OmniOpt2

Setting	Value
Max. nr. evaluations	50184
Max. nr. evaluations (from arguments)	50000
Number random steps	20
Nr. of workers (parameter)	20
Main process memory (GB)	8
Worker memory (GB)	32
Nr. imported jobs	184
Name	Type	Lower bound	Upper bound	Values	Type	Log Scale?
recent_samples_size	int	1	5000		int	No
n_samples	int	1	5000		int	No
confidence	choice			0.25, 0.1, 0.05, 0.025, 0.01, 0.005, 0.001
feature_proportion	float	0.001	0.999		float	No
n_clusters	int	1	50		int	No
Failed	Succeeded	Running	Total
522	527	2	1051
name	min/max
ACCURACY	max
RUNTIME	min
Key	Value
config_yaml	None
config_toml	None
config_json	None
num_random_steps	20
max_eval	50000
run_program	None
experiment_name	None
mem_gb	32
parameter	None
continue_previous_job	/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/
experiment_constraints	None
run_dir	runs
seed	None
decimalrounding	4
enforce_sequential_optimization	False
verbose_tqdm	False
model	None
gridsearch	False
occ	False
show_sixel_scatter	False
show_sixel_general	False
show_sixel_trial_index_result	False
follow	False
send_anonymized_usage_stats	False
ui_url	None
root_venv_dir	/home/s4122485
exclude	None
main_process_gb	8
pareto_front_confidence	1
max_nr_of_zero_results	10
abbreviate_job_names	False
orchestrator_file	None
checkout_to_latest_tested_version	False
live_share	False
disable_tqdm	False
workdir	False
occ_type	euclid
result_names	['RESULT=min']
minkowski_p	2
signed_weighted_euclidean_weights
generation_strategy	None
generate_all_jobs_at_once	False
revert_to_random_when_seemingly_exhausted	True
load_data_from_existing_jobs	[]
n_estimators_randomforest	100
external_generator	None
username	None
max_failed_jobs	None
num_parallel_jobs	20
worker_timeout	120
slurm_use_srun	False
time
partition
reservation	None
force_local_execution	False
slurm_signal_delay_s	0
nodes_per_job	1
cpus_per_task	1
account	None
gpus	0
run_mode	local
verbose	False
verbose_break_run_search_table	False
debug	False
no_sleep	False
tests	False
show_worker_percentage_table_at_end	False
auto_exclude_defective_hosts	False
run_tests_that_fail_on_taurus	False
raise_in_eval	False
show_ram_every_n_seconds	False
Skipped tabs:
Job-Infos
<!DOCTYPE html>
<html lang='en'>
	<head>
		<meta charset='UTF-8'>
		<meta name='viewport' content='width=device-width, initial-scale=1.0'>
		<title>Exported &raquo;s4122485/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1&laquo; from OmniOpt2-Share</title>
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				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #444;
			}
			
			input:checked + .slider:before {
				transform: translateX(24px);
			}
			
			.mode-text {
				position: absolute;
				top: 5px;
				left: 65px;
				font-size: 14px;
				color: black;
				transition: .4s;
				width: 65px;
				display: block;
				font-size: 0.7rem;
				text-align: center;
			}
			
			input:checked + .slider .mode-text {
				content: "Dark Mode";
				color: white;
			}
			
			#mainContent {
				height: fit-content;
				min-height: 100%;
			}
			
			li {
				text-align: left;
			}
			
			#share_path {
				margin-bottom: 20px;
				margin-top: 20px;
			}
			
			#sortForm {
				margin-bottom: 20px;
			}
			
			.share_folder_buttons {
				margin-top: 10px;
				margin-bottom: 10px;
			}
			
			.nav_tab_button {
				margin: 10px;
			}
			
			.header_table {
				margin: 10px;
			}
			
			.no_border {
				border: unset !important;
			}
			
			.gui_table {
				padding: 5px !important;
			}
			
			.gui_parameter_row {
			}
			
			.gui_parameter_row_cell {
				border: unset !important;
			}
			
			.gui_param_table {
				width: 95%;
				margin: unset !important;
			}
			
			table td, table tr,
			.parameterRow table {
				padding: 2px !important;
			}
			
			.parameterRow table {
				margin: 0px;
				border: unset;
			}
			
			.parameterRow > td {
				border: 0px !important;
			}
			
			.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
				border: 0px !important;
			}
			
			.green_text {
				color: green;
			}
			
			.remove_parameter {
				white-space: pre;
			}
			
			select {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				background-color: #fff;
				color: #222;
				padding: 5px 30px 5px 5px;
				border: 1px solid #555;
				border-radius: 5px;
				cursor: pointer;
				outline: none;
				transition: all 0.3s ease;
				background:
					url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
					no-repeat right 10px center,
				linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
				background-size: 12px, auto;
			}
			
			select:hover {
				border-color: #888;
			}
			
			select:focus {
				border-color: #4caf50;
				box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
			}
			
			select::-ms-expand {
				display: none;
			}
			
			input, textarea {
				border-radius: 5px;
			}
			
			#search {
				width: 200px;
				max-width: 70%;
				background-image: url(images/search.svg);
				background-repeat: no-repeat;
				background-size: auto 40px;
				height: 40px;
				line-height: 40px;
				padding-left: 40px;
				box-sizing: border-box;
			}
			
			input[type="checkbox"] {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				width: 25px;
				height: 25px;
				border: 2px solid #3498db;
				border-radius: 5px;
				background-color: #fff;
				position: relative;
				cursor: pointer;
				transition: all 0.3s ease;
				width: 25px !important;
			}
			
			input[type="checkbox"]:checked {
				background-color: #3498db;
				border-color: #2980b9;
			}
			
			input[type="checkbox"]:checked::before {
				content: '✔';
				position: absolute;
				left: 4px;
				top: 2px;
				color: #fff;
			}
			
			input[type="checkbox"]:hover {
				border-color: #2980b9;
				background-color: #3caffc;
			}
			
			.toc {
				margin-bottom: 20px;
			}
			
			.toc li {
				margin-bottom: 5px;
			}
			
			.toc a {
				text-decoration: none;
				color: #007bff;
			}
			
			.toc a:hover {
				text-decoration: underline;
			}
			
			.table-container {
				width: 100%;
				overflow-x: auto;
			}
			
			.section-header {
				background-color: #1d6f9a !important;
				color: white;
			}
			
			.warning {
				color: red;
			}
			
			.li_list a {
				text-decoration: none;
			}
			
			.gridjs-td {
				white-space: nowrap;
			}
			
			th, td {
				border: 1px solid gray !important;
			}
			
			.no_border {
				border: 0px !important;
			}
			
			.no_break {
			
			}
			
			img {
				user-select: none;
				pointer-events: none;
			}
			
			#config_table, #hidden_config_table {
				user-select: none;
			}
			
			.copy_clipboard_button {
				margin-bottom: 10px;
			}
			
			.badge_table {
				background-color: unset !important;
			}
			
			.make_markable {
				user-select: text;
			}
			
			.header-container {
				display: flex;
				flex-wrap: wrap;
				align-items: center;
				justify-content: space-between;
				gap: 1rem;
				padding: 10px;
				background: var(--header-bg, #fff);
				border-bottom: 1px solid #ccc;
			}
			
			.header-logo-group {
				display: flex;
				gap: 1rem;
				align-items: center;
				flex: 1 1 auto;
				min-width: 200px;
			}
			
			.logo-img {
				max-height: 45px;
				height: auto;
				width: auto;
				object-fit: contain;
				pointer-events: unset;
			}
			
			.header-badges {
				flex-direction: column;
				gap: 5px;
				align-items: flex-start;
				flex: 0 1 auto;
				margin-top: auto;
				margin-bottom: auto;
			}
			
			.badge-img {
				height: auto;
				max-width: 130px;
				margin-top: 3px;
			}
			
			.header-tabs {
				margin-top: 10px;
				display: flex;
				flex-wrap: wrap;
				gap: 10px;
				flex: 2 1 100%;
				justify-content: center;
			}
			
			.nav-tab {
				display: inline-block;
				text-decoration: none;
				padding: 8px 16px;
				border-radius: 20px;
				background: linear-gradient(to right, #4a90e2, #357ABD);
				color: white;
				font-weight: bold;
				white-space: nowrap;
				transition: background 0.2s ease-in-out, transform 0.2s;
				box-shadow: 0 2px 4px rgba(0,0,0,0.2);
			}
			
			.nav-tab:hover {
				background: linear-gradient(to right, #5aa0f2, #4a90e2);
				transform: translateY(-2px);
			}
			
			.current-tag {
				padding-left: 10px;
				font-size: 0.9rem;
				color: #666;
			}
			
			.header-theme-toggle {
				flex: 1 1 auto;
				align-items: center;
				margin-top: 20px;
				min-width: 120px;
			}
			
			.switch {
				position: relative;
				display: inline-block;
				width: 60px;
				height: 30px;
			}
			
			.switch input {
				display: none;
			}
			
			.slider {
				position: absolute;
				top: 0; left: 0; right: 0; bottom: 0;
				background-color: #ccc;
				border-radius: 34px;
				cursor: pointer;
			}
			
			.slider::before {
				content: "";
				position: absolute;
				height: 24px;
				width: 24px;
				left: 3px;
				bottom: 3px;
				background-color: white;
				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #2196F3;
			}
			
			input:checked + .slider::before {
				transform: translateX(30px);
			}
			
			@media (max-width: 768px) {
				.header-logo-group,
				.header-badges,
				.header-theme-toggle {
					justify-content: center;
					flex: 1 1 100%;
					text-align: center;
					width: inherit;
				}
			
				.logo-img {
					max-height: 50px;
					pointer-events: unset;
				}
			
				.badge-img {
					max-width: 100px;
				}
			
				.hide_on_mobile {
					display: none;
				}
			
				.nav-tab {
					font-size: 0.9rem;
					padding: 6px 12px;
				}
			
				.header_button {
					white-space: pre;
					font-size: 2em;
				}
			}
			
			.header_button {
				white-space: pre;
				margin-top: 20px;
				margin: 5px;
			}
			
			.line_break_anywhere {
				line-break: anywhere;
			}
			
			.responsive-container {
				display: flex;
				flex-wrap: wrap;
				justify-content: space-between;
				gap: 20px;
			}
			
			.responsive-container .half {
				flex: 1 1 48%;
				box-sizing: border-box;
				min-width: 500px;
			}
			
			.config-section table {
				width: 100%;
				border-collapse: collapse;
			}
			
			@media (max-width: 768px) {
				.responsive-container .half {
					flex: 1 1 100%;
				}
			}
			
			@keyframes spin {
				0% {
					transform: rotate(0deg);
				}
			
				100% {
					transform: rotate(360deg);
				}
			}
			
			.rotate {
				animation: spin 2s linear infinite;
				display: inline-block;
			}
			
			input::placeholder {
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
			}
			
			.gridjs-th-content {
				overflow: visible !important;
			}
			
			.error_text {
				color: red;
			}
			
			h1, h2, h3, h4, h5, h6 {
				margin-top: 1em;
				font-weight: bold;
				color: #333;
				border-left: 5px solid #ccc;
				padding-left: 0.5em;
			}
			
			.no_cursive {
				font-style: normal;	
			}
			
			.caveat {
				background-color: #fff8b3;
				border: 1px solid #f2d600;
				padding: 1em 1em 1em 70px;
				position: relative;
				font-family: sans-serif;
				color: #665500;
				margin: 1em 0;
				border-radius: 4px;
			}
			
			.caveat h1, .caveat h2, .caveat h3, .caveat h4 {
				margin-top: 0;
				margin-bottom: 0.5em;
				font-weight: bold;
			}
			
			.caveat::before {
				content: "⚠️";
				font-size: 50px;
				line-height: 1;
				position: absolute;
				left: 10px;
				top: 50%;
				transform: translateY(-50%);
				pointer-events: none;
				user-select: none;
			}
			
			.caveat.warning::before { content: "⚠️"; }
			.caveat.stop::before { content: "🛑"; }
			.caveat.exclamation::before { content: "❗"; }
			.caveat.alarm::before { content: "🚨"; }
			.caveat.tip::before { content: "💡"; }
			
			.tutorial_icon {
				display: inline-block;
				font-size: 1.3em;
				line-height: 1;
				vertical-align: middle;
				transform: translateY(-10%);
				padding: 0.2em 0;
			}
			
			.highlight {
				background-color: yellow;
				font-weight: bold;
			}
			
			#searchResults li {
				opacity: 0;
				transform: translateY(8px);
				animation: fadeInUp 0.3s ease-out forwards;
				animation-delay: 0.05s;
				list-style: none;
				margin-bottom: 5px;
			}
			
			@keyframes fadeInUp {
				to {
					opacity: 1;
					transform: translateY(0);
				}
			}
			
			.search_headline {
				font-weight: bold;
				margin-top: 1em;
				margin-bottom: 0.3em;
				color: #444;
			}
			
			.search_share_path {
				color: black;
				display: block ruby;
				margin-top: 20px;
			}
			
			@media print {
				#scads_bar {
					display: none !important;
				}
			}
			

			/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
			body{
				color: #222
			}
			
			.surface{
				background: #ece9d8
			}
			
			u{
				text-decoration: none;
				border-bottom: .5px solid #222
			}
			
			a{
				color: #00f
			}
			
			a: focus{
				outline: 1px dotted #00f
			}
			
			code,code *{
				font-family: monospace
			}
			
			pre{
				display: block;
				padding: 12px 8px;
				background-color: #000;
				color: silver;
				font-size: 1rem;
				margin: 0;
				overflow: scroll;
			}
			
			summary: focus{
				outline: 1px dotted #000
			}
			
			: :-webkit-scrollbar{
				width: 16px
			}
			
			: :-webkit-scrollbar: horizontal{
				height: 17px
			}
			
			: :-webkit-scrollbar-track{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-color: #dfdfdf;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
				display: block
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: end{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			button{
				border: none;
				background: #ece9d8;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
				border-radius: 0;
				min-width: 75px;
				min-height: 23px;
				padding: 0 12px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
			}
			
			button.focused,button: focus{
				outline: 1px dotted #000;
				outline-offset: -4px
			}
			
			label{
				display: inline-flex;
				align-items: center
			}
			
			textarea{
				padding: 3px 4px;
				border: none;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0
			}
			
			textarea: focus{
				outline: none
			}
			
			select: focus option{
				color: #000;
				background-color: #fff
			}
			
			.vertical-bar{
				width: 4px;
				height: 20px;
				background: silver;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			&: disabled,&: disabled+label{
				color: grey;
				text-shadow: 1px 1px 0 #fff
			}
			
			input[type=radio]+label{
				line-height: 13px;
				position: relative;
				margin-left: 19px
			}
			
			input[type=radio]+label: before{
				content: "";
				position: absolute;
				top: 0;
				left: -19px;
				display: inline-block;
				width: 13px;
				height: 13px;
				margin-right: 6px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: active+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: checked+label: after{
				content: "";
				display: block;
				width: 5px;
				height: 5px;
				top: 5px;
				left: -14px;
				position: absolute;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password]{
				padding: 3px 4px;
				border: 1px solid #7f9db9;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0;
				height: 21px;
				line-height: 2
			}
			
			input[type=email]: focus,input[type=password]: focus{
				outline: none
			}
			
			input[type=range]{
				-webkit-appearance: none;
				width: 100%;
				background: transparent
			}
			
			input[type=range]: focus{
				outline: none
			}
			
			input[type=range]: :-webkit-slider-thumb{
				-webkit-appearance: none;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range]: :-moz-range-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			.is-vertical{
				display: inline-block;
				width: 4px;
				height: 150px;
				transform: translateY(50%)
			}
			
			.is-vertical>input[type=range]{
				width: 150px;
				height: 4px;
				margin: 0 16px 0 10px;
				transform-origin: left;
				transform: rotate(270deg) translateX(calc(-50% + 8px))
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-thumb{
				transform: translateY(-8px) scaleX(-1)
			}
			
			.is-vertical>input[type=range]: :-moz-range-thumb{
				transform: translateY(2px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
				transform: translateY(-10px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
				transform: translateY(0) scaleX(-1)
			}
			
			.window{
				font-size: 11px;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
				background: #ece9d8;
				padding: 3px
			}
			
			.window fieldset{
				margin-bottom: 9px
			}
			
			.title-bar{
				background: #000;
				padding: 3px 2px 3px 3px;
				display: flex;
				justify-content: space-between;
				align-items: center
			}
			
			.title-bar-text{
				font-weight: 700;
				color: #fff;
				letter-spacing: 0;
				margin-right: 24px
			}
			
			.title-bar-controls button{
				padding: 0;
				display: block;
				min-width: 16px;
				min-height: 14px
			}
			
			.title-bar-controls button: focus{
				outline: none
			}
			
			.window-body{
				margin: 8px
			}
			
			.window-body pre{
				margin: -8px
			}
			
			.status-bar{
				margin: 0 1px;
				display: flex;
				gap: 1px
			}
			
			.status-bar-field{
				box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
				flex-grow: 1;
				padding: 2px 3px;
				margin: 0
			}
			
			ul.tree-view{
				display: block;
				background: #fff;
				padding: 6px;
				margin: 0
			}
			
			ul.tree-view li{
				list-style-type: none;
				margin-top: 3px
			}
			
			ul.tree-view a{
				text-decoration: none;
				color: #000
			}
			
			ul.tree-view a: focus{
				background-color: #2267cb;
				color: #fff
			}
			
			ul.tree-view ul{
				margin-top: 3px;
				margin-left: 16px;
				padding-left: 16px;
				border-left: 1px dotted grey
			}
			
			ul.tree-view ul>li{
				position: relative
			}
			
			ul.tree-view ul>li: before{
				content: "";
				display: block;
				position: absolute;
				left: -16px;
				top: 6px;
				width: 12px;
				border-bottom: 1px dotted grey
			}
			
			ul.tree-view ul>li: last-child: after{
				content: "";
				display: block;
				position: absolute;
				left: -20px;
				top: 7px;
				bottom: 0;
				width: 8px;
				background: #fff
			}
			
			ul.tree-view ul details>summary: before{
				margin-left: -22px;
				position: relative;
				z-index: 1
			}
			
			ul.tree-view details{
				margin-top: 0
			}
			
			ul.tree-view details>summary: before{
				text-align: center;
				display: block;
				float: left;
				content: "+";
				border: 1px solid grey;
				width: 8px;
				height: 9px;
				line-height: 9px;
				margin-right: 5px;
				padding-left: 1px;
				background-color: #fff
			}
			
			ul.tree-view details[open] summary{
				margin-bottom: 0
			}
			
			ul.tree-view details[open]>summary: before{
				content: "-"
			}
			
			fieldset{
				border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
				padding: 10px;
				padding-block-start: 8px;
				margin: 0
			}
			
			legend{
				background: #ece9d8
			}
			
			menu[role=tablist]{
				position: relative;
				margin: 0 0 -2px;
				text-indent: 0;
				list-style-type: none;
				display: flex;
				padding-left: 3px
			}
			
			menu[role=tablist] button{
				z-index: 1;
				display: block;
				color: #222;
				text-decoration: none;
				min-width: unset
			}
			
			menu[role=tablist] button[aria-selected=true]{
				padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
			}
			
			menu[role=tablist] button: focus{
				outline: 1px dotted #222;outline-offset: -4px
			}
			
			menu[role=tablist].justified button{
				flex-grow: 1;text-align: center
			}
			
			[role=tabpanel]{
				padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
			}
			
			: :-webkit-scrollbar{
				width: 17px
			}
			
			: :-webkit-scrollbar-corner{
				background: #dfdfdf
			}
			
			: :-webkit-scrollbar-track: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-track: horizontal{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-position: 50%;
				background-repeat: no-repeat;
				background-color: #c8d6fb;
				background-size: 7px;
				border: 1px solid #fff;
				border-radius: 2px;
				box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
			}
			
			: :-webkit-scrollbar-thumb: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb: horizontal{
				background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				height: 17px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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			}
			
			: :-webkit-scrollbar-button: vertical: end{
				height: 17px;
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			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 17px;
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			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 17px;
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			}
			
			.window{
				box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
				border-top-left-radius: 8px;
				border-top-right-radius: 8px;
				padding: 0 0 3px;
				-webkit-font-smoothing: antialiased
			}
			
			.title-bar{
				background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
				padding: 3px 5px 3px 3px;
				border-top: 1px solid #0831d9;
				border-left: 1px solid #0831d9;
				border-right: 1px solid #001ea0;
				border-top-left-radius: 8px;
				border-top-right-radius: 7px;
				font-size: 13px;
				text-shadow: 1px 1px #0f1089;
				height: 21px
			}
			
			.title-bar-text{
				padding-left: 3px
			}
			
			.title-bar-controls{
				display: flex
			}
			
			.title-bar-controls button{
				min-width: 21px;
				min-height: 21px;
				margin-left: 2px;
				background-repeat: no-repeat;
				background-position: 50%;
				box-shadow: none;
				background-color: #0050ee;
				transition: background .1s;
				border: none
			}
			
			.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
				box-shadow: none!important
			}
			
			.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
			}
			
			.status-bar{
				margin: 0 3px;
				box-shadow: inset 0 1px 2px grey;
				padding: 2px 1px;
				gap: 0
			}
			
			.status-bar-field{
				-webkit-font-smoothing: antialiased;
				box-shadow: none;
				padding: 1px 2px;
				border-right: 1px solid rgba(208,206,191,.75);
				border-left: 1px solid hsla(0,0%,100%,.75)
			}
			
			.status-bar-field: first-of-type{
				border-left: none
			}
			
			.status-bar-field: last-of-type{
				border-right: none
			}
			
			button{
				-webkit-font-smoothing: antialiased;
				box-sizing: border-box;
				border: 1px solid #003c74;
				background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
				box-shadow: none;
				border-radius: 3px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: none;
				background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
			}
			
			button: not(: disabled): hover{
				box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
			}
			
			button.focused,button: focus{
				box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
			}
			
			button: :-moz-focus-inner{
				border: 0
			}
			
			input,label,option,select,textarea{
				-webkit-font-smoothing: antialiased
			}
			
			input[type=radio]{
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				margin: 0;
				background: 0;
				position: fixed;
				opacity: 0;
				border: none
			}
			
			input[type=radio]+label{
				line-height: 16px
			}
			
			input[type=radio]+label: before{
				background: linear-gradient(135deg,#dcdcd7,#fff);
				border-radius: 50%;
				border: 1px solid #1d5281
			}
			
			input[type=radio]: not([disabled]): not(: active)+label: hover: before{
				box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
			}
			
			input[type=radio]: active+label: before{
				background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
			}
			
			input[type=radio]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: focus+label{
				outline: 1px dotted #000
			}
			
			input[type=radio][disabled]+label: before{
				border: 1px solid #cac8bb;
				background: #fff
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password],textarea: :selection{
				background: #2267cb;
				color: #fff
			}
			
			input[type=range]: :-webkit-slider-thumb{
				height: 21px;
				width: 11px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(-8px)
			}
			
			input[type=range]: :-moz-range-thumb{
				height: 21px;
				width: 11px;
				border: 0;
				border-radius: 0;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(2px)
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range]: :-moz-range-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			fieldset{
				box-shadow: none;
				background: #fff;
				border: 1px solid #d0d0bf;
				border-radius: 4px;
				padding-top: 10px
			}
			
			legend{
				background: transparent;
				color: #0046d5
			}
			
			.field-row{
				display: flex;
				align-items: center
			}
			
			.field-row>*+*{
				margin-left: 6px
			}
			
			[class^=field-row]+[class^=field-row]{
				margin-top: 6px
			}
			
			.field-row-stacked{
				display: flex;
				flex-direction: column
			}
			
			.field-row-stacked *+*{
				margin-top: 6px
			}
			
			menu[role=tablist] button{
				background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
				margin-left: -1px;
				margin-right: 2px;
				border-radius: 0;
				border-color: #91a7b4;
				border-top-right-radius: 3px;
				border-top-left-radius: 3px;
				padding: 0 12px 3px
			}
			
			menu[role=tablist] button: hover{
				box-shadow: unset;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]{
				border-color: #919b9c;
				margin-right: -1px;
				border-bottom: 1px solid transparent;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]: first-of-type: before{
				content: "";
				display: block;
				position: absolute;
				z-index: -1;
				top: 100%;
				left: -1px;
				height: 2px;
				width: 0;
				border-left: 1px solid #919b9c
			}
			
			[role=tabpanel]{
				box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
			}
			
			ul.tree-view{
				-webkit-font-smoothing: auto;
				border: 1px solid #7f9db9;
				padding: 2px 5px
			}
			
			@keyframes sliding{
				0%{
					transform: translateX(-30px)
				}
			
				to{
					transform: translateX(100%)
				}
			}
			
			progress{
				box-sizing: border-box;
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				height: 14px;
				border: 1px solid #686868;
				border-radius: 4px;
				padding: 1px 2px 1px 0;
				overflow: hidden;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress,progress: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				height: 14px
			}
			
			progress[value]: :-webkit-progress-bar{
				background-color: transparent
			}
			
			progress[value]: :-webkit-progress-value{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress[value]: :-moz-progress-bar{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]): :-webkit-progress-bar{
				width: 100%;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
				animation: sliding 2s linear 0s infinite
			}
			
			progress: not([value]): :-webkit-progress-bar: not([value]){
				animation: sliding 2s linear 0s infinite;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]){
				position: relative
			}
			
