OmniOpt2

Setting	Value
Max. nr. evaluations	50402
Max. nr. evaluations (from arguments)	50000
Number random steps	20
Nr. of workers (parameter)	20
Main process memory (GB)	8
Worker memory (GB)	1
Nr. imported jobs	402
Name	Type	Lower bound	Upper bound	Type	Log Scale?
recent_samples_size	int	1	5000	int	No
n_samples	int	1	5000	int	No
const	float	0.001	10	float	No
max_depth	int	1	20	int	No
threshold	float	0.001	0.999	float	No
Failed	Succeeded	Running	Total
115	586	125	826
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	1
parameter	None
continue_previous_job	/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/
experiment_constraints	None
run_dir	runs
seed	None
decimalrounding	4
enforce_sequential_optimization	False
verbose_tqdm	False
model	None
gridsearch	False
occ	False
show_sixel_scatter	False
show_sixel_general	False
show_sixel_trial_index_result	False
follow	False
send_anonymized_usage_stats	False
ui_url	None
root_venv_dir	/home/s4122485
exclude	None
main_process_gb	8
pareto_front_confidence	1
max_nr_of_zero_results	10
abbreviate_job_names	False
orchestrator_file	None
checkout_to_latest_tested_version	False
live_share	False
disable_tqdm	False
workdir	False
occ_type	euclid
result_names	['RESULT=min']
minkowski_p	2
signed_weighted_euclidean_weights
generation_strategy	None
generate_all_jobs_at_once	False
revert_to_random_when_seemingly_exhausted	True
load_data_from_existing_jobs	[]
n_estimators_randomforest	100
external_generator	None
username	None
max_failed_jobs	None
num_parallel_jobs	20
worker_timeout	120
slurm_use_srun	False
time
partition
reservation	None
force_local_execution	False
slurm_signal_delay_s	0
nodes_per_job	1
cpus_per_task	1
account	None
gpus	0
run_mode	local
verbose	False
verbose_break_run_search_table	False
debug	False
no_sleep	False
tests	False
show_worker_percentage_table_at_end	False
auto_exclude_defective_hosts	False
run_tests_that_fail_on_taurus	False
raise_in_eval	False
show_ram_every_n_seconds	False
Skipped tabs:
Job-Infos
<!DOCTYPE html>
<html lang='en'>
	<head>
		<meta charset='UTF-8'>
		<meta name='viewport' content='width=device-width, initial-scale=1.0'>
		<title>Exported &raquo;s4122485/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0&laquo; from OmniOpt2-Share</title>
		<script src='https://code.jquery.com/jquery-3.7.1.js'></script>
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				background-color: #007bff; /* Blau, kannst du anpassen */
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			[role="tab"] {
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				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				position: relative;
				padding: 5px 32px 32px 5px;
				background-position: top 50% right 2px;
				background-repeat: no-repeat;
				border-radius: 0;
				border: 1px solid black;
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				background-color: #fafafa;
				text-shadow: 0 0.05em 0.1em rgba(0,0,0,0.2);
				scroll-behavior: smooth;
				text-wrap: balance;
				text-rendering: optimizeLegibility;
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				-moz-osx-font-smoothing: grayscale;
				font-feature-settings: "ss02", "liga", "onum";
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				font-variant: small-caps;
				width: 100%;
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			.time_picker_container > input {
				width: 50px;
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				display: grid;
				justify-content: center;
				align-items: center;
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			.code_bg {
				background-color: #C0C0C0;
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			#commands {
				line-break: anywhere;
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			.color_red {
				color: red;
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			.color_orange {
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				font-size: 16px;
				font-weight: bold;
				text-align: center;
				border-radius: 25px;
				text-decoration: none !important;
				transition: background-color 0.3s, color 0.3s;
				color: unset !important;
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			.tooltipster-base {
				border: 1px solid black;
				position: absolute;
				border-radius: 8px;
				padding: 2px;
				color: white;
				background-color: #61686f;
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				border-left: 8px solid #3498db;
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				width: 50px;
				height: 50px;
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				100% {
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				-webkit-text-stroke: 1px black;
				white !important;
				position: fixed;
				top: 0;
				left: 0;
				width: 100%;
				height: 100%;
				display: flex;
				justify-content: center;
				align-items: center;
				z-index: 9999;
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				color: #fff;
				display: contents;
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			#spinner-text {
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				margin-left: 10px;
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				color: #007bff;
				text-decoration: none;
			}
			
			.copy-container {
				display: inline-block;
				position: relative;
				cursor: pointer;
				margin-left: 10px;
				color: blue;
			}
			
			.copy-container:hover {
				text-decoration: underline;
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			.clipboard-icon {
				position: absolute;
				top: 5px;
				right: 5px;
				font-size: 1.5em;
			}
			
			#main_tab {
				overflow: scroll;
				width: max-content;
			}
			
			.ui-tabs .ui-tabs-nav li {
				user-select: none;
			}
			
			.stacktrace_table {
				background-color: black !important;
				color: white !important;
			}
			
			#breadcrumb {
				user-select: none;
			}
			
			#statusBar {
				user-select: none;
			}
			
			.error_line {
				background-color: red !important;
				color: white !important;
			}
			
			.header_table {
				border: 0px !important;
				padding: 0px !important;
				width: revert !important;
				min-width: revert !important;
			}
			
			.img_auto_width {
				max-width: revert !important;
			}
			
			#main_dir_or_plot_view {
				display: inline-grid;
			}
			
			#refresh_button {
				width: 300px;
			}
			
			._share_link {
				color: black !important;
			}
			
			#footer_element {
				height: 30px;
				background-color: #f8f9fa;
				padding: 0px;
				text-align: center;
				border-top: 1px solid #dee2e6;
				width: 100%;
				box-sizing: border-box;
				position: fixed;
				bottom: 0;
				z-index: 2;
				margin-left: -9px;
				z-index: 99;
			}
			
			.switch {
				position: relative;
				display: inline-block;
				width: 50px;
				height: 26px;
			}
			
			.switch input {
				opacity: 0;
				width: 0;
				height: 0;
			}
			
			.slider {
				position: absolute;
				cursor: pointer;
				top: 0;
				left: 0;
				right: 0;
				bottom: 0;
				background-color: #ccc;
				transition: .4s;
				border-radius: 26px;
			}
			
			.slider:before {
				position: absolute;
				content: "";
				height: 20px;
				width: 20px;
				left: 3px;
				bottom: 3px;
				background-color: white;
				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #444;
			}
			
			input:checked + .slider:before {
				transform: translateX(24px);
			}
			
			.mode-text {
				position: absolute;
				top: 5px;
				left: 65px;
				font-size: 14px;
				color: black;
				transition: .4s;
				width: 65px;
				display: block;
				font-size: 0.7rem;
				text-align: center;
			}
			
			input:checked + .slider .mode-text {
				content: "Dark Mode";
				color: white;
			}
			
			#mainContent {
				height: fit-content;
				min-height: 100%;
			}
			
			li {
				text-align: left;
			}
			
			#share_path {
				margin-bottom: 20px;
				margin-top: 20px;
			}
			
			#sortForm {
				margin-bottom: 20px;
			}
			
			.share_folder_buttons {
				margin-top: 10px;
				margin-bottom: 10px;
			}
			
			.nav_tab_button {
				margin: 10px;
			}
			
			.header_table {
				margin: 10px;
			}
			
			.no_border {
				border: unset !important;
			}
			
			.gui_table {
				padding: 5px !important;
			}
			
			.gui_parameter_row {
			}
			
			.gui_parameter_row_cell {
				border: unset !important;
			}
			
			.gui_param_table {
				width: 95%;
				margin: unset !important;
			}
			
			table td, table tr,
			.parameterRow table {
				padding: 2px !important;
			}
			
			.parameterRow table {
				margin: 0px;
				border: unset;
			}
			
			.parameterRow > td {
				border: 0px !important;
			}
			
			.parameter_config_table td, .parameter_config_table tr, #config_table th, #config_table td, #hidden_config_table th, #hidden_config_table td {
				border: 0px !important;
			}
			
			.green_text {
				color: green;
			}
			
			.remove_parameter {
				white-space: pre;
			}
			
			select {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				background-color: #fff;
				color: #222;
				padding: 5px 30px 5px 5px;
				border: 1px solid #555;
				border-radius: 5px;
				cursor: pointer;
				outline: none;
				transition: all 0.3s ease;
				background:
					url("data:image/svg+xml;charset=UTF-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 10 6'%3E%3Cpath fill='%23888' d='M0 0l5 6 5-6z'/%3E%3C/svg%3E")
					no-repeat right 10px center,
				linear-gradient(180deg, #fff, #ecebe5 86%, #d8d0c4);
				background-size: 12px, auto;
			}
			
			select:hover {
				border-color: #888;
			}
			
			select:focus {
				border-color: #4caf50;
				box-shadow: 0 0 5px rgba(76, 175, 80, 0.5);
			}
			
			select::-ms-expand {
				display: none;
			}
			
			input, textarea {
				border-radius: 5px;
			}
			
			#search {
				width: 200px;
				max-width: 70%;
				background-image: url(images/search.svg);
				background-repeat: no-repeat;
				background-size: auto 40px;
				height: 40px;
				line-height: 40px;
				padding-left: 40px;
				box-sizing: border-box;
			}
			
			input[type="checkbox"] {
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				width: 25px;
				height: 25px;
				border: 2px solid #3498db;
				border-radius: 5px;
				background-color: #fff;
				position: relative;
				cursor: pointer;
				transition: all 0.3s ease;
				width: 25px !important;
			}
			
			input[type="checkbox"]:checked {
				background-color: #3498db;
				border-color: #2980b9;
			}
			
			input[type="checkbox"]:checked::before {
				content: '✔';
				position: absolute;
				left: 4px;
				top: 2px;
				color: #fff;
			}
			
			input[type="checkbox"]:hover {
				border-color: #2980b9;
				background-color: #3caffc;
			}
			
			.toc {
				margin-bottom: 20px;
			}
			
			.toc li {
				margin-bottom: 5px;
			}
			
			.toc a {
				text-decoration: none;
				color: #007bff;
			}
			
			.toc a:hover {
				text-decoration: underline;
			}
			
			.table-container {
				width: 100%;
				overflow-x: auto;
			}
			
			.section-header {
				background-color: #1d6f9a !important;
				color: white;
			}
			
			.warning {
				color: red;
			}
			
			.li_list a {
				text-decoration: none;
			}
			
			.gridjs-td {
				white-space: nowrap;
			}
			
			th, td {
				border: 1px solid gray !important;
			}
			
			.no_border {
				border: 0px !important;
			}
			
			.no_break {
			
			}
			
			img {
				user-select: none;
				pointer-events: none;
			}
			
			#config_table, #hidden_config_table {
				user-select: none;
			}
			
			.copy_clipboard_button {
				margin-bottom: 10px;
			}
			
			.badge_table {
				background-color: unset !important;
			}
			
			.make_markable {
				user-select: text;
			}
			
			.header-container {
				display: flex;
				flex-wrap: wrap;
				align-items: center;
				justify-content: space-between;
				gap: 1rem;
				padding: 10px;
				background: var(--header-bg, #fff);
				border-bottom: 1px solid #ccc;
			}
			
			.header-logo-group {
				display: flex;
				gap: 1rem;
				align-items: center;
				flex: 1 1 auto;
				min-width: 200px;
			}
			
			.logo-img {
				max-height: 45px;
				height: auto;
				width: auto;
				object-fit: contain;
				pointer-events: unset;
			}
			
			.header-badges {
				flex-direction: column;
				gap: 5px;
				align-items: flex-start;
				flex: 0 1 auto;
				margin-top: auto;
				margin-bottom: auto;
			}
			
			.badge-img {
				height: auto;
				max-width: 130px;
				margin-top: 3px;
			}
			
			.header-tabs {
				margin-top: 10px;
				display: flex;
				flex-wrap: wrap;
				gap: 10px;
				flex: 2 1 100%;
				justify-content: center;
			}
			
			.nav-tab {
				display: inline-block;
				text-decoration: none;
				padding: 8px 16px;
				border-radius: 20px;
				background: linear-gradient(to right, #4a90e2, #357ABD);
				color: white;
				font-weight: bold;
				white-space: nowrap;
				transition: background 0.2s ease-in-out, transform 0.2s;
				box-shadow: 0 2px 4px rgba(0,0,0,0.2);
			}
			
			.nav-tab:hover {
				background: linear-gradient(to right, #5aa0f2, #4a90e2);
				transform: translateY(-2px);
			}
			
			.current-tag {
				padding-left: 10px;
				font-size: 0.9rem;
				color: #666;
			}
			
			.header-theme-toggle {
				flex: 1 1 auto;
				align-items: center;
				margin-top: 20px;
				min-width: 120px;
			}
			
			.switch {
				position: relative;
				display: inline-block;
				width: 60px;
				height: 30px;
			}
			
			.switch input {
				display: none;
			}
			
			.slider {
				position: absolute;
				top: 0; left: 0; right: 0; bottom: 0;
				background-color: #ccc;
				border-radius: 34px;
				cursor: pointer;
			}
			
			.slider::before {
				content: "";
				position: absolute;
				height: 24px;
				width: 24px;
				left: 3px;
				bottom: 3px;
				background-color: white;
				transition: .4s;
				border-radius: 50%;
			}
			
			input:checked + .slider {
				background-color: #2196F3;
			}
			
			input:checked + .slider::before {
				transform: translateX(30px);
			}
			
			@media (max-width: 768px) {
				.header-logo-group,
				.header-badges,
				.header-theme-toggle {
					justify-content: center;
					flex: 1 1 100%;
					text-align: center;
					width: inherit;
				}
			
				.logo-img {
					max-height: 50px;
					pointer-events: unset;
				}
			
				.badge-img {
					max-width: 100px;
				}
			
				.hide_on_mobile {
					display: none;
				}
			
				.nav-tab {
					font-size: 0.9rem;
					padding: 6px 12px;
				}
			
				.header_button {
					white-space: pre;
					font-size: 2em;
				}
			}
			
			.header_button {
				white-space: pre;
				margin-top: 20px;
				margin: 5px;
			}
			
			.line_break_anywhere {
				line-break: anywhere;
			}
			
			.responsive-container {
				display: flex;
				flex-wrap: wrap;
				justify-content: space-between;
				gap: 20px;
			}
			
			.responsive-container .half {
				flex: 1 1 48%;
				box-sizing: border-box;
				min-width: 500px;
			}
			
			.config-section table {
				width: 100%;
				border-collapse: collapse;
			}
			
			@media (max-width: 768px) {
				.responsive-container .half {
					flex: 1 1 100%;
				}
			}
			
			@keyframes spin {
				0% {
					transform: rotate(0deg);
				}
			
				100% {
					transform: rotate(360deg);
				}
			}
			
			.rotate {
				animation: spin 2s linear infinite;
				display: inline-block;
			}
			
			input::placeholder {
				font-family: 'IBM Plex Sans', 'Source Sans Pro', sans-serif;
			}
			
			.gridjs-th-content {
				overflow: visible !important;
			}
			
			.error_text {
				color: red;
			}
			
			h1, h2, h3, h4, h5, h6 {
				margin-top: 1em;
				font-weight: bold;
				color: #333;
				border-left: 5px solid #ccc;
				padding-left: 0.5em;
			}
			
			.no_cursive {
				font-style: normal;	
			}
			
			.caveat {
				background-color: #fff8b3;
				border: 1px solid #f2d600;
				padding: 1em 1em 1em 70px;
				position: relative;
				font-family: sans-serif;
				color: #665500;
				margin: 1em 0;
				border-radius: 4px;
			}
			
			.caveat h1, .caveat h2, .caveat h3, .caveat h4 {
				margin-top: 0;
				margin-bottom: 0.5em;
				font-weight: bold;
			}
			
			.caveat::before {
				content: "⚠️";
				font-size: 50px;
				line-height: 1;
				position: absolute;
				left: 10px;
				top: 50%;
				transform: translateY(-50%);
				pointer-events: none;
				user-select: none;
			}
			
			.caveat.warning::before { content: "⚠️"; }
			.caveat.stop::before { content: "🛑"; }
			.caveat.exclamation::before { content: "❗"; }
			.caveat.alarm::before { content: "🚨"; }
			.caveat.tip::before { content: "💡"; }
			
			.tutorial_icon {
				display: inline-block;
				font-size: 1.3em;
				line-height: 1;
				vertical-align: middle;
				transform: translateY(-10%);
				padding: 0.2em 0;
			}
			
			.highlight {
				background-color: yellow;
				font-weight: bold;
			}
			
			#searchResults li {
				opacity: 0;
				transform: translateY(8px);
				animation: fadeInUp 0.3s ease-out forwards;
				animation-delay: 0.05s;
				list-style: none;
				margin-bottom: 5px;
			}
			
			@keyframes fadeInUp {
				to {
					opacity: 1;
					transform: translateY(0);
				}
			}
			
			.search_headline {
				font-weight: bold;
				margin-top: 1em;
				margin-bottom: 0.3em;
				color: #444;
			}
			
			.search_share_path {
				color: black;
				display: block ruby;
				margin-top: 20px;
			}
			
			@media print {
				#scads_bar {
					display: none !important;
				}
			}
			

			/*! XP.css v0.2.6 - https: //botoxparty.github.io/XP.css/ */
			body{
				color: #222
			}
			
			.surface{
				background: #ece9d8
			}
			
			u{
				text-decoration: none;
				border-bottom: .5px solid #222
			}
			
			a{
				color: #00f
			}
			
			a: focus{
				outline: 1px dotted #00f
			}
			
			code,code *{
				font-family: monospace
			}
			
			pre{
				display: block;
				padding: 12px 8px;
				background-color: #000;
				color: silver;
				font-size: 1rem;
				margin: 0;
				overflow: scroll;
			}
			
			summary: focus{
				outline: 1px dotted #000
			}
			
			: :-webkit-scrollbar{
				width: 16px
			}
			
			: :-webkit-scrollbar: horizontal{
				height: 17px
			}
			
			: :-webkit-scrollbar-track{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='2' height='2' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 0H0v1h1v1h1V1H1V0z' fill='silver'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 0H1v1H0v1h1V1h1V0z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-color: #dfdfdf;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			: :-webkit-scrollbar-button: horizontal: end: increment,: :-webkit-scrollbar-button: horizontal: start: decrement,: :-webkit-scrollbar-button: vertical: end: increment,: :-webkit-scrollbar-button: vertical: start: decrement{
				display: block
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 6H7v1H6v1H5v1H4v1h7V9h-1V8H9V7H8V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: end{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 6H4v1h1v1h1v1h1v1h1V9h1V8h1V7h1V6z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 4H8v1H7v1H6v1H5v1h1v1h1v1h1v1h1V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 16px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='16' height='17' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 0H0v16h1V1h14V0z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M2 1H1v14h1V2h12V1H2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M16 17H0v-1h15V0h1v17z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M15 1h-1v14H1v1h14V1z' fill='gray'/%3E%3Cpath fill='silver' d='M2 2h12v13H2z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7 4H6v7h1v-1h1V9h1V8h1V7H9V6H8V5H7V4z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			button{
				border: none;
				background: #ece9d8;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf;
				border-radius: 0;
				min-width: 75px;
				min-height: 23px;
				padding: 0 12px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: inset -1px -1px #fff,inset 1px 1px #0a0a0a,inset -2px -2px #dfdfdf,inset 2px 2px grey
			}
			
			button.focused,button: focus{
				outline: 1px dotted #000;
				outline-offset: -4px
			}
			
			label{
				display: inline-flex;
				align-items: center
			}
			
			textarea{
				padding: 3px 4px;
				border: none;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0
			}
			
			textarea: focus{
				outline: none
			}
			
			select: focus option{
				color: #000;
				background-color: #fff
			}
			
			.vertical-bar{
				width: 4px;
				height: 20px;
				background: silver;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #fff,inset -2px -2px grey,inset 2px 2px #dfdfdf
			}
			
			&: disabled,&: disabled+label{
				color: grey;
				text-shadow: 1px 1px 0 #fff
			}
			
			input[type=radio]+label{
				line-height: 13px;
				position: relative;
				margin-left: 19px
			}
			
			input[type=radio]+label: before{
				content: "";
				position: absolute;
				top: 0;
				left: -19px;
				display: inline-block;
				width: 13px;
				height: 13px;
				margin-right: 6px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='%23fff'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: active+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: checked+label: after{
				content: "";
				display: block;
				width: 5px;
				height: 5px;
				top: 5px;
				left: -14px;
				position: absolute;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]+label: before{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='12' height='12' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 0H4v1H2v1H1v2H0v4h1v2h1V8H1V4h1V2h2V1h4v1h2V1H8V0z' fill='gray'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M8 1H4v1H2v2H1v4h1v1h1V8H2V4h1V3h1V2h4v1h2V2H8V1z' fill='%23000'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 3h1v1H9V3zm1 5V4h1v4h-1zm-2 2V9h1V8h1v2H8zm-4 0v1h4v-1H4zm0 0V9H2v1h2z' fill='%23DFDFDF'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 2h-1v2h1v4h-1v2H8v1H4v-1H2v1h2v1h4v-1h2v-1h1V8h1V4h-1V2z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M4 2h4v1h1v1h1v4H9v1H8v1H4V9H3V8H2V4h1V3h1V2z' fill='silver'/%3E%3C/svg%3E")
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='4' height='4' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M3 0H1v1H0v2h1v1h2V3h1V1H3V0z' fill='gray'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password]{
				padding: 3px 4px;
				border: 1px solid #7f9db9;
				background-color: #fff;
				box-sizing: border-box;
				-webkit-appearance: none;
				-moz-appearance: none;
				appearance: none;
				border-radius: 0;
				height: 21px;
				line-height: 2
			}
			
			input[type=email]: focus,input[type=password]: focus{
				outline: none
			}
			
			input[type=range]{
				-webkit-appearance: none;
				width: 100%;
				background: transparent
			}
			
			input[type=range]: focus{
				outline: none
			}
			
			input[type=range]: :-webkit-slider-thumb{
				-webkit-appearance: none;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v16h2v2h2v2h1v-1H3v-2H1V1h9V0z' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M1 1v15h1v1h1v1h1v1h2v-1h1v-1h1v-1h1V1z' fill='%23C0C7C8'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v15H8v2H6v2H5v-1h2v-2h2z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v16H9v2H7v2H5v1h1v-2h2v-2h2z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range]: :-moz-range-track{
				background: #000;
				border-right: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #a9a9a9,-1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,-1px 1px 0 #fff,1px -1px #a9a9a9
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg width='11' height='21' fill='none' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0v20h1V1h9V0z' fill='%23fff'/%3E%3Cpath fill='%23C0C7C8' d='M1 1h8v18H1z'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9 1h1v19H1v-1h8z' fill='%2387888F'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 0h1v21H0v-1h10z' fill='%23000'/%3E%3C/svg%3E")
			}
			
			.is-vertical{
				display: inline-block;
				width: 4px;
				height: 150px;
				transform: translateY(50%)
			}
			
			.is-vertical>input[type=range]{
				width: 150px;
				height: 4px;
				margin: 0 16px 0 10px;
				transform-origin: left;
				transform: rotate(270deg) translateX(calc(-50% + 8px))
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid grey;
				border-bottom: 1px solid grey;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #a9a9a9,1px -1px 0 #a9a9a9,0 -1px 0 #a9a9a9,1px 1px 0 #fff,-1px -1px #a9a9a9
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-thumb{
				transform: translateY(-8px) scaleX(-1)
			}
			
			.is-vertical>input[type=range]: :-moz-range-thumb{
				transform: translateY(2px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-webkit-slider-thumb{
				transform: translateY(-10px) scaleX(-1)
			}
			
			.is-vertical>input[type=range].has-box-indicator: :-moz-range-thumb{
				transform: translateY(0) scaleX(-1)
			}
			
			.window{
				font-size: 11px;
				box-shadow: inset -1px -1px #0a0a0a,inset 1px 1px #dfdfdf,inset -2px -2px grey,inset 2px 2px #fff;
				background: #ece9d8;
				padding: 3px
			}
			
			.window fieldset{
				margin-bottom: 9px
			}
			
			.title-bar{
				background: #000;
				padding: 3px 2px 3px 3px;
				display: flex;
				justify-content: space-between;
				align-items: center
			}
			
			.title-bar-text{
				font-weight: 700;
				color: #fff;
				letter-spacing: 0;
				margin-right: 24px
			}
			
			.title-bar-controls button{
				padding: 0;
				display: block;
				min-width: 16px;
				min-height: 14px
			}
			
			.title-bar-controls button: focus{
				outline: none
			}
			
			.window-body{
				margin: 8px
			}
			
			.window-body pre{
				margin: -8px
			}
			
			.status-bar{
				margin: 0 1px;
				display: flex;
				gap: 1px
			}
			
			.status-bar-field{
				box-shadow: inset -1px -1px #dfdfdf,inset 1px 1px grey;
				flex-grow: 1;
				padding: 2px 3px;
				margin: 0
			}
			
			ul.tree-view{
				display: block;
				background: #fff;
				padding: 6px;
				margin: 0
			}
			
			ul.tree-view li{
				list-style-type: none;
				margin-top: 3px
			}
			
			ul.tree-view a{
				text-decoration: none;
				color: #000
			}
			
			ul.tree-view a: focus{
				background-color: #2267cb;
				color: #fff
			}
			
			ul.tree-view ul{
				margin-top: 3px;
				margin-left: 16px;
				padding-left: 16px;
				border-left: 1px dotted grey
			}
			
			ul.tree-view ul>li{
				position: relative
			}
			
			ul.tree-view ul>li: before{
				content: "";
				display: block;
				position: absolute;
				left: -16px;
				top: 6px;
				width: 12px;
				border-bottom: 1px dotted grey
			}
			
			ul.tree-view ul>li: last-child: after{
				content: "";
				display: block;
				position: absolute;
				left: -20px;
				top: 7px;
				bottom: 0;
				width: 8px;
				background: #fff
			}
			
			ul.tree-view ul details>summary: before{
				margin-left: -22px;
				position: relative;
				z-index: 1
			}
			
			ul.tree-view details{
				margin-top: 0
			}
			
			ul.tree-view details>summary: before{
				text-align: center;
				display: block;
				float: left;
				content: "+";
				border: 1px solid grey;
				width: 8px;
				height: 9px;
				line-height: 9px;
				margin-right: 5px;
				padding-left: 1px;
				background-color: #fff
			}
			
			ul.tree-view details[open] summary{
				margin-bottom: 0
			}
			
			ul.tree-view details[open]>summary: before{
				content: "-"
			}
			
			fieldset{
				border-image: url("data: image/svg+xml;charset=utf-8,%3Csvg width='5' height='5' fill='gray' xmlns='http: //www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h5v5H0V2h2v1h1V2H0' fill='%23fff'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M0 0h4v4H0V1h1v2h2V1H0'/%3E%3C/svg%3E") 2;
				padding: 10px;
				padding-block-start: 8px;
				margin: 0
			}
			
			legend{
				background: #ece9d8
			}
			
			menu[role=tablist]{
				position: relative;
				margin: 0 0 -2px;
				text-indent: 0;
				list-style-type: none;
				display: flex;
				padding-left: 3px
			}
			
			menu[role=tablist] button{
				z-index: 1;
				display: block;
				color: #222;
				text-decoration: none;
				min-width: unset
			}
			
			menu[role=tablist] button[aria-selected=true]{
				padding-bottom: 2px;margin-top: -2px;background-color: #ece9d8;position: relative;z-index: 8;margin-left: -3px;margin-bottom: 1px
			}
			
			menu[role=tablist] button: focus{
				outline: 1px dotted #222;outline-offset: -4px
			}
			
			menu[role=tablist].justified button{
				flex-grow: 1;text-align: center
			}
			
			[role=tabpanel]{
				padding: 14px;clear: both;background: linear-gradient(180deg,#fcfcfe,#f4f3ee);border: 1px solid #919b9c;position: relative;z-index: 2;margin-bottom: 9px
			}
			
			: :-webkit-scrollbar{
				width: 17px
			}
			
			: :-webkit-scrollbar-corner{
				background: #dfdfdf
			}
			
			: :-webkit-scrollbar-track: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 1' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1'/%3E%3Cpath stroke='%23f3f1ec' d='M1 0h1'/%3E%3Cpath stroke='%23f4f1ec' d='M2 0h1'/%3E%3Cpath stroke='%23f4f3ee' d='M3 0h1'/%3E%3Cpath stroke='%23f5f4ef' d='M4 0h1'/%3E%3Cpath stroke='%23f6f5f0' d='M5 0h1'/%3E%3Cpath stroke='%23f7f7f3' d='M6 0h1'/%3E%3Cpath stroke='%23f9f8f4' d='M7 0h1'/%3E%3Cpath stroke='%23f9f9f7' d='M8 0h1'/%3E%3Cpath stroke='%23fbfbf8' d='M9 0h1'/%3E%3Cpath stroke='%23fbfbf9' d='M10 0h2'/%3E%3Cpath stroke='%23fdfdfa' d='M12 0h1'/%3E%3Cpath stroke='%23fefefb' d='M13 0h3'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-track: horizontal{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 1 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1M0 16h1'/%3E%3Cpath stroke='%23f3f1ec' d='M0 1h1'/%3E%3Cpath stroke='%23f4f1ec' d='M0 2h1'/%3E%3Cpath stroke='%23f4f3ee' d='M0 3h1'/%3E%3Cpath stroke='%23f5f4ef' d='M0 4h1'/%3E%3Cpath stroke='%23f6f5f0' d='M0 5h1'/%3E%3Cpath stroke='%23f7f7f3' d='M0 6h1'/%3E%3Cpath stroke='%23f9f8f4' d='M0 7h1'/%3E%3Cpath stroke='%23f9f9f7' d='M0 8h1'/%3E%3Cpath stroke='%23fbfbf8' d='M0 9h1'/%3E%3Cpath stroke='%23fbfbf9' d='M0 10h1m-1 1h1'/%3E%3Cpath stroke='%23fdfdfa' d='M0 12h1'/%3E%3Cpath stroke='%23fefefb' d='M0 13h1m-1 1h1m-1 1h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb{
				background-position: 50%;
				background-repeat: no-repeat;
				background-color: #c8d6fb;
				background-size: 7px;
				border: 1px solid #fff;
				border-radius: 2px;
				box-shadow: inset -3px 0 #bad1fc,inset 1px 1px #b7caf5
			}
			
