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Angelos Chatzimparmpas 4 years ago
parent 5c58a74d3b
commit 7c673c7155
30 changed files with 403 additions and 252 deletions
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{"duration": 35.12238168716431, "input_args": {"XData": " Fbs Slope Trestbps Exang Thalach Age Chol Sex Oldpeak Restecg Cp Ca Thal\n0 1 0 145 0 150 63 233 1 2.3 0 3 0 1\n1 0 0 130 0 187 37 250 1 3.5 1 2 0 2\n2 0 2 130 0 172 41 204 0 1.4 0 1 0 2\n3 0 2 120 0 178 56 236 1 0.8 1 1 0 2\n4 0 2 120 1 163 57 354 0 0.6 1 0 0 2\n.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n601 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n602 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n603 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n604 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n605 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[606 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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": "KNeighborsClassifier(algorithm='ball_tree', metric='chebyshev', n_neighbors=25,\n weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 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], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "0"}}

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cachedir/joblib/run/randomSearch/f94f6f45ec13ac95464d12dacae2049a/output.pkl
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2
cachedir/joblib/run/randomSearch/func_code.py
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@ -1,4 +1,4 @@
# first line: 585
# first line: 584
@memory.cache
def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
print(clf)

10
frontend/src/components/Ensemble.vue
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@ -83,7 +83,7 @@ export default {
mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[i].replace(/\D/g,'')))
}
modelId = mergedStoreEnsembleLocFormatted.map((item) => modelId[item])
//modelId = mergedStoreEnsembleLocFormatted.map((item) => modelId[item])
colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
parameters = mergedStoreEnsembleLocFormatted.map((item) => parameters[item])
MDSData[0] = mergedStoreEnsembleLocFormatted.map((item) => MDSData[0][item])
@ -103,16 +103,16 @@ export default {
var classifiersInfoProcessing = []
for (let i = 0; i < modelId.length; i++) {
let tempSplit = modelId[i].split(/([0-9]+)/)
if (tempSplit[0] == 'KNN' || tempSplit[0] == 'KNN_C' || tempSplit[0] == 'KNN_M') {
if (tempSplit[0] == 'KNN' || tempSplit[0] == 'KNNC' || tempSplit[0] == 'KNNM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> k-nearest neighbor' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else if (tempSplit[0] == 'LR' || tempSplit[0] == 'LR_C' || tempSplit[0] == 'LR_M') {
else if (tempSplit[0] == 'LR' || tempSplit[0] == 'LRC' || tempSplit[0] == 'LRM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> logistic regression' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else if (tempSplit[0] == 'MLP' || tempSplit[0] == 'MLP_C' || tempSplit[0] == 'MLP_M') {
else if (tempSplit[0] == 'MLP' || tempSplit[0] == 'MLPC' || tempSplit[0] == 'MLPM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> multilayer perceptron' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else if (tempSplit[0] == 'RF' || tempSplit[0] == 'RF_C' || tempSplit[0] == 'RF_M') {
else if (tempSplit[0] == 'RF' || tempSplit[0] == 'RFC' || tempSplit[0] == 'RFM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> random forest' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else {

201
frontend/src/components/History.vue
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@ -36,14 +36,6 @@ export default {
svg.selectAll("*").remove();
},
computePerformanceDiff () {
Array.prototype.max = function() {
return Math.max.apply(null, this);
};
Array.prototype.min = function() {
return Math.min.apply(null, this);
};
var colorsforScatterPlot = this.PerF
var mergedStoreEnsembleLoc = [].concat.apply([], this.storedEnsem)
@ -51,139 +43,149 @@ export default {
for (let i = 0; i < mergedStoreEnsembleLoc.length; i++) {
mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[i].replace(/\D/g,'')))
}
colorsforScatterPlot = mergedStoreEnsembleLocFormatted.map((item) => colorsforScatterPlot[item])
console.log(colorsforScatterPlot)
var min = Math.min.apply(null, colorsforScatterPlot),
max = Math.max.apply(null, colorsforScatterPlot);
console.log(max)
var max = Math.max.apply(Math, colorsforScatterPlot)
var min = Math.min.apply(Math, colorsforScatterPlot)
var countMax = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
var countMin = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
console.log(this.storedEnsem)
console.log(this.storedCM)
console.log(this.PerFCM)
for (let i = 0; i < this.storedCM.length; i++) {
let tempSplit = this.storedCM[i].split(/([0-9]+)/)
console.log(tempSplit[0])
if (tempSplit[0] == 'KNN_C') {
console.log(this.PerFCM[i])
console.log(max)
if (tempSplit[0] == 'KNNC') {
if (this.PerFCM[i] > max) {
countMax[0]++
countMax[0] = countMax[0] + 1
} else if (this.PerFCM[i] < min) {
countMin[0]++
countMin[0] = countMin[0] + 1
} else {
continue
}
}
else if (tempSplit[0] == 'KNN_M') {
else if (tempSplit[0] == 'KNNM') {
if (this.PerFCM[i] > max) {
countMax[1]++
countMax[1] = countMax[1] + 1
} else if (this.PerFCM[i] < min) {
countMin[1]++
countMin[1] = countMin[1] + 1
} else {
continue
}
}
else if (tempSplit[0] == 'LR_C') {
else if (tempSplit[0] == 'LRC') {
if (this.PerFCM[i] > max) {
countMax[2]++
countMax[2] = countMax[2] + 1
}
else if (this.PerFCM[i] < min) {
countMin[2]++
countMin[2] = countMin[2] + 1
} else {
continue
}
}
else if (tempSplit[0] == 'LR_M') {
else if (tempSplit[0] == 'LRM') {
if (this.PerFCM[i] > max) {
