tests/testthat/notrun/pause/testing_train_pipeline.R

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

library(stats)
library(dplyr)
library(recipes)
library(rsample)
library(parsnip)
library(tune)
library(magrittr)
library(future)
library(furrr)
library(workflows)

library(text)
library(tibble)
library(testthat)

context("Testing PM tutorial")

test_that("PM tutorial", {
  skip_on_cran()
colourise <- function(text, fg = "black", bg = NULL) {
  term <- Sys.getenv()["TERM"]
  colour_terms <- c("xterm-color", "xterm-256color", "screen", "screen-256color")

  if (rcmd_running() || !any(term %in% colour_terms, na.rm = TRUE)) {
    return(text)
  }

  col_escape <- function(col) {
    paste0("\033[", col, "m")
  }

  col <- .fg_colours[tolower(fg)]
  if (!is.null(bg)) {
    col <- paste0(col, .bg_colours[tolower(bg)], sep = ";")
  }

  init <- col_escape(col)
  reset <- col_escape("0")
  paste0(init, text, reset)
}

.fg_colours <- c(
  "black" = "0;30",
  "blue" = "0;34",
  "green" = "0;32",
  "cyan" = "0;36",
  "red" = "0;31",
  "purple" = "0;35"
  # "brown" = "0;33",
  # "light gray" = "0;37",
  # "dark gray" = "1;30",
  # "light blue" = "1;34",
  # "light green" = "1;32",
  # "light cyan" = "1;36",
  # "light red" = "1;31",
  # "light purple" = "1;35",
  # "yellow" = "1;33",
  # "white" = "1;37"
)

.bg_colours <- c(
  "black" = "40",
  "red" = "41",
  "green" = "42",
  "brown" = "43",
  "blue" = "44",
  "purple" = "45",
  "cyan" = "46",
  "light gray" = "47"
)

rcmd_running <- function() {
  nchar(Sys.getenv("R_TESTS")) != 0
}



sorting_xs_and_x_append <- function(x, x_append, ...) {
  variable_name_index_pca <- NA

  if (!is.null(x)) {
    # In case the embedding is in list form get the tibble form
    if (!tibble::is_tibble(x) & length(x) == 1) {
      x1 <- x[[1]]
      # Get names for description
      x_name <- names(x)
      # Get embedding info to save for model description
      embedding_description <- comment(x[[1]])
      # In case there are several embeddings in list form get the x_names and
      # embedding description for model description
    } else if (!tibble::is_tibble(x) & length(x) > 1) {
      x_name <- names(x)
      x_name <- paste(x_name, sep = " ", collapse = " & ")
      x_name <- paste("input:", x_name, sep = " ", collapse = " ")

      embedding_description <- comment(x[[1]])
      # In case it is just one word embedding as tibble
    } else {
      x1 <- x
      x_name <- deparse(substitute(x))
      embedding_description <- comment(x)
    }
  }

  # Get names for the added variables to save to description
  x_append_names <- paste(names(x_append), collapse = ", ")
  # Possibility to train without word embeddings
  if (is.null(x)) {
    x1 <- x_append
    x_append <- NULL
    colnames(x1) <- paste0(
      "Dim", "_",
      colnames(x1)
    )
    x_name <- NULL
    embedding_description <- NULL
  }

  ############ Arranging word embeddings to be concatenated from different texts ############
  ##################################################

  if (!tibble::is_tibble(x) & length(x) > 1) {

    # Select all variables that starts with Dim in each dataframe of the list.
    xlist <- lapply(x, function(X) {
      X <- dplyr::select(X, dplyr::starts_with("Dim"))
    })

    Nword_variables <- length(xlist)
    # Give each column specific names with indexes so that they can be handled separately in the PCAs
    for (i in 1:Nword_variables) {
      colnames(xlist[[i]]) <- paste("DimWs", i, ".", colnames(xlist[[i]]), sep = "")
    }

    # Make vector with each index so that we can allocate them separately for the PCAs
    variable_name_index_pca <- list()
    for (i in 1:Nword_variables) {
      variable_name_index_pca[i] <- paste("DimWs", i, sep = "")
    }

    # Make one df rather then list.
    x1 <- dplyr::bind_cols(xlist)
  }
  ############ End for multiple word embeddings ############
  ##########################################################

  #### Add other variables to word embeddings x_append=NULL
  if (!is.null(x_append)) {
    x1 <- add_variables_to_we(
      word_embeddings = x1,
      data = x_append, ...
    )
  }

  x1 <- dplyr::select(x1, dplyr::starts_with("Dim"))
  variables_names <- list(
    x1, x_name, embedding_description,
    x_append_names, variable_name_index_pca
  )
  names(variables_names) <- c(
    "x1", "x_name", "embedding_description",
    "x_append_names", "variable_name_index_pca"
  )

