BranchesTags

Repository files navigation

This repository contains the supplementary information accompanying the manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models".

To set up the scripts, execute the script setup.sh, this will setup a virtual environment and download and install additional dependencies.

To ensure reproducibility, the scripts are organized using snakemake. To execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days to finish. However, it should be easy to adapt this script to run on a cluster, which can finish in a couple of hours depending on available resources.

Running the python benchmark will generate results files in the ./results directory that include optimization results in hdf5 format as well as waterfall, parameter and convergence plots for every model and otimizer . Moreover additional evaluation figures will be generated in the ./evaluation directory.

The MATLAB part of this benchmark can be tun by executing the script ./Hass2019/run_Benchmark.m. The script will take a bit over a week to finish on modern hardware. The folder ./Hass2019 contains a modified version of the script arFit.m that ensures that all optimizer options are corrrectly applied to the fmincon optimizer. These changes will be automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this optimization, which will be loaded instead of rerunning the benchmarks . To rerun a benchmark delete the respective .mat file in ./Hass2019

To compare results across optimizers and methods, the script comparison.py has to be executed after both MATLAB and python part of the benchmark have finished. This will generate additional figures and .csv files which serve as the basis for text and figure in the manuscript.

About

Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
This repository contains the supplementary information accompanying the
manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation
of Ordinary Differential Equation Models".
To set up the scripts, execute the script setup.sh, this will setup a
virtual environment and download and install additional dependencies.
To ensure reproducibility, the scripts are organized using snakemake. To
execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days
to finish. However, it should be easy to adapt this script to run on a
cluster, which can finish in a couple of hours depending on available
resources.
Running the python benchmark will generate results files in the ./results  directory that include optimization results in hdf5 format as well as
waterfall, parameter and convergence plots for every model and otimizer
. Moreover additional evaluation figures will be generated in the
./evaluation directory.
The MATLAB part of this benchmark can be tun by executing the script
./Hass2019/run_Benchmark.m. The script will take a bit over a week to
finish on modern hardware. The folder ./Hass2019 contains a modified
version of the script arFit.m that ensures that all optimizer options
are corrrectly applied to the fmincon optimizer. These changes will be
automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this
optimization, which will be loaded instead of rerunning the benchmarks
. To rerun a benchmark delete the respective .mat file in ./Hass2019
To compare results across optimizers and methods, the script comparison.py  has to be executed after both MATLAB and python part of the benchmark have
finished. This will generate additional figures and .csv files which
serve as the basis for text and figure in the manuscript.
About
Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
This repository contains the supplementary information accompanying the
manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation
of Ordinary Differential Equation Models".
To set up the scripts, execute the script setup.sh, this will setup a
virtual environment and download and install additional dependencies.
To ensure reproducibility, the scripts are organized using snakemake. To
execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days
to finish. However, it should be easy to adapt this script to run on a
cluster, which can finish in a couple of hours depending on available
resources.
Running the python benchmark will generate results files in the ./results  directory that include optimization results in hdf5 format as well as
waterfall, parameter and convergence plots for every model and otimizer
. Moreover additional evaluation figures will be generated in the
./evaluation directory.
The MATLAB part of this benchmark can be tun by executing the script
./Hass2019/run_Benchmark.m. The script will take a bit over a week to
finish on modern hardware. The folder ./Hass2019 contains a modified
version of the script arFit.m that ensures that all optimizer options
are corrrectly applied to the fmincon optimizer. These changes will be
automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this
optimization, which will be loaded instead of rerunning the benchmarks
. To rerun a benchmark delete the respective .mat file in ./Hass2019
To compare results across optimizers and methods, the script comparison.py  has to be executed after both MATLAB and python part of the benchmark have
finished. This will generate additional figures and .csv files which
serve as the basis for text and figure in the manuscript.
About
Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
This repository contains the supplementary information accompanying the
manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation
of Ordinary Differential Equation Models".
To set up the scripts, execute the script setup.sh, this will setup a
virtual environment and download and install additional dependencies.
To ensure reproducibility, the scripts are organized using snakemake. To
execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days
to finish. However, it should be easy to adapt this script to run on a
cluster, which can finish in a couple of hours depending on available
resources.
Running the python benchmark will generate results files in the ./results  directory that include optimization results in hdf5 format as well as
waterfall, parameter and convergence plots for every model and otimizer
. Moreover additional evaluation figures will be generated in the
./evaluation directory.
The MATLAB part of this benchmark can be tun by executing the script
./Hass2019/run_Benchmark.m. The script will take a bit over a week to
finish on modern hardware. The folder ./Hass2019 contains a modified
version of the script arFit.m that ensures that all optimizer options
are corrrectly applied to the fmincon optimizer. These changes will be
automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this
optimization, which will be loaded instead of rerunning the benchmarks
. To rerun a benchmark delete the respective .mat file in ./Hass2019
To compare results across optimizers and methods, the script comparison.py  has to be executed after both MATLAB and python part of the benchmark have
finished. This will generate additional figures and .csv files which
serve as the basis for text and figure in the manuscript.
About
Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
This repository contains the supplementary information accompanying the
manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation
of Ordinary Differential Equation Models".
To set up the scripts, execute the script setup.sh, this will setup a
virtual environment and download and install additional dependencies.
To ensure reproducibility, the scripts are organized using snakemake. To
execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days
to finish. However, it should be easy to adapt this script to run on a
cluster, which can finish in a couple of hours depending on available
resources.
Running the python benchmark will generate results files in the ./results  directory that include optimization results in hdf5 format as well as
waterfall, parameter and convergence plots for every model and otimizer
. Moreover additional evaluation figures will be generated in the
./evaluation directory.
The MATLAB part of this benchmark can be tun by executing the script
./Hass2019/run_Benchmark.m. The script will take a bit over a week to
finish on modern hardware. The folder ./Hass2019 contains a modified
version of the script arFit.m that ensures that all optimizer options
are corrrectly applied to the fmincon optimizer. These changes will be
automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this
optimization, which will be loaded instead of rerunning the benchmarks
. To rerun a benchmark delete the respective .mat file in ./Hass2019
To compare results across optimizers and methods, the script comparison.py  has to be executed after both MATLAB and python part of the benchmark have
finished. This will generate additional figures and .csv files which
serve as the basis for text and figure in the manuscript.
About
Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
This repository contains the supplementary information accompanying the
manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation
of Ordinary Differential Equation Models".
To set up the scripts, execute the script setup.sh, this will setup a
virtual environment and download and install additional dependencies.
To ensure reproducibility, the scripts are organized using snakemake. To
execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days
to finish. However, it should be easy to adapt this script to run on a
cluster, which can finish in a couple of hours depending on available
resources.
Running the python benchmark will generate results files in the ./results  directory that include optimization results in hdf5 format as well as
waterfall, parameter and convergence plots for every model and otimizer
. Moreover additional evaluation figures will be generated in the
./evaluation directory.
The MATLAB part of this benchmark can be tun by executing the script
./Hass2019/run_Benchmark.m. The script will take a bit over a week to
finish on modern hardware. The folder ./Hass2019 contains a modified
version of the script arFit.m that ensures that all optimizer options
are corrrectly applied to the fmincon optimizer. These changes will be
automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this
optimization, which will be loaded instead of rerunning the benchmarks
. To rerun a benchmark delete the respective .mat file in ./Hass2019
To compare results across optimizers and methods, the script comparison.py  has to be executed after both MATLAB and python part of the benchmark have
finished. This will generate additional figures and .csv files which
serve as the basis for text and figure in the manuscript.
About
Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
This repository contains the supplementary information accompanying the
manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation
of Ordinary Differential Equation Models".
To set up the scripts, execute the script setup.sh, this will setup a
virtual environment and download and install additional dependencies.
To ensure reproducibility, the scripts are organized using snakemake. To
execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days
to finish. However, it should be easy to adapt this script to run on a
cluster, which can finish in a couple of hours depending on available
resources.
Running the python benchmark will generate results files in the ./results  directory that include optimization results in hdf5 format as well as
waterfall, parameter and convergence plots for every model and otimizer
. Moreover additional evaluation figures will be generated in the
./evaluation directory.
The MATLAB part of this benchmark can be tun by executing the script
./Hass2019/run_Benchmark.m. The script will take a bit over a week to
finish on modern hardware. The folder ./Hass2019 contains a modified
version of the script arFit.m that ensures that all optimizer options
are corrrectly applied to the fmincon optimizer. These changes will be
automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this
optimization, which will be loaded instead of rerunning the benchmarks
. To rerun a benchmark delete the respective .mat file in ./Hass2019
To compare results across optimizers and methods, the script comparison.py  has to be executed after both MATLAB and python part of the benchmark have
finished. This will generate additional figures and .csv files which
serve as the basis for text and figure in the manuscript.
About
Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
This repository contains the supplementary information accompanying the
manuscript "Fides: Reliable Trust-Region Optimization for Parameter Estimation
of Ordinary Differential Equation Models".
To set up the scripts, execute the script setup.sh, this will setup a
virtual environment and download and install additional dependencies.
To ensure reproducibility, the scripts are organized using snakemake. To
execute the python part of the benchmark, execute benchmarkLocal.sh . This will run the whole benchmark locally and likely need multiple days
to finish. However, it should be easy to adapt this script to run on a
cluster, which can finish in a couple of hours depending on available
resources.
Running the python benchmark will generate results files in the ./results  directory that include optimization results in hdf5 format as well as
waterfall, parameter and convergence plots for every model and otimizer
. Moreover additional evaluation figures will be generated in the
./evaluation directory.
The MATLAB part of this benchmark can be tun by executing the script
./Hass2019/run_Benchmark.m. The script will take a bit over a week to
finish on modern hardware. The folder ./Hass2019 contains a modified
version of the script arFit.m that ensures that all optimizer options
are corrrectly applied to the fmincon optimizer. These changes will be
automatically applied to the downloaded d2d version at ./Hass2019/d2d . This repository is already prepopulated with results from this
optimization, which will be loaded instead of rerunning the benchmarks
. To rerun a benchmark delete the respective .mat file in ./Hass2019
To compare results across optimizers and methods, the script comparison.py  has to be executed after both MATLAB and python part of the benchmark have
finished. This will generate additional figures and .csv files which
serve as the basis for text and figure in the manuscript.
About
Benchmark of the fides optimizer using pypesto based on a subset of models from the petab benchmark suite
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages