OmniOpt2

Setting	Value
Max. nr. evaluations	50080
Max. nr. evaluations (from arguments)	50000
Number random steps	20
Nr. of workers (parameter)	50
Main process memory (GB)	8
Worker memory (GB)	32
Nr. imported jobs	80
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
1050	194	16	1260
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_SensorStream_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	50
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
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		<title>Exported &raquo;s4122485/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1&laquo; from OmniOpt2-Share</title>
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				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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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1220 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 30",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1714 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1536 n_samples 1 confidence 0.001 feature_proportion 0.001 n_clusters 25",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1722 n_samples 1 confidence 0.1 feature_proportion 0.999 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1465 n_samples 1 confidence 0.001 feature_proportion 0.999 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1799 n_samples 1 confidence 0.01 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1636 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1659 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1212 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 30",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1806 n_samples 1 confidence 0.025 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1496 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 25",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1783 n_samples 1 confidence 0.025 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1603 n_samples 1 confidence 0.005 feature_proportion 0.001 n_clusters 25",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1685 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1722 n_samples 1 confidence 0.005 feature_proportion 0.001 n_clusters 27",
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			        1,
			        0.001,
			        27,
			        0.005,
			        "None",
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			        "",
			        "",
			        ""
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1571 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 25",
			        1571,
			        1,
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			        0.025,
			        "None",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1368 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 27",
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			        0.1,
			        "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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1741 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 26",
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			        0.001,
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			        0.01,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1535 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 25",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1105 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 32",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1210 n_samples 2458 confidence 0.25 feature_proportion 0.5123271825096511 n_clusters 1",
			        1210,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1790 n_samples 1 confidence 0.025 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1480 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1529 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 23",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1650 n_samples 1 confidence 0.005 feature_proportion 0.001 n_clusters 25",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1482 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 23",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1603 n_samples 1 confidence 0.001 feature_proportion 0.001 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1589 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1468 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1786 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 26",
			        1786,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1590 n_samples 1 confidence 0.1 feature_proportion 0.001 n_clusters 24",
			        1590,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1467 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 23",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1796 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1495 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1472 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1573 n_samples 1 confidence 0.01 feature_proportion 0.999 n_clusters 23",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1309 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 25",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1763 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1585 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1145 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 30",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1776 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1463 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1790 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1442 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1472 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1785 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1663 n_samples 1 confidence 0.1 feature_proportion 0.001 n_clusters 24",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1810 n_samples 1 confidence 0.025 feature_proportion 0.001 n_clusters 26",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1515 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 23",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1539 n_samples 1 confidence 0.01 feature_proportion 0.001 n_clusters 23",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1480 n_samples 1 confidence 0.025 feature_proportion 0.999 n_clusters 22",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1352 n_samples 2320 confidence 0.005 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1364 n_samples 1 confidence 0.005 feature_proportion 0.999 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1486 n_samples 1 confidence 0.01 feature_proportion 0.999 n_clusters 35",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1408 n_samples 1 confidence 0.01 feature_proportion 0.999 n_clusters 10",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1406 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1326 n_samples 1 confidence 0.001 feature_proportion 0.9842940686435531 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1319 n_samples 1 confidence 0.001 feature_proportion 0.9563541554805162 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1415 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1308 n_samples 1 confidence 0.001 feature_proportion 0.9846756531517721 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1403 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1364 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 7",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4508 n_samples 1665 confidence 0.25 feature_proportion 0.44167408189700863 n_clusters 13",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1406 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1314 n_samples 1 confidence 0.001 feature_proportion 0.8864383288195061 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1433 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1426 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 1",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1431 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1403 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4562 n_samples 10 confidence 0.025 feature_proportion 0.001 n_clusters 36",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1422 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1403 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1452 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 1",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1373 n_samples 1 confidence 0.001 feature_proportion 0.8758310024912581 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1435 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4325 n_samples 19 confidence 0.025 feature_proportion 0.001 n_clusters 36",
			        4325,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1332 n_samples 1 confidence 0.001 feature_proportion 0.882881116334821 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1426 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1403 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1441 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 4",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1308 n_samples 1 confidence 0.001 feature_proportion 0.907676976071096 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1425 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1447 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 1",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1329 n_samples 1 confidence 0.001 feature_proportion 0.8810335123618865 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1435 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1424 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1774 n_samples 1311 confidence 0.001 feature_proportion 0.9095700857447393 n_clusters 7",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1360 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1488 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 2",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1396 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1396,
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			        0.005,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1325 n_samples 1 confidence 0.001 feature_proportion 0.8593258432827535 n_clusters 40",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1423 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1423,
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			        "",
			        ""
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			    [
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1349 n_samples 1 confidence 0.001 feature_proportion 0.8557444178332931 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1425 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1425,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1436 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1436,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1329 n_samples 1 confidence 0.005 feature_proportion 0.8337141033601732 n_clusters 39",
			        1329,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1427 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1427,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1412 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1412,
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			        8,
			        0.005,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1335 n_samples 1 confidence 0.001 feature_proportion 0.8753780743248182 n_clusters 39",
			        1335,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1429 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1429,
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			        6,
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			        "None",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1415 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        1415,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1426 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1426,
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			        8,
			        0.005,
			        "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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1454 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 2",
			        1454,
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			        0.999,
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			        "None",
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			        "",
			        "",
			        ""
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1333 n_samples 1 confidence 0.001 feature_proportion 0.8807458939176019 n_clusters 39",
			        1333,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1374 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 5",
			        1374,
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			        5,
			        0.005,
			        "None",
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			        "",
			        "",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4362 n_samples 16 confidence 0.025 feature_proportion 0.001 n_clusters 37",
			        4362,
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			        0.001,
			        37,
			        0.025,
			        "None",
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			        "",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1414 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1414,
			        1,
			        0.999,
			        5,
			        0.1,
			        "None",
			        "None",
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			        "",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1392 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
			        1392,
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			        0.999,
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			        0.005,
			        "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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1444 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 3",
			        1444,
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			        0.999,
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			        0.1,
			        "None",
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			        "",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1389 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
			        1389,
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			        0.999,
			        9,
			        0.005,
			        "None",
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			        "",
			        "",
			        "",
			        "",
			        ""
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1316 n_samples 1 confidence 0.001 feature_proportion 0.8241896533660558 n_clusters 40",
			        1316,
			        1,
			        0.8241896533660558,
			        40,
			        0.001,
			        "None",
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			        "None",
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			        "",
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			    [
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1443 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1443,
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			        0.999,
			        6,
			        0.1,
			        "None",
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			        "i7184",
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			        "",
			        "",
			        ""
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			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1296 n_samples 1 confidence 0.001 feature_proportion 0.8375764698429752 n_clusters 39",
			        1296,
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			        0.8375764698429752,
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			        "",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1441 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1441,
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			        6,
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			        "None",
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			    ],
			    [
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1408 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1408,
			        1,
			        0.999,
			        8,
			        0.005,
			        "None",
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			        "i7184",
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			        ""
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1288 n_samples 1 confidence 0.001 feature_proportion 0.8313086137949401 n_clusters 40",
			        1288,
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			        0.8313086137949401,
			        40,
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			        "",
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			        ""
			    ],
			    [
			        1747144013,
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1342 n_samples 1 confidence 0.1 feature_proportion 0.8429112746746662 n_clusters 37",
			        1342,
			        1,
			        0.8429112746746662,
			        37,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1434 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1434,
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			        0.999,
			        5,
			        0.1,
			        "None",
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			        "",
			        ""
			    ],
			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1432 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
			        1432,
			        1,
			        0.999,
			        9,
			        0.005,
			        "None",
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			        "",
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			        ""
			    ],
			    [
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			        25,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1423 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 2",
			        1423,
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			        0.1,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4521 n_samples 32 confidence 0.025 feature_proportion 0.001 n_clusters 37",
			        4521,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1432 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1400 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 4506 n_samples 18 confidence 0.025 feature_proportion 0.001 n_clusters 36",
			        4506,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1432 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1432,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1349 n_samples 1 confidence 0.001 feature_proportion 0.848099256444599 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1435 n_samples 1 confidence 0.005 feature_proportion 0.8517399724952058 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1433 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1433,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1293 n_samples 1 confidence 0.005 feature_proportion 0.8663331329537056 n_clusters 39",
			        1293,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1448 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1448,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1327 n_samples 1 confidence 0.001 feature_proportion 0.8346345887560271 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1426 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1426,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1405 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1405,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1312 n_samples 1 confidence 0.001 feature_proportion 0.8578248858300132 n_clusters 40",
			        1312,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1443 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 4",
			        1443,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1405 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
			        1405,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1439 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 4",
			        1439,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1391 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 9",
			        1391,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1433 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1433,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1352 n_samples 1 confidence 0.1 feature_proportion 0.7681217823231377 n_clusters 39",
			        1352,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1445 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 7",
			        1445,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1356 n_samples 1 confidence 0.005 feature_proportion 0.8195946648979335 n_clusters 39",
			        1356,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1423 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1423,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1317 n_samples 1 confidence 0.001 feature_proportion 0.8214992755472571 n_clusters 39",
			        1317,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1428 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1428,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1451 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        1451,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1334 n_samples 1 confidence 0.005 feature_proportion 0.8121308498418017 n_clusters 38",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1428 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1428,
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			    [
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1429 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 2",
			        1429,
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			    [
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			        20,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1353 n_samples 1 confidence 0.1 feature_proportion 0.7816882874068125 n_clusters 38",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1431 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1431,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1324 n_samples 1 confidence 0.005 feature_proportion 0.8143042779376028 n_clusters 38",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1432 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1432,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1404 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1404,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1328 n_samples 1 confidence 0.001 feature_proportion 0.8436049114750099 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1429 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1429,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1327 n_samples 1 confidence 0.005 feature_proportion 0.8396731329000675 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1442 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 5",
			        1442,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1329 n_samples 1 confidence 0.001 feature_proportion 0.8381311279911421 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1431 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1431,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1410 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1410,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1325 n_samples 1 confidence 0.001 feature_proportion 0.8224919633109874 n_clusters 40",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1436 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1436,
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			    [
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1327 n_samples 1 confidence 0.001 feature_proportion 0.8588221830086095 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1462 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1462,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1444 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        1444,
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			        0.999,
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			    [
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			        20,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1317 n_samples 1 confidence 0.001 feature_proportion 0.8316985745927493 n_clusters 39",
			        1317,
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			        "None",
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			        "",
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			        ""
			    ],
			    [
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1412 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1412,
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			        0.999,
			        6,
			        0.1,
			        "None",
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			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1511 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 1",
			        1511,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1445 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
			        1445,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1459 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 2",
			        1459,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1327 n_samples 1 confidence 0.001 feature_proportion 0.8774456764620276 n_clusters 40",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1417 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 7",
			        1417,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1332 n_samples 1 confidence 0.001 feature_proportion 0.7699349316270631 n_clusters 39",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1442 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
			        1442,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1302 n_samples 1 confidence 0.001 feature_proportion 0.8628931157982281 n_clusters 39",
			        1302,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1300 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 6",
			        1300,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1443 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
			        1443,
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			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1315 n_samples 1 confidence 0.001 feature_proportion 0.8697256727062467 n_clusters 39",
			        1315,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1442 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 5",
			        1442,
