OmniOpt2

Setting	Value
Max. nr. evaluations	50498
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	498
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
1	833	4	838
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_Electricity_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
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		<title>Exported &raquo;s4122485/BNDM_Electricity_HoeffdingTreeClassifier_ACCURACY-RUNTIME/2&laquo; from OmniOpt2-Share</title>
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				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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			    "hostname",
			    "OO_Info_runtime",
			    "OO_Info_peak_memory",
			    "OO_Info_mean_memory",
			    "OO_Info_lpd",
			    "OO_Info_portion_req_label",
			    "OO_Info_SLURM_JOB_ID"
			];
			var tab_job_infos_csv_json = [
			    [
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			        1745405679,
			        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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2179 n_samples 1989 const 6.339154988110066 max_depth 15 threshold 0.3520864011645317",
			        2179,
			        1989,
			        6.339154988110066,
			        15,
			        0.3520864011645317,
			        0.67,
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			    [
			        1745405661,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4346 n_samples 3767 const 1.5990370388543234 max_depth 2 threshold 0.5085196899622679",
			        4346,
			        3767,
			        1.5990370388543234,
			        2,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3144 n_samples 692 const 7.999197697264143 max_depth 18 threshold 0.8696807247959077",
			        3144,
			        692,
			        7.999197697264143,
			        18,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 351 n_samples 2627 const 2.7512988179950044 max_depth 6 threshold 0.028082407308742403",
			        351,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3097 n_samples 3385 const 6.126140797570349 max_depth 16 threshold 0.9642155411932617",
			        3097,
			        3385,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 929 n_samples 164 const 0.8794649205524475 max_depth 8 threshold 0.18499292823672295",
			        929,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1602 n_samples 4609 const 9.465750577237458 max_depth 12 threshold 0.4524701127819717",
			        1602,
			        4609,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4903 n_samples 3081 const 9.003164298015646 max_depth 9 threshold 0.30496944417990746",
			        4903,
			        3081,
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			        9,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4395 n_samples 1543 const 4.7268554160501814 max_depth 5 threshold 0.6712767462115735",
			        4395,
			        1543,
			        4.7268554160501814,
			        5,
			        0.6712767462115735,
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			    [
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			        1745405876,
			        40,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1446 n_samples 1107 const 4.252041721758434 max_depth 18 threshold 0.5851757451146841",
			        1446,
			        1107,
			        4.252041721758434,
			        18,
			        0.5851757451146841,
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			    [
			        1745405879,
			        1745405899,
			        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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1025 n_samples 4280 const 5.351056862986647 max_depth 4 threshold 0.7851611709557473",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2607 n_samples 2462 const 0.09219171405676753 max_depth 11 threshold 0.0673396717440337",
			        2607,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 197 n_samples 1665 const 8.47399023402296 max_depth 1 threshold 0.8777520628068596",
			        197,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3654 n_samples 4691 const 3.2139068507365884 max_depth 14 threshold 0.22230341282486915",
			        3654,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3858 n_samples 383 const 7.126444798426704 max_depth 8 threshold 0.3917099936418235",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2276 n_samples 3566 const 2.374103987812996 max_depth 20 threshold 0.7342943971138447",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3587 n_samples 1755 const 5.275914785933681 max_depth 9 threshold 0.5450277243908495",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 130 n_samples 4937 const 0.5333555348841473 max_depth 16 threshold 0.3272090027667582",
			        130,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3942 n_samples 3641 const 3.8731276464676485 max_depth 13 threshold 0.8408751192260534",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2361 n_samples 615 const 9.123467762344516 max_depth 4 threshold 0.06072165586799383",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3204 n_samples 99 const 10 max_depth 1 threshold 0.5118346437757534",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 732 const 10 max_depth 20 threshold 0.2456349363265244",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 19 n_samples 20 const 10 max_depth 1 threshold 0.7430138950059724",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 36 const 0.001 max_depth 1 threshold 0.6540636208881675",
			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2400 n_samples 1 const 0.001 max_depth 1 threshold 0.8723948313099966",
			        2400,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2164 n_samples 411 const 0.001 max_depth 20 threshold 0.999",
			        2164,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 51 const 0.001 max_depth 1 threshold 0.999",
			        1,
			        51,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4856 n_samples 101 const 0.001 max_depth 1 threshold 0.2123690437183894",
			        4856,
			        101,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 657 const 0.001 max_depth 1 threshold 0.5742421932650031",
			        5000,
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			        0.001,
			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 17 n_samples 25 const 10 max_depth 1 threshold 0.6795908734126318",
			        17,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 389 const 0.001 max_depth 1 threshold 0.999",
			        5000,
			        389,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2769 n_samples 109 const 10 max_depth 1 threshold 0.999",
			        2769,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 306 const 10 max_depth 20 threshold 0.9224391414350587",
			        5000,
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			        0.9224391414350587,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 13 const 10 max_depth 1 threshold 0.999",
			        1,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 353 n_samples 1 const 0.001 max_depth 1 threshold 0.999",
			        353,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1758 n_samples 267 const 10 max_depth 1 threshold 0.001",
			        1758,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4860 n_samples 42 const 10 max_depth 20 threshold 0.999",
			        4860,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4876 n_samples 103 const 10 max_depth 1 threshold 0.5731737642110095",
			        4876,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 888 n_samples 1 const 10 max_depth 1 threshold 0.999",
			        888,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 268 const 10 max_depth 1 threshold 0.41722293799981924",
			        5000,
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			        0.41722293799981924,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4431 n_samples 126 const 0.001 max_depth 1 threshold 0.999",
			        4431,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4147 n_samples 85 const 10 max_depth 1 threshold 0.001",
			        4147,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1359 n_samples 789 const 10 max_depth 1 threshold 0.001",
			        1359,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 665 const 10 max_depth 1 threshold 0.001",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 305 const 10 max_depth 20 threshold 0.999",
			        1,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 49 const 0.001 max_depth 1 threshold 0.46527256969927516",
			        1,
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			        1,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2293 n_samples 1 const 10 max_depth 1 threshold 0.999",
			        2293,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 1436 const 0.001 max_depth 20 threshold 0.001",
			        5000,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 963 const 10 max_depth 20 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4041 n_samples 348 const 0.001 max_depth 1 threshold 0.999",
			        4041,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 4908 const 10 max_depth 20 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3624 n_samples 398 const 10 max_depth 1 threshold 0.001",
			        3624,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 842 const 0.001 max_depth 1 threshold 0.001",
			        5000,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 0.001 max_depth 1 threshold 0.8330894583931359",
			        1,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3115 n_samples 272 const 0.001 max_depth 1 threshold 0.001",
			        3115,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 251 const 10 max_depth 20 threshold 0.001",
			        1,
			        251,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 7 const 10 max_depth 1 threshold 0.6552046947439406",
			        1,
			        7,
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			        0.6552046947439406,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1463 n_samples 1 const 10 max_depth 20 threshold 0.7923244133097254",
			        1463,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 2366 const 10 max_depth 20 threshold 0.001",
			        1,
			        2366,
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			        20,
			        0.001,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2428 n_samples 124 const 0.001 max_depth 1 threshold 0.001",
			        2428,
			        124,
			        0.001,
			        1,
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			        0.74,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4686 n_samples 95 const 10 max_depth 1 threshold 0.001",
			        4686,
			        95,
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			        1,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1261 n_samples 1349 const 0.001 max_depth 20 threshold 0.47797120811847105",
			        1261,
			        1349,
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			        20,
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			    [
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 827 n_samples 148 const 10 max_depth 1 threshold 0.001",
			        827,
			        148,
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			        "None",
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2168 n_samples 2974 const 10 max_depth 20 threshold 0.001",
			        2168,
			        2974,
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			        0.001,
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			        2,
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			    [
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			        70,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2914 n_samples 112 const 10 max_depth 19 threshold 0.001",
			        2914,
			        112,
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			    [
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			        114,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 753 n_samples 210 const 10 max_depth 1 threshold 0.001",
			        753,
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			        "None",
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			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4660 n_samples 132 const 10 max_depth 11 threshold 0.999",
			        4660,
			        132,
			        10,
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			        0.999,
			        0.73,
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			        "None",
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			        492.92075892857144,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 768 n_samples 247 const 0.001 max_depth 1 threshold 0.999",
			        768,
			        247,
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			        0.999,
			        0.72,
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			        "None",
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4159 n_samples 2330 const 0.001 max_depth 1 threshold 0.001",
			        4159,
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			        1,
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			        "None",
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			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3891 n_samples 572 const 0.001 max_depth 1 threshold 0.6095631906690081",
			        3891,
			        572,
			        0.001,
			        1,
			        0.6095631906690081,
			        0.73,
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			    [
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			        19,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 633 n_samples 1462 const 10 max_depth 1 threshold 0.17695677415141936",
			        633,
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			        1,
			        0.17695677415141936,
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			        4,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2027 n_samples 601 const 0.001 max_depth 20 threshold 0.999",
			        2027,
			        601,
			        0.001,
			        20,
			        0.999,
			        0.71,
			        2,
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			        "None",
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			        2,
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			    [
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			        51,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1962 n_samples 181 const 0.001 max_depth 1 threshold 0.001",
			        1962,
			        181,
			        0.001,
			        1,
			        0.001,
			        0.74,
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			        0,
			        "None",
			        "i7173",
			        38,
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			        489.9453125,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 398 const 10 max_depth 1 threshold 0.48392461451890756",
			        5000,
			        398,
			        10,
			        1,
			        0.48392461451890756,
			        0.74,
			        22,
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			        "None",
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			        22,
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			        485.08515625,
			        -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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2428 n_samples 470 const 0.001 max_depth 1 threshold 0.999",
			        2428,
			        470,
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			        1,
			        0.999,
			        0.7,
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			        "None",
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			    [
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3006 n_samples 141 const 0.001 max_depth 1 threshold 0.001",
			        3006,
			        141,
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			        1,
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			        "None",
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			    [
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			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 620 n_samples 131 const 0.001 max_depth 1 threshold 0.001",
			        620,
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			        1,
			        0.001,
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			        "None",
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			    [
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			        553,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 10 max_depth 1 threshold 0.4346919109903707",
			        1,
			        1,
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			        1,
			        0.4346919109903707,
			        0.71,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 4693 const 0.001 max_depth 20 threshold 0.999",
			        5000,
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			        20,
			        0.999,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2786 n_samples 206 const 0.001 max_depth 1 threshold 0.8245994759084652",
			        2786,
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			        1,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1114 n_samples 761 const 0.001 max_depth 20 threshold 0.001",
			        1114,
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			    [
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			        126,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3262 n_samples 145 const 0.001 max_depth 20 threshold 0.34097833230664243",
			        3262,
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			    [
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			        259,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2411 n_samples 340 const 10 max_depth 20 threshold 0.30652941782603127",
			        2411,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 1601 const 10 max_depth 1 threshold 0.001",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1699 n_samples 3383 const 0.001 max_depth 1 threshold 0.999",
			        1699,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2798 n_samples 88 const 0.001 max_depth 1 threshold 0.001",
			        2798,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 938 n_samples 4019 const 0.001 max_depth 1 threshold 0.999",
			        938,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4397 n_samples 2124 const 0.001 max_depth 20 threshold 0.48955578827577184",
			        4397,
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			        20,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1658 n_samples 299 const 0.001 max_depth 1 threshold 0.8147471264811459",
			        1658,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1325 n_samples 167 const 0.001 max_depth 1 threshold 0.001",
			        1325,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 676 n_samples 331 const 0.001 max_depth 1 threshold 0.999",
			        676,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 508 n_samples 1 const 0.001 max_depth 9 threshold 0.8891515957067082",
			        508,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2784 n_samples 100 const 0.001 max_depth 1 threshold 0.3172384525790417",
			        2784,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4629 n_samples 117 const 0.001 max_depth 1 threshold 0.801362576583848",
			        4629,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4359 n_samples 292 const 10 max_depth 20 threshold 0.999",
			        4359,
			        292,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2645 n_samples 1 const 0.001 max_depth 1 threshold 0.8416032293394814",
			        2645,
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			        1,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2407 n_samples 749 const 0.001 max_depth 1 threshold 0.26789860758708567",
			        2407,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4247 n_samples 314 const 0.001 max_depth 1 threshold 0.999",
			        4247,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4552 n_samples 328 const 0.001 max_depth 1 threshold 0.3010359032963361",
			        4552,
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			        1,
			        0.3010359032963361,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4465 n_samples 381 const 0.001 max_depth 1 threshold 0.999",
			        4465,
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			        0.999,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 110 const 0.001 max_depth 1 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 82 const 10 max_depth 1 threshold 0.001",
			        5000,
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			        0.73,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3855 n_samples 256 const 0.001 max_depth 20 threshold 0.001",
			        3855,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3143 n_samples 96 const 0.001 max_depth 20 threshold 0.001",
			        3143,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4498 n_samples 104 const 0.001 max_depth 1 threshold 0.001",
			        4498,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2476 n_samples 119 const 10 max_depth 1 threshold 0.3063761123900762",
			        2476,
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			        0.3063761123900762,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 4055 const 0.001 max_depth 1 threshold 0.001",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3481 n_samples 1045 const 10 max_depth 1 threshold 0.999",
			        3481,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4079 n_samples 562 const 10 max_depth 20 threshold 0.5552830058926742",
			        4079,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1376 n_samples 238 const 10 max_depth 1 threshold 0.7782596122734634",
			        1376,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 1192 const 10 max_depth 20 threshold 0.001",
			        5000,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2977 n_samples 153 const 0.001 max_depth 20 threshold 0.999",
			        2977,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2657 n_samples 198 const 10 max_depth 1 threshold 0.999",
			        2657,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 810 n_samples 2375 const 0.001 max_depth 20 threshold 0.999",
			        810,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1173 n_samples 160 const 10 max_depth 20 threshold 0.001",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 1740 const 10 max_depth 1 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4554 n_samples 144 const 0.001 max_depth 20 threshold 0.999",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4485 n_samples 1127 const 0.001 max_depth 20 threshold 0.999",
			        4485,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4181 n_samples 112 const 10 max_depth 5 threshold 0.001",
			        4181,
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			        3753,
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			        3905,
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			        1768,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 157 n_samples 159 const 0.001 max_depth 20 threshold 0.001",
			        157,
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			        4040,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1252 n_samples 206 const 10 max_depth 1 threshold 0.001",
			        1252,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3646 n_samples 251 const 10 max_depth 1 threshold 0.999",
			        3646,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3131 n_samples 578 const 10 max_depth 20 threshold 0.8989661433451961",
			        3131,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3461 n_samples 824 const 0.001 max_depth 1 threshold 0.001",
			        3461,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2850 n_samples 119 const 10 max_depth 1 threshold 0.001",
			        2850,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4209 n_samples 302 const 10 max_depth 1 threshold 0.001",
			        4209,
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			        2735,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 587 n_samples 97 const 0.001 max_depth 1 threshold 0.001",
			        587,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3227 n_samples 2737 const 0.001 max_depth 20 threshold 0.001",
			        3227,
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			        5000,
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			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1953 n_samples 256 const 10 max_depth 1 threshold 0.999",
			        1953,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4601 n_samples 447 const 0.001 max_depth 6 threshold 0.001",
			        4601,
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			        2724,
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			        1100,
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			        4710,
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			        2003,
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			        4133,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3993 n_samples 324 const 0.001 max_depth 20 threshold 0.999",
			        3993,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3466 n_samples 359 const 10 max_depth 1 threshold 0.999",
			        3466,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4936 n_samples 95 const 0.001 max_depth 1 threshold 0.7409389880715015",
			        4936,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2991 n_samples 100 const 10 max_depth 20 threshold 0.7082508736955955",
			        2991,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4963 n_samples 99 const 0.001 max_depth 1 threshold 0.5352749277701959",
			        4963,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4195 n_samples 252 const 0.001 max_depth 1 threshold 0.001",
			        4195,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4739 n_samples 119 const 10 max_depth 20 threshold 0.7181015484610651",
			        4739,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2969 n_samples 82 const 0.001 max_depth 1 threshold 0.999",
			        2969,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4719 n_samples 461 const 10 max_depth 1 threshold 0.999",
			        4719,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4750 n_samples 95 const 10 max_depth 1 threshold 0.3328203915956661",
			        4750,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2637 n_samples 1 const 0.001 max_depth 20 threshold 0.8375979013688104",
			        2637,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2642 n_samples 1 const 10 max_depth 1 threshold 0.999",
			        2642,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2301 n_samples 327 const 10 max_depth 1 threshold 0.7022844976406576",
			        2301,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4827 n_samples 120 const 0.001 max_depth 1 threshold 0.999",
			        4827,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4433 n_samples 574 const 10 max_depth 1 threshold 0.379181975211485",
			        4433,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3748 n_samples 280 const 0.001 max_depth 1 threshold 0.541517016357465",
			        3748,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3814 n_samples 275 const 10 max_depth 1 threshold 0.001",