  return(variables_names)
}


statisticalMode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}


select_eval_measure_val <- function(eval_measure = "bal_accuracy",
                                    holdout_pred = NULL,
                                    truth = y,
                                    estimate = .pred_class, class) {
  # Get accuracy
  if (eval_measure == "accuracy") {
    eval_measure_val <- yardstick::accuracy(holdout_pred, truth = y, estimate = .pred_class)
  } else if (eval_measure == "bal_accuracy") {
    eval_measure_val <- yardstick::bal_accuracy(holdout_pred, truth = y, estimate = .pred_class)
  } else if (eval_measure == "sens") {
    eval_measure_val <- yardstick::sens(holdout_pred, truth = y, estimate = .pred_class)
  } else if (eval_measure == "spec") {
    eval_measure_val <- yardstick::spec(holdout_pred, truth = y, estimate = .pred_class)
  } else if (eval_measure == "precision") {
    eval_measure_val <- yardstick::precision(holdout_pred, truth = y, estimate = .pred_class)
  } else if (eval_measure == "kappa") {
    eval_measure_val <- yardstick::kap(holdout_pred, truth = y, estimate = .pred_class)
  } else if (eval_measure == "f_measure") {
    eval_measure_val <- yardstick::f_meas(holdout_pred, truth = y, estimate = .pred_class)
  } else if (eval_measure == "roc_auc") {
    class1_name <- eval(class)
    eval_measure_val <- yardstick::roc_auc(holdout_pred, truth = y, dplyr::all_of(class1_name))
  } else if (eval_measure == "rmse") {
    eval_measure_val <- yardstick::rmse(holdout_pred, truth = y, estimate = .pred)
  } else if (eval_measure == "rsq") {
    eval_measure_val <- yardstick::rsq(holdout_pred, truth = y, estimate = .pred)
  } else if (eval_measure == "cor_test") {
    cor_testing <- stats::cor.test(holdout_pred$y, holdout_pred$.pred, na.action = na.omit)
    estimate1 <- cor_testing[[4]][[1]]
    metric <- "cor_test"
    estimator <- "standard"
    eval_measure_val <- tibble::tibble(metric, estimator, as.numeric(estimate1))
    colnames(eval_measure_val) <- c(".metric", ".estimator", ".estimate")
  }
  eval_measure_val
}



fit_model_rmse <- function(object,
                           model = "regression",
                           eval_measure = "rmse",
                           penalty = 1,
                           mixture = 0,
                           preprocess_PCA = NA,
                           variable_name_index_pca = NA,
                           first_n_predictors = NA,
                           preprocess_step_center = TRUE,
                           preprocess_step_scale = TRUE,
                           impute_missing = FALSE) {
  data_train <- rsample::analysis(object)
  data_train <- tibble::as_tibble(data_train)

  # If testing N first predictors help(step_scale) first_n_predictors = 3
  if (!is.na(first_n_predictors)) {
    # Select y and id
    Nvariable_totals <- length(data_train)
    variable_names <- colnames(data_train[(first_n_predictors + 1):(Nvariable_totals - 2)])
  } else {
    variable_names <- "id_nr"
  }

  # Get number of embeddings provided
  n_embeddings <- as.numeric(comment(eval_measure))


  # Recipe for one embedding input summary(xy_recipe) help(all_of) library(tidyverse) help(step_naomit)
  if (n_embeddings == 1) {
    xy_recipe <- data_train %>%
      recipes::recipe(y ~ .) %>%
      recipes::update_role(dplyr::all_of(variable_names), new_role = "Not_predictors") %>%
      recipes::update_role(id_nr, new_role = "id variable") %>%
      recipes::update_role(y, new_role = "outcome") # %>%

    if (!impute_missing) {
      xy_recipe <- recipes::step_naomit(xy_recipe, recipes::all_predictors(), skip = TRUE)
    } else if (impute_missing) {
      xy_recipe <- recipes::step_knnimpute(xy_recipe, recipes::all_predictors(), neighbors = 10) # , skip = TRUE
    }


    if (preprocess_step_center) {
      xy_recipe <- recipes::step_center(xy_recipe, recipes::all_predictors())
    }
    if (preprocess_step_scale) {
      xy_recipe <- recipes::step_scale(xy_recipe, recipes::all_predictors())
    }

    # If preprocess_PCA is not NULL add PCA step with number of component of % of variance to retain specification
    xy_recipe <- xy_recipe %>%
      {
        if (!is.na(preprocess_PCA)) {
          if (preprocess_PCA >= 1) {
            recipes::step_pca(., recipes::all_predictors(), num_comp = preprocess_PCA)
          } else if (preprocess_PCA < 1) {
            recipes::step_pca(., recipes::all_predictors(), threshold = preprocess_PCA)
          } else {
            .
          }
        } else {
          .
        }
      }
    xy_recipe_prep <- recipes::prep(xy_recipe)


    # Recipe for multiple word embedding input (with possibility of separate PCAs)
  } else {
    xy_recipe <- data_train %>%
      recipes::recipe(y ~ .) %>%
      # recipes::step_BoxCox(all_predictors()) %>%  preprocess_PCA = NULL, preprocess_PCA = 0.9 preprocess_PCA = 2
      recipes::update_role(id_nr, new_role = "id variable") %>%
      recipes::update_role(-id_nr, new_role = "predictor") %>%
      recipes::update_role(y, new_role = "outcome")

    if (!impute_missing) {
      xy_recipe <- recipes::step_naomit(xy_recipe, recipes::all_predictors(), skip = TRUE)
    } else if (impute_missing) {
      xy_recipe <- recipes::step_knnimpute(xy_recipe, recipes::all_predictors(), neighbors = 10) # , skip = TRUE
    }

    if (preprocess_step_center) {
      xy_recipe <- recipes::step_center(xy_recipe, recipes::all_predictors())
    }
    if (preprocess_step_scale) {
      xy_recipe <- recipes::step_scale(xy_recipe, recipes::all_predictors())
    }

