fixed reset

cachedir/joblib/run/GridSearchForModels/0630790fa81d5b91e3195bd86de458e2/metadata.json

@@ -0,0 +1 @@
+{"duration": 270.6007800102234, "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": "[0, 1, 0, 1, 1]", "AlgorithmsIDsEnd": "0"}}

cachedir/joblib/run/GridSearchForModels/34dd4fa44cf8d83f42cfacc70a3fdd71/metadata.json

@@ -1 +1 @@
-{"duration": 307.1607220172882, "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"}}
+{"duration": 270.70482993125916, "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"}}

cachedir/joblib/run/GridSearchForModels/3cc262c077a39d0c54e4037251751202/metadata.json

@@ -0,0 +1 @@
+{"duration": 367.6226439476013, "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": "[0.46, 1, 0.3, 1, 1]", "AlgorithmsIDsEnd": "576"}}

cachedir/joblib/run/GridSearchForModels/7cd0b89df0e27ae606b59399cc748534/metadata.json

@@ -0,0 +1 @@
+{"duration": 279.6587002277374, "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": "[0.46, 1, 0.3, 1, 1]", "AlgorithmsIDsEnd": "0"}}

cachedir/joblib/run/GridSearchForModels/c426e0f3d8f4e01216f1b2a58820e6ec/metadata.json

@@ -1 +0,0 @@
-{"duration": 393.25071001052856, "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"}}

cachedir/joblib/run/GridSearchForModels/cce4a92f4cbd0e736018f002fd195967/metadata.json

@@ -0,0 +1 @@
+{"duration": 372.7683711051941, "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": "[0, 1, 0, 1, 1]", "AlgorithmsIDsEnd": "576"}}

cachedir/joblib/run/GridSearchForModels/fbe5cd8abe4f095b079dbe1880be140d/metadata.json

@@ -0,0 +1 @@
+{"duration": 367.4382128715515, "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"}}

cachedir/joblib/run/GridSearchForModels/func_code.py

@@ -1,4 +1,4 @@
-# first line: 454
+# first line: 464
 @memory.cache
 def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd):
 

frontend/src/components/AlgorithmHyperParam.vue

@@ -23,6 +23,9 @@ export default {
     }
   },
   methods: {
+    reset () {
+      d3.selectAll("#PCP > *").remove(); 
+    },
     PCPView () {
       d3.selectAll("#PCP > *").remove(); 
       if (this.selAlgorithm != '') {
@@ -87,6 +90,9 @@ export default {
     EventBus.$on('emittedEventCallingModel', this.PCPView)
     EventBus.$on('ResponsiveandChange', this.PCPView)
     EventBus.$on('emittedEventCallingModelClear', this.clear)
+
+    // reset view
+    EventBus.$on('resetViews', this.reset)
   }
 }
 </script>

frontend/src/components/Algorithms.vue

@@ -31,13 +31,16 @@ export default {
       algorithm2: [],
       chart: '',
       flagEmpty: 0,
-      ActiveModels: []
+      ActiveModels: [],
     }
   },
   methods: {
+    reset () {
+      d3.selectAll("#exploding_boxplot > *").remove()
+    },
     boxplot () {
       // reset the boxplot
-      d3.selectAll("#exploding_boxplot > *").remove(); 
+      d3.selectAll("#exploding_boxplot > *").remove()
 
       // retrieve models ID
       const Algor1IDs = this.PerformanceAllModels[0]
@@ -81,6 +84,7 @@ export default {
       const previousColor = ['#8dd3c7','#8da0cb']
       // check for brushing
       var el = document.getElementsByClassName('d3-exploding-boxplot boxcontent')
+      var overall = document.getElementsByClassName('overall')
       this.brushStatus = document.getElementsByClassName('extent')
       // on clicks
       
@@ -121,6 +125,13 @@ export default {
         EventBus.$emit('PCPCall', 'RF')
         EventBus.$emit('updateBarChart', [])
       }
+
+      overall[0].ondblclick = function () {
+        flagEmptyKNN = 0
+        flagEmptyRF = 0
+        EventBus.$emit('alternateFlagLock', flagEmptyKNN)
+      }
+
       // check if brushed through all boxplots and not only one at a time
       const myObserver = new ResizeObserver(entries => {
         EventBus.$emit('brusheAllOn')
@@ -337,6 +348,9 @@ export default {
     EventBus.$on('emittedEventCallingSelectedALgorithm', data => {
       this.selectedAlgorithm = data})
     EventBus.$on('brusheAllOn', this.brushActivationAll)
+
+    // reset the views
+    EventBus.$on('resetViews', this.reset)
   }
 }
 </script>

