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import json
import os
import tempfile
from pathlib import Path
from typing import List
import basico
import pandas as pd
from vcell_opt.data import OptProblem, Vcellopt
from vcell_opt.optUtils import get_reference_data, get_fit_parameters, get_copasi_opt_method_settings, get_progress_report
import vcell_opt.optService as optService
def test_read_opt_problem() -> None:
opt_file = Path(__file__).parent.parent / "test_data" / "optproblem.json"
with open(opt_file, "r") as f_optfile:
vcell_opt_problem: OptProblem = OptProblem.from_json_data(json.load(f_optfile))
def test_run() -> None:
report_file: Path = Path(__file__).parent.parent / "test_data" / "optproblem.report"
if report_file.exists():
os.remove(report_file)
opt_file = Path(__file__).parent.parent / "test_data" / "optproblem.json"
with open(opt_file, "r") as f_optfile:
vcell_opt_problem: OptProblem = OptProblem.from_json_data(json.load(f_optfile))
os.chdir(tempfile.gettempdir())
copasi_model = basico.load_model(vcell_opt_problem.math_model_sbml_contents)
exp_data = get_reference_data(vcell_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(vcell_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 vcell_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(vcell_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()
print(fit_solution)
fit_Kf = fit_solution.loc['Values[Kf]']['sol']
fit_Kr = fit_solution.loc['Values[Kr]']['sol']
fit_s0_init_uM = fit_solution.loc['Values[s0_init_uM]']['sol']
# read last line in report and see that the parameter values match (expected first and last lines as follows)
#
# 20 0.559754 ( 1.2751 0.73804 7.07081e-06 )
# ...
# 3480 4.97871e-14 ( 0.812494 0.687506 3.10832e-05 )
#
with open(report_file, "r") as f_reportfile:
for line in f_reportfile:
pass
last_line = line
#
# verify that last line of report matches returned best fit
#
report_tokens = last_line.split("\t")
last_num_evaluations = int(report_tokens[0])
last_objective_function_report = float(report_tokens[1])
assert abs(float(report_tokens[3]) - fit_Kf) < 1e-5
assert abs(float(report_tokens[4]) - fit_Kr) < 1e-5
assert abs(float(report_tokens[5]) - fit_s0_init_uM) < 1e-5
#
# verify that last line of progress report object matches best fit
#
progress_report = get_progress_report(report_file=report_file)
first_progress_item = progress_report.progress_items[0]
last_progress_item = progress_report.progress_items[len(progress_report.progress_items)-1]
assert first_progress_item.num_function_evaluations == 20
assert first_progress_item.obj_func_value == 0.559754
assert last_progress_item.num_function_evaluations == last_num_evaluations
assert last_progress_item.obj_func_value == last_objective_function_report
assert abs(progress_report.best_param_values["Kf"] - fit_Kf) < 1e-5
assert abs(progress_report.best_param_values["Kr"] - fit_Kr) < 1e-5
assert abs(progress_report.best_param_values["s0_init_uM"] - fit_s0_init_uM) < 1e-5
# remove report file
# if report_file.exists():
# os.remove(report_file)
expected_fit_Kf = 0.812494
expected_fit_Kr = 0.687506
expected_fit_s0_init_uM = 0.000031
#
# using loose tolerances here because this test uses a stochastic method (even with fixed seed)
# TODO: the test model should use a local gradient-based method whose solution is determinstic.
#
assert abs(fit_Kr - expected_fit_Kr) < 1e-4
assert abs(fit_Kf - expected_fit_Kf) < 1e-4
assert abs(fit_s0_init_uM - expected_fit_s0_init_uM) < 1e-4
def test_solver() -> None:
opt_file = Path(__file__).parent.parent / "test_data" / "optproblem.json"
report_file: Path = Path(__file__).parent.parent / "test_data" / "service_optproblem.report"
result_file = Path(__file__).parent.parent / "test_data" / "service_optresults.json"
optService.run_command(opt_file=opt_file, report_file=report_file, result_file=result_file)
with open(opt_file, "r") as f_optfile:
vcell_opt_problem: OptProblem = OptProblem.from_json_data(json.load(f_optfile))
with open(result_file, "r") as f_resultfile:
opt_run: Vcellopt = Vcellopt.from_json_data(json.load(f_resultfile))
assert opt_run is not None
fit_Kf = opt_run.opt_result_set.opt_parameter_values["Kf"]
fit_Kr = opt_run.opt_result_set.opt_parameter_values["Kr"]
fit_s0_init_uM = opt_run.opt_result_set.opt_parameter_values["s0_init_uM"]
# read last line in report and see that the parameter values match (expected first and last lines as follows)
#
# 20 0.559754 ( 1.2751 0.73804 7.07081e-06 )
# ...
# 3480 4.97871e-14 ( 0.812494 0.687506 3.10832e-05 )
#
with open(report_file, "r") as f_reportfile:
for line in f_reportfile:
pass
last_line = line
#
# verify that last line of report matches returned best fit
#
report_tokens = last_line.split("\t")
last_num_evaluations = int(report_tokens[0])
last_objective_function_report = float(report_tokens[1])
assert abs(float(report_tokens[3]) - fit_Kf) < 1e-5
assert abs(float(report_tokens[4]) - fit_Kr) < 1e-5
assert abs(float(report_tokens[5]) - fit_s0_init_uM) < 1e-5
#
# verify that last line of progress report object matches best fit
#
progress_report = get_progress_report(report_file=report_file)
first_progress_item = progress_report.progress_items[0]
last_progress_item = progress_report.progress_items[len(progress_report.progress_items)-1]
assert first_progress_item.num_function_evaluations == 20
assert first_progress_item.obj_func_value == 0.559754
assert last_progress_item.num_function_evaluations == last_num_evaluations
assert last_progress_item.obj_func_value == last_objective_function_report
assert abs(progress_report.best_param_values["Kf"] - fit_Kf) < 1e-5
assert abs(progress_report.best_param_values["Kr"] - fit_Kr) < 1e-5
assert abs(progress_report.best_param_values["s0_init_uM"] - fit_s0_init_uM) < 1e-5
# remove report file
# if report_file.exists():
# os.remove(report_file)
expected_fit_Kf = 0.812494
expected_fit_Kr = 0.687506
expected_fit_s0_init_uM = 0.000031
#
# using loose tolerances here because this test uses a stochastic method (even with fixed seed)
# TODO: the test model should use a local gradient-based method whose solution is determinstic.
#
assert abs(fit_Kr - expected_fit_Kr) < 1e-4
assert abs(fit_Kf - expected_fit_Kf) < 1e-4
assert abs(fit_s0_init_uM - expected_fit_s0_init_uM) < 1e-4
if report_file.exists():
os.remove(report_file)
if result_file.exists():
os.remove(result_file)
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