			        3814,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1026 n_samples 275 const 0.001 max_depth 1 threshold 0.001",
			        1026,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4700 n_samples 91 const 0.001 max_depth 20 threshold 0.001",
			        4700,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4354 n_samples 573 const 0.001 max_depth 20 threshold 0.999",
			        4354,
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			        0.999,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 463 const 10 max_depth 1 threshold 0.4970199757027351",
			        5000,
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			        0.4970199757027351,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4552 n_samples 273 const 0.001 max_depth 20 threshold 0.7685448286254398",
			        4552,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4630 n_samples 130 const 10 max_depth 20 threshold 0.999",
			        4630,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4306 n_samples 127 const 0.001 max_depth 1 threshold 0.312113596243716",
			        4306,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3269 n_samples 128 const 0.001 max_depth 20 threshold 0.001",
			        3269,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4569 n_samples 408 const 10 max_depth 1 threshold 0.001",
			        4569,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4584 n_samples 415 const 0.001 max_depth 1 threshold 0.3004572068946078",
			        4584,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2922 n_samples 103 const 10 max_depth 1 threshold 0.5988083558598358",
			        2922,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1966 n_samples 244 const 0.001 max_depth 20 threshold 0.999",
			        1966,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3530 n_samples 274 const 0.001 max_depth 20 threshold 0.999",
			        3530,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 509 n_samples 182 const 0.001 max_depth 1 threshold 0.001",
			        509,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4571 n_samples 743 const 10 max_depth 1 threshold 0.999",
			        4571,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2651 n_samples 296 const 10 max_depth 1 threshold 0.999",
			        2651,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4491 n_samples 679 const 10 max_depth 20 threshold 0.999",
			        4491,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3964 n_samples 760 const 0.001 max_depth 20 threshold 0.999",
			        3964,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1176 n_samples 5000 const 10 max_depth 1 threshold 0.999",
			        1176,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3835 n_samples 151 const 10 max_depth 1 threshold 0.999",
			        3835,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3040 n_samples 105 const 0.001 max_depth 1 threshold 0.6333144245899963",
			        3040,
			        105,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4021 n_samples 187 const 0.001 max_depth 20 threshold 0.999",
			        4021,
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			        0.001,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2982 n_samples 129 const 10 max_depth 20 threshold 0.5584504640442798",
			        2982,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3148 n_samples 92 const 10 max_depth 20 threshold 0.001",
			        3148,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 340 const 10 max_depth 20 threshold 0.001",
			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3174 n_samples 189 const 0.001 max_depth 20 threshold 0.999",
			        3174,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1920 n_samples 212 const 0.001 max_depth 20 threshold 0.001",
			        1920,
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			        20,
			        0.001,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4417 n_samples 515 const 10 max_depth 20 threshold 0.999",
			        4417,
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			        0.999,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3627 n_samples 665 const 0.001 max_depth 20 threshold 0.001",
			        3627,
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			        20,
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			        0.7,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4013 n_samples 1073 const 10 max_depth 1 threshold 0.18138122082737645",
			        4013,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 2730 const 0.001 max_depth 1 threshold 0.999",
			        5000,
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			        0.001,
			        1,
			        0.999,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4393 n_samples 495 const 0.001 max_depth 1 threshold 0.001",
			        4393,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4429 n_samples 813 const 10 max_depth 20 threshold 0.001",
			        4429,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4870 n_samples 1329 const 10 max_depth 1 threshold 0.29835114197566626",
			        4870,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4495 n_samples 640 const 0.001 max_depth 20 threshold 0.4015809525971704",
			        4495,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4983 n_samples 100 const 10 max_depth 1 threshold 0.4487226947380253",
			        4983,
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			        0.73,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4133 n_samples 364 const 10 max_depth 1 threshold 0.001",
			        4133,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2078 n_samples 1467 const 0.001 max_depth 1 threshold 0.001",
			        2078,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4410 n_samples 590 const 0.001 max_depth 1 threshold 0.8701715273594489",
			        4410,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3348 n_samples 597 const 0.001 max_depth 1 threshold 0.999",
			        3348,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3701 n_samples 118 const 10 max_depth 20 threshold 0.001",
			        3701,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2806 n_samples 83 const 10 max_depth 1 threshold 0.001",
			        2806,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2646 n_samples 178 const 10 max_depth 19 threshold 0.001",
			        2646,
			        178,
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			        19,
			        0.001,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4467 n_samples 105 const 10 max_depth 20 threshold 0.001",
			        4467,
			        105,
			        10,
			        20,
			        0.001,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4371 n_samples 328 const 0.001 max_depth 20 threshold 0.34434637239182125",
			        4371,
			        328,
			        0.001,
			        20,
			        0.34434637239182125,
			        0.73,
			        19,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2639 n_samples 1019 const 0.001 max_depth 1 threshold 0.001",
			        2639,
			        1019,
			        0.001,
			        1,
			        0.001,
			        0.68,
			        3,
			        0,
			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2636 n_samples 1 const 4.921976815304447 max_depth 7 threshold 0.999",
			        2636,
			        1,
			        4.921976815304447,
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			        0.999,
			        0.75,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4021 n_samples 155 const 10 max_depth 1 threshold 0.5124489085160012",
			        4021,
			        155,
			        10,
			        1,
			        0.5124489085160012,
			        0.73,
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			    [
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			        137,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4625 n_samples 342 const 0.001 max_depth 20 threshold 0.001",
			        4625,
			        342,
			        0.001,
			        20,
			        0.001,
			        0.73,
			        124,
			        0,
			        "None",
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			    [
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			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4122 n_samples 341 const 0.001 max_depth 20 threshold 0.001",
			        4122,
			        341,
			        0.001,
			        20,
			        0.001,
			        0.72,
			        19,
			        0,
			        "None",
			        "i7182",
			        19,
			        492.1796875,
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			    [
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			        34,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3430 n_samples 476 const 10 max_depth 15 threshold 0.5718626520298445",
			        3430,
			        476,
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			        15,
			        0.5718626520298445,
			        0.72,
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			        -1,
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			    [
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			        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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2912 n_samples 200 const 10 max_depth 1 threshold 0.29983102907589193",
			        2912,
			        200,
			        10,
			        1,
			        0.29983102907589193,
			        0.71,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2970 n_samples 245 const 0.001 max_depth 1 threshold 0.999",
			        2970,
			        245,
			        0.001,
			        1,
			        0.999,
			        0.73,
			        12,
			        0,
			        "None",
			        "i7183",
			        12,
			        485.25,
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			        -1,
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			        4704382
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			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3468 n_samples 342 const 0.001 max_depth 1 threshold 0.5835465790057645",
			        3468,
			        342,
			        0.001,
			        1,
			        0.5835465790057645,
			        0.72,
			        17,
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			        "None",
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			        -1,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2900 n_samples 1236 const 10 max_depth 1 threshold 0.001",
			        2900,
			        1236,
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			        1,
			        0.001,
			        0.7,
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			        0,
			        "None",
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			        4705125
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 591 n_samples 1217 const 0.001 max_depth 20 threshold 0.999",
			        591,
			        1217,
			        0.001,
			        20,
			        0.999,
			        0.66,
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			        "None",
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			        -1,
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			        4705531
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			    [
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3081 n_samples 138 const 10 max_depth 20 threshold 0.999",
			        3081,
			        138,
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			        20,
			        0.999,
			        0.74,
			        28,
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			        "None",
			        "i7182",
			        28,
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			        -1,
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			        4705989
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4452 n_samples 1168 const 0.001 max_depth 20 threshold 0.001",
			        4452,
			        1168,
			        0.001,
			        20,
			        0.001,
			        0.68,
			        7,
			        0,
			        "None",
			        "i7176",
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			        -1,
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			        4706361
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			    [
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			        32,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4174 n_samples 330 const 10 max_depth 1 threshold 0.6011183978928866",
			        4174,
			        330,
			        10,
			        1,
			        0.6011183978928866,
			        0.72,
			        19,
			        0,
			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 756 n_samples 254 const 0.001 max_depth 1 threshold 0.6466574038823201",
			        756,
			        254,
			        0.001,
			        1,
			        0.6466574038823201,
			        0.71,
			        11,
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			        "None",
			        "i7182",
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			        -1,
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			        4707150
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			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4857 n_samples 548 const 10 max_depth 1 threshold 0.4337871733008759",
			        4857,
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			        1,
			        0.4337871733008759,
			        0.72,
			        13,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4256 n_samples 686 const 0.001 max_depth 1 threshold 0.3228617750143049",
			        4256,
			        686,
			        0.001,
			        1,
			        0.3228617750143049,
			        0.72,
			        11,
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			        "None",
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			    [
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			        97,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 94 const 10 max_depth 1 threshold 0.999",
			        5000,
			        94,
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			        1,
			        0.999,
			        0.74,
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			        "None",
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			    [
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			        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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3045 n_samples 100 const 0.001 max_depth 20 threshold 0.3136851119550377",
			        3045,
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			        20,
			        0.3136851119550377,
			        0.73,
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			    [
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			        26,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 499 const 10 max_depth 1 threshold 0.001",
			        5000,
			        499,
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			        1,
			        0.001,
			        0.72,
			        13,
			        0,
			        "None",
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			    [
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2938 n_samples 111 const 10 max_depth 20 threshold 0.2039367357154023",
			        2938,
			        111,
			        10,
			        20,
			        0.2039367357154023,
			        0.73,
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			    [
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3112 n_samples 122 const 10 max_depth 1 threshold 0.24452129227070557",
			        3112,
			        122,
			        10,
			        1,
			        0.24452129227070557,
			        0.73,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2392 n_samples 155 const 0.001 max_depth 1 threshold 0.001",
			        2392,
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			        0.001,
			        1,
			        0.001,
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			        "None",
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			    [
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			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 2940 const 0.001 max_depth 1 threshold 0.999",
			        5000,
			        2940,
			        0.001,
			        1,
			        0.999,
			        0.68,
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			        "None",
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			    [
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			        13,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1581 const 10 max_depth 1 threshold 0.001",
			        1,
			        1581,
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			        0.001,
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			        "None",
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			    [
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			        98,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4747 n_samples 85 const 0.001 max_depth 1 threshold 0.001",
			        4747,
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			        0.001,
			        1,
			        0.001,
			        0.73,
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			        "None",
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			    [
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			        1745544750,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4518 n_samples 111 const 10 max_depth 1 threshold 0.5543308605597309",
			        4518,
			        111,
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			        1,
			        0.5543308605597309,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3721 n_samples 143 const 10 max_depth 20 threshold 0.37339170400136135",
			        3721,
			        143,
			        10,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 399 const 0.001 max_depth 1 threshold 0.7048508878225677",
			        5000,
			        399,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 2523 const 10 max_depth 20 threshold 0.001",
			        1,
			        2523,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3633 n_samples 217 const 10 max_depth 20 threshold 0.999",
			        3633,
			        217,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1300 n_samples 142 const 0.001 max_depth 1 threshold 0.001",
			        1300,
			        142,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 789 n_samples 121 const 10 max_depth 20 threshold 0.001",
			        789,
			        121,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3394 n_samples 175 const 10 max_depth 1 threshold 0.999",
			        3394,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 84 const 0.001 max_depth 20 threshold 0.001",
			        5000,
			        84,
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			        20,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 562 const 10 max_depth 1 threshold 0.6994086489398458",
			        5000,
			        562,
			        10,
			        1,
			        0.6994086489398458,
			        0.72,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4904 n_samples 844 const 10 max_depth 1 threshold 0.5181988196780649",
			        4904,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3221 n_samples 92 const 0.001 max_depth 1 threshold 0.4162898312743592",
			        3221,
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			        0.001,
			        1,
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			        0.73,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4311 n_samples 455 const 0.001 max_depth 1 threshold 0.001",
			        4311,
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			        0.71,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3819 n_samples 146 const 0.001 max_depth 20 threshold 0.7602323004140825",
			        3819,
			        146,
			        0.001,
			        20,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2619 n_samples 237 const 0.001 max_depth 20 threshold 0.8712993596651286",
			        2619,
			        237,
			        0.001,
			        20,
			        0.8712993596651286,
			        0.73,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3733 n_samples 145 const 10 max_depth 20 threshold 0.001",
			        3733,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2956 n_samples 230 const 10 max_depth 20 threshold 0.999",
			        2956,
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			        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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4378 n_samples 114 const 0.001 max_depth 20 threshold 0.001",
			        4378,
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			        0.001,
			        0.73,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1178 n_samples 253 const 0.001 max_depth 20 threshold 0.999",
			        1178,
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			        20,
			        0.999,
			        0.72,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1764 n_samples 131 const 10 max_depth 1 threshold 0.001",
			        1764,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3150 n_samples 74 const 10 max_depth 1 threshold 0.001",
			        3150,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3503 n_samples 129 const 10 max_depth 1 threshold 0.001",
			        3503,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3369 n_samples 153 const 0.001 max_depth 1 threshold 0.999",
			        3369,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4409 n_samples 129 const 10 max_depth 1 threshold 0.999",
			        4409,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4182 n_samples 129 const 0.001 max_depth 1 threshold 0.001",
			        4182,
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			        0.001,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1216 n_samples 181 const 0.001 max_depth 16 threshold 0.001",
			        1216,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4000 n_samples 404 const 10 max_depth 1 threshold 0.8038145355141877",
			        4000,
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			        0.8038145355141877,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 24 const 10 max_depth 1 threshold 0.999",
			        1,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 1274 const 10 max_depth 1 threshold 0.001",
			        5000,
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			        0.001,
			        0.69,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1851 n_samples 193 const 0.001 max_depth 1 threshold 0.5010140920586574",
			        1851,
			        193,
			        0.001,
			        1,
			        0.5010140920586574,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2421 n_samples 115 const 10 max_depth 1 threshold 0.001",
			        2421,
			        115,
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			        0.001,
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			    [
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			        64,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4916 n_samples 116 const 0.001 max_depth 9 threshold 0.999",
			        4916,
			        116,
			        0.001,
			        9,
			        0.999,
			        0.73,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4560 n_samples 112 const 10 max_depth 20 threshold 0.4305488589445956",
			        4560,
			        112,
			        10,
			        20,
			        0.4305488589445956,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3544 n_samples 3336 const 10 max_depth 1 threshold 0.999",
			        3544,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4803 n_samples 109 const 0.001 max_depth 1 threshold 0.7213831446948192",
			        4803,
			        109,
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			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4322 n_samples 512 const 0.001 max_depth 20 threshold 0.4848649403690247",
			        4322,
			        512,
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			        20,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2500 n_samples 180 const 10 max_depth 1 threshold 0.001",
			        2500,
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			        1,
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			        0.73,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 282 const 10 max_depth 1 threshold 0.999",
			        5000,
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			        0.999,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2085 n_samples 1730 const 10 max_depth 1 threshold 0.999",
			        2085,
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			        0.999,
			        0.68,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 102 const 0.001 max_depth 8 threshold 0.6689841333411877",
			        5000,
			        102,
			        0.001,
			        8,
			        0.6689841333411877,
			        0.74,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4951 n_samples 94 const 10 max_depth 10 threshold 0.001",
			        4951,
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			        "None",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 365 const 10 max_depth 1 threshold 0.2023135296674892",
			        5000,
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			        0.72,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4844 n_samples 86 const 10 max_depth 20 threshold 0.40552582187194247",
			        4844,
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			        0.74,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4337 n_samples 242 const 0.001 max_depth 1 threshold 0.999",
			        4337,
			        242,
			        0.001,
			        1,
			        0.999,
			        0.73,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4642 n_samples 379 const 0.001 max_depth 1 threshold 0.6506554016071571",
			        4642,
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			        0.001,
			        1,
			        0.6506554016071571,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 241 n_samples 270 const 10 max_depth 20 threshold 0.2449302715777827",
			        241,
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			        20,
			        0.2449302715777827,
			        0.7,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3157 n_samples 106 const 0.001 max_depth 20 threshold 0.999",
			        3157,
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			        0.001,
			        20,
			        0.999,
			        0.73,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 15 const 10 max_depth 1 threshold 0.999",
			        1,
			        15,
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			        0.74,
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			        "None",
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4445 n_samples 1393 const 10 max_depth 20 threshold 0.6280402101173124",
			        4445,
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			        0.73,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1255 n_samples 256 const 10 max_depth 1 threshold 0.999",
			        1255,
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			    [
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			        14,
			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1445 const 0.001 max_depth 20 threshold 0.001",
			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4525 n_samples 132 const 10 max_depth 20 threshold 0.7039906132045108",
			        4525,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2754 n_samples 24 const 10 max_depth 1 threshold 0.999",