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1434 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1317 n_samples 1 confidence 0.001 feature_proportion 0.8860445831443919 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1469 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 5",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1402 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1303 n_samples 1 confidence 0.001 feature_proportion 0.8662408945329785 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1440 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1311 n_samples 1 confidence 0.001 feature_proportion 0.8517851098860736 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1439 n_samples 1 confidence 0.1 feature_proportion 0.999 n_clusters 6",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1402 n_samples 1 confidence 0.005 feature_proportion 0.999 n_clusters 8",
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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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1320 n_samples 1 confidence 0.001 feature_proportion 0.9003843244328722 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 SensorStream 2000 HoeffdingTreeClassifier CSDDM recent_samples_size 1464 n_samples 1 confidence 0.1 feature_proportion 0.001 n_clusters 48",
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			    [
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			    [
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			    [
			        1747198884.1539783,
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			];
			var tab_main_worker_cpu_ram_csv_json = [
			    [
			        1747050909,
			        652.453125,
			        3.1
			    ],
			    [
			        1747050909,
			        652.453125,
			        3.4
			    ],
			    [
			        1747050909,
			        652.69921875,
			        2.9
			    ],
			    [
			        1747050909,
			        652.69921875,
			        2.2
			    ],
			    [
			        1747050909,
			        652.69921875,
			        2.2
			    ],
			    [
			        1747050909,
			        652.69921875,
			        3.1
			    ],
			    [
			        1747050909,
			        652.69921875,
			        4.4
			    ],
			    [
			        1747056742,
			        757.8203125,
			        4.2
			    ],
			    [
			        1747056742,
			        757.8203125,
			        4.8
			    ],
			    [
			        1747056743,
			        757.8203125,
			        4.6
			    ],
			    [
			        1747056743,
			        757.8203125,
			        3.1
			    ],
			    [
			        1747063700,
			        768.65625,
			        11.1
			    ],
			    [
			        1747063700,
			        768.65625,
			        3.7
			    ],
			    [
			        1747063700,
			        768.65625,
			        4
			    ],
			    [
			        1747063700,
			        768.65625,
			        5.7
			    ],
			    [
			        1747071536,
			        781.39453125,
			        5.5
			    ],
			    [
			        1747071536,
			        781.39453125,
			        9.5
			    ],
			    [
			        1747071536,
			        781.39453125,
			        9.1
			    ],
			    [
			        1747071536,
			        781.39453125,
			        8.7
			    ],
			    [
			        1747093059,
			        805.51171875,
			        11.8
			    ],
			    [
			        1747093059,
			        805.51171875,
			        16.8
			    ],
			    [
			        1747093059,
			        805.51171875,
			        16.4
			    ],
			    [
			        1747093059,
			        805.51171875,
			        14.7
			    ],
			    [
			        1747122763,
			        812.859375,
			        17.3
			    ],
			    [
			        1747122763,
			        812.859375,
			        18.4
			    ],
			    [
			        1747122764,
			        812.859375,
			        17.4
			    ],
			    [
			        1747122764,
			        812.859375,
			        20.3
			    ],
			    [
			        1747147987,
			        810.89453125,
			        17.1
			    ],
			    [
			        1747147987,
			        810.89453125,
			        18.5
			    ],
			    [
			        1747147987,
			        810.89453125,
			        18.5
			    ],
			    [
			        1747147987,
			        810.89453125,
			        20.4
			    ],
			    [
			        1747172668,
			        816.95703125,
			        16.7
			    ],
			    [
			        1747172668,
			        816.95703125,
			        17.6
			    ],
			    [
			        1747172668,
			        816.95703125,
			        17
			    ],
			    [
			        1747172668,
			        816.95703125,
			        22.2
			    ],
			    [
			        1747198912,
			        842.71484375,
			        17.1
			    ],
			    [
			        1747198912,
			        842.71484375,
			        15.8
			    ],
			    [
			        1747198912,
			        842.71484375,
			        16.4
			    ],
			    [
			        1747198912,
			        842.71484375,
			        20.5
			    ]
			];
			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>50080 </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>50    </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>80    </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>1050</td>
			<td>194</td>
			<td>16</td>
			<td>1260</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,0.160000000000000003330669073875,840.000000000000000000000000000000,1083,2611,0.070542474478483205291290403238,11,0.05
1,1_0,COMPLETED,Sobol,SOBOL,0.190000000000000002220446049250,824.000000000000000000000000000000,3129,1989,0.680266904624179047367249495437,26,0.25
2,2_0,COMPLETED,Sobol,SOBOL,0.149999999999999994448884876874,775.000000000000000000000000000000,4730,4890,0.889683827124536086294881442882,50,0.001
3,3_0,COMPLETED,Sobol,SOBOL,0.410000000000000031086244689504,1734.000000000000000000000000000000,1982,511,0.360119819998741153010968218950,15,0.01
4,4_0,COMPLETED,Sobol,SOBOL,0.140000000000000013322676295502,808.000000000000000000000000000000,1436,3869,0.530711051404476163995127535600,43,0.25
5,5_0,COMPLETED,Sobol,SOBOL,0.359999999999999986677323704498,1194.000000000000000000000000000000,4353,750,0.219490960024297243613489172276,22,0.01
6,6_0,COMPLETED,Sobol,SOBOL,0.110000000000000000555111512313,900.000000000000000000000000000000,2750,3631,0.460092540236189950775269608130,4,0.025
7,7_0,COMPLETED,Sobol,SOBOL,0.149999999999999994448884876874,776.000000000000000000000000000000,540,1750,0.789099973971024160057652352407,33,0.1
8,8_0,COMPLETED,Sobol,SOBOL,0.040000000000000000832667268469,1041.000000000000000000000000000000,160,4393,0.287202520200982691633839749557,35,0.001
9,9_0,FAILED,Sobol,SOBOL,,,3115,11,0.961982364999130368232727050781,2,0.01
10,10_0,COMPLETED,Sobol,SOBOL,0.200000000000000011102230246252,794.000000000000000000000000000000,4013,3107,0.731134507544338729800870169129,24,0.1
11,11_0,COMPLETED,Sobol,SOBOL,0.209999999999999992228438827624,820.000000000000000000000000000000,1763,2489,0.019075132891535759999124266528,41,0.01
12,12_0,COMPLETED,Sobol,SOBOL,0.070000000000000006661338147751,953.000000000000000000000000000000,2320,3190,0.871755753383040454806973684754,19,0.005
13,13_0,COMPLETED,Sobol,SOBOL,0.300000000000000044408920985006,929.000000000000000000000000000000,4407,1312,0.378040294475853466682480075178,46,0.1
14,14_0,COMPLETED,Sobol,SOBOL,0.140000000000000013322676295502,1177.000000000000000000000000000000,3507,4310,0.178377064989879735579236808007,30,0.001
15,15_0,COMPLETED,Sobol,SOBOL,0.230000000000000009992007221626,813.000000000000000000000000000000,721,1188,0.572439912447705867570846294257,7,0.25
16,16_0,COMPLETED,Sobol,SOBOL,0.110000000000000000555111512313,780.000000000000000000000000000000,914,4113,0.197274885172024377899546720982,10,0.025
17,17_0,COMPLETED,Sobol,SOBOL,0.309999999999999997779553950750,920.000000000000000000000000000000,3625,995,0.553418377431109553832300207432,31,0.001
18,18_0,COMPLETED,Sobol,SOBOL,0.200000000000000011102230246252,787.000000000000000000000000000000,4601,3387,0.751373884163796912361021895777,45,0.005
19,19_0,COMPLETED,Sobol,SOBOL,0.140000000000000013322676295502,812.000000000000000000000000000000,2438,1506,0.498294612050056484608973050854,16,0.05
20,20_0,COMPLETED,Sobol,SOBOL,0.179999999999999993338661852249,811.000000000000000000000000000000,1566,2836,0.642030548751354235292865269003,38,0.01
21,21_0,COMPLETED,Sobol,SOBOL,0.230000000000000009992007221626,874.000000000000000000000000000000,3897,2214,0.108295192383229738064542857501,23,0.1
22,22_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,800.000000000000000000000000000000,2918,4665,0.337439163895323857023100799779,3,0.1
23,23_0,COMPLETED,Sobol,SOBOL,0.100000000000000005551115123126,1000.000000000000000000000000000000,44,287,0.911865658814087542971549282811,38,0.001
24,24_0,COMPLETED,Sobol,SOBOL,0.070000000000000006661338147751,913.000000000000000000000000000000,343,3591,0.417711781093850709201120707803,34,0.25
25,25_0,COMPLETED,Sobol,SOBOL,0.160000000000000003330669073875,777.000000000000000000000000000000,2634,1712,0.831600640879944008609925276687,7,0.05
26,26_0,COMPLETED,Sobol,SOBOL,0.170000000000000012212453270877,1106.000000000000000000000000000000,4157,3910,0.596601413853466544523485026730,19,0.05
27,27_0,COMPLETED,Sobol,SOBOL,0.340000000000000024424906541753,998.000000000000000000000000000000,1320,788,0.153716728016734111017527197873,42,0.025
28,28_0,COMPLETED,Sobol,SOBOL,0.040000000000000000832667268469,1114.000000000000000000000000000000,2176,4774,0.986608447656035392192563904246,14,0.1
29,29_0,COMPLETED,Sobol,SOBOL,0.429999999999999993338661852249,2731.000000000000000000000000000000,4849,393,0.263067677564919000232634971326,48,0.25
30,30_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.429999999999999993338661852249,1849.000000000000000000000000000000,2622,220,0.128243726299036020499499954894,37,0.01
31,31_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3763,65,0.254677464487691462835528000141,16,0.25
32,32_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4034,114,0.850435623834162046641438337247,13,0.025
33,33_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3336,9,0.001000000000000000020816681712,22,0.25
34,34_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3525,21,0.027034529484317414149696645609,50,0.01
35,35_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4983,259,0.974162600490920960183416354994,15,0.25
36,36_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3942,197,0.011725494229282875172093447702,36,0.025
37,37_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3516,56,0.998999999999999999111821580300,50,0.025
38,38_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3406,203,0.246878625805959261985123021077,35,0.005
39,39_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.149999999999999994448884876874,864.000000000000000000000000000000,4210,4664,0.056485962029197867018126544281,50,0.25
40,40_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4559,371,0.001000000000000000020816681712,16,0.25
41,41_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3729,78,0.001000000000000000020816681712,1,0.25
42,42_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,2946,132,0.998999999999999999111821580300,37,0.01
43,43_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2580,11,0.249106156354118729590041425581,50,0.01
44,44_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,1023.000000000000000000000000000000,3678,1476,0.405502017489302646335858071325,35,0.01
45,45_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,1159.000000000000000000000000000000,4211,1135,0.998999999999999999111821580300,16,0.25
46,46_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4927,138,0.333475649876999236109753610435,13,0.025
47,47_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3198,1,0.998999999999999999111821580300,9,0.25
48,48_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,2817,123,0.001000000000000000020816681712,38,0.005
49,49_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1689,269,0.001000000000000000020816681712,35,0.01
50,50_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,838.000000000000000000000000000000,5000,4350,0.001000000000000000020816681712,50,0.01
51,51_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4212,57,0.001000000000000000020816681712,34,0.005
52,52_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,1012.000000000000000000000000000000,5000,1657,0.998999999999999999111821580300,17,0.001
53,53_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.220000000000000001110223024625,987.000000000000000000000000000000,5000,2538,0.998999999999999999111821580300,34,0.1
54,54_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1973,1,0.001000000000000000020816681712,17,0.025
55,55_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4999,155,0.998999999999999999111821580300,6,0.025
56,56_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4620,298,0.001000000000000000020816681712,1,0.005
57,57_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.359999999999999986677323704498,1096.000000000000000000000000000000,968,537,0.001000000000000000020816681712,38,0.01
58,58_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3108,1,0.998999999999999999111821580300,2,0.005
59,59_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4997,92,0.032495081365361284941872810350,35,0.01
60,60_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3227,1,0.231125553848025688807865662966,27,0.1
61,61_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,1,0.001000000000000000020816681712,50,0.25
62,62_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3327,1,0.323831116543570551868924667360,28,0.1
63,63_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.059999999999999997779553950750,1173.000000000000000000000000000000,2681,1,0.177366002263627087209840738069,1,0.1
64,64_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3416,1,0.658820351995305975023597966356,40,0.1
65,65_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3970,1,0.044647250281496318746743412476,30,0.1
66,66_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.040000000000000000832667268469,1347.000000000000000000000000000000,1,438,0.037115012918426081023337559373,50,0.01
67,67_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3243,1,0.204773288686230209298955173836,39,0.1
68,68_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.130000000000000004440892098501,2284.000000000000000000000000000000,4910,53,0.133436439922294564075500034050,1,0.001
69,69_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3246,1,0.455277774425458636731178785340,38,0.1
70,70_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.190000000000000002220446049250,1351.000000000000000000000000000000,3812,3016,0.001000000000000000020816681712,50,0.01
71,71_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3256,1,0.514056726630315541370919163455,37,0.1
72,72_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.040000000000000000832667268469,1305.000000000000000000000000000000,1,435,0.001000000000000000020816681712,36,0.25
73,73_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.130000000000000004440892098501,960.000000000000000000000000000000,307,1306,0.001000000000000000020816681712,42,0.01
74,74_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.350000000000000033306690738755,1631.000000000000000000000000000000,4363,841,0.001000000000000000020816681712,7,0.025
75,75_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.359999999999999986677323704498,1843.000000000000000000000000000000,5000,735,0.001000000000000000020816681712,50,0.001
76,76_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1822,1,0.998999999999999999111821580300,50,0.001
77,77_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1810,1,0.998999999999999999111821580300,50,0.01
78,78_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1837,1,0.998999999999999999111821580300,50,0.001
79,79_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1841,1,0.998999999999999999111821580300,50,0.01
80,80_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.340000000000000024424906541753,1729.000000000000000000000000000000,1711,879,0.001000000000000000020816681712,44,0.01
81,81_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1885,1,0.998999999999999999111821580300,43,0.001
82,82_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1831,1,0.998999999999999999111821580300,43,0.005
83,83_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1862,1,0.998999999999999999111821580300,42,0.001
84,84_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.289999999999999980015985556747,1117.000000000000000000000000000000,1479,1150,0.380453336011046916453892663412,49,0.001
85,85_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1855,1,0.998999999999999999111821580300,42,0.001
86,86_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.330000000000000015543122344752,1177.000000000000000000000000000000,4823,1074,0.583140066308557525331934812129,50,0.025
87,87_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1860,1,0.998999999999999999111821580300,43,0.001
88,88_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,1082.000000000000000000000000000000,1559,1018,0.530449996239050647339752231346,45,0.005
89,89_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1858,1,0.998999999999999999111821580300,43,0.001
90,90_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1841,1,0.998999999999999999111821580300,42,0.01
91,91_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1920,1,0.998999999999999999111821580300,42,0.001
92,92_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.230000000000000009992007221626,1145.000000000000000000000000000000,4953,2260,0.512467504042550370257913527894,49,0.01
93,93_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1852,1,0.998999999999999999111821580300,42,0.001
94,94_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,1344.000000000000000000000000000000,4565,1645,0.998999999999999999111821580300,41,0.025
95,95_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,1026.000000000000000000000000000000,1359,1498,0.998999999999999999111821580300,15,0.025
96,96_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.309999999999999997779553950750,1168.000000000000000000000000000000,4652,1302,0.001000000000000000020816681712,35,0.01
97,97_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1517,1,0.354132806888999340788615199926,24,0.001
98,98_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.270000000000000017763568394003,1041.000000000000000000000000000000,3824,1508,0.001000000000000000020816681712,20,0.025
99,99_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1531,1,0.366740018670500489417207745646,24,0.001
100,100_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1557,1,0.740574466743311621286238732864,26,0.001
101,101_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.250000000000000000000000000000,996.000000000000000000000000000000,3120,1304,0.265434309388064837431642217780,43,0.001
102,102_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1597,1,0.566295091289244156840254618146,26,0.001
103,103_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1275,1,0.998999999999999999111821580300,25,0.005
104,104_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1736,1,0.001000000000000000020816681712,26,0.001
105,105_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1322,1,0.998999999999999999111821580300,25,0.005
106,106_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1721,1,0.001000000000000000020816681712,26,0.001
107,107_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1285,1,0.998999999999999999111821580300,25,0.001
108,108_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1723,1,0.001000000000000000020816681712,26,0.005
109,109_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.359999999999999986677323704498,1230.000000000000000000000000000000,1444,701,0.998999999999999999111821580300,26,0.001
110,110_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1598,1,0.998999999999999999111821580300,26,0.005
111,111_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.289999999999999980015985556747,1127.000000000000000000000000000000,1303,1097,0.998999999999999999111821580300,26,0.001
112,112_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.230000000000000009992007221626,1132.000000000000000000000000000000,1267,1698,0.025572645946143713474585368317,28,0.001
113,113_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1568,1,0.998999999999999999111821580300,26,0.005
114,114_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1300,1,0.998999999999999999111821580300,26,0.025
115,115_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,971.000000000000000000000000000000,1445,1587,0.998999999999999999111821580300,30,0.25
116,116_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,916.000000000000000000000000000000,5000,4125,0.998999999999999999111821580300,1,0.25
117,117_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1549,1,0.998999999999999999111821580300,26,0.005
118,118_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1376,1,0.001000000000000000020816681712,24,0.01
119,119_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1477,1,0.001000000000000000020816681712,25,0.005
120,120_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1494,1,0.001000000000000000020816681712,25,0.005
121,121_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1228,1,0.001000000000000000020816681712,21,0.01
122,122_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1572,1,0.001000000000000000020816681712,26,0.005
123,123_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1233,1,0.998999999999999999111821580300,23,0.01
124,124_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1550,1,0.001000000000000000020816681712,26,0.005
125,125_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1114,1,0.001000000000000000020816681712,22,0.01
126,126_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1115,1,0.998999999999999999111821580300,22,0.001
127,127_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1379,1,0.998999999999999999111821580300,24,0.01
128,128_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.300000000000000044408920985006,1158.000000000000000000000000000000,5000,1335,0.001000000000000000020816681712,50,0.001
129,129_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1279,1,0.998999999999999999111821580300,22,0.01
130,130_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1690,1,0.998999999999999999111821580300,27,0.25
131,131_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1110,1,0.998999999999999999111821580300,19,0.01
132,132_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1534,1,0.001000000000000000020816681712,27,0.01
133,133_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1085,1,0.998999999999999999111821580300,23,0.01
134,134_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1578,1,0.001000000000000000020816681712,25,0.005
135,135_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1268,1,0.998999999999999999111821580300,21,0.01
136,136_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1568,1,0.001000000000000000020816681712,26,0.005
137,137_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.300000000000000044408920985006,1011.000000000000000000000000000000,3284,1016,0.998999999999999999111821580300,38,0.005
138,138_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1518,1,0.001000000000000000020816681712,25,0.005
139,139_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1333,1,0.998999999999999999111821580300,25,0.01
140,140_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1527,1,0.001000000000000000020816681712,25,0.005
141,141_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1339,1,0.998999999999999999111821580300,25,0.01
142,142_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1562,1,0.001000000000000000020816681712,26,0.005
143,143_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1300,1,0.998999999999999999111821580300,24,0.01
144,144_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1577,1,0.001000000000000000020816681712,26,0.005
145,145_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1220,1,0.998999999999999999111821580300,20,0.01
146,146_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1602,1,0.001000000000000000020816681712,26,0.001
147,147_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1304,1,0.998999999999999999111821580300,24,0.01
148,148_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.220000000000000001110223024625,1332.000000000000000000000000000000,3253,1891,0.001000000000000000020816681712,50,0.1
149,149_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1525,1,0.001000000000000000020816681712,25,0.005
150,150_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1251,1,0.998999999999999999111821580300,23,0.01
151,151_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1568,1,0.001000000000000000020816681712,26,0.01
152,152_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1407,1,0.001000000000000000020816681712,23,0.01
153,153_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.200000000000000011102230246252,1034.000000000000000000000000000000,4684,2970,0.998999999999999999111821580300,1,0.025
154,154_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1542,1,0.001000000000000000020816681712,25,0.005
155,155_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1371,1,0.998999999999999999111821580300,26,0.01
156,156_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1565,1,0.001000000000000000020816681712,25,0.001
157,157_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1364,1,0.998999999999999999111821580300,24,0.01
158,158_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1302,1,0.001000000000000000020816681712,25,0.005
159,159_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1377,1,0.998999999999999999111821580300,25,0.01
160,160_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1403,1,0.001000000000000000020816681712,25,0.005
161,161_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.340000000000000024424906541753,1276.000000000000000000000000000000,3852,898,0.998999999999999999111821580300,43,0.01
162,162_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1427,1,0.001000000000000000020816681712,25,0.005
163,163_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1367,1,0.998999999999999999111821580300,25,0.01
164,164_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1431,1,0.001000000000000000020816681712,25,0.005
165,165_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1387,1,0.998999999999999999111821580300,25,0.01
166,166_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1395,1,0.001000000000000000020816681712,24,0.005
167,167_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1315,1,0.998999999999999999111821580300,24,0.01
168,168_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1522,1,0.001000000000000000020816681712,25,0.005
