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parent 7aa39aa8ce
commit b0c55e2885
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@ -1 +1 @@
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{"duration": 744.9404788017273, "input_args": {"XData": " sepal_l sepal_w petal_l petal_w\n0 6.3 3.3 6.0 2.5\n1 7.1 3.0 5.9 2.1\n2 5.8 2.7 5.1 1.9\n3 6.3 2.9 5.6 1.8\n4 7.6 3.0 6.6 2.1\n.. ... ... ... ...\n145 5.1 3.8 1.6 0.2\n146 5.0 3.5 1.6 0.6\n147 5.1 3.4 1.5 0.2\n148 4.6 3.2 1.4 0.2\n149 4.8 3.0 1.4 0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n criterion='entropy', max_depth=None, max_features='auto',\n max_leaf_nodes=None, max_samples=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, n_estimators=139,\n n_jobs=None, oob_score=False, random_state=None,\n verbose=0, warm_start=False)", "params": "{'n_estimators': [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "2446"}}

@ -1 +0,0 @@
{"duration": 501.7342348098755, "input_args": {"XData": " sepal_l sepal_w petal_l petal_w\n0 6.3 3.3 6.0 2.5\n1 7.1 3.0 5.9 2.1\n2 5.8 2.7 5.1 1.9\n3 6.3 2.9 5.6 1.8\n4 7.6 3.0 6.6 2.1\n.. ... ... ... ...\n145 5.1 3.8 1.6 0.2\n146 5.0 3.5 1.6 0.6\n147 5.1 3.4 1.5 0.2\n148 4.6 3.2 1.4 0.2\n149 4.8 3.0 1.4 0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "SVC(C=4.39, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n decision_function_shape='ovr', degree=3, gamma='scale', kernel='sigmoid',\n max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n verbose=False)", "params": "{'C': [0.1, 0.21000000000000002, 0.32000000000000006, 0.43000000000000005, 0.54, 0.65, 0.7600000000000001, 0.8700000000000001, 0.9800000000000001, 1.09, 1.2000000000000002, 1.3100000000000003, 1.4200000000000004, 1.5300000000000002, 1.6400000000000003, 1.7500000000000002, 1.8600000000000003, 1.9700000000000004, 2.08, 2.1900000000000004, 2.3000000000000003, 2.4100000000000006, 2.5200000000000005, 2.6300000000000003, 2.7400000000000007, 2.8500000000000005, 2.9600000000000004, 3.0700000000000003, 3.1800000000000006, 3.2900000000000005, 3.4000000000000004, 3.5100000000000007, 3.6200000000000006, 3.7300000000000004, 3.8400000000000007, 3.9500000000000006, 4.0600000000000005, 4.17, 4.28, 4.390000000000001], 'kernel': ['rbf', 'linear', 'poly', 'sigmoid']}", "eachAlgor": "'SVC'", "AlgorithmsIDsEnd": "576"}}

@ -1 +1 @@
{"duration": 910.9788248538971, "input_args": {"XData": " sepal_l sepal_w petal_l petal_w\n0 6.3 3.3 6.0 2.5\n1 7.1 3.0 5.9 2.1\n2 5.8 2.7 5.1 1.9\n3 6.3 2.9 5.6 1.8\n4 7.6 3.0 6.6 2.1\n.. ... ... ... ...\n145 5.1 3.8 1.6 0.2\n146 5.0 3.5 1.6 0.6\n147 5.1 3.4 1.5 0.2\n148 4.6 3.2 1.4 0.2\n149 4.8 3.0 1.4 0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "GradientBoostingClassifier(ccp_alpha=0.0, criterion='mae', init=None,\n learning_rate=0.23, loss='deviance', max_depth=3,\n max_features=None, 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=110,\n n_iter_no_change=None, presort='deprecated',\n random_state=None, subsample=1.0, tol=0.0001,\n validation_fraction=0.1, verbose=0,\n warm_start=False)", "params": "{'n_estimators': [90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110], 'learning_rate': [0.01, 0.12, 0.23], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "3356"}}
{"duration": 814.1466150283813, "input_args": {"XData": " sepal_l sepal_w petal_l petal_w\n0 6.3 3.3 6.0 2.5\n1 7.1 3.0 5.9 2.1\n2 5.8 2.7 5.1 1.9\n3 6.3 2.9 5.6 1.8\n4 7.6 3.0 6.6 2.1\n.. ... ... ... ...\n145 5.1 3.8 1.6 0.2\n146 5.0 3.5 1.6 0.6\n147 5.1 3.4 1.5 0.2\n148 4.6 3.2 1.4 0.2\n149 4.8 3.0 1.4 0.3\n\n[150 rows x 4 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]", "clf": "GradientBoostingClassifier(ccp_alpha=0.0, criterion='mae', init=None,\n learning_rate=0.12, loss='deviance', max_depth=3,\n max_features=None, 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=114,\n n_iter_no_change=None, presort='deprecated',\n random_state=None, subsample=1.0, tol=0.0001,\n validation_fraction=0.1, verbose=0,\n warm_start=False)", "params": "{'n_estimators': [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], 'learning_rate': [0.01, 0.12], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "2926"}}

@ -1,7 +1,7 @@
# first line: 510
# first line: 506
@memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
print('mpike')
print('test')
# instantiate spark session
spark = (
SparkSession

@ -25,7 +25,16 @@ export default {
,0,0,1,0,0
,0,1,1,1
],
KNNModels: 576 //KNN models
SVCModels: 576,
GausNBModels: 736,
MLPModels: 1236,
LRModels: 1356,
LDAModels: 1996,
QDAModels: 2196,
RFModels: 2446,
ExtraTModels: 2606,
AdaBModels: 2766,
GradBModels: 2926,
}
},
methods: {
@ -35,7 +44,7 @@ export default {
PCPView () {
d3.selectAll("#PCP > *").remove();
if (this.selAlgorithm != '') {
var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
var colorGiv = 0
var factorsLocal = this.factors
@ -45,54 +54,214 @@ export default {
divide = element + divide
});
var Mc1 = []
const performanceAlg1 = JSON.parse(this.ModelsPerformance[6])
for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
let sum
sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg1['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg1['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg1['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlg1['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg1['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg1['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg1['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg1['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlg1['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg1['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg1['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg1['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg1['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg1['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlg1['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg1['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg1['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg1['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg1['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg1['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg1['log_loss'])[j]))
Mc1.push((sum/divide)*100)
var McKNN = []
const performanceAlgKNN = JSON.parse(this.ModelsPerformance[6])
for (let j = 0; j < Object.values(performanceAlgKNN['mean_test_accuracy']).length; j++) {
let sumKNN
sumKNN = (factorsLocal[0] * Object.values(performanceAlgKNN['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgKNN['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgKNN['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgKNN['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgKNN['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgKNN['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgKNN['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgKNN['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgKNN['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgKNN['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgKNN['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgKNN['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgKNN['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgKNN['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgKNN['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgKNN['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgKNN['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgKNN['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgKNN['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgKNN['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgKNN['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgKNN['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgKNN['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgKNN['log_loss'])[j]))
McKNN.push((sumKNN/divide)*100)
}
var Mc2 = []
const performanceAlg2 = JSON.parse(this.ModelsPerformance[14])
for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) {
let sum2
sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg2['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg2['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg2['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlg2['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg2['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg2['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg2['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg2['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlg2['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg2['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg2['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg2['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg2['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg2['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlg2['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg2['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg2['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg2['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg2['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg2['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg2['log_loss'])[j]))
Mc2.push((sum2/divide)*100)
var McSVC = []
const performanceAlgSVC = JSON.parse(this.ModelsPerformance[14])
for (let j = 0; j < Object.values(performanceAlgSVC['mean_test_accuracy']).length; j++) {
let sumSVC
sumSVC = (factorsLocal[0] * Object.values(performanceAlgSVC['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgSVC['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgSVC['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgSVC['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgSVC['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgSVC['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgSVC['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgSVC['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgSVC['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgSVC['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgSVC['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgSVC['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgSVC['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgSVC['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgSVC['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgSVC['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgSVC['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgSVC['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgSVC['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgSVC['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgSVC['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgSVC['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgSVC['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgSVC['log_loss'])[j]))
McSVC.push((sumSVC/divide)*100)
}
var McGausNB = []
const performanceAlgGausNB = JSON.parse(this.ModelsPerformance[22])
for (let j = 0; j < Object.values(performanceAlgGausNB['mean_test_accuracy']).length; j++) {
let sumGausNB
sumGausNB = (factorsLocal[0] * Object.values(performanceAlgGausNB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGausNB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGausNB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGausNB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGausNB['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgGausNB['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgGausNB['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgGausNB['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgGausNB['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgGausNB['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgGausNB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGausNB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGausNB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGausNB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGausNB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGausNB['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgGausNB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGausNB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGausNB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGausNB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGausNB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGausNB['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgGausNB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGausNB['log_loss'])[j]))
McGausNB.push((sumGausNB/divide)*100)
}
var McMLP = []
const performanceAlgMLP = JSON.parse(this.ModelsPerformance[30])
for (let j = 0; j < Object.values(performanceAlgMLP['mean_test_accuracy']).length; j++) {
let sumMLP
sumMLP = (factorsLocal[0] * Object.values(performanceAlgMLP['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgMLP['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgMLP['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgMLP['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgMLP['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgMLP['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgMLP['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgMLP['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgMLP['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgMLP['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgMLP['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgMLP['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgMLP['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgMLP['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgMLP['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgMLP['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgMLP['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgMLP['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgMLP['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgMLP['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgMLP['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgMLP['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgMLP['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgMLP['log_loss'])[j]))
McMLP.push((sumMLP/divide)*100)
