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parent 01cfbdc8a6
commit 4887518984
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@ -1 +0,0 @@
{"duration": 291.49311804771423, "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], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "factors": "[1, 1, 1, 1, 1]", "AlgorithmsIDsEnd": "0"}}

@ -1 +0,0 @@
{"duration": 335.52157711982727, "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, class_weight=None, criterion='entropy',\n max_depth=None, max_features='auto', 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=119,\n n_jobs=None, oob_score=False, random_state=None,\n verbose=0, warm_start=False)", "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, 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], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "factors": "[1, 1, 1, 1, 1]", "AlgorithmsIDsEnd": "576"}}

@ -0,0 +1 @@
{"duration": 2253.491542816162, "input_args": {"XData": " Pregnan Glucose BloodPress SkinThick Insulin BMI DPF Age\n0 8 183 64 0 0 23.3 0.672 32\n1 2 197 70 45 543 30.5 0.158 53\n2 8 125 96 0 0 0.0 0.232 54\n3 10 168 74 0 0 38.0 0.537 34\n4 1 189 60 23 846 30.1 0.398 59\n.. ... ... ... ... ... ... ... ...\n763 9 89 62 0 0 22.5 0.142 33\n764 2 122 70 27 0 36.8 0.340 27\n765 10 101 76 48 180 32.9 0.171 63\n766 5 121 72 23 112 26.2 0.245 30\n767 1 93 70 31 0 30.4 0.315 23\n\n[768 rows x 8 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=None, n_neighbors=24, p=2,\n weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}}

@ -0,0 +1 @@
{"duration": 1371.6013679504395, "input_args": {"XData": " Pregnan Glucose BloodPress SkinThick Insulin BMI DPF Age\n0 8 183 64 0 0 23.3 0.672 32\n1 2 197 70 45 543 30.5 0.158 53\n2 8 125 96 0 0 0.0 0.232 54\n3 10 168 74 0 0 38.0 0.537 34\n4 1 189 60 23 846 30.1 0.398 59\n.. ... ... ... ... ... ... ... ...\n763 9 89 62 0 0 22.5 0.142 33\n764 2 122 70 27 0 36.8 0.340 27\n765 10 101 76 48 180 32.9 0.171 63\n766 5 121 72 23 112 26.2 0.245 30\n767 1 93 70 31 0 30.4 0.315 23\n\n[768 rows x 8 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "RandomForestClassifier(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=119,\n n_jobs=None, oob_score=False, random_state=None,\n verbose=0, warm_start=False)", "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, 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], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "576"}}

@ -1,6 +1,6 @@
# first line: 465
# first line: 466
@memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd):
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
# instantiate spark session
spark = (
@ -45,30 +45,13 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
# copy and filter in order to get only the metrics
metrics = df_cv_results_classifiers.copy()
metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
# control the factors
sumperModel = []
for index, row in metrics.iterrows():
rowSum = 0
lengthFactors = len(scoring)
for loop,elements in enumerate(row):
lengthFactors = lengthFactors - 1 + factors[loop]
rowSum = elements*factors[loop] + rowSum
if lengthFactors is 0:
sumperModel = 0
else:
sumperModel.append(rowSum/lengthFactors)
# summarize all models metrics
summarizedMetrics = pd.DataFrame(sumperModel)
summarizedMetrics.rename(columns={0:'sum'})
metrics = metrics.filter(['mean_test_accuracy','mean_test_neg_mean_absolute_error','mean_test_neg_root_mean_squared_error','mean_test_precision_micro','mean_test_precision_macro','mean_test_precision_weighted','mean_test_recall_micro','mean_test_recall_macro','mean_test_recall_weighted','mean_test_roc_auc_ovo_weighted'])
