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cachedir/joblib/run/randomSearch/155d02ea5aff8e95789a41daa7e3053f/metadata.json

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cachedir/joblib/run/randomSearch/1b6d4ac547148ec02682034e93c8ca64/metadata.json

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cachedir/joblib/run/randomSearch/521c6a93866203bedf32aff9533c69f3/metadata.json

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cachedir/joblib/run/randomSearch/658b47b5657ae88d17afeff46270b6f7/metadata.json

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cachedir/joblib/run/randomSearch/6b4dfbb9b4c2f9fdd9d0d54bbf7977a1/metadata.json

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cachedir/joblib/run/randomSearch/d3e7532dea748d4385793888d9104eff/metadata.json

@@ -0,0 +1 @@
+{"duration": 6.86419677734375, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      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='ball_tree', metric='chebyshev', n_neighbors=47)", "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", "crossValidation": "5", "randomSear": "50"}}

cachedir/joblib/run/randomSearch/d4ab81cc65c470639557325fb6452f8a/metadata.json

@@ -0,0 +1 @@
+{"duration": 12.065573930740356, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      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='ball_tree', metric='chebyshev', n_neighbors=18,\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": "500", "crossValidation": "10", "randomSear": "100"}}

cachedir/joblib/run/randomSearch/e0c2d31846e9c13248b2c48bcce6a28a/metadata.json

@@ -0,0 +1 @@
+{"duration": 25.837138891220093, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      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": "LogisticRegression(C=23, max_iter=150, penalty='none', random_state=42,\n                   solver='newton-cg')", "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", "crossValidation": "5", "randomSear": "100"}}

cachedir/joblib/run/randomSearch/e3178c4cf3ca997dadb74d244d909f2b/metadata.json

@@ -0,0 +1 @@
+{"duration": 55.707277059555054, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\n0      1      0       145      0      150   63   233    1      2.3        0   3   0     1\n1      0      0       130      0      187   37   250    1      3.5        1   2   0     2\n2      0      2       130      0      172   41   204    0      1.4        0   1   0     2\n3      0      2       120      0      178   56   236    1      0.8        1   1   0     2\n4      0      2       120      1      163   57   354    0      0.6        1   0   0     2\n..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      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": "RandomForestClassifier(criterion='entropy', max_depth=13, n_estimators=56,\n                       random_state=42)", "params": "{'n_estimators': [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_depth': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "150", "crossValidation": "10", "randomSear": "50"}}

frontend/src/components/History.vue

@@ -25,6 +25,10 @@ export default {
       storedCM: [],
       previouslyIDs: [],
       percentageOverall: [],
+      countShowS1max: [],
+      countShowS1min: [],
+      countShowS2max: [],
+      countShowS2min: [],
       values: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0],
       valuesStage2: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0],
       loop: 0,
@@ -54,36 +58,35 @@ export default {
       var tempDataRFM = []
       var tempDataGradBM = []
       var splitData = []
-      console.log(this.previouslyIDs)
       for (let i = 0; i < this.previouslyIDs.length; i++) {
         let tempSplit = this.previouslyIDs[i].split(/([0-9]+)/)
-        if (tempSplit[0] == 'KNNC') {
+        if (tempSplit[0] == 'KNNC' || tempSplit[0] == 'KNN') {
           tempDataKNNC.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'LRC') {
+        else if (tempSplit[0] == 'LRC' || tempSplit[0] == 'LR') {
           tempDataLRC.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'MLPC') {
+        else if (tempSplit[0] == 'MLPC' || tempSplit[0] == 'MLP') {
           tempDataMLPC.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'RFC') {
+        else if (tempSplit[0] == 'RFC' || tempSplit[0] == 'RF') {
           tempDataRFC.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'GradBC') {
+        else if (tempSplit[0] == 'GradBC' || tempSplit[0] == 'GradB') {
           tempDataGradBC.push(this.previouslyIDs[i])
-        } else if (tempSplit[0] == 'KNNM') {
+        } else if (tempSplit[0] == 'KNNM' || tempSplit[0] == 'KNN') {
           tempDataKNNM.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'LRM') {
+        else if (tempSplit[0] == 'LRM' || tempSplit[0] == 'LR') {
           tempDataLRM.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'MLPM') {
+        else if (tempSplit[0] == 'MLPM' || tempSplit[0] == 'MLP') {
           tempDataMLPM.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'RFM') {
+        else if (tempSplit[0] == 'RFM' || tempSplit[0] == 'RF') {
           tempDataRFM.push(this.previouslyIDs[i])
         }
-        else if (tempSplit[0] == 'GradBM') {
+        else if (tempSplit[0] == 'GradBM' || tempSplit[0] == 'GradB') {
           tempDataGradBM.push(this.previouslyIDs[i])
         }
         else {
@@ -117,9 +120,6 @@ export default {
         
