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cachedir/joblib/run/randomSearch/02358b134734520a2f9f2e6a6700b6b2/metadata.json

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cachedir/joblib/run/randomSearch/0fd67e8b2d136edf1b48240f5b9a01be/metadata.json

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cachedir/joblib/run/randomSearch/45f1c3542a49390ff9a4ef7017811b01/metadata.json

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cachedir/joblib/run/randomSearch/80458ad60b9e9e8bf5a0a61326cda85d/metadata.json

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cachedir/joblib/run/randomSearch/9048f73c37aedd04922355de19ec1bde/metadata.json

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cachedir/joblib/run/randomSearch/a2aa0c0018aabe2a6a5237dcbb542f1d/metadata.json

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cachedir/joblib/run/randomSearch/ec7b79af178a4597718aa6998e3ffb77/metadata.json

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cachedir/joblib/run/randomSearch/func_code.py

@@ -1,4 +1,4 @@
-# first line: 581
+# first line: 595
 @memory.cache
 def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 

frontend/src/components/AlgorithmHyperParam.vue

@@ -1,351 +0,0 @@
-<template>
-  <div id="PCP" class="parcoords" style="min-height: 285px;"></div>
-</template>
-
-<script>
-import 'parcoord-es/dist/parcoords.css';
-import ParCoords from 'parcoord-es';
-import * as d3Base from 'd3'
-
-// attach all d3 plugins to the d3 library
-const d3 = Object.assign(d3Base)
-
-import { EventBus } from '../main.js'
-
-export default {
-  name: 'AlgorithmHyperParam',
-  data () {
-    return {
-      ModelsPerformance: 0,
-      selAlgorithm: 0,
-      keyAllOrClass: true,
-      listClassPerf: [],
-      pc: 0,
-      factors: [1,0,0
-      ,1,0,0,1,0
-      ,0,1,0,0,0
-      ,0,0,1,0,0
-      ,0,1,1,1
-      ],
-      SVCModels: 576,
-      GausNBModels: 736, 
-      MLPModels: 1236,
-      LRModels: 1356, 
-      LDAModels: 1996,
-      QDAModels: 2196,
-      RFModels: 2446,
-      ExtraTModels: 2606,
-      AdaBModels: 2766,
-      GradBModels: 2926,
-    }
-  },
-  methods: {
-    reset () {
-      d3.selectAll("#PCP > *").remove(); 
-    },
-    PCPView () {
-      d3.selectAll("#PCP > *").remove(); 
-      if (this.selAlgorithm != '') {
-        var colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#b15928']
-        var colorGiv = 0
-        
-        var factorsLocal = this.factors
-        var divide = 0
-
-        factorsLocal.forEach(element => {
-          divide = element + divide
-        });
-
-        var McKNN = []
-        const performanceAlgKNN = JSON.parse(this.ModelsPerformance[6])
-        for (let j = 0; j < Object.values(performanceAlgKNN['mean_test_accuracy']).length; j++) {
-          let sumKNN
-          sumKNN = (factorsLocal[0] * Object.values(performanceAlgKNN['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgKNN['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgKNN['geometric_mean_score_macro'])[j])
-            + (factorsLocal[3] * Object.values(performanceAlgKNN['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgKNN['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgKNN['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgKNN['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgKNN['mean_test_recall_micro'])[j])
-            + (factorsLocal[8] * Object.values(performanceAlgKNN['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgKNN['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgKNN['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgKNN['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgKNN['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgKNN['f1_micro'])[j])
-            + (factorsLocal[14] * Object.values(performanceAlgKNN['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgKNN['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgKNN['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgKNN['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgKNN['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgKNN['matthews_corrcoef'])[j]))
-            + (factorsLocal[20] * Object.values(performanceAlgKNN['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgKNN['log_loss'])[j]))
-          McKNN.push((sumKNN/divide)*100)
-        }
-        var McSVC = []
-        const performanceAlgSVC = JSON.parse(this.ModelsPerformance[15])
-        for (let j = 0; j < Object.values(performanceAlgSVC['mean_test_accuracy']).length; j++) {
-          let sumSVC
-          sumSVC = (factorsLocal[0] * Object.values(performanceAlgSVC['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgSVC['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgSVC['geometric_mean_score_macro'])[j])
-            + (factorsLocal[3] * Object.values(performanceAlgSVC['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgSVC['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgSVC['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgSVC['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgSVC['mean_test_recall_micro'])[j])
-            + (factorsLocal[8] * Object.values(performanceAlgSVC['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgSVC['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgSVC['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgSVC['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgSVC['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgSVC['f1_micro'])[j])
-            + (factorsLocal[14] * Object.values(performanceAlgSVC['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgSVC['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgSVC['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgSVC['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgSVC['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgSVC['matthews_corrcoef'])[j]))
-            + (factorsLocal[20] * Object.values(performanceAlgSVC['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgSVC['log_loss'])[j]))
-          McSVC.push((sumSVC/divide)*100)
-        }
-        var McGausNB = []
-        const performanceAlgGausNB = JSON.parse(this.ModelsPerformance[24])
-        for (let j = 0; j < Object.values(performanceAlgGausNB['mean_test_accuracy']).length; j++) {
-          let sumGausNB 
-          sumGausNB = (factorsLocal[0] * Object.values(performanceAlgGausNB['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgGausNB['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgGausNB['geometric_mean_score_macro'])[j])
-            + (factorsLocal[3] * Object.values(performanceAlgGausNB['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGausNB['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgGausNB['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgGausNB['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgGausNB['mean_test_recall_micro'])[j])
-            + (factorsLocal[8] * Object.values(performanceAlgGausNB['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgGausNB['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgGausNB['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGausNB['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGausNB['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGausNB['f1_micro'])[j])
-            + (factorsLocal[14] * Object.values(performanceAlgGausNB['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGausNB['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgGausNB['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGausNB['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGausNB['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgGausNB['matthews_corrcoef'])[j]))
-            + (factorsLocal[20] * Object.values(performanceAlgGausNB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgGausNB['log_loss'])[j]))
-          McGausNB.push((sumGausNB/divide)*100)
-        }
-        var McMLP = []
-        const performanceAlgMLP = JSON.parse(this.ModelsPerformance[33])
-        for (let j = 0; j < Object.values(performanceAlgMLP['mean_test_accuracy']).length; j++) {
-          let sumMLP
-        sumMLP = (factorsLocal[0] * Object.values(performanceAlgMLP['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgMLP['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgMLP['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgMLP['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgMLP['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgMLP['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgMLP['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgMLP['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgMLP['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgMLP['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgMLP['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgMLP['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgMLP['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgMLP['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgMLP['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgMLP['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgMLP['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgMLP['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgMLP['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgMLP['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgMLP['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgMLP['log_loss'])[j]))
-          McMLP.push((sumMLP/divide)*100)
-        }
-        var McLR = []
-        const performanceAlgLR = JSON.parse(this.ModelsPerformance[42])
-        for (let j = 0; j < Object.values(performanceAlgLR['mean_test_accuracy']).length; j++) {
-          let sumLR
-        sumLR = (factorsLocal[0] * Object.values(performanceAlgLR['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgLR['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgLR['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgLR['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLR['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgLR['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgLR['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgLR['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgLR['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgLR['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgLR['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLR['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLR['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLR['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgLR['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLR['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgLR['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLR['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLR['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgLR['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgLR['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgLR['log_loss'])[j]))
