new

cachedir/joblib/run/GridSearchForModels/03316dae023356b400b64061e59b4761/metadata.json

@@ -1 +1 @@
-{"duration": 58.06527900695801, "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=50, store_covariance=False,\n                              tol=0.00081)", "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, 50], 'tol': [1e-05, 0.00021, 0.00041000000000000005, 0.0006100000000000001, 0.0008100000000000001]}", "eachAlgor": "'QDA'", "AlgorithmsIDsEnd": "2196"}}
+{"duration": 50.60136699676514, "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=50, store_covariance=False,\n                              tol=0.00081)", "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, 50], 'tol': [1e-05, 0.00021, 0.00041000000000000005, 0.0006100000000000001, 0.0008100000000000001]}", "eachAlgor": "'QDA'", "AlgorithmsIDsEnd": "2196"}}

cachedir/joblib/run/GridSearchForModels/1508132ed6be760ee3207f8b88753990/metadata.json

@@ -1 +1 @@
-{"duration": 240.78257203102112, "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": "1356"}}
+{"duration": 219.70271110534668, "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": "1356"}}

cachedir/joblib/run/GridSearchForModels/26953ce9acf4a0090b9c0a8938c52a7b/metadata.json

@@ -1 +0,0 @@
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cachedir/joblib/run/GridSearchForModels/33a4787f36e7786c67ea274589dbfcc6/metadata.json

@@ -1 +1 @@
-{"duration": 613.0237910747528, "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": "2606"}}
+{"duration": 554.8381459712982, "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": "2606"}}

cachedir/joblib/run/GridSearchForModels/6373a10bde6134317227151d777f90b0/metadata.json

@@ -1 +1 @@
-{"duration": 814.1466150283813, "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=0.12, 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=114,\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': [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], 'learning_rate': [0.01, 0.12], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "2926"}}
+{"duration": 863.4842360019684, "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=0.12, 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=114,\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': [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], 'learning_rate': [0.01, 0.12], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "2926"}}

cachedir/joblib/run/GridSearchForModels/638e82274ec062236282bd10dc4c7c62/metadata.json

@@ -1 +0,0 @@
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cachedir/joblib/run/GridSearchForModels/9aa930c370944914b87b9f6e9d12113f/metadata.json

@@ -1 +1 @@
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cachedir/joblib/run/GridSearchForModels/a241c963f2aeaa7445819bdd5824462f/metadata.json

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cachedir/joblib/run/GridSearchForModels/a29668b3657493d860b5d05c422ab577/metadata.json

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cachedir/joblib/run/GridSearchForModels/a46dcd4e232a8dcae4536a2e2da671aa/metadata.json

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cachedir/joblib/run/GridSearchForModels/a7e6835baf6490bf5083ce8b2429aaa8/metadata.json

@@ -0,0 +1 @@
+{"duration": 429.9728858470917, "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": "2766"}}

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

@@ -1 +1 @@
-{"duration": 347.06651639938354, "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": "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=24, p=2,\n                     weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}}
+{"duration": 319.8098211288452, "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": "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=24, p=2,\n                     weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}}

cachedir/joblib/run/GridSearchForModels/func_code.py

@@ -1,7 +1,6 @@
-# first line: 506
+# first line: 504
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
-    print('test')
     # instantiate spark session
     spark = (   
         SparkSession    