       } 
 
-      console.log(max)
-      console.log(min)
-
       var countMax = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
       var countMin = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
 
@@ -319,8 +319,9 @@ export default {
           }
         }
       }
-      console.log(countMax)
-      console.log(countMin)
+
+      this.countShowS2max = countMax
+      this.countShowS2min = countMin
       // var percentage = []
       // for (let j = 0; j < countMax.length; j++) {
       //   if (j >= 15) {
@@ -532,61 +533,61 @@ export default {
               if(d.target.node == 40){
                 return colorDiff(percentage[0]);
               } else if(d.target.node == 39){
-                return colorDiff(percentage[1]);
-              } else if(d.target.node == 38){
                 return colorDiff(percentage[2]);
+              } else if(d.target.node == 38){
+                return colorDiff(percentage[4]);
               } else if(d.target.node == 37){
-                return colorDiff(percentage[3]);
+                return colorDiff(percentage[6]);
               } else if(d.target.node == 36){
-                return colorDiff(percentage[4]);
+                return colorDiff(percentage[8]);
               } else if(d.target.node == 34){
-                return colorDiff(percentage[5]);
+                return colorDiff(percentage[1]);
               } else if(d.target.node == 33){
-                return colorDiff(percentage[6]);
+                return colorDiff(percentage[3]);
               } else if(d.target.node == 32){
-                return colorDiff(percentage[7]);
+                return colorDiff(percentage[5]);
               } else if(d.target.node == 31){
-                return colorDiff(percentage[8]);
+                return colorDiff(percentage[7]);
               } else if(d.target.node == 30){
                 return colorDiff(percentage[9]);
               } else if(d.target.node == 28){
                 return colorDiff(percentage[10]);
               } else if(d.target.node == 27){
-                return colorDiff(percentage[11]);
-              } else if(d.target.node == 26){
                 return colorDiff(percentage[12]);
+              } else if(d.target.node == 26){
+                return colorDiff(percentage[14]);
               } else if(d.target.node == 25){
-                return colorDiff(percentage[13]);
+                return colorDiff(percentage[16]);
               } else if(d.target.node == 24){
-                return colorDiff(percentage[14]);
+                return colorDiff(percentage[18]);
               } else if(d.target.node == 22){
-                return colorDiff(percentage[15]);
+                return colorDiff(percentage[11]);
               } else if(d.target.node == 21){
-                return colorDiff(percentage[16]);
+                return colorDiff(percentage[13]);
               } else if(d.target.node == 20){
-                return colorDiff(percentage[17]);
+                return colorDiff(percentage[15]);
               } else if(d.target.node == 19){
-                return colorDiff(percentage[18]);
+                return colorDiff(percentage[17]);
               } else if(d.target.node == 18){
                 return colorDiff(percentage[19]);
               } else if(d.target.node == 16){
                 return colorDiff(previousPercentage[0]);
               } else if(d.target.node == 15){
-                return colorDiff(previousPercentage[1]);
-              } else if(d.target.node == 14){
                 return colorDiff(previousPercentage[2]);
+              } else if(d.target.node == 14){
+                return colorDiff(previousPercentage[4]);
               } else if(d.target.node == 13){
-                return colorDiff(previousPercentage[3]);
+                return colorDiff(previousPercentage[5]);
               } else if(d.target.node == 12){
-                return colorDiff(previousPercentage[4]);
+                return colorDiff(previousPercentage[8]);
               } else if(d.target.node == 10){
-                return colorDiff(previousPercentage[5]);
+                return colorDiff(previousPercentage[1]);
               } else if(d.target.node == 9){
-                return colorDiff(previousPercentage[6]);
+                return colorDiff(previousPercentage[3]);
               } else if(d.target.node == 8){
-                return colorDiff(previousPercentage[7]);
+                return colorDiff(previousPercentage[5]);
               } else if(d.target.node == 7){
-                return colorDiff(previousPercentage[8]);
+                return colorDiff(previousPercentage[7]);
               } else if(d.target.node == 6){
                 return colorDiff(previousPercentage[9]);
               } else {
@@ -598,6 +599,177 @@ export default {
             .on("mouseover",linkmouseover)
             .on("mouseout",linkmouseout);  
 
