SLOPE: Sorted L1 Penalized Estimation

# bodyfat (gaussian) ------------------------------------------------------

temp_file <- tempfile()

download.file(
  "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/bodyfat",
  temp_file
)

tmp <- e1071::read.matrix.csr(temp_file, fac = FALSE)
unlink(temp_file)
tmp_x <- as.data.frame(as.matrix(tmp$x))
colnames(tmp_x) <- c(
  "siri_1956",
  "age",
  "weight",
  "height",
  "neck",
  "chest",
  "abdomen",
  "hip",
  "thigh",
  "knee",
  "ankle",
  "biceps",
  "foream",
  "wrist"
)

# use the Siri 1956 equation as response
bodyfat <- list(x = tmp_x[, -1], y = tmp_x[, 1])

usethis::use_data(bodyfat, overwrite = TRUE)

# abalone (poisson) -------------------------------------------------------

library(SparseM)
library(Matrix)
library(caret)

temp_file <- tempfile()

download.file(
  "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/abalone",
  temp_file
)

tmp <- e1071::read.matrix.csr(temp_file, fac = FALSE)
unlink(temp_file)
tmp_x <- as.data.frame(as.matrix(tmp$x))

data_out <- cbind(tmp$y, tmp_x)

tmp_x$V1 <- as.factor(tmp_x$V1)

readr::write_csv(data_out, "abalone.csv")

tmp_x <- as.data.frame(model.matrix(~., tmp_x))[, -1]
colnames(tmp_x) <- c(
  "sex",
  "infant",
  "length",
  "diameter",
  "height",
  "weight_whole",
  "weight_shucked",
  "weight_viscera",
  "weight_shell"
)

# randomly select a subset of the rows
part <- caret::createDataPartition(tmp$y, p = 0.05, list = FALSE)

abalone <- list(x = tmp_x[part, ], y = tmp$y[part])

usethis::use_data(abalone, overwrite = TRUE)

# heart (binomial) --------------------------------------------------------

library(SparseM)
library(Matrix)
library(fastDummies)
library(tidyverse)

temp_file <- tempfile()

download.file(
  "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/heart",
  temp_file
)

tmp <- e1071::read.matrix.csr(temp_file, fac = FALSE)
x <- as.data.frame(as.matrix(tmp$x))
colnames(x) <- c(
  "age",
  "sex",
  "chest_pain",
  "bp",
  "chol",
  "glucose",
  "ecg",
  "hr",
  "angina",
  "old_peak",
  "slope",
  "vessels",
  "thal"
)

x2 <- x %>%
  mutate(
    sex = factor(sex, labels = c("male", "female")),
    cp = factor(
      chest_pain,
      levels = c(4, 1, 2, 3),
      labels = c("asymtompatic", "typical", "atypical", "nonanginal")
    ),
    ecg = factor(ecg, labels = c("normal", "abnormal", "estes")),
    angina = as.factor(angina),
    glucose = factor(glucose, labels = c("low", "high")),
    slope = factor(
      slope,
      levels = c(1, 2, 3),
      labels = c("upsloping", "flat", "downsloping")
    ),
    thal = factor(thal, labels = c("normal", "fixed", "reversible"))
  )

x3 <- model.matrix(~., x2) %>%
  as.data.frame() %>%
  select(
    age,
    bp,
    chol,
    hr,
    old_peak,
    vessels,
    sex = sexfemale,
    angina = angina1,
    glucose_high = glucosehigh,
    cp_typical = cptypical,
    cp_atypical = cpatypical,
    cp_nonanginal = cpnonanginal,
    ecg_abnormal = ecgabnormal,
    ecg_estes = ecgestes,
    slope_flat = slopeflat,
    slope_downsloping = slopedownsloping,
    thal_fixed = thalfixed,
    thal_reversible = thalreversible
  )

x4 <- Matrix::Matrix(as.matrix(x3), sparse = TRUE)

# response
y <- factor(tmp$y, labels = c("absence", "presence"))

heart <- list(x = x4, y = y)

usethis::use_data(heart, overwrite = TRUE)

# wine (multiclass) -------------------------------------------------------

library(Matrix)
library(SparseM)

temp_file <- tempfile(fileext = ".csv")

download.file(
  "https://raw.githubusercontent.com/hadley/rminds/master/1-data/wine.csv",
  temp_file
)

tmp <- read.csv(temp_file)

x <- as.matrix(tmp[, -1])
y <- as.factor(tmp[, 1])

wine <- list(x = x, y = y)

usethis::use_data(wine, overwrite = TRUE)


# Student (multi-task) ----------------------------------------------------

tmp_file <- tempfile()
tmp_dir <- tempdir()

download.file(
  "https://archive.ics.uci.edu/ml/machine-learning-databases/00320/student.zip",
  tmp_file
)

unzip(tmp_file, exdir = tmp_dir)

d1 <-
  read.table(file.path(tmp_dir, "student-mat.csv"), sep = ";", header = TRUE)
d2 <-
  read.table(file.path(tmp_dir, "student-por.csv"), sep = ";", header = TRUE)
d3 <- merge(
  d1,
  d2,
  by = c(
    "school",
    "sex",
    "age",
    "address",
    "famsize",
    "Pstatus",
    "Medu",
    "Fedu",
    "Mjob",
    "Fjob",
    "reason",
    "nursery",
    "internet"
  ),
  suffixes = c("_math", "_port")
)
y <- with(d3, cbind(G3_math, G3_port))
x1 <- subset(
  d3,
  select = c(
    "school",
    "sex",
    "age",
    "address",
    "famsize",
    "Pstatus",
    "Medu",
    "Fedu",
    "Mjob",
    "Fjob",
    "reason",
    "nursery",
    "internet"
  )
)
x1$famsize <- relevel(x1$famsize, ref = "LE3")
x2 <- model.matrix(~., x1)[, -1]

colnames(x2) <- c(
  "school_ms",
  "sex",
  "age",
  "urban",
  "large_family",
  "cohabitation",
  "Medu",
  "Fedu",
  "Mjob_health",
  "Mjob_other",
  "Mjob_services",
  "Mjob_teacher",
  "Fjob_health",
  "Fjob_other",
  "Fjob_services",
  "Fjob_teacher",
  "reason_home",
  "reason_other",
  "reason_rep",
  "nusery",
  "internet"
)
colnames(y) <- c("math", "portugese")

student <- list(x = x2, y = y)

usethis::use_data(student, overwrite = TRUE)

unlink(tmp_file)