File size: 4,487 Bytes
9d54b72 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
import json
import os
import tempfile
from pathlib import Path
from typing import Dict, List
import basico
import pandas as pd
import typer
from vcell_opt.data import OptProblem, Vcellopt, VcelloptStatus, OptResultSet
from vcell_opt.optUtils import get_reference_data, get_fit_parameters, get_copasi_opt_method_settings, \
result_set_from_fit, get_progress_report
def run_command(opt_file: Path = typer.Argument(..., file_okay=True, dir_okay=False, exists=True,
help="optimization input json file"),
result_file: Path = typer.Argument(..., file_okay=True, dir_okay=False,
help="optimization result output json file"),
report_file: Path = typer.Argument(..., file_okay=True, dir_okay=False,
help="report file with intermediate results")) -> None:
if opt_file is None or result_file is None:
print("use --help for help")
return typer.Exit(-1)
os.chdir(tempfile.gettempdir())
with open(opt_file, "rb") as f_optfile:
opt_file_json = json.load(f_optfile)
opt_problem: OptProblem = OptProblem.from_json_data(opt_file_json)
basico.load_model_from_string(opt_problem.math_model_sbml_contents)
exp_data = get_reference_data(opt_problem)
basico.add_experiment('exp1', data=exp_data)
task_settings = basico.get_task_settings('Parameter Estimation')
task_settings['method'] = get_copasi_opt_method_settings(opt_problem)
basico.set_task_settings('Parameter Estimation', task_settings)
#
# define parameter estimation report format, note that header and footer are omitted to ease parsing
#
basico.add_report('parest report', task=basico.T.PARAMETER_ESTIMATION,
body=[
'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Function Evaluations',
'\\\t',
'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Best Value',
'\\\t',
'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Best Parameters'
],
)
# write the header (easier to do it here than to ask COPASI to do it)
with open(report_file, 'w') as f_report_file:
param_names: List[str] = [param_desc.name for param_desc in opt_problem.parameter_description_list]
f_report_file.write(json.dumps(param_names)+"\n")
basico.assign_report("parest report", task=basico.T.PARAMETER_ESTIMATION, filename=str(report_file), append=True)
fit_items = get_fit_parameters(opt_problem)
basico.set_fit_parameters(fit_items)
results: pd.DataFrame = basico.run_parameter_estimation(update_model=True)
assert results is not None
fit_solution: pd.DataFrame = basico.task_parameterestimation.get_parameters_solution()
opt_parameter_values: Dict[str, float] = result_set_from_fit(fit_solution)
fit_statistics: Dict[str, float] = basico.task_parameterestimation.get_fit_statistic(include_parameters=True,
include_experiments=True,
include_fitted=True)
objective_function = fit_statistics['obj']
num_function_evaluations = fit_statistics['f_evals']
opt_progress_report = get_progress_report(report_file)
result_set = OptResultSet(num_function_evaluations=int(num_function_evaluations),
objective_function=objective_function,
opt_parameter_values=opt_parameter_values,
opt_progress_report=opt_progress_report)
status_message = str(basico.task_parameterestimation.get_fit_statistic(
include_parameters=True, include_experiments=True, include_fitted=True))
opt_run: Vcellopt = Vcellopt(opt_problem=opt_problem, opt_result_set=result_set, status=VcelloptStatus.COMPLETE,
status_message=status_message)
with open(result_file, "w") as f_result_file:
json_data = opt_run.to_json_data()
json.dump(json_data, f_result_file)
if __name__ == "__main__":
typer.run(run_command)
|