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Angelos Chatzimparmpas 5 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 @memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd): def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
# instantiate spark session # instantiate spark session
spark = ( spark = (
@ -45,28 +45,11 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
# copy and filter in order to get only the metrics # copy and filter in order to get only the metrics
metrics = df_cv_results_classifiers.copy() 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'])
# 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'})
# concat parameters and performance # concat parameters and performance
parameters = pd.DataFrame(df_cv_results_classifiers['params']) parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
parametersPerformancePerModel = parametersPerformancePerModel.to_json() parametersPerformancePerModel = parametersPerformancePerModel.to_json()
parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy() parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy()
@ -81,6 +64,22 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
PerFeatureAccuracyAll = [] PerFeatureAccuracyAll = []
PerClassMetric = [] PerClassMetric = []
perModelProb = [] perModelProb = []
resultsMicro = []
resultsMacro = []
resultsWeighted = []
resultsCorrCoef = []
resultsMicroBeta5 = []
resultsMacroBeta5 = []
resultsWeightedBeta5 = []
resultsMicroBeta1 = []
resultsMacroBeta1 = []
resultsWeightedBeta1 = []
resultsMicroBeta2 = []
resultsMacroBeta2 = []
resultsWeightedBeta2 = []
resultsLogLoss = []
loop = 10
for eachModelParameters in parametersLocalNew: for eachModelParameters in parametersLocalNew:
clf.set_params(**eachModelParameters) clf.set_params(**eachModelParameters)
@ -101,6 +100,47 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
yPredictProb = clf.predict_proba(XData) yPredictProb = clf.predict_proba(XData)
perModelProb.append(yPredictProb.tolist()) 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 = pd.DataFrame(perModelProb)
perModelProbPandas = perModelProbPandas.to_json() perModelProbPandas = perModelProbPandas.to_json()

318
frontend/package-lock.json generated
Unescape View File

@ -3057,6 +3057,13 @@
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@ -3068,6 +3075,13 @@
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}
} }
}, },
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@ -9102,12 +9116,19 @@
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}
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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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} }
}, },
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@ -9162,9 +9197,9 @@
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@ -9220,6 +9255,13 @@
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}, },
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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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@ -12485,22 +12553,26 @@
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@ -12509,12 +12581,14 @@
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"d3-array": {
"version": "1.2.4",
"resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz",
"integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw=="
}
} }
}, },
"parent-module": { "parent-module": {
@ -19724,6 +19870,7 @@
"d3-force": "^1.0.6", "d3-force": "^1.0.6",
"d3-hierarchy": "^1.1.8", "d3-hierarchy": "^1.1.8",
"d3-interpolate": "1", "d3-interpolate": "1",
"d3-svg-legend": "^1.13.0",
"delaunay-triangulate": "^1.1.6", "delaunay-triangulate": "^1.1.6",
"es6-promise": "^3.0.2", "es6-promise": "^3.0.2",
"fast-isnumeric": "^1.1.3", "fast-isnumeric": "^1.1.3",
@ -19774,9 +19921,24 @@
}, },
"dependencies": { "dependencies": {
"d3": { "d3": {
"version": , "version": "3.5.17",
"resolved": "https://registry.npmjs.org/d3/-/d3-3.5.17.tgz", "resolved": "https://registry.npmjs.org/d3/-/d3-3.5.17.tgz",
"integrity": "sha1-vEZ0gAQ3iyGjYMn8fPUjF5B2L7g=" "integrity": "sha1-vEZ0gAQ3iyGjYMn8fPUjF5B2L7g=",
"requires": {
"d3-svg-legend": "^1.13.0"
},
"dependencies": {
"d3-svg-legend": {
"version": "1.13.0",
"resolved": "https://registry.npmjs.org/d3-svg-legend/-/d3-svg-legend-1.13.0.tgz",
"integrity": "sha1-YhdHjJrdnWLLMzYX4ZYTEaQaTbM="
}
}
},
"d3-svg-legend": {
"version": "1.13.0",
"resolved": "https://registry.npmjs.org/d3-svg-legend/-/d3-svg-legend-1.13.0.tgz",
"integrity": "sha1-YhdHjJrdnWLLMzYX4ZYTEaQaTbM="
} }
} }
}, },

5
frontend/package.json
Unescape View File

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

38
frontend/src/components/AlgorithmHyperParam.vue
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@ -19,7 +19,12 @@ export default {
ModelsPerformance: 0, ModelsPerformance: 0,
selAlgorithm: 0, selAlgorithm: 0,
pc: 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 KNNModels: 576 //KNN models
} }
}, },
@ -42,9 +47,29 @@ export default {
var Mc1 = [] var Mc1 = []
const performanceAlg1 = JSON.parse(this.ModelsPerformance[6]) 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++) { for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
let sum 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) Mc1.push((sum/divide)*100)
} }
@ -52,7 +77,11 @@ export default {
const performanceAlg2 = JSON.parse(this.ModelsPerformance[14]) const performanceAlg2 = JSON.parse(this.ModelsPerformance[14])
for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) { for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) {
let sum2 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) Mc2.push((sum2/divide)*100)
} }
@ -64,7 +93,8 @@ export default {
Combined = JSON.parse(this.ModelsPerformance[9]) Combined = JSON.parse(this.ModelsPerformance[9])
colorGiv = colors[1] colorGiv = colors[1]
} }
var valuesPerf = Object.values(Combined['0']) var valuesPerf = Object.values(Combined['params'])
var ObjectsParams = Combined['params'] var ObjectsParams = Combined['params']
var newObjectsParams = [] var newObjectsParams = []
var newObjectsParams2 = [] var newObjectsParams2 = []

48
frontend/src/components/Algorithms.vue
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@ -28,7 +28,12 @@ export default {
