emcAdr (version 1.2)

hyperparam_test_genetic_algorithm: This function can be used in order to try different set of parameters for the genetic algorithm in a convenient way. This will run each combination of mutation_rate, nb_elite and alphas possible nb_test_desired times. For each sets of parameters, results will be saved in a file named according to the set of parameter. One can regroup the results of each run in a csv file by using the print_csv function specifying the names of each file that needs to be treated and the number of performed runs on each parameter set

Description

This function can be used in order to try different set of parameters for the genetic algorithm in a convenient way. This will run each combination of mutation_rate, nb_elite and alphas possible nb_test_desired times. For each sets of parameters, results will be saved in a file named according to the set of parameter. One can regroup the results of each run in a csv file by using the print_csv function specifying the names of each file that needs to be treated and the number of performed runs on each parameter set

Usage

hyperparam_test_genetic_algorithm(
  epochs,
  nb_individuals,
  ATCtree,
  observations,
  nb_test_desired,
  mutation_rate,
  nb_elite,
  alphas,
  path = "./",
  num_thread = 1L
)

Value

No return value, this function should output results of the runs of the genetic algorithm in a specific format supported by function print_csv and p_value_csv_file. The files are outputed in path which is current directory by default.

Arguments

epochs

: the number of epochs for the genetic algorithm

nb_individuals

: the size of the population in the genetic algorithm

ATCtree

: ATC tree with upper bound of the DFS (without the root)

observations

: observation of the AE based on the medications of each patients (a DataFrame containing the medication on the first column and the ADR (boolean) on the second) on which we want to compute the risk distribution

nb_test_desired

: number of genetic algorithm runs on each sets of parameters

mutation_rate

: a vector with each mutation_rate to be tested

nb_elite

: a vector with each nb_elite to be tested

alphas

: a vector with each alphas to be tested

path

: the path where the resulting files should be written

num_thread

: Number of thread to run in parallel if openMP is available, 1 by default

Examples

# \donttest{
data("ATC_Tree_UpperBound_2024")
data("FAERS_myopathy")

# different parameter to test for
mutation_rate = c(.1,.2,.3)
nb_elite = c(0,1,2)
alphas = c(0.5,1,2)
hyperparam_test_genetic_algorithm(epochs = 2, nb_individuals = 2,
                              ATCtree = ATC_Tree_UpperBound_2024, 
                              observations = FAERS_myopathy,
                              nb_test_desired = 5, mutation_rate = mutation_rate,
                              nb_elite = nb_elite, alphas = alphas)
# }