fixed data wrangling

parent 2f312728e3
commit 30c79dd9f8
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      frontend/src/components/PCPData.vue
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      frontend/src/components/PerMetricBarChart.vue
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      frontend/src/components/ScatterPlot.vue
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      run.py

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@ -0,0 +1 @@
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@ -0,0 +1 @@
{"duration": 10927.442919969559, "input_args": {"XData": " Age Sex Cp Trestbps Chol Fbs Restecg Thalach Exang Oldpeak Slope Ca Thal\n0 63 1 3 145 233 1 0 150 0 2.3 0 0 1\n1 37 1 2 130 250 0 1 187 0 3.5 0 0 2\n2 41 0 1 130 204 0 0 172 0 1.4 2 0 2\n3 56 1 1 120 236 0 1 178 0 0.8 2 0 2\n4 57 0 0 120 354 0 1 163 1 0.6 2 0 2\n.. ... ... .. ... ... ... ... ... ... ... ... .. ...\n298 57 0 0 140 241 0 1 123 1 0.2 1 0 3\n299 45 1 3 110 264 0 1 132 0 1.2 1 0 3\n300 68 1 0 144 193 1 1 141 0 3.4 1 2 3\n301 57 1 0 130 131 0 1 115 1 1.2 1 1 3\n302 57 0 1 130 236 0 0 174 0 0.0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "SVC(C=4.39, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n decision_function_shape='ovr', degree=3, gamma='scale', kernel='sigmoid',\n max_iter=-1, probability=True, random_state=42, shrinking=True, tol=0.001,\n verbose=False)", "params": "{'C': [0.1, 0.21000000000000002, 0.32000000000000006, 0.43000000000000005, 0.54, 0.65, 0.7600000000000001, 0.8700000000000001, 0.9800000000000001, 1.09, 1.2000000000000002, 1.3100000000000003, 1.4200000000000004, 1.5300000000000002, 1.6400000000000003, 1.7500000000000002, 1.8600000000000003, 1.9700000000000004, 2.08, 2.1900000000000004, 2.3000000000000003, 2.4100000000000006, 2.5200000000000005, 2.6300000000000003, 2.7400000000000007, 2.8500000000000005, 2.9600000000000004, 3.0700000000000003, 3.1800000000000006, 3.2900000000000005, 3.4000000000000004, 3.5100000000000007, 3.6200000000000006, 3.7300000000000004, 3.8400000000000007, 3.9500000000000006, 4.0600000000000005, 4.17, 4.28, 4.390000000000001], 'kernel': ['rbf', 'linear', 'poly', 'sigmoid']}", "eachAlgor": "'SVC'", "AlgorithmsIDsEnd": "576", "toggle": "1"}}

@ -0,0 +1 @@
{"duration": 949.3681681156158, "input_args": {"XData": " Age Sex Cp Trestbps Chol Fbs Restecg Thalach Exang Oldpeak Slope Ca Thal\n0 63 1 3 145 233 1 0 150 0 2.3 0 0 1\n1 37 1 2 130 250 0 1 187 0 3.5 0 0 2\n2 41 0 1 130 204 0 0 172 0 1.4 2 0 2\n3 56 1 1 120 236 0 1 178 0 0.8 2 0 2\n4 57 0 0 120 354 0 1 163 1 0.6 2 0 2\n.. ... ... .. ... ... ... ... ... ... ... ... .. ...\n298 57 0 0 140 241 0 1 123 1 0.2 1 0 3\n299 45 1 3 110 264 0 1 132 0 1.2 1 0 3\n300 68 1 0 144 193 1 1 141 0 3.4 1 2 3\n301 57 1 0 130 131 0 1 115 1 1.2 1 1 3\n302 57 0 1 130 236 0 0 174 0 0.0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "MLPClassifier(activation='tanh', alpha=0.00081, batch_size='auto', beta_1=0.9,\n beta_2=0.999, early_stopping=False, epsilon=1e-08,\n hidden_layer_sizes=(100,), learning_rate='constant',\n learning_rate_init=0.001, max_fun=15000, max_iter=100,\n momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n power_t=0.5, random_state=42, shuffle=True, solver='sgd',\n tol=0.00081, validation_fraction=0.1, verbose=False,\n warm_start=False)", "params": "{'alpha': [1e-05, 0.00021, 0.00041000000000000005, 0.0006100000000000001, 0.0008100000000000001], 'tol': [1e-05, 0.00041000000000000005, 0.0008100000000000001], 'max_iter': [100], 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver': ['adam', 'sgd']}", "eachAlgor": "'MLP'", "AlgorithmsIDsEnd": "1236", "toggle": "1"}}

@ -0,0 +1 @@
{"duration": 1727.4443399906158, "input_args": {"XData": " Age Sex Cp Trestbps Chol Fbs Restecg Thalach Exang Oldpeak Slope Ca Thal\n0 63 1 3 145 233 1 0 150 0 2.3 0 0 1\n1 37 1 2 130 250 0 1 187 0 3.5 0 0 2\n2 41 0 1 130 204 0 0 172 0 1.4 2 0 2\n3 56 1 1 120 236 0 1 178 0 0.8 2 0 2\n4 57 0 0 120 354 0 1 163 1 0.6 2 0 2\n.. ... ... .. ... ... ... ... ... ... ... ... .. ...\n298 57 0 0 140 241 0 1 123 1 0.2 1 0 3\n299 45 1 3 110 264 0 1 132 0 1.2 1 0 3\n300 68 1 0 144 193 1 1 141 0 3.4 1 2 3\n301 57 1 0 130 131 0 1 115 1 1.2 1 1 3\n302 57 0 1 130 236 0 0 174 0 0.0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n criterion='entropy', max_depth=None, max_features='auto',\n max_leaf_nodes=None, max_samples=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, n_estimators=139,\n n_jobs=None, oob_score=False, random_state=42, verbose=0,\n warm_start=False)", "params": "{'n_estimators': [60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'ExtraT'", "AlgorithmsIDsEnd": "2606", "toggle": "1"}}