			progress: not([value]): before{
				box-sizing: border-box;
				content: "";
				position: absolute;
				top: 0;
				left: 0;
				width: 100%;
				height: 100%;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before,progress: not([value]): before: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): after{
				box-sizing: border-box;
				content: "";
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4654 n_samples 3831 confidence 0.05 feature_proportion 0.999 n_clusters 1",
			        4654,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3285 n_samples 1997 confidence 0.05 feature_proportion 0.001 n_clusters 15",
			        3285,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3767 n_samples 2694 confidence 0.05 feature_proportion 0.5215134344312137 n_clusters 5",
			        3767,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1649 n_samples 2809 confidence 0.05 feature_proportion 0.05706742484677252 n_clusters 8",
			        1649,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4277 n_samples 1721 confidence 0.25 feature_proportion 0.9338264754559077 n_clusters 50",
			        4277,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1841 n_samples 2381 confidence 0.1 feature_proportion 0.999 n_clusters 34",
			        1841,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4888 n_samples 686 confidence 0.025 feature_proportion 0.001 n_clusters 1",
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			    [
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			        58,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3646 n_samples 4517 confidence 0.01 feature_proportion 0.5471248122960809 n_clusters 50",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4389 n_samples 1745 confidence 0.025 feature_proportion 0.001 n_clusters 7",
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1189 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 41",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1214 n_samples 284 confidence 0.01 feature_proportion 0.001 n_clusters 50",
			        1214,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1526 n_samples 873 confidence 0.01 feature_proportion 0.001 n_clusters 1",
			        1526,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3301 n_samples 647 confidence 0.025 feature_proportion 0.999 n_clusters 45",
			        3301,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3981 n_samples 2170 confidence 0.05 feature_proportion 0.001 n_clusters 40",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1197 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 41",
			        1197,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4688 n_samples 2721 confidence 0.05 feature_proportion 0.999 n_clusters 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3360 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 44",
			        3360,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1353 n_samples 602 confidence 0.1 feature_proportion 0.001 n_clusters 27",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1703 n_samples 3472 confidence 0.025 feature_proportion 0.36313253440547166 n_clusters 31",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1841 n_samples 2891 confidence 0.1 feature_proportion 0.999 n_clusters 29",
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3834 n_samples 3904 confidence 0.025 feature_proportion 0.910454352436512 n_clusters 30",
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			    [
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			        84,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1788 n_samples 2936 confidence 0.025 feature_proportion 0.001 n_clusters 22",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4310 n_samples 2225 confidence 0.025 feature_proportion 0.001 n_clusters 8",
			        4310,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1186 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 40",
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			    [
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			        81,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4175 n_samples 2542 confidence 0.001 feature_proportion 0.001 n_clusters 1",
			        4175,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4834 n_samples 282 confidence 0.001 feature_proportion 0.001 n_clusters 50",
			        4834,
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			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4044 n_samples 3618 confidence 0.001 feature_proportion 0.001 n_clusters 1",
			        4044,
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3185 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 50",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1168 n_samples 3129 confidence 0.025 feature_proportion 0.001 n_clusters 5",
			        1168,
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			        0.001,
			        5,
			        0.025,
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			    ],
			    [
			        1746209856,
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3502 n_samples 3319 confidence 0.01 feature_proportion 0.001 n_clusters 50",
			        3502,
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			    ],
			    [
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			        88,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4590 n_samples 2708 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        4590,
			        2708,
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			        0.025,
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			    ],
			    [
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			        88,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1790 n_samples 1950 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        1790,
			        1950,
			        0.001,
			        1,
			        0.025,
			        0.57,
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			        "None",
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			    ],
			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4095 n_samples 3994 confidence 0.05 feature_proportion 0.001 n_clusters 3",
			        4095,
			        3994,
			        0.001,
			        3,
			        0.05,
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			        "None",
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			    [
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			        51,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1253 n_samples 5000 confidence 0.05 feature_proportion 0.999 n_clusters 1",
			        1253,
			        5000,
			        0.999,
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			        "None",
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			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3930 n_samples 4523 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        3930,
			        4523,
			        0.999,
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			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3980 n_samples 4193 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        3980,
			        4193,
			        0.999,
			        1,
			        0.005,
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			        "None",
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			    ],
			    [
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			        147,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4925 n_samples 1297 confidence 0.05 feature_proportion 0.999 n_clusters 16",
			        4925,
			        1297,
			        0.999,
			        16,
			        0.05,
			        0.6,
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			        "None",
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			        134,
			        930.16796875,
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			    ],
			    [
			        1746212448,
			        1746212525,
			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4611 n_samples 4612 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        4611,
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			        0.999,
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			        0.025,
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			        "None",
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			        62,
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			    ],
			    [
			        1746212787,
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1603 n_samples 4232 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        1603,
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			        0.1,
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			        "None",
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			    ],
			    [
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			        84,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4734 n_samples 4139 confidence 0.05 feature_proportion 0.001 n_clusters 1",
			        4734,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3497 n_samples 3985 confidence 0.25 feature_proportion 0.001 n_clusters 50",
			        3497,
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			        "None",
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			    ],
			    [
			        1746213867,
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4874 n_samples 4663 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        4874,
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			        "None",
			        "i7181",
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			    [
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			        58,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1663 n_samples 5000 confidence 0.05 feature_proportion 0.123862001657684 n_clusters 1",
			        1663,
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			        1,
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			        "None",
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			    [
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			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4217 n_samples 2858 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        4217,
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			        0.999,
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			        "None",
			        "i7179",
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			    [
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			        88,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4093 n_samples 2482 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        4093,
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			        0.999,
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			    [
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			        202,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1587 n_samples 685 confidence 0.1 feature_proportion 0.001 n_clusters 34",
			        1587,
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			        0.001,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1736 n_samples 4499 confidence 0.05 feature_proportion 0.001 n_clusters 1",
			        1736,
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			    [
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			        145,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1554 n_samples 870 confidence 0.25 feature_proportion 0.999 n_clusters 1",
			        1554,
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			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4007 n_samples 4707 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        4007,
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			        "None",
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			    [
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			        83,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4773 n_samples 3426 confidence 0.01 feature_proportion 0.999 n_clusters 50",
			        4773,
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			    [
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			        411,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1477 n_samples 229 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        1477,
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			        "None",
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			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4771 n_samples 3576 confidence 0.25 feature_proportion 0.001 n_clusters 50",
			        4771,
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			    ],
			    [
			        1746217479,
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			        172,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4702 n_samples 1188 confidence 0.005 feature_proportion 0.5636776128324651 n_clusters 18",
			        4702,
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			    ],
			    [
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			        115,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1869 n_samples 1589 confidence 0.01 feature_proportion 0.999 n_clusters 1",
			        1869,
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			        0,
			        "None",
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			    ],
			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4848 n_samples 4852 confidence 0.1 feature_proportion 0.001 n_clusters 1",
			        4848,
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			        "None",
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			    ],
			    [
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			        108,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4139 n_samples 1784 confidence 0.01 feature_proportion 0.999 n_clusters 1",
			        4139,
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			        0.999,
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			        "None",
			        "i7175",
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			        898.70703125,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3994 n_samples 4312 confidence 0.025 feature_proportion 0.001 n_clusters 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1267 n_samples 583 confidence 0.005 feature_proportion 0.001 n_clusters 30",
			        1267,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4035 n_samples 3015 confidence 0.25 feature_proportion 0.999 n_clusters 16",
			        4035,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 860 n_samples 3047 confidence 0.05 feature_proportion 0.999 n_clusters 11",
			        860,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 715 confidence 0.05 feature_proportion 0.999 n_clusters 1",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2968 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 48",
			        2968,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 723 n_samples 899 confidence 0.25 feature_proportion 0.001 n_clusters 22",
			        723,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1205 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 42",
			        1205,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1834 n_samples 3657 confidence 0.05 feature_proportion 0.9797938461209694 n_clusters 18",
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			    [
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			        95,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4277 n_samples 1884 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        4277,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1484 n_samples 747 confidence 0.05 feature_proportion 0.001 n_clusters 16",
			        1484,
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			    [
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			        158,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4774 n_samples 1322 confidence 0.01 feature_proportion 0.001 n_clusters 1",
			        4774,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3760 n_samples 2534 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        3760,
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			    [
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			        165,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3859 n_samples 2566 confidence 0.001 feature_proportion 0.001 n_clusters 50",
			        3859,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1449 n_samples 3955 confidence 0.005 feature_proportion 0.5034496463259249 n_clusters 1",
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			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2885 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        5000,
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			        0.999,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3815 n_samples 4430 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        3815,
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			        0.999,
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			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1824 n_samples 3731 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        1824,
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			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4801 n_samples 2604 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        4801,
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			    [
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			        108,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4727 n_samples 2382 confidence 0.025 feature_proportion 0.999 n_clusters 50",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1848 n_samples 3148 confidence 0.01 feature_proportion 0.001 n_clusters 50",
			        1848,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3816 n_samples 3149 confidence 0.005 feature_proportion 0.999 n_clusters 50",
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4711 n_samples 1716 confidence 0.025 feature_proportion 0.5023850505871839 n_clusters 34",
			        4711,
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			    [
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			        88,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1792 n_samples 2060 confidence 0.25 feature_proportion 0.999 n_clusters 50",
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			    [
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			        95,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4149 n_samples 2379 confidence 0.005 feature_proportion 0.5871527787056299 n_clusters 1",
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			    [
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			        216,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4789 n_samples 858 confidence 0.001 feature_proportion 0.001 n_clusters 13",
			        4789,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1821 n_samples 3714 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        1821,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4324 n_samples 1279 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        4324,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4153 n_samples 2126 confidence 0.05 feature_proportion 0.001 n_clusters 1",
			        4153,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4607 n_samples 3516 confidence 0.05 feature_proportion 0.001 n_clusters 1",
			        4607,
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			        "None",
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			    ],
			    [
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			        232,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4889 n_samples 468 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        4889,
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			    [
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			        44,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 936 n_samples 3876 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        936,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3578 n_samples 4081 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        3578,
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1286 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 47",
			        1286,
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			        "",
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			        "",
			        "",
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			    [
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			        71,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4237 n_samples 5000 confidence 0.005 feature_proportion 0.999 n_clusters 32",
			        4237,
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			    ],
			    [
			        1746237010,
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4156 n_samples 4654 confidence 0.001 feature_proportion 0.999 n_clusters 50",
			        4156,
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			    ],
			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3946 n_samples 3283 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        3946,
			        3283,
			        0.001,
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			        0.025,
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			        "None",
			        "i7179",
			        58,
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			        738.81982421875,
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			    ],
			    [
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			        114,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1792 n_samples 1676 confidence 0.005 feature_proportion 0.505093020338127 n_clusters 1",
			        1792,
			        1676,
			        0.505093020338127,
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			        "None",
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			        103,
			        890.6484375,
			        790.2635216346154,
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			    ],
			    [
			        1746238290,
			        1746238366,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4073 n_samples 4704 confidence 0.05 feature_proportion 0.001 n_clusters 1",
			        4073,
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			    ],
			    [
			        1746238690,
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4477 confidence 0.001 feature_proportion 0.999 n_clusters 50",
			        5000,
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			        "None",
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			        59,
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			    ],
			    [
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			        197,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1729 n_samples 751 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        1729,
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			        0.1,
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			        "i7186",
			        185,
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			    [
			        1746239611,
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			        83,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1907 n_samples 4500 confidence 0.1 feature_proportion 0.4030474620816641 n_clusters 1",
			        1907,
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			        "None",
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			    [
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			        1746240531,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3752 n_samples 4111 confidence 0.005 feature_proportion 0.999 n_clusters 50",
			        3752,
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			        0.999,
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			        "None",
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			    ],
			    [
			        1746240890,
			        1746240953,
			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1828 n_samples 4115 confidence 0.01 feature_proportion 0.5126096030242938 n_clusters 1",
			        1828,
			        4115,
			        0.5126096030242938,
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			    ],
			    [
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			        183,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3579 n_samples 856 confidence 0.005 feature_proportion 0.999 n_clusters 38",
			        3579,
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			        0.999,
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			        0.005,
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			        "None",
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			    ],
			    [
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			        1746242410,
			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1829 n_samples 2801 confidence 0.05 feature_proportion 0.001 n_clusters 35",
			        1829,
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			        0.001,
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			    ],
			    [
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			        151,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4635 n_samples 1728 confidence 0.005 feature_proportion 0.001 n_clusters 50",
			        4635,
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			        0.001,
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			    [
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			        113,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1794 n_samples 1477 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        1794,
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			        0.999,
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			    ],
			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4153 n_samples 4784 confidence 0.25 feature_proportion 0.999 n_clusters 1",
			        4153,
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			    [
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			        113,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4040 n_samples 1718 confidence 0.005 feature_proportion 0.999 n_clusters 23",
			        4040,
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			        0.999,
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			    ],
			    [
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			        151,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4623 n_samples 1054 confidence 0.25 feature_proportion 0.001 n_clusters 1",
			        4623,
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			        "None",
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4045 n_samples 4830 confidence 0.25 feature_proportion 0.001 n_clusters 50",
			        4045,
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			        0.001,
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			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1679 n_samples 3404 confidence 0.25 feature_proportion 0.999 n_clusters 1",
			        1679,
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			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1774 n_samples 4429 confidence 0.25 feature_proportion 0.001 n_clusters 50",
			        1774,
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			    [
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			        120,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1777 n_samples 1835 confidence 0.1 feature_proportion 0.5356783709250821 n_clusters 1",
			        1777,
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			        0,
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			    ],
			    [
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			        157,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3586 n_samples 724 confidence 0.05 feature_proportion 0.51010195319244 n_clusters 50",
			        3586,
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			    [
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			        1746248573,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3080 n_samples 1830 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        3080,
			        1830,
			        0.999,
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			        0.1,
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			        "None",
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			    [
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			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3995 n_samples 2280 confidence 0.01 feature_proportion 0.36548447590373895 n_clusters 50",
			        3995,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4819 n_samples 967 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        4819,
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			        "None",
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			    ],
			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1815 n_samples 3976 confidence 0.005 feature_proportion 0.4367118163466834 n_clusters 50",
			        1815,
			        3976,
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			    [
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			        126,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4221 n_samples 1365 confidence 0.05 feature_proportion 0.38455855260106314 n_clusters 50",
			        4221,
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			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4509 n_samples 2399 confidence 0.005 feature_proportion 0.001 n_clusters 1",
			        4509,
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			    ],
			    [
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			        58,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1850 n_samples 4066 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        1850,
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			    [
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			        152,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4475 n_samples 1387 confidence 0.01 feature_proportion 0.001 n_clusters 26",
			        4475,
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			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4075 n_samples 2287 confidence 0.05 feature_proportion 0.001 n_clusters 42",
			        4075,
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			        0.001,
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			        0.05,
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			        "i7182",
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			    ],
			    [
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			        121,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2748 n_samples 715 confidence 0.05 feature_proportion 0.001 n_clusters 36",
			        2748,
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			        0.05,
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			    ],
			    [
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			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3188 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        3188,
			        1,
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			        0.025,
			        "None",
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			        "",
			        "",
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			        "",
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			    [
			        1746256244,
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			        107,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4630 n_samples 1633 confidence 0.25 feature_proportion 0.001 n_clusters 1",
			        4630,
			        1633,
			        0.001,
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			    ],
			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4174 n_samples 4171 confidence 0.005 feature_proportion 0.001 n_clusters 50",
			        4174,
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			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3894 n_samples 4255 confidence 0.01 feature_proportion 0.001 n_clusters 1",
			        3894,
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			        0.001,
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			        "None",
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			        781.9375,
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			    ],
			    [
			        1746258513,
			        1746258583,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4799 n_samples 3453 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        4799,
			        3453,
			        0.001,
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			        0.025,
			        0.53,
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			        "None",
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			        -1,
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			    ],
			    [
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			        1746259764,
			        132,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1476 n_samples 1131 confidence 0.05 feature_proportion 0.999 n_clusters 2",
			        1476,
			        1131,
			        0.999,
			        2,
			        0.05,
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			        "None",
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			    ],
			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1742 n_samples 4674 confidence 0.005 feature_proportion 0.415228446503524 n_clusters 50",
			        1742,
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			    ],
			    [
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			        1746261519,
			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4140 n_samples 4330 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        4140,
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			        0.999,
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			        "None",
			        "i7185",
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			    ],
			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4403 n_samples 3431 confidence 0.001 feature_proportion 0.48064354573596846 n_clusters 50",
			        4403,
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			    ],
			    [
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			        114,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1769 n_samples 1660 confidence 0.05 feature_proportion 0.999 n_clusters 34",
			        1769,
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			    ],
			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4797 n_samples 3879 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        4797,
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			    ],
			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1365 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 47",
			        1365,
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			    [
			        1746266075,
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1753 n_samples 4549 confidence 0.25 feature_proportion 0.001 n_clusters 26",
			        1753,
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			        "i7179",
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			    ],
			    [
			        1746267168,
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1741 n_samples 3503 confidence 0.25 feature_proportion 0.999 n_clusters 1",
			        1741,
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			        0.999,
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			        "None",
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			    ],
			    [
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			        71,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4356 n_samples 5000 confidence 0.025 feature_proportion 0.5089082576188598 n_clusters 1",
			        4356,
			        5000,
			        0.5089082576188598,
			        1,
			        0.025,
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			        "None",
			        "i7175",
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			    ],
			    [
			        1746269244,
			        1746269345,
			        101,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3538 n_samples 1321 confidence 0.25 feature_proportion 0.7135766053645493 n_clusters 1",
			        3538,
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			        1,
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			        "None",
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			        906.9453125,
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			    ],
			    [
			        1746270193,
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1818 n_samples 3235 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        1818,
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			        "None",
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			    ],
			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4695 n_samples 4284 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        4695,
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			        0.999,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4897 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        4897,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4377 n_samples 937 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        4377,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4277 n_samples 4882 confidence 0.025 feature_proportion 0.999 n_clusters 18",
			        4277,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1758 n_samples 4646 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        1758,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3976 n_samples 3845 confidence 0.05 feature_proportion 0.39975235712085355 n_clusters 16",
			        3976,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4020 n_samples 4545 confidence 0.25 feature_proportion 0.999 n_clusters 50",
			        4020,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1830 n_samples 3883 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        1830,
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2664 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        2664,
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			        "",
			        "",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3451 n_samples 997 confidence 0.05 feature_proportion 0.999 n_clusters 29",
			        3451,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4243 n_samples 2815 confidence 0.25 feature_proportion 0.43881140094288174 n_clusters 1",
			        4243,
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			    [
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			        184,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1667 n_samples 745 confidence 0.01 feature_proportion 0.999 n_clusters 43",
			        1667,
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			    [
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			        101,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4004 n_samples 1963 confidence 0.05 feature_proportion 0.001 n_clusters 1",
			        4004,
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			    [
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			        101,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1842 n_samples 1849 confidence 0.005 feature_proportion 0.001 n_clusters 1",
			        1842,
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			    [
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			        132,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4160 n_samples 1201 confidence 0.05 feature_proportion 0.999 n_clusters 29",
			        4160,
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			    [
			        1746286755,
			        1746286819,
			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4487 n_samples 4911 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        4487,
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2656 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        2656,
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			    [
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			        121,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1729 n_samples 1282 confidence 0.1 feature_proportion 0.001 n_clusters 50",
			        1729,
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			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1766 n_samples 3230 confidence 0.1 feature_proportion 0.999 n_clusters 36",
			        1766,
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			    [
			        1746291939,
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			        51,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2860 n_samples 4930 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        2860,
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			    [
			        1746292787,
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4176 n_samples 4230 confidence 0.005 feature_proportion 0.6830349442213818 n_clusters 50",
			        4176,
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			    ],
			    [
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			        83,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1791 n_samples 2293 confidence 0.005 feature_proportion 0.001 n_clusters 1",
			        1791,
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			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4689 n_samples 2947 confidence 0.005 feature_proportion 0.001 n_clusters 1",
			        4689,
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1300 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 49",
			        1300,
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			    [
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			        65,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1737 n_samples 4145 confidence 0.05 feature_proportion 0.999 n_clusters 30",
			        1737,
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			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1763 n_samples 3712 confidence 0.025 feature_proportion 0.3156580207720859 n_clusters 1",
			        1763,
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			        "None",
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			    ],
			    [
			        1746298877,
			        1746298959,
			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4869 n_samples 2979 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        4869,
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			        0.999,
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			        "None",
			        "i7180",
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			    ],
			    [
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			        170,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1240 n_samples 485 confidence 0.1 feature_proportion 0.999 n_clusters 34",
			        1240,
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			        0.999,
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			        "None",
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			    [
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			        176,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4197 n_samples 764 confidence 0.001 feature_proportion 0.999 n_clusters 50",
			        4197,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4817 n_samples 843 confidence 0.25 feature_proportion 0.267238103157459 n_clusters 1",
			        4817,
			        843,
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			        1,
			        0.25,
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			        997.88671875,
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			    ],
			    [
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			        141,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1418 n_samples 1335 confidence 0.05 feature_proportion 0.999 n_clusters 1",
			        1418,
			        1335,
			        0.999,
			        1,
			        0.05,
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			        "None",
			        "i7174",
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			    ],
			    [
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			        226,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3259 n_samples 308 confidence 0.01 feature_proportion 0.814937805956197 n_clusters 47",
			        3259,
			        308,
			        0.814937805956197,
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			    ],
			    [
			        1746306587,
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			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4308 n_samples 3352 confidence 0.25 feature_proportion 0.6797383188895489 n_clusters 19",
			        4308,
			        3352,
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			        "None",
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			    ],
			    [
			        1746308147,
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			        259,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3166 n_samples 722 confidence 0.005 feature_proportion 0.001 n_clusters 40",
			        3166,
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			        0.001,
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			    [
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			        114,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1724 n_samples 1993 confidence 0.025 feature_proportion 0.35695655493792877 n_clusters 50",
			        1724,
			        1993,
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			    ],
			    [
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			        114,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1762 n_samples 1878 confidence 0.05 feature_proportion 0.999 n_clusters 10",
			        1762,
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			        0.999,
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			        0.05,
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			        "None",
			        "i7183",
			        88,
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			    ],
			    [
			        1746312797,
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			        165,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4748 n_samples 937 confidence 0.05 feature_proportion 0.999 n_clusters 8",
			        4748,
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			        0.999,
			        8,
			        0.05,
			        0.61,
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			        "None",
			        "i7179",
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			    ],
			    [
			        1746314448,
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			        217,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1475 n_samples 730 confidence 0.1 feature_proportion 0.999 n_clusters 12",
			        1475,
			        730,
			        0.999,
			        12,
			        0.1,
			        0.61,
			        200,
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			        "None",
			        "i7178",
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			    [
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			        69,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1760 n_samples 2942 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        1760,
			        2942,
			        0.999,
			        1,
			        0.001,
			        0.54,
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			        0,
			        "None",
			        "i7183",
			        60,
			        809.875,
			        740.4189453125,
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			    ],
			    [
			        1746317219,
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			        133,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4696 n_samples 1980 confidence 0.001 feature_proportion 0.999 n_clusters 42",
			        4696,
			        1980,
			        0.999,
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			        0.001,
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			        "None",
			        "i7168",
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			    ],
			    [
			        1746318737,
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			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1 n_samples 5000 confidence 0.01 feature_proportion 0.001 n_clusters 1",
			        1,
			        5000,
			        0.001,
			        1,
			        0.01,
			        0.32,
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			        "None",
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			        20,
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			    [
			        1746320319,
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			        215,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1284 n_samples 542 confidence 0.05 feature_proportion 0.999 n_clusters 30",
			        1284,
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			        0.999,
			        30,
			        0.05,
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			        "None",
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			    [
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			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1 n_samples 2729 confidence 0.1 feature_proportion 0.001 n_clusters 1",
			        1,
			        2729,
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			        "None",
			        "i7182",
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			        -1,
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			    ],
			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4649 n_samples 3634 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        4649,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4147 n_samples 5000 confidence 0.01 feature_proportion 0.001 n_clusters 27",
			        4147,
			        5000,
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			        "None",
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			    ],
			    [
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			        115,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 857 n_samples 746 confidence 0.01 feature_proportion 0.999 n_clusters 49",
			        857,
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			    [
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			        102,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4298 n_samples 2235 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        4298,
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			    ],
			    [
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			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4793 n_samples 2635 confidence 0.25 feature_proportion 0.999 n_clusters 50",
			        4793,
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			        0.999,
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			    ],
			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4093 n_samples 4404 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        4093,
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			    ],
			    [
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			        1746331309,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1742 n_samples 3725 confidence 0.25 feature_proportion 0.999 n_clusters 22",
			        1742,
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			        0.999,
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			    ],
			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1753 n_samples 3510 confidence 0.025 feature_proportion 0.45095296043327643 n_clusters 1",
			        1753,
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			        0.45095296043327643,
			        1,
			        0.025,
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			        "None",
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			    ],
			    [
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			        50,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1055 n_samples 4931 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        1055,
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			    ],
			    [
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			        152,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4195 n_samples 1086 confidence 0.001 feature_proportion 0.212089080540408 n_clusters 50",
			        4195,
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			        0.6,
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			        "None",
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			        959.72265625,
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			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3912 n_samples 2789 confidence 0.005 feature_proportion 0.999 n_clusters 23",
			        3912,
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			        0.999,
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2772 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        2772,
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			        "None",
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			    [
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			        188,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1075 n_samples 283 confidence 0.25 feature_proportion 0.001 n_clusters 50",
			        1075,
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			    [
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			        128,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4391 n_samples 2109 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        4391,
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			        0.001,
			        50,
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			        0.58,
			        113,
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			        "None",
			        "i7186",
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			        874.08984375,
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			        4937023
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			    [
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			        1746344014,
			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2666 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        2666,
			        1,
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			        0.025,
			        "None",
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			        "",
			        "",
			        "",
			        "",
			        4937382
			    ],
			    [
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			        121,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4250 n_samples 1872 confidence 0.25 feature_proportion 0.7679567663832477 n_clusters 50",
			        4250,
			        1872,
			        0.7679567663832477,
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			    ],
			    [
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			        196,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1672 n_samples 750 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        1672,
			        750,
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			        0.05,
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			        "None",
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			    ],
			    [
			        1746349901,
			        1746349958,
			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1164 n_samples 3122 confidence 0.05 feature_proportion 0.001 n_clusters 5",
			        1164,
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			        0.001,
			        5,
			        0.05,
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			    [
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			        115,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3715 n_samples 4922 confidence 0.001 feature_proportion 0.001 n_clusters 24",
			        3715,
			        4922,
			        0.001,
			        24,
			        0.001,
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			    [
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			        210,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1063 n_samples 462 confidence 0.005 feature_proportion 0.999 n_clusters 38",
			        1063,
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3107 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        3107,
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			        "None",
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			        "",
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			        "",
			        "",
			        4939626
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			    [
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			        197,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1696 n_samples 743 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        1696,
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			    [
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			        108,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1702 n_samples 2040 confidence 0.05 feature_proportion 0.999 n_clusters 12",
			        1702,
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			        0.999,
			        12,
			        0.05,
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			        "None",
			        "i7183",
			        92,
			        849.41015625,
			        769.8356119791666,
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			    ],
			    [
			        1746360702,
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			        140,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1820 n_samples 1217 confidence 0.05 feature_proportion 0.999 n_clusters 4",
			        1820,
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			        0.999,
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			        0.05,
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			        "i7180",
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			    [
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			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4956 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        4956,
			        1,
			        0.999,
			        1,
			        0.025,
			        "None",
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			        "",
			        "",
			        "",
			        "",
			        4941622
			    ],
			    [
			        1746364662,
			        1746365003,
			        341,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1497 n_samples 478 confidence 0.001 feature_proportion 0.001 n_clusters 28",
			        1497,
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			        0.001,
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			        0.001,
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			    [
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			        122,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4057 n_samples 1703 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        4057,
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			        0.999,
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			        0.05,
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			        "None",
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			        106,
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			    ],
			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1737 n_samples 3507 confidence 0.01 feature_proportion 0.999 n_clusters 50",
			        1737,
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			        0.999,
			        50,
			        0.01,
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			        "None",
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			    [
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			        1746369534,
			        90,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1813 n_samples 3072 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        1813,
			        3072,
			        0.999,
			        1,
			        0.1,
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			        "None",
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			        78,
			        809.265625,
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			        -1,
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			    [
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			        1746371741,
			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4154 n_samples 3504 confidence 0.005 feature_proportion 0.001 n_clusters 8",
			        4154,
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			    [
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			        1746373362,
			        108,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4258 n_samples 2189 confidence 0.01 feature_proportion 0.20633583689181204 n_clusters 1",
			        4258,
			        2189,
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			        1,
			        0.01,
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			        96,
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			        "None",
			        "i7179",
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			        869.66015625,
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			    ],
			    [
			        1746374456,
			        1746374559,
			        103,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1781 n_samples 3273 confidence 0.001 feature_proportion 0.4972270592902 n_clusters 50",
			        1781,
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			    ],
			    [
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			        222,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4895 n_samples 745 confidence 0.05 feature_proportion 0.5160922471679752 n_clusters 50",
			        4895,
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			        0.05,
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			        "None",
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			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4901 n_samples 4041 confidence 0.05 feature_proportion 0.4812191675970336 n_clusters 1",
			        4901,
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			        "None",
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			    ],
			    [
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			        327,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1297 n_samples 281 confidence 0.25 feature_proportion 0.001 n_clusters 50",
			        1297,
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			        0.001,
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			        0.62,
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			        "None",
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			    ],
			    [
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			        84,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4568 n_samples 3188 confidence 0.01 feature_proportion 0.999 n_clusters 14",
			        4568,
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			        0.999,
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			        "None",
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			    [
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			        123,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4200 n_samples 2370 confidence 0.001 feature_proportion 0.999 n_clusters 50",
			        4200,
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			        "None",
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4890 n_samples 6 confidence 0.025 feature_proportion 0.001 n_clusters 26",
			        4890,
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			    [
			        1746387191,
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			        514,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4836 n_samples 1145 confidence 0.001 feature_proportion 0.001 n_clusters 50",
			        4836,
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			        0.001,
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			        0.001,
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			    ],
			    [
			        1746388686,
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			        247,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4876 n_samples 526 confidence 0.025 feature_proportion 0.6550794503754931 n_clusters 26",
			        4876,
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			        "None",
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			    ],
			    [
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			        304,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4657 n_samples 507 confidence 0.01 feature_proportion 0.999 n_clusters 50",
			        4657,
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			        0.61,
			        272,
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			        "None",
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			    ],
			    [
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			        109,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4011 n_samples 2649 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        4011,
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			        0.999,
			        1,
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			        0.55,
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			        "None",
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			    ],
			    [
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			        1746392550,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1837 n_samples 2391 confidence 0.25 feature_proportion 0.999 n_clusters 1",
			        1837,
			        2391,
			        0.999,
			        1,
			        0.25,
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			        "None",
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			        71,
			        835.48046875,
			        760.567578125,
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			        4947242
			    ],
			    [
			        1746394557,
			        1746394639,
			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4655 n_samples 2889 confidence 0.01 feature_proportion 0.001 n_clusters 50",
			        4655,
			        2889,
			        0.001,
			        50,
			        0.01,
			        0.54,
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			        "None",
			        "i7180",
			        68,
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			    ],
			    [
			        1746395509,
			        1746395598,
			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4291 n_samples 4562 confidence 0.001 feature_proportion 0.001 n_clusters 50",
			        4291,
			        4562,
			        0.001,
			        50,
			        0.001,
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			        "None",
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			        72,
			        781.30078125,
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			    ],
			    [
			        1746397508,
			        1746397584,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 4548 confidence 0.01 feature_proportion 0.39550677999908446 n_clusters 25",
			        5000,
			        4548,
			        0.39550677999908446,
			        25,
			        0.01,
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			        60,
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			    ],
			    [
			        1746398906,
			        1746398995,
			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1900 n_samples 2601 confidence 0.01 feature_proportion 0.38673906563153615 n_clusters 1",
			        1900,
			        2601,
			        0.38673906563153615,
			        1,
			        0.01,
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			        "None",
			        "i7184",
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			        832.03515625,
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			        4948435
			    ],
			    [
			        1746401149,
			        1746401270,
			        121,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1714 n_samples 1557 confidence 0.05 feature_proportion 0.999 n_clusters 1",
			        1714,
			        1557,
			        0.999,
			        1,
			        0.05,
			        0.58,
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			        "None",
			        "i7184",
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			    ],
			    [
			        1746402470,
			        1746402540,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1363 n_samples 4804 confidence 0.025 feature_proportion 0.4452099771655368 n_clusters 1",
			        1363,
			        4804,
			        0.4452099771655368,
			        1,
			        0.025,
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			        "None",
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			        51,
			        750.81640625,
			        717.11962890625,
			        -1,
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			        4949062
			    ],
			    [
			        1746404469,
			        1746404552,
			        83,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1856 n_samples 4839 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        1856,
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			        0.999,
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			        0.025,
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			    ],
			    [
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			        69,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1742 n_samples 3391 confidence 0.01 feature_proportion 0.3989259141067631 n_clusters 1",
			        1742,
			        3391,
			        0.3989259141067631,
			        1,
			        0.01,
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			        "None",
			        "i7176",
			        58,
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			        -1,
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			    ],
			    [
			        1746407809,
			        1746407899,
			        90,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1748 n_samples 3574 confidence 0.01 feature_proportion 0.36570980992030766 n_clusters 1",
			        1748,
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			        0.36570980992030766,
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			        "None",
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			        71,
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			    ],
			    [
			        1746409948,
			        1746410024,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1795 n_samples 3392 confidence 0.025 feature_proportion 0.999 n_clusters 29",
			        1795,
			        3392,
			        0.999,
			        29,
			        0.025,
			        0.53,
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			        "None",
			        "i7181",
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			        4950461
			    ],
			    [
			        1746411267,
			        1746411331,
			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4169 n_samples 4217 confidence 0.05 feature_proportion 0.5682648678412265 n_clusters 1",
			        4169,
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			        "None",
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			        52,
			        795.58203125,
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			        -1,
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			        4950697
			    ],
			    [
			        1746412467,
			        1746412524,
			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1700 n_samples 4853 confidence 0.01 feature_proportion 0.6448091994413458 n_clusters 50",
			        1700,
			        4853,
			        0.6448091994413458,
			        50,
			        0.01,
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			        "None",
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			        42,
			        762.41796875,
			        721.8978794642857,
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			        4950917
			    ],
			    [
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			        1746414967,
			        139,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4166 n_samples 1287 confidence 0.25 feature_proportion 0.5981955463704304 n_clusters 26",
			        4166,
			        1287,
			        0.5981955463704304,
			        26,
			        0.25,
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			        124,
			        0,
			        "None",
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			        124,
			        934.28125,
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			        -1,
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			        4951359
			    ],
			    [
			        1746416907,
			        1746416996,
			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1799 n_samples 2044 confidence 0.005 feature_proportion 0.45815022283797235 n_clusters 1",
			        1799,
			        2044,
			        0.45815022283797235,
			        1,
			        0.005,
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			        "None",
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			    ],
			    [
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			        109,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4086 n_samples 2842 confidence 0.001 feature_proportion 0.001 n_clusters 1",
			        4086,
			        2842,
			        0.001,
			        1,
			        0.001,
			        0.53,
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			        "None",
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			    ],
			    [
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			        214,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1781 n_samples 632 confidence 0.05 feature_proportion 0.3658641862119435 n_clusters 1",
			        1781,
			        632,
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			        1,
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			        0.61,
			        200,
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			        "None",
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			        200,
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			    ],
			    [
			        1746421371,
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			        185,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3839 n_samples 1211 confidence 0.25 feature_proportion 0.999 n_clusters 12",
			        3839,
			        1211,
			        0.999,
			        12,
			        0.25,
			        0.6,
			        170,
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			        "None",
			        "i7178",
			        170,
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			    ],
			    [
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			        1746423477,
			        185,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3818 n_samples 1270 confidence 0.025 feature_proportion 0.5152766121280786 n_clusters 1",
			        3818,
			        1270,
			        0.5152766121280786,
			        1,
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			        "None",
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			    ],
			    [
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			        1746425242,
			        71,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1803 n_samples 4023 confidence 0.005 feature_proportion 0.001 n_clusters 23",
			        1803,
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			        0.001,
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			        "None",
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			    ],
			    [
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			        1746427245,
			        674,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4771 n_samples 205 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        4771,
			        205,
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			        1,
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			        0.61,
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			        "None",
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			    ],
			    [
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			        83,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4708 n_samples 3396 confidence 0.01 feature_proportion 0.001 n_clusters 50",
			        4708,
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			        "None",
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			    [
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			        1746430475,
			        103,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1850 n_samples 2349 confidence 0.05 feature_proportion 0.18079046683842404 n_clusters 1",
			        1850,
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			        0.18079046683842404,
			        1,
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			        "None",
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			    ],
			    [
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			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1800 n_samples 2811 confidence 0.01 feature_proportion 0.7679284358191604 n_clusters 1",
			        1800,
			        2811,
			        0.7679284358191604,
			        1,
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			        "None",
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			    ],
			    [
			        1746434861,
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4698 n_samples 4560 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        4698,
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			        0.999,
			        1,
			        0.001,
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			        52,
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			        "None",
			        "i7185",
			        52,
			        780.7265625,
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			    ],
			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1799 n_samples 3697 confidence 0.005 feature_proportion 0.509903028356041 n_clusters 1",
			        1799,
			        3697,
			        0.509903028356041,
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			        0,
			        "None",
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			    ],
			    [
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			        1746438964,
			        241,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4486 n_samples 720 confidence 0.01 feature_proportion 0.999 n_clusters 30",
			        4486,
			        720,
			        0.999,
			        30,
			        0.01,
			        0.62,
			        226,
			        0,
			        "None",
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			        226,
			        1030.91796875,
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			    ],
			    [
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			        214,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4659 n_samples 695 confidence 0.1 feature_proportion 0.999 n_clusters 31",
			        4659,
			        695,
			        0.999,
			        31,
			        0.1,
			        0.62,
			        179,
			        0,
			        "None",
			        "i7175",
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			    ],
			    [
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			        209,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4742 n_samples 689 confidence 0.005 feature_proportion 0.001 n_clusters 11",
			        4742,
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			        11,
			        0.005,
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			        "None",
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			    ],
			    [
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			        240,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4739 n_samples 806 confidence 0.05 feature_proportion 0.999 n_clusters 1",
			        4739,
			        806,
			        0.999,
			        1,
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			        "None",
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			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4895 n_samples 3115 confidence 0.1 feature_proportion 0.47543454568936433 n_clusters 50",
			        4895,
			        3115,
			        0.47543454568936433,
			        50,
			        0.1,
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			        60,
			        0,
			        "None",
			        "i7175",
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			    ],
			    [
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			        96,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3123 n_samples 2616 confidence 0.25 feature_proportion 0.001 n_clusters 1",
			        3123,
			        2616,
			        0.001,
			        1,
			        0.25,
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			        "None",
			        "i7184",
			        75,
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			    ],
			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1115 n_samples 4528 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        1115,
			        4528,
			        0.001,
			        1,
			        0.025,
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			    [
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			        78,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1770 n_samples 4503 confidence 0.05 feature_proportion 0.001 n_clusters 40",
			        1770,
			        4503,
			        0.001,
			        40,
			        0.05,
			        0.48,
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			        "None",
			        "i7182",
			        56,
			        767.75390625,
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			    [
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			        101,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1573 n_samples 1649 confidence 0.1 feature_proportion 0.6612504644373429 n_clusters 50",
			        1573,
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			        "None",
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			    [
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			        179,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4318 n_samples 972 confidence 0.005 feature_proportion 0.001 n_clusters 24",
			        4318,
			        972,
			        0.001,
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			        0.005,
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			        "None",
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			    [
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			        115,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1453 n_samples 5000 confidence 0.005 feature_proportion 0.435209130631594 n_clusters 50",
			        1453,
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			        "None",
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			        713.3642578125,
			        -1,
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			    ],
			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3717 n_samples 3301 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        3717,
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			        0.001,
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			        "None",
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			    ],
			    [
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			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1863 n_samples 2717 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        1863,
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			        0.001,
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			        0.025,
			        0.54,
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			        "None",
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			        78,
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			    ],
			    [
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			        140,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1173 n_samples 785 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        1173,
			        785,
			        0.999,
			        1,
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			        0,
			        "None",
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			        128,
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			    [
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			        71,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4075 n_samples 4776 confidence 0.01 feature_proportion 0.001 n_clusters 22",
			        4075,
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			        0.001,
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			        "None",
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			    ],
			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1295 n_samples 2804 confidence 0.01 feature_proportion 0.001 n_clusters 1",
			        1295,
			        2804,
			        0.001,
			        1,
			        0.01,
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			        0,
			        "None",
			        "i7180",
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			    ],
			    [
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			        51,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2711 n_samples 3828 confidence 0.001 feature_proportion 0.999 n_clusters 24",
			        2711,
			        3828,
			        0.999,
			        24,
			        0.001,
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			        "None",
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			    [
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			        90,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4842 n_samples 4539 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        4842,
			        4539,
			        0.999,
			        1,
			        0.025,
			        0.48,
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			        "None",
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			    [
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			        121,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4346 n_samples 2072 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        4346,
			        2072,
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			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3799 n_samples 3204 confidence 0.1 feature_proportion 0.001 n_clusters 23",
			        3799,
			        3204,
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			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4982 n_samples 4099 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        4982,
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			    [
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			        96,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3992 n_samples 2773 confidence 0.1 feature_proportion 0.999 n_clusters 30",
			        3992,
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			        0.999,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4757 n_samples 4815 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        4757,
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			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4257 n_samples 4913 confidence 0.01 feature_proportion 0.7045522685034176 n_clusters 50",
			        4257,
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			    ],
			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1804 n_samples 2140 confidence 0.005 feature_proportion 0.999 n_clusters 36",
			        1804,
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			        0.999,
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			        "None",
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			    ],
			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3845 n_samples 3198 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        3845,
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			        0.001,
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			        "None",
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			    ],
			    [
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			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4788 n_samples 3667 confidence 0.1 feature_proportion 0.001 n_clusters 23",
			        4788,
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			        0.001,
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			        "None",
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			    ],
			    [
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			        114,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1284 n_samples 1126 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        1284,
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			        0.999,
			        1,
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			        "None",
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			    [
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			        151,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4398 n_samples 1212 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        4398,
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			        0.999,
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			        0.1,
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			        136,
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			        "None",
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			    [
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			        69,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1788 n_samples 3221 confidence 0.05 feature_proportion 0.999 n_clusters 1",
			        1788,
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			        "None",
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			    ],
			    [
			        1746508551,
			        1746508640,
			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4795 n_samples 2699 confidence 0.01 feature_proportion 0.5782745583167382 n_clusters 50",
			        4795,
			        2699,
			        0.5782745583167382,
			        50,
			        0.01,
			        0.55,
			        76,
			        0,
			        "None",
			        "i7176",
			        76,
			        835.609375,
			        758.253125,
			        -1,
			        0.9678255451713396,
			        4969111
			    ],
			    [
			        1746511612,
			        1746511675,
			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1741 n_samples 3761 confidence 0.01 feature_proportion 0.5419856833166743 n_clusters 50",
			        1741,
			        3761,
			        0.5419856833166743,
			        50,
			        0.01,
			        0.47,
			        50,
			        0,
			        "None",
			        "i7185",
			        50,
			        791.66796875,
			        738.59033203125,
			        -1,
			        0.43389408099688476,
			        4969643
			    ],
			    [
			        1746514030,
			        1746514087,
			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1649 n_samples 4745 confidence 0.1 feature_proportion 0.001 n_clusters 50",
			        1649,
			        4745,
			        0.001,
			        50,
			        0.1,
			        0.48,
			        45,
			        0,
			        "None",
			        "i7183",
			        45,
			        750.859375,
			        714.0412946428571,
			        -1,
			        0.30822429906542054,
			        4970043
			    ],
			    [
			        1746516274,
			        1746516387,
			        113,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1713 n_samples 1490 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        1713,
			        1490,
			        0.999,
			        50,
			        0.05,
			        0.59,
			        101,
			        0,
			        "None",
			        "i7180",
			        101,
			        901.671875,
			        794.4672475961538,
			        -1,
			        0.9682616822429907,
			        4970418
			    ],
			    [
			        1746519898,
			        1746520013,
			        115,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4012 n_samples 2755 confidence 0.01 feature_proportion 0.001 n_clusters 1",
			        4012,
			        2755,
			        0.001,
			        1,
			        0.01,
			        0.54,
			        94,
			        0,
			        "None",
			        "i7178",
			        94,
			        831.44921875,
			        753.4794921875,
			        -1,
			        0.9769096573208723,
			        4971029
			    ],
			    [
			        1746522164,
			        1746522367,
			        203,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 923 confidence 0.05 feature_proportion 0.999 n_clusters 28",
			        5000,
			        923,
			        0.999,
			        28,
			        0.05,
			        0.61,
			        178,
			        0,
			        "None",
			        "i7184",
			        178,
			        978.98046875,
			        834.82421875,
			        -1,
			        0.9891339563862929,
			        4971381
			    ],
			    [
			        1746524608,
			        1746524659,
			        51,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 614 n_samples 4077 confidence 0.001 feature_proportion 0.4994519690563973 n_clusters 50",
			        614,
			        4077,
			        0.4994519690563973,
			        50,
			        0.001,
			        0.4,
			        33,
			        0,
			        "None",
			        "i7179",
			        33,
			        706.7109375,
			        695.98046875,
			        -1,
			        0.1377196261682243,
			        4971808
			    ],
			    [
			        1746527169,
			        1746527265,
			        96,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4052 n_samples 4323 confidence 0.1 feature_proportion 0.5573874843914518 n_clusters 50",
			        4052,
			        4323,
			        0.5573874843914518,
			        50,
			        0.1,
			        0.48,
			        70,
			        0,
			        "None",
			        "i7184",
			        70,
			        774.47265625,
			        731.915625,
			        -1,
			        0.8583676012461059,
			        4972248
			    ],
			    [
			        1746529488,
			        1746529552,
			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1743 n_samples 3802 confidence 0.001 feature_proportion 0.5802932748927091 n_clusters 18",
			        1743,
			        3802,
			        0.5802932748927091,
			        18,
			        0.001,
			        0.46,
			        52,
			        0,
			        "None",
			        "i7182",
			        52,
			        786.74609375,
			        733.60791015625,
			        -1,
			        0.43439252336448597,
			        4972651
			    ],
			    [
			        1746532814,
			        1746532890,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4063 n_samples 3143 confidence 0.05 feature_proportion 0.001 n_clusters 1",
			        4063,
			        3143,
			        0.001,
			        1,
			        0.05,
			        0.54,
			        60,
			        0,
			        "None",
			        "i7181",
			        60,
			        805.5,
			        738.22802734375,
			        -1,
			        0.9514267912772586,
			        4973440
			    ],
			    [
			        1746535532,
			        1746535602,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1596 n_samples 4963 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        1596,
			        4963,
			        0.001,
			        50,
			        0.05,
			        0.48,
			        47,
			        0,
			        "None",
			        "i7180",
			        47,
			        750.32421875,
			        712.8498883928571,
			        -1,
			        0.2983177570093458,
			        4973965
			    ],
			    [
			        1746538548,
			        1746538612,
			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1729 n_samples 4473 confidence 0.1 feature_proportion 0.999 n_clusters 50",
			        1729,
			        4473,
			        0.999,
			        50,
			        0.1,
			        0.49,
			        50,
			        0,
			        "None",
			        "i7184",
			        50,
			        774.5625,
			        722.1534598214286,
			        -1,
			        0.3447227414330218,
			        4974507
			    ],
			    [
			        1746540842,
			        1746540943,
			        101,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 3479 confidence 0.001 feature_proportion 0.767427884703277 n_clusters 1",
			        5000,
			        3479,
			        0.767427884703277,
			        1,
			        0.001,
			        0.54,
			        52,
			        0,
			        "None",
			        "i7175",
			        52,
			        803.01171875,
			        743.3671875,
			        -1,
			        0.9726978193146417,
			        4974936
			    ],
			    [
			        1746542775,
			        1746542895,
			        120,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1881 n_samples 2319 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        1881,
			        2319,
			        0.999,
			        1,
			        0.025,
			        0.56,
			        79,
			        0,
			        "None",
			        "i7175",
			        79,
			        852.01953125,
			        761.53125,
			        -1,
			        0.7734953271028038,
			        4975225
			    ],
			    [
			        1746545903,
			        1746546130,
			        227,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1111 n_samples 532 confidence 0.001 feature_proportion 0.999 n_clusters 1",
			        1111,
			        532,
			        0.999,
			        1,
			        0.001,
			        0.61,
			        205,
			        0,
			        "None",
			        "i7181",
			        205,
			        976.07421875,
			        837.01171875,
			        -1,
			        0.994392523364486,
			        4975768
			    ],
			    [
			        1746549780,
			        1746549887,
			        107,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1852 n_samples 4238 confidence 0.01 feature_proportion 0.7143042613524102 n_clusters 1",
			        1852,
			        4238,
			        0.7143042613524102,
			        1,
			        0.01,
			        0.46,
			        57,
			        0,
			        "None",
			        "i7182",
			        57,
			        900.046875,
			        820.6962890625,
			        -1,
			        0.3923239875389408,
			        4976341
			    ],
			    [
			        1746552086,
			        1746552168,
			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1883 n_samples 2807 confidence 0.025 feature_proportion 0.999 n_clusters 20",
			        1883,
			        2807,
			        0.999,
			        20,
			        0.025,
			        0.53,
			        65,
			        0,
			        "None",
			        "i7183",
			        65,
			        827.1015625,
			        753.5985243055555,
			        -1,
			        0.6335327102803738,
			        4976735
			    ],
			    [
			        1746554424,
			        1746554557,
			        133,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3933 n_samples 4432 confidence 0.005 feature_proportion 0.001 n_clusters 19",
			        3933,
			        4432,
			        0.001,
			        19,
			        0.005,
			        0.47,
			        115,
			        0,
			        "None",
			        "i7180",
			        115,
			        775.95703125,
			        712.4514508928571,
			        -1,
			        0.7841495327102804,
			        4977095
			    ],
			    [
			        1746557204,
			        1746557267,
			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4266 n_samples 3931 confidence 0.25 feature_proportion 0.5370370900057451 n_clusters 23",
			        4266,
			        3931,
			        0.5370370900057451,
			        23,
			        0.25,
			        0.47,
			        50,
			        0,
			        "None",
			        "i7181",
			        50,
			        786.7578125,
			        734.47607421875,
			        -1,
			        0.9348785046728972,
			        4977507
			    ],
			    [
			        1746559045,
			        1746559139,
			        94,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1844 n_samples 3957 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        1844,
			        3957,
			        0.999,
			        1,
			        0.1,
			        0.46,
			        45,
			        0,
			        "None",
			        "i7175",
			        45,
			        780.41796875,
			        727.48046875,
			        -1,
			        0.4365856697819315,
			        4977839
			    ],
			    [
			        1746561984,
			        1746562041,
			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3677 n_samples 3796 confidence 0.001 feature_proportion 0.999 n_clusters 50",
			        3677,
			        3796,
			        0.999,
			        50,
			        0.001,
			        0.46,
			        45,
			        0,
			        "None",
			        "i7179",
			        45,
			        779.32421875,
			        728.6049107142857,
			        -1,
			        0.9163862928348909,
			        4978365
			    ],
			    [
			        1746566027,
			        1746566140,
			        113,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1624 n_samples 1548 confidence 0.05 feature_proportion 0.001 n_clusters 4",
			        1624,
			        1548,
			        0.001,
			        4,
			        0.05,
			        0.58,
			        97,
			        0,
			        "None",
			        "i7181",
			        97,
			        888.6953125,
			        786.8440755208334,
			        -1,
			        0.9654330218068535,
			        4978956
			    ],
			    [
			        1746569685,
			        1746569755,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3465 n_samples 5000 confidence 0.1 feature_proportion 0.488488369911279 n_clusters 1",
			        3465,
			        5000,
			        0.488488369911279,
			        1,
			        0.1,
			        0.45,
			        55,
			        0,
			        "None",
			        "i7184",
			        55,
			        751.67578125,
			        716.5791015625,
			        -1,
			        0.6476635514018692,
			        4979495
			    ],
			    [
			        1746573065,
			        1746573116,
			        51,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 818 n_samples 3951 confidence 0.05 feature_proportion 0.001 n_clusters 49",
			        818,
			        3951,
			        0.001,
			        49,
			        0.05,
			        0.42,
			        37,
			        0,
			        "None",
			        "i7180",
			        37,
			        726.984375,
			        701.0162760416666,
			        -1,
			        0.19366978193146417,
			        4980085
			    ],
			    [
			        1746575718,
			        1746575813,
			        95,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1882 n_samples 1863 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        1882,
			        1863,
			        0.001,
			        1,
			        0.025,
			        0.58,
			        82,
			        0,
			        "None",
			        "i7180",
			        82,
			        883.3203125,
			        785.0909090909091,
			        -1,
			        0.9752647975077882,
			        4980502
			    ],
			    [
			        1746579186,
			        1746579262,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3961 n_samples 3798 confidence 0.01 feature_proportion 0.999 n_clusters 1",
			        3961,
			        3798,
			        0.999,
			        1,
			        0.01,
			        0.47,
			        62,
			        0,
			        "None",
			        "i7180",
			        62,
			        802.81640625,
			        743.41796875,
			        -1,
			        0.9485732087227414,
			        4981071
			    ],
			    [
			        1746582255,
			        1746582331,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4691 n_samples 3787 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        4691,
			        3787,
			        0.001,
			        50,
			        0.05,
			        0.49,
			        60,
			        0,
			        "None",
			        "i7185",
			        60,
			        797.33203125,
			        735.2666015625,
			        -1,
			        0.9550654205607476,
			        4981565
			    ],
			    [
			        1746584870,
			        1746584977,
			        107,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4578 n_samples 1801 confidence 0.025 feature_proportion 0.001 n_clusters 50",
			        4578,
			        1801,
			        0.001,
			        50,
			        0.025,
			        0.58,
			        89,
			        0,
			        "None",
			        "i7183",
			        89,
			        896.25,
			        784.2990056818181,
			        -1,
			        0.9878629283489097,
			        4981936
			    ],
			    [
			        1746588319,
			        1746588389,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1846 n_samples 4593 confidence 0.005 feature_proportion 0.7794458354810646 n_clusters 1",
			        1846,
			        4593,
			        0.7794458354810646,
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			    ],
			    [
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			        96,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1803 n_samples 2288 confidence 0.25 feature_proportion 0.4720988596285399 n_clusters 50",
			        1803,
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			    ],
			    [
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			        95,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1830 n_samples 2198 confidence 0.1 feature_proportion 0.001 n_clusters 20",
			        1830,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3696 n_samples 4436 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        3696,
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			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1633 n_samples 4786 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        1633,
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			        0.999,
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			    [
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			        63,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4085 n_samples 5000 confidence 0.05 feature_proportion 0.999 n_clusters 29",
			        4085,
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			    [
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			        71,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1737 n_samples 3398 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        1737,
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			        "None",
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			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4895 n_samples 3718 confidence 0.1 feature_proportion 0.001 n_clusters 1",
			        4895,
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			    [
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			        95,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 3900 confidence 0.25 feature_proportion 0.999 n_clusters 1",
			        5000,
			        3900,
			        0.999,
			        1,
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			        "None",
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			        740.3628472222222,
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			    [
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			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3920 n_samples 4571 confidence 0.05 feature_proportion 0.999 n_clusters 26",
			        3920,
			        4571,
			        0.999,
			        26,
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			        45,
			        780.52734375,
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			    [
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			        96,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1759 n_samples 3308 confidence 0.05 feature_proportion 0.999 n_clusters 50",
			        1759,
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			        0.999,
			        50,
			        0.05,
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			    [
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			        114,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1836 n_samples 1746 confidence 0.001 feature_proportion 0.001 n_clusters 9",
			        1836,
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			        0.001,
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			        "None",
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			        96,
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			        785.6292317708334,
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			    [
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			        95,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 933 n_samples 3598 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        933,
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			    [
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			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1728 n_samples 2801 confidence 0.025 feature_proportion 0.3410327749830845 n_clusters 1",
			        1728,
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			        0.3410327749830845,
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			        "None",
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			    [
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			        115,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1880 n_samples 1497 confidence 0.001 feature_proportion 0.45621247273867077 n_clusters 1",
			        1880,
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			        "None",
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			    [
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			        167,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1708 n_samples 962 confidence 0.01 feature_proportion 0.2897278682826136 n_clusters 50",
			        1708,
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			        "None",
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			    [
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			        109,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4643 n_samples 2354 confidence 0.001 feature_proportion 0.42632079531225436 n_clusters 28",
			        4643,
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			    ],
			    [
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			        120,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1811 n_samples 1664 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        1811,
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			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4398 n_samples 2441 confidence 0.001 feature_proportion 0.999 n_clusters 22",
			        4398,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4796 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 50",
			        4796,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4696 n_samples 706 confidence 0.025 feature_proportion 0.999 n_clusters 33",
			        4696,
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			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3653 n_samples 4719 confidence 0.05 feature_proportion 0.001 n_clusters 50",
			        3653,
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			    [
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			        91,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4942 n_samples 4218 confidence 0.025 feature_proportion 0.001 n_clusters 23",
			        4942,
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			    [
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			        134,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1266 n_samples 806 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        1266,
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			        "None",
			        "i7174",
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			    [
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			        116,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4097 n_samples 2949 confidence 0.005 feature_proportion 0.999 n_clusters 16",
			        4097,
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			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3458 n_samples 2258 confidence 0.05 feature_proportion 0.7116448933955836 n_clusters 20",
			        3458,
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			    [
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			        96,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1619 n_samples 1856 confidence 0.005 feature_proportion 0.999 n_clusters 31",
			        1619,
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			        "None",
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			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1783 n_samples 4011 confidence 0.25 feature_proportion 0.999 n_clusters 50",
			        1783,
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			        "None",
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			    [
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			        1746688128,
			        126,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3411 n_samples 976 confidence 0.01 feature_proportion 0.4667020566298123 n_clusters 1",
			        3411,
			        976,
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			        1,
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			        "None",
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			        113,
			        935.6015625,
			        810.9673549107143,
			        -1,
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			        4999948
			    ],
			    [
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			        1746690497,
			        84,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1773 n_samples 3061 confidence 0.001 feature_proportion 0.999 n_clusters 50",
			        1773,
			        3061,
			        0.999,
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			        0.001,
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			        "None",
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			    ],
			    [
			        1746694610,
			        1746694770,
			        160,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4043 n_samples 1175 confidence 0.025 feature_proportion 0.999 n_clusters 28",
			        4043,
			        1175,
			        0.999,
			        28,
			        0.025,
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			        "None",
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			        144,
			        944.97265625,
			        819.8563878676471,
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			        5000874
			    ],
			    [
			        1746699668,
			        1746699738,
			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4130 n_samples 3462 confidence 0.005 feature_proportion 0.999 n_clusters 6",
			        4130,
			        3462,
			        0.999,
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			        "None",
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			    ],
			    [
			        1746704308,
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			        50,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1616 n_samples 4676 confidence 0.1 feature_proportion 0.999 n_clusters 24",
			        1616,
			        4676,
			        0.999,
			        24,
			        0.1,
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			        "None",
			        "i7172",
			        39,
			        751.9921875,
			        714.6712239583334,
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			    ],
			    [
			        1746709042,
			        1746709181,
			        139,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3481 n_samples 848 confidence 0.005 feature_proportion 0.999 n_clusters 1",
			        3481,
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			        1,
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			        "None",
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			    [
			        1746711774,
			        1746711850,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3949 n_samples 3216 confidence 0.025 feature_proportion 0.001 n_clusters 20",
			        3949,
			        3216,
			        0.001,
			        20,
			        0.025,
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			        "None",
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			        62,
			        811.0078125,
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			    ],
			    [
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			        1746714561,
			        84,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4206 n_samples 4941 confidence 0.01 feature_proportion 0.999 n_clusters 1",
			        4206,
			        4941,
			        0.999,
			        1,
			        0.01,
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			        "None",
			        "i7184",
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			    ],
			    [
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			        82,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4218 n_samples 2192 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        4218,
			        2192,
			        0.001,
			        1,
			        0.025,
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			        "None",
			        "i7172",
			        69,
			        859.9296875,
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			        -1,
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			    ],
			    [
			        1746721821,
			        1746721878,
			        57,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1738 n_samples 3275 confidence 0.25 feature_proportion 0.001 n_clusters 32",
			        1738,
			        3275,
			        0.001,
			        32,
			        0.25,
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			        "None",
			        "i7182",
			        50,
			        788.19921875,
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			    ],
			    [
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			        102,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1028 n_samples 1345 confidence 0.25 feature_proportion 0.26045321754411194 n_clusters 50",
			        1028,
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			        0.26045321754411194,
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			        "None",
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			        5005634
			    ],
			    [
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			        126,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1889 n_samples 1334 confidence 0.01 feature_proportion 0.2648542465162909 n_clusters 50",
			        1889,
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			        0.2648542465162909,
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			        "None",
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			    ],
			    [
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			        397,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1166 n_samples 199 confidence 0.01 feature_proportion 0.999 n_clusters 50",
			        1166,
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			        0.999,
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			        "None",
			        "i7180",
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			    ],
			    [
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			        170,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1662 n_samples 624 confidence 0.05 feature_proportion 0.999 n_clusters 27",
			        1662,
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			        0.999,
			        27,
			        0.05,
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			        "None",
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			        1023.890625,
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			    ],
			    [
			        1746740367,
			        1746740443,
			        76,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1818 n_samples 3064 confidence 0.01 feature_proportion 0.46489394075376356 n_clusters 1",
			        1818,
			        3064,
			        0.46489394075376356,
			        1,
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			    ],
			    [
			        1746744629,
			        1746744737,
			        108,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4322 n_samples 2165 confidence 0.025 feature_proportion 0.999 n_clusters 29",
			        4322,
			        2165,
			        0.999,
			        29,
			        0.025,
			        0.58,
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			        "None",
			        "i7180",
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			    ],
			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1681 n_samples 4183 confidence 0.01 feature_proportion 0.5105230828734826 n_clusters 50",
			        1681,
			        4183,
			        0.5105230828734826,
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			        0.01,
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			        "None",
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			        57,
			        772.66796875,
			        725.77392578125,
			        -1,
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			    ],
			    [
			        1746753100,
			        1746753177,
			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3831 n_samples 4143 confidence 0.25 feature_proportion 0.5141002205526031 n_clusters 22",
			        3831,
			        4143,
			        0.5141002205526031,
			        22,
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			        "None",
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			    [
			        1746755832,
			        1746755942,
			        110,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3958 n_samples 3048 confidence 0.025 feature_proportion 0.999 n_clusters 1",
			        3958,
			        3048,
			        0.999,
			        1,
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			    [
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			        1746758865,
			        242,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 2683 n_samples 249 confidence 0.005 feature_proportion 0.6790527741471297 n_clusters 50",
			        2683,
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			        0.6790527741471297,
			        50,
			        0.005,
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			        224,
			        1039.359375,
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			    [
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			        1746761522,
			        77,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 405 n_samples 1304 confidence 0.05 feature_proportion 0.034841147825029124 n_clusters 1",
			        405,
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			        0.034841147825029124,
			        1,
			        0.05,
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			    [
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			        113,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 3375 n_samples 865 confidence 0.05 feature_proportion 0.5209688174937831 n_clusters 1",
			        3375,
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			        "None",
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			    [
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			        58,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1781 n_samples 4687 confidence 0.025 feature_proportion 0.5284141587078258 n_clusters 1",
			        1781,
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			    ],
			    [
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			        1746773916,
			        51,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1706 n_samples 4602 confidence 0.05 feature_proportion 0.5042770915802844 n_clusters 50",
			        1706,
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			        "None",
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			    ],
			    [
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			        1746776848,
			        102,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 5000 n_samples 2351 confidence 0.025 feature_proportion 0.001 n_clusters 1",
			        5000,
			        2351,
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			        1,
			        0.025,
			        0.56,
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			    [
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			        146,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1788 n_samples 1118 confidence 0.005 feature_proportion 0.999 n_clusters 50",
			        1788,
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			        "None",
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			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1724 n_samples 4466 confidence 0.005 feature_proportion 0.5623693905039256 n_clusters 33",
			        1724,
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			        "None",
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			    ],
			    [
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			        120,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 RialtoBridgeTimelapse 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1717 n_samples 3539 confidence 0.001 feature_proportion 0.001 n_clusters 34",
			        1717,
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			        "None",
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			        789.890625,
			        721.5402644230769,
			        -1,
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			var tab_main_worker_cpu_ram_csv_json = [
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			    [
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			    [
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			    [
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			    [
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			        8.7
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			    [
			        1746616436,
			        1021.828125,
			        8.9
			    ],
			    [
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			    ],
			    [
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			        1021.828125,
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			    [
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			        1043.81640625,
			        12.3
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			    [
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			        1043.81640625,
			        13.7
			    ],
			    [
			        1746690428,
			        1043.81640625,
			        13.4
			    ],
			    [
			        1746690428,
			        1043.81640625,
			        16.7
			    ],
			    [
			        1746764346,
			        1052.44921875,
			        9.3
			    ],
			    [
			        1746764346,
			        1052.44921875,
			        6.6
			    ],
			    [
			        1746764346,
			        1052.44921875,
			        6.5
			    ],
			    [
			        1746764346,
			        1052.44921875,
			        6.1
			    ]
			];
			var tab_main_worker_cpu_ram_headers_json = [
			    "timestamp",
			    "ram_usage_mb",
			    "cpu_usage_percent"
			];
			