# concat parameters and performance
parameters = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = parametersPerformancePerModel.to_json()
parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy()
Models = []
for index, items in enumerate(parametersLocal):
@ -81,13 +64,29 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
PerFeatureAccuracyAll = []
PerClassMetric = []
perModelProb = []
resultsMicro = []
resultsMacro = []
resultsWeighted = []
resultsCorrCoef = []
resultsMicroBeta5 = []
resultsMacroBeta5 = []
resultsWeightedBeta5 = []
resultsMicroBeta1 = []
resultsMacroBeta1 = []
resultsWeightedBeta1 = []
resultsMicroBeta2 = []
resultsMacroBeta2 = []
resultsWeightedBeta2 = []
resultsLogLoss = []
loop = 10
for eachModelParameters in parametersLocalNew:
clf.set_params(**eachModelParameters)
perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData)
permList.append(perm.feature_importances_)
n_feats = XData.shape[1]
PerFeatureAccuracy = []
for i in range(n_feats):
@ -101,6 +100,47 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
yPredictProb = clf.predict_proba(XData)
perModelProb.append(yPredictProb.tolist())
resultsMicro.append(geometric_mean_score(yData, yPredict, average='micro'))
resultsMacro.append(geometric_mean_score(yData, yPredict, average='macro'))
resultsWeighted.append(geometric_mean_score(yData, yPredict, average='weighted'))
resultsCorrCoef.append(matthews_corrcoef(yData, yPredict))
resultsMicroBeta5.append(fbeta_score(yData, yPredict, average='micro', beta=0.5))
resultsMacroBeta5.append(fbeta_score(yData, yPredict, average='macro', beta=0.5))
resultsWeightedBeta5.append(fbeta_score(yData, yPredict, average='weighted', beta=0.5))
resultsMicroBeta1.append(fbeta_score(yData, yPredict, average='micro', beta=1))
resultsMacroBeta1.append(fbeta_score(yData, yPredict, average='macro', beta=1))
resultsWeightedBeta1.append(fbeta_score(yData, yPredict, average='weighted', beta=1))
resultsMicroBeta2.append(fbeta_score(yData, yPredict, average='micro', beta=2))
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))
metrics.insert(loop,'geometric_mean_score_micro',resultsMicro)
metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro)
metrics.insert(loop+2,'geometric_mean_score_weighted',resultsWeighted)
metrics.insert(loop+3,'matthews_corrcoef',resultsCorrCoef)
metrics.insert(loop+4,'f5_micro',resultsMicroBeta5)
metrics.insert(loop+5,'f5_macro',resultsMacroBeta5)
metrics.insert(loop+6,'f5_weighted',resultsWeightedBeta5)
metrics.insert(loop+7,'f1_micro',resultsMicroBeta1)
metrics.insert(loop+8,'f1_macro',resultsMacroBeta1)
metrics.insert(loop+9,'f1_weighted',resultsWeightedBeta1)
metrics.insert(loop+10,'f2_micro',resultsMicroBeta2)
metrics.insert(loop+11,'f2_macro',resultsMacroBeta2)
metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2)
metrics.insert(loop+13,'log_loss',resultsLogLoss)
perModelProbPandas = pd.DataFrame(perModelProb)
perModelProbPandas = perModelProbPandas.to_json()

@ -3057,6 +3057,13 @@
"d3-array": "1",
"d3-collection": "1",
"d3-shape": "^1.2.0"
},
"dependencies": {
"d3-array": {
"version": "1.2.4",
"resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz",
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"optional": true,
"requires": {
"detect-libc": "^1.0.2",
@ -12735,7 +12838,8 @@
},
"nopt": {
"version": "4.0.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/nopt/-/nopt-4.0.1.tgz",
"integrity": "sha1-0NRoWv1UFRk8jHUFYC0NF81kR00=",
"optional": true,
"requires": {
"abbrev": "1",
@ -12744,12 +12848,14 @@
},
"npm-bundled": {
"version": "1.0.6",