frontend/src/components/BalancePredictions.vue

@@ -22,6 +22,10 @@
             }
         },
         methods: {
+          reset () {
+            var svg = d3.select("#my_dataviz");
+            svg.selectAll("*").remove();
+          },
           Balance () {
             // erase histogram
             var svg = d3.select("#my_dataviz");
@@ -213,6 +217,9 @@
           this.responsiveWidthHeight = data})
           EventBus.$on('ResponsiveandChange', data => {
           this.responsiveWidthHeight = data})
+
+          // reset view
+          EventBus.$on('resetViews', this.reset)
         }
     }
 </script>

frontend/src/components/BarChart.vue

@@ -170,7 +170,9 @@ export default {
       },
       reset () 
       {
+        setTimeout(() => {
           Plotly.purge('barChart')
+        }, 50);
       }
     },
     mounted() {
@@ -181,11 +183,13 @@ export default {
       EventBus.$on('emittedEventCallingBarChart', this.BarChartView)
       EventBus.$on('emittedEventCallingUpdateBarChart', data => { this.ModelsChosen = data })
       EventBus.$on('emittedEventCallingUpdateBarChart', this.BarChartView)
-        EventBus.$on('resetViews', this.reset)
       EventBus.$on('Responsive', data => {
           this.WH = data})
       EventBus.$on('ResponsiveandChange', data => {
           this.WH = data})
+
+      // reset view
+      EventBus.$on('resetViews', this.reset)
     }
 }
 </script>

frontend/src/components/DataSetExecController.vue

@@ -78,6 +78,7 @@ export default {
     },
     reset () {
       EventBus.$emit('reset')
+      EventBus.$emit('alternateFlagLock')
     },
     confirm () {
       EventBus.$emit('ConfirmDataSet')

frontend/src/components/DataSpace.vue

@@ -61,6 +61,9 @@ export default {
     }
   },
   methods: {
+    reset () {
+      Plotly.purge('OverviewDataPlotly')
+    },
     selectAppliedFilter () {
       var representationSelectionDocum = document.getElementById('selectFilterID')
       this.userSelectedFilter = representationSelectionDocum.options[representationSelectionDocum.selectedIndex].value
@@ -82,7 +85,6 @@ export default {
       EventBus.$emit('SendProvenance', 'restore')
     },
     scatterPlotDataView () {
-        
       Plotly.purge('OverviewDataPlotly')
 
       // responsive visualization
@@ -227,6 +229,9 @@ export default {
       this.responsiveWidthHeight = data})
     EventBus.$on('ResponsiveandChange', data => {
       this.responsiveWidthHeight = data})
+    
+    // reset view
+    EventBus.$on('resetViews', this.reset)
   }
 }
 </script>

frontend/src/components/FinalResultsLinePlot.vue

@@ -41,6 +41,9 @@ export default {
     }
   },
   methods: {
+    reset () {
+      Plotly.purge('LinePlot')
+    },
     LinePlotView () {
       this.NumberofExecutions ++ 
       this.xaxis.push(this.NumberofExecutions)
@@ -289,6 +292,9 @@ export default {
     EventBus.$on('emittedEventCallingLinePlot', data => {
       this.FinalResultsforLinePlot = data})
     EventBus.$on('emittedEventCallingLinePlot', this.LinePlotView)
+
+    // reset the views
+    EventBus.$on('resetViews', this.reset)
   }
 }
 </script>

frontend/src/components/Heatmap.vue

@@ -30,8 +30,11 @@ export default {
     Refresh () {
       EventBus.$emit('SendSelectedFeaturesEvent', '')
     },
+    reset () {
+      var svg = d3.select("#Heatmap");
+      svg.selectAll("*").remove();
+    },
     Heatmap () {
-
       // Clear Heatmap first
       var svg = d3.select("#Heatmap");
       svg.selectAll("*").remove();
@@ -578,6 +581,9 @@ export default {
       EventBus.$on('resetViews', this.reset)
       EventBus.$on('SendSelectedPointsToBrushHeatmap', data => { this.highlighted = data; })
       EventBus.$on('SendSelectedPointsToBrushHeatmap', this.brush)
+
+      // reset the views
+      EventBus.$on('resetViews', this.reset)
     }
 }
 </script>

frontend/src/components/Main.vue

@@ -696,6 +696,7 @@ export default Vue.extend({
         .then(response => {
           console.log('The server side was reset! Done.')
           this.reset = false
+          EventBus.$emit('resetViews')
         })
         .catch(error => {
           console.log(error)
@@ -733,7 +734,10 @@ export default Vue.extend({
       axios.post(path, postData, axiosConfig)
         .then(response => {
           console.log('The client send the new factors! Done.')
+          // this is if we need to change the factors even before models are there
+          //if (this.OverviewResults != 0) {
             this.RetrieveNewColors()
+         // }
         })
         .catch(error => {
           console.log(error)

