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Angelos Chatzimparmpas 4 years ago
parent 01cfbdc8a6
commit 4887518984
21 changed files with 1227 additions and 1081 deletions
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# first line: 465
# first line: 466
@memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd):
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
# instantiate spark session
spark = (
@ -45,30 +45,13 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
# copy and filter in order to get only the metrics
metrics = df_cv_results_classifiers.copy()
metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
# control the factors
sumperModel = []
for index, row in metrics.iterrows():
rowSum = 0
lengthFactors = len(scoring)
for loop,elements in enumerate(row):
lengthFactors = lengthFactors - 1 + factors[loop]
rowSum = elements*factors[loop] + rowSum
if lengthFactors is 0:
sumperModel = 0
else:
sumperModel.append(rowSum/lengthFactors)
# summarize all models metrics
summarizedMetrics = pd.DataFrame(sumperModel)
summarizedMetrics.rename(columns={0:'sum'})
metrics = metrics.filter(['mean_test_accuracy','mean_test_neg_mean_absolute_error','mean_test_neg_root_mean_squared_error','mean_test_precision_micro','mean_test_precision_macro','mean_test_precision_weighted','mean_test_recall_micro','mean_test_recall_macro','mean_test_recall_weighted','mean_test_roc_auc_ovo_weighted'])
# concat parameters and performance
parameters = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = parametersPerformancePerModel.to_json()
parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy()
Models = []
for index, items in enumerate(parametersLocal):
@ -81,13 +64,29 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
PerFeatureAccuracyAll = []
PerClassMetric = []
perModelProb = []
resultsMicro = []
resultsMacro = []
resultsWeighted = []
resultsCorrCoef = []
resultsMicroBeta5 = []
resultsMacroBeta5 = []
resultsWeightedBeta5 = []
resultsMicroBeta1 = []
resultsMacroBeta1 = []
resultsWeightedBeta1 = []
resultsMicroBeta2 = []
resultsMacroBeta2 = []
resultsWeightedBeta2 = []
resultsLogLoss = []
loop = 10
for eachModelParameters in parametersLocalNew:
clf.set_params(**eachModelParameters)
perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData)
permList.append(perm.feature_importances_)
n_feats = XData.shape[1]
PerFeatureAccuracy = []
for i in range(n_feats):
@ -101,6 +100,47 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
yPredictProb = clf.predict_proba(XData)
perModelProb.append(yPredictProb.tolist())
resultsMicro.append(geometric_mean_score(yData, yPredict, average='micro'))
resultsMacro.append(geometric_mean_score(yData, yPredict, average='macro'))
resultsWeighted.append(geometric_mean_score(yData, yPredict, average='weighted'))
resultsCorrCoef.append(matthews_corrcoef(yData, yPredict))
resultsMicroBeta5.append(fbeta_score(yData, yPredict, average='micro', beta=0.5))
resultsMacroBeta5.append(fbeta_score(yData, yPredict, average='macro', beta=0.5))
resultsWeightedBeta5.append(fbeta_score(yData, yPredict, average='weighted', beta=0.5))
resultsMicroBeta1.append(fbeta_score(yData, yPredict, average='micro', beta=1))
resultsMacroBeta1.append(fbeta_score(yData, yPredict, average='macro', beta=1))
resultsWeightedBeta1.append(fbeta_score(yData, yPredict, average='weighted', beta=1))
resultsMicroBeta2.append(fbeta_score(yData, yPredict, average='micro', beta=2))
resultsMacroBeta2.append(fbeta_score(yData, yPredict, average='macro', beta=2))
resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2))
resultsLogLoss.append(log_loss(yData, yPredict, normalize = True))
metrics.insert(loop,'geometric_mean_score_micro',resultsMicro)
metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro)
metrics.insert(loop+2,'geometric_mean_score_weighted',resultsWeighted)
metrics.insert(loop+3,'matthews_corrcoef',resultsCorrCoef)
metrics.insert(loop+4,'f5_micro',resultsMicroBeta5)
metrics.insert(loop+5,'f5_macro',resultsMacroBeta5)
metrics.insert(loop+6,'f5_weighted',resultsWeightedBeta5)
metrics.insert(loop+7,'f1_micro',resultsMicroBeta1)
metrics.insert(loop+8,'f1_macro',resultsMacroBeta1)
metrics.insert(loop+9,'f1_weighted',resultsWeightedBeta1)
metrics.insert(loop+10,'f2_micro',resultsMicroBeta2)
metrics.insert(loop+11,'f2_macro',resultsMacroBeta2)
metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2)
metrics.insert(loop+13,'log_loss',resultsLogLoss)
perModelProbPandas = pd.DataFrame(perModelProb)
perModelProbPandas = perModelProbPandas.to_json()

322
frontend/package-lock.json generated
Unescape View File

@ -3057,6 +3057,13 @@
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@ -3068,6 +3075,13 @@
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@ -9102,12 +9116,19 @@
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@ -9115,9 +9136,9 @@
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@ -9133,6 +9154,13 @@
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@ -9151,6 +9179,13 @@
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@ -9162,9 +9197,9 @@
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@ -9220,6 +9255,13 @@
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@ -9231,6 +9273,13 @@
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@ -9242,7 +9291,7 @@
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@ -9281,6 +9330,13 @@
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@ -9314,6 +9370,13 @@
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@ -9347,6 +9410,11 @@
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@ -9374,7 +9442,7 @@
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@ -9432,7 +9500,7 @@
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@ -12509,12 +12581,14 @@
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@ -12523,32 +12597,38 @@
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@ -12744,12 +12848,14 @@
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@ -12806,17 +12919,20 @@
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@ -12827,14 +12943,16 @@
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@ -12896,7 +13022,8 @@
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},