parameters: [], parameters: [],
algorithm1: [], algorithm1: [],
algorithm2: [], 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: '', chart: '',
flagEmpty: 0, flagEmpty: 0,
ActiveModels: [], ActiveModels: [],
@ -53,11 +58,32 @@ export default {
divide = element + divide divide = element + divide
}); });
var max
var min
var Mc1 = [] var Mc1 = []
const performanceAlg1 = JSON.parse(this.PerformanceAllModels[6]) 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++) { for (let j = 0; j < Object.values(performanceAlg1['mean_test_accuracy']).length; j++) {
let sum 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) Mc1.push((sum/divide)*100)
} }
@ -65,7 +91,11 @@ export default {
const performanceAlg2 = JSON.parse(this.PerformanceAllModels[14]) const performanceAlg2 = JSON.parse(this.PerformanceAllModels[14])
for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) { for (let j = 0; j < Object.values(performanceAlg2['mean_test_accuracy']).length; j++) {
let sum2 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) Mc2.push((sum2/divide)*100)
} }
@ -78,12 +108,12 @@ export default {
this.algorithm2 = [] this.algorithm2 = []
this.parameters = [] this.parameters = []
for (var i = 0; i < Object.keys(PerformAlgor1['0']).length; i++) { for (var i = 0; i < Object.keys(PerformAlgor1['params']).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]}) 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])) this.parameters.push(JSON.stringify(Object.values(PerformAlgor1['params'])[i]))
} }
for (let j = 0; j < Object.keys(PerformAlgor2['0']).length; j++) { for (let j = 0; j < Object.keys(PerformAlgor2['params']).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]}) 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])) this.parameters.push(JSON.stringify(Object.values(PerformAlgor2['params'])[j]))
} }
@ -97,7 +127,7 @@ export default {
// group : how to group data on x axis // group : how to group data on x axis
// color : color of the point / boxplot // color : color of the point / boxplot
// label : displayed text in toolbox // 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.width(this.WH[0]*3) // interactive visualization
this.chart.height(this.WH[1]*0.9) // interactive visualization this.chart.height(this.WH[1]*0.9) // interactive visualization
//call chart on a div //call chart on a div
@ -181,7 +211,7 @@ export default {
algorithm = this.algorithm2 algorithm = this.algorithm2
} }
for (let k = 0; k < allPoints.length; k++) { 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) modelsActive.push(algorithm[k].ModelID)
} }
} }

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

35
frontend/src/components/Main.vue
Unescape View File

@ -6,7 +6,7 @@
<b-row class="md-3"> <b-row class="md-3">
<b-col cols="3"> <b-col cols="3">
<mdb-card> <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-body>
<mdb-card-text class="text-left"> <mdb-card-text class="text-left">
<DataSetExecController/> <DataSetExecController/>
@ -17,7 +17,7 @@
</b-col> </b-col>
<b-col cols="6"> <b-col cols="6">
<mdb-card> <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> <mdb-card-body>
<Provenance/> <Provenance/>
</mdb-card-body> </mdb-card-body>
@ -25,7 +25,7 @@
</b-col> </b-col>
<b-col cols="3"> <b-col cols="3">
<mdb-card> <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> <mdb-card-body>
<FinalResultsLinePlot/> <FinalResultsLinePlot/>
</mdb-card-body> </mdb-card-body>
@ -49,14 +49,14 @@
<b-col cols="3"> <b-col cols="3">
<mdb-card style="margin-top: 15px"> <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-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/> <Parameters/>
</mdb-card-body> </mdb-card-body>
</mdb-card> </mdb-card>
</b-col> </b-col>
<b-col cols="9"> <b-col cols="9">
<mdb-card style="margin-top: 15px;"> <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> <mdb-card-body>
<Algorithms :width="width" :height="height"/> <Algorithms :width="width" :height="height"/>
</mdb-card-body> </mdb-card-body>
@ -66,7 +66,7 @@
<b-row class="md-3"> <b-row class="md-3">
<b-col cols="3"> <b-col cols="3">
<mdb-card style="margin-top: 15px"> <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> <mdb-card-body>
<AlgorithmHyperParam/> <AlgorithmHyperParam/>
</mdb-card-body> </mdb-card-body>
@ -74,7 +74,7 @@
</b-col> </b-col>
<b-col cols="9"> <b-col cols="9">
<mdb-card style="margin-top: 15px;"> <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> <mdb-card-body>
<BarChart/> <BarChart/>
</mdb-card-body> </mdb-card-body>
@ -86,26 +86,25 @@
<b-row class="md-3"> <b-row class="md-3">
<b-col cols="6"> <b-col cols="6">
<mdb-card style="margin-top: 15px;"> <mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Data Space</mdb-card-header> <mdb-card-header color="primary-color" tag="h5" class="text-center">Features Selection for Each Model</mdb-card-header>
<mdb-card-body> <mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 825px"> <mdb-card-text class="text-center" style="min-height: 845px">
<DataSpace/> <ToggleSelection/>
<PCPData/> <Heatmap/>
</mdb-card-text> </mdb-card-text>
</mdb-card-body> </mdb-card-body>
</mdb-card> </mdb-card>
</b-col> </b-col>
<b-col cols="6"> <b-col cols="6">
<mdb-card style="margin-top: 15px;"> <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> <mdb-card-header color="primary-color" tag="h5" class="text-center">Data Space</mdb-card-header>
<mdb-card-body> <mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 845px"> <mdb-card-text class="text-center" style="min-height: 845px">
<ToggleSelection/> <DataSpace/>
<Heatmap/> <PCPData/>
</mdb-card-text> </mdb-card-text>
</mdb-card-body> </mdb-card-body>
</mdb-card> </mdb-card>
</b-col> </b-col>
</b-row> </b-row>
</div> </div>
@ -113,8 +112,8 @@
<b-row class="md-3"> <b-row class="md-3">
<b-col cols="6"> <b-col cols="6">
<mdb-card style="margin-top: 15px;"> <mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Models Space <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}}] [Sel:{{OverSelLength}}/All:{{OverAllLength}}]
</mdb-card-header> </mdb-card-header>
<mdb-card-body> <mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 845px"> <mdb-card-text class="text-center" style="min-height: 845px">
@ -967,6 +966,7 @@ export default Vue.extend({
</script> </script>
<style> <style>
#nprogress .bar { #nprogress .bar {