			        2754,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2789 n_samples 38 const 10 max_depth 1 threshold 0.999",
			        2789,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2700 n_samples 13 const 10 max_depth 1 threshold 0.999",
			        2700,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2825 n_samples 51 const 10 max_depth 1 threshold 0.999",
			        2825,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2746 n_samples 28 const 0.001 max_depth 1 threshold 0.999",
			        2746,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2829 n_samples 80 const 10 max_depth 1 threshold 0.999",
			        2829,
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			    [
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 64 const 10 max_depth 1 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2999 n_samples 1 const 0.001 max_depth 1 threshold 0.999",
			        2999,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2839 n_samples 27 const 0.001 max_depth 20 threshold 0.999",
			        2839,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2761 n_samples 1 const 10 max_depth 1 threshold 0.001",
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			        3082,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 78 const 10 max_depth 20 threshold 0.999",
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2875 n_samples 96 const 10 max_depth 1 threshold 0.999",
			        2875,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 62 const 10 max_depth 20 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 36 const 0.001 max_depth 20 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3390 n_samples 1 const 10 max_depth 20 threshold 0.999",
			        3390,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 65 const 0.001 max_depth 20 threshold 0.999",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2883 n_samples 69 const 10 max_depth 20 threshold 0.999",
			        2883,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2663 n_samples 7 const 10 max_depth 1 threshold 0.999",
			        2663,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 431 n_samples 3941 const 0.001 max_depth 1 threshold 0.001",
			        431,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2800 n_samples 47 const 0.001 max_depth 1 threshold 0.999",
			        2800,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 200 n_samples 1 const 10 max_depth 1 threshold 0.999",
			        200,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3942 n_samples 1640 const 10 max_depth 20 threshold 0.3241490239578118",
			        3942,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 3929 const 0.001 max_depth 1 threshold 0.001",
			        1,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2892 n_samples 38 const 10 max_depth 20 threshold 0.999",
			        2892,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1 n_samples 1 const 10 max_depth 7 threshold 0.8458809084356678",
			        1,
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			        0.8458809084356678,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 109 const 0.001 max_depth 20 threshold 0.9566033911530513",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 3736 n_samples 4773 const 10 max_depth 1 threshold 0.999",
			        3736,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 4109 n_samples 2856 const 0.001 max_depth 1 threshold 0.001",
			        4109,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2780 n_samples 68 const 10 max_depth 1 threshold 0.999",
			        2780,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2631 n_samples 4671 const 0.001 max_depth 20 threshold 0.999",
			        2631,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2524 n_samples 3315 const 0.001 max_depth 20 threshold 0.48911407482677",
			        2524,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 2657 n_samples 5 const 10 max_depth 20 threshold 0.999",
			        2657,
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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 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 5000 n_samples 1405 const 0.001 max_depth 20 threshold 0.5983351880311628",
			        5000,
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			        "module load GCCcore\/10.3.0 Python && source \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/venv\/bin\/activate && python \/data\/horse\/ws\/s4122485-compPerfDD\/benchmark\/dfki\/benchmarkdd\/main.py 1 1 0 Electricity 2000 HoeffdingTreeClassifier BNDM recent_samples_size 1909 n_samples 96 const 10 max_depth 20 threshold 0.999",
			        1909,
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			        20,
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			    [
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			    [
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			    [
			        1745618892.444642,
			        20,
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			    [
			        1745618893.05072,
			        20,
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			    [
			        1745618911.6257153,
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			    [
			        1745618911.879643,
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			    [
			        1745620568.3174617,
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			    [
			        1745620568.9191113,
			        20,
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			    [
			        1745620570.3540702,
			        20,
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			    ],
			    [
			        1745620570.9480686,
			        20,
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			    ],
			    [
			        1745620589.3502438,
			        20,
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			    [
			        1745620589.8248103,
			        20,
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			    [
			        1745622359.7962086,
			        20,
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			    [
			        1745622360.1521063,
			        20,
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			    ],
			    [
			        1745622362.074689,
			        20,
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			    ],
			    [
			        1745622362.6921222,
			        20,
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			        20,
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			        20,
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			        20,
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			        20,
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			    ],
			    [
			        1745623151.6782756,
			        20,
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			    ],
			    [
			        1745623170.4635773,
			        20,
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			    ],
			    [
			        1745623170.8903322,
			        20,
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			    ],
			    [
			        1745625082.8406038,
			        20,
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			    ],
			    [
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			        20,
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			    [
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			        20,
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			    ],
			    [
			        1745625085.2188163,
			        20,
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			    [
			        1745625103.2842314,
			        20,
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			    [
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			    [
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			    [
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			        20,
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			    [
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			        20,
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			    ],
			    [
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			        20,
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			    ],
			    [
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			        20,
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			    ],
			    [
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			    ],
			    [
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			        20,
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			    ],
			    [
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			        20,
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			    ],
			    [
			        1745631211.178447,
			        20,
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			    ],
			    [
			        1745631212.6576881,
			        20,
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			    ],
			    [
			        1745631213.2362635,
			        20,
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			    ],
			    [
			        1745631233.0449529,
			        20,
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			    ],
			    [
			        1745631233.28483,
			        20,
			        1,
			        5
			    ]
			];
			var tab_main_worker_cpu_ram_csv_json = [
			    [
			        1745405554,
			        633.96484375,
			        34.9
			    ],
			    [
			        1745405554,
			        633.96875,
			        42.6
			    ],
			    [
			        1745405554,
			        633.96875,
			        42.4
			    ],
			    [
			        1745405554,
			        633.96875,
			        44.7
			    ],
			    [
			        1745405554,
			        633.96875,
			        33.3
			    ],
			    [
			        1745405554,
			        633.96875,
			        45.5
			    ],
			    [
			        1745405554,
			        633.96875,
			        42.6
			    ],
			    [
			        1745406104,
			        683.34375,
			        44.6
			    ],
			    [
			        1745406104,
			        683.34375,
			        35
			    ],
			    [
			        1745406104,
			        683.34375,
			        42
			    ],
			    [
			        1745406104,
			        683.34375,
			        48.8
			    ],
			    [
			        1745416073,
			        924.0703125,
			        42.2
			    ],
			    [
			        1745416073,
			        924.0703125,
			        44.2
			    ],
			    [
			        1745416073,
			        924.0703125,
			        44.2
			    ],
			    [
			        1745416073,
			        924.0703125,
			        43.2
			    ],
			    [
			        1745428529,
			        968.9609375,
			        37.5
			    ],
			    [
			        1745428529,
			        968.9609375,
			        34.7
			    ],
			    [
			        1745428529,
			        968.9609375,
			        33.1
			    ],
			    [
			        1745428529,
			        968.9609375,
			        34
			    ],
			    [
			        1745439158,
			        980.9921875,
			        42.2
			    ],
			    [
			        1745439158,
			        980.9921875,
			        43.1
			    ],
			    [
			        1745439158,
			        980.9921875,
			        42.9
			    ],
			    [
			        1745439158,
			        980.9921875,
			        45
			    ],
			    [
			        1745449944,
			        1057.578125,
			        40.8
			    ],
			    [
			        1745449944,
			        1057.578125,
			        38
			    ],
			    [
			        1745449944,
			        1057.578125,
			        39.9
			    ],
			    [
			        1745449944,
			        1057.578125,
			        25.8
			    ],
			    [
			        1745462646,
			        1016.5703125,
			        41.4
			    ],
			    [
			        1745462646,
			        1016.5703125,
			        44.6
			    ],
			    [
			        1745462646,
			        1016.5703125,
			        43.7
			    ],
			    [
			        1745462646,
			        1016.5703125,
			        43.7
			    ],
			    [
			        1745476633,
			        1030.59375,
			        42.8
			    ],
			    [
			        1745476633,
			        1030.59375,
			        36.9
			    ],
			    [
			        1745476633,
			        1030.59375,
			        37.8
			    ],
			    [
			        1745476633,
			        1030.59375,
			        23.7
			    ],
			    [
			        1745489592,
			        1063.625,
			        40.1
			    ],
			    [
			        1745489592,
			        1063.625,
			        39.3
			    ],
			    [
			        1745489592,
			        1063.625,
			        40.7
			    ],
			    [
			        1745489592,
			        1063.625,
			        45.6
			    ],
			    [
			        1745504911,
			        1135.8046875,
			        36.1
			    ],
			    [
			        1745504911,
			        1135.8046875,
			        31.8
			    ],
			    [
			        1745504911,
			        1135.8046875,
			        33.3
			    ],
			    [
			        1745504911,
			        1135.8046875,
			        29.9
			    ],
			    [
			        1745520226,
			        1158.01171875,
			        37.3
			    ],
			    [
			        1745520227,
			        1158.01171875,
			        36
			    ],
			    [
			        1745520227,
			        1158.01171875,
			        35.2
			    ],
			    [
			        1745520227,
			        1158.01171875,
			        35.3
			    ],
			    [
			        1745535616,
			        1170.546875,
			        37.6
			    ],
			    [
			        1745535616,
			        1170.546875,
			        39.8
			    ],
			    [
			        1745535616,
			        1170.546875,
			        38.1
			    ],
			    [
			        1745535616,
			        1170.546875,
			        43.3
			    ],
			    [
			        1745551038,
			        1186.0234375,
			        36.7
			    ],
			    [
			        1745551038,
			        1186.0234375,
			        37.5
			    ],
			    [
			        1745551038,
			        1186.0234375,
			        38.1
			    ],
			    [
			        1745551038,
			        1186.0234375,
			        33.3
			    ],
			    [
			        1745569523,
			        1195.8828125,
			        37.3
			    ],
			    [
			        1745569523,
			        1195.8828125,
			        39.8
			    ],
			    [
			        1745569523,
			        1195.8828125,
			        39.9
			    ],
			    [
			        1745569523,
			        1195.8828125,
			        37.3
			    ],
			    [
			        1745588579,
			        1220.015625,
			        37.6
			    ],
			    [
			        1745588579,
			        1220.015625,
			        37.1
			    ],
			    [
			        1745588579,
			        1220.015625,
			        36.5
			    ],
			    [
			        1745588579,
			        1220.015625,
			        34.6
			    ],
			    [
			        1745603313,
			        1275.97265625,
			        36.8
			    ],
			    [
			        1745603313,
			        1275.97265625,
			        38.8
			    ],
			    [
			        1745603313,
			        1275.97265625,
			        38.2
			    ],
			    [
			        1745603313,
			        1275.97265625,
			        44.2
			    ],
			    [
			        1745631246,
			        1300.24609375,
			        37.3
			    ],
			    [
			        1745631246,
			        1300.24609375,
			        37.7
			    ],
			    [
			        1745631246,
			        1300.24609375,
			        36.5
			    ],
			    [
			        1745631246,
			        1300.24609375,
			        35.3
			    ]
			];
			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>50498 </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>498   </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>1</td>
			<td>833</td>
			<td>4</td>
			<td>838</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_0,0.700000000000000066613381477509,3.000000000000000000000000000000,1620,4143,7.982919480681419877043936139671,4,0.882287493586540239043358724302
1,1_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,27.000000000000000000000000000000,2909,109,4.800845133917406748480516398558,11,0.401071141337975833884854637290
2,2_0,COMPLETED,Sobol,GenerationStep_0,0.690000000000000057731597280508,5.000000000000000000000000000000,4528,3583,6.474530969013460257599490432767,10,0.237508180126547813193482738825
3,3_0,COMPLETED,Sobol,GenerationStep_0,0.640000000000000013322676295502,3.000000000000000000000000000000,660,2012,0.743997874721884722148956825549,18,0.719203989632427709288720052427
4,4_0,COMPLETED,Sobol,GenerationStep_0,0.670000000000000039968028886506,2.000000000000000000000000000000,610,3005,3.434259827704169154571900435258,20,0.509026851631701027223186883930
5,5_0,COMPLETED,Sobol,GenerationStep_0,0.690000000000000057731597280508,5.000000000000000000000000000000,4243,1412,8.803615154411643217713390185963,7,0.027323190048336984392385318188
6,6_0,COMPLETED,Sobol,GenerationStep_0,0.690000000000000057731597280508,3.000000000000000000000000000000,3323,4896,2.422758724032901156419939070474,14,0.363399862712249166474265393845
7,7_0,COMPLETED,Sobol,GenerationStep_0,0.670000000000000039968028886506,2.000000000000000000000000000000,2111,844,5.341073049699888386498969339300,2,0.844608367530629067232439410873
8,8_0,COMPLETED,Sobol,GenerationStep_0,0.719999999999999973354647408996,2.000000000000000000000000000000,2373,3438,1.583753850243054372981532651465,6,0.496270950065925697813895567378
9,9_0,COMPLETED,Sobol,GenerationStep_0,0.670000000000000039968028886506,5.000000000000000000000000000000,3740,2470,6.181586962176487176634509523865,19,0.981872573265805792530613871349
10,10_0,COMPLETED,Sobol,GenerationStep_0,0.690000000000000057731597280508,3.000000000000000000000000000000,3991,3996,2.554970862218179039615506553673,3,0.631299002546817034087212050508
11,11_0,COMPLETED,Sobol,GenerationStep_0,0.640000000000000013322676295502,779.000000000000000000000000000000,203,569,9.681962460685522131598190753721,15,0.146191584344953301455305449963
12,12_0,COMPLETED,Sobol,GenerationStep_0,0.670000000000000039968028886506,2.000000000000000000000000000000,1029,4419,7.001067092803307190251871361397,12,0.123132070150226355376155140675
13,13_0,COMPLETED,Sobol,GenerationStep_0,0.680000000000000048849813083507,7.000000000000000000000000000000,4740,1009,0.215952712245285532999261590703,5,0.608246388662606496389173571515
14,14_0,COMPLETED,Sobol,GenerationStep_0,0.640000000000000013322676295502,4.000000000000000000000000000000,2530,2525,8.549739444735459770186025707517,17,0.757068918446078931161480340961
15,15_0,COMPLETED,Sobol,GenerationStep_0,0.709999999999999964472863211995,4.000000000000000000000000000000,1398,1579,4.234966577643529461738580721430,8,0.271474195556715147681359212584
16,16_0,COMPLETED,Sobol,GenerationStep_0,0.640000000000000013322676295502,3.000000000000000000000000000000,1420,2727,5.798992267700843861177872895496,14,0.713214073034003415152426441637
17,17_0,COMPLETED,Sobol,GenerationStep_0,0.660000000000000031086244689504,3.000000000000000000000000000000,2786,1759,1.359719537043943882181906701589,1,0.196430429929867395033582511132
18,18_0,COMPLETED,Sobol,GenerationStep_0,0.660000000000000031086244689504,3.000000000000000000000000000000,4952,4549,9.905882232663222097812649735715,20,0.406744123253971356923841540265
19,19_0,COMPLETED,Sobol,GenerationStep_0,0.700000000000000066613381477509,5.000000000000000000000000000000,1163,1123,2.937680097915232213523495374830,8,0.923048313748091486807823002891
20,20_0,COMPLETED,Sobol,GenerationStep_0,0.670000000000000039968028886506,2.000000000000000000000000000000,108,3875,0.618076498725451561888633023045,10,0.838893450241535942879522735893
21,21_0,COMPLETED,Sobol,GenerationStep_0,0.739999999999999991118215802999,427.000000000000000000000000000000,3818,466,7.244630460757762513424040662358,17,0.322597111824899918097742101963
22,22_0,COMPLETED,Sobol,GenerationStep_0,0.670000000000000039968028886506,3.000000000000000000000000000000,3445,3227,3.991517750910483552928553763195,3,0.033332207048311829344910961481
23,23_0,COMPLETED,Sobol,GenerationStep_0,0.660000000000000031086244689504,5.000000000000000000000000000000,2312,2280,8.147501117033883133444760460407,11,0.550123702229931899054804489424
24,24_0,COMPLETED,Sobol,GenerationStep_0,0.600000000000000088817841970013,3.000000000000000000000000000000,1938,4697,4.402382960229181740885451290524,16,0.171402555687353019830965195069
25,25_0,COMPLETED,Sobol,GenerationStep_0,0.719999999999999973354647408996,8.000000000000000000000000000000,3228,663,7.738135410612449582856697816169,9,0.683800932487472867826738820440
26,26_0,COMPLETED,Sobol,GenerationStep_0,0.680000000000000048849813083507,4.000000000000000000000000000000,4182,2872,0.988667450130917169204280980921,13,0.956375116348266551824508496793
27,27_0,COMPLETED,Sobol,GenerationStep_0,0.719999999999999973354647408996,3.000000000000000000000000000000,314,1301,6.873107385929674961744240135886,5,0.443482534550130369499498783625
28,28_0,COMPLETED,Sobol,GenerationStep_0,0.680000000000000048849813083507,3.000000000000000000000000000000,916,3706,9.182548273344524503158936568070,2,0.296960047237575075484983244678
29,29_0,COMPLETED,Sobol,GenerationStep_0,0.729999999999999982236431605997,8.000000000000000000000000000000,4550,2113,3.659514554336666947165213059634,15,0.809845728725194891062244550994
30,30_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,17.000000000000000000000000000000,4400,385,0.001000000000000000020816681712,16,0.622981179772556292739693617477
31,31_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6093.000000000000000000000000000000,4308,1,3.276977116741448536885172870825,9,0.540530483495147429096050473163
32,32_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,4.000000000000000000000000000000,5000,3004,10.000000000000000000000000000000,16,0.998999999999999999111821580300
33,33_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,7.000000000000000000000000000000,4818,1094,0.001000000000000000020816681712,16,0.001000000000000000020816681712
34,34_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6437.000000000000000000000000000000,4339,1,2.265543431698116449979352182709,15,0.998999999999999999111821580300
35,35_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1193.000000000000000000000000000000,3456,1,0.001000000000000000020816681712,10,0.001000000000000000020816681712
36,36_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,4.000000000000000000000000000000,1923,2487,10.000000000000000000000000000000,6,0.998999999999999999111821580300
37,37_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,919.000000000000000000000000000000,1989,1,2.578764743662740777097042155219,6,0.001000000000000000020816681712
38,38_0,RUNNING,BoTorch,GenerationStep_1,,,4872,1,0.001000000000000000020816681712,17,0.998999999999999999111821580300
39,39_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6246.000000000000000000000000000000,4648,1,3.180047443483558389232257468393,12,0.998999999999999999111821580300
40,40_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1602.000000000000000000000000000000,3901,1,0.001000000000000000020816681712,15,0.001000000000000000020816681712
41,41_0,RUNNING,BoTorch,GenerationStep_1,,,5000,1,4.666129939426686412673461745726,7,0.538852263434716705070570696989
42,42_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,6.000000000000000000000000000000,5000,1430,10.000000000000000000000000000000,16,0.998999999999999999111821580300
43,43_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,616,5000,0.001000000000000000020816681712,6,0.001000000000000000020816681712
44,44_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,6.000000000000000000000000000000,5000,2192,10.000000000000000000000000000000,7,0.998999999999999999111821580300
45,45_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1601.000000000000000000000000000000,5000,1,0.001000000000000000020816681712,15,0.001000000000000000020816681712
46,46_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,5000,3630,10.000000000000000000000000000000,15,0.998999999999999999111821580300
47,47_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,4647.000000000000000000000000000000,3534,1,0.001000000000000000020816681712,15,0.998999999999999999111821580300
48,48_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,4.000000000000000000000000000000,5000,2644,0.001000000000000000020816681712,16,0.001000000000000000020816681712
49,49_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1133.000000000000000000000000000000,2509,1,0.001000000000000000020816681712,9,0.001000000000000000020816681712
50,50_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,4448.000000000000000000000000000000,3447,1,0.250661656278794942043219862171,10,0.726482584151931454208295235730
51,51_0,FAILED,BoTorch,GenerationStep_1,,,5000,1,0.001000000000000000020816681712,19,0.998999999999999999111821580300
52,52_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1237.000000000000000000000000000000,4861,1,2.389658232350520439268848349457,10,0.290515780915859611610585488961
53,53_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,8.000000000000000000000000000000,4378,948,0.001000000000000000020816681712,16,0.998999999999999999111821580300
54,54_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,623.000000000000000000000000000000,5000,1,4.717391478144433492047937761527,3,0.001000000000000000020816681712
55,55_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,5.000000000000000000000000000000,1,4070,10.000000000000000000000000000000,20,0.998999999999999999111821580300
56,56_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,856.000000000000000000000000000000,1,1,0.001000000000000000020816681712,6,0.001000000000000000020816681712
57,57_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,4210,5000,0.001000000000000000020816681712,7,0.998999999999999999111821580300
58,58_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,4996,5000,10.000000000000000000000000000000,7,0.998999999999999999111821580300
59,59_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5645.000000000000000000000000000000,4875,1,4.181777197771775256285309296800,10,0.998999999999999999111821580300
60,60_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,9.000000000000000000000000000000,4121,871,4.737237682826629381338534585666,12,0.746379054643187656026270815346
61,61_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,220.000000000000000000000000000000,3966,391,4.281001358386227195751416729763,12,0.782628618028717637500335513323
62,62_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,873.000000000000000000000000000000,4142,832,4.512901898045180715257629344705,14,0.424933727214824696893913369422
63,63_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,11.000000000000000000000000000000,3968,677,4.329518160703052842563920421526,10,0.974637108877582591937027700624
64,64_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,4.000000000000000000000000000000,4810,1939,4.731987813585218916045960213523,18,0.001000000000000000020816681712
65,65_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,6.000000000000000000000000000000,3151,1169,1.710987595333870903147044373327,10,0.998999999999999999111821580300
66,66_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,9.000000000000000000000000000000,4524,1084,5.540201882018855883416108554229,13,0.650193741919587564481730623811
67,67_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,101.000000000000000000000000000000,3837,58,3.940393271600024505119108653162,12,0.783080509550259207252054238779
68,68_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,2403,4549,0.001000000000000000020816681712,6,0.998999999999999999111821580300
69,69_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,19.000000000000000000000000000000,4162,537,5.227232416292206806929243612103,12,0.720108349124189861534262036002