countMax[3]++
countMax[3] = countMax[3] + 1
}
else if (this.PerFCM[i] < min) {
countMin[3]++
countMin[3] = countMin[3] + 1
} else {
continue
}
}
else if (tempSplit[0] == 'MLP_C') {
else if (tempSplit[0] == 'MLPC') {
if (this.PerFCM[i] > max) {
countMax[4]++
countMax[4] = countMax[4] + 1
}
else if (this.PerFCM[i] < min) {
countMin[4]++
countMin[4] = countMin[4] + 1
} else {
continue
}
}
else if (tempSplit[0] == 'MLP_M') {
else if (tempSplit[0] == 'MLPM') {
if (this.PerFCM[i] > max) {
countMax[5]++
countMax[5] = countMax[5] + 1
}
else if (this.PerFCM[i] < min) {
countMin[5]++
countMin[5] = countMin[5] + 1
} else {
continue
}
}
else if (tempSplit[0] == 'RF_C') {
else if (tempSplit[0] == 'RFC') {
if (this.PerFCM[i] > max) {
countMax[6]++
countMax[6] = countMax[6] + 1
}
else if (this.PerFCM[i] < min) {
countMin[6]++
countMin[6] = countMin[6] + 1
}
}
else if (tempSplit[0] == 'RF_M') {
else if (tempSplit[0] == 'RFM') {
if (this.PerFCM[i] > max) {
countMax[7]++
countMax[7] = countMax[7] + 1
}
else if (this.PerFCM[i] < min) {
countMin[7]++
countMin[7] = countMin[7] + 1
} else {
continue
}
}
else if (tempSplit[0] == 'GradB_C') {
else if (tempSplit[0] == 'GradBC') {
if (this.PerFCM[i] > max) {
countMax[8]++
countMax[8] = countMax[8] + 1
}
else if (this.PerFCM[i] < min) {
countMin[8]++
countMin[8] = countMin[8] + 1
} else {
continue
}
}
else {
if (this.PerFCM[i] > max) {
countMax[9]++
countMax[9] = countMax[9] + 1
}
else if (this.PerFCM[i] < min) {
countMin[9]++
countMin[9] = countMin[9] + 1
} else {
continue
}
}
}
console.log(countMax)
console.log(countMin)
var percentage = []
for (let j = 0; j < countMax.length; j++) {
if (j >= 5) {
if (countMax[j] == 0) {
percentage.push((countMin[j]/this.values[15-j])*(-1)*100)
percentage.push((countMin[j]/5)*(-1)*100)
} else {
percentage.push(countMax[j]/this.values[15-j] * 100)
percentage.push(countMax[j]/5 * 100)
}
} else {
if (countMax[j] == 0) {
percentage.push((countMin[j]/this.values[16-j])*(-1) * 100)
percentage.push((countMin[j]/5)*(-1) * 100)
} else {
percentage.push(countMax[j]/this.values[16-j] * 100)
percentage.push(countMax[j]/5 * 100)
}
}
}
//CORRECT
// var percentage = []
// for (let j = 0; j < countMax.length; j++) {
// if (j >= 5) {
// if (countMax[j] == 0) {
// percentage.push((countMin[j]/this.values[15-j])*(-1)*100)
// } else {
// percentage.push(countMax[j]/this.values[15-j] * 100)
// }
// } else {
// if (countMax[j] == 0) {
// percentage.push((countMin[j]/this.values[16-j])*(-1) * 100)
// } else {
// percentage.push(countMax[j]/this.values[16-j] * 100)
// }
// }
// }
this.percentageOverall = percentage
@ -369,8 +371,8 @@ export default {
sankey.nodes(graph.nodes)
.links(graph.links)
.layout(0);
var colorDiff = d3v5.scaleLinear().domain([-100,100]).range(['#40004b','#762a83','#9970ab','#c2a5cf','#e7d4e8','#d9f0d3','#a6dba0','#5aae61','#1b7837','#00441b'])
var colorDiff
colorDiff = d3v5.scaleSequential(d3v5.interpolatePRGn).domain([-100, 100])
var percentage = this.percentageOverall
console.log(percentage)
// add in the links
@ -380,37 +382,37 @@ export default {
.attr("class", "link")
.attr("d", path) //d??? look it up later
.style("stroke",function(d){
if(d.source.node == 5){
return "transparent";
}
if(d.source.node == 11){
return "transparent";
}
if(d.source.node == 17){
return "transparent";
}
if(d.source.target == 16){
if(d.source.node == 5){
return "transparent";
}
if(d.source.node == 11){
return "transparent";
}
if(d.source.node == 17){
return "transparent";
}
if(d.target.node == 16){
return colorDiff(percentage[0]);
} else if(d.source.target == 15){
} else if(d.target.node == 15){
return colorDiff(percentage[1]);
} else if(d.source.target == 14){
} else if(d.target.node == 14){
return colorDiff(percentage[2]);
} else if(d.source.target == 13){
} else if(d.target.node == 13){
return colorDiff(percentage[3]);
} else if(d.source.target == 12){
} else if(d.target.node == 12){
return colorDiff(percentage[4]);
} else if(d.source.target == 10){
} else if(d.target.node == 10){
return colorDiff(percentage[5]);
} else if(d.source.target == 9){
} else if(d.target.node == 9){
return colorDiff(percentage[6]);
} else if(d.source.target == 8){
} else if(d.target.node == 8){
return colorDiff(percentage[7]);
} else if(d.source.target == 7){
} else if(d.target.node == 7){
return colorDiff(percentage[8]);
} else if(d.source.target == 6){
} else if(d.target.node == 6){
return colorDiff(percentage[9]);
} else {
return "#000000"
return "#808080"
}
})
.style("stroke-width", function(d) { return Math.max(.5, d.dh); }) //setting the stroke length by the data . d.dh is defined in sankey.js
@ -530,11 +532,11 @@ export default {
}
function linkmouseover(d){
d3.select(this)
.attr("stroke-opacity",.5);
.attr("stroke-opacity",.8);
}
function linkmouseout(d){
d3.select(this)
.attr("stroke-opacity",.05);
.attr("stroke-opacity",.3);
}
//select all of our links and set a new stroke color on the conditioan that the value is =.01.
@ -702,7 +704,7 @@ export default {
if(d.source.node == 5){
return "transparent";
} else {
return "#000000 !important"
return "#808080"
}
})
.style("stroke-width", function(d) { return Math.max(.5, d.dh); }) //setting the stroke length by the data . d.dh is defined in sankey.js
@ -755,7 +757,6 @@ export default {
$("input[type='number']").change( function(d) {
valuesLoc[d.target.id] = parseInt(d.target.value)
console.log(valuesLoc)
EventBus.$emit('changeValues', valuesLoc)
});
}
@ -817,11 +818,11 @@ export default {
}
function linkmouseover(d){
d3.select(this)
.attr("stroke-opacity",.5);
.attr("stroke-opacity",.8);
}
function linkmouseout(d){
d3.select(this)
.attr("stroke-opacity",.05);
.attr("stroke-opacity",.3);
}
//select all of our links and set a new stroke color on the conditioan that the value is =.01.
