fixed the CM process

cachedir/joblib/run/randomSearch/37316eae58a8c74b9165423658201b28/metadata.json

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cachedir/joblib/run/randomSearch/8dd8c905a5a57cf1d9b467f291dd14b2/metadata.json

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cachedir/joblib/run/randomSearch/8ee66eaa63ddb9f764dde4060234b1f9/metadata.json

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cachedir/joblib/run/randomSearch/b7a3520563829a2c45169ede4c18dce6/metadata.json

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+{"duration": 7.2485480308532715, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n298   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n299   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n300   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n301   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n302   57    0   1       130   236    0        0      174      0      0.0      1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='chebyshev',\n                     metric_params=None, n_jobs=None, n_neighbors=26, p=2,\n                     weights='uniform')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}}

cachedir/joblib/run/randomSearch/db2d0ed5e07bad06c380972b056d9c63/metadata.json

@@ -0,0 +1 @@
+{"duration": 31.870266675949097, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n601   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n602   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n603   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n604   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n605   57    0   1       130   236    0        0      174      0      0.0      1   1     2\n\n[606 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]", "clf": "LogisticRegression(C=48, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='auto', n_jobs=None, penalty='none',\n                   random_state=42, solver='saga', tol=0.0001, verbose=0,\n                   warm_start=False)", "params": "{'C': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'max_iter': [50, 100, 150, 200, 250, 300, 350, 400, 450], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "200"}}

cachedir/joblib/run/randomSearch/e4dca276cc6a9475d2aee588523b478e/metadata.json

@@ -0,0 +1 @@
+{"duration": 9.726292848587036, "input_args": {"XData": "     Age  Sex  Cp  Trestbps  Chol  Fbs  Restecg  Thalach  Exang  Oldpeak  Slope  Ca  Thal\n0     63    1   3       145   233    1        0      150      0      2.3      0   0     1\n1     37    1   2       130   250    0        1      187      0      3.5      0   0     2\n2     41    0   1       130   204    0        0      172      0      1.4      2   0     2\n3     56    1   1       120   236    0        1      178      0      0.8      2   0     2\n4     57    0   0       120   354    0        1      163      1      0.6      2   0     2\n..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...    ...  ..   ...\n601   57    0   0       140   241    0        1      123      1      0.2      1   0     3\n602   45    1   3       110   264    0        1      132      0      1.2      1   0     3\n603   68    1   0       144   193    1        1      141      0      3.4      1   2     3\n604   57    1   0       130   131    0        1      115      1      1.2      1   1     3\n605   57    0   1       130   236    0        0      174      0      0.0      1   1     2\n\n[606 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]", "clf": "KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='chebyshev',\n                     metric_params=None, n_jobs=None, n_neighbors=17, 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, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "100"}}

cachedir/joblib/run/randomSearch/eb45ad56de8e367dade6cb77617a3d36/metadata.json

@@ -1 +0,0 @@
-{"duration": 19.771310806274414, "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=64, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=300,\n                   multi_class='auto', n_jobs=None, penalty='none',\n                   random_state=42, solver='newton-cg', tol=0.0001, verbose=0,\n                   warm_start=False)", "params": "{'C': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'max_iter': [50, 100, 150, 200, 250, 300, 350, 400, 450], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "100"}}

cachedir/joblib/run/randomSearch/func_code.py

@@ -1,4 +1,4 @@
-# first line: 501
+# first line: 518
 @memory.cache
 def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 

frontend/src/components/Ensemble.vue

@@ -89,7 +89,7 @@ export default {
         this.clean(parameters[i])
         stringParameters.push(JSON.stringify(parameters[i]).replace(/,/gi, '<br>'))
       }
-
+      // fix that!
       var classifiersInfoProcessing = []
       for (let i = 0; i < modelId.length; i++) {
         if (i < 100) {

frontend/src/components/HyperParameterSpace.vue

@@ -73,7 +73,7 @@ export default {
         this.clean(parameters[i])
         stringParameters.push(JSON.stringify(parameters[i]).replace(/,/gi, '<br>'))
       }
-
+      // fix that!
       var classifiersInfoProcessing = []
       for (let i = 0; i < modelId.length; i++) {
         if (i < 100) {