70,70_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,20.000000000000000000000000000000,4336,697,4.469019307521158168583497172222,16,0.101451945355955253091906342888
71,71_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,529,2611,3.235030082427177600123968659318,5,0.001000000000000000020816681712
72,72_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,4668,865,4.749001571517728059745877544628,19,0.387654585785973115896752005938
73,73_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,3.000000000000000000000000000000,1724,4018,0.001000000000000000020816681712,6,0.001000000000000000020816681712
74,74_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,112.000000000000000000000000000000,4146,210,5.159089695636138728218611504417,12,0.777361697396022677253313304391
75,75_0,COMPLETED,BoTorch,GenerationStep_1,0.650000000000000022204460492503,2.000000000000000000000000000000,142,3762,2.798503527274313729122923177783,4,0.001000000000000000020816681712
76,76_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,3.000000000000000000000000000000,1061,2075,1.273547759532380529989836759341,5,0.001000000000000000020816681712
77,77_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,9.000000000000000000000000000000,4331,738,5.281514468881145063505755388178,6,0.998999999999999999111821580300
78,78_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1028,4700,0.001000000000000000020816681712,1,0.638696920331490947120300916140
79,79_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,2.000000000000000000000000000000,1769,4844,0.001000000000000000020816681712,7,0.789442482276320078860010198696
80,80_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,244.000000000000000000000000000000,4884,531,4.681426275597865505062600277597,19,0.001000000000000000020816681712
81,81_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,4297,1046,5.409622681335242155853393342113,9,0.998999999999999999111821580300
82,82_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,126,3311,2.037391858099973251938763496582,1,0.416910559381018286373432601977
83,83_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,138.000000000000000000000000000000,3650,625,3.274577835351026422472386911977,12,0.744639741967559776192331355560
84,84_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,36.000000000000000000000000000000,4080,168,4.448208025023618894522314803908,13,0.615572563694329888406286954705
85,85_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,2.000000000000000000000000000000,1,2163,4.040885027812313623485351854470,5,0.001000000000000000020816681712
86,86_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,3.000000000000000000000000000000,2675,1669,0.001000000000000000020816681712,11,0.998999999999999999111821580300
87,87_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,5.000000000000000000000000000000,950,737,0.001000000000000000020816681712,3,0.082896226059125388552217827964
88,88_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,13.000000000000000000000000000000,3581,399,3.173088074710329387073670659447,11,0.998999999999999999111821580300
89,89_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,203,564,3.828762672031267300098988926038,1,0.001000000000000000020816681712
90,90_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,26.000000000000000000000000000000,4118,243,0.001000000000000000020816681712,9,0.752417292137260496076578419888
91,91_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,21.000000000000000000000000000000,3562,239,0.001000000000000000020816681712,8,0.492397450899787592071277231298
92,92_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,36.000000000000000000000000000000,4555,176,0.001000000000000000020816681712,9,0.876069924333020000339899979735
93,93_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,23.000000000000000000000000000000,3204,229,10.000000000000000000000000000000,8,0.320699357511880833726536366157
94,94_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,55.000000000000000000000000000000,3743,98,0.001000000000000000020816681712,8,0.684354840095635408303564872767
95,95_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,49.000000000000000000000000000000,3919,127,10.000000000000000000000000000000,8,0.650300433938824529178646116634
96,96_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,267.000000000000000000000000000000,3284,227,10.000000000000000000000000000000,20,0.147185548161598206418076983937
97,97_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,86.000000000000000000000000000000,3439,259,10.000000000000000000000000000000,18,0.874223527231546171378795406781
98,98_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,19.000000000000000000000000000000,5000,317,0.001000000000000000020816681712,10,0.998999999999999999111821580300
99,99_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,113.000000000000000000000000000000,2993,242,10.000000000000000000000000000000,20,0.431090765841456946549925532963
100,100_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,19.000000000000000000000000000000,3367,228,0.001000000000000000020816681712,19,0.686551640685817310405525404349
101,101_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,85.000000000000000000000000000000,4834,261,10.000000000000000000000000000000,8,0.786640028901444576803214658867
102,102_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,14.000000000000000000000000000000,2991,894,10.000000000000000000000000000000,20,0.231636009677679055585031164810
103,103_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,13.000000000000000000000000000000,2882,217,0.001000000000000000020816681712,2,0.001000000000000000020816681712
104,104_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,25.000000000000000000000000000000,3442,178,10.000000000000000000000000000000,16,0.998999999999999999111821580300
105,105_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,83.000000000000000000000000000000,4186,226,10.000000000000000000000000000000,18,0.238953446056642954120619037894
106,106_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,12.000000000000000000000000000000,2750,222,0.001000000000000000020816681712,19,0.001000000000000000020816681712
107,107_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,17.000000000000000000000000000000,2642,249,0.001000000000000000020816681712,20,0.257520447919497852407033633426
108,108_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,23.000000000000000000000000000000,3430,181,10.000000000000000000000000000000,8,0.844401695336755464538214255299
109,109_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,34.000000000000000000000000000000,2375,267,10.000000000000000000000000000000,17,0.998999999999999999111821580300
110,110_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,607.000000000000000000000000000000,2420,226,10.000000000000000000000000000000,20,0.001000000000000000020816681712
111,111_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,5.000000000000000000000000000000,2228,211,0.001000000000000000020816681712,7,0.556912880219845640716869183962
112,112_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,33.000000000000000000000000000000,1169,236,10.000000000000000000000000000000,7,0.478200749972826855138663404432
113,113_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,56.000000000000000000000000000000,4333,109,0.001000000000000000020816681712,9,0.998999999999999999111821580300
114,114_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,20.000000000000000000000000000000,3587,234,0.001000000000000000020816681712,8,0.276431854471228144465300147203
115,115_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,3.000000000000000000000000000000,2833,1588,10.000000000000000000000000000000,20,0.001000000000000000020816681712
116,116_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,2.000000000000000000000000000000,1,3288,0.001000000000000000020816681712,1,0.998999999999999999111821580300
117,117_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,2.000000000000000000000000000000,165,2764,0.001000000000000000020816681712,3,0.953445738882684024062541539024
118,118_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,40.000000000000000000000000000000,3978,218,10.000000000000000000000000000000,6,0.001000000000000000020816681712
119,119_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,50.000000000000000000000000000000,386,233,10.000000000000000000000000000000,9,0.327898151490232547811132235438
120,120_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6112.000000000000000000000000000000,4325,1,9.240970085710779713394913414959,14,0.998999999999999999111821580300
121,121_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5854.000000000000000000000000000000,4295,1,10.000000000000000000000000000000,10,0.998999999999999999111821580300
122,122_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6018.000000000000000000000000000000,4652,1,1.144559977551155016683992471371,14,0.998999999999999999111821580300
123,123_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,57.000000000000000000000000000000,4269,116,3.802424638221551855821189747076,14,0.998999999999999999111821580300
124,124_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,4541.000000000000000000000000000000,3610,1,5.287318965134845782927186519373,14,0.998999999999999999111821580300
125,125_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6495.000000000000000000000000000000,4141,1,8.450454359390683123365306528285,15,0.998999999999999999111821580300
126,126_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6140.000000000000000000000000000000,5000,1,7.963138802523596737614752782974,14,0.998999999999999999111821580300
127,127_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5901.000000000000000000000000000000,4886,1,1.440499818300766010636948522006,11,0.998999999999999999111821580300
128,128_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5985.000000000000000000000000000000,5000,1,10.000000000000000000000000000000,18,0.964048382756507948876389946236
129,129_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6258.000000000000000000000000000000,4715,1,3.757648139152261368423069143319,13,0.998999999999999999111821580300
130,130_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5035.000000000000000000000000000000,3837,1,8.817784042301807900798849004786,10,0.998999999999999999111821580300
131,131_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5946.000000000000000000000000000000,3753,1,4.891569274927675792241643648595,13,0.998999999999999999111821580300
132,132_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5721.000000000000000000000000000000,5000,1,9.344143562242425815611568395980,10,0.998999999999999999111821580300
133,133_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6251.000000000000000000000000000000,4894,1,9.603028786093549484803588711657,9,0.998999999999999999111821580300
134,134_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5889.000000000000000000000000000000,4677,1,3.789011124056584023378491110634,17,0.665606424561819065566226072406
135,135_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1349.000000000000000000000000000000,2436,1,7.295827559415337226766951062018,12,0.998999999999999999111821580300
136,136_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6366.000000000000000000000000000000,5000,1,7.018118190626157826272901729681,13,0.998999999999999999111821580300
137,137_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,590.000000000000000000000000000000,2045,1,10.000000000000000000000000000000,3,0.001000000000000000020816681712
138,138_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,925.000000000000000000000000000000,259,1,7.453475295456022386986205674475,11,0.998999999999999999111821580300
139,139_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,4452.000000000000000000000000000000,3317,1,10.000000000000000000000000000000,14,0.998999999999999999111821580300
140,140_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,7123.000000000000000000000000000000,4039,1,1.394294525377878235516959648521,14,0.998999999999999999111821580300
141,141_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,4571.000000000000000000000000000000,3605,1,6.793581062365734446473197749583,11,0.998999999999999999111821580300
142,142_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5797.000000000000000000000000000000,4066,1,7.603845877818301346451335120946,14,0.998999999999999999111821580300
143,143_0,COMPLETED,BoTorch,GenerationStep_1,0.810000000000000053290705182008,86.000000000000000000000000000000,1,1,8.128844705391392366777836286929,5,0.949762501759429222936148562439
144,144_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5999.000000000000000000000000000000,3986,1,0.914544451382904299840959083667,10,0.998999999999999999111821580300
145,145_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,458.000000000000000000000000000000,5000,1,9.927025514602602740410475234967,1,0.001000000000000000020816681712
146,146_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,674.000000000000000000000000000000,785,1,9.136554769997795588665212562773,4,0.392999771239976980474750689609
147,147_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,7136.000000000000000000000000000000,4314,1,9.538527820295886527901529916562,15,0.998999999999999999111821580300
148,148_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,260.000000000000000000000000000000,4681,29,6.619682501764320825543563842075,16,0.875265281512327963930886198796
149,149_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,793.000000000000000000000000000000,3700,8,6.791362354011568847056423692266,16,0.659972849961133034568661059893
150,150_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,7.000000000000000000000000000000,146,76,10.000000000000000000000000000000,1,0.928794402383602446526822404849
151,151_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,2392,10.000000000000000000000000000000,1,0.998999999999999999111821580300
152,152_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,23.000000000000000000000000000000,1273,365,10.000000000000000000000000000000,4,0.475100751189052428191672561297
153,153_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,3.000000000000000000000000000000,5000,4314,0.001000000000000000020816681712,20,0.998999999999999999111821580300
154,154_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4.000000000000000000000000000000,30,40,8.500455674269851158442179439589,1,0.916073047935597961632936403475
155,155_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,3.000000000000000000000000000000,2102,345,0.001000000000000000020816681712,1,0.001000000000000000020816681712
156,156_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,2.000000000000000000000000000000,1,95,10.000000000000000000000000000000,1,0.990203077059072178656151663745
157,157_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,2.000000000000000000000000000000,1,893,0.001000000000000000020816681712,1,0.001000000000000000020816681712
158,158_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,3.000000000000000000000000000000,5000,4262,0.001000000000000000020816681712,1,0.001000000000000000020816681712
159,159_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,1129.000000000000000000000000000000,1,974,10.000000000000000000000000000000,20,0.687703988583119429911505449127
160,160_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,48.000000000000000000000000000000,4949,128,0.001000000000000000020816681712,16,0.433844804334918399124632060193
161,161_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,20.000000000000000000000000000000,235,377,10.000000000000000000000000000000,6,0.558309114438343745412396401662
162,162_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,2.000000000000000000000000000000,1,1443,0.001000000000000000020816681712,8,0.429666301968924801979454741740
163,163_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,66,0.016403244478537575001819703857,1,0.946919827835912997215928044170
164,164_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,6.000000000000000000000000000000,5000,4160,10.000000000000000000000000000000,20,0.001000000000000000020816681712
165,165_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,1379.000000000000000000000000000000,1,1091,10.000000000000000000000000000000,20,0.001000000000000000020816681712
166,166_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,3.000000000000000000000000000000,5000,4313,0.001000000000000000020816681712,7,0.998999999999999999111821580300
167,167_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,3.000000000000000000000000000000,5000,4179,0.737992334921896753208159225323,1,0.001000000000000000020816681712
168,168_0,COMPLETED,BoTorch,GenerationStep_1,0.630000000000000004440892098501,2.000000000000000000000000000000,1,1145,0.001000000000000000020816681712,10,0.001000000000000000020816681712
169,169_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,2.000000000000000000000000000000,1,97,0.001000000000000000020816681712,2,0.940788194640071573360273760045
170,170_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,5000,3350,10.000000000000000000000000000000,20,0.998999999999999999111821580300
171,171_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,3.000000000000000000000000000000,1920,4221,0.001000000000000000020816681712,1,0.001000000000000000020816681712
172,172_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,68.000000000000000000000000000000,5000,106,10.000000000000000000000000000000,20,0.001000000000000000020816681712
173,173_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,2.000000000000000000000000000000,743,3529,0.001000000000000000020816681712,1,0.670857494395666442699166509556
174,174_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,996,10.000000000000000000000000000000,1,0.001000000000000000020816681712
175,175_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,2.000000000000000000000000000000,1,4198,0.001000000000000000020816681712,8,0.001000000000000000020816681712
176,176_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,48.000000000000000000000000000000,5000,120,0.001000000000000000020816681712,1,0.001000000000000000020816681712
177,177_0,COMPLETED,BoTorch,GenerationStep_1,0.630000000000000004440892098501,3.000000000000000000000000000000,4998,4247,0.001000000000000000020816681712,20,0.019640795788235857616266244463
178,178_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,17.000000000000000000000000000000,1,4298,10.000000000000000000000000000000,20,0.998999999999999999111821580300
179,179_0,COMPLETED,BoTorch,GenerationStep_1,0.650000000000000022204460492503,18.000000000000000000000000000000,5000,4319,10.000000000000000000000000000000,20,0.001000000000000000020816681712
180,180_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,3.000000000000000000000000000000,1,98,10.000000000000000000000000000000,7,0.998999999999999999111821580300
181,181_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,45.000000000000000000000000000000,5000,146,10.000000000000000000000000000000,13,0.998999999999999999111821580300
182,182_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,51.000000000000000000000000000000,1,482,10.000000000000000000000000000000,6,0.998999999999999999111821580300
183,183_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,76.000000000000000000000000000000,4254,83,10.000000000000000000000000000000,12,0.998999999999999999111821580300
184,184_0,COMPLETED,BoTorch,GenerationStep_1,0.810000000000000053290705182008,113.000000000000000000000000000000,1,1,10.000000000000000000000000000000,7,0.998999999999999999111821580300
185,185_0,COMPLETED,BoTorch,GenerationStep_1,0.650000000000000022204460492503,21.000000000000000000000000000000,1,701,10.000000000000000000000000000000,6,0.998999999999999999111821580300
186,186_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,31.000000000000000000000000000000,5000,197,10.000000000000000000000000000000,20,0.998999999999999999111821580300
187,187_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,3054.000000000000000000000000000000,1115,1,10.000000000000000000000000000000,7,0.998999999999999999111821580300
188,188_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,1,1808,10.000000000000000000000000000000,6,0.998999999999999999111821580300
189,189_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,36.000000000000000000000000000000,1905,154,10.000000000000000000000000000000,8,0.998999999999999999111821580300
190,190_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,1,1446,10.000000000000000000000000000000,5,0.998999999999999999111821580300
191,191_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1,136,10.000000000000000000000000000000,6,0.998999999999999999111821580300
192,192_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,14.000000000000000000000000000000,5000,1275,10.000000000000000000000000000000,20,0.001000000000000000020816681712
193,193_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,144.000000000000000000000000000000,5000,525,10.000000000000000000000000000000,14,0.998999999999999999111821580300
194,194_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,98.000000000000000000000000000000,5000,62,10.000000000000000000000000000000,13,0.998999999999999999111821580300
195,195_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,32.000000000000000000000000000000,4643,196,10.000000000000000000000000000000,12,0.998999999999999999111821580300
196,196_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,2.000000000000000000000000000000,1,1283,10.000000000000000000000000000000,5,0.998999999999999999111821580300
197,197_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,39.000000000000000000000000000000,5000,153,0.001000000000000000020816681712,20,0.998999999999999999111821580300
198,198_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,4.000000000000000000000000000000,5000,1831,0.001000000000000000020816681712,20,0.998999999999999999111821580300
199,199_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,9.000000000000000000000000000000,5000,1265,10.000000000000000000000000000000,11,0.998999999999999999111821580300
200,200_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,4.000000000000000000000000000000,5000,2400,10.000000000000000000000000000000,20,0.001000000000000000020816681712
201,201_0,COMPLETED,BoTorch,GenerationStep_1,0.619999999999999995559107901499,2.000000000000000000000000000000,1,2177,10.000000000000000000000000000000,5,0.998999999999999999111821580300
202,202_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,9.000000000000000000000000000000,5000,1701,10.000000000000000000000000000000,20,0.001000000000000000020816681712
203,203_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,2.000000000000000000000000000000,2690,5000,10.000000000000000000000000000000,1,0.001000000000000000020816681712
204,204_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,3.000000000000000000000000000000,1,363,10.000000000000000000000000000000,7,0.998999999999999999111821580300
205,205_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,38.000000000000000000000000000000,3243,94,10.000000000000000000000000000000,10,0.998999999999999999111821580300
206,206_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,19.000000000000000000000000000000,5000,416,10.000000000000000000000000000000,12,0.998999999999999999111821580300
207,207_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,6.000000000000000000000000000000,5000,1204,0.001000000000000000020816681712,20,0.998999999999999999111821580300
208,208_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,5000,4734,10.000000000000000000000000000000,1,0.998999999999999999111821580300
209,209_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,7.000000000000000000000000000000,5000,2151,10.000000000000000000000000000000,17,0.001000000000000000020816681712
210,210_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,23.000000000000000000000000000000,3096,324,10.000000000000000000000000000000,5,0.751929411459826724239974282682
211,211_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,6.000000000000000000000000000000,5000,4020,10.000000000000000000000000000000,19,0.001000000000000000020816681712
212,212_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,4.000000000000000000000000000000,5000,2021,10.000000000000000000000000000000,13,0.998999999999999999111821580300
213,213_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,7.000000000000000000000000000000,5000,3905,10.000000000000000000000000000000,20,0.001000000000000000020816681712
214,214_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,499.000000000000000000000000000000,5000,741,10.000000000000000000000000000000,17,0.998999999999999999111821580300
215,215_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,12.000000000000000000000000000000,2852,351,10.000000000000000000000000000000,3,0.998999999999999999111821580300
216,216_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,3.000000000000000000000000000000,772,3389,4.688031202139906206127761834068,10,0.001000000000000000020816681712
217,217_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,4.000000000000000000000000000000,5000,3076,10.000000000000000000000000000000,20,0.001000000000000000020816681712
218,218_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,6.000000000000000000000000000000,3319,3939,10.000000000000000000000000000000,20,0.001000000000000000020816681712
219,219_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,5.000000000000000000000000000000,2530,4048,10.000000000000000000000000000000,20,0.001000000000000000020816681712
220,220_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,62.000000000000000000000000000000,3764,124,10.000000000000000000000000000000,20,0.628633443953452553110139433556
221,221_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,4.000000000000000000000000000000,5000,3789,10.000000000000000000000000000000,1,0.998999999999999999111821580300
222,222_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,1,2431,10.000000000000000000000000000000,1,0.001000000000000000020816681712
223,223_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,3.000000000000000000000000000000,2887,3367,0.001000000000000000020816681712,13,0.001000000000000000020816681712
224,224_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,2850,4109,10.000000000000000000000000000000,1,0.001000000000000000020816681712
225,225_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,5.000000000000000000000000000000,5000,4070,10.000000000000000000000000000000,20,0.998999999999999999111821580300
226,226_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,6.000000000000000000000000000000,5000,3821,10.000000000000000000000000000000,20,0.865597619125430539632759519009
227,227_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,4.000000000000000000000000000000,5000,4070,10.000000000000000000000000000000,12,0.001000000000000000020816681712
228,228_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,51.000000000000000000000000000000,4965,155,7.434874648367184590824763290584,1,0.998999999999999999111821580300
229,229_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,45.000000000000000000000000000000,3282,103,9.730597639996600989320540975314,1,0.001000000000000000020816681712
230,230_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,4.000000000000000000000000000000,5000,2949,10.000000000000000000000000000000,1,0.001000000000000000020816681712