5
frontend/package.json
Unescape View File

@ -33,12 +33,16 @@
"clean-webpack-plugin": "^3.0.0",
"colorbrewer": "^1.3.0",
"d3": "^5.15.0",
"d3-array": "^2.4.0",
"d3-brush": "^1.1.5",
"d3-dispatch": "^1.0.6",
"d3-drag": "^1.2.5",
"d3-heatmap": "^1.2.1",
"d3-lasso": "0.0.5",
"d3-loom": "^1.0.2",
"d3-selection": "^1.4.1",
"d3-selection-multi": "^1.0.1",
"d3-svg-legend": "^1.13.0",
"d3_exploding_boxplot": "^0.2.1",
"file-saver": "^2.0.2",
"file-system": "^2.2.2",
@ -49,6 +53,7 @@
"interactjs": "^1.8.2",
"jquery": "^3.4.1",
"mdbvue": "^6.3.0",
"merge": "^1.2.1",
"mini-css-extract-plugin": "^0.9.0",
"npm-check-updates": "^4.0.1",
"papaparse": "^5.1.1",

38
frontend/src/components/AlgorithmHyperParam.vue
Unescape View File

@ -19,7 +19,12 @@ export default {
ModelsPerformance: 0,
selAlgorithm: 0,
pc: 0,
factors: [1,1,1,1,1],
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
],
KNNModels: 576 //KNN models
}
},
@ -42,9 +47,29 @@ export default {
var Mc1 = []
const performanceAlg1 = JSON.parse(this.ModelsPerformance[6])
var max
var min
for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
if (j == 0) {
max = Object.values(performanceAlg1['log_loss'])[j]
min = Object.values(performanceAlg1['log_loss'])[j]
}
if (Object.values(performanceAlg1['log_loss'])[j] > max) {
max = Object.values(performanceAlg1['log_loss'])[j]
}
if (Object.values(performanceAlg1['log_loss'])[j] < min) {
min = Object.values(performanceAlg1['log_loss'])[j]
}
}
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_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg1['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg1['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['mean_test_jaccard'])[j])
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 - ((max - Object.values(performanceAlg1['log_loss'])[j])/(max - min))))
Mc1.push((sum/divide)*100)
}
@ -52,7 +77,11 @@ export default {
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_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg2['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg2['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['mean_test_jaccard'])[j])
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 - ((max - Object.values(performanceAlg2['log_loss'])[j])/(max - min))))
Mc2.push((sum2/divide)*100)
}
@ -64,7 +93,8 @@ export default {
Combined = JSON.parse(this.ModelsPerformance[9])
colorGiv = colors[1]
}
var valuesPerf = Object.values(Combined['0'])
var valuesPerf = Object.values(Combined['params'])
var ObjectsParams = Combined['params']
var newObjectsParams = []
var newObjectsParams2 = []

48
frontend/src/components/Algorithms.vue
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@ -28,7 +28,12 @@ export default {
parameters: [],
algorithm1: [],
algorithm2: [],
factors: [1,1,1,1,1],
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
],
chart: '',
flagEmpty: 0,
ActiveModels: [],
@ -53,11 +58,32 @@ export default {
divide = element + divide
});
var max
var min
var Mc1 = []
const performanceAlg1 = JSON.parse(this.PerformanceAllModels[6])
console.log(performanceAlg1)
for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
if (j == 0) {
max = Object.values(performanceAlg1['log_loss'])[j]
min = Object.values(performanceAlg1['log_loss'])[j]
}
if (Object.values(performanceAlg1['log_loss'])[j] > max) {
max = Object.values(performanceAlg1['log_loss'])[j]
}
if (Object.values(performanceAlg1['log_loss'])[j] < min) {
min = Object.values(performanceAlg1['log_loss'])[j]
}
}
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_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg1['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg1['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg1['mean_test_jaccard'])[j])
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 - ((max - Object.values(performanceAlg1['log_loss'])[j])/(max - min))))
Mc1.push((sum/divide)*100)
}
@ -65,7 +91,11 @@ export default {
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_f1_macro'])[j]) + (factorsLocal[2] * Object.values(performanceAlg2['mean_test_precision'])[j]) + (factorsLocal[3] * Object.values(performanceAlg2['mean_test_recall'])[j]) + (factorsLocal[4] * Object.values(performanceAlg2['mean_test_jaccard'])[j])
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 - ((max - Object.values(performanceAlg2['log_loss'])[j])/(max - min))))
Mc2.push((sum2/divide)*100)
}
@ -78,12 +108,12 @@ export default {
this.algorithm2 = []
this.parameters = []
for (var i = 0; i < Object.keys(PerformAlgor1['0']).length; i++) {
this.algorithm1.push({'Performance Metrics': Mc1[i],Algorithm:'KNN',Model:'Model ' + Algor1IDs[i] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor1['params'])[i])+'; Performance Metrics ',ModelID:Algor1IDs[i]})
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 (let j = 0; j < Object.keys(PerformAlgor2['0']).length; j++) {
this.algorithm2.push({'Performance Metrics': Mc2[j],Algorithm:'RF',Model:'Model ' + Algor2IDs[j] + '; Parameters '+JSON.stringify(Object.values(PerformAlgor2['params'])[j])+'; Performance Metrics ',ModelID:Algor2IDs[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]))
}
@ -97,7 +127,7 @@ export default {
// 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 Metrics',group:'Algorithm',color:'Algorithm',label:'Model'})
this.chart = exploding_boxplot(data, {y:'Performance (%)',group:'Algorithm',color:'Algorithm',label:'Model'})
this.chart.width(this.WH[0]*3) // interactive visualization
this.chart.height(this.WH[1]*0.9) // interactive visualization
//call chart on a div
@ -181,7 +211,7 @@ export default {
algorithm = this.algorithm2
}
for (let k = 0; k < allPoints.length; k++) {
if (algorithm[k]['Performance Metrics'] < limiter[0] && algorithm[k]['Performance Metrics'] > limiter[1]) {
if (algorithm[k]['Performance (%)'] < limiter[0] && algorithm[k]['Performance (%)'] > limiter[1]) {
modelsActive.push(algorithm[k].ModelID)
}
}

