R/aggregate_profiles.R

Defines functions check_span check_groups check_type check_variable_type aggregated_profiles_conditional aggregated_profiles_partial aggregated_profiles_accumulated fast_initialisation_for_orginal aggregate_profiles

Documented in aggregate_profiles

#' Aggregates Ceteris Paribus Profiles
#'
#' The function \code{aggregate_profiles()} calculates an aggregate of ceteris paribus profiles.
#' It can be: Partial Dependence Profile (average across Ceteris Paribus Profiles),
#' Conditional Dependence Profile (local weighted average across Ceteris Paribus Profiles) or
#' Accumulated Local Dependence Profile (cummulated average local changes in Ceteris Paribus Profiles).
#'
#' @param x a ceteris paribus explainer produced with function \code{ceteris_paribus()}
#' @param ... other explainers that shall be calculated together
#' @param variables if not \code{NULL} then aggregate only for selected \code{variables} will be calculated
#' @param type either \code{partial/conditional/accumulated} for partial dependence, conditional profiles of accumulated local effects
#' @param groups a variable name that will be used for grouping.
#' By default \code{NULL} which means that no groups shall be calculated
#' @param center by default accumulated profiles start at 0. If \code{center=TRUE}, then they are centered around mean prediction,
#' which is calculated on the observations used in \code{ceteris_paribus}.
#' @param span smoothing coefficient, by default \code{0.25}. It's the sd for gaussian kernel
#' @param variable_type a character. If \code{numerical} then only numerical variables will be calculated.
#' If \code{categorical} then only categorical variables will be calculated.
#'
#' @references Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. \url{https://ema.drwhy.ai/}
#'
#' @importFrom stats na.omit quantile weighted.mean dnorm
#'
#' @return an object of the class \code{aggregated_profiles_explainer}
#'
#' @examples
#' library("DALEX")
#' library("ingredients")
#' library("ranger")
#' head(titanic_imputed)
#' \donttest{
#' model_titanic_rf <- ranger(survived ~.,  data = titanic_imputed, probability = TRUE)
#'
#' explain_titanic_rf <- explain(model_titanic_rf,
#'                               data = titanic_imputed[,-8],
#'                               y = titanic_imputed[,8],
#'                               label = "ranger forest",
#'                               verbose = FALSE)
#'
#' selected_passangers <- select_sample(titanic_imputed, n = 100)
#' cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
#' head(cp_rf)
#'
#' # continuous variable
#' pdp_rf_p <- aggregate_profiles(cp_rf, variables = "age", type = "partial")
#' pdp_rf_p$`_label_` <- "RF_partial"
#' pdp_rf_c <- aggregate_profiles(cp_rf, variables = "age", type = "conditional")
#' pdp_rf_c$`_label_` <- "RF_conditional"
#' pdp_rf_a <- aggregate_profiles(cp_rf, variables = "age", type = "accumulated")
#' pdp_rf_a$`_label_` <- "RF_accumulated"
#'
#' plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_")
#'
#' pdp_rf <- aggregate_profiles(cp_rf, variables = "age",
#'                              groups = "gender")
#'
#' head(pdp_rf)
#' plot(cp_rf, variables = "age") +
#'   show_observations(cp_rf, variables = "age") +
#'   show_rugs(cp_rf, variables = "age", color = "red") +
#'   show_aggregated_profiles(pdp_rf, size = 3, color = "_label_")
#'
#' # categorical variable
#' pdp_rf_p <- aggregate_profiles(cp_rf, variables = "class",
#'                                variable_type = "categorical",  type = "partial")
#' pdp_rf_p$`_label_` <- "RF_partial"
#' pdp_rf_c <- aggregate_profiles(cp_rf, variables = "class",
#'                                variable_type = "categorical", type = "conditional")
#' pdp_rf_c$`_label_` <- "RF_conditional"
#' pdp_rf_a <- aggregate_profiles(cp_rf, variables = "class",
#'                                variable_type = "categorical", type = "accumulated")
#' pdp_rf_a$`_label_` <- "RF_accumulated"
#' plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_")
#'
#' # or maybe flipped?
#' library(ggplot2)
#' plot(pdp_rf_p, pdp_rf_c, pdp_rf_a, color = "_label_") + coord_flip()
#'
#' pdp_rf <- aggregate_profiles(cp_rf, variables = "class", variable_type = "categorical",
#'                              groups = "gender")
#' head(pdp_rf)
#' plot(pdp_rf, variables = "class")
#' # or maybe flipped?
#' plot(pdp_rf, variables = "class") + coord_flip()
#'
#' }
#'
#' @export
#' @rdname aggregate_profiles
aggregate_profiles <- function(x, ...,
                               variable_type = "numerical",
                               groups = NULL,
                               type = "partial",
                               variables = NULL,
                               span = 0.25,
                               center = FALSE) {

  check_variable_type(variable_type)
  check_type(type)
  check_groups(groups)
  check_span(span)

