fix bar chart

cache_dir/joblib/run/GridSearchForModels/6ca078b89780ce51f8c908be394b0a91/metadata.json

@@ -1 +1 @@
-{"duration": 15.238354921340942, "input_args": {"clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\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='warn',\n                       n_jobs=None, oob_score=False, random_state=None,\n                       verbose=0, warm_start=False)", "params": "{'n_estimators': [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']}"}}
+{"duration": 13.885742902755737, "input_args": {"clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\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='warn',\n                       n_jobs=None, oob_score=False, random_state=None,\n                       verbose=0, warm_start=False)", "params": "{'n_estimators': [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, 1]"}}

cache_dir/joblib/run/GridSearchForModels/b32ac658efc96fe80763fbc6cdd8559c/metadata.json

@@ -0,0 +1 @@
+{"duration": 36.23512291908264, "input_args": {"clf": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=5, 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], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "factors": "[1, 1, 1, 1, 1, 1]"}}

cache_dir/joblib/run/GridSearchForModels/func_code.py

@@ -0,0 +1,96 @@
+# first line: 714
+def GridSearchForModels(clf, params, eachAlgor, factors):
+
+    # scoring parameters
+    global scoring 
+
+    # number of scoring parameters
+    global NumberofscoringMetrics
+
+    # crossvalidation number
+    global crossValidation
+
+    # instantiate spark session
+    spark = (   
+        SparkSession    
+        .builder    
+        .getOrCreate()    
+        )
+    sc = spark.sparkContext 
+
+    # this is the grid we use to train the models
+    grid = DistGridSearchCV(    
+        estimator=clf, param_grid=params,     
+        sc=sc, cv=crossValidation, refit='accuracy', scoring=scoring,
+        verbose=0, n_jobs=-1)
+
+    # fit and extract the probabilities
+    grid.fit(XData, yData)
+    yPredict = grid.predict(XData)
+
+    # process the results
+    cv_results = []
+    cv_results.append(grid.cv_results_)
+    df_cv_results = pd.DataFrame.from_dict(cv_results)
+
+    # number of models stored
+    number_of_models = len(df_cv_results.iloc[0][0])
+
+    # initialize results per row
+    df_cv_results_per_row = []
+
+    # loop through number of models
+    for i in range(number_of_models):
+         # initialize results per item
+        df_cv_results_per_item = []
+        for column in df_cv_results.iloc[0]:
+            df_cv_results_per_item.append(column[i])
+        df_cv_results_per_row.append(df_cv_results_per_item)
+
+    # store the results into a pandas dataframe
+    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
+
+    # copy and filter in order to get only the metrics
+    metrics = df_cv_results_classifiers.copy()
+    metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard']) 
+
+    # control the factors
+    sumperModel = []
+    for row in metrics.iterrows():
+        rowSum = 0
+        lengthFactors = NumberofscoringMetrics
+        for loop,elements in enumerate(row):
+            lengthFactors = lengthFactors -  1 + factors[loop]
+            rowSum = elements*factors[loop] + rowSum
+        if lengthFactors is 0:
+            sumperModel = 0
+        else:
+            sumperModel.append(rowSum/lengthFactors)
+    # summarize all models metrics
+    summarizedMetrics = pd.DataFrame(sumperModel)
+    summarizedMetrics.rename(columns={0:'sum'})
+    
+    yPredictProb.append(grid.predict_proba(XData))
+    
+    # retrieve target names (class names)
+    global target_names
+    PerClassMetric = []
+    PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
+    PerClassMetricPandas = pd.DataFrame(PerClassMetric)  
+    print(PerClassMetricPandas)
+    del PerClassMetricPandas['accuracy']  
+    del PerClassMetricPandas['macro avg']  
+    del PerClassMetricPandas['weighted avg']  
+    PerClassMetricPandas = PerClassMetricPandas.to_json()
+
+    # concat parameters and performance
+    parameters = pd.DataFrame(df_cv_results_classifiers['params'])
+    parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
+    parametersPerformancePerModel = parametersPerformancePerModel.to_json()
+    
+    # make global the parameters performance to send it back
+    global allParametersPerformancePerModel
+    allParametersPerformancePerModel.append(parametersPerformancePerModel)
+    allParametersPerformancePerModel.append(PerClassMetricPandas)
+
+    return 'Everything is okay'

cache_dir/joblib/run/GridSearchForParameters/6ca078b89780ce51f8c908be394b0a91/metadata.json

@@ -0,0 +1 @@
+{"duration": 11.80344295501709, "input_args": {"clf": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\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='warn',\n                       n_jobs=None, oob_score=False, random_state=None,\n                       verbose=0, warm_start=False)", "params": "{'n_estimators': [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, 1]"}}

cache_dir/joblib/run/GridSearchForParameters/b32ac658efc96fe80763fbc6cdd8559c/metadata.json

@@ -1 +1 @@
-{"duration": 4.3733649253845215, "input_args": {"clf": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=5, 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], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}"}}
+{"duration": 28.940529108047485, "input_args": {"clf": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=5, 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], 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}", "eachAlgor": "'KNN'", "factors": "[1, 1, 1, 1, 1, 1]"}}

cache_dir/joblib/run/GridSearchForParameters/func_code.py

@@ -1,16 +1,33 @@
-# first line: 484
-def GridSearchForParameters(clf, params):
-    grid = GridSearchCV(estimator=clf, 
-                param_grid=params,
-                scoring='accuracy', 
-                cv=crossValidation,
-                n_jobs = -1)
+# first line: 705
+def GridSearchForParameters(clf, params, eachAlgor, factors):
+
+    global scoring 
+    global NumberofscoringMetrics
+
+    # instantiate spark session
+    spark = (   
+        SparkSession    
+        .builder    
+        .getOrCreate()    
+        )
+    sc = spark.sparkContext 
+
+    scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
+    global crossValidation
+    NumberofscoringMetrics = len(scoring)
+
+    grid = DistGridSearchCV(    
+        estimator=clf, param_grid=params,     
+        sc=sc, cv=crossValidation, refit='accuracy', scoring=scoring,
+        verbose=0, n_jobs=-1)
+
     grid.fit(XData, yData)
+    yPredict = grid.predict(XData)
     cv_results = []
     cv_results.append(grid.cv_results_)
     df_cv_results = pd.DataFrame.from_dict(cv_results)
     number_of_classifiers = len(df_cv_results.iloc[0][0])
-    number_of_columns = len(df_cv_results.iloc[0])
+
     df_cv_results_per_item = []
     df_cv_results_per_row = []
 
@@ -22,7 +39,38 @@ def GridSearchForParameters(clf, params):
     df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
 
     global allParametersPerformancePerModel
-    parametersPerformancePerModel = df_cv_results_classifiers[['mean_test_score','params']]
+    global parametersPerformancePerModel
+
+    metrics = df_cv_results_classifiers.copy()
+    metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
+    sumperModel = []
+    global rowSum
+    for index, row in metrics.iterrows():
+        rowSum = 0
+        lengthFactors = NumberofscoringMetrics
+        for loop,elements in enumerate(row):
+            lengthFactors = lengthFactors -  1 + factors[loop]
+            rowSum = elements*factors[loop] + rowSum
+        if lengthFactors is 0:
+            sumperModel = 0
+        else:
+            sumperModel.append(rowSum/lengthFactors)
+    global target_names
+    global PerClassMetric
+    global PerClassMetricPandas
+    PerClassMetric = []
+    yPredictProb.append(grid.predict_proba(XData))
+    PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
+    PerClassMetricPandas = pd.DataFrame(PerClassMetric)  
+    del PerClassMetricPandas['accuracy']  
+    del PerClassMetricPandas['macro avg']  
+    del PerClassMetricPandas['weighted avg']  
+    summarizedMetrics = pd.DataFrame(sumperModel)
+    summarizedMetrics.rename(columns={0:'sum'})
+    parameters = pd.DataFrame(df_cv_results_classifiers['params'])
+    parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
+    PerClassMetricPandas = PerClassMetricPandas.to_json()
     parametersPerformancePerModel = parametersPerformancePerModel.to_json()
     allParametersPerformancePerModel.append(parametersPerformancePerModel)
+    allParametersPerformancePerModel.append(PerClassMetricPandas)
     return 'Everything is okay'

cachedir/joblib/run/GridSearchForModels/369469c4c53064fdb6191f1f295902f7/metadata.json

@@ -0,0 +1 @@
+{"duration": 288.6949689388275, "input_args": {"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/d44ab74114f87bbccdbdd1a5d904bd4a/metadata.json

@@ -0,0 +1 @@
+{"duration": 227.36859011650085, "input_args": {"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': [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

@@ -0,0 +1,128 @@
+# first line: 634
+@memory.cache
+def GridSearchForModels(clf, params, eachAlgor, factors, AlgorithmsIDsEnd):
+
+    # instantiate spark session
+    spark = (   
+        SparkSession    
+        .builder    
+        .getOrCreate()    
+        )
+    sc = spark.sparkContext 
+
+    # this is the grid we use to train the models
+    grid = DistGridSearchCV(    
+        estimator=clf, param_grid=params,     
+        sc=sc, cv=crossValidation, refit='accuracy', scoring=scoring,
+        verbose=0, n_jobs=-1)
+
+    # fit and extract the probabilities
+    grid.fit(XData, yData)
+
+    # process the results
+    cv_results = []
+    cv_results.append(grid.cv_results_)
+    df_cv_results = pd.DataFrame.from_dict(cv_results)
+
+    # number of models stored
+    number_of_models = len(df_cv_results.iloc[0][0])
+
+    # initialize results per row
+    df_cv_results_per_row = []
+
+    # loop through number of models
+    modelsIDs = []
+    for i in range(number_of_models):
+        modelsIDs.append(AlgorithmsIDsEnd+i)
+         # initialize results per item
+        df_cv_results_per_item = []
+        for column in df_cv_results.iloc[0]:
+            df_cv_results_per_item.append(column[i])
+        df_cv_results_per_row.append(df_cv_results_per_item)
+
+    # store the results into a pandas dataframe
+    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
+
+    # copy and filter in order to get only the metrics
+    metrics = df_cv_results_classifiers.copy()
+    metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard']) 
+
+    # control the factors
+    sumperModel = []
+    for index, row in metrics.iterrows():
+        rowSum = 0
+        lengthFactors = NumberofscoringMetrics
+        for loop,elements in enumerate(row):
+            lengthFactors = lengthFactors -  1 + factors[loop]
+            rowSum = elements*factors[loop] + rowSum
+        if lengthFactors is 0:
+            sumperModel = 0
+        else:
+            sumperModel.append(rowSum/lengthFactors)
+    
+    # summarize all models metrics
+    summarizedMetrics = pd.DataFrame(sumperModel)
+    summarizedMetrics.rename(columns={0:'sum'})
+
+    # concat parameters and performance
+    parameters = pd.DataFrame(df_cv_results_classifiers['params'])
+    parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
+    parametersPerformancePerModel = parametersPerformancePerModel.to_json()
+    
+    parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy()
+    Models = []
+    for index, items in enumerate(parametersLocal):
+        Models.append(str(index))
+
+    parametersLocalNew = [ parametersLocal[your_key] for your_key in Models ]
+
+    permList = []
+    PerFeatureAccuracy = []
+    PerClassMetric = []
+
+    for eachModelParameters in parametersLocalNew:
+        clf.set_params(**eachModelParameters)
+
+        perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData)
+        permList.append(perm.feature_importances_)
+
+        n_feats = XData.shape[1]
+        for i in range(n_feats):
+            scores = model_selection.cross_val_score(clf, XData.values[:, i].reshape(-1, 1), yData, cv=crossValidation)
+            PerFeatureAccuracy.append(scores.mean())
+
+        clf.fit(XData, yData) 
+        yPredict = clf.predict(XData)
+        # retrieve target names (class names)
+        PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
+
+    PerClassMetricPandas = pd.DataFrame(PerClassMetric)  
+    del PerClassMetricPandas['accuracy']  
+    del PerClassMetricPandas['macro avg']  
+    del PerClassMetricPandas['weighted avg']  
+    PerClassMetricPandas = PerClassMetricPandas.to_json()
+
+
+    perm_imp_eli5PD = pd.DataFrame(permList)
+    perm_imp_eli5PD = perm_imp_eli5PD.to_json()
+
+    PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracy)
+    PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
+
+    bestfeatures = SelectKBest(score_func=chi2, k='all')
+    fit = bestfeatures.fit(XData,yData)
+    dfscores = pd.DataFrame(fit.scores_)
+    dfcolumns = pd.DataFrame(XData.columns)
+    featureScores = pd.concat([dfcolumns,dfscores],axis=1)
+    featureScores.columns = ['Specs','Score']  #naming the dataframe columns
+    featureScores = featureScores.to_json()
+
+    # gather the results and send them back
+    results.append(modelsIDs) # Position: 0 and so on
+    results.append(parametersPerformancePerModel) # Position: 1 and so on
+    results.append(PerClassMetricPandas) # Position: 2 and so on
+    results.append(PerFeatureAccuracyPandas) # Position: 3 and so on
+    results.append(perm_imp_eli5PD) # Position: 4 and so on
+    results.append(featureScores) # Position: 5 and so on
+
+    return results