    # If preprocess_PCA is not NULL add PCA step with number of component of % of variance to retain specification
    # Adding a PCA in each loop; first selecting all variables starting with i="Dim_we1"; and then "Dim_we2" etc
    if (!is.na(preprocess_PCA)) {
      if (preprocess_PCA >= 1) {
        for (i in variable_name_index_pca) {
          xy_recipe <-
            xy_recipe %>%
            # !! slices the current name into the `matches()` function.
            # We use a custom prefix so there are no name collisions for the
            # results of each PCA step.
            recipes::step_pca(dplyr::matches(!!i), num_comp = preprocess_PCA, prefix = paste("PCA_", i, "_"))
        }
      } else if (preprocess_PCA < 1) {
        for (i in variable_name_index_pca) {
          xy_recipe <-
            xy_recipe %>%
            recipes::step_pca(dplyr::matches(!!i), threshold = preprocess_PCA, prefix = paste("PCA_", i, "_"))
        }
      }
    }
    xy_recipe_prep <- recipes::prep(xy_recipe)
  }

  # Figure out how many predictors to know whether to use simple or multiple regression, which
  # depend on number of of PCA components that are retrived and/or whether first_n_predictors is used
  if (!is.na(first_n_predictors) & is.na(preprocess_PCA)) {
    # Get number of predictors from receipe
    nr_predictors <- table(xy_recipe_prep[[1]]$role)[["predictor"]]
  } else if (!is.na(preprocess_PCA)) {
    # To load the prepared training data into a variable juice() is used.
    # It extracts the data from the xy_recipe object.
    nr_predictors <- recipes::juice(xy_recipe_prep)
    # Count number of PCAs
    nr_predictors <- length(grep(x = colnames(nr_predictors), pattern = "PC"))
  } else if (is.na(preprocess_PCA) & is.na(first_n_predictors)) {
    nr_predictors <- recipes::juice(xy_recipe_prep)
    nr_predictors <- length(nr_predictors) - 2
  }

  # Ridge and/or Lasso
  if (nr_predictors > 1) {
    # Create and fit model help(linear_reg)
    mod_spec <-
      {
        if (model == "regression") {
          parsnip::linear_reg(penalty = penalty, mixture = mixture)
        } else if (model == "logistic") {
          parsnip::logistic_reg(
            mode = "classification",
            penalty = penalty,
            mixture = mixture
          )
        }
      } %>%
      parsnip::set_engine("glmnet")

    # Create Workflow (to know variable roles from recipes) help(workflow)
    wf <- workflows::workflow() %>%
      workflows::add_model(mod_spec) %>%
      workflows::add_recipe(xy_recipe)

    # Fit model
    mod <- parsnip::fit(wf, data = data_train)

    # Standard regression
  } else if (nr_predictors == 1) {
    mod_spec <- {
      if (model == "regression") {
        parsnip::linear_reg(mode = "regression") %>%
          parsnip::set_engine("lm")
      } else if (model == "logistic") {
        parsnip::logistic_reg(mode = "classification") %>%
          parsnip::set_engine("glm")
      }
    }

    # Create Workflow (to know variable roles from recipes) help(workflow)
    wf <- workflows::workflow() %>%
      workflows::add_model(mod_spec) %>%
      workflows::add_recipe(xy_recipe)

    # Fit model
    mod <- parsnip::fit(wf, data = data_train)
  }

  # Prepare the test data; remove y and according to the recipe
  xy_testing <- rsample::assessment(object) %>%
    dplyr::select(-y)

  if (model == "regression") {
    # Apply model on new data; penalty
    holdout_pred <-
      stats::predict(mod, xy_testing) %>%
      dplyr::bind_cols(rsample::assessment(object) %>%
                         dplyr::select(y, id_nr))

    # Get RMSE; eval_measure = "rmse" library(tidyverse)
    eval_result <- select_eval_measure_val(eval_measure,
                                           holdout_pred = holdout_pred,
                                           truth = y, estimate = .pred
    )$.estimate
    # Sort output of RMSE, predictions and truth (observed y)
    output <- list(
      list(eval_result), list(holdout_pred$.pred), list(holdout_pred$y), list(preprocess_PCA),
      list(holdout_pred$id_nr)
    )
    names(output) <- c("eval_result", "predictions", "y", "preprocess_PCA", "id_nr")
  } else if (model == "logistic") {
    holdout_pred_class <-
      stats::predict(mod, xy_testing, type = c("class")) %>%
      dplyr::bind_cols(rsample::assessment(object) %>%
                         dplyr::select(y, id_nr))

    holdout_pred <-
      stats::predict(mod, xy_testing, type = c("prob")) %>%
      dplyr::bind_cols(rsample::assessment(object) %>%
                         dplyr::select(y, id_nr))

    holdout_pred$.pred_class <- holdout_pred_class$.pred_class

    # Get RMSE; eval_measure = "rmse"
    eval_result <- select_eval_measure_val(eval_measure,
                                           holdout_pred = holdout_pred,
                                           truth = y, estimate = .pred_class
    )$.estimate
    # Sort output of RMSE, predictions and truth (observed y)
    output <- list(
      list(eval_result),
      list(holdout_pred$.pred_class),
      list(holdout_pred$y),
      list(holdout_pred[1]),
      list(holdout_pred[2]),
      list(preprocess_PCA),
      list(holdout_pred$id_nr)
    )
    names(output) <- c(
      "eval_result",
      "estimate",
      "truth",
      ".pred_1",
      ".pred_2",
      "preprocess_PCA",
      "id_nr"
    )
  }
  output
}