			: :-webkit-scrollbar-thumb: vertical{
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 7 8' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h6M0 2h6M0 4h6M0 6h6'/%3E%3Cpath stroke='%23bad1fc' d='M6 0h1M6 2h1M6 4h1'/%3E%3Cpath stroke='%23c8d6fb' d='M0 1h1M0 3h1M0 5h1M0 7h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h6M1 3h6M1 5h6M1 7h6'/%3E%3Cpath stroke='%23bad3fc' d='M6 6h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-thumb: horizontal{
				background-size: 8px;background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 8 7' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eef4fe' d='M0 0h1m1 0h1m1 0h1m1 0h1M0 1h1m1 0h1m1 0h1m1 0h1M0 2h1m1 0h1m1 0h1m1 0h1M0 3h1m1 0h1m1 0h1m1 0h1M0 4h1m1 0h1m1 0h1m1 0h1M0 5h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23c8d6fb' d='M1 0h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%238cb0f8' d='M1 1h1m1 0h1m1 0h1m1 0h1M1 2h1m1 0h1m1 0h1m1 0h1M1 3h1m1 0h1m1 0h1m1 0h1M1 4h1m1 0h1m1 0h1m1 0h1M1 5h1m1 0h1m1 0h1m1 0h1M1 6h1m1 0h1m1 0h1m1 0h1'/%3E%3Cpath stroke='%23bad1fc' d='M0 6h1m1 0h1'/%3E%3Cpath stroke='%23bad3fc' d='M4 6h1m1 0h1'/%3E%3C/svg%3E")
			}
			
			: :-webkit-scrollbar-button: vertical: start{
				height: 17px;
				background-image: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 17 17' shape-rendering='crispEdges'%3E%3Cpath stroke='%23eeede5' d='M0 0h1m15 0h1M0 1h1M0 2h1M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m15 0h1M0 16h1m15 0h1'/%3E%3Cpath stroke='%23fdfdfa' d='M1 0h1'/%3E%3Cpath stroke='%23fff' d='M2 0h14M1 1h1m13 0h1M1 2h1m13 0h1M1 3h1m13 0h1M1 4h1m13 0h1M1 5h1m13 0h1M1 6h1m13 0h1M1 7h1m13 0h1M1 8h1m13 0h1M1 9h1m13 0h1M1 10h1m13 0h1M1 11h1m13 0h1M1 12h1m13 0h1M1 13h1m13 0h1M1 14h1m13 0h1M2 15h13'/%3E%3Cpath stroke='%23e6eefc' d='M2 1h1'/%3E%3Cpath stroke='%23d0dffc' d='M3 1h1M2 2h1'/%3E%3Cpath stroke='%23cad8f9' d='M4 1h1M2 3h1'/%3E%3Cpath stroke='%23c4d2f7' d='M5 1h1'/%3E%3Cpath stroke='%23c0d0f7' d='M6 1h1'/%3E%3Cpath stroke='%23bdcef7' d='M7 1h1M2 6h1'/%3E%3Cpath stroke='%23bbcdf5' d='M8 1h1'/%3E%3Cpath stroke='%23b8cbf6' d='M9 1h1M2 7h1'/%3E%3Cpath stroke='%23b7caf5' d='M10 1h1M2 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			}
			
			: :-webkit-scrollbar-button: vertical: end{
				height: 17px;
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			}
			
			: :-webkit-scrollbar-button: horizontal: start{
				width: 17px;
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			}
			
			: :-webkit-scrollbar-button: horizontal: end{
				width: 17px;
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			}
			
			.window{
				box-shadow: inset -1px -1px #00138c,inset 1px 1px #0831d9,inset -2px -2px #001ea0,inset 2px 2px #166aee,inset -3px -3px #003bda,inset 3px 3px #0855dd;
				border-top-left-radius: 8px;
				border-top-right-radius: 8px;
				padding: 0 0 3px;
				-webkit-font-smoothing: antialiased
			}
			
			.title-bar{
				background: linear-gradient(180deg,#0997ff,#0053ee 8%,#0050ee 40%,#06f 88%,#06f 93%,#005bff 95%,#003dd7 96%,#003dd7);
				padding: 3px 5px 3px 3px;
				border-top: 1px solid #0831d9;
				border-left: 1px solid #0831d9;
				border-right: 1px solid #001ea0;
				border-top-left-radius: 8px;
				border-top-right-radius: 7px;
				font-size: 13px;
				text-shadow: 1px 1px #0f1089;
				height: 21px
			}
			
			.title-bar-text{
				padding-left: 3px
			}
			
			.title-bar-controls{
				display: flex
			}
			
			.title-bar-controls button{
				min-width: 21px;
				min-height: 21px;
				margin-left: 2px;
				background-repeat: no-repeat;
				background-position: 50%;
				box-shadow: none;
				background-color: #0050ee;
				transition: background .1s;
				border: none
			}
			
			.title-bar-controls button: active,.title-bar-controls button: focus,.title-bar-controls button: hover{
				box-shadow: none!important
			}
			
			.title-bar-controls button[aria-label=Minimize]{
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stroke='%239e3217' d='M2 10h1'/%3E%3Cpath stroke='%23b4381a' d='M3 10h1'/%3E%3Cpath stroke='%23df9a87' d='M10 10h1m-2 1h1m-2 2h1'/%3E%3Cpath stroke='%23c6441f' d='M13 10h1m3 0h1m-8 3h1m-1 3h1'/%3E%3Cpath stroke='%23c74520' d='M14 10h2m-6 4h1m-1 1h1m7 2h1m-7 1h1m4 0h1'/%3E%3Cpath stroke='%23c7451f' d='M16 10h1m1 2h1'/%3E%3Cpath stroke='%237b2711' d='M1 11h1'/%3E%3Cpath stroke='%23a13217' d='M2 11h1'/%3E%3Cpath stroke='%23b7391a' d='M3 11h1'/%3E%3Cpath stroke='%23e09b88' d='M11 11h1'/%3E%3Cpath stroke='%23e29d89' d='M12 11h1'/%3E%3Cpath stroke='%23c94621' d='M13 11h1m-3 2h1'/%3E%3Cpath stroke='%23ca4721' d='M14 11h1m2 1h1m-7 2h1m-1 1h1m0 2h1m2 1h1'/%3E%3Cpath stroke='%23ca4821' d='M15 11h1m1 6h1'/%3E%3Cpath stroke='%23c94620' d='M16 11h1m1 3h1m-8 2h1m6 0h1'/%3E%3Cpath stroke='%23c84620' d='M17 11h1m0 2h1'/%3E%3Cpath stroke='%23a53418' d='M2 12h1'/%3E%3Cpath stroke='%23b83a1b' d='M3 12h1'/%3E%3Cpath stroke='%23e19d89' d='M11 12h1'/%3E%3Cpath stroke='%23e39e89' d='M12 12h1'/%3E%3Cpath stroke='%23e0947c' d='M13 12h1'/%3E%3Cpath stroke='%23cc4a22' d='M14 12h1m-3 2h1m4 0h1m-6 1h1'/%3E%3Cpath stroke='%23cd4a22' d='M15 12h1m0 1h1m0 2h1m-5 1h1m1 1h1'/%3E%3Cpath stroke='%23cb4922' d='M16 12h1m0 1h1m-5 4h1'/%3E%3Cpath stroke='%23c3411e' d='M19 12h1m-1 1h1m-1 4h1m-8 2h2m3 0h1'/%3E%3Cpath stroke='%23a93618' d='M2 13h1'/%3E%3Cpath stroke='%23dd9987' d='M7 13h1m-2 2h1'/%3E%3Cpath stroke='%23e39f8a' d='M12 13h1'/%3E%3Cpath stroke='%23e59f8b' d='M13 13h1'/%3E%3Cpath stroke='%23e5a08b' d='M14 13h1m-2 1h1'/%3E%3Cpath stroke='%23ce4c23' d='M15 13h1m0 3h1'/%3E%3Cpath stroke='%23882b13' d='M1 14h1'/%3E%3Cpath stroke='%23e6a08b' d='M14 14h1'/%3E%3Cpath stroke='%23e6a18b' d='M15 14h1m-2 1h1'/%3E%3Cpath stroke='%23ce4b23' d='M16 14h1m-4 1h1'/%3E%3Cpath stroke='%238b2c14' d='M1 15h1m-1 1h1'/%3E%3Cpath stroke='%23ac3619' d='M2 15h1'/%3E%3Cpath stroke='%23d76b48' d='M15 15h1'/%3E%3Cpath stroke='%23cf4c23' d='M16 15h1m-2 1h1'/%3E%3Cpath stroke='%23c94721' d='M18 15h1m-3 3h1'/%3E%3Cpath stroke='%23bb3c1b' d='M3 16h1'/%3E%3Cpath stroke='%23bf3e1d' d='M6 16h1'/%3E%3Cpath stroke='%23cb4821' d='M12 16h1'/%3E%3Cpath stroke='%23cd4b23' d='M14 16h1'/%3E%3Cpath stroke='%23cc4922' d='M17 16h1m-4 1h1m1 0h1'/%3E%3Cpath stroke='%238d2d14' d='M1 17h1'/%3E%3Cpath stroke='%23bc3c1b' d='M3 17h1m-1 1h1'/%3E%3Cpath stroke='%23c84520' d='M11 17h1m1 1h1'/%3E%3Cpath stroke='%23ae3719' d='M2 18h1'/%3E%3Cpath stroke='%23c94720' d='M14 18h1'/%3E%3Cpath stroke='%23c95839' d='M19 18h1'/%3E%3Cpath stroke='%23a7bdf0' d='M0 19h1m0 1h1'/%3E%3Cpath stroke='%23ead7d3' d='M1 19h1'/%3E%3Cpath stroke='%23b34e35' d='M2 19h1'/%3E%3Cpath stroke='%23c03e1c' d='M8 19h1'/%3E%3Cpath stroke='%23c9583a' d='M18 19h1'/%3E%3Cpath stroke='%23f3dbd4' d='M19 19h1'/%3E%3Cpath stroke='%23a7bcef' d='M20 19h1m-2 1h1'/%3E%3C/svg%3E")
			}
			
			.status-bar{
				margin: 0 3px;
				box-shadow: inset 0 1px 2px grey;
				padding: 2px 1px;
				gap: 0
			}
			
			.status-bar-field{
				-webkit-font-smoothing: antialiased;
				box-shadow: none;
				padding: 1px 2px;
				border-right: 1px solid rgba(208,206,191,.75);
				border-left: 1px solid hsla(0,0%,100%,.75)
			}
			
			.status-bar-field: first-of-type{
				border-left: none
			}
			
			.status-bar-field: last-of-type{
				border-right: none
			}
			
			button{
				-webkit-font-smoothing: antialiased;
				box-sizing: border-box;
				border: 1px solid #003c74;
				background: linear-gradient(180deg,#fff,#ecebe5 86%,#d8d0c4);
				box-shadow: none;
				border-radius: 3px
			}
			
			button: not(: disabled).active,button: not(: disabled): active{
				box-shadow: none;
				background: linear-gradient(180deg,#cdcac3,#e3e3db 8%,#e5e5de 94%,#f2f2f1)
			}
			
			button: not(: disabled): hover{
				box-shadow: inset -1px 1px #fff0cf,inset 1px 2px #fdd889,inset -2px 2px #fbc761,inset 2px -2px #e5a01a
			}
			
			button.focused,button: focus{
				box-shadow: inset -1px 1px #cee7ff,inset 1px 2px #98b8ea,inset -2px 2px #bcd4f6,inset 1px -1px #89ade4,inset 2px -2px #89ade4
			}
			
			button: :-moz-focus-inner{
				border: 0
			}
			
			input,label,option,select,textarea{
				-webkit-font-smoothing: antialiased
			}
			
			input[type=radio]{
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				margin: 0;
				background: 0;
				position: fixed;
				opacity: 0;
				border: none
			}
			
			input[type=radio]+label{
				line-height: 16px
			}
			
			input[type=radio]+label: before{
				background: linear-gradient(135deg,#dcdcd7,#fff);
				border-radius: 50%;
				border: 1px solid #1d5281
			}
			
			input[type=radio]: not([disabled]): not(: active)+label: hover: before{
				box-shadow: inset -2px -2px #f8b636,inset 2px 2px #fedf9c
			}
			
			input[type=radio]: active+label: before{
				background: linear-gradient(135deg,#b0b0a7,#e3e1d2)
			}
			
			input[type=radio]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23a9dca6' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%234dbf4a' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23a0d29e' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%2355d551' d='M1 1h1'/%3E%3Cpath stroke='%2343c33f' d='M2 1h1'/%3E%3Cpath stroke='%2329a826' d='M3 1h1'/%3E%3Cpath stroke='%239acc98' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%2342c33f' d='M1 2h1'/%3E%3Cpath stroke='%2338b935' d='M2 2h1'/%3E%3Cpath stroke='%2321a121' d='M3 2h1'/%3E%3Cpath stroke='%23269623' d='M4 2h1'/%3E%3Cpath stroke='%232aa827' d='M1 3h1'/%3E%3Cpath stroke='%2322a220' d='M2 3h1'/%3E%3Cpath stroke='%23139210' d='M3 3h1'/%3E%3Cpath stroke='%2398c897' d='M4 3h1'/%3E%3Cpath stroke='%23249624' d='M2 4h1'/%3E%3Cpath stroke='%2398c997' d='M3 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=radio]: focus+label{
				outline: 1px dotted #000
			}
			
			input[type=radio][disabled]+label: before{
				border: 1px solid #cac8bb;
				background: #fff
			}
			
			input[type=radio][disabled]: checked+label: after{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 5 5' shape-rendering='crispEdges'%3E%3Cpath stroke='%23e8e6da' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23d2ceb5' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23e5e3d4' d='M3 0h1M0 3h1'/%3E%3Cpath stroke='%23d7d3bd' d='M1 1h1'/%3E%3Cpath stroke='%23d0ccb2' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%23c7c2a2' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%23e2dfd0' d='M4 1h1M1 4h1'/%3E%3Cpath stroke='%23cdc8ac' d='M2 2h1'/%3E%3Cpath stroke='%23c5bf9f' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%23c3bd9c' d='M4 2h1'/%3E%3Cpath stroke='%23bfb995' d='M3 3h1'/%3E%3Cpath stroke='%23e2dfcf' d='M4 3h1M3 4h1'/%3E%3Cpath stroke='%23c4be9d' d='M2 4h1'/%3E%3C/svg%3E")
			}
			
			input[type=email],input[type=password],textarea: :selection{
				background: #2267cb;
				color: #fff
			}
			
			input[type=range]: :-webkit-slider-thumb{
				height: 21px;
				width: 11px;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(-8px)
			}
			
			input[type=range]: :-moz-range-thumb{
				height: 21px;
				width: 11px;
				border: 0;
				border-radius: 0;
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 21' shape-rendering='crispEdges'%3E%3Cpath stroke='%23becbd3' d='M1 0h1M0 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M2 0h1M0 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M3 0h5M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M8 0h1M0 14h1'/%3E%3Cpath stroke='%239fb2be' d='M9 0h1M0 15h1'/%3E%3Cpath stroke='%23a6d1b1' d='M1 1h1'/%3E%3Cpath stroke='%236fd16e' d='M2 1h1M1 2h1'/%3E%3Cpath stroke='%2367ce65' d='M3 1h1M1 3h1'/%3E%3Cpath stroke='%2366ce64' d='M4 1h3'/%3E%3Cpath stroke='%2362cd61' d='M7 1h1'/%3E%3Cpath stroke='%2345c343' d='M8 1h1M7 2h1'/%3E%3Cpath stroke='%2363ac76' d='M9 1h1M2 16h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%23879aa6' d='M10 1h1'/%3E%3Cpath stroke='%2363cd62' d='M2 2h1'/%3E%3Cpath stroke='%2349c547' d='M3 2h1M2 3h1'/%3E%3Cpath stroke='%2347c446' d='M4 2h3'/%3E%3Cpath stroke='%2321b71f' d='M8 2h1'/%3E%3Cpath stroke='%231da41c' d='M9 2h1'/%3E%3Cpath stroke='%237d8e99' d='M10 2h1'/%3E%3Cpath stroke='%2325b923' d='M3 3h1'/%3E%3Cpath stroke='%2321b81f' d='M4 3h4M2 15h1'/%3E%3Cpath stroke='%231ea71c' d='M8 3h1'/%3E%3Cpath stroke='%231b9619' d='M9 3h1'/%3E%3Cpath stroke='%23778892' d='M10 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f7f7f4' d='M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h4m-4 1h3m-2 1h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 4h1M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 4h1M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f1f1ed' d='M7 13h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 13h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 13h1'/%3E%3Cpath stroke='%234bc549' d='M1 14h1'/%3E%3Cpath stroke='%23f4f4f1' d='M2 14h1'/%3E%3Cpath stroke='%23e6e6e2' d='M7 14h1m-2 1h1'/%3E%3Cpath stroke='%23cececa' d='M8 14h1'/%3E%3Cpath stroke='%231a9319' d='M9 14h1'/%3E%3Cpath stroke='%23788993' d='M10 14h1'/%3E%3Cpath stroke='%2369b17b' d='M1 15h1'/%3E%3Cpath stroke='%23f2f2ee' d='M3 15h1m0 1h1'/%3E%3Cpath stroke='%23d0d0cc' d='M7 15h1m-2 1h1'/%3E%3Cpath stroke='%231a9118' d='M8 15h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%234c845a' d='M9 15h1'/%3E%3Cpath stroke='%2372838d' d='M10 15h1'/%3E%3Cpath stroke='%2391a6b2' d='M1 16h1m0 1h1m0 1h1m0 1h1'/%3E%3Cpath stroke='%2321b61f' d='M3 16h1m0 1h1'/%3E%3Cpath stroke='%23e7e7e3' d='M5 16h1'/%3E%3Cpath stroke='%234b8259' d='M8 16h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%236e7e88' d='M9 16h1m-2 1h1m-2 1h1m-2 1h1'/%3E%3Cpath stroke='%23d7d7d4' d='M5 17h1'/%3E%3Cpath stroke='%231da21b' d='M5 18h1'/%3E%3Cpath stroke='%23589868' d='M5 19h1'/%3E%3Cpath stroke='%2380929e' d='M5 20h1'/%3E%3C/svg%3E");
				transform: translateY(2px)
			}
			
			input[type=range]: :-webkit-slider-runnable-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range]: :-moz-range-track{
				width: 100%;
				height: 2px;
				box-sizing: border-box;
				background: #ecebe4;
				border-right: 1px solid #f3f2ea;
				border-bottom: 1px solid #f3f2ea;
				border-radius: 2px;
				box-shadow: 1px 0 0 #fff,1px 1px 0 #fff,0 1px 0 #fff,-1px 0 0 #9d9c99,-1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,-1px 1px 0 #fff,1px -1px #9d9c99
			}
			
			input[type=range].has-box-indicator: :-webkit-slider-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(-10px)
			}
			
			input[type=range].has-box-indicator: :-moz-range-thumb{
				background: url("data: image/svg+xml;charset=utf-8,%3Csvg xmlns='http: //www.w3.org/2000/svg' viewBox='0 -0.5 11 22' shape-rendering='crispEdges'%3E%3Cpath stroke='%23f2f1e7' d='M0 0h1m9 0h1M0 21h1m9 0h1'/%3E%3Cpath stroke='%23879aa6' d='M1 0h1m8 20h1'/%3E%3Cpath stroke='%237d8e99' d='M2 0h1m7 19h1'/%3E%3Cpath stroke='%23778892' d='M3 0h5m2 3h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23788993' d='M8 0h1m1 2h1'/%3E%3Cpath stroke='%2372838d' d='M9 0h1m0 1h1'/%3E%3Cpath stroke='%239fb2be' d='M0 1h1m8 20h1'/%3E%3Cpath stroke='%2363af76' d='M1 1h1m7 19h1'/%3E%3Cpath stroke='%231eab1c' d='M2 1h1m6 18h1'/%3E%3Cpath stroke='%231c9d1a' d='M3 1h1'/%3E%3Cpath stroke='%231b9a1a' d='M4 1h3m1 0h1m0 1h1'/%3E%3Cpath stroke='%231b9b1a' d='M7 1h1'/%3E%3Cpath stroke='%234d875b' d='M9 1h1'/%3E%3Cpath stroke='%23afbfc8' d='M0 2h1m7 19h1'/%3E%3Cpath stroke='%2346ca44' d='M1 2h1m5 17h1m0 1h1'/%3E%3Cpath stroke='%2322be20' d='M2 2h1m5 17h1'/%3E%3Cpath stroke='%231faf1d' d='M3 2h1'/%3E%3Cpath stroke='%231fae1d' d='M4 2h3'/%3E%3Cpath stroke='%231fad1d' d='M7 2h1'/%3E%3Cpath stroke='%231da11b' d='M8 2h1'/%3E%3Cpath stroke='%23b5c4cd' d='M0 3h1M0 4h1M0 5h1M0 6h1M0 7h1M0 8h1M0 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m2 3h5'/%3E%3Cpath stroke='%23f7f7f4' d='M1 3h1M1 4h1M1 5h1M1 6h1M1 7h1M1 8h1M1 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f5f5f2' d='M2 3h1M2 4h1M2 5h1M2 6h1M2 7h1M2 8h1M2 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23f3f3ef' d='M3 3h4M3 4h5M3 5h5M3 6h5M3 7h5M3 8h5M3 9h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5m-5 1h5'/%3E%3Cpath stroke='%23f1f1ed' d='M7 3h1'/%3E%3Cpath stroke='%23dbdbd8' d='M8 3h1'/%3E%3Cpath stroke='%23c4c4c1' d='M9 3h1'/%3E%3Cpath stroke='%23ddddd9' d='M8 4h1M8 18h1'/%3E%3Cpath stroke='%23c6c6c3' d='M9 4h1M9 18h1'/%3E%3Cpath stroke='%23dcdcd9' d='M8 5h1M8 6h1M8 7h1M8 8h1M8 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23c3c3c0' d='M9 5h1M9 6h1M9 7h1M9 8h1M9 9h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1m-1 1h1'/%3E%3Cpath stroke='%23b6c5cd' d='M0 19h1m1 2h1'/%3E%3Cpath stroke='%2370d66f' d='M1 19h1m0 1h1'/%3E%3Cpath stroke='%2364d362' d='M2 19h1'/%3E%3Cpath stroke='%234acb48' d='M3 19h1'/%3E%3Cpath stroke='%2348cb46' d='M4 19h3'/%3E%3Cpath stroke='%23becbd3' d='M0 20h1m0 1h1'/%3E%3Cpath stroke='%23a6d2b1' d='M1 20h1'/%3E%3Cpath stroke='%2367d466' d='M3 20h1'/%3E%3Cpath stroke='%2366d465' d='M4 20h3'/%3E%3Cpath stroke='%2363d362' d='M7 20h1'/%3E%3C/svg%3E");transform: translateY(0)
			}
			
			.is-vertical>input[type=range]: :-webkit-slider-runnable-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			.is-vertical>input[type=range]: :-moz-range-track{
				border-left: 1px solid #f3f2ea;
				border-right: 0;
				border-bottom: 1px solid #f3f2ea;
				box-shadow: -1px 0 0 #fff,-1px 1px 0 #fff,0 1px 0 #fff,1px 0 0 #9d9c99,1px -1px 0 #9d9c99,0 -1px 0 #9d9c99,1px 1px 0 #fff,-1px -1px #9d9c99
			}
			
			fieldset{
				box-shadow: none;
				background: #fff;
				border: 1px solid #d0d0bf;
				border-radius: 4px;
				padding-top: 10px
			}
			
			legend{
				background: transparent;
				color: #0046d5
			}
			
			.field-row{
				display: flex;
				align-items: center
			}
			
			.field-row>*+*{
				margin-left: 6px
			}
			
			[class^=field-row]+[class^=field-row]{
				margin-top: 6px
			}
			
			.field-row-stacked{
				display: flex;
				flex-direction: column
			}
			
			.field-row-stacked *+*{
				margin-top: 6px
			}
			
			menu[role=tablist] button{
				background: linear-gradient(180deg,#fff,#fafaf9 26%,#f0f0ea 95%,#ecebe5);
				margin-left: -1px;
				margin-right: 2px;
				border-radius: 0;
				border-color: #91a7b4;
				border-top-right-radius: 3px;
				border-top-left-radius: 3px;
				padding: 0 12px 3px
			}
			
			menu[role=tablist] button: hover{
				box-shadow: unset;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]{
				border-color: #919b9c;
				margin-right: -1px;
				border-bottom: 1px solid transparent;
				border-top: 1px solid #e68b2c;
				box-shadow: inset 0 2px #ffc73c
			}
			
			menu[role=tablist] button[aria-selected=true]: first-of-type: before{
				content: "";
				display: block;
				position: absolute;
				z-index: -1;
				top: 100%;
				left: -1px;
				height: 2px;
				width: 0;
				border-left: 1px solid #919b9c
			}
			
			[role=tabpanel]{
				box-shadow: inset 1px 1px #fcfcfe,inset -1px -1px #fcfcfe,1px 2px 2px 0 rgba(208,206,191,.75)
			}
			
			ul.tree-view{
				-webkit-font-smoothing: auto;
				border: 1px solid #7f9db9;
				padding: 2px 5px
			}
			
			@keyframes sliding{
				0%{
					transform: translateX(-30px)
				}
			
				to{
					transform: translateX(100%)
				}
			}
			
			progress{
				box-sizing: border-box;
				appearance: none;
				-webkit-appearance: none;
				-moz-appearance: none;
				height: 14px;
				border: 1px solid #686868;
				border-radius: 4px;
				padding: 1px 2px 1px 0;
				overflow: hidden;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress,progress: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				height: 14px
			}
			
			progress[value]: :-webkit-progress-bar{
				background-color: transparent
			}
			
			progress[value]: :-webkit-progress-value{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress[value]: :-moz-progress-bar{
				border-radius: 2px;
				background: repeating-linear-gradient(90deg,#fff 0,#fff 2px,transparent 0,transparent 10px),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]): :-webkit-progress-bar{
				width: 100%;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff);
				animation: sliding 2s linear 0s infinite
			}
			
			progress: not([value]): :-webkit-progress-bar: not([value]){
				animation: sliding 2s linear 0s infinite;
				background: repeating-linear-gradient(90deg,transparent 0,transparent 8px,#fff 0,#fff 10px,transparent 0,transparent 18px,#fff 0,#fff 20px,transparent 0,transparent 28px,#fff 0,#fff),linear-gradient(180deg,#acedad 0,#7be47d 14%,#4cda50 28%,#2ed330 42%,#42d845 57%,#76e275 71%,#8fe791 85%,#fff)
			}
			
			progress: not([value]){
				position: relative
			}
			
			progress: not([value]): before{
				box-sizing: border-box;
				content: "";
				position: absolute;
				top: 0;
				left: 0;
				width: 100%;
				height: 100%;
				background-color: #fff;
				-webkit-box-shadow: inset 0 0 1px 0 #686868;
				-moz-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before,progress: not([value]): before: not([value]){
				box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): before: not([value]){
				-moz-box-shadow: inset 0 0 1px 0 #686868;
				-webkit-box-shadow: inset 0 0 1px 0 #686868
			}
			