frontend/package-lock.json

@@ -1807,9 +1807,9 @@
       "integrity": "sha512-tHq6qdbT9U1IRSGf14CL0pUlULksvY9OZ+5eEgl1N7t+OA3tGvNpxJCzuKQlsNgCVwbAs670L1vcVQi8j9HjnA=="
     },
     "@types/node": {
-      "version": "12.7.9",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-12.7.9.tgz",
-      "integrity": "sha512-P57oKTJ/vYivL2BCfxCC5tQjlS8qW31pbOL6qt99Yrjm95YdHgNZwjrTTjMBh+C2/y6PXIX4oz253+jUzxKKfQ=="
+      "version": "12.7.11",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-12.7.11.tgz",
+      "integrity": "sha512-Otxmr2rrZLKRYIybtdG/sgeO+tHY20GxeDjcGmUnmmlCWyEnv2a2x1ZXBo3BTec4OiTXMQCiazB8NMBf0iRlFw=="
     },
     "@types/q": {
       "version": "1.5.2",
@@ -2188,6 +2188,14 @@
         }
       }
     },
+    "allofw": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/allofw/-/allofw-0.1.0.tgz",
+      "integrity": "sha1-AmxSVpoJaAeGrxOwva3uYRLXfPY=",
+      "requires": {
+        "tar": "^4.1.1"
+      }
+    },
     "almost-equal": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/almost-equal/-/almost-equal-1.1.0.tgz",
@@ -3043,31 +3051,77 @@
       "integrity": "sha1-/vF92Erx3NKlYz/UbTsa3LCEvzs="
     },
     "autoprefixer": {
-      "version": "9.6.1",
-      "resolved": "https://registry.npmjs.org/autoprefixer/-/autoprefixer-9.6.1.tgz",
-      "integrity": "sha512-aVo5WxR3VyvyJxcJC3h4FKfwCQvQWb1tSI5VHNibddCVWrcD1NvlxEweg3TSgiPztMnWfjpy2FURKA2kvDE+Tw==",
+      "version": "9.6.4",
+      "resolved": "https://registry.npmjs.org/autoprefixer/-/autoprefixer-9.6.4.tgz",
+      "integrity": "sha512-Koz2cJU9dKOxG8P1f8uVaBntOv9lP4yz9ffWvWaicv9gHBPhpQB22nGijwd8gqW9CNT+UdkbQOQNLVI8jN1ZfQ==",
       "dev": true,
       "requires": {
-        "browserslist": "^4.6.3",
-        "caniuse-lite": "^1.0.30000980",
+        "browserslist": "^4.7.0",
+        "caniuse-lite": "^1.0.30000998",
         "chalk": "^2.4.2",
         "normalize-range": "^0.1.2",
         "num2fraction": "^1.2.2",
-        "postcss": "^7.0.17",
-        "postcss-value-parser": "^4.0.0"
+        "postcss": "^7.0.18",
+        "postcss-value-parser": "^4.0.2"
       },
       "dependencies": {
+        "browserslist": {
+          "version": "4.7.0",
+          "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.7.0.tgz",
+          "integrity": "sha512-9rGNDtnj+HaahxiVV38Gn8n8Lr8REKsel68v1sPFfIGEK6uSXTY3h9acgiT1dZVtOOUtifo/Dn8daDQ5dUgVsA==",
+          "dev": true,
+          "requires": {
+            "caniuse-lite": "^1.0.30000989",
+            "electron-to-chromium": "^1.3.247",
+            "node-releases": "^1.1.29"
+          }
+        },
         "caniuse-lite": {
-          "version": "1.0.30000984",
-          "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30000984.tgz",
-          "integrity": "sha512-n5tKOjMaZ1fksIpQbjERuqCyfgec/m9pferkFQbLmWtqLUdmt12hNhjSwsmPdqeiG2NkITOQhr1VYIwWSAceiA==",
+          "version": "1.0.30000999",
+          "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30000999.tgz",
+          "integrity": "sha512-1CUyKyecPeksKwXZvYw0tEoaMCo/RwBlXmEtN5vVnabvO0KPd9RQLcaAuR9/1F+KDMv6esmOFWlsXuzDk+8rxg==",
           "dev": true
         },
+        "electron-to-chromium": {
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+      "dependencies": {
+        "stardust-core": {
+          "version": "0.1.1",
+          "resolved": "https://registry.npmjs.org/stardust-core/-/stardust-core-0.1.1.tgz",
+          "integrity": "sha1-6gtcxBKrgpnfcf7NrmcN9K5WhPA="
+        }
       }
     },
     "stardust-core": {
@@ -21226,13 +21286,13 @@
       }
     },
     "tar": {
-      "version": "4.4.10",
-      "resolved": "https://registry.npmjs.org/tar/-/tar-4.4.10.tgz",
-      "integrity": "sha512-g2SVs5QIxvo6OLp0GudTqEf05maawKUxXru104iaayWA09551tFCTI8f1Asb4lPfkBr91k07iL4c11XO3/b0tA==",
+      "version": "4.4.13",
+      "resolved": "https://registry.npmjs.org/tar/-/tar-4.4.13.tgz",
+      "integrity": "sha512-w2VwSrBoHa5BsSyH+KxEqeQBAllHhccyMFVHtGtdMpF4W7IRWfZjFiQceJPChOeTsSDVUpER2T8FA93pr0L+QA==",
       "requires": {
         "chownr": "^1.1.1",
         "fs-minipass": "^1.2.5",
-        "minipass": "^2.3.5",
+        "minipass": "^2.8.6",
         "minizlib": "^1.2.1",
         "mkdirp": "^0.5.0",
         "safe-buffer": "^5.1.2",
@@ -21240,9 +21300,9 @@
       },
       "dependencies": {
         "yallist": {
-          "version": "3.0.3",
-          "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.0.3.tgz",
-          "integrity": "sha512-S+Zk8DEWE6oKpV+vI3qWkaK+jSbIK86pCwe2IF/xwIpQ8jEuxpw9NyaGjmp9+BoJv5FV2piqCDcoCtStppiq2A=="
+          "version": "3.1.1",
+          "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
+          "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g=="
         }
       }
     },
@@ -22467,16 +22527,22 @@
       }
     },
     "url-loader": {
-      "version": "2.1.0",
-      "resolved": "https://registry.npmjs.org/url-loader/-/url-loader-2.1.0.tgz",
-      "integrity": "sha512-kVrp/8VfEm5fUt+fl2E0FQyrpmOYgMEkBsv8+UDP1wFhszECq5JyGF33I7cajlVY90zRZ6MyfgKXngLvHYZX8A==",
+      "version": "2.2.0",
+      "resolved": "https://registry.npmjs.org/url-loader/-/url-loader-2.2.0.tgz",
+      "integrity": "sha512-G8nk3np8ZAnwhHXas1JxJEwJyQdqFXAKJehfgZ/XrC48volFBRtO+FIKtF2u0Ma3bw+4vnDVjHPAQYlF9p2vsw==",
       "dev": true,
       "requires": {
         "loader-utils": "^1.2.3",
         "mime": "^2.4.4",
-        "schema-utils": "^2.0.0"
+        "schema-utils": "^2.4.1"
       },
       "dependencies": {
+        "ajv-keywords": {
+          "version": "3.4.1",
+          "resolved": "https://registry.npmjs.org/ajv-keywords/-/ajv-keywords-3.4.1.tgz",
+          "integrity": "sha512-RO1ibKvd27e6FEShVFfPALuHI3WjSVNeK5FIsmme/LYRNxjKuNj+Dt7bucLa6NdSv3JcVTyMlm9kGR84z1XpaQ==",
+          "dev": true
+        },
         "mime": {
           "version": "2.4.4",
           "resolved": "https://registry.npmjs.org/mime/-/mime-2.4.4.tgz",
@@ -22484,13 +22550,13 @@
           "dev": true
         },
         "schema-utils": {
-          "version": "2.0.1",
-          "resolved": "https://registry.npmjs.org/schema-utils/-/schema-utils-2.0.1.tgz",
-          "integrity": "sha512-HJFKJ4JixDpRur06QHwi8uu2kZbng318ahWEKgBjc0ZklcE4FDvmm2wghb448q0IRaABxIESt8vqPFvwgMB80A==",
+          "version": "2.4.1",
+          "resolved": "https://registry.npmjs.org/schema-utils/-/schema-utils-2.4.1.tgz",
+          "integrity": "sha512-RqYLpkPZX5Oc3fw/kHHHyP56fg5Y+XBpIpV8nCg0znIALfq3OH+Ea9Hfeac9BAMwG5IICltiZ0vxFvJQONfA5w==",
           "dev": true,
           "requires": {
-            "ajv": "^6.1.0",
-            "ajv-keywords": "^3.1.0"
+            "ajv": "^6.10.2",
+            "ajv-keywords": "^3.4.1"
           }
         }
       }
@@ -24038,9 +24104,9 @@
       }
     },
     "webpack-dev-middleware": {
-      "version": "3.7.1",
-      "resolved": "https://registry.npmjs.org/webpack-dev-middleware/-/webpack-dev-middleware-3.7.1.tgz",
-      "integrity": "sha512-5MWu9SH1z3hY7oHOV6Kbkz5x7hXbxK56mGHNqHTe6d+ewxOwKUxoUJBs7QIaJb33lPjl9bJZ3X0vCoooUzC36A==",
+      "version": "3.7.2",
+      "resolved": "https://registry.npmjs.org/webpack-dev-middleware/-/webpack-dev-middleware-3.7.2.tgz",
+      "integrity": "sha512-1xC42LxbYoqLNAhV6YzTYacicgMZQTqRd27Sim9wn5hJrX3I5nxYy1SxSd4+gjUFsz1dQFj+yEe6zEVmSkeJjw==",
       "dev": true,
       "requires": {
         "memory-fs": "^0.4.1",
@@ -24059,9 +24125,9 @@
       }
     },
     "webpack-dev-server": {
-      "version": "3.8.1",
-      "resolved": "https://registry.npmjs.org/webpack-dev-server/-/webpack-dev-server-3.8.1.tgz",
-      "integrity": "sha512-9F5DnfFA9bsrhpUCAfQic/AXBVHvq+3gQS+x6Zj0yc1fVVE0erKh2MV4IV12TBewuTrYeeTIRwCH9qLMvdNvTw==",
+      "version": "3.8.2",
+      "resolved": "https://registry.npmjs.org/webpack-dev-server/-/webpack-dev-server-3.8.2.tgz",
+      "integrity": "sha512-0xxogS7n5jHDQWy0WST0q6Ykp7UGj4YvWh+HVN71JoE7BwPxMZrwgraBvmdEMbDVMBzF0u+mEzn8TQzBm5NYJQ==",
       "dev": true,
       "requires": {
         "ansi-html": "0.0.7",
@@ -24073,18 +24139,18 @@
         "del": "^4.1.1",
         "express": "^4.17.1",
         "html-entities": "^1.2.1",
-        "http-proxy-middleware": "^0.19.1",
+        "http-proxy-middleware": "0.19.1",
         "import-local": "^2.0.0",
         "internal-ip": "^4.3.0",
         "ip": "^1.1.5",
-        "is-absolute-url": "^3.0.2",
+        "is-absolute-url": "^3.0.3",
         "killable": "^1.0.1",
         "loglevel": "^1.6.4",
         "opn": "^5.5.0",
         "p-retry": "^3.0.1",
         "portfinder": "^1.0.24",
         "schema-utils": "^1.0.0",
-        "selfsigned": "^1.10.6",
+        "selfsigned": "^1.10.7",
         "semver": "^6.3.0",
         "serve-index": "^1.9.1",
         "sockjs": "0.3.19",
@@ -24093,7 +24159,7 @@
         "strip-ansi": "^3.0.1",
         "supports-color": "^6.1.0",
         "url": "^0.11.0",
-        "webpack-dev-middleware": "^3.7.1",
+        "webpack-dev-middleware": "^3.7.2",
         "webpack-log": "^2.0.0",
         "ws": "^6.2.1",
         "yargs": "12.0.5"
@@ -24129,9 +24195,9 @@
           }
         },
         "is-absolute-url": {
-          "version": "3.0.2",
-          "resolved": "https://registry.npmjs.org/is-absolute-url/-/is-absolute-url-3.0.2.tgz",
-          "integrity": "sha512-+5g/wLlcm1AcxSP7014m6GvbPHswDx980vD/3bZaap8aGV9Yfs7Q6y6tfaupgZ5O74Byzc8dGrSCJ+bFXx0KdA==",
+          "version": "3.0.3",
+          "resolved": "https://registry.npmjs.org/is-absolute-url/-/is-absolute-url-3.0.3.tgz",
+          "integrity": "sha512-opmNIX7uFnS96NtPmhWQgQx6/NYFgsUXYMllcfzwWKUMwfo8kku1TvE6hkNcH+Q1ts5cMVrsY7j0bxXQDciu9Q==",
           "dev": true
         },
         "ms": {