background: red !important; background: red !important;
} }
@ -981,6 +981,7 @@ border-left-color: red !important;
} }
body { body {
font-family: 'Helvetica', 'Arial', sans-serif !important;
left: 0px; left: 0px;
right: 0px; right: 0px;
top: 0px; top: 0px;

713
frontend/src/components/Parameters.vue
Unescape View File

@ -15,6 +15,8 @@
<script> <script>
import { EventBus } from '../main.js' import { EventBus } from '../main.js'
import { legend } from 'd3-svg-legend'
export default { export default {
name: 'Parameters', name: 'Parameters',
data () { data () {
@ -36,18 +38,12 @@ export default {
svg.selectAll("*").remove(); svg.selectAll("*").remove();
}, 50); }, 50);
}, },
/////////////////////////////////////////////////////////
/////////////// The Radar Chart Function ////////////////
/////////////// Written by Nadieh Bremer ////////////////
////////////////// VisualCinnamon.com ///////////////////
/////////// Inspired by the code of alangrafu ///////////
/////////////////////////////////////////////////////////
RadarChart(id, data, options) { RadarChart(id, data, options) {
var cfg = { var cfg = {
w: 600, //Width of the circle w: 600, //Width of the circle
h: 600, //Height of the circle h: 600, //Height of the circle
margin: {top: 20, right: 20, bottom: 20, left: 20}, //The margins of the SVG margin: {top: 20, right: 20, bottom: 20, left: 20}, //The margins around the circle
legendPosition: {x: 20, y: 20}, // the position of the legend, from the top-left corner of the svg
levels: 3, //How many levels or inner circles should there be drawn levels: 3, //How many levels or inner circles should there be drawn
maxValue: 0, //What is the value that the biggest circle will represent maxValue: 0, //What is the value that the biggest circle will represent
labelFactor: 1.25, //How much farther than the radius of the outer circle should the labels be placed labelFactor: 1.25, //How much farther than the radius of the outer circle should the labels be placed
@ -57,7 +53,11 @@ export default {
opacityCircles: 0.1, //The opacity of the circles of each blob opacityCircles: 0.1, //The opacity of the circles of each blob
strokeWidth: 2, //The width of the stroke around each blob strokeWidth: 2, //The width of the stroke around each blob
roundStrokes: false, //If true the area and stroke will follow a round path (cardinal-closed) roundStrokes: false, //If true the area and stroke will follow a round path (cardinal-closed)
color: d3.scale.category10() //Color function color: d3.scale.category10(), //Color function
axisName: "axis",
areaName:"areaName",
value: "value",
sortAreas: true,
}; };
//Put all of the options into a variable called cfg //Put all of the options into a variable called cfg
@ -67,10 +67,16 @@ export default {
}//for i }//for i
}//if }//if
//Map the fields specified in the configuration
// to the axis and value variables
var axisName = cfg["axisName"],
areaName = cfg["areaName"],
value = cfg["value"];
//If the supplied maxValue is smaller than the actual one, replace by the max in the data //If the supplied maxValue is smaller than the actual one, replace by the max in the data
var maxValue = Math.max(cfg.maxValue, d3.max(data, function(i){return d3.max(i.map(function(o){return o.value;}))})); var maxValue = Math.max(cfg.maxValue, d3.max(data, function(i){return d3.max(i.map(function(o){return o.value;}))}))
var allAxis = (data[0].map(function(i, j){return i.axis})), //Names of each axis var allAxis = (data[0].map(function(d, i){ return d[axisName] })), //Names of each axis
total = allAxis.length, //The number of different axes total = allAxis.length, //The number of different axes
radius = Math.min(cfg.w/2, cfg.h/2), //Radius of the outermost circle radius = Math.min(cfg.w/2, cfg.h/2), //Radius of the outermost circle
Format = d3.format('%'), //Percentage formatting Format = d3.format('%'), //Percentage formatting
@ -177,7 +183,7 @@ export default {
//The radial line function //The radial line function
var radarLine = d3.svg.line.radial() var radarLine = d3.svg.line.radial()
.interpolate("linear-closed") .interpolate("linear-closed")
.radius(function(d) { return rScale(d.value); }) .radius(function(d) { return rScale(d[value]); })
.angle(function(d,i) { return i*angleSlice; }); .angle(function(d,i) { return i*angleSlice; });
if(cfg.roundStrokes) { if(cfg.roundStrokes) {
@ -193,7 +199,9 @@ export default {
//Append the backgrounds //Append the backgrounds
blobWrapper blobWrapper
.append("path") .append("path")
.attr("class", "radarArea") .attr("class", function(d) {
return "radarArea" + " " + d[0][areaName].replace(/\s+/g, '') //Remove spaces from the areaName string to make one valid class name
})
.attr("d", function(d,i) { return radarLine(d); }) .attr("d", function(d,i) { return radarLine(d); })
.style("fill", function(d,i) { return cfg.color(i); }) .style("fill", function(d,i) { return cfg.color(i); })
.style("fill-opacity", cfg.opacityArea) .style("fill-opacity", cfg.opacityArea)
@ -229,8 +237,8 @@ export default {
.enter().append("circle") .enter().append("circle")
.attr("class", "radarCircle") .attr("class", "radarCircle")
.attr("r", cfg.dotRadius) .attr("r", cfg.dotRadius)
.attr("cx", function(d,i){ return rScale(d.value) * Math.cos(angleSlice*i - Math.PI/2); }) .attr("cx", function(d,i){ return rScale(d[value]) * Math.cos(angleSlice*i - Math.PI/2); })
.attr("cy", function(d,i){ return rScale(d.value) * Math.sin(angleSlice*i - Math.PI/2); }) .attr("cy", function(d,i){ return rScale(d[value]) * Math.sin(angleSlice*i - Math.PI/2); })
.style("fill", function(d,i,j) { return cfg.color(j); }) .style("fill", function(d,i,j) { return cfg.color(j); })
.style("fill-opacity", 0.8); .style("fill-opacity", 0.8);
@ -250,8 +258,8 @@ export default {
.enter().append("circle") .enter().append("circle")
.attr("class", "radarInvisibleCircle") .attr("class", "radarInvisibleCircle")
.attr("r", cfg.dotRadius*1.5) .attr("r", cfg.dotRadius*1.5)
.attr("cx", function(d,i){ return rScale(d.value) * Math.cos(angleSlice*i - Math.PI/2); }) .attr("cx", function(d,i){ return rScale(d[value]) * Math.cos(angleSlice*i - Math.PI/2); })
.attr("cy", function(d,i){ return rScale(d.value) * Math.sin(angleSlice*i - Math.PI/2); }) .attr("cy", function(d,i){ return rScale(d[value]) * Math.sin(angleSlice*i - Math.PI/2); })
