updated models RandomSearch CV Metrics

4 years ago · 03deab186f
parent 30a2ac5bbe
commit 03deab186f
25 changed files with 593 additions and 85 deletions
--- a/pycache/run.cpython-37.pyc
+++ b/pycache/run.cpython-37.pyc
--- a/cachedir/joblib/run/randomSearch/131a748ed0f5d243f0ea93c7de3fa285/output.pkl
+++ b/cachedir/joblib/run/randomSearch/131a748ed0f5d243f0ea93c7de3fa285/output.pkl
--- a/cachedir/joblib/run/randomSearch/1c55d588f2a725cacd0db75efbba66db/output.pkl
+++ b/cachedir/joblib/run/randomSearch/1c55d588f2a725cacd0db75efbba66db/output.pkl
--- a/cachedir/joblib/run/randomSearch/2352a49cf5a0ab5a678f59beb67dccd8/metadata.json
+++ b/cachedir/joblib/run/randomSearch/2352a49cf5a0ab5a678f59beb67dccd8/metadata.json
@ -1 +0,0 @@
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--- a/cachedir/joblib/run/randomSearch/5b038325dd2fce24b03707e069cb9fec/output.pkl
+++ b/cachedir/joblib/run/randomSearch/5b038325dd2fce24b03707e069cb9fec/output.pkl
--- a/cachedir/joblib/run/randomSearch/72f7fa02379dfc92d4c664b6c36e5abb/metadata.json
+++ b/cachedir/joblib/run/randomSearch/72f7fa02379dfc92d4c664b6c36e5abb/metadata.json
@ -1 +1 @@
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--- a/cachedir/joblib/run/randomSearch/7b7c4f46bd8bbecee35ec82369081165/metadata.json
+++ b/cachedir/joblib/run/randomSearch/7b7c4f46bd8bbecee35ec82369081165/metadata.json
@ -0,0 +1 @@
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--- a/cachedir/joblib/run/randomSearch/9536a6cfa3884d632815dbf101fc6cc0/metadata.json
+++ b/cachedir/joblib/run/randomSearch/9536a6cfa3884d632815dbf101fc6cc0/metadata.json
@ -1 +1 @@
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--- a/cachedir/joblib/run/randomSearch/a72b281c2de36e337e70607faf20f922/metadata.json
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+++ b/cachedir/joblib/run/randomSearch/b2eaebae0c8bac1326b354c9349fb8fc/output.pkl
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+++ b/cachedir/joblib/run/randomSearch/bc948e5112c7458861cc66c261433272/output.pkl
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+++ b/cachedir/joblib/run/randomSearch/f94f6f45ec13ac95464d12dacae2049a/output.pkl
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+++ b/cachedir/joblib/run/randomSearch/fb4e2f9b18c63f6d1b0b6bc39f3853cb/metadata.json
@ -1 +0,0 @@
-{"duration": 93.8413097858429, "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..   ...    ...       ...    ...      ...  ...   ...  ...      ...      ...  ..  ..   ...\n298    0      1       140      1      123   57   241    0      0.2        1   0   0     3\n299    0      1       110      0      132   45   264    1      1.2        1   3   0     3\n300    1      1       144      0      141   68   193    1      3.4        1   0   2     3\n301    0      1       130      1      115   57   131    1      1.2        1   0   1     3\n302    0      1       130      0      174   57   236    0      0.0        0   1   1     2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "LogisticRegression(C=15, max_iter=400, random_state=42)", "params": "{'C': [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], 'max_iter': [50, 100, 150, 200, 250, 300, 350, 400, 450], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "100"}}
--- a/cachedir/joblib/run/randomSearch/func_code.py
+++ b/cachedir/joblib/run/randomSearch/func_code.py
@ -1,9 +1,7 @@
-# first line: 547
+# first line: 548
@memory.cache
 def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):

-    print('inside')
-
    search = RandomizedSearchCV(    
        estimator=clf, param_distributions=params, n_iter=100,
        cv=crossValidation, refit='accuracy', scoring=scoring,
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@ -9418,6 +9418,28 @@
        "d3-path": "1"
      }
    },
+    "d3-simple-slider": {
+      "version": "1.9.0",
+      "resolved": "https://registry.npmjs.org/d3-simple-slider/-/d3-simple-slider-1.9.0.tgz",
+      "integrity": "sha512-gshuzXjoX5mpeuMfAMcHc86/XTL3lvKpepVKwnwozRrnPvM0Zw8uE+VQeIfLbxbtxiC424DXeEWtchO8hpPJ1w==",
+      "requires": {
+        "d3-array": "^1.0.0",
+        "d3-axis": "^1.0.8",
+        "d3-dispatch": "^1.0.0",
+        "d3-drag": "^1.0.0",
+        "d3-ease": "^1.0.0",
+        "d3-scale": "^2.0.0",
+        "d3-selection": "^1.0.0",
+        "d3-transition": "^1.0.0"
+      },
+      "dependencies": {
+        "d3-array": {
+          "version": "1.2.4",
+          "resolved": "https://registry.npmjs.org/d3-array/-/d3-array-1.2.4.tgz",
+          "integrity": "sha512-KHW6M86R+FUPYGb3R5XiYjXPq7VzwxZ22buHhAEVG5ztoEcZZMLov530mmccaqA1GghZArjQV46fuc8kUqhhHw=="
+        }
+      }
+    },
    "d3-svg-legend": {
      "version": "1.13.0",
      "resolved": "https://registry.npmjs.org/d3-svg-legend/-/d3-svg-legend-1.13.0.tgz",
--- a/frontend/package.json
+++ b/frontend/package.json
@ -43,6 +43,7 @@
    "d3-loom": "^1.0.2",
    "d3-selection": "^1.4.1",
    "d3-selection-multi": "^1.0.1",
+    "d3-simple-slider": "^1.9.0",
    "d3-svg-legend": "^1.13.0",
    "d3_exploding_boxplot": "^0.2.1",
    "file-saver": "^2.0.2",
--- a/frontend/src/components/DataSetExecController.vue
+++ b/frontend/src/components/DataSetExecController.vue
@ -1,10 +1,9 @@
 <template>
-  <div>
+  <div class="text-center">
    <label id="data" for="param-dataset" data-toggle="tooltip" data-placement="right" title="Tip: use one of the data sets already provided or upload a new file.">{{ dataset }}</label>
    <select id="selectFile" @change="selectDataSet()">
        <option value="HeartC.csv" selected>Heart Disease</option>
-        <option value="IrisC.csv">Iris</option>
-        <option value="local">Upload New File</option>
+        <option value="local">Upload File</option>
    </select>
    <button class="btn-outline-success"
    id="initializeID"
@ -37,7 +36,7 @@ export default {
      defaultDataSet: 'HeartC', // default value for the first data set
      searchText: 'Hyper-parameter search',
      resetText: 'Reset',
-      dataset: 'Data set'
+      dataset: 'Data set:'
    }
  },
  methods: {
@ -46,7 +45,7 @@ export default {
      this.defaultDataSet = fileName.options[fileName.selectedIndex].value
      this.defaultDataSet = this.defaultDataSet.split('.')[0]