frontend/package.json

@@ -19,7 +19,7 @@
     "@fortawesome/vue-fontawesome": "^0.1.7",
     "@statnett/vue-plotly": "^0.3.2",
     "@types/d3-drag": "^1.2.3",
-    "@types/node": "^12.7.9",
+    "@types/node": "^12.7.11",
     "ajv": "^6.10.2",
     "audit": "0.0.6",
     "axios": "^0.19.0",
@@ -28,7 +28,7 @@
     "blob": "0.0.5",
     "bootstrap": "^4.3.1",
     "bootstrap-toggle": "^2.2.2",
-    "bootstrap-vue": "^2.0.2",
+    "bootstrap-vue": "^2.0.3",
     "circlepack-chart": "^1.3.0",
     "clean-webpack-plugin": "^3.0.0",
     "colorbrewer": "^1.3.0",
@@ -50,14 +50,14 @@
     "jquery": "^3.4.1",
     "mdbvue": "^5.8.3",
     "mini-css-extract-plugin": "^0.8.0",
-    "npm-check-updates": "^3.1.23",
+    "npm-check-updates": "^3.1.24",
     "papaparse": "^5.1.0",
     "parcoord-es": "^2.2.10",
     "plotly.js": "^1.49.5",
     "popper.js": "^1.15.0",
-    "react": "^16.10.1",
-    "react-dom": "^16.10.1",
-    "stardust": "^0.52.5",
+    "react": "^16.10.2",
+    "react-dom": "^16.10.2",
+    "stardust-allofw": "^0.1.1",
     "stardust-core": "^0.2.4",
     "stardust-webgl": "^0.2.4",
     "strip-loader": "^0.1.2",
@@ -88,7 +88,7 @@
     "@babel/plugin-syntax-jsx": "^7.2.0",
     "@babel/plugin-transform-runtime": "^7.6.2",
     "@babel/preset-env": "^7.6.2",
-    "autoprefixer": "^9.6.1",
+    "autoprefixer": "^9.6.4",
     "babel-eslint": "^10.0.3",
     "babel-helper-vue-jsx-merge-props": "^2.0.3",
     "babel-loader": "^8.0.6",
@@ -122,7 +122,7 @@
     "semver": "^6.3.0",
     "shelljs": "^0.8.3",
     "uglifyjs-webpack-plugin": "^2.2.0",
-    "url-loader": "^2.1.0",
+    "url-loader": "^2.2.0",
     "vue-class-component": "^7.1.0",
     "vue-cli-plugin-vuetify": "^0.6.3",
     "vue-loader": "^15.7.1",
@@ -133,7 +133,7 @@
     "vuetify-loader": "^1.3.0",
     "webpack": "^4.41.0",
     "webpack-bundle-analyzer": "^3.5.2",
-    "webpack-dev-server": "^3.8.1",
+    "webpack-dev-server": "^3.8.2",
     "webpack-merge": "^4.2.2"
   },
   "browserslist": [

frontend/src/components/AlgorithmHyperParam.vue

@@ -31,10 +31,10 @@ export default {
         
         var Combined = 0
         if (this.selAlgorithm == 'KNN') {    
-          Combined = JSON.parse(this.ModelsPerformance[0])
+          Combined = JSON.parse(this.ModelsPerformance[1])
           colorGiv = colors[0]
         } else {
-          Combined = JSON.parse(this.ModelsPerformance[2])
+          Combined = JSON.parse(this.ModelsPerformance[7])
           colorGiv = colors[1]
         }
         var valuesPerf = Object.values(Combined['0'])

frontend/src/components/Algorithms.vue

@@ -1,7 +1,7 @@
 <template>
-    <div>
-        <div id="exploding_boxplot" class="exploding_boxplot" ref="myClickable"></div>
-    </div>
+  <div>
+    <div id="exploding_boxplot" class="exploding_boxplot"></div>
+  </div>
 </template>
 
 <script>
@@ -34,70 +34,75 @@ export default {
   },
   methods: {
     boxplot () {
+      // reset the boxplot
       d3.selectAll("#exploding_boxplot > *").remove(); 
-        const PerformAlgor1 = JSON.parse(this.PerformanceAllModels[0])
-        const PerformAlgor2 = JSON.parse(this.PerformanceAllModels[2])
-        this.algorithm1 = []
-        this.algorithm2 = []
-        var parameters = []
-        for (var i = 0; i < Object.keys(PerformAlgor1['0']).length; i++) {
-          this.algorithm1.push({Performance: Object.values(PerformAlgor1['0'])[i]*100,Algorithm:'KNN',Model:'Model ' + i + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance ',ModelID:i})
-          parameters.push(JSON.stringify(Object.values(PerformAlgor1['params'])[i]))
-        }
-        var temp = i
-        for (let j = 0; j < Object.keys(PerformAlgor2['0']).length; j++) {
-          temp = i + j
-          this.algorithm2.push({Performance: Object.values(PerformAlgor2['0'])[j]*100,Algorithm:'RF',Model:'Model ' + temp + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance ',ModelID:temp})
-          parameters.push(JSON.stringify(Object.values(PerformAlgor2['params'])[j]))
-        }
-        EventBus.$emit('ParametersAll', parameters)
-        var data = this.algorithm1.concat(this.algorithm2)
 
-        // aesthetic :
-        // y : point's value on y axis
-        // group : how to group data on x axis
-        // color : color of the point / boxplot
-        // label : displayed text in toolbox
-        this.chart = exploding_boxplot(data, {y:'Performance',group:'Algorithm',color:'Algorithm',label:'Model'})
+      // retrieve models ID
+      const Algor1IDs = this.PerformanceAllModels[0]
+      const Algor2IDs = this.PerformanceAllModels[6]
       
-        this.chart.width(this.WH[0]*3)
-        this.chart.height(this.WH[1])
-        //call chart on a div
-        this.chart('#exploding_boxplot')
-        const previousColor = ['#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#b3de69','#fccde5','#d9d9d9','#bc80bd','#ccebc5','#ffed6f']
-        var el = document.getElementsByClassName('d3-exploding-boxplot boxcontent')
+      // retrieve the results like performance
+      const PerformAlgor1 = JSON.parse(this.PerformanceAllModels[1])
+      const PerformAlgor2 = JSON.parse(this.PerformanceAllModels[7])
 
-        this.brushStatus = document.getElementsByClassName('extent')
+      // initialize/instansiate algorithms and parameters
+      this.algorithm1 = []
+      this.algorithm2 = []
+      var parameters = []
 
-        el[0].onclick = function() {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
-          for (let i = 0; i < allPoints.length; i++) {
-          //if (modelsActive.indexOf(i) == -1) {
-            allPoints[i].style.fill = previousColor[0]
-            allPoints[i].style.opacity = '1.0'
-          //}
-          } 
+      for (var i = 0; i < Object.keys(PerformAlgor1['0']).length; i++) {
+        this.algorithm1.push({Performance: Object.values(PerformAlgor1['0'])[i]*100,Algorithm:'KNN',Model:'Model ' + Algor1IDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance Metrics ',ModelID:Algor1IDs[i]})
+        parameters.push(JSON.stringify(Object.values(PerformAlgor1['params'])[i]))
+      }
+      for (let j = 0; j < Object.keys(PerformAlgor2['0']).length; j++) {
+        this.algorithm2.push({Performance: Object.values(PerformAlgor2['0'])[j]*100,Algorithm:'RF',Model:'Model ' + Algor2IDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance Metrics ',ModelID:Algor2IDs[j]})
+        parameters.push(JSON.stringify(Object.values(PerformAlgor2['params'])[j]))
+      }
 
-          EventBus.$emit('PCPCall', 'KNN')
-        }
-        el[1].onclick = function() {
-          var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
-          for (let i = 0; i < allPoints.length; i++) {
-          //if (modelsActive.indexOf(i) == -1) {
-            allPoints[i].style.fill = previousColor[1]
-            allPoints[i].style.opacity = '1.0'
-          //}
-          }
-          EventBus.$emit('PCPCall', 'RF')
-        }
+      EventBus.$emit('ParametersAll', parameters)
 
-        const myObserver = new ResizeObserver(entries => {
-          EventBus.$emit('brusheAllOn')
-        });
+      // concat the data
+      var data = this.algorithm1.concat(this.algorithm2)
       
-        var brushRect = document.querySelector('.extent');
+      // aesthetic :
+      // y : point's value on y axis
+      // group : how to group data on x axis
+      // color : color of the point / boxplot
+      // label : displayed text in toolbox
+      this.chart = exploding_boxplot(data, {y:'Performance',group:'Algorithm',color:'Algorithm',label:'Model'})
+      this.chart.width(this.WH[0]*3) // interactive visualization
+      this.chart.height(this.WH[1]) // interactive visualization
+      //call chart on a div
+      this.chart('#exploding_boxplot')
 
-        myObserver.observe(brushRect);
+      // colorscale
+      const previousColor = ['#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#b3de69','#fccde5','#d9d9d9','#bc80bd','#ccebc5','#ffed6f']
+      // check for brushing
+      var el = document.getElementsByClassName('d3-exploding-boxplot boxcontent')
+      this.brushStatus = document.getElementsByClassName('extent')
+      // on clicks
+      el[0].onclick = function() {
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
+        for (let i = 0; i < allPoints.length; i++) {
+          allPoints[i].style.fill = previousColor[0]
+          allPoints[i].style.opacity = '1.0'
+        } 
+        EventBus.$emit('PCPCall', 'KNN')
+      }
+      el[1].onclick = function() {
+        var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
+        for (let i = 0; i < allPoints.length; i++) {
+          allPoints[i].style.fill = previousColor[1]
+          allPoints[i].style.opacity = '1.0'
+        }
+        EventBus.$emit('PCPCall', 'RF')
+      }
+      // check if brushed through all boxplots and not only one at a time
+      const myObserver = new ResizeObserver(entries => {
+        EventBus.$emit('brusheAllOn')
+      })
+      var brushRect = document.querySelector('.extent')
+      myObserver.observe(brushRect);
     },
     brushActivationAll () {
       // continue here and select the correct points.
@@ -163,7 +168,6 @@ export default {
           }
         }
       }
-
       this.UpdateBarChart()
     },
     brushed () {
@@ -217,14 +221,13 @@ export default {
           }
         }
       }
-
       this.UpdateBarChart()
     },
     UpdateBarChart () {
       var allPoints = document.getElementsByClassName('d3-exploding-boxplot point')
       var activeModels = []
       var algorithmsSelected = []
-      var parameters = []
+      var modelsSelected =[]
       for (let i = 0; i < allPoints.length; i++) {
         if (allPoints[i].style.fill != "rgb(211, 211, 211)") {
           activeModels.push(allPoints[i].__data__.Model)
@@ -240,18 +243,18 @@ export default {
       } else {
         for (let i = 0; i<activeModels.length; i++) {
           var array = activeModels[i].split(';')
-          var temp2 = array[1].split(' ')
-          parameters.push(temp2[2])
+          var temp = array[0].split(' ')
+          modelsSelected.push(temp[1])
         }
-        EventBus.$emit('ReturningAlgorithmsBar', algorithmsSelected)
-        EventBus.$emit('ReturningBrushedPointsParamsBar', parameters)
       }
+      EventBus.$emit('updateBarChartAlgorithm', algorithmsSelected)
+      EventBus.$emit('updateBarChart', modelsSelected)
     },
     selectedPointsPerAlgorithm () {
       var allPoints = document.getElementsByClassName('d3-exploding-boxplot point')
       var activeModels = []
       var algorithmsSelected = []
-      var parameters = []
+      var models = []
       for (let i = 0; i < allPoints.length; i++) {
         if (allPoints[i].style.fill != "rgb(211, 211, 211)") {
           activeModels.push(allPoints[i].__data__.Model)
@@ -268,11 +271,11 @@ export default {
       } else {
         for (let i = 0; i<activeModels.length; i++) {
           var array = activeModels[i].split(';')
-          var temp2 = array[1].split(' ')
-          parameters.push(temp2[2])
+          var temp = array[0].split(' ')
+          models.push(temp[1])
         }
         EventBus.$emit('ReturningAlgorithms', algorithmsSelected)
-        EventBus.$emit('ReturningBrushedPointsParams', parameters)
+        EventBus.$emit('ReturningBrushedPointsIDs', models)
       }
     },
     previousBoxPlotState () {

frontend/src/components/BalancePredictions.vue

@@ -1,41 +1,128 @@
 <template>
     <div>
-                <p><font-awesome-icon icon="balance-scale"/> Optimize Selection:<b-form-slider ref="basic1" v-model="basicValue" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue }}%</p>
+        <div id="my_dataviz"></div>
     </div>
 </template>
 