+        var countMaxLocal = this.countShowS2max
+        var countMinLocal = this.countShowS2min
+
+        // add the link titles
+        link.append("svg:title") //this is the mouseover stuff title is an svg element you can use "svg:title" or just "title"
+              .text(function(d) {
+              if (d.source.node == 6 && d.target.node == 24) {
+                if (countMaxLocal[18] != 0) {
+                  return '+'+countMaxLocal[18]+'/'+format(d.value);
+                } else if (countMinLocal[18] != 0){
+                  return '-'+countMinLocal[18]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 6 && d.target.node == 18) {
+                if (countMaxLocal[19] != 0) {
+                  return '+'+countMaxLocal[19]+'/'+format(d.value);
+                } else if (countMinLocal[19] != 0) {
+                  return '-'+countMinLocal[19]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 7 && d.target.node == 25) {
+                if (countMaxLocal[16] != 0) {
+                  return '+'+countMaxLocal[16]+'/'+format(d.value);
+                } else if (countMinLocal[16] != 0) {
+                  return '-'+countMinLocal[16]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 7 && d.target.node == 19) {
+                if (countMaxLocal[17] != 0) {
+                  return '+'+countMaxLocal[17]+'/'+format(d.value);
+                } else if (countMinLocal[17] != 0) {
+                  return '-'+countMinLocal[17]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 8 && d.target.node == 26) {
+                if (countMaxLocal[14] != 0) {
+                  return '+'+countMaxLocal[14]+'/'+format(d.value);
+                } else if (countMinLocal[14] != 0) {
+                  return '-'+countMinLocal[14]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 8 && d.target.node == 20) {
+                if (countMaxLocal[15] != 0) {
+                  return '+'+countMaxLocal[15]+'/'+format(d.value);
+                } else if (countMinLocal[15] != 0) {
+                  return '-'+countMinLocal[15]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 9 && d.target.node == 27) {
+                if (countMaxLocal[12] != 0) {
+                  return '+'+countMaxLocal[12]+'/'+format(d.value);
+                } else if (countMinLocal[12] != 0) {
+                  return '-'+countMinLocal[12]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 9 && d.target.node == 21) {
+                if (countMaxLocal[13] != 0) {
+                  return '+'+countMaxLocal[13]+'/'+format(d.value);
+                } else if (countMinLocal[13] != 0) {
+                  return '-'+countMinLocal[13]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 10 && d.target.node == 28) {
+                if (countMaxLocal[10] != 0) {
+                  return '+'+countMaxLocal[10]+'/'+format(d.value);
+                } else if (countMinLocal[10] != 0) {
+                  return '-'+countMinLocal[10]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 10 && d.target.node == 22) {
+                if (countMaxLocal[11] != 0) {
+                  return '+'+countMaxLocal[11]+'/'+format(d.value);
+                } else if (countMinLocal[11] != 0) {
+                  return '-'+countMinLocal[11]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 12 && d.target.node == 36) {
+                if (countMaxLocal[8] != 0) {
+                  return '+'+countMaxLocal[8]+'/'+format(d.value);
+                } else if (countMinLocal[8] != 0){
+                  return '-'+countMinLocal[8]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 12 && d.target.node == 30) {
+                if (countMaxLocal[9] != 0) {
+                  return '+'+countMaxLocal[9]+'/'+format(d.value);
+                } else if (countMinLocal[9] != 0) {
+                  return '-'+countMinLocal[9]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 13 && d.target.node == 37) {
+                if (countMaxLocal[6] != 0) {
+                  return '+'+countMaxLocal[6]+'/'+format(d.value);
+                } else if (countMinLocal[6] != 0) {
+                  return '-'+countMinLocal[6]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 13 && d.target.node == 31) {
+                if (countMaxLocal[7] != 0) {
+                  return '+'+countMaxLocal[7]+'/'+format(d.value);
+                } else if (countMinLocal[7] != 0) {
+                  return '-'+countMinLocal[7]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 14 && d.target.node == 38) {
+                if (countMaxLocal[4] != 0) {
+                  return '+'+countMaxLocal[4]+'/'+format(d.value);
+                } else if (countMinLocal[4] != 0) {
+                  return '-'+countMinLocal[4]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 14 && d.target.node == 32) {
+                if (countMaxLocal[5] != 0) {
+                  return '+'+countMaxLocal[5]+'/'+format(d.value);
+                } else if (countMinLocal[5] != 0) {
+                  return '-'+countMinLocal[5]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 15 && d.target.node == 39) {
+                if (countMaxLocal[2] != 0) {
+                  return '+'+countMaxLocal[2]+'/'+format(d.value);
+                } else if (countMinLocal[2] != 0) {
+                  return '-'+countMinLocal[2]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 15 && d.target.node == 33) {
+                if (countMaxLocal[3] != 0) {
+                  return '+'+countMaxLocal[3]+'/'+format(d.value);
+                } else if (countMinLocal[3] != 0) {
+                  return '-'+countMinLocal[3]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 16 && d.target.node == 40) {
+                if (countMaxLocal[0] != 0) {
+                  return '+'+countMaxLocal[0]+'/'+format(d.value);
+                } else if (countMinLocal[0] != 0) {
+                  return '-'+countMinLocal[0]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 16 && d.target.node == 34) {
+                if (countMaxLocal[1] != 0) {
+                  return '+'+countMaxLocal[1]+'/'+format(d.value);
+                } else if (countMinLocal[1] != 0) {
+                  return '-'+countMinLocal[1]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else {
+                return format(d.value);
+              }
+              });
+
       // add the link titles
         link.append("svg:title") //this is the mouseover stuff title is an svg element you can use "svg:title" or just "title"
               .text(function(d) {
@@ -746,20 +918,16 @@ export default {
           }
 
         colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
-
         max[i] = Math.max.apply(Math, colorsforScatterPlot)
         min[i] = Math.min.apply(Math, colorsforScatterPlot)
         }
         
       } 
-      console.log(max)
-      console.log(min)
+
       var countMax = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
       var countMin = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-
       for (let i = 0; i < this.storedCM.length; i++) {
         let tempSplit = this.storedCM[i].split(/([0-9]+)/)
-
         if (tempSplit[0] == 'KNNC') {
           if (this.PerFCM[i] > max[0]) {
             countMax[0] = countMax[0] + 1
@@ -857,8 +1025,9 @@ export default {
           }
         }
       }
-      console.log(countMax)
-      console.log(countMin)
+      this.countShowS1max = countMax
+      this.countShowS1min = countMin
+
       // var percentage = []
       // for (let j = 0; j < countMax.length; j++) {
       //   if (j >= 5) {
@@ -1059,21 +1228,21 @@ export default {
               if(d.target.node == 16){
                 return colorDiff(percentage[0]);
               } else if(d.target.node == 15){
-                return colorDiff(percentage[1]);
-              } else if(d.target.node == 14){
                 return colorDiff(percentage[2]);
+              } else if(d.target.node == 14){
+                return colorDiff(percentage[4]);
               } else if(d.target.node == 13){
-                return colorDiff(percentage[3]);
+                return colorDiff(percentage[6]);
               } else if(d.target.node == 12){
-                return colorDiff(percentage[4]);
+                return colorDiff(percentage[8]);
               } else if(d.target.node == 10){
-                return colorDiff(percentage[5]);
+                return colorDiff(percentage[1]);
               } else if(d.target.node == 9){
-                return colorDiff(percentage[6]);
+                return colorDiff(percentage[3]);
               } else if(d.target.node == 8){
-                return colorDiff(percentage[7]);
+                return colorDiff(percentage[5]);
               } else if(d.target.node == 7){
-                return colorDiff(percentage[8]);
+                return colorDiff(percentage[7]);
               } else if(d.target.node == 6){
                 return colorDiff(percentage[9]);
               } else {
@@ -1084,11 +1253,98 @@ export default {
             .sort(function(a, b) { return b.dy - a.dy; })
             .on("mouseover",linkmouseover)
             .on("mouseout",linkmouseout);  
+      
+      var countMaxLocal = this.countShowS1max
+      var countMinLocal = this.countShowS1min
 
       // add the link titles
         link.append("svg:title") //this is the mouseover stuff title is an svg element you can use "svg:title" or just "title"
               .text(function(d) {
-              return format(d.value); });
+              if (d.source.node == 0 && d.target.node == 12) {
+                if (countMaxLocal[8] != 0) {
+                  return '+'+countMaxLocal[8]+'/'+format(d.value);
+                } else if (countMinLocal[8] != 0){
+                  return '-'+countMinLocal[8]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 0 && d.target.node == 6) {
+                if (countMaxLocal[9] != 0) {
+                  return '+'+countMaxLocal[9]+'/'+format(d.value);
+                } else if (countMinLocal[9] != 0) {
+                  return '-'+countMinLocal[9]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 1 && d.target.node == 13) {
+                if (countMaxLocal[6] != 0) {
+                  return '+'+countMaxLocal[6]+'/'+format(d.value);
+                } else if (countMinLocal[6] != 0) {
+                  return '-'+countMinLocal[6]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 1 && d.target.node == 7) {
+                if (countMaxLocal[7] != 0) {
+                  return '+'+countMaxLocal[7]+'/'+format(d.value);
+                } else if (countMinLocal[7] != 0) {
+                  return '-'+countMinLocal[7]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 2 && d.target.node == 14) {
+                if (countMaxLocal[4] != 0) {
+                  return '+'+countMaxLocal[4]+'/'+format(d.value);
+                } else if (countMinLocal[4] != 0) {
+                  return '-'+countMinLocal[4]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 2 && d.target.node == 8) {
+                if (countMaxLocal[5] != 0) {
+                  return '+'+countMaxLocal[5]+'/'+format(d.value);
+                } else if (countMinLocal[5] != 0) {
+                  return '-'+countMinLocal[5]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 3 && d.target.node == 15) {
+                if (countMaxLocal[2] != 0) {
+                  return '+'+countMaxLocal[2]+'/'+format(d.value);
+                } else if (countMinLocal[2] != 0) {
+                  return '-'+countMinLocal[2]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 3 && d.target.node == 9) {
+                if (countMaxLocal[3] != 0) {
+                  return '+'+countMaxLocal[3]+'/'+format(d.value);
+                } else if (countMinLocal[3] != 0) {
+                  return '-'+countMinLocal[3]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 4 && d.target.node == 16) {
+                if (countMaxLocal[0] != 0) {
+                  return '+'+countMaxLocal[0]+'/'+format(d.value);
+                } else if (countMinLocal[0] != 0) {
+                  return '-'+countMinLocal[0]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else if (d.source.node == 4 && d.target.node == 10) {
+                if (countMaxLocal[1] != 0) {
+                  return '+'+countMaxLocal[1]+'/'+format(d.value);
+                } else if (countMinLocal[1] != 0) {
+                  return '-'+countMinLocal[1]+'/'+format(d.value);
+                } else {
+                  return format(d.value);
+                }
+              } else {
+                return format(d.value);
+              }
+              });
+              
 