2
frontend/src/components/DataSpace.vue
Unescape View File

@ -28,7 +28,7 @@
<font-awesome-icon icon="eraser" />
{{ removeData }}
</button>
Provenance Controller: <button
History Controller: <button
id="saveID"
v-on:click="save">
<font-awesome-icon icon="save" />

36
frontend/src/components/FinalResultsLinePlot.vue
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@ -105,11 +105,11 @@ export default {
x: this.xaxis,
y: this.scoresMean,
text: text,
line: {color: "rgb(127,201,127)"},
line: {color: "rgb(55,126,184)"},
mode: "lines+markers",
marker : {
symbol: 'pentagon' },
name: "Current Accuracy",
name: "Active Accuracy",
type: "scatter"
}
@ -119,9 +119,9 @@ export default {
text: '',
hoverinfo: 'text',
fill: "tozerox",
fillcolor: "rgba(127,201,127,0)",
fillcolor: "rgba(55,126,184,0)",
line: {color: "transparent"},
name: "Current Accuracy",
name: "Active Accuracy",
showlegend: false,
type: "scatter"
}
@ -130,11 +130,11 @@ export default {
x: this.xaxis,
y: this.scoresMean2,
text: text,
line: {color: "rgb(191,91,23)"},
line: {color: "rgb(55,126,184)"},
mode: "lines+markers",
marker : {
symbol: 'x' },
name: "Current Precision",
name: "Active Precision",
type: "scatter"
}
@ -144,9 +144,9 @@ export default {
text: '',
hoverinfo: 'text',
fill: "tozerox",
fillcolor: "rgba(191,91,23,0)",
fillcolor: "rgba(55,126,184)",
line: {color: "transparent"},
name: "Current Precision",
name: "Active Precision",
showlegend: false,
type: "scatter"
}
@ -155,11 +155,11 @@ export default {
x: this.xaxis,
y: this.scoresMean3,
text: text,
line: {color: "rgb(253,192,134)"},
line: {color: "rgb(55,126,184)"},
mode: "lines+markers",
marker : {
symbol: 'diamond' },
name: "Current Recall",
name: "Active Recall",
type: "scatter"
}
@ -169,9 +169,9 @@ export default {
text: '',
hoverinfo: 'text',
fill: "tozerox",
fillcolor: "rgba(253,192,134,0)",
fillcolor: "rgba(55,126,184,0)",
line: {color: "transparent"},
name: "Current Recall",
name: "Active Recall",
showlegend: false,
type: "scatter"
}
@ -182,7 +182,7 @@ export default {
x: this.xaxis,
y: this.Stack_scoresMean,
text: text,
line: {color: "rgb(190,174,212)"},
line: {color: "rgb(228,26,28)"},
mode: "lines+markers",
marker : {
symbol: 'circle' },
@ -196,7 +196,7 @@ export default {
text: '',
hoverinfo: 'text',
fill: "tozerox",
fillcolor: "rgba(190,174,212,0)",
fillcolor: "rgba(228,26,28,0)",
line: {color: "transparent"},
name: "Stack Accuracy",
showlegend: false,
@ -207,7 +207,7 @@ export default {
x: this.xaxis,
y: this.Stack_scoresMean2,
text: text,
line: {color: "rgb(56,108,176)"},
line: {color: "rgb(228,26,28)"},
mode: "lines+markers",
marker : {
symbol: 'square' },
@ -221,7 +221,7 @@ export default {
text: '',
hoverinfo: 'text',
fill: "tozerox",
fillcolor: "rgba(56,108,176,0)",
fillcolor: "rgba(228,26,28,0)",
line: {color: "transparent"},
name: "Stack Precision",
showlegend: false,
@ -232,7 +232,7 @@ export default {
x: this.xaxis,
y: this.Stack_scoresMean3,
text: text,
line: {color: "rgb(240,2,127)"},
line: {color: "rgb(228,26,28)"},
mode: "lines+markers",
marker : {
symbol: 'star-triangle-up' },
@ -246,7 +246,7 @@ export default {
text: '',
hoverinfo: 'text',
fill: "tozerox",
fillcolor: "rgba(240,2,127,0)",
fillcolor: "rgba(228,26,28,0)",
line: {color: "transparent"},
name: "Stack Recall",
showlegend: false,