.style("fill", "none") .style("fill", "none")
.style("pointer-events", "all") .style("pointer-events", "all")
.on("mouseover", function(d,i) { .on("mouseover", function(d,i) {
@ -261,7 +269,7 @@ export default {
tooltip tooltip
.attr('x', newX) .attr('x', newX)
.attr('y', newY) .attr('y', newY)
.text(Format(d.value)) .text(Format(d[value]))
.transition().duration(200) .transition().duration(200)
.style('opacity', 1); .style('opacity', 1);
}) })
@ -276,7 +284,7 @@ export default {
.style("opacity", 0); .style("opacity", 0);
///////////////////////////////////////////////////////// /////////////////////////////////////////////////////////
/////////////////// Helper Function ///////////////////// /////////////////// Helper Functions ////////////////////
///////////////////////////////////////////////////////// /////////////////////////////////////////////////////////
//Taken from http://bl.ocks.org/mbostock/7555321 //Taken from http://bl.ocks.org/mbostock/7555321
@ -307,189 +315,61 @@ export default {
}); });
}//wrap }//wrap
}, // on mouseover for the legend symbol
overview() { function cellover(d) {
/* Radar chart design created by Nadieh Bremer - VisualCinnamon.com */ //Dim all blobs
d3.selectAll(".radarArea")
.transition().duration(200)
.style("fill-opacity", 0.1);
//Bring back the hovered over blob
d3.select("." + data[d][0][areaName].replace(/\s+/g, ''))
.transition().duration(200)
.style("fill-opacity", 0.7);
}
////////////////////////////////////////////////////////////// // on mouseout for the legend symbol
//////////////////////// Set-Up ////////////////////////////// function cellout() {
////////////////////////////////////////////////////////////// //Bring back all blobs
d3.selectAll(".radarArea")
.transition().duration(200)
.style("fill-opacity", cfg.opacityArea);
}
var margin = {top: 50, right: 50, bottom: 50, left: 50}, /////////////////////////////////////////////////////////
width = Math.min(420, window.innerWidth - 10) - margin.left - margin.right, /////////////////// Draw the Legend /////////////////////
height = Math.min(width, window.innerHeight - margin.top - margin.bottom - 20); /////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////// svg.append("g")
////////////////////////// Data ////////////////////////////// .attr("class", "legendOrdinal")
////////////////////////////////////////////////////////////// .attr("transform", "translate(" + cfg["legendPosition"]["x"] + "," + cfg["legendPosition"]["y"] + ")");
var data = [ var legendOrdinal = legend.color()
[ .shape("path", d3.svg.symbol().type("circle").size(150)())
{axis:"KNN",value:1}, .shapePadding(10)
{axis:"RF",value:0.30}, .scale(cfg.color)
{axis:"Alg3",value:0.55}, .labels(cfg.color.domain().map(function(d){
{axis:"Alg4",value:0.68}, return data[d][0][areaName];
{axis:"Alg5",value:0.22}, }))
{axis:"Alg6",value:0.28}, .on("cellover", function(d){ cellover(d); })
{axis:"Alg7",value:0.55}, .on("cellout", function(d) { cellout(); });
{axis:"Alg9",value:0.68},
{axis:"Alg9",value:0.22},
{axis:"Alg10",value:0.28},
],[
{axis:"KNN",value:0.05},
{axis:"RF",value:0.18},
{axis:"Alg3",value:0.25},
{axis:"Alg4",value:0.28},
{axis:"Alg5",value:0.22},
{axis:"Alg6",value:0.18},
{axis:"Alg7",value:0.45},
{axis:"Alg9",value:0.18},
{axis:"Alg9",value:0.22},
{axis:"Alg10",value:0.18},
],
];
//////////////////////////////////////////////////////////////
//////////////////// Draw the Chart //////////////////////////
//////////////////////////////////////////////////////////////
var color = d3.scale.ordinal() svg.select(".legendOrdinal").call(legendOrdinal);
.range(["#EDC951","#CC333F","#00A0B0"]);
var radarChartOptions = {
w: width,
h: height,
margin: margin,
maxValue: 0.5,
levels: 5,
roundStrokes: true,
color: color
};
//Call function to draw the Radar chart
this.RadarChart("#overview", data, radarChartOptions);
}, },
draw () { overview() {
/* Radar chart design created by Nadieh Bremer - VisualCinnamon.com */
// Clear Heatmap first // Clear Heatmap first
var svg = d3.select("#overview"); var svg = d3.select("#overview");
svg.selectAll("*").remove(); svg.selectAll("*").remove();
var widthinter = this.WH[0]*2 // interactive visualization var widthinter = this.WH[0]*2 // interactive visualization
var heightinter = this.WH[1]*1.23 // interactive visualization var heightinter = this.WH[1]*1.23 // interactive visualization
var margin = 0,
width = widthinter, const max = 576
height = heightinter, const KNNAll = 576
maxBarHeight = height / 2 - (margin + 70); const RFAll = 160
var innerRadius = 0.1 * maxBarHeight; // innermost circle var KNNSelection = 0
var RFSelection = 0
var svg = d3.select('#overview')
.append("svg")
.attr("width", width)
.attr("height", height)
.append("g")
.attr("class", "chart")
.attr("transform", "translate(" + width / 2 + "," + height / 2 + ")");
var defs = svg.append("defs");
var gradients = defs
.append("linearGradient")
.attr("id", "gradient-chart-area")
.attr("x1", "50%")
.attr("y1", "0%")
.attr("x2", "50%")
.attr("y2", "100%")
.attr("spreadMethod", "pad");
gradients.append("stop")
.attr("offset", "0%")
.attr("stop-color", "#EDF0F0")
.attr("stop-opacity", 1);
gradients.append("stop")
.attr("offset", "100%")
.attr("stop-color", "#ACB7BE")
.attr("stop-opacity", 1);
gradients = defs
.append("linearGradient")
.attr("id", "gradient-questions")
.attr("x1", "50%")
.attr("y1", "0%")
.attr("x2", "50%")
.attr("y2", "100%")
.attr("spreadMethod", "pad");
gradients.append("stop")
.attr("offset", "0%")
.attr("stop-color", "#F6F8F9")
.attr("stop-opacity", 1);
gradients.append("stop")
.attr("offset", "100%")
.attr("stop-color", "#D4DAE0")
.attr("stop-opacity", 1);
gradients = defs
.append("radialGradient")
.attr("id", "gradient-bars")
.attr("gradientUnits", "userSpaceOnUse")
.attr("cx", "0")
.attr("cy", "0")
.attr("r", maxBarHeight)
.attr("spreadMethod", "pad");
gradients.append("stop")
.attr("offset", "0%")
.attr("stop-color", "#F3D5AA");
gradients.append("stop")
.attr("offset", "50%")
.attr("stop-color", "#F4A636");
gradients.append("stop")
.attr("offset", "100%")
.attr("stop-color", "#AF4427");
gradients = defs
.append("linearGradient")
.attr("id", "gradient-categorization")
.attr("x1", "0%")
.attr("y1", "50%")
.attr("x2", "100%")
.attr("y2", "50%")
.attr("spreadMethod", "pad");
gradients.append("stop")
