light_profile: Partial Dependence and other Profiles
In flashlight: Shed Light on Black Box Machine Learning Models

light_profile

R Documentation

Partial Dependence and other Profiles

Description

Calculates different types of profiles across covariable values. By default, partial dependence profiles are calculated (see Friedman). Other options are profiles of ALE (accumulated local effects, see Apley), response, predicted values ("M plots" or "marginal plots", see Apley), residuals, and shap. The results are aggregated either by (weighted) means or by (weighted) quartiles.

Note that ALE profiles are calibrated by (weighted) average predictions. In contrast to the suggestions in Apley, we calculate ALE profiles of factors in the same order as the factor levels. They are not being reordered based on similiarity of other variables.

Usage

light_profile(x, ...)

## Default S3 method:
light_profile(x, ...)

## S3 method for class 'flashlight'
light_profile(
  x,
  v = NULL,
  data = NULL,
  by = x$by,
  type = c("partial dependence", "ale", "predicted", "response", "residual", "shap"),
  stats = c("mean", "quartiles"),
  breaks = NULL,
  n_bins = 11L,
  cut_type = c("equal", "quantile"),
  use_linkinv = TRUE,
  counts = TRUE,
  counts_weighted = FALSE,
  v_labels = TRUE,
  pred = NULL,
  pd_evaluate_at = NULL,
  pd_grid = NULL,
  pd_indices = NULL,
  pd_n_max = 1000L,
  pd_seed = NULL,
  pd_center = c("no", "first", "middle", "last", "mean", "0"),
  ale_two_sided = FALSE,
  ...
)

## S3 method for class 'multiflashlight'
light_profile(
  x,
  v = NULL,
  data = NULL,
  type = c("partial dependence", "ale", "predicted", "response", "residual", "shap"),
  breaks = NULL,
  n_bins = 11L,
  cut_type = c("equal", "quantile"),
  pd_evaluate_at = NULL,
  pd_grid = NULL,
  ...
)

Arguments

`x`	An object of class "flashlight" or "multiflashlight".
`...`	Further arguments passed to `cut3()` in forming the cut breaks of the `v` variable.
`v`	The variable name to be profiled.
`data`	An optional `data.frame`. Not used for `type = "shap"`.
`by`	An optional vector of column names used to additionally group the results.
`type`	Type of the profile: Either "partial dependence", "ale", "predicted", "response", "residual", or "shap".
`stats`	Statistic to calculate: "mean" or "quartiles". For ALE profiles, only "mean" makes sense.
`breaks`	Cut breaks for a numeric `v`. Used to overwrite automatic binning via `n_bins` and `cut_type`. Ignored if `v` is not numeric.
`n_bins`	Approximate number of unique values to evaluate for numeric `v`. Ignored if `v` is not numeric or if `breaks` is specified.
`cut_type`	Should a numeric `v` be cut into "equal" or "quantile" bins? Ignored if `v` is not numeric or if `breaks` is specified.
`use_linkinv`	Should retransformation function be applied? Default is `TRUE`. Not used for type "shap".
`counts`	Should observation counts be added?
`counts_weighted`	If `counts = TRUE`: Should counts be weighted by the case weights? If `TRUE`, the sum of `w` is returned by group.
`v_labels`	If `FALSE`, return group centers of `v` instead of labels. Only relevant for types "response", "predicted" or "residual" and if `v` is being binned. In that case useful, for instance, if different flashlights use different data sets and bin labels would not match.
`pred`	Optional vector with predictions (after application of inverse link). Can be used to avoid recalculation of predictions over and over if the functions is to be repeatedly called for different `v` and predictions are computationally expensive to make. Not implemented for multiflashlight.
`pd_evaluate_at`	Vector with values of `v` used to evaluate the profile. Only relevant for type = "partial dependence" and "ale".
`pd_grid`	A `data.frame` with grid values, e.g., generated by `expand.grid()`. Only used for type = "partial dependence".
`pd_indices`	A vector of row numbers to consider in calculating partial dependence profiles and "ale".
`pd_n_max`	Maximum number of ICE profiles to calculate (will be randomly picked from `data`) for partial dependence and ALE.
`pd_seed`	Integer random seed used to select ICE profiles for partial dependence and ALE.
`pd_center`	How should ICE curves be centered? Default is "no". Choose "first", "middle", or "last" to 0-center at specific evaluation points. Choose "mean" to center all profiles at the within-group means. Choose "0" to mean-center curves at 0. Only relevant for partial dependence.
`ale_two_sided`	If `TRUE`, `v` is continuous and `breaks` are passed or being calculated, then two-sided derivatives are calculated for ALE instead of left derivatives. More specifically: Usually, local effects at value x are calculated using points in `[x-e, x]`. Set `ale_two_sided = TRUE` to use points in `[x-e/2, x+e/2]`.

Details

Numeric covariables v with more than n_bins disjoint values are binned into n_bins bins. Alternatively, breaks can be provided to specify the binning. For partial dependence profiles (and partly also ALE profiles), this behaviour can be overwritten either by providing a vector of evaluation points (pd_evaluate_at) or an evaluation pd_grid. By the latter we mean a data frame with column name(s) with a (multi-)variate evaluation grid.

For partial dependence, ALE, and prediction profiles, "model", "predict_function", "linkinv" and "data" are required. For response profiles its "y", "linkinv" and "data", and for shap profiles it is just "shap". "data" can be passed on the fly.

Value

An object of class "light_profile" with the following elements:

data A tibble containing results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).
by Names of group by variable.
v The variable(s) evaluated.
type Same as input type. For information only.
stats Same as input stats.

Methods (by class)

light_profile(default): Default method not implemented yet.
light_profile(flashlight): Profiles for flashlight.
light_profile(multiflashlight): Profiles for multiflashlight.

References

Friedman J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29:1189–1232.
Apley D. W. (2016). Visualizing the effects of predictor variables in black box supervised learning models.

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
light_profile(fl, v = "Species")
light_profile(fl, v = "Petal.Width", type = "residual")

flashlight documentation built on May 31, 2023, 6:19 p.m.