231,231_0,COMPLETED,BoTorch,GenerationStep_1,0.650000000000000022204460492503,4.000000000000000000000000000000,5000,2043,0.001000000000000000020816681712,8,0.998999999999999999111821580300
232,232_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,5000,3153,10.000000000000000000000000000000,20,0.959879516593710846450449025724
233,233_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,4.000000000000000000000000000000,5000,2621,10.000000000000000000000000000000,1,0.998999999999999999111821580300
234,234_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,4.000000000000000000000000000000,5000,2920,10.000000000000000000000000000000,20,0.001000000000000000020816681712
235,235_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,3.000000000000000000000000000000,5000,3092,10.000000000000000000000000000000,8,0.998999999999999999111821580300
236,236_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,2.000000000000000000000000000000,1,3403,10.000000000000000000000000000000,20,0.001000000000000000020816681712
237,237_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,9.000000000000000000000000000000,3695,664,0.001000000000000000020816681712,2,0.001000000000000000020816681712
238,238_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,4.000000000000000000000000000000,1,4048,10.000000000000000000000000000000,20,0.001000000000000000020816681712
239,239_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,497,2448,0.001000000000000000020816681712,1,0.493029689135264459487473232002
240,240_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,5.000000000000000000000000000000,2743,3821,10.000000000000000000000000000000,20,0.001000000000000000020816681712
241,241_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,48.000000000000000000000000000000,3605,104,0.001000000000000000020816681712,1,0.001000000000000000020816681712
242,242_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,5.000000000000000000000000000000,2804,3949,10.000000000000000000000000000000,20,0.998999999999999999111821580300
243,243_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,8.000000000000000000000000000000,5000,935,0.001000000000000000020816681712,20,0.001000000000000000020816681712
244,244_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,5.000000000000000000000000000000,5000,1555,0.001000000000000000020816681712,20,0.379514692210152937423828234387
245,245_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,10.000000000000000000000000000000,1,1664,10.000000000000000000000000000000,17,0.001000000000000000020816681712
246,246_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,4.000000000000000000000000000000,5000,2237,0.001000000000000000020816681712,18,0.998999999999999999111821580300
247,247_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,7.000000000000000000000000000000,1,33,10.000000000000000000000000000000,6,0.998999999999999999111821580300
248,248_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,5.000000000000000000000000000000,5000,1548,0.001000000000000000020816681712,1,0.998999999999999999111821580300
249,249_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,6.000000000000000000000000000000,5000,1533,0.001000000000000000020816681712,20,0.001000000000000000020816681712
250,250_0,COMPLETED,BoTorch,GenerationStep_1,0.630000000000000004440892098501,2.000000000000000000000000000000,1,2555,10.000000000000000000000000000000,1,0.998999999999999999111821580300
251,251_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,4.000000000000000000000000000000,3539,1554,0.001000000000000000020816681712,20,0.781966169970413904621864276123
252,252_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,3.000000000000000000000000000000,3923,3755,10.000000000000000000000000000000,20,0.001000000000000000020816681712
253,253_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,312.000000000000000000000000000000,2549,304,10.000000000000000000000000000000,12,0.001000000000000000020816681712
254,254_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,3.000000000000000000000000000000,1,52,10.000000000000000000000000000000,6,0.998999999999999999111821580300
255,255_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,5.000000000000000000000000000000,1511,3838,10.000000000000000000000000000000,20,0.001000000000000000020816681712
256,256_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,5000,3926,0.001000000000000000020816681712,13,0.998999999999999999111821580300
257,257_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,2.000000000000000000000000000000,1,3162,10.000000000000000000000000000000,1,0.001000000000000000020816681712
258,258_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,1322,1684,0.001000000000000000020816681712,20,0.001000000000000000020816681712
259,259_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,1,1708,10.000000000000000000000000000000,8,0.001000000000000000020816681712
260,260_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,2.000000000000000000000000000000,1,1364,10.000000000000000000000000000000,1,0.001000000000000000020816681712
261,261_0,COMPLETED,BoTorch,GenerationStep_1,0.640000000000000013322676295502,4.000000000000000000000000000000,2274,3820,10.000000000000000000000000000000,20,0.680607056223083484169933399244
262,262_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,1795,3759,10.000000000000000000000000000000,20,0.998999999999999999111821580300
263,263_0,COMPLETED,BoTorch,GenerationStep_1,0.650000000000000022204460492503,3.000000000000000000000000000000,1,1774,10.000000000000000000000000000000,20,0.001000000000000000020816681712
264,264_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,2.000000000000000000000000000000,245,1591,7.258732981560813257715381041635,20,0.998999999999999999111821580300
265,265_0,COMPLETED,BoTorch,GenerationStep_1,0.640000000000000013322676295502,2.000000000000000000000000000000,1,4568,0.001000000000000000020816681712,1,0.001000000000000000020816681712
266,266_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,23.000000000000000000000000000000,5000,930,10.000000000000000000000000000000,16,0.001000000000000000020816681712
267,267_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,15.000000000000000000000000000000,5000,565,0.001000000000000000020816681712,2,0.998999999999999999111821580300
268,268_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,3353,3796,0.001000000000000000020816681712,20,0.001000000000000000020816681712
269,269_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,2.000000000000000000000000000000,1,3627,0.001000000000000000020816681712,20,0.998999999999999999111821580300
270,270_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,19.000000000000000000000000000000,4853,312,10.000000000000000000000000000000,1,0.324107845528441240734451866956
271,271_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,2386.000000000000000000000000000000,791,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
272,272_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,4596.000000000000000000000000000000,1848,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
273,273_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,5000,735,10.000000000000000000000000000000,1,0.001000000000000000020816681712
274,274_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,22.000000000000000000000000000000,1,13,10.000000000000000000000000000000,20,0.998999999999999999111821580300
275,275_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,3.000000000000000000000000000000,5000,2788,0.001000000000000000020816681712,20,0.001000000000000000020816681712
276,276_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,67.000000000000000000000000000000,217,34,10.000000000000000000000000000000,20,0.997342226009621635185453669692
277,277_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,18.000000000000000000000000000000,5000,335,0.001000000000000000020816681712,1,0.036815009015031534278517000303
278,278_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,4971.000000000000000000000000000000,1348,1,0.001000000000000000020816681712,20,0.998999999999999999111821580300
279,279_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,10.000000000000000000000000000000,4928,631,10.000000000000000000000000000000,1,0.310479712580486455486550312344
280,280_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,17.000000000000000000000000000000,4908,358,10.000000000000000000000000000000,1,0.484686175356979831452264306790
281,281_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,8.000000000000000000000000000000,5000,759,0.001000000000000000020816681712,1,0.001000000000000000020816681712
282,282_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,1193.000000000000000000000000000000,5000,468,10.000000000000000000000000000000,20,0.713048423636191586183485924266
283,283_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,49.000000000000000000000000000000,4792,123,10.000000000000000000000000000000,1,0.574778725787663757884615733929
284,284_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,153.000000000000000000000000000000,5000,320,0.001000000000000000020816681712,20,0.001000000000000000020816681712
285,285_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1586.000000000000000000000000000000,559,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
286,286_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,11.000000000000000000000000000000,5000,657,0.001000000000000000020816681712,20,0.001000000000000000020816681712
287,287_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,3.000000000000000000000000000000,5000,3467,0.001000000000000000020816681712,1,0.998999999999999999111821580300
288,288_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,31.000000000000000000000000000000,5000,202,10.000000000000000000000000000000,1,0.041771049228403481223814708301
289,289_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,350.000000000000000000000000000000,4964,328,10.000000000000000000000000000000,20,0.535972939947676918492902586877
290,290_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5851.000000000000000000000000000000,1975,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
291,291_0,COMPLETED,BoTorch,GenerationStep_1,0.810000000000000053290705182008,39.000000000000000000000000000000,1,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
292,292_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,12.000000000000000000000000000000,84,48,10.000000000000000000000000000000,20,0.729200166445189368324975021096
293,293_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,9.000000000000000000000000000000,5000,706,0.001000000000000000020816681712,20,0.383369379065453708133048849049
294,294_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,4745.000000000000000000000000000000,1333,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
295,295_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,6.000000000000000000000000000000,5000,1386,0.001000000000000000020816681712,1,0.614093313713399369113687953359
296,296_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,21.000000000000000000000000000000,5000,295,10.000000000000000000000000000000,1,0.001000000000000000020816681712
297,297_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,5000,10.000000000000000000000000000000,20,0.750692936862546345011537596292
298,298_0,COMPLETED,BoTorch,GenerationStep_1,0.810000000000000053290705182008,328.000000000000000000000000000000,1,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
299,299_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2272.000000000000000000000000000000,2749,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
300,300_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,6.000000000000000000000000000000,1,58,10.000000000000000000000000000000,20,0.526205523606941949488202681096
301,301_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,11.000000000000000000000000000000,55,44,10.000000000000000000000000000000,20,0.998999999999999999111821580300
302,302_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,52.000000000000000000000000000000,4927,120,10.000000000000000000000000000000,1,0.294210468070871755674744463249
303,303_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,79.000000000000000000000000000000,694,27,10.000000000000000000000000000000,1,0.821331474805523376048199679644
304,304_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,8.000000000000000000000000000000,5000,813,0.001000000000000000020816681712,20,0.001000000000000000020816681712
305,305_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,6.000000000000000000000000000000,1,1500,10.000000000000000000000000000000,20,0.001000000000000000020816681712
306,306_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,4572,10.000000000000000000000000000000,20,0.998999999999999999111821580300
307,307_0,COMPLETED,BoTorch,GenerationStep_1,0.810000000000000053290705182008,32.000000000000000000000000000000,1,1,0.001000000000000000020816681712,1,0.998999999999999999111821580300
308,308_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,45.000000000000000000000000000000,5000,132,10.000000000000000000000000000000,20,0.571829070582599130467826853419
309,309_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,13.000000000000000000000000000000,5000,445,0.001000000000000000020816681712,20,0.001000000000000000020816681712
310,310_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,13.000000000000000000000000000000,5000,457,0.001000000000000000020816681712,1,0.998999999999999999111821580300
311,311_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,3.000000000000000000000000000000,5000,3452,10.000000000000000000000000000000,1,0.001000000000000000020816681712
312,312_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,4853,0.001000000000000000020816681712,1,0.998999999999999999111821580300
313,313_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,53.000000000000000000000000000000,5000,108,10.000000000000000000000000000000,1,0.001000000000000000020816681712
314,314_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,5.000000000000000000000000000000,69,59,10.000000000000000000000000000000,1,0.544298329667322722258404610329
315,315_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,6.000000000000000000000000000000,5000,1183,10.000000000000000000000000000000,1,0.065111813925307876482762026171
316,316_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,17.000000000000000000000000000000,5000,355,10.000000000000000000000000000000,1,0.998999999999999999111821580300
317,317_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,3338.000000000000000000000000000000,1307,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
318,318_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,2.000000000000000000000000000000,1,3403,10.000000000000000000000000000000,20,0.998999999999999999111821580300
319,319_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,19.000000000000000000000000000000,1248,317,0.001000000000000000020816681712,20,0.001000000000000000020816681712
320,320_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,19.000000000000000000000000000000,5000,301,0.001000000000000000020816681712,1,0.508224399172210627995127651957
321,321_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,40.000000000000000000000000000000,5000,150,10.000000000000000000000000000000,20,0.001000000000000000020816681712
322,322_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,15.000000000000000000000000000000,5000,410,10.000000000000000000000000000000,1,0.001000000000000000020816681712
323,323_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,12.000000000000000000000000000000,5000,490,0.001000000000000000020816681712,1,0.282821587358941917145216393692
324,324_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,27.000000000000000000000000000000,1,22,0.001000000000000000020816681712,20,0.878089547998334940537290549400
325,325_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,6.000000000000000000000000000000,2,68,10.000000000000000000000000000000,20,0.371622995797189159539186675829
326,326_0,COMPLETED,BoTorch,GenerationStep_1,0.810000000000000053290705182008,555.000000000000000000000000000000,1,1,0.001000000000000000020816681712,20,0.998999999999999999111821580300
327,327_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,9.000000000000000000000000000000,1,4788,10.000000000000000000000000000000,20,0.998999999999999999111821580300
328,328_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,907.000000000000000000000000000000,232,1,10.000000000000000000000000000000,20,0.737454734779045772974370720476
329,329_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,3.000000000000000000000000000000,1,18,10.000000000000000000000000000000,1,0.998999999999999999111821580300
330,330_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,2182.000000000000000000000000000000,600,1,0.001000000000000000020816681712,20,0.998999999999999999111821580300
331,331_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,13.000000000000000000000000000000,3404,1290,10.000000000000000000000000000000,20,0.998999999999999999111821580300
332,332_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,10.000000000000000000000000000000,5000,1295,10.000000000000000000000000000000,20,0.631208425811755846623896104575
333,333_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,39,10.000000000000000000000000000000,1,0.998999999999999999111821580300
334,334_0,COMPLETED,BoTorch,GenerationStep_1,0.640000000000000013322676295502,3.000000000000000000000000000000,5000,3543,0.001000000000000000020816681712,20,0.998999999999999999111821580300
335,335_0,COMPLETED,BoTorch,GenerationStep_1,0.640000000000000013322676295502,2.000000000000000000000000000000,1,4455,0.001000000000000000020816681712,1,0.998999999999999999111821580300
336,336_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,2.000000000000000000000000000000,1,2832,0.001000000000000000020816681712,1,0.001000000000000000020816681712
337,337_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,2.000000000000000000000000000000,1,4945,0.001000000000000000020816681712,1,0.001000000000000000020816681712
338,338_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,1003.000000000000000000000000000000,5000,604,10.000000000000000000000000000000,20,0.998999999999999999111821580300
339,339_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,7.000000000000000000000000000000,5000,1136,10.000000000000000000000000000000,1,0.998999999999999999111821580300
340,340_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4.000000000000000000000000000000,1,13,0.001000000000000000020816681712,1,0.998999999999999999111821580300
341,341_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,16.000000000000000000000000000000,1917,350,0.001000000000000000020816681712,20,0.998999999999999999111821580300
342,342_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,5000,4105,0.001000000000000000020816681712,1,0.998999999999999999111821580300
343,343_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,2389.000000000000000000000000000000,898,1,0.001000000000000000020816681712,1,0.998999999999999999111821580300
344,344_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,18.000000000000000000000000000000,5000,351,0.001000000000000000020816681712,20,0.998999999999999999111821580300
345,345_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,10.000000000000000000000000000000,5000,582,10.000000000000000000000000000000,1,0.001000000000000000020816681712
346,346_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,12.000000000000000000000000000000,5000,559,0.001000000000000000020816681712,1,0.001000000000000000020816681712
347,347_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,4.000000000000000000000000000000,3011,1360,10.000000000000000000000000000000,1,0.998999999999999999111821580300
348,348_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,30,0.001000000000000000020816681712,1,0.998999999999999999111821580300
349,349_0,COMPLETED,BoTorch,GenerationStep_1,0.640000000000000013322676295502,2.000000000000000000000000000000,1,4661,10.000000000000000000000000000000,1,0.998999999999999999111821580300
350,350_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3.000000000000000000000000000000,1,21,0.001000000000000000020816681712,1,0.998999999999999999111821580300
351,351_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,5000,5000,10.000000000000000000000000000000,1,0.001000000000000000020816681712
352,352_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,5000,10.000000000000000000000000000000,20,0.001000000000000000020816681712
353,353_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,6.000000000000000000000000000000,5000,1162,10.000000000000000000000000000000,1,0.701913969693611972111568775290
354,354_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,16.000000000000000000000000000000,5000,1645,10.000000000000000000000000000000,20,0.998999999999999999111821580300
355,355_0,COMPLETED,BoTorch,GenerationStep_1,0.640000000000000013322676295502,2.000000000000000000000000000000,1,4726,0.001000000000000000020816681712,1,0.998999999999999999111821580300
356,356_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,15.000000000000000000000000000000,5000,1047,10.000000000000000000000000000000,20,0.998999999999999999111821580300
357,357_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,27.000000000000000000000000000000,5000,224,0.001000000000000000020816681712,1,0.001000000000000000020816681712
358,358_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,10.000000000000000000000000000000,5000,630,10.000000000000000000000000000000,1,0.998999999999999999111821580300
359,359_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,1643,2869,0.001000000000000000020816681712,1,0.001000000000000000020816681712
360,360_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,48.000000000000000000000000000000,5000,123,10.000000000000000000000000000000,20,0.998999999999999999111821580300
361,361_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,6.000000000000000000000000000000,1,74,0.001000000000000000020816681712,20,0.998999999999999999111821580300
362,362_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,256.000000000000000000000000000000,4534,28,0.001000000000000000020816681712,1,0.998999999999999999111821580300
363,363_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,6.000000000000000000000000000000,1,67,10.000000000000000000000000000000,20,0.998999999999999999111821580300
364,364_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,483.000000000000000000000000000000,5000,348,10.000000000000000000000000000000,20,0.001000000000000000020816681712
365,365_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,64.000000000000000000000000000000,5000,100,10.000000000000000000000000000000,20,0.998999999999999999111821580300
366,366_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,71.000000000000000000000000000000,4951,93,0.001000000000000000020816681712,20,0.998999999999999999111821580300
367,367_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,7.000000000000000000000000000000,1,50,10.000000000000000000000000000000,20,0.998999999999999999111821580300
368,368_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1053.000000000000000000000000000000,2130,1,0.001000000000000000020816681712,20,0.998999999999999999111821580300
369,369_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,15.000000000000000000000000000000,3611,349,0.001000000000000000020816681712,1,0.001000000000000000020816681712
370,370_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,21.000000000000000000000000000000,1,23,10.000000000000000000000000000000,20,0.998999999999999999111821580300
371,371_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,41.000000000000000000000000000000,4,33,10.000000000000000000000000000000,20,0.998999999999999999111821580300
372,372_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,58.000000000000000000000000000000,2930,55,0.001000000000000000020816681712,20,0.998999999999999999111821580300
373,373_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,8.000000000000000000000000000000,5000,794,0.001000000000000000020816681712,1,0.998999999999999999111821580300
374,374_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,42.000000000000000000000000000000,5000,145,0.001000000000000000020816681712,1,0.998999999999999999111821580300
375,375_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,43.000000000000000000000000000000,3965,147,10.000000000000000000000000000000,20,0.998999999999999999111821580300
376,376_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,5000,858,0.001000000000000000020816681712,1,0.998999999999999999111821580300
377,377_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,57.000000000000000000000000000000,5000,124,0.001000000000000000020816681712,20,0.998999999999999999111821580300
378,378_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,16.000000000000000000000000000000,5000,899,10.000000000000000000000000000000,20,0.998999999999999999111821580300
379,379_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,2.000000000000000000000000000000,1,70,10.000000000000000000000000000000,1,0.998999999999999999111821580300
380,380_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,51.000000000000000000000000000000,5000,125,10.000000000000000000000000000000,1,0.998999999999999999111821580300
381,381_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,5.000000000000000000000000000000,1,87,10.000000000000000000000000000000,20,0.998999999999999999111821580300
382,382_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,162.000000000000000000000000000000,4884,42,0.001000000000000000020816681712,20,0.652229103029153156612096609024
383,383_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,59,10.000000000000000000000000000000,1,0.998999999999999999111821580300
384,384_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,110.000000000000000000000000000000,5000,380,0.001000000000000000020816681712,20,0.001000000000000000020816681712
385,385_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,1235.000000000000000000000000000000,375,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
386,386_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,31.000000000000000000000000000000,5000,178,0.001000000000000000020816681712,1,0.998999999999999999111821580300
387,387_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,18.000000000000000000000000000000,5000,385,0.001000000000000000020816681712,1,0.001000000000000000020816681712
388,388_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,51.000000000000000000000000000000,1,12,0.001000000000000000020816681712,20,0.998999999999999999111821580300
389,389_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,63.000000000000000000000000000000,5000,104,0.001000000000000000020816681712,20,0.001000000000000000020816681712
390,390_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,19.000000000000000000000000000000,3664,313,0.001000000000000000020816681712,20,0.537757348633142728822065237182
391,391_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,4.000000000000000000000000000000,2426,753,0.001000000000000000020816681712,20,0.001000000000000000020816681712
392,392_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,1660.000000000000000000000000000000,2581,1,0.001000000000000000020816681712,1,0.998999999999999999111821580300