       // add in the nodes (creating the groups of the rectanlges)
         var node = svg.append("g").selectAll(".node") 

frontend/src/components/Predictions.vue

@@ -3,7 +3,17 @@
     <div id="containerAll"></div>
     <div id="containerSelection"></div>
     <div id="LegendMain"></div>
-    <div id="LegendHeat"></div>
+    <b-row>
+      <b-col cols="2">
+        <div id="HistClass0" style = "margin-top: 42px"></div>
+      </b-col>
+      <b-col cols="8">
+        <div id="LegendHeat"></div>
+      </b-col>
+      <b-col cols="2" style="margin-left: -120px !important">
+        <div id="HistClass1" style = "margin-top: 42px"></div>
+      </b-col>
+    </b-row>
   </div>
 </template>
 
@@ -11,9 +21,10 @@
 import * as d3Base from 'd3'
 import { EventBus } from '../main.js'
 import * as colorbr from 'colorbrewer'
+import * as Plotly from 'plotly.js'
 
 // attach all d3 plugins to the d3 library
-const d3 = Object.assign(d3Base)
+const d3v5 = Object.assign(d3Base)
 const colorbrewer = Object.assign(colorbr)
 
 export default {
@@ -27,19 +38,23 @@ export default {
       classesNumber: 9,
       InfoPred: [],
       flag: false,
+      classOver0: [],
+      classOver1: [],
       RetrieveValueFi: 'biodegC' // default file name
     }
   },
   methods: {
     reset () {
-      var svg = d3.select("#containerAll");
+      var svg = d3v5.select("#containerAll");
       svg.selectAll("*").remove();
-      var svg = d3.select("#containerSelection");
+      var svg = d3v5.select("#containerSelection");
       svg.selectAll("*").remove();
-      var svgLegG = d3.select("#LegendMain");
+      var svgLegG = d3v5.select("#LegendMain");
       svgLegG.selectAll("*").remove();
-      var svgLeg = d3.select("#LegendHeat");
+      var svgLeg = d3v5.select("#LegendHeat");
       svgLeg.selectAll("*").remove();
+      Plotly.purge('HistClass0')
+      Plotly.purge('HistClass1')
       this.GetResultsAll = []
       this.predictSelection = []
       this.StoreIndices = []
@@ -47,15 +62,23 @@ export default {
     },
     Grid () {
 
-      var svg = d3.select("#containerAll");
+      var svg = d3v5.select("#containerAll");
       svg.selectAll("*").remove();
 
       var yValues = JSON.parse(this.GetResultsAll[14])
 
       var targetNames = JSON.parse(this.GetResultsAll[7])
       var getIndices = []
-      if (!this.flag) {
 
+      var predictions = JSON.parse(this.GetResultsAll[12])
+      var KNNPred = predictions[0]
+      var LRPred = predictions[1]
+      var MLPPred = predictions[2]
+      var RFPred = predictions[3]
+      var GradBPred = predictions[4]
+      var PredAver = predictions[5]
+
+      if (!this.flag) {
         for (let i = 0; i < targetNames.length; i++) {
           let clTemp = []
           let j = -1
@@ -64,6 +87,8 @@ export default {
           }
           getIndices.push(clTemp)
         }
+        var Class0 = []
+        var Class1 = []
       }
       else {
         var tempFirst = []
@@ -76,18 +101,13 @@ export default {
         }
         getIndices.push(tempFirst)
         getIndices.push(tempLast)
+        this.classOver0 = predictions[6]
+        this.classOver1 = predictions[7]
       }
       if (this.RetrieveValueFi == "heartC") {
         getIndices.reverse()
       }
 
