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@ -1 +0,0 @@
{"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"}}

@ -1,7 +1,7 @@
# first line: 565 # first line: 632
@memory.cache @memory.cache
def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle): def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle):
print('inside') print('inside models')
# instantiate spark session # instantiate spark session
spark = ( spark = (
SparkSession SparkSession

@ -0,0 +1,27 @@
<template>
<button style="float: right;"
id="updateActiveScatter"
v-on:click="sendUpdateActiveScatter">
<font-awesome-icon icon="calculator" />
{{ valueActive }}
</button>
</template>
<script>
import { EventBus } from '../main.js'
export default {
name: 'ActiveScatter',
data () {
return {
valueActive: 'Compute Metamodel\'s Performance'
}
},
methods: {
sendUpdateActiveScatter () {
EventBus.$emit('sendToServerSelectedScatter')
}
}
}
</script>

@ -130,14 +130,11 @@ 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 originalDataLabels = JSON.parse(this.dataPoints[3]) const originalDataLabels = JSON.parse(this.dataPoints[3])
//console.log(DataSetY) //console.log(DataSetY)
//const originalDataLabels = JSON.parse(this.dataPoints[4]) //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])
@ -210,7 +207,6 @@ 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 = []
@ -482,7 +478,6 @@ 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)

@ -115,7 +115,7 @@
<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"><small class="float-left" style="padding-top: 3px;">Metrics Support: [All]</small>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}}]<small class="float-right"><active-scatter/></small>
</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">
@ -171,6 +171,7 @@ import Controller from './Controller.vue'
import ResetClass from './ResetClass.vue' import ResetClass from './ResetClass.vue'
import Knowledge from './Knowledge.vue' import Knowledge from './Knowledge.vue'
import Active from './Active.vue' import Active from './Active.vue'
import ActiveScatter from './ActiveScatter.vue'
import Export from './Export.vue' import Export from './Export.vue'
import SlidersController from './SlidersController.vue' import SlidersController from './SlidersController.vue'
import ScatterPlot from './ScatterPlot.vue' import ScatterPlot from './ScatterPlot.vue'
@ -210,6 +211,7 @@ export default Vue.extend({
ResetClass, ResetClass,
Knowledge, Knowledge,
Active, Active,
ActiveScatter,
SlidersController, SlidersController,
ScatterPlot, ScatterPlot,
PerMetricBarChart, PerMetricBarChart,
@ -277,6 +279,7 @@ export default Vue.extend({
toggleDeepMain: 1, toggleDeepMain: 1,
keyLoc: 0, keyLoc: 0,
keyData: true, keyData: true,
ClassifierIDsListRemaining: []
} }
}, },
methods: { methods: {
@ -412,6 +415,33 @@ export default Vue.extend({
console.log(error) console.log(error)
}) })
}, },
updateBarChartLocally () {
this.OverSelLength = this.ClassifierIDsList.length
const path = `http://127.0.0.1:5000/data/ServerRequestSelPoinLocally`
const postData = {
ClassifiersList: this.ClassifierIDsList,
}
const axiosConfig = {
headers: {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Headers': 'Origin, Content-Type, X-Auth-Token',
'Access-Control-Allow-Methods': 'GET, PUT, POST, DELETE, OPTIONS'
}
}
axios.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the selected points to the server (scatterplot)!')
if (this.keyNow == 0) {
this.OverAllLength = this.ClassifierIDsList.length
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
}
this.getSelectedModelsMetrics()
})
.catch(error => {
console.log(error)
})
},
SendSelectedPointsToServer () { SendSelectedPointsToServer () {
if (this.ClassifierIDsList === ''){ if (this.ClassifierIDsList === ''){
this.OverSelLength = 0 this.OverSelLength = 0
@ -438,7 +468,7 @@ export default Vue.extend({
this.OverAllLength = this.ClassifierIDsList.length this.OverAllLength = this.ClassifierIDsList.length
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList) EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
} }
this.getSelectedModelsMetrics() //this.getSelectedModelsMetrics()
this.getFinalResults() this.getFinalResults()
}) })
.catch(error => { .catch(error => {
@ -449,7 +479,7 @@ export default Vue.extend({
RemoveFromStackModels () { RemoveFromStackModels () {
const path = `http://127.0.0.1:5000/data/ServerRemoveFromStack` const path = `http://127.0.0.1:5000/data/ServerRemoveFromStack`
const postData = { const postData = {
ClassifiersList: this.ClassifierIDsList, ClassifiersList: this.ClassifierIDsListRemaining,
} }
const axiosConfig = { const axiosConfig = {
headers: { headers: {
@ -462,7 +492,6 @@ export default Vue.extend({
axios.post(path, postData, axiosConfig) axios.post(path, postData, axiosConfig)
.then(response => { .then(response => {
console.log('Sent the selected points to the server (scatterplot)!') console.log('Sent the selected points to the server (scatterplot)!')