}
var McLR = []
const performanceAlgLR = JSON.parse(this.ModelsPerformance[38])
for (let j = 0; j < Object.values(performanceAlgLR['mean_test_accuracy']).length; j++) {
let sumLR
sumLR = (factorsLocal[0] * Object.values(performanceAlgLR['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLR['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLR['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLR['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLR['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgLR['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgLR['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgLR['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgLR['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgLR['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgLR['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLR['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLR['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLR['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLR['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLR['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgLR['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLR['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLR['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLR['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLR['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLR['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgLR['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLR['log_loss'])[j]))
McLR.push((sumLR/divide)*100)
}
var McLDA = []
const performanceAlgLDA = JSON.parse(this.ModelsPerformance[46])
for (let j = 0; j < Object.values(performanceAlgLDA['mean_test_accuracy']).length; j++) {
let sumLDA
sumLDA = (factorsLocal[0] * Object.values(performanceAlgLDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLDA['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgLDA['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgLDA['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgLDA['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgLDA['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgLDA['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgLDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLDA['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgLDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLDA['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgLDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLDA['log_loss'])[j]))
McLDA.push((sumLDA/divide)*100)
}
var McQDA = []
const performanceAlgQDA = JSON.parse(this.ModelsPerformance[54])
for (let j = 0; j < Object.values(performanceAlgQDA['mean_test_accuracy']).length; j++) {
let sumQDA
sumQDA = (factorsLocal[0] * Object.values(performanceAlgQDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgQDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgQDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgQDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgQDA['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgQDA['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgQDA['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgQDA['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgQDA['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgQDA['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgQDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgQDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgQDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgQDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgQDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgQDA['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgQDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgQDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgQDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgQDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgQDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgQDA['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgQDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgQDA['log_loss'])[j]))
McQDA.push((sumQDA/divide)*100)
}
var McRF = []
const performanceAlgRF = JSON.parse(this.ModelsPerformance[62])
for (let j = 0; j < Object.values(performanceAlgRF['mean_test_accuracy']).length; j++) {
let sumRF
sumRF = (factorsLocal[0] * Object.values(performanceAlgRF['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgRF['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgRF['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgRF['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgRF['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgRF['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgRF['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgRF['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgRF['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgRF['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgRF['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgRF['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgRF['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgRF['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgRF['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgRF['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgRF['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgRF['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgRF['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgRF['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgRF['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgRF['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgRF['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgRF['log_loss'])[j]))
McRF.push((sumRF/divide)*100)
}
var McExtraT = []
const performanceAlgExtraT = JSON.parse(this.ModelsPerformance[70])
for (let j = 0; j < Object.values(performanceAlgExtraT['mean_test_accuracy']).length; j++) {
let sumExtraT
sumExtraT = (factorsLocal[0] * Object.values(performanceAlgExtraT['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgExtraT['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgExtraT['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgExtraT['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgExtraT['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgExtraT['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgExtraT['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgExtraT['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgExtraT['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgExtraT['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgExtraT['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgExtraT['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgExtraT['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgExtraT['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgExtraT['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgExtraT['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgExtraT['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgExtraT['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgExtraT['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgExtraT['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgExtraT['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgExtraT['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgExtraT['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgExtraT['log_loss'])[j]))
McExtraT.push((sumExtraT/divide)*100)
}
var McAdaB = []
const performanceAlgAdaB = JSON.parse(this.ModelsPerformance[78])
for (let j = 0; j < Object.values(performanceAlgAdaB['mean_test_accuracy']).length; j++) {
let sumAdaB
sumAdaB = (factorsLocal[0] * Object.values(performanceAlgAdaB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgAdaB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgAdaB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgAdaB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgAdaB['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgAdaB['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgAdaB['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgAdaB['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgAdaB['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgAdaB['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgAdaB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgAdaB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgAdaB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgAdaB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgAdaB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgAdaB['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgAdaB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgAdaB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgAdaB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgAdaB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgAdaB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgAdaB['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgAdaB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgAdaB['log_loss'])[j]))
McAdaB.push((sumAdaB/divide)*100)
}
var McGradB = []
const performanceAlgGradB = JSON.parse(this.ModelsPerformance[86])
for (let j = 0; j < Object.values(performanceAlgGradB['mean_test_accuracy']).length; j++) {
let sumGradB
sumGradB = (factorsLocal[0] * Object.values(performanceAlgGradB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGradB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGradB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGradB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGradB['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgGradB['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgGradB['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgGradB['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgGradB['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgGradB['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgGradB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGradB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGradB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGradB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGradB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGradB['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgGradB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGradB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGradB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGradB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGradB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGradB['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgGradB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGradB['log_loss'])[j]))
McGradB.push((sumGradB/divide)*100)
}
var Combined = 0
if (this.selAlgorithm == 'KNN') {
Combined = JSON.parse(this.ModelsPerformance[1])
colorGiv = colors[0]
} else {
} else if (this.selAlgorithm == 'SVC') {
Combined = JSON.parse(this.ModelsPerformance[9])
colorGiv = colors[1]
} else if (this.selAlgorithm == 'GausNB') {
Combined = JSON.parse(this.ModelsPerformance[17])
colorGiv = colors[2]
} else if (this.selAlgorithm == 'MLP') {
Combined = JSON.parse(this.ModelsPerformance[25])
colorGiv = colors[3]
} else if (this.selAlgorithm == 'LR') {
Combined = JSON.parse(this.ModelsPerformance[33])
colorGiv = colors[4]
} else if (this.selAlgorithm == 'LDA') {
Combined = JSON.parse(this.ModelsPerformance[41])
colorGiv = colors[5]
} else if (this.selAlgorithm == 'QDA') {
Combined = JSON.parse(this.ModelsPerformance[49])
colorGiv = colors[6]
} else if (this.selAlgorithm == 'RF') {
Combined = JSON.parse(this.ModelsPerformance[57])
colorGiv = colors[7]
} else if (this.selAlgorithm == 'ExtraT') {
Combined = JSON.parse(this.ModelsPerformance[65])
colorGiv = colors[8]
} else if (this.selAlgorithm == 'AdaB') {
Combined = JSON.parse(this.ModelsPerformance[73])
colorGiv = colors[9]
} else {
Combined = JSON.parse(this.ModelsPerformance[81])
colorGiv = colors[10]
}
var valuesPerf = Object.values(Combined['params'])
var ObjectsParams = Combined['params']
var newObjectsParams = []
var newObjectsParams2 = []
var newObjectsParamsΚΝΝ = []
var newObjectsParamsSVC = []
var newObjectsParamsGausNB = []
var newObjectsParamsMLP = []
var newObjectsParamsLR = []
var newObjectsParamsLDA = []
var newObjectsParamsQDA = []
var newObjectsParamsRF = []
var newObjectsParamsExtraT = []
var newObjectsParamsAdaB = []
var newObjectsParamsGradB = []
var ArrayCombined = []
var temp
for (var i = 0; i < valuesPerf.length; i++) {
if (this.selAlgorithm === 'KNN') {
// There is a problem here!
newObjectsParams.push({model: i,'performance (%)': Mc1[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
ArrayCombined[i] = newObjectsParams[i]
newObjectsParamsΚΝΝ.push({model: i,'# performance (%) #': McKNN[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
ArrayCombined[i] = newObjectsParamsΚΝΝ[i]
} else if (this.selAlgorithm === 'SVC') {
newObjectsParamsSVC.push({model: this.SVCModels + i,'# performance (%) #': McSVC[i],'C':ObjectsParams[i].C,'kernel':ObjectsParams[i].kernel})
ArrayCombined[i] = newObjectsParamsSVC[i]
} else if (this.selAlgorithm === 'GausNB') {
newObjectsParamsGausNB.push({model: this.GausNBModels + i,'# performance (%) #': McGausNB[i],'var_smoothing':ObjectsParams[i].var_smoothing})
ArrayCombined[i] = newObjectsParamsGausNB[i]
} else if (this.selAlgorithm === 'MLP') {
newObjectsParamsMLP.push({model: this.MLPModels + i,'# performance (%) #': McMLP[i],'alpha':ObjectsParams[i].alpha,'tol':ObjectsParams[i].tol,'activation':ObjectsParams[i].activation,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver})
ArrayCombined[i] = newObjectsParamsMLP[i]
} else if (this.selAlgorithm === 'LR') {
newObjectsParamsLR.push({model: this.LRModels + i,'# performance (%) #': McLR[i],'C':ObjectsParams[i].C,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver,'penalty':ObjectsParams[i].penalty})
ArrayCombined[i] = newObjectsParamsLR[i]
} else if (this.selAlgorithm === 'LDA') {
newObjectsParamsLDA.push({model: this.LDAModels + i,'# performance (%) #': McLDA[i],'shrinkage':ObjectsParams[i].shrinkage,'solver':ObjectsParams[i].solver})
ArrayCombined[i] = newObjectsParamsLDA[i]