169,169_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1204,1,0.998999999999999999111821580300,22,0.01
170,170_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1728,1,0.001000000000000000020816681712,24,0.005
171,171_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1304,1,0.998999999999999999111821580300,26,0.01
172,172_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1713,1,0.001000000000000000020816681712,27,0.005
173,173_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1319,1,0.998999999999999999111821580300,24,0.01
174,174_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1669,1,0.001000000000000000020816681712,27,0.005
175,175_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.998999999999999999111821580300,26,0.01
176,176_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.209999999999999992228438827624,1042.000000000000000000000000000000,4078,2631,0.001000000000000000020816681712,50,0.1
177,177_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1639,1,0.001000000000000000020816681712,26,0.005
178,178_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1368,1,0.998999999999999999111821580300,24,0.01
179,179_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1541,1,0.001000000000000000020816681712,30,0.005
180,180_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1538,1,0.793715407053184440755444484239,27,0.01
181,181_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1517,1,0.001000000000000000020816681712,27,0.005
182,182_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1586,1,0.998999999999999999111821580300,28,0.01
183,183_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1538,1,0.001000000000000000020816681712,27,0.005
184,184_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1660,1,0.998999999999999999111821580300,28,0.01
185,185_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1470,1,0.001000000000000000020816681712,26,0.005
186,186_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1583,1,0.998999999999999999111821580300,28,0.01
187,187_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1426,1,0.001000000000000000020816681712,26,0.005
188,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.998999999999999999111821580300,28,0.01
189,189_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1493,1,0.001000000000000000020816681712,27,0.005
190,190_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1599,1,0.998999999999999999111821580300,28,0.01
191,191_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1414,1,0.001000000000000000020816681712,27,0.005
192,192_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1620,1,0.998999999999999999111821580300,28,0.01
193,193_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1499,1,0.001000000000000000020816681712,27,0.005
194,194_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1633,1,0.870088316494399438560947146470,28,0.01
195,185_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1470,1,0.001000000000000000020816681712,26,0.005
196,196_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1628,1,0.998999999999999999111821580300,28,0.01
197,197_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1507,1,0.001000000000000000020816681712,27,0.005
198,198_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1625,1,0.998999999999999999111821580300,28,0.01
199,199_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1431,1,0.001000000000000000020816681712,26,0.005
200,200_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1574,1,0.731444539851955344289535787539,27,0.01
201,201_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1473,1,0.001000000000000000020816681712,27,0.005
202,202_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1573,1,0.998999999999999999111821580300,27,0.01
203,203_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1501,1,0.001000000000000000020816681712,27,0.01
204,204_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1563,1,0.998999999999999999111821580300,28,0.01
205,205_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1457,1,0.001000000000000000020816681712,26,0.01
206,206_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1555,1,0.998999999999999999111821580300,28,0.005
207,207_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1579,1,0.001000000000000000020816681712,27,0.01
208,208_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1597,1,0.998999999999999999111821580300,28,0.005
209,209_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1603,1,0.001000000000000000020816681712,27,0.01
210,210_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1485,1,0.998999999999999999111821580300,27,0.005
211,211_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1599,1,0.258264801662508758361980198970,27,0.01
212,212_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1419,1,0.001000000000000000020816681712,26,0.005
213,213_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1609,1,0.998999999999999999111821580300,28,0.01
214,214_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3750,1,0.001000000000000000020816681712,40,0.25
215,215_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1565,1,0.751145648190460701876247640030,28,0.01
216,189_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1493,1,0.001000000000000000020816681712,27,0.005
217,217_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1608,1,0.998999999999999999111821580300,28,0.01
218,218_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1466,1,0.001000000000000000020816681712,26,0.005
219,219_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1632,1,0.991194248098206664998599535465,28,0.01
220,220_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1565,1,0.180814335644128826308119073474,27,0.01
221,221_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1527,1,0.998999999999999999111821580300,28,0.005
222,222_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.998999999999999999111821580300,27,0.01
223,223_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1491,1,0.001000000000000000020816681712,27,0.005
224,224_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1607,1,0.998999999999999999111821580300,28,0.01
225,225_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1465,1,0.001000000000000000020816681712,26,0.005
226,226_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1616,1,0.998999999999999999111821580300,28,0.01
227,227_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1411,1,0.001000000000000000020816681712,26,0.005
228,228_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1644,1,0.927930947862149135829668011866,28,0.01
229,229_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1508,1,0.001000000000000000020816681712,27,0.005
230,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.998999999999999999111821580300,28,0.01
231,231_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1424,1,0.001000000000000000020816681712,26,0.005
232,232_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1636,1,0.317666666649199402883141374332,27,0.01
233,233_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1409,1,0.001000000000000000020816681712,26,0.005
234,234_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1617,1,0.628200369400741642778029927285,28,0.01
235,235_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1464,1,0.001000000000000000020816681712,26,0.005
236,236_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1618,1,0.920136122940962475347248528124,28,0.01
237,237_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1438,1,0.001000000000000000020816681712,26,0.005
238,238_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1680,1,0.990829759702948953403733867162,28,0.01
239,239_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1474,1,0.001000000000000000020816681712,26,0.005
240,240_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1564,1,0.669297598265388815619303386484,27,0.01
241,241_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1485,1,0.001000000000000000020816681712,27,0.005
242,242_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1598,1,0.998999999999999999111821580300,28,0.01
243,243_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1514,1,0.001000000000000000020816681712,27,0.005
244,244_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1588,1,0.998999999999999999111821580300,28,0.01
245,245_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1542,1,0.001000000000000000020816681712,27,0.01
246,246_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1516,1,0.998999999999999999111821580300,28,0.005
247,247_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1598,1,0.001000000000000000020816681712,27,0.01
248,248_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1513,1,0.998999999999999999111821580300,27,0.005
249,249_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1584,1,0.001000000000000000020816681712,27,0.01
250,250_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1434,1,0.998999999999999999111821580300,27,0.01
251,251_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1521,1,0.001000000000000000020816681712,26,0.005
252,190_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1599,1,0.998999999999999999111821580300,28,0.01
253,253_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1472,1,0.001000000000000000020816681712,26,0.005
254,254_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1621,1,0.998999999999999999111821580300,28,0.01
255,255_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1476,1,0.001000000000000000020816681712,27,0.005
256,256_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1643,1,0.998999999999999999111821580300,28,0.01
257,257_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1492,1,0.001000000000000000020816681712,27,0.005
258,258_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1611,1,0.998999999999999999111821580300,28,0.01
259,259_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1489,1,0.001000000000000000020816681712,27,0.005
260,260_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1570,1,0.459467035996481643067568256811,27,0.01
261,261_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3814,1,0.001000000000000000020816681712,39,0.25
262,262_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1539,1,0.747927538288890580986389977625,27,0.01
263,263_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1418,1,0.001000000000000000020816681712,26,0.005
264,264_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1623,1,0.533612418273837540994009032147,28,0.01
265,223_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1491,1,0.001000000000000000020816681712,27,0.005
266,266_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1565,1,0.998999999999999999111821580300,27,0.01
267,267_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1471,1,0.001000000000000000020816681712,27,0.005
268,268_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1601,1,0.936145606967785925967007187865,28,0.01
269,269_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3802,1,0.001000000000000000020816681712,36,0.01
270,270_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1572,1,0.246999306575632210369874997014,27,0.01
271,271_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1503,1,0.998999999999999999111821580300,28,0.005
272,272_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1609,1,0.001000000000000000020816681712,27,0.01
273,273_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1506,1,0.998999999999999999111821580300,28,0.005
274,274_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1580,1,0.001000000000000000020816681712,27,0.01
275,275_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1500,1,0.998999999999999999111821580300,27,0.01
276,276_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1565,1,0.001000000000000000020816681712,27,0.01
277,277_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1466,1,0.998999999999999999111821580300,28,0.005
278,278_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1605,1,0.001000000000000000020816681712,27,0.01
279,279_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1480,1,0.998999999999999999111821580300,28,0.005
280,280_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1502,1,0.383634233752573439168997992965,27,0.01
281,281_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1521,1,0.001000000000000000020816681712,27,0.005
282,282_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1614,1,0.998999999999999999111821580300,28,0.01
283,283_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1499,1,0.001000000000000000020816681712,27,0.01
284,284_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1501,1,0.998999999999999999111821580300,27,0.005
285,285_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1588,1,0.001000000000000000020816681712,27,0.01
286,286_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1473,1,0.998999999999999999111821580300,27,0.005
287,287_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1583,1,0.603438650417102095957488927525,27,0.01
288,288_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1511,1,0.001000000000000000020816681712,27,0.005
289,289_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1593,1,0.998999999999999999111821580300,28,0.01
290,290_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1428,1,0.001000000000000000020816681712,26,0.005
291,291_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1645,1,0.904569477913035879801384453458,28,0.01
292,212_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1419,1,0.001000000000000000020816681712,26,0.005
293,293_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1623,1,0.923289160083326621020205493551,28,0.01
294,189_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1493,1,0.001000000000000000020816681712,27,0.005
295,295_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1617,1,0.998999999999999999111821580300,28,0.01
296,187_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1426,1,0.001000000000000000020816681712,26,0.005
297,297_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1612,1,0.998999999999999999111821580300,28,0.01
298,298_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1435,1,0.001000000000000000020816681712,26,0.005
299,299_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1587,1,0.998999999999999999111821580300,28,0.01
300,300_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1557,1,0.524716365477343793521924908418,27,0.01
301,301_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1441,1,0.001000000000000000020816681712,26,0.005
302,299_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1587,1,0.998999999999999999111821580300,28,0.01
303,303_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1476,1,0.001000000000000000020816681712,26,0.005
304,304_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1610,1,0.998999999999999999111821580300,28,0.01
305,305_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1487,1,0.001000000000000000020816681712,27,0.005
306,306_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1618,1,0.904130110114466312154490879038,28,0.01
307,307_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1478,1,0.001000000000000000020816681712,26,0.005
308,308_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1647,1,0.998999999999999999111821580300,28,0.01
309,309_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1463,1,0.001000000000000000020816681712,26,0.005
310,310_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1585,1,0.998999999999999999111821580300,27,0.01
311,311_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1487,1,0.001000000000000000020816681712,26,0.005
312,192_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1620,1,0.998999999999999999111821580300,28,0.01
313,313_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.001000000000000000020816681712,26,0.005
314,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.998999999999999999111821580300,28,0.01
315,315_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1483,1,0.001000000000000000020816681712,26,0.01
316,316_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1562,1,0.998999999999999999111821580300,28,0.01
317,317_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1479,1,0.001000000000000000020816681712,27,0.005
318,318_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1620,1,0.982368649783248781837130536587,28,0.01
319,319_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1425,1,0.001000000000000000020816681712,26,0.005
320,320_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1591,1,0.638344047496977040623278298881,27,0.01
321,321_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1405,1,0.001000000000000000020816681712,26,0.005
322,226_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1616,1,0.998999999999999999111821580300,28,0.01
323,323_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1519,1,0.001000000000000000020816681712,27,0.005
324,224_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1607,1,0.998999999999999999111821580300,28,0.01
325,325_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3682,1,0.001000000000000000020816681712,40,0.25
326,326_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1572,1,0.397541769822993940053379446908,27,0.01
327,327_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1437,1,0.001000000000000000020816681712,27,0.005
328,328_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1606,1,0.650907711902078855992215267179,28,0.01
329,329_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.001000000000000000020816681712,26,0.005
330,330_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1626,1,0.793523690327516906251048567356,28,0.01
331,331_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1450,1,0.001000000000000000020816681712,26,0.005
332,332_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1646,1,0.842416661050871429239350618445,28,0.01
333,333_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1384,1,0.001000000000000000020816681712,26,0.005
334,334_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1633,1,0.998999999999999999111821580300,28,0.01
335,335_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1457,1,0.001000000000000000020816681712,26,0.005
336,336_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1610,1,0.877973296468523289881602522655,28,0.01
337,313_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.001000000000000000020816681712,26,0.005
338,338_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1642,1,0.998999999999999999111821580300,28,0.01
339,298_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1435,1,0.001000000000000000020816681712,26,0.005
340,340_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1555,1,0.688128635440578673154732314288,27,0.01
341,341_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1653,1,0.001000000000000000020816681712,27,0.01
342,342_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1469,1,0.813847873154957768271344775712,26,0.005
343,343_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1555,1,0.001000000000000000020816681712,27,0.01
344,344_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3752,1,0.001000000000000000020816681712,41,0.25
345,345_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1558,1,0.001000000000000000020816681712,27,0.01
346,346_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1558,1,0.998999999999999999111821580300,28,0.005
347,347_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1546,1,0.001000000000000000020816681712,27,0.01
348,348_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1494,1,0.998999999999999999111821580300,27,0.005
349,349_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1635,1,0.048331449502021409103669213891,28,0.01
350,350_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1440,1,0.998999999999999999111821580300,27,0.01
351,351_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1505,1,0.001000000000000000020816681712,26,0.005
352,192_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1620,1,0.998999999999999999111821580300,28,0.01
353,329_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.001000000000000000020816681712,26,0.005
354,354_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1623,1,0.509918744420601810496407324536,28,0.01
355,355_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1469,1,0.001000000000000000020816681712,26,0.005
356,356_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1629,1,0.773370422484878972113619965967,28,0.01
357,357_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1315,1,0.001000000000000000020816681712,24,0.01
358,358_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1803,1,0.998999999999999999111821580300,29,0.01
359,359_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1437,1,0.001000000000000000020816681712,26,0.005
360,360_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1538,1,0.512430814243970522703364167683,27,0.01
361,361_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3757,1,0.001000000000000000020816681712,39,0.25
362,362_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1550,1,0.487140265979398690010526706828,27,0.01
363,363_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1517,1,0.001000000000000000020816681712,26,0.005
364,364_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1634,1,0.953247342235053252146315116988,28,0.01
365,365_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1473,1,0.001000000000000000020816681712,26,0.005
366,366_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1632,1,0.998999999999999999111821580300,28,0.01
367,367_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1470,1,0.001000000000000000020816681712,27,0.005
368,368_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1578,1,0.998999999999999999111821580300,28,0.01
369,369_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1467,1,0.001000000000000000020816681712,26,0.005
370,370_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1626,1,0.998999999999999999111821580300,28,0.01
371,281_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1521,1,0.001000000000000000020816681712,27,0.005
372,372_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1574,1,0.998999999999999999111821580300,28,0.01
373,373_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3698,1,0.001000000000000000020816681712,37,0.25
374,374_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1594,1,0.437954482716237991635210846653,27,0.01
375,375_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1477,1,0.855984986461849395311674015829,27,0.005
376,376_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1616,1,0.001000000000000000020816681712,27,0.01
377,377_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1513,1,0.998999999999999999111821580300,28,0.005
378,378_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1619,1,0.001000000000000000020816681712,27,0.01
379,379_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1505,1,0.998999999999999999111821580300,27,0.005
380,380_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1538,1,0.276162844982334509946753087206,27,0.01
381,381_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1436,1,0.001000000000000000020816681712,26,0.005
382,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.998999999999999999111821580300,28,0.01
383,383_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1523,1,0.001000000000000000020816681712,27,0.005
384,384_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1604,1,0.998999999999999999111821580300,28,0.01
385,385_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1484,1,0.001000000000000000020816681712,27,0.005
386,386_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1622,1,0.519027795004799896716463081248,28,0.01
387,359_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1437,1,0.001000000000000000020816681712,26,0.005
388,388_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1569,1,0.959468204079397479766555534297,27,0.01
389,389_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1501,1,0.001000000000000000020816681712,27,0.005
390,390_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1622,1,0.998999999999999999111821580300,28,0.01
391,331_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1450,1,0.001000000000000000020816681712,26,0.005
392,196_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1628,1,0.998999999999999999111821580300,28,0.01
393,393_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1376,1,0.001000000000000000020816681712,26,0.005
394,394_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1580,1,0.825272058818193210427693884412,27,0.01
395,395_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.001000000000000000020816681712,27,0.005
396,396_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1629,1,0.998999999999999999111821580300,28,0.01
397,183_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1538,1,0.001000000000000000020816681712,27,0.005
398,398_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1619,1,0.998999999999999999111821580300,28,0.01
399,199_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1431,1,0.001000000000000000020816681712,26,0.005
400,400_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1567,1,0.693417444812498784401100238028,27,0.01
401,401_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1483,1,0.001000000000000000020816681712,27,0.005
402,402_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1612,1,0.580042621306449346363365293655,28,0.01
403,235_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1464,1,0.001000000000000000020816681712,26,0.005
404,404_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.998999999999999999111821580300,28,0.01
405,405_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1505,1,0.001000000000000000020816681712,27,0.005
406,192_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1620,1,0.998999999999999999111821580300,28,0.01
407,407_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1531,1,0.001000000000000000020816681712,27,0.01
408,408_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1491,1,0.998999999999999999111821580300,28,0.005
409,409_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1594,1,0.001000000000000000020816681712,28,0.01
410,410_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1429,1,0.998999999999999999111821580300,27,0.005
411,376_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1616,1,0.001000000000000000020816681712,27,0.01
412,412_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1470,1,0.998999999999999999111821580300,27,0.005
413,413_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1615,1,0.017290914134856595618661145863,27,0.01
414,377_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1513,1,0.998999999999999999111821580300,28,0.005
415,278_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1605,1,0.001000000000000000020816681712,27,0.01