EventBus.$emit('updateFlagForFinalResults', 0)
this.updatePredictionsSpace() this.updatePredictionsSpace()
}) })
.catch(error => { .catch(error => {
@ -490,6 +519,8 @@ export default Vue.extend({
EventBus.$emit('GrayOutPoints', this.ClassifierIDsList) EventBus.$emit('GrayOutPoints', this.ClassifierIDsList)
} }
EventBus.$emit('updatePredictionsSpace', this.UpdatePredictions) EventBus.$emit('updatePredictionsSpace', this.UpdatePredictions)
EventBus.$emit('updateFlagForFinalResults', 0)
this.getFinalResults()
}) })
.catch(error => { .catch(error => {
console.log(error) console.log(error)
@ -982,7 +1013,8 @@ export default Vue.extend({
EventBus.$on('ChangeKey', data => { this.keyNow = data }) EventBus.$on('ChangeKey', data => { this.keyNow = data })
EventBus.$on('SendSelectedPointsToServerEvent', data => { this.ClassifierIDsList = data }) EventBus.$on('SendSelectedPointsToServerEvent', data => { this.ClassifierIDsList = data })
EventBus.$on('SendSelectedPointsToServerEvent', this.SendSelectedPointsToServer) EventBus.$on('SendSelectedPointsToServerEvent', this.updateBarChartLocally)
EventBus.$on('sendToServerSelectedScatter', this.SendSelectedPointsToServer)
EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data }) EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data })
EventBus.$on('SendSelectedDataPointsToServerEvent', this.SendSelectedDataPointsToServer) EventBus.$on('SendSelectedDataPointsToServerEvent', this.SendSelectedDataPointsToServer)
@ -1012,7 +1044,7 @@ export default Vue.extend({
EventBus.$on('sendPointsNumber', data => {this.OverAllLength = data}) EventBus.$on('sendPointsNumber', data => {this.OverAllLength = data})
EventBus.$on('AllSelModels', data => {this.valueSel = data}) EventBus.$on('AllSelModels', data => {this.valueSel = data})
EventBus.$on('RemoveFromStack', data => { this.ClassifierIDsList = data }) EventBus.$on('RemoveFromStack', data => { this.ClassifierIDsListRemaining = data })
EventBus.$on('RemoveFromStack', this.RemoveFromStackModels) EventBus.$on('RemoveFromStack', this.RemoveFromStackModels)
EventBus.$on('OpenModal', this.openModalFun) EventBus.$on('OpenModal', this.openModalFun)

@ -14,6 +14,7 @@ export default {
WH: [], WH: [],
barchartmetricsprediction: [], barchartmetricsprediction: [],
SelBarChartMetrics: [], SelBarChartMetrics: [],
boldXAxis: '',
factors: [1,0,0 factors: [1,0,0
,1,0,0,1,0 ,1,0,0,1,0
,0,1,0,0,0 ,0,1,0,0,0
@ -25,7 +26,7 @@ export default {
methods: { methods: {
LineBar () { LineBar () {
Plotly.purge('PerMetricBar') Plotly.purge('PerMetricBar')
var x = [] var x = []
var metricsPerModel = JSON.parse(this.barchartmetrics[9]) var metricsPerModel = JSON.parse(this.barchartmetrics[9])
var metricsPerModelSel = [] var metricsPerModelSel = []
@ -40,7 +41,33 @@ export default {
var perModelAllClear = [] var perModelAllClear = []
var perModelSelectedClear = [] var perModelSelectedClear = []
var resultsColors = [] var resultsColors = []
var chooseFrom = ['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','Log Loss'] if (this.boldXAxis == 'Accuracy') {
var chooseFrom = ['<b>Accuracy</b>','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','Log Loss']
}
else if (this.boldXAxis == 'G-Mean') {
var chooseFrom = ['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']
}
else if (this.boldXAxis == 'Precision') {
var chooseFrom = ['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']
}
else if (this.boldXAxis == 'Recall') {
var chooseFrom = ['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']
}
else if (this.boldXAxis == 'F-Beta Score') {
var chooseFrom = ['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']
}
else if (this.boldXAxis == 'MCC') {
var chooseFrom = ['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']
}
else if (this.boldXAxis == 'ROC AUC') {
var chooseFrom = ['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']
}