.attr("offset", "0%")
.attr("stop-color", "#8dd3c7")
.attr("stop-opacity", 1);
gradients.append("stop")
.attr("offset", "100%")
.attr("stop-color", "#8da0cb")
.attr("stop-opacity", 1);
svg.append("circle")
.attr("r", maxBarHeight + 30)
.classed("category-circle", true);
svg.append("circle")
.attr("r", maxBarHeight + 15)
.classed("question-circle", true);
svg.append("circle")
.attr("r", maxBarHeight)
.classed("chart-area-circle", true);
svg.append("circle")
.attr("r", innerRadius)
.classed("center-circle", true);
var n_neighbors = 0
var metric = 0
var algorithm = 0
var weight = 0
var n_estimators = 0
var criterion = 0
if (this.FlagKNN == 0 && this.FlagRF == 0) { if (this.FlagKNN == 0 && this.FlagRF == 0) {
this.storeActiveModels = [] this.storeActiveModels = []
@ -499,12 +379,16 @@ export default {
if (this.storeActiveModels.length != 0) { if (this.storeActiveModels.length != 0) {
var countkNNRelated = [] var countkNNRelated = []
var countKNN = 0
var countRFRelated = [] var countRFRelated = []
var countRF = 0
for (let i = 0; i < this.storeActiveModels.length; i++) { for (let i = 0; i < this.storeActiveModels.length; i++) {
if (this.storeActiveModels[i] < this.KNNModels) { if (this.storeActiveModels[i] < this.KNNModels) {
countkNNRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]])) countkNNRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countKNN = countKNN + 1
} else { } else {
countRFRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]])) countRFRelated.push(JSON.parse(this.storeParameters[this.storeActiveModels[i]]))
countRF = countRF + 1
} }
} }
if (this.storeActiveModels[0] < this.KNNModels) { if (this.storeActiveModels[0] < this.KNNModels) {
@ -513,409 +397,81 @@ export default {
this.allActiveRF = countRFRelated.slice() this.allActiveRF = countRFRelated.slice()
} }
n_neighbors = ([... new Set(this.allActiveKNN.map(data => data.n_neighbors))].length / 25) * 100 KNNSelection = countKNN
metric = ([... new Set(this.allActiveKNN.map(data => data.metric))].length / 4) * 100 RFSelection = countRF
algorithm = ([... new Set(this.allActiveKNN.map(data => data.algorithm))].length / 3) * 100
weight = ([... new Set(this.allActiveKNN.map(data => data.weight))].length / 2) * 100
n_estimators = ([... new Set(this.allActiveRF.map(data => data.n_estimators))].length / 80) * 100
criterion = ([... new Set(this.allActiveRF.map(data => data.criterion))].length / 2) * 100
} }
if (this.FlagKNN == 1 && this.allActiveKNN.length == 0) { if (this.FlagKNN == 1 && this.allActiveKNN.length == 0) {
n_neighbors = 100 KNNSelection = 576
metric = 100
algorithm = 100
weight = 100
} }
if (this.FlagRF == 1 && this.allActiveRF.length == 0) { if (this.FlagRF == 1 && this.allActiveRF.length == 0) {
n_estimators = 100 RFSelection = 160
criterion = 100
} }
var data = [ //////////////////////////////////////////////////////////////
{ algorithm: 'RF', parameter: 'n_estimators', percentage: n_estimators }, //////////////////////// Set-Up //////////////////////////////
{ algorithm: 'RF', parameter: 'criterion', percentage: criterion }, //////////////////////////////////////////////////////////////
{ algorithm: 'KNN', parameter: 'n_neighbors', percentage: n_neighbors },
{ algorithm: 'KNN', parameter: 'metric', percentage: metric },
{ algorithm: 'KNN', parameter: 'algorithm', percentage: algorithm },
{ algorithm: 'KNN', parameter: 'weight', percentage: weight },
];
var cats = data.map(function(d, i) {
return d.algorithm;
});
var catCounts = {};
for (var i = 0; i < cats.length; i++) {
var num = cats[i];
catCounts[num] = catCounts[num] ? catCounts[num] + 1 : 1;
}
// remove dupes (not exactly the fastest)
cats = cats.filter(function(v, i) {
return cats.indexOf(v) == i;
});
var numCatBars = cats.length;
var angle = 0,
rotate = 0;
data.forEach(function(d, i) {
// bars start and end angles
d.startAngle = angle;
angle += (2 * Math.PI) / numCatBars / catCounts[d.algorithm];
d.endAngle = angle;
// y axis minor lines (i.e. questions) rotation
d.rotate = rotate;
rotate += 360 / numCatBars / catCounts[d.algorithm];
});
// category_label
var arc_category_label = d3.svg.arc()
.startAngle(function(d, i) {
return (i * 2 * Math.PI) / numCatBars;
})
.endAngle(function(d, i) {
return ((i + 1) * 2 * Math.PI) / numCatBars;
})
.innerRadius(maxBarHeight + 32)
.outerRadius(maxBarHeight + 0);
var category_text = svg.selectAll("path.category_label_arc")
.data(cats)
.enter().append("path")
.classed("category-label-arc", true)
.attr("id", function(d, i) {
return "category_label_" + i;
}) //Give each slice a unique ID
.attr("fill", "none")
.attr("d", arc_category_label);
category_text.each(function(d, i) {
//Search pattern for everything between the start and the first capital L
var firstArcSection = /(^.+?)L/;
//Grab everything up to the first Line statement
var newArc = firstArcSection.exec(d3.select(this).attr("d"))[1];
//Replace all the commas so that IE can handle it
newArc = newArc.replace(/,/g, " ");
//If the whole bar lies beyond a quarter of a circle (90 degrees or pi/2)
// and less than 270 degrees or 3 * pi/2, flip the end and start position
var startAngle = (i * 2 * Math.PI) / numCatBars,
endAngle = ((i + 1) * 2 * Math.PI) / numCatBars;
if (startAngle > Math.PI / 2 && startAngle < 3 * Math.PI / 2 && endAngle > Math.PI / 2 && endAngle < 3 * Math.PI / 2) {
var startLoc = /M(.*?)A/, //Everything between the capital M and first capital A
middleLoc = /A(.*?)0 0 1/, //Everything between the capital A and 0 0 1
endLoc = /0 0 1 (.*?)$/; //Everything between the 0 0 1 and the end of the string (denoted by $)
//Flip the direction of the arc by switching the start and end point (and sweep flag)
var newStart = endLoc.exec(newArc)[1];
var newEnd = startLoc.exec(newArc)[1];
var middleSec = middleLoc.exec(newArc)[1];
//Build up the new arc notation, set the sweep-flag to 0
newArc = "M" + newStart + "A" + middleSec + "0 0 0 " + newEnd;
} //if
//Create a new invisible arc that the text can flow along
/* svg.append("path")
.attr("class", "hiddenDonutArcs")
.attr("id", "category_label_"+i)
.attr("d", newArc)
.style("fill", "none");*/
// modifying existing arc instead
d3.select(this).attr("d", newArc);
});
svg.selectAll(".category-label-text")
.data(cats)
.enter().append("text")
.attr("class", "category-label-text")
//.attr("x", 0) //Move the text from the start angle of the arc