416,416_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1409,1,0.843324540513117337781068272307,27,0.005
417,417_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1594,1,0.034637597958127798458694002193,27,0.01
418,418_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1550,1,0.998999999999999999111821580300,28,0.005
419,419_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1613,1,0.001000000000000000020816681712,27,0.01
420,420_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1543,1,0.598530120636348694773687384441,27,0.01
421,421_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1455,1,0.001000000000000000020816681712,26,0.005
422,226_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1616,1,0.998999999999999999111821580300,28,0.01
423,423_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1529,1,0.001000000000000000020816681712,27,0.005
424,190_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1599,1,0.998999999999999999111821580300,28,0.01
425,425_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.001000000000000000020816681712,27,0.005
426,426_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1597,1,0.998999999999999999111821580300,28,0.01
427,425_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.001000000000000000020816681712,27,0.005
428,428_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1615,1,0.372322487242331412460316641955,28,0.01
429,429_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1416,1,0.001000000000000000020816681712,27,0.005
430,430_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1615,1,0.807260944754086384733682280057,28,0.01
431,307_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1478,1,0.001000000000000000020816681712,26,0.005
432,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.998999999999999999111821580300,28,0.01
433,369_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1467,1,0.001000000000000000020816681712,26,0.005
434,434_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1374,1,0.998999999999999999111821580300,25,0.01
435,435_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1621,1,0.001000000000000000020816681712,28,0.005
436,436_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1603,1,0.998999999999999999111821580300,28,0.01
437,437_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1446,1,0.001000000000000000020816681712,26,0.005
438,438_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1579,1,0.998999999999999999111821580300,28,0.01
439,185_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1470,1,0.001000000000000000020816681712,26,0.005
440,440_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1559,1,0.825987408910257236982488393551,28,0.01
441,441_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1461,1,0.001000000000000000020816681712,26,0.005
442,217_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1608,1,0.998999999999999999111821580300,28,0.01
443,309_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1463,1,0.001000000000000000020816681712,26,0.005
444,213_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1609,1,0.998999999999999999111821580300,28,0.01
445,445_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1444,1,0.001000000000000000020816681712,26,0.005
446,446_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1615,1,0.998999999999999999111821580300,28,0.01
447,447_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1451,1,0.089649621480783592275543014694,26,0.01
448,448_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3758,1,0.001000000000000000020816681712,39,0.25
449,449_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1563,1,0.129491013850947606078634066762,27,0.01
450,450_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1397,1,0.499103089207236449986737625295,27,0.005
451,451_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1627,1,0.343368878151771750317777787131,27,0.01
452,452_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1394,1,0.001000000000000000020816681712,26,0.005
453,446_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1615,1,0.998999999999999999111821580300,28,0.01
454,454_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1492,1,0.001000000000000000020816681712,26,0.005
455,455_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1595,1,0.998999999999999999111821580300,28,0.01
456,456_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1495,1,0.001000000000000000020816681712,26,0.005
457,457_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1582,1,0.998999999999999999111821580300,28,0.01
458,458_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1506,1,0.001000000000000000020816681712,27,0.005
459,244_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1588,1,0.998999999999999999111821580300,28,0.01
460,460_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1549,1,0.576620692471713436333402569289,27,0.01
461,461_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1454,1,0.001000000000000000020816681712,26,0.005
462,462_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1635,1,0.998999999999999999111821580300,28,0.01
463,463_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1460,1,0.001000000000000000020816681712,26,0.005
464,426_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1597,1,0.998999999999999999111821580300,28,0.01
465,465_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3800,1,0.001000000000000000020816681712,41,0.25
466,466_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1570,1,0.659925906789398108998057068675,27,0.01
467,405_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1505,1,0.001000000000000000020816681712,27,0.005
468,468_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1586,1,0.998999999999999999111821580300,27,0.01
469,317_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1479,1,0.001000000000000000020816681712,27,0.005
470,304_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1610,1,0.998999999999999999111821580300,28,0.01
471,471_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1510,1,0.001000000000000000020816681712,27,0.01
472,472_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1544,1,0.998999999999999999111821580300,28,0.01
473,185_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1470,1,0.001000000000000000020816681712,26,0.005
474,474_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1596,1,0.998999999999999999111821580300,28,0.01
475,475_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1515,1,0.001000000000000000020816681712,27,0.005
476,476_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.737548633918381768559413558251,28,0.01
477,477_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1482,1,0.001000000000000000020816681712,27,0.005
478,478_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1622,1,0.898096438806562979983993955102,28,0.01
479,239_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1474,1,0.001000000000000000020816681712,26,0.005
480,480_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1530,1,0.411384391158866546955863441326,27,0.01
481,425_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.001000000000000000020816681712,27,0.005
482,482_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1649,1,0.998999999999999999111821580300,28,0.01
483,483_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1432,1,0.001000000000000000020816681712,26,0.005
484,484_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1668,1,0.998999999999999999111821580300,28,0.01
485,485_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1448,1,0.001000000000000000020816681712,26,0.005
486,486_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1648,1,0.998999999999999999111821580300,28,0.01
487,311_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1487,1,0.001000000000000000020816681712,26,0.005
488,488_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1587,1,0.998999999999999999111821580300,27,0.01
489,489_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1540,1,0.005018278513443521354764342846,27,0.005
490,490_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.867658690101562712904126328795,28,0.01
491,463_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1460,1,0.001000000000000000020816681712,26,0.005
492,226_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1616,1,0.998999999999999999111821580300,28,0.01
493,493_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1509,1,0.001000000000000000020816681712,27,0.005
494,494_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1613,1,0.998999999999999999111821580300,28,0.01
495,187_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1426,1,0.001000000000000000020816681712,26,0.005
496,496_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1602,1,0.998999999999999999111821580300,28,0.01
497,497_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1402,1,0.001000000000000000020816681712,26,0.005
498,498_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1626,1,0.489293898263172932772135936830,27,0.01
499,227_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1411,1,0.001000000000000000020816681712,26,0.005
500,500_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1588,1,0.298460251520866304275614311337,27,0.01
501,501_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1407,1,0.001000000000000000020816681712,26,0.005
502,502_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1603,1,0.440566926061923325175229138040,27,0.01
503,298_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1435,1,0.001000000000000000020816681712,26,0.005
504,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.998999999999999999111821580300,28,0.01
505,212_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1419,1,0.001000000000000000020816681712,26,0.005
506,506_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1645,1,0.998999999999999999111821580300,28,0.01
507,507_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1440,1,0.001000000000000000020816681712,26,0.005
508,198_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1625,1,0.998999999999999999111821580300,28,0.01
509,509_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1468,1,0.001000000000000000020816681712,26,0.005
510,510_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1572,1,0.675094330322489555307186037680,28,0.01
511,241_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1485,1,0.001000000000000000020816681712,27,0.005
512,426_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1597,1,0.998999999999999999111821580300,28,0.01
513,513_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1448,1,0.001000000000000000020816681712,27,0.005
514,254_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1621,1,0.998999999999999999111821580300,28,0.01
515,233_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1409,1,0.001000000000000000020816681712,26,0.005
516,516_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1640,1,0.662439818391630241833922809747,28,0.01
517,517_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1450,1,0.001000000000000000020816681712,27,0.01
518,518_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1521,1,0.998999999999999999111821580300,28,0.005
519,519_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1622,1,0.047299730331693261298209307597,27,0.01
520,520_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1568,1,0.509887578364559335142303098110,27,0.01
521,521_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1462,1,0.001000000000000000020816681712,27,0.005
522,222_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.998999999999999999111821580300,27,0.01
523,523_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3805,1,0.001000000000000000020816681712,42,0.25
524,524_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1544,1,0.363697787842798103685737487467,27,0.01
525,401_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1483,1,0.001000000000000000020816681712,27,0.005
526,526_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1594,1,0.744902390376905976232535522286,27,0.01
527,527_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1464,1,0.001000000000000000020816681712,27,0.005
528,528_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1638,1,0.837184997164877486852674337570,28,0.01
529,267_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1471,1,0.001000000000000000020816681712,27,0.005
530,530_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.663125049460775661813727310800,28,0.01
531,531_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1453,1,0.001000000000000000020816681712,27,0.005
532,192_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1620,1,0.998999999999999999111821580300,28,0.01
533,533_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1497,1,0.001000000000000000020816681712,26,0.005
534,534_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1601,1,0.998999999999999999111821580300,28,0.01
535,237_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1438,1,0.001000000000000000020816681712,26,0.005
536,536_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1614,1,0.830379250969013216199243743176,28,0.01
537,425_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.001000000000000000020816681712,27,0.005
538,538_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1630,1,0.839703057641243422182242284180,27,0.01
539,301_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1441,1,0.001000000000000000020816681712,26,0.005
540,540_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1521,1,0.379661004022089187959210221379,27,0.01
541,541_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1492,1,0.902685664612466465150930616801,27,0.005
542,542_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1631,1,0.044321025215655618367804891022,27,0.01
543,543_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1462,1,0.998999999999999999111821580300,27,0.005
544,544_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1597,1,0.001000000000000000020816681712,27,0.01
545,545_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3785,1,0.001000000000000000020816681712,42,0.25
546,546_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1553,1,0.828162144434346547683389871963,27,0.01
547,547_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1401,1,0.001000000000000000020816681712,26,0.005
548,370_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1626,1,0.998999999999999999111821580300,28,0.01
549,235_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1464,1,0.001000000000000000020816681712,26,0.005
550,550_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1604,1,0.972329340734987002115019549819,28,0.01
551,383_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1523,1,0.001000000000000000020816681712,27,0.005
552,552_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1641,1,0.998999999999999999111821580300,28,0.01
553,359_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1437,1,0.001000000000000000020816681712,26,0.005
554,554_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1632,1,0.840879028671121631077767233364,28,0.01
555,555_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1386,1,0.001000000000000000020816681712,26,0.005
556,396_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1629,1,0.998999999999999999111821580300,28,0.01
557,557_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1443,1,0.001000000000000000020816681712,26,0.005
558,558_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.400588126763151974873977678726,28,0.01
559,559_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1445,1,0.001000000000000000020816681712,26,0.005
560,560_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1533,1,0.496210701239882701063521608376,27,0.01
561,561_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.001000000000000000020816681712,25,0.005
562,562_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1595,1,0.998999999999999999111821580300,27,0.01
563,331_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1450,1,0.001000000000000000020816681712,26,0.005
564,564_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1572,1,0.346796954904800058816505270443,28,0.01
565,565_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1458,1,0.001000000000000000020816681712,27,0.01
566,566_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1515,1,0.998999999999999999111821580300,27,0.005
567,376_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1616,1,0.001000000000000000020816681712,27,0.01
568,568_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1433,1,0.461834138987023135047849109469,26,0.005
569,295_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1617,1,0.998999999999999999111821580300,28,0.01
570,570_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1461,1,0.001000000000000000020816681712,27,0.005
571,571_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1626,1,0.846913316557736051137794675014,28,0.01
572,572_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1502,1,0.001000000000000000020816681712,27,0.005
573,573_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1609,1,0.636760034400572494384107358201,27,0.01
574,574_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1468,1,0.001000000000000000020816681712,27,0.005
575,575_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1588,1,0.998999999999999999111821580300,27,0.01
576,576_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1499,1,0.001000000000000000020816681712,26,0.005
577,304_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1610,1,0.998999999999999999111821580300,28,0.01
578,574_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1468,1,0.001000000000000000020816681712,27,0.005
579,579_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1484,1,0.914146489299634268377303669695,27,0.01
580,580_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1546,1,0.427476707929620836079465107105,27,0.01
581,257_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1492,1,0.001000000000000000020816681712,27,0.005
582,582_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1589,1,0.998999999999999999111821580300,28,0.01
583,583_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3863,1,0.001000000000000000020816681712,40,0.25
584,584_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1631,1,0.998999999999999999111821580300,28,0.01
585,585_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1449,1,0.001000000000000000020816681712,26,0.005
586,188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1624,1,0.998999999999999999111821580300,28,0.01
587,477_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1482,1,0.001000000000000000020816681712,27,0.005
588,588_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.875191618043034247342859544005,28,0.01
589,331_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1450,1,0.001000000000000000020816681712,26,0.005
590,289_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1593,1,0.998999999999999999111821580300,28,0.01
591,369_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1467,1,0.001000000000000000020816681712,26,0.005
592,592_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1642,1,0.986247095269500384517868951662,28,0.01
593,593_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1447,1,0.001000000000000000020816681712,26,0.005
594,594_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3706,1,0.001000000000000000020816681712,37,0.01
595,595_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1574,1,0.385671298212246327352659136523,27,0.01
596,223_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1491,1,0.001000000000000000020816681712,27,0.005
597,213_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1609,1,0.998999999999999999111821580300,28,0.01
598,454_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1492,1,0.001000000000000000020816681712,26,0.005
599,599_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1629,1,0.879636490492993083911699159216,28,0.01
600,600_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1549,1,0.565376832374504312284102525155,27,0.01
601,601_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3813,1,0.001000000000000000020816681712,38,0.25
602,602_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1572,1,0.490636170634659307676628259287,27,0.01
603,253_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1472,1,0.001000000000000000020816681712,26,0.005
604,338_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1642,1,0.998999999999999999111821580300,28,0.01
605,307_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1478,1,0.001000000000000000020816681712,26,0.005
606,404_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.998999999999999999111821580300,28,0.01
607,267_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1471,1,0.001000000000000000020816681712,27,0.005
608,608_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1618,1,0.438067121798308090063756026211,28,0.01
609,609_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1475,1,0.001000000000000000020816681712,27,0.005
610,224_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1607,1,0.998999999999999999111821580300,28,0.01
611,235_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1464,1,0.001000000000000000020816681712,26,0.005
612,612_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1590,1,0.998999999999999999111821580300,28,0.01
613,509_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1468,1,0.001000000000000000020816681712,26,0.005
614,614_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1622,1,0.944475703637688068781130823481,28,0.01
615,615_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1702,1,0.001000000000000000020816681712,28,0.005
616,299_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1587,1,0.998999999999999999111821580300,28,0.01
617,617_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1463,1,0.001000000000000000020816681712,26,0.01
618,618_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3668,1,0.001000000000000000020816681712,39,0.25
619,619_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1552,1,0.294191751300386328260572099680,27,0.01
620,620_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1527,1,0.517225806846111746395422414935,27,0.01
621,621_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1472,1,0.001000000000000000020816681712,27,0.005
622,622_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1594,1,0.998999999999999999111821580300,28,0.01
623,623_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1518,1,0.001000000000000000020816681712,26,0.01
624,624_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1557,1,0.998999999999999999111821580300,28,0.005
625,625_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1639,1,0.151866781766602038095115290162,27,0.01
626,626_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1502,1,0.985869451506783622818375079078,27,0.005
627,627_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1598,1,0.092230222430580058312621360983,27,0.01
628,628_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1508,1,0.998999999999999999111821580300,28,0.005
629,629_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1607,1,0.235603409254413143081308135152,27,0.01
630,630_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1446,1,0.998999999999999999111821580300,27,0.005
631,631_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1598,1,0.002779991809944639888363404623,27,0.01
632,632_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1528,1,0.506114334043405444951702065737,27,0.005
633,633_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.635754498085264629914092893159,27,0.01
634,634_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1434,1,0.001000000000000000020816681712,26,0.005
635,635_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1592,1,0.998999999999999999111821580300,28,0.01
636,636_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1481,1,0.001000000000000000020816681712,26,0.005
637,637_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1583,1,0.863694136976660975513198081899,27,0.01
638,197_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1507,1,0.001000000000000000020816681712,27,0.005
639,639_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1613,1,0.737346314956601744938780029770,27,0.01
640,640_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1549,1,0.492321428800856164542665283079,27,0.01
641,641_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3792,1,0.001000000000000000020816681712,39,0.01
642,642_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1549,1,0.236318690506951617491537831484,27,0.01
643,643_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.400000000000000022204460492503,1586.000000000000000000000000000000,1199,413,0.001000000000000000020816681712,24,0.005
644,644_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1581,1,0.502748160156803902687272511685,27,0.01
645,645_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3843,1,0.001000000000000000020816681712,40,0.005
646,646_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1582,1,0.393974737063276458925997758342,27,0.01
647,647_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1449,1,0.001000000000000000020816681712,26,0.025
648,648_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1652,1,0.956860391080657013418431233731,29,0.01
649,649_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1482,1,0.001000000000000000020816681712,26,0.01
650,650_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1575,1,0.998999999999999999111821580300,28,0.005
651,651_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1604,1,0.018606173903741488051544195059,27,0.01
652,652_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1573,1,0.998999999999999999111821580300,28,0.01
653,653_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,1171.000000000000000000000000000000,4687,1851,0.603677719212855512509463551396,36,0.1
654,654_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2020,1,0.998999999999999999111821580300,28,0.25
655,655_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1372,1,0.998999999999999999111821580300,25,0.01
656,656_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1434,1,0.001000000000000000020816681712,31,0.01
657,657_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2123,1,0.001000000000000000020816681712,23,0.025
658,658_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1364,1,0.998999999999999999111821580300,28,0.01
659,659_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2322,1,0.998999999999999999111821580300,25,0.25
660,660_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1900,1,0.239454833129206623443252510697,25,0.025