  # if there is more ceteris paribuses, they should be merged into a single data frame
  elist <- list(...)
  if (length(elist) > 1) {
    # only ceteris_paribus_explainer objects
    elist <-  elist[sapply(elist, function(x) "ceteris_paribus_explainer" %in% class(x))]
  } else {
    elist <- NULL
  }
  dfl <- c(list(x), elist)
  mean_prediction <-
    mean(do.call(rbind, lapply(dfl, function(x){ attr(x, "observations")}))$`_yhat_`, na.rm = TRUE)

  all_profiles <- do.call(rbind, dfl)
  class(all_profiles) <- "data.frame"

  all_profiles$`_ids_` <- factor(all_profiles$`_ids_`)

  # variables to use
  all_variables <- na.omit(as.character(unique(all_profiles$`_vname_`)))
  if (!is.null(variables)) {
    all_variables_intersect <- intersect(all_variables, variables)
    if (length(all_variables_intersect) == 0) stop(paste0("parameter variables do not overlap with ", paste(all_variables, collapse = ", ")))
    all_variables <- all_variables_intersect
  }

  # only numerical or only factors?
  is_numeric <- sapply(all_profiles[, all_variables, drop = FALSE], is.numeric)

  if (variable_type == "numerical") {
    vnames <- names(which(is_numeric))
    all_profiles$`_x_` <- 0

    # there are no numerical variables
    if (length(vnames) == 0) {
      # change to categorical
      variable_type <- "categorical"
      all_profiles$`_x_` <- ""
      # send message
      message("'variable_type' changed to 'categorical' due to lack of numerical variables.")
      # take all
      vnames <- all_variables
    } else if (!is.null(variables) && length(vnames) != length(variables)) {
      message("Non-numerical variables (from the 'variables' argument) are rejected.")
    }
  } else {
    vnames <- names(which(!is_numeric))
    all_profiles$`_x_` <- ""

    # there are variables selected
    if (!is.null(variables)) {
      # take all
      vnames <- all_variables
    } else if (length(vnames) == 0) {
      # there were no variables selected and there are no categorical variables
      stop("There are no non-numerical variables.")
    }
  }

  # select only suitable variables
  all_profiles <- all_profiles[all_profiles$`_vname_` %in% vnames, ]
  # create _x_
  tmp <- as.character(all_profiles$`_vname_`)
  for (viname in unique(tmp)) {
    all_profiles$`_x_`[tmp == viname] <- all_profiles[tmp == viname, viname]
  }

  if (class(all_profiles) != "data.frame") {
    all_profiles <- as.data.frame(all_profiles)
  }

  # change x column to proper character values
  if (variable_type == "categorical") {
    all_profiles$`_x_` <- as.character(apply(all_profiles, 1, function(x) x[x["_vname_"]]))
  }

  if (!is.null(groups) && ! groups %in% colnames(all_profiles)) {
    stop("groups parameter is not a name of any column")
  }

  # standard partial profiles
  # just average
  if (type == "partial") {
    aggregated_profiles <- aggregated_profiles_partial(all_profiles, groups)
    class(aggregated_profiles) <- c("aggregated_profiles_explainer",
                                    "partial_dependence_explainer", "data.frame")
  }
  if (type == "conditional") {
    aggregated_profiles <- aggregated_profiles_conditional(all_profiles, groups, span = span)
    class(aggregated_profiles) <- c("aggregated_profiles_explainer",
                                    "conditional_dependence_explainer", "data.frame")
  }
  if (type == "accumulated") {
    aggregated_profiles <- aggregated_profiles_accumulated(all_profiles, groups, span = span,
                                                           center = center, mean_prediction = mean_prediction)
    class(aggregated_profiles) <- c("aggregated_profiles_explainer",
                                    "accumulated_dependence_explainer", "data.frame")
  }

  # calculate mean(all observation's _yhat_), mean of prediction
  attr(aggregated_profiles, "mean_prediction") <- mean_prediction

  aggregated_profiles
}

# faster version of
# all_profiles$`_orginal_` <- 0
# for (i in 1:nrow(all_profiles)) {
#   all_profiles$`_orginal_`[i] <- observations[as.character(all_profiles$`_ids_`[i]) ,
#                                               as.character(all_profiles$`_vname_`[i])]
# }
fast_initialisation_for_orginal <- function(all_profiles, observations) {
  if (is.numeric(all_profiles$`_x_`)) {
    init_value = 0
    transform_value = I
  } else {
    init_value = ""
    transform_value = as.character
  }