@ -896,28 +897,26 @@ export default {
.link {
fill: none;
stroke-opacity: .05;
stroke-opacity: .3;
}
.link:hover {
stroke-opacity: .5;
stroke-opacity: .8;
}
#clicked {
stroke-opacity: .5 !important;
}
/* #unclicked {
stroke-opacity: .05;
}*/
.axis path,
.axis line {
fill: none;
stroke: black;
shape-rendering: crispEdges;
margin-left:60px;
}
.axis text {
font-family: sans-serif;
font-size: 11px;
}
stroke-opacity: .8;
}
.axis path,
.axis line {
fill: none;
stroke: #808080;
shape-rendering: crispEdges;
margin-left:60px;
}
.axis text {
font-family: sans-serif;
font-size: 11px;
}
</style>

12
frontend/src/components/HyperParameterSpace.vue
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@ -66,7 +66,7 @@ export default {
var MDSData= JSON.parse(this.ScatterPlotResults[9])
var TSNEData = JSON.parse(this.ScatterPlotResults[10])
var UMAPData = JSON.parse(this.ScatterPlotResults[11])
EventBus.$emit('sendPointsNumber', modelId.length)
var stringParameters = []
@ -78,16 +78,16 @@ export default {
var classifiersInfoProcessing = []
for (let i = 0; i < modelId.length; i++) {
let tempSplit = modelId[i].split(/([0-9]+)/)
if (tempSplit[0] == 'KNN' || tempSplit[0] == 'KNN_C' || tempSplit[0] == 'KNN_M') {
if (tempSplit[0] == 'KNN' || tempSplit[0] == 'KNNC' || tempSplit[0] == 'KNNM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> k-nearest neighbor' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else if (tempSplit[0] == 'LR' || tempSplit[0] == 'LR_C' || tempSplit[0] == 'LR_M') {
else if (tempSplit[0] == 'LR' || tempSplit[0] == 'LRC' || tempSplit[0] == 'LRM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> logistic regression' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else if (tempSplit[0] == 'MLP' || tempSplit[0] == 'MLP_C' || tempSplit[0] == 'MLP_M') {
else if (tempSplit[0] == 'MLP' || tempSplit[0] == 'MLPC' || tempSplit[0] == 'MLPM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> multilayer perceptron' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else if (tempSplit[0] == 'RF' || tempSplit[0] == 'RF_C' || tempSplit[0] == 'RF_M') {
else if (tempSplit[0] == 'RF' || tempSplit[0] == 'RFC' || tempSplit[0] == 'RFM') {
classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> random forest' + '<br><b>Parameters:</b> ' + stringParameters[i]
}
else {
@ -298,7 +298,9 @@ export default {
pushModelsRemainingTemp.push(allModels[i])
}
}
console.log(pushModelsRemainingTemp)
EventBus.$emit('RemainingPoints', pushModelsRemainingTemp)
console.log(ClassifierIDsList)
EventBus.$emit('SendSelectedPointsUpdateIndicator', ClassifierIDsList)
EventBus.$emit('SendSelectedPointsToServerEvent', ClassifierIDsList)
}

11
frontend/src/components/Main.vue
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@ -169,7 +169,7 @@
</b-col>
<b-col cols="3">
<mdb-card style="margin-top: 15px;">
<mdb-card-header color="primary-color" tag="h5" class="text-center">Predictive Results for Majority-Voting Ensemble<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">2</span></span>
<mdb-card-header color="primary-color" tag="h5" class="text-center"><span class="float-left"><font-awesome-icon icon="calculator" /></span>Predictive Results for Majority-Voting Ensemble<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">2</span></span>
</mdb-card-header>
<mdb-card-body>
<mdb-card-text class="text-center" style="min-height: 270px">
@ -233,7 +233,7 @@ export default Vue.extend({
CMNumberofModelsOFFICIAL: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0],
CMNumberofModels: [0,0,0,0,0,0,5,5,5,5,5,0,5,5,5,5,5,0], // Remove that!
CMNumberofModelsOFFICIALS2: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0,25,25,25,25,25,0],
CMNumberofModelsS2: [0,0,0,0,0,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0,5,5,5,5,5,0], // Remove that!
CMNumberofModelsS2: [0,0,0,0,0,0,5,5,5,5,5,0,5,5,5,5,5,0,2,2,2,2,2,0,2,2,2,2,2,0,2,2,2,2,2,0,2,2,2,2,2,0], // Remove that!
CurrentStage: 1,
projectionID_A: 1,
projectionID_B: 1,
@ -353,9 +353,9 @@ export default Vue.extend({
var IDsLocal = JSON.parse(this.OverviewResults[0])
var Performance = JSON.parse(this.OverviewResults[1])
EventBus.$emit('SendIDs', IDsLocal)
EventBus.$emit('SendPerformance', Performance)
EventBus.$emit('SendStoredEnsembleHist', this.ModelsLocal)
EventBus.$emit('SendStoredEnsembleHist', this.storeEnsemble)
EventBus.$emit('SendStoredEnsemble', this.storeEnsemble)
EventBus.$emit('SendPerformance', Performance)
EventBus.$emit('emittedEventCallingCrossoverMutation', this.OverviewResults)
this.PredictSelEnsem = []
EventBus.$emit('emittedEventCallingSankeyStage2')
@ -527,6 +527,7 @@ export default Vue.extend({
} else {
this.OverSelLengthCM = this.ClassifierIDsListCM.length
const path = `http://127.0.0.1:5000/data/ServerRequestSelPoin`
console.log(this.ClassifierIDsListCM)
const postData = {
ClassifiersList: this.ClassifierIDsListCM,
}
@ -568,7 +569,7 @@ export default Vue.extend({
}
axios.post(path, postData, axiosConfig)
.then(response => {
console.log('Sent the selected points to the server (scatterplot)!')
console.log('Remove from Ensemble (scatterplot)!')