661,661_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2742,1,0.001000000000000000020816681712,22,0.25
662,662_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1922,1,0.001000000000000000020816681712,25,0.025
663,663_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2182,1,0.001000000000000000020816681712,23,0.25
664,664_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1909,1,0.747413038544377683614072793716,25,0.025
665,665_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2110,1,0.001000000000000000020816681712,26,0.25
666,666_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1918,1,0.998999999999999999111821580300,23,0.025
667,667_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2795,1,0.001000000000000000020816681712,23,0.025
668,668_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1499,1,0.885275870103499062935270558228,25,0.01
669,669_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,1044.000000000000000000000000000000,3864,1478,0.998999999999999999111821580300,32,0.25
670,670_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1930,1,0.011981861225485420166525507568,24,0.025
671,671_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2179,1,0.001000000000000000020816681712,26,0.25
672,672_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1893,1,0.001000000000000000020816681712,24,0.025
673,673_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2071,1,0.001000000000000000020816681712,26,0.25
674,674_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1862,1,0.998999999999999999111821580300,27,0.025
675,675_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2135,1,0.001000000000000000020816681712,26,0.25
676,676_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.239999999999999991118215802999,1046.000000000000000000000000000000,4102,2193,0.992018328226981882522750311182,1,0.001
677,677_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4577,1,0.001000000000000000020816681712,50,0.01
678,678_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,945.000000000000000000000000000000,2578,820,0.001000000000000000020816681712,20,0.025
679,679_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1383,1,0.001000000000000000020816681712,26,0.01
680,680_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2017,1,0.456574142066395183281457548219,26,0.25
681,681_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1856,1,0.998999999999999999111821580300,27,0.025
682,682_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2038,1,0.001000000000000000020816681712,26,0.25
683,683_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1936,1,0.998999999999999999111821580300,28,0.025
684,684_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2009,1,0.001000000000000000020816681712,27,0.25
685,685_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1885,1,0.998999999999999999111821580300,28,0.025
686,686_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.998999999999999999111821580300,25,0.01
687,687_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3729,1,0.001000000000000000020816681712,44,0.25
688,688_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2040,1,0.382454517567754836981919197569,26,0.25
689,689_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1854,1,0.998999999999999999111821580300,28,0.025
690,690_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2037,1,0.001000000000000000020816681712,26,0.25
691,691_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1828,1,0.998999999999999999111821580300,26,0.025
692,692_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2163,1,0.001000000000000000020816681712,26,0.25
693,693_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1815,1,0.998999999999999999111821580300,28,0.025
694,694_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2031,1,0.165128291377723029897950368650,26,0.25
695,695_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1924,1,0.998999999999999999111821580300,28,0.025
696,696_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1649,1,0.998999999999999999111821580300,26,0.25
697,697_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2138,1,0.001000000000000000020816681712,25,0.25
698,698_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1854,1,0.998999999999999999111821580300,24,0.025
699,699_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1466,1,0.998999999999999999111821580300,25,0.01
700,700_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2081,1,0.245312172591840654822803458046,26,0.25
701,701_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1855,1,0.998999999999999999111821580300,26,0.025
702,702_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2020,1,0.130528520482230514510035845888,26,0.25
703,703_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1857,1,0.998999999999999999111821580300,26,0.025
704,704_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2051,1,0.001000000000000000020816681712,26,0.25
705,705_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1825,1,0.998999999999999999111821580300,29,0.025
706,706_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1369,1,0.998999999999999999111821580300,24,0.01
707,673_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2071,1,0.001000000000000000020816681712,26,0.25
708,708_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3714,1,0.001000000000000000020816681712,44,0.25
709,709_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2127,1,0.018850828608224581839358791058,26,0.25
710,710_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1788,1,0.998999999999999999111821580300,26,0.025
711,711_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2034,1,0.020726337249918534710868556203,26,0.25
712,712_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3720,1,0.001000000000000000020816681712,45,0.25
713,713_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2047,1,0.272215073558463249714378662247,27,0.25
714,714_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1328,1,0.998999999999999999111821580300,23,0.01
715,715_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2087,1,0.001000000000000000020816681712,27,0.25
716,716_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1355,1,0.998999999999999999111821580300,25,0.01
717,717_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2086,1,0.001000000000000000020816681712,27,0.25
718,718_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1879,1,0.998999999999999999111821580300,25,0.025
719,719_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1469,1,0.998999999999999999111821580300,25,0.01
720,720_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2047,1,0.486545082934917327843749035310,26,0.25
721,721_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1775,1,0.998999999999999999111821580300,27,0.025
722,722_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1455,1,0.998999999999999999111821580300,23,0.01
723,723_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2090,1,0.001000000000000000020816681712,27,0.25
724,724_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1913,1,0.998999999999999999111821580300,27,0.025
725,725_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1553,1,0.998999999999999999111821580300,25,0.25
726,726_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2115,1,0.001000000000000000020816681712,27,0.005
727,727_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1896,1,0.998999999999999999111821580300,27,0.25
728,728_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1956,1,0.596321534621649718133085116278,27,0.025
729,729_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2056,1,0.001000000000000000020816681712,27,0.25
730,730_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.340000000000000024424906541753,1854.000000000000000000000000000000,3989,943,0.643407978820752401993843250239,48,0.1
731,731_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2048,1,0.362189917224511825910582274446,26,0.25
732,732_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2026,1,0.998999999999999999111821580300,26,0.025
733,733_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2028,1,0.210011206017661017364517306305,26,0.25
734,734_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3686,1,0.001000000000000000020816681712,45,0.25
735,735_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2005,1,0.606276452727556036670364392194,27,0.025
736,736_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2036,1,0.253511547656335045619613310919,26,0.25
737,737_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1340,1,0.998999999999999999111821580300,23,0.01
738,738_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4440,1,0.001000000000000000020816681712,50,0.01
739,739_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2051,1,0.213622442491774755524147622054,26,0.25
740,740_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1708,1,0.001000000000000000020816681712,24,0.01
741,741_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1833,1,0.001000000000000000020816681712,26,0.1
742,742_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.359999999999999986677323704498,1530.000000000000000000000000000000,1787,647,0.001000000000000000020816681712,24,0.1
743,743_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1811,1,0.001000000000000000020816681712,25,0.005
744,744_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.300000000000000044408920985006,1083.000000000000000000000000000000,5000,1331,0.998999999999999999111821580300,41,0.1
745,745_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1814,1,0.001000000000000000020816681712,26,0.1
746,746_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2162,1,0.998999999999999999111821580300,33,0.25
747,747_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1763,1,0.001000000000000000020816681712,24,0.005
748,748_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1787,1,0.001000000000000000020816681712,25,0.01
749,749_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1851,1,0.001000000000000000020816681712,26,0.1
750,750_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2241,1,0.998999999999999999111821580300,34,0.25
751,751_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4326,1,0.998999999999999999111821580300,40,0.1
752,752_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1487,1,0.001000000000000000020816681712,26,0.1
753,753_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,907.000000000000000000000000000000,839,1030,0.001000000000000000020816681712,22,0.025
754,754_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1485,1,0.001000000000000000020816681712,26,0.01
755,755_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4357,1,0.998999999999999999111821580300,42,0.01
756,756_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1360,1,0.001000000000000000020816681712,25,0.01
757,757_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1501,1,0.001000000000000000020816681712,27,0.1
758,758_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1497,1,0.001000000000000000020816681712,26,0.01
759,759_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1509,1,0.001000000000000000020816681712,26,0.005
760,760_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1530,1,0.001000000000000000020816681712,26,0.1
761,761_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1537,1,0.001000000000000000020816681712,26,0.01
762,762_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1448,1,0.499465466185934570120252828929,24,0.1
763,763_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4150,1,0.001000000000000000020816681712,43,0.1
764,764_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1536,1,0.001000000000000000020816681712,26,0.01
765,765_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4485,1,0.078431644533835873089877566144,47,0.01
766,760_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1530,1,0.001000000000000000020816681712,26,0.1
767,767_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1549,1,0.001000000000000000020816681712,26,0.01
768,768_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1476,1,0.340811663461267400077048250751,26,0.1
769,769_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4362,1,0.001000000000000000020816681712,45,0.01
770,770_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1545,1,0.001000000000000000020816681712,26,0.01
771,771_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4388,1,0.001000000000000000020816681712,44,0.01
772,772_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1539,1,0.001000000000000000020816681712,26,0.01
773,773_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1493,1,0.001000000000000000020816681712,26,0.1
774,774_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1543,1,0.001000000000000000020816681712,26,0.01
775,775_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1492,1,0.625998657144022474518862964032,26,0.1
776,774_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1543,1,0.001000000000000000020816681712,26,0.01
777,777_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1476,1,0.001000000000000000020816681712,26,0.1
778,778_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4433,1,0.004714045415447756966209613694,45,0.1
779,779_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1425,1,0.001000000000000000020816681712,25,0.01
780,780_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1518,1,0.001000000000000000020816681712,26,0.1
781,781_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4339,1,0.001000000000000000020816681712,45,0.1
782,782_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1515,1,0.001000000000000000020816681712,26,0.01
783,783_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1461,1,0.021543680950271391905115336840,25,0.1
784,784_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1538,1,0.021030974670767862283460303274,26,0.01
785,785_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1478,1,0.001000000000000000020816681712,25,0.1
786,786_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1534,1,0.009028166096223691136635203236,26,0.01
787,787_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4489,1,0.001000000000000000020816681712,46,0.01
788,788_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1529,1,0.001000000000000000020816681712,26,0.01
789,789_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1466,1,0.624167864217004209059780350799,25,0.1
790,788_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1529,1,0.001000000000000000020816681712,26,0.01
791,791_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1542,1,0.001000000000000000020816681712,27,0.1
792,764_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1536,1,0.001000000000000000020816681712,26,0.01
793,793_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4472,1,0.001000000000000000020816681712,46,0.01
794,794_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1516,1,0.001000000000000000020816681712,26,0.01
795,795_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1518,1,0.406345660933504548850692117412,26,0.005
796,796_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4464,1,0.442382515408805687684434815310,48,0.1
797,797_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4414,1,0.001000000000000000020816681712,45,0.01
798,798_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1511,1,0.001000000000000000020816681712,26,0.01
799,799_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.001000000000000000020816681712,26,0.1
800,800_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1530,1,0.013346447795483719692133384171,26,0.01
801,801_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.190000000000000002220446049250,1031.000000000000000000000000000000,4174,3349,0.998999999999999999111821580300,37,0.1
802,802_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1496,1,0.001000000000000000020816681712,26,0.1
803,803_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1531,1,0.001000000000000000020816681712,26,0.01
804,804_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1508,1,0.264731416293616739210392552195,26,0.005
805,805_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1531,1,0.001000000000000000020816681712,26,0.1
806,806_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1494,1,0.001000000000000000020816681712,24,0.01
807,807_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1606,1,0.376442622097279078197118451499,26,0.005
808,808_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1550,1,0.156960545605557721948741800588,25,0.01
809,809_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1673,1,0.001000000000000000020816681712,28,0.1
810,810_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1553,1,0.001000000000000000020816681712,26,0.01
811,811_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.289999999999999980015985556747,1441.000000000000000000000000000000,4303,1403,0.001000000000000000020816681712,12,0.1
812,812_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1598,1,0.013675645203584710016264658350,26,0.1
813,813_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1534,1,0.035409156639667732635601282709,26,0.01
814,814_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1530,1,0.088447765420339413688921581524,26,0.005
815,815_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1449,1,0.001000000000000000020816681712,25,0.01
816,816_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1551,1,0.204727147128072689552524821011,27,0.1
817,817_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1613,1,0.001000000000000000020816681712,25,0.01
818,818_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.330000000000000015543122344752,1092.000000000000000000000000000000,1496,944,0.998999999999999999111821580300,24,0.01
819,819_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1434,1,0.998999999999999999111821580300,26,0.01
820,820_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1782,1,0.998999999999999999111821580300,26,0.025
821,821_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.050000000000000002775557561563,1091.000000000000000000000000000000,1,2238,0.998999999999999999111821580300,1,0.05
822,822_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1808,1,0.998999999999999999111821580300,26,0.025
823,823_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.309999999999999997779553950750,1412.000000000000000000000000000000,1599,1054,0.998999999999999999111821580300,1,0.25
824,824_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1798,1,0.998999999999999999111821580300,26,0.01
825,825_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1396,1,0.998999999999999999111821580300,23,0.025
826,826_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1959,1,0.998999999999999999111821580300,30,0.005
827,827_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1424,1,0.998999999999999999111821580300,25,0.025
828,828_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1783,1,0.998999999999999999111821580300,27,0.025
829,829_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1542,1,0.998999999999999999111821580300,23,0.01
830,830_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1926,1,0.998999999999999999111821580300,30,0.01
831,831_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1646,1,0.998999999999999999111821580300,25,0.1
832,832_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1892,1,0.998999999999999999111821580300,28,0.005
833,833_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1609,1,0.998999999999999999111821580300,24,0.025
834,834_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1124,1,0.998999999999999999111821580300,32,0.025
835,835_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1950,1,0.998999999999999999111821580300,31,0.005
836,836_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.340000000000000024424906541753,1190.000000000000000000000000000000,1941,931,0.998999999999999999111821580300,27,0.025
837,837_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,763.000000000000000000000000000000,5000,5000,0.217670256183479099432176440132,50,0.05
838,838_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1926,1,0.998999999999999999111821580300,29,0.025
839,839_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1425,1,0.998999999999999999111821580300,23,0.1
840,840_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1889,1,0.001000000000000000020816681712,28,0.025
841,841_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1149,1,0.998999999999999999111821580300,31,0.005
842,842_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1920,1,0.998999999999999999111821580300,29,0.01
843,843_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1154,1,0.998999999999999999111821580300,33,0.001
844,844_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1873,1,0.001000000000000000020816681712,27,0.025
845,845_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1485,1,0.998999999999999999111821580300,24,0.1
846,846_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1752,1,0.001000000000000000020816681712,28,0.025
847,847_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1812,1,0.998999999999999999111821580300,28,0.01
848,848_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1638,1,0.998999999999999999111821580300,26,0.1
849,849_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1765,1,0.998999999999999999111821580300,28,0.005
850,850_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.200000000000000011102230246252,870.000000000000000000000000000000,1495,2445,0.998999999999999999111821580300,50,0.1
851,851_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1556,1,0.998999999999999999111821580300,26,0.01
852,852_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1708,1,0.998999999999999999111821580300,28,0.025
853,853_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1525,1,0.998999999999999999111821580300,27,0.01
854,854_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1526,1,0.998999999999999999111821580300,25,0.005
855,855_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1644,1,0.998999999999999999111821580300,26,0.025
856,856_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1569,1,0.998999999999999999111821580300,26,0.1
857,857_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1742,1,0.001000000000000000020816681712,28,0.025
858,858_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1527,1,0.998999999999999999111821580300,26,0.01
859,859_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1808,1,0.001000000000000000020816681712,28,0.01
860,860_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1563,1,0.998999999999999999111821580300,26,0.005
861,861_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1688,1,0.001000000000000000020816681712,26,0.001
862,862_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1654,1,0.998999999999999999111821580300,26,0.01
863,863_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1608,1,0.998999999999999999111821580300,26,0.1
864,864_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1739,1,0.001000000000000000020816681712,27,0.005
865,865_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1538,1,0.998999999999999999111821580300,25,0.1
866,866_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1799,1,0.001000000000000000020816681712,27,0.01
867,867_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1621,1,0.998999999999999999111821580300,26,0.001
868,868_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1634,1,0.998999999999999999111821580300,27,0.005
869,869_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1605,1,0.001000000000000000020816681712,25,0.1
870,870_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1844,1,0.998999999999999999111821580300,28,0.01
871,871_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1452,1,0.998999999999999999111821580300,25,0.1
872,872_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1791,1,0.998999999999999999111821580300,28,0.01
873,873_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1577,1,0.001000000000000000020816681712,25,0.1
874,874_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1820,1,0.001000000000000000020816681712,27,0.01
875,875_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1608,1,0.998999999999999999111821580300,26,0.025
876,876_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1674,1,0.001000000000000000020816681712,27,0.005
877,877_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1591,1,0.998999999999999999111821580300,25,0.025
878,878_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.309999999999999997779553950750,901.000000000000000000000000000000,4281,1357,0.998999999999999999111821580300,11,0.005
879,879_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1798,1,0.998999999999999999111821580300,27,0.01
880,880_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,806.000000000000000000000000000000,4155,4960,0.001000000000000000020816681712,1,0.025
881,881_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1779,1,0.001000000000000000020816681712,28,0.025
882,882_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1600,1,0.998999999999999999111821580300,25,0.005
883,883_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1584,1,0.001000000000000000020816681712,25,0.025
884,884_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1617,1,0.998999999999999999111821580300,26,0.01
885,885_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1784,1,0.001000000000000000020816681712,27,0.01
886,886_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1638,1,0.998999999999999999111821580300,26,0.025
887,887_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1089,1,0.998999999999999999111821580300,32,0.1
888,888_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1786,1,0.998999999999999999111821580300,28,0.001
889,889_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1220,1,0.998999999999999999111821580300,30,0.1
890,890_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1714,1,0.998999999999999999111821580300,26,0.025
891,891_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1536,1,0.001000000000000000020816681712,25,0.001
892,892_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1722,1,0.998999999999999999111821580300,27,0.1
893,893_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1465,1,0.998999999999999999111821580300,24,0.001
894,866_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1799,1,0.001000000000000000020816681712,27,0.01
895,895_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1636,1,0.001000000000000000020816681712,26,0.01
896,896_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1659,1,0.998999999999999999111821580300,26,0.025
897,897_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1212,1,0.998999999999999999111821580300,30,0.1
898,898_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1806,1,0.001000000000000000020816681712,27,0.025