  # fill up the _orginal_ column
  all_profiles$`_orginal_` <- init_value
  all_profiles_per_vname <- split(all_profiles, all_profiles$`_vname_`, drop = TRUE)
  profiles_per_vname <- lapply(all_profiles_per_vname, function(profile_per_vname) {
    profile_per_vname$`_orginal_` <- transform_value(observations[as.character(profile_per_vname$`_ids_`),
                                                                  as.character(profile_per_vname$`_vname_`[1])])
    profile_per_vname
  })
  do.call(rbind, profiles_per_vname)
}

aggregated_profiles_accumulated <- function(all_profiles, groups = NULL, span = 0.25, center = FALSE,
                                            mean_prediction = 0) {
  observations <- attr(all_profiles, "observations")
  # just initialisation
  all_profiles <- fast_initialisation_for_orginal(all_profiles, observations)

  # split all_profiles into groups
  tmp <- all_profiles[,c("_vname_", "_label_", "_x_", "_yhat_", "_ids_", "_orginal_", groups)]

  split_profiles <- split(tmp, tmp[,c("_vname_", "_label_")], drop = TRUE)

  # calculate for each group
  chunks <- lapply(split_profiles, function(split_profile) {
    if (is.numeric(split_profile$`_x_`)) {
      # for continuous variables we will calculate weighted average
      # where weights come from gaussian kernel and distance between points

      # scaling factor, range if the range i > 0
      range_x <- diff(range(split_profile$`_x_`))
      if (range_x == 0) range_x <- 1
      # scalled differences
      diffs <- (split_profile$`_orginal_` - split_profile$`_x_`) /range_x
      split_profile$`_w_` <- dnorm(diffs, sd = span)
    } else {
      # for categorical variables we will calculate weighted average
      # but weights are 0-1, 1 if it's the same level and 0 otherwise
      split_profile$`_w_` <- split_profile$`_orginal_` == split_profile$`_x_`
    }

    #  diffs
    per_points <- split(split_profile, split_profile$`_ids_`)
    chunks <- lapply(per_points, function(per_point) {
      per_point$`_yhat_` <- c(0, diff(per_point$`_yhat_`))
      per_point

    })
    split_profile <- do.call(rbind, chunks)

    # for factors, keep proper order
    # as in https://github.com/ModelOriented/ingredients/issues/82
    if (!is.numeric(split_profile$`_x_`)) {
      split_profile$`_x_` <- factor(split_profile$`_x_`, levels = unique(split_profile$`_x_`))
    }
    # weighed means
    per_points <- split(split_profile, split_profile[, c("_x_", groups)])
    chunks <- lapply(per_points, function(per_point) {
      avg <- weighted.mean(per_point$`_yhat_`, w = per_point$`_w_`, na.rm = TRUE)
      res <- per_point[1, c("_vname_", "_label_", "_x_", "_yhat_", groups)]
      # NaN occurs when all weights are 0 , #43
      res$`_yhat_` <- ifelse(is.nan(avg), 0, avg)
      res
    })
    par_profile <- do.call(rbind, chunks)
    # for factors, keep it charagetr again
    split_profile$`_x_` <- as.character( split_profile$`_x_`)
    # postprocessing
    if (is.null(groups)) {
      par_profile$`_yhat_` <- cumsum(par_profile$`_yhat_`)
    } else {
      # cumsum per group
      par_points <- split(par_profile, par_profile[, groups])
      chunks <- lapply(par_points, function(par_point) {
        par_point$`_yhat_` <- cumsum(par_point$`_yhat_`)
        par_point
      })

      par_profile <- do.call(rbind, chunks)
    }

    # should accumulated profiles be centered at 0?
    if (center) {
      par_profile$`_yhat_` <- par_profile$`_yhat_` -
                          mean(par_profile$`_yhat_`, na.rm = TRUE) +
                          mean_prediction
    }

    par_profile
  })

  aggregated_profiles <- do.call(rbind, chunks)

  # postprocessing
  if (!is.null(groups)) {
    colnames(aggregated_profiles)[5] = "_groups_"
    aggregated_profiles$`_label_` <- paste(aggregated_profiles$`_label_`, aggregated_profiles$`_groups_`, sep = "_")
  }

  aggregated_profiles$`_ids_` <- 0
  rownames(aggregated_profiles) <- 1:nrow(aggregated_profiles)