43
frontend/src/components/Main.vue
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@ -6,7 +6,7 @@
<b-row class="md-3">
<b-col cols="3">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Data Set and Validation Metrics Manager</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Data Sets and Performance Metrics Manager</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-left">
<DataSetExecController/>
@ -17,7 +17,7 @@
</b-col>
<b-col cols="6">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Current Stacking Ensemble</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Stacking Ensemble Provenance</mdb-card-header>
<mdb-card-body>
<Provenance/>
</mdb-card-body>
@ -25,7 +25,7 @@
</b-col>
<b-col cols="3">
<mdb-card>
<mdb-card-header color="primary-color" tag="h5" class="text-center">Meta-Model Performance</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [1, 5, 6]</small>Meta-Model Performance</mdb-card-header>
<mdb-card-body>
<FinalResultsLinePlot/>
</mdb-card-body>
@ -49,14 +49,14 @@
<b-col cols="3">
<mdb-card style="margin-top: 15px">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Overview of the Algorithms Space</mdb-card-header>
<mdb-card-body class="text-center">
<mdb-card-body class="text-left">
<Parameters/>
</mdb-card-body>
</mdb-card>
</b-col>
<b-col cols="9">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Performance of the Algorithms [Sel.:{{valueSel}}/All:{{valueAll}}]<small class="float-right"><Controller/></small></mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Performance of the Algorithms [Sel:{{valueSel}}/All:{{valueAll}}]<small class="float-right"><Controller/></small></mdb-card-header>
<mdb-card-body>
<Algorithms :width="width" :height="height"/>
</mdb-card-body>
@ -66,7 +66,7 @@
<b-row class="md-3">
<b-col cols="3">
<mdb-card style="margin-top: 15px">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Parameters Manipulation for Each Algorithm</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Parameters Manipulation for Each Algorithm</mdb-card-header>
<mdb-card-body>
<AlgorithmHyperParam/>
</mdb-card-body>
@ -74,7 +74,7 @@
</b-col>
<b-col cols="9">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Performance of the Algorithms for Each Class</mdb-card-header>
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [5, 6, 7 (F1)]</small>Performance of the Algorithms for Each Class</mdb-card-header>
<mdb-card-body>
<BarChart/>
</mdb-card-body>
@ -84,17 +84,6 @@
</div>
<div class="tab-pane fade" id="profile" role="tabpanel" aria-labelledby="profile-tab">
<b-row class="md-3">
<b-col cols="6">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Data Space</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 825px">
<DataSpace/>
<PCPData/>
</mdb-card-text>
</mdb-card-body>
</mdb-card>
</b-col>
<b-col cols="6">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Features Selection for Each Model</mdb-card-header>
@ -105,7 +94,17 @@
</mdb-card-text>
</mdb-card-body>
</mdb-card>
</b-col>
<b-col cols="6">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Data Space</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 845px">
<DataSpace/>
<PCPData/>
</mdb-card-text>
</mdb-card-body>
</mdb-card>
</b-col>
</b-row>
</div>
@ -113,8 +112,8 @@
<b-row class="md-3">
<b-col cols="6">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Models Space
[Sel.:{{OverSelLength}}/All:{{OverAllLength}}]
<mdb-card-header color="primary-color" tag="h5" class="text-center"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>Models Space
[Sel:{{OverSelLength}}/All:{{OverAllLength}}]
</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 845px">
@ -967,6 +966,7 @@ export default Vue.extend({
</script>
<style>
#nprogress .bar {
background: red !important;
}
@ -981,6 +981,7 @@ border-left-color: red !important;
}
body {
font-family: 'Helvetica', 'Arial', sans-serif !important;
left: 0px;
right: 0px;
top: 0px;