899,899_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1496,1,0.998999999999999999111821580300,25,0.1
900,900_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1783,1,0.001000000000000000020816681712,27,0.025
901,901_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1603,1,0.001000000000000000020816681712,25,0.005
902,902_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1685,1,0.001000000000000000020816681712,26,0.025
903,903_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.179999999999999993338661852249,824.000000000000000000000000000000,1210,2458,0.512327182509651146702367441321,1,0.25
904,904_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1722,1,0.001000000000000000020816681712,27,0.005
905,905_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1571,1,0.998999999999999999111821580300,25,0.025
906,906_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1368,1,0.998999999999999999111821580300,27,0.1
907,907_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1741,1,0.001000000000000000020816681712,26,0.01
908,908_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1535,1,0.998999999999999999111821580300,25,0.025
909,909_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1105,1,0.998999999999999999111821580300,32,0.1
910,910_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1790,1,0.001000000000000000020816681712,27,0.025
911,911_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1480,1,0.998999999999999999111821580300,26,0.005
912,912_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1529,1,0.998999999999999999111821580300,23,0.1
913,913_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1650,1,0.001000000000000000020816681712,25,0.005
914,914_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1482,1,0.998999999999999999111821580300,23,0.025
915,915_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1603,1,0.001000000000000000020816681712,24,0.001
916,916_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1589,1,0.001000000000000000020816681712,24,0.01
917,917_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1468,1,0.998999999999999999111821580300,24,0.1
918,918_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1786,1,0.001000000000000000020816681712,26,0.01
919,919_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1590,1,0.001000000000000000020816681712,24,0.1
920,920_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1467,1,0.998999999999999999111821580300,23,0.1
921,921_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1796,1,0.001000000000000000020816681712,26,0.01
922,922_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1495,1,0.001000000000000000020816681712,24,0.025
923,923_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1472,1,0.001000000000000000020816681712,24,0.01
924,924_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1573,1,0.998999999999999999111821580300,23,0.01
925,925_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1309,1,0.998999999999999999111821580300,25,0.1
926,926_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1763,1,0.001000000000000000020816681712,26,0.01
927,927_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1585,1,0.998999999999999999111821580300,24,0.005
928,928_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1145,1,0.998999999999999999111821580300,30,0.1
929,929_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1776,1,0.001000000000000000020816681712,26,0.025
930,930_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1463,1,0.998999999999999999111821580300,24,0.1
931,931_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1790,1,0.001000000000000000020816681712,26,0.01
932,932_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.001000000000000000020816681712,24,0.025
933,933_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1472,1,0.998999999999999999111821580300,24,0.1
934,934_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1785,1,0.001000000000000000020816681712,26,0.01
935,935_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1663,1,0.001000000000000000020816681712,24,0.1
936,936_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1810,1,0.001000000000000000020816681712,26,0.025
937,937_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1515,1,0.998999999999999999111821580300,23,0.005
938,938_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,995.000000000000000000000000000000,2050,184,0.001000000000000000020816681712,33,0.025
939,939_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1539,1,0.001000000000000000020816681712,23,0.01
940,940_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.200000000000000011102230246252,966.000000000000000000000000000000,1352,2320,0.001000000000000000020816681712,50,0.005
941,941_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1480,1,0.998999999999999999111821580300,22,0.025
942,942_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1764,1,0.001000000000000000020816681712,26,0.025
943,943_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1471,1,0.998999999999999999111821580300,24,0.1
944,944_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1694,1,0.001000000000000000020816681712,25,0.01
945,945_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1569,1,0.001000000000000000020816681712,23,0.025
946,946_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1099,1,0.998999999999999999111821580300,32,0.1
947,947_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1700,1,0.001000000000000000020816681712,25,0.01
948,948_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.998999999999999999111821580300,37,0.01
949,949_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1364,1,0.998999999999999999111821580300,31,0.005
950,950_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1486,1,0.998999999999999999111821580300,35,0.01
951,951_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1342,1,0.998999999999999999111821580300,31,0.1
952,952_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1513,1,0.998999999999999999111821580300,36,0.01
953,953_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1395,1,0.998999999999999999111821580300,32,0.005
954,954_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1509,1,0.998999999999999999111821580300,36,0.01
955,955_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1384,1,0.998999999999999999111821580300,32,0.1
956,956_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1503,1,0.998999999999999999111821580300,35,0.005
957,957_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1467,1,0.998999999999999999111821580300,35,0.01
958,958_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.149999999999999994448884876874,822.000000000000000000000000000000,5000,4530,0.001000000000000000020816681712,50,0.025
959,959_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1490,1,0.998999999999999999111821580300,35,0.01
960,960_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1408,1,0.998999999999999999111821580300,32,0.1
961,961_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.170000000000000012212453270877,856.000000000000000000000000000000,5000,3852,0.001000000000000000020816681712,50,0.025
962,962_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1493,1,0.998999999999999999111821580300,36,0.01
963,963_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,1136.000000000000000000000000000000,1481,634,0.998999999999999999111821580300,29,0.01
964,964_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.220000000000000001110223024625,2482.000000000000000000000000000000,1236,318,0.001000000000000000020816681712,1,0.025
965,965_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,838.000000000000000000000000000000,3638,4983,0.001000000000000000020816681712,50,0.01
966,966_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.419999999999999984456877655248,1764.000000000000000000000000000000,1259,336,0.998999999999999999111821580300,1,0.1
967,967_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.289999999999999980015985556747,982.000000000000000000000000000000,4252,1388,0.998999999999999999111821580300,1,0.05
968,968_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,801.000000000000000000000000000000,5000,5000,0.001000000000000000020816681712,50,0.25
969,969_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.289999999999999980015985556747,930.000000000000000000000000000000,1881,1390,0.998999999999999999111821580300,1,0.005
970,970_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,925.000000000000000000000000000000,4267,1514,0.998999999999999999111821580300,1,0.05
971,971_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.440000000000000002220446049250,1876.000000000000000000000000000000,1235,301,0.998999999999999999111821580300,1,0.025
972,972_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.289999999999999980015985556747,2254.000000000000000000000000000000,1184,315,0.001000000000000000020816681712,1,0.1
973,973_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,924.000000000000000000000000000000,4098,1532,0.998999999999999999111821580300,1,0.005
974,974_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.359999999999999986677323704498,1045.000000000000000000000000000000,2650,421,0.998999999999999999111821580300,13,0.01
975,975_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.409999999999999975575093458247,2142.000000000000000000000000000000,4484,451,0.001000000000000000020816681712,50,0.025
976,976_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1119,1,0.998999999999999999111821580300,30,0.001
977,977_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1057,1,0.998999999999999999111821580300,30,0.1
978,978_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.230000000000000009992007221626,2315.000000000000000000000000000000,1208,272,0.001000000000000000020816681712,1,0.025
979,979_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.220000000000000001110223024625,847.000000000000000000000000000000,1500,2047,0.998999999999999999111821580300,1,0.25
980,980_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.340000000000000024424906541753,1078.000000000000000000000000000000,2704,522,0.001000000000000000020816681712,50,0.01
981,981_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,989.000000000000000000000000000000,1877,1078,0.314947751553451726902466134561,28,0.025
982,982_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,3763.000000000000000000000000000000,1472,208,0.998999999999999999111821580300,23,0.01
983,983_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.289999999999999980015985556747,872.000000000000000000000000000000,1344,1128,0.456260109604418151452165375304,21,0.005
984,984_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.369999999999999995559107901499,1488.000000000000000000000000000000,4059,663,0.001000000000000000020816681712,31,0.025
985,985_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,822.000000000000000000000000000000,1343,1456,0.380192059477222565888610006368,50,0.025
986,986_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.250000000000000000000000000000,899.000000000000000000000000000000,1210,1445,0.194874728317937895294420513892,50,0.025
987,987_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.359999999999999986677323704498,1183.000000000000000000000000000000,1373,668,0.998999999999999999111821580300,5,0.01
988,988_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.239999999999999991118215802999,892.000000000000000000000000000000,1184,1401,0.643258819762501654615505231050,50,0.025
989,989_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.130000000000000004440892098501,768.000000000000000000000000000000,1593,5000,0.001000000000000000020816681712,50,0.025
990,990_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.349999999999999977795539507497,1026.000000000000000000000000000000,1284,669,0.998999999999999999111821580300,4,0.005
991,991_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,1120.000000000000000000000000000000,1333,580,0.998999999999999999111821580300,5,0.01
992,992_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.220000000000000001110223024625,952.000000000000000000000000000000,4922,2559,0.001000000000000000020816681712,1,0.05
993,993_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.050000000000000002775557561563,673.000000000000000000000000000000,1,5000,0.001000000000000000020816681712,1,0.025
994,994_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,1076.000000000000000000000000000000,4359,1155,0.756729743206114635611925223202,17,0.01
995,995_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.419999999999999984456877655248,1693.000000000000000000000000000000,1666,435,0.341623320562915666620540378062,30,0.1
996,996_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.400000000000000022204460492503,1533.000000000000000000000000000000,1671,516,0.418108293325782975902171756388,31,0.01
997,997_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,865.000000000000000000000000000000,1567,3509,0.001000000000000000020816681712,50,0.025
998,998_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.369999999999999995559107901499,1262.000000000000000000000000000000,1651,698,0.464563344120994647923339471163,30,0.01
999,999_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.050000000000000002775557561563,1075.000000000000000000000000000000,2099,1222,0.998999999999999999111821580300,2,0.01
1000,1000_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.119999999999999995559107901499,772.000000000000000000000000000000,3267,5000,0.001000000000000000020816681712,50,0.05
1001,1001_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,2964.000000000000000000000000000000,1672,312,0.442057610393094602141417226449,30,0.01
1002,1002_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,831.000000000000000000000000000000,2285,1011,0.783589734097740908680407301290,23,0.01
1003,1003_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.119999999999999995559107901499,832.000000000000000000000000000000,2261,1395,0.998999999999999999111821580300,13,0.01
1004,1004_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.190000000000000002220446049250,943.000000000000000000000000000000,2274,644,0.998999999999999999111821580300,24,0.005
1005,1005_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.349999999999999977795539507497,1107.000000000000000000000000000000,1178,687,0.504367961000837805585206297110,28,0.1
1006,1006_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,1436.000000000000000000000000000000,1237,473,0.998999999999999999111821580300,21,0.1
1007,1007_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.340000000000000024424906541753,1320.000000000000000000000000000000,4321,974,0.448137173978192027146150167027,35,0.005
1008,1008_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.369999999999999995559107901499,1501.000000000000000000000000000000,1447,587,0.892151708912277574547999847709,41,0.1
1009,1009_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4498,108,0.001000000000000000020816681712,50,0.25
1010,1010_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.390000000000000013322676295502,1874.000000000000000000000000000000,1409,478,0.998999999999999999111821580300,43,0.1
1011,1011_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,926.000000000000000000000000000000,1130,1272,0.998999999999999999111821580300,50,0.005
1012,1012_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,1347.000000000000000000000000000000,3812,1459,0.998999999999999999111821580300,50,0.005
1013,1013_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.400000000000000022204460492503,1590.000000000000000000000000000000,3414,463,0.998999999999999999111821580300,18,0.01
1014,1014_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2000,1,0.998999999999999999111821580300,15,0.01
1015,1015_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.419999999999999984456877655248,2104.000000000000000000000000000000,3708,390,0.489886290505814281370788876302,50,0.01
1016,1016_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.409999999999999975575093458247,2364.000000000000000000000000000000,3512,434,0.705876927060089975896062242100,1,0.01
1017,1017_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.359999999999999986677323704498,1141.000000000000000000000000000000,3154,595,0.998999999999999999111821580300,13,0.01
1018,1018_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.390000000000000013322676295502,1392.000000000000000000000000000000,1412,519,0.001000000000000000020816681712,50,0.005
1019,1019_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.250000000000000000000000000000,1165.000000000000000000000000000000,1880,1997,0.998999999999999999111821580300,34,0.005
1020,1020_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,910.000000000000000000000000000000,4647,1761,0.569874554770941843528930803586,23,0.1
1021,1021_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1610,1,0.998999999999999999111821580300,5,0.01
1022,1022_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1523,78,0.948358191828172603088376035885,38,0.001
1023,1023_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.340000000000000024424906541753,1169.000000000000000000000000000000,4275,952,0.316226283885189640709967306975,21,0.25
1024,1024_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.270000000000000017763568394003,1031.000000000000000000000000000000,1136,1180,0.818643644781014701017340939870,43,0.25
1025,1025_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.260000000000000008881784197001,864.000000000000000000000000000000,4200,1836,0.841294354541999411800645702897,1,0.025
1026,1026_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.419999999999999984456877655248,1809.000000000000000000000000000000,1397,396,0.998999999999999999111821580300,11,0.1
1027,1027_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,945.000000000000000000000000000000,1071,1037,0.373425499636659996571808051158,23,0.01
1028,1028_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.429999999999999993338661852249,2932.000000000000000000000000000000,1405,357,0.998999999999999999111821580300,22,0.001
1029,1029_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.250000000000000000000000000000,893.000000000000000000000000000000,4332,2093,0.373832167565780359996807646894,2,0.25
1030,1030_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.239999999999999991118215802999,887.000000000000000000000000000000,1644,1931,0.998999999999999999111821580300,12,0.005
1031,1031_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3386,1,0.001000000000000000020816681712,42,0.1
1032,1032_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,917.000000000000000000000000000000,3643,4384,0.263342094076290778037474638040,18,0.01
1033,1033_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.479999999999999982236431605997,4868.000000000000000000000000000000,1380,180,0.998999999999999999111821580300,24,0.001
1034,1034_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.179999999999999993338661852249,1307.000000000000000000000000000000,4823,3384,0.139674848864999007203024916635,16,0.005
1035,1035_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1323,1,0.998999999999999999111821580300,38,0.001
1036,1036_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,1003.000000000000000000000000000000,4306,1596,0.772394340739445350862979466910,30,0.1
1037,1037_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.998999999999999999111821580300,38,0.001
1038,1038_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,1287.000000000000000000000000000000,1401,569,0.998999999999999999111821580300,23,0.025
1039,1039_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1408,1,0.998999999999999999111821580300,10,0.01
1040,1040_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1340,2,0.891922242832283607150145599007,41,0.001
1041,1041_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.998999999999999999111821580300,6,0.1
1042,1042_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1390,1,0.998999999999999999111821580300,8,0.005
1043,1043_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,1178.000000000000000000000000000000,1522,608,0.543148431634367656606343643944,37,0.001
1044,1044_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1297,1,0.824364330098790842882294782612,40,0.001
1045,1045_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1429,1,0.998999999999999999111821580300,6,0.1
1046,1046_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1326,1,0.819977138136086969311122629733,40,0.001
1047,1047_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1430,1,0.998999999999999999111821580300,6,0.1
1048,1048_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1314,1,0.843650543769294380958001511317,40,0.001
1049,1049_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1406,1,0.998999999999999999111821580300,6,0.1
1050,1050_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1321,1,0.877066640726682056339313930948,40,0.001
1051,1051_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1334,1,0.998999999999999999111821580300,7,0.005
1052,1052_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.100000000000000005551115123126,907.000000000000000000000000000000,2490,2572,0.923023507930481157401914060756,43,0.01
1053,1053_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1331,1,0.815816273825260562801986452541,39,0.005
1054,1054_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1411,1,0.998999999999999999111821580300,7,0.005
1055,1055_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4458,6,0.001000000000000000020816681712,36,0.025
1056,1056_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1319,1,0.996451994520346828743129208306,40,0.001
1057,1057_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1427,1,0.998999999999999999111821580300,6,0.1
1058,1058_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1326,1,0.984294068643553066166873577458,40,0.001
1059,1059_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1424,1,0.998999999999999999111821580300,6,0.1
1060,1060_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1280,1,0.988159484895871464971150999190,40,0.001
1061,1061_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1416,1,0.998999999999999999111821580300,6,0.1
1062,1062_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1393,1,0.998999999999999999111821580300,8,0.005
1063,1063_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1316,1,0.943799788293939512229258070874,40,0.001
1064,1064_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1414,1,0.998999999999999999111821580300,6,0.1
1065,1065_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1319,1,0.956354155480516165965809705085,40,0.001
1066,1066_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1414,1,0.998999999999999999111821580300,7,0.01
1067,1067_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1415,1,0.998999999999999999111821580300,5,0.1
1068,1068_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1308,1,0.984675653151772078963688272779,40,0.001
1069,1069_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1403,1,0.998999999999999999111821580300,5,0.1
1070,1070_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,934.000000000000000000000000000000,4508,1665,0.441674081897008630193113276619,13,0.25
1071,1071_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1364,1,0.998999999999999999111821580300,7,0.1
1072,1072_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1406,1,0.998999999999999999111821580300,8,0.005
1073,1073_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1314,1,0.886438328819506127231875325378,40,0.001
1074,1074_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1433,1,0.998999999999999999111821580300,5,0.1
1075,1075_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1426,1,0.998999999999999999111821580300,1,0.005
1076,1076_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1431,1,0.998999999999999999111821580300,6,0.1
1077,1077_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1403,1,0.998999999999999999111821580300,9,0.005
1078,1078_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4562,10,0.001000000000000000020816681712,36,0.025
1079,1079_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1422,1,0.998999999999999999111821580300,5,0.1
1080,1080_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1403,1,0.998999999999999999111821580300,8,0.005
1081,1081_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1452,1,0.998999999999999999111821580300,1,0.1
1082,1082_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1373,1,0.875831002491258114872607620782,39,0.001
1083,1083_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1435,1,0.998999999999999999111821580300,6,0.1
1084,1084_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4325,19,0.001000000000000000020816681712,36,0.025
1085,1085_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1332,1,0.882881116334821025581902631529,40,0.001
1086,1086_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1426,1,0.998999999999999999111821580300,6,0.1
1087,1077_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1403,1,0.998999999999999999111821580300,9,0.005
1088,1088_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1441,1,0.998999999999999999111821580300,4,0.1
1089,1089_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1308,1,0.907676976071095986675629774254,40,0.001
1090,1090_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1425,1,0.998999999999999999111821580300,6,0.005
1091,1091_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1447,1,0.998999999999999999111821580300,1,0.1
1092,1092_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.881033512361886539387967332004,40,0.001
1093,1083_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1435,1,0.998999999999999999111821580300,6,0.1
1094,1094_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.280000000000000026645352591004,856.000000000000000000000000000000,1774,1311,0.909570085744739298405647787149,7,0.001
1095,1095_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1424,1,0.998999999999999999111821580300,5,0.1
1096,1096_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1360,1,0.998999999999999999111821580300,8,0.005
1097,1097_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1488,1,0.998999999999999999111821580300,2,0.1
1098,1098_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1396,1,0.998999999999999999111821580300,8,0.005
1099,1099_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1325,1,0.859325843282753520924188705976,40,0.001
1100,1100_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1423,1,0.998999999999999999111821580300,6,0.1
1101,1101_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1349,1,0.855744417833293136155248248542,39,0.001