-      if (this.defaultDataSet == "DiabetesC" || this.defaultDataSet == "HeartC" || this.defaultDataSet == "IrisC" || this.defaultDataSet == "StanceC") { // This is a function that handles a new file, which users can upload.
+      if (this.defaultDataSet == "HeartC") { // This is a function that handles a new file, which users can upload.
        this.dataset = "Data set"
        d3.select("#data").select("input").remove(); // Remove the selection field.
        EventBus.$emit('SendToServerDataSetConfirmation', this.defaultDataSet)
--- a/frontend/src/components/Ensemble.vue
+++ b/frontend/src/components/Ensemble.vue
@ -40,6 +40,8 @@ export default {
  },
  methods: {
    reset () {
+      this.ScatterPlotResults = ''
+      this.storeEnsembleLoc = []
      Plotly.purge('OverviewPlotlyCM')
    },
    selectVisualRepresentationCM () {
--- a/frontend/src/components/GlobalParamController.vue
+++ b/frontend/src/components/GlobalParamController.vue
@ -0,0 +1,99 @@
+<template>
+  <div style="margin: -20px 0 -20px 0 !important">
+    <div class="row align-items-center" style="margin: 0 0 -25px 0 !important">
+      <div class="col-lg-4"><p>Random search:</p></div>
+      <div class="col-lg-6"><div id="slider-stepPos"></div></div>
+      <div class="col-lg-2"><p id="value-stepPos"></p></div>
+    </div>
+    <div class="row align-items-center" style="margin: 0 0 -25px 0 !important">
+      <div class="col-lg-4"><p>Cross validation:</p></div>
+      <div class="col-lg-6"><div id="slider-stepNeg"></div></div>
+      <div class="col-lg-2"><p id="value-stepNeg"></p></div>
+    </div>
+  </div>
+</template>
+
+<script>
+import { EventBus } from '../main.js'
+import { sliderBottom } from 'd3-simple-slider'
+import * as d3Base from 'd3'
+
+// attach all d3 plugins to the d3 library
+const d3 = Object.assign(d3Base, { sliderBottom })
+
+export default {
+  name: 'GlobalParamController',
+  data () {
+    return {
+    }
+  },
+  methods: {
+    InitSliders () { 
+      var svg = d3.select("#slider-stepPos");
+      svg.selectAll("*").remove();
+
+      var svg = d3.select("#slider-stepNeg");
+      svg.selectAll("*").remove();
+
+      var dataCorrect = [50.0, 100.0, 150.0, 200.0, 250.0, 300.0];
+      var dataWrong = [5.0, 10.0, 15.0];
+
+        var sliderStepPos = d3
+          .sliderBottom()
+          .min(d3.min(dataCorrect))
+          .max(d3.max(dataCorrect))
+          .width(200)
+          .tickFormat(d3.format(".0f"))
+          .ticks(6)
+          .step(50)
+          .default(100.0)
+          .on('onchange', val => {
+            d3.select('p#value-stepPos').text(d3.format(".0f")(val));
+            EventBus.$emit('SendtheChangeinRangePos', d3.format(".0f")(val))
+          });
+
+        var gStepPos = d3
+          .select('div#slider-stepPos')
+          .append('svg')
+          .attr('width', 500)
+          .attr('height', 100)
+          .append('g')
+          .attr('transform', 'translate(30,30)');
+
+        gStepPos.call(sliderStepPos);
+
+        d3.select('p#value-stepPos').text(d3.format(".0f")(sliderStepPos.value()));
+
+        var sliderStepNeg = d3
+          .sliderBottom()
+          .min(d3.min(dataWrong))
+          .max(d3.max(dataWrong))
+          .width(200)
+          .tickFormat(d3.format(".0f"))
+          .ticks(3)
+          .step(5)
+          .default(5.0)
+          .on('onchange', val => {
+            d3.select('p#value-stepNeg').text(d3.format(".0f")(val));
+            EventBus.$emit('SendtheChangeinRangeNeg', d3.format(".0f")(val))
+          });
+
+        var gStepNeg = d3
+          .select('div#slider-stepNeg')
+          .append('svg')
+          .attr('width', 500)
+          .attr('height', 100)
+          .append('g')
+          .attr('transform', 'translate(30,30)');
+
+        gStepNeg.call(sliderStepNeg);
+
+        d3.select('p#value-stepNeg').text(d3.format(".0f")(sliderStepNeg.value()));
+    },
+  },
+  mounted () {
+    this.InitSliders()
+    EventBus.$on('reset', this.InitSliders)
+  },
+}
+</script>
--- a/frontend/src/components/HyperParameterSpace.vue
+++ b/frontend/src/components/HyperParameterSpace.vue
@ -39,6 +39,7 @@ export default {
  },
  methods: {
    reset () {
+      this.ScatterPlotResults = ''
      Plotly.purge('OverviewPlotly')
    },
    clean(obj) {
@ -59,6 +60,7 @@ 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)
--- a/frontend/src/components/Main.vue
+++ b/frontend/src/components/Main.vue
@ -10,6 +10,7 @@
              <mdb-card-body>
                <mdb-card-text class="text-left" style="font-size: 18.5px;">
                  <PerformanceMetrics/>
+                  <GlobalParamController/>
                  <DataSetExecController/>
                </mdb-card-text>
              </mdb-card-body>
@ -19,7 +20,7 @@
          <mdb-card>
            <mdb-card-header color="primary-color" tag="h5" class="text-center">Provenance</mdb-card-header>
            <mdb-card-body>
-              <mdb-card-text class="text-left" style="font-size: 18.5px; min-height: 230px">
+              <mdb-card-text class="text-left" style="font-size: 18.5px; min-height: 359px">
              </mdb-card-text>
            </mdb-card-body>
          </mdb-card>
@ -28,7 +29,7 @@
            <mdb-card >
              <mdb-card-header color="primary-color" tag="h5" class="text-center">Majority-Voting Ensemble's Results</mdb-card-header>
              <mdb-card-body>
-                <mdb-card-text class="text-left" style="font-size: 18.5px; min-height: 230px">
+                <mdb-card-text class="text-left" style="font-size: 18.5px; min-height: 359px">
                </mdb-card-text>
              </mdb-card-body>