else if (this.boldXAxis == 'Log Loss') {
var chooseFrom = ['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>']
}
else {
var chooseFrom = ['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','Log Loss']
}
for (let i = 0; i < metricsPerModel.length; i++) { for (let i = 0; i < metricsPerModel.length; i++) {
if (factorsLocal[i] != 0) { if (factorsLocal[i] != 0) {
resultsColors.push(metricsPerModel[i]) resultsColors.push(metricsPerModel[i])
@ -57,7 +84,6 @@ 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 = {
@ -116,37 +142,30 @@ export default {
xAxisHovered = eventData.points[0].x xAxisHovered = eventData.points[0].x
var index var index
if (xAxisHovered == 'Accuracy') { if (xAxisHovered == 'Accuracy') {
Plotly.restyle(boxPlot, 'x', [['<b>Accuracy</b>','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','Log Loss']]);
index = 0 index = 0
} }
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']]);
index = 1 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']]);
index = 2 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']]);
index = 3 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']]);
index = 4 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']]);
index = 5 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']]);
index = 6 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>']]);
index = 7 index = 7
} }
EventBus.$emit('updateBold', xAxisHovered)
EventBus.$emit('updateMetricsScatter', resultsColors[index]) EventBus.$emit('updateMetricsScatter', resultsColors[index])
}); });
}, },
@ -155,6 +174,9 @@ export default {
} }
}, },
mounted () { mounted () {
EventBus.$on('updateBold', data => {this.boldXAxis = data;})
EventBus.$on('updateBold', this.LineBar)
EventBus.$on('InitializeMetricsBarChart', data => {this.barchartmetrics = data;}) EventBus.$on('InitializeMetricsBarChart', data => {this.barchartmetrics = data;})
EventBus.$on('InitializeMetricsBarChart', this.LineBar) EventBus.$on('InitializeMetricsBarChart', this.LineBar)

@ -51,14 +51,18 @@ export default {
} }
}, },
ScatterPlotPredView () { ScatterPlotPredView () {
Plotly.purge('OverviewPredPlotly') Plotly.purge('OverviewPredPlotly')
// responsive visualization // responsive visualization
var width = this.WH[0]*6.5 // interactive visualization var width = this.WH[0]*6.5 // interactive visualization
var height = this.WH[1]*1.22 // interactive visualization var height = this.WH[1]*1.22 // interactive visualization
var XandYCoordinatesMDS
var target_names = JSON.parse(this.PredictionsData[4]) var target_names = JSON.parse(this.PredictionsData[4])
const XandYCoordinatesMDS = JSON.parse(this.PredictionsData[8]) if (this.UpdatedData.length != 0) {
XandYCoordinatesMDS = JSON.parse(this.UpdatedData[0])
} else {
XandYCoordinatesMDS = JSON.parse(this.PredictionsData[8])
}
const DataSet = JSON.parse(this.PredictionsData[14]) const DataSet = JSON.parse(this.PredictionsData[14])
const originalDataLabels = 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])
@ -66,6 +70,7 @@ export default {
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])
stringParameters.push(JSON.stringify(DataSetParse[i]).replace(/,/gi, '<br>')) stringParameters.push(JSON.stringify(DataSetParse[i]).replace(/,/gi, '<br>'))
} }
const XandYCoordinatesTSNE = JSON.parse(this.PredictionsData[18]) const XandYCoordinatesTSNE = JSON.parse(this.PredictionsData[18])
@ -280,37 +285,47 @@ 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) { //if (this.onlyOnce) {
this.selectedPointsOverview() this.selectedPointsOverview()
} // }
this.onlyOnce = false // this.onlyOnce = false
}, },
UpdateScatterPlot () { // UpdateScatterPlot () {
const XandYCoordinates = JSON.parse(this.UpdatedData[0]) // const XandYCoordinates = JSON.parse(this.UpdatedData[0])
// const aux_X = result.Xax.filter((item, index) => originalDataLabels[index] == target_names[i]);