this.getDatafromtheBackEnd()
})
.catch(error => {

7
frontend/src/components/Predictions.vue
Unescape View File

@ -274,6 +274,7 @@ export default {
if (this.predictSelection.length != 0) {
var predictions = this.predictSelection
console.log(predictions)
var KNNPred = predictions[0]
var LRPred = predictions[1]
var MLPPred = predictions[2]
@ -281,6 +282,7 @@ export default {
var GradBPred = predictions[4]
var PredAver = predictions[5]
} else {
console.log(predictionsAll)
var KNNPred = predictionsAll[0]
var LRPred = predictionsAll[1]
var MLPPred = predictionsAll[2]
@ -294,10 +296,11 @@ export default {
var RFPredAll = predictionsAll[3]
var GradBPredAll = predictionsAll[4]
var PredAverAll = predictionsAll[5]
console.log(PredAverAll)
console.log(PredAver)
var yValues = JSON.parse(this.GetResultsSelection[6])
var targetNames = JSON.parse(this.GetResultsSelection[7])
console.log(yValues)
var getIndices = []
for (let i = 0; i < targetNames.length; i++) {
let clTemp = []

47
frontend/src/components/ValidationController.vue
Unescape View File

@ -25,13 +25,34 @@ export default {
var chart2
var data = []
for (let i=0; i<500; i++){
data.push({Algorithm:"Accuracy",value:randomIntFromInterval(40,100)})
data.push({Algorithm:"Precision",value:randomIntFromInterval(60,100)})
data.push({Algorithm:"Recall",value:randomIntFromInterval(30,100)})
data.push({Algorithm:"F1-score",value:randomIntFromInterval(0,100)})
for (let i=0; i<400; i++){
if (i < 100){
data.push({Algorithm:"Accuracy",value:randomIntFromInterval(10,60), category:"#ff7f00"})
data.push({Algorithm:"Accuracy",value:randomIntFromInterval(10,30), category:"#fdbf6f"})
data.push({Algorithm:"Accuracy",value:randomIntFromInterval(15,25), category:"#fb9a99"})
data.push({Algorithm:"Accuracy",value:randomIntFromInterval(35,45), category:"#b15928"})
data.push({Algorithm:"Accuracy",value:randomIntFromInterval(55,70), category:"#a6cee3"})
} else if (i < 200){
data.push({Algorithm:"Precision",value:randomIntFromInterval(10,60), category:"#ff7f00"})
data.push({Algorithm:"Precision",value:randomIntFromInterval(40,70), category:"#fdbf6f"})
data.push({Algorithm:"Precision",value:randomIntFromInterval(60,100), category:"#fb9a99"})
data.push({Algorithm:"Precision",value:randomIntFromInterval(60,79), category:"#b15928"})
data.push({Algorithm:"Precision",value:randomIntFromInterval(40,45), category:"#a6cee3"})
} else if (i < 300){
data.push({Algorithm:"Recall",value:randomIntFromInterval(30,40), category:"#ff7f00"})
data.push({Algorithm:"Recall",value:randomIntFromInterval(30,40), category:"#fdbf6f"})
data.push({Algorithm:"Recall",value:randomIntFromInterval(12,30), category:"#fb9a99"})
data.push({Algorithm:"Recall",value:randomIntFromInterval(30,40), category:"#b15928"})
data.push({Algorithm:"Recall",value:randomIntFromInterval(30,70), category:"#a6cee3"})
} else {
data.push({Algorithm:"F1-score",value:randomIntFromInterval(20,80), category:"#ff7f00"})
data.push({Algorithm:"F1-score",value:randomIntFromInterval(30,40), category:"#fdbf6f"})
data.push({Algorithm:"F1-score",value:randomIntFromInterval(50,70), category:"#fb9a99"})
data.push({Algorithm:"F1-score",value:randomIntFromInterval(60,70), category:"#b15928"})
data.push({Algorithm:"F1-score",value:randomIntFromInterval(80,100), category:"#a6cee3"})
}
}
function randomIntFromInterval(min, max) { // min and max included
return Math.floor(Math.random() * (max - min + 1) + min);
}
@ -44,7 +65,7 @@ export default {
selector:"#violin",
constrainExtremes:true});
chart2.renderBoxPlot({showBox:false});
chart2.renderDataPlots({showBeanLines:true,beanWidth:15,showPlot:false,colors:['#ff7f00','#fdbf6f','#fb9a99','#b15928','#a6cee3'],showLines:['median']});
chart2.renderDataPlots({showBeanLines:true,beanWidth:15,showPlot:false,showLines:['median']});
chart2.renderNotchBoxes({showNotchBox:false});
chart2.renderViolinPlot({reset:true, width:75, clamp:0, resolution:30, bandwidth:50});
@ -131,7 +152,7 @@ export default {
return colorOptions[group];
}
} else {
return d3.scale.ordinal().range(['#555','#555','#555','#555','#ffff99'])
return d3.scale.ordinal().range(['#c0c0c0','#c0c0c0','#c0c0c0','#c0c0c0','#ffff99'])
}
}
@ -1288,7 +1309,6 @@ export default {
cNotch = chart.groupObjs[cName].notchBox;
cNotch.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "notch-plot");
// Plot Box (default show)
if (nOpts.showNotchBox) {
cNotch.objs.notch = cNotch.objs.g.append("polygon")
@ -1346,7 +1366,8 @@ export default {
showBeanLines: false,
beanWidth: 20,
colors: null
};
};
chart.dataPlots.options = shallowCopy(defaultOptions);
for (var option in options) {
chart.dataPlots.options[option] = options[option]
@ -1540,6 +1561,8 @@ export default {
}
// mention active models number
var loopValue = 0
for (cName in chart.groupObjs) {
@ -1567,9 +1590,11 @@ export default {
cPlot.objs.bean.lines.push(cPlot.objs.bean.g.append("line")
.attr("class", "bean line")
.style("stroke-width", '1')
.style("stroke", chart.dataPlots.colorFunct(cName)));
.style("stroke", function () { return chart.data[pt+loopValue].category; }));
}
}
loopValue = loopValue + 500
}
};

350
run.py
Unescape View File

@ -542,7 +542,6 @@ def retrieveModel():
global HistoryPreservation
for eachAlgor in algorithms:
print(eachAlgor)
if (eachAlgor) == 'KNN':
clf = KNeighborsClassifier()
params = {'n_neighbors': list(range(1, 100)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}