            </mdb-card>
@ -119,7 +120,7 @@
              <b-col cols="6">
                <mdb-card style="margin-top: 15px;">
                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Hyper-Parameters' Space
-                    [Sel: {{OverSelLength}} / All: {{OverAllLength}}]<small class="float-right"><active-scatter/></small>
+                    [Sel: {{OverSelLength}} / All: {{OverAllLength}}]<small class="float-right"><active-scatter/></small><span class="badge badge-info badge-pill float-right">Projection<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">1</span></span>
                  </mdb-card-header>
                  <mdb-card-body>
                    <mdb-card-text class="text-center"  style="min-height: 600px">
@ -130,8 +131,8 @@
              </b-col>
              <b-col cols="6">
                <mdb-card style="margin-top: 15px;">
-                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Models Included in the Majority-Voting Ensemble
-                    [Sel: {{OverSelLengthCM}} / All: {{OverAllLengthCM}}]<small class="float-right"><active-scatter/></small>
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Majority-Voting Ensemble
+                    [Sel: {{OverSelLengthCM}} / All: {{OverAllLengthCM}}]<small class="float-right"><active-scatter/></small><span class="badge badge-info badge-pill float-right">Projection<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: 600px">
@ -144,7 +145,7 @@
            <b-row class="md-3">
              <b-col cols="3">
                <mdb-card style="margin-top: 15px;">
-                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Manipulation of Algorithms
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Manipulation of Algorithms<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">1&2</span></span>
                    </mdb-card-header>
                    <mdb-card-body>
                      <mdb-card-text class="text-center"  style="min-height: 270px">
@ -155,7 +156,7 @@
              </b-col>
              <b-col cols="6">
                <mdb-card style="margin-top: 15px;">
-                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Predictive Results for Each Data Instance
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Predictive Results for Each Data Instance<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">{{projectionID_A}}</span></span>
                    </mdb-card-header>
                    <mdb-card-body>
                      <mdb-card-text class="text-center"  style="min-height: 270px">
@ -166,7 +167,7 @@
              </b-col>
              <b-col cols="3">
                <mdb-card style="margin-top: 15px;">
-                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Performance for Each Validation Metric
+                  <mdb-card-header color="primary-color" tag="h5" class="text-center">Performance for Each Validation Metric<span class="badge badge-primary badge-pill float-right">Active<span class="badge badge-light" style="margin-left:4px; margin-bottom:1px">{{projectionID_B}}</span></span>
                    </mdb-card-header>
                    <mdb-card-body>
                      <mdb-card-text class="text-center"  style="min-height: 270px">   
@ -189,6 +190,7 @@ 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'
 import VotingResults from './VotingResults.vue'
 import Parameters from './Parameters.vue'
@ -216,6 +218,7 @@ export default Vue.extend({
    AlgorithmsController,
    AlgorithmHyperParam,
    HyperParameterSpace,
+    GlobalParamController,
    Ensemble,
    Parameters,
    Predictions,
@ -227,6 +230,8 @@ export default Vue.extend({
  },
  data () {
    return {
+      projectionID_A: 1,
+      projectionID_B: 1,
      storeEnsemble: [],
      PredictSelEnsem: [],
      firstTimeExec: true,
@ -281,6 +286,8 @@ export default Vue.extend({
      ClassifierIDsListRemaining: [],
      PredictSel: [],
      storeBothEnsCM: [],
+      crossVal: '5',
+      RandomSear: '100',
    }
  },
  methods: {
@ -673,8 +680,12 @@ export default Vue.extend({
    },
    fileNameSend () {
      const path = `http://127.0.0.1:5000/data/ServerRequest`
+      
      const postData = {
        fileName: this.RetrieveValueFile,
+        RandomSearch: this.RandomSear,
+        CrossValidation: this.crossVal,
+        Factors: this.basicValuesFact
      }
      const axiosConfig = {
        headers: {
@ -773,6 +784,11 @@ export default Vue.extend({
    Reset () {
      const path = `http://127.0.0.1:5000/data/Reset`
      this.reset = true
+      this.firstTimeExec = true
+      this.OverSelLength = 0
+      this.OverAllLength = 0
+      this.OverSelLengthCM = 0
+      this.OverAllLengthCM = 0
      const postData = {
        ClassifiersList: this.reset
      }
@ -909,6 +925,14 @@ export default Vue.extend({
        .catch(error => {
          console.log(error)
        })
+    },
+    changeActiveTo1 () {
+      this.projectionID_A = 1 
+      this.projectionID_B = 1
+    },
+    changeActiveTo2 () {
+      this.projectionID_A = 2 
+      this.projectionID_B = 2
    }
  },
  created () {
@ -948,13 +972,17 @@ export default Vue.extend({
    EventBus.$on('ReturningBrushedPointsIDs',  data => { this.modelsUpdate = data })
    //EventBus.$on('ReturningBrushedPointsIDs',  this.UpdateBarChartFeatures )
    EventBus.$on('ConfirmDataSet', this.fileNameSend)
+    EventBus.$on('reset', this.changeActiveTo1)
    EventBus.$on('reset', this.Reset)
    EventBus.$on('ReturningAlgorithms', data => { this.selectedAlgorithms = data })
    EventBus.$on('ReturningBrushedPointsParams', data => { this.parametersofModels = data; })