			progress: not([value]): after{
				box-sizing: border-box;
				content: "";
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1632 n_samples 4206 const 9.354374468216024 max_depth 20 threshold 0.001",
			        1632,
			        4206,
			        9.354374468216024,
			        20,
			        0.001,
			        0.73,
			        18,
			        0,
			        "None",
			        "i7176",
			        18,
			        524.51171875,
			        523.9619140625,
			        -1,
			        0.1009963487839594,
			        4631190
			    ],
			    [
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			        35,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 20 const 6.844923229951603 max_depth 14 threshold 0.999",
			        1,
			        20,
			        6.844923229951603,
			        14,
			        0.999,
			        0.72,
			        11,
			        0,
			        "None",
			        "i7175",
			        11,
			        525.83203125,
			        525.7021484375,
			        -1,
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			    ],
			    [
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			        1745416776,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1546 const 10 max_depth 1 threshold 0.001",
			        1,
			        1546,
			        10,
			        1,
			        0.001,
			        0.73,
			        7,
			        0,
			        "None",
			        "i7171",
			        7,
			        525.06640625,
			        524.890625,
			        -1,
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			        4632655
			    ],
			    [
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			        83,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 10 max_depth 15 threshold 0.999",
			        1,
			        1,
			        10,
			        15,
			        0.999,
			        0.74,
			        14,
			        0,
			        "None",
			        "i7175",
			        14,
			        523.875,
			        521.50390625,
			        -1,
			        0.49996905749118137,
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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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3747 n_samples 577 const 1.6076739076269428 max_depth 3 threshold 0.999",
			        3747,
			        577,
			        1.6076739076269428,
			        3,
			        0.999,
			        0.75,
			        5,
			        0,
			        "None",
			        "i7180",
			        5,
			        524.99609375,
			        524.8177083333334,
			        -1,
			        0.9998143449470883,
			        4633893
			    ],
			    [
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			        4837,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3206 n_samples 1263 const 3.2458475829316304 max_depth 8 threshold 0.8957893443722278",
			        3206,
			        1263,
			        3.2458475829316304,
			        8,
			        0.8957893443722278,
			        0.73,
			        4655,
			        0,
			        "None",
			        "i7176",
			        4655,
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			        474.97372462606836,
			        -1,
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			    ],
			    [
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			        6824,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 443 n_samples 3129 const 8.05610312634334 max_depth 20 threshold 0.7458556071352214",
			        443,
			        3129,
			        8.05610312634334,
			        20,
			        0.7458556071352214,
			        0.73,
			        6794,
			        0,
			        "None",
			        "i7183",
			        6794,
			        523.99609375,
			        521.5662972650514,
			        -1,
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			    ],
			    [
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			        1745423273,
			        39,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1022 n_samples 4732 const 6.529952822124694 max_depth 15 threshold 0.001",
			        1022,
			        4732,
			        6.529952822124694,
			        15,
			        0.001,
			        0.73,
			        12,
			        0,
			        "None",
			        "i7177",
			        12,
			        524.19140625,
			        524.0341796875,
			        -1,
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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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4572 const 3.427880853460206 max_depth 7 threshold 0.001",
			        1,
			        4572,
			        3.427880853460206,
			        7,
			        0.001,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7186",
			        2,
			        525.76953125,
			        525.5416666666666,
			        -1,
			        6.188501763723002e-5,
			        4642028
			    ],
			    [
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			        39,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1028 n_samples 277 const 6.118769442363483 max_depth 12 threshold 0.999",
			        1028,
			        277,
			        6.118769442363483,
			        12,
			        0.999,
			        0.74,
			        28,
			        0,
			        "None",
			        "i7182",
			        28,
			        524.85546875,
			        524.71640625,
			        -1,
			        0.9944303484126493,
			        4642623
			    ],
			    [
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			        1745426175,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1194 n_samples 2201 const 2.3445250723355047 max_depth 4 threshold 0.999",
			        1194,
			        2201,
			        2.3445250723355047,
			        4,
			        0.999,
			        0.74,
			        1,
			        0,
			        "None",
			        "i7183",
			        1,
			        525.33984375,
			        525.1848958333334,
			        -1,
			        0.22167213317655796,
			        4643244
			    ],
			    [
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			        33,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4897 n_samples 167 const 1.6954311768257337 max_depth 7 threshold 0.8731260716792513",
			        4897,
			        167,
			        1.6954311768257337,
			        7,
			        0.8731260716792513,
			        0.74,
			        23,
			        0,
			        "None",
			        "i7176",
			        23,
			        526.2421875,
			        525.9359375,
			        -1,
			        0.9926975679188068,
			        4644298
			    ],
			    [
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			        1745431641,
			        259,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 939 const 0.001 max_depth 3 threshold 0.001",
			        1,
			        939,
			        0.001,
			        3,
			        0.001,
			        0.73,
			        251,
			        0,
			        "None",
			        "i7178",
			        251,
			        524.90625,
			        524.8759765625,
			        -1,
			        0.00037131010582338014,
			        4647685
			    ],
			    [
			        1745432177,
			        1745432191,
			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1210 n_samples 2204 const 7.004003683018396 max_depth 4 threshold 0.999",
			        1210,
			        2204,
			        7.004003683018396,
			        4,
			        0.999,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7174",
			        2,
			        524.85546875,
			        524.6809895833334,
			        -1,
			        0.224642614023145,
			        4648401
			    ],
			    [
			        1745432992,
			        1745433864,
			        872,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1087 n_samples 294 const 0.7177603693701413 max_depth 13 threshold 0.999",
			        1087,
			        294,
			        0.7177603693701413,
			        13,
			        0.999,
			        0.74,
			        856,
			        0,
			        "None",
			        "i7174",
			        856,
			        524.62890625,
			        524.6229137073864,
			        -1,
			        0.9988241846648926,
			        4649054
			    ],
			    [
			        1745433962,
			        1745433982,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 706 n_samples 3271 const 3.4995304184204366 max_depth 5 threshold 0.001",
			        706,
			        3271,
			        3.4995304184204366,
			        5,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7174",
			        1,
			        524.09765625,
			        523.9427083333334,
			        -1,
			        0.0873816449037688,
			        4649747
			    ],
			    [
			        1745434783,
			        1745434810,
			        27,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4043 const 8.625278163102992 max_depth 20 threshold 0.999",
			        1,
			        4043,
			        8.625278163102992,
			        20,
			        0.999,
			        0.73,
			        16,
			        0,
			        "None",
			        "i7178",
			        16,
			        524.1484375,
			        524.0166015625,
			        -1,
			        6.188501763723002e-5,
			        4650348
			    ],
			    [
			        1745435795,
			        1745435842,
			        47,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 2772 const 0.001 max_depth 4 threshold 0.999",
			        5000,
			        2772,
			        0.001,
			        4,
			        0.999,
			        0.73,
			        30,
			        0,
			        "None",
			        "i7186",
			        30,
			        523.796875,
			        523.708984375,
			        -1,
			        0.6188501763723002,
			        4651107
			    ],
			    [
			        1745436173,
			        1745436238,
			        65,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1492 n_samples 585 const 4.932684739917858 max_depth 3 threshold 0.001",
			        1492,
			        585,
			        4.932684739917858,
			        3,
			        0.001,
			        0.74,
			        54,
			        0,
			        "None",
			        "i7173",
			        54,
			        524.42578125,
			        524.359375,
			        -1,
			        0.9252428986942262,
			        4651372
			    ],
			    [
			        1745436600,
			        1745436645,
			        45,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3935 n_samples 673 const 10 max_depth 1 threshold 0.001",
			        3935,
			        673,
			        10,
			        1,
			        0.001,
			        0.75,
			        32,
			        0,
			        "None",
			        "i7185",
			        32,
			        523.828125,
			        523.7096354166666,
			        -1,
			        0.9214060276007179,
			        4651677
			    ],
			    [
			        1745437009,
			        1745437029,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3777 n_samples 552 const 7.078273639683896 max_depth 1 threshold 0.36210394598973233",
			        3777,
			        552,
			        7.078273639683896,
			        1,
			        0.36210394598973233,
			        0.75,
			        5,
			        0,
			        "None",
			        "i7186",
			        5,
			        525.23828125,
			        525.0026041666666,
			        -1,
			        0.9564948326010273,
			        4651961
			    ],
			    [
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			        1745439344,
			        39,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3710 n_samples 570 const 0.9890058308422881 max_depth 3 threshold 0.001",
			        3710,
			        570,
			        0.9890058308422881,
			        3,
			        0.001,
			        0.75,
			        26,
			        0,
			        "None",
			        "i7178",
			        26,
			        525.17578125,
			        525.03515625,
			        -1,
			        0.9462219196732471,
			        4653886
			    ],
			    [
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			        1745440047,
			        45,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4234 n_samples 3676 const 10 max_depth 20 threshold 0.001",
			        4234,
			        3676,
			        10,
			        20,
			        0.001,
			        0.73,
			        35,
			        0,
			        "None",
			        "i7176",
			        35,
			        524.12890625,
			        524.0618489583334,
			        -1,
			        0.5240423293520639,
			        4654490
			    ],
			    [
			        1745440930,
			        1745440950,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4166 n_samples 663 const 3.916828312958884 max_depth 1 threshold 0.999",
			        4166,
			        663,
			        3.916828312958884,
			        1,
			        0.999,
			        0.74,
			        5,
			        0,
			        "None",
			        "i7175",
			        5,
			        524.109375,
			        523.9518229166666,
			        -1,
			        0.9847144006436042,
			        4655286
			    ],
			    [
			        1745441801,
			        1745441854,
			        53,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4775 n_samples 120 const 1.4951635467596356 max_depth 10 threshold 0.548595526080069",
			        4775,
			        120,
			        1.4951635467596356,
			        10,
			        0.548595526080069,
			        0.74,
			        36,
			        0,
			        "None",
			        "i7185",
			        36,
			        528.2265625,
			        525.0299479166666,
			        -1,
			        0.9951110836066588,
			        4655906
			    ],
			    [
			        1745442612,
			        1745442924,
			        312,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3946 n_samples 766 const 8.218094473965188 max_depth 3 threshold 0.001",
			        3946,
			        766,
			        8.218094473965188,
			        3,
			        0.001,
			        0.74,
			        298,
			        0,
			        "None",
			        "i7178",
			        298,
			        525.3359375,
			        525.3194580078125,
			        -1,
			        0.8855127173711245,
			        4656456
			    ],
			    [
			        1745444478,
			        1745444498,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4793 n_samples 1345 const 8.313669612612612 max_depth 2 threshold 0.999",
			        4793,
			        1345,
			        8.313669612612612,
			        2,
			        0.999,
			        0.74,
			        4,
			        0,
			        "None",
			        "i7186",
			        4,
			        524.56640625,
			        524.390625,
			        -1,
			        0.9990098397178043,
			        4657692
			    ],
			    [
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			        1745445119,
			        33,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1186 n_samples 295 const 8.039835570900577 max_depth 8 threshold 0.999",
			        1186,
			        295,
			        8.039835570900577,
			        8,
			        0.999,
			        0.75,
			        18,
			        0,
			        "None",
			        "i7185",
			        18,
			        523.93359375,
			        523.8017578125,
			        -1,
			        0.9858902159787115,
			        4658122
			    ],
			    [
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			        1745446265,
			        66,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1197 n_samples 511 const 7.9054035332901655 max_depth 7 threshold 0.999",
			        1197,
			        511,
			        7.9054035332901655,
			        7,
			        0.999,
			        0.75,
			        35,
			        0,
			        "None",
			        "i7182",
			        35,
			        524.8515625,
			        524.7708333333334,
			        -1,
			        0.9511727210842255,
			        4658947
			    ],
			    [
			        1745447153,
			        1745447185,
			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1595 n_samples 5000 const 2.586778476039295 max_depth 3 threshold 0.001",
			        1595,
			        5000,
			        2.586778476039295,
			        3,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7182",
			        1,
			        523.8203125,
			        523.58984375,
			        -1,
			        0.0987066031313819,
			        4659667
			    ],
			    [
			        1745448046,
			        1745448059,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1884 n_samples 5000 const 10 max_depth 3 threshold 0.001",
			        1884,
			        5000,
			        10,
			        3,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7176",
			        1,
			        524.79296875,
			        524.6380208333334,
			        -1,
			        0.11659137322854136,
			        4660411
			    ],
			    [
			        1745448940,
			        1745448973,
			        33,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 147 const 9.29194724506755 max_depth 1 threshold 0.001",
			        5000,
			        147,
			        9.29194724506755,
			        1,
			        0.001,
			        0.74,
			        21,
			        0,
			        "None",
			        "i7178",
			        21,
			        526.72265625,
			        525.28203125,
			        -1,
			        0.9891701219134847,
			        4661121
			    ],
			    [
			        1745449784,
			        1745449797,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3940 n_samples 3475 const 1.3833342566173448 max_depth 4 threshold 0.001",
			        3940,
			        3475,
			        1.3833342566173448,
			        4,
			        0.001,
			        0.74,
			        2,
			        0,
			        "None",
			        "i7174",
			        2,
			        525.4140625,
			        525.2395833333334,
			        -1,
			        0.4876539389813726,
			        4661720
			    ],
			    [
			        1745450733,
			        1745450747,
			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3858 n_samples 3395 const 2.455504135342066 max_depth 8 threshold 0.001",
			        3858,
			        3395,
			        2.455504135342066,
			        8,
			        0.001,
			        0.74,
			        3,
			        0,
			        "None",
			        "i7175",
			        3,
			        524.8671875,
			        524.6302083333334,
			        -1,
			        0.47750479608886687,
			        4662333
			    ],
			    [
			        1745454453,
			        1745454466,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4813 n_samples 4720 const 9.143043644721473 max_depth 9 threshold 0.001",
			        4813,
			        4720,
			        9.143043644721473,
			        9,
			        0.001,
			        0.73,
			        3,
			        0,
			        "None",
			        "i7182",
			        3,
			        524.03515625,
			        523.8450520833334,
			        -1,
			        0.29785258988798813,
			        4664845
			    ],
			    [
			        1745464064,
			        1745464077,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4893 n_samples 2324 const 7.001943146693152 max_depth 3 threshold 0.999",
			        4893,
			        2324,
			        7.001943146693152,
			        3,
			        0.999,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7180",
			        2,
			        524.625,
			        524.4674479166666,
			        -1,
			        0.8629246859335354,
			        4671400
			    ],
			    [
			        1745464975,
			        1745464989,
			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4025 n_samples 1925 const 4.998639273041189 max_depth 4 threshold 0.001",
			        4025,
			        1925,
			        4.998639273041189,
			        4,
			        0.001,
			        0.74,
			        4,
			        0,
			        "None",
			        "i7186",
			        4,
			        524.59375,
			        524.3645833333334,
			        -1,
			        0.9536481217897147,
			        4672052
			    ],
			    [
			        1745466815,
			        1745466932,
			        117,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3927 n_samples 506 const 2.4510249725207185 max_depth 4 threshold 0.001",
			        3927,
			        506,
			        2.4510249725207185,
			        4,
			        0.001,
			        0.75,
			        104,
			        0,
			        "None",
			        "i7175",
			        104,
			        524.21484375,
			        524.1784855769231,
			        -1,
			        0.9446129092146791,
			        4673313
			    ],
			    [
			        1745467725,
			        1745467738,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 4043 const 8.54386700166326 max_depth 1 threshold 0.999",
			        5000,
			        4043,
			        8.54386700166326,
			        1,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7175",
			        1,
			        524.1640625,
			        523.9505208333334,
			        -1,
			        0.3094250881861501,
			        4673875
			    ],
			    [
			        1745468535,
			        1745468555,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3842 n_samples 603 const 5.425640159475038 max_depth 1 threshold 0.001",
			        3842,
			        603,
			        5.425640159475038,
			        1,
			        0.001,
			        0.75,
			        7,
			        0,
			        "None",
			        "i7175",
			        7,
			        524.77734375,
			        524.5989583333334,
			        -1,
			        0.9787734389504301,
			        4674341
			    ],
			    [
			        1745465744,
			        1745469799,
			        4055,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4302 n_samples 3129 const 5.019096234478818 max_depth 15 threshold 0.001",
			        4302,
			        3129,
			        5.019096234478818,
			        15,
			        0.001,
			        0.73,
			        4046,
			        0,
			        "None",
			        "i7176",
			        4046,
			        525.1328125,
			        514.4046471898034,
			        -1,
			        0.5324586917507271,
			        4672614
			    ],
			    [
			        1745473417,
			        1745473436,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4569 n_samples 636 const 7.378350070551063 max_depth 1 threshold 0.001",
			        4569,
			        636,
			        7.378350070551063,
			        1,
			        0.001,
			        0.75,
			        7,
			        0,
			        "None",
			        "i7178",
			        7,
			        524.92578125,
			        524.7630208333334,
			        -1,
			        0.9584751531654186,
			        4677408
			    ],
			    [
			        1745474087,
			        1745474100,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1633 const 3.101366885738281 max_depth 2 threshold 0.999",
			        1,
			        1633,
			        3.101366885738281,
			        2,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7178",
			        1,
			        523.953125,
			        523.77734375,
			        -1,
			        0.0002475400705489201,
			        4677859
			    ],
			    [
			        1745474778,
			        1745474869,
			        91,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4932 n_samples 485 const 10 max_depth 2 threshold 0.001",
			        4932,
			        485,
			        10,
			        2,
			        0.001,
			        0.75,
			        80,
			        0,
			        "None",
			        "i7176",
			        80,
			        524.703125,
			        524.6544744318181,
			        -1,
			        0.9647874249644162,
			        4678306
			    ],
			    [
			        1745476117,
			        1745476143,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4429 n_samples 561 const 1.6141132516385033 max_depth 3 threshold 0.001",
			        4429,
			        561,
			        1.6141132516385033,
			        3,
			        0.001,
			        0.75,
			        17,
			        0,
			        "None",
			        "i7181",
			        17,
			        524.69921875,
			        524.5673828125,
			        -1,
			        0.9899127421251315,
			        4679049
			    ],
			    [
			        1745476728,
			        1745476749,
			        21,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4968 n_samples 1169 const 4.305085745359978 max_depth 2 threshold 0.999",
			        4968,
			        1169,
			        4.305085745359978,
			        2,
			        0.999,
			        0.74,
			        4,
			        0,
			        "None",
			        "i7186",
			        4,
			        525.86328125,
			        525.6080729166666,
			        -1,
			        0.8681230274150629,
			        4679365
			    ],
			    [
			        1745477268,
			        1745477288,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3015 n_samples 216 const 2.937058645619547 max_depth 5 threshold 0.999",
			        3015,
			        216,
			        2.937058645619547,
			        5,
			        0.999,
			        0.74,
			        9,
			        0,
			        "None",
			        "i7185",
			        9,
			        524.578125,
			        524.34765625,
			        -1,
			        0.9891701219134847,
			        4679660
			    ],
			    [
			        1745486791,
			        1745486981,
			        190,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4804 n_samples 307 const 0.001 max_depth 2 threshold 0.5028958796629459",
			        4804,
			        307,
			        0.001,
			        2,
			        0.5028958796629459,
			        0.74,
			        176,
			        0,
			        "None",
			        "i7182",
			        176,
			        530.09375,
			        526.915234375,
			        -1,
			        0.9594034284299771,
			        4684385
			    ],
			    [
			        1745489496,
			        1745489516,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3735 n_samples 1658 const 6.731610584578516 max_depth 5 threshold 0.999",
			        3735,
			        1658,
			        6.731610584578516,
			        5,
			        0.999,
			        0.73,
			        9,
			        0,
			        "None",
			        "i7182",
			        9,
			        524.30078125,
			        524.0872395833334,
			        -1,
			        0.8229469645398849,
			        4685294
			    ],
			    [
			        1745494779,
			        1745495406,
			        627,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2656 n_samples 150 const 6.240397914305971 max_depth 20 threshold 0.001",
			        2656,
			        150,
			        6.240397914305971,
			        20,
			        0.001,
			        0.75,
			        610,
			        0,
			        "None",
			        "i7182",
			        610,
			        525.0625,
			        525.055183531746,
			        -1,
			        0.9953586236772077,
			        4687044
			    ],
			    [
			        1745495391,
			        1745495424,
			        33,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4214 n_samples 568 const 8.736740358329818 max_depth 2 threshold 0.001",
			        4214,
			        568,
			        8.736740358329818,
			        2,
			        0.001,
			        0.74,
			        21,
			        0,
			        "None",
			        "i7181",
			        21,
			        524.703125,
			        524.5625,
			        -1,
			        0.9583513831301442,
			        4687263
			    ],
			    [
			        1745496172,
			        1745496230,
			        58,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4500 n_samples 590 const 5.040423403704067 max_depth 3 threshold 0.11556670384270012",
			        4500,
			        590,
			        5.040423403704067,
			        3,
			        0.11556670384270012,
			        0.74,
			        44,
			        0,
			        "None",
			        "i7176",
			        44,
			        524.48046875,
			        524.4040178571429,
			        -1,
			        0.9326691008106938,
			        4687526
			    ],
			    [
			        1745497406,
			        1745497840,
			        434,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 712 const 1.3239189081767935 max_depth 4 threshold 0.001",
			        5000,
			        712,
			        1.3239189081767935,
			        4,
			        0.001,
			        0.73,
			        418,
			        0,
			        "None",
			        "i7181",
			        418,
			        525.1171875,
			        522.0612571022727,
			        -1,
			        0.9119376199022217,
			        4687935
			    ],
			    [
			        1745498036,
			        1745498055,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4995 n_samples 440 const 6.366083338566762 max_depth 1 threshold 0.905122537869337",
			        4995,
			        440,
			        6.366083338566762,
			        1,
			        0.905122537869337,
			        0.74,
			        7,
			        0,
			        "None",
			        "i7186",
			        7,
			        525.7890625,
			        525.5768229166666,
			        -1,
			        0.9802586793737236,
			        4688144
			    ],
			    [
			        1745499024,
			        1745499135,
			        111,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 599 const 1.7221350046756414 max_depth 4 threshold 0.001",
			        5000,
			        599,
			        1.7221350046756414,
			        4,
			        0.001,
			        0.74,
			        98,
			        0,
			        "None",
			        "i7181",
			        98,
			        524.34765625,
			        524.3072916666666,
			        -1,
			        0.9500587907667554,
			        4688564
			    ],
			    [
			        1745498726,
			        1745499260,
			        534,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3746 n_samples 562 const 10 max_depth 7 threshold 0.001",
			        3746,
			        562,
			        10,
			        7,
			        0.001,
			        0.74,
			        525,
			        0,
			        "None",
			        "i7175",
			        525,
			        524.09375,
			        519.2717329545454,
			        -1,
			        0.9657157002289746,
			        4688380
			    ],
			    [
			        1745509078,
			        1745509144,
			        66,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1086 n_samples 1544 const 0.001 max_depth 3 threshold 0.001",
			        1086,
			        1544,
			        0.001,
			        3,
			        0.001,
			        0.74,
			        56,
			        0,
			        "None",
			        "i7185",
			        56,
			        524.4609375,
			        524.40283203125,
			        -1,
			        0.2688285166161272,
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			    ],
			    [
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			        1745509598,
			        2150,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4804 n_samples 335 const 2.8223054482217234 max_depth 5 threshold 0.001",
			        4804,
			        335,
			        2.8223054482217234,
			        5,
			        0.001,
			        0.74,
			        2136,
			        0,
			        "None",
			        "i7186",
			        2136,
			        523.87890625,
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			        -1,
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			    ],
			    [
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			        125,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3672 n_samples 660 const 1.2487456994412225 max_depth 2 threshold 0.001",
			        3672,
			        660,
			        1.2487456994412225,
			        2,
			        0.001,
			        0.74,
			        114,
			        0,
			        "None",
			        "i7185",
			        114,
			        524.0546875,
			        524.0047433035714,
			        -1,
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			    ],
			    [
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			        1745511260,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1165 n_samples 250 const 5.372314669906302 max_depth 7 threshold 0.999",
			        1165,
			        250,
			        5.372314669906302,
			        7,
			        0.999,
			        0.75,
			        11,
			        0,
			        "None",
			        "i7182",
			        11,
			        524.89453125,
			        524.763671875,
			        -1,
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			    ],
			    [
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			        118,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1221 n_samples 484 const 6.07533373538386 max_depth 7 threshold 0.999",
			        1221,
			        484,
			        6.07533373538386,
			        7,
			        0.999,
			        0.74,
			        106,
			        0,
			        "None",
			        "i7181",
			        106,
			        524.3671875,
			        524.3266225961538,
			        -1,
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			        4695306
			    ],
			    [
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			        1745512380,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 711 n_samples 484 const 8.243159643933344 max_depth 1 threshold 0.999",
			        711,
			        484,
			        8.243159643933344,
			        1,
			        0.999,
			        0.74,
			        3,
			        0,
			        "None",
			        "i7181",
			        3,
			        525.80859375,
			        525.6002604166666,
			        -1,
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			    ],
			    [
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			        58,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4329 n_samples 867 const 1.664137594098345 max_depth 3 threshold 0.34622971420787807",
			        4329,
			        867,
			        1.664137594098345,
			        3,
			        0.34622971420787807,
			        0.74,
			        49,
			        0,
			        "None",
			        "i7180",
			        49,
			        524.61328125,
			        524.5435267857143,
			        -1,
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			    ],
			    [
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			        1745513826,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4333 n_samples 532 const 10 max_depth 1 threshold 0.999",
			        4333,
			        532,
			        10,
			        1,
			        0.999,
			        0.75,
			        6,
			        0,
			        "None",
			        "i7181",
			        6,
			        524.1640625,
			        523.98828125,
			        -1,
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			        4696357
			    ],
			    [
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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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2607 n_samples 156 const 1.2764969693654553 max_depth 2 threshold 0.001",
			        2607,
			        156,
			        1.2764969693654553,
			        2,
			        0.001,
			        0.75,
			        8,
			        0,
			        "None",
			        "i7185",
			        8,
			        524.8359375,
			        524.60546875,
			        -1,
			        0.9873135713843678,
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			    ],
			    [
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			        33,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3979 n_samples 4642 const 10 max_depth 20 threshold 0.001",
			        3979,
			        4642,
			        10,
			        20,
			        0.001,
			        0.74,
			        19,
			        0,
			        "None",
			        "i7183",
			        19,
			        524.82421875,
			        524.666015625,
			        -1,
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			        4697460
			    ],
			    [
			        1745516879,
			        1745517098,
			        219,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 846 n_samples 1574 const 10 max_depth 4 threshold 0.001",
			        846,
			        1574,
			        10,
			        4,
			        0.001,
			        0.73,
			        206,
			        0,
			        "None",
			        "i7183",
			        206,
			        525.68359375,
			        525.6606657608696,
			        -1,
			        0.2094188996843864,
			        4697907
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			    [
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			        1745518405,
			        143,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3654 n_samples 989 const 0.001 max_depth 3 threshold 0.999",
			        3654,
			        989,
			        0.001,
			        3,
			        0.999,
			        0.74,
			        130,
			        0,
			        "None",
			        "i7183",
			        130,
			        524.1484375,
			        524.1028645833334,
			        -1,
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			        4698531
			    ],
			    [
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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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1278 n_samples 546 const 1.8031161511802056 max_depth 2 threshold 0.999",
			        1278,
			        546,
			        1.8031161511802056,
			        2,
			        0.999,
			        0.74,
			        4,
			        0,
			        "None",
			        "i7181",
			        4,
			        525.2265625,
			        525.0703125,
			        -1,
			        0.9460981496379727,
			        4699506
			    ],
			    [
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			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 974 n_samples 242 const 6.111782301446027 max_depth 14 threshold 0.999",
			        974,
			        242,
			        6.111782301446027,
			        14,
			        0.999,
			        0.74,
			        20,
			        0,
			        "None",
			        "i7180",
			        20,
			        524.74609375,
			        524.653125,
			        -1,
			        0.988427501701838,
			        4699896
			    ],
			    [
			        1745521803,
			        1745521842,
			        39,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4130 n_samples 3496 const 8.05254533258215 max_depth 15 threshold 0.001",
			        4130,
			        3496,
			        8.05254533258215,
			        15,
			        0.001,
			        0.74,
			        23,
			        0,
			        "None",
			        "i7185",
			        23,
			        523.80078125,
			        523.67421875,
			        -1,
			        0.51117024568352,
			        4700291
			    ],
			    [
			        1745518919,
			        1745523844,
			        4925,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1303 n_samples 583 const 1.8025829548889756 max_depth 20 threshold 0.999",
			        1303,
			        583,
			        1.8025829548889756,
			        20,
			        0.999,
			        0.75,
			        4910,
			        0,
			        "None",
			        "i7183",
			        4910,
			        525.41015625,
			        523.0701144143002,
			        -1,
			        0.9722755120985209,
			        4698826
			    ],
			    [
			        1745519670,
			        1745525453,
			        5783,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 482 const 9.146605430082193 max_depth 8 threshold 0.001",
			        5000,
			        482,
			        9.146605430082193,
			        8,
			        0.001,
			        0.74,
			        5772,
			        0,
			        "None",
			        "i7185",
			        5772,
			        525.14453125,
			        522.8972636010362,
			        -1,
			        0.964230459805681,
			        4699181
			    ],
			    [
			        1745527051,
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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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 2.2319406244907154 max_depth 20 threshold 0.9397679594065287",
			        1,
			        1,
			        2.2319406244907154,
			        20,
			        0.9397679594065287,
			        0.74,
			        13,
			        0,
			        "None",
			        "i7175",
			        13,
			        523.97265625,
			        523.8583984375,
			        -1,
			        0.49996905749118137,
			        4702980
			    ],
			    [
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			        1745528146,
			        195,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 4.5944120482679915 max_depth 6 threshold 0.001",
			        1,
			        1,
			        4.5944120482679915,
			        6,
			        0.001,
			        0.73,
			        187,
			        0,
			        "None",
			        "i7181",
			        187,
			        523.91796875,
			        523.8934151785714,
			        -1,
			        0,
			        4703399
			    ],
			    [
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			        1745529115,
			        233,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4901 n_samples 132 const 0.001 max_depth 6 threshold 0.001",
			        4901,
			        132,
			        0.001,
			        6,
			        0.001,
			        0.74,
			        220,
			        0,
			        "None",
			        "i7181",
			        220,
			        524.5234375,
			        523.1984049479166,
			        -1,
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			        4703862
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			    [
			        1745529962,
			        1745529982,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1171 n_samples 4671 const 4.367189173034898 max_depth 11 threshold 0.001",
			        1171,
			        4671,
			        4.367189173034898,
			        11,
			        0.001,
			        0.73,
			        6,
			        0,
			        "None",
			        "i7185",
			        6,
			        524.71875,
			        524.5026041666666,
			        -1,
			        0.07246735565319636,
			        4704398
			    ],
			    [
			        1745531031,
			        1745531070,
			        39,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1753 n_samples 3782 const 10 max_depth 20 threshold 0.999",
			        1753,
			        3782,
			        10,
			        20,
			        0.999,
			        0.74,
			        30,
			        0,
			        "None",
			        "i7183",
			        30,
			        524.75,
			        524.662109375,
			        -1,
			        0.21696887183612848,
			        4704891
			    ],
			    [
			        1745531892,
			        1745531911,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1150 n_samples 4565 const 0.001 max_depth 13 threshold 0.001",
			        1150,
			        4565,
			        0.001,
			        13,
			        0.001,
			        0.73,
			        7,
			        0,
			        "None",
			        "i7183",
			        7,
			        524.38671875,
			        524.2317708333334,
			        -1,
			        0.07116777028281453,
			        4705301
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			    [
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			        1745532559,
			        45,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3611 n_samples 1082 const 7.852233822954185 max_depth 2 threshold 0.001",
			        3611,
			        1082,
			        7.852233822954185,
			        2,
			        0.001,
			        0.74,
			        36,
			        0,
			        "None",
			        "i7181",
			        36,
			        525.71875,
			        525.6022135416666,
			        -1,
			        0.8769725849371867,
			        4705596
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			    [
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			        1745534018,
			        46,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4614 n_samples 1323 const 0.001 max_depth 2 threshold 0.001",
			        4614,
			        1323,
			        0.001,
			        2,
			        0.001,
			        0.74,
			        35,
			        0,
			        "None",
			        "i7185",
			        35,
			        524.90234375,
			        524.8235677083334,
			        -1,
			        0.8797574107308621,
			        4706353
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			    [
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			        1745535547,
			        33,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1344 n_samples 326 const 10 max_depth 5 threshold 0.999",
			        1344,
			        326,
			        10,
			        5,
			        0.999,
			        0.75,
			        18,
			        0,
			        "None",
			        "i7182",
			        18,
			        525.25,
			        525.119140625,
			        -1,
			        0.9722136270808838,
			        4707105
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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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1059 const 4.949273649215842 max_depth 5 threshold 0.999",
			        1,
			        1059,
			        4.949273649215842,
			        5,
			        0.999,
			        0.73,
			        6,
			        0,
			        "None",
			        "i7183",
			        6,
			        525.453125,
			        525.27734375,
			        -1,
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			        4707431
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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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1174 const 2.6427585524363146 max_depth 4 threshold 0.999",
			        1,
			        1174,
			        2.6427585524363146,
			        4,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7181",
			        1,
			        525.5546875,
			        525.3815104166666,
			        -1,
			        0.00037131010582338014,
			        4707785
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			    [
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			        1745538647,
			        99,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1444 n_samples 604 const 9.212794908157784 max_depth 6 threshold 0.999",
			        1444,
			        604,
			        9.212794908157784,
			        6,
			        0.999,
			        0.74,
			        83,
			        0,
			        "None",
			        "i7185",
			        83,
			        524.4140625,
			        524.3544034090909,
			        -1,
			        0.9848381706788787,
			        4708602
			    ],
			    [
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			        1745540135,
			        692,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4779 n_samples 195 const 2.8339041830567657 max_depth 16 threshold 0.001",
			        4779,
			        195,
			        2.8339041830567657,
			        16,
			        0.001,
			        0.74,
			        683,
			        0,
			        "None",
			        "i7178",
			        683,
			        524.859375,
			        524.4131822183099,
			        -1,
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			        4709033
			    ],
			    [
			        1745540165,
			        1745540191,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2661 n_samples 155 const 2.626344440479946 max_depth 4 threshold 0.001",
			        2661,
			        155,
			        2.626344440479946,
			        4,
			        0.001,
			        0.75,
			        12,
			        0,
			        "None",
			        "i7182",
			        12,
			        525.84765625,
			        525.6767578125,
			        -1,
			        0.9824246549910267,
			        4709347
			    ],
			    [
			        1745541997,
			        1745542010,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1252 n_samples 1541 const 2.202930639091097 max_depth 1 threshold 0.999",
			        1252,
			        1541,
			        2.202930639091097,
			        1,
			        0.999,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7178",
			        2,
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			        524.27734375,
			        -1,
			        0.38740021040906,
			        4710216
			    ],
			    [
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			        1068,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1186 n_samples 552 const 6.481306332891947 max_depth 20 threshold 0.999",
			        1186,
			        552,
			        6.481306332891947,
			        20,
			        0.999,
			        0.74,
			        1058,
			        0,
			        "None",
			        "i7180",
			        1058,
			        524.22265625,
			        522.1049985532408,
			        -1,
			        0.9633640695587599,
			        4709741
			    ],
			    [
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			        1745546308,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3871 n_samples 557 const 10 max_depth 2 threshold 0.999",
			        3871,
			        557,
			        10,
			        2,
			        0.999,
			        0.75,
			        4,
			        0,
			        "None",
			        "i7183",
			        4,
			        524.140625,
			        523.9622395833334,
			        -1,
			        0.9651587350702395,
			        4712299
			    ],
			    [
			        1745547876,
			        1745547889,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 960 n_samples 1646 const 8.744361792471672 max_depth 1 threshold 0.001",
			        960,
			        1646,
			        8.744361792471672,
			        1,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7182",
			        1,
			        524.078125,
			        523.8645833333334,
			        -1,
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			        4713075
			    ],
			    [
			        1745549316,
			        1745549329,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3937 n_samples 528 const 6.4027003063333385 max_depth 1 threshold 0.999",
			        3937,
			        528,
			        6.4027003063333385,
			        1,
			        0.999,
			        0.75,
			        5,
			        0,
			        "None",
			        "i7180",
			        5,
			        524.44140625,
			        524.2669270833334,
			        -1,
			        0.9802586793737236,
			        4713723
			    ],
			    [
			        1745549936,
			        1745549956,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2578 n_samples 189 const 10 max_depth 5 threshold 0.09956569354123923",
			        2578,
			        189,
			        10,
			        5,
			        0.09956569354123923,
			        0.75,
			        10,
			        0,
			        "None",
			        "i7182",
			        10,
			        525.19921875,
			        525.068359375,
			        -1,
			        0.9845287455906925,
			        4714009
			    ],
			    [
			        1745550777,
			        1745550877,
			        100,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4108 n_samples 1004 const 4.56940400201298 max_depth 2 threshold 0.001",
			        4108,
			        1004,
			        4.56940400201298,
			        2,
			        0.001,
			        0.74,
			        88,
			        0,
			        "None",
			        "i7185",
			        88,
			        524.14453125,
			        524.0802556818181,
			        -1,
			        0.993006993006993,
			        4714390
			    ],
			    [
			        1745551557,
			        1745551570,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 3951 const 5.480567963475388 max_depth 4 threshold 0.001",
			        5000,
			        3951,
			        5.480567963475388,
			        4,
			        0.001,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7186",
			        2,
			        525.40625,
			        525.24609375,
			        -1,
			        0.6188501763723002,
			        4714751
			    ],
			    [
			        1745552647,
			        1745552660,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 4015 const 0.001 max_depth 1 threshold 0.001",
			        5000,
			        4015,
			        0.001,
			        1,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7178",
			        1,
			        524.546875,
			        524.37109375,
			        -1,
			        0.6188501763723002,
			        4715268
			    ],
			    [
			        1745554617,
			        1745554630,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2645 n_samples 205 const 1.1949277430553469 max_depth 1 threshold 0.999",
			        2645,
			        205,
			        1.1949277430553469,
			        1,
			        0.999,
			        0.75,
			        4,
			        0,
			        "None",
			        "i7183",
			        4,
			        525.1640625,
			        524.953125,
			        -1,
			        0.9895414320193081,
			        4716207
			    ],
			    [
			        1745555437,
			        1745555602,
			        165,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4075 n_samples 3335 const 10 max_depth 17 threshold 0.001",
			        4075,
			        3335,
			        10,
			        17,
			        0.001,
			        0.73,
			        25,
			        0,
			        "None",
			        "i7178",
			        25,
			        525.08984375,
			        524.996875,
			        -1,
			        0.5043628937434247,
			        4716618
			    ],
			    [
			        1745556429,
			        1745556482,
			        53,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 3557 const 8.291559068299 max_depth 20 threshold 0.999",
			        5000,
			        3557,
			        8.291559068299,
			        20,
			        0.999,
			        0.74,
			        42,
			        0,
			        "None",
			        "i7180",
			        42,
			        524.9140625,
			        524.8387276785714,
			        -1,
			        0.6188501763723002,
			        4717084
			    ],
			    [
			        1745557419,
			        1745557446,
			        27,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 0.9490514255205863 max_depth 16 threshold 0.999",
			        1,
			        1,
			        0.9490514255205863,
			        16,
			        0.999,
			        0.74,
			        16,
			        0,
			        "None",
			        "i7180",
			        16,
			        524.2890625,
			        524.1728515625,
			        -1,
			        0.49996905749118137,
			        4717517
			    ],
			    [
			        1745561800,
			        1745561826,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 2.1697327084897227 max_depth 13 threshold 0.999",
			        1,
			        1,
			        2.1697327084897227,
			        13,
			        0.999,
			        0.74,
			        14,
			        0,
			        "None",
			        "i7183",
			        14,
			        525.00390625,
			        524.8515625,
			        -1,
			        0.49996905749118137,
			        4719450
			    ],
			    [
			        1745562881,
			        1745562934,
			        53,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 2746 const 8.08071682440557 max_depth 3 threshold 0.001",
			        5000,
			        2746,
			        8.08071682440557,
			        3,
			        0.001,
			        0.73,
			        39,
			        0,
			        "None",
			        "i7185",
			        39,
			        524.94921875,
			        524.8326822916666,
			        -1,
			        0.6188501763723002,
			        4719941
			    ],
			    [
			        1745564099,
			        1745564113,
			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2036 n_samples 3089 const 10 max_depth 1 threshold 0.001",
			        2036,
			        3089,
			        10,
			        1,
			        0.001,
			        0.72,
			        1,
			        0,
			        "None",
			        "i7181",
			        1,
			        525.87890625,
			        525.7200520833334,
			        -1,
			        0.25199579181880066,
			        4720522
			    ],
			    [
			        1745569182,
			        1745569195,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1249 n_samples 2244 const 0.001 max_depth 3 threshold 0.001",
			        1249,
			        2244,
			        0.001,
			        3,
			        0.001,
			        0.74,
			        1,
			        0,
			        "None",
			        "i7170",
			        1,
			        525.46484375,
			        525.2890625,
			        -1,
			        0.2318831610867009,
			        4722863
			    ],
			    [
			        1745570040,
			        1745570067,
			        27,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 7.891611466037118 max_depth 1 threshold 0.999",
			        1,
			        1,
			        7.891611466037118,
			        1,
			        0.999,
			        0.74,
			        14,
			        0,
			        "None",
			        "i7186",
			        14,
			        525.2109375,
			        525.068359375,
			        -1,
			        0.49996905749118137,
			        4723247
			    ],
			    [
			        1745571222,
			        1745571235,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4557 const 9.930702701871775 max_depth 5 threshold 0.001",
			        1,
			        4557,
			        9.930702701871775,
			        5,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7182",
			        1,
			        525.2265625,
			        524.9986979166666,
			        -1,
			        6.188501763723002e-5,
			        4723785
			    ],
			    [
			        1745572306,
			        1745572319,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 715 n_samples 3264 const 10 max_depth 4 threshold 0.999",
			        715,
			        3264,
			        10,
			        4,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7185",
			        1,
			        524.40625,
			        524.23046875,
			        -1,
			        0.08849557522123894,
			        4724234
			    ],
			    [
			        1745573402,
			        1745573415,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3660 n_samples 2442 const 10 max_depth 5 threshold 0.999",
			        3660,
			        2442,
			        10,
			        5,
			        0.999,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7185",
			        2,
			        524.578125,
			        524.40234375,
			        -1,
			        0.6794974936567857,
			        4724717
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			    [
			        1745574402,
			        1745574415,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4768 const 0.49731331739402246 max_depth 4 threshold 0.001",
			        1,
			        4768,
			        0.49731331739402246,
			        4,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7185",
			        1,
			        524.86328125,
			        524.6875,
			        -1,
			        6.188501763723002e-5,
			        4725173
			    ],
			    [
			        1745575583,
			        1745575672,
			        89,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4318 n_samples 4188 const 0.001 max_depth 5 threshold 0.001",
			        4318,
			        4188,
			        0.001,
			        5,
			        0.001,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7178",
			        2,
			        523.89453125,
			        523.625,
			        -1,
			        0.26721950615755924,
			        4725732
			    ],
			    [
			        1745576863,
			        1745576876,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4371 const 0.001 max_depth 6 threshold 0.001",
			        1,
			        4371,
			        0.001,
			        6,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7186",
			        1,
			        524.5390625,
			        524.3645833333334,
			        -1,
			        6.188501763723002e-5,
			        4726280
			    ],
			    [
			        1745578182,
			        1745578195,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 534 n_samples 4640 const 2.972155706032389 max_depth 2 threshold 0.999",
			        534,
			        4640,
			        2.972155706032389,
			        2,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7183",
			        1,
			        525.96875,
			        525.7578125,
			        -1,
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			        4726858
			    ],
			    [
			        1745579352,
			        1745579365,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 725 n_samples 2224 const 10 max_depth 1 threshold 0.001",
			        725,
			        2224,
			        10,
			        1,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7181",
			        1,
			        525.32421875,
			        525.1510416666666,
			        -1,
			        0.13459991336097532,
			        4727410
			    ],
			    [
			        1745581923,
			        1745581943,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2441 n_samples 2742 const 7.189347985104828 max_depth 3 threshold 0.999",
			        2441,
			        2742,
			        7.189347985104828,
			        3,
			        0.999,
			        0.73,
			        11,
			        0,
			        "None",
			        "i7185",
			        11,
			        524.2421875,
			        524.1240234375,
			        -1,
			        0.302122656104957,
			        4728520
			    ],
			    [
			        1745583183,
			        1745583210,
			        27,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1518 n_samples 4480 const 8.144696216793449 max_depth 16 threshold 0.001",
			        1518,
			        4480,
			        8.144696216793449,
			        16,
			        0.001,
			        0.73,
			        14,
			        0,
			        "None",
			        "i7185",
			        14,
			        525.1328125,
			        525.0009765625,
			        -1,
			        0.09394145677331518,
			        4729076
			    ],
			    [
			        1745584243,
			        1745584256,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1142 n_samples 4841 const 1.472729736118242 max_depth 7 threshold 0.001",
			        1142,
			        4841,
			        1.472729736118242,
			        7,
			        0.001,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7181",
			        1,
			        525.12890625,
			        524.9557291666666,
			        -1,
			        0.07067269014171669,
			        4729547
			    ],
			    [
			        1745585603,
			        1745585623,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 4110 const 10 max_depth 16 threshold 0.999",
			        5000,
			        4110,
			        10,
			        16,
			        0.999,
			        0.73,
			        11,
			        0,
			        "None",
			        "i7181",
			        11,
			        524.84765625,
			        524.7177734375,
			        -1,
			        0.3094250881861501,
			        4730140
			    ],
			    [
			        1745586725,
			        1745586896,
			        171,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3013 n_samples 2862 const 1.0732334621521118 max_depth 4 threshold 0.001",
			        3013,
			        2862,
			        1.0732334621521118,
			        4,
			        0.001,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7178",
			        2,
			        467.0390625,
			        466.6822916666667,
			        -1,
			        0.3729191162819481,
			        4730635
			    ],
			    [
			        1745587984,
			        1745588011,
			        27,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 9.66365197273571 max_depth 4 threshold 0.999",
			        1,
			        1,
			        9.66365197273571,
			        4,
			        0.999,
			        0.74,
			        14,
			        0,
			        "None",
			        "i7182",
			        14,
			        525.39453125,
			        525.279296875,
			        -1,
			        0.49996905749118137,
			        4731189
			    ],
			    [
			        1745589104,
			        1745589124,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4098 n_samples 3950 const 6.248205559626692 max_depth 10 threshold 0.999",
			        4098,
			        3950,
			        6.248205559626692,
			        10,
			        0.999,
			        0.73,
			        6,
			        0,
			        "None",
			        "i7186",
			        6,
			        524.1171875,
			        523.8815104166666,
			        -1,
			        0.5072096045547373,
			        4731652
			    ],
			    [
			        1745590231,
			        1745590244,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4579 const 9.391628592715517 max_depth 1 threshold 0.999",
			        1,
			        4579,
			        9.391628592715517,
			        1,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7185",
			        1,
			        524.52734375,
			        524.2994791666666,
			        -1,
			        6.188501763723002e-5,
			        4732120
			    ],
			    [
			        1745600088,
			        1745600101,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 3443 const 4.034236304365566 max_depth 8 threshold 0.999",
			        1,
			        3443,
			        4.034236304365566,
			        8,
			        0.999,
			        0.73,
			        3,
			        0,
			        "None",
			        "i7185",
			        3,
			        524.875,
			        524.6653645833334,
			        -1,
			        0.00012377003527446005,
			        4736228
			    ],
			    [
			        1745601410,
			        1745601442,
			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 145 const 5.199096058573954 max_depth 1 threshold 0.10676508308058826",
			        5000,
			        145,
			        5.199096058573954,
			        1,
			        0.10676508308058826,
			        0.74,
			        19,
			        0,
			        "None",
			        "i7186",
			        19,
			        526.19140625,
			        524.646484375,
			        -1,
			        0.9873754564020051,
			        4736759
			    ],
			    [
			        1745602488,
			        1745602515,
			        27,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4390 const 0.001 max_depth 17 threshold 0.999",
			        1,
			        4390,
			        0.001,
			        17,
			        0.999,
			        0.73,
			        17,
			        0,
			        "None",
			        "i7185",
			        17,
			        524.1015625,
			        523.9697265625,
			        -1,
			        6.188501763723002e-5,
			        4737175
			    ],
			    [
			        1745603748,
			        1745603781,
			        33,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 4720 const 8.822659783425578 max_depth 20 threshold 0.001",
			        1,
			        4720,
			        8.822659783425578,
			        20,
			        0.001,
			        0.73,
			        20,
			        0,
			        "None",
			        "i7180",
			        20,
			        524.84765625,
			        522.9404296875,
			        -1,
			        6.188501763723002e-5,
			        4737706
			    ],
			    [
			        1745604978,
			        1745605005,
			        27,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 3.8056327983972906 max_depth 10 threshold 0.999",
			        1,
			        1,
			        3.8056327983972906,
			        10,
			        0.999,
			        0.74,
			        16,
			        0,
			        "None",
			        "i7185",
			        16,
			        524.8046875,
			        524.6884765625,
			        -1,
			        0.49996905749118137,
			        4738254
			    ],
			    [
			        1745606208,
			        1745606228,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3802 n_samples 3400 const 10 max_depth 10 threshold 0.001",
			        3802,
			        3400,
			        10,
			        10,
			        0.001,
			        0.74,
			        7,
			        0,
			        "None",
			        "i7178",
			        7,
			        524.359375,
			        524.2018229166666,
			        -1,
			        0.4705736741134971,
			        4738785
			    ],
			    [
			        1745607410,
			        1745607423,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3161 n_samples 4188 const 2.2380672206458776 max_depth 4 threshold 0.001",
			        3161,
			        4188,
			        2.2380672206458776,
			        4,
			        0.001,
			        0.72,
			        1,
			        0,
			        "None",
			        "i7185",
			        1,
			        524.609375,
			        524.43359375,
			        -1,
			        0.19561854075128413,
			        4739275
			    ],
			    [
			        1745608591,
			        1745608604,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1712 n_samples 4545 const 5.501348292712335 max_depth 1 threshold 0.999",
			        1712,
			        4545,
			        5.501348292712335,
			        1,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7176",
			        1,
			        525.39453125,
			        525.1809895833334,
			        -1,
			        0.1059471501949378,
			        4739770
			    ],
			    [
			        1745611611,
			        1745611624,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4 n_samples 4087 const 7.462609427801542 max_depth 8 threshold 0.999",
			        4,
			        4087,
			        7.462609427801542,
			        8,
			        0.999,
			        0.73,
			        1,
			        0,
			        "None",
			        "i7004",
			        1,
			        524.96484375,
			        524.8072916666666,
			        -1,
			        0.0002475400705489201,
			        4741854
			    ],
			    [
			        1745612411,
			        1745612437,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 3530 const 10 max_depth 15 threshold 0.001",
			        5000,
			        3530,
			        10,
			        15,
			        0.001,
			        0.74,
			        18,
			        0,
			        "None",
			        "i7181",
			        18,
			        525.3359375,
			        525.220703125,
			        -1,
			        0.6188501763723002,
			        4742394
			    ],
			    [
			        1745613892,
			        1745613905,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 3510 const 0.001 max_depth 5 threshold 0.999",
			        5000,
			        3510,
			        0.001,
			        5,
			        0.999,
			        0.74,
			        2,
			        0,
			        "None",
			        "i7183",
			        2,
			        525.6796875,
			        525.5247395833334,
			        -1,
			        0.6188501763723002,
			        4743024
			    ],
			    [
			        1745616211,
			        1745616231,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3224 n_samples 4684 const 6.137911707715111 max_depth 13 threshold 0.001",
			        3224,
			        4684,
			        6.137911707715111,
			        13,
			        0.001,
			        0.73,
			        7,
			        0,
			        "None",
			        "i7180",
			        7,
			        524.7109375,
			        524.53515625,
			        -1,
			        0.1995172968624296,
			        4744012
			    ],
			    [
			        1745617453,
			        1745617466,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4859 n_samples 3551 const 2.143282906674681 max_depth 1 threshold 0.010946654628968804",
			        4859,
			        3551,
			        2.143282906674681,
			        1,
			        0.010946654628968804,
			        0.74,
			        1,
			        0,
			        "None",
			        "i7186",
			        1,
			        525.4453125,
			        524.3828125,
			        -1,
			        0.6013986013986014,
			        4744490
			    ],
			    [
			        1745618552,
			        1745618578,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1980 n_samples 213 const 8.912222043666304 max_depth 7 threshold 0.999",
			        1980,
			        213,
			        8.912222043666304,
			        7,
			        0.999,
			        0.74,
			        14,
			        0,
			        "None",
			        "i7183",
			        14,
			        524.8984375,
			        524.7236328125,
			        -1,
			        0.9754316479980196,
			        4744937
			    ],
			    [
			        1745619692,
			        1745619706,
			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 3550 const 7.6411536963603455 max_depth 3 threshold 0.001",
			        5000,
			        3550,
			        7.6411536963603455,
			        3,
			        0.001,
			        0.74,
			        2,
			        0,
			        "None",
			        "i7183",
			        2,
			        524.85546875,
			        524.63671875,
			        -1,
			        0.6188501763723002,
			        4745412
			    ],
			    [
			        1745621092,
			        1745621105,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4381 n_samples 2371 const 4.993916676264071 max_depth 2 threshold 0.001",
			        4381,
			        2371,
			        4.993916676264071,
			        2,
			        0.001,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7181",
			        2,
			        524.2578125,
			        524.1041666666666,
			        -1,
			        0.8133547868061143,
			        4746001
			    ],
			    [
			        1745622072,
			        1745622111,
			        39,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 189 const 10 max_depth 20 threshold 0.999",
			        5000,
			        189,
			        10,
			        20,
			        0.999,
			        0.74,
			        28,
			        0,
			        "None",
			        "i7180",
			        28,
			        525.53125,
			        525.42578125,
			        -1,
			        0.9824865400086639,
			        4746380
			    ],
			    [
			        1745623113,
			        1745623126,
			        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 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3811 n_samples 1887 const 2.860391486320654 max_depth 3 threshold 0.001",
			        3811,
			        1887,
			        2.860391486320654,
			        3,
			        0.001,
			        0.73,
			        2,
			        0,
			        "None",
			        "i7181",
			        2,
			        525.54296875,
			        525.3684895833334,
			        -1,
			        0.9342162262516245,
			        4746822
			    ],
			    [
			        1745624475,
			        1745624507,
			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 NOAAWeather 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 5000 const 8.89059710567169 max_depth 19 threshold 0.001",
			        5000,
			        5000,
			        8.89059710567169,
			        19,
			        0.001,
			        0.73,
			        21,
			        0,
			        "None",
			        "i7178",
			        21,
			        525.38671875,
			        525.2453125,
			        -1,
			        0.3094250881861501,
			        4747398
			    ],
			    [
			        1745625623,
			        1745625636,
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			        5000,
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			        1,
			        5
			    ],
			    [
			        1745643837.384431,
			        20,
			        2,
			        10
			    ],
			    [
			        1745643850.837762,
			        20,
			        2,
			        10
			    ],
			    [
			        1745644864.7409012,
			        20,
			        2,
			        10
			    ],
			    [
			        1745644865.0140326,
			        20,
			        2,
			        10
			    ],
			    [
			        1745644866.3806093,
			        20,
			        3,
			        15
			    ],
			    [
			        1745644884.233663,
			        20,
			        3,
			        15
			    ],
			    [
			        1745646144.6525068,
			        20,
			        3,
			        15
			    ],
			    [
			        1745646144.9538436,
			        20,
			        3,
			        15
			    ],
			    [
			        1745646146.327002,
			        20,
			        4,
			        20
			    ],
			    [
			        1745646155.824462,
			        20,
			        4,
			        20
			    ],
			    [
			        1745646170.447335,
			        20,
			        3,
			        15
			    ],
			    [
			        1745646170.5315163,
			        20,
			        3,
			        15
			    ],
			    [
			        1745647662.022274,
			        20,
			        3,
			        15
			    ],
			    [
			        1745647662.3216147,
			        20,
			        3,
			        15
			    ],
			    [
			        1745647664.0401554,
			        20,
			        4,
			        20
			    ],
			    [
			        1745647673.7260478,
			        20,
			        4,
			        20
			    ],
			    [
			        1745647687.3800259,
			        20,
			        3,
			        15
			    ],
			    [
			        1745647687.4630015,
			        20,
			        3,
			        15
			    ],
			    [
			        1745648761.3800504,
			        20,
			        3,
			        15
			    ],
			    [
			        1745648762.1993496,
			        20,
			        3,
			        15
			    ],
			    [
			        1745648763.593347,
			        20,
			        4,
			        20
			    ],
			    [
			        1745648773.0651019,
			        20,
			        4,
			        20
			    ],
			    [
			        1745648787.9081523,
			        20,
			        3,
			        15
			    ],
			    [
			        1745648788.0831513,
			        20,
			        3,
			        15
			    ]
			];
			var tab_main_worker_cpu_ram_csv_json = [
			    [
			        1745405819,
			        646.6015625,
			        39.3
			    ],
			    [
			        1745405819,
			        646.60546875,
			        34.5
			    ],
			    [
			        1745405819,
			        646.73046875,
			        35.8
			    ],
			    [
			        1745405819,
			        646.73046875,
			        31.7
			    ],
			    [
			        1745405819,
			        646.73046875,
			        39
			    ],
			    [
			        1745405819,
			        646.73046875,
			        33.3
			    ],
			    [
			        1745405819,
			        646.73046875,
			        42.5
			    ],
			    [
			        1745406633,
			        700.96875,
			        39.4
			    ],
			    [
			        1745406633,
			        700.96875,
			        36.2
			    ],
			    [
			        1745406633,
			        700.96875,
			        39.8
			    ],
			    [
			        1745406633,
			        700.96875,
			        26.7
			    ],
			    [
			        1745418370,
			        910.01171875,
			        34.1
			    ],
			    [
			        1745418370,
			        910.01171875,
			        29.5
			    ],
			    [
			        1745418370,
			        910.01171875,
			        29.4
			    ],
			    [
			        1745418370,
			        910.01171875,
			        31.4
			    ],
			    [
			        1745437028,
			        961.546875,
			        37.1
			    ],
			    [
			        1745437028,
			        961.546875,
			        29.4
			    ],
			    [
			        1745437028,
			        961.546875,
			        28.1
			    ],
			    [
			        1745437028,
			        961.546875,
			        28
			    ],
			    [
			        1745455398,
			        938.3671875,
			        35.6
			    ],
			    [
			        1745455398,
			        938.3671875,
			        33.9
			    ],
			    [
			        1745455398,
			        938.3671875,
			        33.5
			    ],
			    [
			        1745455398,
			        938.3671875,
			        38.5
			    ],
			    [
			        1745478080,
			        923.56640625,
			        35.5
			    ],
			    [
			        1745478080,
			        923.56640625,
			        36.5
			    ],
			    [
			        1745478080,
			        923.56640625,
			        36.3
			    ],
			    [
			        1745478080,
			        923.56640625,
			        25.8
			    ],
			    [
			        1745499646,
			        926.51953125,
			        31.3
			    ],
			    [
			        1745499646,
			        926.51953125,
			        26
			    ],
			    [
			        1745499646,
			        926.51953125,
			        25
			    ],
			    [
			        1745499646,
			        926.51953125,
			        31.4
			    ],
			    [
			        1745521823,
			        1020.203125,
			        29.9
			    ],
			    [
			        1745521823,
			        1020.203125,
			        26.8
			    ],
			    [
			        1745521823,
			        1020.203125,
			        29.4
			    ],
			    [
			        1745521823,
			        1020.203125,
			        30
			    ],
			    [
			        1745542028,
			        1023.43359375,
			        30.7
			    ],
			    [
			        1745542028,
			        1023.43359375,
			        36.8
			    ],
			    [
			        1745542028,
			        1023.43359375,
			        35.4
			    ],
			    [
			        1745542028,
			        1023.43359375,
			        37.8
			    ],
			    [
			        1745564134,
			        1287.953125,
			        30.5
			    ],
			    [
			        1745564134,
			        1287.953125,
			        29.9
			    ],
			    [
			        1745564134,
			        1287.953125,
			        28.7
			    ],
			    [
			        1745564134,
			        1287.953125,
			        30
			    ],
			    [
			        1745591436,
			        1027.765625,
			        32.9
			    ],
			    [
			        1745591436,
			        1027.765625,
			        25.5
			    ],
			    [
			        1745591436,
			        1027.765625,
			        24.4
			    ],
			    [
			        1745591436,
			        1027.765625,
			        26.1
			    ],
			    [
			        1745623148,
			        1055.0390625,
			        32.6
			    ],
			    [
			        1745623148,
			        1055.0390625,
			        32.6
			    ],
			    [
			        1745623148,
			        1055.0390625,
			        32.2
			    ],
			    [
			        1745623148,
			        1055.0390625,
			        35.6
			    ],
			    [
			        1745648807,
			        1086.02734375,
			        31.6
			    ],
			    [
			        1745648807,
			        1086.02734375,
			        30.8
			    ],
			    [
			        1745648807,
			        1086.02734375,
			        31.2
			    ],
			    [
			        1745648807,
			        1086.02734375,
			        31.5
			    ]
			];
			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();