 <script>
-    import bFormSlider from 'vue-bootstrap-slider/es/form-slider';
-    import 'bootstrap-slider/dist/css/bootstrap-slider.css'
     import { EventBus } from '../main.js'
+    import * as d3Base from 'd3'
+
+    // attach all d3 plugins to the d3 library
+    const d3 = Object.assign(d3Base)
 
     export default {
         name: 'BalancePredictions',
         data () {
             return {
-            basicValue: 0,
+            resultsfromOverview: 0,
             }
         },
         methods: {
-            slideStart () {
-            },
-            slideStop () {
-                //var basicValues = []
-                //EventBus.$emit('CallFactorsView', basicValues)
-            },
+            Balance () {
+                            // set the dimensions and margins of the graph
+            var margin = {top: 30, right: 30, bottom: 30, left: 60},
+                width = 600 - margin.left - margin.right,
+                height = 200 - margin.top - margin.bottom;
+
+            // append the svg object to the body of the page
+            var svg = d3.select("#my_dataviz")
+            .append("svg")
+                .attr("width", width + margin.left + margin.right)
+                .attr("height", height + margin.top + margin.bottom)
+            .append("g")
+                .attr("transform",
+                    "translate(" + margin.left + "," + margin.top + ")");
+
+            // get the data
+            d3.csv("https://raw.githubusercontent.com/holtzy/D3-graph-gallery/master/DATA/data_doubleHist.csv").then( function(data) {
+            // add the x Axis
+            var x = d3.scaleLinear()
+                .domain([-10,15])
+                .range([0, width]);
+            svg.append("g")
+                .attr("transform", "translate(0," + height + ")")
+                .call(d3.axisBottom(x));
+
+            // add the first y Axis
+            var y1 = d3.scaleLinear()
+                        .range([height/2, 0])
+                        .domain([0, 0.12]);
+            svg.append("g")
+                .attr("transform", "translate(-20,0)")
+                .call(d3.axisLeft(y1).tickValues([0.05, 0.1]));
+
+            // add the first y Axis
+            var y2 = d3.scaleLinear()
+                        .range([height/2, height])
+                        .domain([0, 0.12]);
+            svg.append("g")
+                .attr("transform", "translate(-20,0)")
+                .call(d3.axisLeft(y2).ticks(2).tickSizeOuter(0));
+
+            // Compute kernel density estimation
+            var kde = kernelDensityEstimator(kernelEpanechnikov(7), x.ticks(60))
+            var density1 =  kde( data.filter( function(d){return d.type === "variable 1"} ).map(function(d){  return d.value; }) )
+            var density2 =  kde( data.filter( function(d){return d.type === "variable 2"} ).map(function(d){  return d.value; }) )
+
+            // Plot the area
+            svg.append("path")
+                .attr("class", "mypath")
+                .datum(density1)
+                .attr("fill", "#69b3a2")
+                .attr("opacity", ".6")
+                .attr("stroke", "#000")
+                .attr("stroke-width", 1)
+                .attr("stroke-linejoin", "round")
+                .attr("d",  d3.line()
+                    .curve(d3.curveBasis)
+                    .x(function(d) { return x(d[0]); })
+                    .y(function(d) { return y1(d[1]); })
+                );
+
+            // Plot the area
+            svg.append("path")
+                .attr("class", "mypath")
+                .datum(density2)
+                .attr("fill", "#404080")
+                .attr("opacity", ".6")
+                .attr("stroke", "#000")
+                .attr("stroke-width", 1)
+                .attr("stroke-linejoin", "round")
+                .attr("d",  d3.line()
+                    .curve(d3.curveBasis)
+                    .x(function(d) { return x(d[0]); })
+                    .y(function(d) { return y2(d[1]); })
+                );
+
+            }).catch(function(error){
+                // handle error   
+            })
+
+            // Handmade legend
+            svg.append("circle").attr("cx",290).attr("cy",30).attr("r", 6).style("fill", "#69b3a2")
+            svg.append("circle").attr("cx",290).attr("cy",60).attr("r", 6).style("fill", "#404080")
+            svg.append("text").attr("x", 310).attr("y", 30).text("variable A").style("font-size", "15px").attr("alignment-baseline","middle")
+            svg.append("text").attr("x", 310).attr("y", 60).text("variable B").style("font-size", "15px").attr("alignment-baseline","middle")
+
+            // Function to compute density
+            function kernelDensityEstimator(kernel, X) {
+            return function(V) {
+                return X.map(function(x) {
+                return [x, d3.mean(V, function(v) { return kernel(x - v); })];
+                });
+            };
+            }
+            function kernelEpanechnikov(k) {
+            return function(v) {
+                return Math.abs(v /= k) <= 1 ? 0.75 * (1 - v * v) / k : 0;
+            };
+            }
+            }
         },
+        mounted () {
+            EventBus.$on('emittedEventCallingBalanceView', data => { this.resultsfromOverview = data} )
+            EventBus.$on('emittedEventCallingBalanceView', this.Balance)
+        }
     }
 </script>
-
-<style>
-p {
-    margin: 0 !important;
-    padding: 0 !important;
-}
-.slider.slider-horizontal {
-    width: 150px !important;
-}
-.slider-handle {
-    background: black;
-}
-</style>

frontend/src/components/BarChart.vue

@@ -16,99 +16,101 @@ export default {
     return {
       PerformanceResults: '',
       ClassNamesOverview: '',
-      PerformanceResultsSel: []
+      algorithmsinBar: [],
+      modelsSelectedinBar: [],
+      KNNModels: 576, //KNN models
+      WH: []
     }
   },
   methods: {
     BarChartView () {
+        const PerClassMetrics = JSON.parse(this.PerformanceResults[2])
+        const PerClassMetrics2 = JSON.parse(this.PerformanceResults[8])
 
-        const PerClassMetrics = JSON.parse(this.PerformanceResults[1])
-        const PerClassMetrics2 = JSON.parse(this.PerformanceResults[3])
-        var UpdateFeatures = 0
-        UpdateFeatures = JSON.parse(this.PerformanceResultsSel[0])
-        var UpdateFeatures2 = 0
-        UpdateFeatures2 = JSON.parse(this.PerformanceResultsSel[1])
+        var KNNModels = []
+        var RFModels = []
+        for (let i=0; i<this.algorithmsinBar.length;i++) {
+            if (this.algorithmsinBar[i] === "KNN") {
+                KNNModels.push(JSON.parse(this.modelsSelectedinBar[i]))
+            } else {
+                RFModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.KNNModels)
+            }
+        }
+        console.log(KNNModels)
+        console.log(RFModels)
         var target_names
         target_names = Object.keys(PerClassMetrics)
+
         var sum = []
         var temp = 0
+        var temp2 = 0
         for (var i=0;i<target_names.length;i++) {
             temp = 0
-            temp = (Object.values(PerClassMetrics)[i][0]['f1-score']+Object.values(PerClassMetrics)[i][0]['precision']+Object.values(PerClassMetrics)[i][0]['recall'])/3
-            sum.push(temp)
-            temp = 0
-            temp = (Object.values(PerClassMetrics2)[i][0]['f1-score']+Object.values(PerClassMetrics2)[i][0]['precision']+Object.values(PerClassMetrics2)[i][0]['recall'])/3
+            temp2 = 0
+            for (var j=0;j<Object.keys(PerClassMetrics[target_names[i]]).length;j++){
+                temp = temp + (Object.values(PerClassMetrics)[i][j]['f1-score']+Object.values(PerClassMetrics)[i][j]['precision']+Object.values(PerClassMetrics)[i][j]['recall'])/3
+            }
+            temp = temp/Object.keys(PerClassMetrics[target_names[i]]).length
             sum.push(temp)
+            for (var k=0;k<Object.keys(PerClassMetrics2[target_names[i]]).length;k++){
+                temp2 = temp2 + (Object.values(PerClassMetrics2)[i][k]['f1-score']+Object.values(PerClassMetrics2)[i][k]['precision']+Object.values(PerClassMetrics2)[i][k]['recall'])/3
+            }
+            temp2 = temp2/Object.keys(PerClassMetrics2[target_names[i]]).length
+            sum.push(temp2)
         }
+
         var sumLine = []
         var temp = 0
+        var temp2 = 0
         for (var i=0;i<target_names.length;i++) {
-            if (UpdateFeatures != 0) {
-                temp = 0
-                temp = (Object.values(UpdateFeatures)[i][0]['f1-score']+Object.values(UpdateFeatures)[i][0]['precision']+Object.values(UpdateFeatures)[i][0]['recall'])/3
-                sumLine.push(temp)
+            temp = 0
+            temp2 = 0
+
+            if (KNNModels.length == 0) {
+                for (var j=0;j<Object.keys(PerClassMetrics[target_names[i]]).length;j++){
+                    temp = temp + (Object.values(PerClassMetrics)[i][j]['f1-score']+Object.values(PerClassMetrics)[i][j]['precision']+Object.values(PerClassMetrics)[i][j]['recall'])/3
+                }
+                temp = temp/Object.keys(PerClassMetrics[target_names[i]]).length
             } else {
-                temp = 0
-                temp = (Object.values(PerClassMetrics)[i][0]['f1-score']+Object.values(PerClassMetrics)[i][0]['precision']+Object.values(PerClassMetrics)[i][0]['recall'])/3
-                sumLine.push(temp)
+                for (var j=0;j<KNNModels.length;j++){
+                    temp = temp + (Object.values(PerClassMetrics)[i][KNNModels[j]]['f1-score']+Object.values(PerClassMetrics)[i][KNNModels[j]]['precision']+Object.values(PerClassMetrics)[i][KNNModels[j]]['recall'])/3
+                    temp = temp/KNNModels.length
+                }
             }
-            if (UpdateFeatures2 != 0) {
-                temp = 0
-                temp = (Object.values(UpdateFeatures2)[i][0]['f1-score']+Object.values(UpdateFeatures2)[i][0]['precision']+Object.values(UpdateFeatures2)[i][0]['recall'])/3
-                sumLine.push(temp)
+            sumLine.push(temp)
+
+            if (KNNModels.length == 0) {
+                for (var k=0;k<Object.keys(PerClassMetrics2[target_names[i]]).length;k++){
+                    temp2 = temp2 + (Object.values(PerClassMetrics2)[i][k]['f1-score']+Object.values(PerClassMetrics2)[i][k]['precision']+Object.values(PerClassMetrics2)[i][k]['recall'])/3
+                }
+                temp2 = temp2/Object.keys(PerClassMetrics2[target_names[i]]).length
             } else {
-                temp = 0
-                temp = (Object.values(PerClassMetrics2)[i][0]['f1-score']+Object.values(PerClassMetrics2)[i][0]['precision']+Object.values(PerClassMetrics2)[i][0]['recall'])/3
-                sumLine.push(temp)
+                for (var k=0;k<RFModels.length;k++){
+                    temp2 = temp2 + (Object.values(PerClassMetrics2)[i][RFModels[k]]['f1-score']+Object.values(PerClassMetrics2)[i][RFModels[k]]['precision']+Object.values(PerClassMetrics2)[i][RFModels[k]]['recall'])/3
+                    temp2 = temp2/RFModels.length
+                }
             }
+            sumLine.push(temp2)
+
         }
-       /* console.log(PerClassMetrics)
-        if (this.ModelsChosen != []) {
-            console.log('mpike')
-            for (var i=0;i<target_names.length;i++) {
-            temp = 0
-            temp = (Object.values(PerClassMetrics)[i][0]['f1-score']+Object.values(PerClassMetrics)[i][0]['f1-score']+Object.values(PerClassMetrics)[i][0]['f1-score'])/3
-            sum.push(temp)
-            temp = 0
-            temp = (Object.values(PerClassMetrics2)[i][0]['f1-score']+Object.values(PerClassMetrics2)[i][0]['f1-score']+Object.values(PerClassMetrics2)[i][0]['f1-score'])/3
-            sum.push(temp)
-        }
-        }*/
+        console.log(sumLine)
         Plotly.purge('barChart')
         