+    EventBus.$on('RemainingPoints', this.changeActiveTo1)
    EventBus.$on('RemainingPoints', data => { this.unselectedRemainingPoints = data })
+    EventBus.$on('InitializeCrossoverMutation', this.changeActiveTo2)
    EventBus.$on('InitializeCrossoverMutation', this.sendPointsCrossMutat)

+    EventBus.$on('RemainingPointsCM', this.changeActiveTo2)
    EventBus.$on('RemainingPointsCM', data => { this.unselectedRemainingPointsEnsem = data })

    EventBus.$on('ChangeKey', data => { this.keyNow = data })
@ -1015,6 +1043,10 @@ export default Vue.extend({

    EventBus.$on('toggleDeep', data => {this.toggleDeepMain = data})

+    EventBus.$on('SendtheChangeinRangePos', data => { this.RandomSear = data })
+    EventBus.$on('SendtheChangeinRangeNeg', data => { this.crossVal = data })
+    EventBus.$on('factorsChanged', data => { this.basicValuesFact = data })
+
    //Prevent double click to search for a word. 
    document.addEventListener('mousedown', function (event) {
      if (event.detail > 1) {
--- a/frontend/src/components/PerformanceMetrics.vue
+++ b/frontend/src/components/PerformanceMetrics.vue
@ -14,7 +14,7 @@
              <b-form-checkbox
                id="checkboxAcc"
                v-model="checkedAcc"
-                @click="clickAcc"
+                @change="clickAcc()"
              >
              </b-form-checkbox>
            </td>
@ -23,7 +23,7 @@
              <b-form-checkbox
                id="checkboxGM"
                v-model="checkedGM"
-                @click="clickGM"
+                @change="clickGM()"
              >
              </b-form-checkbox>
            </td>
@ -34,7 +34,7 @@
              <b-form-checkbox
                id="checkboxPrec"
                v-model="checkedPrec"
-                @click="clickPrec"
+                @change="clickPrec()"
              >
              </b-form-checkbox>
            </td>
@ -43,7 +43,7 @@
              <b-form-checkbox
                id="checkboxRA"
                v-model="checkedRA"
-                @click="clickRA"
+                @change="clickRA()"
              >
              </b-form-checkbox>
            </td>
@ -54,7 +54,7 @@
              <b-form-checkbox
                id="checkboxRec"
                v-model="checkedRec"
-                @click="clickRec"
+                @change="clickRec()"
              >
              </b-form-checkbox>
            </td>
@ -63,7 +63,7 @@
              <b-form-checkbox
                id="checkboxLog"
                v-model="checkedLog"
-                @click="clickLog"
+                @change="clickLog()"
              >
              </b-form-checkbox>
            </td>
@ -74,7 +74,7 @@
              <b-form-checkbox
                id="checkboxF1"
                v-model="checkedF1"
-                @click="clickF1"
+                @change="clickF1()"
              >
              </b-form-checkbox>
            </td>
@ -83,7 +83,7 @@
              <b-form-checkbox
                id="checkboxMCC"
                v-model="checkedMCC"
-                @click="clickMCC"
+                @change="clickMCC()"
              >
              </b-form-checkbox>
            </td>
@ -97,6 +97,7 @@

 <script>
    import { EventBus } from '../main.js'
+    import $ from 'jquery'

    export default {
        name: 'PerformanceMetrics',
@ -110,36 +111,67 @@
              checkedRA: false,
              checkedLog: false,
              checkedMCC: false,
+              factorsLocal: [1,1,1,1,0,0,0,0]
          }
        },
        methods: {
          clickAcc () {
            this.checkedAcc = !this.checkedAcc
+            console.log(this.checkedAcc)
+            this.factorsRegisterChange(0,this.checkedAcc)
          },
          clickPrec () {
            this.checkedPrec = !this.checkedPrec
+            this.factorsRegisterChange(1,this.checkedPrec)
          },
          clickRec () {
-            this.checkedRec = !this.checkedRec  
+            this.checkedRec = !this.checkedRec
+            this.factorsRegisterChange(2,this.checkedRec)
          },
          clickF1 () {
-            this.checkedF1 = !this.checkedF1 
+            this.checkedF1 = !this.checkedF1
+            this.factorsRegisterChange(3,this.checkedF1)
          },
          clickGM () {
            this.checkedGM = !this.checkedGM
+            this.factorsRegisterChange(4,this.checkedGM)
          },
          clickRA () {
            this.checkedRA = !this.checkedRA
+            this.factorsRegisterChange(5,this.checkedRA)
          },
          clickLog () {
            this.checkedLog = !this.checkedLog
+            this.factorsRegisterChange(6,this.checkedLog)
          },
          clickMCC () {
            this.checkedMCC = !this.checkedMCC
+            this.factorsRegisterChange(7,this.checkedMCC)
          },
+          factorsRegisterChange (position,value) {
+            if (value == true) {
+              this.factorsLocal[position] = 1
+            } else {
+              this.factorsLocal[position] = 0
+            }
+            EventBus.$emit('factorsChanged', this.factorsLocal)
+          },
+          resetBoxes () {
+            this.checkedAcc = true
+            this.checkedPrec = true
+            this.checkedRec = true
+            this.checkedF1 = true
+            this.checkedGM = false
+            this.checkedRA = false
+            this.checkedLog = false
+            this.checkedMCC = false
+
+            this.factorsLocal = [1,1,1,1,0,0,0,0]
+            EventBus.$emit('factorsChanged', this.factorsLocal)
+          }
        },
        mounted () {
-
+          EventBus.$on('reset', this.resetBoxes)
        }
    }
 </script>
--- a/frontend/src/components/Predictions.vue
+++ b/frontend/src/components/Predictions.vue
@ -32,6 +32,10 @@ export default {
      svg.selectAll("*").remove();
      var svg = d3.select("#containerSelection");
      svg.selectAll("*").remove();
+      this.GetResultsAll = []
+      this.GetResultsSelection = []
+      this.predictSelection = []
+      this.StoreIndices = []
    },
    Grid () {

@ -60,13 +64,22 @@ export default {
      var predictions = JSON.parse(this.GetResultsAll[12])
      var KNNPred = predictions[0]
      var LRPred = predictions[1]
-      var PredAver = predictions[2]
+      var MLPPred = predictions[2]
+      var RFPred = predictions[3]
+      var GradBPred = predictions[4]
+      var PredAver = predictions[5]
      var dataAver = []
      var dataAverGetResults = []
      var dataKNN = []
      var dataKNNResults = []
      var dataLR = []
      var dataLRResults = []
+      var dataMLP = []
+      var dataMLPResults = []
+      var dataRF = []
+      var dataRFResults = []
+      var dataGradB = []
+      var dataGradBResults = []

      var max = 0
      for (let i = 0; i < targetNames.length; i++) {
@ -79,26 +92,41 @@ export default {
      var size = sqrtSize * sqrtSize

      for (let i = 0; i < targetNames.length; i++) {
-        dataAver = [];
+        dataAver = []
        dataKNN = []
        dataLR = []
+        dataMLP = []
+        dataRF = []
+        dataGradB = []
        getIndices[targetNames[i]].forEach(element => {
          dataAver.push({ id: element, value: PredAver[element][targetNames[i]] })
          dataKNN.push({ id: element, value: KNNPred[element][targetNames[i]] })
          dataLR.push({ id: element, value: LRPred[element][targetNames[i]] })
+          dataMLP.push({ id: element, value: MLPPred[element][targetNames[i]] })
+          dataRF.push({ id: element, value: RFPred[element][targetNames[i]] })
+          dataGradB.push({ id: element, value: GradBPred[element][targetNames[i]] })
        });
        for (let j = 0; j < size - getIndices[targetNames[i]].length; j++) {
          dataAver.push({ id: null, value: 1.0 })
          dataKNN.push({ id: null, value: 1.0 })
          dataLR.push({ id: null, value: 1.0 })
+          dataMLP.push({ id: null, value: 1.0 })
+          dataRF.push({ id: null, value: 1.0 })
+          dataGradB.push({ id: null, value: 1.0 })
        }
        dataAverGetResults.push(dataAver)
        dataKNNResults.push(dataKNN)
        dataLRResults.push(dataLR)
+        dataMLPResults.push(dataMLP)
+        dataRFResults.push(dataRF)
+        dataGradBResults.push(dataGradB)
      }
    dataAverGetResults.reverse()
    dataKNNResults.reverse()
    dataLRResults.reverse()
+    dataMLPResults.reverse()
+    dataRFResults.reverse()
+    dataGradBResults.reverse()
    
    var classArray = []
    this.StoreIndices = []
@ -119,15 +147,27 @@ export default {
        return indices.indexOf(a.id) - indices.indexOf(b.id)
      });