fit_model_rmse_wrapper <- function(penalty = penalty,
                                   mixture = mixture,
                                   object,
                                   model,
                                   eval_measure,
                                   preprocess_PCA = preprocess_PCA,
                                   variable_name_index_pca = variable_name_index_pca,
                                   first_n_predictors = first_n_predictors,
                                   preprocess_step_center = preprocess_step_center,
                                   preprocess_step_scale = preprocess_step_scale,
                                   impute_missing = impute_missing) {
  fit_model_rmse(object,
                 model,
                 eval_measure,
                 penalty,
                 mixture,
                 preprocess_PCA = preprocess_PCA,
                 variable_name_index_pca = variable_name_index_pca,
                 first_n_predictors = first_n_predictors,
                 preprocess_step_center = preprocess_step_center,
                 preprocess_step_scale = preprocess_step_scale,
                 impute_missing = impute_missing
  )
}


tune_over_cost <- function(object,
                           model,
                           eval_measure,
                           penalty,
                           mixture,
                           preprocess_PCA = preprocess_PCA,
                           variable_name_index_pca = variable_name_index_pca,
                           first_n_predictors = first_n_predictors,
                           preprocess_step_center = preprocess_step_center,
                           preprocess_step_scale = preprocess_step_scale,
                           impute_missing = impute_missing) {
  T1 <- Sys.time()

  # Number of components or percent of variance to attain; min_halving; preprocess_PCA = NULL
  if (!is.na(preprocess_PCA[1])) {
    if (preprocess_PCA[1] == "min_halving") {
      num_features <- length(rsample::analysis(object)) - 1
      num_users <- nrow(rsample::analysis(object))
      preprocess_PCA_value <- round(max(min(num_features / 2, num_users / 1.5), min(50, num_features)))
      preprocess_PCA_value
    } else if (preprocess_PCA[1] >= 1) {
      preprocess_PCA_value <- preprocess_PCA
    } else if (preprocess_PCA[1] < 1) {
      preprocess_PCA_value <- preprocess_PCA
    } else {
      preprocess_PCA_value <- NA
    }
  }

  if (is.na(preprocess_PCA[1])) {
    preprocess_PCA_value <- NA
  }

  ## Sequence to select dimensions from the semreps. SM-article state:
  # "Adding 1, then multiplying by 1.3 and finally rounding to the nearest
  # integer (e.g., 1, 3, 5, 8, where the next number of dimensions to be tested are the first 12;
  # in other words ([8 +􏰄 1*] 􏱡 1.3)
  if (!is.na(first_n_predictors)) {
    stop <- first_n_predictors
    new_num <- 1
    selection_vector <- 1
    while (new_num < stop) {
      new_num <- round((new_num + 1) * 1.3)
      selection_vector <- c(selection_vector, new_num)
    }
    # Changing the last number to the maximum number of dimensions
    selection_vector[length(selection_vector)] <- first_n_predictors
    first_n_predictors <- selection_vector
    first_n_predictors
  }


  grid_inner <- base::expand.grid(
    penalty = penalty,
    mixture = mixture,
    preprocess_PCA = preprocess_PCA_value,
    first_n_predictors = first_n_predictors
  )

  #  Test models with the different hyperparameters for the inner samples
  tune_results <- purrr::pmap(list(
    grid_inner$penalty,
    grid_inner$mixture,
    grid_inner$preprocess_PCA,
    grid_inner$first_n_predictors
  ),
  fit_model_rmse_wrapper,
  object = object,
  model = model,
  eval_measure = eval_measure,
  variable_name_index_pca = variable_name_index_pca,
  preprocess_step_center = preprocess_step_center,
  preprocess_step_scale = preprocess_step_scale,
  impute_missing = impute_missing
  )

  # Sort the output to separate the rmse, predictions and truth
  tune_outputlist <- tune_results %>%
    dplyr::bind_rows() %>%
    split.default(names(.)) %>%
    purrr::map(na.omit)

  # Extract the RMSE
  tune_eval_result <- unlist(tune_outputlist$eval_result$eval_result)

  # Add RMSE to the grid
  grid_inner_eval_result <- grid_inner %>%
    dplyr::mutate(eval_result = tune_eval_result)


  # Progression output
  best_eval <- bestParameters(
    data = grid_inner_eval_result,
    eval_measure = eval_measure
  )

  T2 <- Sys.time()
  time <- round(T2 - T1, digits = 2)

  variable_time <- sprintf(
    "(duration: %s %s).",
    time,
    units(time)
  )

  description_text <- paste(
    "Fold:", eval_measure,
    round(best_eval$eval_result, digits = 3),
    variable_time, "\n"
  )

  cat(colourise(description_text, "green"))


  return(grid_inner_eval_result)
}


bestParameters <- function(data, eval_measure) {
  if (eval_measure %in% c(
    "accuracy", "bal_accuracy", "sens", "spec",
    "precision", "kappa", "f_measure", "roc_auc",
    "rsq", "cor_test"
  )) {
    bestParametersFunction <- function(data) data[which.max(data$eval_result), ]
  } else if (eval_measure == "rmse") {
    bestParametersFunction <- function(data) data[which.min(data$eval_result), ]
  }
  results <- bestParametersFunction(data)
  return(results)
}

summarize_tune_results <- function(object,
                                   model,
                                   eval_measure,
                                   penalty,
                                   mixture,
                                   preprocess_PCA = preprocess_PCA,
                                   variable_name_index_pca = variable_name_index_pca,
                                   first_n_predictors = first_n_predictors,
                                   preprocess_step_center = preprocess_step_center,
                                   preprocess_step_scale = preprocess_step_scale,
                                   impute_missing = impute_missing) {