"bundled": true,
"resolved": "https://registry.npmjs.org/npm-bundled/-/npm-bundled-1.0.6.tgz",
"integrity": "sha512-8/JCaftHwbd//k6y2rEWp6k1wxVfpFzB6t1p825+cUb7Ym2XQfhwIC5KwhrvzZRJu+LtDE585zVaS32+CGtf0g==",
"optional": true
},
"npm-packlist": {
"version": "1.4.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/npm-packlist/-/npm-packlist-1.4.1.tgz",
"integrity": "sha512-+TcdO7HJJ8peiiYhvPxsEDhF3PJFGUGRcFsGve3vxvxdcpO2Z4Z7rkosRM0kWj6LfbK/P0gu3dzk5RU1ffvFcw==",
"optional": true,
"requires": {
"ignore-walk": "^3.0.1",
@ -12758,7 +12864,8 @@
},
"npmlog": {
"version": "4.1.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/npmlog/-/npmlog-4.1.2.tgz",
"integrity": "sha512-2uUqazuKlTaSI/dC8AzicUck7+IrEaOnN/e0jd3Xtt1KcGpwx30v50mL7oPyr/h9bL3E4aZccVwpwP+5W9Vjkg==",
"optional": true,
"requires": {
"are-we-there-yet": "~1.1.2",
@ -12769,17 +12876,20 @@
},
"number-is-nan": {
"version": "1.0.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/number-is-nan/-/number-is-nan-1.0.1.tgz",
"integrity": "sha1-CXtgK1NCKlIsGvuHkDGDNpQaAR0=",
"optional": true
},
"object-assign": {
"version": "4.1.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/object-assign/-/object-assign-4.1.1.tgz",
"integrity": "sha1-IQmtx5ZYh8/AXLvUQsrIv7s2CGM=",
"optional": true
},
"once": {
"version": "1.4.0",
"bundled": true,
"resolved": "https://registry.npmjs.org/once/-/once-1.4.0.tgz",
"integrity": "sha1-WDsap3WWHUsROsF9nFC6753Xa9E=",
"optional": true,
"requires": {
"wrappy": "1"
@ -12787,17 +12897,20 @@
},
"os-homedir": {
"version": "1.0.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/os-homedir/-/os-homedir-1.0.2.tgz",
"integrity": "sha1-/7xJiDNuDoM94MFox+8VISGqf7M=",
"optional": true
},
"os-tmpdir": {
"version": "1.0.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/os-tmpdir/-/os-tmpdir-1.0.2.tgz",
"integrity": "sha1-u+Z0BseaqFxc/sdm/lc0VV36EnQ=",
"optional": true
},
"osenv": {
"version": "0.1.5",
"bundled": true,
"resolved": "https://registry.npmjs.org/osenv/-/osenv-0.1.5.tgz",
"integrity": "sha512-0CWcCECdMVc2Rw3U5w9ZjqX6ga6ubk1xDVKxtBQPK7wis/0F2r9T6k4ydGYhecl7YUBxBVxhL5oisPsNxAPe2g==",
"optional": true,
"requires": {
"os-homedir": "^1.0.0",
@ -12806,17 +12919,20 @@
},
"path-is-absolute": {
"version": "1.0.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/path-is-absolute/-/path-is-absolute-1.0.1.tgz",
"integrity": "sha1-F0uSaHNVNP+8es5r9TpanhtcX18=",
"optional": true
},
"process-nextick-args": {
"version": "2.0.0",
"bundled": true,
"resolved": "https://registry.npmjs.org/process-nextick-args/-/process-nextick-args-2.0.0.tgz",
"integrity": "sha512-MtEC1TqN0EU5nephaJ4rAtThHtC86dNN9qCuEhtshvpVBkAW5ZO7BASN9REnF9eoXGcRub+pFuKEpOHE+HbEMw==",
"optional": true
},
"rc": {
"version": "1.2.8",
"bundled": true,
"resolved": "https://registry.npmjs.org/rc/-/rc-1.2.8.tgz",
"integrity": "sha512-y3bGgqKj3QBdxLbLkomlohkvsA8gdAiUQlSBJnBhfn+BPxg4bc62d8TcBW15wavDfgexCgccckhcZvywyQYPOw==",
"optional": true,
"requires": {
"deep-extend": "^0.6.0",
@ -12827,14 +12943,16 @@
"dependencies": {
"minimist": {
"version": "1.2.0",
"bundled": true,
"resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
"integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
"optional": true
}
}
},
"readable-stream": {
"version": "2.3.6",
"bundled": true,
"resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz",
"integrity": "sha512-tQtKA9WIAhBF3+VLAseyMqZeBjW0AHJoxOtYqSUZNJxauErmLbVm2FW1y+J/YA9dUrAC39ITejlZWhVIwawkKw==",
"optional": true,
"requires": {
"core-util-is": "~1.0.0",
@ -12848,7 +12966,8 @@
},
"rimraf": {
"version": "2.6.3",
"bundled": true,
"resolved": "https://registry.npmjs.org/rimraf/-/rimraf-2.6.3.tgz",