//Move the labels below the arcs for those slices with an end angle greater than 90 degrees
.attr("dy", function(d, i) {
var startAngle = (i * 2 * Math.PI) / numCatBars,
endAngle = ((i + 1) * 2 * Math.PI) / numCatBars;
return (startAngle > Math.PI / 2 && startAngle < 3 * Math.PI / 2 && endAngle > Math.PI / 2 && endAngle < 3 * Math.PI / 2 ? -4 : 14);
})
.append("textPath")
.attr("startOffset", "50%")
.style("text-anchor", "middle")
.attr("xlink:href", function(d, i) {
return "#category_label_" + i;
})
.text(function(d) {
return d;
});
// parameter
var arc_parameter = d3.svg.arc()
.startAngle(function(d, i) {
return d.startAngle;
})
.endAngle(function(d, i) {
return d.endAngle;
})
//.innerRadius(maxBarHeight + 2)
.outerRadius(maxBarHeight - 9);
var question_text = svg.selectAll("path.parameter_arc")
.data(data)
.enter().append("path")
.classed("question-label-arc", true)
.attr("id", function(d, i) {
return "parameter_" + i;
}) //Give each slice a unique ID
.attr("fill", "none")
.attr("d", arc_parameter);
question_text.each(function(d, i) {
//Search pattern for everything between the start and the first capital L
var firstArcSection = /(^.+?)L/;
//Grab everything up to the first Line statement
var newArc = firstArcSection.exec(d3.select(this).attr("d"))[1];
//Replace all the commas so that IE can handle it
newArc = newArc.replace(/,/g, " ");
//If the end angle lies beyond a quarter of a circle (90 degrees or pi/2)
//flip the end and start position
if (d.startAngle > Math.PI / 2 && d.startAngle < 3 * Math.PI / 2 && d.endAngle > Math.PI / 2 && d.endAngle < 3 * Math.PI / 2) {
var startLoc = /M(.*?)A/, //Everything between the capital M and first capital A
middleLoc = /A(.*?)0 0 1/, //Everything between the capital A and 0 0 1
endLoc = /0 0 1 (.*?)$/; //Everything between the 0 0 1 and the end of the string (denoted by $)
//Flip the direction of the arc by switching the start and end point (and sweep flag)
var newStart = endLoc.exec(newArc)[1];
var newEnd = startLoc.exec(newArc)[1];
var middleSec = middleLoc.exec(newArc)[1];
//Build up the new arc notation, set the sweep-flag to 0
newArc = "M" + newStart + "A" + middleSec + "0 0 0 " + newEnd;
} //if
//Create a new invisible arc that the text can flow along
/* svg.append("path")
.attr("class", "hiddenDonutArcs")
.attr("id", "parameter_"+i)
.attr("d", newArc)
.style("fill", "none");*/
// modifying existing arc instead
d3.select(this).attr("d", newArc);
});
question_text = svg.selectAll(".question-label-text")
.data(data)
.enter().append("text")
.attr("class", "question-label-text")
//.attr("x", 0) //Move the text from the start angle of the arc
//.attr("y", 0)
//Move the labels below the arcs for those slices with an end angle greater than 90 degrees
/* .attr("dy", function (d, i) {
return (d.startAngle > Math.PI / 2 && d.startAngle < 3 * Math.PI / 2 && d.endAngle > Math.PI / 2 && d.endAngle < 3 * Math.PI / 2 ? 10 : -10);
})*/
.append("textPath")
//.attr("startOffset", "50%")
//.style("text-anchor", "middle")
//.style("dominant-baseline", "central")
.style('font-size', '7px')
.style('font-family', 'sans-serif')
.attr("xlink:href", function(d, i) {
return "#parameter_" + i;
})
.text(function(d) {
return d.parameter.toUpperCase();
})
.call(wrapTextOnArc, maxBarHeight);
// adjust dy (labels vertical start) based on number of lines (i.e. tspans)
question_text.each(function(d, i) {
var textPath = d3.select(this)[0][0],
tspanCount = textPath.childNodes.length;
if (d.startAngle > Math.PI / 2 && d.startAngle < 3 * Math.PI / 2 && d.endAngle > Math.PI / 2 && d.endAngle < 3 * Math.PI / 2) {
// set baseline for one line and adjust if greater than one line
d3.select(textPath.childNodes[0]).attr("dy", 3 + (tspanCount - 1) * -0.6 + 'em');
} else {
d3.select(textPath.childNodes[0]).attr("dy", -2.1 + (tspanCount - 1) * -0.6 + 'em');
}
});
/* bars */
var arc = d3.svg.arc()
.startAngle(function(d, i) {
return d.startAngle;
})
.endAngle(function(d, i) {
return d.endAngle;
})
.innerRadius(innerRadius);
var bars = svg.selectAll("path.bar")
.data(data)
.enter().append("path")
.classed("bars", true)
.each(function(d) {
d.outerRadius = innerRadius;
})
.attr("d", arc);
bars.transition().ease("elastic").duration(1000).delay(function(d, i) {
return i * 100;
})
.attrTween("d", function(d, index) {
var i = d3.interpolate(d.outerRadius, x_scale(+d.percentage));
return function(t) {
d.outerRadius = i(t);
return arc(d, index);
};
});
var x_scale = d3.scale.linear()
.domain([0, 100])
.range([innerRadius, maxBarHeight]);
var y_scale = d3.scale.linear()
.domain([0, 100])
.range([-innerRadius, -maxBarHeight]);
svg.selectAll("circle.x.minor")
.data(y_scale.ticks(10))
.enter().append("circle")
.classed("gridlines minor", true)
.attr("r", function(d) {
return x_scale(d);
});
// question lines var margin = {top: 50, right: 120, bottom: 50, left: 60},
svg.selectAll("line.y.minor") legendPosition = {x: 425, y: 185},
.data(data) width = Math.min(510, window.innerWidth - 10) - margin.left - margin.right,
.enter().append("line") height = Math.min(width, window.innerHeight - margin.top - margin.bottom - 20);
.classed("gridlines minor", true)
.attr("y1", -innerRadius)
.attr("y2", -maxBarHeight - 15)
.attr("transform", function(d, i) {
return "rotate(" + (d.rotate) + ")";
});
// category lines //////////////////////////////////////////////////////////////
svg.selectAll("line.y.major") ////////////////////////// Data //////////////////////////////
.data(cats) //////////////////////////////////////////////////////////////
.enter().append("line")
.classed("gridlines major", true)
.attr("y1", -innerRadius)
.attr("y2", -maxBarHeight - 100)
.attr("transform", function(d, i) {
return "rotate(" + (i * 360 / numCatBars) + ")";
});
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 [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},
],
];
//////////////////////////////////////////////////////////////
//////////////////// Draw the Chart //////////////////////////
//////////////////////////////////////////////////////////////
function wrapTextOnArc(text, radius) { var color = d3.scale.ordinal()
// note getComputedTextLength() doesn't work correctly for text on an arc, .range(["#b3cde3","#fbb4ae"]);
// hence, using a hidden text element for measuring text length.