frontend/src/components/AlgorithmHyperParam.vue

@@ -63,6 +63,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgKNN['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgKNN['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgKNN['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgKNN['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgKNN['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgKNN['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgKNN['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgKNN['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgKNN['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgKNN['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgKNN['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgKNN['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgKNN['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgKNN['log_loss'])[j]))
+          if (sumKNN <= 0) {
+            sumKNN = 0
+          }
           McKNN.push((sumKNN/divide)*100)
         }
         var McSVC = []
@@ -74,6 +77,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgSVC['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgSVC['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgSVC['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgSVC['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgSVC['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgSVC['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgSVC['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgSVC['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgSVC['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgSVC['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgSVC['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgSVC['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgSVC['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgSVC['log_loss'])[j]))
+          if (sumSVC <= 0) {
+            sumSVC = 0
+          }
           McSVC.push((sumSVC/divide)*100)
         }
         var McGausNB = []
@@ -85,6 +91,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgGausNB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGausNB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGausNB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGausNB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGausNB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGausNB['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgGausNB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGausNB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGausNB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGausNB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGausNB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGausNB['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgGausNB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGausNB['log_loss'])[j]))
+          if (sumGausNB <= 0) {
+            sumGausNB = 0
+          }
           McGausNB.push((sumGausNB/divide)*100)
         }
         var McMLP = []
@@ -96,6 +105,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgMLP['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgMLP['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgMLP['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgMLP['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgMLP['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgMLP['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgMLP['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgMLP['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgMLP['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgMLP['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgMLP['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgMLP['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgMLP['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgMLP['log_loss'])[j]))
+          if (sumMLP <= 0) {
+            sumMLP = 0
+          }
           McMLP.push((sumMLP/divide)*100)
         }
         var McLR = []
@@ -107,6 +119,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgLR['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLR['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLR['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLR['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLR['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLR['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgLR['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLR['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLR['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLR['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLR['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLR['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgLR['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLR['log_loss'])[j]))
+          if (sumLR <= 0) {
+            sumLR = 0
+          }
           McLR.push((sumLR/divide)*100)
         }
         var McLDA = []
@@ -118,6 +133,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgLDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLDA['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgLDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLDA['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgLDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLDA['log_loss'])[j]))
+          if (sumLR <= 0) {
+            sumLR = 0
+          }
           McLDA.push((sumLDA/divide)*100)
         }
         var McQDA = []
@@ -129,6 +147,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgQDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgQDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgQDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgQDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgQDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgQDA['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgQDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgQDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgQDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgQDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgQDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgQDA['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgQDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgQDA['log_loss'])[j]))
+          if (sumQDA <= 0) {
+            sumQDA = 0
+          }
           McQDA.push((sumQDA/divide)*100)
         }
         var McRF = []
@@ -140,6 +161,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgRF['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgRF['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgRF['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgRF['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgRF['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgRF['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgRF['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgRF['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgRF['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgRF['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgRF['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgRF['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgRF['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgRF['log_loss'])[j]))
+          if (sumRF <= 0) {
+            sumRF = 0
+          }
           McRF.push((sumRF/divide)*100)
         }
         var McExtraT = []
@@ -151,6 +175,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgExtraT['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgExtraT['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgExtraT['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgExtraT['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgExtraT['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgExtraT['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgExtraT['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgExtraT['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgExtraT['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgExtraT['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgExtraT['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgExtraT['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgExtraT['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgExtraT['log_loss'])[j]))
+          if (sumExtraT <= 0) {
+            sumExtraT = 0
+          }
           McExtraT.push((sumExtraT/divide)*100)
         }
         var McAdaB = []
@@ -162,6 +189,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgAdaB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgAdaB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgAdaB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgAdaB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgAdaB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgAdaB['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgAdaB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgAdaB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgAdaB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgAdaB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgAdaB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgAdaB['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgAdaB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgAdaB['log_loss'])[j]))
+          if (sumAdaB <= 0) {
+            sumAdaB = 0
+          }
           McAdaB.push((sumAdaB/divide)*100)
         }
         var McGradB = []
@@ -173,6 +203,9 @@ export default {
             + (factorsLocal[10] * Object.values(performanceAlgGradB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGradB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGradB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGradB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGradB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGradB['f1_micro'])[j])
             + (factorsLocal[16] * Object.values(performanceAlgGradB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGradB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGradB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGradB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGradB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGradB['matthews_corrcoef'])[j])
             + (factorsLocal[22] * Object.values(performanceAlgGradB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGradB['log_loss'])[j]))
+          if (sumGradB <= 0) {
+            sumGradB = 0
+          }
           McGradB.push((sumGradB/divide)*100)
         }
 