1251
frontend/src/components/Parameters.vue
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231
frontend/src/components/SlidersController.vue
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@ -1,16 +1,35 @@
<template>
<div id="WrapSliders">
<p>(1) Accuracy<b-form-slider ref="basic1" v-model="basicValue1" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue1 }}%</p>
<p>(2) F1-Score<b-form-slider ref="basic2" v-model="basicValue2" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider >{{ basicValue2 }}%</p>
<p>(3) Precision<b-form-slider ref="basic3" v-model="basicValue3" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue3 }}%</p>
<p>(4) Recall<b-form-slider ref="basic4" v-model="basicValue4" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue4 }}%</p>
<p>(5) Jaccard Similarity<b-form-slider ref="basic5" v-model="basicValue5" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue5 }}%</p>
<p>(6) Metric 6<b-form-slider ref="basic1" v-model="basicValue6" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue6 }}%</p>
<p>(7) Metric 7<b-form-slider ref="basic1" v-model="basicValue7" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue7 }}%</p>
<p>(8) Metric 8<b-form-slider ref="basic1" v-model="basicValue8" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue8 }}%</p>
<p>(9) Metric 9<b-form-slider ref="basic1" v-model="basicValue9" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue9 }}%</p>
<p>(10) Metric 10<b-form-slider ref="basic1" v-model="basicValue10" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue10 }}%</p>
</div>
<b-row>
<b-col cols="9">
<div id="WrapSliders">
<p>(1) Accuracy:<b-form-slider ref="basic1" v-model="basicValue1" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue1 }}%</p>
<p>(2) MAE:<b-form-slider ref="basic2" v-model="basicValue2" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue2 }}%</p>
<p>(3) RMSE:<b-form-slider ref="basic3" v-model="basicValue3" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue3 }}%</p>
<p>(4*) G-Mean:<b-form-slider ref="basic4" v-model="basicValue4" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue4 }}%</p>
<p>(5*) Precision:<b-form-slider ref="basic5" v-model="basicValue5" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue5 }}%</p>
<p>(6*) Recall:<b-form-slider ref="basic6" v-model="basicValue6" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue6 }}%</p>
<p>(7*) F-Beta Score:<b-form-slider ref="basic7" v-model="basicValue7" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider >{{ basicValue7 }}%</p>
<p>(8) MCC:<b-form-slider ref="basic8" v-model="basicValue8" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px; padding-left:15px"></b-form-slider>{{ basicValue8 }}%</p>
<p>(9) ROC AUC:<b-form-slider ref="basic9" v-model="basicValue9" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue9 }}%</p>
<p>(10) Log Loss:<b-form-slider ref="basic10" v-model="basicValue10" :min="0" :max="100" trigger-change-event @slide-start="slideStart" @slide-stop="slideStop" style="padding-right: 15px;padding-left:15px"></b-form-slider>{{ basicValue10 }}%</p>
</div>
</b-col>
<b-col cols="3" style="padding-top: 55px">
<p>(*) Average:
<select id="selectFilterAverage" @change="selectAppliedFilterAverage()">
<option value="weighted" selected>Weighted</option>
<option value="micro">Micro</option>
<option value="macro">Macro</option>
</select></p>
<br />
<p>(7) Beta Value:
<select id="selectFilterBeta" @change="selectAppliedFilterBeta()">
<option value="one" selected>F1 Score</option>
<option value="half">F0.5 Score</option>
<option value="two">F2 Score</option>
</select></p>
</b-col>
</b-row>
</template>
<script>
@ -21,31 +40,173 @@
export default {
name: 'SlidersController',
data () {
return {
basicValue1: 100,
basicValue2: 100,
basicValue3: 100,
basicValue4: 100,
basicValue5: 100,
basicValue6: 100,
basicValue7: 100,
basicValue8: 100,
basicValue9: 100,
basicValue10: 100
}
return {
basicValue1: 100,
basicValue2: 100,
basicValue3: 100,
basicValue4: 100,
basicValue5: 100,
basicValue6: 100,
basicValue7: 100,
basicValue8: 100,
basicValue9: 100,
basicValue10: 100,
factorsLocal: [1,1,1,0,0
,1,0,0,1,0
,0,1,0,0,0
,0,0,1,0,0
,0,1,1,1
],
userSelectedFilterAver: 'weighted',
userSelectedFilterBeta: 'one'
}
},
methods: {
slideStart () {
},
slideStop () {
var basicValues = []
basicValues.push(this.basicValue1/100)
basicValues.push(this.basicValue2/100)
basicValues.push(this.basicValue3/100)
basicValues.push(this.basicValue4/100)