} else if (this.selAlgorithm === 'QDA') {
newObjectsParamsQDA.push({model: this.QDAModels + i,'# performance (%) #': McQDA[i],'reg_param':ObjectsParams[i].reg_param,'tol':ObjectsParams[i].tol})
ArrayCombined[i] = newObjectsParamsQDA[i]
} else if (this.selAlgorithm === 'RF') {
newObjectsParamsRF.push({model: this.RFModels + i,'# performance (%) #': McRF[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
ArrayCombined[i] = newObjectsParamsRF[i]
} else if (this.selAlgorithm === 'ExtraT') {
newObjectsParamsExtraT.push({model: this.ExtraTModels + i,'# performance (%) #': McExtraT[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
ArrayCombined[i] = newObjectsParamsExtraT[i]
} else if (this.selAlgorithm === 'AdaB') {
newObjectsParamsAdaB.push({model: this.AdaBModels + i,'# performance (%) #': McAdaB[i],'n_estimators':ObjectsParams[i].n_estimators,'learning_rate':ObjectsParams[i].learning_rate,'algorithm':ObjectsParams[i].algorithm})
ArrayCombined[i] = newObjectsParamsAdaB[i]
} else {
newObjectsParams2.push({model: this.KNNModels + i,'performance (%)': Mc2[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
ArrayCombined[i] = newObjectsParams2[i]
newObjectsParamsGradB.push({model: this.GradBModels + i,'# performance (%) #': McGradB[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion,'learning_rate':ObjectsParams[i].learning_rate})
ArrayCombined[i] = newObjectsParamsGradB[i]
}
}
EventBus.$emit('AllAlModels', ArrayCombined.length)

@ -22,8 +22,17 @@ export default {
brushedBoxPl: [],
previousColor: 0,
selectedAlgorithm: 0,
AllAlgorithms: ['KNN','RF'],
KNNModels: 576, //KNN models
AllAlgorithms: ['KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
SVCModels: 576,
GausNBModels: 736,
MLPModels: 1236,
LRModels: 1356,
LDAModels: 1996,
QDAModels: 2196,
RFModels: 2446,
ExtraTModels: 2606,
AdaBModels: 2766,
GradBModels: 2926,
WH: [],
parameters: [],
algorithm1: [],
@ -48,8 +57,17 @@ export default {
d3.selectAll("#exploding_boxplot > *").remove()
// retrieve models ID
const Algor1IDs = this.PerformanceAllModels[0]
const Algor2IDs = this.PerformanceAllModels[8]
const AlgorKNNIDs = this.PerformanceAllModels[0]
const AlgorSVCIDs = this.PerformanceAllModels[8]
const AlgorGausNBIDs = this.PerformanceAllModels[16]
const AlgorMLPIDs = this.PerformanceAllModels[24]
const AlgorLRIDs = this.PerformanceAllModels[32]
const AlgorLDAIDs = this.PerformanceAllModels[40]
const AlgorQDAIDs = this.PerformanceAllModels[48]
const AlgorRFIDs = this.PerformanceAllModels[56]
const AlgorExtraTIDs = this.PerformanceAllModels[64]
const AlgorAdaBIDs = this.PerformanceAllModels[72]
const AlgorGradBIDs = this.PerformanceAllModels[80]
var factorsLocal = this.factors
var divide = 0
@ -58,67 +76,226 @@ export default {
divide = element + divide
});
var Mc1 = []
const performanceAlg1 = JSON.parse(this.PerformanceAllModels[6])
for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
let sum
sum = (factorsLocal[0] * Object.values(performanceAlg1['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg1['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg1['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg1['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlg1['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg1['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg1['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg1['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg1['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlg1['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg1['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg1['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg1['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg1['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg1['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlg1['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg1['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg1['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg1['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg1['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg1['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlg1['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg1['log_loss'])[j]))
Mc1.push((sum/divide)*100)
var McKNN = []
const performanceAlgKNN = JSON.parse(this.PerformanceAllModels[6])
for (let j = 0; j < Object.values(performanceAlgKNN['mean_test_accuracy']).length; j++) {
let sumKNN
sumKNN = (factorsLocal[0] * Object.values(performanceAlgKNN['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgKNN['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgKNN['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgKNN['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgKNN['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgKNN['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgKNN['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgKNN['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgKNN['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgKNN['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgKNN['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgKNN['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgKNN['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgKNN['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgKNN['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgKNN['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgKNN['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgKNN['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgKNN['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgKNN['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgKNN['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgKNN['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgKNN['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgKNN['log_loss'])[j]))
McKNN.push((sumKNN/divide)*100)
}
var Mc2 = []
const performanceAlg2 = JSON.parse(this.PerformanceAllModels[14])
for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) {
let sum2
sum2 = (factorsLocal[0] * Object.values(performanceAlg2['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlg2['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlg2['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlg2['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlg2['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlg2['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlg2['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlg2['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlg2['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlg2['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlg2['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlg2['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlg2['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlg2['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlg2['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlg2['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlg2['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlg2['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlg2['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlg2['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlg2['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlg2['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlg2['log_loss'])[j]))
Mc2.push((sum2/divide)*100)
var McSVC = []
const performanceAlgSVC = JSON.parse(this.PerformanceAllModels[14])
for (let j = 0; j < Object.values(performanceAlgSVC['mean_test_accuracy']).length; j++) {
let sumSVC
sumSVC = (factorsLocal[0] * Object.values(performanceAlgSVC['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgSVC['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgSVC['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgSVC['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgSVC['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgSVC['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgSVC['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgSVC['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgSVC['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgSVC['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgSVC['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgSVC['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgSVC['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgSVC['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgSVC['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgSVC['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgSVC['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgSVC['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgSVC['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgSVC['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgSVC['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgSVC['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgSVC['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgSVC['log_loss'])[j]))
McSVC.push((sumSVC/divide)*100)
}
var McGausNB = []
const performanceAlgGausNB = JSON.parse(this.PerformanceAllModels[22])
for (let j = 0; j < Object.values(performanceAlgGausNB['mean_test_accuracy']).length; j++) {
let sumGausNB
sumGausNB = (factorsLocal[0] * Object.values(performanceAlgGausNB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGausNB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGausNB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGausNB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGausNB['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgGausNB['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgGausNB['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgGausNB['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgGausNB['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgGausNB['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgGausNB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGausNB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGausNB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGausNB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGausNB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGausNB['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgGausNB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGausNB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGausNB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGausNB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGausNB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGausNB['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgGausNB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGausNB['log_loss'])[j]))
McGausNB.push((sumGausNB/divide)*100)
}
var McMLP = []
const performanceAlgMLP = JSON.parse(this.PerformanceAllModels[30])
for (let j = 0; j < Object.values(performanceAlgMLP['mean_test_accuracy']).length; j++) {
let sumMLP
sumMLP = (factorsLocal[0] * Object.values(performanceAlgMLP['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgMLP['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgMLP['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgMLP['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgMLP['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgMLP['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgMLP['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgMLP['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgMLP['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgMLP['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgMLP['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgMLP['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgMLP['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgMLP['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgMLP['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgMLP['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgMLP['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgMLP['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgMLP['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgMLP['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgMLP['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgMLP['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgMLP['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgMLP['log_loss'])[j]))
McMLP.push((sumMLP/divide)*100)
}
var McLR = []
const performanceAlgLR = JSON.parse(this.PerformanceAllModels[38])
for (let j = 0; j < Object.values(performanceAlgLR['mean_test_accuracy']).length; j++) {
let sumLR
sumLR = (factorsLocal[0] * Object.values(performanceAlgLR['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLR['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLR['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLR['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLR['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgLR['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgLR['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgLR['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgLR['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgLR['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgLR['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLR['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLR['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLR['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLR['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLR['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgLR['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLR['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLR['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLR['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLR['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLR['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgLR['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLR['log_loss'])[j]))
McLR.push((sumLR/divide)*100)
}
var McLDA = []
const performanceAlgLDA = JSON.parse(this.PerformanceAllModels[46])
for (let j = 0; j < Object.values(performanceAlgLDA['mean_test_accuracy']).length; j++) {
let sumLDA