frontend/src/components/Main.vue

@@ -323,7 +323,7 @@ export default Vue.extend({
           } else {
             EventBus.$emit('emittedEventCallingCrossoverMutation', this.OverviewResults)
             EventBus.$emit('emittedEventCallingGridCrossoverMutation', this.OverviewResults)
-            EventBus.$emit('emittedEventCallingGridSelectionGridCrossoverMutation', this.OverviewResults)
+            EventBus.$emit('emittedEventCallingGridSelectionCrossoverMutation', this.OverviewResults)
             //this.getFinalResults()
           }
         })
@@ -349,7 +349,6 @@ export default Vue.extend({
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResultsCM)
           EventBus.$emit('emittedEventCallingGrid', this.OverviewResultsCM)
           EventBus.$emit('emittedEventCallingGridSelection', this.OverviewResultsCM)
-          //this.getFinalResults()
         })
         .catch(error => {
           console.log(error)

frontend/src/components/Predictions.vue

@@ -61,6 +61,7 @@ export default {
       var KNNPred = predictions[0]
       var LRPred = predictions[1]
       var PredAver = predictions[2]
+      console.log(PredAver)
 
       var dataAver = []
       var dataAverGetResults = []
@@ -124,7 +125,7 @@ export default {
     }
     
     var classStore = [].concat.apply([], classArray);
-
+    console.log(classStore)
 		// === Set up canvas === //
 
 		var width = 1200,

frontend/src/components/PredictionsCM.vue

@@ -431,8 +431,8 @@ export default {
       EventBus.$on('emittedEventCallingGridCrossoverMutation', data => { this.GetResultsAllCM = data; })
       EventBus.$on('emittedEventCallingGridCrossoverMutation', this.Grid)
 
-      EventBus.$on('emittedEventCallingGridSelectionGridCrossoverMutation', data => { this.GetResultsSelectionCM = data; })
-      EventBus.$on('emittedEventCallingGridSelectionGridCrossoverMutation', this.GridSelection)
+      EventBus.$on('emittedEventCallingGridSelectionCrossoverMutation', data => { this.GetResultsSelectionCM = data; })
+      EventBus.$on('emittedEventCallingGridSelectionCrossoverMutation', this.GridSelection)
 
       EventBus.$on('SendSelectedPointsToServerEventCM', data => { this.predictSelectionCM = data; })
       EventBus.$on('SendSelectedPointsToServerEventCM', this.GridSelection)

run.py

@@ -76,6 +76,15 @@ def reset():
     global yData
     yData = []
 
+    global addKNN
+    addKNN = 0
+
+    global addLR
+    addLR = 100
+
+    global countAllModels
+    countAllModels = 0
+
     global XDataStored
     XDataStored = []
     global yDataStored
@@ -197,6 +206,12 @@ def retrieveFileName():
     global detailsParams
     detailsParams = []
 
+    global addKNN
+    addKNN = 0
+
+    global addLR
+    addLR = 100
+
     # Initializing models
 
     global RetrieveModelsList
@@ -475,6 +490,7 @@ def retrieveModel():
     global XData
     global yData
     global LRModelsCount
+    global countAllModels
 
     # loop through the algorithms
     global allParametersPerformancePerModel
@@ -484,11 +500,12 @@ def retrieveModel():
         if (eachAlgor) == 'KNN':
             clf = KNeighborsClassifier()
             params = {'n_neighbors': list(range(1, 100)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}
-            AlgorithmsIDsEnd = KNNModelsCount
+            AlgorithmsIDsEnd = countAllModels
         else:
             clf = LogisticRegression(random_state=RANDOM_SEED)
             params = {'C': list(np.arange(1,100,1)), 'max_iter': list(np.arange(50,500,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}
-            AlgorithmsIDsEnd = LRModelsCount
+            AlgorithmsIDsEnd = countAllModels
+        countAllModels = countAllModels + 100
         allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd)
     HistoryPreservation = allParametersPerformancePerModel.copy()
     # call the function that sends the results to the frontend
@@ -652,6 +669,7 @@ def PreprocessingParam():
     dfLR = dfLR.T
 
     df_params = pd.concat([dfKNN, dfLR])
+    df_params = df_params.reset_index(drop=True)
     return df_params
 
 def PreprocessingParamSep():
@@ -788,7 +806,6 @@ def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel)
     Results.append(json.dumps(ModelSpaceTSNE))
     Results.append(json.dumps(ModelSpaceUMAP))
     Results.append(json.dumps(PredictionProbSel))
-    print('mpike')
 
     return Results
 
@@ -806,6 +823,9 @@ def CrossoverMutateFun():
     global XData
     global yData
     global LRModelsCount
+    global addKNN
+    global addLR
+    global countAllModels
 