-          McLR.push((sumLR/divide)*100)
-        }
-        var McLDA = []
-        const performanceAlgLDA = JSON.parse(this.ModelsPerformance[51])
-        for (let j = 0; j < Object.values(performanceAlgLDA['mean_test_accuracy']).length; j++) {
-          let sumLDA
-        sumLDA = (factorsLocal[0] * Object.values(performanceAlgLDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgLDA['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgLDA['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgLDA['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgLDA['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgLDA['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgLDA['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgLDA['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgLDA['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgLDA['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgLDA['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgLDA['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgLDA['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgLDA['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgLDA['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgLDA['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgLDA['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgLDA['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgLDA['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgLDA['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgLDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgLDA['log_loss'])[j]))
-          McLDA.push((sumLDA/divide)*100)
-        }
-        var McQDA = []
-        const performanceAlgQDA = JSON.parse(this.ModelsPerformance[60])
-        for (let j = 0; j < Object.values(performanceAlgQDA['mean_test_accuracy']).length; j++) {
-          let sumQDA
-        sumQDA = (factorsLocal[0] * Object.values(performanceAlgQDA['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgQDA['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgQDA['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgQDA['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgQDA['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgQDA['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgQDA['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgQDA['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgQDA['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgQDA['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgQDA['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgQDA['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgQDA['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgQDA['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgQDA['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgQDA['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgQDA['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgQDA['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgQDA['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgQDA['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgQDA['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgQDA['log_loss'])[j]))
-          McQDA.push((sumQDA/divide)*100)
-        }
-        var McRF = []
-        const performanceAlgRF = JSON.parse(this.ModelsPerformance[69])
-        for (let j = 0; j < Object.values(performanceAlgRF['mean_test_accuracy']).length; j++) {
-          let sumRF
-        sumRF = (factorsLocal[0] * Object.values(performanceAlgRF['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgRF['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgRF['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgRF['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgRF['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgRF['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgRF['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgRF['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgRF['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgRF['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgRF['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgRF['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgRF['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgRF['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgRF['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgRF['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgRF['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgRF['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgRF['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgRF['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgRF['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgRF['log_loss'])[j]))
-          McRF.push((sumRF/divide)*100)
-        }
-        var McExtraT = []
-        const performanceAlgExtraT = JSON.parse(this.ModelsPerformance[78])
-        for (let j = 0; j < Object.values(performanceAlgExtraT['mean_test_accuracy']).length; j++) {
-          let sumExtraT
-        sumExtraT = (factorsLocal[0] * Object.values(performanceAlgExtraT['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgExtraT['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgExtraT['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgExtraT['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgExtraT['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgExtraT['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgExtraT['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgExtraT['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgExtraT['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgExtraT['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgExtraT['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgExtraT['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgExtraT['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgExtraT['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgExtraT['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgExtraT['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgExtraT['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgExtraT['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgExtraT['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgExtraT['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgExtraT['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgExtraT['log_loss'])[j]))
-          McExtraT.push((sumExtraT/divide)*100)
-        }
-        var McAdaB = []
-        const performanceAlgAdaB = JSON.parse(this.ModelsPerformance[87])
-        for (let j = 0; j < Object.values(performanceAlgAdaB['mean_test_accuracy']).length; j++) {
-        let sumAdaB
-        sumAdaB = (factorsLocal[0] * Object.values(performanceAlgAdaB['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgAdaB['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgAdaB['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgAdaB['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgAdaB['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgAdaB['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgAdaB['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgAdaB['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgAdaB['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgAdaB['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgAdaB['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgAdaB['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgAdaB['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgAdaB['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgAdaB['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgAdaB['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgAdaB['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgAdaB['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgAdaB['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgAdaB['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgAdaB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgAdaB['log_loss'])[j]))
-          McAdaB.push((sumAdaB/divide)*100)
-        }
-        var McGradB = []
-        const performanceAlgGradB = JSON.parse(this.ModelsPerformance[96])
-        for (let j = 0; j < Object.values(performanceAlgGradB['mean_test_accuracy']).length; j++) {
-          let sumGradB
-        sumGradB = (factorsLocal[0] * Object.values(performanceAlgGradB['mean_test_accuracy'])[j]) + (factorsLocal[1] * Object.values(performanceAlgGradB['geometric_mean_score_micro'])[j]) + (factorsLocal[2] * Object.values(performanceAlgGradB['geometric_mean_score_macro'])[j])
-          + (factorsLocal[3] * Object.values(performanceAlgGradB['geometric_mean_score_weighted'])[j]) + (factorsLocal[4] * Object.values(performanceAlgGradB['mean_test_precision_micro'])[j]) + (factorsLocal[5] * Object.values(performanceAlgGradB['mean_test_precision_macro'])[j]) + (factorsLocal[6] * Object.values(performanceAlgGradB['mean_test_precision_weighted'])[j]) + (factorsLocal[7] * Object.values(performanceAlgGradB['mean_test_recall_micro'])[j])
-          + (factorsLocal[8] * Object.values(performanceAlgGradB['mean_test_recall_macro'])[j]) + (factorsLocal[9] * Object.values(performanceAlgGradB['mean_test_recall_weighted'])[j]) + (factorsLocal[10] * Object.values(performanceAlgGradB['f5_micro'])[j]) + (factorsLocal[11] * Object.values(performanceAlgGradB['f5_macro'])[j]) + (factorsLocal[12] * Object.values(performanceAlgGradB['f5_weighted'])[j]) + (factorsLocal[13] * Object.values(performanceAlgGradB['f1_micro'])[j])
-          + (factorsLocal[14] * Object.values(performanceAlgGradB['f1_macro'])[j]) + (factorsLocal[15] * Object.values(performanceAlgGradB['f1_weighted'])[j]) + (factorsLocal[16] * Object.values(performanceAlgGradB['f2_micro'])[j]) + (factorsLocal[17] * Object.values(performanceAlgGradB['f2_macro'])[j]) + (factorsLocal[18] * Object.values(performanceAlgGradB['f2_weighted'])[j]) + (factorsLocal[19] * Math.abs(Object.values(performanceAlgGradB['matthews_corrcoef'])[j]))
-          + (factorsLocal[20] * Object.values(performanceAlgGradB['mean_test_roc_auc_ovo_weighted'])[j]) + (factorsLocal[21] * (1 - Object.values(performanceAlgGradB['log_loss'])[j]))