				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>50402 </td></tr><tr><td> Max. nr. evaluations (from arguments)</td><td>50000 </td></tr><tr><td> Number random steps</td><td>20    </td></tr><tr><td> Nr. of workers (parameter)</td><td>20    </td></tr><tr><td> Main process memory (GB)</td><td>8     </td></tr><tr><td> Worker memory (GB)</td><td>1     </td></tr><tr><td> Nr. imported jobs</td><td>402   </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>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>int</td><td>No         </td></tr><tr><td> n_samples</td><td>int</td><td>1</td><td>5000</td><td>int</td><td>No         </td></tr><tr><td> const</td><td>float</td><td>0.001</td><td>10</td><td>float</td><td>No         </td></tr><tr><td> max_depth</td><td>int</td><td>1</td><td>20</td><td>int</td><td>No         </td></tr><tr><td> threshold</td><td>float</td><td>0.001</td><td>0.999</td><td>float</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>115</td>
			<td>586</td>
			<td>125</td>
			<td>826</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,const,max_depth,threshold
0,0_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,3727.000000000000000000000000000000,3947,4776,2.426852101355791102577086348902,15,0.911944730758666999292927357601
1,1_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,64.000000000000000000000000000000,477,1294,7.059569869057275681711871584412,3,0.115188466887921101000635815126
2,2_0,RUNNING,Sobol,GenerationStep_0,,,2324,2937,4.984084343263879723906484286999,19,0.261974101036787032636965477650
3,3_0,COMPLETED,Sobol,GenerationStep_0,0.739999999999999991118215802999,606.000000000000000000000000000000,3214,627,9.578959563182666059333314478863,6,0.710763393921777564266051285813
4,4_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,3.000000000000000000000000000000,2575,3461,5.133793137258850336479554243851,9,0.530782207138836392701364275126
5,5_0,COMPLETED,Sobol,GenerationStep_0,0.739999999999999991118215802999,209.000000000000000000000000000000,1597,145,0.382688906221650582129001350040,16,0.449386862760409733219546524197
6,6_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,1.000000000000000000000000000000,1234,4122,7.613523780889809522420819121180,5,0.175444864289835106285764254608
7,7_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,4048.000000000000000000000000000000,4637,1978,2.824566339492797784060940102790,13,0.844500157017260777436717944511
8,8_0,RUNNING,Sobol,GenerationStep_0,,,4758,2549,8.211337752038613047034232295118,18,0.028722819142043592294255560660
9,9_0,RUNNING,Sobol,GenerationStep_0,,,662,1032,3.461448887171223898207017555251,10,0.952177326759323428007064649137
10,10_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,6.000000000000000000000000000000,1552,4388,5.771207156204619437289693451021,12,0.672601819606497897119368190033
11,11_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,2.000000000000000000000000000000,3067,1698,0.981034646631218532064622195321,4,0.346612363640218967830719520862
12,12_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,1.000000000000000000000000000000,3639,3904,4.347280614845455026795661979122,1,0.411589822629466639547501927154
13,13_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,651.000000000000000000000000000000,2036,2220,8.981224113300442013496649451554,14,0.615786892589181622525984494132
14,14_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,2.000000000000000000000000000000,131,3242,1.828427904865704389791858375247,8,0.757424419209361099447619380953
15,15_0,RUNNING,Sobol,GenerationStep_0,,,4158,386,6.422077030851506052044896932784,20,0.215069304434582581064816508842
16,16_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,2.000000000000000000000000000000,4271,3325,3.330138141836971321652072219877,4,0.748226797904819274087628855341
17,17_0,COMPLETED,Sobol,GenerationStep_0,0.719999999999999973354647408996,1803.000000000000000000000000000000,173,557,8.733042489860206814000775921158,12,0.294560630628839137390428959407
18,18_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,1436.000000000000000000000000000000,1998,3908,0.809602960999123766328011697624,9,0.082719816980883473234165137455
19,19_0,RUNNING,Sobol,GenerationStep_0,,,3520,2468,6.177105988535099179159715276910,16,0.874606845691800138808957854053
20,20_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,13.000000000000000000000000000000,2881,4676,9.074495104392990896258197608404,18,0.819219392528757417437645926839
21,21_0,COMPLETED,Sobol,GenerationStep_0,0.739999999999999991118215802999,421.000000000000000000000000000000,1271,1742,3.863231815738603636134484986542,6,0.145294835828244678355147811999
22,22_0,RUNNING,Sobol,GenerationStep_0,,,930,2760,6.631472184409387793380119546782,15,0.479418966922909028927080044014
23,23_0,COMPLETED,Sobol,GenerationStep_0,0.750000000000000000000000000000,11.000000000000000000000000000000,4960,1154,1.384796847504563555020240528393,2,0.555937481524422794088025057135
24,24_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,1.000000000000000000000000000000,4437,4313,7.229741452257149703086724912282,7,0.317189360594376912327874151742
25,25_0,RUNNING,Sobol,GenerationStep_0,,,964,2081,2.022134691881947166081090472289,20,0.648055705823004202237314075319
26,26_0,COMPLETED,Sobol,GenerationStep_0,0.739999999999999991118215802999,1.000000000000000000000000000000,1871,3730,9.711453752141446571499727724586,1,0.976605337787419536255129060009
27,27_0,COMPLETED,Sobol,GenerationStep_0,0.750000000000000000000000000000,217.000000000000000000000000000000,2768,170,4.461122184950858660101857822156,13,0.058020034657791257071135504475
28,28_0,RUNNING,Sobol,GenerationStep_0,,,3340,3000,0.172034022723324603321159997904,11,0.247903403114527465245231496738
29,29_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,38.000000000000000000000000000000,2355,934,5.578605328795500639671445242129,3,0.795127796733751890378982807306
30,30_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,37.000000000000000000000000000000,3459,233,4.859764101842775296802301454591,6,0.046773648931516274840358704523
31,31_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,219.000000000000000000000000000000,2921,1,4.438542062683248801135960093234,18,0.001000000000000000020816681712
32,32_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4176,3135,0.871784182632703963378162370645,1,0.731029952627939905163145795086
33,33_0,FAILED,BoTorch,GenerationStep_1,,,1826,2323,4.083102594671124130343287106371,19,0.001000000000000000020816681712
34,34_0,FAILED,BoTorch,GenerationStep_1,,,4129,1512,3.732983422048613331156730055227,20,0.023414483120109356312221748908
35,35_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,810,5000,9.447953651288884202585904859006,6,0.998999999999999999111821580300
36,36_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,172.000000000000000000000000000000,4893,1,1.820662893179034735879895379185,4,0.352538460450783797117679796429
37,37_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1,297,9.729088664446484457926089817192,3,0.998999999999999999111821580300
38,38_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,402.000000000000000000000000000000,4223,168,4.642425954750168060058967967052,9,0.001000000000000000020816681712
39,39_0,FAILED,BoTorch,GenerationStep_1,,,123,2090,0.271247538805505783443550171796,20,0.396143042827735603061967140093
40,40_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,6891.000000000000000000000000000000,3501,2487,5.202348421245671872270577296149,10,0.001000000000000000020816681712
41,41_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,11.000000000000000000000000000000,2835,4716,4.900992539832213168438102002256,16,0.034303712692629467762017725363
42,42_0,FAILED,BoTorch,GenerationStep_1,,,1,1124,0.001000000000000000020816681712,15,0.607716774471307785709939253138
43,43_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,179.000000000000000000000000000000,2818,379,4.760956304235917535550015600165,2,0.001000000000000000020816681712
44,44_0,FAILED,BoTorch,GenerationStep_1,,,280,3787,4.732724231286397298390511423349,18,0.998999999999999999111821580300
45,45_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,205.000000000000000000000000000000,5000,1,4.423867155395075911883395747282,14,0.001000000000000000020816681712
46,46_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3528,5000,0.001000000000000000020816681712,1,0.913083696838623559699499310227
47,47_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4535,5000,9.555187280847842856701390701346,1,0.998999999999999999111821580300
48,48_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,5000,4973,1.411825813895269066122750700742,4,0.419389501328674085822711958826
49,49_0,FAILED,BoTorch,GenerationStep_1,,,2503,407,4.205522438276515728716731246095,20,0.173417428532577577726669915137
50,50_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,29.000000000000000000000000000000,498,3567,10.000000000000000000000000000000,20,0.998999999999999999111821580300
51,51_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,54.000000000000000000000000000000,606,4287,6.993981165201246241736043884885,20,0.998999999999999999111821580300
52,52_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1757.000000000000000000000000000000,1575,219,4.156578069447977696881935116835,17,0.001000000000000000020816681712
53,53_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,5000,4970,1.116429832258156729096754133934,1,0.647294155775218538195758810616
54,54_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2887,4670,10.000000000000000000000000000000,1,0.562329068550748600863187220966
55,55_0,FAILED,BoTorch,GenerationStep_1,,,2061,4753,3.262549297392496328029665164649,20,0.268007701674113862910786565408
56,56_0,FAILED,BoTorch,GenerationStep_1,,,902,2157,3.484140943478941210287302965298,20,0.028308060882234954702685314487
57,57_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3508.000000000000000000000000000000,730,4478,5.418289989864391031915147323161,20,0.001000000000000000020816681712
58,58_0,FAILED,BoTorch,GenerationStep_1,,,1185,3862,4.866153822538765716387842985569,17,0.296207850141983697334069347562
59,59_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,12.000000000000000000000000000000,1,1,4.687756077228742768170377530623,14,0.998999999999999999111821580300
60,60_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,3160,1152,1.013936259713969167961522543919,1,0.434165160297506569353487293483
61,61_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,112.000000000000000000000000000000,2709,191,4.548646604030056828094075171975,15,0.457041815054654632088926291544
62,62_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,16.000000000000000000000000000000,2416,174,3.652114975493442106113661793643,8,0.424751957750007513858747643098
63,63_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,30.000000000000000000000000000000,1087,1760,4.154732290604834510361342836404,3,0.508698157869207800985122958082
64,64_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,3.000000000000000000000000000000,4004,1147,0.001000000000000000020816681712,1,0.300525006754298718014695168677
65,65_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,32.000000000000000000000000000000,1501,165,4.850998767071728501321103976807,12,0.787540035366897872570746130805
66,66_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,5.000000000000000000000000000000,1684,3741,10.000000000000000000000000000000,10,0.726275664539537402220048534218
67,67_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,433.000000000000000000000000000000,3519,205,3.780902361426735414084987496608,20,0.325834294889829345631682144813
68,68_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,59.000000000000000000000000000000,1344,174,5.115067689930387295760283450363,20,0.826591769371103235464204317395
69,69_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,3698,1174,3.232721768402695428079596240423,1,0.483687747137744317171836883062
70,70_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,52.000000000000000000000000000000,2057,188,4.395011550377971865088966296753,15,0.936777192972326422371054377436
71,71_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,7.000000000000000000000000000000,759,147,3.736274106251862292538135079667,4,0.594308368328731906338191492978
72,72_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,13.000000000000000000000000000000,1508,186,3.602691753020237364069089380791,8,0.998999999999999999111821580300
73,73_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2319,1171,0.001000000000000000020816681712,1,0.445916788700499056030679412288
74,74_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,29.000000000000000000000000000000,2989,199,1.635974091803096719388577184873,8,0.524444281144226143176467758167
75,75_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,139.000000000000000000000000000000,476,1792,5.470243294041359227719567570603,5,0.645341581372832928664706741984
76,76_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,3.000000000000000000000000000000,5000,1151,0.001000000000000000020816681712,1,0.998999999999999999111821580300
77,77_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,657,1790,1.056741089232478669757142597518,4,0.521606618686266787499050678889
78,78_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,57.000000000000000000000000000000,1525,159,7.178709756313385703663243475603,15,0.803732980142241815180170760868
79,79_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2030,1688,6.171821739752903823728047427721,1,0.488145719763166119342656656954
80,80_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,22.000000000000000000000000000000,2625,3779,10.000000000000000000000000000000,17,0.586668047025132199934205345926
81,81_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,51.000000000000000000000000000000,1170,224,2.214432997420789916986905154772,20,0.998999999999999999111821580300
82,82_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,10.000000000000000000000000000000,2171,165,7.155820346237795170907247666037,13,0.998999999999999999111821580300
83,83_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,4.000000000000000000000000000000,4994,1115,0.229780731920786934763256681435,1,0.213164079032129333279854677130
84,84_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,3600,1199,0.001000000000000000020816681712,1,0.518268866581045206665123714629
85,85_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,142.000000000000000000000000000000,2371,196,5.464249452653472616248109261505,19,0.404021014556149082874725309011
86,86_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,3.000000000000000000000000000000,4637,1126,3.181202584628357143969878961798,1,0.320871395243942814090587489773
87,87_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4866,1201,3.029664598534731911883000066155,1,0.469220887931314445395969414676
88,88_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,776.000000000000000000000000000000,2081,1119,0.001000000000000000020816681712,6,0.467829365932089347168698623136
89,89_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,456,3764,10.000000000000000000000000000000,6,0.611901942462721115667534377280
90,90_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,13.000000000000000000000000000000,1016,113,2.918769287447466442841914613382,7,0.960520192686573071050304406526
91,91_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,65.000000000000000000000000000000,1552,34,8.879614714796861818513207254000,13,0.597692319668157501233451966982
92,92_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,3.000000000000000000000000000000,4343,1009,1.718322407596805323493072137353,1,0.435380056034906148809682235878
93,93_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4.000000000000000000000000000000,128,166,3.126048660302101378505312823108,7,0.998999999999999999111821580300
94,94_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,2266,4318,0.001000000000000000020816681712,1,0.948249611299602723768487066991
95,95_0,FAILED,BoTorch,GenerationStep_1,,,2604,321,1.267379984230151190871538346983,20,0.842357576116417594569441007479
96,96_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,5.000000000000000000000000000000,2041,94,3.031797511172410697355417141807,8,0.774540148145078544139607856778
97,97_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,7.000000000000000000000000000000,1,143,10.000000000000000000000000000000,13,0.998999999999999999111821580300
98,98_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2806,4413,0.001000000000000000020816681712,6,0.998999999999999999111821580300
99,99_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,2636,4313,10.000000000000000000000000000000,1,0.839890023291309684161376480915
100,100_0,FAILED,BoTorch,GenerationStep_1,,,3459,315,4.741613716667973044138761906652,17,0.138169028730024873174997424030
101,101_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,7.000000000000000000000000000000,3325,3678,10.000000000000000000000000000000,11,0.683614735230954839728667593590
102,102_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2060,4302,0.001000000000000000020816681712,8,0.998999999999999999111821580300
103,103_0,FAILED,BoTorch,GenerationStep_1,,,4694,340,3.998544004297386322832608129829,16,0.034678226904843116595866803209
104,104_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,3.000000000000000000000000000000,4615,1045,0.760917219016992940616717078228,2,0.540348506838694730980421354616
105,105_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,895.000000000000000000000000000000,2715,305,4.327736732254049201173984329216,8,0.916668770636145269214978270611
106,106_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,2494,4263,10.000000000000000000000000000000,14,0.134041231165303337258265514720
107,107_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,12.000000000000000000000000000000,2796,4367,0.001000000000000000020816681712,16,0.998999999999999999111821580300
108,108_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,2641,4263,0.001000000000000000020816681712,9,0.534092958141472728961218763288
109,109_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2791,4521,0.001000000000000000020816681712,6,0.998999999999999999111821580300
110,110_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3.000000000000000000000000000000,2891,4244,10.000000000000000000000000000000,10,0.998999999999999999111821580300
111,111_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,1588,4247,10.000000000000000000000000000000,14,0.001000000000000000020816681712
112,112_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,14.000000000000000000000000000000,3141,4496,0.001000000000000000020816681712,17,0.998999999999999999111821580300
113,113_0,FAILED,BoTorch,GenerationStep_1,,,2097,1297,10.000000000000000000000000000000,17,0.001000000000000000020816681712
114,114_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3457,3659,10.000000000000000000000000000000,1,0.809133085905287430250609759241
115,115_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,9.000000000000000000000000000000,2259,3634,10.000000000000000000000000000000,12,0.370578917516691974665121733779
116,116_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,3122,4210,10.000000000000000000000000000000,14,0.998999999999999999111821580300
117,117_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2165,4261,10.000000000000000000000000000000,1,0.173990135602116013924245407907
118,118_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,3832,3680,10.000000000000000000000000000000,8,0.998999999999999999111821580300
119,119_0,FAILED,BoTorch,GenerationStep_1,,,1,204,0.001000000000000000020816681712,20,0.998999999999999999111821580300
120,120_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,38.000000000000000000000000000000,2849,163,6.999124660647745521657725475961,8,0.209264120357786298498581345484
121,121_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,1838,3769,6.485074304519098831178780528717,5,0.874912674730019657154400647414
122,122_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,16.000000000000000000000000000000,3078,181,7.769454949332877724543777731014,20,0.342359129039994491972009882375
123,123_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,6.000000000000000000000000000000,2843,181,0.001000000000000000020816681712,1,0.130408808456970087830129045869
124,124_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,7.000000000000000000000000000000,1296,239,10.000000000000000000000000000000,1,0.998999999999999999111821580300
125,125_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2303,4081,6.858076499134932113577178824926,4,0.799360133545771067709040380578
126,126_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,3.000000000000000000000000000000,4448,1123,7.469788479631874977826555550564,1,0.131879658058694809730226893407
127,127_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1680,1733,0.001000000000000000020816681712,1,0.001000000000000000020816681712
128,128_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,7.000000000000000000000000000000,4710,1130,10.000000000000000000000000000000,1,0.001000000000000000020816681712
129,129_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,5000,4782,0.001000000000000000020816681712,1,0.178526290473858650909022571796
130,130_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,1738,3763,4.815154987998652380554176488658,1,0.998999999999999999111821580300
131,131_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,855.000000000000000000000000000000,2966,197,10.000000000000000000000000000000,20,0.001000000000000000020816681712
132,132_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,13.000000000000000000000000000000,1718,3689,6.825341233959388631546971737407,13,0.998999999999999999111821580300
133,133_0,RUNNING,BoTorch,GenerationStep_1,,,2895,200,0.001000000000000000020816681712,20,0.076804239872480375983521128092
134,134_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,10.000000000000000000000000000000,3036,150,7.538113634528371242993216583272,1,0.001000000000000000020816681712
135,135_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,2475,4005,6.426300892946943399408610275714,1,0.998999999999999999111821580300
136,136_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4531,5000,0.001000000000000000020816681712,1,0.074003317571130725061046007340
137,137_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,2722.000000000000000000000000000000,3358,173,0.001000000000000000020816681712,20,0.548206065852483437339515148778
138,138_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,24.000000000000000000000000000000,3562,154,10.000000000000000000000000000000,20,0.998999999999999999111821580300
139,139_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,2295,1360,7.758815963303011820073606941151,1,0.836822371018262844977186887263
140,140_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,26.000000000000000000000000000000,1206,260,10.000000000000000000000000000000,17,0.998999999999999999111821580300
141,141_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1797,3863,6.322912076673159020856473944150,1,0.998999999999999999111821580300
142,142_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,8.000000000000000000000000000000,4338,1127,6.383162903401643717415936407633,1,0.001000000000000000020816681712
143,143_0,RUNNING,BoTorch,GenerationStep_1,,,4631,1124,9.759584882619927626024036726449,18,0.001000000000000000020816681712
144,144_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2027,1849,0.001000000000000000020816681712,1,0.001000000000000000020816681712
145,145_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1742,3655,3.069072302742101410188979571103,1,0.998999999999999999111821580300
146,146_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3.000000000000000000000000000000,2063,211,0.001000000000000000020816681712,1,0.001000000000000000020816681712
147,147_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1447.000000000000000000000000000000,3593,175,0.001000000000000000020816681712,17,0.998999999999999999111821580300
148,148_0,RUNNING,BoTorch,GenerationStep_1,,,1699,3775,5.150500998246559269944100378780,20,0.998999999999999999111821580300
149,149_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,317.000000000000000000000000000000,3547,142,8.280450329510866680493563762866,20,0.079602586774756220466819911508
150,150_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,6960.000000000000000000000000000000,1201,241,0.001000000000000000020816681712,7,0.529271524711949936659038939979
151,151_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,9.000000000000000000000000000000,1170,247,6.764737111428604343643655738560,1,0.998999999999999999111821580300
152,152_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,35.000000000000000000000000000000,2526,146,7.066705492422057588441930420231,20,0.898645695112825104722276137181
153,153_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3035,4894,6.289874448970982356854619865771,7,0.001000000000000000020816681712
154,154_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,4104.000000000000000000000000000000,1431,264,0.001000000000000000020816681712,6,0.643561203248694480016922625509
155,155_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,2833,1180,6.846988150051072530288820416899,1,0.167204701823000928539997289590
156,156_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,3529,4659,5.632647580281184396255866886349,5,0.001000000000000000020816681712
157,157_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,12.000000000000000000000000000000,2494,150,10.000000000000000000000000000000,12,0.998999999999999999111821580300
158,158_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,16.000000000000000000000000000000,4345,169,0.001000000000000000020816681712,1,0.998999999999999999111821580300
159,159_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,436,4639,6.325458473471387677022903517354,4,0.998999999999999999111821580300
160,160_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3.000000000000000000000000000000,1279,4592,7.690329724188904592097060231026,9,0.998999999999999999111821580300
161,161_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2672,5000,5.298203266665863608864128764253,2,0.001000000000000000020816681712
162,162_0,FAILED,BoTorch,GenerationStep_1,,,1380,260,0.001000000000000000020816681712,11,0.998999999999999999111821580300
163,163_0,FAILED,BoTorch,GenerationStep_1,,,4889,1123,10.000000000000000000000000000000,13,0.609083619376945328838246496161
164,164_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,18.000000000000000000000000000000,1504,4675,0.001000000000000000020816681712,20,0.629347460725696472572110451438
165,165_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1098,1325,5.027125821543634742738504428416,1,0.720254408194180961721997391578
166,166_0,FAILED,BoTorch,GenerationStep_1,,,4223,1153,8.027942632177351711675328260753,13,0.001000000000000000020816681712
167,167_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,2322,4775,6.656542335458770054401611560024,5,0.190145975294729013826966479428
168,168_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,4.000000000000000000000000000000,4256,1144,5.976935319922044520524195831968,1,0.998999999999999999111821580300
169,169_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1721,4774,6.821769497149514194234143360518,3,0.518734011202464206924389600317
170,170_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,12.000000000000000000000000000000,1515,251,0.001000000000000000020816681712,1,0.650814078454431843567817850271
171,171_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,9.000000000000000000000000000000,3330,4819,7.321407164679680334984368528239,13,0.001000000000000000020816681712
172,172_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,13.000000000000000000000000000000,3764,4610,7.101494348934270561812809319235,16,0.001000000000000000020816681712
173,173_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,5624.000000000000000000000000000000,4073,4079,5.793602618086338473801788495621,17,0.001000000000000000020816681712
174,174_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,15.000000000000000000000000000000,1,1,0.001000000000000000020816681712,13,0.364425641799726196534692235218
175,175_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,7.000000000000000000000000000000,1057,293,0.778353328090424012586368007760,1,0.477412970615624265846577145567
176,176_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,198.000000000000000000000000000000,4929,1109,10.000000000000000000000000000000,5,0.785266267420965502488172660378
177,177_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,23.000000000000000000000000000000,1152,4700,0.001000000000000000020816681712,19,0.725022790240480996537542068836
178,178_0,FAILED,BoTorch,GenerationStep_1,,,4185,1161,10.000000000000000000000000000000,17,0.998999999999999999111821580300
179,179_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,12.000000000000000000000000000000,4444,4801,7.244657019753569571207663102541,15,0.001000000000000000020816681712
180,180_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,325,2314,0.017603290543783397387178624172,6,0.001000000000000000020816681712
181,181_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1074.000000000000000000000000000000,2609,174,8.750415389978634550516289891675,16,0.001000000000000000020816681712
182,182_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,60.000000000000000000000000000000,3041,221,8.297228189677202436769221094437,8,0.682310366879790075600453747029
183,183_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,302.000000000000000000000000000000,3415,210,6.588016411303290631451545777963,14,0.509746208063717953784532710415
184,184_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,90.000000000000000000000000000000,2598,160,0.539001077937343753276877578173,9,0.001000000000000000020816681712
185,185_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4339,2314,0.017282396316155156090754729803,6,0.008191788130919471638824447268
186,186_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1822,2420,0.001000000000000000020816681712,6,0.001000000000000000020816681712
187,187_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,250,2192,0.415588233371108672908889047903,6,0.853732702124537112808866368141
188,188_0,FAILED,BoTorch,GenerationStep_1,,,4113,1143,7.954758150484550149883489211788,18,0.175996911623061774720966354835
189,189_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,52.000000000000000000000000000000,1387,233,1.830635450638218975072391003778,15,0.998999999999999999111821580300
190,190_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,382.000000000000000000000000000000,3042,214,2.656643987345915114417493896326,19,0.721519800191476101680621013656
191,191_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,10.000000000000000000000000000000,2532,181,9.348691011206227585716987960041,15,0.601495127339678137268208502064
192,192_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,4294,2605,0.755862678330930903847217905422,6,0.001000000000000000020816681712
193,193_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,7.000000000000000000000000000000,3151,221,8.834455602694266218577467952855,2,0.690019440306716580479928779823
194,194_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,395.000000000000000000000000000000,4144,1150,0.001000000000000000020816681712,5,0.136719392263140671062160436122
195,195_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,7.000000000000000000000000000000,2926,181,2.069370087711045247402807945036,3,0.719734767179860179808770226373
196,196_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,7.000000000000000000000000000000,3201,246,7.095270102031539849463115388062,2,0.642774750927520566179396155349
197,197_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,68.000000000000000000000000000000,4909,1070,0.001000000000000000020816681712,3,0.347360280378901986608752849861
198,198_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,31.000000000000000000000000000000,3401,229,7.686798648617527263127158221323,7,0.544222944207530923144133794267
199,199_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,320,2345,0.001000000000000000020816681712,6,0.998999999999999999111821580300
200,200_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,192,2563,0.001000000000000000020816681712,6,0.270443165970518595297988895254
201,201_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,270.000000000000000000000000000000,2532,218,10.000000000000000000000000000000,18,0.620450097790610888282003543281
202,202_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,502.000000000000000000000000000000,3023,182,0.001000000000000000020816681712,12,0.715905838781500158951587309275
203,203_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,22.000000000000000000000000000000,4066,1137,9.747729133674889467897628492210,2,0.191768457350884158341131069392
204,204_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2453.000000000000000000000000000000,4205,1196,8.561199137704997852438282279763,8,0.322206366633464413862242281539
205,205_0,FAILED,BoTorch,GenerationStep_1,,,4705,1088,8.708335847756636383110162569210,18,0.176794105152183800289122928007
206,206_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,244.000000000000000000000000000000,2570,136,8.078651869791222850381018361077,17,0.009649153852934203023927750564
207,207_0,FAILED,BoTorch,GenerationStep_1,,,3430,2582,7.887822061193467071404938906198,20,0.998999999999999999111821580300
208,208_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,6.000000000000000000000000000000,2543,156,9.086002136900216186177203780971,4,0.727287964945878884215346715791
209,209_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1796,2355,1.043315692403675942756535732769,6,0.001000000000000000020816681712
210,210_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,90.000000000000000000000000000000,4644,1125,2.878200450094253959321122238180,4,0.001000000000000000020816681712
211,211_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,5814.000000000000000000000000000000,4579,1136,2.426920766613552604695769332466,8,0.001000000000000000020816681712
212,212_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,8.000000000000000000000000000000,3153,241,4.662721225552121318003173655597,5,0.998999999999999999111821580300
213,213_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,53.000000000000000000000000000000,2521,173,5.777821905750599285056523513049,13,0.330338387426990509609936452762
214,214_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,17.000000000000000000000000000000,3585,4599,8.267507260208445174498592677992,20,0.319801549290721076612697970631
215,215_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,155.000000000000000000000000000000,1023,251,7.924588239316905813325320195872,19,0.998999999999999999111821580300
216,216_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,14.000000000000000000000000000000,3175,236,2.906549833510712499418104926008,10,0.998999999999999999111821580300
217,217_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,5.000000000000000000000000000000,2506,170,7.888849296571624591933868941851,3,0.221486585232591515204347842882
218,218_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,23.000000000000000000000000000000,1004,228,9.147842013783938241999749152455,14,0.998999999999999999111821580300
219,219_0,FAILED,BoTorch,GenerationStep_1,,,4689,1126,2.267426719991279426835717458744,9,0.001000000000000000020816681712
220,220_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,233.000000000000000000000000000000,4560,1134,6.150419387166332185756800754461,4,0.001000000000000000020816681712
221,221_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,108.000000000000000000000000000000,3181,221,1.512987532366835141672822828696,10,0.718220275035342803349180940131
222,222_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,39.000000000000000000000000000000,1039,227,7.363695006460547531901283946354,18,0.998999999999999999111821580300
223,223_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,17.000000000000000000000000000000,1097,243,10.000000000000000000000000000000,11,0.998999999999999999111821580300
224,224_0,FAILED,BoTorch,GenerationStep_1,,,3215,234,0.001000000000000000020816681712,15,0.998999999999999999111821580300
225,225_0,FAILED,BoTorch,GenerationStep_1,,,4578,1145,8.330135570791425081438319466542,18,0.001000000000000000020816681712
226,226_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,102.000000000000000000000000000000,1626,230,3.192390491641548866397215533652,11,0.668932253558026213013931737805
227,227_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,7.000000000000000000000000000000,3200,200,2.697912816043044781366688766866,2,0.998999999999999999111821580300
228,228_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,552.000000000000000000000000000000,2472,167,7.627177995264018406373907055240,9,0.001000000000000000020816681712
229,229_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,9.000000000000000000000000000000,1533,241,7.561128247902432875093836628366,5,0.998999999999999999111821580300
230,230_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,7.000000000000000000000000000000,3160,258,7.203767115523593389525558450259,4,0.998999999999999999111821580300
231,231_0,FAILED,BoTorch,GenerationStep_1,,,4566,1135,6.461403803175121751678489090409,20,0.001000000000000000020816681712
232,232_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,8.000000000000000000000000000000,3218,189,6.006698160790054252800018730341,4,0.998999999999999999111821580300
233,233_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,40.000000000000000000000000000000,2508,184,7.863883145286068021562186913798,10,0.490359579596231165421471587251
234,234_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,462.000000000000000000000000000000,4605,1129,7.599550300668897584444039239315,5,0.001000000000000000020816681712
235,235_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,1744,3763,5.371782706991779399174902209779,8,0.001000000000000000020816681712
236,236_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3.000000000000000000000000000000,4851,3612,4.240142694179531268616756278789,8,0.998999999999999999111821580300
237,237_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,137.000000000000000000000000000000,2507,164,0.577251505190471858597334176011,14,0.306899388254874339043709596808
238,238_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,30.000000000000000000000000000000,1895,3754,8.150671713851753708013347932138,20,0.998999999999999999111821580300
239,239_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,3677,4595,5.123895788793259242765998351388,9,0.001000000000000000020816681712
240,240_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,27.000000000000000000000000000000,1444,256,10.000000000000000000000000000000,15,0.998999999999999999111821580300
241,241_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,6.000000000000000000000000000000,2549,170,6.332997508350337589888567890739,6,0.985413041431913772072448409745
242,242_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,35.000000000000000000000000000000,2532,155,3.734020590846502951620777821518,6,0.001000000000000000020816681712
243,243_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,12.000000000000000000000000000000,2552,173,6.703964112600647062834013922838,11,0.998999999999999999111821580300
244,244_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,78.000000000000000000000000000000,3177,215,0.001000000000000000020816681712,4,0.610517601635312323615778495878
245,245_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,218.000000000000000000000000000000,2589,179,6.471444168533754925931589241372,16,0.500282788426074098708795645507
246,246_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,19.000000000000000000000000000000,2555,176,0.001000000000000000020816681712,3,0.998999999999999999111821580300
247,247_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3475,4680,9.077456618343205718701938167214,3,0.028611367792671208581944952698
248,248_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,43.000000000000000000000000000000,3119,230,9.775206524155878540227604389656,9,0.644682172800471686713308372418
249,249_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,5.000000000000000000000000000000,1930,3790,4.645101960086776671232655644417,10,0.271155116071727819715420082503
250,250_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,109.000000000000000000000000000000,2506,109,1.402978180405198838087699186872,9,0.001000000000000000020816681712
251,251_0,RUNNING,BoTorch,GenerationStep_1,,,4034,2283,5.517084401755794509369934530696,16,0.785642155997020141811049143143
252,252_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,17.000000000000000000000000000000,1538,3803,8.445775147814403638335534196813,15,0.261583447306394323295108961247
253,253_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,17.000000000000000000000000000000,2505,142,9.900629095257633238702510425355,8,0.331818052821733933122061444010
254,254_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,11.000000000000000000000000000000,3574,4652,8.448274561564355522591540648136,16,0.001000000000000000020816681712
255,255_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3241,3048,2.612999584228113736372733910684,6,0.615439351866056472140087407752
256,256_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,14.000000000000000000000000000000,1442,252,10.000000000000000000000000000000,9,0.998999999999999999111821580300
257,257_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,7.000000000000000000000000000000,1909,3769,7.933770384638901163043556152843,11,0.847711486001036740312031270150
258,258_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,3539,4683,5.361099915133380910958749154815,14,0.998999999999999999111821580300
259,259_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,9.000000000000000000000000000000,2060,5000,5.556344394314094436992945702514,15,0.642679832740109424626950840320
260,260_0,RUNNING,BoTorch,GenerationStep_1,,,978,2596,5.603281667363146922866690147202,16,0.779436503012458259398442805832
261,261_0,RUNNING,BoTorch,GenerationStep_1,,,693,2727,7.481011124141303092471844138345,16,0.960437973446450543590913184744
262,262_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,335.000000000000000000000000000000,2522,159,6.332097522517154075671896862332,17,0.001000000000000000020816681712
263,263_0,RUNNING,BoTorch,GenerationStep_1,,,641,2940,4.973235476613772299003812804585,19,0.259802877323909608975327500957