-      classArray.push(dataAverGetResults[i].concat(dataKNNResults[i], dataLRResults[i]));
+      dataMLPResults[i].sort(function(a, b){
+        return indices.indexOf(a.id) - indices.indexOf(b.id)
+      });
+
+      dataRFResults[i].sort(function(a, b){
+        return indices.indexOf(a.id) - indices.indexOf(b.id)
+      });
+
+      dataGradBResults[i].sort(function(a, b){
+        return indices.indexOf(a.id) - indices.indexOf(b.id)
+      });
+
+      classArray.push(dataAverGetResults[i].concat(dataKNNResults[i], dataLRResults[i],dataMLPResults[i],dataRFResults[i],dataGradBResults[i]));
    }
-    
+
    var classStore = [].concat.apply([], classArray);

 		// === Set up canvas === //

 		var width = 1200,
-				height = 125;
+				height = 85;
 		var colourScale;


@ -144,9 +184,9 @@ export default {
 		var custom = d3.select(customBase); // this is our svg replacement

    // settings for a grid with 40 cells in a row and 2x5 cells in a group
-		var groupSpacing = 60;
+		var groupSpacing = 40;
 		var cellSpacing = 2;
-    var cellSize = Math.floor((width - 1 * groupSpacing) / (10 * sqrtSize)) - cellSpacing;
+    var cellSize = Math.floor((width - 1 * groupSpacing) / (13 * sqrtSize)) - cellSpacing;

 		// === First call === //
 		databind(classStore, size, sqrtSize); // ...then update the databind function
@ -187,6 +227,13 @@ export default {
 				.attr('width', cellSize)
 				.attr('height', cellSize)
        .attr('fillStyle', function(d) { return colourScale(d.value); })
+        .attr('fill-opacity', function(d) { 
+          if (d.id == null) {
+            return "0.0";
+          } else {
+            return "1.0"; 
+          } 
+        });

 			var exitSel = join.exit()
 				.transition()
@ -240,21 +287,29 @@ export default {
      svg.selectAll("*").remove();

      var predictionsAll = JSON.parse(this.GetResultsAll[12])
-      console.log(predictionsAll)

      if (this.predictSelection.length != 0) {
        var predictions = this.predictSelection
        var KNNPred = predictions[0]
        var LRPred = predictions[1]
-        var PredAver = predictions[2]
+        var MLPPred = predictions[2]
+        var RFPred = predictions[3]
+        var GradBPred = predictions[4]
+        var PredAver = predictions[5]
      } else {
        var KNNPred = predictionsAll[0]
        var LRPred = predictionsAll[1]
-        var PredAver = predictionsAll[2]
+        var MLPPred = predictionsAll[2]
+        var RFPred = predictionsAll[3]
+        var GradBPred = predictionsAll[4]
+        var PredAver = predictionsAll[5]
      }
      var KNNPredAll = predictionsAll[0]
      var LRPredAll = predictionsAll[1]
-      var PredAverAll = predictionsAll[2]
+      var MLPPredAll = predictionsAll[2]
+      var RFPredAll = predictionsAll[3]
+      var GradBPredAll = predictionsAll[4]
+      var PredAverAll = predictionsAll[5]

      var yValues = JSON.parse(this.GetResultsSelection[6])
      var targetNames = JSON.parse(this.GetResultsSelection[7])
@ -271,6 +326,12 @@ export default {
      var dataKNNResults = []
      var dataLR = []
      var dataLRResults = []
+      var dataMLP = []
+      var dataMLPResults = []
+      var dataRF = []
+      var dataRFResults = []
+      var dataGradB = []
+      var dataGradBResults = []

      var max = 0
      for (let i = 0; i < targetNames.length; i++) {
@ -283,27 +344,42 @@ export default {
      var size = sqrtSize * sqrtSize

      for (let i = 0; i < targetNames.length; i++) {
-        dataAver = [];
+        dataAver = []
        dataKNN = []
        dataLR = []
+        dataMLP = []
+        dataRF = []
+        dataGradB = []
        getIndices[targetNames[i]].forEach(element => {
          dataAver.push({ id: element, value: PredAver[element][targetNames[i]] - PredAverAll[element][targetNames[i]] })
          dataKNN.push({ id: element, value: KNNPred[element][targetNames[i]] - KNNPredAll[element][targetNames[i]] })
          dataLR.push({ id: element, value: LRPred[element][targetNames[i]] - LRPredAll[element][targetNames[i]] })
+          dataMLP.push({ id: element, value: MLPPred[element][targetNames[i]] - MLPPredAll[element][targetNames[i]] })
+          dataRF.push({ id: element, value: RFPred[element][targetNames[i]] - RFPredAll[element][targetNames[i]] })
+          dataGradB.push({ id: element, value: GradBPred[element][targetNames[i]] - GradBPredAll[element][targetNames[i]] })
        });
        for (let j = 0; j < size - getIndices[targetNames[i]].length; j++) {
          dataAver.push({ id: null, value: 0 })
          dataKNN.push({ id: null, value: 0 })
          dataLR.push({ id: null, value: 0 })
+          dataMLP.push({ id: null, value: 0 })
+          dataRF.push({ id: null, value: 0 })
+          dataGradB.push({ id: null, value: 0 })
        }
        dataAverGetResults.push(dataAver)
        dataKNNResults.push(dataKNN)
        dataLRResults.push(dataLR)
+        dataMLPResults.push(dataMLP)
+        dataRFResults.push(dataRF)
+        dataGradBResults.push(dataGradB)
      }
    dataAverGetResults.reverse()
    dataKNNResults.reverse()
    dataLRResults.reverse()
-    
+    dataMLPResults.reverse()
+    dataRFResults.reverse()
+    dataGradBResults.reverse()
+
    var classArray = []

    for (let i = 0; i < dataAverGetResults.length; i++) {
@ -321,14 +397,27 @@ export default {
        return indices.indexOf(a.id) - indices.indexOf(b.id)
      });

-      classArray.push(dataAverGetResults[i].concat(dataKNNResults[i], dataLRResults[i]));
+      dataMLPResults[i].sort(function(a, b){
+        return indices.indexOf(a.id) - indices.indexOf(b.id)
+      });
+
+      dataRFResults[i].sort(function(a, b){
+        return indices.indexOf(a.id) - indices.indexOf(b.id)
+      });
+
+      dataGradBResults[i].sort(function(a, b){
+        return indices.indexOf(a.id) - indices.indexOf(b.id)
+      });
+
+      classArray.push(dataAverGetResults[i].concat(dataKNNResults[i], dataLRResults[i], dataMLPResults[i], dataRFResults[i], dataGradBResults[i]));
    }
    
    var classStore = [].concat.apply([], classArray);
+
 		// === Set up canvas === //

 		var width = 1200,
-				height = 125;
+				height = 85;
 		var colourScale;