@ -569,7 +568,7 @@ def retrieveModel():
AlgorithmsIDsEnd = countAllModels
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': list(range(20, 100)), 'learning_rate': list(np.arange(0.01,0.56,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']}
params = {'n_estimators': list(range(20, 100)), 'loss': ['deviance', 'exponential'], 'learning_rate': list(np.arange(0.01,0.56,0.11)), 'subsample': list(np.arange(0.1,1,0.1)), 'criterion': ['friedman_mse', 'mse', 'mae']}
countAllModels = countAllModels + randomSearchVar
AlgorithmsIDsEnd = countAllModels
countAllModels = countAllModels + randomSearchVar
@ -684,6 +683,7 @@ def PreprocessingIDs():
return df_concatIDs
def PreprocessingMetrics():
global allParametersPerformancePerModel
dicKNN = allParametersPerformancePerModel[2]
dicLR = allParametersPerformancePerModel[6]
dicMLP = allParametersPerformancePerModel[10]
@ -700,6 +700,24 @@ def PreprocessingMetrics():
df_concatMetrics = df_concatMetrics.reset_index(drop=True)
return df_concatMetrics
def PreprocessingMetricsEnsem():
global allParametersPerformancePerModelEnsem
dicKNN = allParametersPerformancePerModelEnsem[2]
dicLR = allParametersPerformancePerModelEnsem[6]
dicMLP = allParametersPerformancePerModelEnsem[10]
dicRF = allParametersPerformancePerModelEnsem[14]
dicGradB = allParametersPerformancePerModelEnsem[18]
dfKNN = pd.DataFrame.from_dict(dicKNN)
dfLR = pd.DataFrame.from_dict(dicLR)
dfMLP = pd.DataFrame.from_dict(dicMLP)
dfRF = pd.DataFrame.from_dict(dicRF)
dfGradB = pd.DataFrame.from_dict(dicGradB)
df_concatMetrics = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])
df_concatMetrics = df_concatMetrics.reset_index(drop=True)
return df_concatMetrics
def PreprocessingPred():
dicKNN = allParametersPerformancePerModel[3]
dicLR = allParametersPerformancePerModel[7]
@ -765,23 +783,23 @@ def EnsembleIDs():
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
if match:
items = match.groups()
if (items[0] == 'KNN'):
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM")):
numberIDKNNGlob.append(int(items[1]))
elif (items[0] == 'LR'):
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM")):
numberIDLRGlob.append(int(items[1]))
elif (items[0] == 'MLP'):
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM")):
numberIDMLPGlob.append(int(items[1]))
elif (items[0] == 'RF'):
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM")):
numberIDRFGlob.append(int(items[1]))
else:
numberIDGradBGlob.append(int(items[1]))
EnsembleIdsAll = numberIDKNNGlob + numberIDLRGlob + numberIDMLPGlob + numberIDRFGlob + numberIDGradBGlob
return EnsembleIdsAll
def PreprocessingPredEnsemble():
global EnsembleActive
global allParametersPerformancePerModelEnsem
numberIDKNN = []
numberIDLR = []
numberIDMLP = []
@ -792,22 +810,22 @@ def PreprocessingPredEnsemble():
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
if match:
items = match.groups()
if (items[0] == 'KNN'):
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM")):
numberIDKNN.append(int(items[1]))
elif (items[0] == 'LR'):
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM")):
numberIDLR.append(int(items[1]))
elif (items[0] == 'MLP'):
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM")):
numberIDMLP.append(int(items[1]))
elif (items[0] == 'RF'):
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM")):
numberIDRF.append(int(items[1]))
else:
numberIDGradB.append(int(items[1]))
dicKNN = allParametersPerformancePerModel[3]
dicLR = allParametersPerformancePerModel[7]
dicMLP = allParametersPerformancePerModel[11]
dicRF = allParametersPerformancePerModel[15]
dicGradB = allParametersPerformancePerModel[19]
dicKNN = allParametersPerformancePerModelEnsem[3]
dicLR = allParametersPerformancePerModelEnsem[7]
dicMLP = allParametersPerformancePerModelEnsem[11]
dicRF = allParametersPerformancePerModelEnsem[15]
dicGradB = allParametersPerformancePerModelEnsem[19]
dfKNN = pd.DataFrame.from_dict(dicKNN)
dfLR = pd.DataFrame.from_dict(dicLR)
@ -895,6 +913,41 @@ def PreprocessingParam():
df_params = df_params.reset_index(drop=True)
return df_params
def PreprocessingParamEnsem():
dicKNN = allParametersPerformancePerModelEnsem[1]
dicLR = allParametersPerformancePerModelEnsem[5]
dicMLP = allParametersPerformancePerModelEnsem[9]
dicRF = allParametersPerformancePerModelEnsem[13]
dicGradB = allParametersPerformancePerModelEnsem[17]
dicKNN = dicKNN['params']
dicLR = dicLR['params']
dicMLP = dicMLP['params']
dicRF = dicRF['params']
dicGradB = dicGradB['params']
dicKNN = {int(k):v for k,v in dicKNN.items()}
dicLR = {int(k):v for k,v in dicLR.items()}
dicMLP = {int(k):v for k,v in dicMLP.items()}
dicRF = {int(k):v for k,v in dicRF.items()}
dicGradB = {int(k):v for k,v in dicGradB.items()}
dfKNN = pd.DataFrame.from_dict(dicKNN)
dfLR = pd.DataFrame.from_dict(dicLR)
dfMLP = pd.DataFrame.from_dict(dicMLP)
dfRF = pd.DataFrame.from_dict(dicRF)
dfGradB = pd.DataFrame.from_dict(dicGradB)
dfKNN = dfKNN.T
dfLR = dfLR.T
dfMLP = dfMLP.T
dfRF = dfRF.T
dfGradB = dfGradB.T
df_params = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])
df_params = df_params.reset_index(drop=True)
return df_params
def PreprocessingParamSep():
dicKNN = allParametersPerformancePerModel[1]
dicLR = allParametersPerformancePerModel[5]
@ -928,7 +981,6 @@ def PreprocessingParamSep():
return [dfKNN, dfLR, dfMLP, dfRF, dfGradB]