			"use strict";
			
			function add_default_layout_data (layout, no_height = 0) {
				layout["width"] = get_graph_width();
				if (!no_height) {
					layout["height"] = get_graph_height();
				}
				layout["paper_bgcolor"] = 'rgba(0,0,0,0)';
				layout["plot_bgcolor"] = 'rgba(0,0,0,0)';
			
				return layout;
			}
			
			function get_marker_size() {
				return 12;
			}
			
			function get_text_color() {
				return theme == "dark" ? "white" : "black";
			}
			
			function get_font_size() {
				return 14;
			}
			
			function get_graph_height() {
				return 800;
			}
			
			function get_font_data() {
				return {
					size: get_font_size(),
					color: get_text_color()
				}
			}
			
			function get_axis_title_data(name, axis_type = "") {
				if(axis_type) {
					return {
						text: name,
						type: axis_type,
						font: get_font_data()
					};
				}
			
				return {
					text: name,
					font: get_font_data()
				};
			}
			
			function get_graph_width() {
				var width = document.body.clientWidth || window.innerWidth || document.documentElement.clientWidth;
				return Math.max(800, Math.floor(width * 0.9));
			}
			
			function createTable(data, headers, table_name) {
				if (!$("#" + table_name).length) {
					console.error("#" + table_name + " not found");
					return;
				}
			
				new gridjs.Grid({
					columns: headers,
					data: data,
					search: true,
					sort: true,
					ellipsis: false
				}).render(document.getElementById(table_name));
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			
				colorize_table_entries();
			
				add_colorize_to_gridjs_table();
			}
			
			function download_as_file(id, filename) {
				var text = $("#" + id).text();
				var blob = new Blob([text], {
					type: "text/plain"
				});
				var link = document.createElement("a");
				link.href = URL.createObjectURL(blob);
				link.download = filename;
				document.body.appendChild(link);
				link.click();
				document.body.removeChild(link);
			}
			
			function copy_to_clipboard_from_id (id) {
				var text = $("#" + id).text();
			
				copy_to_clipboard(text);
			}
			
			function copy_to_clipboard(text) {
				if (!navigator.clipboard) {
					let textarea = document.createElement("textarea");
					textarea.value = text;
					document.body.appendChild(textarea);
					textarea.select();
					try {
						document.execCommand("copy");
					} catch (err) {
						console.error("Copy failed:", err);
					}
					document.body.removeChild(textarea);
					return;
				}
			
				navigator.clipboard.writeText(text).then(() => {
					console.log("Text copied to clipboard");
				}).catch(err => {
					console.error("Failed to copy text:", err);
				});
			}
			
			function filterNonEmptyRows(data) {
				var new_data = [];
				for (var row_idx = 0; row_idx <  data.length; row_idx++) {
					var line = data[row_idx];
			
					var line_has_empty_data = false;
			
					for (var col_idx = 0; col_idx < line.length; col_idx++) {
						var col_header_name = tab_results_headers_json[col_idx];
						var single_data_point = line[col_idx];
			
						if(single_data_point === "" && !special_col_names.includes(col_header_name)) {
							line_has_empty_data = true;
							continue;
						}
					}
			
					if(!line_has_empty_data) {
						new_data.push(line);
					}
				}
			
				return new_data;
			}
			
			function make_text_in_parallel_plot_nicer() {
				$(".parcoords g > g > text").each(function() {
					if (theme == "dark") {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "white")
							.css("stroke", "black")
							.css("stroke-width", "2px")
							.css("paint-order", "stroke fill");
					} else {
						$(this)
							.css("text-shadow", "unset")
							.css("font-size", "0.9em")
							.css("fill", "black")
							.css("stroke", "unset")
							.css("stroke-width", "unset")
							.css("paint-order", "stroke fill");
					}
				});
			}
			
			function createParallelPlot(dataArray, headers, resultNames, ignoreColumns = []) {
				if ($("#parallel-plot").data("loaded") == "true") {
					return;
				}
			
				dataArray = filterNonEmptyRows(dataArray);
				const ignoreSet = new Set(ignoreColumns);
				const numericalCols = [];
				const categoricalCols = [];
				const categoryMappings = {};
			
				headers.forEach((header, colIndex) => {
					if (ignoreSet.has(header)) return;
			
					const values = dataArray.map(row => row[colIndex]);
					if (values.every(val => !isNaN(parseFloat(val)))) {
						numericalCols.push({ name: header, index: colIndex });
					} else {
						categoricalCols.push({ name: header, index: colIndex });
						const uniqueValues = [...new Set(values)];
						categoryMappings[header] = Object.fromEntries(uniqueValues.map((val, i) => [val, i]));
					}
				});
			
				const dimensions = [];
			
				numericalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => parseFloat(row[col.index])),
						range: [
							Math.min(...dataArray.map(row => parseFloat(row[col.index]))),
							Math.max(...dataArray.map(row => parseFloat(row[col.index])))
						]
					});
				});
			
				categoricalCols.forEach(col => {
					dimensions.push({
						label: col.name,
						values: dataArray.map(row => categoryMappings[col.name][row[col.index]]),
						tickvals: Object.values(categoryMappings[col.name]),
						ticktext: Object.keys(categoryMappings[col.name])
					});
				});
			
				let colorScale = null;
				let colorValues = null;
			
				if (resultNames.length > 1) {
					let selectBox = '<select id="result-select" style="margin-bottom: 10px;">';
					selectBox += '<option value="none">No color</option>';
					var k = 0;
					resultNames.forEach(resultName => {
						var minMax = result_min_max[k];
						if(minMax === undefined) {
							minMax = "min [automatically chosen]"
						}
						selectBox += `<option value="${resultName}">${resultName} (${minMax})</option>`;
						k = k + 1;
					});
					selectBox += '</select>';
					$("#parallel-plot").before(selectBox);
			
					$("#result-select").change(function() {
						const selectedResult = $(this).val();
						if (selectedResult === "none") {
							colorValues = null;
							colorScale = null;
						} else {
							const resultCol = numericalCols.find(col => col.name.toLowerCase() === selectedResult.toLowerCase());
							colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
			
							let minResult = Math.min(...colorValues);
							let maxResult = Math.max(...colorValues);
			
							var _result_min_max_idx = result_names.indexOf(selectedResult);
			
							let invertColor = false;
							if (result_min_max.length > _result_min_max_idx) {
								invertColor = result_min_max[_result_min_max_idx] === "max";
							}
			
							colorScale = invertColor
								? [[0, 'red'], [1, 'green']]
								: [[0, 'green'], [1, 'red']];
						}
						updatePlot();
					});
				} else {
					let invertColor = false;
					if (Object.keys(result_min_max).length == 1) {
						invertColor = result_min_max[0] === "max";
					}
			
					colorScale = invertColor
						? [[0, 'red'], [1, 'green']]
						: [[0, 'green'], [1, 'red']];
			
					const resultCol = numericalCols.find(col => col.name.toLowerCase() === resultNames[0].toLowerCase());
					colorValues = dataArray.map(row => parseFloat(row[resultCol.index]));
				}
			
				function updatePlot() {
					const trace = {
						type: 'parcoords',
						dimensions: dimensions,
						line: colorValues ? { color: colorValues, colorscale: colorScale } : {},
						unselected: {
							line: {
								color: get_text_color(),
								opacity: 0
							}
						},
					};
			
					dimensions.forEach(dim => {
						if (!dim.line) {
							dim.line = {};
						}
						if (!dim.line.color) {
							dim.line.color = 'rgba(169,169,169, 0.01)';
						}
					});
			
					Plotly.newPlot('parallel-plot', [trace], add_default_layout_data({}));
			
					make_text_in_parallel_plot_nicer();
				}
			
				updatePlot();
			
