R/interpret.R

Defines functions print.interpret plot.interpret add_refs interpret

Documented in interpret plot.interpret

#' Interpret a model via regularized coefficient estimates
#'
#' @param x a model_list object containing a glmnet model
#' @param sparsity If NULL (default) coefficients for the best-performing model
#'   will be returned. Otherwise, a value in [0, 1] that determines the
#'   sparseness of the model for which coefficients will be returned, with 0
#'   being maximally sparse (i.e. having the fewest non-zero coefficients) and 1
#'   being minimally sparse
#' @param remove_zeros Remove features with coefficients equal to 0? Default is
#'   TRUE
#' @param top_n Integer: How many coefficients to return? The largest top_n
#'   absolute-value coefficients will be returned. If missing (default), all
#'   coefficients are returned
#'
#' @return A data frame of variables and their regularized regression
#'   coefficient estimates with parent class "interpret"
#'
#' @details **WARNING** Coefficients are on the scale of the predictors; they
#'   are not standardized, so unless features were scaled before training (e.g.
#'   with \code{prep_data(..., scale = TRUE)}, the magnitude of coefficients
#'   does not necessarily reflect their importance.
#'
#'   If x was trained with more than one value of alpha the best value of alpha
#'   is used; sparsity is determined only via the selection of lambda. Using
#'   only lasso regression (i.e. alpha = 1) will produce a sparser set of
#'   coefficients and can be obtained by not tuning hyperparameters.
#'
#' @export
#' @seealso \code{\link{plot.interpret}}
#'
#' @examples
#' m <- machine_learn(pima_diabetes, patient_id, outcome = diabetes, models = "glm")
#' interpret(m)
#' interpret(m, .2)
#' interpret(m) %>%
#'   plot()
interpret <- function(x, sparsity = NULL, remove_zeros = TRUE, top_n) {
  if (!is.model_list(x))
    stop("x must be a model_list")
  if (!"glmnet" %in% names(x))
    stop("Only glmnet models can be interpreted, and there's no glmnet model ",
         "in x. If there's a random forest, you could use get_variable_importance.")
  mi <- extract_model_info(x)
  if (mi$m_class == "Multiclass")
    stop("Interpret doesn't support multiclass models. If you're interested in ",
         "how one class is chosen, use interpret on a regular classification ",
         "model in a one-vs-all fashion. Alternatively, you can use ",
         "get_variable_importance().")
  if (mi$best_model_name != "glmnet")
    warning("Interpreting glmnet model, but ", mi$best_model_name, " performed ",
            "best in cross-validation and will be used to make predictions. ",
            "To use the glmnet model for predictions, extract it with ",
            "x['glmnet'].")

  g <- x$glmnet$finalModel
  coefs <- stats::coef(g)

  # If user didn't specify sparsity, use best lambda
  if (is.null(sparsity)) {
    lambda <- g$lambdaOpt
    coefs <- stats::coef(g, s = lambda)
  } else {
    if (!is.numeric(sparsity) || sparsity < 0 || sparsity > 1)
      stop("sparsity must be a numeric value between 1 and 100")
    j <- round(stats::quantile(seq_len(ncol(coefs)), sparsity), 0)
    coefs <- coefs[, j, drop = FALSE]
    lambda <- g$lambda[j]
  }

  # find the dummy object
  dummy_step_object <- get_recipe_step(x, "step_dummy_hcai")

  coefs <-
    tibble::tibble(variable = rownames(coefs), coefficient = coefs[, 1]) %>%
    dplyr::arrange(desc(variable == "(Intercept)"), desc(abs(coefficient))) %>%
    add_refs(dummy_step_object)

  # caret and glmnet take opposite approaches to positive outcome handling,
  # so need to flip the signs of coefficients for classification models
  if (mi$m_class == "Classification")
    coefs$coefficient <- - coefs$coefficient

  if (remove_zeros)
    coefs <- dplyr::filter(coefs, coefficient != 0)

  if (!missing(top_n)) {
    if (top_n < nrow(coefs))
      coefs <- coefs[seq_len(top_n), ]
  }

  structure(coefs,
            class = c("interpret", class(coefs)),
            m_class = mi$m_class,
            target = mi$target,
            lambda = lambda,
            alpha = g$tuneValue[["alpha"]],
            ref_levels = dummy_step_object$ref_levels)
}

