context("Test negative log likelihood functions")
if (FALSE) {
setwd("..")
setwd("..")
getwd()
library("devtools")
document()
load_all("./") # load all R files in /R and datasets in /data. Ignores NAMESPACE:
# devtools::check() # runs full check
setwd("..")
install("cvma", build_vignettes = FALSE, dependencies = FALSE) # INSTALL W/ devtools:
}
library(testthat)
library(SuperLearner)
library(future)
library(cvma)
#Simulate some data:
set.seed(1234)
X <- data.frame(x1=runif(n=100,0,5), x2=runif(n=100,0,5))
Y1 <- rbinom(100, 1, plogis(-2 + 0.1*X$x1 + 0.2*X$x2))
Y2 <- rbinom(100, 1, plogis(-2 + 0.1*X$x1))
Y <- data.frame(Y1 = Y1, Y2 = Y2)
test_that("Negative log-likelihood with default settings works", {
fit <- cvma(Y = Y, X = X, V = 5,
learners = c("SL.glm","SL.mean"),
sl_control = list(ensemble_fn = "ensemble_linear",
optim_risk_fn = "optim_risk_sl_nloglik",
weight_fn = "weight_sl_convex",
cv_risk_fn = "cv_risk_sl_nloglik",
family = binomial(),
alpha = 0.05),
y_weight_control = list(ensemble_fn = "ensemble_linear",
weight_fn = "weight_y_01",
optim_risk_fn = "optim_risk_y_nloglik",
cv_risk_fn = "cv_risk_y_nloglik",
alpha = 0.05))
expect_equal(fit$cv_assoc$cv_measure, 0.6731192, tolerance = 0.01)
})
test_that("Negative log-likelihood with mean squared-error SL risk works", {
fit <- cvma(Y = Y, X = X, V = 5,
learners = c("SL.glm","SL.mean"),
sl_control = list(ensemble_fn = "ensemble_linear",
optim_risk_fn = "optim_risk_sl_se",
weight_fn = "weight_sl_convex",
cv_risk_fn = "cv_risk_sl_nloglik",
family = binomial(),
alpha = 0.05),
y_weight_control = list(ensemble_fn = "ensemble_linear",
weight_fn = "weight_y_01",
optim_risk_fn = "optim_risk_y_nloglik",
cv_risk_fn = "cv_risk_y_nloglik",
alpha = 0.05))
expect_equal(fit$cv_assoc$cv_measure, 0.582109, tolerance = 0.01)
})
test_that("Negative log-likelihood with AUC SL risk and 01 weight works", {
fit <- cvma(Y = Y, X = X, V = 5,
learners = c("SL.glm","SL.mean"),
sl_control = list(ensemble_fn = "ensemble_linear",
optim_risk_fn = "optim_risk_sl_auc",
weight_fn = "weight_sl_01",
cv_risk_fn = "cv_risk_sl_nloglik",
family = binomial(),
alpha = 0.05),
y_weight_control = list(ensemble_fn = "ensemble_linear",
weight_fn = "weight_y_01",
optim_risk_fn = "optim_risk_y_nloglik",
cv_risk_fn = "cv_risk_y_nloglik",
alpha = 0.05))
expect_equal(fit$cv_assoc$cv_measure, 0.5507803, tolerance = 0.01)
})
test_that("Negative log-likelihood with AUC y risk risk works", {
fit <- cvma(Y = Y, X = X, V = 5,
learners = c("SL.glm","SL.mean"),
sl_control = list(ensemble_fn = "ensemble_linear",
optim_risk_fn = "optim_risk_sl_se",
weight_fn = "weight_sl_convex",
cv_risk_fn = "cv_risk_sl_nloglik",
family = binomial(),
alpha = 0.05),
y_weight_control = list(ensemble_fn = "ensemble_linear",
weight_fn = "weight_y_01",
optim_risk_fn = "optim_risk_y_auc",
cv_risk_fn = "cv_risk_y_nloglik",
alpha = 0.05))
expect_equal(fit$cv_assoc$cv_measure, 0.4877479, tolerance = 0.01)
})
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