1102,1102_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1425,1,0.998999999999999999111821580300,6,0.1
1103,1103_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1436,1,0.998999999999999999111821580300,5,0.1
1104,1104_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.833714103360173153234313758730,39,0.005
1105,1057_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1427,1,0.998999999999999999111821580300,6,0.1
1106,1106_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1412,1,0.998999999999999999111821580300,8,0.005
1107,1107_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1335,1,0.875378074324818178375551269710,39,0.001
1108,1045_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1429,1,0.998999999999999999111821580300,6,0.1
1109,1109_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1415,1,0.998999999999999999111821580300,1,0.1
1110,1110_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1426,1,0.998999999999999999111821580300,8,0.005
1111,1111_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1454,1,0.998999999999999999111821580300,2,0.1
1112,1112_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1333,1,0.880745893917601940792394543678,39,0.001
1113,1113_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1374,1,0.998999999999999999111821580300,5,0.005
1114,1114_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4362,16,0.001000000000000000020816681712,37,0.025
1115,1115_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1414,1,0.998999999999999999111821580300,5,0.1
1116,1116_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1392,1,0.998999999999999999111821580300,9,0.005
1117,1117_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1444,1,0.998999999999999999111821580300,3,0.1
1118,1118_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1389,1,0.998999999999999999111821580300,9,0.005
1119,1119_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1316,1,0.824189653366055763328290595382,40,0.001
1120,1120_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1443,1,0.998999999999999999111821580300,6,0.1
1121,1121_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1296,1,0.837576469842975179780353300885,39,0.001
1122,1122_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1441,1,0.998999999999999999111821580300,6,0.1
1123,1123_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1408,1,0.998999999999999999111821580300,8,0.005
1124,1124_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1288,1,0.831308613794940143293388246093,40,0.001
1125,1125_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1342,1,0.842911274674666199935302302038,37,0.1
1126,1126_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1434,1,0.998999999999999999111821580300,5,0.1
1127,1127_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1432,1,0.998999999999999999111821580300,9,0.005
1128,1128_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1423,1,0.998999999999999999111821580300,2,0.1
1129,1129_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4521,32,0.001000000000000000020816681712,37,0.025
1130,1130_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1432,1,0.998999999999999999111821580300,5,0.1
1131,1131_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1400,1,0.998999999999999999111821580300,9,0.005
1132,1132_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4506,18,0.001000000000000000020816681712,36,0.025
1133,1130_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1432,1,0.998999999999999999111821580300,5,0.1
1134,1134_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1349,1,0.848099256444599047810584124818,39,0.001
1135,1135_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1435,1,0.851739972495205832636600007390,39,0.005
1136,1074_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1433,1,0.998999999999999999111821580300,5,0.1
1137,1137_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1293,1,0.866333132953705620238338269701,39,0.005
1138,1138_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1448,1,0.998999999999999999111821580300,6,0.1
1139,1139_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.834634588756027140377113937575,39,0.001
1140,1086_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1426,1,0.998999999999999999111821580300,6,0.1
1141,1141_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1405,1,0.998999999999999999111821580300,8,0.005
1142,1142_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1312,1,0.857824885830013150744832728378,40,0.001
1143,1143_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1443,1,0.998999999999999999111821580300,4,0.1
1144,1144_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1405,1,0.998999999999999999111821580300,9,0.005
1145,1145_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.998999999999999999111821580300,4,0.1
1146,1146_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1391,1,0.998999999999999999111821580300,9,0.005
1147,1147_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4444,47,0.998999999999999999111821580300,41,0.001
1148,1148_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1433,1,0.998999999999999999111821580300,6,0.1
1149,1149_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1352,1,0.768121782323137658465839194832,39,0.1
1150,1150_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1445,1,0.998999999999999999111821580300,7,0.1
1151,1151_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1356,1,0.819594664897933533609375444939,39,0.005
1152,1100_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1423,1,0.998999999999999999111821580300,6,0.1
1153,1153_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1317,1,0.821499275547257101770526332984,39,0.001
1154,1154_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1428,1,0.998999999999999999111821580300,6,0.1
1155,1155_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1451,1,0.998999999999999999111821580300,1,0.1
1156,1156_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1334,1,0.812130849841801727428958201926,38,0.005
1157,1154_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1428,1,0.998999999999999999111821580300,6,0.1
1158,1158_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1429,1,0.998999999999999999111821580300,2,0.1
1159,1159_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1353,1,0.781688287406812509772180419532,38,0.1
1160,1076_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1431,1,0.998999999999999999111821580300,6,0.1
1161,1161_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1324,1,0.814304277937602849490872358729,38,0.005
1162,1162_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1432,1,0.998999999999999999111821580300,6,0.1
1163,1163_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1404,1,0.998999999999999999111821580300,8,0.005
1164,1164_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1328,1,0.843604911475009933141677720414,39,0.001
1165,1165_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1429,1,0.998999999999999999111821580300,5,0.1
1166,1166_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.839673132900067509254427022825,39,0.005
1167,1167_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.998999999999999999111821580300,5,0.005
1168,1168_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1329,1,0.838131127991142088617948502360,39,0.001
1169,1076_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1431,1,0.998999999999999999111821580300,6,0.1
1170,1170_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1410,1,0.998999999999999999111821580300,8,0.005
1171,1171_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1325,1,0.822491963310987395097129137866,40,0.001
1172,1103_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1436,1,0.998999999999999999111821580300,5,0.1
1173,1173_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.858822183008609463250593307748,39,0.001
1174,1174_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1462,1,0.998999999999999999111821580300,5,0.1
1175,1175_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1444,1,0.998999999999999999111821580300,1,0.1
1176,1176_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1317,1,0.831698574592749317879736281611,39,0.001
1177,1177_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1412,1,0.998999999999999999111821580300,6,0.1
1178,1178_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1511,1,0.998999999999999999111821580300,1,0.1
1179,1179_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1445,1,0.998999999999999999111821580300,8,0.005
1180,1180_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1459,1,0.998999999999999999111821580300,2,0.005
1181,1181_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1327,1,0.877445676462027646103081224283,40,0.001
1182,1182_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1417,1,0.998999999999999999111821580300,7,0.005
1183,1183_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1332,1,0.769934931627063057213433694415,39,0.001
1184,1184_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.998999999999999999111821580300,6,0.1
1185,1185_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1302,1,0.862893115798228138579872847913,39,0.001
1186,1186_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1300,1,0.998999999999999999111821580300,6,0.005
1187,1187_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1443,1,0.998999999999999999111821580300,5,0.1
1188,1188_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1315,1,0.869725672706246677456931593042,39,0.001
1189,1167_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.998999999999999999111821580300,5,0.005
1190,1190_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.239999999999999991118215802999,1105.000000000000000000000000000000,1632,1831,0.998999999999999999111821580300,29,0.1
1191,1191_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1296,1,0.853089844264941610241237412993,39,0.001
1192,1126_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1434,1,0.998999999999999999111821580300,5,0.1
1193,1193_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1317,1,0.886044583144391939555362114334,40,0.001
1194,1194_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1469,1,0.998999999999999999111821580300,5,0.1
1195,1195_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1324,1,0.844211716259428701647493653581,40,0.001
1196,1196_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1385,1,0.998999999999999999111821580300,6,0.005
1197,1197_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1435,1,0.998999999999999999111821580300,5,0.1
1198,1198_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1402,1,0.998999999999999999111821580300,8,0.005
1199,1199_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1303,1,0.866240894532978478181917125767,40,0.001
1200,1200_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1440,1,0.998999999999999999111821580300,6,0.005
1201,1201_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1311,1,0.851785109886073588114641097491,40,0.001
1202,1041_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.998999999999999999111821580300,6,0.1
1203,1203_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4408,1,0.001000000000000000020816681712,36,0.025
1204,1204_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1430,1,0.998999999999999999111821580300,5,0.1
1205,1205_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1412,1,0.998999999999999999111821580300,9,0.005
1206,1206_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.998999999999999999111821580300,3,0.1
1207,1198_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1402,1,0.998999999999999999111821580300,8,0.005
1208,1208_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1320,1,0.900384324432872196197763514647,40,0.001
1209,1209_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.998999999999999999111821580300,5,0.1
1210,1210_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.250000000000000000000000000000,998.000000000000000000000000000000,4463,1989,0.226007366411098920000455336776,20,0.25
1211,1209_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1442,1,0.998999999999999999111821580300,5,0.1
1212,1212_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1424,1,0.998999999999999999111821580300,7,0.005
1213,1213_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1311,1,0.861202903719500589829749515047,40,0.001
1214,1214_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,5815.000000000000000000000000000000,1461,165,0.998999999999999999111821580300,10,0.005
1215,1215_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.489999999999999991118215802999,6722.000000000000000000000000000000,1395,150,0.998999999999999999111821580300,38,0.001
1216,1216_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.400000000000000022204460492503,1919.000000000000000000000000000000,4471,538,0.998999999999999999111821580300,9,0.001
1217,1217_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.040000000000000000832667268469,1203.000000000000000000000000000000,2114,1874,0.998999999999999999111821580300,5,0.01
1218,1218_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.230000000000000009992007221626,942.000000000000000000000000000000,4708,2222,0.998999999999999999111821580300,26,0.05
1219,1219_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.179999999999999993338661852249,914.000000000000000000000000000000,4784,3626,0.762607877415813040222758445452,44,0.01
1220,1220_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.409999999999999975575093458247,2033.000000000000000000000000000000,4526,502,0.900279312023803512943231908139,38,0.025
1221,1221_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1428,1,0.998999999999999999111821580300,7,0.1
1222,1222_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1464,1,0.001000000000000000020816681712,48,0.1
1223,1223_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.390000000000000013322676295502,1947.000000000000000000000000000000,4516,573,0.998999999999999999111821580300,50,0.25
1224,1224_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1422,1,0.001000000000000000020816681712,50,0.005
1225,1225_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,2074.000000000000000000000000000000,4301,615,0.998999999999999999111821580300,39,0.005
1226,1226_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1314,33,0.001000000000000000020816681712,38,0.001
1227,1227_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1361,1,0.998999999999999999111821580300,41,0.001
1228,1228_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.440000000000000002220446049250,3512.000000000000000000000000000000,4401,360,0.998999999999999999111821580300,50,0.25
1229,1229_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.380000000000000004440892098501,1507.000000000000000000000000000000,3322,505,0.001000000000000000020816681712,19,0.01
1230,1230_0,FAILED,BoTorch,BOTORCH_MODULAR,,,2155,1,0.001000000000000000020816681712,2,0.01
1231,1231_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.450000000000000011102230246252,2795.000000000000000000000000000000,1394,291,0.998999999999999999111821580300,19,0.005
1232,1232_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,900.000000000000000000000000000000,1507,3986,0.001000000000000000020816681712,50,0.01
1233,1233_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.149999999999999994448884876874,937.000000000000000000000000000000,4555,4523,0.998999999999999999111821580300,50,0.25
1234,1234_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.190000000000000002220446049250,885.000000000000000000000000000000,4213,3488,0.998999999999999999111821580300,1,0.005
1235,1235_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.140000000000000013322676295502,903.000000000000000000000000000000,4257,5000,0.998999999999999999111821580300,1,0.005
1236,1236_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.320000000000000006661338147751,1017.000000000000000000000000000000,4260,1124,0.001000000000000000020816681712,11,0.025
1237,1237_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1439,1,0.679975109203141969693717783230,27,0.005
1238,1238_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1875,1,0.001000000000000000020816681712,18,0.01
1239,1239_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.209999999999999992228438827624,834.000000000000000000000000000000,4604,2750,0.998999999999999999111821580300,1,0.25
1240,1240_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1433,1,0.661906184464946090173498305376,26,0.005
1241,1241_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,1132.000000000000000000000000000000,5000,4830,0.160243384046608594584171214592,50,0.01
1242,1242_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1456,1,0.672522311609197953607974795887,26,0.005
1243,1243_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.179999999999999993338661852249,910.000000000000000000000000000000,1436,2768,0.001000000000000000020816681712,50,0.005
1244,1244_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.089999999999999996669330926125,883.000000000000000000000000000000,798,5000,0.708598253721837045837617097277,50,0.01
1245,1245_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1889,1,0.001000000000000000020816681712,20,0.01
1246,1246_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,890.000000000000000000000000000000,3922,3764,0.998999999999999999111821580300,50,0.025
1247,1247_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.179999999999999993338661852249,1071.000000000000000000000000000000,4533,3661,0.001000000000000000020816681712,50,0.25
1248,1248_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.220000000000000001110223024625,1192.000000000000000000000000000000,4422,2409,0.001000000000000000020816681712,1,0.01
1249,1249_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,890.000000000000000000000000000000,5000,4109,0.001000000000000000020816681712,1,0.01
1250,1250_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.209999999999999992228438827624,1032.000000000000000000000000000000,4782,2829,0.998999999999999999111821580300,50,0.1
1251,1251_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.149999999999999994448884876874,959.000000000000000000000000000000,1585,3786,0.001000000000000000020816681712,1,0.01
1252,1252_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.160000000000000003330669073875,1040.000000000000000000000000000000,853,2125,0.001000000000000000020816681712,50,0.005
1253,1253_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.149999999999999994448884876874,999.000000000000000000000000000000,4978,4325,0.998999999999999999111821580300,50,0.01
1254,1254_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.270000000000000017763568394003,962.000000000000000000000000000000,5000,1599,0.001000000000000000020816681712,1,0.25
1255,1255_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1515,1,0.001000000000000000020816681712,50,0.005
1256,1256_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.200000000000000011102230246252,861.000000000000000000000000000000,4685,2718,0.504475082313234346464980717428,1,0.01
1257,1257_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1769,1,0.001000000000000000020816681712,37,0.01
1258,1258_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,5000,2076,0.998999999999999999111821580300,50,0.05
1259,1259_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1296,2640,0.579279401434295793116291406477,1,0.025
</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_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026632/5026632_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026638/5026638_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026639/5026639_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026680/5026680_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026683/5026683_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026686/5026686_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026688/5026688_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026689/5026689_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026690/5026690_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026691/5026691_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026693/5026693_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026695/5026695_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026696/5026696_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026698/5026698_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026699/5026699_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026701/5026701_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026702/5026702_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026703/5026703_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026707/5026707_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026710/5026710_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026711/5026711_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026712/5026712_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026713/5026713_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026714/5026714_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026715/5026715_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026716/5026716_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026718/5026718_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026724/5026724_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026729/5026729_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026730/5026730_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026732/5026732_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026734/5026734_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026735/5026735_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026736/5026736_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026738/5026738_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026741/5026741_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026742/5026742_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026744/5026744_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026746/5026746_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026748/5026748_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026754/5026754_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026758/5026758_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026759/5026759_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026763/5026763_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026764/5026764_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026827/5026827_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026828/5026828_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026829/5026829_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026830/5026830_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026831/5026831_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026832/5026832_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026889/5026889_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026890/5026890_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026892/5026892_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026897/5026897_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026902/5026902_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026904/5026904_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026908/5026908_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026910/5026910_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026912/5026912_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026913/5026913_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026914/5026914_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026917/5026917_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026922/5026922_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026924/5026924_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026925/5026925_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026926/5026926_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026927/5026927_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026929/5026929_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026930/5026930_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026931/5026931_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026932/5026932_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026933/5026933_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026934/5026934_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026938/5026938_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026939/5026939_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026943/5026943_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026946/5026946_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026949/5026949_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026952/5026952_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026953/5026953_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026954/5026954_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026958/5026958_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026959/5026959_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026960/5026960_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026961/5026961_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026963/5026963_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026965/5026965_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026969/5026969_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026971/5026971_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026973/5026973_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026975/5026975_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026979/5026979_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026981/5026981_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026983/5026983_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026985/5026985_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026986/5026986_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026996/5026996_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5026998/5026998_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027000/5027000_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027002/5027002_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027005/5027005_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027006/5027006_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027007/5027007_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027009/5027009_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027012/5027012_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027013/5027013_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027014/5027014_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027016/5027016_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027018/5027018_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027021/5027021_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027024/5027024_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027028/5027028_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027033/5027033_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027037/5027037_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027049/5027049_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027052/5027052_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027057/5027057_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027076/5027076_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027078/5027078_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027158/5027158_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027219/5027219_0_log.err not found