sumLDA = (factorsLocal[0] * Object.values(performanceAlgLDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgLDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgLDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgLDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLDA['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgLDA['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgLDA['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgLDA['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgLDA['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgLDA['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgLDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgLDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLDA['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgLDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgLDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgLDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgLDA['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgLDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgLDA['log_loss'])[j]))
McLDA.push((sumLDA/divide)*100)
}
var McQDA = []
const performanceAlgQDA = JSON.parse(this.PerformanceAllModels[54])
for (let j = 0; j < Object.values(performanceAlgQDA['mean_test_accuracy']).length; j++) {
let sumQDA
sumQDA = (factorsLocal[0] * Object.values(performanceAlgQDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgQDA['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgQDA['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgQDA['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgQDA['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgQDA['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgQDA['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgQDA['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgQDA['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgQDA['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgQDA['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgQDA['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgQDA['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgQDA['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgQDA['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgQDA['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgQDA['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgQDA['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgQDA['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgQDA['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgQDA['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgQDA['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgQDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgQDA['log_loss'])[j]))
McQDA.push((sumQDA/divide)*100)
}
var McRF = []
const performanceAlgRF = JSON.parse(this.PerformanceAllModels[62])
for (let j = 0; j < Object.values(performanceAlgRF['mean_test_accuracy']).length; j++) {
let sumRF
sumRF = (factorsLocal[0] * Object.values(performanceAlgRF['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgRF['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgRF['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgRF['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgRF['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgRF['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgRF['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgRF['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgRF['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgRF['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgRF['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgRF['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgRF['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgRF['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgRF['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgRF['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgRF['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgRF['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgRF['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgRF['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgRF['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgRF['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgRF['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgRF['log_loss'])[j]))
McRF.push((sumRF/divide)*100)
}
var McExtraT = []
const performanceAlgExtraT = JSON.parse(this.PerformanceAllModels[70])
for (let j = 0; j < Object.values(performanceAlgExtraT['mean_test_accuracy']).length; j++) {
let sumExtraT
sumExtraT = (factorsLocal[0] * Object.values(performanceAlgExtraT['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgExtraT['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgExtraT['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgExtraT['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgExtraT['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgExtraT['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgExtraT['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgExtraT['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgExtraT['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgExtraT['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgExtraT['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgExtraT['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgExtraT['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgExtraT['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgExtraT['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgExtraT['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgExtraT['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgExtraT['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgExtraT['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgExtraT['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgExtraT['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgExtraT['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgExtraT['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgExtraT['log_loss'])[j]))
McExtraT.push((sumExtraT/divide)*100)
}
var McAdaB = []
const performanceAlgAdaB = JSON.parse(this.PerformanceAllModels[78])
for (let j = 0; j < Object.values(performanceAlgAdaB['mean_test_accuracy']).length; j++) {
let sumAdaB
sumAdaB = (factorsLocal[0] * Object.values(performanceAlgAdaB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgAdaB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgAdaB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgAdaB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgAdaB['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgAdaB['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgAdaB['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgAdaB['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgAdaB['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgAdaB['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgAdaB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgAdaB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgAdaB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgAdaB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgAdaB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgAdaB['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgAdaB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgAdaB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgAdaB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgAdaB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgAdaB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgAdaB['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgAdaB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgAdaB['log_loss'])[j]))
McAdaB.push((sumAdaB/divide)*100)
}
var McGradB = []
const performanceAlgGradB = JSON.parse(this.PerformanceAllModels[86])
for (let j = 0; j < Object.values(performanceAlgGradB['mean_test_accuracy']).length; j++) {
let sumGradB
sumGradB = (factorsLocal[0] * Object.values(performanceAlgGradB['mean_test_accuracy'])[j]) + (factorsLocal[1] * (Object.values(performanceAlgGradB['mean_test_neg_mean_absolute_error'])[j]) + 1) + (factorsLocal[2] * (Object.values(performanceAlgGradB['mean_test_neg_root_mean_squared_error'])[j]) + 1) + (factorsLocal[3] * Object.values(performanceAlgGradB['geometric_mean_score_micro'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGradB['geometric_mean_score_macro'])[j])
+ (factorsLocal[5] * Object.values(performanceAlgGradB['geometric_mean_score_weighted'])[j]) + (factorsLocal[6] * Object.values(performanceAlgGradB['mean_test_precision_micro'])[j]) + (factorsLocal[7] * Object.values(performanceAlgGradB['mean_test_precision_macro'])[j]) + (factorsLocal[8] * Object.values(performanceAlgGradB['mean_test_precision_weighted'])[j]) + (factorsLocal[9] * Object.values(performanceAlgGradB['mean_test_recall_micro'])[j])
+ (factorsLocal[10] * Object.values(performanceAlgGradB['mean_test_recall_macro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGradB['mean_test_recall_weighted'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGradB['f5_micro'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGradB['f5_macro'])[j]) + (factorsLocal[14] * Object.values(performanceAlgGradB['f5_weighted'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGradB['f1_micro'])[j])
+ (factorsLocal[16] * Object.values(performanceAlgGradB['f1_macro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGradB['f1_weighted'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGradB['f2_micro'])[j]) + (factorsLocal[19] * Object.values(performanceAlgGradB['f2_macro'])[j]) + (factorsLocal[20] * Object.values(performanceAlgGradB['f2_weighted'])[j]) + (factorsLocal[21] * Object.values(performanceAlgGradB['matthews_corrcoef'])[j])
+ (factorsLocal[22] * Object.values(performanceAlgGradB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[23] * (1 - Object.values(performanceAlgGradB['log_loss'])[j]))
McGradB.push((sumGradB/divide)*100)
}
// retrieve the results like performance
const PerformAlgor1 = JSON.parse(this.PerformanceAllModels[1])
const PerformAlgor2 = JSON.parse(this.PerformanceAllModels[9])
const PerformAlgorKNN = JSON.parse(this.PerformanceAllModels[1])
const PerformAlgorSVC = JSON.parse(this.PerformanceAllModels[9])
const PerformAlgorGausNB = JSON.parse(this.PerformanceAllModels[17])
const PerformAlgorMLP = JSON.parse(this.PerformanceAllModels[25])
const PerformAlgorLR = JSON.parse(this.PerformanceAllModels[33])
const PerformAlgorLDA = JSON.parse(this.PerformanceAllModels[41])
const PerformAlgorQDA = JSON.parse(this.PerformanceAllModels[49])
const PerformAlgorRF = JSON.parse(this.PerformanceAllModels[57])
const PerformAlgorExtraT = JSON.parse(this.PerformanceAllModels[65])
const PerformAlgorAdaB = JSON.parse(this.PerformanceAllModels[73])
const PerformAlgorGradB = JSON.parse(this.PerformanceAllModels[81])
// initialize/instansiate algorithms and parameters
this.algorithm1 = []
this.algorithm2 = []
this.algorithmKNN = []
this.algorithmSVC = []
this.algorithmGausNB = []
this.algorithmMLP = []
this.algorithmLR = []
this.algorithmLDA = []
this.algorithmQDA = []
this.algorithmRF = []
this.algorithmExtraT = []
this.algorithmAdaB = []
this.algorithmGradB = []
this.parameters = []
for (var i = 0; i < Object.keys(PerformAlgor1['params']).length; i++) {
this.algorithm1.push({'Performance (%)': Mc1[i],Algorithm:'KNN',Model:'Model ' + Algor1IDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance (%) ',ModelID:Algor1IDs[i]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgor1['params'])[i]))
for (var i = 0; i < Object.keys(PerformAlgorKNN['params']).length; i++) {
this.algorithmKNN.push({'# Performance (%) #': McKNN[i],Algorithm:'KNN',Model:'Model ' + AlgorKNNIDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorKNN['params'])[i])+'; # Performance (%) # ',ModelID:AlgorKNNIDs[i]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorKNN['params'])[i]))
}
for (let j = 0; j < Object.keys(PerformAlgorSVC['params']).length; j++) {
this.algorithmSVC.push({'# Performance (%) #': McSVC[j],Algorithm:'SVC',Model:'Model ' + AlgorSVCIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorSVC['params'])[j])+'; # Performance (%) # ',ModelID:AlgorSVCIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorSVC['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorGausNB['params']).length; j++) {
this.algorithmGausNB.push({'# Performance (%) #': McGausNB[j],Algorithm:'GausNB',Model:'Model ' + AlgorGausNBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGausNBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorGausNB['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgor2['params']).length; j++) {
this.algorithm2.push({'Performance (%)': Mc2[j],Algorithm:'RF',Model:'Model ' + Algor2IDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance (%) ',ModelID:Algor2IDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgor2['params'])[j]))
for (let j = 0; j < Object.keys(PerformAlgorMLP['params']).length; j++) {
this.algorithmMLP.push({'# Performance (%) #': McMLP[j],Algorithm:'MLP',Model:'Model ' + AlgorMLPIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorMLP['params'])[j])+'; # Performance (%) # ',ModelID:AlgorMLPIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorMLP['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorLR['params']).length; j++) {
this.algorithmLR.push({'# Performance (%) #': McLR[j],Algorithm:'LR',Model:'Model ' + AlgorLRIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLR['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLRIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorLR['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorLDA['params']).length; j++) {
this.algorithmLDA.push({'# Performance (%) #': McLDA[j],Algorithm:'LDA',Model:'Model ' + AlgorLDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorLDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorLDAIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorLDA['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorQDA['params']).length; j++) {
this.algorithmQDA.push({'# Performance (%) #': McQDA[j],Algorithm:'QDA',Model:'Model ' + AlgorQDAIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorQDA['params'])[j])+'; # Performance (%) # ',ModelID:AlgorQDAIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorQDA['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorRF['params']).length; j++) {
this.algorithmRF.push({'# Performance (%) #': McRF[j],Algorithm:'RF',Model:'Model ' + AlgorRFIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorRF['params'])[j])+'; # Performance (%) # ',ModelID:AlgorRFIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorRF['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorExtraT['params']).length; j++) {
this.algorithmExtraT.push({'# Performance (%) #': McExtraT[j],Algorithm:'ExtraT',Model:'Model ' + AlgorExtraTIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j])+'; # Performance (%) # ',ModelID:AlgorExtraTIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorExtraT['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorAdaB['params']).length; j++) {
this.algorithmAdaB.push({'# Performance (%) #': McAdaB[j],Algorithm:'AdaB',Model:'Model ' + AlgorAdaBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorAdaBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorAdaB['params'])[j]))
}
for (let j = 0; j < Object.keys(PerformAlgorGradB['params']).length; j++) {
this.algorithmGradB.push({'# Performance (%) #': McGradB[j],Algorithm:'GradB',Model:'Model ' + AlgorGradBIDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgorGradB['params'])[j])+'; # Performance (%) # ',ModelID:AlgorGradBIDs[j]})
this.parameters.push(JSON.stringify(Object.values(PerformAlgorGradB['params'])[j]))
}
EventBus.$emit('ParametersAll', this.parameters)
// concat the data
var data = this.algorithm1.concat(this.algorithm2)
var data = this.algorithmKNN.concat(this.algorithmSVC)
var data = data.concat(this.algorithmGausNB)
var data = data.concat(this.algorithmMLP)
var data = data.concat(this.algorithmLR)
var data = data.concat(this.algorithmLDA)
var data = data.concat(this.algorithmQDA)
var data = data.concat(this.algorithmRF)
var data = data.concat(this.algorithmExtraT)
var data = data.concat(this.algorithmAdaB)
var data = data.concat(this.algorithmGradB)
// 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 = exploding_boxplot(data, {y:'# Performance (%) #',group:'Algorithm',color:'Algorithm',label:'Model'})
this.chart.width(this.WH[0]*10) // interactive visualization
this.chart.height(this.WH[1]*0.9) // interactive visualization
//call chart on a div
this.chart('#exploding_boxplot')
// colorscale
const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
// check for brushing
var el = document.getElementsByClassName('d3-exploding-boxplot boxcontent')
var overall = document.getElementsByClassName('overall')
@ -126,7 +303,16 @@ export default {
// on clicks
var flagEmptyKNN = 0
var flagEmptySVC = 0
var flagEmptyGausNB = 0
var flagEmptyMLP = 0
var flagEmptyLR = 0
var flagEmptyLDA = 0
var flagEmptyQDA = 0
var flagEmptyRF = 0
var flagEmptyExtraT = 0
var flagEmptyAdaB = 0
var flagEmptyGradB = 0
el[0].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
@ -146,12 +332,114 @@ export default {
EventBus.$emit('updateBarChart', [])
}
el[1].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[1]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptySVC == 0) {
flagEmptySVC = 1
} else {
flagEmptySVC = 0
}
EventBus.$emit('updateFlagSVC', flagEmptySVC)
EventBus.$emit('PCPCall', 'SVC')
EventBus.$emit('updateBarChart', [])
}
el[2].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GausNB')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[2]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyGausNB == 0) {
flagEmptyGausNB = 1
} else {
flagEmptyGausNB = 0
}
EventBus.$emit('updateFlagGausNB', flagEmptyGausNB)
EventBus.$emit('PCPCall', 'GausNB')
EventBus.$emit('updateBarChart', [])
}
el[3].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[3]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyMLP == 0) {
flagEmptyMLP = 1
} else {
flagEmptyMLP = 0
}
EventBus.$emit('updateFlagMLP', flagEmptyMLP)
EventBus.$emit('PCPCall', 'MLP')
EventBus.$emit('updateBarChart', [])
}
el[4].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[4]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyLR == 0) {
flagEmptyLR = 1
} else {
flagEmptyLR = 0
}
EventBus.$emit('updateFlagLR', flagEmptyLR)
EventBus.$emit('PCPCall', 'LR')
EventBus.$emit('updateBarChart', [])
}
el[5].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[5]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyLDA == 0) {
flagEmptyLDA = 1
} else {
flagEmptyLDA = 0
}
EventBus.$emit('updateFlagLDA', flagEmptyLDA)
EventBus.$emit('PCPCall', 'LDA')
EventBus.$emit('updateBarChart', [])
}
el[6].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[6]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyQDA == 0) {
flagEmptyQDA = 1
} else {
flagEmptyQDA = 0
}
EventBus.$emit('updateFlagQDA', flagEmptyQDA)
EventBus.$emit('PCPCall', 'QDA')
EventBus.$emit('updateBarChart', [])
}
el[7].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[7]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyRF == 0) {
flagEmptyRF = 1
} else {
@ -162,13 +450,72 @@ export default {
EventBus.$emit('PCPCall', 'RF')
EventBus.$emit('updateBarChart', [])
}
el[8].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[8]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyExtraT == 0) {
flagEmptyExtraT = 1
} else {
flagEmptyExtraT = 0
}
EventBus.$emit('updateFlagExtraT', flagEmptyExtraT)
EventBus.$emit('PCPCall', 'ExtraT')
EventBus.$emit('updateBarChart', [])
}
el[9].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[9]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyAdaB == 0) {
flagEmptyAdaB = 1
} else {
flagEmptyAdaB = 0
}
EventBus.$emit('updateFlagAdaB', flagEmptyAdaB)
EventBus.$emit('PCPCall', 'AdaB')
EventBus.$emit('updateBarChart', [])
}
el[10].onclick = function() {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.fill = previousColor[10]
allPoints[i].style.opacity = '1.0'
}
if (flagEmptyGradB == 0) {
flagEmptyGradB = 1
} else {
flagEmptyGradB = 0
}
EventBus.$emit('updateFlagGradB', flagEmptyGradB)
EventBus.$emit('PCPCall', 'GradB')
EventBus.$emit('updateBarChart', [])
}
overall[0].ondblclick = function () {
flagEmptyKNN = 0
flagEmptySVC = 0
flagEmptyGausNB = 0
flagEmptyMLP = 0
flagEmptyLR = 0
flagEmptyLDA = 0
flagEmptyQDA = 0
flagEmptyRF = 0
flagEmptyExtraT = 0
flagEmptyAdaB = 0
flagEmptyGradB = 0
EventBus.$emit('clearPCP')
EventBus.$emit('alternateFlagLock', flagEmptyKNN)
EventBus.$emit('alternateFlagLock', flagEmptyKNN)
EventBus.$emit('alternateFlagLock')
}
// check if brushed through all boxplots and not only one at a time
@ -183,30 +530,74 @@ export default {
var limiter = this.chart.returnBrush()
var algorithm = []
const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
var modelsActive = []
for (var j = 0; j < this.AllAlgorithms.length; j++) {
algorithm = []
if (this.AllAlgorithms[j] === 'KNN') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
algorithm = this.algorithm1
} else {
algorithm = this.algorithmKNN
} else if (this.AllAlgorithms[j] === 'SVC') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
algorithm = this.algorithmSVC
} else if (this.AllAlgorithms[j] === 'GausNB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GausNB')
algorithm = this.algorithmGausNB
} else if (this.AllAlgorithms[j] === 'MLP') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
algorithm = this.algorithmMLP
} else if (this.AllAlgorithms[j] === 'LR') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
algorithm = this.algorithmLR
} else if (this.AllAlgorithms[j] === 'LDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
algorithm = this.algorithmLDA
} else if (this.AllAlgorithms[j] === 'QDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
algorithm = this.algorithmQDA
} else if (this.AllAlgorithms[j] === 'RF') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
algorithm = this.algorithm2
algorithm = this.algorithmRF
} else if (this.AllAlgorithms[j] === 'ExtraT') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
algorithm = this.algorithmExtraT
} else if (this.AllAlgorithms[j] === 'AdaB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
algorithm = this.algorithmAdaB
} else {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
algorithm = this.algorithmGradB
}
for (let k = 0; k < allPoints.length; k++) {
if (algorithm[k]['Performance (%)'] < limiter[0] && algorithm[k]['Performance (%)'] > limiter[1]) {
if (algorithm[k]['# Performance (%) #'] < limiter[0] && algorithm[k]['# Performance (%) #'] > limiter[1]) {
modelsActive.push(algorithm[k].ModelID)
}
}
for (let i = 0; i < allPoints.length; i++) {
if (this.AllAlgorithms[j] === 'KNN') {
allPoints[i].style.fill = previousColor[0]
} else {
} else if (this.AllAlgorithms[j] === 'SVC') {
allPoints[i].style.fill = previousColor[1]
} else if (this.AllAlgorithms[j] === 'GausNB') {
allPoints[i].style.fill = previousColor[2]
} else if (this.AllAlgorithms[j] === 'MLP') {
allPoints[i].style.fill = previousColor[3]
} else if (this.AllAlgorithms[j] === 'LR') {
allPoints[i].style.fill = previousColor[4]
} else if (this.AllAlgorithms[j] === 'LDA') {
allPoints[i].style.fill = previousColor[5]
} else if (this.AllAlgorithms[j] === 'QDA') {
allPoints[i].style.fill = previousColor[6]
} else if (this.AllAlgorithms[j] === 'RF') {
allPoints[i].style.fill = previousColor[7]
} else if (this.AllAlgorithms[j] === 'ExtraT') {
allPoints[i].style.fill = previousColor[8]
} else if (this.AllAlgorithms[j] === 'AdaB') {
allPoints[i].style.fill = previousColor[9]
} else {
allPoints[i].style.fill = previousColor[10]
}
}
if (modelsActive.length == 0) {
for (let i = 0; i < allPoints.length; i++) {
//if (modelsActive.indexOf(i) == -1) {
@ -219,9 +610,36 @@ export default {
if (this.AllAlgorithms[j] === 'KNN') {
allPoints[i].style.fill = previousColor[0]
allPoints[i].style.opacity = '1.0'
} else {
} else if (this.AllAlgorithms[j] === 'SVC') {
allPoints[i].style.fill = previousColor[1]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'GausNB') {
allPoints[i].style.fill = previousColor[2]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'MLP') {
allPoints[i].style.fill = previousColor[3]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'LR') {
allPoints[i].style.fill = previousColor[4]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'LDA') {
allPoints[i].style.fill = previousColor[5]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'QDA') {
allPoints[i].style.fill = previousColor[6]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'RF') {
allPoints[i].style.fill = previousColor[7]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'ExtraT') {
allPoints[i].style.fill = previousColor[8]
allPoints[i].style.opacity = '1.0'
} else if (this.AllAlgorithms[j] === 'AdaB') {
allPoints[i].style.fill = previousColor[9]
allPoints[i].style.opacity = '1.0'
} else {
allPoints[i].style.fill = previousColor[10]
allPoints[i].style.opacity = '1.0'
}
}
} else {
@ -232,8 +650,53 @@ export default {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'SVC') {
if (modelsActive.indexOf(i+this.SVCModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'GausNB') {
if (modelsActive.indexOf(i+this.GausNBModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'MLP') {
if (modelsActive.indexOf(i+this.MLPModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'LR') {
if (modelsActive.indexOf(i+this.LRModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'LDA') {
if (modelsActive.indexOf(i+this.LDAModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'QDA') {
if (modelsActive.indexOf(i+this.QDAModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'RF') {
if (modelsActive.indexOf(i+this.RFModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'ExtraT') {
if (modelsActive.indexOf(i+this.ExtraTModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.AllAlgorithms[j] === 'AdaB') {
if (modelsActive.indexOf(i+this.AdaBModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else {
if (modelsActive.indexOf(i+this.KNNModels) == -1) {
if (modelsActive.indexOf(i+this.GradBModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
@ -246,21 +709,59 @@ export default {
this.UpdateBarChart()
},
brushed () {
if (this.selectedAlgorithm == 'KNN') {
if (this.selectedAlgorithm === 'KNN') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point KNN')
} else {
} else if (this.selectedAlgorithm === 'SVC') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point SVC')
} else if (this.selectedAlgorithm === 'GausNB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GausNB')
} else if (this.selectedAlgorithm === 'MLP') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point MLP')
} else if (this.selectedAlgorithm === 'LR') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LR')
} else if (this.selectedAlgorithm === 'LDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point LDA')
} else if (this.selectedAlgorithm === 'QDA') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point QDA')
} else if (this.selectedAlgorithm === 'RF') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point RF')
} else if (this.selectedAlgorithm === 'ExtraT') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point ExtraT')
} else if (this.selectedAlgorithm === 'AdaB') {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point AdaB')
} else {
var allPoints = document.getElementsByClassName('d3-exploding-boxplot point GradB')
}
const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']
const previousColor = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
var modelsActive = []
for (let j = 0; j < this.brushedBoxPl.length; j++) {
modelsActive.push(this.brushedBoxPl[j].model)
}
console.log(modelsActive)
console.log(this.selectedAlgorithm)
for (let i = 0; i < allPoints.length; i++) {
if (this.selectedAlgorithm == 'KNN') {
if (this.selectedAlgorithm === 'KNN') {
allPoints[i].style.fill = previousColor[0]
} else {
} else if (this.selectedAlgorithm === 'SVC') {
allPoints[i].style.fill = previousColor[1]
} else if (this.selectedAlgorithm === 'GausNB') {
allPoints[i].style.fill = previousColor[2]
} else if (this.selectedAlgorithm === 'MLP') {
allPoints[i].style.fill = previousColor[3]
} else if (this.selectedAlgorithm === 'LR') {
allPoints[i].style.fill = previousColor[4]
} else if (this.selectedAlgorithm === 'LDA') {
allPoints[i].style.fill = previousColor[5]
} else if (this.selectedAlgorithm === 'QDA') {
allPoints[i].style.fill = previousColor[6]