Plotly.animate('OverviewPredPlotly', { // const aux_Y = result.Yax.filter((item, index) => originalDataLabels[index] == target_names[i]);
data: [ // const aux_ID = result.ID.filter((item, index) => originalDataLabels[index] == target_names[i]);
{x: XandYCoordinates[0], y: XandYCoordinates[1]}
], // var Text = aux_ID.map((item, index) => {
traces: [0], // let popup = 'Data Point ID: ' + item + '<br> Details: ' + stringParameters[item]
layout: {} // return popup;
}, { // });
transition: {
duration: 1000,
easing: 'cubic-in-out' // Plotly.animate('OverviewPredPlotly', {
}, // data: [
frame: { // {x: XandYCoordinates[0], y: XandYCoordinates[1]}
duration: 1000 // ],
} // traces: [0],
}) // layout: {}
//this.selectedPointsOverview() // }, {
}, // transition: {
// duration: 1000,
// easing: 'cubic-in-out'
// },
// frame: {
// duration: 1000
// }
// })
// //this.selectedPointsOverview()
// },
selectedPointsOverview () { selectedPointsOverview () {
const OverviewPlotly = document.getElementById('OverviewPredPlotly') const OverviewPlotly = document.getElementById('OverviewPredPlotly')
OverviewPlotly.on('plotly_selected', function (evt) { OverviewPlotly.on('plotly_selected', function (evt) {
if (typeof evt !== 'undefined') { if (typeof evt !== 'undefined') {
const DataPoints = [] const DataPoints = []
console.log(evt)
for (let i = 0; evt.points.length; i++) { for (let i = 0; evt.points.length; i++) {
if (evt.points[i] === undefined) { if (evt.points[i] === undefined) {
break break
@ -332,7 +347,7 @@ export default {
mounted() { mounted() {
EventBus.$on('onlyOnce', data => { this.onlyOnce = data }) EventBus.$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.ScatterPlotPredView)
EventBus.$on('emittedEventCallingPredictionsSpacePlotView', data => { EventBus.$on('emittedEventCallingPredictionsSpacePlotView', data => {
this.PredictionsData = data}) this.PredictionsData = data})
EventBus.$on('emittedEventCallingPredictionsSpacePlotView', this.ScatterPlotPredView) EventBus.$on('emittedEventCallingPredictionsSpacePlotView', this.ScatterPlotPredView)

@ -69,8 +69,15 @@ export default {
methods: { methods: {
resetSelection () { resetSelection () {
this.newColorsUpdate = [] this.newColorsUpdate = []
this.ScatterPlotView() EventBus.$emit('updateBold', '')
EventBus.$emit('updateBoxPlots') EventBus.$emit('updateBoxPlots')
this.colorsStore = []
this.MDSStore = []
this.parametersStore = []
this.TSNEStore = []
this.modelIDStore = []
this.UMAPStore = []
this.ScatterPlotView()
}, },
reset () { reset () {
Plotly.purge('OverviewPlotly') Plotly.purge('OverviewPlotly')
@ -94,32 +101,25 @@ export default {
}, },
ScatterPlotView () { ScatterPlotView () {
Plotly.purge('OverviewPlotly') Plotly.purge('OverviewPlotly')
var colorsforScatterPlot
var MDSData
var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[0]) 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 = [] 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]) MDSData= JSON.parse(this.ScatterPlotResults[1])
var parametersLoc = JSON.parse(this.ScatterPlotResults[2]) var parametersLoc = JSON.parse(this.ScatterPlotResults[2])
var parameters = JSON.parse(parametersLoc) var parameters = JSON.parse(parametersLoc)
var TSNEData = JSON.parse(this.ScatterPlotResults[12]) var TSNEData = JSON.parse(this.ScatterPlotResults[12])
var modelId = JSON.parse(this.ScatterPlotResults[13]) var modelId = JSON.parse(this.ScatterPlotResults[13])
var UMAPData = JSON.parse(this.ScatterPlotResults[17]) var UMAPData = JSON.parse(this.ScatterPlotResults[17])
if (this.keyLocal == 0) { if (this.keyLocal == 0) {
this.colorsStore.push(colorsforScatterPlot) this.colorsStore.push(colorsforScatterPlot)
this.MDSStore.push(MDSData) this.MDSStore.push(MDSData)
@ -133,8 +133,6 @@ 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)
@ -162,6 +160,7 @@ export default {
var listofNumbersModelsIDs = [] var listofNumbersModelsIDs = []