-          McGradB.push((sumGradB/divide)*100)
-        }
-
-        var Combined = 0
-        if (this.selAlgorithm == 'KNN') {    
-          Combined = JSON.parse(this.ModelsPerformance[1])
-          colorGiv = colors[0]
-        } else if (this.selAlgorithm == 'SVC') {    
-          Combined = JSON.parse(this.ModelsPerformance[10])
-          colorGiv = colors[1]
-        } else if (this.selAlgorithm == 'GauNB') {    
-          Combined = JSON.parse(this.ModelsPerformance[19])
-          colorGiv = colors[2]
-        } else if (this.selAlgorithm == 'MLP') {    
-          Combined = JSON.parse(this.ModelsPerformance[28])
-          colorGiv = colors[3]
-        } else if (this.selAlgorithm == 'LR') {    
-          Combined = JSON.parse(this.ModelsPerformance[37])
-          colorGiv = colors[4]
-        } else if (this.selAlgorithm == 'LDA') {    
-          Combined = JSON.parse(this.ModelsPerformance[46])
-          colorGiv = colors[5]
-        } else if (this.selAlgorithm == 'QDA') {    
-          Combined = JSON.parse(this.ModelsPerformance[55])
-          colorGiv = colors[6]
-        } else if (this.selAlgorithm == 'RF') {    
-          Combined = JSON.parse(this.ModelsPerformance[64])
-          colorGiv = colors[7]
-        } else if (this.selAlgorithm == 'ExtraT') {    
-          Combined = JSON.parse(this.ModelsPerformance[73])
-          colorGiv = colors[8]
-        } else if (this.selAlgorithm == 'AdaB') {    
-          Combined = JSON.parse(this.ModelsPerformance[82])
-          colorGiv = colors[9]
-        } else {
-          Combined = JSON.parse(this.ModelsPerformance[91])
-          colorGiv = colors[10]
-        }
-        var valuesPerf = Object.values(Combined['params'])
-        
-        var ObjectsParams = Combined['params']
-        var newObjectsParamsΚΝΝ = []
-        var newObjectsParamsSVC = []
-        var newObjectsParamsGausNB = []
-        var newObjectsParamsMLP = []
-        var newObjectsParamsLR = []
-        var newObjectsParamsLDA = []
-        var newObjectsParamsQDA = []
-        var newObjectsParamsRF = []
-        var newObjectsParamsExtraT = []
-        var newObjectsParamsAdaB = []
-        var newObjectsParamsGradB = []
-        var ArrayCombined = []
-        var temp
-        for (var i = 0; i < valuesPerf.length; i++) {
-          if (this.keyAllOrClass) {
-            if (this.selAlgorithm === 'KNN') {
-              newObjectsParamsΚΝΝ.push({model: i,'# Perf (%) #': McKNN[i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
-              ArrayCombined[i] = newObjectsParamsΚΝΝ[i]
-            } else if (this.selAlgorithm === 'SVC') {
-              newObjectsParamsSVC.push({model: this.SVCModels + i,'# Perf (%) #': McSVC[i],'C':ObjectsParams[i].C,'kernel':ObjectsParams[i].kernel})
-              ArrayCombined[i] = newObjectsParamsSVC[i]
-            } else if (this.selAlgorithm === 'GauNB') {
-              newObjectsParamsGausNB.push({model: this.GausNBModels + i,'# Perf (%) #': McGausNB[i],'var_smoothing':ObjectsParams[i].var_smoothing})
-              ArrayCombined[i] = newObjectsParamsGausNB[i]
-            } else if (this.selAlgorithm === 'MLP') {
-              newObjectsParamsMLP.push({model: this.MLPModels + i,'# Perf (%) #': McMLP[i],'alpha':ObjectsParams[i].alpha,'tol':ObjectsParams[i].tol,'activation':ObjectsParams[i].activation,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver})
-              ArrayCombined[i] = newObjectsParamsMLP[i]
-            } else if (this.selAlgorithm === 'LR') {
-              newObjectsParamsLR.push({model: this.LRModels + i,'# Perf (%) #': McLR[i],'C':ObjectsParams[i].C,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver,'penalty':ObjectsParams[i].penalty})
-              ArrayCombined[i] = newObjectsParamsLR[i]
-            } else if (this.selAlgorithm === 'LDA') {
-              newObjectsParamsLDA.push({model: this.LDAModels + i,'# Perf (%) #': McLDA[i],'shrinkage':ObjectsParams[i].shrinkage,'solver':ObjectsParams[i].solver})
-              ArrayCombined[i] = newObjectsParamsLDA[i]
-            } else if (this.selAlgorithm === 'QDA') {
-              newObjectsParamsQDA.push({model: this.QDAModels + i,'# Perf (%) #': McQDA[i],'reg_param':ObjectsParams[i].reg_param,'tol':ObjectsParams[i].tol})
-              ArrayCombined[i] = newObjectsParamsQDA[i]
-            } else if (this.selAlgorithm === 'RF') {
-              newObjectsParamsRF.push({model: this.RFModels + i,'# Perf (%) #': McRF[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
-              ArrayCombined[i] = newObjectsParamsRF[i]
-            } else if (this.selAlgorithm === 'ExtraT') {
-              newObjectsParamsExtraT.push({model: this.ExtraTModels + i,'# Perf (%) #': McExtraT[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
-              ArrayCombined[i] = newObjectsParamsExtraT[i]
-            } else if (this.selAlgorithm === 'AdaB') {
-              newObjectsParamsAdaB.push({model: this.AdaBModels + i,'# Perf (%) #': McAdaB[i],'n_estimators':ObjectsParams[i].n_estimators,'learning_rate':ObjectsParams[i].learning_rate,'algorithm':ObjectsParams[i].algorithm})
-              ArrayCombined[i] = newObjectsParamsAdaB[i]
-            } else {
-              newObjectsParamsGradB.push({model: this.GradBModels + i,'# Perf (%) #': McGradB[i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion,'learning_rate':ObjectsParams[i].learning_rate})
-              ArrayCombined[i] = newObjectsParamsGradB[i]
-            }
-          } else {
-            if (this.selAlgorithm === 'KNN') {
-              newObjectsParamsΚΝΝ.push({model: i,'# Perf (%) #': this.listClassPerf[0][i],'n_neighbors':ObjectsParams[i].n_neighbors,'metric':ObjectsParams[i].metric,'algorithm':ObjectsParams[i].algorithm,'weights':ObjectsParams[i].weights})
-              ArrayCombined[i] = newObjectsParamsΚΝΝ[i]
-            } else if (this.selAlgorithm === 'SVC') {
-              newObjectsParamsSVC.push({model: this.SVCModels + i,'# Perf (%) #': this.listClassPerf[1][i],'C':ObjectsParams[i].C,'kernel':ObjectsParams[i].kernel})
-              ArrayCombined[i] = newObjectsParamsSVC[i]
-            } else if (this.selAlgorithm === 'GauNB') {
-              newObjectsParamsGausNB.push({model: this.GausNBModels + i,'# Perf (%) #': this.listClassPerf[2][i],'var_smoothing':ObjectsParams[i].var_smoothing})
-              ArrayCombined[i] = newObjectsParamsGausNB[i]
-            } else if (this.selAlgorithm === 'MLP') {
-              newObjectsParamsMLP.push({model: this.MLPModels + i,'# Perf (%) #': this.listClassPerf[3][i],'alpha':ObjectsParams[i].alpha,'tol':ObjectsParams[i].tol,'activation':ObjectsParams[i].activation,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver})
-              ArrayCombined[i] = newObjectsParamsMLP[i]
-            } else if (this.selAlgorithm === 'LR') {
-              newObjectsParamsLR.push({model: this.LRModels + i,'# Perf (%) #': this.listClassPerf[4][i],'C':ObjectsParams[i].C,'max_iter':ObjectsParams[i].max_iter,'solver':ObjectsParams[i].solver,'penalty':ObjectsParams[i].penalty})
-              ArrayCombined[i] = newObjectsParamsLR[i]
-            } else if (this.selAlgorithm === 'LDA') {
-              newObjectsParamsLDA.push({model: this.LDAModels + i,'# Perf (%) #': this.listClassPerf[5][i],'shrinkage':ObjectsParams[i].shrinkage,'solver':ObjectsParams[i].solver})
-              ArrayCombined[i] = newObjectsParamsLDA[i]
-            } else if (this.selAlgorithm === 'QDA') {
-              newObjectsParamsQDA.push({model: this.QDAModels + i,'# Perf (%) #': this.listClassPerf[6][i],'reg_param':ObjectsParams[i].reg_param,'tol':ObjectsParams[i].tol})
-              ArrayCombined[i] = newObjectsParamsQDA[i]
-            } else if (this.selAlgorithm === 'RF') {
-              newObjectsParamsRF.push({model: this.RFModels + i,'# Perf (%) #': this.listClassPerf[7][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
-              ArrayCombined[i] = newObjectsParamsRF[i]
-            } else if (this.selAlgorithm === 'ExtraT') {
-              newObjectsParamsExtraT.push({model: this.ExtraTModels + i,'# Perf (%) #': this.listClassPerf[8][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion})
-              ArrayCombined[i] = newObjectsParamsExtraT[i]
-            } else if (this.selAlgorithm === 'AdaB') {
-              newObjectsParamsAdaB.push({model: this.AdaBModels + i,'# Perf (%) #': this.listClassPerf[9][i],'n_estimators':ObjectsParams[i].n_estimators,'learning_rate':ObjectsParams[i].learning_rate,'algorithm':ObjectsParams[i].algorithm})
-              ArrayCombined[i] = newObjectsParamsAdaB[i]
-            } else {
-              newObjectsParamsGradB.push({model: this.GradBModels + i,'# Perf (%) #': this.listClassPerf[10][i],'n_estimators':ObjectsParams[i].n_estimators,'criterion':ObjectsParams[i].criterion,'learning_rate':ObjectsParams[i].learning_rate})
-              ArrayCombined[i] = newObjectsParamsGradB[i]
-            }
-          }
-        }
-        EventBus.$emit('AllAlModels', ArrayCombined.length)
-        this.pc = ParCoords()("#PCP")
-            .data(ArrayCombined)
-            .color(colorGiv)
-            .hideAxis(['model'])
-            .bundlingStrength(0) // set bundling strength
-            .smoothness(0)
-            .showControlPoints(false)
-            .render()
-            .brushMode('1D-axes')
-            .reorderable()
-            .interactive();
-
-        this.pc.on("brushend", function(d) {
-          EventBus.$emit('AllSelModels', d.length)
-          EventBus.$emit('UpdateBoxPlot', d)
-        });
-      }
-    },
-    sliders () {
-
-    },
-
-    clear () {
-        d3.selectAll("#PCP > *").remove(); 
-    },
-  },
-  mounted() {
-    EventBus.$on('ReturningBrushedPointsModels', this.brushed)
-    EventBus.$on('emittedEventCallingModelSelect', data => { this.selAlgorithm = data })
-    EventBus.$on('emittedEventCallingModel', data => { this.ModelsPerformance = data })
-    EventBus.$on('emittedEventCallingModel', this.PCPView)
-    EventBus.$on('ResponsiveandChange', this.PCPView)
-    EventBus.$on('emittedEventCallingModelClear', this.clear)
-
-    EventBus.$on('CallFactorsView', data => { this.factors = data })
-    EventBus.$on('CallFactorsView', this.PCPView)
-
-    EventBus.$on('boxplotSet', data => { this.listClassPerf = data })
-    EventBus.$on('boxplotCall', data => { this.keyAllOrClass = data })
-
-    // reset view
-    EventBus.$on('resetViews', this.reset)
-    EventBus.$on('clearPCP', this.reset)
-  }
-}
-</script>