} else if (this.selectedAlgorithm === 'RF') {
allPoints[i].style.fill = previousColor[7]
} else if (this.selectedAlgorithm === 'ExtraT') {
allPoints[i].style.fill = previousColor[8]
} else if (this.selectedAlgorithm === 'AdaB') {
allPoints[i].style.fill = previousColor[9]
} else {
allPoints[i].style.fill = previousColor[10]
}
}
if (modelsActive.length == 0) {
@ -272,24 +773,96 @@ export default {
}
} else if (modelsActive.length == allPoints.length) {
for (let i = 0; i < allPoints.length; i++) {
if (this.selectedAlgorithm == 'KNN') {
if (this.selectedAlgorithm === 'KNN') {
allPoints[i].style.fill = previousColor[0]
allPoints[i].style.opacity = '1.0'
} else {
} else if (this.selectedAlgorithm === 'SVC') {
allPoints[i].style.fill = previousColor[1]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'GausNB') {
allPoints[i].style.fill = previousColor[2]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'MLP') {
allPoints[i].style.fill = previousColor[3]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'LR') {
allPoints[i].style.fill = previousColor[4]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'LDA') {
allPoints[i].style.fill = previousColor[5]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'QDA') {
allPoints[i].style.fill = previousColor[6]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'RF') {
allPoints[i].style.fill = previousColor[7]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'ExtraT') {
allPoints[i].style.fill = previousColor[8]
allPoints[i].style.opacity = '1.0'
} else if (this.selectedAlgorithm === 'AdaB') {
allPoints[i].style.fill = previousColor[9]
allPoints[i].style.opacity = '1.0'
} else {
allPoints[i].style.fill = previousColor[10]
allPoints[i].style.opacity = '1.0'
}
}
} else {
for (let i = 0; i < allPoints.length; i++) {
allPoints[i].style.opacity = '1.0'
if (this.selectedAlgorithm == 'KNN') {
if (this.selectedAlgorithm === 'KNN') {
if (modelsActive.indexOf(i) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'SVC') {
if (modelsActive.indexOf(i+this.SVCModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'GausNB') {
if (modelsActive.indexOf(i+this.GausNBModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'MLP') {
if (modelsActive.indexOf(i+this.MLPModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'LR') {
if (modelsActive.indexOf(i+this.LRModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'LDA') {
if (modelsActive.indexOf(i+this.LDAModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'QDA') {
if (modelsActive.indexOf(i+this.QDAModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'RF') {
if (modelsActive.indexOf(i+this.RFModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'ExtraT') {
if (modelsActive.indexOf(i+this.ExtraTModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else if (this.selectedAlgorithm === 'AdaB') {
if (modelsActive.indexOf(i+this.AdaBModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
} else {
if (modelsActive.indexOf(i+this.KNNModels) == -1) {
if (modelsActive.indexOf(i+this.GradBModels) == -1) {
allPoints[i].style.fill = "#d3d3d3"
allPoints[i].style.opacity = '0.4'
}
@ -310,9 +883,26 @@ export default {
activeModels.push(allPoints[i].__data__.Model)
if (allPoints[i].__data__.Algorithm === 'KNN') {
algorithmsSelected.push('KNN')
}
else {
} else if (allPoints[i].__data__.Algorithm === 'SVC') {
algorithmsSelected.push('SVC')
} else if (allPoints[i].__data__.Algorithm === 'GausNB') {
algorithmsSelected.push('GausNB')
} else if (allPoints[i].__data__.Algorithm === 'MLP') {
algorithmsSelected.push('MLP')
} else if (allPoints[i].__data__.Algorithm === 'LR') {
algorithmsSelected.push('LR')
} else if (allPoints[i].__data__.Algorithm === 'LDA') {
algorithmsSelected.push('LDA')
} else if (allPoints[i].__data__.Algorithm === 'QDA') {
algorithmsSelected.push('QDA')
} else if (allPoints[i].__data__.Algorithm === 'RF') {
algorithmsSelected.push('RF')
} else if (allPoints[i].__data__.Algorithm === 'ExtraT') {
algorithmsSelected.push('ExtraT')
} else if (allPoints[i].__data__.Algorithm === 'AdaB') {
algorithmsSelected.push('AdaB')
} else {
algorithmsSelected.push('GradB')
}
}
}
@ -337,9 +927,26 @@ export default {
activeModels.push(allPoints[i].__data__.Model)
if (allPoints[i].__data__.Algorithm === 'KNN') {
algorithmsSelected.push('KNN')
}
else {
} else if (allPoints[i].__data__.Algorithm === 'SVC') {
algorithmsSelected.push('SVC')
} else if (allPoints[i].__data__.Algorithm === 'GausNB') {
algorithmsSelected.push('GausNB')
} else if (allPoints[i].__data__.Algorithm === 'MLP') {
algorithmsSelected.push('MLP')
} else if (allPoints[i].__data__.Algorithm === 'LR') {
algorithmsSelected.push('LR')
} else if (allPoints[i].__data__.Algorithm === 'LDA') {
algorithmsSelected.push('LDA')
} else if (allPoints[i].__data__.Algorithm === 'QDA') {
algorithmsSelected.push('QDA')
} else if (allPoints[i].__data__.Algorithm === 'RF') {
algorithmsSelected.push('RF')
} else if (allPoints[i].__data__.Algorithm === 'ExtraT') {
algorithmsSelected.push('ExtraT')
} else if (allPoints[i].__data__.Algorithm === 'AdaB') {
algorithmsSelected.push('AdaB')
} else {
algorithmsSelected.push('GradB')
}
}
}
@ -362,10 +969,26 @@ export default {
} else {
if (this.selectedAlgorithm == 'KNN') {
$(el)[0].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'RF') {
} else if (this.selectedAlgorithm == 'SVC') {
$(el)[1].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'GausNB') {
$(el)[2].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'MLP') {
$(el)[3].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'LR') {
$(el)[4].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'LDA') {
$(el)[5].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'QDA') {
$(el)[6].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'RF') {
$(el)[7].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'ExtraT') {
$(el)[8].dispatchEvent(new Event('click'))
} else if (this.selectedAlgorithm == 'AdaB') {
$(el)[9].dispatchEvent(new Event('click'))
} else {
$(el)[10].dispatchEvent(new Event('click'))
}
}
}

@ -18,153 +18,436 @@ export default {
ClassNamesOverview: '',
algorithmsinBar: [],
modelsSelectedinBar: [],
factors: [1,1,1,1,1],
KNNModels: 576, //KNN models,
colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc'],
factors: [1,1,1,0,0
,1,0,0,1,0
,0,1,0,0,0
,0,0,1,0,0
,0,1,1,1
],
SVCModels: 576,
GausNBModels: 736,
MLPModels: 1236,
LRModels: 1356,
LDAModels: 1996,
QDAModels: 2196,
RFModels: 2446,
ExtraTModels: 2606,
AdaBModels: 2766,
GradBModels: 2926,
colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
WH: []
}
},
methods: {
BarChartView () {
const PerClassMetrics = JSON.parse(this.PerformanceResults[2])
const PerClassMetrics2 = JSON.parse(this.PerformanceResults[10])
const PerClassMetricsKNN = JSON.parse(this.PerformanceResults[2])
const PerClassMetricsSVC = JSON.parse(this.PerformanceResults[10])
const PerClassMetricsGausNB = JSON.parse(this.PerformanceResults[18])
const PerClassMetricsMLP = JSON.parse(this.PerformanceResults[26])
const PerClassMetricsLR = JSON.parse(this.PerformanceResults[34])
const PerClassMetricsLDA = JSON.parse(this.PerformanceResults[42])
const PerClassMetricsQDA = JSON.parse(this.PerformanceResults[50])
const PerClassMetricsRF = JSON.parse(this.PerformanceResults[58])
const PerClassMetricsExtraT = JSON.parse(this.PerformanceResults[66])
const PerClassMetricsAdaB = JSON.parse(this.PerformanceResults[74])
const PerClassMetricsGradB = JSON.parse(this.PerformanceResults[82])
var KNNModels = []
var SVCModels = []
var GausNBModels = []
var MLPModels = []
var LRModels = []
var LDAModels = []
var QDAModels = []
var RFModels = []
var ExtraTModels = []
var AdaBModels = []
var GradBModels = []
var factorsLocal = this.factors
var divide = factorsLocal[1] + factorsLocal[2] + factorsLocal[3]
var divide = factorsLocal[6] + factorsLocal[7] + factorsLocal[8] + factorsLocal[9] + factorsLocal[10] + factorsLocal[11] + factorsLocal[15] + factorsLocal[16] + factorsLocal[17]
var factorF1 = 1
var factorPrec = 1
var factorRecall = 1
if (factorsLocal[15]!=0) {
factorF1 = factorsLocal[15]
} else if (factorsLocal[16]!=0) {
factorF1 = factorsLocal[16]
} else if (factorsLocal[17]!=0){
factorF1 = factorsLocal[17]
} else {
factorF1 = 0
}
if (factorsLocal[6]!=0) {
factorPrec = factorsLocal[6]
} else if (factorsLocal[7]!=0) {
factorPrec = factorsLocal[7]
} else if (factorsLocal[8]!=0){
factorPrec = factorsLocal[8]
} else {
factorPrec = 0
}
if (factorsLocal[9]!=0) {
factorRecall = factorsLocal[9]
} else if (factorsLocal[10]!=0) {
factorRecall = factorsLocal[10]
} else if (factorsLocal[11]!=0){
factorRecall = factorsLocal[11]
} else {
factorRecall = 0
}
if (this.modelsSelectedinBar.length != 0){
for (let i=0; i<this.algorithmsinBar.length;i++) {
if (this.algorithmsinBar[i] === "KNN") {
KNNModels.push(JSON.parse(this.modelsSelectedinBar[i]))
} else if (this.algorithmsinBar[i] === "SVC") {
SVCModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.SVCModels)
} else if (this.algorithmsinBar[i] === "GausNB") {
GausNBModels.push(JSON.parse(this.modelsSelectedinBar[i] - this.GausNBModels))
} else if (this.algorithmsinBar[i] === "MLP") {
MLPModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.MLPModels)
} else if (this.algorithmsinBar[i] === "LR") {
LRModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.LRModels)
} else if (this.algorithmsinBar[i] === "LDA") {
LDAModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.LDAModels)
} else if (this.algorithmsinBar[i] === "QDA") {
QDAModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.QDAModels)
} else if (this.algorithmsinBar[i] === "RF") {
RFModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.RFModels)
} else if (this.algorithmsinBar[i] === "ExtraT") {
ExtraTModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.ExtraTModels)
} else if (this.algorithmsinBar[i] === "AdaB") {
AdaBModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.AdaBModels)
} else {
RFModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.KNNModels)
GradBModels.push(JSON.parse(this.modelsSelectedinBar[i]) - this.GradBModels)
}
}
}
var target_names
target_names = Object.keys(PerClassMetrics)
target_names = Object.keys(PerClassMetricsKNN)
var sum = []
var temp = 0
var temp2 = 0
var tempKNN = 0
var tempSVC = 0
var tempGausNB = 0
var tempMLP = 0
var tempLR = 0
var tempLDA = 0
var tempQDA = 0
var tempRF = 0
var tempExtraT = 0
var tempAdaB = 0
var tempGradB = 0
for (var i=0;i<target_names.length;i++) {
temp = 0
temp2 = 0
for (var j=0;j<Object.keys(PerClassMetrics[target_names[i]]).length;j++){
temp = temp + ((Object.values(PerClassMetrics)[i][j]['f1-score']*factorsLocal[1])+(Object.values(PerClassMetrics)[i][j]['precision']*factorsLocal[2])+(Object.values(PerClassMetrics)[i][j]['recall']*factorsLocal[3]))/divide
}
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']*factorsLocal[1])+(Object.values(PerClassMetrics2)[i][k]['precision']*factorsLocal[2])+(Object.values(PerClassMetrics2)[i][k]['recall']*factorsLocal[3]))/divide
}
temp2 = temp2/Object.keys(PerClassMetrics2[target_names[i]]).length
sum.push(temp2)
tempKNN = 0
tempSVC = 0
tempGausNB = 0
tempMLP = 0
tempLR = 0
tempLDA = 0
tempQDA = 0
tempRF = 0
tempExtraT = 0
tempAdaB = 0
tempGradB = 0
for (var j=0;j<Object.keys(PerClassMetricsKNN[target_names[i]]).length;j++){
tempKNN = tempKNN + ((Object.values(PerClassMetricsKNN)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsKNN)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsKNN)[i][j]['recall']*factorRecall))/divide
}
tempKNN = tempKNN/Object.keys(PerClassMetricsKNN[target_names[i]]).length
sum.push(tempKNN)
for (var k=0;k<Object.keys(PerClassMetricsSVC[target_names[i]]).length;k++){
tempSVC = tempSVC + ((Object.values(PerClassMetricsSVC)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsSVC)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsSVC)[i][k]['recall']*factorRecall))/divide
}
tempSVC = tempSVC/Object.keys(PerClassMetricsSVC[target_names[i]]).length
sum.push(tempSVC)
for (var k=0;k<Object.keys(PerClassMetricsGausNB[target_names[i]]).length;k++){
tempGausNB = tempGausNB + ((Object.values(PerClassMetricsGausNB)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsGausNB)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsGausNB)[i][k]['recall']*factorRecall))/divide
}
tempGausNB = tempGausNB/Object.keys(PerClassMetricsGausNB[target_names[i]]).length
sum.push(tempGausNB)
for (var k=0;k<Object.keys(PerClassMetricsMLP[target_names[i]]).length;k++){
tempMLP = tempMLP + ((Object.values(PerClassMetricsMLP)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsMLP)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsMLP)[i][k]['recall']*factorRecall))/divide
}
tempMLP = tempMLP/Object.keys(PerClassMetricsMLP[target_names[i]]).length
sum.push(tempMLP)
for (var k=0;k<Object.keys(PerClassMetricsLR[target_names[i]]).length;k++){
tempLR = tempLR + ((Object.values(PerClassMetricsLR)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsLR)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsLR)[i][k]['recall']*factorRecall))/divide
}
tempLR = tempLR/Object.keys(PerClassMetricsLR[target_names[i]]).length
sum.push(tempLR)
for (var k=0;k<Object.keys(PerClassMetricsLDA[target_names[i]]).length;k++){
tempLDA = tempLDA + ((Object.values(PerClassMetricsLDA)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsLDA)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsLDA)[i][k]['recall']*factorRecall))/divide
}
tempLDA = tempLDA/Object.keys(PerClassMetricsLDA[target_names[i]]).length
sum.push(tempLDA)
for (var k=0;k<Object.keys(PerClassMetricsQDA[target_names[i]]).length;k++){
tempQDA = tempQDA + ((Object.values(PerClassMetricsQDA)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsQDA)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsQDA)[i][k]['recall']*factorRecall))/divide
}
tempQDA = tempQDA/Object.keys(PerClassMetricsQDA[target_names[i]]).length
sum.push(tempQDA)
for (var k=0;k<Object.keys(PerClassMetricsRF[target_names[i]]).length;k++){
tempRF = tempRF + ((Object.values(PerClassMetricsRF)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsRF)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsRF)[i][k]['recall']*factorRecall))/divide
}
tempRF = tempRF/Object.keys(PerClassMetricsRF[target_names[i]]).length
sum.push(tempRF)