# remove that maybe!
def preProcsumPerMetric(factors):
sumPerClassifier = []
loopThroughMetrics = PreprocessingMetrics()
@ -945,6 +997,22 @@ def preProcsumPerMetric(factors):
sumPerClassifier.append(rowSum/sum(factors) * 100)
return sumPerClassifier
def preProcsumPerMetricEnsem(factors):
sumPerClassifier = []
loopThroughMetrics = PreprocessingMetricsEnsem()
loopThroughMetrics = loopThroughMetrics.fillna(0)
loopThroughMetrics.loc[:, 'log_loss'] = 1 - loopThroughMetrics.loc[:, 'log_loss']
for row in loopThroughMetrics.iterrows():
rowSum = 0
name, values = row
for loop, elements in enumerate(values):
rowSum = elements*factors[loop] + rowSum
if sum(factors) is 0:
sumPerClassifier = 0
else:
sumPerClassifier.append(rowSum/sum(factors) * 100)
return sumPerClassifier
def preProcMetricsAllAndSel():
loopThroughMetrics = PreprocessingMetrics()
loopThroughMetrics = loopThroughMetrics.fillna(0)
@ -1004,6 +1072,7 @@ def InitializeEnsemble():
global ModelSpaceTSNE
global allParametersPerformancePerModel
global EnsembleActive
global ModelsIDs
global keySend
XModels = XModels.fillna(0)
@ -1014,16 +1083,24 @@ def InitializeEnsemble():
ModelSpaceUMAP = FunUMAP(XModels)
if (len(EnsembleActive) == 0):
parametersGen = PreprocessingParam()
PredictionProbSel = PreprocessingPred()
ModelsIDs = PreprocessingIDs()
sumPerClassifier = preProcsumPerMetric(factors)
else:
parametersGen = PreprocessingParamEnsem()
PredictionProbSel = PreprocessingPredEnsemble()
ModelsIds = EnsembleIDs()
EnsembleModel(ModelsIds, keySend)
ModelsIDs = EnsembleActive
modelsIdsCuts = EnsembleIDs()
sumPerClassifier = preProcsumPerMetricEnsem(factors)
EnsembleModel(modelsIdsCuts, keySend)
keySend=1
returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel)
returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,parametersGen,sumPerClassifier,PredictionProbSel)
def EnsembleModel (Models, keyRetrieved):
global XDataTest, yDataTest
global scores
global previousState
@ -1105,6 +1182,7 @@ def EnsembleModel (Models, keyRetrieved):
}
dfParamRF = pd.DataFrame.from_dict(tempDic)
dfParamRFFilt = dfParamRF.iloc[:,0]
for eachelem in numberIDRFGlob:
if (eachelem >= greater):
arg = dfParamRFFilt[eachelem-addRF]
@ -1245,23 +1323,20 @@ def classification_report_with_accuracy_score(y_true, y_pred):
PerClassResultsClass1.append(Filter_PerClassResultsLocal1)
return accuracy_score(y_true, y_pred) # return accuracy score
def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel):
def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,parametersGen,sumPerClassifier,PredictionProbSel):
global Results
global AllTargets
global names_labels
global EnsembleActive
global ModelsIDs
Results = []
parametersGen = PreprocessingParam()
metricsPerModel = preProcMetricsAllAndSel()
sumPerClassifier = preProcsumPerMetric(factors)
ModelsIDs = PreprocessingIDs()
parametersGenPD = parametersGen.to_json(orient='records')
XDataJSONEntireSet = XData.to_json(orient='records')
XDataColumns = XData.columns.tolist()
Results.append(json.dumps(ModelsIDs))
Results.append(json.dumps(sumPerClassifier))
Results.append(json.dumps(parametersGenPD))
@ -1332,16 +1407,16 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
global randomSearchVar
greater = randomSearchVar*5
KNNIDsC = list(filter(lambda k: 'KNN_C' in k, RemainingIds))
LRIDsC = list(filter(lambda k: 'LR_C' in k, RemainingIds))
MLPIDsC = list(filter(lambda k: 'MLP_C' in k, RemainingIds))
RFIDsC = list(filter(lambda k: 'RF_C' in k, RemainingIds))
GradBIDsC = list(filter(lambda k: 'GradB_C' in k, RemainingIds))
KNNIDsM = list(filter(lambda k: 'KNN_M' in k, RemainingIds))
LRIDsM = list(filter(lambda k: 'LR_M' in k, RemainingIds))
MLPIDsM = list(filter(lambda k: 'MLP_M' in k, RemainingIds))
RFIDsM = list(filter(lambda k: 'RF_M' in k, RemainingIds))
GradBIDsM = list(filter(lambda k: 'GradB_M' in k, RemainingIds))
KNNIDsC = list(filter(lambda k: 'KNNC' in k, RemainingIds))
LRIDsC = list(filter(lambda k: 'LRC' in k, RemainingIds))
MLPIDsC = list(filter(lambda k: 'MLPC' in k, RemainingIds))
RFIDsC = list(filter(lambda k: 'RFC' in k, RemainingIds))
GradBIDsC = list(filter(lambda k: 'GradBC' in k, RemainingIds))
KNNIDsM = list(filter(lambda k: 'KNNM' in k, RemainingIds))
LRIDsM = list(filter(lambda k: 'LRM' in k, RemainingIds))
MLPIDsM = list(filter(lambda k: 'MLPM' in k, RemainingIds))
RFIDsM = list(filter(lambda k: 'RFM' in k, RemainingIds))
GradBIDsM = list(filter(lambda k: 'GradBM' in k, RemainingIds))
countKNN = 0
countLR = 0
@ -1380,7 +1455,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_C_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNNCC', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
@ -1396,7 +1471,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
allParametersPerfCrossMutrKNNCC.append(localCrossMutr[1])
allParametersPerfCrossMutrKNNCC.append(localCrossMutr[2])
allParametersPerfCrossMutrKNNCC.append(localCrossMutr[3])
HistoryPreservation = HistoryPreservation + allParametersPerfCrossMutrKNNCC
countKNN = 0
@ -1430,7 +1505,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_C_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNNCM', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
@ -1473,7 +1548,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_C_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LRCC', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
@ -1523,7 +1598,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_C_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LRCM', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
@ -1567,7 +1642,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP_C_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLPCC', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
@ -1617,7 +1692,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP_C_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLPCM', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
@ -1661,7 +1736,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'max_depth': [crossoverDF['max_depth'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF_C_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RFCC', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
@ -1711,7 +1786,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'max_depth': [crossoverDF['max_depth'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF_C_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RFCM', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
@ -1749,13 +1824,13 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
valuePerColumn = pairDF[column].iloc[randomZeroOne]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['loss'] == crossoverDF['loss'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['subsample'] == crossoverDF['subsample'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'loss': [crossoverDF['loss'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'subsample': [crossoverDF['subsample'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB_C_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradBCC', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
@ -1799,13 +1874,13 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['loss'] == crossoverDF['loss'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['subsample'] == crossoverDF['subsample'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'loss': [crossoverDF['loss'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'subsample': [crossoverDF['subsample'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB_C_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradBCM', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
@ -1861,7 +1936,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_M_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNNMC', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
@ -1911,7 +1986,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_M_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNNMM', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
@ -1954,7 +2029,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_M_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LRMC', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
@ -2004,7 +2079,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_M_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LRMM', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