-      var predictions = JSON.parse(this.GetResultsAll[12])
-      var KNNPred = predictions[0]
-      var LRPred = predictions[1]
-      var MLPPred = predictions[2]
-      var RFPred = predictions[3]
-      var GradBPred = predictions[4]
-      var PredAver = predictions[5]
       var dataAver = []
       var dataAverGetResults = []
       var dataKNN = []
@@ -207,7 +227,7 @@ export default {
 		var colourScale;
 
 
-		var canvas = d3.select('#containerAll')
+		var canvas = d3v5.select('#containerAll')
 			.append('canvas')
 			.attr('width', width)
       .attr('height', height);
@@ -217,7 +237,7 @@ export default {
 		// === Bind data to custom elements === //
 
 		var customBase = document.createElement('custom');
-		var custom = d3.select(customBase); // this is our svg replacement
+		var custom = d3v5.select(customBase); // this is our svg replacement
 
     // settings for a grid with 40 cells in a row and 2x5 cells in a group
 		var groupSpacing = 42;
@@ -232,7 +252,7 @@ export default {
 		// === First call === //
 		databind(classStore, size, sqrtSize, lengthOverall); // ...then update the databind function
 
-		var t = d3.timer(function(elapsed) {
+		var t = d3v5.timer(function(elapsed) {
 			draw();
 			if (elapsed > 2500) t.stop();
 		}); // start a timer that runs the draw function for 500 ms (this needs to be higher than the transition in the databind function)
@@ -242,7 +262,7 @@ export default {
 
 		function databind(data, size, sqrtSize, lengthOverallLocal) {
 
-			colourScale = d3.scaleSequential(d3.interpolateGreens).domain([0, 100])
+			colourScale = d3v5.scaleSequential(d3v5.interpolateGreens).domain([0, 100])
 
 			var join = custom.selectAll('custom.rect')
         .data(data);
@@ -301,7 +321,7 @@ export default {
 
 				// for each virtual/custom element...
 
-				var node = d3.select(this);
+				var node = d3v5.select(this);
 				context.fillStyle = node.attr('fillStyle');
 				context.fillRect(node.attr('x'), node.attr('y'), node.attr('width'), node.attr('height'))
 
@@ -312,7 +332,7 @@ export default {
   },
   GridSelection () {
 
-      var svg = d3.select("#containerSelection");
+      var svg = d3v5.select("#containerSelection");
       svg.selectAll("*").remove();
 
       var predictionsAll = JSON.parse(this.GetResultsAll[12])
@@ -488,7 +508,7 @@ export default {
 		var colourScale;
 
 
-		var canvas = d3.select('#containerSelection')
+		var canvas = d3v5.select('#containerSelection')
 			.append('canvas')
 			.attr('width', width)
 			.attr('height', height);
@@ -498,7 +518,7 @@ export default {
 		// === Bind data to custom elements === //
 
 		var customBase = document.createElement('custom');
-		var custom = d3.select(customBase); // this is our svg replacement
+		var custom = d3v5.select(customBase); // this is our svg replacement
 
     // settings for a grid with 40 cells in a row and 2x5 cells in a group
 		var groupSpacing = 42;
@@ -514,7 +534,7 @@ export default {
 		// === First call === //
 		databind(classStore, size, sqrtSize, lengthOverall); // ...then update the databind function
 		
-		var t = d3.timer(function(elapsed) {
+		var t = d3v5.timer(function(elapsed) {
 			draw();
 			if (elapsed > 2500) t.stop();
 		}); // start a timer that runs the draw function for 500 ms (this needs to be higher than the transition in the databind function)
@@ -524,7 +544,7 @@ export default {
 
 		function databind(data, size, sqrtSize, lengthOverallLocal) {
 
-			colourScale = d3.scaleSequential(d3.interpolatePRGn).domain([-100, 100])
+			colourScale = d3v5.scaleSequential(d3v5.interpolatePRGn).domain([-100, 100])
 
 			var join = custom.selectAll('custom.rect')
         .data(data);
@@ -584,7 +604,7 @@ export default {
 
 				// for each virtual/custom element...
 