  # Return row-bound tibble containing the INNER results
  results <- purrr::map_df(
    .x = object$splits,
    .f = tune_over_cost,
    penalty = penalty,
    mixture = mixture,
    preprocess_PCA = preprocess_PCA,
    variable_name_index_pca = variable_name_index_pca,
    model = model,
    eval_measure = eval_measure,
    first_n_predictors = first_n_predictors,
    preprocess_step_center = preprocess_step_center,
    preprocess_step_scale = preprocess_step_scale,
    impute_missing = impute_missing
  )

  return(results)
}



x = word_embeddings_4$texts$harmonytext
y = Language_based_assessment_data_8$hilstotal
x_append = NULL
cv_method = "validation_split"
outside_folds = 10
outside_strata_y = "y"
outside_breaks = 4
inside_folds = 3 / 4
inside_strata_y = "y"
inside_breaks = 4
model = "regression"
eval_measure = "default"
preprocess_step_center = TRUE
preprocess_step_scale = TRUE
preprocess_PCA = NA
penalty = 10^seq(-16, 16)
mixture = c(0)
first_n_predictors = NA
impute_missing = FALSE
method_cor = "pearson"
model_description = "Consider writing a description of your model here"
multi_cores = "multi_cores_sys_default"
save_output = "all"
seed = 2020
  T1_textTrainRegression <- Sys.time()
  set.seed(seed)

  random_number <- runif(5)
  expect_equal(random_number[1], 0.6469028, tolerance = 0.00001)
  expect_equal(random_number[5], 0.1360972, tolerance = 0.00001)


  # Select correct eval_measure depending on model when default
  if (model == "regression" & eval_measure == "default") {
    eval_measure <- "rmse"
  } else if (model == "logistic" & eval_measure == "default") {
    eval_measure <- "bal_accuracy"
  }

  # The fit_model_rmse function need to number of word embeddings -- instead of
  # sending a separate parameter number of embeddings are give as a comment in "model"
  if (tibble::is_tibble(x)) {
    comment(eval_measure) <- "1"
  } else {
    comment(eval_measure) <- paste(length(x))
  }

  # Sorting out y
  if (tibble::is_tibble(y) | is.data.frame(y)) {
    y_name <- colnames(y)

    y <- tibble::as_tibble_col(y[[1]], column_name = "y")
  } else {
    y_name <- deparse(substitute(y))
    y <- tibble::as_tibble_col(y, column_name = "y")
  }

  # Sorting out x's
  variables_and_names <- sorting_xs_and_x_append(x = x, x_append = x_append)
  x2 <- variables_and_names$x1
  x_name <- variables_and_names$x_name
  embedding_description <- variables_and_names$embedding_description
  x_append_names <- variables_and_names$x_append_names
  variable_name_index_pca <- variables_and_names$variable_name_index_pca
  rm(variables_and_names)

  xy <- dplyr::bind_cols(x2, y)
  # xy <- tibble::as_tibble(xy) xy[1537]
  xy$id_nr <- c(seq_len(nrow(xy)))

  # complete.cases is not neccassary
  # Cross-Validation inside_folds = 3/4; results_nested_resampling[[1]][[1]][[1]]
  if (cv_method == "cv_folds") {
    results_nested_resampling <- rlang::expr(rsample::nested_cv(xy,
                                                                outside = rsample::vfold_cv(
                                                                  v = !!outside_folds,
                                                                  repeats = 1,
                                                                  strata = !!outside_strata_y,
                                                                  breaks = !!outside_breaks
                                                                ), #
                                                                inside = rsample::vfold_cv(
                                                                  v = !!inside_folds,
                                                                  repeats = 1,
                                                                  strata = !!inside_strata_y,
                                                                  breaks = !!inside_breaks
                                                                )
    ))
  }
  if (cv_method == "validation_split") {
    results_nested_resampling <- rlang::expr(rsample::nested_cv(xy,
                                                                outside = rsample::vfold_cv(
                                                                  v = !!outside_folds,
                                                                  repeats = 1,
                                                                  strata = !!outside_strata_y,
                                                                  breaks = !!outside_breaks
                                                                ),
                                                                inside = rsample::validation_split(
                                                                  prop = !!inside_folds,
                                                                  strata = !!inside_strata_y,
                                                                  breaks = !!inside_breaks
                                                                )
    ))
  }

  results_nested_resampling <- rlang::eval_tidy(results_nested_resampling)
  expect_equal(results_nested_resampling$splits[[1]][[1]][[1]][[1]], 0.4212412, tolerance = 0.00001)
  expect_equal(results_nested_resampling$splits[[1]][[1]][[1]][[2]], -0.00678613, tolerance = 0.00001)

  # Deciding whether to use multicores depending on system and settings.
  if (multi_cores == "multi_cores_sys_default") {
    if (.Platform$OS.type == "unix") {
      multi_cores_use <- TRUE
    } else if (.Platform$OS.type == "windows") {
      multi_cores_use <- FALSE
    }
  } else if (multi_cores == TRUE) {
    multi_cores_use <- TRUE
  } else if (multi_cores == FALSE) {
    multi_cores_use <- FALSE
  }


  if (multi_cores_use == FALSE) {
    tuning_results <- purrr::map(
      .x = results_nested_resampling$inner_resamples,
      .f = summarize_tune_results,
      model = model,
      eval_measure = eval_measure,
      penalty = penalty,
      mixture = mixture,
      preprocess_PCA = preprocess_PCA,
      variable_name_index_pca = variable_name_index_pca,
      first_n_predictors = first_n_predictors,
      preprocess_step_center = preprocess_step_center,
      preprocess_step_scale = preprocess_step_scale,
      impute_missing = impute_missing
    )
  } else if (multi_cores_use == TRUE) {
    # The multisession plan uses the local cores to process the inner resampling loop.
    future::plan(future::multisession)
    # The object tuning_results is a list of data frames for each of the OUTER resamples.
    tuning_results <- furrr::future_map(
      .options = furrr::furrr_options(seed = seed),
      .x = results_nested_resampling$inner_resamples,
      .f = summarize_tune_results,
      model = model,
      eval_measure = eval_measure,
      penalty = penalty,
      mixture = mixture,
      preprocess_PCA = preprocess_PCA,
      variable_name_index_pca = variable_name_index_pca,
      first_n_predictors = first_n_predictors,
      preprocess_step_center = preprocess_step_center,
      preprocess_step_scale = preprocess_step_scale,
      impute_missing = impute_missing
    )
  }
  expect_equal(tuning_results[[1]]$eval_result[[1]], 7.434478, tolerance = 0.00001)