@ -345,9 +434,9 @@ export default {
 		var custom = d3.select(customBase); // this is our svg replacement

    // settings for a grid with 40 cells in a row and 2x5 cells in a group
-		var groupSpacing = 60;
+		var groupSpacing = 40;
 		var cellSpacing = 2;
-    var cellSize = Math.floor((width - 1 * groupSpacing) / (10 * sqrtSize)) - cellSpacing;
+    var cellSize = Math.floor((width - 1 * groupSpacing) / (13 * sqrtSize)) - cellSpacing;

 		// === First call === //
 		databind(classStore, size, sqrtSize); // ...then update the databind function
@ -388,6 +477,13 @@ export default {
 				.attr('width', cellSize)
 				.attr('height', cellSize)
        .attr('fillStyle', function(d) { return colourScale(d.value); })
+        .attr('fill-opacity', function(d) { 
+          if (d.id == null) {
+            return "0.0";
+          } else {
+            return "1.0"; 
+          } 
+        });

 			var exitSel = join.exit()
 				.transition()
--- a/run.py
+++ b/run.py
@ -42,6 +42,12 @@ cors = CORS(app, resources={r"/data/*": {"origins": "*"}})
@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@app.route('/data/Reset', methods=["GET", "POST"])
 def reset():
+
+    global Results
+    Results = []
+    global ResultsCM
+    ResultsCM = []
+
    global DataRawLength
    global DataResultsRaw
    global previousState
@ -68,11 +74,20 @@ def reset():
    global factors
    factors = [1,1,1,1,0,0,0,0]

+    global crossValidation
+    crossValidation = 5
+
+    global randomSearchVar
+    randomSearchVar = 100
+
    global keyData
    keyData = 0

    KNNModelsCount = 0
-    LRModelsCount = 100
+    LRModelsCount = KNNModelsCount+randomSearchVar
+    MLPModelsCount = LRModelsCount+randomSearchVar
+    RFModelsCount = MLPModelsCount+randomSearchVar
+    GradBModelsCount = RFModelsCount+randomSearchVar

    global XData
    XData = []
@ -86,7 +101,16 @@ def reset():
    addKNN = 0

    global addLR
-    addLR = 100
+    addLR = addKNN+randomSearchVar
+
+    global addMLP
+    addMLP = addLR+randomSearchVar
+
+    global addRF
+    addRF = addMLP+randomSearchVar
+
+    global addGradB
+    addGradB = addRF+randomSearchVar

    global countAllModels
    countAllModels = 0
@ -124,9 +148,6 @@ def reset():

    global all_classifiers
    all_classifiers = []
-
-    global crossValidation
-    crossValidation = 5
    
    # models
    global KNNModels
@ -166,6 +187,7 @@ def retrieveFileName():
    global DataRawLengthTest

    fileName = request.get_data().decode('utf8').replace("'", '"')
+    data = json.loads(fileName)  

    global keySpecInternal
    keySpecInternal = 1
@ -182,6 +204,27 @@ def retrieveFileName():
    global keyData
    keyData = 0

+    global KNNModelsCount
+    global LRModelsCount
+    global MLPModelsCount
+    global RFModelsCount
+    global GradBModelsCount
+
+    global factors
+    factors = data['Factors']
+
+    global crossValidation
+    crossValidation = int(data['CrossValidation'])
+
+    global randomSearchVar
+    randomSearchVar = int(data['RandomSearch'])
+
+    KNNModelsCount = 0
+    LRModelsCount = KNNModelsCount+randomSearchVar
+    MLPModelsCount = LRModelsCount+randomSearchVar
+    RFModelsCount = MLPModelsCount+randomSearchVar
+    GradBModelsCount = RFModelsCount+randomSearchVar
+
    global XData
    XData = []

@ -219,7 +262,16 @@ def retrieveFileName():
    addKNN = 0

    global addLR
-    addLR = 100
+    addLR = addKNN+randomSearchVar
+
+    global addMLP
+    addMLP = addLR+randomSearchVar
+
+    global addRF
+    addRF = addMLP+randomSearchVar
+
+    global addGradB
+    addGradB = addRF+randomSearchVar

    # Initializing models

@ -241,9 +293,6 @@ def retrieveFileName():
    global all_classifiers
    all_classifiers = []

-    global crossValidation
-    crossValidation = 5
-
    global scoring
    scoring = {'accuracy': 'accuracy', 'precision_weighted': 'precision_weighted', 'recall_weighted': 'recall_weighted', 'f1_weighted': 'f1_weighted', 'roc_auc_ovo_weighted': 'roc_auc_ovo_weighted'}

@ -297,7 +346,7 @@ def retrieveFileName():

    DataRawLength = -1
    DataRawLengthTest = -1
-    data = json.loads(fileName)  
+
    if data['fileName'] == 'HeartC':
        CollectionDB = mongo.db.HeartC.find()
    elif data['fileName'] == 'StanceC':
@ -514,26 +563,27 @@ def retrieveModel():
        elif (eachAlgor) == 'LR':
            clf = LogisticRegression(random_state=RANDOM_SEED)
            params = {'C': list(np.arange(1,100,1)), 'max_iter': list(np.arange(50,500,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}
-            countAllModels = countAllModels + 100
+            countAllModels = countAllModels + randomSearchVar
            AlgorithmsIDsEnd = countAllModels
        elif (eachAlgor) == 'MLP':
            start = 60
            stop = 120
            step = 1
+            random.seed(RANDOM_SEED)
            ranges = [(n, random.randint(1,3)) for n in range(start, stop, step)]
            clf = MLPClassifier(random_state=RANDOM_SEED)
            params = {'hidden_layer_sizes': ranges,'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0004)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']}
-            countAllModels = countAllModels + 100
+            countAllModels = countAllModels + randomSearchVar
            AlgorithmsIDsEnd = countAllModels
        elif (eachAlgor) == 'RF':
            clf = RandomForestClassifier(random_state=RANDOM_SEED)
            params = {'n_estimators': list(range(20, 100)), 'criterion': ['gini', 'entropy']}
-            countAllModels = countAllModels + 100
+            countAllModels = countAllModels + randomSearchVar
            AlgorithmsIDsEnd = countAllModels
        else: 
            clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
            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 + 100
+            countAllModels = countAllModels + randomSearchVar
            AlgorithmsIDsEnd = countAllModels
        allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd)
    HistoryPreservation = allParametersPerformancePerModel.copy()
@ -547,8 +597,6 @@ memory = Memory(location, verbose=0)
@memory.cache
 def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):