@ -37,7 +37,6 @@
var performancePerModel = JSON.parse(this.resultsfromOverview[0]) var performancePerModel = JSON.parse(this.resultsfromOverview[0])
var performancePerModelSelection = [] var performancePerModelSelection = []
console.log(this.newResultsFromSelection)
if (this.newResultsFromSelection.length != 0) { if (this.newResultsFromSelection.length != 0) {
var performancePerModelSelection = JSON.parse(this.newResultsFromSelection[0]) var performancePerModelSelection = JSON.parse(this.newResultsFromSelection[0])
} }

@ -468,8 +468,8 @@ export default {
beautifyLabels.push('Presence of Hypotheticality') beautifyLabels.push('Presence of Hypotheticality')
} }
else if (this.RetrieveDataSet == 'HeartC') { else if (this.RetrieveDataSet == 'HeartC') {
beautifyLabels.push('< 50% diameter narrowing / Healthy') beautifyLabels.push('< 50% Diameter Narrowing / Healthy')
beautifyLabels.push('> 50% diameter narrowing / Diseased') beautifyLabels.push('> 50% Diameter Narrowing / Diseased')
} else { } else {
target_names.forEach(element => { target_names.forEach(element => {
beautifyLabels.push(element) beautifyLabels.push(element)
@ -544,10 +544,10 @@ export default {
eventData.points.forEach((e) => { eventData.points.forEach((e) => {
tName = e.data.name.replace(/ *\([^)]*\) */g, "") tName = e.data.name.replace(/ *\([^)]*\) */g, "")
}); });
if (tName == "< 50% diameter narrowing / Healthy") { if (tName == "< 50% Diameter Narrowing / Healthy") {
tName = 0 tName = 0
this.tNameAll = 0 this.tNameAll = 0
} else if (tName == "> 50% diameter narrowing / Diseased"){ } else if (tName == "> 50% Diameter Narrowing / Diseased"){
tName = 1 tName = 1
this.tNameAll = 1 this.tNameAll = 1
} else { } else {

@ -94,18 +94,23 @@ export default {
}, },
merge() { merge() {
EventBus.$emit('SendAction', 'merge') EventBus.$emit('SendAction', 'merge')
EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
}, },
remove () { remove () {
EventBus.$emit('SendAction', 'remove') EventBus.$emit('SendAction', 'remove')
EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
}, },
compose () { compose () {
EventBus.$emit('SendAction', 'compose') EventBus.$emit('SendAction', 'compose')
EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
}, },
save () { save () {
EventBus.$emit('SendProvenance', 'save') EventBus.$emit('SendProvenance', 'save')
EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
}, },
restore () { restore () {
EventBus.$emit('SendProvenance', 'restore') EventBus.$emit('SendProvenance', 'restore')
EventBus.$emit('SendSelectedPointsToServerEventfromData', [])
}, },
clean(obj) { clean(obj) {
var propNames = Object.getOwnPropertyNames(obj); var propNames = Object.getOwnPropertyNames(obj);
@ -125,10 +130,14 @@ export default {
var target_names = JSON.parse(this.dataPoints[0]) var target_names = JSON.parse(this.dataPoints[0])
const XandYCoordinatesMDS = JSON.parse(this.dataPoints[1]) const XandYCoordinatesMDS = JSON.parse(this.dataPoints[1])
console.log(XandYCoordinatesMDS)
const DataSet = JSON.parse(this.dataPoints[2]) const DataSet = JSON.parse(this.dataPoints[2])
const DataSetY = JSON.parse(this.dataPoints[3]) const originalDataLabels = JSON.parse(this.dataPoints[3])
const originalDataLabels = JSON.parse(this.dataPoints[4]) //console.log(DataSetY)
//const originalDataLabels = JSON.parse(this.dataPoints[4])
console.log(originalDataLabels)
var DataSetParse = JSON.parse(DataSet) var DataSetParse = JSON.parse(DataSet)
console.log(DataSetParse)
var stringParameters = [] var stringParameters = []
for (let i = 0; i < DataSetParse.length; i++) { for (let i = 0; i < DataSetParse.length; i++) {
this.clean(DataSetParse[i]) this.clean(DataSetParse[i])
@ -170,8 +179,8 @@ export default {
beautifyLabels.push('Presence of Hypotheticality') beautifyLabels.push('Presence of Hypotheticality')
} }
else if (this.RetrieveDataSet == 'HeartC') { else if (this.RetrieveDataSet == 'HeartC') {
beautifyLabels.push('< 50% diameter narrowing / Healthy') beautifyLabels.push('< 50% Diameter Narrowing / Healthy')
beautifyLabels.push('> 50% diameter narrowing / Diseased') beautifyLabels.push('> 50% Diameter Narrowing / Diseased')
} else { } else {
target_names.forEach(element => { target_names.forEach(element => {
beautifyLabels.push(element) beautifyLabels.push(element)
@ -201,6 +210,7 @@ export default {
result.Yax = Yaxs result.Yax = Yaxs
result.ID = IDs result.ID = IDs
result.colorUpdates = colorUpdate result.colorUpdates = colorUpdate
console.log(result)
var traces = [] var traces = []
var layout = [] var layout = []
@ -453,6 +463,7 @@ export default {
this.selectedDataPoints() this.selectedDataPoints()
} }
this.onlyOnce = false this.onlyOnce = false
}, },
selectedDataPoints () { selectedDataPoints () {
const OverviewDataPlotly = document.getElementById('OverviewDataPlotly') const OverviewDataPlotly = document.getElementById('OverviewDataPlotly')
@ -471,6 +482,7 @@ export default {
ClassifierIDsListCleared.push(numberNumb) ClassifierIDsListCleared.push(numberNumb)
} }
} }
console.log(ClassifierIDsListCleared)
if (ClassifierIDsList != '') { if (ClassifierIDsList != '') {
EventBus.$emit('ChangeKey', 1) EventBus.$emit('ChangeKey', 1)
EventBus.$emit('SendSelectedPointsToServerEventfromData', ClassifierIDsListCleared) EventBus.$emit('SendSelectedPointsToServerEventfromData', ClassifierIDsListCleared)
@ -482,6 +494,7 @@ export default {
} }
}, },
mounted() { mounted() {
EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
// initialize the first data space projection based on the data set // initialize the first data space projection based on the data set
EventBus.$on('emittedEventCallingDataSpacePlotView', data => { EventBus.$on('emittedEventCallingDataSpacePlotView', data => {
this.dataPoints = data}) this.dataPoints = data})