frontend/src/components/Ensemble.vue

@@ -8,8 +8,8 @@
             </select>
             &nbsp;&nbsp;
             Actions: <button
-            id="Remove"
-            v-on:click="Remove">
+            id="RemoveEnsembleID"
+            v-on:click="RemoveEnsemble">
             <font-awesome-icon icon="eraser" />
             {{ CrossoverMutateText }}
             </button>
@@ -43,7 +43,8 @@ export default {
       ScatterPlotResults: '',
       representationDef: 'mdsCM',
       storeEnsembleLoc: [],
-      valueActive: 'Compute performance for active ensemble'
+      valueActive: 'Compute performance for active ensemble',
+      pushModelsTempCMSame: [],
     }
   },
   methods: {
@@ -76,7 +77,7 @@ export default {
       var MDSData= JSON.parse(this.ScatterPlotResults[9])
       var TSNEData = JSON.parse(this.ScatterPlotResults[10])
       var UMAPData = JSON.parse(this.ScatterPlotResults[11])
-
+      console.log(modelId)
       var mergedStoreEnsembleLoc = [].concat.apply([], this.storeEnsembleLoc)
       var mergedStoreEnsembleLocFormatted = []
       for (let i = 0; i < mergedStoreEnsembleLoc.length; i++) {
@@ -99,15 +100,25 @@ export default {
         this.clean(parameters[i])
         stringParameters.push(JSON.stringify(parameters[i]).replace(/,/gi, '<br>'))
       }
-      // fix that!
+
       var classifiersInfoProcessing = []
       for (let i = 0; i < modelId.length; i++) {
-        if (i < 100) {
+        let tempSplit = modelId[i].split(/([0-9]+)/)
+        if (tempSplit[0] == 'KNN') {
           classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> k-nearest neighbor' + '<br><b>Parameters:</b> ' + stringParameters[i]
         }
-        else {
+        else if (tempSplit[0] == 'LR') {
           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') {
+          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') {
+          classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> random forest' + '<br><b>Parameters:</b> ' + stringParameters[i]
+        }
+        else {
+          classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> gradient boosting' + '<br><b>Parameters:</b> ' + stringParameters[i]
+        }
       }
 
       var DataGeneral, maxX, minX, maxY, minY, layout
@@ -133,7 +144,7 @@ export default {
           marker: {
            line: { color: 'rgb(0, 0, 0)', width: 3 },
             color: colorsforScatterPlot,
-            size: 12,
+            size: 16,
             colorscale: 'Viridis',
             colorbar: {
               title: '# Performance (%) #',
@@ -197,7 +208,7 @@ export default {
           marker: {
               line: { color: 'rgb(0, 0, 0)', width: 3 },
               color: colorsforScatterPlot,
-              size: 12,
+              size: 16,
               colorscale: 'Viridis',
               colorbar: {
                 title: '# Performance (%) #',
@@ -249,7 +260,7 @@ export default {
           marker: {
            line: { color: 'rgb(0, 0, 0)', width: 3 },
             color: colorsforScatterPlot,
-            size: 12,
+            size: 16,
             colorscale: 'Viridis',
             colorbar: {
               title: '# Performance (%) #',
@@ -319,11 +330,17 @@ export default {
           }
         })
       },
+      RemoveEnsemble () {
+        EventBus.$emit('RemoveFromEnsemble', this.pushModelsTempCMSame)
+      },
       sendUpdateActiveScatter () {
         EventBus.$emit('sendToServerSelectedScatter')
       }
   },
   mounted() {
+
+    EventBus.$on('SendSelectedPointsUpdateIndicatorCM', data => { this.pushModelsTempCMSame = data })
+
     EventBus.$on('SendStoredEnsemble', data => { this.storeEnsembleLoc = data })
     
     EventBus.$on('emittedEventCallingCrossoverMutation', data => {

frontend/src/components/History.vue

@@ -18,7 +18,10 @@ export default {
   data () {
     return {
       WH: [],
-      RandomSearLoc : 100
+      RandomSearLoc : 100,
+      step: 2,
+      values: [0,0,0,0,0,0,50,50,50,50,50,0,50,50,50,50,50,0],
+      loop: 0
     }
   },
   methods: {
@@ -27,13 +30,16 @@ export default {
       svg.selectAll("*").remove();
     },
     SankeyView () {
+    var valuesLoc = this.values
+    var localStep = this.step
+    var numberofModels = 6
     var units = "Models";
     var initialModels = this.RandomSearLoc * 5
     var months = [{month:"RandSear",value:initialModels,loss:null},
                   {month:"Crossover",value:250,loss:null},
                   {month:"Mutate",value:250,loss:null}];
     //this is the svg canvas attributes: (not buidlign abything just seeting up varaibels)
-    var margin = {top: 40, right: 40, bottom: 40, left: 100}, //comma is the equivalent of var : 
+    var margin = {top: 10, right: 40, bottom: 10, left: 100}, //comma is the equivalent of var : 
         width = 1200 - margin.left - margin.right,
         height = 350 - margin.top - margin.bottom;
 
@@ -44,9 +50,9 @@ export default {
       format = function(d) { return formatNumber(d) + " " + units; }
     var color = d3.scale.category20b()
 
-    var startingAxis = this.RandomSearLoc * 6
+    var startingAxis = this.RandomSearLoc
     var axisScale = d3.scale.linear()
-                      .domain([startingAxis,0])
+                      .domain([startingAxis*6,0])
                       .range([0, height]);
 
     //Create the Axis
@@ -55,6 +61,8 @@ export default {
                   .orient("left")
                   .ticks(10);
 
+
+
     // var lossScale = d3.scale.linear()
     //                   .domain([.95,1,1.05])
     //                   .range(["red","black","green"]);
@@ -66,6 +74,17 @@ export default {
         .append("g") //group everything on the vancas together.  will edit down on ln38 below
         .attr("transform", 
               "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + -height + ")");
+    // Fix that!
+    d3.select("svg")
+      .append("text")
+      .text("Player Count")
+      .attr("x",30)
+      .attr("y",17)
+      .attr("font-family","Pontano Sans")
+      .attr("font-size",18.5)
+      .attr("fill","black")
+      .attr("transform", function(d){ 
+          return "translate(" + 0 + "," + 0 + ") rotate(-90 150 150)";});
 
     // Set the sankey diagram properties
     var sankey = d3Sankey() //calling the function
@@ -112,39 +131,39 @@ export default {
     // load the data
     var graph = {
       "nodes":[
-      {"name":"GradB","node":0,"month":"RandSear","color":"#e41a1c"},
-      {"name":"RF","node":1,"month":"RandSear","color":"#377eb8"},
-      {"name":"MLP","node":2,"month":"RandSear","color":"#4daf4a"},
-      {"name":"LR","node":3,"month":"RandSear","color":"#984ea3"},
-      {"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00"},
-      {"name":"Random search","node":5,"month":"RandSear","color":"#ffffff"},
-      {"name":"GradB","node":6,"month":"Crossover","color":"#e41a1c"},
-      {"name":"RF","node":7,"month":"Crossover","color":"#377eb8"},
-      {"name":"MLP","node":8,"month":"Crossover","color":"#4daf4a"},
-      {"name":"LR","node":9,"month":"Crossover","color":"#984ea3"},
-      {"name":"KNN","node":10,"month":"Crossover","color":"#ff7f00"},
-      {"name":"Mutate","node":11,"month":"Crossover","color":"#ffffff"},
-      {"name":"GradB","node":12,"month":"Mutate","color":"#e41a1c"},
-      {"name":"RF","node":13,"month":"Mutate","color":"#377eb8"},
-      {"name":"MLP","node":14,"month":"Mutate","color":"#4daf4a"},
-      {"name":"LR","node":15,"month":"Mutate","color":"#984ea3"},
-      {"name":"KNN","node":16,"month":"Mutate","color":"#ff7f00"},
-      {"name":"Crossover","node":17,"month":"Crossover","color":"#ffffff"},
+      {"name":"GradB","node":0,"month":"RandSear","color":"#e41a1c","dh":height/numberofModels},
+      {"name":"RF","node":1,"month":"RandSear","color":"#377eb8","dh":height/numberofModels},
+      {"name":"MLP","node":2,"month":"RandSear","color":"#4daf4a","dh":height/numberofModels},
+      {"name":"LR","node":3,"month":"RandSear","color":"#984ea3","dh":height/numberofModels},
+      {"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00","dh":height/numberofModels},
+      {"name":"Random search","node":5,"month":"RandSear","color":"#ffffff","dh":height/numberofModels},
+      {"name":"GradB","node":6,"month":"Crossover","color":"#e41a1c","dh":height/(numberofModels*localStep)},
+      {"name":"RF","node":7,"month":"Crossover","color":"#377eb8","dh":height/(numberofModels*localStep)},
+      {"name":"MLP","node":8,"month":"Crossover","color":"#4daf4a","dh":height/(numberofModels*localStep)},
+      {"name":"LR","node":9,"month":"Crossover","color":"#984ea3","dh":height/(numberofModels*localStep)},
+      {"name":"KNN","node":10,"month":"Crossover","color":"#ff7f00","dh":height/(numberofModels*localStep)},
+      {"name":"Mutate","node":11,"month":"Crossover","color":"#ffffff","dh":height/(numberofModels*localStep)},
+      {"name":"GradB","node":12,"month":"Mutate","color":"#e41a1c","dh":height/(numberofModels*localStep)},
+      {"name":"RF","node":13,"month":"Mutate","color":"#377eb8","dh":height/(numberofModels*localStep)},
+      {"name":"MLP","node":14,"month":"Mutate","color":"#4daf4a","dh":height/(numberofModels*localStep)},
+      {"name":"LR","node":15,"month":"Mutate","color":"#984ea3","dh":height/(numberofModels*localStep)},
+      {"name":"KNN","node":16,"month":"Mutate","color":"#ff7f00","dh":height/(numberofModels*localStep)},
+      {"name":"Crossover","node":17,"month":"Crossover","color":"#ffffff","dh":height/(numberofModels*localStep)},
       ],
 