264,264_0,RUNNING,BoTorch,GenerationStep_1,,,1380,3036,4.468210326000484400310597266071,19,0.481255144990883565458972270790
265,265_0,RUNNING,BoTorch,GenerationStep_1,,,4044,1686,6.044513461481997751434391830117,16,0.998999999999999999111821580300
266,266_0,RUNNING,BoTorch,GenerationStep_1,,,1338,1634,6.033679625397618373483510367805,16,0.998999999999999999111821580300
267,267_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,11.000000000000000000000000000000,5000,4202,8.585438480859002297052029462066,16,0.001000000000000000020816681712
268,268_0,RUNNING,BoTorch,GenerationStep_1,,,2956,1162,7.010972281217740409431371517712,16,0.413956101526252728817212300783
269,269_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,840.000000000000000000000000000000,4197,2288,8.648665933349528600615485629532,17,0.440329233957960031187184313239
270,270_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,29.000000000000000000000000000000,1397,748,8.582218032404627550135955971200,3,0.998999999999999999111821580300
271,271_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,658.000000000000000000000000000000,1442,491,0.172278333568139041531708244293,10,0.998999999999999999111821580300
272,272_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,205.000000000000000000000000000000,2474,1,9.637758568822217597471535555087,14,0.001000000000000000020816681712
273,273_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,13.000000000000000000000000000000,3208,622,3.330560840828262314516905462369,3,0.998999999999999999111821580300
274,274_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1542.000000000000000000000000000000,3239,1,8.089744143829349809493578504771,14,0.998999999999999999111821580300
275,275_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,853.000000000000000000000000000000,3229,114,0.001000000000000000020816681712,10,0.998999999999999999111821580300
276,276_0,RUNNING,BoTorch,GenerationStep_1,,,1422,813,7.379127603979042326898252213141,20,0.998999999999999999111821580300
277,277_0,RUNNING,BoTorch,GenerationStep_1,,,3156,650,0.001000000000000000020816681712,15,0.998999999999999999111821580300
278,278_0,RUNNING,BoTorch,GenerationStep_1,,,3885,1496,10.000000000000000000000000000000,17,0.001000000000000000020816681712
279,279_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1905.000000000000000000000000000000,3232,630,0.740850563739126055651240676525,10,0.998999999999999999111821580300
280,280_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,203.000000000000000000000000000000,2488,1,1.773825316273061725169668534363,13,0.001000000000000000020816681712
281,281_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,569.000000000000000000000000000000,2479,1,5.767559606713633435504107183078,2,0.998999999999999999111821580300
282,282_0,RUNNING,BoTorch,GenerationStep_1,,,3212,706,4.080370875853424728063600923633,14,0.998999999999999999111821580300
283,283_0,FAILED,BoTorch,GenerationStep_1,,,1372,894,2.281274392776289072770623533870,20,0.998999999999999999111821580300
284,284_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1,5000,9.357105042733714128644351148978,8,0.001000000000000000020816681712
285,285_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,220.000000000000000000000000000000,2488,1,1.591244402870753527068359289842,16,0.001000000000000000020816681712
286,286_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1806.000000000000000000000000000000,3218,1,8.460932148079884029812092194334,11,0.998999999999999999111821580300
287,287_0,FAILED,BoTorch,GenerationStep_1,,,3400,800,1.432037690862859147244989799219,16,0.001000000000000000020816681712
288,288_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4017,1713,5.885094802026737070832496101502,3,0.998999999999999999111821580300
289,289_0,FAILED,BoTorch,GenerationStep_1,,,1407,1002,9.932107590574744904188264627010,18,0.998999999999999999111821580300
290,290_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4050,1929,7.805872649475146474173925525974,3,0.616609678434299257610007316543
291,291_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,77.000000000000000000000000000000,4232,1783,9.067037892943980992299657373223,4,0.001000000000000000020816681712
292,292_0,FAILED,BoTorch,GenerationStep_1,,,5000,1689,4.899556593155010908446911344072,17,0.001000000000000000020816681712
293,293_0,FAILED,BoTorch,GenerationStep_1,,,3949,1228,6.118133983451073731885117013007,20,0.001000000000000000020816681712
294,294_0,FAILED,BoTorch,GenerationStep_1,,,1426,838,4.880909457430805176159083202947,11,0.998999999999999999111821580300
295,295_0,FAILED,BoTorch,GenerationStep_1,,,1456,991,6.694401101334604753390067344299,17,0.998999999999999999111821580300
296,296_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,164.000000000000000000000000000000,2458,1,4.125285195292131668054480542196,1,0.467971144790514603784004066256
297,297_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,4074.000000000000000000000000000000,3935,1785,0.001000000000000000020816681712,11,0.867756938969099111957916647953
298,298_0,FAILED,BoTorch,GenerationStep_1,,,1334,777,10.000000000000000000000000000000,17,0.998999999999999999111821580300
299,299_0,FAILED,BoTorch,GenerationStep_1,,,1499,1019,5.213222796200745356998140778160,15,0.998999999999999999111821580300
300,300_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,54.000000000000000000000000000000,2883,191,5.782847915353857004561177745927,10,0.524118903854265849595606141520
301,301_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,51.000000000000000000000000000000,1744,295,1.696238810979487032781776179036,9,0.998999999999999999111821580300
302,302_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,5.000000000000000000000000000000,1656,486,8.497768702284846042971366841812,1,0.998999999999999999111821580300
303,303_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,239.000000000000000000000000000000,2040,511,9.146630820940130135454637638759,10,0.998999999999999999111821580300
304,304_0,RUNNING,BoTorch,GenerationStep_1,,,1509,449,6.832191192451614547564986423822,20,0.998999999999999999111821580300
305,305_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,792.000000000000000000000000000000,1663,548,7.015045931115419186596682266099,15,0.998999999999999999111821580300
306,306_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1375.000000000000000000000000000000,3466,488,6.390353807680478226416198594961,10,0.967608806757010553312170486606
307,307_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,377.000000000000000000000000000000,1164,467,6.714245216799539228702542459359,10,0.998999999999999999111821580300
308,308_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,496.000000000000000000000000000000,2235,500,5.860706296709775386943874764256,11,0.998999999999999999111821580300
309,309_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2009.000000000000000000000000000000,1658,431,10.000000000000000000000000000000,10,0.998999999999999999111821580300
310,310_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,10.000000000000000000000000000000,1630,461,4.941275509609140748068512039026,4,0.998999999999999999111821580300
311,311_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,6.000000000000000000000000000000,1618,529,0.692391720748141370478379030828,3,0.998999999999999999111821580300
312,312_0,FAILED,BoTorch,GenerationStep_1,,,2615,622,2.178153593541737986782891312032,20,0.998999999999999999111821580300
313,313_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,6.000000000000000000000000000000,1963,569,10.000000000000000000000000000000,2,0.998999999999999999111821580300
314,314_0,FAILED,BoTorch,GenerationStep_1,,,1076,2811,8.605951349903810054797759221401,17,0.001000000000000000020816681712
315,315_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1296.000000000000000000000000000000,2231,506,4.605612304164046655330366775161,14,0.998999999999999999111821580300
316,316_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,5000,1815,9.934426712320652441690072009806,2,0.925514001324245882962316045450
317,317_0,FAILED,BoTorch,GenerationStep_1,,,2071,624,8.253442242099509940089774318039,15,0.998999999999999999111821580300
318,318_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,16.000000000000000000000000000000,4215,4202,8.761646872464430302329674304929,20,0.998999999999999999111821580300
319,319_0,RUNNING,BoTorch,GenerationStep_1,,,3004,2704,3.778764699131170079482444634777,16,0.998999999999999999111821580300
320,320_0,RUNNING,BoTorch,GenerationStep_1,,,4627,2971,9.808442582336342141502427693922,17,0.001000000000000000020816681712
321,321_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2276.000000000000000000000000000000,1423,581,7.693524089112304231719008384971,14,0.998999999999999999111821580300
322,322_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,149.000000000000000000000000000000,2854,677,5.338108851029091717066421551863,5,0.998999999999999999111821580300
323,323_0,RUNNING,BoTorch,GenerationStep_1,,,3516,2459,2.691475943524368474868424527813,16,0.998999999999999999111821580300
324,324_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,348.000000000000000000000000000000,1750,533,7.941642570833712611033661232796,10,0.998999999999999999111821580300
325,325_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,6.000000000000000000000000000000,1342,469,1.765357056715351813380721068825,4,0.998999999999999999111821580300
326,326_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,367.000000000000000000000000000000,2023,501,5.440086789033208525268037192291,10,0.998999999999999999111821580300
327,327_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,6.000000000000000000000000000000,1573,396,0.721239331823008922306428303273,2,0.998999999999999999111821580300
328,328_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2624.000000000000000000000000000000,2014,403,1.602696001968660910463881918986,9,0.998999999999999999111821580300
329,329_0,RUNNING,BoTorch,GenerationStep_1,,,2473,3182,3.935565828868622162417523213662,19,0.998999999999999999111821580300
330,330_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,8.000000000000000000000000000000,1366,558,7.007364879055167250498925568536,4,0.998999999999999999111821580300
331,331_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,4439,2336,3.428280946135094264803910846240,4,0.572946869831680971607568153559
332,332_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,169.000000000000000000000000000000,2711,80,2.352721480870644921168377550202,9,0.001000000000000000020816681712
333,333_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1,4797,0.001000000000000000020816681712,9,0.001000000000000000020816681712
334,334_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,4820,2282,0.001000000000000000020816681712,3,0.001000000000000000020816681712
335,335_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,3823,1834,2.356868890829822316135278015281,1,0.877978875399522906697313828772
336,336_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1.000000000000000000000000000000,2269,3743,0.693204897484774207327973272186,4,0.180204607022724699882942900331
337,337_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,4.000000000000000000000000000000,1339,558,5.747884021322549941146462515462,1,0.998999999999999999111821580300
338,338_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,8.000000000000000000000000000000,1684,634,0.001000000000000000020816681712,1,0.998999999999999999111821580300
339,339_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2332.000000000000000000000000000000,1274,2846,0.001000000000000000020816681712,10,0.355502497870175238059431421789
340,340_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1102,2748,1.136112115460044647008430729329,3,0.621807073541113530801283104665
341,341_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,4.000000000000000000000000000000,1405,544,2.571156169521008116873872495489,1,0.998999999999999999111821580300
342,342_0,RUNNING,BoTorch,GenerationStep_1,,,3385,869,10.000000000000000000000000000000,17,0.998999999999999999111821580300
343,343_0,RUNNING,BoTorch,GenerationStep_1,,,5000,2604,5.373519271524727081157379871001,18,0.998999999999999999111821580300
344,344_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,368.000000000000000000000000000000,4048,483,0.001000000000000000020816681712,3,0.883850347631669919223895703908
345,345_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,28.000000000000000000000000000000,977,2755,9.403308873623178243406073306687,3,0.949623854431412617316254909383
346,346_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,4.000000000000000000000000000000,1459,590,7.172946998983504407476630149176,2,0.998999999999999999111821580300
347,347_0,RUNNING,BoTorch,GenerationStep_1,,,4230,2506,7.886466660516348703424682753393,19,0.998999999999999999111821580300
348,348_0,RUNNING,BoTorch,GenerationStep_1,,,2062,2903,2.704950801011711813970350704039,18,0.293204685225323002928377036369
349,349_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2003,3266,0.305029431826188413001688104487,5,0.724137362610965062970080907689
350,350_0,RUNNING,BoTorch,GenerationStep_1,,,4613,2357,5.188757169194640361808978923364,18,0.998999999999999999111821580300
351,351_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1848,2865,4.065991671290184683584811864421,2,0.932517067104638330832244719204
352,352_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1217.000000000000000000000000000000,3746,2870,0.001000000000000000020816681712,9,0.412312917244901377955557109090
353,353_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,68.000000000000000000000000000000,2442,126,10.000000000000000000000000000000,6,0.001000000000000000020816681712
354,354_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,4624,2147,3.217589371042620438601034038584,4,0.001000000000000000020816681712
355,355_0,RUNNING,BoTorch,GenerationStep_1,,,5000,2898,4.828817208253542325735452322988,15,0.998999999999999999111821580300
356,356_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,1920,3755,0.725433311385376033975092013861,2,0.998999999999999999111821580300
357,357_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4431,2410,2.677219545804471412253633388900,1,0.760007584194765439811192209163
358,358_0,RUNNING,BoTorch,GenerationStep_1,,,4111,1700,5.811955061028196034556003723992,20,0.873413724110297162717131413956
359,359_0,FAILED,BoTorch,GenerationStep_1,,,2200,3006,9.361393931754209418727441516239,16,0.001000000000000000020816681712
360,360_0,RUNNING,BoTorch,GenerationStep_1,,,3656,2296,0.026245010767179412919380965263,18,0.998999999999999999111821580300
361,361_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,16.000000000000000000000000000000,1193,389,8.402385772217357384761271532625,4,0.998999999999999999111821580300
362,362_0,RUNNING,BoTorch,GenerationStep_1,,,2162,709,0.011790176090587549048716020650,15,0.998999999999999999111821580300
363,363_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,153.000000000000000000000000000000,2150,117,1.828873796354113068218794069253,10,0.001000000000000000020816681712
364,364_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,5.000000000000000000000000000000,1224,384,3.514644265843393178272435761755,1,0.998999999999999999111821580300
365,365_0,RUNNING,BoTorch,GenerationStep_1,,,4924,3899,0.001000000000000000020816681712,18,0.998999999999999999111821580300
366,366_0,RUNNING,BoTorch,GenerationStep_1,,,3332,1173,3.485850834813120968647126574069,20,0.001000000000000000020816681712
367,367_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,86.000000000000000000000000000000,1974,658,2.292500903960617453236636720248,3,0.428820939780847454336765167682
368,368_0,RUNNING,BoTorch,GenerationStep_1,,,4084,720,0.001000000000000000020816681712,14,0.889008583986459544057368020731
369,369_0,RUNNING,BoTorch,GenerationStep_1,,,3967,1533,5.248709557500708555721757875290,19,0.002761623568269289206306149609
370,370_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,4704,2542,6.210608698170146269035285513382,4,0.998999999999999999111821580300
371,371_0,RUNNING,BoTorch,GenerationStep_1,,,2048,691,10.000000000000000000000000000000,17,0.998999999999999999111821580300
372,372_0,FAILED,BoTorch,GenerationStep_1,,,3956,2487,1.651896346354787104004913089739,20,0.998999999999999999111821580300
373,373_0,FAILED,BoTorch,GenerationStep_1,,,3446,474,6.196256619328281978198447177419,20,0.653123299263931533786831096222
374,374_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1114.000000000000000000000000000000,3457,510,10.000000000000000000000000000000,18,0.998999999999999999111821580300
375,375_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,14.000000000000000000000000000000,3763,366,1.051516622469356621039082710922,2,0.980576405457441291346754042024
376,376_0,FAILED,BoTorch,GenerationStep_1,,,1630,2659,4.799958077719900906288330588723,17,0.001000000000000000020816681712
377,377_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,5288.000000000000000000000000000000,1079,2046,4.720624390154340765946017199894,17,0.286206530830022065625684035695
378,378_0,FAILED,BoTorch,GenerationStep_1,,,868,1620,5.337667520473994287044661177788,17,0.001000000000000000020816681712
379,379_0,FAILED,BoTorch,GenerationStep_1,,,4815,2466,5.061956163212975035037288762396,20,0.474924558510962935287125219475
380,380_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,127.000000000000000000000000000000,2186,2670,8.198008747460935552453520358540,5,0.001000000000000000020816681712
381,381_0,FAILED,BoTorch,GenerationStep_1,,,1738,1548,4.246144188946220587865809648065,20,0.001000000000000000020816681712
382,382_0,FAILED,BoTorch,GenerationStep_1,,,2995,2645,2.401672659815052401910406842944,18,0.998999999999999999111821580300
383,383_0,FAILED,BoTorch,GenerationStep_1,,,3336,1175,0.222342331429510431384244384390,20,0.001000000000000000020816681712
384,384_0,RUNNING,BoTorch,GenerationStep_1,,,2856,2891,7.380362472949883567707729525864,16,0.001000000000000000020816681712
385,385_0,RUNNING,BoTorch,GenerationStep_1,,,3584,404,6.721052582353941318160650553182,20,0.984822577137266730318287955015
386,386_0,FAILED,BoTorch,GenerationStep_1,,,4373,1803,5.298306136760099427363002178026,18,0.032878164911360911737414625122
387,387_0,FAILED,BoTorch,GenerationStep_1,,,225,1576,3.285746569577544740070607076632,20,0.001000000000000000020816681712
388,388_0,FAILED,BoTorch,GenerationStep_1,,,4937,2594,1.478432126945172120358051870426,20,0.998999999999999999111821580300
389,389_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,17.000000000000000000000000000000,2101,722,3.409392497413522793436868596473,3,0.998999999999999999111821580300
390,390_0,RUNNING,BoTorch,GenerationStep_1,,,662,2976,1.033692519790843089566578782978,16,0.749842587601757704085514433245
391,391_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,4066,2387,7.480229895404292506100318860263,1,0.363096782942059104204446384756
392,392_0,RUNNING,BoTorch,GenerationStep_1,,,785,2554,7.980986904770652756724302889779,20,0.998999999999999999111821580300
393,393_0,RUNNING,BoTorch,GenerationStep_1,,,2294,2900,0.001000000000000000020816681712,16,0.001000000000000000020816681712
394,394_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,102.000000000000000000000000000000,3623,464,10.000000000000000000000000000000,4,0.947332321324175108401277611847
395,395_0,RUNNING,BoTorch,GenerationStep_1,,,336,2957,9.932676610298011965483055973891,16,0.378642123750463466258509015461
396,396_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4161,1840,9.039981320555920518700077082030,1,0.862828293116247646565852846834
397,397_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4027.000000000000000000000000000000,1470,3073,9.106887622622643618797155795619,15,0.457674067842062881616982394917
398,398_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,4.000000000000000000000000000000,1679,578,7.548230792325080429350236954633,1,0.711385778875662899700671459868
399,399_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,5003.000000000000000000000000000000,640,3028,9.851094683670602591973874950781,15,0.998999999999999999111821580300
400,400_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,906,3070,7.012810123148440766271960455924,2,0.799476802698245947453870030586
401,401_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,3.000000000000000000000000000000,3666,1946,10.000000000000000000000000000000,5,0.349766219572547887306512848227
402,402_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2661,3187,6.958804974895517858612947748043,3,0.998999999999999999111821580300
403,403_0,RUNNING,BoTorch,GenerationStep_1,,,1713,621,0.001000000000000000020816681712,14,0.505929613725944804514256247785
404,404_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2939,2623,0.383901153724636734487773992441,1,0.998999999999999999111821580300
405,405_0,RUNNING,BoTorch,GenerationStep_1,,,4971,2931,10.000000000000000000000000000000,14,0.883360179629515740451495275920
406,406_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1464,3258,7.169522144874471258901849068934,3,0.330251633655427756508515813039
407,407_0,FAILED,BoTorch,GenerationStep_1,,,1738,632,3.047555792594246248938816279406,20,0.998999999999999999111821580300
408,408_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4049.000000000000000000000000000000,1078,3053,9.150996599359052297018024546560,15,0.998999999999999999111821580300
409,409_0,FAILED,BoTorch,GenerationStep_1,,,4132,2411,8.190823183912193883315921993926,20,0.034772937062807936225983240774
410,410_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,6.000000000000000000000000000000,3656,452,8.893312390535440314920379023533,1,0.719891523432594770959269681043
411,411_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2611,3253,8.341586023156862594873928173911,4,0.998999999999999999111821580300
412,412_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2718,2363,8.564582128616471834448020672426,1,0.001000000000000000020816681712
413,413_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2800,3280,2.516878513789331250904979242478,2,0.998999999999999999111821580300
414,414_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3860,4723,2.814392715014940815621002911939,7,0.001000000000000000020816681712
415,415_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2725,2512,6.272528124532945170699349546339,1,0.942827746285942680515290703624
416,416_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,12.000000000000000000000000000000,3032,3289,8.389872558761018339623660722282,13,0.998999999999999999111821580300
417,417_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,915,3407,8.008240470909658625942029175349,4,0.327419562204851521958204330076
418,418_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,7.000000000000000000000000000000,4601,4041,7.709825704803209056592550041387,13,0.001000000000000000020816681712
419,419_0,FAILED,BoTorch,GenerationStep_1,,,813,2914,9.857653126125352827102688024752,19,0.445255098236235880015954080591
420,420_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1148.000000000000000000000000000000,3595,378,6.123439427474686169716733274981,5,0.188394349445743680693254873404
421,421_0,RUNNING,BoTorch,GenerationStep_1,,,4654,1718,10.000000000000000000000000000000,15,0.998999999999999999111821580300
422,422_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3217.000000000000000000000000000000,3272,2003,10.000000000000000000000000000000,16,0.129519694365121590395872885892
423,423_0,RUNNING,BoTorch,GenerationStep_1,,,3539,411,1.041009796599750680812235259509,14,0.748408197559333743242859782185
424,424_0,RUNNING,BoTorch,GenerationStep_1,,,4204,1540,0.394863843317764917628664989024,20,0.316479685680530531399767824041
425,425_0,RUNNING,BoTorch,GenerationStep_1,,,2950,2784,0.830960625766838489880683482625,19,0.818522599612523604761804563168
426,426_0,RUNNING,BoTorch,GenerationStep_1,,,639,2929,0.001000000000000000020816681712,17,0.063016348663762031256929674328
427,427_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,5336.000000000000000000000000000000,2810,1992,9.194486847496685655301007500384,20,0.582919145513041914163920864667
428,428_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3001.000000000000000000000000000000,3196,2052,10.000000000000000000000000000000,17,0.954390383249319462777293665567
429,429_0,RUNNING,BoTorch,GenerationStep_1,,,3478,993,0.972787779164508537022015843831,17,0.484740989284603962516939645866
430,430_0,RUNNING,BoTorch,GenerationStep_1,,,2545,2808,1.007998627551093040466412276146,19,0.001000000000000000020816681712
431,431_0,RUNNING,BoTorch,GenerationStep_1,,,3625,449,0.001000000000000000020816681712,15,0.267426443471645403082703751352
432,432_0,RUNNING,BoTorch,GenerationStep_1,,,4531,1692,8.340846607684420987993689777795,15,0.764133859642180279792000874295
433,433_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,13.000000000000000000000000000000,4885,3820,8.708079746565443812755802355241,13,0.511918916660562328679873189685
434,434_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,126.000000000000000000000000000000,2599,154,6.673637614346051627478573209373,19,0.388940894222917166178632442097
435,435_0,RUNNING,BoTorch,GenerationStep_1,,,4555,1629,10.000000000000000000000000000000,12,0.998999999999999999111821580300
436,436_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,4.000000000000000000000000000000,1449,541,10.000000000000000000000000000000,1,0.152366892205836906848404055381
437,437_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,48.000000000000000000000000000000,1160,469,8.457897510615936198519193567336,3,0.455853171175655325075837254190
438,438_0,RUNNING,BoTorch,GenerationStep_1,,,1467,641,2.657499308632835788301918000798,20,0.001000000000000000020816681712
439,439_0,RUNNING,BoTorch,GenerationStep_1,,,1558,587,3.849732192444363931826956104487,20,0.312307379340901802855512414681
440,440_0,RUNNING,BoTorch,GenerationStep_1,,,4178,3538,0.001000000000000000020816681712,18,0.001000000000000000020816681712
441,441_0,RUNNING,BoTorch,GenerationStep_1,,,3332,1219,7.073355299127985773566251737066,20,0.001000000000000000020816681712
442,442_0,RUNNING,BoTorch,GenerationStep_1,,,4674,1755,10.000000000000000000000000000000,19,0.862452921748006473556813489267
443,443_0,RUNNING,BoTorch,GenerationStep_1,,,1,1160,6.154376286344658275595520535717,20,0.001000000000000000020816681712
444,444_0,FAILED,BoTorch,GenerationStep_1,,,3212,966,2.191963321249253304756621218985,20,0.946222906407868546985184821096
445,445_0,FAILED,BoTorch,GenerationStep_1,,,1049,2584,0.001000000000000000020816681712,16,0.998999999999999999111821580300
446,446_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4017,3506,7.062470167760997341588335984852,6,0.001000000000000000020816681712
447,447_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,2489,2954,10.000000000000000000000000000000,7,0.119624518623273085049518726919
448,448_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,9.000000000000000000000000000000,3866,297,5.281077701760873210901081620250,5,0.998999999999999999111821580300
449,449_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,871.000000000000000000000000000000,1306,2556,10.000000000000000000000000000000,7,0.998999999999999999111821580300
450,450_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,186.000000000000000000000000000000,542,1,2.474639272010057400308369324193,9,0.001000000000000000020816681712
451,451_0,RUNNING,BoTorch,GenerationStep_1,,,4404,768,6.637212825163158136376750917407,18,0.998999999999999999111821580300
452,452_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3077.000000000000000000000000000000,152,2930,10.000000000000000000000000000000,11,0.001000000000000000020816681712
453,453_0,RUNNING,BoTorch,GenerationStep_1,,,5000,3032,2.051440729444488741961549749249,16,0.998999999999999999111821580300
454,454_0,RUNNING,BoTorch,GenerationStep_1,,,1,1240,7.261566448200146517422126635211,16,0.998999999999999999111821580300
455,455_0,RUNNING,BoTorch,GenerationStep_1,,,828,929,0.094911675438754339317881658644,15,0.998999999999999999111821580300
456,456_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4591,4535,9.787214877831686976605851668864,5,0.001000000000000000020816681712
457,457_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1,3033,10.000000000000000000000000000000,5,0.001000000000000000020816681712
458,458_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1,4044,3.088276292387677646189558799961,4,0.998999999999999999111821580300
459,459_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,10.000000000000000000000000000000,4735,3467,7.761471998156723728357064828742,12,0.001000000000000000020816681712
460,460_0,RUNNING,BoTorch,GenerationStep_1,,,3564,478,5.097805614911355753804400592344,20,0.804990399999725325663746389182
461,461_0,RUNNING,BoTorch,GenerationStep_1,,,1,1314,0.001000000000000000020816681712,19,0.998999999999999999111821580300
462,462_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,223.000000000000000000000000000000,5000,1630,0.001000000000000000020816681712,5,0.352575755228457354828464076490
463,463_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4881,4534,10.000000000000000000000000000000,4,0.633985555473354334132807252900
464,464_0,RUNNING,BoTorch,GenerationStep_1,,,5000,2780,0.447502364098640581513421921045,20,0.998999999999999999111821580300
465,465_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1,4053,8.534526785573403273588155570906,2,0.001000000000000000020816681712
466,466_0,RUNNING,BoTorch,GenerationStep_1,,,3723,2204,0.001000000000000000020816681712,20,0.001000000000000000020816681712
467,467_0,RUNNING,BoTorch,GenerationStep_1,,,344,957,2.106312492955036752562136825873,15,0.001000000000000000020816681712
468,468_0,RUNNING,BoTorch,GenerationStep_1,,,1793,2971,0.532761379952384817038080200291,18,0.001000000000000000020816681712
469,469_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1,3446,8.396303971271903421325077943038,8,0.001000000000000000020816681712
470,470_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,19.000000000000000000000000000000,1267,351,3.160064767233302873705724778119,4,0.998999999999999999111821580300
471,471_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1,4145,2.677668545824144707978575752350,3,0.001000000000000000020816681712
472,472_0,RUNNING,BoTorch,GenerationStep_1,,,827,2526,0.367695838836283039885444168249,19,0.685815187065616616024499307969
473,473_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1146.000000000000000000000000000000,3693,551,6.190392722692186211475018353667,20,0.998999999999999999111821580300
474,474_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,9.000000000000000000000000000000,4229,1516,10.000000000000000000000000000000,3,0.998999999999999999111821580300
475,475_0,RUNNING,BoTorch,GenerationStep_1,,,4658,2972,0.659885739023865713903660434880,19,0.001000000000000000020816681712
476,476_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,10.000000000000000000000000000000,3274,2278,10.000000000000000000000000000000,11,0.998999999999999999111821580300
477,477_0,RUNNING,BoTorch,GenerationStep_1,,,978,1536,0.001000000000000000020816681712,17,0.797279626905566973782413242589
478,478_0,RUNNING,BoTorch,GenerationStep_1,,,4817,3459,0.217839729305479928855859839132,15,0.001000000000000000020816681712
479,479_0,RUNNING,BoTorch,GenerationStep_1,,,3489,921,5.868831610823952438238393369829,17,0.998999999999999999111821580300
480,480_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4002,3517,3.056108547176975331893800102989,6,0.001000000000000000020816681712
481,481_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,3271.000000000000000000000000000000,4101,1864,10.000000000000000000000000000000,11,0.998999999999999999111821580300
482,482_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2939.000000000000000000000000000000,4064,3564,0.001000000000000000020816681712,14,0.001000000000000000020816681712
483,483_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,10.000000000000000000000000000000,3621,1484,2.334094745000881765406575141242,3,0.001000000000000000020816681712
484,484_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3433,4564,2.852411264435280813245299214032,1,0.001000000000000000020816681712
485,485_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,11.000000000000000000000000000000,1787,4152,10.000000000000000000000000000000,16,0.001000000000000000020816681712
486,486_0,RUNNING,BoTorch,GenerationStep_1,,,3669,3398,0.967813393142174183125803210714,18,0.001000000000000000020816681712
487,487_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1515.000000000000000000000000000000,1,1974,10.000000000000000000000000000000,12,0.998999999999999999111821580300
488,488_0,RUNNING,BoTorch,GenerationStep_1,,,3463,1037,1.502885877783196821866340542329,20,0.001000000000000000020816681712
489,489_0,RUNNING,BoTorch,GenerationStep_1,,,3494,769,1.690743598914392409326978850004,19,0.001000000000000000020816681712
490,490_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4590,1421,1.583522180326011330464552884223,2,0.001000000000000000020816681712
491,491_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,3493,4578,1.744002631380833667762431105075,9,0.158596869847343335813150133617
492,492_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1976.000000000000000000000000000000,4437,1801,10.000000000000000000000000000000,9,0.998999999999999999111821580300
493,493_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,26.000000000000000000000000000000,4086,1407,4.091204691642098545401040610159,3,0.001000000000000000020816681712
494,494_0,RUNNING,BoTorch,GenerationStep_1,,,4122,1290,1.196886650132307217120342102135,20,0.998999999999999999111821580300
495,495_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,16.000000000000000000000000000000,1981,4137,0.001000000000000000020816681712,18,0.001000000000000000020816681712
496,496_0,RUNNING,BoTorch,GenerationStep_1,,,3223,1027,1.125324178827438181826892105164,15,0.998999999999999999111821580300
497,497_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4362,3699,4.608285445763911525318690109998,3,0.001000000000000000020816681712
498,498_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3392,4562,0.001000000000000000020816681712,3,0.001000000000000000020816681712
499,499_0,RUNNING,BoTorch,GenerationStep_1,,,4112,1717,1.326596018169855728530137639609,20,0.001000000000000000020816681712
500,500_0,RUNNING,BoTorch,GenerationStep_1,,,3631,1474,1.245044198230084830925079586450,19,0.998999999999999999111821580300
501,501_0,FAILED,BoTorch,GenerationStep_1,,,3744,1183,0.790682052509150024022233083087,20,0.230730511068332033719485707479
502,502_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,3972,4613,3.167254705696610717069461315987,3,0.001000000000000000020816681712
503,503_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1931,4514,0.001000000000000000020816681712,3,0.001000000000000000020816681712
504,504_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,3786,3480,8.209091302770394449339619313832,1,0.001000000000000000020816681712
505,505_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3447,4297,4.613718536824668703388852009084,7,0.001000000000000000020816681712
506,506_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,3881,4536,10.000000000000000000000000000000,7,0.998999999999999999111821580300
507,507_0,FAILED,BoTorch,GenerationStep_1,,,4427,2970,7.678116781199058493712072959170,19,0.998999999999999999111821580300
508,508_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2162.000000000000000000000000000000,3868,1871,10.000000000000000000000000000000,10,0.001000000000000000020816681712
509,509_0,FAILED,BoTorch,GenerationStep_1,,,1394,1607,0.001000000000000000020816681712,19,0.998999999999999999111821580300
510,510_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,3947,2159,10.000000000000000000000000000000,3,0.998999999999999999111821580300
511,511_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4118,2004,8.149837578794333126097626518458,6,0.980386482346412457822282249253
512,512_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,3836,3475,10.000000000000000000000000000000,3,0.001000000000000000020816681712
513,513_0,RUNNING,BoTorch,GenerationStep_1,,,5000,2989,8.194043801626309075913923152257,20,0.308763198903562463559779871503
514,514_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,3706,3422,0.001000000000000000020816681712,7,0.001000000000000000020816681712
515,515_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,5000,2970,9.839228014125334098594066745136,1,0.998999999999999999111821580300
516,516_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,1087,2309,10.000000000000000000000000000000,3,0.998999999999999999111821580300
517,517_0,RUNNING,BoTorch,GenerationStep_1,,,4283,1149,0.001000000000000000020816681712,17,0.998999999999999999111821580300
518,518_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,3790,3417,10.000000000000000000000000000000,6,0.001000000000000000020816681712
519,519_0,RUNNING,BoTorch,GenerationStep_1,,,3476,1079,2.186842523151914630119563298649,18,0.633479033132960056562410500192
520,520_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,3.000000000000000000000000000000,1997,3788,7.915943438570739232318373979069,8,0.998999999999999999111821580300
521,521_0,RUNNING,BoTorch,GenerationStep_1,,,1488,608,0.001000000000000000020816681712,13,0.001000000000000000020816681712
522,522_0,RUNNING,BoTorch,GenerationStep_1,,,3344,1023,4.469641460699337542905595910270,18,0.998999999999999999111821580300
523,523_0,FAILED,BoTorch,GenerationStep_1,,,3309,1281,0.001000000000000000020816681712,15,0.998999999999999999111821580300
524,524_0,FAILED,BoTorch,GenerationStep_1,,,3490,761,0.430997673782833212730025707060,17,0.001000000000000000020816681712
525,525_0,FAILED,BoTorch,GenerationStep_1,,,244,879,2.458916912962906042849908772041,20,0.998999999999999999111821580300
526,526_0,FAILED,BoTorch,GenerationStep_1,,,855,1178,0.483043264381904979831716673289,19,0.998999999999999999111821580300
527,527_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,13.000000000000000000000000000000,2774,5000,6.043468677137105338204037252581,17,0.998999999999999999111821580300
528,528_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4179,1873,0.001000000000000000020816681712,3,0.559269611098489050071691508492
529,529_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,439.000000000000000000000000000000,4003,1488,10.000000000000000000000000000000,5,0.001000000000000000020816681712
530,530_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,6.000000000000000000000000000000,4081,4642,10.000000000000000000000000000000,12,0.001000000000000000020816681712
531,531_0,FAILED,BoTorch,GenerationStep_1,,,3986,3479,0.001000000000000000020816681712,20,0.998999999999999999111821580300
532,532_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4.000000000000000000000000000000,958,4810,10.000000000000000000000000000000,11,0.998999999999999999111821580300
533,533_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,1248,2242,5.388752231383983293255823809886,2,0.998999999999999999111821580300
534,534_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,93.000000000000000000000000000000,3354,1005,10.000000000000000000000000000000,3,0.001000000000000000020816681712
535,535_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1182.000000000000000000000000000000,2562,171,10.000000000000000000000000000000,14,0.001000000000000000020816681712
536,536_0,FAILED,BoTorch,GenerationStep_1,,,5000,1141,0.001000000000000000020816681712,20,0.001000000000000000020816681712
537,537_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,3999,4617,6.423899661855496212581329018576,1,0.001000000000000000020816681712
538,538_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,3837,2184,0.886862850441088479414020184777,1,0.001000000000000000020816681712
539,539_0,FAILED,BoTorch,GenerationStep_1,,,4945,3044,0.001000000000000000020816681712,19,0.998999999999999999111821580300
540,540_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,36.000000000000000000000000000000,4441,1880,2.564816630936368468951513932552,5,0.998999999999999999111821580300
541,541_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,3889,3435,0.001000000000000000020816681712,2,0.001000000000000000020816681712
542,542_0,RUNNING,BoTorch,GenerationStep_1,,,3358,2766,5.097326546227673560451876255684,20,0.998999999999999999111821580300
543,543_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,8.000000000000000000000000000000,2747,201,8.949814931329996170461527071893,7,0.998999999999999999111821580300
544,544_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,10.000000000000000000000000000000,3740,3469,5.853962912481627256511274026707,12,0.606001525817160602294109139621
545,545_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,4061,4626,0.001000000000000000020816681712,8,0.001000000000000000020816681712
546,546_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,5000,3135,8.197979578126526334358459280338,6,0.998999999999999999111821580300
547,547_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,25,2771,8.206145059848649836453660100233,1,0.001000000000000000020816681712
548,548_0,RUNNING,BoTorch,GenerationStep_1,,,3038,2899,2.610574104102213688349820586154,20,0.998999999999999999111821580300
549,549_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,16.000000000000000000000000000000,3661,3440,5.951994032663095346435966348508,14,0.998999999999999999111821580300
550,550_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4458,3972,10.000000000000000000000000000000,4,0.998999999999999999111821580300
551,551_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1.000000000000000000000000000000,3913,3440,5.091136690435690503875321155647,1,0.001000000000000000020816681712
552,552_0,RUNNING,BoTorch,GenerationStep_1,,,373,931,1.268353496931750923337745007302,18,0.001000000000000000020816681712
553,553_0,FAILED,BoTorch,GenerationStep_1,,,4059,3482,0.797789739814594112132795089565,20,0.017808528366675749343706058880
554,554_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,14.000000000000000000000000000000,1,1,8.110371716482097426137443108018,6,0.998999999999999999111821580300
555,555_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,15.000000000000000000000000000000,2570,155,0.001000000000000000020816681712,2,0.001000000000000000020816681712
556,556_0,FAILED,BoTorch,GenerationStep_1,,,3014,3411,5.221567287552929315097571816295,20,0.001000000000000000020816681712
557,557_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1.000000000000000000000000000000,4445,2972,5.822592415399761911487530596787,1,0.001000000000000000020816681712