#' Adds existing reference levels to variable names
#' @param d A tibble with a variable and coefficient column
#' @param x a model_list object containing a glmnet model
#' @return A tibble with reference levels added to variable names
#' @noRd
add_refs <- function(d, dummy_step_object) {
  if (!is.null(dummy_step_object)) {
    dummies <- dummy_step_object$dummies

    d$variable <- map_chr(d$variable, ~{
      if (.x %in% dummies$dummy) {
        var <- paste0(.x, " (vs. ", dummies$ref[dummies$dummy == .x], ")")
      } else {
        var <- .x
      }
      return(var)
    })
  }
  return(d)
}

#' Plot regularized model coefficients
#'
#' @param x A \code{interpret} object or a data frame with columns "variable"
#'   and "coefficient"
#' @param include_intercept If FALSE (default) the intercept estimate will not
#'   be plotted
#' @param max_char Maximum length of variable names to leave untruncated.
#'   Default = 40; use \code{Inf} to prevent truncation. Variable names longer
#'   than this will be truncated to leave the beginning and end of each variable
#'   name, bridged by " ... ".
#' @param title Plot title. NULL for no title; character for custom title. If
#'   left blank contains the model class and outcome variable
#' @param caption Plot caption, appears in lower-right. NULL for no caption;
#'   character for custom caption. If left blank the caption will contain info
#'   including the hyperparameter values of the model used by
#'   \code{\link{interpret}} to determine coefficient estimates.
#' @param font_size Relative size of all fonts in plot, default = 11
#' @param point_size Size of dots, default = 3
#' @param print Print the plot? Default = TRUE
#' @param ... Unused
#'
#'
#' @return A ggplot object, invisibly.
#' @export
#' @seealso \code{\link{interpret}}
#'
#' @examples
#' machine_learn(mtcars, outcome = mpg, models = "glm", tune = FALSE) %>%
#'   interpret() %>%
#'   plot(font_size = 14)
plot.interpret <- function(x, include_intercept = FALSE, max_char = 40,
                           title, caption, font_size = 11, point_size = 3,
                           print = TRUE, ...) {

  ats <- attributes(x)

  if (!is.data.frame(x))
    stop("x must be a data frame from interpret, or at least look like one!")
  if (any(names(x) != c("variable", "coefficient")))
    stop("x must be a data frame from interpret, or at least look like one!")

  if (!include_intercept)
    x <- dplyr::filter(x, variable != "(Intercept)")

  # If attributes are missing (probably means plot.coefs was called explicitly)
  # can't generate the usual title and caption
  if (is.null(ats)) {
    if (missing(title)) title <- NULL
    if (missing(caption)) caption <- NULL
  } else {
    if (missing(title))
      title <- paste("Coefficients for regularized", tolower(ats$m_class),
                     "model of", ats$target)
    if (missing(caption))
      caption <- paste("Hyperparameter values alpha =", signif(ats$alpha, 1),
                       "and lambda =", signif(ats$lambda, 3))
  }
  # Truncate long variable names
  x$variable <- trunc_char(x$variable, max_char)

  limits <- max(abs(x$coefficient)) * c(-1.05, 1.05)
  the_plot <-
    x %>%
    ggplot(aes(x = reorder(variable, coefficient), y = coefficient)) +
    geom_hline(yintercept = 0, linetype = "dashed", alpha = .6) +
    geom_point(size = point_size) +
    coord_flip() +
    scale_x_discrete(name = NULL) +
    scale_y_continuous(name = "Coefficient Estimate",
                       limits = limits) +
    labs(title = title, caption = caption) +
    theme_gray(base_size = font_size)

  if (print)
    print(the_plot)
  return(invisible(the_plot))
}

# Print interpret objects
#' @export
print.interpret <- function(x, ...) {
  ref_levels <- attr(x, "ref_levels")
  out <-
    if (!purrr::is_empty(ref_levels)) {
      paste0(c("Reference Levels:\n",
               paste0("All `", names(ref_levels),
                      "` estimates are relative to `", ref_levels, "`\n"),
               "\n"), collapse = "")
    }
  cat(out)
  NextMethod(x)
  return(invisible(x))
}

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healthcareai documentation built on Aug. 6, 2020, 1:06 a.m.