var StackModelsIDs = [] var StackModelsIDs = []
console.log(this.ModelsIDGray.length)
if (this.ModelsIDGray.length != 0) { if (this.ModelsIDGray.length != 0) {
for (let j = 0; j < this.ModelsIDGray.length; j++){ for (let j = 0; j < this.ModelsIDGray.length; j++){
listofNumbersModelsIDs.push(parseInt(this.ModelsIDGray[j])) listofNumbersModelsIDs.push(parseInt(this.ModelsIDGray[j]))
@ -171,18 +170,39 @@ export default {
var MDSDataNewX = [] var MDSDataNewX = []
var MDSDataNewY = [] var MDSDataNewY = []
var colorsforScatterPlotNew = [] var colorsforScatterPlotNew = []
if (this.DataPointsSelUpdate.length != 0) {
this.colorsStore.pop()
this.MDSStore.pop()
this.parametersStore.pop()
this.modelIDStore.pop()
this.UMAPStore.pop()
}
for (let i = 0; i < modelId.length; i++) { for (let i = 0; i < modelId.length; i++) {
if (listofNumbersModelsIDs.includes(modelId[i])) { if (listofNumbersModelsIDs.includes(modelId[i])) {
} else {
StackModelsIDs.push(modelId[i]) StackModelsIDs.push(modelId[i])
parametersNew.push(parameters[i]) parametersNew.push(parameters[i])
colorsforScatterPlotNew.push(colorsforScatterPlot[i]) if (this.DataPointsSelUpdate.length != 0) {
MDSDataNewX.push(MDSData[0][i]) colorsforScatterPlot = JSON.parse(this.DataPointsSelUpdate[0])
MDSDataNewY.push(MDSData[1][i]) colorsforScatterPlotNew.push(colorsforScatterPlot[i])
} else {
colorsforScatterPlotNew.push(colorsforScatterPlot[i])
}
if (this.DataPointsSelUpdate.length != 0) {
MDSData = JSON.parse(this.DataPointsSelUpdate[1])
MDSDataNewX.push(MDSData[0][i])
MDSDataNewY.push(MDSData[1][i])
} else {
MDSDataNewX.push(MDSData[0][i])
MDSDataNewY.push(MDSData[1][i])
}
} }
} }
this.DataPointsSelUpdate = []
MDSData[0] = MDSDataNewX MDSData[0] = MDSDataNewX
MDSData[1] = MDSDataNewY MDSData[1] = MDSDataNewY
console.log(StackModelsIDs)
modelId = StackModelsIDs modelId = StackModelsIDs
parameters = parametersNew parameters = parametersNew
colorsforScatterPlot = colorsforScatterPlotNew colorsforScatterPlot = colorsforScatterPlotNew
@ -193,13 +213,19 @@ export default {
//this.TSNEStore.push(TSNEData) //this.TSNEStore.push(TSNEData)
this.modelIDStore.push(modelId) this.modelIDStore.push(modelId)
this.UMAPStore.push(UMAPData) this.UMAPStore.push(UMAPData)
console.log(this.activeModels)
colorsforScatterPlot = this.colorsStore.slice(this.activeModels,this.activeModels+1)[0] colorsforScatterPlot = this.colorsStore.slice(this.activeModels,this.activeModels+1)[0]
MDSData = this.MDSStore.slice(this.activeModels,this.activeModels+1)[0] MDSData = this.MDSStore.slice(this.activeModels,this.activeModels+1)[0]
parameters = this.parametersStore.slice(this.activeModels,this.activeModels+1)[0] parameters = this.parametersStore.slice(this.activeModels,this.activeModels+1)[0]
//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]
console.log(modelId)
//UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0] //UMAPData = this.UMAPStore.slice(this.activeModels,this.activeModels+1)[0]
} }
console.log(this.colorsStore)
console.log(colorsforScatterPlot)
EventBus.$emit('sendPointsNumber', modelId.length) EventBus.$emit('sendPointsNumber', modelId.length)
var classifiersInfoProcessing = [] var classifiersInfoProcessing = []
for (let i = 0; i < modelId.length; i++) { for (let i = 0; i < modelId.length; i++) {
@ -397,7 +423,6 @@ export default {
if (this.onlyOnce) { if (this.onlyOnce) {
this.selectedPointsOverview() this.selectedPointsOverview()
} }
this.onlyOnce = false
}, },
selectedPointsOverview () { selectedPointsOverview () {
const OverviewPlotly = document.getElementById('OverviewPlotly') const OverviewPlotly = document.getElementById('OverviewPlotly')
@ -418,11 +443,13 @@ export default {
ClassifierIDsListCleared.push(numberNumb) ClassifierIDsListCleared.push(numberNumb)
} }
} }
console.log(ClassifierIDsListCleared)
for (let i = 0; i < allModels.length; i++) { for (let i = 0; i < allModels.length; i++) {