"integrity": "sha512-mwqeW5XsA2qAejG46gYdENaxXjx9onRNCfn7L0duuP4hCuTIi/QO7PDK07KJfp1d+izWPrzEJDcSqBa0OZQriA==",
"optional": true,
"requires": {
"glob": "^7.1.3"
@ -12856,37 +12975,44 @@
},
"safe-buffer": {
"version": "5.1.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz",
"integrity": "sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g==",
"optional": true
},
"safer-buffer": {
"version": "2.1.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/safer-buffer/-/safer-buffer-2.1.2.tgz",
"integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==",
"optional": true
},
"sax": {
"version": "1.2.4",
"bundled": true,
"resolved": "https://registry.npmjs.org/sax/-/sax-1.2.4.tgz",
"integrity": "sha512-NqVDv9TpANUjFm0N8uM5GxL36UgKi9/atZw+x7YFnQ8ckwFGKrl4xX4yWtrey3UJm5nP1kUbnYgLopqWNSRhWw==",
"optional": true
},
"semver": {
"version": "5.7.0",
"bundled": true,
"resolved": "https://registry.npmjs.org/semver/-/semver-5.7.0.tgz",
"integrity": "sha512-Ya52jSX2u7QKghxeoFGpLwCtGlt7j0oY9DYb5apt9nPlJ42ID+ulTXESnt/qAQcoSERyZ5sl3LDIOw0nAn/5DA==",
"optional": true
},
"set-blocking": {
"version": "2.0.0",
"bundled": true,
"resolved": "https://registry.npmjs.org/set-blocking/-/set-blocking-2.0.0.tgz",
"integrity": "sha1-BF+XgtARrppoA93TgrJDkrPYkPc=",
"optional": true
},
"signal-exit": {
"version": "3.0.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/signal-exit/-/signal-exit-3.0.2.tgz",
"integrity": "sha1-tf3AjxKH6hF4Yo5BXiUTK3NkbG0=",
"optional": true
},
"string-width": {
"version": "1.0.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/string-width/-/string-width-1.0.2.tgz",
"integrity": "sha1-EYvfW4zcUaKn5w0hHgfisLmxB9M=",
"optional": true,
"requires": {
"code-point-at": "^1.0.0",
@ -12896,7 +13022,8 @@
},
"string_decoder": {
"version": "1.1.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.1.1.tgz",
"integrity": "sha512-n/ShnvDi6FHbbVfviro+WojiFzv+s8MPMHBczVePfUpDJLwoLT0ht1l4YwBCbi8pJAveEEdnkHyPyTP/mzRfwg==",
"optional": true,
"requires": {
"safe-buffer": "~5.1.0"
@ -12904,7 +13031,8 @@
},
"strip-ansi": {
"version": "3.0.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz",
"integrity": "sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=",
"optional": true,
"requires": {
"ansi-regex": "^2.0.0"
@ -12912,12 +13040,14 @@
},
"strip-json-comments": {
"version": "2.0.1",
"bundled": true,
"resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz",
"integrity": "sha1-PFMZQukIwml8DsNEhYwobHygpgo=",
"optional": true
},
"tar": {
"version": "4.4.8",
"bundled": true,
"resolved": "https://registry.npmjs.org/tar/-/tar-4.4.8.tgz",
"integrity": "sha512-LzHF64s5chPQQS0IYBn9IN5h3i98c12bo4NCO7e0sGM2llXQ3p2FGC5sdENN4cTW48O915Sh+x+EXx7XW96xYQ==",
"optional": true,
"requires": {
"chownr": "^1.1.1",
@ -12931,12 +13061,14 @@
},
"util-deprecate": {
"version": "1.0.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
"integrity": "sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=",
"optional": true
},
"wide-align": {
"version": "1.1.3",
"bundled": true,
"resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.3.tgz",
"integrity": "sha512-QGkOQc8XL6Bt5PwnsExKBPuMKBxnGxWWW3fU55Xt4feHozMUhdUMaBCk290qpm/wG5u/RSKzwdAC4i51YigihA==",
"optional": true,
"requires": {
"string-width": "^1.0.2 || 2"
@ -12944,12 +13076,14 @@
},
"wrappy": {
"version": "1.0.2",
"bundled": true,
"resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
"integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=",
"optional": true
},
"yallist": {
"version": "3.0.3",
"bundled": true,
"resolved": "https://registry.npmjs.org/yallist/-/yallist-3.0.3.tgz",