@ -393,7 +393,7 @@ export default {
d3.select('#rowLabel_' + k).classed("hover", true); d3.select('#rowLabel_' + k).classed("hover", true);
if (d != null) { if (d != null) {
tooltip.style("visibility", "visible"); tooltip.style("visibility", "visible");
tooltip.html('<div class="heatmap_tooltip">' + d.toFixed(3) + '</div>'); tooltip.html('<div class="heatmap_tooltip">' + d.toFixed(2) + '</div>');
} else } else
tooltip.style("visibility", "hidden"); tooltip.style("visibility", "hidden");
}) })
@ -478,6 +478,8 @@ export default {
}) })
.attr("y", viewerPosTop + cellSize); .attr("y", viewerPosTop + cellSize);
svgLeg.append("text").attr("x", 220).attr("y", 50).text("Importance (Normalized)").style("font-size", "16px").attr("alignment-baseline","top")
//================================================== //==================================================
// Change ordering of cells // Change ordering of cells
function sortByValues(rORc, i, sortOrder) { function sortByValues(rORc, i, sortOrder) {

@ -325,6 +325,7 @@ export default Vue.extend({
.then(response => { .then(response => {
this.OverviewResults = response.data.OverviewResults this.OverviewResults = response.data.OverviewResults
console.log('Server successfully sent all the data related to visualizations!') console.log('Server successfully sent all the data related to visualizations!')
EventBus.$emit('onlyOnce', true)
EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults) EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults)
// if (this.firstTimeFlag == 1) { // if (this.firstTimeFlag == 1) {
// this.selectedModels_Stack.push(0) // this.selectedModels_Stack.push(0)
@ -874,8 +875,10 @@ export default Vue.extend({
}) })
}, },
ActionFun () { ActionFun () {
console.log(this.actionData)
const path = `http://127.0.0.1:5000/data/UpdateAction` const path = `http://127.0.0.1:5000/data/UpdateAction`
const postData = { const postData = {
points: this.dataPointsSelfromDataSpace,
action: this.actionData action: this.actionData
} }
const axiosConfig = { const axiosConfig = {

@ -46,7 +46,7 @@ export default {
} }
} }
if (DataSetParse.length == this.ClassifierIDsListClearedData.length || this.ClassifierIDsListClearedData.length == 0) if (DataSetParse.length == this.ClassifierIDsListClearedData.length || this.ClassifierIDsListClearedData.length == 0) {
var pc = ParCoords()("#PCPDataView") var pc = ParCoords()("#PCPDataView")
.data(DataSetParse) .data(DataSetParse)
.width(1200) .width(1200)
@ -60,6 +60,7 @@ export default {
.brushMode('1D-axes') .brushMode('1D-axes')
.reorderable() .reorderable()
.interactive(); .interactive();
}
else { else {
var pc = ParCoords()("#PCPDataView") var pc = ParCoords()("#PCPDataView")
.data(DataSetParse) .data(DataSetParse)

@ -57,6 +57,7 @@ export default {
} }
} }
} }
console.log(resultsColors)
var width = this.WH[0]*6.5 // interactive visualization var width = this.WH[0]*6.5 // interactive visualization
var height = this.WH[1]*0.5 // interactive visualization var height = this.WH[1]*0.5 // interactive visualization
var trace1 = { var trace1 = {
@ -120,33 +121,33 @@ export default {
} }
else if (xAxisHovered == 'G-Mean') { else if (xAxisHovered == 'G-Mean') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','<b>G-Mean</b>','<b>G-Mean</b>','<b>G-Mean</b>','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','<b>G-Mean</b>','<b>G-Mean</b>','<b>G-Mean</b>','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
index = 3 index = 1
} }
else if (xAxisHovered == 'Precision') { else if (xAxisHovered == 'Precision') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','<b>Precision</b>','<b>Precision</b>','<b>Precision</b>','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','<b>Precision</b>','<b>Precision</b>','<b>Precision</b>','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
index = 4 index = 2
} }
else if (xAxisHovered == 'Recall') { else if (xAxisHovered == 'Recall') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','<b>Recall</b>','<b>Recall</b>','<b>Recall</b>','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','<b>Recall</b>','<b>Recall</b>','<b>Recall</b>','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','Log Loss']]);
index = 5 index = 3
} }
else if (xAxisHovered == 'F-Beta Score') { else if (xAxisHovered == 'F-Beta Score') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','MCC','ROC AUC','Log Loss']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','<b>F-Beta Score</b>','MCC','ROC AUC','Log Loss']]);
index = 6 index = 4
} }
else if (xAxisHovered == 'MCC') { else if (xAxisHovered == 'MCC') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','<b>MCC</b>','ROC AUC','Log Loss']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','<b>MCC</b>','ROC AUC','Log Loss']]);
index = 7 index = 5
} }
else if (xAxisHovered == 'ROC AUC') { else if (xAxisHovered == 'ROC AUC') {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','<b>ROC AUC</b>','Log Loss']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','<b>ROC AUC</b>','Log Loss']]);
index = 8 index = 6
} }
else { else {
Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','<b>Log Loss</b>']]); Plotly.restyle(boxPlot, 'x', [['Accuracy','G-Mean','G-Mean','G-Mean','Precision','Precision','Precision','Recall','Recall','Recall','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','F-Beta Score','MCC','ROC AUC','<b>Log Loss</b>']]);
index = 9 index = 7
} }
//EventBus.$emit('updateMetricsScatter', resultsColors[index]) EventBus.$emit('updateMetricsScatter', resultsColors[index])
}); });
}, },
reset () { reset () {