-				var node = d3.select(this);
+				var node = d3v5.select(this);
 				context.fillStyle = node.attr('fillStyle');
 				context.fillRect(node.attr('x'), node.attr('y'), node.attr('width'), node.attr('height'))
 
@@ -606,13 +626,13 @@ export default {
     // http://bl.ocks.org/mbostock/5577023
     var colors = colorbrewer.PRGn[this.classesNumber];
 
-    var svgLegGl = d3.select("#LegendMain");
+    var svgLegGl = d3v5.select("#LegendMain");
       svgLegGl.selectAll("*").remove();
 
-    var svgLeg = d3.select("#LegendHeat");
+    var svgLeg = d3v5.select("#LegendHeat");
       svgLeg.selectAll("*").remove();
 
-    var svgLegGl = d3.select("#LegendMain").append("svg")
+    var svgLegGl = d3v5.select("#LegendMain").append("svg")
       .attr("width", viewerWidth)
       .attr("height", viewerHeight*0.35)
       .style("margin-top", "0")
@@ -651,7 +671,7 @@ export default {
     svgLegGl.append("text").attr("x", 275).attr("y", heightText+30).text(info[0]).style("font-size", "16px").style("font-weight", "bold").attr("alignment-baseline","top")
     svgLegGl.append("text").attr("x", 882).attr("y", heightText+30).text(info[1]).style("font-size", "16px").style("font-weight", "bold").attr("alignment-baseline","top")
 
-      var svgLeg = d3.select("#LegendHeat").append("svg")
+      var svgLeg = d3v5.select("#LegendHeat").append("svg")
         .attr("width", viewerWidth/2)
         .attr("height", viewerHeight*0.10)
         .style("margin-top", "35px")
@@ -701,6 +721,111 @@ export default {
           .attr("y", (viewerPosTop + cellSizeHeat) + 5);
 
       svgLeg.append("text").attr("x", 220).attr("y", 30).text("Difference in predictive power").style("font-size", "16px").attr("alignment-baseline","top")
+      
+      Plotly.purge('HistClass0')
+      Plotly.purge('HistClass1')
+
+      if (this.classOver0 !=0 && this.classOver1 != 0) {
+        function range(start, end) {
+          var ans = [];
+          for (let i = start; i < end; i++) {
+              ans.push(i+1);
+          }
+          return ans;
+        }
+        var max_of_array = Math.max.apply(Math, this.classOver0)
+        var min_of_array = Math.min.apply(Math, this.classOver0)
+        var class0Local = Object.values(this.classOver0)
+        var class1Local = Object.values(this.classOver1)
+
+        var indicesClass0 = this.StoreIndices[0].map( function(value) { 
+            return value - 100; 
+        } );
+        var indicesClass1 = this.StoreIndices[1]
+
+        var indicesClass0Trim = indicesClass0.slice(0,100)
+        var indicesClass1Trim = indicesClass1.slice(0,100)
+
+        var histogram0 = []
+        var histogram1 = []
+
+        indicesClass0Trim.forEach(element => {
+          histogram0.push(class0Local[element])
+        });
+        indicesClass1Trim.forEach(element => {
+          histogram1.push(class1Local[element])    
+        });
+
+        var trace = {
+          x: range(0,100),
+          y: histogram0, 
+          marker: {
+            color: "rgba(192, 192, 192, 1)", 
+            line: {
+              color:  "rgba(85, 85, 85, 1)", 
+              width: 1
+            }
+          },  
+          type: "bar", 
+        };
+
+        var layout = {
+          width: 300,
+          height: 62,
+          bargap: 0.1,
+          showlegend: false,
+          margin: {
+            l: 40,
+            r: 15,
+            b: 14,
+            t: 10,
+            pad: 0
+          },
+          title: "Distribution of Instances (Sorted)",
+          yaxis: {title: "Instances"}
+        };
+        var data = [trace]
+        var config = {'displayModeBar': false}
+
+        Plotly.newPlot('HistClass0', data, layout, config);
+
+        var max_of_array = Math.max.apply(Math, this.classOver1);
+        var min_of_array = Math.min.apply(Math, this.classOver1);
+
+        var trace = {
+          x: range(0,100),
+          y: histogram1,
+          marker: {
+            color: "rgba(192, 192, 192, 1)", 
+            line: {
+              color:  "rgba(85, 85, 85, 1)", 
+              width: 1
+            }
+          },  
+          type: "bar", 
+        };
+
+        var layout = {
+          width: 300,
+          height: 62,
+          bargap: 0.1,
+          showlegend: false,
+          margin: {
+            l: 40,
+            r: 15,
+            b: 14,
+            t: 10,
+            pad: 0
+          },
+          title: "Distribution of Instances (Sorted)",
+          yaxis: {title: "Instances"}
+        };
+        var data = [trace]
+        var config = {'displayModeBar': false}
+
+        Plotly.newPlot('HistClass1', data, layout, config);
+      }
+     
   },
   },
   mounted () {
@@ -730,22 +855,26 @@ export default {
 </script>
 