for (var k=0;k<Object.keys(PerClassMetricsExtraT[target_names[i]]).length;k++){
tempExtraT = tempExtraT + ((Object.values(PerClassMetricsExtraT)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsExtraT)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsExtraT)[i][k]['recall']*factorRecall))/divide
}
tempExtraT = tempExtraT/Object.keys(PerClassMetricsExtraT[target_names[i]]).length
sum.push(tempExtraT)
for (var k=0;k<Object.keys(PerClassMetricsAdaB[target_names[i]]).length;k++){
tempAdaB = tempAdaB + ((Object.values(PerClassMetricsAdaB)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsAdaB)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsAdaB)[i][k]['recall']*factorRecall))/divide
}
tempAdaB = tempAdaB/Object.keys(PerClassMetricsAdaB[target_names[i]]).length
sum.push(tempAdaB)
for (var k=0;k<Object.keys(PerClassMetricsGradB[target_names[i]]).length;k++){
tempGradB = tempGradB + ((Object.values(PerClassMetricsGradB)[i][k]['f1-score']*factorF1)+(Object.values(PerClassMetricsGradB)[i][k]['precision']*factorPrec)+(Object.values(PerClassMetricsGradB)[i][k]['recall']*factorRecall))/divide
}
tempGradB = tempGradB/Object.keys(PerClassMetricsGradB[target_names[i]]).length
sum.push(tempGradB)
}
var sumLine = []
var temp = 0
var temp2 = 0
tempKNN = 0
tempSVC = 0
tempGausNB = 0
tempMLP = 0
tempLR = 0
tempLDA = 0
tempQDA = 0
tempRF = 0
tempExtraT = 0
tempAdaB = 0
tempGradB = 0
for (var i=0;i<target_names.length;i++) {
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']*factorsLocal[1])+(Object.values(PerClassMetrics)[i][j]['precision']*factorsLocal[2])+(Object.values(PerClassMetrics)[i][j]['recall']*factorsLocal[3]))/divide
}
temp = temp/Object.keys(PerClassMetrics[target_names[i]]).length
} else {
for (var j=0;j<KNNModels.length;j++){
temp = temp + ((Object.values(PerClassMetrics)[i][j]['f1-score']*factorsLocal[1])+(Object.values(PerClassMetrics)[i][j]['precision']*factorsLocal[2])+(Object.values(PerClassMetrics)[i][j]['recall']*factorsLocal[3]))/divide
}
temp = temp/KNNModels.length
}
sumLine.push(temp)
if (RFModels.length == 0) {
for (var k=0;k<Object.keys(PerClassMetrics2[target_names[i]]).length;k++){
temp2 = temp2 + ((Object.values(PerClassMetrics2)[i][k]['f1-score']*factorsLocal[1])+(Object.values(PerClassMetrics2)[i][k]['precision']*factorsLocal[2])+(Object.values(PerClassMetrics2)[i][k]['recall']*factorsLocal[3]))/divide
}
temp2 = temp2/Object.keys(PerClassMetrics2[target_names[i]]).length
} else {
for (var k=0;k<RFModels.length;k++){
temp2 = temp2 + ((Object.values(PerClassMetrics2)[i][k]['f1-score']*factorsLocal[1])+(Object.values(PerClassMetrics2)[i][k]['precision']*factorsLocal[2])+(Object.values(PerClassMetrics2)[i][k]['recall']*factorsLocal[3]))/divide
}
temp2 = temp2/RFModels.length
}
sumLine.push(temp2)
tempKNN = 0
tempSVC = 0
tempGausNB = 0
tempMLP = 0
tempLR = 0
tempLDA = 0
tempQDA = 0
tempRF = 0
tempExtraT = 0
tempAdaB = 0
tempGradB = 0
if (KNNModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsKNN[target_names[i]]).length;j++){
tempKNN = tempKNN + ((Object.values(PerClassMetricsKNN)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsKNN)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsKNN)[i][j]['recall']*factorRecall))/divide
}
tempKNN = tempKNN/Object.keys(PerClassMetricsKNN[target_names[i]]).length
} else {
for (var j=0;j<KNNModels.length;j++){
tempKNN = tempKNN + ((Object.values(PerClassMetricsKNN)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsKNN)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsKNN)[i][j]['recall']*factorRecall))/divide
}
tempKNN = tempKNN/KNNModels.length
}
sumLine.push(tempKNN)
if (SVCModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsSVC[target_names[i]]).length;j++){
tempSVC = tempSVC + ((Object.values(PerClassMetricsSVC)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsSVC)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsSVC)[i][j]['recall']*factorRecall))/divide
}
tempSVC = tempSVC/Object.keys(PerClassMetricsSVC[target_names[i]]).length
} else {
for (var j=0;j<SVCModels.length;j++){
tempSVC = tempSVC + ((Object.values(PerClassMetricsSVC)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsSVC)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsSVC)[i][j]['recall']*factorRecall))/divide
}
tempSVC = tempSVC/SVCModels.length
}
sumLine.push(tempSVC)
if (GausNBModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsGausNB[target_names[i]]).length;j++){
tempGausNB = tempGausNB + ((Object.values(PerClassMetricsGausNB)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsGausNB)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsGausNB)[i][j]['recall']*factorRecall))/divide
}
tempGausNB = tempGausNB/Object.keys(PerClassMetricsGausNB[target_names[i]]).length
} else {
for (var j=0;j<GausNBModels.length;j++){
tempGausNB = tempGausNB + ((Object.values(PerClassMetricsGausNB)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsGausNB)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsGausNB)[i][j]['recall']*factorRecall))/divide
}
tempGausNB = tempGausNB/GausNBModels.length
}
sumLine.push(tempGausNB)
if (MLPModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsMLP[target_names[i]]).length;j++){
tempMLP = tempMLP + ((Object.values(PerClassMetricsMLP)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsMLP)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsMLP)[i][j]['recall']*factorRecall))/divide
}
tempMLP = tempMLP/Object.keys(PerClassMetricsMLP[target_names[i]]).length
} else {
for (var j=0;j<MLPModels.length;j++){
tempMLP = tempMLP + ((Object.values(PerClassMetricsMLP)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsMLP)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsMLP)[i][j]['recall']*factorRecall))/divide
}
tempMLP = tempMLP/MLPModels.length
}
sumLine.push(tempMLP)
if (LRModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsLR[target_names[i]]).length;j++){
tempLR = tempLR + ((Object.values(PerClassMetricsLR)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsLR)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsLR)[i][j]['recall']*factorRecall))/divide
}
tempLR = tempLR/Object.keys(PerClassMetricsLR[target_names[i]]).length
} else {
for (var j=0;j<LRModels.length;j++){
tempLR = tempLR + ((Object.values(PerClassMetricsLR)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsLR)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsLR)[i][j]['recall']*factorRecall))/divide
}
tempLR = tempLR/LRModels.length
}
sumLine.push(tempLR)
if (LDAModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsLDA[target_names[i]]).length;j++){
tempLDA = tempLDA + ((Object.values(PerClassMetricsLDA)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsLDA)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsLDA)[i][j]['recall']*factorRecall))/divide
}
tempLDA = tempLDA/Object.keys(PerClassMetricsLDA[target_names[i]]).length
} else {
for (var j=0;j<LDAModels.length;j++){
tempLDA = tempLDA + ((Object.values(PerClassMetricsLDA)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsLDA)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsLDA)[i][j]['recall']*factorRecall))/divide
}
tempLDA = tempLDA/LDAModels.length
}
sumLine.push(tempLDA)
if (QDAModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsQDA[target_names[i]]).length;j++){
tempQDA = tempQDA + ((Object.values(PerClassMetricsQDA)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsQDA)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsQDA)[i][j]['recall']*factorRecall))/divide
}
tempQDA = tempQDA/Object.keys(PerClassMetricsQDA[target_names[i]]).length
} else {
for (var j=0;j<QDAModels.length;j++){
tempQDA = tempQDA + ((Object.values(PerClassMetricsQDA)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsQDA)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsQDA)[i][j]['recall']*factorRecall))/divide
}
tempQDA = tempQDA/QDAModels.length
}
sumLine.push(tempQDA)
if (RFModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsRF[target_names[i]]).length;j++){
tempRF = tempRF + ((Object.values(PerClassMetricsRF)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsRF)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsRF)[i][j]['recall']*factorRecall))/divide
}
tempRF = tempRF/Object.keys(PerClassMetricsRF[target_names[i]]).length
} else {
for (var j=0;j<RFModels.length;j++){
tempRF = tempRF + ((Object.values(PerClassMetricsRF)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsRF)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsRF)[i][j]['recall']*factorRecall))/divide
}
tempRF = tempRF/RFModels.length
}
sumLine.push(tempRF)
if (ExtraTModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsExtraT[target_names[i]]).length;j++){
tempExtraT = tempExtraT + ((Object.values(PerClassMetricsExtraT)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsExtraT)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsExtraT)[i][j]['recall']*factorRecall))/divide
}
tempExtraT = tempExtraT/Object.keys(PerClassMetricsExtraT[target_names[i]]).length
} else {
for (var j=0;j<ExtraTModels.length;j++){
tempExtraT = tempExtraT + ((Object.values(PerClassMetricsExtraT)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsExtraT)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsExtraT)[i][j]['recall']*factorRecall))/divide
}
tempExtraT = tempExtraT/ExtraTModels.length
}
sumLine.push(tempExtraT)
if (AdaBModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsAdaB[target_names[i]]).length;j++){
tempAdaB = tempAdaB + ((Object.values(PerClassMetricsAdaB)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsAdaB)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsAdaB)[i][j]['recall']*factorRecall))/divide
}
tempAdaB = tempAdaB/Object.keys(PerClassMetricsAdaB[target_names[i]]).length
} else {
for (var j=0;j<AdaBModels.length;j++){
tempAdaB = tempAdaB + ((Object.values(PerClassMetricsAdaB)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsAdaB)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsAdaB)[i][j]['recall']*factorRecall))/divide
}
tempAdaB = tempAdaB/AdaBModels.length
}
sumLine.push(tempAdaB)
if (GradBModels.length == 0) {
for (var j=0;j<Object.keys(PerClassMetricsGradB[target_names[i]]).length;j++){
tempGradB = tempGradB + ((Object.values(PerClassMetricsGradB)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsGradB)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsGradB)[i][j]['recall']*factorRecall))/divide
}
tempGradB = tempGradB/Object.keys(PerClassMetricsGradB[target_names[i]]).length
} else {
for (var j=0;j<GradBModels.length;j++){
tempGradB = tempGradB + ((Object.values(PerClassMetricsGradB)[i][j]['f1-score']*factorF1)+(Object.values(PerClassMetricsGradB)[i][j]['precision']*factorPrec)+(Object.values(PerClassMetricsGradB)[i][j]['recall']*factorRecall))/divide
}
tempGradB = tempGradB/GradBModels.length
}
sumLine.push(tempGradB)
}
Plotly.purge('barChart')
var layout = {
autosize: true,
barmode: 'group',
width: this.WH[0]*3,
height: this.WH[1]*0.635,
xaxis: {
title: 'Algorithm',
type:"category",
showticklabels: true,
tickangle: 'auto',
exponentformat: 'e',
showexponent: 'all'
},
yaxis: {
title: 'Performance Metrics',
},
xaxis2: {
overlaying: 'x',
type:"category",
showticklabels: true,
tickangle: 'auto',
exponentformat: 'e',
showexponent: 'all'
},
margin: {
l: 50,
r: 0,
b: 30,
t: 30,
pad: 0
}
autosize: true,
barmode: 'group',
width: this.WH[0]*10,
height: this.WH[1]*0.635,
xaxis: {
title: 'Algorithm',
type:"category",
showticklabels: true,
tickangle: 'auto',
exponentformat: 'e',
showexponent: 'all',
side: 'top'
},
yaxis: {
title: '# Performance (%) #',
},
xaxis2: {
overlaying: 'x',
type:"category",
showticklabels: true,
tickangle: 'auto',
exponentformat: 'e',
showexponent: 'all',
},
bargap:0.1,
bargroupgap: 0.2,
margin: {
l: 50,
r: 0,
b: 30,
t: 30,
pad: 0
},
legend: {"orientation": "h"}
}
var traces = []
var tracesSel = []
var data = []
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: this.colorsValues[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: this.colorsValues[i],
},
width: [0.1, 0.1]
};
data.push(traces[i])
data.push(tracesSel[i])
var traces = []
var tracesSel = []
var data = []
var sumList = []
var sumLineList = []
var keepSum
var keepSumLine
for (var i = 0; i < target_names.length; i++) {
keepSum = []
keepSumLine = []
for (var j = i; j < sum.length; j+=target_names.length) {
keepSum.push(sum[j]*100)
keepSumLine.push(sumLine[j]*100)
}
sumList[i] = keepSum
sumLineList[i] = keepSumLine
}
for (var i = 0; i < target_names.length; i++) {
traces[i] = {
x: ['KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
y: sumList[i],
name: target_names[i],
opacity: 0.5,
marker: {
opacity: 0.5,
color: this.colorsValues[i]
},
type: 'bar'
};
tracesSel[i] = {
type: 'bar',
x: ['KNN','SVC','GausNB','MLP','LR','LDA','QDA','RF','ExtraT','AdaB','GradB'],
y: sumLineList[i],
name: target_names[i]+' (Sel)',
xaxis: 'x2',
mode: 'markers',
marker: {
opacity: 1.0,
color: this.colorsValues[i],
},
width: [0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06]
};
data.push(traces[i])
data.push(tracesSel[i])
}
Plotly.newPlot('barChart', data, layout)
},

@ -64,7 +64,7 @@ export default {
restoreData: 'Restore Step',
userSelectedFilter: 'mean',
responsiveWidthHeight: [],
colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc']
colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
}
},
methods: {

@ -17,7 +17,7 @@ export default {
data () {
return {
PCPDataReceived: '',
colorsValues: ['#6a3d9a','#b15928','#e31a1c']
colorsValues: ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99']
}
},
methods: {

@ -4,10 +4,6 @@
<b-col cols="12">
<div id="overview"></div>
</b-col>
<!--
<b-col cols="4">
<div id="encodings"></div>
</b-col>-->
</b-row>
</div>
</template>
@ -24,11 +20,38 @@ export default {
WH: [],
storeActiveModels: [],
allActiveKNN: [],
allActiveSVC: [],
allActiveGausNB: [],
allActiveMLP: [],
allActiveLR: [],
allActiveLDA: [],
allActiveQDA: [],
allActiveRF: [],
allActiveExtraT: [],
allActiveAdaB: [],
allActiveGradB: [],
storeParameters: [],
FlagKNN: 0,
FlagSVC: 0,
FlagGausNB: 0,
FlagMLP: 0,
FlagLR: 0,
FlagLDA: 0,
FlagQDA: 0,
FlagRF: 0,
KNNModels: 576, //KNN models
FlagExtraT: 0,
FlagAdaB: 0,
FlagGradB: 0,
SVCModels: 576,
GausNBModels: 736,
MLPModels: 1236,
LRModels: 1356,
LDAModels: 1996,
QDAModels: 2196,
RFModels: 2446,
ExtraTModels: 2606,
AdaBModels: 2766,
GradBModels: 2926,
}
},
methods: {
@ -263,6 +286,8 @@ export default {