-        const f1ScorePerClass = []
-
-       /* for (let j = 0; j < ClassNames.length; j++) {
-            f1ScorePerClass[j] = []
-                for (let i = 0; i < limitList.length; i++) { // Fix this tomorrow! 0 to 16 and we want the ids.. 
-                    f1ScorePerClass[j].push(PerClassMetrics[limitList[i]][ClassNames[j]]['f1-score'])
-                }
-        }*/
         var layout = {
         autosize: true,
         barmode: 'group',
-        width: 650,
-        height: 400,
+        width: this.WH[0]*3,
+        height: this.WH[1],
             xaxis: {
                 title: 'Algorithm',
                 type:"category",
-                titlefont: {
-                family: 'Arial, sans-serif',
-                size: 18,
-                color: 'grey'
-                },
                 showticklabels: true,
                 tickangle: 'auto',
-                tickfont: {
-                family: 'Old Standard TT, serif',
-                size: 14,
-                color: 'black'
-                },
                 exponentformat: 'e',
                 showexponent: 'all'
             },
             yaxis: {
-                title: 'Per Class Performance',
+                title: 'Per Class Performance Metrics',
                 titlefont: {
                 family: 'Arial, sans-serif',
                 size: 18,
@@ -118,18 +120,8 @@ export default {
             xaxis2: {
                 overlaying: 'x',
                 type:"category",
-                titlefont: {
-                family: 'Arial, sans-serif',
-                size: 18,
-                color: 'grey'
-                },
                 showticklabels: true,
                 tickangle: 'auto',
-                tickfont: {
-                family: 'Old Standard TT, serif',
-                size: 14,
-                color: 'black'
-                },
                 exponentformat: 'e',
                 showexponent: 'all'
             },
@@ -141,110 +133,38 @@ export default {
             pad: 0
             }
         }
-        /*for (let j = 0; j < ClassNames.length; j++) {
-            let len = f1ScorePerClass[j].length
-            let indices = new Array(len)
-            for (let i = 0; i < len; ++i) indices[i] = i
-            indices.sort(function (a, b) { return f1ScorePerClass[j][b] < f1ScorePerClass[j][a] ? -1 : f1ScorePerClass[j][b] > f1ScorePerClass[j][a] ? 1 : 0 })
-            f1ScorePerClass[j].sort((function(a, b){return b-a}))
-            f1ScoreData = [
-                {
-                x: indices.map(String),
-                y: f1ScorePerClass[j],
-                type: 'bar',
-                marker: {
-                    color: 'rgb(158,202,225)',
-                    opacity: 0.6,
-                    line: {
-                    color: 'rgb(8,48,107)',
-                    width: 1.5
-                    }
-                }
-                }
-            ]*/
-
+            var traces = []
+            var tracesSel = []
+            var data = []
             var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']
-
-            var trace1 = {
-            x: ['KNN', 'RF'],
-            y: [sum[0],sum[1]],
-            name: target_names[0],
-            opacity: 0.5,
-            marker: {
-                opacity: 0.5,
-                color: colors[0]
-            },
-            type: 'bar'
-            };
-
-            var trace2 = {
-            x: ['KNN', 'RF'],
-            y: [sum[2],sum[3]],
-            name: target_names[1],
-            opacity: 0.5,
-            marker: {
-                opacity: 0.5,
-                color: colors[1]
-            },
-            type: 'bar'
-            };
-
-            var trace3 = {
-            x: ['KNN', 'RF'],
-            y: [sum[4],sum[5]],
-            name: target_names[2],
-            opacity: 0.5,
-            marker: {
-                opacity: 0.5,
-                color: colors[2]
-            },
-            type: 'bar'
-            };
-
-            var trace4 = {
-            type: 'bar',
-            x: ['KNN', 'RF'],
-            y: [sumLine[0],sumLine[1]],
-            name: target_names[0]+' (Sel)',
-            xaxis: 'x2',
-            mode: 'markers',
-            marker: {
-                opacity: 1.0,
-                color: colors[0],
-            },
-            width: [0.1, 0.1]
-            };
-
-            var trace5 = {
-            type: 'bar',
-            x: ['KNN', 'RF'],
-            y: [sumLine[2],sumLine[3]],
-            name: target_names[1]+' (Sel)',
-            xaxis: 'x2',
-            mode: 'markers',
-            marker: {
-                opacity: 1.0,
-                color: colors[1],
-            },
-            width: [0.1, 0.1]
-            };
-
-            var trace6 = {
-            type: 'bar',
-            x: ['KNN', 'RF'],
-            y: [sumLine[4],sumLine[5]],
-            name: target_names[2]+' (Sel)',
-            xaxis: 'x2',
-            mode: 'markers',
-            marker: {
-                opacity: 1.0,
-                color: colors[2],
-            },
-            width: [0.1, 0.1]
-            };
-
-            var data = [trace1, trace4, trace2, trace5, trace3, trace6];
-
+            for (var i = 0; i < target_names.length; i++) {
+                traces[i] = {
+                    x: ['KNN', 'RF'],
+                    y: [sum[i+i],sum[i+i+1]],
+                    name: target_names[i],
+                    opacity: 0.5,
+                    marker: {
+                        opacity: 0.5,
+                        color: colors[i]
+                    },
+                    type: 'bar'
+                    };
+                tracesSel[i] = {
+                    type: 'bar',
+                    x: ['KNN', 'RF'],
+                    y: [sumLine[i+i],sumLine[i+i+1]],
+                    name: target_names[i]+' (Sel)',
+                    xaxis: 'x2',
+                    mode: 'markers',
+                    marker: {
+                        opacity: 1.0,
+                        color: colors[i],
+                    },
+                    width: [0.1, 0.1]
+                    };
+                    data.push(traces[i])
+                    data.push(tracesSel[i])
+            }
             Plotly.newPlot('barChart', data, layout)
         },
         reset () 
@@ -253,15 +173,18 @@ export default {
         }
     },
     mounted() {
-        this.PerformanceResultsSel[0] = 0
-        this.PerformanceResultsSel[1] = 0
-        EventBus.$on('emittedEventCallingBarChartUpdatedFeatures', data => { this.PerformanceResultsSel = data })
-        EventBus.$on('emittedEventCallingBarChartUpdatedFeatures', this.BarChartView)
+        EventBus.$on('updateBarChartAlgorithm', data => { this.algorithmsinBar = data })
+        EventBus.$on('updateBarChart', data => { this.modelsSelectedinBar = data })
+        EventBus.$on('updateBarChart', this.BarChartView)
         EventBus.$on('emittedEventCallingBarChart', data => { this.PerformanceResults = data })
         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})
     }
 }
 </script>

frontend/src/components/Controller.vue

@@ -17,7 +17,6 @@ export default {
   name: 'Controller',
   data () {
     return {
-      value: 'Add to Stack',
       valueExecute: 'Execute Stacking Ensemble'
     }
   },

frontend/src/components/FinalResultsLinePlot.vue

@@ -46,7 +46,7 @@ export default {
         text: text,
         line: {color: "rgb(0,100,80)"}, 
         mode: "lines+markers", 
-        name: "Accuracy", 
+        name: "Current Accuracy", 
         type: "scatter"
       }
 
@@ -58,7 +58,7 @@ export default {
         fill: "tozerox", 
         fillcolor: "rgba(0,100,80,0.2)", 
         line: {color: "transparent"}, 
-        name: "Accuracy", 
+        name: "Current Accuracy", 
         showlegend: false, 
         type: "scatter"
       }

frontend/src/components/Main.vue

@@ -75,9 +75,11 @@
               </mdb-card-body>
             </b-col>
           </b-row>
+            <mdb-card-text>
               <mdb-card-body>        
                 <ToggleSelection/>
               </mdb-card-body>
+            </mdb-card-text>
           </mdb-card>
         </b-col>
         <b-col cols="3">
@@ -197,15 +199,13 @@ export default Vue.extend({
       toggle2: 1,
       toggle3: 1,
       modelsUpdate: [],
-      parametersUpdate: [],
-      AlgorithmsUpdate: []
+      AlgorithmsUpdate: [],
     }
   },
   methods: {
     selectVisualRepresentation () {
       const representationSelectionDocum = document.getElementById('selectBarChart')
       this.representationSelection = representationSelectionDocum.options[representationSelectionDocum.selectedIndex].value
-      console.log(this.representationSelection)
       EventBus.$emit('RepresentationSelection', this.representationSelection)
     },
     getCollection () {
@@ -251,6 +251,7 @@ export default Vue.extend({
           this.OverviewResults = response.data.OverviewResults
           console.log('Server successfully sent all the data related to visualizations!')
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults)
+          EventBus.$emit('InitializeProvenance', this.OverviewResults)
           EventBus.$emit('InitializeMetricsBarChart', this.OverviewResults)
           this.valueSel = 0
           this.valueAll = 0
@@ -264,6 +265,7 @@ export default Vue.extend({
           EventBus.$emit('emittedEventCallingTableView', this.OverviewResults)
           EventBus.$emit('emittedEventCallingDataSpacePlotView', this.OverviewResults)
           EventBus.$emit('emittedEventCallingPredictionsSpacePlotView', this.OverviewResults)
+          EventBus.$emit('emittedEventCallingBalanceView', this.OverviewResults)
           this.getFinalResults()
         })
         .catch(error => {
@@ -295,27 +297,6 @@ export default Vue.extend({
           console.log(error)
         })
     },
-    UpdateModelsFeaturePerformance () {
-      const path = `http://localhost:5000/data/UpdatePerFeaturePerformance`
-
-      const axiosConfig = {
-        headers: {
-          'Content-Type': 'application/json',
-          'Access-Control-Allow-Origin': '*',
-          'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
-          'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
-        }
-      }
-      axios.get(path, axiosConfig)
-        .then(response => {
-          this.PerformanceCheck = response.data.PerformanceCheck
-          console.log('Server successfully sent all the performance data related to models!')
-          EventBus.$emit('emittedEventCallingBarChartUpdatedFeatures', this.PerformanceCheck)
-        })
-        .catch(error => {
-          console.log(error)
-        })
-    },
     SendSelectedPointsToServer () {
       if (this.ClassifierIDsList === ''){
         this.OverSelLength = 0
@@ -342,6 +323,7 @@ export default Vue.extend({
             EventBus.$emit('emittedEventCallingTableView', this.OverviewResults)
             EventBus.$emit('emittedEventCallingDataSpacePlotView', this.OverviewResults)
             EventBus.$emit('emittedEventCallingPredictionsSpacePlotView', this.OverviewResults)
+            EventBus.$emit('emittedEventCallingBalanceView', this.OverviewResults)
             this.getFinalResults()
           })
           .catch(error => {
@@ -376,7 +358,6 @@ export default Vue.extend({
         const path = `http://127.0.0.1:5000/data/ServerRequest`
           const postData = {
             fileName: this.RetrieveValueFile,
-            featureSelection: this.SelectedFeaturesPerClassifier
           }
           const axiosConfig = {
             headers: {
@@ -421,7 +402,7 @@ export default Vue.extend({
       EventBus.$emit('emittedEventCallingModelBrushed')
       const path = `http://127.0.0.1:5000/data/SendBrushedParam`
       const postData = {
-        parameters: this.parametersofModels,
+        models: this.modelsUpdate,
         algorithms: this.selectedAlgorithms
       }
       const axiosConfig = {
@@ -448,7 +429,7 @@ export default Vue.extend({
     UpdateBarChartFeatures () {
       const path = `http://127.0.0.1:5000/data/FeaturesScoresUpdate`
       const postData = {
-        parameters: this.parametersUpdate,
+        models: this.modelsUpdate,
         algorithms: this.AlgorithmsUpdate
       }
       const axiosConfig = {
@@ -612,9 +593,8 @@ export default Vue.extend({
     $(window).on("unload", function(e) {
       alert('Handler for .unload() called.');
     })
-    EventBus.$on('ReturningAlgorithmsBar',  data => { this.AlgorithmsUpdate = data })
-    EventBus.$on('ReturningBrushedPointsParamsBar',  data => { this.parametersUpdate = data })
-    EventBus.$on('ReturningBrushedPointsParamsBar',  this.UpdateBarChartFeatures )
+    EventBus.$on('ReturningBrushedPointsIDs',  data => { this.modelsUpdate = data })
+    //EventBus.$on('ReturningBrushedPointsIDs',  this.UpdateBarChartFeatures )
     EventBus.$on('ConfirmDataSet', this.fileNameSend)
     EventBus.$on('reset', this.Reset)
     EventBus.$on('UploadedFile', this.Reset)