if (!ClassifierIDsListCleared.includes(allModels[i])) { if (!ClassifierIDsListCleared.includes(allModels[i])) {
pushModelsRemainingTemp.push(allModels[i]) pushModelsRemainingTemp.push(allModels[i])
} }
} }
console.log(pushModelsRemainingTemp)
EventBus.$emit('updateRemaining', pushModelsRemainingTemp) EventBus.$emit('updateRemaining', pushModelsRemainingTemp)
if (allModels != '') { if (allModels != '') {
EventBus.$emit('ChangeKey', 1) EventBus.$emit('ChangeKey', 1)
@ -437,47 +464,47 @@ export default {
UpdateScatter () { UpdateScatter () {
this.ScatterPlotView() this.ScatterPlotView()
}, },
animate() { // animate() {
var maxX // var maxX
var minX // var minX
var maxY // var maxY
var minY // var minY
var colorsforScatterPlot = JSON.parse(this.DataPointsSelUpdate[0]) // var colorsforScatterPlot = JSON.parse(this.DataPointsSelUpdate[0])
var MDSData = JSON.parse(this.DataPointsSelUpdate[1]) // var MDSData = JSON.parse(this.DataPointsSelUpdate[1])
maxX = Math.max(MDSData[0]) // maxX = Math.max(MDSData[0])
minX = Math.min(MDSData[0]) // minX = Math.min(MDSData[0])
maxY = Math.max(MDSData[1]) // maxY = Math.max(MDSData[1])
minY = Math.max(MDSData[1]) // minY = Math.max(MDSData[1])
Plotly.animate('OverviewPlotly', { // Plotly.animate('OverviewPlotly', {
data: [ // data: [
{x: MDSData[0], y: MDSData[1],marker: { // {x: MDSData[0], y: MDSData[1],marker: {
color: colorsforScatterPlot, // color: colorsforScatterPlot,
}} // }}
], // ],
traces: [0], // traces: [0],
layout: { // layout: {
xaxis: { // xaxis: {
visible: false, // visible: false,
range: [minX, maxX] // range: [minX, maxX]
}, // },
yaxis: { // yaxis: {
visible: false, // visible: false,
range: [minY, maxY] // range: [minY, maxY]
}, // },
} // }
}, { // }, {
transition: { // transition: {
duration: 3000, // duration: 3000,
easing: 'cubic-in-out' // easing: 'cubic-in-out'
}, // },
frame: { // frame: {
duration: 3000 // duration: 3000
} // }
}) // })
} // }
}, },
mounted() { mounted() {
EventBus.$on('onlyOnce', data => { this.onlyOnce = data }) EventBus.$on('onlyOnce', data => { this.onlyOnce = data })
@ -513,7 +540,7 @@ export default {
EventBus.$on('RepresentationSelection', data => {this.representationDef = data}) EventBus.$on('RepresentationSelection', data => {this.representationDef = data})
EventBus.$on('RepresentationSelection', this.ScatterPlotView) EventBus.$on('RepresentationSelection', this.ScatterPlotView)
EventBus.$on('UpdateModelsScatterplot', data => {this.DataPointsSelUpdate = data}) EventBus.$on('UpdateModelsScatterplot', data => {this.DataPointsSelUpdate = data})
EventBus.$on('UpdateModelsScatterplot', this.animate) EventBus.$on('UpdateModelsScatterplot', this.ScatterPlotView)
// reset view // reset view
EventBus.$on('resetViews', this.reset) EventBus.$on('resetViews', this.reset)

@ -9,7 +9,7 @@ import os
def import_content(filepath): def import_content(filepath):
mng_client = pymongo.MongoClient('localhost', 27017) mng_client = pymongo.MongoClient('localhost', 27017)
mng_db = mng_client['mydb'] mng_db = mng_client['mydb']
collection_name = 'StanceC' collection_name = 'StanceCTest'
db_cm = mng_db[collection_name] db_cm = mng_db[collection_name]
cdir = os.path.dirname(__file__) cdir = os.path.dirname(__file__)
file_res = os.path.join(cdir, filepath) file_res = os.path.join(cdir, filepath)
@ -20,5 +20,5 @@ def import_content(filepath):
db_cm.insert(data_json) db_cm.insert(data_json)
if __name__ == "__main__": if __name__ == "__main__":
filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stance.csv' filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stancetest.csv'
import_content(filepath) import_content(filepath)

@ -32,6 +32,10 @@ from sklearn.manifold import TSNE
from sklearn.metrics import matthews_corrcoef from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import log_loss from sklearn.metrics import log_loss
