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Drop: remove tests related to osqp
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@mihaiconstantin
mihaiconstantin committed Apr 12, 2023
1 parent d90e193 commit 1487a88
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Showing 3 changed files with 10 additions and 102 deletions.
32 changes: 0 additions & 32 deletions tests/testthat/helpers.R
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Original file line number Diff line number Diff line change
Expand Up @@ -13,38 +13,6 @@ solve_qp <- function(basis_matrix, y, a_mat, b_vec, meq = 0) {
return(quadprog::solve.QP(Dmat = d_mat, dvec = d_vec, Amat = t(a_mat), bvec = b_vec, meq = meq)$solution)
}

# Helper based on `osqp` for testing the `Solver` class.
solve_osqp <- function(basis_matrix, y, lower, upper) {
# Set settings.
settings <- osqp::osqpSettings(
verbose = FALSE,
eps_abs = 1e-10,
eps_rel = 1e-10,
linsys_solver = 0L,
warm_start = FALSE
)

# Create matrices for `osqp`.
p_mat <- crossprod(basis_matrix, basis_matrix)
q_vec <- -crossprod(basis_matrix, y)

# Create constraint matrix.
a_mat <- diag(1, ncol(basis_matrix))

# Create model.
model <- osqp::osqp(
P = p_mat,
q = q_vec,
A = a_mat,
l = lower,
u = upper,
pars = settings
)

# Optimize.
return(model$Solve()$x)
}

# Compute performance measures.
compute_measure <- function(true_parameters, estimated_parameters, measure) {
# Extract the true and estimated parameters from the weights matrices.
Expand Down
58 changes: 0 additions & 58 deletions tests/testthat/test-solver.R
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Original file line number Diff line number Diff line change
Expand Up @@ -10,17 +10,11 @@ test_that("'Solver' implementations set correct constraints for monotone non-dec
n <- ncol(ispline$matrix)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(ispline, y, increasing = TRUE)
qp$setup(ispline, y, increasing = TRUE)

# Test bounds for 'OsqpSolver'.
expect_equal(osqp$.__enclos_env__$private$.lower, c(-Inf, rep(0, n - 1)))
expect_equal(osqp$.__enclos_env__$private$.upper, rep(Inf, n))

# Constraints for 'QuadprogSolver'.
b_vec <- rep(0, n)
a_mat <- diag(1, n)
Expand All @@ -42,17 +36,11 @@ test_that("'Solver' implementations set correct constraints for monotone non-inc
n <- ncol(ispline$matrix)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(ispline, y, increasing = FALSE)
qp$setup(ispline, y, increasing = FALSE)

# Test bounds for 'OsqpSolver'.
expect_equal(osqp$.__enclos_env__$private$.lower, rep(-Inf, n))
expect_equal(osqp$.__enclos_env__$private$.upper, c(Inf, rep(0, n - 1)))

# Constraints for 'QuadprogSolver'.
b_vec <- rep(0, n)
a_mat <- diag(-1, n)
Expand All @@ -74,17 +62,11 @@ test_that("'Solver' implementations set correct constraints for non-monotone spl
n <- ncol(bspline$matrix)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(bspline, y)
qp$setup(bspline, y)

# Test bounds for 'OsqpSolver'.
expect_equal(osqp$.__enclos_env__$private$.lower, rep(-Inf, n))
expect_equal(osqp$.__enclos_env__$private$.upper, rep(Inf, n))

# Constraints for 'QuadprogSolver'.
b_vec <- rep(0, n)
a_mat <- diag(0, n)
Expand All @@ -105,15 +87,12 @@ test_that("'Solver' implementations give correct solution for monotone non-decre
n <- ncol(ispline$matrix)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(ispline, y, increasing = TRUE)
qp$setup(ispline, y, increasing = TRUE)

# Solve using own implementations.
osqp_impl_alpha <- osqp$solve()
qp_impl_alpha <- qp$solve()

# Solve using 'quadprog'.
Expand All @@ -122,15 +101,8 @@ test_that("'Solver' implementations give correct solution for monotone non-decre
b_vec <- rep(0, n)
qp_alpha <- solve_qp(ispline$matrix, y, a_mat, b_vec)

# Solve using 'osqp'.
osqp_alpha <- solve_osqp(ispline$matrix, y, c(-Inf, rep(0, n - 1)), rep(Inf, n))

# Each implementation should be equal with its counterpart.
expect_equal(osqp_impl_alpha, osqp_alpha, tolerance = 1e-6)
expect_equal(qp_impl_alpha, qp_alpha, tolerance = 1e-6)

# The implementations should also give the same solution.
expect_equal(osqp_impl_alpha, qp_impl_alpha, tolerance = 1e-6)
})


Expand All @@ -144,15 +116,12 @@ test_that("'Solver' implementations give correct solution for monotone non-incre
n <- ncol(ispline$matrix)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(ispline, y, increasing = FALSE)
qp$setup(ispline, y, increasing = FALSE)

# Solve using own implementations.
osqp_impl_alpha <- osqp$solve()
qp_impl_alpha <- qp$solve()

# Solve using 'quadprog'.
Expand All @@ -161,15 +130,8 @@ test_that("'Solver' implementations give correct solution for monotone non-incre
b_vec <- rep(0, n)
qp_alpha <- solve_qp(ispline$matrix, y, a_mat, b_vec)

# Solve using 'osqp'.
osqp_alpha <- solve_osqp(ispline$matrix, y, rep(-Inf, n), c(Inf, rep(0, n - 1)))

# Each implementation should be equal with its counterpart.
expect_equal(osqp_impl_alpha, osqp_alpha, tolerance = 1e-6)
expect_equal(qp_impl_alpha, qp_alpha, tolerance = 1e-6)