frontend/src/components/PerMetricBarChart.vue

@@ -17,7 +17,6 @@ export default {
         LineBar () {
         var  metricsPerModel = JSON.parse(this.barchartmetrics[9])
         var vh = 80
-
         /*if (this.representationDefault === 'bar'){
         var type = 'bar';
         } else if (this.representationDefault === 'line'){
@@ -73,7 +72,7 @@ export default {
             x: ['Acc','F1s','Pre','Rec','Jac'], 
             y: metricsPerModel, 
             name: 'Projection average', 
-            type: type,
+            type: 'bar',
             marker: {
                 color: 'rgb(0,0,0)'
             }
@@ -82,7 +81,7 @@ export default {
             x: ['Acc','F1s','Pre','Rec','Jac'], 
             y: metricsPerModel, 
             name: 'Selected points', 
-            type: type,
+            type: 'bar',
             marker: {
                 color: 'rgb(0, 187, 187)'
             }

frontend/src/components/Provenance.vue

@@ -2,39 +2,98 @@
     <div>
         <div class="squares-container">
             <canvas id="main-canvas"></canvas>
-            <svg id="main-svg"></svg>
         </div>
     </div>
 </template>
 
 <script>
+import * as d3Base from 'd3'
+
 import { EventBus } from '../main.js'
 
+// attach all d3 plugins to the d3 library
+const d3 = Object.assign(d3Base)
+
+import * as Stardust from 'stardust-core'
+import * as StardustGL from 'stardust-webgl'
+
 export default {
   name: 'Provenance',
   data () {
     return {
+        stackInformation: ''
     }
   },
   methods: {
     provenance () {
-        var container = d3.select(".squares-container");
-        var vis = new SquaresVisualization(container.node());
-        loadData("mnist.csv", DATA => {
-            vis.layout();
-            vis.setInstances(DATA.slice(0, 4000));
-            vis.setLayoutParameter({
-            y0: 10,
-            numberBins: 10,
-            squaresPerBin: 11,
-            squareSize: 3,
-            squareSpacing: 4,
-            xSpacing: 88
-            });
-        });
+      var canvas = document.getElementById("main-canvas");
+      var width = 960;
+      var height = 500;
+
+      // Create a WebGL 2D platform on the canvas:
+      var platform = Stardust.platform("webgl-2d", canvas, width, height);
+      var data = [];
+  [27, 53, 91, 52, 112, 42, 107, 91, 68, 56, 115, 86, 26, 102, 28, 23, 119, 110].forEach((x, index) => {
+    for (let i = 0; i < x; i++) {
+      data.push({
+        type: index % 3,
+        column: Math.floor(index / 3)
+      });
     }
+  });
+  var typeCounter = [0, 0, 0];
+  var typeColumnCounter = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
+  data.forEach(d => {
+    d.typeIndex = typeCounter[d.type]++;
+    d.typeColumnIndex = typeColumnCounter[3 * d.column + d.type]++;
+  });
+      // Convert the SVG file to Stardust mark spec.
+      let isotype = new Stardust.mark.circle();
+
+      // Create the mark object.
+      let isotypes = Stardust.mark.create(isotype, platform);
+
+      let isotypeHeight = 18;
+     let colors = [[141,211,199], [255,255,179], [190,186,218]];
+    colors = colors.map(x => [x[0] / 255, x[1] / 255, x[2] / 255, 1]);
+
+    let pScale = Stardust.scale.custom(`
+            Vector2(
+                20 + column * 160 + type * 45 + typeColumnIndex % 5 * 8,
+                460 - floor(typeColumnIndex / 5) * 10
+            )
+        `);
+    pScale.attr("typeColumnIndex", d => d.typeColumnIndex);
+    pScale.attr("column", d => d.column);
+    pScale.attr("typeIndex", d => d.typeIndex);
+    pScale.attr("type", d => d.type);
+
+    let qScale = Stardust.scale.custom(`
+            Vector2(
+                65 + type * 300 + typeIndex % 30 * 8,
+                460 - floor(typeIndex / 15) * 18
+            )
+        `);
+    qScale.attr("typeIndex", d => d.typeIndex);
+    qScale.attr("type", d => d.type);
+
+    let interpolateScale = Stardust.scale.interpolate("Vector2");
+    interpolateScale.t(0);
+
+      isotypes.attr("center", interpolateScale(pScale(), qScale()));
+      isotypes.attr("radius", 4.0);
+      isotypes.attr("color", d => colors[d.type]);
+
+      isotypes.data(data);
+
+      isotypes.render();
+
+  }
   },
-  mount () {
+  mounted () {
+    EventBus.$on('InitializeProvenance', data => {this.stackInformation = data})
+    EventBus.$on('InitializeProvenance', this.provenance)
   }
+
 }
 </script>

frontend/src/components/ScatterPlot.vue

@@ -1,5 +1,21 @@
 <template>
+<div>
+  <div align="center">
+            <button
+            id="AddStack"
+            v-on:click="AddStack">
+            <font-awesome-icon icon="plus" />
+            {{ valueStackAdd }}
+            </button>
+            <button
+            id="RemoveStack"
+            v-on:click="RemoveStack">
+            <font-awesome-icon icon="minus" />
+            {{ valueStackRemove }}
+            </button>
+  </div>
   <div id="OverviewPlotly" class="OverviewPlotly"></div>
+</div>
 </template>
 
 <script>
@@ -16,16 +32,24 @@ export default {
   data () {
     return {
       ScatterPlotResults: '',
-      representationDef: 'MDS',
+      representationDef: 'mds',
       colorsforOver: [],
       brushedBox : [],
       max: 0,
       min: 0,
       parametersAll: [],
       length: 0,
+      valueStackAdd: 'Add to Stack',
+      valueStackRemove: 'Remove from Stack'
     }
   },
   methods: {
+    AddStack () {
+      //EventBus.$emit('PCPCallDB')
+    },
+    RemoveStack () {
+      //EventBus.$emit('PCPCallDB')
+    },
     ScatterPlotView () {
 
       function isEquivalent(a, b) {
@@ -57,8 +81,20 @@ export default {
       Plotly.purge('OverviewPlotly')
       var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
       var MDSData = JSON.parse(this.ScatterPlotResults[1])
+      console.log(colorsforScatterPlot)
       var parameters = JSON.parse(this.ScatterPlotResults[2])
-      var tSNEData = JSON.parse(this.ScatterPlotResults[12])
+      var TSNEData = JSON.parse(this.ScatterPlotResults[12])
+
+
+      if (this.colorsforOver.length != 0) {
+        if (this.colorsforOver[1].length != 0) {
+          MDSData = this.colorsforOver[1]
+          TSNEData = this.colorsforOver[2]
+        }
+        if (this.colorsforOver[0].length != 0) {
+          colorsforScatterPlot = this.colorsforOver[0]
+        }
+      }
 
       parameters = JSON.parse(parameters)
       var classifiersInfo = this.brushedBox
@@ -97,23 +133,13 @@ export default {
         }
       }
 
-      if (this.colorsforOver.length != 0) {
-        if (this.colorsforOver[1].length != 0) {
-          MDSData = this.colorsforOver[1]
-        }
-        if (this.colorsforOver[0].length != 0) {
-          colorsforScatterPlot = this.colorsforOver[0]
-        }
-      }
-      console.log(MDSData)
-      console.log(tSNEData)
       var classifiersInfoProcessing = []
       for (let i = 0; i < modelsDetails.length; i++) {
         classifiersInfoProcessing[i] = 'Model ID: ' + modelsIDs[i] + '; Details: ' + modelsDetails[i]
       }
       var DataGeneral
       var layout
-      if (this.representationDef == 'MDS') {
+      if (this.representationDef == 'mds') {
         DataGeneral = [{
           type: 'scatter',
           mode: 'markers',
@@ -150,11 +176,22 @@ export default {
           legend: {orientation: 'h', y: -0.3},
         }
       } else {
+        var result = TSNEData.reduce(function(r, a) {
+            a.forEach(function(s, i) {
+                var key = i === 0 ? 'Xax' : 'Yax';
+
+                r[key] || (r[key] = []); // if key not found on result object, add the key with empty array as the value
+
+                r[key].push(s);
+            })
+            return r;
+        }, {})
+
         DataGeneral = [{
           type: 'scatter',
           mode: 'markers',
-          x: tSNEData[0],
-          y: tSNEData[1],
+          x: result.Xax,
+          y: result.Yax,
           hovertemplate: 
                 "<b>%{text}</b><br><br>" +
                 "<extra></extra>",
@@ -170,21 +207,19 @@ export default {
           }
         }]
         layout = {
-          title: 'Models Performance (MDS)',
+          title: 'Models Performance (t-SNE)',
           xaxis: {
               visible: false
           },
           yaxis: {
               visible: false
           },
-          autosize: true,
-          width: 400,
-          height: 400,
           dragmode: 'lasso',
           hovermode: "closest",
           hoverlabel: { bgcolor: "#FFF" },
           legend: {orientation: 'h', y: -0.3},
         }
+
       }
      
       var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage', 'toggleSpikelines', 'autoScale2d', 'hoverClosestGl2d','hoverCompareCartesian','select2d','hoverClosestCartesian','zoomIn2d','zoomOut2d','zoom2d'], responsive: true}

frontend/src/main.js

@@ -6,10 +6,10 @@ import 'bootstrap-vue/dist/bootstrap-vue.css'
 import router from './router'
 import { library } from '@fortawesome/fontawesome-svg-core'
 import { FontAwesomeIcon } from '@fortawesome/vue-fontawesome'
-import { faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale} from '@fortawesome/free-solid-svg-icons'
+import { faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus} from '@fortawesome/free-solid-svg-icons'
 import bFormSlider from 'vue-bootstrap-slider'
 
-library.add(faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale)
+library.add(faUpload, faPlay, faCheck, faSave, faTrash, faPlus, faBalanceScale, faMinus)
 
 Vue.component('font-awesome-icon', FontAwesomeIcon)
 

package-lock.json

@@ -0,0 +1,3 @@
+{
+  "lockfileVersion": 1
+}

run.py

@@ -25,8 +25,6 @@ from sklearn.manifold import MDS
 from sklearn.manifold import TSNE
 from sklearn.metrics import classification_report
 from sklearn.preprocessing import scale
-#from sklearn.metrics import r2_score
-#from rfpimp import permutation_importances
 import eli5
 from eli5.sklearn import PermutationImportance
 from sklearn.feature_selection import SelectKBest
@@ -37,6 +35,9 @@ from sklearn.decomposition import PCA
 from mlxtend.classifier import StackingCVClassifier
 from mlxtend.feature_selection import ColumnSelector
 
+from skdist.distribute.search import DistGridSearchCV
+from pyspark.sql import SparkSession
+
 # This block of code is for the connection between the server, the database, and the client (plus routing).
 