"integrity": "sha512-S+Zk8DEWE6oKpV+vI3qWkaK+jSbIK86pCwe2IF/xwIpQ8jEuxpw9NyaGjmp9+BoJv5FV2piqCDcoCtStppiq2A==",
"optional": true
}
}
@ -17208,6 +17342,11 @@
"readable-stream": "^2.0.1"
}
},
"merge": {
"version": "1.2.1",
"resolved": "https://registry.npmjs.org/merge/-/merge-1.2.1.tgz",
"integrity": "sha512-VjFo4P5Whtj4vsLzsYBu5ayHhoHJ0UqNm7ibvShmbmoz7tGi0vXaoJbGdB+GmDMLUdg8DpQXEIeVDAe8MaABvQ=="
},
"merge-deep": {
"version": "3.0.2",
"resolved": "https://registry.npmjs.org/merge-deep/-/merge-deep-3.0.2.tgz",
@ -19201,6 +19340,13 @@
"d3-transition": "^1.1.1",
"requestanimationframe": "0.0.23",
"sylvester-es6": "0.0.2"
},
"dependencies": {
"d3-array": {
"version": "1.2.4",
"resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz",
"integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw=="
}
}
},
"parent-module": {
@ -19724,6 +19870,7 @@
"d3-force": "^1.0.6",
"d3-hierarchy": "^1.1.8",
"d3-interpolate": "1",
"d3-svg-legend": "^1.13.0",
"delaunay-triangulate": "^1.1.6",
"es6-promise": "^3.0.2",
"fast-isnumeric": "^1.1.3",
@ -19774,9 +19921,24 @@
},
"dependencies": {
"d3": {
"version": ,
"version": "3.5.17",
"resolved": "https://registry.npmjs.org/d3/-/d3-3.5.17.tgz",
"integrity": "sha1-vEZ0gAQ3iyGjYMn8fPUjF5B2L7g="
"integrity": "sha1-vEZ0gAQ3iyGjYMn8fPUjF5B2L7g=",
"requires": {
"d3-svg-legend": "^1.13.0"
},
"dependencies": {
"d3-svg-legend": {
"version": "1.13.0",
"resolved": "https://registry.npmjs.org/d3-svg-legend/-/d3-svg-legend-1.13.0.tgz",
"integrity": "sha1-YhdHjJrdnWLLMzYX4ZYTEaQaTbM="
}
}
},
"d3-svg-legend": {
"version": "1.13.0",
"resolved": "https://registry.npmjs.org/d3-svg-legend/-/d3-svg-legend-1.13.0.tgz",
"integrity": "sha1-YhdHjJrdnWLLMzYX4ZYTEaQaTbM="
}
}
},

@ -33,12 +33,16 @@
"clean-webpack-plugin": "^3.0.0",
"colorbrewer": "^1.3.0",
"d3": "^5.15.0",
"d3-array": "^2.4.0",
"d3-brush": "^1.1.5",
"d3-dispatch": "^1.0.6",
"d3-drag": "^1.2.5",
"d3-heatmap": "^1.2.1",
"d3-lasso": "0.0.5",
"d3-loom": "^1.0.2",
"d3-selection": "^1.4.1",
"d3-selection-multi": "^1.0.1",
"d3-svg-legend": "^1.13.0",
"d3_exploding_boxplot": "^0.2.1",
"file-saver": "^2.0.2",
"file-system": "^2.2.2",
@ -49,6 +53,7 @@
"interactjs": "^1.8.2",
"jquery": "^3.4.1",
"mdbvue": "^6.3.0",
"merge": "^1.2.1",
"mini-css-extract-plugin": "^0.9.0",
"npm-check-updates": "^4.0.1",
"papaparse": "^5.1.1",

@ -19,7 +19,12 @@ export default {
ModelsPerformance: 0,
selAlgorithm: 0,
pc: 0,
factors: [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
],
KNNModels: 576 //KNN models
}
},
@ -42,9 +47,29 @@ 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
sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg1['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg1['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg1['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['mean_test_jaccard'])[j])
sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg1['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg1['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg1['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['geometric_mean_score_macro'])[j])
+ (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))))
Mc1.push((sum/divide)*100)
}
@ -52,7 +77,11 @@ export default {
const performanceAlg2 = JSON.parse(this.ModelsPerformance[14])
for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) {
let sum2
sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg2['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg2['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg2['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['mean_test_jaccard'])[j])
sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg2['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg2['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg2['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['geometric_mean_score_macro'])[j])
+ (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))))
Mc2.push((sum2/divide)*100)
}
@ -64,7 +93,8 @@ export default {
Combined = JSON.parse(this.ModelsPerformance[9])
colorGiv = colors[1]
}
var valuesPerf = Object.values(Combined['0'])
var valuesPerf = Object.values(Combined['params'])
var ObjectsParams = Combined['params']
var newObjectsParams = []
var newObjectsParams2 = []

@ -28,7 +28,12 @@ export default {
parameters: [],
algorithm1: [],
algorithm2: [],
factors: [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
],
chart: '',
flagEmpty: 0,
ActiveModels: [],
@ -53,11 +58,32 @@ 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
sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg1['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg1['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg1['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['mean_test_jaccard'])[j])
sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg1['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg1['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg1['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['geometric_mean_score_macro'])[j])
+ (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))))
Mc1.push((sum/divide)*100)
}
@ -65,7 +91,11 @@ export default {
const performanceAlg2 = JSON.parse(this.PerformanceAllModels[14])
for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) {
let sum2
sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlg2['mean_test_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg2['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg2['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['mean_test_jaccard'])[j])
sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg2['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg2['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg2['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['geometric_mean_score_macro'])[j])
+ (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))))
Mc2.push((sum2/divide)*100)
}
@ -78,12 +108,12 @@ export default {
this.algorithm2 = []
this.parameters = []
for (var i = 0; i < Object.keys(PerformAlgor1['0']).length; i++) {
this.algorithm1.push({'Performance Metrics': Mc1[i],Algorithm:'KNN',Model:'Model ' + Algor1IDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance Metrics ',ModelID:Algor1IDs[i]})
for (var i = 0; i < Object.keys(PerformAlgor1['params']).length; i++) {
this.algorithm1.push({'Performance (%)': Mc1[i],Algorithm:'KNN',Model:'Model ' + Algor1IDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance (%) ',ModelID:Algor1IDs[i]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgor1['params'])[i]))
}
for (let j = 0; j < Object.keys(PerformAlgor2['0']).length; j++) {
this.algorithm2.push({'Performance Metrics': Mc2[j],Algorithm:'RF',Model:'Model ' + Algor2IDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance Metrics ',ModelID:Algor2IDs[j]})
for (let j = 0; j < Object.keys(PerformAlgor2['params']).length; j++) {
this.algorithm2.push({'Performance (%)': Mc2[j],Algorithm:'RF',Model:'Model ' + Algor2IDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance (%) ',ModelID:Algor2IDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgor2['params'])[j]))
}
@ -97,7 +127,7 @@ export default {
// group : how to group data on x axis
// color : color of the poi