5] * Object.values(performanceAlg1['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg1['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg1['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg1['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg1['mean_test_recall_micro'])[j])
           + (factorsLocal[10] * Object.values(performanceAlg1['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg1['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg1['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg1['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg1['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg1['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlg1['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg1['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg1['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg1['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg1['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg1['matthews_corrcoef'])[j])
-          + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg1['log_loss'])[j])/(max - min))))
+          + (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg1['log_loss'])[j]))
         Mc1.push((sum/divide)*100)
       }
 
@@ -95,7 +79,7 @@ export default {
           + (factorsLocal[5] * Object.values(performanceAlg2['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg2['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg2['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg2['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg2['mean_test_recall_micro'])[j])
           + (factorsLocal[10] * Object.values(performanceAlg2['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg2['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg2['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg2['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg2['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg2['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlg2['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg2['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg2['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg2['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg2['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg2['matthews_corrcoef'])[j])
-          + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - ((max - Object.values(performanceAlg2['log_loss'])[j])/(max - min))))
+          + (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg2['log_loss'])[j]))
         Mc2.push((sum2/divide)*100)
       }
       
@@ -134,7 +118,7 @@ export default {
       this.chart('#exploding_boxplot')
 
       // colorscale
-      const previousColor = ['#8dd3c7','#8da0cb']
+      const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
       // check for brushing
       var el = document.getElementsByClassName('d3-exploding-boxplot boxcontent')
       var overall = document.getElementsByClassName('overall')
@@ -199,7 +183,7 @@ export default {
       var limiter = this.chart.returnBrush()
 
       var algorithm = []
-      const previousColor = ['#8dd3c7','#8da0cb']
+      const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
       var modelsActive = []
       for (var j = 0; j < this.AllAlgorithms.length; j++) {
         algorithm = []
@@ -267,7 +251,7 @@ export default {
       } else {
         var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
       }
-      const previousColor = ['#8dd3c7','#8da0cb']
+      const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
       var modelsActive = []
       for (let j = 0; j < this.brushedBoxPl.length; j++) {
         modelsActive.push(this.brushedBoxPl[j].model)

frontend/src/components/BarChart.vue

@@ -20,7 +20,7 @@ export default {
       modelsSelectedinBar: [],
       factors: [1,1,1,1,1],
       KNNModels: 576, //KNN models,
-      colorsValues: ['#6a3d9a','#b15928','#e31a1c'],
+      colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc'],
       WH: []
     }
   },

frontend/src/components/DataSpace.vue

@@ -64,7 +64,7 @@ export default {
       restoreData: 'Restore Step',
       userSelectedFilter: 'mean',
       responsiveWidthHeight: [],
-      colorsValues: ['#6a3d9a','#b15928','#e31a1c']
+      colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc']
     }
   },
   methods: {

frontend/src/components/FinalResultsLinePlot.vue

@@ -144,7 +144,7 @@ export default {
         text: '',
         hoverinfo: 'text',
         fill: "tozerox", 
-        fillcolor: "rgba(55,126,184)", 
+        fillcolor: "rgba(55,126,184,0)", 
         line: {color: "transparent"}, 
         name: "Active Precision", 
         showlegend: false, 

frontend/src/components/Main.vue

@@ -210,7 +210,7 @@ export default Vue.extend({
       ClassifierIDsList: '',
       SelectedFeaturesPerClassifier: '',
       FinalResults: 0,
-      Algorithms: ['KNN','RF'],
+      Algorithms: ['GPC','KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','BaggingClassifier','AdaB','GradB'],
       selectedAlgorithm: '',
       PerformancePerModel: '',
       PerformanceCheck: '',

frontend/src/components/Parameters.vue

@@ -455,7 +455,7 @@ export default {
 			////////////////////////////////////////////////////////////// 
 
 			var color = d3.scale.ordinal()
-				.range(["#b3cde3","#fbb4ae"]);
+				.range(["#808000","#008080"]);
       
 			var radarChartOptions = {
 			  w: width,

frontend/src/components/PredictionsSpace.vue

@@ -26,7 +26,7 @@ export default {
       UpdatedData: '',
       representationDef: 'mds',
       representationSelection: 'mds',
-      colorsValues: ['#6a3d9a','#b15928','#e31a1c'],
+      colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc'],
       WH: []
     }
   },

frontend/src/components/Provenance.vue

@@ -85,7 +85,7 @@ export default {
       let isotypes = Stardust.mark.create(isotype, this.platform);
 
       let isotypeHeight = 18;
-      let colors = [[141,211,199], [141,160,203]];
+      let colors = [[166,206,227], [31,120,180], [178,223,138], [51,160,44], [251,154,153], [227,26,28], [253,191,111], [255,127,0], [202,178,214], [106,61,154], [255,255,153], [177,89,40]];
       colors = colors.map(x => [x[0] / 255, x[1] / 255, x[2] / 255, 1]);
 
       let pScale = Stardust.scale.custom(`

frontend/src/components/ScatterPlot.vue

@@ -60,6 +60,7 @@ export default {
     ScatterPlotView () {
       Plotly.purge('OverviewPlotly')
       var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
+      console.log(colorsforScatterPlot)
       var MDSData = JSON.parse(this.ScatterPlotResults[1])
       var parameters = JSON.parse(this.ScatterPlotResults[2])
       var TSNEData = JSON.parse(this.ScatterPlotResults[12])

run.py

@@ -15,11 +15,14 @@ from joblib import Memory
 from itertools import chain
 import ast
 