       "links":[
-      {"source":5,"target":11,"value":50},
-      {"source":5,"target":17,"value":50},
-      {"source":0,"target":6,"value":50},
-      {"source":0,"target":12,"value":50},
-      {"source":1,"target":7,"value":50},
-      {"source":1,"target":13,"value":50},
-      {"source":2,"target":8,"value":50},
-      {"source":2,"target":14,"value":50},
-      {"source":3,"target":9,"value":50},
-      {"source":3,"target":15,"value":50},
-      {"source":4,"target":10,"value":50},
-      {"source":4,"target":16,"value":50},
+      {"source":5,"target":11,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[6]+valuesLoc[7]+valuesLoc[8]+valuesLoc[9]+valuesLoc[10]))},
+      {"source":5,"target":17,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[12]+valuesLoc[13]+valuesLoc[14]+valuesLoc[15]+valuesLoc[16]))},
+      {"source":0,"target":6,"value":valuesLoc[6],"dh":height/(numberofModels*localStep)*(valuesLoc[6]/50)},
+      {"source":0,"target":12,"value":valuesLoc[12],"dh":height/(numberofModels*localStep)*(valuesLoc[12]/50)},
+      {"source":1,"target":7,"value":valuesLoc[7],"dh":height/(numberofModels*localStep)*(valuesLoc[7]/50)},
+      {"source":1,"target":13,"value":valuesLoc[13],"dh":height/(numberofModels*localStep)*(valuesLoc[13]/50)},
+      {"source":2,"target":8,"value":valuesLoc[8],"dh":height/(numberofModels*localStep)*(valuesLoc[8]/50)},
+      {"source":2,"target":14,"value":valuesLoc[14],"dh":height/(numberofModels*localStep)*(valuesLoc[14]/50)},
+      {"source":3,"target":9,"value":valuesLoc[9],"dh":height/(numberofModels*localStep)*(valuesLoc[9]/50)},
+      {"source":3,"target":15,"value":valuesLoc[15],"dh":height/(numberofModels*localStep)*(valuesLoc[15]/50)},
+      {"source":4,"target":10,"value":valuesLoc[10],"dh":height/(numberofModels*localStep)*(valuesLoc[10]/50)},
+      {"source":4,"target":16,"value":valuesLoc[16],"dh":height/(numberofModels*localStep)*(valuesLoc[16]/50)},
     ]}
 
       sankey.nodes(graph.nodes)
@@ -161,8 +180,8 @@ export default {
             if(d.source.node == 5){
             return "transparent";
           }})
-          .style("stroke-width", function(d) { return Math.max(.5, d.dy); })   //setting the stroke length by the data . d.dy is defined in sankey.js
-          .sort(function(a, b) { return b.dy - a.dy; })
+          .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
+          .sort(function(a, b) { return b.dh - a.dh; })
           .on("mouseover",linkmouseover)
           .on("mouseout",linkmouseout);  
 
@@ -187,7 +206,7 @@ export default {
       
     // add the rectangles for the nodes
       node.append("rect")
-          .attr("height", function(d) {return d.dy; })
+          .attr("height", function(d) { return d.dh; })
           .attr("width", sankey.nodeWidth(  ))
           .style("fill", function(d) { return d.color; }) //matches name with the colors here! inside the replace is some sort of regex
           // .style("stroke",function(d) { return d3.rgb(d.color).darker(1); }) //line around the box formatting
@@ -195,18 +214,27 @@ export default {
           .on("mouseover", nodemouseover)
           .on("mouseout", nodemouseout);
 
-      node.append("foreignObject")
+      if (this.loop == 0) {
+        node.append("foreignObject")
           .attr("x", 28)
-          .attr("y", -21)
+          .attr("y", -24)
           .attr("height", 18)
           .attr("width", 40)
           .attr("transform", "scale(1,-1)")
           .append("xhtml:body")
             .html(function(d) {
               if (d.node > 5 && d.node != 11 && d.node != 17) {
-                return '<input id='+d.node+' type="text" value="50" />'
+                return '<input id='+d.node+' type="number" value='+valuesLoc[d.node]+' min="0" max='+Math.floor(startingAxis/localStep)+' required/>'
               }
             });
+
+          $("input[type='number']").change( function(d) {
+            valuesLoc[d.target.id] = parseInt(d.target.value)
+            console.log(valuesLoc)
+            EventBus.$emit('changeValues', valuesLoc)
+            // your code
+          });
+      }
           // .append("title")
           // .text(function(d) { 
           // return d.name + "\n" + format(d.value); });
@@ -216,9 +244,9 @@ export default {
           .attr("x", -6)
           .attr("y", function(d) {
             if (d.node <= 5) {
-              return d.dy - 70; 
+              return d.dh - 81; 
             } else {
-              return d.dy - 35; 
+              return d.dh - 41; 
             }
           })
           .attr("dy", ".35em")
@@ -285,9 +313,12 @@ export default {
           .attr("transform", 
             "translate(" + -45 + "," + 0 + ") scale(1,-1) translate(" + 0 + "," + -(height) + ")");
 
-  }
+  },
   },
   mounted() {
+    EventBus.$on('changeValues', data => { this.values = data })
+    EventBus.$on('changeValues', this.SankeyView )
+
     EventBus.$on('SendtheChangeinRangePos', data => { this.RandomSearLoc = data })
 
     EventBus.$on('emittedEventCallingSankey', this.SankeyView)

frontend/src/components/HyperParameterSpace.vue

@@ -60,7 +60,6 @@ export default {
       Plotly.purge('OverviewPlotly')
 
       var modelId = JSON.parse(this.ScatterPlotResults[0])
-
       var colorsforScatterPlot = JSON.parse(this.ScatterPlotResults[1])
       var parametersLoc = JSON.parse(this.ScatterPlotResults[2])
       var parameters = JSON.parse(parametersLoc)
@@ -75,15 +74,25 @@ export default {
         this.clean(parameters[i])
         stringParameters.push(JSON.stringify(parameters[i]).replace(/,/gi, '<br>'))
       }
-      // fix that!
+
       var classifiersInfoProcessing = []
       for (let i = 0; i < modelId.length; i++) {
-        if (i < 100) {
+        let tempSplit = modelId[i].split(/([0-9]+)/)
+        if (tempSplit[0] == 'KNN') {
           classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> k-nearest neighbor' + '<br><b>Parameters:</b> ' + stringParameters[i]
         }
-        else {
+        else if (tempSplit[0] == 'LR') {
           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') {
+          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') {
+          classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> random forest' + '<br><b>Parameters:</b> ' + stringParameters[i]
+        }
+        else {
+          classifiersInfoProcessing[i] = '<b>Model ID:</b> ' + modelId[i] + '<br><b>Algorithm:</b> gradient boosting' + '<br><b>Parameters:</b> ' + stringParameters[i]
+        }
       }
 