     # loop through the algorithms
     global allParametersPerfCrossMutr
@@ -819,17 +839,16 @@ def CrossoverMutateFun():
     countLR = 0
     setMaxLoopValue = 5
     paramAllAlgs = PreprocessingParam()
+
     KNNIntIndex = []
     LRIntIndex = []
     
     localCrossMutr = []
     allParametersPerfCrossMutrKNNC = []
-
     while countKNN < setMaxLoopValue:
         for dr in KNNIDs:
             KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
         KNNPickPair = random.sample(KNNIntIndex,2)
-
         pairDF = paramAllAlgs.iloc[KNNPickPair]
         crossoverDF = pd.DataFrame()
         for column in pairDF:
@@ -843,11 +862,13 @@ def CrossoverMutateFun():
         else:
             clf = KNeighborsClassifier()
             params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
-            AlgorithmsIDsEnd = 200 + countKNN
+            AlgorithmsIDsEnd = countAllModels + countKNN
             localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN', AlgorithmsIDsEnd)
             countKNN += 1
             crossoverDF = pd.DataFrame()
 
+    countAllModels = countAllModels + 5
+
     for loop in range(setMaxLoopValue - 1):
         localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
         localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
@@ -888,11 +909,13 @@ def CrossoverMutateFun():
         else:
             clf = KNeighborsClassifier()
             params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
-            AlgorithmsIDsEnd = 205 + countKNN
+            AlgorithmsIDsEnd = countAllModels + countKNN
             localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN', AlgorithmsIDsEnd)
             countKNN += 1
             crossoverDF = pd.DataFrame()
 
+    countAllModels = countAllModels + 5
+
     for loop in range(setMaxLoopValue - 1):
         localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
         localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
@@ -927,11 +950,13 @@ def CrossoverMutateFun():
         else:
             clf = LogisticRegression(random_state=RANDOM_SEED)
             params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
-            AlgorithmsIDsEnd = 210 + countLR
+            AlgorithmsIDsEnd = countAllModels + countLR
             localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR', AlgorithmsIDsEnd)
             countLR += 1
             crossoverDF = pd.DataFrame()
 
+    countAllModels = countAllModels + 5
+
     for loop in range(setMaxLoopValue - 1):
         localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
         localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
@@ -972,11 +997,13 @@ def CrossoverMutateFun():
         else:
             clf = LogisticRegression(random_state=RANDOM_SEED)
             params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
-            AlgorithmsIDsEnd = 215 + countLR
+            AlgorithmsIDsEnd = countAllModels + countLR
             localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR', AlgorithmsIDsEnd)
             countLR += 1
             crossoverDF = pd.DataFrame()
 
+    countAllModels = countAllModels + 5
+
     for loop in range(setMaxLoopValue - 1):
         localCrossMutr[0] = localCrossMutr[0] + localCrossMutr[(loop+1)*4]
         localCrossMutr[1] = pd.concat([localCrossMutr[1], localCrossMutr[(loop+1)*4+1]], ignore_index=True)
@@ -990,15 +1017,15 @@ def CrossoverMutateFun():
 
     HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrLRM
 
-    allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNC + allParametersPerfCrossMutrKNNM + allParametersPerfCrossMutrLRC + allParametersPerfCrossMutrLRM
-
-    allParametersPerformancePerModel[0] = allParametersPerformancePerModel[0] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0]
+    localCrossMutr.clear()
 
-    allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNC[1]], ignore_index=True)
-    allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNM[1]], ignore_index=True)
+    allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNC + allParametersPerfCrossMutrKNNM + allParametersPerfCrossMutrLRC + allParametersPerfCrossMutrLRM
 
-    allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNC[2]], ignore_index=True)
-    allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNM[2]], ignore_index=True)
+    allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0]
+    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNC[1]], ignore_index=True)
+    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNM[1]], ignore_index=True)
+    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrKNNC[2]], ignore_index=True)
+    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrKNNM[2]], ignore_index=True)
     
     allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNC[3]], ignore_index=True)
     allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNM[3]], ignore_index=True)
@@ -1008,14 +1035,15 @@ def CrossoverMutateFun():
     
     allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrLRC[1]], ignore_index=True)
     allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrLRM[1]], ignore_index=True)
-    
     allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrLRC[2]], ignore_index=True)
     allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrLRM[2]], ignore_index=True)
     
     allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRC[3]], ignore_index=True)
     allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRM[3]], ignore_index=True)
 
