csv.pre_parallel¶

Predict With Specified Maximum Number of Processes¶

Prototype¶

# at_cascade.csv.pre_parallel
def pre_parallel(
    fit_dir,
    sim_dir,
    all_covariate_table,
    fit_goal_set,
    start_job_name,
    max_job_depth,
    option_predict,
) :
    assert type(fit_dir)                     == str
    assert sim_dir == None or type(sim_dir)  == str
    assert type(all_covariate_table)         == list
    assert type( all_covariate_table[0] )    == dict
    assert type(fit_goal_set)                == set
    assert type( next(iter(fit_goal_set) ))  == str
    assert type( option_predict )            == dict

fit_dir¶

Same as the csv fit fit_dir .

sim_dir¶

Same as sim_dir .

all_covariate_table¶

This is an in memory representation of covariate.csv .

fit_goal_set¶

This set contains the node that we are required to fit; i.e., the nodes in fit_goal.csv . Ancestors between these nodes and the root node are also fit.

start_job_name¶

Is the name of the job (fit) that the predictions should start at. This is a node name, followed by a period, followed by a sex. Only this fit, and its descendants, will be included in the predictions. If this argument is None, all of the jobs (fits) will be included.

max_job_depth¶

This is the number of generations below start_job_name that are included in the predictions. If max_job_depth is zero, only the start job will be included. If max_job_depth is None, start job and all its descendants are included.

option_predict¶

This is an in memory representation of option_predict.csv .