Rcpp::sourceCpp('tmp-tests/lasso.cpp')
X2 <- matrix(0, 1000, 1000)
X2[] <- rnorm(length(X2))
n <- nrow(X2)
m <- ncol(X2)
require(glmnet)
X3 <- sweep(X2, 2, colMeans(X2), '-')
X <- sweep(X3, 2, sqrt(colSums(X3^2) / (n-1)), '/')
# parameters
h2 <- 0.8 # heritability
h2.lims <- c(0.7, 0.9)
M <- 100
K <- 0.3
# simulation
v <- 1
while (v < h2.lims[1] || v > h2.lims[2]) {
set <- sample(m, size = M)
effects <- rnorm(M, sd = sqrt(h2 / M))
y.simu <- X[, set] %*% effects
print(v <- var(y.simu))
}
y.simu <- y.simu + rnorm(n, sd = sqrt(1 - v))
y.simu <- y.simu - mean(y.simu)
norm.y <- sqrt(sum(y.simu^2))
# rescale to unit norm (not variance)
X <- X / sqrt(n - 1) * sqrt(n)
print(colSums(X^2))
all_cross <- crossprod(X)
# sc <- sqrt(n * (n - 1))
sc <- n
tol <- sqrt(1e-7)
cp0 <- abs(crossprod(X, y.simu)) / sc
# sc <- 999
lseq <- function(from, to, N) {
exp(seq(log(from), log(to), length.out = N))
}
lam <- lseq(max(cp0), max(cp0) / 1000, 100)
print(lam[1])
print(glmnet(X, y.simu)$lambda[1])
b <- integer(length(lam))
printf <- function(...) cat(sprintf(...))
# sink("log.txt")
time <- proc.time()
in_A <- (cp0 > (2 * lam[2] - lam[1]))
all_betas <- numeric(ncol(X))
cp <- cp0
ratios <- numeric(length(lam))
r <- y.simu
for (i in 2:length(lam)) {
printf("\ni = %d\n", i)
seq_strong_thr <- (2 * lam[i] - lam[i - 1]) * 1.00
KKT_thr <- lam[i] * 1.00
in_S <- (cp > seq_strong_thr)
repeat {
eps_ind <- which(in_A)
printf("Length of eps_ind: %d\n", length(eps_ind))
mat <- X[, eps_ind, drop = FALSE]
betas.old <- all_betas[eps_ind]
# printf("Before: "); print(betas)
betas.new <- CD_lasso_Cpp(mat, r, all_betas[eps_ind], lam[i], sc, tol)
all_betas[eps_ind] <- betas.new
# printf("After: "); print(betas)
# if (i == 10) stop("Greve!")
ind0 <- (betas.old == 0)
printf("Ratio: %.3g\n", ratio <- median(betas.new[!ind0] / betas.old[!ind0]))
if (ratios[i] == 0) ratios[i] <- ratio
r <- y.simu - mat %*% betas.new # reupdating (due to possible floating errors)
cp <- abs(crossprod(X, r)) / sc
# step c
printf("c - "); print((bad_KKT_c <- which(!in_A & in_S & (cp > KKT_thr))))
if (length(bad_KKT_c)) {
# Add these predictors
# sorting by appearance? and avoing recopying the matrix
in_A[bad_KKT_c] <- TRUE
next
}
# step d
printf("d - "); print((bad_KKT_d <- which(!in_A & !in_S & (cp > KKT_thr))))
if (length(bad_KKT_d)) {
# Add these predictors
# sorting by appearance? and avoing recopying the matrix
in_A[bad_KKT_d] <- TRUE
in_S <- (cp > seq_strong_thr)
next
}
in_A[all_betas != 0] <- TRUE # really that?
b[i] <- sum(betas.new != 0)
break
}
}
print(proc.time() - time)
sink()
plot(cp)
abline(h = lam[i], col = "red")
print(system.time(mod <- glmnet(X, y.simu, lambda = lam)))
print(colSums(mod$beta != 0))
be <- mod$beta
ratios2 <- numeric(ncol(be))
for (i in 2:ncol(be)) {
ind0 <- (be[, i - 1] == 0)
ratios2[i] <- print(median(be[!ind0, i] / be[!ind0, i - 1]))
}
require(biglasso)
X.big <- as.big.matrix(X)
lam2 <- glmnet(X, y.simu)$lambda
print(system.time(mod2 <- biglasso(X.big, y.simu, lambda = lam)))
print(all.equal(mod$beta, mod2$beta[-1, ]))
print(cbind(b, colSums(mod$beta != 0), colSums(mod2$beta != 0)))
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