frontend/src/components/Algorithms.vue

@@ -85,6 +85,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgKNN['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgKNN['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgKNN['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgKNN['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgKNN['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgKNN['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgKNN['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgKNN['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgKNN['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgKNN['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgKNN['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgKNN['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgKNN['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgKNN['log_loss'])[j]))
+        if (sumKNN <= 0) {
+          sumKNN = 0
+        }
         McKNN.push((sumKNN/divide)*100)
       }
       var McSVC = []
@@ -96,6 +99,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgSVC['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgSVC['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgSVC['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgSVC['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgSVC['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgSVC['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgSVC['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgSVC['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgSVC['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgSVC['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgSVC['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgSVC['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgSVC['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgSVC['log_loss'])[j]))
+        if (sumSVC <= 0) {
+          sumSVC = 0
+        }
         McSVC.push((sumSVC/divide)*100)
       }
       var McGausNB = []
@@ -107,6 +113,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgGausNB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGausNB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGausNB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGausNB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGausNB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGausNB['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgGausNB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGausNB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGausNB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGausNB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGausNB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGausNB['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgGausNB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGausNB['log_loss'])[j]))
+        if (sumGausNB <= 0) {
+          sumGausNB = 0
+        }
         McGausNB.push((sumGausNB/divide)*100)
       }
       var McMLP = []
@@ -118,7 +127,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgMLP['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgMLP['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgMLP['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgMLP['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgMLP['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgMLP['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgMLP['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgMLP['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgMLP['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgMLP['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgMLP['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgMLP['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgMLP['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgMLP['log_loss'])[j]))
-        McMLP.push((sumMLP/divide)*100)
+       if (sumMLP <= 0) {
+          sumMLP = 0
+        }
+       McMLP.push((sumMLP/divide)*100)
       }
       var McLR = []
       const performanceAlgLR = JSON.parse(this.PerformanceAllModels[38])
@@ -129,7 +141,10 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgLR['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLR['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLR['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLR['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLR['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLR['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgLR['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLR['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLR['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLR['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLR['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLR['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgLR['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLR['log_loss'])[j]))
-        McLR.push((sumLR/divide)*100)
+       if (sumLR <= 0) {
+          sumLR = 0
+        }
+       McLR.push((sumLR/divide)*100)
       }
       var McLDA = []
       const performanceAlgLDA = JSON.parse(this.PerformanceAllModels[46])
@@ -140,6 +155,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgLDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLDA['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgLDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLDA['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgLDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLDA['log_loss'])[j]))
+        if (sumLDA <= 0) {
+          sumLDA = 0
+        }
         McLDA.push((sumLDA/divide)*100)
       }
       var McQDA = []
@@ -151,6 +169,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgQDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgQDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgQDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgQDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgQDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgQDA['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgQDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgQDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgQDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgQDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgQDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgQDA['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgQDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgQDA['log_loss'])[j]))
+        if (sumQDA <= 0) {
+          sumQDA = 0
+        }
         McQDA.push((sumQDA/divide)*100)
       }
       var McRF = []
@@ -162,6 +183,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgRF['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgRF['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgRF['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgRF['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgRF['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgRF['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgRF['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgRF['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgRF['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgRF['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgRF['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgRF['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgRF['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgRF['log_loss'])[j]))
+        if (sumRF <= 0) {
+          sumRF = 0
+        }
         McRF.push((sumRF/divide)*100)
       }
       var McExtraT = []
@@ -173,6 +197,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgExtraT['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgExtraT['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgExtraT['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgExtraT['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgExtraT['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgExtraT['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgExtraT['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgExtraT['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgExtraT['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgExtraT['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgExtraT['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgExtraT['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgExtraT['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgExtraT['log_loss'])[j]))
+        if (sumExtraT <= 0) {
+          sumExtraT = 0
+        }
         McExtraT.push((sumExtraT/divide)*100)
       }
       var McAdaB = []
@@ -184,6 +211,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgAdaB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgAdaB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgAdaB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgAdaB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgAdaB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgAdaB['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgAdaB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgAdaB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgAdaB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgAdaB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgAdaB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgAdaB['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgAdaB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgAdaB['log_loss'])[j]))
+        if (sumAdaB <= 0) {
+          sumAdaB = 0
+        }
         McAdaB.push((sumAdaB/divide)*100)
       }
       var McGradB = []
@@ -195,6 +225,9 @@ export default {
           + (factorsLocal[10] * Object.values(performanceAlgGradB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGradB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGradB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGradB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGradB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGradB['f1_micro'])[j])
           + (factorsLocal[16] * Object.values(performanceAlgGradB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGradB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGradB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGradB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGradB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGradB['matthews_corrcoef'])[j])
           + (factorsLocal[22] * Object.values(performanceAlgGradB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGradB['log_loss'])[j]))
+        if (sumGradB <= 0) {
+          sumGradB = 0
+        }
         McGradB.push((sumGradB/divide)*100)
       }
       

run.py

@@ -503,7 +503,6 @@ memory = Memory(location, verbose=0)
 # calculating for all algorithms and models the performance and other results
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
-    print('test')
     # instantiate spark session
     spark = (   
         SparkSession