from sklearn.metrics import fbeta_score from sklearn.metrics import fbeta_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from imblearn.metrics import geometric_mean_score from imblearn.metrics import geometric_mean_score
import umap import umap
from sklearn.metrics import classification_report from sklearn.metrics import classification_report
@ -79,6 +83,9 @@ def Reset():
global RANDOM_SEED global RANDOM_SEED
RANDOM_SEED = 42 RANDOM_SEED = 42
global StanceTest
StanceTest = False
global factors global factors
factors = [1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1] factors = [1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1]
@ -176,6 +183,8 @@ def Reset():
def RetrieveFileName(): def RetrieveFileName():
global DataRawLength global DataRawLength
global DataResultsRaw global DataResultsRaw
global DataResultsRawTest
global DataRawLengthTest
fileName = request.get_data().decode('utf8').replace("'", '"') fileName = request.get_data().decode('utf8').replace("'", '"')
@ -275,14 +284,22 @@ def RetrieveFileName():
global parametersSelData global parametersSelData
parametersSelData = [] parametersSelData = []
global StanceTest
StanceTest = False
global target_names global target_names
target_names = [] target_names = []
DataRawLength = -1 DataRawLength = -1
DataRawLengthTest = -1
data = json.loads(fileName) data = json.loads(fileName)
if data['fileName'] == 'HeartC': if data['fileName'] == 'HeartC':
CollectionDB = mongo.db.HeartC.find() CollectionDB = mongo.db.HeartC.find()
elif data['fileName'] == 'StanceC': elif data['fileName'] == 'StanceC':
StanceTest = True
CollectionDB = mongo.db.StanceC.find() CollectionDB = mongo.db.StanceC.find()
CollectionDBTest = mongo.db.StanceCTest.find()
elif data['fileName'] == 'DiabetesC': elif data['fileName'] == 'DiabetesC':
CollectionDB = mongo.db.DiabetesC.find() CollectionDB = mongo.db.DiabetesC.find()
else: else:
@ -293,6 +310,15 @@ def RetrieveFileName():
item['InstanceID'] = index item['InstanceID'] = index
DataResultsRaw.append(item) DataResultsRaw.append(item)
DataRawLength = len(DataResultsRaw) DataRawLength = len(DataResultsRaw)
DataResultsRawTest = []
if (StanceTest):
for index, item in enumerate(CollectionDBTest):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawTest.append(item)
DataRawLengthTest = len(DataResultsRawTest)
DataSetSelection() DataSetSelection()
return 'Everything is okay' return 'Everything is okay'
@ -364,6 +390,49 @@ def CollectionData():
return jsonify(response) return jsonify(response)
def DataSetSelection(): def DataSetSelection():
global XDataTest, yDataTest
XDataTest = pd.DataFrame()
yDataTest = []
global StanceTest
if (StanceTest):
DataResultsTest = copy.deepcopy(DataResultsRawTest)
for dictionary in DataResultsRawTest:
for key in dictionary.keys():
if (key.find('*') != -1):
target = key
continue
continue
DataResultsRawTest.sort(key=lambda x: x[target], reverse=True)
DataResultsTest.sort(key=lambda x: x[target], reverse=True)
for dictionary in DataResultsTest:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[target]
AllTargetsTest = [o[target] for o in DataResultsRaw]
AllTargetsFloatValuesTest = []
previous = None
Class = 0
for i, value in enumerate(AllTargetsTest):
if (i == 0):
previous = value
target_namesLoc.append(value)
if (value == previous):
AllTargetsFloatValuesTest.append(Class)
else:
Class = Class + 1
target_namesLoc.append(value)
AllTargetsFloatValuesTest.append(Class)
previous = value
ArrayDataResultsTest = pd.DataFrame.from_dict(DataResultsTest)
XDataTest, yDataTest = ArrayDataResultsTest, AllTargetsFloatValuesTest
DataResults = copy.deepcopy(DataResultsRaw) DataResults = copy.deepcopy(DataResultsRaw)
for dictionary in DataResultsRaw: for dictionary in DataResultsRaw:
@ -564,7 +633,7 @@ 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, AlgorithmsIDsEnd, toggle): def GridSearchForModels(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd, toggle):