393,393_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,54.000000000000000000000000000000,5000,108,10.000000000000000000000000000000,1,0.998999999999999999111821580300
394,394_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,45.000000000000000000000000000000,4212,128,0.001000000000000000020816681712,1,0.653947647460837444555181718897
395,395_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,44.000000000000000000000000000000,5000,339,0.001000000000000000020816681712,20,0.389993087749901434779786768559
396,396_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,60.000000000000000000000000000000,5000,95,0.001000000000000000020816681712,1,0.001000000000000000020816681712
397,397_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,6.000000000000000000000000000000,5000,1125,0.001000000000000000020816681712,1,0.001000000000000000020816681712
398,398_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,298,10.000000000000000000000000000000,1,0.001000000000000000020816681712
399,399_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,379.000000000000000000000000000000,4758,20,10.000000000000000000000000000000,1,0.998999999999999999111821580300
400,400_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,55.000000000000000000000000000000,5000,129,0.001000000000000000020816681712,20,0.603269904114436261721721166396
401,401_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,8.000000000000000000000000000000,3248,494,10.000000000000000000000000000000,1,0.998999999999999999111821580300
402,402_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,5.000000000000000000000000000000,1,9,10.000000000000000000000000000000,1,0.998999999999999999111821580300
403,403_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,59.000000000000000000000000000000,5000,262,0.001000000000000000020816681712,20,0.001000000000000000020816681712
404,404_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,2.000000000000000000000000000000,1,1525,0.001000000000000000020816681712,1,0.001000000000000000020816681712
405,405_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,5000,0.001000000000000000020816681712,1,0.998999999999999999111821580300
406,406_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,68.000000000000000000000000000000,5000,118,0.001000000000000000020816681712,1,0.552642418235704591111812078452
407,407_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4.000000000000000000000000000000,1,21,0.001000000000000000020816681712,1,0.591806268175156580113593918213
408,408_0,COMPLETED,BoTorch,GenerationStep_1,0.619999999999999995559107901499,2.000000000000000000000000000000,1,3128,0.001000000000000000020816681712,1,0.998999999999999999111821580300
409,409_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,29.000000000000000000000000000000,3044,127,0.001000000000000000020816681712,1,0.998999999999999999111821580300
410,410_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,39.000000000000000000000000000000,1,8,10.000000000000000000000000000000,20,0.998999999999999999111821580300
411,411_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,45.000000000000000000000000000000,5000,129,10.000000000000000000000000000000,1,0.744294579166857594998418790055
412,412_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,15.000000000000000000000000000000,3358,302,10.000000000000000000000000000000,1,0.001000000000000000020816681712
413,413_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,833.000000000000000000000000000000,501,2,10.000000000000000000000000000000,1,0.998999999999999999111821580300
414,414_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,5000,3279,0.001000000000000000020816681712,20,0.998999999999999999111821580300
415,415_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,9.000000000000000000000000000000,2919,333,0.001000000000000000020816681712,1,0.998999999999999999111821580300
416,416_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,7.000000000000000000000000000000,5000,967,10.000000000000000000000000000000,1,0.001000000000000000020816681712
417,417_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,5.000000000000000000000000000000,5000,1832,10.000000000000000000000000000000,20,0.001000000000000000020816681712
418,418_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,311.000000000000000000000000000000,5000,261,10.000000000000000000000000000000,20,0.001000000000000000020816681712
419,419_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,19.000000000000000000000000000000,4535,302,10.000000000000000000000000000000,1,0.998999999999999999111821580300
420,420_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,6117.000000000000000000000000000000,5000,1,0.001000000000000000020816681712,1,0.998999999999999999111821580300
421,421_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,57.000000000000000000000000000000,3529,91,10.000000000000000000000000000000,20,0.379869534478735393356885197136
422,422_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,42.000000000000000000000000000000,4373,140,10.000000000000000000000000000000,14,0.001000000000000000020816681712
423,423_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,146.000000000000000000000000000000,5000,43,0.001000000000000000020816681712,1,0.998999999999999999111821580300
424,424_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,58.000000000000000000000000000000,3932,111,0.001000000000000000020816681712,20,0.001000000000000000020816681712
425,425_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,77.000000000000000000000000000000,5000,77,0.001000000000000000020816681712,1,0.998999999999999999111821580300
426,426_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,249.000000000000000000000000000000,4480,322,10.000000000000000000000000000000,10,0.001000000000000000020816681712
427,427_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,144.000000000000000000000000000000,5000,289,10.000000000000000000000000000000,10,0.001000000000000000020816681712
428,428_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,66.000000000000000000000000000000,4498,90,0.001000000000000000020816681712,1,0.998999999999999999111821580300
429,429_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,65.000000000000000000000000000000,4015,96,10.000000000000000000000000000000,20,0.610641175041206074602939679608
430,430_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,54.000000000000000000000000000000,4438,115,0.001000000000000000020816681712,20,0.406247287637847120933543010324
431,431_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,48.000000000000000000000000000000,3217,77,0.001000000000000000020816681712,20,0.748587045121917316059523272997
432,432_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,14.000000000000000000000000000000,3973,428,10.000000000000000000000000000000,1,0.508145961518002953205552785221
433,433_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,79.000000000000000000000000000000,4665,74,10.000000000000000000000000000000,1,0.998999999999999999111821580300
434,434_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,43.000000000000000000000000000000,4123,142,0.001000000000000000020816681712,20,0.001000000000000000020816681712
435,435_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,238.000000000000000000000000000000,2872,12,10.000000000000000000000000000000,20,0.998999999999999999111821580300
436,436_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,2724.000000000000000000000000000000,3015,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
437,437_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,58.000000000000000000000000000000,4145,106,10.000000000000000000000000000000,1,0.998999999999999999111821580300
438,438_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,2736.000000000000000000000000000000,2855,1,0.001000000000000000020816681712,20,0.998999999999999999111821580300
439,439_0,COMPLETED,BoTorch,GenerationStep_1,0.760000000000000008881784197001,5716.000000000000000000000000000000,5000,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
440,440_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,24.000000000000000000000000000000,5000,330,0.001000000000000000020816681712,10,0.001000000000000000020816681712
441,441_0,COMPLETED,BoTorch,GenerationStep_1,0.810000000000000053290705182008,32.000000000000000000000000000000,1,1,10.000000000000000000000000000000,1,0.683826882269863767227491280210
442,442_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,51.000000000000000000000000000000,5000,614,0.001000000000000000020816681712,11,0.001000000000000000020816681712
443,443_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,308.000000000000000000000000000000,4917,25,10.000000000000000000000000000000,1,0.998999999999999999111821580300
444,444_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,30.000000000000000000000000000000,3461,327,10.000000000000000000000000000000,20,0.001000000000000000020816681712
445,445_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,12.000000000000000000000000000000,3142,408,10.000000000000000000000000000000,20,0.998999999999999999111821580300
446,446_0,COMPLETED,BoTorch,GenerationStep_1,0.739999999999999991118215802999,56.000000000000000000000000000000,3893,103,0.001000000000000000020816681712,20,0.398515044119732209093598385152
447,447_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,45.000000000000000000000000000000,3294,89,0.001000000000000000020816681712,20,0.608503288224623739566254698730
448,448_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,154.000000000000000000000000000000,2943,23,10.000000000000000000000000000000,20,0.998999999999999999111821580300
449,449_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,2892,1531,10.000000000000000000000000000000,1,0.001000000000000000020816681712
450,450_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,4.000000000000000000000000000000,5000,2108,0.001000000000000000020816681712,20,0.495027393340531474130727929150
451,451_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,2.000000000000000000000000000000,1,1589,0.001000000000000000020816681712,20,0.001000000000000000020816681712
452,452_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,37.000000000000000000000000000000,1,4187,10.000000000000000000000000000000,20,0.998999999999999999111821580300
453,453_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,4332,0.001000000000000000020816681712,20,0.001000000000000000020816681712
454,454_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,4.000000000000000000000000000000,5000,2016,10.000000000000000000000000000000,1,0.122659812476311566586950618785
455,455_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,3.000000000000000000000000000000,1,279,0.001000000000000000020816681712,20,0.998999999999999999111821580300
456,456_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,3.000000000000000000000000000000,5000,4630,10.000000000000000000000000000000,20,0.998999999999999999111821580300
457,457_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,3.000000000000000000000000000000,4882,3402,10.000000000000000000000000000000,1,0.687128207605233076726847230020
458,458_0,COMPLETED,BoTorch,GenerationStep_1,0.619999999999999995559107901499,3.000000000000000000000000000000,5000,4570,0.001000000000000000020816681712,1,0.998999999999999999111821580300
459,459_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,1.000000000000000000000000000000,1,2907,10.000000000000000000000000000000,1,0.998999999999999999111821580300
460,460_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,7.000000000000000000000000000000,5000,994,0.001000000000000000020816681712,1,0.998999999999999999111821580300
461,461_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,5000,2731,10.000000000000000000000000000000,1,0.001000000000000000020816681712
462,462_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,2330,1041,0.001000000000000000020816681712,20,0.998999999999999999111821580300
463,463_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,49,10.000000000000000000000000000000,1,0.001000000000000000020816681712
464,464_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,3.000000000000000000000000000000,5000,2501,0.001000000000000000020816681712,1,0.001000000000000000020816681712
465,465_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,1664,2305,0.001000000000000000020816681712,20,0.001000000000000000020816681712
466,466_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,3007,0.001000000000000000020816681712,1,0.001000000000000000020816681712
467,467_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,8.000000000000000000000000000000,4926,787,0.001000000000000000020816681712,1,0.579928974447770695377357696998
468,468_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,2.000000000000000000000000000000,1,4922,10.000000000000000000000000000000,20,0.998999999999999999111821580300
469,469_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,3.000000000000000000000000000000,4652,3647,0.001000000000000000020816681712,1,0.001000000000000000020816681712
470,470_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,5000,3342,10.000000000000000000000000000000,1,0.544521993908610224544020184112
471,471_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,4.000000000000000000000000000000,5000,2849,0.001000000000000000020816681712,20,0.459898420131524154896851541707
472,472_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,5.000000000000000000000000000000,4960,1661,0.001000000000000000020816681712,1,0.479550643227101058840844416409
473,473_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,1.000000000000000000000000000000,1,735,10.000000000000000000000000000000,1,0.001000000000000000020816681712
474,474_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,131,10.000000000000000000000000000000,1,0.001000000000000000020816681712
475,475_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,2.000000000000000000000000000000,471,1609,0.001000000000000000020816681712,1,0.512815672247756149104702672048
476,476_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,3.000000000000000000000000000000,3069,2362,10.000000000000000000000000000000,1,0.998999999999999999111821580300
477,477_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,34.000000000000000000000000000000,157,15,10.000000000000000000000000000000,1,0.708495654541757891031750205002
478,478_0,COMPLETED,BoTorch,GenerationStep_1,0.640000000000000013322676295502,4.000000000000000000000000000000,5000,2556,0.001000000000000000020816681712,1,0.001000000000000000020816681712
479,479_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,4.000000000000000000000000000000,5000,1913,10.000000000000000000000000000000,1,0.369676258317329919922400449650
480,480_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3.000000000000000000000000000000,5000,4853,10.000000000000000000000000000000,1,0.998999999999999999111821580300
481,481_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,3.000000000000000000000000000000,5000,3712,10.000000000000000000000000000000,1,0.998999999999999999111821580300
482,482_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,3.000000000000000000000000000000,2205,452,0.001000000000000000020816681712,1,0.001000000000000000020816681712
483,483_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,2.000000000000000000000000000000,1,1407,0.001000000000000000020816681712,1,0.001000000000000000020816681712
484,484_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,8.000000000000000000000000000000,1,45,0.001000000000000000020816681712,20,0.473259753428767471206128902850
485,485_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,6.000000000000000000000000000000,5000,1259,0.001000000000000000020816681712,1,0.618848468925940031759580506332
486,486_0,COMPLETED,BoTorch,GenerationStep_1,0.729999999999999982236431605997,3.000000000000000000000000000000,5000,3011,0.001000000000000000020816681712,1,0.001000000000000000020816681712
487,487_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,27.000000000000000000000000000000,5000,4870,10.000000000000000000000000000000,20,0.001000000000000000020816681712
488,488_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,4.000000000000000000000000000000,5000,2391,0.001000000000000000020816681712,1,0.998999999999999999111821580300
489,489_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,16.000000000000000000000000000000,1,3528,10.000000000000000000000000000000,20,0.001000000000000000020816681712
490,490_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,11.000000000000000000000000000000,5000,4802,10.000000000000000000000000000000,20,0.998999999999999999111821580300
491,491_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,2.000000000000000000000000000000,1,4011,0.001000000000000000020816681712,1,0.998999999999999999111821580300
492,492_0,COMPLETED,BoTorch,GenerationStep_1,0.709999999999999964472863211995,6.000000000000000000000000000000,3701,993,0.001000000000000000020816681712,20,0.001000000000000000020816681712
493,493_0,COMPLETED,BoTorch,GenerationStep_1,0.750000000000000000000000000000,10.000000000000000000000000000000,105,35,10.000000000000000000000000000000,1,0.459702317922291325835715269932
494,494_0,COMPLETED,BoTorch,GenerationStep_1,0.700000000000000066613381477509,5.000000000000000000000000000000,4196,1307,0.001000000000000000020816681712,1,0.001000000000000000020816681712
495,495_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,3.000000000000000000000000000000,5000,4798,0.001000000000000000020816681712,1,0.001000000000000000020816681712
496,496_0,COMPLETED,BoTorch,GenerationStep_1,0.690000000000000057731597280508,7.000000000000000000000000000000,2120,419,10.000000000000000000000000000000,20,0.001000000000000000020816681712
497,497_0,COMPLETED,BoTorch,GenerationStep_1,0.670000000000000039968028886506,3.000000000000000000000000000000,5000,3913,0.001000000000000000020816681712,1,0.001000000000000000020816681712
498,498_0,COMPLETED,BoTorch,GenerationStep_1,0.719999999999999973354647408996,12.000000000000000000000000000000,4552,504,0.001000000000000000020816681712,1,0.671565545696383714435739875626
499,499_0,COMPLETED,BoTorch,GenerationStep_1,0.680000000000000048849813083507,3.000000000000000000000000000000,5000,3691,10.000000000000000000000000000000,20,0.001000000000000000020816681712
500,500_0,COMPLETED,BoTorch,GenerationStep_1,0.660000000000000031086244689504,6.000000000000000000000000000000,4154,4135,10.000000000000000000000000000000,20,0.998999999999999999111821580300
501,501_0,COMPLETED,Sobol,SOBOL,0.670000000000000039968028886506,5.000000000000000000000000000000,2179,1989,6.339154988110066391016061970731,15,0.352086401164531714869099232601
502,502_0,COMPLETED,Sobol,SOBOL,0.670000000000000039968028886506,3.000000000000000000000000000000,4346,3767,1.599037038854323355963060748763,2,0.508519689962267928962091900758
503,503_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,25.000000000000000000000000000000,3144,692,7.999197697264142981055101699894,18,0.869680724795907744706369157939
504,504_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,4.000000000000000000000000000000,351,2627,2.751298817995004419856286403956,6,0.028082407308742403373447160675
505,505_0,COMPLETED,Sobol,SOBOL,0.729999999999999982236431605997,23.000000000000000000000000000000,929,164,0.879464920552447515511573783442,8,0.184992928236722947721304421975
506,506_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,4.000000000000000000000000000000,3097,3385,6.126140797570348794920391810592,16,0.964215541193261693742044826649
507,507_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,6.000000000000000000000000000000,4395,1543,4.726855416050181446507849614136,5,0.671276746211573516198711786274
508,508_0,COMPLETED,Sobol,SOBOL,0.619999999999999995559107901499,5.000000000000000000000000000000,1602,4609,9.465750577237457719093072228134,12,0.452470112781971689486226750887
509,509_0,COMPLETED,Sobol,SOBOL,0.689999999999999946709294817992,17.000000000000000000000000000000,1446,1107,4.252041721758433645561581215588,18,0.585175745114684064951404707244
510,510_0,COMPLETED,Sobol,SOBOL,0.650000000000000022204460492503,5.000000000000000000000000000000,4903,3081,9.003164298015645883310753561091,9,0.304969444179907456238254326308
511,511_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,4.000000000000000000000000000000,2607,2462,0.092191714056767529172908837154,11,0.067339671744033699485321164957
512,512_0,COMPLETED,Sobol,SOBOL,0.670000000000000039968028886506,4.000000000000000000000000000000,1025,4280,5.351056862986647288948915957008,4,0.785161170955747333977114976733
513,513_0,COMPLETED,Sobol,SOBOL,0.699999999999999955591079014994,3.000000000000000000000000000000,197,1665,8.473990234022959810999964247458,1,0.877752062806859578714124836551
514,514_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,3.000000000000000000000000000000,3654,4691,3.213906850736588438621765817516,14,0.222303412824869150998807754149
515,515_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,38.000000000000000000000000000000,3858,383,7.126444798426703641780477482826,8,0.391709993641823517318556469036
516,516_0,COMPLETED,Sobol,SOBOL,0.660000000000000031086244689504,23.000000000000000000000000000000,2276,3566,2.374103987812996141570920372033,20,0.734294397113844699021001360961
517,517_0,COMPLETED,Sobol,SOBOL,0.680000000000000048849813083507,7.000000000000000000000000000000,2361,615,9.123467762344516174266573216300,4,0.060721655867993828969986225275
518,518_0,COMPLETED,Sobol,SOBOL,0.709999999999999964472863211995,6.000000000000000000000000000000,3942,3641,3.873127646467648510508752224268,13,0.840875119226053402421428017988
519,519_0,COMPLETED,Sobol,SOBOL,0.719999999999999973354647408996,10.000000000000000000000000000000,3587,1755,5.275914785933681017127128143329,9,0.545027724390849455105012566491
520,520_0,COMPLETED,Sobol,SOBOL,0.699999999999999955591079014994,2.000000000000000000000000000000,130,4937,0.533355534884147330920711738145,16,0.327209002766758183522455283310
521,521_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,586.000000000000000000000000000000,5000,732,10.000000000000000000000000000000,20,0.245634936326524389027525785423
522,522_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,48.000000000000000000000000000000,3204,99,10.000000000000000000000000000000,1,0.511834643775753383998505796626
523,523_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,90.000000000000000000000000000000,4703,91,10.000000000000000000000000000000,1,0.703182209900971022520366204844
524,524_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,1140.000000000000000000000000000000,2400,1,0.001000000000000000020816681712,1,0.872394831309996621193647570180
525,525_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,9.000000000000000000000000000000,19,20,10.000000000000000000000000000000,1,0.743013895005972369034452640335
526,526_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,2.000000000000000000000000000000,1,36,0.001000000000000000020816681712,1,0.654063620888167496225662489451
527,527_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,47.000000000000000000000000000000,2164,411,0.001000000000000000020816681712,20,0.998999999999999999111821580300
528,528_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,3.000000000000000000000000000000,1,51,0.001000000000000000020816681712,1,0.998999999999999999111821580300
529,529_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,69.000000000000000000000000000000,4856,101,0.001000000000000000020816681712,1,0.212369043718389410857483312611
530,530_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,10.000000000000000000000000000000,5000,657,0.001000000000000000020816681712,1,0.574242193265003142421676329832
531,531_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,6.000000000000000000000000000000,17,25,10.000000000000000000000000000000,1,0.679590873412631801819827614963
532,532_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,15.000000000000000000000000000000,5000,389,0.001000000000000000020816681712,1,0.998999999999999999111821580300
533,533_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,2769,109,10.000000000000000000000000000000,1,0.998999999999999999111821580300
534,534_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,1268.000000000000000000000000000000,353,1,0.001000000000000000020816681712,1,0.998999999999999999111821580300
535,535_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,49.000000000000000000000000000000,5000,306,10.000000000000000000000000000000,20,0.922439141435058673756941516331
536,536_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,6.000000000000000000000000000000,1,13,10.000000000000000000000000000000,1,0.998999999999999999111821580300
537,537_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,1758,267,10.000000000000000000000000000000,1,0.001000000000000000020816681712
538,538_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,212.000000000000000000000000000000,4860,42,10.000000000000000000000000000000,20,0.998999999999999999111821580300
539,539_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.760000000000000008881784197001,2538.000000000000000000000000000000,888,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
540,540_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,60.000000000000000000000000000000,4876,103,10.000000000000000000000000000000,1,0.573173764211009517666184365225
541,541_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,24.000000000000000000000000000000,5000,268,10.000000000000000000000000000000,1,0.417222937999819243337640273239
542,542_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,54.000000000000000000000000000000,4431,126,0.001000000000000000020816681712,1,0.998999999999999999111821580300
543,543_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,95.000000000000000000000000000000,4147,85,10.000000000000000000000000000000,1,0.001000000000000000020816681712
544,544_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,8.000000000000000000000000000000,1359,789,10.000000000000000000000000000000,1,0.001000000000000000020816681712
545,545_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,12.000000000000000000000000000000,5000,665,10.000000000000000000000000000000,1,0.001000000000000000020816681712
546,546_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,854.000000000000000000000000000000,2293,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
547,547_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,82.000000000000000000000000000000,1,305,10.000000000000000000000000000000,20,0.998999999999999999111821580300
548,548_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,2.000000000000000000000000000000,1,49,0.001000000000000000020816681712,1,0.465272569699275162502516423046
549,549_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,1265.000000000000000000000000000000,5000,963,10.000000000000000000000000000000,20,0.998999999999999999111821580300
550,550_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,6.000000000000000000000000000000,5000,1436,0.001000000000000000020816681712,20,0.001000000000000000020816681712
551,551_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.760000000000000008881784197001,5041.000000000000000000000000000000,1463,1,10.000000000000000000000000000000,20,0.792324413309725361642676944030
552,552_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,22.000000000000000000000000000000,4041,348,0.001000000000000000020816681712,1,0.998999999999999999111821580300