@ -2048,7 +2123,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP_M_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLPMC', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
@ -2098,7 +2173,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP_M_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLPMM', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
@ -2142,7 +2217,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'max_depth': [crossoverDF['max_depth'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF_M_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RFMC', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
@ -2192,7 +2267,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'max_depth': [crossoverDF['max_depth'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF_M_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RFMM', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
@ -2223,7 +2298,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
GradBPickPair = random.sample(GradBIntIndex,2)
pairDF = paramAllAlgs.iloc[GradBPickPair]
print(pairDF)
crossoverDF = pd.DataFrame()
for column in pairDF:
listData = []
@ -2231,13 +2306,13 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
valuePerColumn = pairDF[column].iloc[randomZeroOne]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['loss'] == crossoverDF['loss'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['subsample'] == crossoverDF['subsample'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'loss': [crossoverDF['loss'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'subsample': [crossoverDF['subsample'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB_M_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradBMC', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
@ -2281,13 +2356,13 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['loss'] == crossoverDF['loss'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['subsample'] == crossoverDF['subsample'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'loss': [crossoverDF['loss'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'subsample': [crossoverDF['subsample'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB_M_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradBMM', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
@ -2315,7 +2390,7 @@ def InitializeSecondStageCM (RemainingIds, setMaxLoopValue):
allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNCM[1]], ignore_index=True)
allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNCC[2]], ignore_index=True)
allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNCM[2]], ignore_index=True)
allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNCC[3]], ignore_index=True)
allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNCM[3]], ignore_index=True)
@ -2437,6 +2512,8 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
global allParametersPerfCrossMutr
global HistoryPreservation
global allParametersPerformancePerModel
global randomSearchVar
greater = randomSearchVar*5
@ -2483,7 +2560,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNNC', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
@ -2533,7 +2610,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = KNeighborsClassifier()
params = {'n_neighbors': [crossoverDF['n_neighbors'].iloc[0]], 'metric': [crossoverDF['metric'].iloc[0]], 'algorithm': [crossoverDF['algorithm'].iloc[0]], 'weights': [crossoverDF['weights'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countKNN
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNN_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'KNNM', AlgorithmsIDsEnd)
countKNN += 1
crossoverDF = pd.DataFrame()
@ -2576,7 +2653,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LRC', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
@ -2626,7 +2703,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = LogisticRegression(random_state=RANDOM_SEED)
params = {'C': [crossoverDF['C'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]], 'penalty': [crossoverDF['penalty'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countLR
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LR_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'LRM', AlgorithmsIDsEnd)
countLR += 1
crossoverDF = pd.DataFrame()
@ -2670,7 +2747,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLPC', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
@ -2720,7 +2797,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = MLPClassifier(random_state=RANDOM_SEED)
params = {'hidden_layer_sizes': [crossoverDF['hidden_layer_sizes'].iloc[0]], 'alpha': [crossoverDF['alpha'].iloc[0]], 'tol': [crossoverDF['tol'].iloc[0]], 'max_iter': [crossoverDF['max_iter'].iloc[0]], 'activation': [crossoverDF['activation'].iloc[0]], 'solver': [crossoverDF['solver'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countMLP
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLP_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'MLPM', AlgorithmsIDsEnd)
countMLP += 1
crossoverDF = pd.DataFrame()
@ -2764,7 +2841,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'max_depth': [crossoverDF['max_depth'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RFC', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
@ -2814,7 +2891,7 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
clf = RandomForestClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'max_depth': [crossoverDF['max_depth'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countRF
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RFM', AlgorithmsIDsEnd)
countRF += 1
crossoverDF = pd.DataFrame()
@ -2852,13 +2929,13 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
valuePerColumn = pairDF[column].iloc[randomZeroOne]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['loss'] == crossoverDF['loss'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['subsample'] == crossoverDF['subsample'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'loss': [crossoverDF['loss'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'subsample': [crossoverDF['subsample'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB_C', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradBC', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
@ -2902,13 +2979,13 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
valuePerColumn = pairDF[column].iloc[0]
listData.append(valuePerColumn)
crossoverDF[column] = listData
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
if (((paramAllAlgs['n_estimators'] == crossoverDF['n_estimators'].iloc[0]) & (paramAllAlgs['loss'] == crossoverDF['loss'].iloc[0]) & (paramAllAlgs['learning_rate'] == crossoverDF['learning_rate'].iloc[0]) & (paramAllAlgs['subsample'] == crossoverDF['subsample'].iloc[0]) & (paramAllAlgs['criterion'] == crossoverDF['criterion'].iloc[0])).any()):
crossoverDF = pd.DataFrame()
else:
clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
params = {'n_estimators': [crossoverDF['n_estimators'].iloc[0]], 'loss': [crossoverDF['loss'].iloc[0]], 'learning_rate': [crossoverDF['learning_rate'].iloc[0]], 'subsample': [crossoverDF['subsample'].iloc[0]], 'criterion': [crossoverDF['criterion'].iloc[0]]}
AlgorithmsIDsEnd = countAllModels + countGradB
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB_M', AlgorithmsIDsEnd)
localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradBM', AlgorithmsIDsEnd)
countGradB += 1
crossoverDF = pd.DataFrame()
@ -2929,6 +3006,49 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
localCrossMutr.clear()
global allParametersPerformancePerModelEnsem
allParametersPerformancePerModelEnsem = allParametersPerformancePerModel.copy()