-    print(allParametersPerformancePerModel[7])
+    addKNN = addLR
+
+    addLR = addLR + 10
 
     # KNNIntIndex = []
     # for dr in KNNIDs:
@@ -1038,7 +1066,6 @@ def CrossoverMutateFun():
     return 'Everything Okay'
 
 def crossoverMutation(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
-
     search = GridSearchCV(    
     estimator=clf, param_grid=params, cv=crossValidation, refit='accuracy', 
     scoring=scoring, verbose=0, n_jobs=-1)
@@ -1134,7 +1161,6 @@ def PreprocessingIDsCM():
     dicLRM = allParametersPerfCrossMutr[12]
 
     df_concatIDs = dicKNNC + dicKNNM + dicLRC + dicLRM
-
     return df_concatIDs
 
 def PreprocessingMetricsCM():
@@ -1163,13 +1189,28 @@ def PreprocessingPredCM():
     dfLRC = pd.DataFrame.from_dict(dicLRC)
     dfLRM = pd.DataFrame.from_dict(dicLRM)
 
+    dfKNN = pd.concat([dfKNNC, dfKNNM])
+
+    dfLR = pd.concat([dfLRC, dfLRM])
+
     df_concatProbs = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM])
+
+    predictionsKNN = []
+    for column, content in dfKNN.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsKNN.append(el)
+
+    predictionsLR = []
+    for column, content in dfLR.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsLR.append(el)
+    
     predictions = []
     for column, content in df_concatProbs.items():
         el = [sum(x)/len(x) for x in zip(*content)]
         predictions.append(el)
 
-    return predictions
+    return [predictionsKNN, predictionsLR, predictions]
 
 def PreprocessingParamCM():
     dicKNNC = allParametersPerfCrossMutr[1]
@@ -1198,6 +1239,7 @@ def PreprocessingParamCM():
     dfLRM = dfLRM.T
 
     df_params = pd.concat([dfKNNC, dfKNNM, dfLRC, dfLRM])
+    df_params = df_params.reset_index(drop=True)
     return df_params
 
 def PreprocessingParamSepCM():
@@ -1326,30 +1368,37 @@ def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,Predict
     ResultsCM.append(json.dumps(ModelSpaceUMAPCM))
     ResultsCM.append(json.dumps(PredictionProbSel))
 
-    return Results
+    return ResultsCM
 
 def PreprocessingPredSel(SelectedIDs):
 
+    global addKNN
+    global addLR
+
     numberIDKNN = []
     numberIDLR = []
+    print(SelectedIDs)
     for el in SelectedIDs:
         match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
         if match:
             items = match.groups()
             if (items[0] == 'KNN'):
-                numberIDKNN.append(int(items[1]))
+                numberIDKNN.append(int(items[1]) - addKNN)
             else:
-                numberIDLR.append(int(items[1]) - 100)
-
+                numberIDLR.append(int(items[1]) - addLR)
+    print(numberIDKNN)
     dicKNN = allParametersPerformancePerModel[3]
     dicLR = allParametersPerformancePerModel[7]
 
     dfKNN = pd.DataFrame.from_dict(dicKNN)
+    print(dfKNN)
     dfKNN = dfKNN.loc[numberIDKNN]
     dfLR = pd.DataFrame.from_dict(dicLR)
     dfLR = dfLR.loc[numberIDLR]
-    dfLR.index += 100
+    print(dfLR)
+    dfLR.index += addKNN
     df_concatProbs = pd.concat([dfKNN, dfLR])
+    print(df_concatProbs)
 
     predictionsKNN = []
     for column, content in dfKNN.items():
@@ -1360,6 +1409,7 @@ def PreprocessingPredSel(SelectedIDs):
     for column, content in dfLR.items():
         el = [sum(x)/len(x) for x in zip(*content)]
         predictionsLR.append(el)
+
     predictions = []
     for column, content in df_concatProbs.items():
         el = [sum(x)/len(x) for x in zip(*content)]
@@ -1375,6 +1425,7 @@ def RetrieveSelIDsPredict():
     RetrieveIDsSelection = request.get_data().decode('utf8').replace("'", '"')
     RetrieveIDsSelection = json.loads(RetrieveIDsSelection)
     RetrieveIDsSelection = RetrieveIDsSelection['predictSelectionIDs']
+    
     ResultsSelPred = PreprocessingPredSel(RetrieveIDsSelection)
 
     return 'Everything Okay'