-from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier # 1 neighbors
-from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC # 1 svm
-from yellowbrick.regressor import CooksDistance
+from sklearn.naive_bayes import GaussianNB # 1 naive bayes
-from sklearn.naive_bayes import GaussianNB 
+from sklearn.neural_network import MLPClassifier # 1 neural network
-from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression # 1 linear model
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis # 2 discriminant analysis
+from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier # 5 ensemble models
+from sklearn.calibration import CalibratedClassifierCV
 from sklearn.pipeline import make_pipeline
 from sklearn import model_selection
 from sklearn.manifold import MDS
@@ -66,7 +69,7 @@ def Reset():
     RANDOM_SEED = 42
 
     global factors
-    factors = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    factors = [1,1,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1]
 
     global XData
     XData = []
@@ -444,14 +447,55 @@ def RetrieveModel():
     # loop through the algorithms
     global allParametersPerformancePerModel
     for eachAlgor in algorithms:
+        print(eachAlgor)
         if (eachAlgor) == 'KNN':
             clf = KNeighborsClassifier()
-            params = {'n_neighbors': list(range(1, 25)), 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}
+            params = {'n_neighbors': list(range(1, 25)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}
             AlgorithmsIDsEnd = 0
-        else: 
+        elif (eachAlgor) == 'SVC':
-            clf = RandomForestClassifier()
+            clf = SVC(probability=True)
-            params = {'n_estimators': list(range(40, 120)), 'criterion': ['gini', 'entropy']}
+            params = {'C': list(np.arange(0.1,4.43,0.11)), 'kernel': ['rbf','linear', 'poly', 'sigmoid']}
             AlgorithmsIDsEnd = 576
+        elif (eachAlgor) == 'GausNB':
+            clf = GaussianNB()
+            params = {'var_smoothing': list(np.arange(0.00000000001,0.0000001,0.0000000001))}
+            AlgorithmsIDsEnd = 736
+        elif (eachAlgor) == 'MLP':
+            clf = MLPClassifier()
+            params = {'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0005)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']}
+            AlgorithmsIDsEnd = 1736
+        elif (eachAlgor) == 'LR':
+            clf = LogisticRegression()
+            params = {'C': list(np.arange(0.5,2,0.075)), 'max_iter': list(np.arange(50,250,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}
+            AlgorithmsIDsEnd = 1816
+        elif (eachAlgor) == 'LDA':
+            clf = LinearDiscriminantAnalysis()
+            params = {'shrinkage': list(np.arange(0,1,0.018)), 'solver': ['lsqr', 'eigen']}
+            AlgorithmsIDsEnd = 2536
+        elif (eachAlgor) == 'QDA':
+            clf = QuadraticDiscriminantAnalysis()
+            params = {'reg_param': list(range(1, 50)), 'tol': list(np.arange(0.00001,0.001,0.0005))}
+            AlgorithmsIDsEnd = 2716
+        elif (eachAlgor) == 'RF':
+            clf = RandomForestClassifier()
+            params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']}
+            AlgorithmsIDsEnd = 2876
+        elif (eachAlgor) == 'ExtraT':
+            clf = ExtraTreesClassifier()
+            params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']}
+            AlgorithmsIDsEnd = 3036
+        elif (eachAlgor) == 'BagC':
+            clf = BaggingClassifier()
+            params = {'n_estimators': list(range(90,110)), 'base_estimator': ['KNeighborsClassifier()', 'DummyClassifier()', 'DecisionTreeClassifier()', 'SVC()', 'BernoulliNB()', 'LogisticRegression()', 'Ridge()', 'Perceptron()', 'LDA()','QDA()']}
+            AlgorithmsIDsEnd = 1896
+        elif (eachAlgor) == 'AdaB':
+            clf = AdaBoostClassifier()
+            params = {'n_estimators': list(range(40, 80)), 'learning_rate': list(np.arange(0.1,2.3,1.1)), 'algorithm': ['SAMME.R', 'SAMME']}
+            AlgorithmsIDsEnd = 3196
+        else: 
+            clf = GradientBoostingClassifier()
+            params = {'n_estimators': list(range(90, 110)), 'learning_rate': list(np.arange(0.01,0.34,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']}
+            AlgorithmsIDsEnd = 3356
         allParametersPerformancePerModel = GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd)
 
     # call the function that sends the results to the frontend 
@@ -542,6 +586,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     resultsMacroBeta2 = []
     resultsWeightedBeta2 = []
     resultsLogLoss = []
+    resultsLogLossFinal = []
 
     loop = 10
 
@@ -582,8 +627,12 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
         resultsMacroBeta2.append(fbeta_score(yData, yPredict, average='macro', beta=2))
         resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2))
   