@ -28,7 +28,8 @@ export default {
representationSelection: 'mds', representationSelection: 'mds',
RetrieveDataSet: 'HeartC', RetrieveDataSet: 'HeartC',
colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'], colorsValues: ['#808000','#008080','#bebada','#fccde5','#d9d9d9','#bc80bd','#ccebc5'],
WH: [] WH: [],
onlyOnce: true,
} }
}, },
methods: { methods: {
@ -59,8 +60,8 @@ export default {
var target_names = JSON.parse(this.PredictionsData[4]) var target_names = JSON.parse(this.PredictionsData[4])
const XandYCoordinatesMDS = JSON.parse(this.PredictionsData[8]) const XandYCoordinatesMDS = JSON.parse(this.PredictionsData[8])
const DataSet = JSON.parse(this.PredictionsData[14]) const DataSet = JSON.parse(this.PredictionsData[14])
const DataSetY = JSON.parse(this.PredictionsData[15]) const originalDataLabels = JSON.parse(this.PredictionsData[15])
const originalDataLabels = JSON.parse(this.PredictionsData[16]) //const originalDataLabels = JSON.parse(this.PredictionsData[16])
var DataSetParse = JSON.parse(DataSet) var DataSetParse = JSON.parse(DataSet)
var stringParameters = [] var stringParameters = []
for (let i = 0; i < DataSetParse.length; i++) { for (let i = 0; i < DataSetParse.length; i++) {
@ -81,8 +82,8 @@ export default {
beautifyLabels.push('Presence of Hypotheticality') beautifyLabels.push('Presence of Hypotheticality')
} }
else if (this.RetrieveDataSet == 'HeartC') { else if (this.RetrieveDataSet == 'HeartC') {
beautifyLabels.push('< 50% diameter narrowing / Healthy') beautifyLabels.push('< 50% Diameter Narrowing / Healthy')
beautifyLabels.push('> 50% diameter narrowing / Diseased') beautifyLabels.push('> 50% Diameter Narrowing / Diseased')
} else { } else {
target_names.forEach(element => { target_names.forEach(element => {
beautifyLabels.push(element) beautifyLabels.push(element)
@ -107,7 +108,7 @@ export default {
const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]); const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
const aux_Y = result.Yax.filter((item, index) => originalDataLabels[index] == target_names[i]); const aux_Y = result.Yax.filter((item, index) => originalDataLabels[index] == target_names[i]);
const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]); const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
console.log(aux_X)
var Text = aux_ID.map((item, index) => { var Text = aux_ID.map((item, index) => {
let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item] let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
return popup; return popup;
@ -279,11 +280,14 @@ export default {
var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage', 'toggleSpikelines', 'autoScale2d', 'hoverClosestGl2d','hoverCompareCartesian','select2d','hoverClosestCartesian','zoomIn2d','zoomOut2d','zoom2d'], responsive: true} var config = {scrollZoom: true, displaylogo: false, showLink: false, showSendToCloud: false, modeBarButtonsToRemove: ['toImage', 'toggleSpikelines', 'autoScale2d', 'hoverClosestGl2d','hoverCompareCartesian','select2d','hoverClosestCartesian','zoomIn2d','zoomOut2d','zoom2d'], responsive: true}
Plotly.newPlot('OverviewPredPlotly', traces, layout, config) Plotly.newPlot('OverviewPredPlotly', traces, layout, config)
if (this.onlyOnce) {
this.selectedPointsOverview() this.selectedPointsOverview()
}
this.onlyOnce = false
}, },
UpdateScatterPlot () { UpdateScatterPlot () {
const XandYCoordinates = JSON.parse(this.UpdatedData[0]) const XandYCoordinates = JSON.parse(this.UpdatedData[0])
console.log(XandYCoordinates)
Plotly.animate('OverviewPredPlotly', { Plotly.animate('OverviewPredPlotly', {
data: [ data: [
@ -326,6 +330,7 @@ export default {
}, },
}, },
mounted() { mounted() {
EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
EventBus.$on('updatePredictionsSpace', data => { this.UpdatedData = data }) EventBus.$on('updatePredictionsSpace', data => { this.UpdatedData = data })
EventBus.$on('updatePredictionsSpace', this.UpdateScatterPlot) EventBus.$on('updatePredictionsSpace', this.UpdateScatterPlot)
EventBus.$on('emittedEventCallingPredictionsSpacePlotView', data => { EventBus.$on('emittedEventCallingPredictionsSpacePlotView', data => {

