Releases¶
pyDarwin-Certara 3.0.0¶
What’s New¶
Python 3.12 support.
Additional support for RDarwin .
pyDarwin-Certara 2.0.1¶
Issues Corrected¶
Estimated OMEGAS are now used when calculating OMEGA penalty, fixed OMEGAS are ignored.
Estimated SIGMAS are now used when calculating SIGMA penalty, fixed SIGMAS are ignored.
Unnecessary memory allocation when calculating size of total search space.
pyDarwin-Certara 2.0¶
What’s New¶
Added NLME Engine support, including Omega Block Search.
Introduced {darwin_cmd} alias for Linux Grid runs.
Introduced search_info command.
Introduced estimations of number of models and remaining time. The former is shown in the beginning of the search as well as in the
search_info
output (except for Exhaustive search), the latter – in the beginning of every but first iteration. Those may be not too accurate since they assume a certain amount of Downhill Search iterations (if it is requested) and that a model run time is on average the same (which is not always the case due to duplicates that sometimes make up a large part of the iteration).Added tokens consistency check before running the search.
Added NONMEM license expiration error to the visible errors.
Updated and renamed columns in results.csv.
Introduced new model statuses:
Clone – a sibling with the same genotype
Twin – a sibling with the same phenotype
Restored – a model run picked from the cache, when the cache was loaded form a file and the model was picked for the first time
Cache – a model run picked from the cache in all other cases
Improved Omega Structure Search, e.g.:
increased number of possible omega block patterns
reduced number of model runs by adding the omega structure to the model phenotype, detecting twins, and looking for duplicates in model cache by phenotype
Introduced key models retention: best models from every iteration can be saved with all the necessary output in a separate folder for further analysis.
Changed Final Downhill and 2-bit search iteration names.
Removed unnecessary 0 generation from Genetic Algorithm.
Reorganized examples folder, added NONMEM and NLME subfolders.
If random_seed is not set in the options file it will be initialized with a random number.
working_dir must be an absolute path.
Issues Fixed¶
Removed commas and new lines from translation and runtime messages so they won’t break CSV structure anymore.
Corrected calculation of ‘Number of unique models to best model’.
pyDarwin-Certara 1.1.1¶
What’s New¶
The condition_number penalty is now added to the fitness value for the case when the covariance step is successful and condition_number > 1000. Previously, the condition_number penalty was added to fitness value only for the case when covariance was unsuccessful or not requested.
Issues Fixed¶
An issue was corrected where the correlation penalty does not get added to the resulting fitness value for cases when it should.
pyDarwin-Certara 1.1¶
What’s New¶
pyDarwin now supports additional options for Searching Omega Structure, including:
Band matrix
Submatrices
pyDarwin now supports usage of the Particle Swarms Optimization (PSO) algorithm to search for a global optimal solution in the candidate model search space.
Issues Fixed¶
An issue has been fixed that could cause the search to fail if the covariance step ($COV) was not specified in template.txt.
pyDarwin-Certara 1.0¶
Initial release of pyDarwin-Certara offers the Python module darwin to perform a search over a candidate model space using one of the following machine learning algorithms:
Users can alternatively select the Exhaustive Search (EX) to execute all potential candidate models in a given search space without machine learning optimization.
Two primary functions to execute the search have been made available:
python -m darwin.run_search <template_path> <tokens_path> <options_path>
To execute, call the darwin.run_search
function and provide the paths to the following files as arguments:
Template file (e.g., template.txt) - basic shell for NONMEM control files
Tokens file (e.g., tokens.json) - json file describing the dimensions of the search space and the options in each dimension
Options file (e.g., options.json) - json file describing algorithm, run options, and post-run penalty code configurations.
Alternatively, you may execute the darwin.run_search_in_folder function,
specifying the path to the folder containing the template.txt
, tokens.json
, and options.json
files
as a single argument:
python -m darwin.run_search_in_folder <folder_path>