basicValues.push(this.basicValue5/100)
EventBus.$emit('CallFactorsView', basicValues)
},
selectAppliedFilterAverage () {
var representationSelectionDocum = document.getElementById('selectFilterAverage')
this.userSelectedFilterAver = representationSelectionDocum.options[representationSelectionDocum.selectedIndex].value
this.slideStop()
},
selectAppliedFilterBeta () {
var representationSelectionDocum = document.getElementById('selectFilterBeta')
this.userSelectedFilterBeta = representationSelectionDocum.options[representationSelectionDocum.selectedIndex].value
this.slideStop()
},
slideStart () {
},
slideStop () {
if (this.userSelectedFilterAver == 'micro') {
this.factorsLocal[3] = this.basicValue4
this.factorsLocal[4] = 0
this.factorsLocal[5] = 0
this.factorsLocal[6] = this.basicValue5
this.factorsLocal[7] = 0
this.factorsLocal[8] = 0
this.factorsLocal[9] = this.basicValue6
this.factorsLocal[10] = 0
this.factorsLocal[11] = 0
if (this.userSelectedFilterBeta == 'half') {
this.factorsLocal[12] = this.basicValue7
this.factorsLocal[13] = 0
this.factorsLocal[14] = 0
this.factorsLocal[15] = 0
this.factorsLocal[16] = 0
this.factorsLocal[17] = 0
this.factorsLocal[18] = 0
this.factorsLocal[19] = 0
this.factorsLocal[20] = 0
} else if (this.userSelectedFilterBeta == 'two') {
this.factorsLocal[12] = 0
this.factorsLocal[13] = 0
this.factorsLocal[14] = 0
this.factorsLocal[15] = 0
this.factorsLocal[16] = 0
this.factorsLocal[17] = 0
this.factorsLocal[18] = this.basicValue7
this.factorsLocal[19] = 0
this.factorsLocal[20] = 0
} else {
this.factorsLocal[12] = 0
this.factorsLocal[13] = 0
this.factorsLocal[14] = 0
this.factorsLocal[15] = this.basicValue7
this.factorsLocal[16] = 0
this.factorsLocal[17] = 0
this.factorsLocal[18] = 0
this.factorsLocal[19] = 0
this.factorsLocal[20] = 0
}
} else if (this.userSelectedFilterAver == 'macro') {
this.factorsLocal[3] = 0
this.factorsLocal[4] = this.basicValue4
this.factorsLocal[5] = 0
this.factorsLocal[6] = 0
this.factorsLocal[7] = this.basicValue5
this.factorsLocal[8] = 0
this.factorsLocal[9] = 0
this.factorsLocal[10] = this.basicValue6
this.factorsLocal[11] = 0
if (this.userSelectedFilterBeta == 'half') {
this.factorsLocal[12] = 0
this.factorsLocal[13] = this.basicValue7
this.factorsLocal[14] = 0
this.factorsLocal[15] = 0
this.factorsLocal[16] = 0
this.factorsLocal[17] = 0
this.factorsLocal[18] = 0
this.factorsLocal[19] = 0
this.factorsLocal[20] = 0
} else if (this.userSelectedFilterBeta == 'two') {
this.factorsLocal[12] = 0
this.factorsLocal[13] = 0
this.factorsLocal[14] = 0
this.factorsLocal[15] = 0
this.factorsLocal[16] = 0
this.factorsLocal[17] = 0
this.factorsLocal[18] = 0
this.factorsLocal[19] = this.basicValue7
this.factorsLocal[20] = 0
} else {
this.factorsLocal[12] = 0
this.factorsLocal[13] = 0
this.factorsLocal[14] = 0
this.factorsLocal[15] = 0
this.factorsLocal[16] = this.basicValue7
this.factorsLocal[17] = 0
this.factorsLocal[18] = 0
this.factorsLocal[19] = 0
this.factorsLocal[20] = 0
}
} else {
this.factorsLocal[3] = 0
this.factorsLocal[4] = 0
this.factorsLocal[5] = this.basicValue4
this.factorsLocal[6] = 0
this.factorsLocal[7] = 0
this.factorsLocal[8] = this.basicValue5
this.factorsLocal[9] = 0
this.factorsLocal[10] = 0
this.factorsLocal[11] = this.basicValue6
if (this.userSelectedFilterBeta == 'half') {
this.factorsLocal[12] = 0
this.factorsLocal[13] = 0
this.factorsLocal[14] = this.basicValue7
this.factorsLocal[15] = 0
this.factorsLocal[16] = 0
this.factorsLocal[17] = 0
this.factorsLocal[18] = 0
this.factorsLocal[19] = 0
this.factorsLocal[20] = 0
} else if (this.userSelectedFilterBeta == 'two') {
this.factorsLocal[12] = 0
this.factorsLocal[13] = 0
this.factorsLocal[14] = 0
this.factorsLocal[15] = 0
this.factorsLocal[16] = 0
this.factorsLocal[17] = 0
this.factorsLocal[18] = 0
this.factorsLocal[19] = 0
this.factorsLocal[20] = this.basicValue7
} else {
this.factorsLocal[12] = 0
this.factorsLocal[13] = 0
this.factorsLocal[14] = 0
this.factorsLocal[15] = 0
this.factorsLocal[16] = 0
this.factorsLocal[17] = this.basicValue7
this.factorsLocal[18] = 0
this.factorsLocal[19] = 0
this.factorsLocal[20] = 0
}
}
this.factorsLocal[0] = this.basicValue1/100
this.factorsLocal[1] = this.basicValue2/100
this.factorsLocal[2] = this.basicValue3/100
this.factorsLocal[21] = this.basicValue8/100
this.factorsLocal[22] = this.basicValue9/100
this.factorsLocal[23] = this.basicValue10/100
EventBus.$emit('CallFactorsView', this.factorsLocal)
},
},
}
</script>