@ -96,13 +96,22 @@ export default {
Plotly.purge('OverviewPlotly') Plotly.purge('OverviewPlotly')
var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0]) var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0])
console.log(this.newColorsUpdate)
/*if (this.newColorsUpdate.length != 0) { if (this.newColorsUpdate.length != 0) {
console.log('den mpike')
colorsforScatterPlot = []
let resultsClear = JSON.parse(this.newColorsUpdate) let resultsClear = JSON.parse(this.newColorsUpdate)
for (let j = 0; j < Object.values(resultsClear).length; j++) { for (let j = 0; j < Object.values(resultsClear).length; j++) {
colorsforScatterPlot.push(Object.values(resultsClear)[j]) colorsforScatterPlot.push(Object.values(resultsClear)[j])
} }
}*/ this.colorsStore = []
this.MDSStore = []
this.parametersStore = []
this.TSNEStore = []
this.modelIDStore = []
this.UMAPStore = []
}
console.log(colorsforScatterPlot)
var MDSData = JSON.parse(this.ScatterPlotResults[1]) var MDSData = JSON.parse(this.ScatterPlotResults[1])
var parametersLoc = JSON.parse(this.ScatterPlotResults[2]) var parametersLoc = JSON.parse(this.ScatterPlotResults[2])
@ -124,6 +133,8 @@ export default {
TSNEData = this.TSNEStore.slice(this.activeModels,this.activeModels+1)[0] TSNEData = this.TSNEStore.slice(this.activeModels,this.activeModels+1)[0]
modelId = this.modelIDStore.slice(this.activeModels,this.activeModels+1)[0] modelId = this.modelIDStore.slice(this.activeModels,this.activeModels+1)[0]
UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0] UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0]
console.log(colorsforScatterPlot)
} }
EventBus.$emit('sendPointsNumber', modelId.length) EventBus.$emit('sendPointsNumber', modelId.length)
@ -197,7 +208,6 @@ export default {
EventBus.$emit('NewHeatmapAccordingtoNewStack', modelId) EventBus.$emit('NewHeatmapAccordingtoNewStack', modelId)
} }
var DataGeneral var DataGeneral
var maxX var maxX
@ -470,8 +480,11 @@ export default {
} }
}, },
mounted() { mounted() {
/*EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data }) EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
EventBus.$on('updateMetricsScatter', this.ScatterPlotView)*/
EventBus.$on('updateMetricsScatter', data => { this.newColorsUpdate = data })
EventBus.$on('updateMetricsScatter', this.ScatterPlotView)
EventBus.$on('updateRemaining', data => { this.pushModelsRemaining = data }) EventBus.$on('updateRemaining', data => { this.pushModelsRemaining = data })
EventBus.$on('requestProven', data => { this.activeModels = data }) EventBus.$on('requestProven', data => { this.activeModels = data })

@ -47,7 +47,7 @@ export default {
$('#toggle-uni').change(function() { $('#toggle-uni').change(function() {
var toggleIDUni = document.getElementById('toggle-uni') var toggleIDUni = document.getElementById('toggle-uni')
var toggleIDPer = document.getElementById('toggle-per') var toggleIDPer = document.getElementById('toggle-per')
var toggleIDFi = document.getElementById('toggle-per') var toggleIDFi = document.getElementById('toggle-fi')
if (toggleIDUni.checked === false) { if (toggleIDUni.checked === false) {
EventBus.$emit('toggle1', 0) EventBus.$emit('toggle1', 0)
} else { } else {
@ -62,7 +62,7 @@ export default {
$('#toggle-per').change(function() { $('#toggle-per').change(function() {
var toggleIDUni = document.getElementById('toggle-uni') var toggleIDUni = document.getElementById('toggle-uni')
var toggleIDPer = document.getElementById('toggle-per') var toggleIDPer = document.getElementById('toggle-per')
var toggleIDFi = document.getElementById('toggle-per') var toggleIDFi = document.getElementById('toggle-fi')
if (toggleIDPer.checked === false) { if (toggleIDPer.checked === false) {
EventBus.$emit('toggle2', 0) EventBus.$emit('toggle2', 0)
} else { } else {
@ -77,7 +77,7 @@ export default {
$('#toggle-fi').change(function() { $('#toggle-fi').change(function() {
var toggleIDUni = document.getElementById('toggle-uni') var toggleIDUni = document.getElementById('toggle-uni')
var toggleIDPer = document.getElementById('toggle-per') var toggleIDPer = document.getElementById('toggle-per')
var toggleIDFi = document.getElementById('toggle-per') var toggleIDFi = document.getElementById('toggle-fi')
if (toggleIDFi.checked === false) { if (toggleIDFi.checked === false) {
EventBus.$emit('toggle3', 0) EventBus.$emit('toggle3', 0)
} else { } else {