				$("#parallel-plot").data("loaded", "true");
			
				make_text_in_parallel_plot_nicer();
			}
			
			function plotWorkerUsage() {
				if($("#workerUsagePlot").data("loaded") == "true") {
					return;
				}
				var data = tab_worker_usage_csv_json;
				if (!Array.isArray(data) || data.length === 0) {
					console.error("Invalid or empty data provided.");
					return;
				}
			
				let timestamps = [];
				let desiredWorkers = [];
				let realWorkers = [];
			
				for (let i = 0; i < data.length; i++) {
					let entry = data[i];
			
					if (!Array.isArray(entry) || entry.length < 3) {
						console.warn("Skipping invalid entry:", entry);
						continue;
					}
			
					let unixTime = parseFloat(entry[0]);
					let desired = parseInt(entry[1], 10);
					let real = parseInt(entry[2], 10);
			
					if (isNaN(unixTime) || isNaN(desired) || isNaN(real)) {
						console.warn("Skipping invalid numerical values:", entry);
						continue;
					}
			
					timestamps.push(new Date(unixTime * 1000).toISOString());
					desiredWorkers.push(desired);
					realWorkers.push(real);
				}
			
				let trace1 = {
					x: timestamps,
					y: desiredWorkers,
					mode: 'lines+markers',
					name: 'Desired Workers',
					line: {
						color: 'blue'
					}
				};
			
				let trace2 = {
					x: timestamps,
					y: realWorkers,
					mode: 'lines+markers',
					name: 'Real Workers',
					line: {
						color: 'red'
					}
				};
			
				let layout = {
					title: "Worker Usage Over Time",
					xaxis: {
						title: get_axis_title_data("Time", "date")
					},
					yaxis: {
						title: get_axis_title_data("Number of Workers")
					},
					legend: {
						x: 0,
						y: 1
					}
				};
			
				Plotly.newPlot('workerUsagePlot', [trace1, trace2], add_default_layout_data(layout));
				$("#workerUsagePlot").data("loaded", "true");
			}
			
			function plotCPUAndRAMUsage() {
				if($("#mainWorkerCPURAM").data("loaded") == "true") {
					return;
				}
				var timestamps = tab_main_worker_cpu_ram_csv_json.map(row => new Date(row[0] * 1000));
				var ramUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[1]);
				var cpuUsage = tab_main_worker_cpu_ram_csv_json.map(row => row[2]);
			
				var trace1 = {
					x: timestamps,
					y: cpuUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'CPU Usage (%)',
					type: 'scatter',
					yaxis: 'y1'
				};
			
				var trace2 = {
					x: timestamps,
					y: ramUsage,
					mode: 'lines+markers',
					marker: {
						size: get_marker_size(),
					},
					name: 'RAM Usage (MB)',
					type: 'scatter',
					yaxis: 'y2'
				};
			
				var layout = {
					title: 'CPU and RAM Usage Over Time',
					xaxis: {
						title: get_axis_title_data("Timestamp", "date"),
						tickmode: 'array',
						tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
						ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
						tickangle: -45
					},
					yaxis: {
						title: get_axis_title_data("CPU Usage (%)"),
						rangemode: 'tozero'
					},
					yaxis2: {
						title: get_axis_title_data("RAM Usage (MB)"),
						overlaying: 'y',
						side: 'right',
						rangemode: 'tozero'
					},
					legend: {
						x: 0.1,
						y: 0.9
					}
				};
			
				var data = [trace1, trace2];
				Plotly.newPlot('mainWorkerCPURAM', data, add_default_layout_data(layout));
				$("#mainWorkerCPURAM").data("loaded", "true");
			}
			
			function plotScatter2d() {
				if ($("#plotScatter2d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter2d");
			
				var minInput = document.getElementById("minValue");
				var maxInput = document.getElementById("maxValue");
			
				if (!minInput || !maxInput) {
					minInput = document.createElement("input");
					minInput.id = "minValue";
					minInput.type = "number";
					minInput.placeholder = "Min Value";
					minInput.step = "any";
			
					maxInput = document.createElement("input");
					maxInput.id = "maxValue";
					maxInput.type = "number";
					maxInput.placeholder = "Max Value";
					maxInput.step = "any";
			
					var inputContainer = document.createElement("div");
					inputContainer.style.marginBottom = "10px";
					inputContainer.appendChild(minInput);
					inputContainer.appendChild(maxInput);
					plotDiv.appendChild(inputContainer);
				}
			
				var resultSelect = document.getElementById("resultSelect");
				if (result_names.length > 1 && !resultSelect) {
					resultSelect = document.createElement("select");
					resultSelect.id = "resultSelect";
					resultSelect.style.marginBottom = "10px";
			
					var sortedResults = [...result_names].sort();
					sortedResults.forEach(result => {
						var option = document.createElement("option");
						option.value = result;
						option.textContent = result;
						resultSelect.appendChild(option);
					});
			
					var selectContainer = document.createElement("div");
					selectContainer.style.marginBottom = "10px";
					selectContainer.appendChild(resultSelect);
					plotDiv.appendChild(selectContainer);
				}
			
				minInput.addEventListener("input", updatePlots);
				maxInput.addEventListener("input", updatePlots);
				if (resultSelect) {
					resultSelect.addEventListener("change", updatePlots);
				}
			
				updatePlots();
			
				async function updatePlots() {
					var minValue = parseFloat(minInput.value);
					var maxValue = parseFloat(maxInput.value);
					if (isNaN(minValue)) minValue = -Infinity;
					if (isNaN(maxValue)) maxValue = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var selectedResult = resultSelect ? resultSelect.value : result_names[0];
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue !== -Infinity) minResult = Math.max(minResult, minValue);
					if (maxValue !== Infinity) maxResult = Math.min(maxResult, maxValue);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 2) {
						console.error("Not enough columns for Scatter-Plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							let xCol = numericColumns[i];
							let yCol = numericColumns[j];
			
							let xIndex = tab_results_headers_json.indexOf(xCol);
							let yIndex = tab_results_headers_json.indexOf(yCol);
			
							let data = tab_results_csv_json.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
							}));
			
							data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
							let layoutTitle = `${xCol} (x) vs ${yCol} (y), result: ${selectedResult}`;
							let layout = {
								title: layoutTitle,
								xaxis: {
									title: get_axis_title_data(xCol)
								},
								yaxis: {
									title: get_axis_title_data(yCol)
								},
								showlegend: false
							};
							let subDiv = document.createElement("div");
			
							let spinnerContainer = document.createElement("div");
							spinnerContainer.style.display = "flex";
							spinnerContainer.style.alignItems = "center";
							spinnerContainer.style.justifyContent = "center";
							spinnerContainer.style.width = layout.width + "px";
							spinnerContainer.style.height = layout.height + "px";
							spinnerContainer.style.position = "relative";
			
							let spinner = document.createElement("div");
							spinner.className = "spinner";
							spinner.style.width = "40px";
							spinner.style.height = "40px";
			
							let loadingText = document.createElement("span");
							loadingText.innerText = `Loading ${layoutTitle}`;
							loadingText.style.marginLeft = "10px";
			
							spinnerContainer.appendChild(spinner);
							spinnerContainer.appendChild(loadingText);
			
							plotDiv.appendChild(spinnerContainer);
			
							await new Promise(resolve => setTimeout(resolve, 50));
			
							let colors = data.map(d => {
								if (d.result === null) {
									return 'rgb(0, 0, 0)';
								} else {
									let norm = (d.result - minResult) / (maxResult - minResult);
									if (invertColor) {
										norm = 1 - norm;
									}
									return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
								}
							});
			
							let trace = {
								x: data.map(d => d.x),
								y: data.map(d => d.y),
								mode: 'markers',
								marker: {
									size: get_marker_size(),
									color: data.map(d => d.result !== null ? d.result : null),
									colorscale: invertColor ? [
										[0, 'red'],
										[1, 'green']
									] : [
										[0, 'green'],
										[1, 'red']
									],
									colorbar: {
										title: 'Result',
										tickvals: [minResult, maxResult],
										ticktext: [`${minResult}`, `${maxResult}`]
									},
									symbol: data.map(d => d.result === null ? 'x' : 'circle'),
								},
								text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
								type: 'scatter',
								showlegend: false
							};
			
							try {
								plotDiv.replaceChild(subDiv, spinnerContainer);
							} catch (err) {
								//
							}
							Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
						}
					}
				}
			
				$("#plotScatter2d").data("loaded", "true");
			}
			
			function plotScatter3d() {
				if ($("#plotScatter3d").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotScatter3d");
				if (!plotDiv) {
					console.error("Div element with id 'plotScatter3d' not found");
					return;
				}
				plotDiv.innerHTML = "";
			
				var minInput3d = document.getElementById("minValue3d");
				var maxInput3d = document.getElementById("maxValue3d");
			
				if (!minInput3d || !maxInput3d) {
					minInput3d = document.createElement("input");
					minInput3d.id = "minValue3d";
					minInput3d.type = "number";
					minInput3d.placeholder = "Min Value";
					minInput3d.step = "any";
			
					maxInput3d = document.createElement("input");
					maxInput3d.id = "maxValue3d";
					maxInput3d.type = "number";
					maxInput3d.placeholder = "Max Value";
					maxInput3d.step = "any";
			
					var inputContainer3d = document.createElement("div");
					inputContainer3d.style.marginBottom = "10px";
					inputContainer3d.appendChild(minInput3d);
					inputContainer3d.appendChild(maxInput3d);
					plotDiv.appendChild(inputContainer3d);
				}
			
				var select3d = document.getElementById("select3dScatter");
				if (result_names.length > 1 && !select3d) {
					if (!select3d) {
						select3d = document.createElement("select");
						select3d.id = "select3dScatter";
						select3d.style.marginBottom = "10px";
						select3d.innerHTML = result_names.map(name => `<option value="${name}">${name}</option>`).join("");
			
						select3d.addEventListener("change", updatePlots3d);
			
						plotDiv.appendChild(select3d);
					}
				}
			
				minInput3d.addEventListener("input", updatePlots3d);
				maxInput3d.addEventListener("input", updatePlots3d);
			
				updatePlots3d();
			
				async function updatePlots3d() {
					var selectedResult = select3d ? select3d.value : result_names[0];
					var minValue3d = parseFloat(minInput3d.value);
					var maxValue3d = parseFloat(maxInput3d.value);
			
					if (isNaN(minValue3d)) minValue3d = -Infinity;
					if (isNaN(maxValue3d)) maxValue3d = Infinity;
			
					while (plotDiv.children.length > 2) {
						plotDiv.removeChild(plotDiv.lastChild);
					}
			
					var resultIndex = tab_results_headers_json.findIndex(header =>
						header.toLowerCase() === selectedResult.toLowerCase()
					);
					var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
			
					var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
					var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
					if (minValue3d !== -Infinity) minResult = Math.max(minResult, minValue3d);
					if (maxValue3d !== Infinity) maxResult = Math.min(maxResult, maxValue3d);
			
					var invertColor = result_min_max[result_names.indexOf(selectedResult)] === "max";
			
					var numericColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !result_names.includes(col) &&
						tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
					);
			
					if (numericColumns.length < 3) {
						console.error("Not enough columns for 3D scatter plots");
						return;
					}
			
					for (let i = 0; i < numericColumns.length; i++) {
						for (let j = i + 1; j < numericColumns.length; j++) {
							for (let k = j + 1; k < numericColumns.length; k++) {
								let xCol = numericColumns[i];
								let yCol = numericColumns[j];
								let zCol = numericColumns[k];
			
								let xIndex = tab_results_headers_json.indexOf(xCol);
								let yIndex = tab_results_headers_json.indexOf(yCol);
								let zIndex = tab_results_headers_json.indexOf(zCol);
			
								let data = tab_results_csv_json.map(row => ({
									x: parseFloat(row[xIndex]),
									y: parseFloat(row[yIndex]),
									z: parseFloat(row[zIndex]),
									result: row[resultIndex] !== "" ? parseFloat(row[resultIndex]) : null
								}));
			
								data = data.filter(d => d.result >= minResult && d.result <= maxResult);
			
								let layoutTitle = `${xCol} (x) vs ${yCol} (y) vs ${zCol} (z), result: ${selectedResult}`;
								let layout = {
									title: layoutTitle,
									scene: {
										xaxis: {
											title: get_axis_title_data(xCol)
										},
										yaxis: {
											title: get_axis_title_data(yCol)
										},
										zaxis: {
											title: get_axis_title_data(zCol)
										}
									},
									showlegend: false
								};
			
								let spinnerContainer = document.createElement("div");
								spinnerContainer.style.display = "flex";
								spinnerContainer.style.alignItems = "center";
								spinnerContainer.style.justifyContent = "center";
								spinnerContainer.style.width = layout.width + "px";
								spinnerContainer.style.height = layout.height + "px";
								spinnerContainer.style.position = "relative";
			
								let spinner = document.createElement("div");
								spinner.className = "spinner";
								spinner.style.width = "40px";
								spinner.style.height = "40px";
			
								let loadingText = document.createElement("span");
								loadingText.innerText = `Loading ${layoutTitle}`;
								loadingText.style.marginLeft = "10px";
			
								spinnerContainer.appendChild(spinner);
								spinnerContainer.appendChild(loadingText);
			
								plotDiv.appendChild(spinnerContainer);
			
								await new Promise(resolve => setTimeout(resolve, 50));
			
								let colors = data.map(d => {
									if (d.result === null) {
										return 'rgb(0, 0, 0)';
									} else {
										let norm = (d.result - minResult) / (maxResult - minResult);
										if (invertColor) {
											norm = 1 - norm;
										}
										return `rgb(${Math.round(255 * norm)}, ${Math.round(255 * (1 - norm))}, 0)`;
									}
								});
			
								let trace = {
									x: data.map(d => d.x),
									y: data.map(d => d.y),
									z: data.map(d => d.z),
									mode: 'markers',
									marker: {
										size: get_marker_size(),
										color: data.map(d => d.result !== null ? d.result : null),
										colorscale: invertColor ? [
											[0, 'red'],
											[1, 'green']
										] : [
											[0, 'green'],
											[1, 'red']
										],
										colorbar: {
											title: 'Result',
											tickvals: [minResult, maxResult],
											ticktext: [`${minResult}`, `${maxResult}`]
										},
									},
									text: data.map(d => d.result !== null ? `Result: ${d.result}` : 'No result'),
									type: 'scatter3d',
									showlegend: false
								};
			
								let subDiv = document.createElement("div");
								try {
									plotDiv.replaceChild(subDiv, spinnerContainer);
								} catch (err) {
									//
								}
								Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
							}
						}
					}
				}
			
				$("#plotScatter3d").data("loaded", "true");
			}
			
			async function plot_worker_cpu_ram() {
				if($("#worker_cpu_ram_pre").data("loaded") == "true") {
					return;
				}
			
				const logData = $("#worker_cpu_ram_pre").text();
				const regex = /^Unix-Timestamp: (\d+), Hostname: ([\w-]+), CPU: ([\d.]+)%, RAM: ([\d.]+) MB \/ ([\d.]+) MB$/;
			
				const hostData = {};
			
				logData.split("\n").forEach(line => {
					line = line.trim();
					const match = line.match(regex);
					if (match) {
						const timestamp = new Date(parseInt(match[1]) * 1000);
						const hostname = match[2];
						const cpu = parseFloat(match[3]);
						const ram = parseFloat(match[4]);
			
						if (!hostData[hostname]) {
							hostData[hostname] = { timestamps: [], cpuUsage: [], ramUsage: [] };
						}
			
						hostData[hostname].timestamps.push(timestamp);
						hostData[hostname].cpuUsage.push(cpu);
						hostData[hostname].ramUsage.push(ram);
					}
				});
			
				if (!Object.keys(hostData).length) {
					console.log("No valid data found");
					return;
				}
			
				const container = document.getElementById("cpuRamWorkerChartContainer");
				container.innerHTML = "";
			
				var i = 1;
			
				Object.entries(hostData).forEach(([hostname, { timestamps, cpuUsage, ramUsage }], index) => {
					const chartId = `workerChart_${index}`;
					const chartDiv = document.createElement("div");
					chartDiv.id = chartId;
					chartDiv.style.marginBottom = "40px";
					container.appendChild(chartDiv);
			
					const cpuTrace = {
						x: timestamps,
						y: cpuUsage,
						mode: "lines+markers",
						name: "CPU Usage (%)",
						yaxis: "y1",
						line: {
							color: "red"
						}
					};
			
					const ramTrace = {
						x: timestamps,
						y: ramUsage,
						mode: "lines+markers",
						name: "RAM Usage (MB)",
						yaxis: "y2",
						line: {
							color: "blue"
						}
					};
			
					const layout = {
						title: `Worker CPU and RAM Usage - ${hostname}`,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date")
						},
						yaxis: {
							title: get_axis_title_data("CPU Usage (%)"),
							side: "left",
							color: "red"
						},
						yaxis2: {
							title: get_axis_title_data("RAM Usage (MB)"),
							side: "right",
							overlaying: "y",
							color: "blue"
						},
						showlegend: true
					};
			
					Plotly.newPlot(chartId, [cpuTrace, ramTrace], add_default_layout_data(layout));
					i++;
				});
			
				$("#plot_worker_cpu_ram_button").remove();
				$("#worker_cpu_ram_pre").data("loaded", "true");
			}
			
			function load_log_file(log_nr, filename) {
				var pre_id = `single_run_${log_nr}_pre`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}&filename=${filename}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							if (data.data) {
								$("#" + pre_id).html(data.data);
								$("#" + pre_id).data("loaded", true);
							} else {
								log(`No 'data' key found in response.`);
							}
			
							$("#spinner_log_" + log_nr).remove();
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#spinner_log_" + log_nr).remove();
						});
				}
			}
			
			function load_debug_log () {
				var pre_id = `here_debuglogs_go`;
			
				if (!$("#" + pre_id).data("loaded")) {
					const params = new URLSearchParams(window.location.search);
			
					const user_id = params.get('user_id');
					const experiment_name = params.get('experiment_name');
					const run_nr = params.get('run_nr');
			
					var url = `get_debug_log?user_id=${user_id}&experiment_name=${experiment_name}&run_nr=${run_nr}`;
			
					fetch(url)
						.then(response => response.json())
						.then(data => {
							$("#debug_log_spinner").remove();
			
							if (data.data) {
								try {
									$("#" + pre_id).html(data.data);
								} catch (err) {
									$("#" + pre_id).text(`Error loading data: ${err}`);
								}
			
								$("#" + pre_id).data("loaded", true);
			
								if (typeof apply_theme_based_on_system_preferences === 'function') {
									apply_theme_based_on_system_preferences();
								}
							} else {
								log(`No 'data' key found in response.`);
							}
						})
						.catch(error => {
							log(`Error loading log: ${error}`);
							$("#debug_log_spinner").remove();
						});
				}
			}
			
			function plotBoxplot() {
				if ($("#plotBoxplot").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough numeric columns for Boxplot");
					return;
				}
			
				var resultIndex = tab_results_headers_json.findIndex(function(header) {
					return result_names.includes(header.toLowerCase());
				});
				var resultValues = tab_results_csv_json.map(row => row[resultIndex]);
				var minResult = Math.min(...resultValues.filter(value => value !== null && value !== ""));
				var maxResult = Math.max(...resultValues.filter(value => value !== null && value !== ""));
			
				var plotDiv = document.getElementById("plotBoxplot");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'box',
						name: col,
						boxmean: 'sd',
						marker: {
							color: 'rgb(0, 255, 0)'
						},
					};
				});
			
				let layout = {
					title: 'Boxplot of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Value")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotBoxplot").data("loaded", "true");
			}
			
			function plotHeatmap() {
				if ($("#plotHeatmap").data("loaded") === "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col => {
					if (special_col_names.includes(col) || result_names.includes(col)) {
						return false;
					}
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.every(row => {
						let value = parseFloat(row[index]);
						return !isNaN(value) && isFinite(value);
					});
				});
			
				if (numericColumns.length < 2) {
					console.error("Not enough valid numeric columns for Heatmap");
					return;
				}
			
				var columnData = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					return tab_results_csv_json.map(row => parseFloat(row[index]));
				});
			
				var dataMatrix = numericColumns.map((_, i) =>
					numericColumns.map((_, j) => {
						let values = columnData[i].map((val, index) => (val + columnData[j][index]) / 2);
						return values.reduce((a, b) => a + b, 0) / values.length;
					})
				);
			
				var trace = {
					z: dataMatrix,
					x: numericColumns,
					y: numericColumns,
					colorscale: 'Viridis',
					type: 'heatmap'
				};
			
				var layout = {
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					yaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				var plotDiv = document.getElementById("plotHeatmap");
				plotDiv.innerHTML = "";
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotHeatmap").data("loaded", "true");
			}
			
			function plotHistogram() {
				if ($("#plotHistogram").data("loaded") == "true") {
					return;
				}
			
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Histogram");
					return;
				}
			
				var plotDiv = document.getElementById("plotHistogram");
				plotDiv.innerHTML = "";
			
				const colorPalette = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6'];
			
				let traces = numericColumns.map((col, index) => {
					let data = tab_results_csv_json.map(row => parseFloat(row[tab_results_headers_json.indexOf(col)]));
			
					return {
						x: data,
						type: 'histogram',
						name: col,
						opacity: 0.7,
						marker: {
							color: colorPalette[index % colorPalette.length]
						},
						autobinx: true
					};
				});
			
				let layout = {
					title: 'Histogram of Numerical Columns',
					xaxis: {
						title: get_axis_title_data("Value")
					},
					yaxis: {
						title: get_axis_title_data("Frequency")
					},
					showlegend: true,
					barmode: 'overlay'
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotHistogram").data("loaded", "true");
			}
			
			function plotViolin() {
				if ($("#plotViolin").data("loaded") == "true") {
					return;
				}
				var numericColumns = tab_results_headers_json.filter(col =>
					!special_col_names.includes(col) && !result_names.includes(col) &&
					tab_results_csv_json.every(row => !isNaN(parseFloat(row[tab_results_headers_json.indexOf(col)])))
				);
			
				if (numericColumns.length < 1) {
					console.error("Not enough columns for Violin Plot");
					return;
				}
			
				var plotDiv = document.getElementById("plotViolin");
				plotDiv.innerHTML = "";
			
				let traces = numericColumns.map(col => {
					let index = tab_results_headers_json.indexOf(col);
					let data = tab_results_csv_json.map(row => parseFloat(row[index]));
			
					return {
						y: data,
						type: 'violin',
						name: col,
						box: {
							visible: true
						},
						line: {
							color: 'rgb(0, 255, 0)'
						},
						marker: {
							color: 'rgb(0, 255, 0)'
						},
						meanline: {
							visible: true
						},
					};
				});
			
				let layout = {
					title: 'Violin Plot of Numerical Columns',
					yaxis: {
						title: get_axis_title_data("Value")
					},
					xaxis: {
						title: get_axis_title_data("Columns")
					},
					showlegend: false
				};
			
				Plotly.newPlot(plotDiv, traces, add_default_layout_data(layout));
				$("#plotViolin").data("loaded", "true");
			}
			
			function plotExitCodesPieChart() {
				if ($("#plotExitCodesPieChart").data("loaded") == "true") {
					return;
				}
			
				var exitCodes = tab_job_infos_csv_json.map(row => row[tab_job_infos_headers_json.indexOf("exit_code")]);
			
				var exitCodeCounts = exitCodes.reduce(function(counts, exitCode) {
					counts[exitCode] = (counts[exitCode] || 0) + 1;
					return counts;
				}, {});
			
				var labels = Object.keys(exitCodeCounts);
				var values = Object.values(exitCodeCounts);
			
				var plotDiv = document.getElementById("plotExitCodesPieChart");
				plotDiv.innerHTML = "";
			
				var trace = {
					labels: labels,
					values: values,
					type: 'pie',
					hoverinfo: 'label+percent',
					textinfo: 'label+value',
					marker: {
						colors: ['#ff9999','#66b3ff','#99ff99','#ffcc99','#c2c2f0']
					}
				};
			
				var layout = {
					title: 'Exit Code Distribution',
					showlegend: true
				};
			
				Plotly.newPlot(plotDiv, [trace], add_default_layout_data(layout));
				$("#plotExitCodesPieChart").data("loaded", "true");
			}
			
			function plotResultEvolution() {
				if ($("#plotResultEvolution").data("loaded") == "true") {
					return;
				}
			
				result_names.forEach(resultName => {
					var relevantColumns = tab_results_headers_json.filter(col =>
						!special_col_names.includes(col) && !col.startsWith("OO_Info") && col.toLowerCase() !== resultName.toLowerCase()
					);
			
					var xColumnIndex = tab_results_headers_json.indexOf("trial_index");
					var resultIndex = tab_results_headers_json.indexOf(resultName);
			
					let data = tab_results_csv_json.map(row => ({
						x: row[xColumnIndex],
						y: parseFloat(row[resultIndex])
					}));
			
					data.sort((a, b) => a.x - b.x);
			
					let xData = data.map(item => item.x);
					let yData = data.map(item => item.y);
			
					let trace = {
						x: xData,
						y: yData,
						mode: 'lines+markers',
						name: resultName,
						line: {
							shape: 'linear'
						},
						marker: {
							size: get_marker_size()
						}
					};
			
					let layout = {
						title: `Evolution of ${resultName} over time`,
						xaxis: {
							title: get_axis_title_data("Trial-Index")
						},
						yaxis: {
							title: get_axis_title_data(resultName)
						},
						showlegend: true
					};
			
					let subDiv = document.createElement("div");
					document.getElementById("plotResultEvolution").appendChild(subDiv);
			
					Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
				});
			
				$("#plotResultEvolution").data("loaded", "true");
			}
			
			function plotResultPairs() {
				if ($("#plotResultPairs").data("loaded") == "true") {
					return;
				}
			
				var plotDiv = document.getElementById("plotResultPairs");
				plotDiv.innerHTML = "";
			
				for (let i = 0; i < result_names.length; i++) {
					for (let j = i + 1; j < result_names.length; j++) {
						let xName = result_names[i];
						let yName = result_names[j];
			
						let xIndex = tab_results_headers_json.indexOf(xName);
						let yIndex = tab_results_headers_json.indexOf(yName);
			
						let data = tab_results_csv_json
							.filter(row => row[xIndex] !== "" && row[yIndex] !== "")
							.map(row => ({
								x: parseFloat(row[xIndex]),
								y: parseFloat(row[yIndex]),
								status: row[tab_results_headers_json.indexOf("trial_status")]
							}));
			
						let colors = data.map(d => d.status === "COMPLETED" ? 'green' : (d.status === "FAILED" ? 'red' : 'gray'));
			
						let trace = {
							x: data.map(d => d.x),
							y: data.map(d => d.y),
							mode: 'markers',
							marker: {
								size: get_marker_size(),
								color: colors
							},
							text: data.map(d => `Status: ${d.status}`),
							type: 'scatter',
							showlegend: false
						};
			
						let layout = {
							xaxis: {
								title: get_axis_title_data(xName)
							},
							yaxis: {
								title: get_axis_title_data(yName)
							},
							showlegend: false
						};
			
						let subDiv = document.createElement("div");
						plotDiv.appendChild(subDiv);
			
						Plotly.newPlot(subDiv, [trace], add_default_layout_data(layout));
					}
				}
			
				$("#plotResultPairs").data("loaded", "true");
			}
			
			function add_up_down_arrows_for_scrolling () {
				const upArrow = document.createElement('div');
				const downArrow = document.createElement('div');
			
				const style = document.createElement('style');
				style.innerHTML = `
					.scroll-arrow {
						position: fixed;
						right: 10px;
						z-index: 100;
						cursor: pointer;
						font-size: 25px;
						display: none;
						background-color: green;
						color: white;
						padding: 5px;
						outline: 2px solid white;
						box-shadow: 0 0 10px rgba(0, 0, 0, 0.5);
						transition: background-color 0.3s, transform 0.3s;
					}
					.scroll-arrow:hover {
						background-color: darkgreen;
						transform: scale(1.1);
					}
					#up-arrow {
						top: 10px;
					}
					#down-arrow {
						bottom: 10px;
					}
				`;
				document.head.appendChild(style);
			
				upArrow.id = "up-arrow";
				upArrow.classList.add("scroll-arrow");
				upArrow.classList.add("invert_in_dark_mode");
				upArrow.innerHTML = "&#8593;";
			
				downArrow.id = "down-arrow";
				downArrow.classList.add("scroll-arrow");
				downArrow.classList.add("invert_in_dark_mode");
				downArrow.innerHTML = "&#8595;";
			
				document.body.appendChild(upArrow);
				document.body.appendChild(downArrow);
			
				function checkScrollPosition() {
					const scrollPosition = window.scrollY;
					const pageHeight = document.documentElement.scrollHeight;
					const windowHeight = window.innerHeight;
			
					if (scrollPosition > 0) {
						upArrow.style.display = "block";
					} else {
						upArrow.style.display = "none";
					}
			
					if (scrollPosition + windowHeight < pageHeight) {
						downArrow.style.display = "block";
					} else {
						downArrow.style.display = "none";
					}
				}
			
				window.addEventListener("scroll", checkScrollPosition);
			
				upArrow.addEventListener("click", function () {
					window.scrollTo({ top: 0, behavior: 'smooth' });
				});
			
				downArrow.addEventListener("click", function () {
					window.scrollTo({ top: document.documentElement.scrollHeight, behavior: 'smooth' });
				});
			
				checkScrollPosition();
			
				if (typeof apply_theme_based_on_system_preferences === 'function') {
					apply_theme_based_on_system_preferences();
				}
			}
			
			function plotGPUUsage() {
				if ($("#tab_gpu_usage").data("loaded") === "true") {
					return;
				}
			
				Object.keys(gpu_usage).forEach(node => {
					const nodeData = gpu_usage[node];
			
					var timestamps = [];
					var gpuUtilizations = [];
					var temperatures = [];
			
					nodeData.forEach(entry => {
						try {
							var timestamp = new Date(entry[0]* 1000);
							var utilization = parseFloat(entry[1]);
							var temperature = parseFloat(entry[2]);
			
							if (!isNaN(timestamp) && !isNaN(utilization) && !isNaN(temperature)) {
								timestamps.push(timestamp);
								gpuUtilizations.push(utilization);
								temperatures.push(temperature);
							} else {
								console.warn("Invalid data point:", entry);
							}
						} catch (error) {
							console.error("Error processing GPU data entry:", error, entry);
						}
					});
			
					var trace1 = {
						x: timestamps,
						y: gpuUtilizations,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Utilization (%)',
						type: 'scatter',
						yaxis: 'y1'
					};
			
					var trace2 = {
						x: timestamps,
						y: temperatures,
						mode: 'lines+markers',
						marker: {
							size: get_marker_size(),
						},
						name: 'GPU Temperature (°C)',
						type: 'scatter',
						yaxis: 'y2'
					};
			
					var layout = {
						title: 'GPU Usage Over Time - ' + node,
						xaxis: {
							title: get_axis_title_data("Timestamp", "date"),
							tickmode: 'array',
							tickvals: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0),
							ticktext: timestamps.filter((_, index) => index % Math.max(Math.floor(timestamps.length / 10), 1) === 0).map(t => t.toLocaleString()),
							tickangle: -45
						},
						yaxis: {
							title: get_axis_title_data("GPU Utilization (%)"),
							overlaying: 'y',
							rangemode: 'tozero'
						},
						yaxis2: {
							title: get_axis_title_data("GPU Temperature (°C)"),
							overlaying: 'y',
							side: 'right',
							position: 0.85,
							rangemode: 'tozero'
						},
						legend: {
							x: 0.1,
							y: 0.9
						}
					};
			
					var divId = 'gpu_usage_plot_' + node;
			
					if (!document.getElementById(divId)) {
						var div = document.createElement('div');
						div.id = divId;
						div.className = 'gpu-usage-plot';
						document.getElementById('tab_gpu_usage').appendChild(div);
					}
			
					var plotData = [trace1, trace2];
			
					Plotly.newPlot(divId, plotData, add_default_layout_data(layout));
				});
			
				$("#tab_gpu_usage").data("loaded", "true");
			}
			
			function plotResultsDistributionByGenerationMethod() {
				if ("true" === $("#plotResultsDistributionByGenerationMethod").data("loaded")) {
					return;
				}
			
				var res_col = result_names[0];
				var gen_method_col = "generation_node";
			
				var data = {};
			
				tab_results_csv_json.forEach(row => {
					var gen_method = row[tab_results_headers_json.indexOf(gen_method_col)];
					var result = row[tab_results_headers_json.indexOf(res_col)];
			
					if (!data[gen_method]) {
						data[gen_method] = [];
					}
					data[gen_method].push(result);
				});
			
				var traces = Object.keys(data).map(method => {
					return {
						y: data[method],
						type: 'box',
						name: method,
						boxpoints: 'outliers',
						jitter: 0.5,
						pointpos: 0
					};
				});
			
				var layout = {
					title: 'Distribution of Results by Generation Method',
					yaxis: {
						title: get_axis_title_data(res_col)
					},
					xaxis: {
						title: get_axis_title_data("Generation Method")
					},
					boxmode: 'group'
				};
			
				Plotly.newPlot("plotResultsDistributionByGenerationMethod", traces, add_default_layout_data(layout));
				$("#plotResultsDistributionByGenerationMethod").data("loaded", "true");
			}
			
			function plotJobStatusDistribution() {
				if ($("#plotJobStatusDistribution").data("loaded") === "true") {
					return;
				}
			
				var status_col = "trial_status";
				var status_counts = {};
			
				tab_results_csv_json.forEach(row => {
					var status = row[tab_results_headers_json.indexOf(status_col)];
					if (status) {
						status_counts[status] = (status_counts[status] || 0) + 1;
					}
				});
			
				var statuses = Object.keys(status_counts);
				var counts = Object.values(status_counts);
			
				var colors = statuses.map((status, i) =>
					status === "FAILED" ? "#FF0000" : `hsl(${30 + ((i * 137) % 330)}, 70%, 50%)`
				);
			
				var trace = {
					x: statuses,
					y: counts,
					type: 'bar',
					marker: { color: colors }
				};
			
				var layout = {
					title: 'Distribution of Job Status',
					xaxis: { title: 'Trial Status' },
					yaxis: { title: 'Nr. of jobs' }
				};
			