122
package-lock.json generated
Unescape View File

@ -1,3 +1,123 @@
{
"lockfileVersion": 1
"requires": true,
"lockfileVersion": 1,
"dependencies": {
"@types/d3-selection": {
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"resolved": "https://registry.npmjs.org/@types/d3-selection/-/d3-selection-1.0.10.tgz",
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},
"d3-array": {
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"resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.0.1.tgz",
"integrity": "sha1-N1wCh0/NlsFu2fG89bSnvlPzWOc="
},
"d3-collection": {
"version": "1.0.7",
"resolved": "https://registry.npmjs.org/d3-collection/-/d3-collection-1.0.7.tgz",
"integrity": "sha512-ii0/r5f4sjKNTfh84Di+DpztYwqKhEyUlKoPrzUFfeSkWxjW49xU2QzO9qrPrNkpdI0XJkfzvmTu8V2Zylln6A=="
},
"d3-color": {
"version": "1.4.0",
"resolved": "https://registry.npmjs.org/d3-color/-/d3-color-1.4.0.tgz",
"integrity": "sha512-TzNPeJy2+iEepfiL92LAAB7fvnp/dV2YwANPVHdDWmYMm23qIJBYww3qT8I8C1wXrmrg4UWs7BKc2tKIgyjzHg=="
},
"d3-dispatch": {
"version": "1.0.1",
"resolved": "https://registry.npmjs.org/d3-dispatch/-/d3-dispatch-1.0.1.tgz",
"integrity": "sha1-S9ZaQ87P9DGN653yRVKqi/KBqEA="
},
"d3-ease": {
"version": "1.0.6",
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"d3-format": {
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"resolved": "https://registry.npmjs.org/d3-format/-/d3-format-1.0.2.tgz",
"integrity": "sha1-E4YYMgtLvrQ7XA/zBRkHn7vXN14="
},
"d3-interpolate": {
"version": "1.4.0",
"resolved": "https://registry.npmjs.org/d3-interpolate/-/d3-interpolate-1.4.0.tgz",
"integrity": "sha512-V9znK0zc3jOPV4VD2zZn0sDhZU3WAE2bmlxdIwwQPPzPjvyLkd8B3JUVdS1IDUFDkWZ72c9qnv1GK2ZagTZ8EA==",
"requires": {
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}
},
"d3-legend": {
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"resolved": "https://registry.npmjs.org/d3-legend/-/d3-legend-1.0.0.tgz",
"integrity": "sha1-KtwzKEMy/5bSIdSTL3kXRj+i8Xk="
},
"d3-scale": {
"version": "1.0.3",
"resolved": "https://registry.npmjs.org/d3-scale/-/d3-scale-1.0.3.tgz",
"integrity": "sha1-T56PDMLqDzkl/wSsJ63AkEX6TJA=",
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"d3-collection": "1",
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"d3-time": "1",
"d3-time-format": "2"
}
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"resolved": "https://registry.npmjs.org/d3-selection/-/d3-selection-1.0.2.tgz",
"integrity": "sha1-rmYq/UcCrJxdoDmyEHoXZPockHA="
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"d3-svg-legend": {
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"integrity": "sha1-jY3BvWk8N47ki2+CPook5o8uGtI=",
"requires": {
"@types/d3-selection": "1.0.10",
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"d3-dispatch": "1.0.1",
"d3-format": "1.0.2",
"d3-scale": "1.0.3",
"d3-selection": "1.0.2",
"d3-transition": "1.0.3"
}
},
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"resolved": "https://registry.npmjs.org/d3-time/-/d3-time-1.1.0.tgz",
"integrity": "sha512-Xh0isrZ5rPYYdqhAVk8VLnMEidhz5aP7htAADH6MfzgmmicPkTo8LhkLxci61/lCB7n7UmE3bN0leRt+qvkLxA=="
},
"d3-time-format": {
"version": "2.2.3",
"resolved": "https://registry.npmjs.org/d3-time-format/-/d3-time-format-2.2.3.tgz",
"integrity": "sha512-RAHNnD8+XvC4Zc4d2A56Uw0yJoM7bsvOlJR33bclxq399Rak/b9bhvu/InjxdWhPtkgU53JJcleJTGkNRnN6IA==",
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"d3-time": "1"
}
},
"d3-timer": {
"version": "1.0.10",
"resolved": "https://registry.npmjs.org/d3-timer/-/d3-timer-1.0.10.tgz",
"integrity": "sha512-B1JDm0XDaQC+uvo4DT79H0XmBskgS3l6Ve+1SBCfxgmtIb1AVrPIoqd+nPSv+loMX8szQ0sVUhGngL7D5QPiXw=="
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"resolved": "https://registry.npmjs.org/d3-transition/-/d3-transition-1.0.3.tgz",
"integrity": "sha1-kdyYa92zCXNjkyCoXbcs5KsaJ7s=",
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"d3-color": "1",
"d3-dispatch": "1",
"d3-ease": "1",
"d3-interpolate": "1",
"d3-selection": "1",
"d3-timer": "1"
}
},
"d3v3": {
"version": "1.0.3",
"resolved": "https://registry.npmjs.org/d3v3/-/d3v3-1.0.3.tgz",
"integrity": "sha1-37Ddsh7cPkXD62CzheZYfTFdClk="
}
}
}