var temporaryText = d3.select('svg')
.append("text")
.attr("class", "temporary-text") // used to select later
.style("font", "7px sans-serif")
.style("opacity", 0); // hide element
var getTextLength = function(string) { var radarChartOptions = {
temporaryText.text(string); w: width,
return temporaryText.node().getComputedTextLength(); h: height,
margin: margin,
legendPosition: legendPosition,
maxValue: 0.5,
levels: 5,
roundStrokes: true,
color: color,
axisName: "axis",
areaName: "legend",
value: "value"
}; };
//Call function to draw the Radar chart
text.each(function(d) { this.RadarChart("#overview", data, radarChartOptions);
var text = d3.select(this),
words = text.text().split(/[ \f\n\r\t\v]+/).reverse(), //Don't cut non-breaking space (\xA0), as well as the Unicode characters \u00A0 \u2028 \u2029)
word,
wordCount = words.length,
line = [],
textLength,
lineHeight = 1.1, // ems
x = 0,
y = 0,
dy = 0,
tspan = text.text(null).append("tspan").attr("x", x).attr("y", y).attr("dy", dy + "em"),
arcLength = ((d.endAngle - d.startAngle) / (2 * Math.PI)) * (2 * Math.PI * radius),
paddedArcLength = arcLength - 16;
while (word = words.pop()) {
line.push(word);
tspan.text(line.join(" "));
textLength = getTextLength(tspan.text());
tspan.attr("x", (arcLength - textLength) / 2);
if (textLength > paddedArcLength && line.length > 1) {
// remove last word
line.pop();
tspan.text(line.join(" "));
textLength = getTextLength(tspan.text());
tspan.attr("x", (arcLength - textLength) / 2);
// start new line with last word
line = [word];
tspan = text.append("tspan").attr("dy", lineHeight + dy + "em").text(word);
textLength = getTextLength(tspan.text());
tspan.attr("x", (arcLength - textLength) / 2);
}
}
});
d3.selectAll("text.temporary-text").remove()
}
},
drawEncodings () {
// Clear Heatmap first
var svg = d3.select("#encodings");
svg.selectAll("*").remove();
var widthinter = this.WH[0]*0.95 // interactive visualization
var heightinter = this.WH[1]*0.5 // interactive visualization
/*
// Create the SVG element and set its dimensions.
var width = widthinter,
height = heightinter,
padding = 15;
var div = d3.select('#encodings'),
svg = div.append('svg');
svg.attr('width', width).attr('height', height);
// Create the svg:defs element and the main gradient definition.
var svgDefs = svg.append('defs');
var mainGradient = svgDefs.append('linearGradient')
.attr("id", "mainGradient")
.attr("x1", "0%")
.attr("x2", "100%")
.attr("y1", "0%")
.attr("y2", "100%");
mainGradient.append("stop")
.attr('class', 'start')
.attr("offset", "0%")
.attr("stop-color", "red")
.attr("stop-opacity", 1);
mainGradient.append("stop")
.attr('class', 'end')
.attr("offset", "100%")
.attr("stop-color", "blue")
.attr("stop-opacity", 1);
// Use the gradient to set the shape fill, via CSS.
svg.append('rect')
.classed('filled', true)
.attr('x', padding)
.attr('y', padding)
.attr('width', width - 2 * padding)
.attr('height', height - 2 * padding);
*/
}, },
updateFlags () { updateFlags () {
this.FlagKNN = 0 this.FlagKNN = 0
@ -930,7 +486,6 @@ export default {
EventBus.$on('sendParameters', data => { this.storeParameters = data }) EventBus.$on('sendParameters', data => { this.storeParameters = data })
EventBus.$on('updateActiveModels', data => { this.storeActiveModels = data }) EventBus.$on('updateActiveModels', data => { this.storeActiveModels = data })
EventBus.$on('updateActiveModels', this.overview) EventBus.$on('updateActiveModels', this.overview)
//EventBus.$on('updateActiveModels', this.drawEncodings)
EventBus.$on('Responsive', data => { EventBus.$on('Responsive', data => {
this.WH = data}) this.WH = data})

197
frontend/src/components/SlidersController.vue
Unescape View File

@ -1,16 +1,35 @@
<template> <template>
<b-row>
<b-col cols="9">
<div id="WrapSliders"> <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>(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>(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) 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>(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) 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>(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) 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>(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) 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>(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) 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>(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) 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>(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) 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>(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) 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> <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> </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>
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@ -31,20 +50,162 @@
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userSelectedFilterBeta: 'one'
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slideStart () { slideStart () {
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slideStop () { slideStop () {
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122
package-lock.json generated
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@ -1,3 +1,123 @@
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112
run.py
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@ -24,6 +24,10 @@ from sklearn.pipeline import make_pipeline
from sklearn import model_selection from sklearn import model_selection
from sklearn.manifold import MDS from sklearn.manifold import MDS
from sklearn.manifold import TSNE 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 import umap
from sklearn.metrics import classification_report from sklearn.metrics import classification_report
from sklearn.preprocessing import scale from sklearn.preprocessing import scale
@ -62,7 +66,7 @@ def Reset():
RANDOM_SEED = 42 RANDOM_SEED = 42
global factors 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 global XData
XData = [] XData = []
@ -100,7 +104,7 @@ def Reset():
all_classifiers = [] all_classifiers = []
global crossValidation global crossValidation
crossValidation = 3 crossValidation = 5
# models # models
global KNNModels global KNNModels
@ -109,8 +113,7 @@ def Reset():
RFModels = [] RFModels = []
global scoring 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', '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'}
scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'}