⚠ Job 5027133 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027133/5027133_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027133/5027133_0_log.out
----------------------------------------
submitit INFO (2025-05-12 22:18:51,273) - Starting with JobEnvironment(job_id=5027133, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-05-12 22:18:51,294) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027133/5027133_submitted.pkl
slurmstepd: error: *** JOB 5027133 ON i7186 CANCELLED AT 2025-05-13T00:18:50 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027133/5027133_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027323/5027323_0_log.err not found


⚠ Job 5027228 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027228/5027228_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027228/5027228_0_log.out
----------------------------------------
submitit INFO (2025-05-12 23:51:54,304) - Starting with JobEnvironment(job_id=5027228, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-05-12 23:51:54,314) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027228/5027228_submitted.pkl
slurmstepd: error: *** JOB 5027228 ON i7186 CANCELLED AT 2025-05-13T01:51:52 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027228/5027228_0_log.err not found


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/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027576/5027576_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027584/5027584_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027595/5027595_0_log.err not found


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/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027637/5027637_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027649/5027649_0_log.err not found


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/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027671/5027671_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027679/5027679_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027691/5027691_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027699/5027699_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027709/5027709_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027721/5027721_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027730/5027730_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027742/5027742_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027751/5027751_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027759/5027759_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027775/5027775_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027782/5027782_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027795/5027795_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027803/5027803_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027822/5027822_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027833/5027833_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027843/5027843_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027852/5027852_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027863/5027863_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027870/5027870_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027883/5027883_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027897/5027897_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027905/5027905_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027916/5027916_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027928/5027928_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027938/5027938_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027948/5027948_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027959/5027959_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027968/5027968_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027977/5027977_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027987/5027987_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5027997/5027997_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028012/5028012_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028023/5028023_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028033/5028033_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028041/5028041_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028054/5028054_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028078/5028078_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028088/5028088_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028101/5028101_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028117/5028117_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028128/5028128_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028142/5028142_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028153/5028153_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028166/5028166_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028176/5028176_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028197/5028197_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028211/5028211_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028224/5028224_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028233/5028233_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028255/5028255_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028270/5028270_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028285/5028285_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028295/5028295_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028306/5028306_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028318/5028318_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028327/5028327_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028340/5028340_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028352/5028352_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028359/5028359_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028368/5028368_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028375/5028375_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028385/5028385_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028394/5028394_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028407/5028407_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028536/5028536_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028576/5028576_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028586/5028586_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028595/5028595_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028607/5028607_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028614/5028614_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028624/5028624_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028635/5028635_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028643/5028643_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028655/5028655_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028660/5028660_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028669/5028669_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028682/5028682_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028694/5028694_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028869/5028869_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028882/5028882_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028892/5028892_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028902/5028902_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028911/5028911_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028923/5028923_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028933/5028933_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028945/5028945_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028954/5028954_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028964/5028964_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028988/5028988_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028996/5028996_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029006/5029006_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029015/5029015_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029024/5029024_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029035/5029035_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029047/5029047_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029056/5029056_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029064/5029064_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029075/5029075_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029084/5029084_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029092/5029092_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029100/5029100_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029107/5029107_0_log.err not found


⚠ Job 5028976 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028976/5028976_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028976/5028976_0_log.out
----------------------------------------
submitit INFO (2025-05-13 18:49:09,526) - Starting with JobEnvironment(job_id=5028976, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-05-13 18:49:09,529) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028976/5028976_submitted.pkl
slurmstepd: error: *** JOB 5028976 ON i7186 CANCELLED AT 2025-05-13T20:49:27 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5028976/5028976_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029117/5029117_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029122/5029122_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029135/5029135_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029140/5029140_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029149/5029149_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029160/5029160_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029169/5029169_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029178/5029178_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029188/5029188_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029497/5029497_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029506/5029506_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029514/5029514_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029524/5029524_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029534/5029534_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029543/5029543_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029555/5029555_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029564/5029564_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029573/5029573_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029585/5029585_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029594/5029594_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029600/5029600_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029614/5029614_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029622/5029622_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029634/5029634_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029644/5029644_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029656/5029656_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029665/5029665_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029674/5029674_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029695/5029695_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029706/5029706_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029713/5029713_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029725/5029725_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029734/5029734_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029747/5029747_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029760/5029760_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029778/5029778_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029799/5029799_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029818/5029818_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029837/5029837_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029859/5029859_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029878/5029878_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029889/5029889_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029906/5029906_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029917/5029917_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029927/5029927_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029935/5029935_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029948/5029948_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029970/5029970_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029978/5029978_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5029987/5029987_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030070/5030070_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030080/5030080_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030099/5030099_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030117/5030117_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030129/5030129_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030162/5030162_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030263/5030263_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030274/5030274_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030294/5030294_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030321/5030321_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5030948/5030948_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5031060/5031060_0_log.err not found


/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/CSDDM_SensorStream_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2/single_runs/5031088/5031088_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_SensorStream_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>50                                                                                                                                </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">1747050909.5044847,50,0,0
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1747160274.6275933,50,2,4
1747160276.1574411,50,3,6
1747160281.643593,50,3,6
1747160299.8612695,50,2,4
1747160299.9555662,50,2,4
1747160664.307889,50,2,4
1747160664.9393022,50,2,4
1747160666.4713492,50,3,6
1747160672.0440447,50,3,6
1747160689.5694613,50,2,4
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1747161102.1225471,50,2,4
1747161102.3764777,50,2,4
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1747162060.1211598,50,3,6
1747162066.026281,50,3,6
1747162083.9540656,50,2,4
1747162084.0612404,50,2,4
1747162542.3067617,50,2,4
1747162542.8203804,50,2,4
1747162544.3291872,50,3,6
1747162559.9860892,50,3,6
1747162567.1978254,50,2,4
1747162574.8896115,50,1,2
1747162587.4104898,50,0,0
1747162587.5103593,50,0,0
1747163045.121536,50,0,0
1747163045.445104,50,0,0
1747163047.142201,50,1,2
1747163057.8754857,50,1,2
1747163429.0895596,50,1,2
1747163429.5456257,50,1,2
1747163431.143296,50,2,4
1747163431.5881917,50,2,4
1747163449.7636783,50,1,2
1747163449.842248,50,1,2
1747163797.9499674,50,1,2
1747163798.3823864,50,1,2
1747163801.166851,50,2,4
1747163801.6542838,50,2,4
1747163819.9738178,50,1,2
1747163820.168224,50,1,2
1747164284.7971568,50,1,2
1747164285.2284548,50,1,2
1747164286.9188473,50,2,4
1747164287.4038796,50,2,4
1747164306.1645348,50,1,2
1747164306.2814372,50,1,2
1747164779.604597,50,1,2
1747164780.119623,50,1,2
1747164781.5981681,50,2,4
1747164782.1384165,50,2,4
1747164800.7264943,50,1,2
1747164800.9348822,50,1,2
1747165241.9908774,50,1,2
1747165242.4304636,50,1,2
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1747165244.8139153,50,2,4
1747165263.4788444,50,1,2
1747165263.8831372,50,1,2
1747165710.709135,50,1,2
1747165711.165652,50,1,2
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1747165713.4590943,50,2,4
1747165732.738048,50,1,2
1747165732.9841163,50,1,2
1747166195.100488,50,1,2
1747166195.5344598,50,1,2
1747166197.1750767,50,2,4
1747166197.6512105,50,2,4
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1747169713.9086657,50,2,4
1747169733.5254107,50,1,2
1747169733.784371,50,1,2
1747170232.140683,50,1,2
1747170232.711966,50,1,2
1747170234.3359034,50,2,4
1747170234.9258966,50,2,4
1747170253.5241864,50,1,2
1747170253.9315999,50,1,2
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1747170756.2136335,50,1,2
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1747170758.6491184,50,2,4
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1747170778.190712,50,1,2
1747171259.2049706,50,1,2
1747171260.7219267,50,1,2
1747171262.3085525,50,2,4
1747171262.9032066,50,2,4
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1747171669.1824925,50,1,2
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1747171672.101416,50,2,4
1747171672.4330854,50,2,4
1747171689.848677,50,1,2
1747171690.0817945,50,1,2
1747172178.2023664,50,1,2
1747172178.7457604,50,1,2
1747172180.3345404,50,2,4
1747172180.8804848,50,2,4
1747172200.620578,50,1,2
1747172200.7221599,50,1,2
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1747173271.3116257,50,1,2
1747173746.7660487,50,1,2
1747173747.2402217,50,1,2
1747173749.2542512,50,2,4
1747173749.812059,50,2,4
1747173769.7427127,50,1,2
1747173769.8576252,50,1,2
1747174245.1561618,50,1,2
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1747174247.20843,50,2,4
1747174247.7782354,50,2,4
1747174266.9207792,50,1,2
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1747174774.4040372,50,1,2
1747174775.173315,50,1,2
1747174777.0923693,50,2,4
1747174777.649906,50,2,4
1747174797.602857,50,1,2
1747174797.900949,50,1,2
1747175282.6240819,50,1,2
1747175283.1456926,50,1,2
1747175285.0988393,50,2,4
1747175285.6932797,50,2,4
1747175304.6935647,50,1,2
1747175304.8025568,50,1,2
1747175795.8455126,50,1,2
1747175796.142561,50,1,2
1747175797.5812905,50,2,4
1747175797.888335,50,2,4
1747175815.9523237,50,1,2
1747175816.1247935,50,1,2
1747176305.4720879,50,1,2
1747176306.1597145,50,1,2
1747176307.8020318,50,2,4
1747176324.3535836,50,2,4
1747176821.8176253,50,2,4
1747176822.1160574,50,2,4
1747176823.547413,50,3,6
1747176830.1426387,50,3,6
1747176848.507333,50,2,4
1747176848.5978243,50,2,4
1747177288.3787272,50,2,4
1747177289.1755185,50,2,4
1747177291.1432047,50,3,6
1747177292.4900167,50,3,6
1747177301.2702122,50,2,4
1747177322.4639401,50,1,2
1747177322.9024403,50,1,2
1747177835.62433,50,1,2
1747177836.09437,50,1,2
1747177837.7777474,50,2,4
1747177838.4811704,50,2,4
1747177858.4084878,50,1,2
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1747178387.8319607,50,1,2
1747178388.4018667,50,1,2
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1747178391.0548692,50,2,4
1747178410.9034498,50,1,2
1747178411.006711,50,1,2
1747178935.4859793,50,1,2
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1747178938.285506,50,2,4
1747178938.9813912,50,2,4
1747178958.8122745,50,1,2
1747178958.931418,50,1,2
1747179483.8703105,50,1,2
1747179484.4936185,50,1,2
1747179486.3047645,50,2,4
1747179487.0415237,50,2,4
1747179509.8290334,50,1,2
1747179509.9423492,50,1,2
1747180014.900318,50,1,2
1747180015.4284592,50,1,2
1747180017.2448823,50,2,4
1747180017.8726897,50,2,4
1747180037.3820589,50,1,2
1747180037.8422267,50,1,2
1747180560.750363,50,1,2
1747180561.3566666,50,1,2
1747180563.3236375,50,2,4
1747180564.081426,50,2,4
1747180584.0078294,50,1,2
1747180584.1133642,50,1,2
1747181091.1853034,50,1,2
1747181091.7496684,50,1,2
1747181093.384332,50,2,4
1747181094.0942206,50,2,4
1747181114.093699,50,1,2
1747181114.190287,50,1,2
1747181601.3930109,50,1,2
1747181602.220336,50,1,2
1747181604.1715555,50,2,4
1747181604.9071088,50,2,4
1747181624.2951381,50,1,2
1747181624.7014127,50,1,2
1747182157.348444,50,1,2
1747182158.2461288,50,1,2
1747182160.3259206,50,2,4
1747182161.04295,50,2,4
1747182181.428928,50,1,2
1747182181.5355015,50,1,2
1747182682.0822053,50,1,2
1747182682.7046738,50,1,2
1747182684.5393438,50,2,4
1747182685.2819693,50,2,4
1747182706.4677732,50,1,2
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1747183254.7927032,50,1,2
1747183255.2759478,50,1,2
1747183257.0518816,50,2,4
1747183257.3880577,50,2,4
1747183276.6564016,50,1,2
1747183276.8801873,50,1,2
1747183751.3750381,50,1,2
1747183752.2599363,50,1,2
1747183754.3550816,50,2,4
1747183755.0567877,50,2,4
1747183775.9689653,50,1,2
1747183776.0604608,50,1,2
1747184303.2214885,50,1,2
1747184303.8109415,50,1,2
1747184305.468086,50,2,4
1747184306.2148647,50,2,4
1747184327.5387487,50,1,2
1747184327.6657226,50,1,2
1747184837.3158011,50,1,2
1747184839.6694329,50,1,2
1747184841.49163,50,2,4
1747184842.229375,50,2,4
1747184863.322266,50,1,2
1747184863.8048215,50,1,2
1747185380.5161784,50,1,2
1747185381.2725945,50,1,2
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1747185403.6358032,50,1,2
1747185403.747487,50,1,2
1747185880.9026604,50,1,2
1747185881.4774945,50,1,2
1747185883.3406627,50,2,4
1747185884.0651069,50,2,4
1747185904.6627102,50,1,2
1747185904.8813586,50,1,2
1747186409.7987392,50,1,2
1747186410.2563744,50,1,2
1747186413.1912324,50,2,4
1747186413.8667028,50,2,4
1747186434.4069226,50,1,2
1747186434.516288,50,1,2
1747186917.4099264,50,1,2
1747186918.224232,50,1,2
1747186920.1707246,50,2,4
1747186937.100366,50,2,4
1747187441.3494625,50,2,4
1747187442.142199,50,2,4
1747187443.8353662,50,3,6
1747187450.2409348,50,3,6
1747187471.00232,50,2,4
1747187471.0899777,50,2,4
1747187969.1208272,50,2,4
1747187969.6741507,50,2,4
1747187971.37086,50,3,6
1747187972.582211,50,3,6
1747187981.0327232,50,2,4
1747188001.9030926,50,1,2
1747188002.1022072,50,1,2
1747188573.433367,50,1,2
1747188574.430563,50,1,2
1747188576.152351,50,2,4
1747188576.53341,50,2,4
1747188595.9823167,50,1,2
1747188596.0678427,50,1,2
1747189094.4815683,50,1,2
1747189095.243944,50,1,2
1747189097.1817815,50,2,4
1747189114.8436544,50,2,4
1747189607.6555092,50,2,4
1747189608.1811624,50,2,4
1747189609.8631623,50,3,6
1747189632.8452733,50,3,6
1747189929.3387828,50,3,6
1747189930.1486852,50,3,6
1747189931.8654308,50,4,8
1747189960.474167,50,4,8
1747190416.9298744,50,4,8
1747190417.4218946,50,4,8
1747190419.2329977,50,5,10
1747190453.1652374,50,5,10
1747190915.2469907,50,5,10
1747190915.6337428,50,5,10
1747190917.105598,50,6,12
1747190956.1023815,50,6,12
1747191367.1849515,50,6,12
1747191367.560313,50,6,12
1747191369.1195455,50,7,14
1747191386.6231096,50,7,14
1747191394.9387386,50,6,12
1747191417.6713157,50,5,10
1747191417.77402,50,5,10
1747191929.8218348,50,5,10
1747191930.225981,50,5,10
1747191932.050692,50,6,12
1747191944.2420175,50,6,12
1747191952.9560485,50,5,10
1747191966.848316,50,4,8
1747191980.5093148,50,3,6
1747191980.8615923,50,3,6
1747192415.4559247,50,3,6
1747192416.0789602,50,3,6
1747192417.6284316,50,4,8
1747192445.5560868,50,4,8
1747192856.086194,50,4,8
1747192856.4787147,50,4,8
1747192858.2156115,50,5,10
1747192875.353388,50,5,10
1747192895.121644,50,4,8
1747192895.2940817,50,4,8
1747193293.3858554,50,4,8
1747193294.1240153,50,4,8
1747193295.6730273,50,5,10
1747193318.7754748,50,5,10
1747193333.4235282,50,4,8
1747193333.8620718,50,4,8
1747193705.574502,50,4,8
1747193705.9503508,50,4,8
1747193707.3360372,50,5,10
1747193718.4235563,50,5,10
1747193742.496171,50,4,8
1747193742.6039186,50,4,8
1747194179.6474662,50,4,8
1747194180.1352887,50,4,8
1747194181.7122712,50,5,10
1747194215.9073071,50,5,10
1747194776.963353,50,5,10
1747194777.4795284,50,5,10
1747194779.2082818,50,6,12
1747194780.3240907,50,6,12
1747194806.0155861,50,5,10
1747194814.7682137,50,4,8
1747194829.5037658,50,3,6
1747194829.8546782,50,3,6
1747195363.4764173,50,3,6
1747195364.1753488,50,3,6
1747195366.028141,50,4,8
1747195373.6814396,50,4,8
1747195398.0579832,50,3,6
1747195398.1659808,50,3,6
1747196009.001764,50,3,6
1747196009.440329,50,3,6
1747196011.2309623,50,4,8
1747196012.3290958,50,4,8
1747196021.7855358,50,3,6
1747196046.011043,50,2,4
1747196046.1112895,50,2,4
1747196577.473982,50,2,4
1747196578.142645,50,2,4
1747196579.703258,50,3,6
1747196603.3532155,50,3,6
1747197218.8044176,50,3,6
1747197219.0694165,50,3,6
1747197220.4762452,50,4,8
1747197231.7728052,50,4,8
1747197252.0232027,50,3,6
1747197252.182842,50,3,6
1747197727.7771325,50,3,6
1747197728.2203572,50,3,6
1747197730.119561,50,4,8
1747197736.6452622,50,4,8
1747197760.253021,50,3,6
1747197760.4223707,50,3,6
1747198333.6972902,50,3,6
1747198334.155057,50,3,6
1747198336.063593,50,4,8
1747198365.1945422,50,4,8
1747198847.2330496,50,4,8
1747198847.54125,50,4,8
1747198849.0425491,50,5,10
1747198860.56483,50,5,10
1747198883.9968355,50,4,8
1747198884.1539783,50,4,8
</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
1747050909,652.453125,3.1
1747050909,652.453125,3.4
1747050909,652.69921875,2.9
1747050909,652.69921875,2.2
1747050909,652.69921875,2.2
1747050909,652.69921875,3.1
1747050909,652.69921875,4.4
1747056742,757.8203125,4.2
1747056742,757.8203125,4.8
1747056743,757.8203125,4.6
1747056743,757.8203125,3.1
1747063700,768.65625,11.1
1747063700,768.65625,3.7
1747063700,768.65625,4.0
1747063700,768.65625,5.7
1747071536,781.39453125,5.5
1747071536,781.39453125,9.5
1747071536,781.39453125,9.1
1747071536,781.39453125,8.7
1747093059,805.51171875,11.8
1747093059,805.51171875,16.8
1747093059,805.51171875,16.4
1747093059,805.51171875,14.7
1747122763,812.859375,17.3
1747122763,812.859375,18.4
1747122764,812.859375,17.4
1747122764,812.859375,20.3
1747147987,810.89453125,17.1
1747147987,810.89453125,18.5
1747147987,810.89453125,18.5
1747147987,810.89453125,20.4
1747172668,816.95703125,16.7
1747172668,816.95703125,17.6
1747172668,816.95703125,17.0
1747172668,816.95703125,22.2
1747198912,842.71484375,17.1
1747198912,842.71484375,15.8
1747198912,842.71484375,16.4
1747198912,842.71484375,20.5
</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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