@ -418,7 +418,7 @@ def callPreResults():
global yData global yData
global target_names global target_names
global impDataInst global impDataInst
print(XData)
DataSpaceResMDS = FunMDS(XData) DataSpaceResMDS = FunMDS(XData)
DataSpaceResTSNE = FunTsne(XData) DataSpaceResTSNE = FunTsne(XData)
DataSpaceResTSNE = DataSpaceResTSNE.tolist() DataSpaceResTSNE = DataSpaceResTSNE.tolist()
@ -442,7 +442,7 @@ def callPreResults():
@app.route('/data/requestDataSpaceResults', methods=["GET", "POST"]) @app.route('/data/requestDataSpaceResults', methods=["GET", "POST"])
def SendDataSpaceResults(): def SendDataSpaceResults():
global preResults global preResults
callPreResults()
response = { response = {
'preDataResults': preResults, 'preDataResults': preResults,
} }
@ -1689,8 +1689,6 @@ def RetrieveSelClassifiersIDandRemoveFromStack():
PredictionProbSelUpdate = PreprocessingPredUpdate(ClassifierIDsList) PredictionProbSelUpdate = PreprocessingPredUpdate(ClassifierIDsList)
print(PredictionProbSelUpdate)
global resultsUpdatePredictionSpace global resultsUpdatePredictionSpace
resultsUpdatePredictionSpace = [] resultsUpdatePredictionSpace = []
resultsUpdatePredictionSpace.append(json.dumps(PredictionProbSelUpdate[0])) # Position: 0 resultsUpdatePredictionSpace.append(json.dumps(PredictionProbSelUpdate[0])) # Position: 0
@ -1932,7 +1930,7 @@ def RetrieveSelDataPoints():
if (len(paramsListSeptoDicGradB['n_estimators']) is 0): if (len(paramsListSeptoDicGradB['n_estimators']) is 0):
RetrieveParamsClearedListGradB = [] RetrieveParamsClearedListGradB = []
print(algorithms)
for eachAlgor in algorithms: for eachAlgor in algorithms:
if (eachAlgor) == 'KNN': if (eachAlgor) == 'KNN':
clf = KNeighborsClassifier() clf = KNeighborsClassifier()
@ -2093,6 +2091,17 @@ def RetrieveSelDataPoints():
df_concatMetrics = pd.concat([dfKNNCleared, dfSVCCleared, dfGausNBCleared, dfMLPCleared, dfLRCleared, dfLDACleared, dfQDACleared, dfRFCleared, dfExtraTCleared, dfAdaBCleared, dfGradBCleared]) df_concatMetrics = pd.concat([dfKNNCleared, dfSVCCleared, dfGausNBCleared, dfMLPCleared, dfLRCleared, dfLDACleared, dfQDACleared, dfRFCleared, dfExtraTCleared, dfAdaBCleared, dfGradBCleared])
else: else:
dfSVCCleared = pd.DataFrame()
dfKNNCleared = pd.DataFrame()
dfGausNBCleared = pd.DataFrame()
dfMLPCleared = pd.DataFrame()
dfLRCleared = pd.DataFrame()
dfLDACleared = pd.DataFrame()
dfQDACleared = pd.DataFrame()
dfRFCleared = pd.DataFrame()
dfExtraTCleared = pd.DataFrame()
dfAdaBCleared = pd.DataFrame()
dfGradBCleared = pd.DataFrame()
if (len(metricsSelList[0]) != 0): if (len(metricsSelList[0]) != 0):
dicKNN = json.loads(metricsSelList[0]) dicKNN = json.loads(metricsSelList[0])
dfKNN = pd.DataFrame.from_dict(dicKNN) dfKNN = pd.DataFrame.from_dict(dicKNN)
@ -2103,8 +2112,7 @@ def RetrieveSelDataPoints():
dfKNNCleared = dfKNN dfKNNCleared = dfKNN
else: else:
dfKNNCleared = dfKNN.drop(dfKNN.index[set_diff_df]) dfKNNCleared = dfKNN.drop(dfKNN.index[set_diff_df])
df_concatMetrics = dfKNNCleared if (len(metricsSelList[1]) != 0):
elif (len(metricsSelList[1]) != 0):
dicSVC = json.loads(metricsSelList[1]) dicSVC = json.loads(metricsSelList[1])
dfSVC = pd.DataFrame.from_dict(dicSVC) dfSVC = pd.DataFrame.from_dict(dicSVC)
parametersSelDataPD = parametersSelData[1].apply(pd.Series) parametersSelDataPD = parametersSelData[1].apply(pd.Series)
@ -2114,8 +2122,7 @@ def RetrieveSelDataPoints():
dfSVCCleared = dfSVC dfSVCCleared = dfSVC
else: else:
dfSVCCleared = dfSVC.drop(dfSVC.index[set_diff_df]) dfSVCCleared = dfSVC.drop(dfSVC.index[set_diff_df])
df_concatMetrics = dfSVCCleared if (len(metricsSelList[2]) != 0):
elif (len(metricsSelList[2]) != 0):
dicGausNB = json.loads(metricsSelList[2]) dicGausNB = json.loads(metricsSelList[2])
dfGausNB = pd.DataFrame.from_dict(dicGausNB) dfGausNB = pd.DataFrame.from_dict(dicGausNB)
parametersSelDataPD = parametersSelData[2].apply(pd.Series) parametersSelDataPD = parametersSelData[2].apply(pd.Series)
@ -2125,8 +2132,7 @@ def RetrieveSelDataPoints():
dfGausNBCleared = dfGausNB dfGausNBCleared = dfGausNB
else: else:
dfGausNBCleared = dfGausNB.drop(dfGausNB.index[set_diff_df]) dfGausNBCleared = dfGausNB.drop(dfGausNB.index[set_diff_df])
df_concatMetrics = dfGausNBCleared if (len(metricsSelList[3]) != 0):
elif (len(metricsSelList[3]) != 0):
dicMLP = json.loads(metricsSelList[3]) dicMLP = json.loads(metricsSelList[3])
dfMLP = pd.DataFrame.from_dict(dicMLP) dfMLP = pd.DataFrame.from_dict(dicMLP)