				Plotly.newPlot("plotJobStatusDistribution", [trace], add_default_layout_data(layout));
				$("#plotJobStatusDistribution").data("loaded", "true");
			}
			
			function _colorize_table_entries_by_generation_method () {
				document.querySelectorAll('[data-column-id="generation_node"]').forEach(el => {
					let text = el.textContent.toLowerCase();
					let color = text.includes("manual") ? "green" :
						text.includes("sobol") ? "orange" :
						text.includes("saasbo") ? "pink" :
						text.includes("uniform") ? "lightblue" :
						text.includes("legacy_gpei") ? "sienna" :
						text.includes("bo_mixed") ? "aqua" :
						text.includes("randomforest") ? "darkseagreen" :
						text.includes("external_generator") ? "purple" :
						text.includes("botorch") ? "yellow" : "";
					if (color !== "") {
						el.style.backgroundColor = color;
					}
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_trial_status () {
				document.querySelectorAll('[data-column-id="trial_status"]').forEach(el => {
					let color = el.textContent.includes("COMPLETED") ? "lightgreen" :
						el.textContent.includes("RUNNING") ? "orange" :
						el.textContent.includes("FAILED") ? "red" :
						el.textContent.includes("ABANDONED") ? "yellow" : "";
					if (color) el.style.backgroundColor = color;
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_run_time() {
				let cells = [...document.querySelectorAll('[data-column-id="run_time"]')];
				if (cells.length === 0) return;
			
				let values = cells.map(el => parseFloat(el.textContent)).filter(v => !isNaN(v));
				if (values.length === 0) return;
			
				let min = Math.min(...values);
				let max = Math.max(...values);
				let range = max - min || 1;
			
				cells.forEach(el => {
					let value = parseFloat(el.textContent);
					if (isNaN(value)) return;
			
					let ratio = (value - min) / range;
					let red = Math.round(255 * ratio);
					let green = Math.round(255 * (1 - ratio));
			
					el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
					el.classList.add("invert_in_dark_mode");
				});
			}
			
			function _colorize_table_entries_by_results() {
				result_names.forEach((name, index) => {
					let minMax = result_min_max[index];
					let selector_query = `[data-column-id="${name}"]`;
					let cells = [...document.querySelectorAll(selector_query)];
					if (cells.length === 0) return;
			
					let values = cells.map(el => parseFloat(el.textContent)).filter(v => v > 0 && !isNaN(v));
					if (values.length === 0) return;
			
					let logValues = values.map(v => Math.log(v));
					let logMin = Math.min(...logValues);
					let logMax = Math.max(...logValues);
					let logRange = logMax - logMin || 1;
			
					cells.forEach(el => {
						let value = parseFloat(el.textContent);
						if (isNaN(value) || value <= 0) return;
			
						let logValue = Math.log(value);
						let ratio = (logValue - logMin) / logRange;
						if (minMax === "max") ratio = 1 - ratio;
			
						let red = Math.round(255 * ratio);
						let green = Math.round(255 * (1 - ratio));
			
						el.style.backgroundColor = `rgb(${red}, ${green}, 0)`;
						el.classList.add("invert_in_dark_mode");
					});
				});
			}
			
			function _colorize_table_entries_by_generation_node_or_hostname() {
				["hostname", "generation_node"].forEach(element => {
					let selector_query = '[data-column-id="' + element + '"]:not(.gridjs-th)';
					let cells = [...document.querySelectorAll(selector_query)];
					if (cells.length === 0) return;
			
					let uniqueValues = [...new Set(cells.map(el => el.textContent.trim()))];
					let colorMap = {};
			
					uniqueValues.forEach((value, index) => {
						let hue = Math.round((360 / uniqueValues.length) * index);
						colorMap[value] = `hsl(${hue}, 70%, 60%)`;
					});
			
					cells.forEach(el => {
						let value = el.textContent.trim();
						if (colorMap[value]) {
							el.style.backgroundColor = colorMap[value];
							el.classList.add("invert_in_dark_mode");
						}
					});
				});
			}
			
			function colorize_table_entries () {
				setTimeout(() => {
					if (typeof result_names !== "undefined" && Array.isArray(result_names) && result_names.length > 0) {
						_colorize_table_entries_by_trial_status();
						_colorize_table_entries_by_results();
						_colorize_table_entries_by_run_time();
						_colorize_table_entries_by_generation_method();
						_colorize_table_entries_by_generation_node_or_hostname();
						if (typeof apply_theme_based_on_system_preferences === 'function') {
							apply_theme_based_on_system_preferences();
						}
					}
				}, 300);
			}
			
			function add_colorize_to_gridjs_table () {
				let searchInput = document.querySelector(".gridjs-search-input");
				if (searchInput) {
					searchInput.addEventListener("input", colorize_table_entries);
				}
			}
			
			function updatePreWidths() {
				var width = window.innerWidth * 0.95;
				var pres = document.getElementsByTagName('pre');
				for (var i = 0; i < pres.length; i++) {
					pres[i].style.width = width + 'px';
				}
			}
			
			function demo_mode(nr_sec = 3) {
				let i = 0;
				let tabs = $('menu[role="tablist"] > button');
			
				setInterval(() => {
					tabs.attr('aria-selected', 'false').removeClass('active');
			
					let tab = tabs.eq(i % tabs.length);
					tab.attr('aria-selected', 'true').addClass('active');
			
					tab.trigger('click');
			
					i++;
				}, nr_sec * 1000);
			}
			
			function resizePlotlyCharts() {
				const plotlyElements = document.querySelectorAll('.js-plotly-plot');
			
				if (plotlyElements.length) {
					const windowWidth = window.innerWidth;
					const windowHeight = window.innerHeight;
			
					const newWidth = windowWidth * 0.9;
					const newHeight = windowHeight * 0.9;
			
					plotlyElements.forEach(function(element, index) {
						const layout = {
							width: newWidth,
							height: newHeight,
							plot_bgcolor: 'rgba(0, 0, 0, 0)',
							paper_bgcolor: 'rgba(0, 0, 0, 0)',
						};
			
						Plotly.relayout(element, layout)
					});
				}
			
				make_text_in_parallel_plot_nicer();
				apply_theme_based_on_system_preferences();
			}
			
			window.addEventListener('load', updatePreWidths);
			window.addEventListener('resize', updatePreWidths);
			
			$(document).ready(function() {
				colorize_table_entries();
			
				add_up_down_arrows_for_scrolling();
			
				add_colorize_to_gridjs_table();
			});
			
			window.addEventListener('resize', function() {
				resizePlotlyCharts();
			});
			
			"use strict";
			
			function get_row_by_index(idx) {
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
			
				var trial_index_col_idx = tab_results_headers_json.indexOf("trial_index");
			
				if(trial_index_col_idx == -1) {
					error(`"trial_index" could not be found in tab_results_headers_json. Cannot continue`);
			
					return null;
				}
			
				for (var i = 0; i < tab_results_csv_json.length; i++) {
					var row = tab_results_csv_json[i];
					var trial_index = row[trial_index_col_idx];
			
					if (trial_index == idx) {
						return row;
					}
				}
			
				return null;
			}
			
			function load_pareto_graph_from_idxs () {
				if (!Object.keys(window).includes("pareto_idxs")) {
					error("pareto_idxs is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
				if(pareto_idxs === null) {
					var err_msg = "pareto_idxs is null. Cannot plot or create tables from empty data. This can be caused by a defective <tt>pareto_idxs.json</tt> file. Please try reloading, or re-calculating the pareto-front and re-submitting if this problem persists.";
					$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
					return;	
				}
			
				var table = get_pareto_table_data_from_idx();
			
				var html_tables = createParetoTablesFromData(table);
			
				$("#pareto_from_idxs_table").html(html_tables);
			
				renderParetoFrontPlots(table);
			
				apply_theme_based_on_system_preferences();
			}
			
			function renderParetoFrontPlots(data) {
				try {
					let container = document.getElementById("pareto_front_idxs_plot_container");
					if (!container) {
						console.error("DIV with id 'pareto_front_idxs_plot_container' not found.");
						return;
					}
			
					container.innerHTML = "";
			
					if(data === undefined || data === null) {
						var err_msg = "There was an error getting the data for Pareto-Fronts. See the developer's console to see further details.";
						$("#pareto_from_idxs_table").html(`<div class="caveat alarm">${err_msg}</div>`);
						return;
					}
			
					Object.keys(data).forEach((key, idx) => {
						if (!key.startsWith("Pareto front for ")) return;
			
						let label = key.replace("Pareto front for ", "");
						let [xKey, yKey] = label.split("/");
			
						if (!xKey || !yKey) {
							console.warn("Could not extract two objectives from key:", key);
							return;
						}
			
						let entries = data[key];
						let x = [];
						let y = [];
						let hoverTexts = [];
			
						entries.forEach((entry) => {
							let results = entry.results || {};
							let values = entry.values || {};
			
							let xVal = (results[xKey] || [])[0];
							let yVal = (results[yKey] || [])[0];
			
							if (xVal === undefined || yVal === undefined) {
								console.warn("Missing values for", xKey, yKey, "in", entry);
								return;
							}
			
							x.push(xVal);
							y.push(yVal);
			
							let hoverInfo = [];
			
							if ("trial_index" in values) {
								hoverInfo.push(`<b>Trial Index:</b> ${values.trial_index[0]}`);
							}
			
							Object.keys(values)
								.filter(k => k !== "trial_index")
								.sort()
								.forEach(k => {
									hoverInfo.push(`<b>${k}:</b> ${values[k][0]}`);
								});
			
							Object.keys(results)
								.sort()
								.forEach(k => {
									hoverInfo.push(`<b>${k}:</b> ${results[k][0]}`);
								});
			
							hoverTexts.push(hoverInfo.join("<br>"));
						});
			
						let wrapper = document.createElement("div");
						wrapper.style.marginBottom = "30px";
			
						let titleEl = document.createElement("h3");
						titleEl.textContent = `Pareto Front: ${xKey} (${getMinMaxByResultName(xKey)}) vs ${yKey} (${getMinMaxByResultName(yKey)})`;
						wrapper.appendChild(titleEl);
			
						let divId = `pareto_plot_${idx}`;
						let plotDiv = document.createElement("div");
						plotDiv.id = divId;
						plotDiv.style.width = "100%";
						plotDiv.style.height = "400px";
			
						wrapper.appendChild(plotDiv);
						container.appendChild(wrapper);
			
						let trace = {
							x: x,
							y: y,
							text: hoverTexts,
							hoverinfo: "text",
							mode: "markers",
							type: "scatter",
							marker: {
								size: 8,
								color: 'rgb(31, 119, 180)',
								line: {
									width: 1,
									color: 'black'
								}
							},
							name: label
						};
			
						let layout = {
							xaxis: { title: { text: xKey } },
							yaxis: { title: { text: yKey } },
							margin: { t: 10, l: 60, r: 20, b: 50 },
							hovermode: "closest",
							showlegend: false
						};
			
						Plotly.newPlot(divId, [trace], add_default_layout_data(layout, 1));
					});
				} catch (e) {
					console.error("Error while rendering Pareto front plots:", e);
				}
			}
			
			function createParetoTablesFromData(data) {
				try {
					var container = document.createElement("div");
			
					var parsedData;
					try {
						parsedData = typeof data === "string" ? JSON.parse(data) : data;
					} catch (e) {
						console.error("JSON parsing failed:", e);
						return container;
					}
			
					for (var sectionTitle in parsedData) {
						if (!parsedData.hasOwnProperty(sectionTitle)) {
							continue;
						}
			
						var sectionData = parsedData[sectionTitle];
						var heading = document.createElement("h2");
						heading.textContent = sectionTitle;
						container.appendChild(heading);
			
						var table = document.createElement("table");
						table.style.borderCollapse = "collapse";
						table.style.marginBottom = "2em";
						table.style.width = "100%";
			
						var thead = document.createElement("thead");
						var headerRow = document.createElement("tr");
			
						var allValueKeys = new Set();
						var allResultKeys = new Set();
			
						sectionData.forEach(entry => {
							var values = entry.values || {};
							var results = entry.results || {};
			
							Object.keys(values).forEach(key => {
								allValueKeys.add(key);
							});
			
							Object.keys(results).forEach(key => {
								allResultKeys.add(key);
							});
						});
			
						var sortedValueKeys = Array.from(allValueKeys).sort();
						var sortedResultKeys = Array.from(allResultKeys).sort();
			
						if (sortedValueKeys.includes("trial_index")) {
							sortedValueKeys = sortedValueKeys.filter(k => k !== "trial_index");
							sortedValueKeys.unshift("trial_index");
						}
			
						var allColumns = [...sortedValueKeys, ...sortedResultKeys];
			
						allColumns.forEach(col => {
							var th = document.createElement("th");
							th.textContent = col;
							th.style.border = "1px solid black";
							th.style.padding = "4px";
							headerRow.appendChild(th);
						});
			
						thead.appendChild(headerRow);
						table.appendChild(thead);
			
						var tbody = document.createElement("tbody");
			
						sectionData.forEach(entry => {
							var tr = document.createElement("tr");
			
							allColumns.forEach(col => {
								var td = document.createElement("td");
								td.style.border = "1px solid black";
								td.style.padding = "4px";
			
								var value = null;
			
								if (col in entry.values) {
									value = entry.values[col];
								} else if (col in entry.results) {
									value = entry.results[col];
								}
			
								if (Array.isArray(value)) {
									td.textContent = value.join(", ");
								} else {
									td.textContent = value !== null && value !== undefined ? value : "";
								}
			
								tr.appendChild(td);
							});
			
							tbody.appendChild(tr);
						});
			
						table.appendChild(tbody);
						container.appendChild(table);
					}
			
					return container;
				} catch (err) {
					console.error("Unexpected error:", err);
					var errorDiv = document.createElement("div");
					errorDiv.textContent = "Error generating tables.";
					return errorDiv;
				}
			}
			
			function get_pareto_table_data_from_idx () {
				if (!Object.keys(window).includes("pareto_idxs")) {
					error("pareto_idxs is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_csv_json")) {
					error("tab_results_csv_json is not defined");
					return;
				}
			
				if (!Object.keys(window).includes("tab_results_headers_json")) {
					error("tab_results_headers_json is not defined");
					return;
				}
			
				var x_keys = Object.keys(pareto_idxs);
			
				var tables = {};
			
				for (var i = 0; i < x_keys.length; i++) {
					var x_key = x_keys[i];
					var y_keys = Object.keys(pareto_idxs[x_key]);
			
					for (var j = 0; j < y_keys.length; j++) {
						var y_key = y_keys[j];
			
						var indices = pareto_idxs[x_key][y_key];
			
						for (var k = 0; k < indices.length; k++) {
							var idx = indices[k];
							var row = get_row_by_index(idx);
			
							if(row === null) {
								error(`Error getting the row for index ${idx}`);
								return;
							}
			
							var row_dict = {
								"results": {},
								"values": {},
							};
			
							for (var l = 0; l < tab_results_headers_json.length; l++) {
								var header = tab_results_headers_json[l];
			
								if (!special_col_names.includes(header) || header == "trial_index") {
									var val = row[l];
			
									if (result_names.includes(header)) {
										if (!Object.keys(row_dict["results"]).includes(header)) {
											row_dict["results"][header] = [];
										}
			
										row_dict["results"][header].push(val);
									} else {
										if (!Object.keys(row_dict["values"]).includes(header)) {
											row_dict["values"][header] = [];
										}
										row_dict["values"][header].push(val);
									}
								}
								
							}
			
							var table_key = `Pareto front for ${x_key}/${y_key}`;
			
							if(!Object.keys(tables).includes(table_key)) {
								tables[table_key] = [];
							}
			
							tables[table_key].push(row_dict);
						}
			
					}
				}
			
				return tables;
			}
			
			function getMinMaxByResultName(resultName) {
				try {
					if (typeof resultName !== "string") {
						error("Parameter resultName must be a string");
						return;
					}
			
					if (!Array.isArray(result_names)) {
						error("Global variable result_names is not an array or undefined");
						return;
					}
					if (!Array.isArray(result_min_max)) {
						error("Global variable result_min_max is not an array or undefined");
						return;
					}
			
					if (result_names.length !== result_min_max.length) {
						error("Global arrays result_names and result_min_max must have the same length");
						return;
					}
			
					var index = result_names.indexOf(resultName);
					if (index === -1) {
						error("Result name '" + resultName + "' not found in result_names");
						return;
					}
			
					var minMaxValue = result_min_max[index];
			
					if (minMaxValue !== "min" && minMaxValue !== "max") {
						error("Value for result name '" + resultName + "' is invalid: expected 'min' or 'max'");
						return;
					}
			
					return minMaxValue;
			
				} catch (e) {
					error("Unexpected error: " + e.message);
				}
			}
			

			$(document).ready(function() {
				colorize_table_entries();;
				plotWorkerUsage();;
				plotCPUAndRAMUsage();;
				createParallelPlot(tab_results_csv_json, tab_results_headers_json, result_names, special_col_names);;
				plotScatter2d();;
				plotScatter3d();
				plotJobStatusDistribution();;
				plotBoxplot();;
				plotViolin();;
				plotHistogram();;
				plotHeatmap();;
				plotResultPairs();;
				plotResultEvolution();;
				plotExitCodesPieChart();