  # Function to get the lowest eval_measure_val
  # Determine the best parameter estimate from each INNER sample to be used
  # for each of the outer resampling iterations:
  hyper_parameter_vals <-
    tuning_results %>%
    purrr::map_df(bestParameters, eval_measure) %>%
    dplyr::select(c(penalty, mixture, preprocess_PCA, first_n_predictors))

  # Bind best results
  results_split_parameter <-
    dplyr::bind_cols(results_nested_resampling, hyper_parameter_vals)


  # Compute the outer re-sampling results for each of the comment(model)
  # splits using the corresponding tuning parameter value from results_split_parameter.
  results_outer <- purrr::pmap(
    list(
      object = results_nested_resampling$splits,
      penalty = results_split_parameter$penalty,
      mixture = results_split_parameter$mixture,
      preprocess_PCA = results_split_parameter$preprocess_PCA,
      first_n_predictors = results_split_parameter$first_n_predictors,
      variable_name_index_pca = list(variable_name_index_pca),
      model = model,
      eval_measure = list(eval_measure),
      preprocess_step_center = preprocess_step_center,
      preprocess_step_scale = preprocess_step_scale,
      impute_missing = impute_missing
    ),
    fit_model_rmse
  )

  # Separate RMSE, predictions and observed y
  outputlist_results_outer <- results_outer %>%
    dplyr::bind_rows() %>%
    split.default(names(.)) %>%
    purrr::map(na.omit)


  # Get overall evaluation measure between predicted and observed values
  if (model == "regression") {
    # Unnest predictions and y
    predy_y <- tibble::tibble(
      tidyr::unnest(outputlist_results_outer$predictions, cols = c(predictions)),
      tidyr::unnest(outputlist_results_outer$y, cols = c(y)),
      tidyr::unnest(outputlist_results_outer$id_nr, cols = c(id_nr))
    )
    predy_y <- predy_y %>% dplyr::arrange(id_nr)
    # Correlate predictions and observed correlation
    collected_results <- stats::cor.test(predy_y$predictions, predy_y$y, method = method_cor, alternative = "greater")


    collected_results <- list(predy_y, collected_results)
  } else if (model == "logistic") {
    collected_results <- classification_results(outputlist_results_outer = outputlist_results_outer, ...)

    #  Save predictions outside list to make similar structure as model == regression output.
    predy_y <- collected_results$predy_y
    # Remove the predictions from list
    collected_results[[1]] <- NULL
  }

  ##### Construct final model to be saved and applied on other data  ########
  ############################################################################
  xy_all <- xy

  ######### One word embedding as input
  n_embbeddings <- as.numeric(comment(eval_measure))
  if (n_embbeddings == 1) {

    # If testing N first predictors help(step_scale) first_n_predictors = 3
    if (!is.na(first_n_predictors)) {
      # Select y and id
      Nvariable_totals <- length(xy_all)
      variable_names <- colnames(xy_all[(first_n_predictors + 1):(Nvariable_totals - 2)])
    } else {
      variable_names <- "id_nr"
    }

    # [0,] is added to just get the col names (and avoid saving all the data with the receipt) help(step_naomit)
    final_recipe <- # xy %>%
      recipes::recipe(y ~ ., xy_all[0, ]) %>%
      recipes::update_role(all_of(variable_names), new_role = "Not_predictors") %>%
      recipes::update_role(id_nr, new_role = "id variable") %>%
      # recipes::update_role(-id_nr, new_role = "predictor") %>%
      recipes::update_role(y, new_role = "outcome")

    if (!impute_missing) {
      final_recipe <- recipes::step_naomit(final_recipe, recipes::all_predictors(), skip = TRUE)
    } else if (impute_missing) {
      final_recipe <- recipes::step_knnimpute(final_recipe, recipes::all_predictors(), neighbors = 10)
    }

    if (preprocess_step_center) {
      final_recipe <- recipes::step_center(final_recipe, recipes::all_predictors())
    }
    if (preprocess_step_scale) {
      final_recipe <- recipes::step_scale(final_recipe, recipes::all_predictors())
    }
    # If preprocess_PCA is not NULL add PCA step with number of component of % of variance to retain specification
    final_recipe <- final_recipe %>%
      {
        if (!is.na(preprocess_PCA[1])) {
          if (preprocess_PCA[1] >= 1) {
            recipes::step_pca(., recipes::all_predictors(),
                              num_comp = statisticalMode(results_split_parameter$preprocess_PCA)
            )
          } else if (preprocess_PCA[1] < 1) {
            recipes::step_pca(., recipes::all_predictors(),
                              threshold = statisticalMode(results_split_parameter$preprocess_PCA)
            )
          } else {
            .
          }
        } else {
          .
        }
      }

