tests/testthat/test_interpretable_rule.R

In tlverse/tmle3mopttx: Targeted Maximum Likelihood Estimation of the Mean under Optimal Individualized Treatment

context("Test interpretable rule")

library(sl3)
library(data.table)
library(tmle3mopttx)
library(tmle3)
library(R6)
library(uuid)
library(hal9001)

set.seed(1234)

data("data_bin")
data <- data_bin
data <- data[1:100,]

# Define sl3 library and metalearners:
qlib <- make_learner_stack(
  "Lrnr_mean",
  "Lrnr_glm_fast"
)

glib <- make_learner_stack(
  "Lrnr_mean",
  "Lrnr_glmnet",
  "Lrnr_xgboost"
)

blib <- qlib

#blib <- make_learner_stack(
#  list("Lrnr_hal9001",
#       reduce_basis = 1/sqrt(nrow(data))))

metalearner <- make_learner(Lrnr_nnls)
Q_learner <- make_learner(Lrnr_sl, qlib, metalearner)
g_learner <- make_learner(Lrnr_sl, glib, metalearner)
b_learner <- make_learner(Lrnr_sl, blib, metalearner)
learner_list <- list(Y = Q_learner, A = g_learner, B = b_learner)

# Define nodes:
node_list <- list(W = c("W1", "W2", "W3"), A = "A", Y = "Y")

test_that("Binary rule, interpretable rule", {
  tmle_spec <- tmle3_mopttx_blip_revere(
    V = c("W1"), type = "blip1",
    learners = learner_list, maximize = TRUE,
    complex = TRUE, realistic = TRUE, interpret=TRUE
  )
  
  tmle_task <- tmle_spec$make_tmle_task(data, node_list)
  initial_likelihood <- tmle_spec$make_initial_likelihood(tmle_task, learner_list)
  updater <- tmle_spec$make_updater()
  targeted_likelihood <- tmle_spec$make_targeted_likelihood(initial_likelihood, updater)
  tmle_params <- tmle_spec$make_params(tmle_task, likelihood=targeted_likelihood)
  
  dim(tmle_spec$blip_fit_interpret)
  expect_equal(dim(tmle_spec$blip_fit_interpret)[2], 2, tolerance = 0)
})