				colorize_table_entries();
			});
		</script>

					<h1>&nbsp;Overview</h1>
			<h2>Experiment overview:               </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Setting</th><th>Value </th></tr></thead><tbody><tr><td> Max. nr. evaluations</td><td>50184 </td></tr><tr><td> Max. nr. evaluations (from arguments)</td><td>50000 </td></tr><tr><td> Number random steps</td><td>20    </td></tr><tr><td> Nr. of workers (parameter)</td><td>20    </td></tr><tr><td> Main process memory (GB)</td><td>8     </td></tr><tr><td> Worker memory (GB)</td><td>32    </td></tr><tr><td> Nr. imported jobs</td><td>184   </td></tr></tbody></table><h2>Experiment parameters:                                                    </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Name</th><th>Type</th><th>Lower bound</th><th>Upper bound</th><th>Values</th><th>Type</th><th>Log Scale? </th></tr></thead><tbody><tr><td> recent_samples_size</td><td>int</td><td>1</td><td>5000</td><td></td><td>int</td><td>No         </td></tr><tr><td> n_samples</td><td>int</td><td>1</td><td>5000</td><td></td><td>int</td><td>No         </td></tr><tr><td> confidence</td><td>choice</td><td></td><td></td><td>0.25, 0.1, 0.05, 0.025, 0.01, 0.005, 0.001</td><td></td><td></td></tr><tr><td> feature_proportion</td><td>float</td><td>0.001</td><td>0.999</td><td></td><td>float</td><td>No         </td></tr><tr><td> n_clusters</td><td>int</td><td>1</td><td>50</td><td></td><td>int</td><td>No         </td></tr></tbody></table><h2>Number of evaluations</h2>
			<table>
			<tbody>
			<tr>
			<th>Failed</th>
			<th>Succeeded</th>
			<th>Running</th>
			<th>Total</th>
			</tr>
			<tr>
			<td>522</td>
			<td>527</td>
			<td>2</td>
			<td>1051</td>
			</tr>
			</tbody>
			</table>
			<h2>Result names and types</h2>
			<table>
			<tr><th>name</th><th>min/max</th></tr>
			<tr>
			<td>ACCURACY</td>
			<td>max</td>
			</tr>
			<tr>
			<td>RUNTIME</td>
			<td>min</td>
			</tr>
			</table>
			<br>
			<h2>Git-Version</h2>
			<tt>Commit: 2223ae6553abdd3e288f4b391080b763a7a48477
			</tt>
			<h1>&nbsp;Results</h1>
			<div id='tab_results_csv_table'></div>
			<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download &raquo;results.csv&laquo; as file</button>
<pre id='tab_results_csv_table_pre'>trial_index,arm_name,trial_status,generation_method,generation_node,ACCURACY,RUNTIME,recent_samples_size,n_samples,feature_proportion,n_clusters,confidence
0,0_0,COMPLETED,Sobol,SOBOL,0.350000000000000033306690738755,25.000000000000000000000000000000,181,3860,0.822555312514305136950554242503,33,0.05
1,1_0,COMPLETED,Sobol,SOBOL,0.540000000000000035527136788005,57.000000000000000000000000000000,2932,1704,0.148036698935553434619549761919,15,0.25
2,2_0,COMPLETED,Sobol,SOBOL,0.550000000000000044408920985006,63.000000000000000000000000000000,4834,2997,0.252346722556278102445048716618,6,0.001
3,3_0,COMPLETED,Sobol,SOBOL,0.520000000000000017763568394003,45.000000000000000000000000000000,2038,231,0.716174733797088292064358938660,48,0.005
4,4_0,COMPLETED,Sobol,SOBOL,0.520000000000000017763568394003,72.000000000000000000000000000000,1522,3433,0.013702073752880097184947416622,10,0.001
5,5_0,COMPLETED,Sobol,SOBOL,0.609999999999999986677323704498,145.000000000000000000000000000000,4118,1193,0.953326972957700524702318034542,43,0.005
6,6_0,COMPLETED,Sobol,SOBOL,0.460000000000000019984014443253,56.000000000000000000000000000000,3509,4752,0.568314072351902677127100105281,27,0.005
7,7_0,COMPLETED,Sobol,SOBOL,0.520000000000000017763568394003,70.000000000000000000000000000000,871,1913,0.400785573139786743812607028303,19,0.05
8,8_0,COMPLETED,Sobol,SOBOL,0.500000000000000000000000000000,65.000000000000000000000000000000,1034,2677,0.626035108476877266703297664208,24,0.001
9,9_0,COMPLETED,Sobol,SOBOL,0.609999999999999986677323704498,179.000000000000000000000000000000,3355,540,0.341606548991054304043046840889,29,0.05
10,10_0,COMPLETED,Sobol,SOBOL,0.479999999999999982236431605997,62.000000000000000000000000000000,3959,4179,0.198122332345694290856030761461,39,0.25
11,11_0,COMPLETED,Sobol,SOBOL,0.589999999999999968913755310496,108.000000000000000000000000000000,1671,1394,0.771347163841128335981522923248,9,0.01
12,12_0,COMPLETED,Sobol,SOBOL,0.359999999999999986677323704498,29.000000000000000000000000000000,2192,4438,0.444129850411787652220141353610,47,0.025
13,13_0,COMPLETED,Sobol,SOBOL,0.570000000000000062172489379009,89.000000000000000000000000000000,4671,2218,0.525857446042820808607132221368,2,0.01
14,14_0,COMPLETED,Sobol,SOBOL,0.410000000000000031086244689504,35.000000000000000000000000000000,2783,3747,0.887639202101156099544709832116,17,0.1
15,15_0,COMPLETED,Sobol,SOBOL,0.520000000000000017763568394003,70.000000000000000000000000000000,339,889,0.080520196149125700113557968507,38,0.25
16,16_0,COMPLETED,Sobol,SOBOL,0.419999999999999984456877655248,44.000000000000000000000000000000,543,3577,0.985878579951822708871134182118,35,0.01
17,17_0,COMPLETED,Sobol,SOBOL,0.589999999999999968913755310496,105.000000000000000000000000000000,2597,734,0.046448975149542097440313881407,18,0.005
18,18_0,COMPLETED,Sobol,SOBOL,0.489999999999999991118215802999,68.000000000000000000000000000000,4483,4607,0.431385555941611542607461160515,3,0.1
19,19_0,COMPLETED,Sobol,SOBOL,0.440000000000000002220446049250,62.000000000000000000000000000000,2396,2372,0.599115052722394514361781148182,45,0.05
20,20_0,COMPLETED,Sobol,SOBOL,0.479999999999999982236431605997,93.000000000000000000000000000000,1858,4317,0.177847689228132377348146064833,8,0.05
21,21_0,COMPLETED,Sobol,SOBOL,0.579999999999999960031971113494,170.000000000000000000000000000000,3754,1557,0.852196095457300573094983064948,41,0.025
22,22_0,COMPLETED,Sobol,SOBOL,0.500000000000000000000000000000,103.000000000000000000000000000000,3152,2539,0.747931578194722557206830515497,32,0.25
23,23_0,COMPLETED,Sobol,SOBOL,0.619999999999999995559107901499,299.000000000000000000000000000000,1220,378,0.283939093442633738728630987680,24,0.001
24,24_0,COMPLETED,Sobol,SOBOL,0.410000000000000031086244689504,42.000000000000000000000000000000,744,4917,0.555726107465103313920451455488,22,0.25
25,25_0,COMPLETED,Sobol,SOBOL,0.560000000000000053290705182008,99.000000000000000000000000000000,3618,2054,0.473711728831753109414393065890,26,0.025
26,26_0,COMPLETED,Sobol,SOBOL,0.540000000000000035527136788005,80.000000000000000000000000000000,4225,3268,0.112341433530673384666442871094,42,0.05
27,27_0,COMPLETED,Sobol,SOBOL,0.589999999999999968913755310496,158.000000000000000000000000000000,1397,1053,0.919167953314259600361424418224,13,0.005
28,28_0,COMPLETED,Sobol,SOBOL,0.530000000000000026645352591004,101.000000000000000000000000000000,1930,2844,0.374094952709972838889029844722,49,0.01
29,29_0,COMPLETED,Sobol,SOBOL,0.589999999999999968913755310496,1133.000000000000000000000000000000,4959,64,0.658845867488533243339077216660,4,0.001
30,30_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,176.000000000000000000000000000000,1641,818,0.014929985744551047335826332585,50,0.01
31,31_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,149.000000000000000000000000000000,1902,1008,0.001000000000000000020816681712,50,0.05
32,32_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,47.000000000000000000000000000000,3130,2858,0.001000000000000000020816681712,8,0.1
33,33_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,61.000000000000000000000000000000,4385,3038,0.001000000000000000020816681712,1,0.005
34,34_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.369999999999999995559107901499,28.000000000000000000000000000000,401,4558,0.092634902555904258258934191872,50,0.1
35,35_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,0.058891226683714603673536203132,50,0.01
36,36_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,139.000000000000000000000000000000,1854,882,0.001000000000000000020816681712,50,0.1
37,37_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.429999999999999993338661852249,127.000000000000000000000000000000,2065,849,0.001000000000000000020816681712,50,0.005
38,38_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,49.000000000000000000000000000000,2913,2697,0.001000000000000000020816681712,4,0.05
39,39_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,48.000000000000000000000000000000,3762,4761,0.998999999999999999111821580300,44,0.1
40,40_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,104.000000000000000000000000000000,4173,1543,0.157534273744074720946528600507,1,0.01
41,41_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,41.000000000000000000000000000000,1216,4806,0.998999999999999999111821580300,50,0.005
42,42_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,48.000000000000000000000000000000,4932,4462,0.001000000000000000020816681712,37,0.1
43,43_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.309999999999999997779553950750,23.000000000000000000000000000000,1,1352,0.120508926629064719304729180749,50,0.01
44,44_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.470000000000000028865798640254,49.000000000000000000000000000000,4647,3968,0.998999999999999999111821580300,32,0.005
45,45_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,67.000000000000000000000000000000,3474,2134,0.998999999999999999111821580300,1,0.005
46,46_0,FAILED,BoTorch,BOTORCH_MODULAR,,,835,1,0.001000000000000000020816681712,50,0.05
47,47_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.410000000000000031086244689504,31.000000000000000000000000000000,614,4953,0.072195215349455651998589189589,50,0.1
48,48_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.300000000000000044408920985006,24.000000000000000000000000000000,1,1638,0.998999999999999999111821580300,50,0.005
49,49_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.440000000000000002220446049250,38.000000000000000000000000000000,2609,2837,0.001000000000000000020816681712,1,0.1
50,50_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,53.000000000000000000000000000000,4997,3818,0.001000000000000000020816681712,30,0.1
51,51_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,58.000000000000000000000000000000,828,1487,0.001000000000000000020816681712,50,0.05
52,52_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.390000000000000013322676295502,34.000000000000000000000000000000,293,3441,0.001000000000000000020816681712,50,0.1
53,53_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,45.000000000000000000000000000000,1197,4860,0.998999999999999999111821580300,50,0.1
54,54_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,143.000000000000000000000000000000,1877,1050,0.001000000000000000020816681712,50,0.01
55,55_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,29.000000000000000000000000000000,2261,5000,0.998999999999999999111821580300,50,0.1
56,56_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.309999999999999997779553950750,21.000000000000000000000000000000,1,4514,0.235919420054758438576314460988,50,0.005
57,57_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.300000000000000044408920985006,37.000000000000000000000000000000,1,701,0.998999999999999999111821580300,50,0.01
58,58_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,46.000000000000000000000000000000,1744,4752,0.998999999999999999111821580300,50,0.25
59,59_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,48.000000000000000000000000000000,1784,4515,0.155891787326828096249542454643,50,0.1
60,60_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2935,1,0.640761267415340673991863695846,50,0.005
61,61_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,56.000000000000000000000000000000,4601,3662,0.598394201801831782105978163600,37,0.001
62,62_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1568,1,0.647688177078188709323569582921,50,0.05
63,63_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1029,1,0.299020273131795744081529164760,50,0.005
64,64_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1653,1,0.998999999999999999111821580300,50,0.05
65,65_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,48.000000000000000000000000000000,2885,1,0.001000000000000000020816681712,1,0.005
66,66_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2738,1,0.897505808032030172327608852356,50,0.005
67,67_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1364,1,0.203595558136698440154788158907,38,0.25
68,68_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1762,1,0.520471371873533650287413365731,24,0.005
69,69_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.161159076827445235657876310142,38,0.25
70,70_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,0.001000000000000000020816681712,26,0.1
71,71_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1387,1,0.219877893489058118259293905794,39,0.25
72,72_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1773,1,0.532531938464215315320871013682,23,0.005
73,73_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,91.000000000000000000000000000000,1353,1975,0.001000000000000000020816681712,44,0.25
74,74_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1752,1,0.504034221414319971721340607473,24,0.005
75,75_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2426,1,0.998999999999999999111821580300,50,0.005
76,76_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1356,1,0.357370553345331076755542198953,50,0.1
77,77_0,FAILED,BoTorch,BOTORCH_MODULAR,,,574,1,0.001000000000000000020816681712,50,0.025
78,78_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1716,1,0.998999999999999999111821580300,50,0.1
79,79_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,85.000000000000000000000000000000,4030,3304,0.958653512072230706841935443663,1,0.025
80,80_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1776,1,0.675238669049019857304472225223,23,0.1
81,81_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1847,1,0.695601120769149372513595608325,23,0.005
82,82_0,FAILED,BoTorch,BOTORCH_MODULAR,,,792,1,0.001000000000000000020816681712,31,0.005
83,83_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1905,1,0.695764344538252865746130737534,25,0.1
84,84_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.309999999999999997779553950750,24.000000000000000000000000000000,1,3916,0.998999999999999999111821580300,1,0.005
85,85_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1816,1,0.639771619002576508172808189556,24,0.1
86,86_0,FAILED,BoTorch,BOTORCH_MODULAR,,,567,1,0.001000000000000000020816681712,50,0.025
87,87_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1743,1,0.998999999999999999111821580300,50,0.1
88,88_0,FAILED,BoTorch,BOTORCH_MODULAR,,,575,1,0.001000000000000000020816681712,50,0.25
89,89_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1769,1,0.998999999999999999111821580300,50,0.1
90,90_0,FAILED,BoTorch,BOTORCH_MODULAR,,,572,1,0.001000000000000000020816681712,50,0.25
91,91_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2434,1,0.998999999999999999111821580300,50,0.005
92,92_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1359,1,0.090390172711860825027763155504,50,0.1
93,93_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1757,1,0.998999999999999999111821580300,50,0.25
94,94_0,FAILED,BoTorch,BOTORCH_MODULAR,,,574,1,0.001000000000000000020816681712,50,0.1
95,95_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1798,1,0.998999999999999999111821580300,50,0.005
96,96_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1351,1,0.239435221504455841845526720135,50,0.1
97,97_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2429,1,0.998999999999999999111821580300,50,0.005
98,98_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1349,1,0.068121443377983578737477898812,50,0.1
99,99_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1742,1,0.998999999999999999111821580300,50,0.25
100,100_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1407,1,0.377291031207270532998876433339,50,0.1
101,94_0,FAILED,BoTorch,BOTORCH_MODULAR,,,574,1,0.001000000000000000020816681712,50,0.1
102,102_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1754,1,0.998999999999999999111821580300,50,0.1
103,103_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,57.000000000000000000000000000000,1536,3384,0.001000000000000000020816681712,1,0.025
104,89_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1769,1,0.998999999999999999111821580300,50,0.1
105,105_0,FAILED,BoTorch,BOTORCH_MODULAR,,,584,1,0.001000000000000000020816681712,50,0.1
106,106_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1356,1,0.407075991120631974773402816936,50,0.025
107,107_0,FAILED,BoTorch,BOTORCH_MODULAR,,,594,1,0.001000000000000000020816681712,50,0.1
108,108_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1367,1,0.390675928279951401034253422040,50,0.1
109,109_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1609,1,0.998999999999999999111821580300,50,0.25
110,110_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1299,1,0.025109052946932219896325477748,50,0.1
111,111_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2433,1,0.998999999999999999111821580300,50,0.005
112,112_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1288,1,0.015757813841910613128494134116,50,0.1
113,113_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1274,1,0.998999999999999999111821580300,50,0.25
114,114_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.440000000000000002220446049250,45.000000000000000000000000000000,2519,3155,0.998999999999999999111821580300,1,0.005
115,115_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1324,1,0.334148493961586579015232700840,50,0.1
116,116_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1837,1,0.998999999999999999111821580300,50,0.25
117,117_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1330,1,0.001000000000000000020816681712,50,0.1
118,118_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1865,1,0.998999999999999999111821580300,50,0.25
119,119_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1315,1,0.001000000000000000020816681712,50,0.1
120,120_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1889,1,0.998999999999999999111821580300,50,0.1
121,121_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2449,1,0.998999999999999999111821580300,50,0.25
122,122_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1343,1,0.001000000000000000020816681712,50,0.1
123,123_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1848,1,0.998999999999999999111821580300,50,0.25
124,124_0,FAILED,BoTorch,BOTORCH_MODULAR,,,585,1,0.001000000000000000020816681712,50,0.025
125,125_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2442,1,0.998999999999999999111821580300,50,0.1
126,126_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1352,1,0.001000000000000000020816681712,50,0.1
127,127_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1854,1,0.998999999999999999111821580300,50,0.25
128,128_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1325,1,0.001000000000000000020816681712,50,0.1
129,118_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1865,1,0.998999999999999999111821580300,50,0.25
130,130_0,FAILED,BoTorch,BOTORCH_MODULAR,,,583,1,0.001000000000000000020816681712,50,0.1
131,131_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1872,1,0.998999999999999999111821580300,50,0.25
132,132_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1320,1,0.001000000000000000020816681712,50,0.025
133,133_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1840,1,0.998999999999999999111821580300,50,0.25
134,134_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.001000000000000000020816681712,50,0.1
135,135_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1852,1,0.998999999999999999111821580300,50,0.25
136,136_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1333,1,0.001000000000000000020816681712,50,0.1
137,137_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1842,1,0.998999999999999999111821580300,50,0.25
138,117_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1330,1,0.001000000000000000020816681712,50,0.1
139,139_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2443,1,0.998999999999999999111821580300,50,0.25
140,140_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1836,1,0.998999999999999999111821580300,50,0.1
141,141_0,FAILED,BoTorch,BOTORCH_MODULAR,,,589,1,0.001000000000000000020816681712,50,0.1
142,142_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1364,1,0.073919421326621853607363732408,50,0.025
143,143_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2432,1,0.998999999999999999111821580300,50,0.1
144,144_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1353,1,0.001000000000000000020816681712,50,0.1
145,145_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1674,1,0.998999999999999999111821580300,50,0.025
146,146_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1356,1,0.001000000000000000020816681712,50,0.1
147,139_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2443,1,0.998999999999999999111821580300,50,0.25
148,148_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1360,1,0.001000000000000000020816681712,50,0.1
149,149_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1870,1,0.998999999999999999111821580300,50,0.25
150,150_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1332,1,0.001000000000000000020816681712,50,0.025
151,151_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1824,1,0.998999999999999999111821580300,50,0.25
152,134_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.001000000000000000020816681712,50,0.1
153,153_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1884,1,0.998999999999999999111821580300,50,0.25
154,154_0,FAILED,BoTorch,BOTORCH_MODULAR,,,584,1,0.001000000000000000020816681712,50,0.25
155,155_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1875,1,0.998999999999999999111821580300,50,0.1
156,156_0,FAILED,BoTorch,BOTORCH_MODULAR,,,590,1,0.001000000000000000020816681712,50,0.025
157,157_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1314,1,0.001000000000000000020816681712,50,0.1
158,158_0,FAILED,BoTorch,BOTORCH_MODULAR,,,587,1,0.001000000000000000020816681712,50,0.25
159,159_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1865,1,0.998999999999999999111821580300,50,0.1
160,160_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1829,1,0.998999999999999999111821580300,50,0.1
161,161_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.001000000000000000020816681712,50,0.1
162,162_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2440,1,0.998999999999999999111821580300,50,0.1
163,163_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1337,1,0.001000000000000000020816681712,50,0.1
164,133_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1840,1,0.998999999999999999111821580300,50,0.25
165,165_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1313,1,0.001000000000000000020816681712,50,0.1
166,166_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1904,1,0.998999999999999999111821580300,50,0.25
167,128_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1325,1,0.001000000000000000020816681712,50,0.1
168,168_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1839,1,0.998999999999999999111821580300,50,0.25
169,169_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1324,1,0.001000000000000000020816681712,50,0.025
170,162_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2440,1,0.998999999999999999111821580300,50,0.1
171,171_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1346,1,0.001000000000000000020816681712,50,0.1
172,172_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.470000000000000028865798640254,40.000000000000000000000000000000,1603,5000,0.001000000000000000020816681712,50,0.025
173,173_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1823,1,0.998999999999999999111821580300,50,0.1
174,174_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1300,1,0.001000000000000000020816681712,50,0.005
175,175_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1873,1,0.998999999999999999111821580300,50,0.25
176,176_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,53.000000000000000000000000000000,507,1750,0.001000000000000000020816681712,50,0.025
177,177_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1355,1,0.082879966001670496433817447723,50,0.1
178,178_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3493,1,0.664550912764349077654912889557,50,0.05
179,179_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1889,1,0.998999999999999999111821580300,50,0.01
180,180_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1692,1,0.668698996434498260654777368472,50,0.01
181,181_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,41.000000000000000000000000000000,3530,1,0.004883661179148107017722324485,1,0.001
182,182_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1841,1,0.670986009348317424816343645944,50,0.01
183,183_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1840,1,0.998999999999999999111821580300,50,0.05
184,184_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1847,1,0.672001232993433839091323989123,50,0.01
185,185_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,101.000000000000000000000000000000,1302,1209,0.001000000000000000020816681712,1,0.001
186,186_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2528,1,0.665450079573546848799026065535,50,0.01
187,187_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,128.000000000000000000000000000000,1769,1868,0.141653591983212867599917217376,50,0.05
188,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1828,1,0.671794977964650263935197926912,50,0.05
189,189_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.470000000000000028865798640254,48.000000000000000000000000000000,1569,4310,0.553934229698349489545705637283,36,0.001
190,190_0,FAILED,BoTorch,BOTORCH_MODULAR,,,565,1,0.651906311792785131409289078874,50,0.25
191,191_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,199.000000000000000000000000000000,1439,524,0.001000000000000000020816681712,1,0.1
192,192_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1222,1,0.001000000000000000020816681712,50,0.001
193,193_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,115.000000000000000000000000000000,1507,1258,0.188414252347174432378196229365,11,0.005
194,194_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1854,1,0.915011369485264514089806198172,50,0.05
195,195_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,63.000000000000000000000000000000,4193,3803,0.605662844620268092654669089825,50,0.1
196,196_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1841,1,0.873356149933436243237849794241,50,0.01
197,197_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2443,1,0.552818465837306427523856200423,50,0.005
198,198_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1805,1,0.876166472611600299913447997824,50,0.01
199,199_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2442,1,0.547943408839026924184167910425,50,0.005
200,200_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1859,1,0.871033837681214451187372560526,50,0.05
201,201_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2446,1,0.550108395973429753489369886665,50,0.005
202,202_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1798,1,0.870281820339020462107271214336,50,0.01
203,203_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2446,1,0.538718691934576665580891585705,50,0.005
204,204_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1794,1,0.874364194797671356873536296916,50,0.05
205,205_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1250,1,0.001000000000000000020816681712,50,0.001
206,206_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1855,1,0.873335857704903739673341078742,50,0.05
207,207_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1272,1,0.001000000000000000020816681712,50,0.001
208,208_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1805,1,0.869398243210775523159838940046,50,0.05
209,209_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1346,22,0.001000000000000000020816681712,50,0.001
210,210_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1840,1,0.870935965522624755408287455793,50,0.05
211,207_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1272,1,0.001000000000000000020816681712,50,0.001
212,212_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1847,1,0.871813753770980137680624011409,50,0.05
213,213_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1277,1,0.001000000000000000020816681712,50,0.001
214,214_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1799,1,0.866883229729062732005218094855,50,0.05
215,215_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1249,1,0.001000000000000000020816681712,50,0.001
216,216_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2440,1,0.852859785356848809634300323523,50,0.01
217,217_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.570000000000000062172489379009,100.000000000000000000000000000000,1539,1415,0.115046938912590684944703411929,1,0.25
218,218_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1827,1,0.869345175630027444668712632847,50,0.05
219,219_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1831,1,0.668846847498033136858452962770,50,0.25
220,220_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1825,1,0.081067884585931879182219006452,50,0.05
221,221_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1802,1,0.055052554995186679664964657377,50,0.005
222,222_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1825,1,0.998999999999999999111821580300,50,0.05
223,223_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1825,1,0.672200709730002565756024068833,50,0.25
224,224_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1814,1,0.055733311337458048273507671411,39,0.05
225,225_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1825,1,0.687232080829594527138226567331,50,0.05
226,226_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.510000000000000008881784197001,67.000000000000000000000000000000,1409,3219,0.532304942702914951624393324892,1,0.001
227,227_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1830,1,0.690048218259697088150517174654,50,0.05
228,228_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,69.000000000000000000000000000000,4791,3311,0.892189460999842975930107513705,48,0.001
229,229_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1829,1,0.998999999999999999111821580300,50,0.05
230,230_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1559,1,0.693159397813588595838041328534,50,0.1
231,231_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1570,1,0.998999999999999999111821580300,50,0.01
232,232_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1266,1,0.009103408252092217226025816501,50,0.001
233,233_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1640,1,0.714779359511274470229125199694,50,0.05
234,234_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1284,1,0.002585350636466209744979138918,50,0.001
235,235_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1617,1,0.700830029547216071250659297220,50,0.01
236,236_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1159,1,0.005088711847833609844271585132,50,0.001
237,237_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1658,1,0.711984406356903520851631128608,50,0.05
238,238_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.998999999999999999111821580300,50,0.01
239,239_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1786,1,0.712061905641153236601326170785,50,0.1
240,240_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1733,1,0.716830607777477535336174696567,50,0.05
241,241_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1282,1,0.007635067543390428729255248186,50,0.001
242,242_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1608,1,0.998999999999999999111821580300,50,0.1
243,243_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1487,1,0.700043476712594481270457436040,50,0.01
244,244_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1308,1,0.006309457581203179420137328037,50,0.001
245,245_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1652,1,0.713520038248145360704199902102,50,0.05
246,246_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1322,1,0.004035566162652388932929881094,50,0.001
247,247_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2437,1,0.998999999999999999111821580300,50,0.01
248,248_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1644,1,0.710454830659833724837426416343,50,0.01
249,249_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1345,1,0.001000000000000000020816681712,50,0.001
250,250_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1615,1,0.715559059308566558499364873569,50,0.01
251,251_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1283,1,0.006745057057253559147647070660,50,0.001
252,252_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1688,1,0.703540952527063723209721501917,50,0.05
253,253_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1267,1,0.001000000000000000020816681712,50,0.001
254,254_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3490,1,0.998999999999999999111821580300,50,0.01
255,255_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1291,1,0.001199193434991824335281163094,50,0.001
256,256_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1631,1,0.705924337574787452709301760478,50,0.05
257,257_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1235,1,0.004421792067035990383971899575,50,0.001
258,258_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1635,1,0.714209799046394699928441696102,50,0.05
259,259_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1518,1,0.704688482439617014385646598384,50,0.01
260,260_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1795,1,0.998999999999999999111821580300,50,0.05
261,261_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1306,1,0.006508550854639699387305107336,50,0.001
262,262_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1661,1,0.708501586958073636957067265030,50,0.01
263,263_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1353,1,0.001000000000000000020816681712,50,0.001
264,264_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1631,1,0.719178002713259156841729691223,50,0.05
265,265_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1313,1,0.017303610318918846711078174394,50,0.001
266,266_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2446,1,0.065725056472743584312645737100,50,0.25
267,267_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1579,1,0.998999999999999999111821580300,50,0.05
268,268_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1235,1,0.004452665723558844532159461949,50,0.001
269,269_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2474,1,0.068650841494566877676319904822,50,0.25
270,270_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1247,1,0.001000000000000000020816681712,50,0.001
271,271_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1582,1,0.704793039688000999554162717686,50,0.01
272,272_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1263,1,0.001000000000000000020816681712,50,0.001
273,273_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1667,1,0.711642774219366724963720116648,50,0.05
274,274_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1414,1,0.018031028702360420851169564571,50,0.001
275,275_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1647,1,0.998999999999999999111821580300,50,0.05
276,276_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1273,1,0.003217701284819401898878998836,50,0.001
277,277_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1652,1,0.998999999999999999111821580300,50,0.05
278,278_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1281,1,0.002111844755140980869900779737,50,0.001
279,279_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1737,1,0.710120104779632943525768951076,50,0.01
280,280_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2418,1,0.696769409705131570476055458130,50,0.01
281,281_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1731,1,0.737938222341005145565873135638,50,0.1
282,282_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1278,1,0.005187706787638886665736670523,50,0.001
283,283_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1719,1,0.998999999999999999111821580300,50,0.01
284,284_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1478,1,0.690468169392444930387853219145,50,0.01
285,285_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1263,1,0.010062681104895910069729758618,50,0.001
286,286_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,71.000000000000000000000000000000,3927,2969,0.001000000000000000020816681712,50,0.01
287,287_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1623,1,0.998999999999999999111821580300,50,0.05
288,288_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1646,1,0.680528065672998172530583360640,50,0.05
289,289_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1357,1,0.002644851518806399029437592674,50,0.001
290,290_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3652,1,0.693266382374022938073210298171,50,0.1
291,291_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1309,1,0.001000000000000000020816681712,50,0.001
292,292_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3463,1,0.699053070356776218297056857409,50,0.25
293,293_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1571,1,0.703143897785573290803995405440,50,0.1
294,294_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2438,1,0.998999999999999999111821580300,50,0.05
295,295_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1642,1,0.704206610603147487026376438735,50,0.05
296,296_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1317,1,0.008002393406972957076717101188,50,0.001
297,297_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1570,1,0.708540547801206010980479277350,50,0.05
298,298_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1306,1,0.008992926534939885835351347509,50,0.001
299,299_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1664,1,0.700205189975500297272503757995,50,0.01
300,300_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1767,1,0.700395446756498452067773996532,50,0.05
301,301_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1415,1,0.998999999999999999111821580300,50,0.01
302,302_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1622,1,0.687312011122370614124577059556,50,0.01
303,303_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,50.000000000000000000000000000000,4902,4790,0.243361076555769745288770877778,50,0.001
304,304_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1595,1,0.998999999999999999111821580300,50,0.05
305,305_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4655,1,0.667291447186880049002866144292,50,0.25
306,306_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1814,1,0.713643453327723609191934883711,50,0.01
307,307_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4730,1,0.998999999999999999111821580300,50,0.05
308,308_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1808,1,0.701035623193346446591078802157,50,0.05
309,309_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1828,1,0.998999999999999999111821580300,50,0.01
310,310_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3525,1,0.109745613890400356416066074416,50,0.005
311,311_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1264,1,0.001000000000000000020816681712,40,0.005
312,312_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1822,1,0.710253668263683302086519688601,50,0.05
313,313_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3499,1,0.100382008409408612914504033142,1,0.005
314,314_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1824,1,0.998999999999999999111821580300,50,0.01
315,315_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3453,1,0.683277251221528714175690311095,50,0.05
316,316_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1802,1,0.075933506591877961144909647828,50,0.25
317,317_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1828,1,0.712009478922016714186327135394,50,0.05
318,318_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1826,1,0.998999999999999999111821580300,50,0.05
319,319_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1825,1,0.998999999999999999111821580300,50,0.01
320,320_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1800,1,0.074574483117918283214820007743,50,0.005
321,321_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4009,1,0.702099363646119467574635564233,50,0.25
322,322_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1815,1,0.707610417588101503483244414383,50,0.05
323,323_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4705,1,0.998999999999999999111821580300,50,0.25
324,324_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1821,1,0.998999999999999999111821580300,50,0.01
325,325_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1817,1,0.681523389625604769648248293379,50,0.05
326,326_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3507,1,0.789665739504358810130213441880,50,0.05
327,327_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1823,1,0.696501972557675985697756004811,50,0.05
328,328_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1830,1,0.719489399280168417938341463014,50,0.01
329,329_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1818,1,0.051918569228591116004878358581,50,0.05
330,330_0,FAILED,BoTorch,BOTORCH_MODULAR,,,968,1,0.701797602899705830203913592413,50,0.01
331,331_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1811,1,0.714499637928333708458694673027,50,0.05
332,332_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1814,1,0.656247522815804562590358273155,50,0.025
333,333_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1811,1,0.747753903737091052406071867154,50,0.01
334,334_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1823,1,0.060883310629188794327326661460,48,0.05
335,335_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,80.000000000000000000000000000000,1400,1946,0.998999999999999999111821580300,50,0.01
336,336_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1810,1,0.710141086964361067934703442006,50,0.01
337,337_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3990,1,0.998999999999999999111821580300,50,0.01
338,338_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1850,1,0.708072590338721497005280980375,50,0.01
339,339_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1824,1,0.998999999999999999111821580300,50,0.05
340,340_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,815.000000000000000000000000000000,1858,110,0.998999999999999999111821580300,50,0.05
341,341_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1829,1,0.998999999999999999111821580300,50,0.01
342,342_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4784,1,0.667481400516954370694122644636,50,0.1
343,343_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,194.000000000000000000000000000000,2505,175,0.658240597849692465892701420671,50,0.025
344,344_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4021,1,0.998999999999999999111821580300,50,0.1
345,345_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1822,1,0.998999999999999999111821580300,50,0.05
346,346_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1349,1,0.430035816665602421693392898305,50,0.005
347,347_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4019,1,0.470645468372954289826282092690,50,0.005
348,348_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2528,1,0.457055111133504832210405766091,50,0.005
349,349_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,223.000000000000000000000000000000,3450,484,0.203853618128552377397610939624,50,0.025
350,350_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2522,1,0.438077168582680753772251591727,50,0.005
351,351_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4768,1,0.998999999999999999111821580300,50,0.01
352,352_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,157.000000000000000000000000000000,1804,963,0.596820693322494322252680376550,50,0.005
353,353_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2517,27,0.491453889808104005254563162453,50,0.005
354,354_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,122.000000000000000000000000000000,2537,460,0.998999999999999999111821580300,50,0.01
355,355_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2531,1,0.422740690790100792639805149520,50,0.005
356,356_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4650,1,0.439414456543955056488925947633,1,0.005
357,357_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1282,1,0.456290957256872409253389832884,50,0.005
358,358_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2538,1,0.312754914294430141907810138946,39,0.005
359,359_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1276,1,0.529475247156931616565600506874,50,0.005
360,360_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1325,1,0.420477432992695854263587307287,50,0.005
361,361_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2535,1,0.494275959821844912411847872136,50,0.005
362,362_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3998,1,0.556174455443036563906389346812,50,0.005
363,363_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2536,1,0.405185271160342308505164510279,1,0.005
364,364_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1291,1,0.556768470627712619425153661723,50,0.005
365,365_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2528,1,0.410911265482629572964867747942,1,0.005
366,366_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4003,1,0.489264783989678098130582384329,50,0.005
367,367_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1298,1,0.457746115373307393969781742271,50,0.005
368,368_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2531,1,0.446165495530613909203054845420,13,0.005
369,369_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4002,1,0.515866153512030800598608948349,50,0.005
370,370_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3438,1,0.433579354909104763837746077115,50,0.005
371,371_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4635,1,0.432184772461701061097016918211,1,0.005
372,372_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1271,1,0.468122721214015824475751514910,50,0.005
373,373_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2528,1,0.424552970321952938270726463088,1,0.005
374,374_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4000,1,0.542429289447107221278088218241,50,0.005
375,375_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2530,1,0.398666640354809587165618722793,1,0.005
376,376_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1322,1,0.001000000000000000020816681712,50,0.001
377,377_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2538,1,0.412529939697277636678762746669,47,0.005
378,378_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3465,1,0.487936028944047750499635185406,50,0.005
379,379_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2532,1,0.426972898282819646187391526837,1,0.005
380,380_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2517,1,0.410524649670652830302230995585,20,0.005
381,381_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4003,1,0.521804767621129816923541966389,50,0.005
382,382_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1278,1,0.470031890092403814573884801575,50,0.005
383,383_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2516,1,0.412746643835891657836612012034,1,0.005
384,384_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1296,1,0.500547933356076724109584574762,50,0.005
385,385_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4700,1,0.433021873303130910848324219842,1,0.005
386,386_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2521,1,0.447203877067926136579245621760,50,0.005
387,387_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4013,1,0.534668794783711098794753979746,50,0.005
388,388_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,93.000000000000000000000000000000,1482,1508,0.282860110692362554107859295982,50,0.01
389,389_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2523,1,0.396984221383834767760134809578,2,0.005
390,390_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4523,1,0.431890621532674601201762243363,50,0.005
391,391_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1243,1,0.413608705689873568100978218354,1,0.005
392,392_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4523,1,0.459939650102576480517058143960,50,0.005
393,393_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1314,1,0.410573064850464852781897207024,12,0.005
394,394_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4692,1,0.403319608513975857988498319173,6,0.005
395,395_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1264,1,0.102871104819691666643066696452,2,0.005
396,396_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1268,1,0.424368855848881454395638002097,50,0.005
397,397_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4466,1,0.444518376706223461436451316331,50,0.005
398,398_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.570000000000000062172489379009,89.000000000000000000000000000000,4860,2318,0.001000000000000000020816681712,50,0.025
399,399_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1312,1,0.408970440295916060069458808357,30,0.005
400,400_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4607,1,0.403378490848376070054825959232,50,0.005
401,401_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1385,1,0.036193194376562071168379475239,50,0.001
402,402_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4665,1,0.405578338497504076176625176231,50,0.005
403,403_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1341,1,0.069506272015924172591461172033,50,0.001
404,404_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4750,1,0.399289428895887699155764494208,50,0.005
405,405_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1202,1,0.401597065675597331146917667866,11,0.005
406,406_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4769,1,0.480573802499320190673159913786,50,0.005
407,407_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,67.000000000000000000000000000000,4638,3614,0.768292064530889162732307795523,1,0.01
408,408_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4569,1,0.402159950700463508788118360826,50,0.005
409,409_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,86.000000000000000000000000000000,1456,2055,0.001000000000000000020816681712,50,0.01
410,410_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1418,1,0.395840816795979821840489876195,21,0.005
411,411_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1311,1,0.045604887932895063484828312994,50,0.001
412,412_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1345,1,0.396650402252635037037009624328,2,0.005
413,413_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2539,1,0.487871677427527128401152367587,50,0.005
414,414_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1318,1,0.404265703139137677624148636824,1,0.005
415,415_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2525,1,0.480683082674246975329879205674,50,0.005
416,416_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1348,1,0.474669252246118722471379669514,50,0.005
417,417_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1333,1,0.424400497385746811040974080242,11,0.005
418,418_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3994,1,0.446725207931679579864692186675,50,0.005
419,419_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1308,1,0.407408595823932406787548643479,2,0.005
420,420_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2523,1,0.400065549354878535748269996475,27,0.005
421,421_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1294,1,0.496936024358315742155411953718,50,0.005
422,422_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1332,1,0.428993127554542441615126335819,15,0.005
423,423_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2543,1,0.439549859369340101267198406276,50,0.005
424,424_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1313,1,0.062338725268890435304847130737,50,0.001
425,425_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2519,1,0.402050416513602681956029982757,49,0.005
426,426_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1371,1,0.480245779976306130798491267342,50,0.005
427,427_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4838,1,0.454608905379159100590413800091,50,0.005
428,428_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2522,1,0.420372279939830117978516454968,27,0.005
429,429_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.517281350476878176714023993554,50,0.005
430,430_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1400,1,0.409749618881272870130061392047,31,0.005
431,431_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4414,65,0.439071631967267450580294507745,49,0.005
432,432_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2536,1,0.439163807935626537837237037820,39,0.005
433,433_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4805,1,0.320823991059530078118200435711,1,0.005
434,434_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2520,1,0.488450668976831259282533892474,50,0.005
435,435_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4810,1,0.386950576064564844003257348959,5,0.005
436,436_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2532,1,0.449435878433248670038580030450,50,0.005
437,437_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3987,1,0.504498668794120197489405654778,50,0.005
438,438_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2530,1,0.431609050491980950070569633681,44,0.005
439,439_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3974,1,0.453608478443076168495906586031,1,0.005
440,440_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1346,1,0.402331542866212199527353732265,23,0.005
441,441_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2524,1,0.488268435740326411931278016709,50,0.005
442,442_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1357,1,0.447205578158007543976282249787,50,0.005
443,443_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4812,1,0.528293128032335745913883329195,50,0.005
444,444_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2523,1,0.376815364000324171112055182675,26,0.005
445,445_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1324,1,0.433260702825646160096795256322,25,0.005
446,446_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3997,1,0.441352184983501094617253102115,1,0.005
447,447_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2513,1,0.448432502161696500131427001179,50,0.005
448,448_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2528,1,0.396797310581056328349092154895,1,0.005
449,449_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1342,1,0.474800972747781269145406213283,50,0.005
450,450_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3460,1,0.445814300022491338815200379031,50,0.005
451,451_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1338,1,0.436992390635801641884938817384,33,0.005
452,452_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2526,1,0.458905940730698569396395214426,50,0.005
453,453_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1237,1,0.488860636623055122562675478548,50,0.005
454,454_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4013,1,0.398354774386069310221358819035,3,0.005
455,455_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2529,1,0.449572910549736137753029652231,50,0.005
456,456_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1314,1,0.477310930815422418405091775639,20,0.005
457,457_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2524,1,0.423725677400737466005153919468,50,0.005
458,458_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4004,1,0.434472334520448311145912612119,2,0.005
459,459_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2522,1,0.470639238750895361196313615437,50,0.005
460,460_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2521,1,0.402532592749429651846071465116,21,0.005
461,461_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1362,1,0.503673550831063354671357501502,50,0.005
462,462_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2521,1,0.443783785024831123333655114038,43,0.005
463,463_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4017,1,0.442854609626423811885587156212,40,0.005
464,464_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1363,1,0.456233159909740948467771204378,50,0.005
465,465_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,51.000000000000000000000000000000,1738,3152,0.602483083728453272520653172251,31,0.025
466,466_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1328,1,0.397445286915654782955442669845,25,0.005
467,467_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2523,1,0.459819433600250848659385383144,50,0.005
468,468_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3991,1,0.400931728192496228402319502493,1,0.005
469,469_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1326,1,0.014413725433974938078263150487,35,0.001
470,470_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2524,1,0.397789399078895655748056015000,38,0.005
471,471_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1310,1,0.473495576316034727515358326855,50,0.005
472,472_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2521,1,0.425312191612872370072295780119,17,0.005
473,473_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1316,1,0.445392663868949489902604454983,50,0.005
474,474_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4016,1,0.426931773368127320811282743307,1,0.005
475,475_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2519,1,0.431941273751725918206290089074,50,0.005
476,476_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.430354907402555497686336138941,26,0.005
477,477_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3459,1,0.458093180560686707991635557846,50,0.005
478,478_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4001,1,0.405245835206829108088300017698,1,0.005
479,479_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1256,1,0.496211923783617614436280973678,50,0.005
480,480_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2525,1,0.411611676794893521780238643260,50,0.005
481,481_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3995,1,0.456593333732297657068244234324,1,0.005
482,482_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2525,1,0.423903900093434982121465282034,50,0.005
483,483_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1322,1,0.419907055417393260121627918124,4,0.005
484,484_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.445118475738137175934383549247,50,0.005
485,485_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4834,1,0.396786729031487594987481770659,1,0.005
486,486_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1347,1,0.473144580759886246301704204598,50,0.005
487,487_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2522,1,0.444817747369118665634601939018,44,0.005
488,488_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3982,1,0.430282303255595577340386626020,1,0.005
489,489_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2522,1,0.425285956852921309678805528165,50,0.005
490,490_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1314,1,0.001000000000000000020816681712,50,0.001
491,491_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2535,1,0.401069901561030117687067786392,49,0.005
492,492_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4007,1,0.450988277382767976941835286198,1,0.005
493,493_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2521,1,0.449292430277704601948585150240,50,0.005
494,494_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1326,1,0.403446410899149066064950375221,1,0.005
495,495_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2527,1,0.456344118054830827357903899610,50,0.005
496,496_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1360,1,0.496869958907121378466342775937,50,0.005
497,497_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2516,1,0.474562675571493175485926485635,50,0.005
498,498_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1305,1,0.398532087129838119565761189733,19,0.005
499,499_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3986,1,0.455062977052320793980300095427,41,0.005
500,500_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2520,1,0.402863822938424209763041972110,50,0.005
501,501_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1322,1,0.484592431069580331648438686898,50,0.005
502,502_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1413,1,0.416576486065960482907399864416,13,0.005
503,503_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2525,1,0.453393544076749754889732457741,50,0.005
504,504_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3999,1,0.379574162959571081987775187372,1,0.005
505,505_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1372,1,0.430910095003934345836427155518,50,0.005
506,506_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2526,1,0.445069275302483524647811918840,27,0.005
507,507_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1251,1,0.472902795046046819926743864926,50,0.005
508,508_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2524,1,0.400808958342810828234803466330,9,0.005
509,509_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1182,1,0.471389854779709516208185959840,50,0.005
510,510_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1362,1,0.417994663618341921740295674681,7,0.005
511,511_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2524,1,0.407553387749823914365521204672,50,0.005
512,512_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4007,1,0.451667782189854261076789043727,1,0.005
513,513_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2524,1,0.523607634721565129432008234289,50,0.005
514,514_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1328,1,0.425262513660263519188475811461,11,0.005
515,515_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1382,1,0.477462347776757944917136455842,50,0.005
516,516_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1350,1,0.399044311672358431586360438814,17,0.005
517,517_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2523,1,0.464088683060629059173862742682,50,0.005
518,518_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1277,1,0.365819772555645905676158236020,1,0.005
519,519_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2520,1,0.454282191760729314733424644146,50,0.005
520,520_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2519,1,0.402212251024816780375914504475,47,0.005
521,521_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4005,1,0.462133278760715526800595398527,1,0.005
522,522_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2529,1,0.423414423459108679725915180825,50,0.005
523,523_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4828,1,0.414168118288269049287464440567,50,0.005
524,524_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2521,1,0.436515506325127422559972956151,30,0.005
525,525_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1298,1,0.477631092995699724212954606628,50,0.005
526,526_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2527,1,0.397110686333740026121574828721,33,0.005
527,527_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,339.000000000000000000000000000000,1251,265,0.364739029876184173151187906115,25,0.005
528,528_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4014,1,0.437444709522435526416472839628,8,0.005
529,529_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1350,1,0.420399091566084515125822917980,50,0.005
530,530_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.570000000000000062172489379009,78.000000000000000000000000000000,4763,2454,0.290856029544249494733776373323,50,0.25
531,531_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1293,1,0.709609382894468732061454829818,50,0.005
532,532_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1661,1,0.011124305649254264197201003128,49,0.005
533,533_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,72.000000000000000000000000000000,1793,2369,0.133738037654066416193288091563,50,0.1
534,534_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1722,1,0.014300114415810361023773111810,50,0.005
535,535_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,73.000000000000000000000000000000,4049,3078,0.781691847366915215644667114248,50,0.025
536,536_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1776,1,0.703096494886611966812495211343,50,0.005
537,537_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1233,1,0.001000000000000000020816681712,50,0.25
538,538_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.470000000000000028865798640254,53.000000000000000000000000000000,1657,4898,0.841597650243126715885466637701,1,0.1
539,539_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1233,1,0.002286822136550610215510737078,50,0.25
540,540_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,82.000000000000000000000000000000,3941,3176,0.221226584010760674026130345737,1,0.005
541,541_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1328,1,0.007542971305455265125516906011,50,0.25
542,542_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1273,1,0.001000000000000000020816681712,50,0.005
543,543_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1119,1,0.814065493681150775806543151702,50,0.01
544,544_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1313,1,0.001000000000000000020816681712,50,0.005
545,545_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2529,1,0.001000000000000000020816681712,50,0.005
546,546_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1261,1,0.001000000000000000020816681712,50,0.005
547,547_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1268,1,0.998999999999999999111821580300,50,0.01
548,548_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1241,1,0.004396298803823053998052206026,50,0.005
549,549_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1299,1,0.001000000000000000020816681712,37,0.005
550,550_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,40.000000000000000000000000000000,1480,4886,0.308500170043674082354101528836,50,0.005
551,551_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1251,1,0.001000000000000000020816681712,50,0.005
552,552_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,230.000000000000000000000000000000,1212,552,0.998999999999999999111821580300,50,0.01
553,553_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,93.000000000000000000000000000000,4040,2202,0.998999999999999999111821580300,50,0.005
554,554_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,61.000000000000000000000000000000,2548,1,0.001000000000000000020816681712,1,0.25
555,555_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1273,1,0.835201971567614886993169420748,50,0.005
556,556_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,47.000000000000000000000000000000,3997,1,0.001000000000000000020816681712,1,0.05
557,557_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1277,1,0.818226551567374227325046831538,50,0.005
558,558_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,94.000000000000000000000000000000,3363,1327,0.188801102121698427538376563461,50,0.05
559,559_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,168.000000000000000000000000000000,3371,1009,0.813842153604528939503381934628,50,0.005
560,560_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,200.000000000000000000000000000000,1479,615,0.998999999999999999111821580300,44,0.005
561,561_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2936,1,0.001000000000000000020816681712,37,0.05
562,562_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2992,1,0.001000000000000000020816681712,39,0.25
563,563_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2907,1,0.001000000000000000020816681712,37,0.05
564,564_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2878,1,0.998999999999999999111821580300,39,0.025
565,565_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2899,1,0.001000000000000000020816681712,38,0.05
566,566_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2909,1,0.998999999999999999111821580300,39,0.025
567,567_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2914,1,0.001000000000000000020816681712,38,0.25
568,568_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2867,1,0.998999999999999999111821580300,38,0.025
569,569_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2915,1,0.001000000000000000020816681712,39,0.05
570,570_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.470000000000000028865798640254,45.000000000000000000000000000000,1670,4350,0.774658600577105338480521368183,10,0.025
571,571_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2900,1,0.062692692347069492453393024789,38,0.05
572,572_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2795,19,0.998999999999999999111821580300,37,0.025
573,573_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,997.000000000000000000000000000000,2904,50,0.001000000000000000020816681712,38,0.05
574,574_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2890,1,0.998999999999999999111821580300,39,0.025