-        resultsLogLoss.append(log_loss(yData, yPredict, normalize = True))
+        resultsLogLoss.append(log_loss(yData, yPredictProb, normalize=True))
 
+    maxLog = max(resultsLogLoss)
+    minLog = min(resultsLogLoss)
+    for each in resultsLogLoss:
+        resultsLogLossFinal.append((each-minLog)/(maxLog-minLog))
 
     metrics.insert(loop,'geometric_mean_score_micro',resultsMicro)
     metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro)
@@ -603,7 +652,7 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
     metrics.insert(loop+11,'f2_macro',resultsMacroBeta2)
     metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2)
 
-    metrics.insert(loop+13,'log_loss',resultsLogLoss)
+    metrics.insert(loop+13,'log_loss',resultsLogLossFinal)
 
     perModelProbPandas = pd.DataFrame(perModelProb)
     perModelProbPandas = perModelProbPandas.to_json()
@@ -890,29 +939,51 @@ def preProcessFeatSc():
 def preProcsumPerMetric(factors):
     sumPerClassifier = []
     loopThroughMetrics = PreprocessingMetrics()
-    print(loopThroughMetrics)
     for row in loopThroughMetrics.iterrows():
         rowSum = 0
-        lengthFactors = len(scoring)
         name, values = row
         for loop, elements in enumerate(values):
-            lengthFactors = lengthFactors -  1 + factors[loop]
             rowSum = elements*factors[loop] + rowSum
-        if lengthFactors is 0:
+        if sum(factors) is 0:
             sumPerClassifier = 0
         else:
-            sumPerClassifier.append(rowSum/lengthFactors)
+            sumPerClassifier.append(rowSum/sum(factors))
     return sumPerClassifier
 
 def preProcMetricsAllAndSel():
     loopThroughMetrics = PreprocessingMetrics()
+    global factors
     metricsPerModelColl = []
     metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'].sum()/loopThroughMetrics['mean_test_accuracy'].count())
     metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_mean_absolute_error'].sum()/loopThroughMetrics['mean_test_neg_mean_absolute_error'].count())
     metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_root_mean_squared_error'].sum()/loopThroughMetrics['mean_test_neg_root_mean_squared_error'].count())
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_micro'].sum()/loopThroughMetrics['geometric_mean_score_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'].sum()/loopThroughMetrics['geometric_mean_score_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_weighted'].sum()/loopThroughMetrics['geometric_mean_score_weighted'].count())
     metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_micro'].sum()/loopThroughMetrics['mean_test_precision_micro'].count())
-    metricsPerModelColl.append(loopThroughMetrics['mean_test_jaccard'].sum()/loopThroughMetrics['mean_test_jaccard'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_macro'].sum()/loopThroughMetrics['mean_test_precision_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_weighted'].sum()/loopThroughMetrics['mean_test_precision_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_micro'].sum()/loopThroughMetrics['mean_test_recall_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_macro'].sum()/loopThroughMetrics['mean_test_recall_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_recall_weighted'].sum()/loopThroughMetrics['mean_test_recall_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f5_micro'].sum()/loopThroughMetrics['f5_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f5_macro'].sum()/loopThroughMetrics['f5_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f5_weighted'].sum()/loopThroughMetrics['f5_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f1_micro'].sum()/loopThroughMetrics['f1_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f1_macro'].sum()/loopThroughMetrics['f1_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f1_weighted'].sum()/loopThroughMetrics['f1_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f2_micro'].sum()/loopThroughMetrics['f2_micro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f2_macro'].sum()/loopThroughMetrics['f2_macro'].count())
+    metricsPerModelColl.append(loopThroughMetrics['f2_weighted'].sum()/loopThroughMetrics['f2_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['matthews_corrcoef'].sum()/loopThroughMetrics['matthews_corrcoef'].count())
+    metricsPerModelColl.append(loopThroughMetrics['mean_test_roc_auc_ovo_weighted'].sum()/loopThroughMetrics['mean_test_roc_auc_ovo_weighted'].count())
+    metricsPerModelColl.append(loopThroughMetrics['log_loss'].sum()/loopThroughMetrics['log_loss'].count())
     for index, metric in enumerate(metricsPerModelColl):
+        if (index == 1 or index == 2):
+            metricsPerModelColl[index] = (metric + 1)*factors[index]
+        elif (index == 23):
+            metricsPerModelColl[index] = (1 - metric)*factors[index]
+        else:  
             metricsPerModelColl[index] = metric*factors[index]
     return metricsPerModelColl
 
@@ -932,7 +1003,7 @@ def FunTsne (data):
     return tsne
 
 def FunUMAP (data):
-    trans = umap.UMAP(n_neighbors=5, random_state=RANDOM_SEED).fit(data)
+    trans = umap.UMAP(n_neighbors=15, random_state=RANDOM_SEED).fit(data)
     Xpos = trans.embedding_[:, 0].tolist()
     Ypos = trans.embedding_[:, 1].tolist()
     return [Xpos,Ypos]