# The implementations should also give the same solution.
expect_equal(osqp_impl_alpha, qp_impl_alpha, tolerance = 1e-6)
})


Expand All @@ -182,22 +144,18 @@ test_that("'Solver' implementations give correct solution for non-monotone splin
bspline <- Basis$new(x, df = 0, monotone = FALSE)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(bspline, y)
qp$setup(bspline, y)

# Solve using own implementations.
osqp_impl_alpha <- osqp$solve()
qp_impl_alpha <- qp$solve()

# Solve using 'lm'.
lm_alpha <- as.numeric(lm.fit(bspline$matrix, y)$coefficients)

# Test.
expect_equal(osqp_impl_alpha, lm_alpha, tolerance = 1e-6)
expect_equal(qp_impl_alpha, lm_alpha, tolerance = 1e-6)
})

Expand All @@ -212,36 +170,25 @@ test_that("'Solver' implementations gives correct solution for updated statistic
n <- ncol(ispline$matrix)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(ispline, y, increasing = TRUE)
qp$setup(ispline, y, increasing = TRUE)

# Solve first time with original data.
osqp$solve()
qp$solve()

# Create new data to update the solver.
y_new <- sample(y, length(y), TRUE)

# Update solvers and solve problem.
osqp_impl_alpha <- osqp$solve_update(y_new)
qp_impl_alpha <- qp$solve_update(y_new)

# Solve problem with new data using 'osqp' helper.
osqp_alpha <- solve_osqp(ispline$matrix, y_new, osqp$.__enclos_env__$private$.lower, osqp$.__enclos_env__$private$.upper)

# Solve problem with new data using 'quadprog' helper.
qp_alpha <- solve_qp(ispline$matrix, y_new, qp$.__enclos_env__$private$.a_mat, qp$.__enclos_env__$private$.b_vec)

# The solver implementations should agree with their counterpart helpers.
expect_equal(osqp_impl_alpha, osqp_alpha, tolerance = 1e-6)
expect_equal(qp_impl_alpha, qp_alpha, tolerance = 1e-6)

# The solver implementations should give the same solution.
expect_equal(osqp_impl_alpha, qp_impl_alpha, tolerance = 1e-6)
})


Expand All @@ -254,26 +201,21 @@ test_that("'Solver' implementations still give original solution after solving w
ispline <- Basis$new(x, df = 0, monotone = TRUE)

# Create solvers.
osqp <- OsqpSolver$new()
qp <- QuadprogSolver$new()

# Setup solvers.
osqp$setup(ispline, y, increasing = TRUE)
qp$setup(ispline, y, increasing = TRUE)

# Solve first time with original data.
osqp_first_alpha <- osqp$solve()
qp_first_alpha <- qp$solve()

# Create new data to update the solver.
y_new <- sample(y, length(y), TRUE)

# Solve with updated data.
osqp$solve_update(y_new)
qp$solve_update(y_new)

# Solve again and expect to recover the original solution.
expect_equal(osqp_first_alpha, osqp$solve())
expect_equal(qp_first_alpha, qp$solve())
})

Expand Down
22 changes: 10 additions & 12 deletions tests/testthat/test-step-two.R
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Original file line number Diff line number Diff line change
Expand Up @@ -53,11 +53,8 @@ test_that("'StepTwo' correctly performs the LOOCV procedure", {
# Create 'StepTwo' mock instance.
step_2 <- StepTwoTester$new(step_1)

# Flip a coin to decide which solver to use.
solver_type <- ifelse(rbinom(1, 1, .5), "quadprog", "osqp")

# Perform LOOCV via the mock instance.
step_2$run_cv(monotone = TRUE, increasing = TRUE, df = NULL, solver_type = solver_type)
step_2$run_cv(monotone = TRUE, increasing = TRUE, df = NULL, solver_type = "quadprog")

# The dimensions if the LOOCV result should match the number of sample sizes and DF tested.
expect_equal(nrow(step_2$cv$se), range$available_samples)
Expand Down Expand Up @@ -91,11 +88,8 @@ test_that("'StepTwo' fits and interpolates a spline correctly", {
# Fit a spline via step two.
step_2 <- StepTwo$new(step_1)

# Flip a coin to decide which solver to use.
solver_type <- ifelse(rbinom(1, 1, .5), "quadprog", "osqp")

# Fit the spline.
step_2$fit(monotone = TRUE, increasing = TRUE, df = NULL, solver_type = solver_type)
step_2$fit(monotone = TRUE, increasing = TRUE, df = NULL, solver_type = "quadprog")

# Extract the DF selected.
df <- step_2$cv$df[which.min(step_2$cv$mse)]
Expand All @@ -105,12 +99,16 @@ test_that("'StepTwo' fits and interpolates a spline correctly", {
knots <- attributes(basis)$knots
basis <- cbind(1, basis)

# Create box constraints for 'osqp'.
lower <- c(-Inf, rep(0, ncol(basis) - 1))
upper <- rep(Inf, ncol(basis))
# Number of basis functions.
n <- ncol(basis)

# Create box constraints.
a_mat <- diag(1, n)
a_mat[1, 1] <- 0
b_vec <- rep(0, n)

# Estimate alpha.
alpha <- solve_osqp(basis, step_1$statistics, lower, upper)
alpha <- solve_qp(basis, step_1$statistics, a_mat, b_vec)

# Predict.
fitted <- basis %*% alpha
Expand Down

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