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrKNNC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrKNNM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrKNNC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrKNNM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrKNNC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrKNNM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrLRC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrLRM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrLRC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrLRM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrLRC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrLRM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrMLPC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrMLPM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrMLPC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrMLPM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrMLPC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrMLPM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrRFC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrRFM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrRFC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrRFM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrRFC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrRFM[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrGradBC[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[17] = pd.concat([allParametersPerformancePerModelEnsem[17], allParametersPerfCrossMutrGradBM[1]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrGradBC[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[18] = pd.concat([allParametersPerformancePerModelEnsem[18], allParametersPerfCrossMutrGradBM[2]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrGradBC[3]], ignore_index=True)
allParametersPerformancePerModelEnsem[19] = pd.concat([allParametersPerformancePerModelEnsem[19], allParametersPerfCrossMutrGradBM[3]], ignore_index=True)
allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNC + allParametersPerfCrossMutrKNNM + allParametersPerfCrossMutrLRC + allParametersPerfCrossMutrLRM + allParametersPerfCrossMutrMLPC + allParametersPerfCrossMutrMLPM + allParametersPerfCrossMutrRFC + allParametersPerfCrossMutrRFM + allParametersPerfCrossMutrGradBC + allParametersPerfCrossMutrGradBM
allParametersPerformancePerModel[0] = allParametersPerformancePerModel[0] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0]
@ -2936,11 +3056,10 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNM[1]], ignore_index=True)
allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNC[2]], ignore_index=True)
allParametersPerformancePerModel[2] = pd.concat([allParametersPerformancePerModel[2], allParametersPerfCrossMutrKNNM[2]], ignore_index=True)
allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNC[3]], ignore_index=True)
allParametersPerformancePerModel[3] = pd.concat([allParametersPerformancePerModel[3], allParametersPerfCrossMutrKNNM[3]], ignore_index=True)
allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrLRC[0] + allParametersPerfCrossMutrLRM[0]
allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrLRC[1]], ignore_index=True)
@ -2972,12 +3091,12 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
allParametersPerformancePerModel[15] = pd.concat([allParametersPerformancePerModel[15], allParametersPerfCrossMutrRFM[3]], ignore_index=True)
allParametersPerformancePerModel[16] = allParametersPerformancePerModel[16] + allParametersPerfCrossMutrGradBC[0] + allParametersPerfCrossMutrGradBM[0]
allParametersPerformancePerModel[17] = pd.concat([allParametersPerformancePerModel[17], allParametersPerfCrossMutrGradBC[1]], ignore_index=True)
allParametersPerformancePerModel[17] = pd.concat([allParametersPerformancePerModel[17], allParametersPerfCrossMutrGradBM[1]], ignore_index=True)
allParametersPerformancePerModel[18] = pd.concat([allParametersPerformancePerModel[18], allParametersPerfCrossMutrGradBC[2]], ignore_index=True)
allParametersPerformancePerModel[18] = pd.concat([allParametersPerformancePerModel[18], allParametersPerfCrossMutrGradBM[2]], ignore_index=True)
allParametersPerformancePerModel[19] = pd.concat([allParametersPerformancePerModel[19], allParametersPerfCrossMutrGradBC[3]], ignore_index=True)
allParametersPerformancePerModel[19] = pd.concat([allParametersPerformancePerModel[19], allParametersPerfCrossMutrGradBM[3]], ignore_index=True)
@ -3376,29 +3495,28 @@ def PreProcessingInitial():
ModelSpaceTSNECM = ModelSpaceTSNECM.tolist()
ModelSpaceUMAPCM = FunUMAP(XModels)
PredictionProbSel = PreprocessingPredCM()
PredictionProbSelCM = PreprocessingPredCM()
CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSel)
CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSelCM)
def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSel):
def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,PredictionProbSelCM):
global ResultsCM
global AllTargets
ResultsCM = []
parametersGen = PreprocessingParamCM()
metricsPerModel = preProcMetricsAllAndSelCM()
sumPerClassifier = preProcsumPerMetricCM(factors)
ModelsIDs = PreprocessingIDsCM()
parametersGenPD = parametersGen.to_json(orient='records')
parametersGenCM = PreprocessingParamCM()
metricsPerModelCM = preProcMetricsAllAndSelCM()
sumPerClassifierCM = preProcsumPerMetricCM(factors)
ModelsIDsCM = PreprocessingIDsCM()
parametersGenPDGM = parametersGenCM.to_json(orient='records')
XDataJSONEntireSet = XData.to_json(orient='records')
XDataColumns = XData.columns.tolist()
ResultsCM.append(json.dumps(ModelsIDs))
ResultsCM.append(json.dumps(sumPerClassifier))
ResultsCM.append(json.dumps(parametersGenPD))
ResultsCM.append(json.dumps(metricsPerModel))
ResultsCM.append(json.dumps(ModelsIDsCM))
ResultsCM.append(json.dumps(sumPerClassifierCM))
ResultsCM.append(json.dumps(parametersGenPDGM))
ResultsCM.append(json.dumps(metricsPerModelCM))
ResultsCM.append(json.dumps(XDataJSONEntireSet))
ResultsCM.append(json.dumps(XDataColumns))
ResultsCM.append(json.dumps(yData))
@ -3407,7 +3525,7 @@ def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,Predict
ResultsCM.append(json.dumps(ModelSpaceMDSCM))
ResultsCM.append(json.dumps(ModelSpaceTSNECM))
ResultsCM.append(json.dumps(ModelSpaceUMAPCM))
ResultsCM.append(json.dumps(PredictionProbSel))
ResultsCM.append(json.dumps(PredictionProbSelCM))
ResultsCM.append(json.dumps(names_labels))
return ResultsCM
@ -3425,18 +3543,20 @@ def PreprocessingPredSel(SelectedIDs):
numberIDMLP = []
numberIDRF = []
numberIDGradB = []
print(SelectedIDs)
for el in SelectedIDs:
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
print(match)
if match:
items = match.groups()
if (items[0] == 'KNN'):
print(items)
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM")):
numberIDKNN.append(int(items[1]) - addKNN)
elif (items[0] == 'LR'):
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM")):
numberIDLR.append(int(items[1]) - addLR)
elif (items[0] == 'MLP'):
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM")):
numberIDMLP.append(int(items[1]) - addMLP)
elif (items[0] == 'RF'):
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM")):
numberIDRF.append(int(items[1]) - addRF)
else:
numberIDGradB.append(int(items[1]) - addGradB)
@ -3464,10 +3584,11 @@ def PreprocessingPredSel(SelectedIDs):
dfRF = dfRF.loc[numberIDRF]
dfRF.index += addKNN + addLR + addMLP
print(numberIDGradB)
dfGradB = pd.DataFrame.from_dict(dicGradB)
print(dfGradB)
dfGradB = dfGradB.loc[numberIDGradB]
print(dfGradB)
dfGradB.index += addKNN + addLR + addMLP + addRF
df_concatProbs = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])
@ -3512,7 +3633,7 @@ def RetrieveSelIDsPredict():
RetrieveIDsSelection = request.get_data().decode('utf8').replace("'", '"')
RetrieveIDsSelection = json.loads(RetrieveIDsSelection)
RetrieveIDsSelection = RetrieveIDsSelection['predictSelectionIDs']
print(RetrieveIDsSelection)
ResultsSelPred = PreprocessingPredSel(RetrieveIDsSelection)
return 'Everything Okay'
@ -3537,13 +3658,13 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
if match:
items = match.groups()
if (items[0] == 'KNN'):
if ((items[0] == "KNN") | (items[0] == "KNNC") | (items[0] == "KNNM")):
numberIDKNN.append(int(items[1]))
elif (items[0] == 'LR'):
elif ((items[0] == "LR") | (items[0] == "LRC") | (items[0] == "LRM")):
numberIDLR.append(int(items[1]))
elif (items[0] == 'MLP'):
elif ((items[0] == "MLP") | (items[0] == "MLPC") | (items[0] == "MLPM")):
numberIDMLP.append(int(items[1]))
elif (items[0] == 'RF'):
elif ((items[0] == "RF") | (items[0] == "RFC") | (items[0] == "RFM")):
numberIDRF.append(int(items[1]))
else:
numberIDGradB.append(int(items[1]))
@ -3629,10 +3750,15 @@ def SendPredictSelEnsem():
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@app.route('/data/ServerRequestSelPoin', methods=["GET", "POST"])
def RetrieveSelClassifiersID():
global EnsembleActive
ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
#ComputeMetricsForSel(ClassifierIDsList)
ClassifierIDCleaned = json.loads(ClassifierIDsList)
ClassifierIDCleaned = ClassifierIDCleaned['ClassifiersList']
EnsembleActive = []
EnsembleActive = ClassifierIDCleaned.copy()
EnsembleIDs()
EnsembleModel(ClassifierIDsList, 1)
return 'Everything Okay'

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