    ######### More than one word embeddings as input
  } else {
    final_recipe <- recipes::recipe(y ~ ., xy_all[0, ]) %>%
      recipes::update_role(id_nr, new_role = "id variable") %>%
      recipes::update_role(-id_nr, new_role = "predictor") %>%
      recipes::update_role(y, new_role = "outcome")

    if (!impute_missing) {
      final_recipe <- recipes::step_naomit(final_recipe, recipes::all_predictors(), skip = TRUE)
    } else if (impute_missing) {
      final_recipe <- recipes::step_knnimpute(final_recipe, recipes::all_predictors(), neighbors = 10)
    }

    if (preprocess_step_center) {
      final_recipe <- recipes::step_center(final_recipe, recipes::all_predictors())
    }
    if (preprocess_step_scale) {
      final_recipe <- recipes::step_scale(final_recipe, recipes::all_predictors())
    }

    # Adding a PCA in each loop; first selecting all variables starting with i="Dim_we1"; and then "Dim_we2" etc
    if (!is.na(preprocess_PCA)) {
      if (preprocess_PCA >= 1) {
        for (i in variable_name_index_pca) {
          final_recipe <-
            final_recipe %>%
            # !! slices the current name into the `matches()` function.
            # We use a custom prefix so there are no name collisions for the
            # results of each PCA step.
            recipes::step_pca(dplyr::matches(!!i), num_comp = preprocess_PCA, prefix = paste("PCA_", i, "_"))
        }
        # }
      } else if (preprocess_PCA < 1) {
        for (i in variable_name_index_pca) {
          final_recipe <-
            final_recipe %>%
            recipes::step_pca(dplyr::matches(!!i), threshold = preprocess_PCA, prefix = paste("PCA_", i, "_"))
        }
      }
    }
  }


  # Creating recipe in another environment to avoid saving unnecessarily large parts of the environment
  # when saving the object to rda, rds or Rdata.
  # http://r.789695.n4.nabble.com/Model-object-when-generated-in-a-function-saves-
  # entire-environment-when-saved-td4723192.html
  recipe_save_small_size <- function(final_recipe, xy_all) {
    env_final_recipe <- new.env(parent = globalenv())
    env_final_recipe$xy_all <- xy_all
    env_final_recipe$final_recipe <- final_recipe

    with(env_final_recipe, preprocessing_recipe_save <- suppressWarnings(recipes::prep(final_recipe,
                                                                                       xy_all,
                                                                                       retain = FALSE
    )))
  }

  preprocessing_recipe_save <- recipe_save_small_size(
    final_recipe = final_recipe,
    xy_all = xy_all
  )

  # Check number of predictors (to later decide standard or multiple regression)
  # To load the prepared training data into a variable juice() is used.
  # It extracts the data from the xy_recipe object.
  preprocessing_recipe_prep <- recipes::prep(final_recipe, xy_all)
  nr_predictors <- recipes::juice(preprocessing_recipe_prep)
  nr_predictors <- length(nr_predictors)

  ####### NEW ENVIRONMENT
  model_save_small_size <- function(xy_all, final_recipe, results_split_parameter, model, nr_predictors) {
    env_final_model <- new.env(parent = globalenv())
    env_final_model$xy_all <- xy_all
    env_final_model$final_recipe <- final_recipe
    env_final_model$penalty_mode <- statisticalMode(results_split_parameter$penalty)
    env_final_model$mixture_mode <- statisticalMode(results_split_parameter$mixture)
    env_final_model$model <- model
    env_final_model$nr_predictors <- nr_predictors
    env_final_model$statisticalMode <- statisticalMode
    env_final_model$`%>%` <- `%>%`

    with(
      env_final_model,
      if (nr_predictors > 3) {
        # Create and fit model; penalty=NULL mixture = NULL
        final_predictive_model_spec <-
          {
            if (model == "regression") {
              parsnip::linear_reg(penalty = penalty_mode, mixture = mixture_mode)
            } else if (model == "logistic") {
              parsnip::logistic_reg(
                mode = "classification",
                penalty = penalty_mode,
                mixture = mixture_mode
              )
            }
          } %>%
          parsnip::set_engine("glmnet")

        # Create Workflow (to know variable roles from recipes) help(workflow)
        wf_final <- workflows::workflow() %>%
          workflows::add_model(final_predictive_model_spec) %>%
          workflows::add_recipe(final_recipe)

        # Fit model
        final_predictive_model <- parsnip::fit(wf_final, data = xy_all)

        # Standard regression
      } else if (nr_predictors == 3) {
        final_predictive_model_spec <- {
          if (model == "regression") {
            parsnip::linear_reg(mode = "regression") %>%
              parsnip::set_engine("lm")
          } else if (model == "logistic") {
            parsnip::logistic_reg(mode = "classification") %>%
              parsnip::set_engine("glm")
          }
        }


        # Create Workflow (to know variable roles from recipes) help(workflow)
        wf_final <- workflows::workflow() %>%
          workflows::add_model(final_predictive_model_spec) %>%
          workflows::add_recipe(final_recipe)

        # Fit model
        final_predictive_model <- parsnip::fit(wf_final, data = xy_all)
      }
    )
  }

  final_predictive_model <- model_save_small_size(xy_all, final_recipe, results_split_parameter, model, nr_predictors)


