library(tensorPredictors)
library(logisticPCA)
# library(RGCCA)
# Use modified version of `RGCCA`
# Reasons (on Ubuntu 22.04 LTS):
#   - compatible with `Rscript`
#   - about 4 times faster for small problems
# Changes:
#   - Run in main thread, avoid `parallel::makeCluster` if `n_cores == 1`
#     (file "R/mgccak.R" lines 81:103)
#   - added `Encoding: UTF-8`
#     (file "DESCRIPTION")
suppressWarnings({
    devtools::load_all("~/Work/tensorPredictors/References/Software/TGCCA-modified", export_all = FALSE)
})

setwd("~/Work/tensorPredictors/sim/")
base.name <- format(Sys.time(), "sim_2b_ising-%Y%m%dT%H%M")

# Source utility function used in most simulations (extracted for convenience)
source("./sim_utils.R")

# Set PRNG seed for reproducability
# Note: `0x` is the HEX number prefix and the trailing `L` stands for "long"
# which is `R`s way if indicating an integer.
set.seed(seed <- 0x2bL, "Mersenne-Twister", "Inversion", "Rejection")


reps <- 100                     # number of simulation replications
sample.sizes <- c(100, 200, 300, 500, 750)  # sample sizes `n`
dimX <- c(2, 3)                 # predictor `X` dimension
dimF <- c(2, 2)                 # "function" `F(y)` of responce `y` dimension

betas <- Map(diag, 1, dimX, dimF)
Omegas <- list(toeplitz(c(0, -2)), toeplitz(seq(1, 0, by = -0.5)))

# compute true (full) model parameters to compair estimates against
B.true <- Reduce(`%x%`, rev(betas))

# data sampling routine
sample.data <- function(sample.size, betas, Omegas) {
    dimX <- mapply(nrow, betas)
    dimF <- mapply(ncol, betas)

    # generate response (sample axis is last axis)
    y <- runif(sample.size, -1, 1)
    F <- aperm(array(c(
        sinpi(y), -cospi(y),
        cospi(y), sinpi(y)
    ), dim = c(length(y), 2, 2)), c(2, 3, 1))

    Omega <- Reduce(kronecker, rev(Omegas))

    X <- apply(F, 3, function(Fi) {
        dim(Fi) <- dimF
        params <- diag(as.vector(mlm(Fi, betas))) + Omega
        tensorPredictors::ising_sample(1, params)
    })
    dim(X) <- c(dimX, sample.size)

    list(X = X, F = F, y = y, sample.axis = 3)
}

# # has been run once with initial seed
# lpca.hyper.param <- local({
#     c(X, F, y, sample.axis) %<-% sample.data(1e3, betas, Omegas)
#     vecX <- mat(X, sample.axis)
#     CV <- cv.lpca(vecX, ks = prod(dimF), ms = seq(1, 30, by = 1))
#     # plot(CV)
#     as.numeric(colnames(CV))[which.min(CV)]
# })
# set.seed(seed, "Mersenne-Twister", "Inversion", "Rejection")
lpca.hyper.param <- 23


# Create a CSV logger to write simulation results to
log.file <- paste(base.name, "csv", sep = ".")
logger <- CSV.logger(
    file.name = log.file,
    header = c("sample.size", "rep", outer(
        c("time", "dist.subspace", "dist.projection"), # < measures, v methods
        c("gmlm", "tnormal", "pca", "hopca", "lpca", "clpca", "tsir", "mgcca"),
        paste, sep = "."
    ))
)


### for each sample size
for (sample.size in sample.sizes) {
    # repeate every simulation
    for (rep in seq_len(reps)) {
        # Sample training data
        c(X, F, y, sample.axis) %<-% sample.data(sample.size, betas, Omegas)