558,558_0,FAILED,BoTorch,GenerationStep_1,,,3644,3193,5.899093391968474264785982086323,20,0.001000000000000000020816681712
559,559_0,FAILED,BoTorch,GenerationStep_1,,,1,780,0.001000000000000000020816681712,14,0.998999999999999999111821580300
560,560_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,5.000000000000000000000000000000,3645,3460,7.111319877875974526659774710424,10,0.998999999999999999111821580300
561,561_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,5.000000000000000000000000000000,4164,4681,6.069760993363793488697410793975,11,0.001000000000000000020816681712
562,562_0,FAILED,BoTorch,GenerationStep_1,,,3863,1349,6.601649359275390871459876507288,13,0.998999999999999999111821580300
563,563_0,FAILED,BoTorch,GenerationStep_1,,,3437,662,0.001000000000000000020816681712,15,0.001000000000000000020816681712
564,564_0,FAILED,BoTorch,GenerationStep_1,,,4276,3538,0.001000000000000000020816681712,20,0.001000000000000000020816681712
565,565_0,FAILED,BoTorch,GenerationStep_1,,,3685,3398,0.497302839621918735257821708728,20,0.001000000000000000020816681712
566,566_0,FAILED,BoTorch,GenerationStep_1,,,3865,1653,0.001000000000000000020816681712,18,0.998999999999999999111821580300
567,567_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2.000000000000000000000000000000,3884,1903,5.301577506265772399274283088744,1,0.001000000000000000020816681712
568,568_0,FAILED,BoTorch,GenerationStep_1,,,3139,1209,1.939337247992448665101505866915,16,0.328838916822334736966837454020
569,569_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,366.000000000000000000000000000000,3450,3327,5.785487018733995512320689158514,20,0.001000000000000000020816681712
570,570_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,9.000000000000000000000000000000,2495,160,10.000000000000000000000000000000,2,0.001000000000000000020816681712
571,571_0,RUNNING,BoTorch,GenerationStep_1,,,3388,3319,2.593988440564713116032180550974,20,0.001000000000000000020816681712
572,572_0,FAILED,BoTorch,GenerationStep_1,,,3536,815,0.380936710753080554070493235486,17,0.001000000000000000020816681712
573,573_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4.000000000000000000000000000000,4110,4556,8.245846563226217895703484828118,11,0.001000000000000000020816681712
574,574_0,COMPLETED,Sobol,SOBOL,0.739999999999999991118215802999,5450.000000000000000000000000000000,3391,584,7.631954748511315500536511535756,15,0.792847869634628277246690686297
575,575_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,2496.000000000000000000000000000000,1039,2834,4.625938686680980183041356212925,9,0.416944089017808439123768948775
576,576_0,FAILED,Sobol,SOBOL,,,2192,1398,5.483694824459031558205879264278,19,0.042337236819788813535492266737
577,577_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,2.000000000000000000000000000000,4543,3973,2.243324974490329637433205789421,5,0.668454859392717426480601261574
578,578_0,COMPLETED,Sobol,SOBOL,0.739999999999999991118215802999,2.000000000000000000000000000000,3842,2378,2.612650971758179618831263724132,2,0.511130058655515306931249597255
579,579_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,13.000000000000000000000000000000,1798,4791,9.684376849948428400693956064060,16,0.139236256306990990871952362795
580,580_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,1467.000000000000000000000000000000,490,700,0.542432405192404987204213284713,6,0.263640704482793808427487647350
581,581_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,13.000000000000000000000000000000,2533,3423,7.223569961935282890408416278660,12,0.885725489649921704504720310069
582,582_0,FAILED,Sobol,SOBOL,,,2933,1763,0.959868397393263905748028719245,18,0.441941105190664540458556075464
583,583_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,1.000000000000000000000000000000,284,4196,6.786604132746346884630384010961,1,0.814617486774921406578187088598
584,584_0,COMPLETED,Sobol,SOBOL,0.699999999999999955591079014994,332.000000000000000000000000000000,1476,6,3.420673478821292690099653555080,11,0.689552393352612869037443488196
585,585_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,1133.000000000000000000000000000000,4124,2749,8.856825660621748141920761554502,8,0.068006081501021983792831804294
586,586_0,FAILED,Sobol,SOBOL,,,4983,1219,5.979253330792301035501168371411,10,0.222708793984726066694790347356
587,587_0,FAILED,Sobol,SOBOL,,,2026,3488,1.728237748644314786261588778871,13,0.599173899618908767372715828969
588,588_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,2.000000000000000000000000000000,679,1955,8.361864044424146769074468465988,4,0.973097502104938061329164611379
589,589_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,6746.000000000000000000000000000000,3635,4549,3.876499283723533473278166638920,19,0.347785308610647936333748475590
590,590_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,5.000000000000000000000000000000,3550,2091,6.284337012261152821679388580378,6,0.643179271066561364378344478610
591,591_0,FAILED,Sobol,SOBOL,,,881,4531,0.856726662892848267460976785515,13,0.020816361745819449841077286578
592,592_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,132.000000000000000000000000000000,2034,966,9.370081400274299809893818746787,3,0.395689708694815622802565258098
593,593_0,FAILED,Sobol,SOBOL,,,4702,3702,3.551882729458622822704683130723,17,0.767305803287774312870794801711
594,594_0,RUNNING,Sobol,SOBOL,,,4308,301,1.304092024816200012793387941201,18,0.926853723462671030830506424536
595,595_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,236.000000000000000000000000000000,1331,2572,5.797989082159475060507247690111,5,0.300466007269918899957872326922
596,596_0,RUNNING,Sobol,SOBOL,,,23,1664,4.311643237006850704062799195526,15,0.176343282116577043661109769346
597,597_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,2.000000000000000000000000000000,2999,4256,8.571186347902752800109738018364,8,0.551976569445803755264989831630
598,598_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,221.000000000000000000000000000000,2775,877,4.807196509830654385098114289576,4,0.120874360861256718857603686956
599,599_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,6794.000000000000000000000000000000,443,3129,8.056103126343339582149383204523,20,0.745855607135221365666666315519
600,600_0,RUNNING,Sobol,SOBOL,,,1635,2318,2.033997474751435330375670673675,10,0.871263336665928367352762506926
601,601_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,8.000000000000000000000000000000,3966,4891,5.048554912711494147004032129189,14,0.494466748442500825255763174937
602,602_0,FAILED,Sobol,SOBOL,,,4516,1417,9.787522886728867277383869804908,11,0.342048544365912698950182857516
603,603_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,2.000000000000000000000000000000,2492,3837,3.114912560740485947263778143679,7,0.963255759492516516750981736550
604,604_0,FAILED,Sobol,SOBOL,,,1144,370,7.092363858853467206699860980734,17,0.589659564843401318157134483045
605,605_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,1.000000000000000000000000000000,3168,3087,0.029169979312457145620429699306,1,0.216644621903076761704198816005
606,606_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,4655.000000000000000000000000000000,3206,1263,3.245847582931630448399573651841,8,0.895789344372227791524210260832
607,607_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,20.000000000000000000000000000000,1182,3992,8.990227472356520976859428628813,14,0.266719340665265924883442494320
608,608_0,COMPLETED,Sobol,SOBOL,0.739999999999999991118215802999,53.000000000000000000000000000000,2374,525,1.097433158602565406170015194220,5,0.141380255468189719447735797075
609,609_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,18.000000000000000000000000000000,1632,4206,9.354374468216024141042908013333,20,0.001000000000000000020816681712
610,610_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,11.000000000000000000000000000000,1,20,6.844923229951603182996677787742,14,0.998999999999999999111821580300
611,611_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,7.000000000000000000000000000000,1,1546,10.000000000000000000000000000000,1,0.001000000000000000020816681712
612,612_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,14.000000000000000000000000000000,1,1,10.000000000000000000000000000000,15,0.998999999999999999111821580300
613,613_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,5.000000000000000000000000000000,3747,577,1.607673907626942844117934328096,3,0.998999999999999999111821580300
614,614_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,12.000000000000000000000000000000,1022,4732,6.529952822124694300498504162533,15,0.001000000000000000020816681712
615,615_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1011,408,0.393554376244728443268172668468,13,0.998999999999999999111821580300
616,616_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,1,4572,3.427880853460206100891127789509,7,0.001000000000000000020816681712
617,617_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,28.000000000000000000000000000000,1028,277,6.118769442363483079816433019005,12,0.998999999999999999111821580300
618,618_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1.000000000000000000000000000000,1194,2201,2.344525072335504667364602937596,4,0.998999999999999999111821580300
619,619_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1,1659,0.001000000000000000020816681712,20,0.998999999999999999111821580300
620,620_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,23.000000000000000000000000000000,4897,167,1.695431176825733743385171692353,7,0.873126071679251269941346436099
621,621_0,FAILED,BoTorch,BOTORCH_MODULAR,,,5000,1313,3.165501579147921518142538843676,20,0.006401894735810911320206262332
622,622_0,FAILED,BoTorch,BOTORCH_MODULAR,,,5000,2556,9.465331967636002374888448684942,14,0.001000000000000000020816681712
623,623_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1207,525,0.001000000000000000020816681712,14,0.998999999999999999111821580300
624,624_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1196,626,0.001000000000000000020816681712,16,0.998999999999999999111821580300
625,625_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,251.000000000000000000000000000000,1,939,0.001000000000000000020816681712,3,0.001000000000000000020816681712
626,626_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,1210,2204,7.004003683018395598480765329441,4,0.998999999999999999111821580300
627,627_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,856.000000000000000000000000000000,1087,294,0.717760369370141315670252879499,13,0.998999999999999999111821580300
628,628_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,706,3271,3.499530418420436550519525553682,5,0.001000000000000000020816681712
629,629_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,16.000000000000000000000000000000,1,4043,8.625278163102992223798537452240,20,0.998999999999999999111821580300
630,630_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,30.000000000000000000000000000000,5000,2772,0.001000000000000000020816681712,4,0.998999999999999999111821580300
631,631_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,54.000000000000000000000000000000,1492,585,4.932684739917857719149196782382,3,0.001000000000000000020816681712
632,632_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,32.000000000000000000000000000000,3935,673,10.000000000000000000000000000000,1,0.001000000000000000020816681712
633,633_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,5.000000000000000000000000000000,3777,552,7.078273639683896334418022888713,1,0.362103945989732334265198687717
634,634_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,26.000000000000000000000000000000,3710,570,0.989005830842288102822124074009,3,0.001000000000000000020816681712
635,635_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,35.000000000000000000000000000000,4234,3676,10.000000000000000000000000000000,20,0.001000000000000000020816681712
636,636_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3740,597,2.566101943941078822319923347095,20,0.244727394358833422183252537252
637,637_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,5.000000000000000000000000000000,4166,663,3.916828312958883895333883629064,1,0.998999999999999999111821580300
638,638_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,36.000000000000000000000000000000,4775,120,1.495163546759635586624881398166,10,0.548595526080069006980011181440
639,639_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,298.000000000000000000000000000000,3946,766,8.218094473965187773956131422892,3,0.001000000000000000020816681712
640,640_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3571,3205,4.201517333015769573023590055527,18,0.001000000000000000020816681712
641,641_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,4.000000000000000000000000000000,4793,1345,8.313669612612612169755266222637,2,0.998999999999999999111821580300
642,642_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,18.000000000000000000000000000000,1186,295,8.039835570900576655617442156654,8,0.998999999999999999111821580300
643,643_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,35.000000000000000000000000000000,1197,511,7.905403533290165540847738157026,7,0.998999999999999999111821580300
644,644_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1595,5000,2.586778476039294893951137055410,3,0.001000000000000000020816681712
645,645_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1884,5000,10.000000000000000000000000000000,3,0.001000000000000000020816681712
646,646_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,21.000000000000000000000000000000,5000,147,9.291947245067550653629950829782,1,0.001000000000000000020816681712
647,647_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2.000000000000000000000000000000,3940,3475,1.383334256617344770745603454998,4,0.001000000000000000020816681712
648,648_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,3.000000000000000000000000000000,3858,3395,2.455504135342065907821051951032,8,0.001000000000000000020816681712
649,649_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3712,3367,3.730578966492122283682419947581,20,0.001000000000000000020816681712
650,650_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4768,1064,2.988523342947288607973632679204,20,0.001000000000000000020816681712
651,651_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3738,608,0.687515375925208394036758363654,16,0.001000000000000000020816681712
652,652_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,3.000000000000000000000000000000,4813,4720,9.143043644721473128811339847744,9,0.001000000000000000020816681712
653,653_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3826,3188,4.061072370248937524195298465202,19,0.001000000000000000020816681712
654,654_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,4893,2324,7.001943146693151653892073227325,3,0.998999999999999999111821580300
655,655_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,4.000000000000000000000000000000,4025,1925,4.998639273041188602064721635543,4,0.001000000000000000020816681712
656,656_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,4046.000000000000000000000000000000,4302,3129,5.019096234478817919466564489994,15,0.001000000000000000020816681712
657,657_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,104.000000000000000000000000000000,3927,506,2.451024972520718492319247161504,4,0.001000000000000000020816681712
658,658_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,5000,4043,8.543867001663260296595581166912,1,0.998999999999999999111821580300
659,659_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,7.000000000000000000000000000000,3842,603,5.425640159475038082348419266054,1,0.001000000000000000020816681712
660,660_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4814,1136,0.001000000000000000020816681712,20,0.397943174971079560719999790308
661,661_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3333,3349,3.840513173109687983242110931315,18,0.001000000000000000020816681712
662,662_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4260,643,0.832342874011282263246869206341,17,0.001000000000000000020816681712
663,663_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3741,695,0.001000000000000000020816681712,19,0.998999999999999999111821580300
664,664_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3621,986,0.089210478540130172531164021166,20,0.998999999999999999111821580300
665,665_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3817,610,2.446398893380139050179877813207,20,0.013450285047943905114564699943
666,666_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,7.000000000000000000000000000000,4569,636,7.378350070551062955814813903999,1,0.001000000000000000020816681712
667,667_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1,1633,3.101366885738281009565753265633,2,0.998999999999999999111821580300
668,668_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,80.000000000000000000000000000000,4932,485,10.000000000000000000000000000000,2,0.001000000000000000020816681712
669,669_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4508,1540,0.001000000000000000020816681712,18,0.998999999999999999111821580300
670,670_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,17.000000000000000000000000000000,4429,561,1.614113251638503276907954386843,3,0.001000000000000000020816681712
671,671_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,4.000000000000000000000000000000,4968,1169,4.305085745359978233182118856348,2,0.998999999999999999111821580300
672,672_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,9.000000000000000000000000000000,3015,216,2.937058645619547103677859922755,5,0.998999999999999999111821580300
673,673_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4759,592,2.010082772868059475257496160339,20,0.001000000000000000020816681712
674,674_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,176.000000000000000000000000000000,4804,307,0.001000000000000000020816681712,2,0.502895879662945910482108047290
675,675_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4098,1371,0.874289237165640664706245388516,17,0.998999999999999999111821580300
676,676_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3951,525,2.449574057426062712039538382669,20,0.001000000000000000020816681712
677,677_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,9.000000000000000000000000000000,3735,1658,6.731610584578516132125969306799,5,0.998999999999999999111821580300
678,678_0,FAILED,BoTorch,BOTORCH_MODULAR,,,5000,1269,2.150226141271637025909058138495,16,0.001000000000000000020816681712
679,679_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4237,1097,2.321099593052762255496190846316,15,0.998999999999999999111821580300
680,680_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3897,612,2.593425148261335966282103981939,20,0.905996134812591336071818659548
681,681_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3454,1130,4.916229261190849797458213288337,20,0.001000000000000000020816681712
682,682_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4188,527,0.001000000000000000020816681712,12,0.001000000000000000020816681712
683,683_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4955,1082,0.877436903739452267458887035900,19,0.561651210648870247155173274223
684,684_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,610.000000000000000000000000000000,2656,150,6.240397914305971127646444074344,20,0.001000000000000000020816681712
685,685_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,21.000000000000000000000000000000,4214,568,8.736740358329818434413027716801,2,0.001000000000000000020816681712
686,686_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,44.000000000000000000000000000000,4500,590,5.040423403704067162323099182686,3,0.115566703842700124749143242298
687,687_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4113,460,8.743837295701759160237998003140,20,0.001000000000000000020816681712
688,688_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,418.000000000000000000000000000000,5000,712,1.323918908176793474140708894993,4,0.001000000000000000020816681712
689,689_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,7.000000000000000000000000000000,4995,440,6.366083338566761895549461769406,1,0.905122537869337051574802899268
690,690_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,525.000000000000000000000000000000,3746,562,10.000000000000000000000000000000,7,0.001000000000000000020816681712
691,691_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,98.000000000000000000000000000000,5000,599,1.722135004675641400950780735002,4,0.001000000000000000020816681712
692,692_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3634,583,2.388139316626426200684818468289,20,0.001000000000000000020816681712
693,693_0,FAILED,BoTorch,BOTORCH_MODULAR,,,5000,1280,4.600817441021989750993270718027,14,0.005008779975116601709816954724
694,694_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2136.000000000000000000000000000000,4804,335,2.822305448221723356283519024146,5,0.001000000000000000020816681712
695,695_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4180,1201,2.019700047947447174578883277718,18,0.001000000000000000020816681712
696,696_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,56.000000000000000000000000000000,1086,1544,0.001000000000000000020816681712,3,0.001000000000000000020816681712
697,697_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3857,1347,0.001000000000000000020816681712,17,0.001000000000000000020816681712
698,698_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,114.000000000000000000000000000000,3672,660,1.248745699441222489767255865445,2,0.001000000000000000020816681712
699,699_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,11.000000000000000000000000000000,1165,250,5.372314669906302242452511563897,7,0.998999999999999999111821580300
700,700_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,106.000000000000000000000000000000,1221,484,6.075333735383860123135946196271,7,0.998999999999999999111821580300
701,701_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,3.000000000000000000000000000000,711,484,8.243159643933344327138001972344,1,0.998999999999999999111821580300
702,702_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,49.000000000000000000000000000000,4329,867,1.664137594098344941073719382985,3,0.346229714207878069842649892962
703,703_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,6.000000000000000000000000000000,4333,532,10.000000000000000000000000000000,1,0.998999999999999999111821580300
704,704_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,8.000000000000000000000000000000,2607,156,1.276496969365455314004975662101,2,0.001000000000000000020816681712
705,705_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,19.000000000000000000000000000000,3979,4642,10.000000000000000000000000000000,20,0.001000000000000000020816681712
706,706_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,206.000000000000000000000000000000,846,1574,10.000000000000000000000000000000,4,0.001000000000000000020816681712
707,707_0,FAILED,BoTorch,BOTORCH_MODULAR,,,228,1102,0.456567811722239313976245966842,20,0.998999999999999999111821580300
708,708_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,130.000000000000000000000000000000,3654,989,0.001000000000000000020816681712,3,0.998999999999999999111821580300
709,709_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,4910.000000000000000000000000000000,1303,583,1.802582954888975574903042797814,20,0.998999999999999999111821580300
710,710_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,5772.000000000000000000000000000000,5000,482,9.146605430082193066709805862047,8,0.001000000000000000020816681712
711,711_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,4.000000000000000000000000000000,1278,546,1.803116151180205628534736206348,2,0.998999999999999999111821580300
712,712_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,20.000000000000000000000000000000,974,242,6.111782301446027076963218860328,14,0.998999999999999999111821580300
713,713_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,23.000000000000000000000000000000,4130,3496,8.052545332582150550138067046646,15,0.001000000000000000020816681712
714,714_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4166,2491,10.000000000000000000000000000000,14,0.998999999999999999111821580300
715,715_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,13.000000000000000000000000000000,1,1,2.231940624490715396888163013500,20,0.939767959406528685661896815873
716,716_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,187.000000000000000000000000000000,1,1,4.594412048267991544037158746505,6,0.001000000000000000020816681712
717,717_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,220.000000000000000000000000000000,4901,132,0.001000000000000000020816681712,6,0.001000000000000000020816681712
718,718_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,6.000000000000000000000000000000,1171,4671,4.367189173034898175274065579288,11,0.001000000000000000020816681712
719,719_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,30.000000000000000000000000000000,1753,3782,10.000000000000000000000000000000,20,0.998999999999999999111821580300
720,720_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,7.000000000000000000000000000000,1150,4565,0.001000000000000000020816681712,13,0.001000000000000000020816681712
721,721_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,36.000000000000000000000000000000,3611,1082,7.852233822954184816467204655055,2,0.001000000000000000020816681712
722,722_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1406,602,10.000000000000000000000000000000,20,0.372144360580139943461830398519
723,723_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,35.000000000000000000000000000000,4614,1323,0.001000000000000000020816681712,2,0.001000000000000000020816681712
724,724_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4558,1162,2.532068963073648149020300479606,17,0.268704035199428203917193513917
725,725_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,18.000000000000000000000000000000,1344,326,10.000000000000000000000000000000,5,0.998999999999999999111821580300
726,726_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,6.000000000000000000000000000000,1,1059,4.949273649215841786030978255440,5,0.998999999999999999111821580300
727,727_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1,1174,2.642758552436314634093150743865,4,0.998999999999999999111821580300
728,728_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4924,423,2.783754275038518244400620460510,16,0.044610109946505813183215138906
729,729_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,83.000000000000000000000000000000,1444,604,9.212794908157784234958853630815,6,0.998999999999999999111821580300
730,730_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,683.000000000000000000000000000000,4779,195,2.833904183056765724302294984227,16,0.001000000000000000020816681712
731,731_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,12.000000000000000000000000000000,2661,155,2.626344440479945863131661099032,4,0.001000000000000000020816681712
732,732_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1058.000000000000000000000000000000,1186,552,6.481306332891946908603131305426,20,0.998999999999999999111821580300
733,733_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,1252,1541,2.202930639091097209103509158012,1,0.998999999999999999111821580300
734,734_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,4.000000000000000000000000000000,3871,557,10.000000000000000000000000000000,2,0.998999999999999999111821580300
735,735_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3972,2475,10.000000000000000000000000000000,14,0.998999999999999999111821580300
736,736_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,960,1646,8.744361792471671890325524145737,1,0.001000000000000000020816681712
737,737_0,FAILED,BoTorch,BOTORCH_MODULAR,,,5000,2458,9.350802117033882865371197112836,15,0.001000000000000000020816681712
738,738_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,5.000000000000000000000000000000,3937,528,6.402700306333338531317167507950,1,0.998999999999999999111821580300
739,739_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,10.000000000000000000000000000000,2578,189,10.000000000000000000000000000000,5,0.099565693541239230768269408145
740,740_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,88.000000000000000000000000000000,4108,1004,4.569404002012979582048046722775,2,0.001000000000000000020816681712
741,741_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,5000,3951,5.480567963475388104654939525062,4,0.001000000000000000020816681712
742,742_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,5000,4015,0.001000000000000000020816681712,1,0.001000000000000000020816681712
743,743_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3380,1325,0.900211544096783611301759719936,20,0.001000000000000000020816681712
744,744_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,4.000000000000000000000000000000,2645,205,1.194927743055346880396427877713,1,0.998999999999999999111821580300
745,745_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,4075,3335,10.000000000000000000000000000000,17,0.001000000000000000020816681712
746,746_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,42.000000000000000000000000000000,5000,3557,8.291559068298999690682649088558,20,0.998999999999999999111821580300
747,747_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,16.000000000000000000000000000000,1,1,0.949051425520586300343950370006,16,0.998999999999999999111821580300
748,748_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,5000,2538,10.000000000000000000000000000000,15,0.001000000000000000020816681712
749,749_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4207,2462,10.000000000000000000000000000000,14,0.998999999999999999111821580300
750,750_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4528,1733,3.157967451184891860549441844341,20,0.998999999999999999111821580300
751,751_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,14.000000000000000000000000000000,1,1,2.169732708489722661227006028639,13,0.998999999999999999111821580300
752,752_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,39.000000000000000000000000000000,5000,2746,8.080716824405570264389098156244,3,0.001000000000000000020816681712
753,753_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,1.000000000000000000000000000000,2036,3089,10.000000000000000000000000000000,1,0.001000000000000000020816681712
754,754_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1.000000000000000000000000000000,1249,2244,0.001000000000000000020816681712,3,0.001000000000000000020816681712
755,755_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,14.000000000000000000000000000000,1,1,7.891611466037118383098913909635,1,0.998999999999999999111821580300
756,756_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1,4557,9.930702701871775062159031222109,5,0.001000000000000000020816681712
757,757_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,715,3264,10.000000000000000000000000000000,4,0.998999999999999999111821580300
758,758_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,3660,2442,10.000000000000000000000000000000,5,0.998999999999999999111821580300
759,759_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1,4768,0.497313317394022458284297272257,4,0.001000000000000000020816681712
760,760_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,4318,4188,0.001000000000000000020816681712,5,0.001000000000000000020816681712
761,761_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1,4371,0.001000000000000000020816681712,6,0.001000000000000000020816681712
762,762_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,534,4640,2.972155706032388877702032914385,2,0.998999999999999999111821580300
763,763_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,725,2224,10.000000000000000000000000000000,1,0.001000000000000000020816681712
764,764_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1,3262,0.001000000000000000020816681712,20,0.001000000000000000020816681712
765,765_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,11.000000000000000000000000000000,2441,2742,7.189347985104827642999225645326,3,0.998999999999999999111821580300
766,766_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,14.000000000000000000000000000000,1518,4480,8.144696216793448684256873093545,16,0.001000000000000000020816681712
767,767_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1142,4841,1.472729736118242094278230069904,7,0.001000000000000000020816681712
768,768_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,11.000000000000000000000000000000,5000,4110,10.000000000000000000000000000000,16,0.998999999999999999111821580300
769,769_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,3013,2862,1.073233462152111794551956336363,4,0.001000000000000000020816681712
770,770_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,14.000000000000000000000000000000,1,1,9.663651972735710415918219950981,4,0.998999999999999999111821580300
771,771_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,6.000000000000000000000000000000,4098,3950,6.248205559626692107144663168583,10,0.998999999999999999111821580300
772,772_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1,4579,9.391628592715516532507535885088,1,0.998999999999999999111821580300
773,773_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,4328,2486,0.001000000000000000020816681712,19,0.001000000000000000020816681712
774,774_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,3.000000000000000000000000000000,1,3443,4.034236304365565572993546084035,8,0.998999999999999999111821580300
775,775_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,19.000000000000000000000000000000,5000,145,5.199096058573953627046648762189,1,0.106765083080588255026732724673
776,776_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,17.000000000000000000000000000000,1,4390,0.001000000000000000020816681712,17,0.998999999999999999111821580300
777,777_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,20.000000000000000000000000000000,1,4720,8.822659783425578439164382871240,20,0.001000000000000000020816681712
778,778_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,16.000000000000000000000000000000,1,1,3.805632798397290628145128721371,10,0.998999999999999999111821580300
779,779_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,7.000000000000000000000000000000,3802,3400,10.000000000000000000000000000000,10,0.001000000000000000020816681712
780,780_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,1.000000000000000000000000000000,3161,4188,2.238067220645877597462458652444,4,0.001000000000000000020816681712
781,781_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1712,4545,5.501348292712335386056565766921,1,0.998999999999999999111821580300
782,782_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4556,2782,9.425859708734494191162411880214,15,0.998999999999999999111821580300
783,783_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,4,4087,7.462609427801542238967158482410,8,0.998999999999999999111821580300
784,784_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,18.000000000000000000000000000000,5000,3530,10.000000000000000000000000000000,15,0.001000000000000000020816681712
785,785_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2.000000000000000000000000000000,5000,3510,0.001000000000000000020816681712,5,0.998999999999999999111821580300
786,786_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1581,2171,0.001000000000000000020816681712,20,0.001000000000000000020816681712
787,787_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,7.000000000000000000000000000000,3224,4684,6.137911707715111298000465467339,13,0.001000000000000000020816681712
788,788_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1.000000000000000000000000000000,4859,3551,2.143282906674680798886356569710,1,0.010946654628968804229272038242
789,789_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,14.000000000000000000000000000000,1980,213,8.912222043666304216458229348063,7,0.998999999999999999111821580300
790,790_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2.000000000000000000000000000000,5000,3550,7.641153696360345470850461424561,3,0.001000000000000000020816681712
791,791_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,4381,2371,4.993916676264070986235310556367,2,0.001000000000000000020816681712
792,792_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,28.000000000000000000000000000000,5000,189,10.000000000000000000000000000000,20,0.998999999999999999111821580300
793,793_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,3811,1887,2.860391486320653875452535430668,3,0.001000000000000000020816681712
794,794_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,5000,5000,8.890597105671689703854099207092,19,0.001000000000000000020816681712
795,795_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,3913,4669,7.722432901608008215532663598424,5,0.001000000000000000020816681712
796,796_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4499,1869,0.001000000000000000020816681712,20,0.998999999999999999111821580300
797,797_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4782,1152,5.077347979390888887962773878826,20,0.001000000000000000020816681712
798,798_0,FAILED,BoTorch,BOTORCH_MODULAR,,,1711,3723,4.071553110502144079418940236792,13,0.998999999999999999111821580300
799,799_0,FAILED,BoTorch,BOTORCH_MODULAR,,,3629,3233,0.001000000000000000020816681712,20,0.001000000000000000020816681712
800,800_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,3.000000000000000000000000000000,5000,3546,8.509557850387841071437833306845,8,0.998999999999999999111821580300
801,801_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,1543.000000000000000000000000000000,2641,174,7.984606969811506971268499910366,20,0.001000000000000000020816681712
802,802_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,10.000000000000000000000000000000,5000,1830,3.632705817558471217409987730207,3,0.001000000000000000020816681712
803,803_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,30.000000000000000000000000000000,5000,128,6.341248725121127804982279485557,20,0.998999999999999999111821580300
804,804_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,1813,3707,3.752582250450356315951694341493,4,0.998999999999999999111821580300
805,805_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,3834,3532,8.437004859935209211130313633475,13,0.001000000000000000020816681712
806,806_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,2.000000000000000000000000000000,4349,5000,9.689772665577448762519452429842,5,0.001000000000000000020816681712
807,807_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2.000000000000000000000000000000,3697,2333,0.988548012805980258477234201564,4,0.001000000000000000020816681712
808,808_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3989,447,2.073570637522046400391673159902,13,0.001000000000000000020816681712
809,809_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,1,844,0.001000000000000000020816681712,13,0.998999999999999999111821580300
810,810_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,40.000000000000000000000000000000,5000,76,3.071482978668945040823246017681,19,0.998999999999999999111821580300
811,811_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,4.000000000000000000000000000000,3335,3422,8.883131744696699527708005916793,9,0.001000000000000000020816681712
812,812_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,31.000000000000000000000000000000,1594,636,1.410432935100853768517481512390,2,0.001000000000000000020816681712
813,813_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,8.000000000000000000000000000000,4334,417,8.548060271368365548028123157565,1,0.998999999999999999111821580300
814,814_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1.000000000000000000000000000000,1398,2215,2.293052341711073971453060948988,1,0.001000000000000000020816681712
815,815_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4572,1263,0.411325632168959398615726286152,20,0.001000000000000000020816681712
816,816_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,274,2702,5.397786815259408399469975847751,2,0.998999999999999999111821580300
817,817_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1.000000000000000000000000000000,1,2691,2.541827703786625924919917451916,5,0.998999999999999999111821580300
818,818_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2.000000000000000000000000000000,5000,3479,1.338350981813196005631994012219,6,0.001000000000000000020816681712
819,819_0,FAILED,BoTorch,BOTORCH_MODULAR,,,4208,1910,0.001000000000000000020816681712,20,0.001000000000000000020816681712
820,820_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,16.000000000000000000000000000000,1,1,7.668848811818926414218822174007,17,0.998999999999999999111821580300
821,821_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2.000000000000000000000000000000,4312,3511,6.358603079263808410814817762002,1,0.001000000000000000020816681712
822,822_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,5000,2727,10.000000000000000000000000000000,15,0.001000000000000000020816681712
823,823_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,3.000000000000000000000000000000,1877,2075,7.553368504456837051463935495121,5,0.001000000000000000020816681712
824,824_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,11.000000000000000000000000000000,374,3521,4.230633678111437312452380865579,12,0.001000000000000000020816681712
825,825_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,6.000000000000000000000000000000,1026,550,3.418442168374661971341765820398,4,0.998999999999999999111821580300
</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">
⚠ Job 4621459 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621459/4621459_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621459/4621459_0_log.out
----------------------------------------
submitit INFO (2025-04-23 12:58:26,069) - Starting with JobEnvironment(job_id=4621459, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 12:58:26,074) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621459/4621459_submitted.pkl
slurmstepd: error: *** JOB 4621459 ON i7183 CANCELLED AT 2025-04-23T14:58:21 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621459/4621459_0_log.err not found