parametersSelDataPD = parametersSelData[3].apply(pd.Series) parametersSelDataPD = parametersSelData[3].apply(pd.Series)
@ -2136,8 +2142,7 @@ def RetrieveSelDataPoints():
dfMLPCleared = dfMLP dfMLPCleared = dfMLP
else: else:
dfMLPCleared = dfMLP.drop(dfMLP.index[set_diff_df]) dfMLPCleared = dfMLP.drop(dfMLP.index[set_diff_df])
df_concatMetrics = dfMLPCleared if (len(metricsSelList[4]) != 0):
elif (len(metricsSelList[4]) != 0):
dicLR = json.loads(metricsSelList[4]) dicLR = json.loads(metricsSelList[4])
dfLR = pd.DataFrame.from_dict(dicLR) dfLR = pd.DataFrame.from_dict(dicLR)
parametersSelDataPD = parametersSelData[4].apply(pd.Series) parametersSelDataPD = parametersSelData[4].apply(pd.Series)
@ -2147,8 +2152,7 @@ def RetrieveSelDataPoints():
dfLRCleared = dfLR dfLRCleared = dfLR
else: else:
dfLRCleared = dfLR.drop(dfLR.index[set_diff_df]) dfLRCleared = dfLR.drop(dfLR.index[set_diff_df])
df_concatMetrics = dfLRCleared if (len(metricsSelList[5]) != 0):
elif (len(metricsSelList[5]) != 0):
dicLDA = json.loads(metricsSelList[5]) dicLDA = json.loads(metricsSelList[5])
dfLDA = pd.DataFrame.from_dict(dicLDA) dfLDA = pd.DataFrame.from_dict(dicLDA)
parametersSelDataPD = parametersSelData[5].apply(pd.Series) parametersSelDataPD = parametersSelData[5].apply(pd.Series)
@ -2158,8 +2162,7 @@ def RetrieveSelDataPoints():
dfLDACleared = dfLDA dfLDACleared = dfLDA
else: else:
dfLDACleared = dfLDA.drop(dfLDA.index[set_diff_df]) dfLDACleared = dfLDA.drop(dfLDA.index[set_diff_df])
df_concatMetrics = dfLDACleared if (len(metricsSelList[6]) != 0):
elif (len(metricsSelList[6]) != 0):
dicQDA = json.loads(metricsSelList[6]) dicQDA = json.loads(metricsSelList[6])
dfQDA = pd.DataFrame.from_dict(dicQDA) dfQDA = pd.DataFrame.from_dict(dicQDA)
parametersSelDataPD = parametersSelData[6].apply(pd.Series) parametersSelDataPD = parametersSelData[6].apply(pd.Series)
@ -2169,8 +2172,7 @@ def RetrieveSelDataPoints():
dfQDACleared = dfQDA dfQDACleared = dfQDA
else: else:
dfQDACleared = dfQDA.drop(dfQDA.index[set_diff_df]) dfQDACleared = dfQDA.drop(dfQDA.index[set_diff_df])
df_concatMetrics = dfQDACleared if (len(metricsSelList[7]) != 0):
elif (len(metricsSelList[7]) != 0):
dicRF = json.loads(metricsSelList[7]) dicRF = json.loads(metricsSelList[7])
dfRF = pd.DataFrame.from_dict(dicRF) dfRF = pd.DataFrame.from_dict(dicRF)
parametersSelDataPD = parametersSelData[7].apply(pd.Series) parametersSelDataPD = parametersSelData[7].apply(pd.Series)
@ -2180,8 +2182,7 @@ def RetrieveSelDataPoints():
dfRFCleared = dfRF dfRFCleared = dfRF
else: else:
dfRFCleared = dfRF.drop(dfRF.index[set_diff_df]) dfRFCleared = dfRF.drop(dfRF.index[set_diff_df])
df_concatMetrics = dfRFCleared if (len(metricsSelList[8]) != 0):
elif (len(metricsSelList[8]) != 0):
dicExtraT = json.loads(metricsSelList[8]) dicExtraT = json.loads(metricsSelList[8])
dfExtraT = pd.DataFrame.from_dict(dicExtraT) dfExtraT = pd.DataFrame.from_dict(dicExtraT)
parametersSelDataPD = parametersSelData[8].apply(pd.Series) parametersSelDataPD = parametersSelData[8].apply(pd.Series)
@ -2191,8 +2192,7 @@ def RetrieveSelDataPoints():
dfExtraTCleared = dfExtraT dfExtraTCleared = dfExtraT
else: else:
dfExtraTCleared = dfExtraT.drop(dfExtraT.index[set_diff_df]) dfExtraTCleared = dfExtraT.drop(dfExtraT.index[set_diff_df])
df_concatMetrics = dfExtraTCleared if (len(metricsSelList[9]) != 0):
elif (len(metricsSelList[9]) != 0):
dicAdaB = json.loads(metricsSelList[9]) dicAdaB = json.loads(metricsSelList[9])
dfAdaB = pd.DataFrame.from_dict(dicAdaB) dfAdaB = pd.DataFrame.from_dict(dicAdaB)
parametersSelDataPD = parametersSelData[9].apply(pd.Series) parametersSelDataPD = parametersSelData[9].apply(pd.Series)
@ -2202,8 +2202,7 @@ def RetrieveSelDataPoints():
dfAdaBCleared = dfAdaB dfAdaBCleared = dfAdaB
else: else:
dfAdaBCleared = dfAdaB.drop(dfAdaB.index[set_diff_df]) dfAdaBCleared = dfAdaB.drop(dfAdaB.index[set_diff_df])
df_concatMetrics = dfAdaBCleared if (len(metricsSelList[10]) != 0):
else:
dicGradB = json.loads(metricsSelList[10]) dicGradB = json.loads(metricsSelList[10])
dfGradB = pd.DataFrame.from_dict(dicGradB) dfGradB = pd.DataFrame.from_dict(dicGradB)
parametersSelDataPD = parametersSelData[10].apply(pd.Series) parametersSelDataPD = parametersSelData[10].apply(pd.Series)
@ -2213,14 +2212,15 @@ def RetrieveSelDataPoints():
dfGradBCleared = dfGradB dfGradBCleared = dfGradB
else: else:
dfGradBCleared = dfGradB.drop(dfGradB.index[set_diff_df]) dfGradBCleared = dfGradB.drop(dfGradB.index[set_diff_df])