  ##### NEW ENVIRONMENT END

  ##########  DESCRIBING THE MODEL  ##########
  ############################################

  x_name_description <- paste("x word_embeddings = ", x_name)
  x_append_names_description <- paste("x_append = ", x_append_names)
  y_name_description <- paste("y = ", y_name)
  cv_method_description <- paste("cv_method = ", deparse(cv_method))
  outside_folds_description <- paste("outside_folds = ", deparse(outside_folds))
  outside_strata_y_description <- paste("outside_strata_y = ", deparse(outside_strata_y))
  inside_folds_description <- paste("inside_folds = ", deparse(inside_folds))
  inside_strata_y_description <- paste("inside_strata_y = ", deparse(inside_strata_y))

  penalty_setting <- paste("penalty_setting = ", deparse(penalty))
  mixture_setting <- paste("mixture_setting = ", deparse(mixture))
  preprocess_PCA_setting <- paste("preprocess_PCA_setting = ", deparse(preprocess_PCA))
  first_n_predictors_setting <- paste("first_n_predictors_setting = ", deparse(first_n_predictors))

  # Saving the final mtry and min_n used for the final model.
  penalty_description <- paste("penalty in final model = ", deparse(statisticalMode(results_split_parameter$penalty)))
  penalty_fold_description <- paste("penalty in each fold = ", deparse(results_split_parameter$penalty))

  mixture_description <- paste("mixture in final model = ", deparse(statisticalMode(results_split_parameter$mixture)))
  mixture_fold_description <- paste("mixture in each fold = ", deparse(results_split_parameter$mixture))

  preprocess_PCA_description <- paste(
    "preprocess_PCA in final model = ",
    deparse(statisticalMode(results_split_parameter$preprocess_PCA))
  )
  preprocess_PCA_fold_description <- paste(
    "preprocess_PCA in each fold = ",
    deparse(results_split_parameter$preprocess_PCA)
  )

  first_n_predictors_description <- paste(
    "first_n_predictors in final model = ",
    deparse(statisticalMode(results_split_parameter$first_n_predictors))
  )
  first_n_predictors_fold_description <- paste(
    "first_n_predictors in each fold = ",
    deparse(results_split_parameter$first_n_predictors)
  )

  preprocess_step_center <- paste("preprocess_step_center_setting = ", deparse(preprocess_step_center))
  preprocess_step_scale <- paste("preprocess_step_scale_setting = ", deparse(preprocess_step_scale))

  impute_missing <- paste("impute_missing_setting = ", deparse(impute_missing))

  # Getting time and date
  T2_textTrainRegression <- Sys.time()
  Time_textTrainRegression <- T2_textTrainRegression - T1_textTrainRegression
  Time_textTrainRegression <- sprintf(
    "Duration to train text: %f %s",
    Time_textTrainRegression,
    units(Time_textTrainRegression)
  )
  Date_textTrainRegression <- Sys.time()
  time_date <- paste(Time_textTrainRegression,
                     "; Date created: ", Date_textTrainRegression,
                     sep = "",
                     collapse = " "
  )


  # Describe model; adding user's-description + the name of the x and y
  model_description_detail <- c(
    x_name_description,
    x_append_names_description,
    y_name_description,
    cv_method_description,
    outside_folds_description,
    outside_strata_y_description,
    inside_folds_description,
    inside_strata_y_description,
    penalty_setting,
    penalty_description,
    penalty_fold_description,
    mixture_setting,
    mixture_description,
    mixture_fold_description,
    preprocess_step_center,
    preprocess_step_scale,
    preprocess_PCA_setting,
    preprocess_PCA_description,
    preprocess_PCA_fold_description,
    first_n_predictors_setting,
    first_n_predictors_description,
    first_n_predictors_fold_description,
    impute_missing,
    embedding_description,
    model_description,
    time_date
  )

  ###### Saving and arranging output ######
  ##########################################

  if (model == "regression") {
    if (save_output == "all") {
      final_results <- list(
        predy_y, preprocessing_recipe_save, final_predictive_model, model_description_detail,
        collected_results[[2]]
      )
      names(final_results) <- c(
        "predictions", "final_recipe", "final_model", "model_description",
        "results"
      )
    } else if (save_output == "only_results_predictions") {
      final_results <- list(
        predy_y, model_description_detail,
        collected_results[[2]]
      )
      names(final_results) <- c("predictions", "model_description", "results")
    } else if (save_output == "only_results") {
      final_results <- list(
        model_description_detail,
        collected_results[[2]]
      )
      names(final_results) <- c("model_description", "results")
    }
  } else if (model == "logistic") {
    if (save_output == "all") {
      final_results <- list(
        predy_y, preprocessing_recipe_save, final_predictive_model, model_description_detail,
        collected_results$roc_curve_data, collected_results$roc_curve_plot, collected_results$fisher,
        collected_results$chisq, collected_results$results_collected
      )
      names(final_results) <- c(
        "predictions", "final_recipe", "final_model", "model_description",
        "roc_curve_data", "roc_curve_plot", "fisher", "chisq", "results_metrics"
      )
      final_results
    } else if (save_output == "only_results_predictions") {
      final_results <- list(
        predy_y, model_description_detail,
        collected_results$roc_curve_data, collected_results$roc_curve_plot, collected_results$fisher,
        collected_results$chisq, collected_results$results_collected
      )
      names(final_results) <- c(
        "predictions", "model_description",
        "roc_curve_data", "roc_curve_plot", "fisher", "chisq", "results_metrics"
      )
      final_results
    } else if (save_output == "only_results") {
      final_results <- list(
        model_description_detail,
        collected_results$roc_curve_data, collected_results$roc_curve_plot, collected_results$fisher,
        collected_results$chisq, collected_results$results_collected
      )
      names(final_results) <- c(
        "model_description",
        "roc_curve_data", "roc_curve_plot", "fisher", "chisq", "results_metrics"
      )
      final_results
    }
    final_results
  }


  expect_equal(final_results$results$estimate[[1]], 0.3590022, tolerance = 0.00001)


})