       var DataGeneral, maxX, minX, maxY, minY, layout

frontend/src/components/Main.vue

@@ -188,9 +188,7 @@
 import Vue from 'vue'
 import DataSetExecController from './DataSetExecController.vue'
 import PerformanceMetrics from './PerformanceMetrics.vue'
-import Algorithms from './Algorithms.vue'
 import AlgorithmsController from './AlgorithmsController.vue'
-import AlgorithmHyperParam from './AlgorithmHyperParam.vue'
 import HyperParameterSpace from './HyperParameterSpace.vue'
 import GlobalParamController from './GlobalParamController'
 import Ensemble from './Ensemble.vue'
@@ -217,9 +215,7 @@ export default Vue.extend({
   components: {
     DataSetExecController,
     PerformanceMetrics,
-    Algorithms,
     AlgorithmsController,
-    AlgorithmHyperParam,
     HyperParameterSpace,
     GlobalParamController,
     Ensemble,
@@ -348,6 +344,7 @@ export default Vue.extend({
             this.storeBothEnsCM[0] = this.OverviewResults
             this.firstTimeExec = false
           } else {   
+            EventBus.$emit('SendStoredEnsemble', this.storeEnsemble)
             EventBus.$emit('emittedEventCallingCrossoverMutation', this.OverviewResults)
             this.PredictSelEnsem = []
             EventBus.$emit('emittedEventCallingGrid', this.OverviewResults)
@@ -379,6 +376,7 @@ export default Vue.extend({
           console.log('Server successfully sent all the data related to visualizations!')
           EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResultsCM)
           this.storeBothEnsCM[0] = this.OverviewResultsCM
+          EventBus.$emit('emittedEventCallingSankey', this.OverviewResultsCM)
           //this.PredictSel = []
           //EventBus.$emit('emittedEventCallingGrid', this.OverviewResultsCM)
           //EventBus.$emit('SendSelectedPointsToServerEvent', this.PredictSel)
@@ -539,8 +537,8 @@ export default Vue.extend({
           })
       }
     },
-    RemoveFromStackModels () {
-      const path = `http://127.0.0.1:5000/data/ServerRemoveFromStack`
+    RemoveFromEnsembleModels () {
+      const path = `http://127.0.0.1:5000/data/ServerRemoveFromEnsemble`
       const postData = {
         ClassifiersList: this.ClassifierIDsListRemaining,
       }
@@ -555,7 +553,7 @@ export default Vue.extend({
       axios.post(path, postData, axiosConfig)
       .then(response => {
       console.log('Sent the selected points to the server (scatterplot)!')
-      this.updatePredictionsSpace()
+      this.getDatafromtheBackEnd()
       })
       .catch(error => {
       console.log(error)
@@ -904,8 +902,6 @@ export default Vue.extend({
     },
     sendPointsCrossMutat () {
       const path = `http://127.0.0.1:5000/data/CrossoverMutation`
-      
-      EventBus.$emit('SendStoredEnsemble', this.storeEnsemble)
 
       var mergedStoreEnsembleLoc = [].concat.apply([], this.storeEnsemble)
       
@@ -992,11 +988,11 @@ export default Vue.extend({
     EventBus.$on('RemainingPointsCM', data => { this.unselectedRemainingPointsEnsem = data })
 
     EventBus.$on('ChangeKey', data => { this.keyNow = data })
-    EventBus.$on('SendSelectedPointsUpdateIndicator', data => { this.ClassifierIDsList = data; this.storeEnsemble.push(this.ClassifierIDsList)})
+    EventBus.$on('SendSelectedPointsUpdateIndicator', data => { this.ClassifierIDsList = data; this.storeEnsemble.push(this.ClassifierIDsList) })
     EventBus.$on('SendSelectedPointsUpdateIndicator', this.SelectedPoints)
     EventBus.$on('sendToServerSelectedScatter', this.SendSelectedPointsToServer)
 
-    EventBus.$on('SendSelectedPointsUpdateIndicatorCM', data => { this.ClassifierIDsListCM = data })
+    EventBus.$on('SendSelectedPointsUpdateIndicatorCM', data => { this.ClassifierIDsListCM = data; this.storeEnsemble = []; this.storeEnsemble.push(this.ClassifierIDsListCM) })
     EventBus.$on('SendSelectedPointsUpdateIndicatorCM', this.SelectedPointsCM)
 
     EventBus.$on('SendSelectedDataPointsToServerEvent', data => { this.DataPointsSel = data })
@@ -1030,8 +1026,8 @@ export default Vue.extend({
   
     EventBus.$on('AllSelModels', data => {this.valueSel = data})
 
-    EventBus.$on('RemoveFromStack', data => { this.ClassifierIDsListRemaining = data })
-    EventBus.$on('RemoveFromStack', this.RemoveFromStackModels)
+    EventBus.$on('RemoveFromEnsemble', data => { this.ClassifierIDsListRemaining = data })
+    EventBus.$on('RemoveFromEnsemble', this.RemoveFromEnsembleModels)
 
     EventBus.$on('OpenModal', this.openModalFun)
 

run.py

@@ -76,6 +76,9 @@ def reset():
 
     global KNNModelsCount
     global LRModelsCount
+    global MLPModelsCount
+    global RFModelsCount
+    global GradBModelsCount
 
     global factors
     factors = [1,1,1,1,0,0,0,0]
@@ -182,6 +185,9 @@ def reset():
     global names_labels
     names_labels = []
 
+    global keySend
+    keySend=0
+
     return 'The reset was done!'
 
 # retrieve data from client and select the correct data set
@@ -208,12 +214,6 @@ def retrieveFileName():
     global keyData
     keyData = 0
 
-    global KNNModelsCount
-    global LRModelsCount
-    global MLPModelsCount
-    global RFModelsCount
-    global GradBModelsCount
-
     global factors
     factors = data['Factors']
 
@@ -223,12 +223,6 @@ def retrieveFileName():
     global randomSearchVar
     randomSearchVar = int(data['RandomSearch'])
 
-    KNNModelsCount = 0
-    LRModelsCount = KNNModelsCount+randomSearchVar
-    MLPModelsCount = LRModelsCount+randomSearchVar
-    RFModelsCount = MLPModelsCount+randomSearchVar
-    GradBModelsCount = RFModelsCount+randomSearchVar
-
     global XData
     XData = []
 
@@ -333,6 +327,9 @@ def retrieveFileName():
     global names_labels
     names_labels = []
 
+    global keySend
+    keySend=0
+
     DataRawLength = -1
     DataRawLengthTest = -1
 
@@ -538,7 +535,6 @@ def retrieveModel():
 
     global XData
     global yData
-    global LRModelsCount
     global countAllModels
 
     # loop through the algorithms
@@ -576,6 +572,7 @@ def retrieveModel():
             params = {'n_estimators': list(range(20, 100)), 'learning_rate': list(np.arange(0.01,0.23,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']}
             countAllModels = countAllModels + randomSearchVar
             AlgorithmsIDsEnd = countAllModels
+            countAllModels = countAllModels + randomSearchVar
         allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd)
     HistoryPreservation = allParametersPerformancePerModel.copy()
     # call the function that sends the results to the frontend
@@ -758,7 +755,6 @@ def EnsembleIDs():
     global numberIDMLPGlob
     global numberIDRFGlob
     global numberIDGradBGlob
-    
     numberIDKNNGlob = []
     numberIDLRGlob = []
     numberIDMLPGlob = []
@@ -786,7 +782,6 @@ def EnsembleIDs():
 def PreprocessingPredEnsemble():
 
     global EnsembleActive
-
     numberIDKNN = []
     numberIDLR = []
     numberIDMLP = []
@@ -1009,6 +1004,7 @@ def InitializeEnsemble():
     global ModelSpaceTSNE
     global allParametersPerformancePerModel
     global EnsembleActive
+    global keySend
 
     XModels = XModels.fillna(0)
 
@@ -1022,8 +1018,8 @@ def InitializeEnsemble():
     else:
         PredictionProbSel = PreprocessingPredEnsemble()
         ModelsIds = EnsembleIDs()
-        key = 0
-        EnsembleModel(ModelsIds, key)
+        EnsembleModel(ModelsIds, keySend)
+        keySend=1
 
     returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,PredictionProbSel)
 
@@ -1044,12 +1040,14 @@ def EnsembleModel (Models, keyRetrieved):
     global yData
     global sclf
 