print('inside') print('inside models')
# instantiate spark session # instantiate spark session
spark = ( spark = (
SparkSession SparkSession
@ -1712,7 +1781,7 @@ def SendPredBacktobeUpdated():
@app.route('/data/ServerRequestSelPoin', methods=["GET", "POST"]) @app.route('/data/ServerRequestSelPoin', methods=["GET", "POST"])
def RetrieveSelClassifiersID(): def RetrieveSelClassifiersID():
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"') ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
ComputeMetricsForSel(ClassifierIDsList) #ComputeMetricsForSel(ClassifierIDsList)
ClassifierIDCleaned = json.loads(ClassifierIDsList) ClassifierIDCleaned = json.loads(ClassifierIDsList)
global keySpecInternal global keySpecInternal
@ -1722,6 +1791,15 @@ def RetrieveSelClassifiersID():
EnsembleModel(ClassifierIDsList, 1) EnsembleModel(ClassifierIDsList, 1)
return 'Everything Okay' return 'Everything Okay'
# Retrieve data from client
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@app.route('/data/ServerRequestSelPoinLocally', methods=["GET", "POST"])
def RetrieveSelClassifiersIDLocally():
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
ComputeMetricsForSel(ClassifierIDsList)
return 'Everything Okay'
def ComputeMetricsForSel(Models): def ComputeMetricsForSel(Models):
Models = json.loads(Models) Models = json.loads(Models)
MetricsAlltoSel = PreprocessingMetrics() MetricsAlltoSel = PreprocessingMetrics()
@ -2473,6 +2551,8 @@ def FeatureSelPerModel():
return 'Everything Okay' return 'Everything Okay'
def EnsembleModel(Models, keyRetrieved): def EnsembleModel(Models, keyRetrieved):
global XDataTest, yDataTest
global scores global scores
global previousState global previousState
global previousStateActive global previousStateActive
@ -2852,11 +2932,12 @@ def EnsembleModel(Models, keyRetrieved):
# if (keyRetrieved == 0): # if (keyRetrieved == 0):
# pass # pass
# else: # else:
global StanceTest
if (keySpec == 0 or keySpec == 1): if (keySpec == 0 or keySpec == 1):
num_cores = multiprocessing.cpu_count() num_cores = multiprocessing.cpu_count()
inputsSc = ['accuracy','precision_weighted','recall_weighted','f1_weighted'] inputsSc = ['accuracy','precision_weighted','recall_weighted','f1_weighted']
flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc)) flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(StanceTest,XDataTest,yDataTest,sclf,keyData,keySpec,keySpecInternal,previousState,previousStateActive,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
scores = [item for sublist in flat_results for item in sublist] scores = [item for sublist in flat_results for item in sublist]
if (keySpec == 0): if (keySpec == 0):
@ -2929,7 +3010,7 @@ def EnsembleModel(Models, keyRetrieved):
return 'Okay' return 'Okay'
def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop): def solve(StanceTest,y_Test,y_true,sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateActiveLoc,XData,yData,crossValidation,scoringIn,loop):
scoresLoc = [] scoresLoc = []
if (keySpec == 0): if (keySpec == 0):
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1) temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
@ -2974,6 +3055,16 @@ def solve(sclf,keyData,keySpec,keySpecInternal,previousStateLoc,previousStateAct
scoresLoc.append(temp.std()) scoresLoc.append(temp.std())
scoresLoc.append(temp.mean()) scoresLoc.append(temp.mean())
scoresLoc.append(temp.std()) scoresLoc.append(temp.std())
if (StanceTest):
y_pred = sclf.predict(y_Test)
if (loop == 0):
print(accuracy_score(y_true, y_pred))
elif (loop == 1):
print(precision_score(y_true, y_pred, average='weighted'))
elif (loop == 2):
print(recall_score(y_true, y_pred, average='weighted'))
else:
print(f1_score(y_true, y_pred, average='weighted'))
return scoresLoc return scoresLoc

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