575,575_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2968,1,0.001000000000000000020816681712,38,0.001
576,576_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,88.000000000000000000000000000000,4146,2973,0.607452686109176132056575170282,2,0.05
577,577_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2877,1,0.998999999999999999111821580300,39,0.01
578,578_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,136.000000000000000000000000000000,1714,1047,0.412626072298239832125688053566,19,0.05
579,579_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,123.000000000000000000000000000000,1883,1532,0.140519922118221091134060429795,28,0.1
580,580_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,55.000000000000000000000000000000,2709,1250,0.204145180421658828384678940893,35,0.005
581,581_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,86.000000000000000000000000000000,1561,1679,0.998999999999999999111821580300,16,0.1
582,582_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,71.000000000000000000000000000000,1559,2037,0.001000000000000000020816681712,4,0.001
583,583_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,72.000000000000000000000000000000,1720,2953,0.524797862356759625690472148563,27,0.05
584,584_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,105.000000000000000000000000000000,1243,963,0.001000000000000000020816681712,1,0.05
585,585_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,95.000000000000000000000000000000,3998,1440,0.058916050266513371458376724377,50,0.25
586,586_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,52.000000000000000000000000000000,1653,2983,0.898492213313135557051225532632,41,0.05
587,587_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3672,1,0.684130439691058467310824653396,50,0.005
588,588_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,121.000000000000000000000000000000,4328,2491,0.188272096297886937232846094048,10,0.025
589,589_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1219,1,0.001000000000000000020816681712,12,0.025
590,590_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,141.000000000000000000000000000000,1227,985,0.849283169384614344821216036507,38,0.05
591,591_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,191.000000000000000000000000000000,1458,497,0.819930573812733154426268811221,19,0.025
592,592_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.570000000000000062172489379009,80.000000000000000000000000000000,3514,1700,0.828908822695276104042250153725,33,0.05
593,593_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,144.000000000000000000000000000000,1898,1039,0.623019077628370387955669684743,31,0.01
594,594_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,69.000000000000000000000000000000,3013,1572,0.707373296837493170663435648748,22,0.005
595,595_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,74.000000000000000000000000000000,1694,3575,0.554471478209584645036045458255,40,0.05
596,596_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,103.000000000000000000000000000000,3008,946,0.606149230954711848440297217167,31,0.05
597,597_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,124.000000000000000000000000000000,4085,1513,0.494592411582152446936078149520,35,0.05
598,598_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,95.000000000000000000000000000000,4538,2147,0.740223609614996336247827457555,11,0.01
599,599_0,FAILED,BoTorch,BOTORCH_MODULAR,,,957,1,0.847611624857968037893840573815,27,0.05
600,600_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,140.000000000000000000000000000000,1570,989,0.956211082046512839127672123141,23,0.025
601,601_0,FAILED,BoTorch,BOTORCH_MODULAR,,,921,1,0.737411940430879098151706330100,37,0.1
602,602_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,178.000000000000000000000000000000,1082,638,0.426570839661309098023167507563,41,0.25
603,603_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1101,1,0.542710863333791038165543341165,42,0.1
604,604_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,99.000000000000000000000000000000,4239,1819,0.001000000000000000020816681712,36,0.01
605,605_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,57.000000000000000000000000000000,4832,4347,0.332580883093735180100480874898,1,0.001
606,606_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1014,1,0.767027123985929981664355636894,37,0.25
607,607_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,73.000000000000000000000000000000,1784,3106,0.753376912221547789805242700822,23,0.01
608,608_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,69.000000000000000000000000000000,1667,2350,0.001000000000000000020816681712,18,0.25
609,609_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,44.000000000000000000000000000000,3669,4429,0.780048314418072985532148777565,46,0.01
610,610_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1021,1,0.766129166927527527342078883521,37,0.25
611,611_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1243,1,0.133606134736232312620884954413,1,0.05
612,612_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3679,1,0.770849736910836469405694515444,39,0.1
613,613_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,117.000000000000000000000000000000,1475,1045,0.998999999999999999111821580300,18,0.05
614,614_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.470000000000000028865798640254,42.000000000000000000000000000000,1462,4646,0.001000000000000000020816681712,1,0.025
615,615_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,65.000000000000000000000000000000,4095,3433,0.968698519898311904974264052726,20,0.001
616,616_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1197,1,0.773335919063679377849496177078,38,0.1
617,617_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3249,1,0.802265737947582380229505361058,38,0.01
618,618_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1263,1,0.001000000000000000020816681712,10,0.025
619,619_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1028,1,0.800600889233421542101609702513,37,0.001
620,620_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,53.000000000000000000000000000000,1713,3734,0.001000000000000000020816681712,42,0.025
621,621_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.470000000000000028865798640254,44.000000000000000000000000000000,1599,4759,0.141970205776356328541965012846,17,0.025
622,622_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,45.000000000000000000000000000000,1278,1,0.001000000000000000020816681712,1,0.001
623,623_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,48.000000000000000000000000000000,3809,4015,0.973419367844927219124429029762,14,0.01
624,624_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,91.000000000000000000000000000000,4399,1948,0.998999999999999999111821580300,50,0.05
625,625_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,48.000000000000000000000000000000,3899,4056,0.998999999999999999111821580300,12,0.05
626,626_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1714,1,0.798692407048741559449922533531,36,0.1
627,627_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,105.000000000000000000000000000000,1575,1138,0.998999999999999999111821580300,50,0.1
628,628_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,93.000000000000000000000000000000,1482,1373,0.128646560778194501972748753360,43,0.001
629,629_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,74.000000000000000000000000000000,1093,1594,0.032320777739681762208423521088,16,0.001
630,630_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,60.000000000000000000000000000000,3690,3561,0.852209973066680670505945727200,34,0.025
631,631_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3738,1,0.782690229017617533635586823948,40,0.01
632,632_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,152.000000000000000000000000000000,1147,780,0.728177683020164390192974224192,29,0.1
633,633_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,48.000000000000000000000000000000,5000,5000,0.671146118717867778968866332434,47,0.1
634,634_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.510000000000000008881784197001,60.000000000000000000000000000000,4998,3735,0.001000000000000000020816681712,50,0.25
635,635_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4707,1,0.813513751477055957472828140453,37,0.01
636,636_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,163.000000000000000000000000000000,1107,905,0.001000000000000000020816681712,32,0.025
637,637_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3273,1,0.786790498310724562003315440961,41,0.01
638,638_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,76.000000000000000000000000000000,3691,4370,0.862400335419407304549110904190,29,0.001
639,639_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,1872.000000000000000000000000000000,1298,69,0.780439572384673763139062430128,40,0.01
640,640_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1319,270,0.998999999999999999111821580300,50,0.025
641,641_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,290.000000000000000000000000000000,3101,269,0.998999999999999999111821580300,50,0.1
642,642_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3312,270,0.998999999999999999111821580300,50,0.025
643,643_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,131.000000000000000000000000000000,4016,1402,0.998999999999999999111821580300,50,0.01
644,644_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,114.000000000000000000000000000000,2947,803,0.998999999999999999111821580300,50,0.025
645,645_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,138.000000000000000000000000000000,3946,1231,0.001000000000000000020816681712,43,0.01
646,646_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,300.000000000000000000000000000000,3314,260,0.269024451797957142584749590242,50,0.025
647,647_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,71.000000000000000000000000000000,4291,2719,0.998999999999999999111821580300,1,0.01
648,648_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,56.000000000000000000000000000000,3527,3811,0.245571004152352195815112168020,1,0.01
649,649_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,212.000000000000000000000000000000,1250,311,0.998999999999999999111821580300,50,0.005
650,650_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,286.000000000000000000000000000000,3381,281,0.998999999999999999111821580300,50,0.005
651,651_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,1938.000000000000000000000000000000,1166,84,0.764026982879914884350114334666,50,0.005
652,652_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1156,29,0.387425961115542671908684724258,50,0.005
653,653_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,225.000000000000000000000000000000,2719,302,0.998999999999999999111821580300,50,0.025
654,654_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,66.000000000000000000000000000000,4880,4082,0.691713709224799799812899436802,48,0.025
655,655_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,270.000000000000000000000000000000,4515,1553,0.001000000000000000020816681712,50,0.001
656,656_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,52.000000000000000000000000000000,4042,3554,0.998999999999999999111821580300,2,0.005
657,657_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,432.000000000000000000000000000000,1618,281,0.645748855671260280075784976361,47,0.025
658,658_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1220,296,0.659700288161630066241514214198,38,0.025
659,659_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4954,287,0.643996506978077420946249276312,50,0.025
660,660_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,331.000000000000000000000000000000,1710,322,0.681790081034168826690233800036,46,0.05
661,661_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1232,323,0.692214079076246768806868203683,37,0.005
662,662_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.570000000000000062172489379009,89.000000000000000000000000000000,3982,2292,0.998999999999999999111821580300,13,0.25
663,663_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,88.000000000000000000000000000000,1775,1818,0.998999999999999999111821580300,40,0.025
664,664_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1728,1,0.001000000000000000020816681712,45,0.025
665,665_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,146.000000000000000000000000000000,3325,742,0.998999999999999999111821580300,43,0.05
666,666_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,59.000000000000000000000000000000,1752,3395,0.280641569680610158954436883505,25,0.05
667,667_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,117.000000000000000000000000000000,4749,2428,0.001000000000000000020816681712,27,0.05
668,668_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,63.000000000000000000000000000000,4234,3780,0.998999999999999999111821580300,1,0.025
669,669_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,138.000000000000000000000000000000,4381,1962,0.998999999999999999111821580300,1,0.001
670,670_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,60.000000000000000000000000000000,2620,1237,0.998999999999999999111821580300,10,0.025
671,671_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1226,289,0.020627013040106119412531171520,50,0.001
672,672_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,61.000000000000000000000000000000,1790,3202,0.998999999999999999111821580300,25,0.05
673,673_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,122.000000000000000000000000000000,4448,1736,0.001000000000000000020816681712,6,0.05
674,674_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,88.000000000000000000000000000000,4584,3049,0.001000000000000000020816681712,46,0.05
675,675_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,143.000000000000000000000000000000,4547,2718,0.001000000000000000020816681712,39,0.001
676,676_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3220,16,0.001000000000000000020816681712,44,0.025
677,677_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.600000000000000088817841970013,118.000000000000000000000000000000,1767,1172,0.998999999999999999111821580300,50,0.05
678,678_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,142.000000000000000000000000000000,1764,1238,0.001000000000000000020816681712,50,0.025
679,679_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1159,21,0.001000000000000000020816681712,42,0.025
680,680_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.570000000000000062172489379009,103.000000000000000000000000000000,3854,2209,0.998999999999999999111821580300,8,0.05
681,681_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,127.000000000000000000000000000000,1400,1192,0.998999999999999999111821580300,3,0.1
682,682_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1615,698,0.001000000000000000020816681712,5,0.05
683,683_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,139.000000000000000000000000000000,1613,1400,0.001000000000000000020816681712,50,0.005
684,684_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,44.000000000000000000000000000000,1794,4937,0.001000000000000000020816681712,5,0.005
685,685_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,53.000000000000000000000000000000,4654,3831,0.998999999999999999111821580300,1,0.05
686,686_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,62.000000000000000000000000000000,3285,1997,0.001000000000000000020816681712,15,0.05
687,687_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,73.000000000000000000000000000000,3767,2694,0.521513434431213673825311616383,5,0.05
688,688_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,62.000000000000000000000000000000,1649,2809,0.057067424846772518698401199799,8,0.05
689,689_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,98.000000000000000000000000000000,4277,1721,0.933826475455907689671164462197,50,0.25
690,690_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,69.000000000000000000000000000000,1841,2381,0.998999999999999999111821580300,34,0.1
691,691_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,160.000000000000000000000000000000,4888,686,0.001000000000000000020816681712,1,0.025
692,692_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,46.000000000000000000000000000000,3646,4517,0.547124812296080897944250409637,50,0.01
693,693_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,86.000000000000000000000000000000,4389,1745,0.001000000000000000020816681712,7,0.025
694,694_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1189,1,0.001000000000000000020816681712,41,0.025
695,695_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1214,284,0.001000000000000000020816681712,50,0.01
696,696_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,140.000000000000000000000000000000,1526,873,0.001000000000000000020816681712,1,0.01
697,697_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,148.000000000000000000000000000000,3301,647,0.998999999999999999111821580300,45,0.025
698,698_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,95.000000000000000000000000000000,3981,2170,0.001000000000000000020816681712,40,0.05
699,699_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1197,1,0.001000000000000000020816681712,41,0.025
700,700_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,87.000000000000000000000000000000,4688,2721,0.998999999999999999111821580300,50,0.05
701,701_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3360,1,0.001000000000000000020816681712,44,0.025
702,702_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1353,602,0.001000000000000000020816681712,27,0.1
703,703_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,52.000000000000000000000000000000,1703,3472,0.363132534405471663063735832111,31,0.025
704,704_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,56.000000000000000000000000000000,1841,2891,0.998999999999999999111821580300,29,0.1
705,705_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,61.000000000000000000000000000000,3834,3904,0.910454352436511960000586896058,30,0.025
706,706_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,69.000000000000000000000000000000,1788,2936,0.001000000000000000020816681712,22,0.025
707,707_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,74.000000000000000000000000000000,4310,2225,0.001000000000000000020816681712,8,0.025
708,708_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1186,1,0.998999999999999999111821580300,40,0.025
709,709_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,69.000000000000000000000000000000,4175,2542,0.001000000000000000020816681712,1,0.001
710,710_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4834,282,0.001000000000000000020816681712,50,0.001
711,711_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,65.000000000000000000000000000000,4044,3618,0.001000000000000000020816681712,1,0.001
712,712_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3185,1,0.998999999999999999111821580300,50,0.025
713,713_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,60.000000000000000000000000000000,1168,3129,0.001000000000000000020816681712,5,0.025
714,714_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,58.000000000000000000000000000000,3502,3319,0.001000000000000000020816681712,50,0.01
715,715_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,79.000000000000000000000000000000,4590,2708,0.001000000000000000020816681712,1,0.025
716,716_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,77.000000000000000000000000000000,1790,1950,0.001000000000000000020816681712,1,0.025
717,717_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,66.000000000000000000000000000000,4095,3994,0.001000000000000000020816681712,3,0.05
718,718_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,41.000000000000000000000000000000,1253,5000,0.998999999999999999111821580300,1,0.05
719,719_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,54.000000000000000000000000000000,3930,4523,0.998999999999999999111821580300,1,0.005
720,720_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,61.000000000000000000000000000000,3980,4193,0.998999999999999999111821580300,1,0.005
721,721_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,134.000000000000000000000000000000,4925,1297,0.998999999999999999111821580300,16,0.05
722,722_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,62.000000000000000000000000000000,4611,4612,0.998999999999999999111821580300,50,0.025
723,723_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.440000000000000002220446049250,49.000000000000000000000000000000,1603,4232,0.998999999999999999111821580300,50,0.1
724,724_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,72.000000000000000000000000000000,4734,4139,0.001000000000000000020816681712,1,0.05
725,725_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,46.000000000000000000000000000000,3497,3985,0.001000000000000000020816681712,50,0.25
726,726_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,51.000000000000000000000000000000,4874,4663,0.001000000000000000020816681712,50,0.025
727,727_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,44.000000000000000000000000000000,1663,5000,0.123862001657684003830262042811,1,0.05
728,728_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,72.000000000000000000000000000000,4217,2858,0.998999999999999999111821580300,1,0.001
729,729_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,77.000000000000000000000000000000,4093,2482,0.998999999999999999111821580300,1,0.001
730,730_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,185.000000000000000000000000000000,1587,685,0.001000000000000000020816681712,34,0.1
731,731_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,46.000000000000000000000000000000,1736,4499,0.001000000000000000020816681712,1,0.05
732,732_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,129.000000000000000000000000000000,1554,870,0.998999999999999999111821580300,1,0.25
733,733_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,50.000000000000000000000000000000,4007,4707,0.998999999999999999111821580300,1,0.025
734,734_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,396.000000000000000000000000000000,1477,229,0.998999999999999999111821580300,50,0.05
735,735_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,68.000000000000000000000000000000,4773,3426,0.998999999999999999111821580300,50,0.01
736,736_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,70.000000000000000000000000000000,4771,3576,0.001000000000000000020816681712,50,0.25
737,737_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,157.000000000000000000000000000000,4702,1188,0.563677612832465091408096213854,18,0.005
738,738_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,105.000000000000000000000000000000,1869,1589,0.998999999999999999111821580300,1,0.01
739,739_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,45.000000000000000000000000000000,4848,4852,0.001000000000000000020816681712,1,0.1
740,740_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,92.000000000000000000000000000000,4139,1784,0.998999999999999999111821580300,1,0.01
741,741_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,49.000000000000000000000000000000,3994,4312,0.001000000000000000020816681712,50,0.025
742,742_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,249.000000000000000000000000000000,1267,583,0.001000000000000000020816681712,30,0.005
743,743_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,59.000000000000000000000000000000,4035,3015,0.998999999999999999111821580300,16,0.25
744,744_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,62.000000000000000000000000000000,860,3047,0.998999999999999999111821580300,11,0.05
745,745_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,175.000000000000000000000000000000,5000,715,0.998999999999999999111821580300,1,0.05
746,746_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2968,1,0.001000000000000000020816681712,48,0.025
747,747_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,79.000000000000000000000000000000,723,899,0.001000000000000000020816681712,22,0.25
748,748_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1205,1,0.001000000000000000020816681712,42,0.025
749,749_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,61.000000000000000000000000000000,1834,3657,0.979793846120969424973168315773,18,0.05
750,750_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,81.000000000000000000000000000000,4277,1884,0.001000000000000000020816681712,50,0.025
751,751_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,141.000000000000000000000000000000,1484,747,0.001000000000000000020816681712,16,0.05
752,752_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,140.000000000000000000000000000000,4774,1322,0.001000000000000000020816681712,1,0.01
753,753_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,91.000000000000000000000000000000,3760,2534,0.998999999999999999111821580300,50,0.05
754,754_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,152.000000000000000000000000000000,3859,2566,0.001000000000000000020816681712,50,0.001
755,755_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.440000000000000002220446049250,46.000000000000000000000000000000,1449,3955,0.503449646325924926593131658592,1,0.005
756,756_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,64.000000000000000000000000000000,5000,2885,0.998999999999999999111821580300,50,0.025
757,757_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,45.000000000000000000000000000000,3815,4430,0.998999999999999999111821580300,1,0.1
758,758_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,52.000000000000000000000000000000,1824,3731,0.001000000000000000020816681712,1,0.025
759,759_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,66.000000000000000000000000000000,4801,2604,0.998999999999999999111821580300,1,0.1
760,760_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,94.000000000000000000000000000000,4727,2382,0.998999999999999999111821580300,50,0.025
761,761_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,63.000000000000000000000000000000,1848,3148,0.001000000000000000020816681712,50,0.01
762,762_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,70.000000000000000000000000000000,3816,3149,0.998999999999999999111821580300,50,0.005
763,763_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,102.000000000000000000000000000000,4711,1716,0.502385050587183856762862887990,34,0.025
764,764_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,75.000000000000000000000000000000,1792,2060,0.998999999999999999111821580300,50,0.25
765,765_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,82.000000000000000000000000000000,4149,2379,0.587152778705629874167470916291,1,0.005
766,766_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,202.000000000000000000000000000000,4789,858,0.001000000000000000020816681712,13,0.001
767,767_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,61.000000000000000000000000000000,1821,3714,0.998999999999999999111821580300,50,0.025
768,768_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,131.000000000000000000000000000000,4324,1279,0.998999999999999999111821580300,50,0.025
769,769_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,79.000000000000000000000000000000,4153,2126,0.001000000000000000020816681712,1,0.05
770,770_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,73.000000000000000000000000000000,4607,3516,0.001000000000000000020816681712,1,0.05
771,771_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,218.000000000000000000000000000000,4889,468,0.998999999999999999111821580300,1,0.001
772,772_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.409999999999999975575093458247,34.000000000000000000000000000000,936,3876,0.001000000000000000020816681712,50,0.025
773,773_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,45.000000000000000000000000000000,3578,4081,0.001000000000000000020816681712,50,0.05
774,774_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1286,1,0.998999999999999999111821580300,47,0.025
775,775_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,56.000000000000000000000000000000,4237,5000,0.998999999999999999111821580300,32,0.005
776,776_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,61.000000000000000000000000000000,4156,4654,0.998999999999999999111821580300,50,0.001
777,777_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,58.000000000000000000000000000000,3946,3283,0.001000000000000000020816681712,1,0.025
778,778_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,103.000000000000000000000000000000,1792,1676,0.505093020338126974522197087936,1,0.005
779,779_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,62.000000000000000000000000000000,4073,4704,0.001000000000000000020816681712,1,0.05
780,780_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,59.000000000000000000000000000000,5000,4477,0.998999999999999999111821580300,50,0.001
781,781_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,185.000000000000000000000000000000,1729,751,0.998999999999999999111821580300,50,0.1
782,782_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,66.000000000000000000000000000000,1907,4500,0.403047462081664087385490802262,1,0.1
783,783_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,57.000000000000000000000000000000,3752,4111,0.998999999999999999111821580300,50,0.005
784,784_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,51.000000000000000000000000000000,1828,4115,0.512609603024293813966494326451,1,0.01
785,785_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,167.000000000000000000000000000000,3579,856,0.998999999999999999111821580300,38,0.005
786,786_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,78.000000000000000000000000000000,1829,2801,0.001000000000000000020816681712,35,0.05
787,787_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,135.000000000000000000000000000000,4635,1728,0.001000000000000000020816681712,50,0.005
788,788_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,101.000000000000000000000000000000,1794,1477,0.998999999999999999111821580300,1,0.005
789,789_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,46.000000000000000000000000000000,4153,4784,0.998999999999999999111821580300,1,0.25
790,790_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,101.000000000000000000000000000000,4040,1718,0.998999999999999999111821580300,23,0.005
791,791_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,138.000000000000000000000000000000,4623,1054,0.001000000000000000020816681712,1,0.25
792,792_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,45.000000000000000000000000000000,4045,4830,0.001000000000000000020816681712,50,0.25
793,793_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,52.000000000000000000000000000000,1679,3404,0.998999999999999999111821580300,1,0.25
794,794_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,54.000000000000000000000000000000,1774,4429,0.001000000000000000020816681712,50,0.25
795,795_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,104.000000000000000000000000000000,1777,1835,0.535678370925082081299706260324,1,0.1
796,796_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,143.000000000000000000000000000000,3586,724,0.510101953192440005224739252299,50,0.05
797,797_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,69.000000000000000000000000000000,3080,1830,0.998999999999999999111821580300,50,0.1
798,798_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,79.000000000000000000000000000000,3995,2280,0.365484475903738947888399479780,50,0.01
799,799_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,145.000000000000000000000000000000,4819,967,0.998999999999999999111821580300,1,0.025
800,800_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,57.000000000000000000000000000000,1815,3976,0.436711816346683379119752999031,50,0.005
801,801_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,114.000000000000000000000000000000,4221,1365,0.384558552601063141640480580463,50,0.05
802,802_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,72.000000000000000000000000000000,4509,2399,0.001000000000000000020816681712,1,0.005
803,803_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,47.000000000000000000000000000000,1850,4066,0.998999999999999999111821580300,1,0.005
804,804_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,139.000000000000000000000000000000,4475,1387,0.001000000000000000020816681712,26,0.01
805,805_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,73.000000000000000000000000000000,4075,2287,0.001000000000000000020816681712,42,0.05
806,806_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,110.000000000000000000000000000000,2748,715,0.001000000000000000020816681712,36,0.05
807,807_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3188,1,0.001000000000000000020816681712,50,0.025
808,808_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,93.000000000000000000000000000000,4630,1633,0.001000000000000000020816681712,1,0.25
809,809_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,61.000000000000000000000000000000,4174,4171,0.001000000000000000020816681712,50,0.005
810,810_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,50.000000000000000000000000000000,3894,4255,0.001000000000000000020816681712,1,0.01
811,811_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,58.000000000000000000000000000000,4799,3453,0.001000000000000000020816681712,50,0.025
812,812_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,117.000000000000000000000000000000,1476,1131,0.998999999999999999111821580300,2,0.05
813,813_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,55.000000000000000000000000000000,1742,4674,0.415228446503523973554194981261,50,0.005
814,814_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,53.000000000000000000000000000000,4140,4330,0.998999999999999999111821580300,50,0.05
815,815_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,69.000000000000000000000000000000,4403,3431,0.480643545735968458210152221000,50,0.001
816,816_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,99.000000000000000000000000000000,1769,1660,0.998999999999999999111821580300,34,0.05
817,817_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,65.000000000000000000000000000000,4797,3879,0.998999999999999999111821580300,50,0.025
818,818_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1365,1,0.998999999999999999111821580300,47,0.025
819,819_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,50.000000000000000000000000000000,1753,4549,0.001000000000000000020816681712,26,0.25
820,820_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,52.000000000000000000000000000000,1741,3503,0.998999999999999999111821580300,1,0.25
821,821_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,57.000000000000000000000000000000,4356,5000,0.508908257618859827253743333131,1,0.025
822,822_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,85.000000000000000000000000000000,3538,1321,0.713576605364549254595374350174,1,0.25
823,823_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,55.000000000000000000000000000000,1818,3235,0.998999999999999999111821580300,50,0.05
824,824_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,52.000000000000000000000000000000,4695,4284,0.998999999999999999111821580300,1,0.001
825,825_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4897,1,0.998999999999999999111821580300,1,0.025
826,826_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,206.000000000000000000000000000000,4377,937,0.001000000000000000020816681712,50,0.025
827,827_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,43.000000000000000000000000000000,4277,4882,0.998999999999999999111821580300,18,0.025
828,828_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,47.000000000000000000000000000000,1758,4646,0.998999999999999999111821580300,1,0.005
829,829_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,62.000000000000000000000000000000,3976,3845,0.399752357120853551997186059452,16,0.05
830,830_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,63.000000000000000000000000000000,4020,4545,0.998999999999999999111821580300,50,0.25
831,831_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,66.000000000000000000000000000000,1830,3883,0.001000000000000000020816681712,1,0.025
832,832_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2664,1,0.001000000000000000020816681712,50,0.025
833,833_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,117.000000000000000000000000000000,3451,997,0.998999999999999999111821580300,29,0.05
834,834_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,69.000000000000000000000000000000,4243,2815,0.438811400942881735254275099578,1,0.25
835,835_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,167.000000000000000000000000000000,1667,745,0.998999999999999999111821580300,43,0.01
836,836_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,90.000000000000000000000000000000,4004,1963,0.001000000000000000020816681712,1,0.05
837,837_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,83.000000000000000000000000000000,1842,1849,0.001000000000000000020816681712,1,0.005
838,838_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,114.000000000000000000000000000000,4160,1201,0.998999999999999999111821580300,29,0.05
839,839_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,52.000000000000000000000000000000,4487,4911,0.001000000000000000020816681712,50,0.05
840,840_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2656,1,0.001000000000000000020816681712,50,0.025
841,841_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,109.000000000000000000000000000000,1729,1282,0.001000000000000000020816681712,50,0.1
842,842_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,58.000000000000000000000000000000,1766,3230,0.998999999999999999111821580300,36,0.1
843,843_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.409999999999999975575093458247,36.000000000000000000000000000000,2860,4930,0.001000000000000000020816681712,1,0.025
844,844_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,57.000000000000000000000000000000,4176,4230,0.683034944221381778994839351071,50,0.005
845,845_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,67.000000000000000000000000000000,1791,2293,0.001000000000000000020816681712,1,0.005
846,846_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,65.000000000000000000000000000000,4689,2947,0.001000000000000000020816681712,1,0.005
847,847_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1300,1,0.998999999999999999111821580300,49,0.025
848,848_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,54.000000000000000000000000000000,1737,4145,0.998999999999999999111821580300,30,0.05
849,849_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,54.000000000000000000000000000000,1763,3712,0.315658020772085878835611083559,1,0.025
850,850_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,67.000000000000000000000000000000,4869,2979,0.998999999999999999111821580300,50,0.1
851,851_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,157.000000000000000000000000000000,1240,485,0.998999999999999999111821580300,34,0.1
852,852_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,165.000000000000000000000000000000,4197,764,0.998999999999999999111821580300,50,0.001
853,853_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,161.000000000000000000000000000000,4817,843,0.267238103157458994907358373894,1,0.25
854,854_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,125.000000000000000000000000000000,1418,1335,0.998999999999999999111821580300,1,0.05
855,855_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,211.000000000000000000000000000000,3259,308,0.814937805956197025558651603205,47,0.01
856,856_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,73.000000000000000000000000000000,4308,3352,0.679738318889548942536293907324,19,0.25
857,857_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,243.000000000000000000000000000000,3166,722,0.001000000000000000020816681712,40,0.005
858,858_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,102.000000000000000000000000000000,1724,1993,0.356956554937928771664701343980,50,0.025
859,859_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,88.000000000000000000000000000000,1762,1878,0.998999999999999999111821580300,10,0.05
860,860_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,153.000000000000000000000000000000,4748,937,0.998999999999999999111821580300,8,0.05
861,861_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,200.000000000000000000000000000000,1475,730,0.998999999999999999111821580300,12,0.1
862,862_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,60.000000000000000000000000000000,1760,2942,0.998999999999999999111821580300,1,0.001
863,863_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,106.000000000000000000000000000000,4696,1980,0.998999999999999999111821580300,42,0.001
864,864_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,20.000000000000000000000000000000,1,5000,0.001000000000000000020816681712,1,0.01
865,865_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,199.000000000000000000000000000000,1284,542,0.998999999999999999111821580300,30,0.05
866,866_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.309999999999999997779553950750,23.000000000000000000000000000000,1,2729,0.001000000000000000020816681712,1,0.1
867,867_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,65.000000000000000000000000000000,4649,3634,0.998999999999999999111821580300,50,0.1
868,868_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,47.000000000000000000000000000000,4147,5000,0.001000000000000000020816681712,27,0.01
869,869_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,104.000000000000000000000000000000,857,746,0.998999999999999999111821580300,49,0.01
870,870_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,86.000000000000000000000000000000,4298,2235,0.998999999999999999111821580300,50,0.05
871,871_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,79.000000000000000000000000000000,4793,2635,0.998999999999999999111821580300,50,0.25
872,872_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,52.000000000000000000000000000000,4093,4404,0.001000000000000000020816681712,1,0.025
873,873_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,58.000000000000000000000000000000,1742,3725,0.998999999999999999111821580300,22,0.25
874,874_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,50.000000000000000000000000000000,1753,3510,0.450952960433276428542370695141,1,0.025
875,875_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.429999999999999993338661852249,40.000000000000000000000000000000,1055,4931,0.998999999999999999111821580300,50,0.025
876,876_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,142.000000000000000000000000000000,4195,1086,0.212089080540408009278863232794,50,0.001
877,877_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,67.000000000000000000000000000000,3912,2789,0.998999999999999999111821580300,23,0.005
878,878_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2772,1,0.998999999999999999111821580300,50,0.025
879,879_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1075,283,0.001000000000000000020816681712,50,0.25
880,880_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,113.000000000000000000000000000000,4391,2109,0.001000000000000000020816681712,50,0.05
881,881_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2666,1,0.001000000000000000020816681712,50,0.025
882,882_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,106.000000000000000000000000000000,4250,1872,0.767956766383247724228056085849,50,0.25
883,883_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,180.000000000000000000000000000000,1672,750,0.998999999999999999111821580300,50,0.05
884,884_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,47.000000000000000000000000000000,1164,3122,0.001000000000000000020816681712,5,0.05
885,885_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,101.000000000000000000000000000000,3715,4922,0.001000000000000000020816681712,24,0.001
886,886_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,198.000000000000000000000000000000,1063,462,0.998999999999999999111821580300,38,0.005
887,887_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3107,1,0.001000000000000000020816681712,50,0.025
888,888_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,181.000000000000000000000000000000,1696,743,0.998999999999999999111821580300,50,0.025
889,889_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,92.000000000000000000000000000000,1702,2040,0.998999999999999999111821580300,12,0.05
890,890_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,125.000000000000000000000000000000,1820,1217,0.998999999999999999111821580300,4,0.05
891,891_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4956,1,0.998999999999999999111821580300,1,0.025
892,892_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,330.000000000000000000000000000000,1497,478,0.001000000000000000020816681712,28,0.001
893,893_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,106.000000000000000000000000000000,4057,1703,0.998999999999999999111821580300,50,0.05
894,894_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,60.000000000000000000000000000000,1737,3507,0.998999999999999999111821580300,50,0.01
895,895_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,78.000000000000000000000000000000,1813,3072,0.998999999999999999111821580300,1,0.1
896,896_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,62.000000000000000000000000000000,4154,3504,0.001000000000000000020816681712,8,0.005
897,897_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,96.000000000000000000000000000000,4258,2189,0.206335836891812035576165840212,1,0.01
898,898_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,85.000000000000000000000000000000,1781,3273,0.497227059290200001928639039761,50,0.001
899,899_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,209.000000000000000000000000000000,4895,745,0.516092247167975193278266488051,50,0.05
900,900_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,54.000000000000000000000000000000,4901,4041,0.481219167597033592453925621157,1,0.05
901,901_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,273.000000000000000000000000000000,1297,281,0.001000000000000000020816681712,50,0.25
902,902_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,66.000000000000000000000000000000,4568,3188,0.998999999999999999111821580300,14,0.01
903,903_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,104.000000000000000000000000000000,4200,2370,0.998999999999999999111821580300,50,0.001
904,904_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4890,6,0.001000000000000000020816681712,26,0.025
905,905_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,497.000000000000000000000000000000,4836,1145,0.001000000000000000020816681712,50,0.001
906,906_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,237.000000000000000000000000000000,4876,526,0.655079450375493088642997463467,26,0.025
907,907_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,272.000000000000000000000000000000,4657,507,0.998999999999999999111821580300,50,0.01
908,908_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,92.000000000000000000000000000000,4011,2649,0.998999999999999999111821580300,1,0.025
909,909_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,71.000000000000000000000000000000,1837,2391,0.998999999999999999111821580300,1,0.25
910,910_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,68.000000000000000000000000000000,4655,2889,0.001000000000000000020816681712,50,0.01
911,911_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,72.000000000000000000000000000000,4291,4562,0.001000000000000000020816681712,50,0.001
912,912_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,60.000000000000000000000000000000,5000,4548,0.395506779999084456811431209644,25,0.01
913,913_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,77.000000000000000000000000000000,1900,2601,0.386739065631536149680869129952,1,0.01
914,914_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,108.000000000000000000000000000000,1714,1557,0.998999999999999999111821580300,1,0.05
915,915_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,51.000000000000000000000000000000,1363,4804,0.445209977165536785470578706736,1,0.025
916,916_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,68.000000000000000000000000000000,1856,4839,0.998999999999999999111821580300,1,0.025
917,917_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,58.000000000000000000000000000000,1742,3391,0.398925914106763090938301274946,1,0.01
918,918_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,71.000000000000000000000000000000,1748,3574,0.365709809920307660213723011111,1,0.01
919,919_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,64.000000000000000000000000000000,1795,3392,0.998999999999999999111821580300,29,0.025
920,920_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,52.000000000000000000000000000000,4169,4217,0.568264867841226539724175381707,1,0.05
921,921_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,42.000000000000000000000000000000,1700,4853,0.644809199441345848313744681946,50,0.01
922,922_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,124.000000000000000000000000000000,4166,1287,0.598195546370430375482385443320,26,0.25
923,923_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,77.000000000000000000000000000000,1799,2044,0.458150222837972354028579502483,1,0.005
924,924_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,95.000000000000000000000000000000,4086,2842,0.001000000000000000020816681712,1,0.001
925,925_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,200.000000000000000000000000000000,1781,632,0.365864186211943498427956455998,1,0.05
926,926_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,170.000000000000000000000000000000,3839,1211,0.998999999999999999111821580300,12,0.25
927,927_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,166.000000000000000000000000000000,3818,1270,0.515276612128078626007265938824,1,0.025
928,928_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,55.000000000000000000000000000000,1803,4023,0.001000000000000000020816681712,23,0.005
929,929_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,656.000000000000000000000000000000,4771,205,0.001000000000000000020816681712,1,0.025
930,930_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,68.000000000000000000000000000000,4708,3396,0.001000000000000000020816681712,50,0.01
931,931_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,91.000000000000000000000000000000,1850,2349,0.180790466838424035422860924882,1,0.05
932,932_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,73.000000000000000000000000000000,1800,2811,0.767928435819160370989777675277,1,0.01
933,933_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,52.000000000000000000000000000000,4698,4560,0.998999999999999999111821580300,1,0.001
934,934_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,61.000000000000000000000000000000,1799,3697,0.509903028356040954882644200552,1,0.005
935,935_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,226.000000000000000000000000000000,4486,720,0.998999999999999999111821580300,30,0.01
936,936_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,179.000000000000000000000000000000,4659,695,0.998999999999999999111821580300,31,0.1
937,937_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,193.000000000000000000000000000000,4742,689,0.001000000000000000020816681712,11,0.005
938,938_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,222.000000000000000000000000000000,4739,806,0.998999999999999999111821580300,1,0.05
939,939_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,60.000000000000000000000000000000,4895,3115,0.475434545689364329579262857806,50,0.1
940,940_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.510000000000000008881784197001,75.000000000000000000000000000000,3123,2616,0.001000000000000000020816681712,1,0.25
941,941_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,37.000000000000000000000000000000,1115,4528,0.001000000000000000020816681712,1,0.025
942,942_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,56.000000000000000000000000000000,1770,4503,0.001000000000000000020816681712,40,0.05
943,943_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,90.000000000000000000000000000000,1573,1649,0.661250464437342921897311498469,50,0.1
944,944_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,165.000000000000000000000000000000,4318,972,0.001000000000000000020816681712,24,0.005
945,945_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,59.000000000000000000000000000000,1453,5000,0.435209130631593998916883947459,50,0.005
946,946_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,54.000000000000000000000000000000,3717,3301,0.001000000000000000020816681712,50,0.025
947,947_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,78.000000000000000000000000000000,1863,2717,0.001000000000000000020816681712,50,0.025
948,948_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,128.000000000000000000000000000000,1173,785,0.998999999999999999111821580300,1,0.001
949,949_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,57.000000000000000000000000000000,4075,4776,0.001000000000000000020816681712,22,0.01
950,950_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.510000000000000008881784197001,63.000000000000000000000000000000,1295,2804,0.001000000000000000020816681712,1,0.01
951,951_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.419999999999999984456877655248,38.000000000000000000000000000000,2711,3828,0.998999999999999999111821580300,24,0.001
952,952_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,72.000000000000000000000000000000,4842,4539,0.998999999999999999111821580300,1,0.025
953,953_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,104.000000000000000000000000000000,4346,2072,0.001000000000000000020816681712,1,0.025
954,954_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,60.000000000000000000000000000000,3799,3204,0.001000000000000000020816681712,23,0.1
955,955_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,65.000000000000000000000000000000,4982,4099,0.001000000000000000020816681712,1,0.025
956,956_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,83.000000000000000000000000000000,3992,2773,0.998999999999999999111821580300,30,0.1
957,957_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,44.000000000000000000000000000000,4757,4815,0.001000000000000000020816681712,50,0.05
958,958_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,58.000000000000000000000000000000,4257,4913,0.704552268503417566947177874681,50,0.01
959,959_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,71.000000000000000000000000000000,1804,2140,0.998999999999999999111821580300,36,0.005
960,960_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,63.000000000000000000000000000000,3845,3198,0.001000000000000000020816681712,50,0.025
961,961_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.510000000000000008881784197001,61.000000000000000000000000000000,4788,3667,0.001000000000000000020816681712,23,0.1
962,962_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,101.000000000000000000000000000000,1284,1126,0.998999999999999999111821580300,1,0.025
963,963_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,136.000000000000000000000000000000,4398,1212,0.998999999999999999111821580300,50,0.1
964,964_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,56.000000000000000000000000000000,1788,3221,0.998999999999999999111821580300,1,0.05
965,965_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,76.000000000000000000000000000000,4795,2699,0.578274558316738240826282435592,50,0.01
966,966_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,50.000000000000000000000000000000,1741,3761,0.541985683316674293763526293333,50,0.01
967,967_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,45.000000000000000000000000000000,1649,4745,0.001000000000000000020816681712,50,0.1
968,968_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,101.000000000000000000000000000000,1713,1490,0.998999999999999999111821580300,50,0.05
969,969_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,94.000000000000000000000000000000,4012,2755,0.001000000000000000020816681712,1,0.01
970,970_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,178.000000000000000000000000000000,5000,923,0.998999999999999999111821580300,28,0.05
971,971_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.400000000000000022204460492503,33.000000000000000000000000000000,614,4077,0.499451969056397326873764086486,50,0.001
972,972_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,70.000000000000000000000000000000,4052,4323,0.557387484391451804022210581024,50,0.1
973,973_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,52.000000000000000000000000000000,1743,3802,0.580293274892709098899956643436,18,0.001
974,974_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,60.000000000000000000000000000000,4063,3143,0.001000000000000000020816681712,1,0.05
975,975_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,47.000000000000000000000000000000,1596,4963,0.001000000000000000020816681712,50,0.05
976,976_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,50.000000000000000000000000000000,1729,4473,0.998999999999999999111821580300,50,0.1
977,977_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,52.000000000000000000000000000000,5000,3479,0.767427884703277052302894389868,1,0.001
978,978_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,79.000000000000000000000000000000,1881,2319,0.998999999999999999111821580300,1,0.025
979,979_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,205.000000000000000000000000000000,1111,532,0.998999999999999999111821580300,1,0.001
980,980_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,57.000000000000000000000000000000,1852,4238,0.714304261352410208019136916846,1,0.01
981,981_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,65.000000000000000000000000000000,1883,2807,0.998999999999999999111821580300,20,0.025
982,982_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,115.000000000000000000000000000000,3933,4432,0.001000000000000000020816681712,19,0.005
983,983_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,50.000000000000000000000000000000,4266,3931,0.537037090005745065823816730699,23,0.25
984,984_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,45.000000000000000000000000000000,1844,3957,0.998999999999999999111821580300,1,0.1
985,985_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,45.000000000000000000000000000000,3677,3796,0.998999999999999999111821580300,50,0.001
986,986_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,97.000000000000000000000000000000,1624,1548,0.001000000000000000020816681712,4,0.05
987,987_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,55.000000000000000000000000000000,3465,5000,0.488488369911278974555557397252,1,0.1
988,988_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.419999999999999984456877655248,37.000000000000000000000000000000,818,3951,0.001000000000000000020816681712,49,0.05
989,989_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,82.000000000000000000000000000000,1882,1863,0.001000000000000000020816681712,1,0.025
990,990_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,62.000000000000000000000000000000,3961,3798,0.998999999999999999111821580300,1,0.01
991,991_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,60.000000000000000000000000000000,4691,3787,0.001000000000000000020816681712,50,0.05
992,992_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,89.000000000000000000000000000000,4578,1801,0.001000000000000000020816681712,50,0.025
993,993_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,52.000000000000000000000000000000,1846,4593,0.779445835481064608352141931391,1,0.005
994,994_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.550000000000000044408920985006,82.000000000000000000000000000000,1803,2288,0.472098859628539924138124206365,50,0.25
995,995_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,80.000000000000000000000000000000,1830,2198,0.001000000000000000020816681712,20,0.1
996,996_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,46.000000000000000000000000000000,3696,4436,0.998999999999999999111821580300,50,0.05
997,997_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,50.000000000000000000000000000000,1633,4786,0.998999999999999999111821580300,50,0.05
998,998_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,45.000000000000000000000000000000,4085,5000,0.998999999999999999111821580300,29,0.05
999,999_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.520000000000000017763568394003,53.000000000000000000000000000000,1737,3398,0.001000000000000000020816681712,50,0.05
1000,1000_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,53.000000000000000000000000000000,4895,3718,0.001000000000000000020816681712,1,0.1
1001,1001_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,63.000000000000000000000000000000,5000,3900,0.998999999999999999111821580300,1,0.25
1002,1002_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,45.000000000000000000000000000000,3920,4571,0.998999999999999999111821580300,26,0.05
1003,1003_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,73.000000000000000000000000000000,1759,3308,0.998999999999999999111821580300,50,0.05
1004,1004_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,96.000000000000000000000000000000,1836,1746,0.001000000000000000020816681712,9,0.001
1005,1005_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,57.000000000000000000000000000000,933,3598,0.998999999999999999111821580300,1,0.025
1006,1006_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,63.000000000000000000000000000000,1728,2801,0.341032774983084485675988162257,1,0.025
1007,1007_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,102.000000000000000000000000000000,1880,1497,0.456212472738670771210678367424,1,0.001
1008,1008_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,154.000000000000000000000000000000,1708,962,0.289727868282613598704955393259,50,0.01
1009,1009_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,97.000000000000000000000000000000,4643,2354,0.426320795312254363640391829904,28,0.001
1010,1010_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,104.000000000000000000000000000000,1811,1664,0.001000000000000000020816681712,1,0.025
1011,1011_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,69.000000000000000000000000000000,4398,2441,0.998999999999999999111821580300,22,0.001
1012,1012_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4796,1,0.998999999999999999111821580300,50,0.025
1013,1013_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,193.000000000000000000000000000000,4696,706,0.998999999999999999111821580300,33,0.025
1014,1014_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,50.000000000000000000000000000000,3653,4719,0.001000000000000000020816681712,50,0.05
1015,1015_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,75.000000000000000000000000000000,4942,4218,0.001000000000000000020816681712,23,0.025
1016,1016_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,116.000000000000000000000000000000,1266,806,0.998999999999999999111821580300,1,0.005
1017,1017_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,96.000000000000000000000000000000,4097,2949,0.998999999999999999111821580300,16,0.005
1018,1018_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,66.000000000000000000000000000000,3458,2258,0.711644893395583566508832973341,20,0.05
1019,1019_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,81.000000000000000000000000000000,1619,1856,0.998999999999999999111821580300,31,0.005
1020,1020_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,53.000000000000000000000000000000,1783,4011,0.998999999999999999111821580300,50,0.25
1021,1021_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,113.000000000000000000000000000000,3411,976,0.466702056629812289667569302765,1,0.01
1022,1022_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,70.000000000000000000000000000000,1773,3061,0.998999999999999999111821580300,50,0.001
1023,1023_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,144.000000000000000000000000000000,4043,1175,0.998999999999999999111821580300,28,0.025
1024,1024_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,61.000000000000000000000000000000,4130,3462,0.998999999999999999111821580300,6,0.005
1025,1025_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,39.000000000000000000000000000000,1616,4676,0.998999999999999999111821580300,24,0.1
1026,1026_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,127.000000000000000000000000000000,3481,848,0.998999999999999999111821580300,1,0.005
1027,1027_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,62.000000000000000000000000000000,3949,3216,0.001000000000000000020816681712,20,0.025
1028,1028_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,67.000000000000000000000000000000,4206,4941,0.998999999999999999111821580300,1,0.01
1029,1029_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,69.000000000000000000000000000000,4218,2192,0.001000000000000000020816681712,1,0.025
1030,1030_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,50.000000000000000000000000000000,1738,3275,0.001000000000000000020816681712,32,0.25
1031,1031_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.569999999999999951150186916493,87.000000000000000000000000000000,1028,1345,0.260453217544111936820883101973,50,0.25
1032,1032_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.599999999999999977795539507497,114.000000000000000000000000000000,1889,1334,0.264854246516290914303937142904,50,0.01
1033,1033_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,381.000000000000000000000000000000,1166,199,0.998999999999999999111821580300,50,0.01
1034,1034_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,159.000000000000000000000000000000,1662,624,0.998999999999999999111821580300,27,0.05
1035,1035_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.530000000000000026645352591004,62.000000000000000000000000000000,1818,3064,0.464893940753763557083289015281,1,0.01
1036,1036_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.579999999999999960031971113494,94.000000000000000000000000000000,4322,2165,0.998999999999999999111821580300,29,0.025
1037,1037_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.460000000000000019984014443253,57.000000000000000000000000000000,1681,4183,0.510523082873482580978929945559,50,0.01
1038,1038_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,61.000000000000000000000000000000,3831,4143,0.514100220552603071055841610359,22,0.25
1039,1039_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.540000000000000035527136788005,94.000000000000000000000000000000,3958,3048,0.998999999999999999111821580300,1,0.025
1040,1040_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,224.000000000000000000000000000000,2683,249,0.679052774147129722948079688649,50,0.005
1041,1041_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,64.000000000000000000000000000000,405,1304,0.034841147825029124274198721878,1,0.05
1042,1042_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,103.000000000000000000000000000000,3375,865,0.520968817493783098449000590335,1,0.05
1043,1043_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,46.000000000000000000000000000000,1781,4687,0.528414158707825820648906756105,1,0.025
1044,1044_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.469999999999999973354647408996,43.000000000000000000000000000000,1706,4602,0.504277091580284442251524978929,50,0.05
1045,1045_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.560000000000000053290705182008,86.000000000000000000000000000000,5000,2351,0.001000000000000000020816681712,1,0.025
1046,1046_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.589999999999999968913755310496,123.000000000000000000000000000000,1788,1118,0.998999999999999999111821580300,50,0.005
1047,1047_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,51.000000000000000000000000000000,1724,4466,0.562369390503925647983862745605,33,0.005
1048,1048_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.500000000000000000000000000000,106.000000000000000000000000000000,1717,3539,0.001000000000000000020816681712,34,0.001
1049,1049_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,182.000000000000000000000000000000,3927,996,0.367589279616386654936377453851,1,0.05
1050,1050_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3510,4321,0.998999999999999999111821580300,50,0.025
</pre>
			<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("tab_results_csv_table_pre")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("tab_results_csv_table_pre", "results.csv")'> Download &raquo;results.csv&laquo; as file</button>
			<script>
				createTable(tab_results_csv_json, tab_results_headers_json, 'tab_results_csv_table');</script>
			