frontend/src/components/PCPData.vue

@@ -21,13 +21,15 @@ export default {
     }
   },
   methods: {
+    reset () {
+      d3.selectAll("#PCPDataView > *").remove();
+    },
     PCPView () {
       d3.selectAll("#PCPDataView > *").remove();
       const DataSetNew = JSON.parse(this.PCPDataReceived[2])
       var DataSetParse = JSON.parse(DataSetNew)
       const target_names = JSON.parse(this.PCPDataReceived[3])
       var colors = this.colorsValues
-      console.log(target_names)
 
       this.pc = ParCoords()("#PCPDataView")
           .data(DataSetParse)
@@ -46,6 +48,9 @@ export default {
     EventBus.$on('emittedEventCallingDataPCP', data => { this.PCPDataReceived = data })
     EventBus.$on('emittedEventCallingDataPCP', this.PCPView)
     EventBus.$on('ResponsiveandChange', this.PCPView)
+
+    // reset the views
+    EventBus.$on('resetViews', this.reset)
   }
 }
 </script>

frontend/src/components/Parameters.vue

@@ -28,8 +28,13 @@ export default {
     }
   },
   methods: {
+    reset () {
+      setTimeout(() => {
+         var svg = d3.select("#overview");
+        svg.selectAll("*").remove();
+      }, 50);
+    },
     draw () {
-
       // Clear Heatmap first
       var svg = d3.select("#overview");
       svg.selectAll("*").remove();
@@ -176,16 +181,13 @@ export default {
               countRFRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
             }
           }
-              //console.log(countkNNRelated)
+
-              //console.log(countRFRelated)
           n_neighbors = ([... new Set(countkNNRelated.map(data => data.n_neighbors))].length / 25) * 100
           metric = ([... new Set(countkNNRelated.map(data => data.metric))].length / 4) * 100
           algorithm = ([... new Set(countkNNRelated.map(data => data.algorithm))].length / 3) * 100
           weight = ([... new Set(countkNNRelated.map(data => data.weight))].length / 2) * 100
           n_estimators = ([... new Set(countRFRelated.map(data => data.n_estimators))].length / 80) * 100
           criterion = ([... new Set(countRFRelated.map(data => data.criterion))].length / 2) * 100
-
-              //console.log(algorithm)
         }
 
         var data = [
@@ -388,7 +390,6 @@ export default {
 
         // adjust dy (labels vertical start) based on number of lines (i.e. tspans)
         question_text.each(function(d, i) {
-              //console.log(d3.select(this)[0]);
           var textPath = d3.select(this)[0][0],
             tspanCount = textPath.childNodes.length;
 
@@ -572,6 +573,10 @@ export default {
               .attr('width', width - 2 * padding)
               .attr('height', height - 2 * padding);
           */
+      },
+      updateFlags () {
+        this.FlagKNN = 0
+        this.FlagRF = 0
       }
   },
   mounted () {
@@ -587,6 +592,12 @@ export default {
     this.WH = data})
     EventBus.$on('ResponsiveandChange', data => {
     this.WH = data})
+
+    // reset the views
+    EventBus.$on('resetViews', this.reset)
+
+    EventBus.$on('alternateFlagLock', this.updateFlags)
+    EventBus.$on('alternateFlagLock', this.draw)
   }
 
 }
@@ -595,7 +606,6 @@ export default {
 <style>
 /* Styles go here */
 
-
 .category-circle {
   fill: url(#gradient-categorization);
 }

frontend/src/components/PerMetricBarChart.vue

@@ -17,7 +17,6 @@ export default {
   },
   methods: {
     LineBar () {
-
       Plotly.purge('PerMetricBar')
 
       var metricsPerModel = JSON.parse(this.barchartmetrics[9])
@@ -73,7 +72,9 @@ export default {
               }}};
 
         Plotly.newPlot('PerMetricBar', data, layout, {displayModeBar:false}, {staticPlot: true});
-        //}
+      },
+      reset () {
+        Plotly.purge('PerMetricBar')
       }
     },
     mounted () {
@@ -86,6 +87,9 @@ export default {
       EventBus.$on('UpdateBarChartperMetric', data => {
       this.SelBarChartMetrics = data})
       EventBus.$on('UpdateBarChartperMetric', this.LineBar)
+
+      // reset view
+      EventBus.$on('resetViews', this.reset)
     }
 }
 </script>

frontend/src/components/PredictionsSpace.vue

@@ -17,6 +17,9 @@ export default {
     }
   },
   methods: {
+    reset () {
+      Plotly.purge('OverviewPredPlotly')
+    },
     ScatterPlotDataView () {
        Plotly.purge('OverviewPredPlotly')
 