df_concatMetrics = dfGradBCleared df_concatMetrics = pd.concat([dfKNNCleared, dfSVCCleared, dfGausNBCleared, dfMLPCleared, dfLRCleared, dfLDACleared, dfQDACleared, dfRFCleared, dfExtraTCleared, dfAdaBCleared, dfGradBCleared])
df_concatMetrics = df_concatMetrics.reset_index(drop=True)
print(df_concatMetrics)
global foreachMetricResults global foreachMetricResults
foreachMetricResults = [] foreachMetricResults = []
foreachMetricResults = preProcSumForEachMetric(factors, df_concatMetrics) foreachMetricResults = preProcSumForEachMetric(factors, df_concatMetrics)
df_concatMetrics.loc[:, 'log_loss'] = 1 - df_concatMetrics.loc[:, 'log_loss'] df_concatMetrics.loc[:, 'log_loss'] = 1 - df_concatMetrics.loc[:, 'log_loss']
print(df_concatMetrics)
global sumPerClassifierSelUpdate global sumPerClassifierSelUpdate
sumPerClassifierSelUpdate = [] sumPerClassifierSelUpdate = []
sumPerClassifierSelUpdate = preProcsumPerMetricAccordingtoData(factors, df_concatMetrics) sumPerClassifierSelUpdate = preProcsumPerMetricAccordingtoData(factors, df_concatMetrics)
@ -2230,7 +2230,8 @@ def RetrieveSelDataPoints():
ModelSpaceMDSNewSel = FunMDS(df_concatMetrics) ModelSpaceMDSNewSel = FunMDS(df_concatMetrics)
ModelSpaceMDSNewSelComb = [list(a) for a in zip(ModelSpaceMDSNewSel[0], ModelSpaceMDSNewSel[1])] ModelSpaceMDSNewSelComb = [list(a) for a in zip(ModelSpaceMDSNewSel[0], ModelSpaceMDSNewSel[1])]
print(len(ModelSpaceMDSNewComb))
print(len(ModelSpaceMDSNewSelComb))
global mt2xFinal global mt2xFinal
mt2xFinal = [] mt2xFinal = []
mtx1, mtx2, disparity = procrustes(ModelSpaceMDSNewComb, ModelSpaceMDSNewSelComb) mtx1, mtx2, disparity = procrustes(ModelSpaceMDSNewComb, ModelSpaceMDSNewSelComb)
@ -2242,6 +2243,8 @@ def RetrieveSelDataPoints():
def GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, DataPointsSel): def GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, DataPointsSel):
global XData
global yData
if (len(params) == 0): if (len(params) == 0):
resultsMetrics.append([]) # Position: 0 and so on resultsMetrics.append([]) # Position: 0 and so on
parametersSelData.append([]) parametersSelData.append([])
@ -2253,8 +2256,7 @@ def GridSearchSel(clf, params, factors, AlgorithmsIDsEnd, DataPointsSel):
.getOrCreate() .getOrCreate()
) )
sc = spark.sparkContext sc = spark.sparkContext
XDatasubset = XData.iloc[DataPointsSel,:]
XDatasubset = XData.loc[DataPointsSel,:]
yDataSubset = [yData[i] for i in DataPointsSel] yDataSubset = [yData[i] for i in DataPointsSel]
@ -3022,19 +3024,19 @@ def RetrieveAction():
filterActionCleared = json.loads(filterAction) filterActionCleared = json.loads(filterAction)
global filterActionFinal global filterActionFinal
global dataSpacePointsIDs
global filterDataFinal global filterDataFinal
global XData global XData
global yData global yData
filterActionFinal = filterActionCleared['action'] filterActionFinal = filterActionCleared['action']
dataSpacePointsIDs = filterActionCleared['points']
print(dataSpacePointsIDs)
if (filterActionFinal == 'merge'): if (filterActionFinal == 'merge'):
if (filterDataFinal == 'mean' or filterDataFinal == ''): if (filterDataFinal == 'mean' or filterDataFinal == ''):
mean = XData.iloc[dataSpacePointsIDs, :].mean() mean = XData.loc[dataSpacePointsIDs, :].mean()
XData.loc[len(XData)]= mean XData.loc[len(XData)]= mean
else: else:
median = XData.iloc[dataSpacePointsIDs, :].median() median = XData.loc[dataSpacePointsIDs, :].median()
XData.loc[len(XData)]= median XData.loc[len(XData)]= median
yDataSelected = [yData[i] for i in dataSpacePointsIDs] yDataSelected = [yData[i] for i in dataSpacePointsIDs]
storeMode = mode(yDataSelected) storeMode = mode(yDataSelected)
@ -3044,10 +3046,10 @@ def RetrieveAction():
XData.reset_index(drop=True, inplace=True) XData.reset_index(drop=True, inplace=True)
elif (filterActionFinal == 'compose'): elif (filterActionFinal == 'compose'):
if (filterDataFinal == 'mean' or filterDataFinal == ''): if (filterDataFinal == 'mean' or filterDataFinal == ''):
mean = XData.iloc[dataSpacePointsIDs, :].mean() mean = XData.loc[dataSpacePointsIDs, :].mean()
XData.loc[len(XData)]= mean XData.loc[len(XData)]= mean
else: else:
median = XData.iloc[dataSpacePointsIDs, :].median() median = XData.loc[dataSpacePointsIDs, :].median()
XData.loc[len(XData)]= median XData.loc[len(XData)]= median
yDataSelected = [yData[i] for i in dataSpacePointsIDs] yDataSelected = [yData[i] for i in dataSpacePointsIDs]
storeMode = mode(yDataSelected) storeMode = mode(yDataSelected)

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