 # Access MongoDB 
@@ -108,16 +109,20 @@ def Reset():
     target_names = []
     return 'The reset was done!'
 
-# Retrieve data from client 
+# Retrieve data from client and select the correct data set
 @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 
-    featureSelection = request.get_data().decode('utf8').replace("'", '"')
-    featureSelection = json.loads(featureSelection)
+
+    #global featureSelection 
+    #featureSelection = request.get_data().decode('utf8').replace("'", '"')
+    #featureSelection = json.loads(featureSelection)
+
     global DataRawLength
     global DataResultsRaw
+
     global RANDOM_SEED
     RANDOM_SEED = 42
 
@@ -153,14 +158,22 @@ def RetrieveFileName():
     global crossValidation
     crossValidation = 3
 
+    global scoring
+    scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
     #scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'}
 
+    global NumberofscoringMetrics
+    NumberofscoringMetrics = len(scoring)
+
     global yPredictProb
     yPredictProb = []
 
     global loopFeatures
     loopFeatures = 2
 
+    global factors
+    factors = [1,1,1,1,1]
+
     global columns 
     columns = []
 
@@ -255,112 +268,6 @@ def column_index(df, query_cols):
     sidx = np.argsort(cols)
     return sidx[np.searchsorted(cols,query_cols,sorter=sidx)].tolist()
 
-global mem
-mem = Memory("./cache_dir")
-
-def GridSearch(clf, params):
-    global XData
-    global yData
-    global scoring
-    global target_names
-    grid = GridSearchCV(estimator=clf, 
-                param_grid=params,
-                scoring=scoring, 
-                cv=crossValidation,
-                refit='accuracy',
-                n_jobs = -1)
-    grid.fit(XData, yData)
-    cv_results = []
-    cv_results.append(grid.cv_results_)
-    df_cv_results = pd.DataFrame.from_dict(cv_results)
-    number_of_classifiers = len(df_cv_results.iloc[0][0])
-    number_of_columns = len(df_cv_results.iloc[0])
-    df_cv_results_per_item = []
-    df_cv_results_per_row = []
-    
-    for i in range(number_of_classifiers):
-        df_cv_results_per_item = []
-        for column in df_cv_results.iloc[0]:
-            df_cv_results_per_item.append(column[i])
-        df_cv_results_per_row.append(df_cv_results_per_item)
-
-    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
-    parameters = df_cv_results_classifiers['params']
-    PerClassMetrics = []
-    #perm_imp_rfpimp = []
-    #FeatureImp = []
-    #RFEList = []
-    permList = []
-    PerFeatureAccuracy = []
-    global subset
-    global loopFeatures
-    global yPredictProb
-    global columns
-    columns = []
-    counter = 0
-    subset = XData
-    for i, eachClassifierParams in enumerate(grid.cv_results_['params']):
-        eachClassifierParamsDictList = {}
-        for key, value in eachClassifierParams.items():
-            Listvalue = []
-            Listvalue.append(value)
-            eachClassifierParamsDictList[key] = Listvalue
-            counter = counter + 1
-        grid = GridSearchCV(estimator=clf, 
-            param_grid=eachClassifierParamsDictList,
-            scoring=scoring, 
-            cv=crossValidation,
-            refit='accuracy',
-            n_jobs = -1)
-        if (featureSelection['featureSelection'] == ''):
-            subset = XData
-        else:
-            featureSelected = []
-            for indices, each in enumerate(XData.columns):
-                if (int(''.join(x for x in featureSelection['featureSelection'][loopFeatures] if x.isdigit())) == 1):
-                    featureSelected.append(each)
-                loopFeatures = loopFeatures + 3
-            subset = XData[featureSelected]
-            element = (column_index(XData, featureSelected))
-            columns.append(element)
-        grid.fit(subset, yData) 
-        #perm_imp_rfpimp.append(permutation_importances(grid.best_estimator_, subset, yData, r2)['Importance']) 
-        perm = PermutationImportance(grid.best_estimator_, cv = None, refit = True, n_iter = 50).fit(subset, yData)
-        permList.append(perm.feature_importances_)
-        n_feats = subset.shape[1]
-        for i in range(n_feats):
-            scores = model_selection.cross_val_score(grid.best_estimator_, subset.values[:, i].reshape(-1, 1), yData, cv=crossValidation)
-            PerFeatureAccuracy.append(scores.mean())
-
-        yPredict = grid.predict(subset)
-        yPredictProb.append(grid.predict_proba(subset))
-        PerClassMetrics.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
-        #if (FI == 1):
-        #    X = subset.values
-        #    Y = array(yData)
-        #    FeatureImp.append(class_feature_importance(X, Y, grid.best_estimator_.feature_importances_))
-        #    rfe = RFE(grid.best_estimator_, 3)
-        #    fit = rfe.fit(subset, yData)
-        #    RFEList.append(fit.ranking_)
-
-    bestfeatures = SelectKBest(score_func=chi2, k='all')
-    fit = bestfeatures.fit(subset,yData)
-    dfscores = pd.DataFrame(fit.scores_)
-    dfcolumns = pd.DataFrame(subset.columns)
-    #concat two dataframes for better visualization 
-    featureScores = pd.concat([dfcolumns,dfscores],axis=1)
-    featureScores.columns = ['Specs','Score']  #naming the dataframe columns
-    #FeatureImpPandas = pd.DataFrame(FeatureImp)
-    #RFEListPD = pd.DataFrame(RFEList)
-    #perm_imp_rfpimp = pd.DataFrame(perm_imp_rfpimp)
-    perm_imp_eli5PD = pd.DataFrame(permList)
-    PerClassMetricsPandas = pd.DataFrame(PerClassMetrics)
-    PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracy)
-    return df_cv_results_classifiers, parameters, PerClassMetricsPandas, PerFeatureAccuracyPandas, perm_imp_eli5PD, featureScores
-
-#def r2(rf, X_train, y_train):
-#    return r2_score(y_train, rf.predict(X_train))
-
 def class_feature_importance(X, Y, feature_importances):
     N, M = X.shape
     X = scale(X)
@@ -403,12 +310,8 @@ def Preprocessing():
     global factors
     factors = [1,1,1,1,1,1]
     global df_cv_results_classifiers_metrics
-    global NumberofscoringMetrics
-    del df_cv_results_classifiers['params']
     df_cv_results_classifiers_metrics = df_cv_results_classifiers.copy()
-    del df_cv_results_classifiers_metrics['mean_fit_time']
-    del df_cv_results_classifiers_metrics['mean_score_time']
-    df_cv_results_classifiers_metrics = df_cv_results_classifiers_metrics.ix[:, 0:NumberofscoringMetrics]
+    df_cv_results_classifiers_metrics = df_cv_results_classifiers_metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
     return [parameters,PerClassMetrics,FeatureAccuracy,df_cv_results_classifiers_metrics,perm_imp_eli5PDCon,featureScoresCon]
 
 def sumPerMetric(factors):
@@ -416,7 +319,6 @@ def sumPerMetric(factors):
     preProcessResults = []
     preProcessResults = Preprocessing()
     loopThroughMetrics = preProcessResults[3]
-
     global scoring
     global metricsPerModel
     metricsPerModel = []
@@ -425,7 +327,7 @@ def sumPerMetric(factors):
     metricsPerModel.append(loopThroughMetrics['mean_test_precision'].sum()/loopThroughMetrics['mean_test_precision'].count())
     metricsPerModel.append(loopThroughMetrics['mean_test_recall'].sum()/loopThroughMetrics['mean_test_recall'].count())
     metricsPerModel.append(loopThroughMetrics['mean_test_jaccard'].sum()/loopThroughMetrics['mean_test_jaccard'].count())
-    for index, row in loopThroughMetrics.iterrows():
+    for row in loopThroughMetrics.iterrows():
         rowSum = 0
         lengthFactors = len(scoring)
         for loop,elements in enumerate(row):
@@ -496,7 +398,6 @@ def InitializeEnsemble():
     ModelSpaceMDS = FunMDS(XClassifiers)
     ModelSpaceTSNE = FunTsne(XClassifiers)
     ModelSpaceTSNE = ModelSpaceTSNE.tolist()
-    print(ModelSpaceTSNE)
     global ClassifierIDsList
     key = 0
     EnsembleModel(ClassifierIDsList, key)
@@ -697,70 +598,89 @@ def SendToPlot():
     }
     return jsonify(response)
 
-# Retrieve data from client 
+# Initialize every model for each algorithm
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
 @app.route('/data/ServerRequestSelParameters', methods=["GET", "POST"])
 def RetrieveModel():
-    global RetrievedModel
+
+    # get the models from the frontend
     RetrievedModel = request.get_data().decode('utf8').replace("'", '"')
     RetrievedModel = json.loads(RetrievedModel)
-    global parametersPerformancePerModel
-    parametersPerformancePerModel = []
-    global algorithms 
-    global factors
-    factors = [1,1,1,1,1,1]
+
     algorithms = RetrievedModel['Algorithms']
+
+    # loop through the algorithms
+    global allParametersPerformancePerModel
     for eachAlgor in algorithms:
         if (eachAlgor) == 'KNN':
             clf = KNeighborsClassifier()
             params = {'n_neighbors': list(range(1, 25)), 'weights': ['uniform', 'distance'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski']}
+            AlgorithmsIDsEnd = 0
         else: 
             clf = RandomForestClassifier()
             params = {'n_estimators': list(range(80, 120)), 'criterion': ['gini', 'entropy']}
-        GridSearchForParameters(clf, params, eachAlgor, factors)
+            AlgorithmsIDsEnd = 576
+        allParametersPerformancePerModel = GridSearchForModels(clf, params, eachAlgor, factors, AlgorithmsIDsEnd)
+
+    # call the function that sends the results to the frontend 
     SendEachClassifiersPerformanceToVisualize()
+
     return 'Everything Okay'
 
-def GridSearchForParameters(clf, params, eachAlgor, factors):
-    global scoring 
-    global NumberofscoringMetrics
+location = './cachedir'
+memory = Memory(location, verbose=0)
 
-    scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
-    NumberofscoringMetrics = len(scoring)
+# calculating for all algorithms and models the performance and other results
+@memory.cache
+def GridSearchForModels(clf, params, eachAlgor, factors, AlgorithmsIDsEnd):
 
-    grid = GridSearchCV(estimator=clf, 
-                param_grid=params,
-                scoring=scoring, 
-                cv=crossValidation,
-                refit='accuracy',
-                n_jobs = -1)
+    # instantiate spark session
+    spark = (   
+        SparkSession    
+        .builder    
+        .getOrCreate()    
+        )
+    sc = spark.sparkContext 
+
+    # this is the grid we use to train the models
+    grid = DistGridSearchCV(    
+        estimator=clf, param_grid=params,     
+        sc=sc, cv=crossValidation, refit='accuracy', scoring=scoring,
+        verbose=0, n_jobs=-1)
+
+    # fit and extract the probabilities
     grid.fit(XData, yData)
-    yPredict = grid.predict(XData)
+
+    # process the results
     cv_results = []
     cv_results.append(grid.cv_results_)
     df_cv_results = pd.DataFrame.from_dict(cv_results)
-    number_of_classifiers = len(df_cv_results.iloc[0][0])
-    number_of_columns = len(df_cv_results.iloc[0])
 
-    df_cv_results_per_item = []
+    # number of models stored
+    number_of_models = len(df_cv_results.iloc[0][0])
+
+    # initialize results per row
     df_cv_results_per_row = []
 
-    for i in range(number_of_classifiers):
+    # loop through number of models
+    modelsIDs = []
+    for i in range(number_of_models):
+        modelsIDs.append(AlgorithmsIDsEnd+i)
+         # initialize results per item
         df_cv_results_per_item = []
         for column in df_cv_results.iloc[0]:
             df_cv_results_per_item.append(column[i])
         df_cv_results_per_row.append(df_cv_results_per_item)
-    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
 
-    global allParametersPerformancePerModel
-    global parametersPerformancePerModel
+    # store the results into a pandas dataframe
+    df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
 