global loopFeatures global loopFeatures
loopFeatures = 2 loopFeatures = 2
@ -183,11 +186,10 @@ def RetrieveFileName():
all_classifiers = [] all_classifiers = []
global crossValidation global crossValidation
crossValidation = 3 crossValidation = 5
global scoring global scoring
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'}
#scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'}
global loopFeatures global loopFeatures
loopFeatures = 2 loopFeatures = 2
@ -436,7 +438,6 @@ def RetrieveModel():
global algorithms global algorithms
algorithms = RetrievedModel['Algorithms'] algorithms = RetrievedModel['Algorithms']
global factors
global XData global XData
global yData global yData
@ -451,7 +452,7 @@ def RetrieveModel():
clf = RandomForestClassifier() clf = RandomForestClassifier()
params = {'n_estimators': list(range(40, 120)), 'criterion': ['gini', 'entropy']} params = {'n_estimators': list(range(40, 120)), 'criterion': ['gini', 'entropy']}
AlgorithmsIDsEnd = 576 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 # call the function that sends the results to the frontend
SendEachClassifiersPerformanceToVisualize() SendEachClassifiersPerformanceToVisualize()
@ -463,7 +464,7 @@ memory = Memory(location, verbose=0)
# calculating for all algorithms and models the performance and other results # calculating for all algorithms and models the performance and other results
@memory.cache @memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, AlgorithmsIDsEnd): def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
# instantiate spark session # instantiate spark session
spark = ( spark = (
@ -508,28 +509,11 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
# copy and filter in order to get only the metrics # copy and filter in order to get only the metrics
metrics = df_cv_results_classifiers.copy() 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 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'])
summarizedMetrics = pd.DataFrame(sumperModel)
summarizedMetrics.rename(columns={0:'sum'})
# concat parameters and performance # concat parameters and performance
parameters = pd.DataFrame(df_cv_results_classifiers['params']) parametersPerformancePerModel = pd.DataFrame(df_cv_results_classifiers['params'])
parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
parametersPerformancePerModel = parametersPerformancePerModel.to_json() parametersPerformancePerModel = parametersPerformancePerModel.to_json()
parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy() parametersLocal = json.loads(parametersPerformancePerModel)['params'].copy()
@ -544,6 +528,22 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
PerFeatureAccuracyAll = [] PerFeatureAccuracyAll = []
PerClassMetric = [] PerClassMetric = []
perModelProb = [] perModelProb = []
resultsMicro = []
resultsMacro = []
resultsWeighted = []
resultsCorrCoef = []
resultsMicroBeta5 = []
resultsMacroBeta5 = []
resultsWeightedBeta5 = []
resultsMicroBeta1 = []
resultsMacroBeta1 = []
resultsWeightedBeta1 = []
resultsMicroBeta2 = []
resultsMacroBeta2 = []
resultsWeightedBeta2 = []
resultsLogLoss = []
loop = 10
for eachModelParameters in parametersLocalNew: for eachModelParameters in parametersLocalNew:
clf.set_params(**eachModelParameters) clf.set_params(**eachModelParameters)
@ -564,6 +564,47 @@ def GridSearchForModels(XData, yData, clf, params, eachAlgor, factors, Algorithm
yPredictProb = clf.predict_proba(XData) yPredictProb = clf.predict_proba(XData)
perModelProb.append(yPredictProb.tolist()) 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 = pd.DataFrame(perModelProb)
perModelProbPandas = perModelProbPandas.to_json() perModelProbPandas = perModelProbPandas.to_json()
@ -849,6 +890,7 @@ def preProcessFeatSc():
def preProcsumPerMetric(factors): def preProcsumPerMetric(factors):
sumPerClassifier = [] sumPerClassifier = []
loopThroughMetrics = PreprocessingMetrics() loopThroughMetrics = PreprocessingMetrics()
print(loopThroughMetrics)
for row in loopThroughMetrics.iterrows(): for row in loopThroughMetrics.iterrows():
rowSum = 0 rowSum = 0
lengthFactors = len(scoring) lengthFactors = len(scoring)
@ -866,9 +908,9 @@ def preProcMetricsAllAndSel():
loopThroughMetrics = PreprocessingMetrics() loopThroughMetrics = PreprocessingMetrics()
metricsPerModelColl = [] metricsPerModelColl = []
metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'].sum()/loopThroughMetrics['mean_test_accuracy'].count()) 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_neg_mean_absolute_error'].sum()/loopThroughMetrics['mean_test_neg_mean_absolute_error'].count())
metricsPerModelColl.append(loopThroughMetrics['mean_test_precision'].sum()/loopThroughMetrics['mean_test_precision'].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_recall'].sum()/loopThroughMetrics['mean_test_recall'].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()) metricsPerModelColl.append(loopThroughMetrics['mean_test_jaccard'].sum()/loopThroughMetrics['mean_test_jaccard'].count())
for index, metric in enumerate(metricsPerModelColl): for index, metric in enumerate(metricsPerModelColl):
metricsPerModelColl[index] = metric*factors[index] 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 # copy and filter in order to get only the metrics
metrics = df_cv_results_classifiers.copy() 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() metrics = metrics.to_json()
resultsMetrics.append(metrics) # Position: 0 and so on resultsMetrics.append(metrics) # Position: 0 and so on
@ -1284,7 +1326,7 @@ def EnsembleModel(Models, keyRetrieved):
temp = json.loads(allParametersPerformancePerModel[1]) temp = json.loads(allParametersPerformancePerModel[1])
dfParamKNN = pd.DataFrame.from_dict(temp) dfParamKNN = pd.DataFrame.from_dict(temp)
dfParamKNNFilt = dfParamKNN.iloc[:,1] dfParamKNNFilt = dfParamKNN.iloc[:,0]
for eachelem in KNNModels: for eachelem in KNNModels:
arg = dfParamKNNFilt[eachelem] arg = dfParamKNNFilt[eachelem]
@ -1292,7 +1334,7 @@ def EnsembleModel(Models, keyRetrieved):
temp = json.loads(allParametersPerformancePerModel[9]) temp = json.loads(allParametersPerformancePerModel[9])
dfParamRF = pd.DataFrame.from_dict(temp) dfParamRF = pd.DataFrame.from_dict(temp)
dfParamRFFilt = dfParamRF.iloc[:,1] dfParamRFFilt = dfParamRF.iloc[:,0]
for eachelem in RFModels: for eachelem in RFModels:
arg = dfParamRFFilt[eachelem-576] arg = dfParamRFFilt[eachelem-576]
all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier().set_params(**arg))) all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier().set_params(**arg)))

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