⚠ Job 4621763 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621763/4621763_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621763/4621763_0_log.out
----------------------------------------
submitit INFO (2025-04-23 13:03:14,132) - Starting with JobEnvironment(job_id=4621763, hostname=i7181, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 13:03:14,137) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621763/4621763_submitted.pkl
slurmstepd: error: *** JOB 4621763 ON i7181 CANCELLED AT 2025-04-23T15:01:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4621763/4621763_0_log.err not found


⚠ Job 4622055 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622055/4622055_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622055/4622055_0_log.out
----------------------------------------
submitit INFO (2025-04-23 13:03:53,788) - Starting with JobEnvironment(job_id=4622055, hostname=i7180, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 13:03:53,791) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622055/4622055_submitted.pkl
slurmstepd: error: *** JOB 4622055 ON i7180 CANCELLED AT 2025-04-23T15:03:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622055/4622055_0_log.err not found


⚠ Job 4622114 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622114/4622114_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622114/4622114_0_log.out
----------------------------------------
submitit INFO (2025-04-23 13:04:56,047) - Starting with JobEnvironment(job_id=4622114, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 13:04:56,049) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622114/4622114_submitted.pkl
slurmstepd: error: *** JOB 4622114 ON i7185 CANCELLED AT 2025-04-23T15:04:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622114/4622114_0_log.err not found