+    global randomSearchVar
+    greater = randomSearchVar*5
+
     global numberIDKNNGlob
     global numberIDLRGlob
     global numberIDMLPGlob
     global numberIDRFGlob
     global numberIDGradBGlob
-
     all_classifiers = []
     columnsInit = []
     columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData]
@@ -1064,7 +1062,10 @@ def EnsembleModel (Models, keyRetrieved):
     dfParamKNN = pd.DataFrame.from_dict(tempDic)
     dfParamKNNFilt = dfParamKNN.iloc[:,0]
     for eachelem in numberIDKNNGlob:
-        arg = dfParamKNNFilt[eachelem]
+        if (eachelem >= greater):
+            arg = dfParamKNNFilt[eachelem-addKNN]
+        else:
+            arg = dfParamKNNFilt[eachelem-KNNModelsCount]
         all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), KNeighborsClassifier().set_params(**arg)))
     temp = allParametersPerformancePerModel[5]
     temp = temp['params']
@@ -1075,7 +1076,10 @@ def EnsembleModel (Models, keyRetrieved):
     dfParamLR = pd.DataFrame.from_dict(tempDic)
     dfParamLRFilt = dfParamLR.iloc[:,0]
     for eachelem in numberIDLRGlob:
-        arg = dfParamLRFilt[eachelem-LRModelsCount]
+        if (eachelem >= greater):
+            arg = dfParamLRFilt[eachelem-addLR]
+        else:
+            arg = dfParamLRFilt[eachelem-LRModelsCount]
         all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LogisticRegression(random_state=RANDOM_SEED).set_params(**arg)))
 
     temp = allParametersPerformancePerModel[9]
@@ -1087,7 +1091,10 @@ def EnsembleModel (Models, keyRetrieved):
     dfParamMLP = pd.DataFrame.from_dict(tempDic)
     dfParamMLPFilt = dfParamMLP.iloc[:,0]
     for eachelem in numberIDMLPGlob:
-        arg = dfParamMLPFilt[eachelem-MLPModelsCount]
+        if (eachelem >= greater):
+            arg = dfParamMLPFilt[eachelem-addMLP]
+        else:
+            arg = dfParamMLPFilt[eachelem-MLPModelsCount]
         all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), MLPClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
     temp = allParametersPerformancePerModel[13]
@@ -1099,7 +1106,10 @@ def EnsembleModel (Models, keyRetrieved):
     dfParamRF = pd.DataFrame.from_dict(tempDic)
     dfParamRFFilt = dfParamRF.iloc[:,0]
     for eachelem in numberIDRFGlob:
-        arg = dfParamRFFilt[eachelem-RFModelsCount]
+        if (eachelem >= greater):
+            arg = dfParamRFFilt[eachelem-addRF]
+        else:
+            arg = dfParamRFFilt[eachelem-RFModelsCount]
         all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), RandomForestClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
     temp = allParametersPerformancePerModel[17]
@@ -1111,7 +1121,10 @@ def EnsembleModel (Models, keyRetrieved):
     dfParamGradB = pd.DataFrame.from_dict(tempDic)
     dfParamGradBFilt = dfParamGradB.iloc[:,0]
     for eachelem in numberIDGradBGlob:
-        arg = dfParamGradBFilt[eachelem-GradBModelsCount]
+        if (eachelem >= greater):
+            arg = dfParamGradBFilt[eachelem-addGradB]
+        else:
+            arg = dfParamGradBFilt[eachelem-GradBModelsCount]
         all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), GradientBoostingClassifier(random_state=RANDOM_SEED).set_params(**arg)))
 
     global sclf 
@@ -1283,6 +1296,7 @@ def CrossoverMutateFun():
     EnsembleActive = json.loads(EnsembleActive)
 
     EnsembleActive = EnsembleActive['StoreEnsemble']
+
     random.seed(RANDOM_SEED)
     
     global XData
@@ -1298,6 +1312,9 @@ def CrossoverMutateFun():
     global allParametersPerfCrossMutr
     global HistoryPreservation
 
+    global randomSearchVar
+    greater = randomSearchVar*5
+
     KNNIDs = list(filter(lambda k: 'KNN' in k, RemainingIds))
     LRIDs = list(filter(lambda k: 'LR' in k, RemainingIds))
     MLPIDs = list(filter(lambda k: 'MLP' in k, RemainingIds))
@@ -1320,9 +1337,13 @@ def CrossoverMutateFun():
     
     localCrossMutr = []
     allParametersPerfCrossMutrKNNC = []
+
     while countKNN < setMaxLoopValue:
         for dr in KNNIDs:
-            KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                KNNIntIndex.append(int(re.findall('\d+', dr)[0])-addKNN)
+            else:
+                KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
         KNNPickPair = random.sample(KNNIntIndex,2)
         pairDF = paramAllAlgs.iloc[KNNPickPair]
         crossoverDF = pd.DataFrame()
@@ -1364,7 +1385,10 @@ def CrossoverMutateFun():
 
     while countKNN < setMaxLoopValue:
         for dr in KNNIDs:
-            KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                KNNIntIndex.append(int(re.findall('\d+', dr)[0])-addKNN)
+            else:
+                KNNIntIndex.append(int(re.findall('\d+', dr)[0]))
         KNNPickPair = random.sample(KNNIntIndex,1)
 
         pairDF = paramAllAlgs.iloc[KNNPickPair]
@@ -1409,10 +1433,15 @@ def CrossoverMutateFun():
 
     while countLR < setMaxLoopValue:
         for dr in LRIDs:
-            LRIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                LRIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar))
+            else:
+                LRIntIndex.append(int(re.findall('\d+', dr)[0]))
+        print(LRIntIndex)
         LRPickPair = random.sample(LRIntIndex,2)
-
+        print(paramAllAlgs)
         pairDF = paramAllAlgs.iloc[LRPickPair]
+        print(pairDF)
         crossoverDF = pd.DataFrame()
         for column in pairDF:
             listData = []
@@ -1452,7 +1481,10 @@ def CrossoverMutateFun():
 
     while countLR < setMaxLoopValue:
         for dr in LRIDs:
-            LRIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                LRIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar))
+            else:
+                LRIntIndex.append(int(re.findall('\d+', dr)[0]))
         LRPickPair = random.sample(LRIntIndex,1)
 
         pairDF = paramAllAlgs.iloc[LRPickPair]
@@ -1497,7 +1529,10 @@ def CrossoverMutateFun():
 
     while countMLP < setMaxLoopValue:
         for dr in MLPIDs:
-            MLPIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                MLPIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*2))
+            else:
+                MLPIntIndex.append(int(re.findall('\d+', dr)[0]))
         MLPPickPair = random.sample(MLPIntIndex,2)
 
         pairDF = paramAllAlgs.iloc[MLPPickPair]
@@ -1540,7 +1575,10 @@ def CrossoverMutateFun():
 
     while countMLP < setMaxLoopValue:
         for dr in MLPIDs:
-            MLPIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                MLPIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*2))
+            else:
+                MLPIntIndex.append(int(re.findall('\d+', dr)[0]))
         MLPPickPair = random.sample(MLPIntIndex,1)
 
         pairDF = paramAllAlgs.iloc[MLPPickPair]
@@ -1585,7 +1623,10 @@ def CrossoverMutateFun():
 
     while countRF < setMaxLoopValue:
         for dr in RFIDs:
-            RFIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                RFIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*3))
+            else:
+                RFIntIndex.append(int(re.findall('\d+', dr)[0]))
         RFPickPair = random.sample(RFIntIndex,2)
 
         pairDF = paramAllAlgs.iloc[RFPickPair]
@@ -1627,7 +1668,9 @@ def CrossoverMutateFun():
     allParametersPerfCrossMutrRFM = []
 
     while countRF < setMaxLoopValue:
-        for dr in RFIDs:
+        if (int(re.findall('\d+', dr)[0]) >= greater):
+            RFIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*3))
+        else:
             RFIntIndex.append(int(re.findall('\d+', dr)[0]))
         RFPickPair = random.sample(RFIntIndex,1)
 
@@ -1673,7 +1716,10 @@ def CrossoverMutateFun():
 
     while countGradB < setMaxLoopValue:
         for dr in GradBIDs:
-            GradBIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                GradBIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*4))
+            else:
+                GradBIntIndex.append(int(re.findall('\d+', dr)[0]))
         GradBPickPair = random.sample(GradBIntIndex,2)
 
         pairDF = paramAllAlgs.iloc[GradBPickPair]
@@ -1716,7 +1762,10 @@ def CrossoverMutateFun():
             
     while countGradB < setMaxLoopValue:
         for dr in GradBIDs:
-            GradBIntIndex.append(int(re.findall('\d+', dr)[0]))
+            if (int(re.findall('\d+', dr)[0]) >= greater):
+                GradBIntIndex.append(int(re.findall('\d+', dr)[0])-(addKNN-randomSearchVar*4))
+            else:
+                GradBIntIndex.append(int(re.findall('\d+', dr)[0]))
         GradPickPair = random.sample(GradBIntIndex,1)
 
         pairDF = paramAllAlgs.iloc[GradBPickPair]
@@ -1737,7 +1786,7 @@ def CrossoverMutateFun():
             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]]}
             AlgorithmsIDsEnd = countAllModels + countGradB
-            localCrossMutr = crossoverMutation(XData, yData, clf, params, 'RF', AlgorithmsIDsEnd)
+            localCrossMutr = crossoverMutation(XData, yData, clf, params, 'GradB', AlgorithmsIDsEnd)
             countGradB += 1
             crossoverDF = pd.DataFrame()
 