@@ -26,7 +29,6 @@ export default {
 
       var target_names = JSON.parse(this.PredictionsData[4])
       const XandYCoordinates = JSON.parse(this.PredictionsData[8])
-      console.log(XandYCoordinates)
       const DataSet = JSON.parse(this.PredictionsData[14])
       const DataSetY = JSON.parse(this.PredictionsData[15])
       const originalDataLabels = JSON.parse(this.PredictionsData[16])
@@ -148,6 +150,9 @@ export default {
     this.WH = data})
     EventBus.$on('ResponsiveandChange', data => {
     this.WH = data})
+
+    // reset the views
+    EventBus.$on('resetViews', this.reset)
   }
 }
 </script>

frontend/src/components/Provenance.vue

@@ -28,10 +28,17 @@ export default {
         typeCounter: [],
         typeColumnCounter: [],
         KNNModels: 576, //KNN models
-
+        platform: ''
     }
   },
   methods: {
+    reset () {
+      if (this.platform == '') {
+
+      } else {
+        this.platform.clear();
+      }
+    },
     provenance () {
       var canvas = document.getElementById("main-canvas");
       var width = this.WH[0]*9 // interactive visualization
@@ -41,7 +48,7 @@ export default {
       var flagRF = 0
       var StackInfo = JSON.parse(this.stackInformation[1])
       // Create a WebGL 2D platform on the canvas:
-      var platform = Stardust.platform("webgl-2d", canvas, width, height);
+      this.platform = Stardust.platform("webgl-2d", canvas, width, height);
     
       for (let i = 0; i < StackInfo.length; i++) {
         if (StackInfo[i] < this.KNNModels){
@@ -75,7 +82,7 @@ export default {
       let isotype = new Stardust.mark.circle();
 
       // Create the mark object.
-      let isotypes = Stardust.mark.create(isotype, platform);
+      let isotypes = Stardust.mark.create(isotype, this.platform);
 
       let isotypeHeight = 18;
       let colors = [[141,211,199], [141,160,203]];
@@ -123,6 +130,9 @@ export default {
     this.WH = data})
     EventBus.$on('ResponsiveandChange', data => {
     this.WH = data})
+
+    // reset the views
+    EventBus.$on('resetViews', this.reset)
   }
 
 }

frontend/src/components/ScatterPlot.vue

@@ -45,6 +45,9 @@ export default {
     }
   },
   methods: {
+    reset () {
+      Plotly.purge('OverviewPlotly')
+    },
     selectVisualRepresentation () {
       const representationSelectionDocum = document.getElementById('selectBarChart')
       this.representationSelection = representationSelectionDocum.options[representationSelectionDocum.selectedIndex].value
@@ -280,6 +283,9 @@ export default {
     EventBus.$on('RepresentationSelection', this.ScatterPlotView)
     EventBus.$on('UpdateModelsScatterplot', data => {this.DataPointsSelUpdate = data})
     EventBus.$on('UpdateModelsScatterplot', this.animate)
+
+    // reset view
+    EventBus.$on('resetViews', this.reset)
   }
 }
 </script>

run.py

@@ -131,7 +131,6 @@ def Reset():
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
 @app.route('/data/ServerRequest', methods=["GET", "POST"])
 def RetrieveFileName():
-
     fileName = request.get_data().decode('utf8').replace("'", '"')
 
     #global featureSelection 
@@ -195,9 +194,6 @@ def RetrieveFileName():
     global loopFeatures
     loopFeatures = 2
 
-    global factors
-    factors = [1,1,1,1,1]
-
     # models
     global KNNModels
     KNNModels = []
@@ -438,6 +434,7 @@ def RetrieveModel():
     global algorithms
     algorithms = RetrievedModel['Algorithms']
 
+    global factors
     global XData
     global yData
 
@@ -653,10 +650,16 @@ def RetrieveModelsParam():
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
 @app.route('/data/factors', methods=["GET", "POST"])
 def RetrieveFactors():
+    global factors
+    global allParametersPerformancePerModel
     Factors = request.get_data().decode('utf8').replace("'", '"')
     FactorsInt = json.loads(Factors)
     factors = FactorsInt['Factors']
 
+    # this is if we want to change the factors before running the search
+    #if (len(allParametersPerformancePerModel) == 0):
+    #    pass
+    #else:
     global sumPerClassifierSel
     global ModelSpaceMDSNew
     global ModelSpaceTSNENew
@@ -1459,7 +1462,7 @@ def RetrieveAction():
 
     filterActionFinal = filterActionCleared['action']
 
-    if (filterActionFinal == 'merge'): # fix merge
+    if (filterActionFinal == 'merge'):
         if (filterDataFinal == 'mean' or filterDataFinal == ''):
             mean = XData.iloc[dataSpacePointsIDs, :].mean()
             XData.loc[len(XData)]= mean