library(tensorPredictors)
# library(RGCCA)
### Load modified version which _does not_ create a clusster in case of
### `n_cores == 1` allowing huge speed improvements! (at least on Ubuntu 22.04 LTS)
### Moreover, it is compatible with `Rscript`
### Also added `Encoding: UTF-8` in `DESCRIPTION`
devtools::load_all("~/Work/tensorPredictors/References/Software/TGCCA-modified", export_all = FALSE)


setwd("~/Work/tensorPredictors/sim/")
base.name <- format(Sys.time(), "sim_1c_normal-%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(0x1cL, "Mersenne-Twister", "Inversion", "Rejection")

### Simulation configuration
reps <- 100                     # number of simulation replications
sample.sizes <- c(100, 200, 300, 500, 750)  # sample sizes `n`
dimX <- c(2, 3, 5)              # predictor `X` dimension
dimF <- rep(2, length(dimX))    # "function" `F(y)` of responce `y` dimension

# setup true model parameters (rank 1 betas)
betas <- list(
    `[<-`(matrix(0, dimX[1], dimF[1]), 1, , c(1, 1)),
    `[<-`(matrix(0, dimX[2], dimF[2]), 2, , c(1, 0)),
    `[<-`(matrix(0, dimX[3], dimF[3]), 3, , c(1, 2))
)
# invisible(Map(print.table, betas, zero.print = "."))
Omegas <- Map(function(pj) 0.5^abs(outer(1:pj, 1:pj, `-`)), dimX)   # AR(0.5)
eta1 <- 0

# True (full) reduction matrix to compair against
B.true <- as.matrix(as.numeric(15 == (1:30)))

# define projections onto rank 1 matrices for betas
proj.betas <- Map(.projRank, rep(1L, length(betas)))


# data sampling routine
sample.data <- function(sample.size, eta1, betas, Omegas) {
    # responce is a standard normal variable
    y <- rnorm(sample.size)
    # F(y) is a tensor of monomials
    y.pow <- Reduce(function(a, b) outer(a, b, `+`), Map(seq, 0L, len = dimF))
    F <- t(outer(y, as.vector(y.pow), `^`))
    dim(F) <- c(dimF, sample.size)

    # sample predictors from tensor normal X | Y = y (last axis is sample axis)
    sample.axis <- length(betas) + 1L
    Deltas <- Map(solve, Omegas)                            # normal covariances
    mu_y <- mlm(mlm(F, betas) + as.vector(eta1), Deltas)    # conditional mean
    X <- mu_y + rtensornorm(sample.size, 0, Deltas, sample.axis)           # responses X

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

# 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"),                     # measures
        c("gmlm", "pca", "hopca", "tsir", "mgcca"),     # methods
        paste, sep = "."
    ))
)

# # true reduction (1D, select first component)
# B.true <- as.matrix(as.numeric(1 == (1:30)))



# B.true <- Reduce(`%x%`, rev(betas)) #[, 1L, drop = FALSE]
# print.table(B.true, zero.print = ".")

# print.table(Reduce(kronecker, rev(betas)), zero.print = ".")
# mu1 <- function(y) 1 + 3 * y + y^2

# matX <- mat(X, 1:3)
# x1 <- matX[1, ]
# x2 <- matX[2, ]

# plot(mu1(y), x1, col = cut(y, 10L))
# plot(y, x1, col = cut(y, 10L))

# plot(mu1(y), x2, col = cut(y, 10L))
# plot(y, matX[1, ], col = cut(y, 10L))
# plot(y, matX[10, ], col = cut(y, 10L))

#         c(X, F, y, sample.axis) %<-% sample.data(sample.size, eta1, betas, Omegas)


# colnames(B.true) <- c("1", "y", "y", "y^2", "y", "y^2", "y^2", "y^3")

# mu_y <- function(y) {
#     B.min <- cbind(
#         B.true[, 1],
#         rowSums(B.true[, c(2:3, 5)]),
#         rowSums(B.true[, c(4, 6:7)]),
#         B.true[, 8]
#     )

#     B.min %*% rbind(1, y, y^2, y^3)
# }

# plot((mat(X, 4) %*% B.min)[, 2], y)

### 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, eta1, betas, Omegas)

        # 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
        tryCatch({
            time.gmlm <- system.time(
                fit.gmlm <- gmlm_tensor_normal(X, F, sample.axis = sample.axis,
                    proj.betas = proj.betas)
            )["user.self"]
            time.pca <- system.time(
                fit.pca <- prcomp(mat(X, sample.axis), rank. = 1L)
            )["user.self"]
            time.hopca <- system.time(
                fit.hopca <- HOPCA(X, npc = c(1L, 1L, 1L), sample.axis = sample.axis)
            )["user.self"]
            time.tsir <- system.time(
                fit.tsir <- TSIR(X, y, d = c(1L, 1L, 1L), 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(y, y^2, y^3)
            time.mgcca <- system.time(
                fit.mgcca <- mgcca(
                    list(X1, F1), # `drop` removes 1D axis
                    quiet = TRUE,
                    scheme = "factorial",
                    ncomp = c(1L, 1L)
                )
            )["user.self"]
        }, error = function(ex) {
            print(ex)
        })

        # Compute true reduction matrix
        B.gmlm <- Reduce(kronecker, Map(
                      function(beta) qr.Q(qr(beta))[, 1L, drop = FALSE],
                  rev(fit.gmlm$betas)))
        B.pca <- fit.pca$rotation
        B.hopca <- Reduce(`%x%`, rev(fit.hopca))
        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.pca   <- dist.subspace(B.true, B.pca,   normalize = TRUE)
        dist.subspace.hopca <- dist.subspace(B.true, B.hopca, normalize = TRUE)
        dist.subspace.tsir  <- dist.subspace(B.true, B.tsir,  normalize = TRUE)
        dist.subspace.mgcca <- dist.subspace(B.true, B.mgcca, normalize = TRUE)

        # No projection distacne as in this case the Subspace and Projection
        # distances are identical

        # Call CSV logger writing results to file
        logger()

        # print progress
        cat(sprintf("sample size (%d): %d/%d - rep: %d/%d\n",
            sample.size, which(sample.size == sample.sizes),
            length(sample.sizes), rep, reps))
    }
}


### 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, "time", main = "Runtime",
    xlab = "Sample Size", ylab = "Time [s]", ylim = c(0, 18))

# aggr <- aggregate(sim[, names(sim) != "sample.size"], list(sample.size = sim$sample.size), mean)