 <style type="text/css">
-		canvas {
-			border:  1px dotted #ccc;
-		}
+canvas {
+  border:  1px dotted #ccc;
+}
 
-    #containerForAll {
-      height: 100px;
-      position: relative;
-    }
-    #LegendMain {
-      width: 100%;
-      height: 100%;
-      position: absolute;
-      top: 0;
-      left: 0;
-    }
-    #LegendMain {
-      z-index: 10;
-    }
+#containerForAll {
+  height: 100px;
+  position: relative;
+}
+#LegendMain {
+  width: 100%;
+  height: 100%;
+  position: absolute;
+  top: 0;
+  left: 0;
+}
+#LegendMain {
+  z-index: 10;
+}
+
+.gtitle {
+  transform: translate(-432px, -99px) !important;
+}
 </style>

frontend/src/components/VotingResults.vue

@@ -172,9 +172,9 @@ export default {
               .attr('class', 'score')
               .text(function(d){return d[rCol];});
 
-      chart.append("text").attr("x",width/3).attr("y", 20).attr("class","title").text(info[0]+' (%)');
-      chart.append("text").attr("x",width/3+rightOffset).attr("y", 20).attr("class","title").text(info[1]+' (%)');
-      chart.append("text").attr("x",width+labelArea/3).attr("y", 20).attr("class","title").text("Metrics");
+      chart.append("text").attr("x",width/3).attr("y", 20).attr("class","title").text(info[0]);
+      chart.append("text").attr("x",width/3+rightOffset).attr("y", 20).attr("class","title").text(info[1]);
+      chart.append("text").attr("x",width+labelArea/3).attr("y", 20).attr("class","title").text("Metrics (%)");
     },
     legendColFinal () {        
     //==================================================

run.py

@@ -943,6 +943,11 @@ def PreprocessingPred():
     lastElPredAv = []
     yDataSortedFirst = []
     yDataSortedLast = []
+    gatherPointsAllClass0 = []
+    gatherPointsAllClass1 = []
+
+    ResultsGatheredFirst = [0,0,0,0,0,0,0]
+    ResultsGatheredLast = [0,0,0,0,0,0,0]
 
     for index, item in enumerate(yData):
             if (item == 1):
@@ -996,7 +1001,7 @@ def PreprocessingPred():
         predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
         predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
         yDataSorted = yDataSortedFirst + yDataSortedLast
-    return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
+    return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions, ResultsGatheredLast[6], ResultsGatheredFirst[6]]
 
 def computeClusters(dataLocal,one,two,three,four,five,flagLocal):
     if (len(dataLocal) != 0):
@@ -1032,6 +1037,11 @@ def computeClusters(dataLocal,one,two,three,four,five,flagLocal):
         gatherPointsRF = []
         gatherPointsGradB = []
 
+        gatherPointsAll = [0] * 100
+        for ind, val in enumerate(labels):
+            for k in range(100):
+                if (k == val):
+                    gatherPointsAll[k] = gatherPointsAll[val] + 1
         for k in range(100):
             my_members = labels == k
             if (len(X[my_members, 0]) == 0):
@@ -1071,7 +1081,7 @@ def computeClusters(dataLocal,one,two,three,four,five,flagLocal):
     else:
         gatherPointsAv = []
         
-    return [gatherPointsAv,gatherPointsKNN,gatherPointsLR,gatherPointsMLP,gatherPointsRF,gatherPointsGradB]
+    return [gatherPointsAv,gatherPointsKNN,gatherPointsLR,gatherPointsMLP,gatherPointsRF,gatherPointsGradB, gatherPointsAll]
 
 def EnsembleIDs():
     global EnsembleActive
@@ -1917,7 +1927,7 @@ def CrossoverMutateFun():
     EnsembleActive = json.loads(EnsembleActive)
 
     EnsembleActive = EnsembleActive['StoreEnsemble']
-
+    print(EnsembleActive)
     setMaxLoopValue = request.get_data().decode('utf8').replace("'", '"')
     setMaxLoopValue = json.loads(setMaxLoopValue)
 
@@ -1927,7 +1937,7 @@ def CrossoverMutateFun():
     CurStage = json.loads(CurStage)
 
     CurStage = CurStage['Stage']
-
+    print(CurStage)
     if (CurStage == 1):
         InitializeFirstStageCM(RemainingIds, setMaxLoopValue)
     elif (CurStage == 2):
@@ -1938,7 +1948,7 @@ def CrossoverMutateFun():
 
 def RemoveSelected(RemainingIds):
     global allParametersPerfCrossMutr
-
+    print(RemainingIds)
     for loop in range(20): 
         indexes = []
         for i, val in enumerate(allParametersPerfCrossMutr[loop*4]):