			<h1>&nbsp;Errors</h1>
			<button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download &raquo;oo_errors.txt&laquo; as file</button>
<pre id='simple_pre_tab_tab_errors'><span style="background-color: black; color: white">
/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4904360/4904360_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4904472/4904472_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4904953/4904953_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4905143/4905143_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4905237/4905237_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4905940/4905940_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4906139/4906139_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4906339/4906339_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4909460/4909460_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4909788/4909788_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4912420/4912420_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4916526/4916526_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4919555/4919555_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4921530/4921530_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4923124/4923124_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4925153/4925153_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4927022/4927022_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4936286/4936286_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4936684/4936684_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4937382/4937382_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4939626/4939626_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4941622/4941622_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4945932/4945932_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/4995000/4995000_0_log.err not found

</span></pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("simple_pre_tab_tab_errors")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("simple_pre_tab_tab_errors", "oo_errors.txt")'> Download &raquo;oo_errors.txt&laquo; as file</button>
			
			<h1>&nbsp;Args Overview</h1>
			<h2>Arguments Overview:                                                                                    </h2><table cellspacing="0" cellpadding="5"><thead><tr><th> Key</th><th>Value                                                                                                                                      </th></tr></thead><tbody><tr><td> config_yaml</td><td>None                                                                                                                                       </td></tr><tr><td> config_toml</td><td>None                                                                                                                                       </td></tr><tr><td> config_json</td><td>None                                                                                                                                       </td></tr><tr><td> num_random_steps</td><td>20                                                                                                                                         </td></tr><tr><td> max_eval</td><td>50000                                                                                                                                      </td></tr><tr><td> run_program</td><td>None                                                                                                                                       </td></tr><tr><td> experiment_name</td><td>None                                                                                                                                       </td></tr><tr><td> mem_gb</td><td>32                                                                                                                                         </td></tr><tr><td> parameter</td><td>None                                                                                                                                       </td></tr><tr><td> continue_previous_job</td><td>/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_RialtoBridgeTimelapse_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/ </td></tr><tr><td> experiment_constraints</td><td>None                                                                                                                                       </td></tr><tr><td> run_dir</td><td>runs                                                                                                                                       </td></tr><tr><td> seed</td><td>None                                                                                                                                       </td></tr><tr><td> decimalrounding</td><td>4                                                                                                                                          </td></tr><tr><td> enforce_sequential_optimization</td><td>False                                                                                                                                      </td></tr><tr><td> verbose_tqdm</td><td>False                                                                                                                                      </td></tr><tr><td> model</td><td>None                                                                                                                                       </td></tr><tr><td> gridsearch</td><td>False                                                                                                                                      </td></tr><tr><td> occ</td><td>False                                                                                                                                      </td></tr><tr><td> show_sixel_scatter</td><td>False                                                                                                                                      </td></tr><tr><td> show_sixel_general</td><td>False                                                                                                                                      </td></tr><tr><td> show_sixel_trial_index_result</td><td>False                                                                                                                                      </td></tr><tr><td> follow</td><td>False                                                                                                                                      </td></tr><tr><td> send_anonymized_usage_stats</td><td>False                                                                                                                                      </td></tr><tr><td> ui_url</td><td>None                                                                                                                                       </td></tr><tr><td> root_venv_dir</td><td>/home/s4122485                                                                                                                             </td></tr><tr><td> exclude</td><td>None                                                                                                                                       </td></tr><tr><td> main_process_gb</td><td>8                                                                                                                                          </td></tr><tr><td> pareto_front_confidence</td><td>1                                                                                                                                          </td></tr><tr><td> max_nr_of_zero_results</td><td>10                                                                                                                                         </td></tr><tr><td> abbreviate_job_names</td><td>False                                                                                                                                      </td></tr><tr><td> orchestrator_file</td><td>None                                                                                                                                       </td></tr><tr><td> checkout_to_latest_tested_version</td><td>False                                                                                                                                      </td></tr><tr><td> live_share</td><td>False                                                                                                                                      </td></tr><tr><td> disable_tqdm</td><td>False                                                                                                                                      </td></tr><tr><td> workdir</td><td>False                                                                                                                                      </td></tr><tr><td> occ_type</td><td>euclid                                                                                                                                     </td></tr><tr><td> result_names</td><td>[&#039;RESULT=min&#039;]                                                                                                                             </td></tr><tr><td> minkowski_p</td><td>2                                                                                                                                          </td></tr><tr><td> signed_weighted_euclidean_weights</td><td></td></tr><tr><td> generation_strategy</td><td>None                                                                                                                                       </td></tr><tr><td> generate_all_jobs_at_once</td><td>False                                                                                                                                      </td></tr><tr><td> revert_to_random_when_seemingly_exhausted</td><td>True                                                                                                                                       </td></tr><tr><td> load_data_from_existing_jobs</td><td>[]                                                                                                                                         </td></tr><tr><td> n_estimators_randomforest</td><td>100                                                                                                                                        </td></tr><tr><td> external_generator</td><td>None                                                                                                                                       </td></tr><tr><td> username</td><td>None                                                                                                                                       </td></tr><tr><td> max_failed_jobs</td><td>None                                                                                                                                       </td></tr><tr><td> num_parallel_jobs</td><td>20                                                                                                                                         </td></tr><tr><td> worker_timeout</td><td>120                                                                                                                                        </td></tr><tr><td> slurm_use_srun</td><td>False                                                                                                                                      </td></tr><tr><td> time</td><td></td></tr><tr><td> partition</td><td></td></tr><tr><td> reservation</td><td>None                                                                                                                                       </td></tr><tr><td> force_local_execution</td><td>False                                                                                                                                      </td></tr><tr><td> slurm_signal_delay_s</td><td>0                                                                                                                                          </td></tr><tr><td> nodes_per_job</td><td>1                                                                                                                                          </td></tr><tr><td> cpus_per_task</td><td>1                                                                                                                                          </td></tr><tr><td> account</td><td>None                                                                                                                                       </td></tr><tr><td> gpus</td><td>0                                                                                                                                          </td></tr><tr><td> run_mode</td><td>local                                                                                                                                      </td></tr><tr><td> verbose</td><td>False                                                                                                                                      </td></tr><tr><td> verbose_break_run_search_table</td><td>False                                                                                                                                      </td></tr><tr><td> debug</td><td>False                                                                                                                                      </td></tr><tr><td> no_sleep</td><td>False                                                                                                                                      </td></tr><tr><td> tests</td><td>False                                                                                                                                      </td></tr><tr><td> show_worker_percentage_table_at_end</td><td>False                                                                                                                                      </td></tr><tr><td> auto_exclude_defective_hosts</td><td>False                                                                                                                                      </td></tr><tr><td> run_tests_that_fail_on_taurus</td><td>False                                                                                                                                      </td></tr><tr><td> raise_in_eval</td><td>False                                                                                                                                      </td></tr><tr><td> show_ram_every_n_seconds</td><td>False                                                                                                                                      </td></tr></tbody></table>
			<h1>&nbsp;Worker-Usage</h1>
			<div class='invert_in_dark_mode' id='workerUsagePlot'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
<pre id="pre_tab_worker_usage">1746192598.5694206,20,0,0
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1746608753.5835125,20,1,5
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1746612745.166097,20,1,5
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1746616410.2118866,20,1,5
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1746616427.2112901,20,1,5
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1746620109.1737576,20,1,5
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1746623778.061795,20,1,5
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1746626625.0780857,20,1,5
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1746640182.3003445,20,1,5
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1746644497.5761852,20,1,5
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1746669443.3288102,20,1,5
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1746704302.5577488,20,1,5
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1746711781.636287,20,1,5
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1746714448.3208368,20,1,5
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1746714450.608208,20,2,10
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1746737541.5119467,20,2,10
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1746740337.7386,20,2,10
1746740351.3612938,20,1,5
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1746764336.1011145,20,1,5
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;CPU/RAM-Usage (main)</h1>
			<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download &raquo;cpu_ram_usage.csv&laquo; as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1746192598,645.9609375,32.8
1746192598,645.9609375,33.1
1746192598,646.02734375,32.1
1746192598,646.02734375,36.1
1746192598,646.02734375,32.4
1746192598,646.02734375,32.8
1746192598,646.02734375,35.9
1746202970,814.671875,32.2
1746202970,814.671875,29.4
1746202970,814.671875,27.5
1746202970,814.671875,31.4
1746212460,839.984375,24.3
1746212460,839.984375,18.6
1746212460,839.984375,17.7
1746212460,839.984375,12.1
1746219441,798.5703125,13.6
1746219441,798.5703125,8.9
1746219441,798.5703125,8.9
1746219441,798.5703125,8.8
1746230583,828.97265625,8.1
1746230583,828.97265625,7.4
1746230583,828.97265625,7.3
1746230583,828.97265625,10.9
1746239616,851.41015625,8.5
1746239616,851.41015625,8.2
1746239617,851.41015625,7.9
1746239617,851.41015625,6.2
1746251400,874.14453125,8.3
1746251400,874.14453125,8.7
1746251400,874.14453125,9.0
1746251400,874.14453125,8.9
1746269244,868.3203125,10.1
1746269244,868.3203125,9.2
1746269244,868.3203125,9.3
1746269244,868.3203125,13.2
1746291233,923.89453125,9.9
1746291233,923.89453125,8.4
1746291233,923.89453125,8.5
1746291233,923.89453125,4.9
1746315438,890.3359375,8.4
1746315438,890.3359375,6.7
1746315438,890.3359375,6.9
1746315438,890.3359375,7.9
1746345777,945.58203125,8.2
1746345777,945.58203125,7.7
1746345777,945.58203125,7.6
1746345777,945.58203125,5.6
1746380660,926.9140625,14.6
1746380660,926.9140625,18.4
1746380661,926.9140625,18.7
1746380661,926.9140625,14.0
1746414841,948.7109375,14.3
1746414841,948.7109375,8.5
1746414841,948.7109375,7.9
1746414841,948.7109375,6.4
1746454199,1109.96484375,12.1
1746454199,1109.96484375,6.4
1746454199,1109.96484375,6.7
1746454199,1109.96484375,5.1
1746500619,1013.875,8.5
1746500619,1013.875,8.5
1746500619,1013.875,8.5
1746500619,1013.875,7.7
1746554440,987.48046875,8.4
1746554440,987.48046875,7.0
1746554440,987.48046875,6.9
1746554440,987.48046875,9.3
1746616436,1021.828125,8.7
1746616436,1021.828125,8.9
1746616436,1021.828125,9.1
1746616437,1021.828125,8.5
1746690428,1043.81640625,12.3
1746690428,1043.81640625,13.7
1746690428,1043.81640625,13.4
1746690428,1043.81640625,16.7
1746764346,1052.44921875,9.3
1746764346,1052.44921875,6.6
1746764346,1052.44921875,6.5
1746764346,1052.44921875,6.1
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download &raquo;cpu_ram_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;Parallel Plot</h1>
			<div class="invert_in_dark_mode" id="parallel-plot"></div>
			<h1>&nbsp;Scatter-2D</h1>
			<div class='invert_in_dark_mode' id='plotScatter2d'></div>
			<h1>&nbsp;Scatter-3D</h1>
			<div class='invert_in_dark_mode' id='plotScatter3d'></div>
			<h1>&nbsp;Job Status Distribution</h1>
			<div class="invert_in_dark_mode" id="plotJobStatusDistribution"></div>
			<h1>&nbsp;Boxplots</h1>
			<div class="invert_in_dark_mode" id="plotBoxplot"></div>
			<h1>&nbsp;Violin</h1>
			<div class="invert_in_dark_mode" id="plotViolin"></div>
			<h1>&nbsp;Histogram</h1>
			<div class="invert_in_dark_mode" id="plotHistogram"></div>
			<h1>&nbsp;Heatmap</h1>
			<div class="invert_in_dark_mode" id="plotHeatmap"></div><br>
			    <h1>Correlation Heatmap Explanation</h1>
			
			    <p>
			        This is a heatmap that visualizes the correlation between numerical columns in a dataset. The values represented in the heatmap show the strength and direction of relationships between different variables.
			    </p>
			
			    <h2>How It Works</h2>
			    <p>
			        The heatmap uses a matrix to represent correlations between each pair of numerical columns. The calculation behind this is based on the concept of "correlation," which measures how strongly two variables are related. A correlation can be positive, negative, or zero:
			    </p>
			    <ul>
			        <li><strong>Positive correlation</strong>: Both variables increase or decrease together (e.g., if the temperature rises, ice cream sales increase).</li>
			        <li><strong>Negative correlation</strong>: As one variable increases, the other decreases (e.g., as the price of a product rises, the demand for it decreases).</li>
			        <li><strong>Zero correlation</strong>: There is no relationship between the two variables (e.g., height and shoe size might show zero correlation in some contexts).</li>
			    </ul>
			
			    <h2>Color Scale: Yellow to Purple (Viridis)</h2>
			    <p>
			        The heatmap uses a color scale called "Viridis," which ranges from yellow to purple. Here's what the colors represent:
			    </p>
			    <ul>
			        <li><strong>Yellow (brightest)</strong>: A strong positive correlation (close to +1). This indicates that as one variable increases, the other increases in a very predictable manner.</li>
			        <li><strong>Green</strong>: A moderate positive correlation. Variables are still positively related, but the relationship is not as strong.</li>
			        <li><strong>Blue</strong>: A weak or near-zero correlation. There is a small or no discernible relationship between the variables.</li>
			        <li><strong>Purple (darkest)</strong>: A strong negative correlation (close to -1). This indicates that as one variable increases, the other decreases in a very predictable manner.</li>
			    </ul>
			
			    <h2>What the Heatmap Shows</h2>
			    <p>
			        In the heatmap, each cell represents the correlation between two numerical columns. The color of the cell is determined by the correlation coefficient: from yellow for strong positive correlations, through green and blue for weaker correlations, to purple for strong negative correlations.
			    </p>
			
			<h1>&nbsp;Result-Pairs</h1>
			<div class="invert_in_dark_mode" id="plotResultPairs"></div>
			<h1>&nbsp;Evolution</h1>
			<div class="invert_in_dark_mode" id="plotResultEvolution"></div>
			<h1>&nbsp;Exit-Codes</h1>
			<div class="invert_in_dark_mode" id="plotExitCodesPieChart"></div>
	</body>
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