118
run.py
Unescape View File

@ -24,6 +24,10 @@ from sklearn.pipeline import make_pipeline
from sklearn import model_selection
from sklearn.manifold import MDS
from sklearn.manifold import TSNE
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import log_loss
from sklearn.metrics import fbeta_score
from imblearn.metrics import geometric_mean_score
import umap
from sklearn.metrics import classification_report
from sklearn.preprocessing import scale
@ -62,7 +66,7 @@ def Reset():
RANDOM_SEED = 42
global factors
factors = [1,1,1,1,1]
factors = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
global XData
XData = []
@ -100,7 +104,7 @@ def Reset():
all_classifiers = []
global crossValidation
crossValidation = 3
crossValidation = 5
# models
global KNNModels
@ -109,9 +113,8 @@ def Reset():
RFModels = []
global scoring
#scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'}
scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
scoring = {'accuracy': 'accuracy', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_root_mean_squared_error': 'neg_root_mean_squared_error', 'precision_micro': 'precision_micro', 'precision_macro': 'precision_macro', 'precision_weighted': 'precision_weighted', 'recall_micro': 'recall_micro', 'recall_macro': 'recall_macro', 'recall_weighted': 'recall_weighted', 'roc_auc_ovo_weighted': 'roc_auc_ovo_weighted'}
global loopFeatures
loopFeatures = 2
@ -183,11 +186,10 @@ def RetrieveFileName():
all_classifiers = []
global crossValidation
crossValidation = 3
crossValidation = 5
global scoring
scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
#scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'}
scoring = {'accuracy': 'accuracy', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_root_mean_squared_error': 'neg_root_mean_squared_error', 'precision_micro': 'precision_micro', 'precision_macro': 'precision_macro', 'precision_weighted': 'precision_weighted', 'recall_micro': 'recall_micro', 'recall_macro': 'recall_macro', 'recall_weighted': 'recall_weighted', 'roc_auc_ovo_weighted': 'roc_auc_ovo_weighted'}
global loopFeatures
loopFeatures = 2
@ -436,7 +438,6 @@ def RetrieveModel():
global algorithms
algorithms = RetrievedModel['Algorithms']
global factors
global XData
global yData
@ -451,7 +452,7 @@ def RetrieveModel():
clf = RandomForestClassifier()
params = {'n_estimators': list(range(40, 120)), 'criterion': ['gini', 'entropy']}
AlgorithmsIDsEnd = 576
allParametersPerformancePerModel = GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd)
allParametersPerformancePerModel = GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd)
# call the function that sends the results to the frontend
SendEachClassifiersPerformanceToVisualize()
@ -463,7 +464,7 @@ 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, factors, AlgorithmsIDsEnd):
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
# instantiate spark session
spark = (
@ -508,30 +509,13 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
# copy and filter in order to get only the metrics
metrics = df_cv_results_classifiers.copy()
metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
# control the factors
sumperModel = []
for index, row in metrics.iterrows():
rowSum = 0
lengthFactors = len(scoring)
for loop,elements in enumerate(row):
lengthFactors = lengthFactors - 1 + factors[loop]
rowSum = elements*factors[loop] + rowSum
if lengthFactors is 0:
sumperModel = 0
else:
sumperModel.append(rowSum/lengthFactors)
# summarize all models metrics
summarizedMetrics = pd.DataFrame(sumperModel)
summarizedMetrics.rename(columns={0:'sum'})
metrics = metrics.filter(['mean_test_accuracy','mean_test_neg_mean_absolute_error','mean_test_neg_root_mean_squared_error','mean_test_precision_micro','mean_test_precision_macro','mean_test_precision_weighted','mean_test_recall_micro','mean_test_recall_macro','mean_test_recall_weighted','mean_test_roc_auc_ovo_weighted'])
# concat parameters and performance
parameters = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = parametersPerformancePerModel.to_json()
parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy()
Models = []
for index, items in enumerate(parametersLocal):
@ -544,13 +528,29 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
PerFeatureAccuracyAll = []
PerClassMetric = []
perModelProb = []
resultsMicro = []
resultsMacro = []
resultsWeighted = []
resultsCorrCoef = []
resultsMicroBeta5 = []
resultsMacroBeta5 = []
resultsWeightedBeta5 = []
resultsMicroBeta1 = []
resultsMacroBeta1 = []
resultsWeightedBeta1 = []
resultsMicroBeta2 = []
resultsMacroBeta2 = []
resultsWeightedBeta2 = []
resultsLogLoss = []
loop = 10
for eachModelParameters in parametersLocalNew:
clf.set_params(**eachModelParameters)
perm = PermutationImportance(clf, cv = None, refit = True, n_iter = 25).fit(XData, yData)
permList.append(perm.feature_importances_)
n_feats = XData.shape[1]
PerFeatureAccuracy = []
for i in range(n_feats):
@ -564,6 +564,47 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
yPredictProb = clf.predict_proba(XData)
perModelProb.append(yPredictProb.tolist())
resultsMicro.append(geometric_mean_score(yData, yPredict, average='micro'))
resultsMacro.append(geometric_mean_score(yData, yPredict, average='macro'))
resultsWeighted.append(geometric_mean_score(yData, yPredict, average='weighted'))
resultsCorrCoef.append(matthews_corrcoef(yData, yPredict))
resultsMicroBeta5.append(fbeta_score(yData, yPredict, average='micro', beta=0.5))
resultsMacroBeta5.append(fbeta_score(yData, yPredict, average='macro', beta=0.5))
resultsWeightedBeta5.append(fbeta_score(yData, yPredict, average='weighted', beta=0.5))
resultsMicroBeta1.append(fbeta_score(yData, yPredict, average='micro', beta=1))
resultsMacroBeta1.append(fbeta_score(yData, yPredict, average='macro', beta=1))
resultsWeightedBeta1.append(fbeta_score(yData, yPredict, average='weighted', beta=1))
resultsMicroBeta2.append(fbeta_score(yData, yPredict, average='micro', beta=2))
resultsMacroBeta2.append(fbeta_score(yData, yPredict, average='macro', beta=2))
resultsWeightedBeta2.append(fbeta_score(yData, yPredict, average='weighted', beta=2))
resultsLogLoss.append(log_loss(yData, yPredict, normalize = True))
metrics.insert(loop,'geometric_mean_score_micro',resultsMicro)
metrics.insert(loop+1,'geometric_mean_score_macro',resultsMacro)
metrics.insert(loop+2,'geometric_mean_score_weighted',resultsWeighted)
metrics.insert(loop+3,'matthews_corrcoef',resultsCorrCoef)
metrics.insert(loop+4,'f5_micro',resultsMicroBeta5)
metrics.insert(loop+5,'f5_macro',resultsMacroBeta5)
metrics.insert(loop+6,'f5_weighted',resultsWeightedBeta5)
metrics.insert(loop+7,'f1_micro',resultsMicroBeta1)
metrics.insert(loop+8,'f1_macro',resultsMacroBeta1)
metrics.insert(loop+9,'f1_weighted',resultsWeightedBeta1)
metrics.insert(loop+10,'f2_micro',resultsMicroBeta2)
metrics.insert(loop+11,'f2_macro',resultsMacroBeta2)
metrics.insert(loop+12,'f2_weighted',resultsWeightedBeta2)
metrics.insert(loop+13,'log_loss',resultsLogLoss)
perModelProbPandas = pd.DataFrame(perModelProb)
perModelProbPandas = perModelProbPandas.to_json()
@ -849,6 +890,7 @@ def preProcessFeatSc():
def preProcsumPerMetric(factors):
sumPerClassifier = []
loopThroughMetrics = PreprocessingMetrics()
print(loopThroughMetrics)
for row in loopThroughMetrics.iterrows():
rowSum = 0
lengthFactors = len(scoring)
@ -866,9 +908,9 @@ def preProcMetricsAllAndSel():
loopThroughMetrics = PreprocessingMetrics()
metricsPerModelColl = []
metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'].sum()/loopThroughMetrics['mean_test_accuracy'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_f1_macro'].sum()/loopThroughMetrics['mean_test_f1_macro'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_precision'].sum()/loopThroughMetrics['mean_test_precision'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_recall'].sum()/loopThroughMetrics['mean_test_recall'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_mean_absolute_error'].sum()/loopThroughMetrics['mean_test_neg_mean_absolute_error'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_neg_root_mean_squared_error'].sum()/loopThroughMetrics['mean_test_neg_root_mean_squared_error'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_precision_micro'].sum()/loopThroughMetrics['mean_test_precision_micro'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_jaccard'].sum()/loopThroughMetrics['mean_test_jaccard'].count())
for index, metric in enumerate(metricsPerModelColl):
metricsPerModelColl[index] = metric*factors[index]
@ -1214,7 +1256,7 @@ def GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, DataPointsSel):
# copy and filter in order to get only the metrics
metrics = df_cv_results_classifiers.copy()
metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
metrics = metrics.filter(['mean_test_accuracy','mean_test_neg_mean_absolute_error','mean_test_neg_root_mean_squared_error','mean_test_precision_micro','mean_test_precision_macro','mean_test_precision_weighted','mean_test_recall_micro','mean_test_recall_macro','mean_test_recall_weighted','mean_test_roc_auc_ovo_weighted'])
metrics = metrics.to_json()
resultsMetrics.append(metrics) # Position: 0 and so on
@ -1284,7 +1326,7 @@ def EnsembleModel(Models, keyRetrieved):
temp = json.loads(allParametersPerformancePerModel[1])
dfParamKNN = pd.DataFrame.from_dict(temp)
dfParamKNNFilt = dfParamKNN.iloc[:,1]
dfParamKNNFilt = dfParamKNN.iloc[:,0]
for eachelem in KNNModels:
arg = dfParamKNNFilt[eachelem]
@ -1292,7 +1334,7 @@ def EnsembleModel(Models, keyRetrieved):
temp = json.loads(allParametersPerformancePerModel[9])
dfParamRF = pd.DataFrame.from_dict(temp)
dfParamRFFilt = dfParamRF.iloc[:,1]
dfParamRFFilt = dfParamRF.iloc[:,0]
for eachelem in RFModels:
arg = dfParamRFFilt[eachelem-576]
all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier().set_params(**arg)))

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