-    print('inside')
-
    search = RandomizedSearchCV(    
        estimator=clf, param_distributions=params, n_iter=100,
        cv=crossValidation, refit='accuracy', scoring=scoring,
@ -640,30 +688,45 @@ def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
 def PreprocessingIDs():
    dicKNN = allParametersPerformancePerModel[0]
    dicLR = allParametersPerformancePerModel[4]
+    dicMLP = allParametersPerformancePerModel[8]
+    dicRF = allParametersPerformancePerModel[12]
+    dicGradB = allParametersPerformancePerModel[16]

-    df_concatIDs = dicKNN + dicLR
+    df_concatIDs = dicKNN + dicLR + dicMLP + dicRF + dicGradB

    return df_concatIDs

 def PreprocessingMetrics():
    dicKNN = allParametersPerformancePerModel[2]
    dicLR = allParametersPerformancePerModel[6]
+    dicMLP = allParametersPerformancePerModel[10]
+    dicRF = allParametersPerformancePerModel[14]
+    dicGradB = allParametersPerformancePerModel[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])
+    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]
+    dicMLP = allParametersPerformancePerModel[11]
+    dicRF = allParametersPerformancePerModel[15]
+    dicGradB = allParametersPerformancePerModel[19]

    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_concatProbs = pd.concat([dfKNN, dfLR])
+    df_concatProbs = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])
    df_concatProbs.reset_index(drop=True, inplace=True)

    predictionsKNN = []
@ -675,13 +738,28 @@ def PreprocessingPred():
    for column, content in dfLR.items():
        el = [sum(x)/len(x) for x in zip(*content)]
        predictionsLR.append(el)
+
+    predictionsMLP = []
+    for column, content in dfMLP.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsMLP.append(el)
+
+    predictionsRF = []
+    for column, content in dfRF.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsRF.append(el)
+
+    predictionsGradB = []
+    for column, content in dfGradB.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsGradB.append(el)
    
    predictions = []
    for column, content in df_concatProbs.items():
        el = [sum(x)/len(x) for x in zip(*content)]
        predictions.append(el)

-    return [predictionsKNN, predictionsLR, predictions]
+    return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]

 def PreprocessingPredEnsemble():

@ -689,29 +767,49 @@ def PreprocessingPredEnsemble():

    numberIDKNN = []
    numberIDLR = []
+    numberIDMLP = []
+    numberIDRF = []
+    numberIDGradB = []
+
    for el in EnsembleActive:
        match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
        if match:
            items = match.groups()
            if (items[0] == 'KNN'):
                numberIDKNN.append(int(items[1]))
-            else:
+            elif (items[0] == 'LR'):
                numberIDLR.append(int(items[1]))
+            elif (items[0] == 'MLP'):
+                numberIDMLP.append(int(items[1]))
+            elif (items[0] == 'RF'):
+                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]

    dfKNN = pd.DataFrame.from_dict(dicKNN)
    dfLR = pd.DataFrame.from_dict(dicLR)
-    df_concatProbs = pd.concat([dfKNN, dfLR])
+    dfMLP = pd.DataFrame.from_dict(dicMLP)
+    dfRF = pd.DataFrame.from_dict(dicRF)
+    dfGradB = pd.DataFrame.from_dict(dicGradB)
+
+    df_concatProbs = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])
    df_concatProbs = df_concatProbs.reset_index(drop=True)

    dfKNN = df_concatProbs.loc[numberIDKNN]
    dfLR = df_concatProbs.loc[numberIDLR]
+    dfMLP = df_concatProbs.loc[numberIDMLP]
+    dfRF = df_concatProbs.loc[numberIDRF]
+    dfGradB = df_concatProbs.loc[numberIDGradB]

    df_concatProbs = pd.DataFrame()
    df_concatProbs = df_concatProbs.iloc[0:0]
-    df_concatProbs = pd.concat([dfKNN, dfLR])
+    df_concatProbs = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])

    predictionsKNN = []
    for column, content in dfKNN.items():
@ -723,50 +821,95 @@ def PreprocessingPredEnsemble():
        el = [sum(x)/len(x) for x in zip(*content)]
        predictionsLR.append(el)

+    predictionsMLP = []
+    for column, content in dfMLP.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsMLP.append(el)
+
+    predictionsRF = []
+    for column, content in dfRF.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsRF.append(el)
+
+    predictionsGradB = []
+    for column, content in dfGradB.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsGradB.append(el)
+
    predictions = []
    for column, content in df_concatProbs.items():
        el = [sum(x)/len(x) for x in zip(*content)]
        predictions.append(el)

-    return [predictionsKNN, predictionsLR, predictions]
+    return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]

 def PreprocessingParam():
    dicKNN = allParametersPerformancePerModel[1]
    dicLR = allParametersPerformancePerModel[5]
+    dicMLP = allParametersPerformancePerModel[9]
+    dicRF = allParametersPerformancePerModel[13]
+    dicGradB = allParametersPerformancePerModel[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])
+    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]
+    dicMLP = allParametersPerformancePerModel[9]
+    dicRF = allParametersPerformancePerModel[13]
+    dicGradB = allParametersPerformancePerModel[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

-    return [dfKNN, dfLR]
+    return [dfKNN, dfLR, dfMLP, dfRF, dfGradB]

 # remove that maybe!
 def preProcsumPerMetric(factors):
@ -1463,28 +1606,61 @@ def PreprocessingPredSel(SelectedIDs):

    global addKNN
    global addLR
+    global addMLP
+    global addRF
+    global addGradB

    numberIDKNN = []
    numberIDLR = []
+    numberIDMLP = []
+    numberIDRF = []
+    numberIDGradB = []
+
    for el in SelectedIDs:
        match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
        if match:
            items = match.groups()
            if (items[0] == 'KNN'):
-                numberIDKNN.append(int(items[1]) -addKNN)
-            else:
+                numberIDKNN.append(int(items[1]) - addKNN)
+            elif (items[0] == 'LR'):
                numberIDLR.append(int(items[1]) - addLR)
+            elif (items[0] == 'MLP'):
+                numberIDMLP.append(int(items[1]) - addMLP)
+            elif (items[0] == 'RF'):
+                numberIDRF.append(int(items[1]) - addRF)
+            else:
+                numberIDGradB.append(int(items[1]) - addGradB)

    dicKNN = allParametersPerformancePerModel[3]
    dicLR = allParametersPerformancePerModel[7]
+    dicMLP = allParametersPerformancePerModel[11]
+    dicRF = allParametersPerformancePerModel[15]
+    dicGradB = allParametersPerformancePerModel[19]

    dfKNN = pd.DataFrame.from_dict(dicKNN)
-
    dfKNN = dfKNN.loc[numberIDKNN]
+
    dfLR = pd.DataFrame.from_dict(dicLR)
    dfLR = dfLR.loc[numberIDLR]
+
    dfLR.index += addKNN
-    df_concatProbs = pd.concat([dfKNN, dfLR])
+
+    dfMLP = pd.DataFrame.from_dict(dicMLP)
+    dfMLP = dfMLP.loc[numberIDMLP]
+
+    dfMLP.index += addKNN + addLR
+
+    dfRF = pd.DataFrame.from_dict(dicRF)
+    dfRF = dfRF.loc[numberIDRF]
+
+    dfRF.index += addKNN + addLR + addMLP
+
+    dfGradB = pd.DataFrame.from_dict(dicGradB)
+    dfGradB = dfGradB.loc[numberIDGradB]
+
+    dfGradB.index += addKNN + addLR + addMLP + addRF
+
+    df_concatProbs = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])

    predictionsKNN = []
    for column, content in dfKNN.items():
@ -1496,12 +1672,27 @@ def PreprocessingPredSel(SelectedIDs):
        el = [sum(x)/len(x) for x in zip(*content)]
        predictionsLR.append(el)

