tests/test_smallmodel.R

In mccallm/ternarynet: Ternary Network Estimation

library(testthat)
library(ternarynet) 

smallmodel_score <- function() {

    library(ternarynet)

    i_exp <- as.integer(c(0,0,0, 0,0,0, 0,0,0, 0,0,0,
                          1,1,1, 1,1,1, 1,1,1, 1,1,1,
                          2,2,2, 2,2,2, 2,2,2, 2,2,2,
                          3,3,3, 3,3,3, 3,3,3, 3,3,3,
                          4,4,4, 4,4,4, 4,4,4, 4,4,4,
                          5,5,5, 5,5,5, 5,5,5, 5,5,5,
                          6,6,6, 6,6,6, 6,6,6, 6,6,6,
                          7,7,7, 7,7,7, 7,7,7, 7,7,7))

    i_node <- as.integer(c(0,0,0, 1,1,1, 2,2,2, 3,3,3,
                           0,0,0, 1,1,1, 2,2,2, 3,3,3,
                           0,0,0, 1,1,1, 2,2,2, 3,3,3,
                           0,0,0, 1,1,1, 2,2,2, 3,3,3,
                           0,0,0, 1,1,1, 2,2,2, 3,3,3,
                           0,0,0, 1,1,1, 2,2,2, 3,3,3,
                           0,0,0, 1,1,1, 2,2,2, 3,3,3,
                           0,0,0, 1,1,1, 2,2,2, 3,3,3))

    outcome <- as.integer(c(-1,0,1, -1,0,1, -1,0,1, -1,0,1,
                            -1,0,1, -1,0,1, -1,0,1, -1,0,1,
                            -1,0,1, -1,0,1, -1,0,1, -1,0,1,
                            -1,0,1, -1,0,1, -1,0,1, -1,0,1,
                            -1,0,1, -1,0,1, -1,0,1, -1,0,1,
                            -1,0,1, -1,0,1, -1,0,1, -1,0,1,
                            -1,0,1, -1,0,1, -1,0,1, -1,0,1,
                            -1,0,1, -1,0,1, -1,0,1, -1,0,1))

    value <- c(0,1,2, 0,1,2, 0,1,2, 0,1,2,
               2,1,0, 0,1,2, 0,1,2, 0,1,2,
               2,1,0, 2,1,0, 0,1,2, 0,1,2,
               2,1,0, 2,1,0, 2,1,0, 0,1,2,
               2,1,0, 2,1,0, 2,1,0, 2,1,0,
               0,1,2, 2,1,0, 2,1,0, 2,1,0,
               0,1,2, 0,1,2, 2,1,0, 2,1,0,
               0,1,2, 0,1,2, 0,1,2, 2,1,0)

    is_perturbation <- c(TRUE,TRUE,TRUE,  FALSE,FALSE,FALSE, FALSE,FALSE,FALSE, FALSE,FALSE,FALSE,
                         FALSE,FALSE,FALSE,  TRUE,TRUE,TRUE, FALSE,FALSE,FALSE, FALSE,FALSE,FALSE,
                         FALSE,FALSE,FALSE,  FALSE,FALSE,FALSE, TRUE,TRUE,TRUE, FALSE,FALSE,FALSE,
                         FALSE,FALSE,FALSE,  FALSE,FALSE,FALSE, FALSE,FALSE,FALSE, TRUE,TRUE,TRUE,
                         TRUE,TRUE,TRUE,  FALSE,FALSE,FALSE, FALSE,FALSE,FALSE, FALSE,FALSE,FALSE,
                         FALSE,FALSE,FALSE,  TRUE,TRUE,TRUE, FALSE,FALSE,FALSE, FALSE,FALSE,FALSE,
                         FALSE,FALSE,FALSE,  FALSE,FALSE,FALSE, TRUE,TRUE,TRUE, FALSE,FALSE,FALSE,
                         FALSE,FALSE,FALSE,  FALSE,FALSE,FALSE, FALSE,FALSE,FALSE, TRUE,TRUE,TRUE)

    indata <- data.frame(i_exp,i_node,outcome,value,is_perturbation)

    results <- parallelFit(indata,
                            max_parents=1,
                            n_cycles=1000000,
                            n_write=10,
                            T_lo=0.001,
                            T_hi=1.0,
                            target_score=0,
                            n_proc=1,
                            logfile='try.log')
    
    lowest_temp_results <- results[[1]]

    lowest_temp_results$unnormalized_score

}
   
test_that("smallmodel", {
    expect_true(smallmodel_score() == 0)
})