@@ -1759,13 +1808,12 @@ def CrossoverMutateFun():
     localCrossMutr.clear()
 
     allParametersPerfCrossMutr = allParametersPerfCrossMutrKNNC + allParametersPerfCrossMutrKNNM + allParametersPerfCrossMutrLRC + allParametersPerfCrossMutrLRM + allParametersPerfCrossMutrMLPC + allParametersPerfCrossMutrMLPM + allParametersPerfCrossMutrRFC + allParametersPerfCrossMutrRFM + allParametersPerfCrossMutrGradBC + allParametersPerfCrossMutrGradBM
+    allParametersPerformancePerModel[0] = allParametersPerformancePerModel[0] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0]
 
-    allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrKNNC[0] + allParametersPerfCrossMutrKNNM[0]
-
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNC[1]], ignore_index=True)
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrKNNM[1]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrKNNC[2]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrKNNM[2]], ignore_index=True)
+    allParametersPerformancePerModel[1] = pd.concat([allParametersPerformancePerModel[1], allParametersPerfCrossMutrKNNC[1]], ignore_index=True)
+    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)
@@ -1781,39 +1829,39 @@ def CrossoverMutateFun():
     allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRC[3]], ignore_index=True)
     allParametersPerformancePerModel[7] = pd.concat([allParametersPerformancePerModel[7], allParametersPerfCrossMutrLRM[3]], ignore_index=True)
 
-    allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrMLPC[0] + allParametersPerfCrossMutrMLPM[0]
+    allParametersPerformancePerModel[8] = allParametersPerformancePerModel[8] + allParametersPerfCrossMutrMLPC[0] + allParametersPerfCrossMutrMLPM[0]
     
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrMLPC[1]], ignore_index=True)
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrMLPM[1]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrMLPC[2]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrMLPM[2]], ignore_index=True)
+    allParametersPerformancePerModel[9] = pd.concat([allParametersPerformancePerModel[9], allParametersPerfCrossMutrMLPC[1]], ignore_index=True)
+    allParametersPerformancePerModel[9] = pd.concat([allParametersPerformancePerModel[9], allParametersPerfCrossMutrMLPM[1]], ignore_index=True)
+    allParametersPerformancePerModel[10] = pd.concat([allParametersPerformancePerModel[10], allParametersPerfCrossMutrMLPC[2]], ignore_index=True)
+    allParametersPerformancePerModel[10] = pd.concat([allParametersPerformancePerModel[10], allParametersPerfCrossMutrMLPM[2]], ignore_index=True)
     
     allParametersPerformancePerModel[11] = pd.concat([allParametersPerformancePerModel[11], allParametersPerfCrossMutrMLPC[3]], ignore_index=True)
     allParametersPerformancePerModel[11] = pd.concat([allParametersPerformancePerModel[11], allParametersPerfCrossMutrMLPM[3]], ignore_index=True)
 
-    allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrRFC[0] + allParametersPerfCrossMutrRFM[0]
+    allParametersPerformancePerModel[12] = allParametersPerformancePerModel[12] + allParametersPerfCrossMutrRFC[0] + allParametersPerfCrossMutrRFM[0]
     
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrRFC[1]], ignore_index=True)
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrRFM[1]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrRFC[2]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrRFM[2]], ignore_index=True)
+    allParametersPerformancePerModel[13] = pd.concat([allParametersPerformancePerModel[13], allParametersPerfCrossMutrRFC[1]], ignore_index=True)
+    allParametersPerformancePerModel[13] = pd.concat([allParametersPerformancePerModel[13], allParametersPerfCrossMutrRFM[1]], ignore_index=True)
+    allParametersPerformancePerModel[14] = pd.concat([allParametersPerformancePerModel[14], allParametersPerfCrossMutrRFC[2]], ignore_index=True)
+    allParametersPerformancePerModel[14] = pd.concat([allParametersPerformancePerModel[14], allParametersPerfCrossMutrRFM[2]], ignore_index=True)
     
     allParametersPerformancePerModel[15] = pd.concat([allParametersPerformancePerModel[15], allParametersPerfCrossMutrRFC[3]], ignore_index=True)
     allParametersPerformancePerModel[15] = pd.concat([allParametersPerformancePerModel[15], allParametersPerfCrossMutrRFM[3]], ignore_index=True)
 
-    allParametersPerformancePerModel[4] = allParametersPerformancePerModel[4] + allParametersPerfCrossMutrGradBC[0] + allParametersPerfCrossMutrGradBM[0]
+    allParametersPerformancePerModel[16] = allParametersPerformancePerModel[16] + allParametersPerfCrossMutrGradBC[0] + allParametersPerfCrossMutrGradBM[0]
     
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrGradBC[1]], ignore_index=True)
-    allParametersPerformancePerModel[5] = pd.concat([allParametersPerformancePerModel[5], allParametersPerfCrossMutrGradBM[1]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrGradBC[2]], ignore_index=True)
-    allParametersPerformancePerModel[6] = pd.concat([allParametersPerformancePerModel[6], allParametersPerfCrossMutrGradBM[2]], ignore_index=True)
+    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)
 
-    addKNN = addLR
+    addKNN = addGradB
 
-    addLR = addLR + setMaxLoopValue*2
+    addLR = addKNN + setMaxLoopValue*2
 
     addMLP = addLR + setMaxLoopValue*2
 
@@ -1824,6 +1872,9 @@ def CrossoverMutateFun():
     return 'Everything Okay'
 
 def crossoverMutation(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
+    print(AlgorithmsIDsEnd)
+    print(clf)
+    print(params)
     search = GridSearchCV(    
     estimator=clf, param_grid=params, cv=crossValidation, refit='accuracy', 
     scoring=scoring, verbose=0, n_jobs=-1)
@@ -2219,7 +2270,6 @@ def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,Predict
     metricsPerModel = preProcMetricsAllAndSelCM()
     sumPerClassifier = preProcsumPerMetricCM(factors)
     ModelsIDs = PreprocessingIDsCM()
-    
 
     parametersGenPD = parametersGen.to_json(orient='records')
     XDataJSONEntireSet = XData.to_json(orient='records')
@@ -2342,7 +2392,7 @@ def RetrieveSelIDsPredict():
     RetrieveIDsSelection = request.get_data().decode('utf8').replace("'", '"')
     RetrieveIDsSelection = json.loads(RetrieveIDsSelection)
     RetrieveIDsSelection = RetrieveIDsSelection['predictSelectionIDs']
-    
+
     ResultsSelPred = PreprocessingPredSel(RetrieveIDsSelection)
 
     return 'Everything Okay'
@@ -2372,11 +2422,11 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
             elif (items[0] == 'LR'):
                 numberIDLR.append(int(items[1]))
             elif (items[0] == 'MLP'):
-                numberIDLR.append(int(items[1]))
+                numberIDMLP.append(int(items[1]))
             elif (items[0] == 'RF'):
-                numberIDLR.append(int(items[1]))
+                numberIDRF.append(int(items[1]))
             else:
-                numberIDLR.append(int(items[1]))
+                numberIDGradB.append(int(items[1]))
 
     dicKNN = allParametersPerformancePerModel[3]
     dicLR = allParametersPerformancePerModel[7]
@@ -2465,4 +2515,17 @@ def RetrieveSelClassifiersID():
 
     EnsembleModel(ClassifierIDsList, 1)
 
+    return 'Everything Okay'
+
+@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
+@app.route('/data/ServerRemoveFromEnsemble', methods=["GET", "POST"])
+def RetrieveSelClassifiersIDandRemoveFromEnsemble():
+    global EnsembleActive
+    ClassifierIDsList = request.get_data().decode('utf8').replace("'", '"')
+    ClassifierIDsList = json.loads(ClassifierIDsList)
+    ClassifierIDsListCleaned = ClassifierIDsList['ClassifiersList']
+
+    EnsembleActive = []
+    EnsembleActive = ClassifierIDsListCleaned.copy()
+
     return 'Everything Okay'