+    predictionsMLP = []
+    for column, content in dfMLP.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsMLP.append(el)
+
+    predictionsRF = []
+    for column, content in dfRF.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsRF.append(el)
+
+    predictionsGradB = []
+    for column, content in dfGradB.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsGradB.append(el)
+
    predictions = []
    for column, content in df_concatProbs.items():
        el = [sum(x)/len(x) for x in zip(*content)]
        predictions.append(el)

-    return [predictionsKNN, predictionsLR, predictions]
+    return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]

@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@app.route('/data/SendtoSeverSelIDs', methods=["GET", "POST"])
@ -1528,30 +1719,49 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):

    numberIDKNN = []
    numberIDLR = []
+    numberIDMLP = []
+    numberIDRF = []
+    numberIDGradB = []
+
    for el in SelectedIDsEnsem:
        match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
        if match:
            items = match.groups()
            if (items[0] == 'KNN'):
                numberIDKNN.append(int(items[1]))
+            elif (items[0] == 'LR'):
+                numberIDLR.append(int(items[1]))
+            elif (items[0] == 'MLP'):
+                numberIDLR.append(int(items[1]))
+            elif (items[0] == 'RF'):
+                numberIDLR.append(int(items[1]))
            else:
                numberIDLR.append(int(items[1]))

    dicKNN = allParametersPerformancePerModel[3]
    dicLR = allParametersPerformancePerModel[7]
+    dicMLP = allParametersPerformancePerModel[11]
+    dicRF = allParametersPerformancePerModel[15]
+    dicGradB = allParametersPerformancePerModel[19]

    dfKNN = pd.DataFrame.from_dict(dicKNN)
    dfLR = pd.DataFrame.from_dict(dicLR)
-    df_concatProbs = pd.concat([dfKNN, dfLR])
+    dfMLP = pd.DataFrame.from_dict(dicMLP)
+    dfRF = pd.DataFrame.from_dict(dicRF)
+    dfGradB = pd.DataFrame.from_dict(dicGradB)
+
+    df_concatProbs = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])
    df_concatProbs = df_concatProbs.reset_index(drop=True)

    dfKNN = df_concatProbs.loc[numberIDKNN]
-
    dfLR = df_concatProbs.loc[numberIDLR]
+    dfMLP = df_concatProbs.loc[numberIDMLP]
+    dfRF = df_concatProbs.loc[numberIDRF]
+    dfGradB = df_concatProbs.loc[numberIDGradB]

    df_concatProbs = pd.DataFrame()
    df_concatProbs = df_concatProbs.iloc[0:0]
-    df_concatProbs = pd.concat([dfKNN, dfLR])
+    df_concatProbs = pd.concat([dfKNN, dfLR, dfMLP, dfRF, dfGradB])

    predictionsKNN = []
    for column, content in dfKNN.items():
@ -1563,12 +1773,27 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
        el = [sum(x)/len(x) for x in zip(*content)]
        predictionsLR.append(el)

+    predictionsMLP = []
+    for column, content in dfMLP.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsMLP.append(el)
+
+    predictionsRF = []
+    for column, content in dfRF.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsRF.append(el)
+
+    predictionsGradB = []
+    for column, content in dfGradB.items():
+        el = [sum(x)/len(x) for x in zip(*content)]
+        predictionsGradB.append(el)
+
    predictions = []
    for column, content in df_concatProbs.items():
        el = [sum(x)/len(x) for x in zip(*content)]
        predictions.append(el)

-    return [predictionsKNN, predictionsLR, predictions]
+    return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]

@cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@app.route('/data/SendtoSeverSelIDsEnsem', methods=["GET", "POST"])
				`@ -1 +0,0 @@`
				{"duration": 236.167307138443, "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.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "MLPClassifier(alpha=0.00021, hidden_layer_sizes=(113, 2), max_iter=100,\n random_state=42, solver='sgd', tol=0.00041000000000000005)", "params": "{'hidden_layer_sizes': [(60, 2), (61, 1), (62, 2), (63, 3), (64, 3), (65, 1), (66, 2), (67, 2), (68, 1), (69, 2), (70, 2), (71, 3), (72, 2), (73, 3), (74, 2), (75, 1), (76, 2), (77, 2), (78, 1), (79, 3), (80, 1), (81, 3), (82, 3), (83, 1), (84, 2), (85, 1), (86, 1), (87, 2), (88, 3), (89, 2), (90, 2), (91, 3), (92, 1), (93, 1), (94, 1), (95, 1), (96, 2), (97, 1), (98, 3), (99, 2), (100, 1), (101, 3), (102, 1), (103, 3), (104, 3), (105, 1), (106, 1), (107, 1), (108, 3), (109, 1), (110, 1), (111, 3), (112, 2), (113, 2), (114, 1), (115, 2), (116, 2), (117, 3), (118, 3), (119, 2)], 'alpha': [1e-05, 0.00021, 0.00041000000000000005, 0.0006100000000000001, 0.0008100000000000001], 'tol': [1e-05, 0.00041000000000000005, 0.0008100000000000001], 'max_iter': [100], 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver': ['adam', 'sgd']}", "eachAlgor": "'MLP'", "AlgorithmsIDsEnd": "200"}}
				`@ -0,0 +1 @@`
				{"duration": 262.5104908943176, "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.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "RandomForestClassifier(criterion='entropy', n_estimators=96, random_state=42)", "params": "{'n_estimators': [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], 'criterion': ['gini', 'entropy']}", "eachAlgor": "'RF'", "AlgorithmsIDsEnd": "300"}}
				`@ -0,0 +1 @@`
				{"duration": 72.83721280097961, "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.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "LogisticRegression(C=28, max_iter=350, penalty='none', random_state=42,\n solver='saga')", "params": "{'C': [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], 'max_iter': [50, 100, 150, 200, 250, 300, 350, 400, 450], 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']}", "eachAlgor": "'LR'", "AlgorithmsIDsEnd": "100"}}
				`@ -1 +0,0 @@`
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