diff --git a/__pycache__/run.cpython-38.pyc b/__pycache__/run.cpython-38.pyc
index 91acbed..d246dc1 100644
Binary files a/__pycache__/run.cpython-38.pyc and b/__pycache__/run.cpython-38.pyc differ
diff --git a/frontend/src/components/DataSpace.vue b/frontend/src/components/DataSpace.vue
index b8f73d4..07914b3 100644
--- a/frontend/src/components/DataSpace.vue
+++ b/frontend/src/components/DataSpace.vue
@@ -3,7 +3,7 @@
-
+
@@ -32,7 +32,7 @@ export default {
var tooltip = d3.select("#tooltip")
var width = 2525
- var height = 225
+ var height = 250
var rectWidth = 20
var rectHeight = 10
@@ -226,7 +226,7 @@ export default {
svg.selectAll("*").remove();
var width = 2525
- var height = 225
+ var height = 250
var xScaleOp = d3.scale.linear()
.domain([0, width-50])
@@ -388,7 +388,7 @@ export default {
svg.selectAll("*").remove();
var width = 2525
- var height = 225
+ var height = 250
var svgLines = d3.select('#TextLabels').append('svg');
svgLines.attr('width', width);
diff --git a/frontend/src/components/FeatureSpaceDetail.vue b/frontend/src/components/FeatureSpaceDetail.vue
index 9b39243..1a8fdbe 100644
--- a/frontend/src/components/FeatureSpaceDetail.vue
+++ b/frontend/src/components/FeatureSpaceDetail.vue
@@ -833,7 +833,7 @@ export default {
var MIVar = JSON.parse(this.dataFS[28+this.quadrantNumber])
MIVar = MIVar.concat(this.MIRemaining)
//var colorCateg = d3.scaleOrdinal(d3.schemeDark2)
- var colorCateg = d3.scaleOrdinal().domain([0, 1, 2, 4]).range(['#808000','#7570b3','#1b9e77','#d95f02'])
+ var colorCateg = d3.scaleOrdinal().domain([0, 1, 2, 4]).range(['#808000','#7570b3','#d95f02','#1b9e77'])
var corrTargetFormatted = []
for (let i = 0; i < Object.keys(corrTarget).length; i++) {
diff --git a/frontend/src/components/FeatureSpaceOverview.vue b/frontend/src/components/FeatureSpaceOverview.vue
index bd73b66..3127837 100644
--- a/frontend/src/components/FeatureSpaceOverview.vue
+++ b/frontend/src/components/FeatureSpaceOverview.vue
@@ -1186,7 +1186,7 @@ export default {
var legendRectSize = 14;
var legendSpacing = 3;
var labelsData = JSON.parse(this.overallData[1])
- var color = d3v5.scaleOrdinal().domain(labelsData).range(['#808000','#7570b3','#1b9e77','#d95f02'])
+ var color = d3v5.scaleOrdinal().domain(labelsData).range(['#808000','#7570b3','#d95f02','#1b9e77'])
var svgLegend = d3v5.select('#legendTarget').append('svg')
.attr('width', 130)
diff --git a/frontend/src/components/Main.vue b/frontend/src/components/Main.vue
index ca26e97..8573750 100755
--- a/frontend/src/components/Main.vue
+++ b/frontend/src/components/Main.vue
@@ -8,7 +8,7 @@
-
+
@@ -149,7 +149,7 @@ export default Vue.extend({
DataResults: '',
keyNow: 1,
instancesImportance: '',
- RetrieveValueFile: 'IrisC', // this is for the default data set
+ RetrieveValueFile: 'HeartC', // this is for the default data set
SelectedFeaturesPerClassifier: '',
FinalResults: 0,
selectedAlgorithm: '',
@@ -862,7 +862,7 @@ body {
top: 0px;
bottom: 0px;
margin-top: -4px !important;
- //overflow: hidden !important; # remove scrolling
+ overflow: hidden !important; // remove scrolling
}
.modal-backdrop {
diff --git a/run.py b/run.py
index 5df6387..70e205c 100644
--- a/run.py
+++ b/run.py
@@ -502,11 +502,11 @@ def dataSetSelection():
def create_global_function():
global estimator
- def estimator(n_estimators, eta, max_depth):
+ def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
# initialize model
n_estimators = int(n_estimators)
max_depth = int(max_depth)
- model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
+ model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, subsample=subsample, colsample_bytree=colsample_bytree, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
# set in cross-validation
result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
# result is mean of test_score
@@ -568,11 +568,11 @@ def executeModel(exeCall, flagEx, nodeTransfName):
# Bayesian Optimization CHANGE INIT_POINTS!
if (keyFirstTime):
create_global_function()
- params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "max_depth": (6,12)}
+ params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "max_depth": (6,12), "subsample": (0.8,1), "colsample_bytree": (0.8,1)}
bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
- bayesopt.maximize(init_points=10, n_iter=5, acq='ucb') # 35 and 15
+ bayesopt.maximize(init_points=35, n_iter=15, acq='ucb') # 35 and 15
bestParams = bayesopt.max['params']
- estimator = XGBClassifier(n_estimators=int(bestParams.get('n_estimators')), eta=bestParams.get('eta'), max_depth=int(bestParams.get('max_depth')), probability=True, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
+ estimator = XGBClassifier(n_estimators=int(bestParams.get('n_estimators')), eta=bestParams.get('eta'), max_depth=int(bestParams.get('max_depth')), subsample=bestParams.get('subsample'), colsample_bytree=bestParams.get('colsample_bytree'), probability=True, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
columnsNewGen = OrignList
if (len(exeCall) != 0):