553,553_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.619999999999999995559107901499,5.000000000000000000000000000000,5000,4908,10.000000000000000000000000000000,20,0.998999999999999999111821580300
554,554_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,3624,398,10.000000000000000000000000000000,1,0.001000000000000000020816681712
555,555_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,9.000000000000000000000000000000,5000,842,0.001000000000000000020816681712,1,0.001000000000000000020816681712
556,556_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.810000000000000053290705182008,39.000000000000000000000000000000,1,1,0.001000000000000000020816681712,1,0.833089458393135862124267987383
557,557_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,13.000000000000000000000000000000,3115,272,0.001000000000000000020816681712,1,0.001000000000000000020816681712
558,558_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,196.000000000000000000000000000000,1,251,10.000000000000000000000000000000,20,0.001000000000000000020816681712
559,559_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,13.000000000000000000000000000000,1,7,10.000000000000000000000000000000,1,0.655204694743940607715160240332
560,560_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,2.000000000000000000000000000000,1,2366,10.000000000000000000000000000000,20,0.001000000000000000020816681712
561,561_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,16.000000000000000000000000000000,2428,124,0.001000000000000000020816681712,1,0.001000000000000000020816681712
562,562_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,90.000000000000000000000000000000,4686,95,10.000000000000000000000000000000,1,0.001000000000000000020816681712
563,563_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,6.000000000000000000000000000000,1261,1349,0.001000000000000000020816681712,20,0.477971208118471047043840371771
564,564_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,22.000000000000000000000000000000,827,148,10.000000000000000000000000000000,1,0.001000000000000000020816681712
565,565_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,2.000000000000000000000000000000,2168,2974,10.000000000000000000000000000000,20,0.001000000000000000020816681712
566,566_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,2914,112,10.000000000000000000000000000000,19,0.001000000000000000020816681712
567,567_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,753,210,10.000000000000000000000000000000,1,0.001000000000000000020816681712
568,568_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,48.000000000000000000000000000000,4660,132,10.000000000000000000000000000000,11,0.998999999999999999111821580300
569,569_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,11.000000000000000000000000000000,768,247,0.001000000000000000020816681712,1,0.998999999999999999111821580300
570,570_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,4.000000000000000000000000000000,4159,2330,0.001000000000000000020816681712,1,0.001000000000000000020816681712
571,571_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,12.000000000000000000000000000000,3891,572,0.001000000000000000020816681712,1,0.609563190669008103661496988934
572,572_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,4.000000000000000000000000000000,633,1462,10.000000000000000000000000000000,1,0.176956774151419360396886304443
573,573_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,2.000000000000000000000000000000,2027,601,0.001000000000000000020816681712,20,0.998999999999999999111821580300
574,574_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,38.000000000000000000000000000000,1962,181,0.001000000000000000020816681712,1,0.001000000000000000020816681712
575,575_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,22.000000000000000000000000000000,5000,398,10.000000000000000000000000000000,1,0.483924614518907558746718677867
576,576_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,5.000000000000000000000000000000,2428,470,0.001000000000000000020816681712,1,0.998999999999999999111821580300
577,577_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,24.000000000000000000000000000000,3006,141,0.001000000000000000020816681712,1,0.001000000000000000020816681712
578,578_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,17.000000000000000000000000000000,620,131,0.001000000000000000020816681712,1,0.001000000000000000020816681712
579,579_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,542.000000000000000000000000000000,1,1,10.000000000000000000000000000000,1,0.434691910990370689038542195703
580,580_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,3.000000000000000000000000000000,5000,4693,0.001000000000000000020816681712,20,0.998999999999999999111821580300
581,581_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,13.000000000000000000000000000000,2786,206,0.001000000000000000020816681712,1,0.824599475908465162632410283550
582,582_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,6.000000000000000000000000000000,1114,761,0.001000000000000000020816681712,20,0.001000000000000000020816681712
583,583_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,28.000000000000000000000000000000,3262,145,0.001000000000000000020816681712,20,0.340978332306642428761733754072
584,584_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,249.000000000000000000000000000000,2411,340,10.000000000000000000000000000000,20,0.306529417826031269633801912278
585,585_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,7.000000000000000000000000000000,5000,1601,10.000000000000000000000000000000,1,0.001000000000000000020816681712
586,586_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,3.000000000000000000000000000000,1699,3383,0.001000000000000000020816681712,1,0.998999999999999999111821580300
587,587_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,27.000000000000000000000000000000,2798,88,0.001000000000000000020816681712,1,0.001000000000000000020816681712
588,588_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,2.000000000000000000000000000000,938,4019,0.001000000000000000020816681712,1,0.998999999999999999111821580300
589,589_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,6.000000000000000000000000000000,4397,2124,0.001000000000000000020816681712,20,0.489555788275771841533412498393
590,590_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,29.000000000000000000000000000000,1658,299,0.001000000000000000020816681712,1,0.814747126481145889087542855123
591,591_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1874.000000000000000000000000000000,508,1,0.001000000000000000020816681712,9,0.889151595706708208766144707624
592,592_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,27.000000000000000000000000000000,1325,167,0.001000000000000000020816681712,1,0.001000000000000000020816681712
593,593_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,9.000000000000000000000000000000,676,331,0.001000000000000000020816681712,1,0.998999999999999999111821580300
594,594_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1743.000000000000000000000000000000,2645,1,0.001000000000000000020816681712,1,0.841603229339481395676614283730
595,595_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,24.000000000000000000000000000000,2784,100,0.001000000000000000020816681712,1,0.317238452579041707579676767637
596,596_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,54.000000000000000000000000000000,4629,117,0.001000000000000000020816681712,1,0.801362576583848018607625363074
597,597_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,54.000000000000000000000000000000,4359,292,10.000000000000000000000000000000,20,0.998999999999999999111821580300
598,598_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,4.000000000000000000000000000000,2407,749,0.001000000000000000020816681712,1,0.267898607587085668679804939529
599,599_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,27.000000000000000000000000000000,4247,314,0.001000000000000000020816681712,1,0.998999999999999999111821580300
600,600_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,19.000000000000000000000000000000,4552,328,0.001000000000000000020816681712,1,0.301035903296336115886333573144
601,601_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,18.000000000000000000000000000000,4465,381,0.001000000000000000020816681712,1,0.998999999999999999111821580300
602,602_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,81.000000000000000000000000000000,5000,110,0.001000000000000000020816681712,1,0.998999999999999999111821580300
603,603_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,128.000000000000000000000000000000,5000,82,10.000000000000000000000000000000,1,0.001000000000000000020816681712
604,604_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,27.000000000000000000000000000000,3855,256,0.001000000000000000020816681712,20,0.001000000000000000020816681712
605,605_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,45.000000000000000000000000000000,3143,96,0.001000000000000000020816681712,20,0.001000000000000000020816681712
606,606_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,65.000000000000000000000000000000,4498,104,0.001000000000000000020816681712,1,0.001000000000000000020816681712
607,607_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,20.000000000000000000000000000000,2476,119,10.000000000000000000000000000000,1,0.306376112390076227320889756811
608,608_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,5.000000000000000000000000000000,5000,4055,0.001000000000000000020816681712,1,0.001000000000000000020816681712
609,609_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,7.000000000000000000000000000000,3481,1045,10.000000000000000000000000000000,1,0.998999999999999999111821580300
610,610_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,4079,562,10.000000000000000000000000000000,20,0.555283005892674208503478894272
611,611_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,1376,238,10.000000000000000000000000000000,1,0.778259612273463408094187343522
612,612_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,20.000000000000000000000000000000,5000,1192,10.000000000000000000000000000000,20,0.001000000000000000020816681712
613,613_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,22.000000000000000000000000000000,2977,153,0.001000000000000000020816681712,20,0.998999999999999999111821580300
614,614_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,10.000000000000000000000000000000,2657,198,10.000000000000000000000000000000,1,0.998999999999999999111821580300
615,615_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,3.000000000000000000000000000000,810,2375,0.001000000000000000020816681712,20,0.998999999999999999111821580300
616,616_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,32.000000000000000000000000000000,1173,160,10.000000000000000000000000000000,20,0.001000000000000000020816681712
617,617_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,6.000000000000000000000000000000,5000,1740,10.000000000000000000000000000000,1,0.998999999999999999111821580300
618,618_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,53.000000000000000000000000000000,4554,144,0.001000000000000000020816681712,20,0.998999999999999999111821580300
619,619_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,7.000000000000000000000000000000,4485,1127,0.001000000000000000020816681712,20,0.998999999999999999111821580300
620,620_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,62.000000000000000000000000000000,4181,112,10.000000000000000000000000000000,5,0.001000000000000000020816681712
621,621_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,3.000000000000000000000000000000,3753,5000,0.001000000000000000020816681712,20,0.001000000000000000020816681712
622,622_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,13.000000000000000000000000000000,3905,1238,10.000000000000000000000000000000,20,0.001000000000000000020816681712
623,623_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,5.000000000000000000000000000000,1768,1791,10.000000000000000000000000000000,20,0.998999999999999999111821580300
624,624_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,7.000000000000000000000000000000,157,159,0.001000000000000000020816681712,20,0.001000000000000000020816681712
625,625_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,11.000000000000000000000000000000,4040,562,10.000000000000000000000000000000,1,0.998999999999999999111821580300
626,626_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,24.000000000000000000000000000000,1252,206,10.000000000000000000000000000000,1,0.001000000000000000020816681712
627,627_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,3646,251,10.000000000000000000000000000000,1,0.998999999999999999111821580300
628,628_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,3131,578,10.000000000000000000000000000000,20,0.898966143345196089242676862341
629,629_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,10.000000000000000000000000000000,3461,824,0.001000000000000000020816681712,1,0.001000000000000000020816681712
630,630_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,29.000000000000000000000000000000,2850,119,10.000000000000000000000000000000,1,0.001000000000000000020816681712
631,631_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,27.000000000000000000000000000000,4209,302,10.000000000000000000000000000000,1,0.001000000000000000020816681712
632,632_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,3.000000000000000000000000000000,2735,1835,10.000000000000000000000000000000,20,0.998999999999999999111821580300
633,633_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,21.000000000000000000000000000000,587,97,0.001000000000000000020816681712,1,0.001000000000000000020816681712
634,634_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.650000000000000022204460492503,3.000000000000000000000000000000,3227,2737,0.001000000000000000020816681712,20,0.001000000000000000020816681712
635,635_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,4.000000000000000000000000000000,5000,2749,10.000000000000000000000000000000,1,0.399549027042126081443740304167
636,636_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,4.000000000000000000000000000000,5000,2811,0.001000000000000000020816681712,1,0.998999999999999999111821580300
637,637_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,1953,256,10.000000000000000000000000000000,1,0.998999999999999999111821580300
638,638_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,23.000000000000000000000000000000,4601,447,0.001000000000000000020816681712,6,0.001000000000000000020816681712
639,639_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,26.000000000000000000000000000000,2724,94,0.001000000000000000020816681712,20,0.001000000000000000020816681712
640,640_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,4.000000000000000000000000000000,1100,2397,10.000000000000000000000000000000,1,0.998999999999999999111821580300
641,641_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,65.000000000000000000000000000000,4710,123,10.000000000000000000000000000000,1,0.998999999999999999111821580300
642,642_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,3.000000000000000000000000000000,2003,193,10.000000000000000000000000000000,1,0.066506047225541042866581165072
643,643_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,4133,444,10.000000000000000000000000000000,1,0.998999999999999999111821580300
644,644_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,25.000000000000000000000000000000,3993,324,0.001000000000000000020816681712,20,0.998999999999999999111821580300
645,645_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,14.000000000000000000000000000000,3466,359,10.000000000000000000000000000000,1,0.998999999999999999111821580300
646,646_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,65.000000000000000000000000000000,4936,95,0.001000000000000000020816681712,1,0.740938988071501491994297339261
647,647_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,33.000000000000000000000000000000,2991,100,10.000000000000000000000000000000,20,0.708250873695595473122921248432
648,648_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,98.000000000000000000000000000000,4963,99,0.001000000000000000020816681712,1,0.535274927770195918519391398149
649,649_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,30.000000000000000000000000000000,4195,252,0.001000000000000000020816681712,1,0.001000000000000000020816681712
650,650_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,65.000000000000000000000000000000,4739,119,10.000000000000000000000000000000,20,0.718101548461065108952539048914
651,651_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,44.000000000000000000000000000000,2969,82,0.001000000000000000020816681712,1,0.998999999999999999111821580300
652,652_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,2356.000000000000000000000000000000,2637,1,0.001000000000000000020816681712,20,0.837597901368810404676423786441
653,653_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,1864.000000000000000000000000000000,2642,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
654,654_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,14.000000000000000000000000000000,4719,461,10.000000000000000000000000000000,1,0.998999999999999999111821580300
655,655_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,81.000000000000000000000000000000,4750,95,10.000000000000000000000000000000,1,0.332820391595666120743146620953
656,656_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,5.000000000000000000000000000000,2301,327,10.000000000000000000000000000000,1,0.702284497640657590089574568992
657,657_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,58.000000000000000000000000000000,4827,120,0.001000000000000000020816681712,1,0.998999999999999999111821580300
658,658_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,4433,574,10.000000000000000000000000000000,1,0.379181975211485000620825758233
659,659_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,3748,280,0.001000000000000000020816681712,1,0.541517016357464964215751024312
660,660_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,26.000000000000000000000000000000,3814,275,10.000000000000000000000000000000,1,0.001000000000000000020816681712
661,661_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,16.000000000000000000000000000000,1026,275,0.001000000000000000020816681712,1,0.001000000000000000020816681712
662,662_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,78.000000000000000000000000000000,4700,91,0.001000000000000000020816681712,20,0.001000000000000000020816681712
663,663_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,12.000000000000000000000000000000,4354,573,0.001000000000000000020816681712,20,0.998999999999999999111821580300
664,664_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,13.000000000000000000000000000000,5000,463,10.000000000000000000000000000000,1,0.497019975702735095079987104327
665,665_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,23.000000000000000000000000000000,4552,273,0.001000000000000000020816681712,20,0.768544828625439824243414932425
666,666_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,49.000000000000000000000000000000,4630,130,10.000000000000000000000000000000,20,0.998999999999999999111821580300
667,667_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,47.000000000000000000000000000000,4306,127,0.001000000000000000020816681712,1,0.312113596243715984979161248702
668,668_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,33.000000000000000000000000000000,3269,128,0.001000000000000000020816681712,20,0.001000000000000000020816681712
669,669_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,4569,408,10.000000000000000000000000000000,1,0.001000000000000000020816681712
670,670_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,14.000000000000000000000000000000,4584,415,0.001000000000000000020816681712,1,0.300457206894607808944641647031
671,671_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,28.000000000000000000000000000000,2922,103,10.000000000000000000000000000000,1,0.598808355859835828027826210018
672,672_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,1966,244,0.001000000000000000020816681712,20,0.998999999999999999111821580300
673,673_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,24.000000000000000000000000000000,3530,274,0.001000000000000000020816681712,20,0.998999999999999999111821580300
674,674_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,14.000000000000000000000000000000,509,182,0.001000000000000000020816681712,1,0.001000000000000000020816681712
675,675_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,10.000000000000000000000000000000,4571,743,10.000000000000000000000000000000,1,0.998999999999999999111821580300
676,676_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,1219.000000000000000000000000000000,4491,679,10.000000000000000000000000000000,20,0.998999999999999999111821580300
677,677_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,8.000000000000000000000000000000,2651,296,10.000000000000000000000000000000,1,0.998999999999999999111821580300
678,678_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,11.000000000000000000000000000000,3964,760,0.001000000000000000020816681712,20,0.998999999999999999111821580300
679,679_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,2.000000000000000000000000000000,1176,5000,10.000000000000000000000000000000,1,0.998999999999999999111821580300
680,680_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,46.000000000000000000000000000000,3835,151,10.000000000000000000000000000000,1,0.998999999999999999111821580300
681,681_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,36.000000000000000000000000000000,3040,105,0.001000000000000000020816681712,1,0.633314424589996316505846607470
682,682_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,36.000000000000000000000000000000,4021,187,0.001000000000000000020816681712,20,0.998999999999999999111821580300
683,683_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,32.000000000000000000000000000000,2982,129,10.000000000000000000000000000000,20,0.558450464044279781816726426769
684,684_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,40.000000000000000000000000000000,3148,92,10.000000000000000000000000000000,20,0.001000000000000000020816681712
685,685_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,321.000000000000000000000000000000,1,340,10.000000000000000000000000000000,20,0.001000000000000000020816681712
686,686_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,35.000000000000000000000000000000,3174,189,0.001000000000000000020816681712,20,0.998999999999999999111821580300
687,687_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,41.000000000000000000000000000000,1920,212,0.001000000000000000020816681712,20,0.001000000000000000020816681712
688,688_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,25.000000000000000000000000000000,4417,515,10.000000000000000000000000000000,20,0.998999999999999999111821580300
689,689_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,13.000000000000000000000000000000,3627,665,0.001000000000000000020816681712,20,0.001000000000000000020816681712
690,690_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,6.000000000000000000000000000000,4013,1073,10.000000000000000000000000000000,1,0.181381220827376454796109328527
691,691_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,4.000000000000000000000000000000,5000,2730,0.001000000000000000020816681712,1,0.998999999999999999111821580300
692,692_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,4393,495,0.001000000000000000020816681712,1,0.001000000000000000020816681712
693,693_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,234.000000000000000000000000000000,4429,813,10.000000000000000000000000000000,20,0.001000000000000000020816681712
694,694_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,6.000000000000000000000000000000,4870,1329,10.000000000000000000000000000000,1,0.298351141975666256378474372468
695,695_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,10.000000000000000000000000000000,4495,640,0.001000000000000000020816681712,20,0.401580952597170415252492148284
696,696_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,72.000000000000000000000000000000,4983,100,10.000000000000000000000000000000,1,0.448722694738025285055016411206
697,697_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,4133,364,10.000000000000000000000000000000,1,0.001000000000000000020816681712
698,698_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,2.000000000000000000000000000000,2078,1467,0.001000000000000000020816681712,1,0.001000000000000000020816681712
699,699_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,4410,590,0.001000000000000000020816681712,1,0.870171527359448915284190206876
700,700_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,8.000000000000000000000000000000,3348,597,0.001000000000000000020816681712,1,0.998999999999999999111821580300
701,701_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,52.000000000000000000000000000000,3701,118,10.000000000000000000000000000000,20,0.001000000000000000020816681712
702,702_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,32.000000000000000000000000000000,2806,83,10.000000000000000000000000000000,1,0.001000000000000000020816681712
703,703_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,16.000000000000000000000000000000,2646,178,10.000000000000000000000000000000,19,0.001000000000000000020816681712
704,704_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,2267.000000000000000000000000000000,2636,1,4.921976815304446972731966525316,7,0.998999999999999999111821580300
705,705_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,61.000000000000000000000000000000,4467,105,10.000000000000000000000000000000,20,0.001000000000000000020816681712
706,706_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,19.000000000000000000000000000000,4371,328,0.001000000000000000020816681712,20,0.344346372391821253788890544456
707,707_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,3.000000000000000000000000000000,2639,1019,0.001000000000000000020816681712,1,0.001000000000000000020816681712
708,708_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,40.000000000000000000000000000000,4021,155,10.000000000000000000000000000000,1,0.512448908516001222501756728889
709,709_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,124.000000000000000000000000000000,4625,342,0.001000000000000000020816681712,20,0.001000000000000000020816681712
710,710_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,4122,341,0.001000000000000000020816681712,20,0.001000000000000000020816681712
711,711_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,22.000000000000000000000000000000,3430,476,10.000000000000000000000000000000,15,0.571862652029844542589387401677
712,712_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,16.000000000000000000000000000000,2912,200,10.000000000000000000000000000000,1,0.299831029075891930535391338708
713,713_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,12.000000000000000000000000000000,2970,245,0.001000000000000000020816681712,1,0.998999999999999999111821580300