⚠ Job 4622465 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622465/4622465_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622465/4622465_0_log.out
----------------------------------------
submitit INFO (2025-04-23 13:07:05,865) - Starting with JobEnvironment(job_id=4622465, hostname=i7174, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 13:07:05,896) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622465/4622465_submitted.pkl
slurmstepd: error: *** JOB 4622465 ON i7174 CANCELLED AT 2025-04-23T15:06:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622465/4622465_0_log.err not found


⚠ Job 4622663 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622663/4622663_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622663/4622663_0_log.out
----------------------------------------
submitit INFO (2025-04-23 13:10:34,499) - Starting with JobEnvironment(job_id=4622663, hostname=i7177, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 13:10:34,501) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622663/4622663_submitted.pkl
slurmstepd: error: *** JOB 4622663 ON i7177 CANCELLED AT 2025-04-23T15:10:55 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4622663/4622663_0_log.err not found


⚠ Job 4630040 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630040/4630040_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630040/4630040_0_log.out
----------------------------------------
submitit INFO (2025-04-23 15:19:23,814) - Starting with JobEnvironment(job_id=4630040, hostname=i7180, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 15:19:23,816) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630040/4630040_submitted.pkl
slurmstepd: error: *** JOB 4630040 ON i7180 CANCELLED AT 2025-04-23T17:19:29 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630040/4630040_0_log.err not found


⚠ Job 4630143 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630143/4630143_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630143/4630143_0_log.out
----------------------------------------
submitit INFO (2025-04-23 15:21:23,771) - Starting with JobEnvironment(job_id=4630143, hostname=i7180, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 15:21:23,774) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630143/4630143_submitted.pkl
slurmstepd: error: *** JOB 4630143 ON i7180 CANCELLED AT 2025-04-23T17:21:29 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4630143/4630143_0_log.err not found


⚠ Job 4640966 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4640966/4640966_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4640966/4640966_0_log.out
----------------------------------------
submitit INFO (2025-04-23 17:56:00,208) - Starting with JobEnvironment(job_id=4640966, hostname=i7173, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 17:56:00,345) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4640966/4640966_submitted.pkl
slurmstepd: error: *** JOB 4640966 ON i7173 CANCELLED AT 2025-04-23T19:55:03 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4640966/4640966_0_log.err not found


⚠ Job 4644872 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4644872/4644872_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4644872/4644872_0_log.out
----------------------------------------
submitit INFO (2025-04-23 19:10:28,249) - Starting with JobEnvironment(job_id=4644872, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 19:10:28,252) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4644872/4644872_submitted.pkl
slurmstepd: error: *** JOB 4644872 ON i7186 CANCELLED AT 2025-04-23T21:10:33 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4644872/4644872_0_log.err not found


⚠ Job 4645536 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4645536/4645536_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4645536/4645536_0_log.out
----------------------------------------
submitit INFO (2025-04-23 19:24:58,524) - Starting with JobEnvironment(job_id=4645536, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 19:24:58,527) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4645536/4645536_submitted.pkl
slurmstepd: error: *** JOB 4645536 ON i7186 CANCELLED AT 2025-04-23T21:25:04 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4645536/4645536_0_log.err not found


⚠ Job 4654836 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4654836/4654836_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4654836/4654836_0_log.out
----------------------------------------
submitit INFO (2025-04-23 22:33:02,319) - Starting with JobEnvironment(job_id=4654836, hostname=i7176, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 22:33:02,321) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4654836/4654836_submitted.pkl
slurmstepd: error: *** JOB 4654836 ON i7176 CANCELLED AT 2025-04-24T00:33:06 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4654836/4654836_0_log.err not found


⚠ Job 4656997 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4656997/4656997_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4656997/4656997_0_log.out
----------------------------------------
submitit INFO (2025-04-23 23:23:29,033) - Starting with JobEnvironment(job_id=4656997, hostname=i7178, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-23 23:23:29,038) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4656997/4656997_submitted.pkl
slurmstepd: error: *** JOB 4656997 ON i7178 CANCELLED AT 2025-04-24T01:23:36 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4656997/4656997_0_log.err not found


⚠ Job 4665481 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4665481/4665481_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4665481/4665481_0_log.out
----------------------------------------
submitit INFO (2025-04-24 02:42:38,466) - Starting with JobEnvironment(job_id=4665481, hostname=i7180, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 02:42:38,469) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4665481/4665481_submitted.pkl
slurmstepd: error: *** JOB 4665481 ON i7180 CANCELLED AT 2025-04-24T04:42:39 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4665481/4665481_0_log.err not found


⚠ Job 4674866 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4674866/4674866_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4674866/4674866_0_log.out
----------------------------------------
submitit INFO (2025-04-24 06:36:53,518) - Starting with JobEnvironment(job_id=4674866, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 06:36:53,523) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4674866/4674866_submitted.pkl
slurmstepd: error: *** JOB 4674866 ON i7185 CANCELLED AT 2025-04-24T08:37:17 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4674866/4674866_0_log.err not found


⚠ Job 4675456 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4675456/4675456_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4675456/4675456_0_log.out
----------------------------------------
submitit INFO (2025-04-24 06:51:12,703) - Starting with JobEnvironment(job_id=4675456, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 06:51:12,705) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4675456/4675456_submitted.pkl
slurmstepd: error: *** JOB 4675456 ON i7186 CANCELLED AT 2025-04-24T08:51:17 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4675456/4675456_0_log.err not found


⚠ Job 4676976 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4676976/4676976_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4676976/4676976_0_log.out
----------------------------------------
submitit INFO (2025-04-24 07:31:53,492) - Starting with JobEnvironment(job_id=4676976, hostname=i7180, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 07:31:53,495) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4676976/4676976_submitted.pkl
slurmstepd: error: *** JOB 4676976 ON i7180 CANCELLED AT 2025-04-24T09:32:18 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4676976/4676976_0_log.err not found


⚠ Job 4684643 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684643/4684643_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684643/4684643_0_log.out
----------------------------------------
submitit INFO (2025-04-24 11:38:58,854) - Starting with JobEnvironment(job_id=4684643, hostname=i7182, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 11:38:58,856) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684643/4684643_submitted.pkl
slurmstepd: error: *** JOB 4684643 ON i7182 CANCELLED AT 2025-04-24T13:39:07 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684643/4684643_0_log.err not found


⚠ Job 4684958 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684958/4684958_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684958/4684958_0_log.out
----------------------------------------
submitit INFO (2025-04-24 11:55:01,164) - Starting with JobEnvironment(job_id=4684958, hostname=i7175, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 11:55:01,166) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684958/4684958_submitted.pkl
slurmstepd: error: *** JOB 4684958 ON i7175 CANCELLED AT 2025-04-24T13:55:08 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4684958/4684958_0_log.err not found


⚠ Job 4685513 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685513/4685513_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685513/4685513_0_log.out
----------------------------------------
submitit INFO (2025-04-24 12:22:34,822) - Starting with JobEnvironment(job_id=4685513, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 12:22:34,826) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685513/4685513_submitted.pkl
slurmstepd: error: *** JOB 4685513 ON i7185 CANCELLED AT 2025-04-24T14:22:39 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685513/4685513_0_log.err not found


⚠ Job 4685806 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685806/4685806_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685806/4685806_0_log.out
----------------------------------------
submitit INFO (2025-04-24 12:37:08,458) - Starting with JobEnvironment(job_id=4685806, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 12:37:08,460) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685806/4685806_submitted.pkl
slurmstepd: error: *** JOB 4685806 ON i7185 CANCELLED AT 2025-04-24T14:37:10 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4685806/4685806_0_log.err not found


⚠ Job 4686234 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4686234/4686234_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4686234/4686234_0_log.out
----------------------------------------
submitit INFO (2025-04-24 12:58:43,130) - Starting with JobEnvironment(job_id=4686234, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 12:58:43,132) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4686234/4686234_submitted.pkl
slurmstepd: error: *** JOB 4686234 ON i7186 CANCELLED AT 2025-04-24T14:58:41 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4686234/4686234_0_log.err not found


⚠ Job 4689008 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4689008/4689008_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4689008/4689008_0_log.out
----------------------------------------
submitit INFO (2025-04-24 14:59:39,685) - Starting with JobEnvironment(job_id=4689008, hostname=i7182, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 14:59:39,688) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4689008/4689008_submitted.pkl
slurmstepd: error: *** JOB 4689008 ON i7182 CANCELLED AT 2025-04-24T16:59:43 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4689008/4689008_0_log.err not found


⚠ Job 4693491 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4693491/4693491_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4693491/4693491_0_log.out
----------------------------------------
submitit INFO (2025-04-24 17:24:43,221) - Starting with JobEnvironment(job_id=4693491, hostname=i7176, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 17:24:43,225) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4693491/4693491_submitted.pkl
slurmstepd: error: *** JOB 4693491 ON i7176 CANCELLED AT 2025-04-24T19:24:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4693491/4693491_0_log.err not found


⚠ Job 4694228 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4694228/4694228_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4694228/4694228_0_log.out
----------------------------------------
submitit INFO (2025-04-24 17:48:54,908) - Starting with JobEnvironment(job_id=4694228, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 17:48:54,910) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4694228/4694228_submitted.pkl
slurmstepd: error: *** JOB 4694228 ON i7183 CANCELLED AT 2025-04-24T19:49:22 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4694228/4694228_0_log.err not found


⚠ Job 4698255 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4698255/4698255_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4698255/4698255_0_log.out
----------------------------------------
submitit INFO (2025-04-24 20:00:45,661) - Starting with JobEnvironment(job_id=4698255, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 20:00:45,663) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4698255/4698255_submitted.pkl
slurmstepd: error: *** JOB 4698255 ON i7185 CANCELLED AT 2025-04-24T22:00:55 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4698255/4698255_0_log.err not found


⚠ Job 4702697 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4702697/4702697_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4702697/4702697_0_log.out
----------------------------------------
submitit INFO (2025-04-24 22:27:08,478) - Starting with JobEnvironment(job_id=4702697, hostname=i7182, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-24 22:27:08,482) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4702697/4702697_submitted.pkl
slurmstepd: error: *** JOB 4702697 ON i7182 CANCELLED AT 2025-04-25T00:27:29 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4702697/4702697_0_log.err not found


⚠ Job 4706023 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4706023/4706023_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4706023/4706023_0_log.out
----------------------------------------
submitit INFO (2025-04-25 00:21:29,346) - Starting with JobEnvironment(job_id=4706023, hostname=i7182, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-25 00:21:29,348) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4706023/4706023_submitted.pkl
slurmstepd: error: *** JOB 4706023 ON i7182 CANCELLED AT 2025-04-25T02:21:32 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4706023/4706023_0_log.err not found


⚠ Job 4712651 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4712651/4712651_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4712651/4712651_0_log.out
----------------------------------------
submitit INFO (2025-04-25 04:09:53,198) - Starting with JobEnvironment(job_id=4712651, hostname=i7181, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-25 04:09:53,201) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4712651/4712651_submitted.pkl
slurmstepd: error: *** JOB 4712651 ON i7181 CANCELLED AT 2025-04-25T06:10:09 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4712651/4712651_0_log.err not found


⚠ Job 4713384 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4713384/4713384_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4713384/4713384_0_log.out
----------------------------------------
submitit INFO (2025-04-25 04:35:53,187) - Starting with JobEnvironment(job_id=4713384, hostname=i7180, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-25 04:35:53,191) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4713384/4713384_submitted.pkl
slurmstepd: error: *** JOB 4713384 ON i7180 CANCELLED AT 2025-04-25T06:36:09 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4713384/4713384_0_log.err not found


⚠ Job 4715708 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4715708/4715708_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4715708/4715708_0_log.out
----------------------------------------
submitit INFO (2025-04-25 05:59:54,125) - Starting with JobEnvironment(job_id=4715708, hostname=i7181, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-25 05:59:54,139) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4715708/4715708_submitted.pkl
slurmstepd: error: *** JOB 4715708 ON i7181 CANCELLED AT 2025-04-25T08:00:09 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4715708/4715708_0_log.err not found


⚠ Job 4727890 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4727890/4727890_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4727890/4727890_0_log.out
----------------------------------------
submitit INFO (2025-04-25 13:27:19,345) - Starting with JobEnvironment(job_id=4727890, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-25 13:27:19,348) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4727890/4727890_submitted.pkl
slurmstepd: error: *** JOB 4727890 ON i7186 CANCELLED AT 2025-04-25T15:27:23 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4727890/4727890_0_log.err not found


⚠ Job 4740960 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4740960/4740960_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4740960/4740960_0_log.out
----------------------------------------
submitit INFO (2025-04-25 21:41:46,614) - Starting with JobEnvironment(job_id=4740960, hostname=i7176, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-25 21:41:46,617) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4740960/4740960_submitted.pkl
slurmstepd: error: *** JOB 4740960 ON i7176 CANCELLED AT 2025-04-25T23:42:13 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4740960/4740960_0_log.err not found


⚠ Job 4743487 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4743487/4743487_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4743487/4743487_0_log.out
----------------------------------------
submitit INFO (2025-04-25 23:03:28,335) - Starting with JobEnvironment(job_id=4743487, hostname=i7180, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-25 23:03:28,338) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4743487/4743487_submitted.pkl
slurmstepd: error: *** JOB 4743487 ON i7180 CANCELLED AT 2025-04-26T01:03:43 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4743487/4743487_0_log.err not found


⚠ Job 4748300 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748300/4748300_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748300/4748300_0_log.out
----------------------------------------
submitit INFO (2025-04-26 02:19:10,834) - Starting with JobEnvironment(job_id=4748300, hostname=i7185, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-26 02:19:10,837) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748300/4748300_submitted.pkl
slurmstepd: error: *** JOB 4748300 ON i7185 CANCELLED AT 2025-04-26T04:19:21 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748300/4748300_0_log.err not found


⚠ Job 4748753 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748753/4748753_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748753/4748753_0_log.out
----------------------------------------
submitit INFO (2025-04-26 02:38:30,695) - Starting with JobEnvironment(job_id=4748753, hostname=i7183, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-26 02:38:30,697) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748753/4748753_submitted.pkl
slurmstepd: error: *** JOB 4748753 ON i7183 CANCELLED AT 2025-04-26T04:38:51 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4748753/4748753_0_log.err not found


⚠ Job 4749316 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749316/4749316_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749316/4749316_0_log.out
----------------------------------------
submitit INFO (2025-04-26 03:01:51,084) - Starting with JobEnvironment(job_id=4749316, hostname=i7181, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-26 03:01:51,087) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749316/4749316_submitted.pkl
slurmstepd: error: *** JOB 4749316 ON i7181 CANCELLED AT 2025-04-26T05:01:53 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749316/4749316_0_log.err not found


⚠ Job 4749710 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749710/4749710_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749710/4749710_0_log.out
----------------------------------------
submitit INFO (2025-04-26 03:17:51,076) - Starting with JobEnvironment(job_id=4749710, hostname=i7175, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-26 03:17:51,078) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749710/4749710_submitted.pkl
slurmstepd: error: *** JOB 4749710 ON i7175 CANCELLED AT 2025-04-26T05:17:53 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4749710/4749710_0_log.err not found


⚠ Job 4758846 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4758846/4758846_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4758846/4758846_0_log.out
----------------------------------------
submitit INFO (2025-04-26 09:48:55,313) - Starting with JobEnvironment(job_id=4758846, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-26 09:48:55,315) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4758846/4758846_submitted.pkl
slurmstepd: error: *** JOB 4758846 ON i7186 CANCELLED AT 2025-04-26T11:49:09 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4758846/4758846_0_log.err not found


⚠ Job 4760954 (task: 0) with path /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4760954/4760954_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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4760954/4760954_0_log.out
----------------------------------------
submitit INFO (2025-04-26 11:24:16,104) - Starting with JobEnvironment(job_id=4760954, hostname=i7186, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-04-26 11:24:16,110) - Loading pickle: /data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4760954/4760954_submitted.pkl
slurmstepd: error: *** JOB 4760954 ON i7186 CANCELLED AT 2025-04-26T13:24:15 DUE TO TIME LIMIT ***



/data/horse/ws/s4122485-compPerfDD/benchmark/dfki/benchmarkdd/runs/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/1/single_runs/4760954/4760954_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>1                                                                                                                               </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/BNDM_NOAAWeather_HoeffdingTreeClassifier_ACCURACY-RUNTIME/0/ </td></tr><tr><td> experiment_constraints</td><td>None                                                                                                                            </td></tr><tr><td> run_dir</td><td>runs                                                                                                                            </td></tr><tr><td> seed</td><td>None                                                                                                                            </td></tr><tr><td> decimalrounding</td><td>4                                                                                                                               </td></tr><tr><td> enforce_sequential_optimization</td><td>False                                                                                                                           </td></tr><tr><td> verbose_tqdm</td><td>False                                                                                                                           </td></tr><tr><td> model</td><td>None                                                                                                                            </td></tr><tr><td> gridsearch</td><td>False                                                                                                                           </td></tr><tr><td> occ</td><td>False                                                                                                                           </td></tr><tr><td> show_sixel_scatter</td><td>False                                                                                                                           </td></tr><tr><td> show_sixel_general</td><td>False                                                                                                                           </td></tr><tr><td> show_sixel_trial_index_result</td><td>False                                                                                                                           </td></tr><tr><td> follow</td><td>False                                                                                                                           </td></tr><tr><td> send_anonymized_usage_stats</td><td>False                                                                                                                           </td></tr><tr><td> ui_url</td><td>None                                                                                                                            </td></tr><tr><td> root_venv_dir</td><td>/home/s4122485                                                                                                                  </td></tr><tr><td> exclude</td><td>None                                                                                                                            </td></tr><tr><td> main_process_gb</td><td>8                                                                                                                               </td></tr><tr><td> pareto_front_confidence</td><td>1                                                                                                                               </td></tr><tr><td> max_nr_of_zero_results</td><td>10                                                                                                                              </td></tr><tr><td> abbreviate_job_names</td><td>False                                                                                                                           </td></tr><tr><td> orchestrator_file</td><td>None                                                                                                                            </td></tr><tr><td> checkout_to_latest_tested_version</td><td>False                                                                                                                           </td></tr><tr><td> live_share</td><td>False                                                                                                                           </td></tr><tr><td> disable_tqdm</td><td>False                                                                                                                           </td></tr><tr><td> workdir</td><td>False                                                                                                                           </td></tr><tr><td> occ_type</td><td>euclid                                                                                                                          </td></tr><tr><td> result_names</td><td>[&#039;RESULT=min&#039;]                                                                                                                  </td></tr><tr><td> minkowski_p</td><td>2                                                                                                                               </td></tr><tr><td> signed_weighted_euclidean_weights</td><td></td></tr><tr><td> generation_strategy</td><td>None                                                                                                                            </td></tr><tr><td> generate_all_jobs_at_once</td><td>False                                                                                                                           </td></tr><tr><td> revert_to_random_when_seemingly_exhausted</td><td>True                                                                                                                            </td></tr><tr><td> load_data_from_existing_jobs</td><td>[]                                                                                                                              </td></tr><tr><td> n_estimators_randomforest</td><td>100                                                                                                                             </td></tr><tr><td> external_generator</td><td>None                                                                                                                            </td></tr><tr><td> username</td><td>None                                                                                                                            </td></tr><tr><td> max_failed_jobs</td><td>None                                                                                                                            </td></tr><tr><td> num_parallel_jobs</td><td>20                                                                                                                              </td></tr><tr><td> worker_timeout</td><td>120                                                                                                                             </td></tr><tr><td> slurm_use_srun</td><td>False                                                                                                                           </td></tr><tr><td> time</td><td></td></tr><tr><td> partition</td><td></td></tr><tr><td> reservation</td><td>None                                                                                                                            </td></tr><tr><td> force_local_execution</td><td>False                                                                                                                           </td></tr><tr><td> slurm_signal_delay_s</td><td>0                                                                                                                               </td></tr><tr><td> nodes_per_job</td><td>1                                                                                                                               </td></tr><tr><td> cpus_per_task</td><td>1                                                                                                                               </td></tr><tr><td> account</td><td>None                                                                                                                            </td></tr><tr><td> gpus</td><td>0                                                                                                                               </td></tr><tr><td> run_mode</td><td>local                                                                                                                           </td></tr><tr><td> verbose</td><td>False                                                                                                                           </td></tr><tr><td> verbose_break_run_search_table</td><td>False                                                                                                                           </td></tr><tr><td> debug</td><td>False                                                                                                                           </td></tr><tr><td> no_sleep</td><td>False                                                                                                                           </td></tr><tr><td> tests</td><td>False                                                                                                                           </td></tr><tr><td> show_worker_percentage_table_at_end</td><td>False                                                                                                                           </td></tr><tr><td> auto_exclude_defective_hosts</td><td>False                                                                                                                           </td></tr><tr><td> run_tests_that_fail_on_taurus</td><td>False                                                                                                                           </td></tr><tr><td> raise_in_eval</td><td>False                                                                                                                           </td></tr><tr><td> show_ram_every_n_seconds</td><td>False                                                                                                                           </td></tr></tbody></table>
			<h1>&nbsp;Worker-Usage</h1>
			<div class='invert_in_dark_mode' id='workerUsagePlot'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
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1745648788.0831513,20,3,15
</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
1745405819,646.6015625,39.3
1745405819,646.60546875,34.5
1745405819,646.73046875,35.8
1745405819,646.73046875,31.7
1745405819,646.73046875,39.0
1745405819,646.73046875,33.3
1745405819,646.73046875,42.5
1745406633,700.96875,39.4
1745406633,700.96875,36.2
1745406633,700.96875,39.8
1745406633,700.96875,26.7
1745418370,910.01171875,34.1
1745418370,910.01171875,29.5
1745418370,910.01171875,29.4
1745418370,910.01171875,31.4
1745437028,961.546875,37.1
1745437028,961.546875,29.4
1745437028,961.546875,28.1
1745437028,961.546875,28.0
1745455398,938.3671875,35.6
1745455398,938.3671875,33.9
1745455398,938.3671875,33.5
1745455398,938.3671875,38.5
1745478080,923.56640625,35.5
1745478080,923.56640625,36.5
1745478080,923.56640625,36.3
1745478080,923.56640625,25.8
1745499646,926.51953125,31.3
1745499646,926.51953125,26.0
1745499646,926.51953125,25.0
1745499646,926.51953125,31.4
1745521823,1020.203125,29.9
1745521823,1020.203125,26.8
1745521823,1020.203125,29.4
1745521823,1020.203125,30.0
1745542028,1023.43359375,30.7
1745542028,1023.43359375,36.8
1745542028,1023.43359375,35.4
1745542028,1023.43359375,37.8
1745564134,1287.953125,30.5
1745564134,1287.953125,29.9
1745564134,1287.953125,28.7
1745564134,1287.953125,30.0
1745591436,1027.765625,32.9
1745591436,1027.765625,25.5
1745591436,1027.765625,24.4
1745591436,1027.765625,26.1
1745623148,1055.0390625,32.6
1745623148,1055.0390625,32.6
1745623148,1055.0390625,32.2
1745623148,1055.0390625,35.6
1745648807,1086.02734375,31.6
1745648807,1086.02734375,30.8
1745648807,1086.02734375,31.2
1745648807,1086.02734375,31.5
</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>
	</body>
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