+    # copy and filter in order to get only the metrics
     metrics = df_cv_results_classifiers.copy()
-    del metrics['mean_fit_time']
-    del metrics['mean_score_time']
-    metrics = metrics.ix[:, 0:NumberofscoringMetrics]
+    metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard']) 
+
+    # control the factors
     sumperModel = []
-    global rowSum
     for index, row in metrics.iterrows():
         rowSum = 0
         lengthFactors = NumberofscoringMetrics
@@ -771,31 +691,77 @@ def GridSearchForParameters(clf, params, eachAlgor, factors):
             sumperModel = 0
         else:
             sumperModel.append(rowSum/lengthFactors)
-    global target_names
-    global PerClassMetric
-    global PerClassMetricPandas
+    
+    # summarize all models metrics
+    summarizedMetrics = pd.DataFrame(sumperModel)
+    summarizedMetrics.rename(columns={0:'sum'})
+
+    # concat parameters and performance
+    parameters = pd.DataFrame(df_cv_results_classifiers['params'])
+    parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
+    parametersPerformancePerModel = parametersPerformancePerModel.to_json()
+    
+    parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy()
+    Models = []
+    for index, items in enumerate(parametersLocal):
+        Models.append(str(index))
+
+    parametersLocalNew = [ parametersLocal[your_key] for your_key in Models ]
+
+    permList = []
+    PerFeatureAccuracy = []
     PerClassMetric = []
-    yPredictProb.append(grid.predict_proba(XData))
-    PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
+
+    for eachModelParameters in parametersLocalNew:
+        clf.set_params(**eachModelParameters)
+
+        perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData)
+        permList.append(perm.feature_importances_)
+
+        n_feats = XData.shape[1]
+        for i in range(n_feats):
+            scores = model_selection.cross_val_score(clf, XData.values[:, i].reshape(-1, 1), yData, cv=crossValidation)
+            PerFeatureAccuracy.append(scores.mean())
+
+        clf.fit(XData, yData) 
+        yPredict = clf.predict(XData)
+        # retrieve target names (class names)
+        PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
+
     PerClassMetricPandas = pd.DataFrame(PerClassMetric)  
     del PerClassMetricPandas['accuracy']  
     del PerClassMetricPandas['macro avg']  
     del PerClassMetricPandas['weighted avg']  
-    summarizedMetrics = pd.DataFrame(sumperModel)
-    summarizedMetrics.rename(columns={0:'sum'})
-    parameters = pd.DataFrame(df_cv_results_classifiers['params'])
-    parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
     PerClassMetricPandas = PerClassMetricPandas.to_json()
-    parametersPerformancePerModel = parametersPerformancePerModel.to_json()
-    allParametersPerformancePerModel.append(parametersPerformancePerModel)
-    allParametersPerformancePerModel.append(PerClassMetricPandas)
-    return 'Everything is okay'
 
-#GridSearchForParameters = mem.cache(GridSearchForParameters)
 
-# Sending each model's results
+    perm_imp_eli5PD = pd.DataFrame(permList)
+    perm_imp_eli5PD = perm_imp_eli5PD.to_json()
+
+    PerFeatureAccuracyPandas = pd.DataFrame(PerFeatureAccuracy)
+    PerFeatureAccuracyPandas = PerFeatureAccuracyPandas.to_json()
+
+    bestfeatures = SelectKBest(score_func=chi2, k='all')
+    fit = bestfeatures.fit(XData,yData)
+    dfscores = pd.DataFrame(fit.scores_)
+    dfcolumns = pd.DataFrame(XData.columns)
+    featureScores = pd.concat([dfcolumns,dfscores],axis=1)
+    featureScores.columns = ['Specs','Score']  #naming the dataframe columns
+    featureScores = featureScores.to_json()
+
+    # gather the results and send them back
+    results.append(modelsIDs) # Position: 0 and so on
+    results.append(parametersPerformancePerModel) # Position: 1 and so on
+    results.append(PerClassMetricPandas) # Position: 2 and so on
+    results.append(PerFeatureAccuracyPandas) # Position: 3 and so on
+    results.append(perm_imp_eli5PD) # Position: 4 and so on
+    results.append(featureScores) # Position: 5 and so on
+
+    return results
+
+# Sending each model's results to frontend
 @app.route('/data/PerformanceForEachModel', methods=["GET", "POST"])
-def SendEachClassifiersPerformanceToVisualize ():
+def SendEachClassifiersPerformanceToVisualize():
     response = {    
         'PerformancePerModel': allParametersPerformancePerModel,
     }
@@ -833,161 +799,55 @@ def RetrieveModelsParam():
     results = []
     counter1 = 0
     counter2 = 0
+    KNNModels = []
+    RFModels = []
+    parametersLocalKNN = json.loads(allParametersPerformancePerModel[1])['params'].copy()
+    your_key = '496'
+    parametersLocalRF = json.loads(allParametersPerformancePerModel[4])['params'].copy()
     for index, items in enumerate(algorithmList):
         if (items == 'KNN'):
             counter1 = counter1 + 1
+            KNNModels.append(RetrieveModelsPar['models'][index])
         else:
             counter2 = counter2 + 1
-
-    output = pd.DataFrame()
-    for d in RetrieveModelsPar['parameters']:  
-        output = output.append(json.loads(d), ignore_index=True)
-    RetrieveModelsPandSel = output.loc[0:counter1,:]
-    RetrieveModelsPandSel2 = output.loc[counter1:counter1+counter2,:]
-    RetrieveModelsPandSelDic = RetrieveModelsPandSel.to_dict(orient='list')
-    RetrieveModelsPandSelDic2 = RetrieveModelsPandSel2.to_dict(orient='list')
-
-    RetrieveModels = {}
-    for key, value in RetrieveModelsPandSelDic.items():
-        withoutDuplicates = Remove(value)
-        RetrieveModels[key] = withoutDuplicates
-
-    RetrieveModels2 = {}
-    for key, value in RetrieveModelsPandSelDic2.items():
-        withoutDuplicates = Remove(value)
-        RetrieveModels2[key] = withoutDuplicates
+            RFModels.append(str(int(RetrieveModelsPar['models'][index])-576))
+
+    parametersLocalKNNNew = [ parametersLocalKNN[your_key] for your_key in KNNModels ]
+    parametersLocalRFNew = [ parametersLocalRF[your_key] for your_key in RFModels ]
+
+    #output = pd.DataFrame()
+    #print(RetrieveModelsPar['models'])
+    #for d in RetrieveModelsPar['parameters']:  
+    #    output = output.append(json.loads(d), ignore_index=True)
+    #RetrieveModelsPandSel = output.loc[0:counter1,:]
+    #RetrieveModelsPandSel2 = output.loc[counter1:counter1+counter2,:]
+    #RetrieveModelsPandSelDic = RetrieveModelsPandSel.to_dict(orient='list')
+    #RetrieveModelsPandSelDic2 = RetrieveModelsPandSel2.to_dict(orient='list')
+    #print(RetrieveModelsPandSelDic)
+    #RetrieveModels = {}
+    #for key, value in RetrieveModelsPandSelDic.items():
+    #    withoutDuplicates = Remove(value)
+    #    RetrieveModels[key] = withoutDuplicates
+
+    #RetrieveModels2 = {}
+    #for key, value in RetrieveModelsPandSelDic2.items():
+    #    withoutDuplicates = Remove(value)
+    #    RetrieveModels2[key] = withoutDuplicates
     global resultsList
     resultsList = []
     for alg in count:
         if (alg == 'KNN'):
             clf = KNeighborsClassifier()
-            params = RetrieveModels
-            detailsParams.append(params)
-            results.append(GridSearch(clf, params))
+            #params = RetrieveModels
+            #detailsParams.append(params)
+            results.append(GridSearch(clf, parametersLocalKNNNew))
             resultsList.append(results[0])
         elif (alg == 'RF'):
             clf = RandomForestClassifier()
-            params = RetrieveModels2
-            detailsParams.append(params)
-            results.append(GridSearch(clf, params))
+            #params = RetrieveModels2
+            #detailsParams.append(params)
+            results.append(GridSearch(clf, parametersLocalRFNew))
             resultsList.append(results[0])
         else:
             pass
     return 'Everything Okay'
-
-# Retrieve data from client 
-@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
-@app.route('/data/FeaturesScoresUpdate', methods=["GET", "POST"])
-def UpdateBarChartLine():
-    RetrieveModelsforUpdate = request.get_data().decode('utf8').replace("'", '"')
-    RetrieveModelsforUpdate = json.loads(RetrieveModelsforUpdate)
-    algorithms = RetrieveModelsforUpdate['algorithms']
-    count = []
-    if ('KNN' in algorithms):
-        count.append('KNN')
-    else:
-        count.append(0)
-    if ('RF' in algorithms):
-        count.append('RF')
-    else:
-        count.append(0)
-
-    results = []
-    counter1 = 0
-    counter2 = 0
-    for index, items in enumerate(algorithms):
-        if (items == 'KNN'):
-            counter1 = counter1 + 1
-        else:
-            counter2 = counter2 + 1
-
-    output = pd.DataFrame()
-    output2 = pd.DataFrame()
-    loop = 0
-    for d in RetrieveModelsforUpdate['parameters']:  
-        if (loop < counter1):
-            output = output.append(json.loads(d), ignore_index=True)
-        else:
-            output2 = output2.append(json.loads(d), ignore_index=True)
-        loop = loop + 1
-    output.dropna(axis='columns')
-    output2.dropna(axis='columns')
-    
-    if (output.empty):
-        pass
-    else:
-        RetrieveModelsPandSel = output.loc[0:counter1,:]
-        RetrieveModelsPandSelDic = RetrieveModelsPandSel.to_dict(orient='list')
-        RetrieveModels = {}
-        for key, value in RetrieveModelsPandSelDic.items():
-            withoutDuplicates = Remove(value)
-            RetrieveModels[key] = withoutDuplicates
-
-    if (output2.empty):
-        pass
-    else:
-        RetrieveModelsPandSel2 = output2.loc[0:counter2,:]
-        RetrieveModelsPandSelDic2 = RetrieveModelsPandSel2.to_dict(orient='list')
-        RetrieveModels2 = {}
-        for key, value in RetrieveModelsPandSelDic2.items():
-            withoutDuplicates = Remove(value)
-            RetrieveModels2[key] = withoutDuplicates
-
-    factors = [1,1,1,1,1,1]
-    global allParametersPerformancePerModelUpdate
-    allParametersPerformancePerModelUpdate = []
-    for alg in count:
-        if (alg == 'KNN'):
-            clf = KNeighborsClassifier()
-            params = RetrieveModels
-            GridSearchForUpdate(clf, params, factors)
-        elif (alg == 'RF'):
-            clf = RandomForestClassifier()
-            params = RetrieveModels2
-            GridSearchForUpdate(clf, params, factors)
-        else:
-            allParametersPerformancePerModelUpdate.append(0)
-    SendEachClassifiersPerformanceToVisualizeLinePlot()
-    return 'Everything Okay'
-
-
-def GridSearchForUpdate(clf, params, factors):
-    global scoring 
-    global NumberofscoringMetrics
-    scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
-    NumberofscoringMetrics = len(scoring)
-
-    grid = GridSearchCV(estimator=clf, 
-                param_grid=params,
-                scoring=scoring, 
-                cv=crossValidation,
-                refit='accuracy',
-                n_jobs = -1)
-    grid.fit(XData, yData)
-    yPredict = grid.predict(XData)
-
-    global allParametersPerformancePerModelUpdate
-
-    global target_names
-    global PerClassUpd
-    global PerClassMetricUpdate
-    PerClassUpd = []
-    PerClassMetricUpdate = []
-    PerClassUpd.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
-    PerClassMetricUpdate = pd.DataFrame(PerClassUpd)   
-    del PerClassMetricUpdate['accuracy']  
-    del PerClassMetricUpdate['macro avg']  
-    del PerClassMetricUpdate['weighted avg']  
-    PerClassMetricUpdate = PerClassMetricUpdate.to_json()
-    allParametersPerformancePerModelUpdate.append(PerClassMetricUpdate)
-    return 'Everything is okay'
-
-
-# Sending each model's results
-@app.route('/data/UpdatePerFeaturePerformance', methods=["GET", "POST"])
-def SendEachClassifiersPerformanceToVisualizeLinePlot ():
-    global allParametersPerformancePerModelUpdate
-    response = {    
-        'PerformanceCheck': allParametersPerformancePerModelUpdate,
-    }
-    return jsonify(response)