Example 6: PK Model, DMAG by RF with post-run Python code

Example 6 is again the same data and search as Example 4 and 5, but using the Random Forest search algorithm and python code for post run PPC penalty calculation.

The template file can be downloaded here and the tokens file here.

As before, to run in the environment used for this example, the directories are set to:

"working_dir": "u:/pyDarwin/example5/working",
"temp_dir": "u:/pyDarwin/example5/rundir",
"output_dir": "u:/pyDarwin/example5/output",

It is recommended that the user set the directories to something appropriate for their environment. If directories are not set, the default is:

{user_dir}\pydarwin\{project_name}

In either case, the folder names are given in the initial and final output to facilitate finding the files and debugging.

The options file looks like:

{
"author": "Certara",
"algorithm": "RF",
"num_opt_chains": 4,

"random_seed": 11,
"population_size": 80,
"num_parallel": 4,
"num_generations": 7,

"downhill_period": 5,
"num_niches": 2,
"niche_radius": 2,
"local_2_bit_search": true,
"final_downhill_search": true,

"crash_value": 99999999,

"penalty": {
    "theta": 10,
    "omega": 10,
    "sigma": 10,
    "convergence": 100,
    "covariance": 100,
    "correlation": 100,
    "condition_number": 100,
    "non_influential_tokens": 0.00001
},

"remove_run_dir": false,

"nmfe_path": "c:/nm744/util/nmfe74.bat",
"model_run_timeout": 1200,

"postprocess": {
    "use_python": true,
    "post_run_python_code": "{project_dir}/CmaxPPC.py"
}
}

and can be downloaded here.