        # start timing for reporting
        start.timer()

        # fit different models
        # Wrapped in try-catch clock to ensure the simulation continues,
        # if an error occures continue with nest resplication and log an error message
        try.catch.block <- tryCatch({
            time.gmlm <- system.time(
                fit.gmlm <- gmlm_ising(X, F, y, sample.axis = sample.axis)
            )["user.self"]
            time.tnormal <- -1  # part of Ising gmlm (not relevent here)
            time.pca <- system.time(
                fit.pca <- prcomp(mat(X, sample.axis), rank. = prod(dimF))
            )["user.self"]
            time.hopca <- system.time(
                fit.hopca <- HOPCA(X, npc = dimF, sample.axis = sample.axis)
            )["user.self"]
            time.lpca <- system.time(
                fit.lpca <- logisticPCA(mat(X, sample.axis), k = prod(dimF),
                                        m = lpca.hyper.param)
            )["user.self"]
            time.clpca <- system.time(
                fit.clpca <- convexLogisticPCA(mat(X, sample.axis), k = prod(dimF),
                                               m = lpca.hyper.param)
            )["user.self"]
            time.tsir <- system.time(
                fit.tsir <- TSIR(X, y, dimF, sample.axis = sample.axis)
            )["user.self"]
            # `mgcca` expects the first axis to be the sample axis
            X1 <- aperm(X, c(sample.axis, seq_along(dim(X))[-sample.axis]))
            F1 <- cbind(sinpi(y), cospi(y))
            time.mgcca <- system.time(
                fit.mgcca <- mgcca(list(X1, F1), ncomp = c(prod(dimF), 1),
                                   quiet = TRUE, scheme = "factorial")
            )["user.self"]
        }, error = print)

        # Get elapsed time from last timer start and the accumulated total time
        # (_not_ a precide timing, only to get an idea)
        c(elapsed, total.time) %<-% end.timer()

        # Drop comparison in case any error (in any fitting routine)
        if (inherits(try.catch.block, "error")) { next }

        # Compute true reduction matrix
        B.gmlm <- with(fit.gmlm, Reduce(`%x%`, rev(betas)))
        B.tnormal <- with(attr(fit.gmlm, "tensor_normal"), Reduce(`%x%`, rev(betas)))
        B.pca <- fit.pca$rotation
        B.hopca <- Reduce(`%x%`, rev(fit.hopca))
        B.lpca <- fit.lpca$U
        B.clpca <- fit.clpca$U
        B.tsir <- Reduce(`%x%`, rev(fit.tsir))
        B.mgcca <- fit.mgcca$astar[[1]]

        # Subspace Distances: Normalized `|| P_A - P_B ||_F` where
        #   `P_A = A (A' A)^-1 A'` and the normalization means that with
        #   respect to the dimensions of `A, B` the subspace distance is in the
        #   range `[0, 1]`.
        dist.subspace.gmlm    <- dist.subspace(B.true, B.gmlm,    normalize = TRUE)
        dist.subspace.tnormal <- dist.subspace(B.true, B.tnormal, normalize = TRUE)
        dist.subspace.pca     <- dist.subspace(B.true, B.pca,     normalize = TRUE)
        dist.subspace.hopca   <- dist.subspace(B.true, B.hopca,   normalize = TRUE)
        dist.subspace.lpca    <- dist.subspace(B.true, B.lpca,    normalize = TRUE)
        dist.subspace.clpca   <- dist.subspace(B.true, B.clpca,   normalize = TRUE)
        dist.subspace.tsir    <- dist.subspace(B.true, B.tsir,    normalize = TRUE)
        dist.subspace.mgcca   <- dist.subspace(B.true, B.mgcca,   normalize = TRUE)

        # # Projection Distances: Spectral norm (2-norm) `|| P_A - P_B ||_2`.
        dist.projection.gmlm    <- dist.projection(B.true, B.gmlm)
        dist.projection.tnormal <- dist.projection(B.true, B.tnormal)
        dist.projection.pca     <- dist.projection(B.true, B.pca)
        dist.projection.hopca   <- dist.projection(B.true, B.hopca)
        dist.projection.lpca    <- dist.projection(B.true, B.lpca)
        dist.projection.clpca   <- dist.projection(B.true, B.clpca)
        dist.projection.tsir    <- dist.projection(B.true, B.tsir)
        dist.projection.mgcca   <- dist.projection(B.true, B.mgcca)

        # Call CSV logger writing results to file
        logger()

        # print progress
        cat(sprintf("sample size (%d): %d/%d - rep: %d/%d - elapsed: %.1f [s], total: %.0f [s]\n",
            sample.size, which(sample.size == sample.sizes),
            length(sample.sizes), rep, reps, elapsed, total.time))
    }
}


### read simulation results generate plots
if (!interactive()) { pdf(file = paste(base.name, "pdf", sep = ".")) }

sim <- read.csv(log.file)


plot.sim(sim, "dist.subspace", main = "Subspace Distance",
    xlab = "Sample Size", ylab = "Distance")

plot.sim(sim, "dist.projection", main = "Projection Distance",
    xlab = "Sample Size", ylab = "Distance")

plot.sim(sim, "time", main = "Runtime",
    xlab = "Sample Size", ylab = "Time [s]",
    ylim = c(0, max(sim[startsWith(names(sim), "time")])))