  aggregated_profiles
}


aggregated_profiles_partial <- function(all_profiles, groups = NULL) {

  if (is.null(groups)) {
    tmp <- all_profiles[,c("_vname_", "_label_", "_x_", "_yhat_")]
    aggregated_profiles <- aggregate(tmp$`_yhat_`, by = list(tmp$`_vname_`, tmp$`_label_`, tmp$`_x_`), FUN = mean, na.rm = TRUE)
    colnames(aggregated_profiles) <- c("_vname_", "_label_", "_x_", "_yhat_")
  } else {
    tmp <- all_profiles[,c("_vname_", "_label_", "_x_", "_yhat_",groups)]
    aggregated_profiles <- aggregate(tmp$`_yhat_`, by = list(tmp$`_vname_`, tmp$`_label_`, tmp$`_x_`, tmp[,groups]), FUN = mean, na.rm = TRUE)
    colnames(aggregated_profiles) <- c("_vname_", "_label_", "_x_", "_groups_", "_yhat_")
    aggregated_profiles$`_label_` <- paste(aggregated_profiles$`_label_`, aggregated_profiles$`_groups_`, sep = "_")
  }
  aggregated_profiles$`_ids_` <- 0

  # for factors, keep proper order
  # as in https://github.com/ModelOriented/ingredients/issues/82
  if (!is.numeric(all_profiles$`_x_`)) {
    aggregated_profiles$`_x_` <- factor(aggregated_profiles$`_x_`, levels = unique(all_profiles$`_x_`))
    aggregated_profiles <- aggregated_profiles[order(aggregated_profiles$`_x_`),]
  }

  aggregated_profiles
}

aggregated_profiles_conditional <- function(all_profiles, groups = NULL, span = 0.25) {

  observations <- attr(all_profiles, "observations")
  # just initialisation
  all_profiles <- fast_initialisation_for_orginal(all_profiles, observations)

  # split all_profiles into groups
  tmp <- all_profiles[,c("_vname_", "_label_", "_x_", "_yhat_", "_ids_", "_orginal_", groups)]

  split_profiles <- split(tmp, tmp[,c("_vname_", "_label_")], drop = TRUE)

  # calculate for each group
  chunks <- lapply(split_profiles, function(split_profile) {

    if (is.numeric(split_profile$`_x_`)) {
      # for continuous variables we will calculate weighted average
      # where weights come from gaussian kernel and distance between points

      # scaling factor, range if the range i > 0
      range_x <- diff(range(split_profile$`_x_`))
      if (range_x == 0) range_x <- 1

      # scalled differences
      diffs <- (split_profile$`_orginal_` - split_profile$`_x_`) /range_x
      split_profile$`_w_` <- dnorm(diffs, sd = span)
    } else {
      # for categorical variables we will calculate weighted average
      # but weights are 0-1, 1 if it's the same level and 0 otherwise
      split_profile$`_w_` <- split_profile$`_orginal_` == split_profile$`_x_`

      # for factors, keep proper order
      # as in https://github.com/ModelOriented/ingredients/issues/82
      split_profile$`_x_` <- factor(split_profile$`_x_`, levels = unique(split_profile$`_x_`))
    }

    per_points <- split(split_profile, split_profile[, c("_x_", groups)])

    chunks <- lapply(per_points, function(per_point) {
      avg <- weighted.mean(per_point$`_yhat_`, w = per_point$`_w_`, na.rm = TRUE)
      res <- per_point[1, c("_vname_", "_label_", "_x_", "_yhat_", groups)]
      # NaN occurs when all weights are 0 , #43
      res$`_yhat_` <- ifelse(is.nan(avg), 0, avg)
      res
    })
    do.call(rbind, chunks)
  })

  aggregated_profiles <- do.call(rbind, chunks)

  if (!is.null(groups)) {
    colnames(aggregated_profiles)[5] = "_groups_"
    aggregated_profiles$`_label_` <- paste(aggregated_profiles$`_label_`, aggregated_profiles$`_groups_`, sep = "_")
  }

  aggregated_profiles$`_ids_` <- 0

  aggregated_profiles
}


#'@noRd
#'@title Check if variable_type is "numerical" or "categorical"
#'
#'@param variable_type a character
#'
check_variable_type <- function(variable_type) {
  if (!(variable_type %in% c("numerical", "categorical")))
    stop("variable_type needs to be 'numerical' or 'categorical'")
}

#'@noRd
#'@title Check if type is partial/conditional/accumulated
#'
#'@param type a character
#'
check_type <- function(type) {
  if (!(type %in% c("partial", "conditional", "accumulated")))
    stop("type needs to be 'partial', 'conditional' or 'accumulated'")
}

#'@noRd
#'@title Check if group parameter is correct
#'
#'@param groups
#'
check_groups <- function(groups) {
  if (! is.null(groups) && (! is.character(groups) || length(groups) != 1))
    stop("groups must be a character vector of length 1")
}

#'@noRd
#'@title Check if span is correct
#'
#'@param span
#'
check_span <- function(span) {
  if (! is.numeric(span) || length(span) != 1 || span <= 0)
    stop("span must be positive number")
}

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ingredients documentation built on April 10, 2021, 5:06 p.m.