714,714_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,17.000000000000000000000000000000,3468,342,0.001000000000000000020816681712,1,0.583546579005764454173288413585
715,715_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,3.000000000000000000000000000000,2900,1236,10.000000000000000000000000000000,1,0.001000000000000000020816681712
716,716_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,4.000000000000000000000000000000,591,1217,0.001000000000000000020816681712,20,0.998999999999999999111821580300
717,717_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,28.000000000000000000000000000000,3081,138,10.000000000000000000000000000000,20,0.998999999999999999111821580300
718,718_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,7.000000000000000000000000000000,4452,1168,0.001000000000000000020816681712,20,0.001000000000000000020816681712
719,719_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,4174,330,10.000000000000000000000000000000,1,0.601118397892886568989467832580
720,720_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,756,254,0.001000000000000000020816681712,1,0.646657403882320069854472421866
721,721_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,4857,548,10.000000000000000000000000000000,1,0.433787173300875905646023511508
722,722_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,11.000000000000000000000000000000,4256,686,0.001000000000000000020816681712,1,0.322861775014304908104634250776
723,723_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,87.000000000000000000000000000000,5000,94,10.000000000000000000000000000000,1,0.998999999999999999111821580300
724,724_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,38.000000000000000000000000000000,3045,100,0.001000000000000000020816681712,20,0.313685111955037676523261325201
725,725_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,5000,499,10.000000000000000000000000000000,1,0.001000000000000000020816681712
726,726_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,30.000000000000000000000000000000,2938,111,10.000000000000000000000000000000,20,0.203936735715402300828102966079
727,727_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,3112,122,10.000000000000000000000000000000,1,0.244521292270705570270905582220
728,728_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,9.000000000000000000000000000000,2392,155,0.001000000000000000020816681712,1,0.001000000000000000020816681712
729,729_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,4.000000000000000000000000000000,5000,2940,0.001000000000000000020816681712,1,0.998999999999999999111821580300
730,730_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,2.000000000000000000000000000000,1,1581,10.000000000000000000000000000000,1,0.001000000000000000020816681712
731,731_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,89.000000000000000000000000000000,4747,85,0.001000000000000000020816681712,1,0.001000000000000000020816681712
732,732_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,75.000000000000000000000000000000,4518,111,10.000000000000000000000000000000,1,0.554330860559730909109532603907
733,733_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,37.000000000000000000000000000000,3721,143,10.000000000000000000000000000000,20,0.373391704001361346421106190974
734,734_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,5000,399,0.001000000000000000020816681712,1,0.704850887822567662155393009016
735,735_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,4.000000000000000000000000000000,1,2523,10.000000000000000000000000000000,20,0.001000000000000000020816681712
736,736_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,35.000000000000000000000000000000,3633,217,10.000000000000000000000000000000,20,0.998999999999999999111821580300
737,737_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,32.000000000000000000000000000000,1300,142,0.001000000000000000020816681712,1,0.001000000000000000020816681712
738,738_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,22.000000000000000000000000000000,789,121,10.000000000000000000000000000000,20,0.001000000000000000020816681712
739,739_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,25.000000000000000000000000000000,3394,175,10.000000000000000000000000000000,1,0.998999999999999999111821580300
740,740_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,87.000000000000000000000000000000,5000,84,0.001000000000000000020816681712,20,0.001000000000000000020816681712
741,741_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,13.000000000000000000000000000000,5000,562,10.000000000000000000000000000000,1,0.699408648939845778436108503229
742,742_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,9.000000000000000000000000000000,4904,844,10.000000000000000000000000000000,1,0.518198819678064914739934465615
743,743_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,44.000000000000000000000000000000,3221,92,0.001000000000000000020816681712,1,0.416289831274359201085388804131
744,744_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,14.000000000000000000000000000000,4311,455,0.001000000000000000020816681712,1,0.001000000000000000020816681712
745,745_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,42.000000000000000000000000000000,3819,146,0.001000000000000000020816681712,20,0.760232300414082518003056065936
746,746_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,142.000000000000000000000000000000,2619,237,0.001000000000000000020816681712,20,0.871299359665128614338414081431
747,747_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,38.000000000000000000000000000000,3733,145,10.000000000000000000000000000000,20,0.001000000000000000020816681712
748,748_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,61.000000000000000000000000000000,2956,230,10.000000000000000000000000000000,20,0.998999999999999999111821580300
749,749_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,83.000000000000000000000000000000,4378,114,0.001000000000000000020816681712,20,0.001000000000000000020816681712
750,750_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,19.000000000000000000000000000000,1178,253,0.001000000000000000020816681712,20,0.998999999999999999111821580300
751,751_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,55.000000000000000000000000000000,1764,131,10.000000000000000000000000000000,1,0.001000000000000000020816681712
752,752_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,50.000000000000000000000000000000,3150,74,10.000000000000000000000000000000,1,0.001000000000000000020816681712
753,753_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,34.000000000000000000000000000000,3503,129,10.000000000000000000000000000000,1,0.001000000000000000020816681712
754,754_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,28.000000000000000000000000000000,3369,153,0.001000000000000000020816681712,1,0.998999999999999999111821580300
755,755_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,46.000000000000000000000000000000,4409,129,10.000000000000000000000000000000,1,0.998999999999999999111821580300
756,756_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,49.000000000000000000000000000000,4182,129,0.001000000000000000020816681712,1,0.001000000000000000020816681712
757,757_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,30.000000000000000000000000000000,1216,181,0.001000000000000000020816681712,16,0.001000000000000000020816681712
758,758_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,4000,404,10.000000000000000000000000000000,1,0.803814535514187689102527656360
759,759_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,5.000000000000000000000000000000,1,24,10.000000000000000000000000000000,1,0.998999999999999999111821580300
760,760_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,5.000000000000000000000000000000,5000,1274,10.000000000000000000000000000000,1,0.001000000000000000020816681712
761,761_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,42.000000000000000000000000000000,1851,193,0.001000000000000000020816681712,1,0.501014092058657434236579319986
762,762_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,12.000000000000000000000000000000,2421,115,10.000000000000000000000000000000,1,0.001000000000000000020816681712
763,763_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,54.000000000000000000000000000000,4916,116,0.001000000000000000020816681712,9,0.998999999999999999111821580300
764,764_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,80.000000000000000000000000000000,4560,112,10.000000000000000000000000000000,20,0.430548858944595602693539149186
765,765_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.630000000000000004440892098501,3.000000000000000000000000000000,3544,3336,10.000000000000000000000000000000,1,0.998999999999999999111821580300
766,766_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,60.000000000000000000000000000000,4803,109,0.001000000000000000020816681712,1,0.721383144694819233855298534763
767,767_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,15.000000000000000000000000000000,4322,512,0.001000000000000000020816681712,20,0.484864940369024721711355141451
768,768_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,10.000000000000000000000000000000,2500,180,10.000000000000000000000000000000,1,0.001000000000000000020816681712
769,769_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,23.000000000000000000000000000000,5000,282,10.000000000000000000000000000000,1,0.998999999999999999111821580300
770,770_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.680000000000000048849813083507,2.000000000000000000000000000000,2085,1730,10.000000000000000000000000000000,1,0.998999999999999999111821580300
771,771_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,63.000000000000000000000000000000,5000,102,0.001000000000000000020816681712,8,0.668984133341187670040994817100
772,772_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,69.000000000000000000000000000000,4951,94,10.000000000000000000000000000000,10,0.001000000000000000020816681712
773,773_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,28.000000000000000000000000000000,5000,365,10.000000000000000000000000000000,1,0.202313529667489194752150183376
774,774_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,74.000000000000000000000000000000,4844,86,10.000000000000000000000000000000,20,0.405525821871942471208427605234
775,775_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,27.000000000000000000000000000000,4337,242,0.001000000000000000020816681712,1,0.998999999999999999111821580300
776,776_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,25.000000000000000000000000000000,4642,379,0.001000000000000000020816681712,1,0.650655401607157068966102997365
777,777_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,127.000000000000000000000000000000,241,270,10.000000000000000000000000000000,20,0.244930271577782687764113234152
778,778_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,39.000000000000000000000000000000,3157,106,0.001000000000000000020816681712,20,0.998999999999999999111821580300
779,779_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,4.000000000000000000000000000000,1,15,10.000000000000000000000000000000,1,0.998999999999999999111821580300
780,780_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,23.000000000000000000000000000000,4445,1393,10.000000000000000000000000000000,20,0.628040210117312369675346417353
781,781_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,17.000000000000000000000000000000,1255,256,10.000000000000000000000000000000,1,0.998999999999999999111821580300
782,782_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.609999999999999986677323704498,2.000000000000000000000000000000,1,1445,0.001000000000000000020816681712,20,0.001000000000000000020816681712
783,783_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,50.000000000000000000000000000000,4525,132,10.000000000000000000000000000000,20,0.703990613204510840006378202816
784,784_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,141.000000000000000000000000000000,2754,24,10.000000000000000000000000000000,1,0.998999999999999999111821580300
785,785_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,75.000000000000000000000000000000,2789,38,10.000000000000000000000000000000,1,0.998999999999999999111821580300
786,786_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,173.000000000000000000000000000000,2700,13,10.000000000000000000000000000000,1,0.998999999999999999111821580300
787,787_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,49.000000000000000000000000000000,2825,51,10.000000000000000000000000000000,1,0.998999999999999999111821580300
788,788_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.760000000000000008881784197001,3201.000000000000000000000000000000,2999,1,0.001000000000000000020816681712,1,0.998999999999999999111821580300
789,789_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,100.000000000000000000000000000000,2746,28,0.001000000000000000020816681712,1,0.998999999999999999111821580300
790,790_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,53.000000000000000000000000000000,2829,80,10.000000000000000000000000000000,1,0.998999999999999999111821580300
791,791_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,3534.000000000000000000000000000000,3082,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
792,792_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,173.000000000000000000000000000000,5000,64,10.000000000000000000000000000000,1,0.998999999999999999111821580300
793,793_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,299.000000000000000000000000000000,2839,27,0.001000000000000000020816681712,20,0.998999999999999999111821580300
794,794_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,639.000000000000000000000000000000,2761,1,10.000000000000000000000000000000,1,0.001000000000000000020816681712
795,795_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,10.000000000000000000000000000000,5000,4734,10.000000000000000000000000000000,20,0.001000000000000000020816681712
796,796_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,107.000000000000000000000000000000,5000,78,10.000000000000000000000000000000,20,0.998999999999999999111821580300
797,797_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,35.000000000000000000000000000000,2875,96,10.000000000000000000000000000000,1,0.998999999999999999111821580300
798,798_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.760000000000000008881784197001,4456.000000000000000000000000000000,3390,1,10.000000000000000000000000000000,20,0.998999999999999999111821580300
799,799_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,131.000000000000000000000000000000,5000,62,10.000000000000000000000000000000,20,0.998999999999999999111821580300
800,800_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,293.000000000000000000000000000000,5000,36,0.001000000000000000020816681712,20,0.998999999999999999111821580300
801,801_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,173.000000000000000000000000000000,5000,65,0.001000000000000000020816681712,20,0.998999999999999999111821580300
802,802_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,48.000000000000000000000000000000,2883,69,10.000000000000000000000000000000,20,0.998999999999999999111821580300
803,803_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,331.000000000000000000000000000000,2663,7,10.000000000000000000000000000000,1,0.998999999999999999111821580300
804,804_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,3.000000000000000000000000000000,431,3941,0.001000000000000000020816681712,1,0.001000000000000000020816681712
805,805_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,91.000000000000000000000000000000,2800,47,0.001000000000000000020816681712,1,0.998999999999999999111821580300
806,806_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,583.000000000000000000000000000000,200,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
807,807_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,17.000000000000000000000000000000,3942,1640,10.000000000000000000000000000000,20,0.324149023957811810436879795816
808,808_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,2.000000000000000000000000000000,1,3929,0.001000000000000000020816681712,1,0.001000000000000000020816681712
809,809_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,86.000000000000000000000000000000,2892,38,10.000000000000000000000000000000,20,0.998999999999999999111821580300
810,810_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.810000000000000053290705182008,131.000000000000000000000000000000,1,1,10.000000000000000000000000000000,7,0.845880908435667766553933688556
811,811_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,60.000000000000000000000000000000,5000,109,0.001000000000000000020816681712,20,0.956603391153051263451345676003
812,812_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,4.000000000000000000000000000000,3736,4773,10.000000000000000000000000000000,1,0.998999999999999999111821580300
813,813_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,4.000000000000000000000000000000,4109,2856,0.001000000000000000020816681712,1,0.001000000000000000020816681712
814,814_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.739999999999999991118215802999,57.000000000000000000000000000000,2780,68,10.000000000000000000000000000000,1,0.998999999999999999111821580300
815,815_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,3.000000000000000000000000000000,2631,4671,0.001000000000000000020816681712,20,0.998999999999999999111821580300
816,816_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.670000000000000039968028886506,2.000000000000000000000000000000,2524,3315,0.001000000000000000020816681712,20,0.489114074826769995585351580303
817,817_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,624.000000000000000000000000000000,2657,5,10.000000000000000000000000000000,20,0.998999999999999999111821580300
818,818_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,6.000000000000000000000000000000,5000,1405,0.001000000000000000020816681712,20,0.598335188031162812372087955737
819,819_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,78.000000000000000000000000000000,1909,96,10.000000000000000000000000000000,20,0.998999999999999999111821580300
820,820_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.750000000000000000000000000000,1700.000000000000000000000000000000,5000,4,10.000000000000000000000000000000,20,0.998999999999999999111821580300
821,821_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,19.000000000000000000000000000000,403,86,10.000000000000000000000000000000,10,0.001000000000000000020816681712
822,822_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.729999999999999982236431605997,15.000000000000000000000000000000,2512,110,0.001000000000000000020816681712,10,0.001000000000000000020816681712
823,823_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,117.000000000000000000000000000000,5000,556,10.000000000000000000000000000000,10,0.001000000000000000020816681712
824,824_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,6.000000000000000000000000000000,5000,1355,10.000000000000000000000000000000,11,0.998999999999999999111821580300
825,825_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,5.000000000000000000000000000000,5000,1725,0.001000000000000000020816681712,10,0.001000000000000000020816681712
826,826_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.719999999999999973354647408996,4.000000000000000000000000000000,1,75,0.001000000000000000020816681712,11,0.001000000000000000020816681712
827,827_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,5.000000000000000000000000000000,5000,2007,10.000000000000000000000000000000,20,0.001000000000000000020816681712
828,828_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.699999999999999955591079014994,8.000000000000000000000000000000,5000,1161,0.001000000000000000020816681712,11,0.998999999999999999111821580300
829,829_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,8.000000000000000000000000000000,5000,930,0.001000000000000000020816681712,1,0.998999999999999999111821580300
830,830_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.660000000000000031086244689504,3.000000000000000000000000000000,2338,3952,10.000000000000000000000000000000,10,0.001000000000000000020816681712
831,831_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,4.000000000000000000000000000000,5000,2892,0.001000000000000000020816681712,10,0.001000000000000000020816681712
832,832_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,4.000000000000000000000000000000,1,82,0.001000000000000000020816681712,12,0.998999999999999999111821580300
833,833_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,11.000000000000000000000000000000,4987,1224,10.000000000000000000000000000000,11,0.366253154210592180017869168296
834,834_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,3772,1,10.000000000000000000000000000000,1,0.998999999999999999111821580300
835,835_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.709999999999999964472863211995,2.000000000000000000000000000000,1,3181,0.001000000000000000020816681712,20,0.001000000000000000020816681712
836,836_0,COMPLETED,BoTorch,BOTORCH_MODULAR,0.689999999999999946709294817992,2.000000000000000000000000000000,1,1005,10.000000000000000000000000000000,1,0.998999999999999999111821580300
837,837_0,RUNNING,BoTorch,BOTORCH_MODULAR,,,5000,57,0.001000000000000000020816681712,1,0.998999999999999999111821580300
</pre>
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			<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_Electricity_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>
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<pre id="pre_tab_worker_usage">1745405554.9346569,20,0,0
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1745608647.0319042,20,1,5
1745608648.502837,20,2,10
1745608649.1559954,20,2,10
1745608670.2122319,20,1,5
1745608670.414766,20,1,5
1745610424.9133594,20,1,5
1745610425.2743397,20,1,5
1745610427.0671203,20,2,10
1745610427.69074,20,2,10
1745610446.6407757,20,1,5
1745610446.7297728,20,1,5
1745611059.0997748,20,1,5
1745611059.4487944,20,1,5
1745611061.0762691,20,2,10
1745611061.719206,20,2,10
1745611082.6328769,20,1,5
1745611082.8739214,20,1,5
1745611706.4928644,20,1,5
1745611707.0158713,20,1,5
1745611708.4869192,20,2,10
1745611709.0833564,20,2,10
1745611727.4557817,20,1,5
1745611727.8896105,20,1,5
1745613589.7828243,20,1,5
1745613590.128069,20,1,5
1745613591.5675592,20,2,10
1745613592.212217,20,2,10
1745613612.957692,20,1,5
1745613613.1488874,20,1,5
1745615262.494417,20,1,5
1745615263.260843,20,1,5
1745615265.0661805,20,2,10
1745615265.6864476,20,2,10
1745615286.6538868,20,1,5
1745615286.882278,20,1,5
1745615916.3503287,20,1,5
1745615916.5797627,20,1,5
1745615918.0719697,20,2,10
1745615918.6623814,20,2,10
1745615937.296647,20,1,5
1745615937.6059134,20,1,5
1745617481.3400497,20,1,5
1745617481.9544268,20,1,5
1745617483.3758984,20,2,10
1745617483.9840627,20,2,10
1745617503.0667849,20,1,5
1745617503.260829,20,1,5
1745618890.611493,20,1,5
1745618891.0107865,20,1,5
1745618892.444642,20,2,10
1745618893.05072,20,2,10
1745618911.6257153,20,1,5
1745618911.879643,20,1,5
1745620568.3174617,20,1,5
1745620568.9191113,20,1,5
1745620570.3540702,20,2,10
1745620570.9480686,20,2,10
1745620589.3502438,20,1,5
1745620589.8248103,20,1,5
1745622359.7962086,20,1,5
1745622360.1521063,20,1,5
1745622362.074689,20,2,10
1745622362.6921222,20,2,10
1745622381.588911,20,1,5
1745622381.8675988,20,1,5
1745623147.8942788,20,1,5
1745623149.2729585,20,1,5
1745623151.046506,20,2,10
1745623151.6782756,20,2,10
1745623170.4635773,20,1,5
1745623170.8903322,20,1,5
1745625082.8406038,20,1,5
1745625083.1621556,20,1,5
1745625084.6438117,20,2,10
1745625085.2188163,20,2,10
1745625103.2842314,20,1,5
1745625103.5339563,20,1,5
1745626905.9541614,20,1,5
1745626906.2935712,20,1,5
1745626908.0627356,20,2,10
1745626908.653515,20,2,10
1745626927.1996331,20,1,5
1745626927.3868928,20,1,5
1745627638.6323175,20,1,5
1745627638.8666382,20,1,5
1745627640.3100414,20,2,10
1745627640.9113636,20,2,10
1745627660.2427206,20,1,5
1745627660.4434175,20,1,5
1745629591.385515,20,1,5
1745629591.9961963,20,1,5
1745629593.4426868,20,2,10
1745629594.0478067,20,2,10
1745629613.8964477,20,1,5
1745629614.0989423,20,1,5
1745630426.499666,20,1,5
1745630427.0161016,20,1,5
1745630428.467163,20,2,10
1745630429.0921292,20,2,10
1745630448.582061,20,1,5
1745630448.883157,20,1,5
1745631210.8306327,20,1,5
1745631211.178447,20,1,5
1745631212.6576881,20,2,10
1745631213.2362635,20,2,10
1745631233.0449529,20,1,5
1745631233.28483,20,1,5
</pre><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_worker_usage")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_worker_usage", "worker_usage.csv")'> Download &raquo;worker_usage.csv&laquo; as file</button>
			
			<h1>&nbsp;CPU/RAM-Usage (main)</h1>
			<div class='invert_in_dark_mode' id='mainWorkerCPURAM'></div><button class='copy_clipboard_button' onclick='copy_to_clipboard_from_id("pre_tab_main_worker_cpu_ram")'> Copy raw data to clipboard</button>
			<button onclick='download_as_file("pre_tab_main_worker_cpu_ram", "cpu_ram_usage.csv")'> Download &raquo;cpu_ram_usage.csv&laquo; as file</button>
<pre id="pre_tab_main_worker_cpu_ram">timestamp,ram_usage_mb,cpu_usage_percent
1745405554,633.96484375,34.9
1745405554,633.96875,42.6
1745405554,633.96875,42.4
1745405554,633.96875,44.7
1745405554,633.96875,33.3
1745405554,633.96875,45.5
1745405554,633.96875,42.6
1745406104,683.34375,44.6
1745406104,683.34375,35.0
1745406104,683.34375,42.0
1745406104,683.34375,48.8
1745416073,924.0703125,42.2
1745416073,924.0703125,44.2
1745416073,924.0703125,44.2
1745416073,924.0703125,43.2
1745428529,968.9609375,37.5
1745428529,968.9609375,34.7
1745428529,968.9609375,33.1
1745428529,968.9609375,34.0
1745439158,980.9921875,42.2
1745439158,980.9921875,43.1
1745439158,980.9921875,42.9
1745439158,980.9921875,45.0
1745449944,1057.578125,40.8
1745449944,1057.578125,38.0
1745449944,1057.578125,39.9
1745449944,1057.578125,25.8
1745462646,1016.5703125,41.4
1745462646,1016.5703125,44.6
1745462646,1016.5703125,43.7
1745462646,1016.5703125,43.7
1745476633,1030.59375,42.8
1745476633,1030.59375,36.9
1745476633,1030.59375,37.8
1745476633,1030.59375,23.7
1745489592,1063.625,40.1
1745489592,1063.625,39.3
1745489592,1063.625,40.7
1745489592,1063.625,45.6
1745504911,1135.8046875,36.1
1745504911,1135.8046875,31.8
1745504911,1135.8046875,33.3
1745504911,1135.8046875,29.9
1745520226,1158.01171875,37.3
1745520227,1158.01171875,36.0
1745520227,1158.01171875,35.2
1745520227,1158.01171875,35.3
1745535616,1170.546875,37.6
1745535616,1170.546875,39.8
1745535616,1170.546875,38.1
1745535616,1170.546875,43.3
1745551038,1186.0234375,36.7
1745551038,1186.0234375,37.5
1745551038,1186.0234375,38.1
1745551038,1186.0234375,33.3
1745569523,1195.8828125,37.3
1745569523,1195.8828125,39.8
1745569523,1195.8828125,39.9
1745569523,1195.8828125,37.3
1745588579,1220.015625,37.6
1745588579,1220.015625,37.1
1745588579,1220.015625,36.5
1745588579,1220.015625,34.6
1745603313,1275.97265625,36.8
1745603313,1275.97265625,38.8
1745603313,1275.97265625,38.2
1745603313,1275.97265625,44.2
1745631246,1300.24609375,37.3
1745631246,1300.24609375,37.7
1745631246,1300.24609375,36.5
1745631246,1300.24609375,35.3
</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>
</html>