Adding check_parameter_positivity() function to seir.py

[emprzy]

Describe your changes.

This pull request introduces a function called check_parameter_positivity() to seir.py. check_parameter_positivity() takes in an array of parsed parameters, as well as parameter names, subpopulation names, and dates, checks for the existence of negative parameter values, and throws a ValueError error if they are found. When throwing the error, check_parameter_positivity() will only print the earliest (w.r.t date) negative parameter to avoid redundant feedback. Example output will resemble:

The earliest date negative for each subpop and unique parameter are:
subpop: 50000, parameter eta_X0toX3_highIE*1*1*nuage18to64HR: 2023-07-21
subpop: 32000, parameter eta_X1toX4_highIE*1*1*nuage0to17: 2024-02-15
subpop: 28000, parameter eta_X1toX4_highIE*1*1*nuage18to64LR: 2025-06-01

A test function, test_check_parameter_positivity() has also been added to test_seir.py to test check_parameter_positivity().

Does this pull request make any user interface changes? If so please describe.

No changes to the user interface.

What does your pull request address? Tag relevant issues.

This pull request addresses GH #215.

Tag relevant team members.

@jcblemai

[emprzy]

Still need to add the test function (not ready yet for review)

[emprzy]

@jcblemai I have initial concerns that I did not properly instantiate ModelInfo in my test_seir.py::test_neg_params() function. Also, I'm hesitant about the parameters I passed into my neg_params() (line ~163 of seir.py) function call being correct extractions of the specific data that the function calls for. Haven't run checks yet but wanted to say that's what I will likely need help on.

[jcblemai]

I'm very, very bad at naming things (Carl, Tim are definitely very good at this) but neg_param() is not informative enough as a function name, something like check_parameter_positivity or something seems more suitable.

[jcblemai]

parsed_parameter has shape (n_parsed_parameters(unique_strings) X n_times X n_subpop), is that what's causing the CI error ?

EDIT: no, see below:

[jcblemai]

oh and you are right, this is not passing the right parameters. Basically, within flepimop the workflow is

1. parameters are drawn > parameters array, same for all subpop and times (except if the parameter is a time series), an array of dimensions n_parameters, n_times, n_subpop (unsure of the order)
2. Modifiers are applied, this changes the array (not the dimensions) so parameters varies in time and subpop according to their modifiers
3. Then, the compartments module computes the way to aggregate parameters for the sampler, e.g if on transition has a rate of beta * phi / gamma we do NOT want to pass beta, phi, and gamma to the simulator because

• it's a jit compiled function so parsing this formula there is a big hassle and waste of time
• we don't use these values separately anyway
  So instead compartments compute unique_strings (each one being a formula, a constant, or a a parameter name), and then if you give a parameter array (as above, modified by NPI) to the compartments module it will pre-compute these unique_strings (which are different parameters in some sort) by parsing each formula. This array is called, bad name, parsed_parameter and has size n_unique_strings, n_days, n_subpop

Your function checks the parsed_parameters because each of these steps can input a negative value. You then want to test the fully parsed thing.

Hence, to test, once you have modinf you need to:

# draw some parameters
p_draw = modinf.parameters.parameters_quick_draw(n_days=modinf.n_days, nsubpops=modinf.nsubpops)

# p_draw > SEE SCREENSHOT 2!!! A  SINGLE LINE

# build a modifier object
npi_seir = seir.build_npi_SEIR(modinf=modinf, load_ID=False, sim_id2load=None, config=config)
# apply the NPI to the parameters
reduced_parameters = modinf.parameters.parameters_reduce(p_draw, npi_seir)

# reduced_parameters > SEE SCREENSHOT 2!!! A TIMESERIE

# parse the compartments:
unique_strings, transition_array,proportion_array, proportion_info  = self.modinf.compartments.get_transition_array()

# parse the unique_string and compute the parsed parameter objects
parsed_parameters = modinf.compartments.parse_parameters(reduced_parameters, modinf.parameters.pnames, unique_strings)

# parsed_parameters > SEE SCREENSHOT 3!!! now it's a time series that is computed from a formula