.style("fill", "none")
.style("pointer-events", "all")
.on("mouseover", function(d,i) {
var newX
var newY
newX = parseFloat(d3.select(this).attr('cx')) - 10;
newY = parseFloat(d3.select(this).attr('cy')) - 10;
@ -365,56 +390,182 @@ export default {
var widthinter = this.WH[0]*2 // interactive visualization
var heightinter = this.WH[1]*1.23 // interactive visualization
const max = 576
const max = 640
const KNNAll = 576
const SVCAll = 160
const GausNBAll = 500
const MLPAll = 120
const LRAll = 640
const LDAAll = 200
const QDAAll = 250
const RFAll = 160
const ExtraTAll = 160
const AdaBAll = 160
const GradBAll = 180
var KNNSelection = 0
var SVCSelection = 0
var GausNBSelection = 0
var MLPSelection = 0
var LRSelection = 0
var LDASelection = 0
var QDASelection = 0
var RFSelection = 0
var ExtraTSelection = 0
var AdaBSelection = 0
var GradBSelection = 0
if (this.FlagKNN == 0 && this.FlagRF == 0) {
if (this.FlagKNN == 0 && this.FlagSVC == 0 && this.FlagGausNB == 0 && this.FlagMLP == 0 && this.FlagLR == 0 && this.FlagLDA == 0 && this.FlagQDA == 0 && this.FlagRF == 0 && this.FlagExtraT == 0 && this.FlagAdaB == 0 && this.FlagGradB == 0) {
this.storeActiveModels = []
this.allActiveKNN = []
this.allActiveSVC = []
this.allActiveGausNB = []
this.allActiveMLP = []
this.allActiveLR = []
this.allActiveLDA = []
this.allActiveQDA = []
this.allActiveRF = []
this.allActiveExtraT = []
this.allActiveAdaB = []
this.allActiveGradB = []
}
if (this.storeActiveModels.length != 0) {
var countkNNRelated = []
var countKNN = 0
var countSVCRelated = []
var countSVC = 0
var countGausNBRelated = []
var countGausNB = 0
var countMLPRelated = []
var countMLP = 0
var countLRRelated = []
var countLR = 0
var countLDARelated = []
var countLDA = 0
var countQDARelated = []
var countQDA = 0
var countRFRelated = []
var countRF = 0
var countExtraTRelated = []
var countExtraT = 0
var countAdaBRelated = []
var countAdaB = 0
var countGradBRelated = []
var countGradB = 0
for (let i = 0; i < this.storeActiveModels.length; i++) {
if (this.storeActiveModels[i] < this.KNNModels) {
countkNNRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countKNN = countKNN + 1
} else {
if (this.storeActiveModels[i] > this.GradBModels) {
countGradBRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countGradB++
} else if (this.storeActiveModels[i] > this.AdaBModels) {
countAdaBRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countAdaB++
} else if (this.storeActiveModels[i] > this.ExtraTModels) {
countExtraT.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countExtraT++
} else if (this.storeActiveModels[i] > this.RFModels) {
countRFRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countRF = countRF + 1
countRF++
} else if (this.storeActiveModels[i] > this.QDAModels) {
countQDARelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countQDA++
} else if (this.storeActiveModels[i] > this.LDAModels) {
countLDARelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countLDA++
} else if (this.storeActiveModels[i] > this.LRModels) {
countLRRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countLR++
} else if (this.storeActiveModels[i] > this.MLPModels) {
countMLPRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countMLP++
} else if (this.storeActiveModels[i] > this.GausNBModels) {
countGausNBRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countGausNB++
} else if (this.storeActiveModels[i] > this.SVCModels) {
countSVCRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countSVC++
} else {
countkNNRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countKNN++
}
}
if (this.storeActiveModels[0] < this.KNNModels) {
this.allActiveKNN = countkNNRelated.slice()
} else {
if (this.storeActiveModels[0] > this.GradBModels) {
this.allActiveGradB = countGradBRelated.slice()
} else if (this.storeActiveModels[0] > this.AdaBModels) {
this.allActiveAdaB = countAdaBRelated.slice()
} else if (this.storeActiveModels[0] > this.ExtraTModels) {
this.allActiveExtraT = countExtraT.slice()
} else if (this.storeActiveModels[0] > this.RFModels) {
this.allActiveRF = countRFRelated.slice()
} else if (this.storeActiveModels[0] > this.QDAModels) {
this.allActiveQDA = countQDARelated.slice()
} else if (this.storeActiveModels[0] > this.LDAModels) {
this.allActiveLDA = countLDARelated.slice()
} else if (this.storeActiveModels[0] > this.LRModels) {
this.allActiveLR = countLRRelated.slice()
} else if (this.storeActiveModels[0] > this.MLPModels) {
this.allActiveMLP = countMLPRelated.slice()
} else if (this.storeActiveModels[0] > this.GausNBModels) {
this.allActiveGausNB = countGausNBRelated.slice()
} else if (this.storeActiveModels[0] > this.SVCModels) {
this.allActiveSVC = countSVCRelated.slice()
} else {
this.allActiveKNN = countkNNRelated.slice()
}
KNNSelection = countKNN
SVCSelection = countSVC
GausNBSelection = countGausNB
MLPSelection = countMLP
LRSelection = countLR
LDASelection = countLDA
QDASelection = countQDA
RFSelection = countRF
ExtraTSelection = countExtraT
AdaBSelection = countAdaB
GradBSelection = countGradB
}
if (this.FlagKNN == 1 && this.allActiveKNN.length == 0) {
KNNSelection = 576
}
if (this.FlagSVC == 1 && this.allActiveSVC.length == 0) {
SVCSelection = 160
}
if (this.FlagGausNB == 1 && this.allActiveGausNB.length == 0) {
GausNBSelection = 500
}
if (this.FlagMLP == 1 && this.allActiveMLP.length == 0) {
MLPSelection = 120
}
if (this.FlagLR == 1 && this.allActiveLR.length == 0) {
LRSelection = 640
}
if (this.FlagLDA == 1 && this.allActiveLDA.length == 0) {
LDASelection = 200
}
if (this.FlagQDA == 1 && this.allActiveQDA.length == 0) {
QDASelection = 250
}
if (this.FlagRF == 1 && this.allActiveRF.length == 0) {
RFSelection = 160
}
if (this.FlagExtraT == 1 && this.allActiveExtraT.length == 0) {
ExtraTSelection = 160
}
if (this.FlagAdaB == 1 && this.allActiveAdaB.length == 0) {
AdaBSelection = 160
}
if (this.FlagGradB == 1 && this.allActiveGradB.length == 0) {
GradBSelection = 180
}
//////////////////////////////////////////////////////////////
//////////////////////// Set-Up //////////////////////////////
//////////////////////////////////////////////////////////////
var margin = {top: 50, right: 120, bottom: 50, left: 60},
var margin = {top: 50, right: 120, bottom: 55, left: 65},
legendPosition = {x: 425, y: 185},
width = Math.min(510, window.innerWidth - 10) - margin.left - margin.right,
height = Math.min(width, window.innerHeight - margin.top - margin.bottom - 20);
@ -425,29 +576,29 @@ export default {
var data = [
[
{axis:"KNN [Models 576]",legend:"Entire",value:KNNAll/max},
{axis:"RF [Models 160]",legend:"Entire",value:RFAll/max},
{axis:"Alg3",legend:"Entire",value:0.55},
{axis:"Alg4",legend:"Entire",value:0.68},
{axis:"Alg5",legend:"Entire",value:0.22},
{axis:"Alg6",legend:"Entire",value:0.28},
{axis:"Alg7",legend:"Entire",value:0.55},
{axis:"Alg8",legend:"Entire",value:0.68},
{axis:"Alg9",legend:"Entire",value:0.22},
{axis:"Alg10",legend:"Entire",value:0.28},
{axis:"Alg11",legend:"Entire",value:0.28},
{axis:"KNN [M 576]",legend:"Entire",value:KNNAll/max},
{axis:"SVC [M 160]",legend:"Entire",value:SVCAll/max},
{axis:"GausNB [M 500]",legend:"Entire",value:GausNBAll/max},
{axis:"MLP [M 120]",legend:"Entire",value:MLPAll/max},
{axis:"LR [M 640]",legend:"Entire",value:LRAll/max},
{axis:"LDA [M 200]",legend:"Entire",value:LDAAll/max},
{axis:"QDA [M 250]",legend:"Entire",value:QDAAll/max},
{axis:"RF [M 160]",legend:"Entire",value:RFAll/max},
{axis:"ExtraT [M 160]",legend:"Entire",value:ExtraTAll/max},
{axis:"AdaB [M 160]",legend:"Entire",value:AdaBAll/max},
{axis:"GradB [M 180]",legend:"Entire",value:GradBAll/max},
],[
{axis:"KNN [Models 576]",legend:"Selection",value:KNNSelection/max},
{axis:"RF [Models 160]",legend:"Selection",value:RFSelection/max},
{axis:"Alg3",legend:"Selection",value:0.25},
{axis:"Alg4",legend:"Selection",value:0.28},
{axis:"Alg5",legend:"Selection",value:0.22},
{axis:"Alg6",legend:"Selection",value:0.18},
{axis:"Alg7",legend:"Selection",value:0.45},
{axis:"Alg8",legend:"Selectionn",value:0.18},
{axis:"Alg9",legend:"Selection",value:0.22},
{axis:"Alg10",legend:"Selection",value:0.18},
{axis:"Alg11",legend:"Selection",value:0.18},
{axis:"KNN [M 576]",legend:"Selection",value:KNNSelection/max},
{axis:"SVC [M 160]",legend:"Selection",value:SVCSelection/max},
{axis:"GausNB [M 500]",legend:"Selection",value:GausNBSelection/max},
{axis:"MLP [M 120]",legend:"Selection",value:MLPSelection/max},
{axis:"LR [M 640]",legend:"Selection",value:LRSelection/max},
{axis:"LDA [M 200]",legend:"Selection",value:LDASelection/max},
{axis:"QDA [M 250]",legend:"Selection",value:QDASelection/max},
{axis:"RF [M 160]",legend:"Selectionn",value:RFSelection/max},
{axis:"ExtraT [M 160]",legend:"Selection",value:ExtraTSelection/max},
{axis:"AdaB [M 160]",legend:"Selection",value:AdaBSelection/max},
{axis:"GradB [M 180]",legend:"Selection",value:GradBSelection/max},
],
];
//////////////////////////////////////////////////////////////
@ -455,7 +606,7 @@ export default {
//////////////////////////////////////////////////////////////
var color = d3.scale.ordinal()
.range(["#808000","#008080"]);
.range(["#ffed6f","#b3de69"]);
var radarChartOptions = {
w: width,
@ -475,14 +626,43 @@ export default {
},
updateFlags () {
this.FlagKNN = 0
this.FlagSVC = 0
this.FlagGausNB = 0
this.FlagMLP = 0
this.FlagLR = 0
this.FlagLDA = 0
this.FlagQDA = 0
this.FlagRF = 0
this.FlagExtraT = 0
this.FlagAdaB = 0
this.FlagGradB = 0
}
},
mounted () {
EventBus.$on('updateFlagKNN', data => { this.FlagKNN = data })
EventBus.$on('updateFlagSVC', data => { this.FlagSVC = data })
EventBus.$on('updateFlagGausNB', data => { this.FlagGausNB = data })
EventBus.$on('updateFlagMLP', data => { this.FlagMLP = data })
EventBus.$on('updateFlagLR', data => { this.FlagLR = data })
EventBus.$on('updateFlagLDA', data => { this.FlagLDA = data })
EventBus.$on('updateFlagQDA', data => { this.FlagQDA = data })
EventBus.$on('updateFlagRF', data => { this.FlagRF = data })
EventBus.$on('updateFlagExtraT', data => { this.FlagExtraT = data })
EventBus.$on('updateFlagAdaB', data => { this.FlagAdaB = data })
EventBus.$on('updateFlagGradB', data => { this.FlagGradB = data })
EventBus.$on('updateFlagKNN', this.overview)
EventBus.$on('updateFlagSVC', this.overview)
EventBus.$on('updateFlagGausNB', this.overview)
EventBus.$on('updateFlagMLP', this.overview)
EventBus.$on('updateFlagLR', this.overview)
EventBus.$on('updateFlagLDA', this.overview)
EventBus.$on('updateFlagQDA', this.overview)
EventBus.$on('updateFlagRF', this.overview)
EventBus.$on('updateFlagExtraT', this.overview)
EventBus.$on('updateFlagAdaB', this.overview)
EventBus.$on('updateFlagGradB', this.overview)
EventBus.$on('sendParameters', data => { this.storeParameters = data })
EventBus.$on('updateActiveModels', data => { this.storeActiveModels = data })
EventBus.$on('updateActiveModels', this.overview)

@ -26,7 +26,7 @@ export default {
UpdatedData: '',
representationDef: 'mds',
representationSelection: 'mds',
colorsValues: ['#b3e2cd','#fdcdac','#cbd5e8','#f4cae4','#e6f5c9','#fff2ae','#f1e2cc'],
colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
WH: []
}
},

@ -447,7 +447,6 @@ def RetrieveModel():
# loop through the algorithms
global allParametersPerformancePerModel
for eachAlgor in algorithms:
print(eachAlgor)
if (eachAlgor) == 'KNN':
clf = KNeighborsClassifier()
params = {'n_neighbors': list(range(1, 25)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}
@ -458,42 +457,41 @@ def RetrieveModel():
AlgorithmsIDsEnd = 576
elif (eachAlgor) == 'GausNB':
clf = GaussianNB()
params = {'var_smoothing': list(np.arange(0.00000000001,0.0000001,0.0000000001))}
params = {'var_smoothing': list(np.arange(0.00000000001,0.0000001,0.0000000002))}
AlgorithmsIDsEnd = 736
elif (eachAlgor) == 'MLP':
clf = MLPClassifier()
params = {'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0005)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']}
AlgorithmsIDsEnd = 1736
params = {'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0004)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']}
AlgorithmsIDsEnd = 1236
elif (eachAlgor) == 'LR':
clf = LogisticRegression()
params = {'C': list(np.arange(0.5,2,0.075)), 'max_iter': list(np.arange(50,250,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}
AlgorithmsIDsEnd = 1816
AlgorithmsIDsEnd = 1356
elif (eachAlgor) == 'LDA':
clf = LinearDiscriminantAnalysis()
params = {'shrinkage': list(np.arange(0,1,0.018)), 'solver': ['lsqr', 'eigen']}
AlgorithmsIDsEnd = 2536
params = {'shrinkage': list(np.arange(0,1,0.01)), 'solver': ['lsqr', 'eigen']}
AlgorithmsIDsEnd = 1996
elif (eachAlgor) == 'QDA':
clf = QuadraticDiscriminantAnalysis()
params = {'reg_param': list(range(1, 50)), 'tol': list(np.arange(0.00001,0.001,0.0005))}
AlgorithmsIDsEnd = 2716
params = {'reg_param': list(range(1, 51)), 'tol': list(np.arange(0.00001,0.001,0.0002))}
AlgorithmsIDsEnd = 2196
elif (eachAlgor) == 'RF':
clf = RandomForestClassifier()
params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']}
AlgorithmsIDsEnd = 2876
AlgorithmsIDsEnd = 2446
elif (eachAlgor) == 'ExtraT':
clf = ExtraTreesClassifier()
params = {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']}
AlgorithmsIDsEnd = 3036
AlgorithmsIDsEnd = 2606
elif (eachAlgor) == 'AdaB':
clf = AdaBoostClassifier()
params = {'n_estimators': list(range(40, 80)), 'learning_rate': list(np.arange(0.1,2.3,1.1)), 'algorithm': ['SAMME.R', 'SAMME']}
AlgorithmsIDsEnd = 3196
AlgorithmsIDsEnd = 2766
else:
clf = GradientBoostingClassifier()
params = {'n_estimators': list(range(90, 111)), 'learning_rate': list(np.arange(0.01,0.34,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']}
AlgorithmsIDsEnd = 3356
params = {'n_estimators': list(range(85, 115)), 'learning_rate': list(np.arange(0.01,0.23,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']}
AlgorithmsIDsEnd = 2926
allParametersPerformancePerModel = GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd)
# call the function that sends the results to the frontend
SendEachClassifiersPerformanceToVisualize()
@ -505,6 +503,7 @@ memory = Memory(location, verbose=0)
# calculating for all algorithms and models the performance and other results
@memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
print('test')
# instantiate spark session
spark = (
SparkSession

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