plot_shap_dependence-methods: Create SHAP dependence plot.
In familiar: End-to-End Automated Machine Learning and Model Evaluation

plot_shap_dependence

R Documentation

Create SHAP dependence plot.

Description

This method creates a SHAP dependence plot that shows the dependence of the SHAP value of a feature against its value.

Usage

plot_shap_dependence(
  object,
  draw = FALSE,
  dir_path = NULL,
  split_by = NULL,
  facet_by = NULL,
  facet_wrap_cols = NULL,
  ggtheme = NULL,
  discrete_palette = NULL,
  gradient_palette = NULL,
  x_label = waiver(),
  y_label = waiver(),
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5L,
  x_breaks = NULL,
  y_range = NULL,
  y_n_breaks = 5L,
  y_breaks = NULL,
  shap_feature = NULL,
  interaction_feature = NULL,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'ANY'
plot_shap_dependence(
  object,
  draw = FALSE,
  dir_path = NULL,
  split_by = NULL,
  facet_by = NULL,
  facet_wrap_cols = NULL,
  ggtheme = NULL,
  discrete_palette = NULL,
  gradient_palette = NULL,
  x_label = waiver(),
  y_label = waiver(),
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5L,
  x_breaks = NULL,
  y_range = NULL,
  y_n_breaks = 5L,
  y_breaks = NULL,
  shap_feature = NULL,
  interaction_feature = NULL,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'familiarCollection'
plot_shap_dependence(
  object,
  draw = FALSE,
  dir_path = NULL,
  split_by = NULL,
  facet_by = NULL,
  facet_wrap_cols = NULL,
  ggtheme = NULL,
  discrete_palette = NULL,
  gradient_palette = NULL,
  x_label = waiver(),
  y_label = waiver(),
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5L,
  x_breaks = NULL,
  y_range = NULL,
  y_n_breaks = 5L,
  y_breaks = NULL,
  shap_feature = NULL,
  interaction_feature = NULL,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

Arguments

`object`	`familiarCollection` object, or one or more `familiarData` objects, that will be internally converted to a `familiarCollection` object. It is also possible to provide a `familiarEnsemble` or one or more `familiarModel` objects together with the data from which data is computed prior to export. Paths to such files can also be provided. Additionally, some `familiarData` objects can be created from prediction tables (`familiarDataElementPredictionTable`). Other `familiarData` objects can be created from data (`dataObject`, or `data.table`). Please check details for more information.
`draw`	(optional) Draws the plot if TRUE.
`dir_path`	(optional) Path to the directory where created SHAP dependence plots are saved to. Output is saved in the `explanation` subdirectory. If `NULL` no figures are saved, but are returned instead.
`split_by`	(optional) Splitting variables. This refers to column names on which datasets are split. A separate figure is created for each split. See details for available variables.
`facet_by`	(optional) Variables used to determine how and if facets of each figure appear. In case the `facet_wrap_cols` argument is `NULL`, the first variable is used to define columns, and the remaing variables are used to define rows of facets. The variables cannot overlap with those provided to the `split_by` argument, but may overlap with other arguments. See details for available variables.
`facet_wrap_cols`	(optional) Number of columns to generate when facet wrapping. If NULL, a facet grid is produced instead.
`ggtheme`	(optional) `ggplot` theme to use for plotting.
`discrete_palette`	(optional) Divergent or sequential palette used to colour the elements of dependence plots for interactions with another categorical feature. `familiar` has a default palette. Other palettes are supported by the `paletteer` package, `grDevices::palette.pals()` (requires R >= 4.0.0), `grDevices::hcl.pals()` (requires R >= 3.6.0) and `rainbow`, `heat.colors`, `terrain.colors`, `topo.colors` and `cm.colors`, which correspond to the palettes of the same name in `grDevices`. You may also specify your own palette by providing a vector of colour names listed by `grDevices::colors()` or through hexadecimal RGB strings. If no `interaction_feature` is set, or is a numerical feature, the gradient palette is not used.
`gradient_palette`	(optional) Divergent or sequential palette used to colour the elements of dependence plots for interactions with another numeric feature. `familiar` has a default palette. Other palettes are supported by the `paletteer` package, `grDevices::palette.pals()` (requires R >= 4.0.0), `grDevices::hcl.pals()` (requires R >= 3.6.0) and `rainbow`, `heat.colors`, `terrain.colors`, `topo.colors` and `cm.colors`, which correspond to the palettes of the same name in `grDevices`. You may also specify your own palette by providing a vector of colour names listed by `grDevices::colors()` or through hexadecimal RGB strings. If no `interaction_feature` is set, or is a categorical feature, the gradient palette is not used.
`x_label`	(optional) Label to provide to the x-axis. If NULL, no label is shown.
`y_label`	(optional) Label to provide to the y-axis. If NULL, no label is shown.
`legend_label`	(optional) Label to provide to the legend. If NULL, the legend will not have a name.
`plot_title`	(optional) Label to provide as figure title. If NULL, no title is shown.
`plot_sub_title`	(optional) Label to provide as figure subtitle. If NULL, no subtitle is shown.
`caption`	(optional) Label to provide as figure caption. If NULL, no caption is shown.
`x_range`	(optional) Value range for the x-axis.
`x_n_breaks`	(optional) Number of breaks to show on the x-axis of the plot. `x_n_breaks` is used to determine the `x_breaks` argument in case it is unset.
`x_breaks`	(optional) Break points on the x-axis of the plot.
`y_range`	(optional) Value range for the y-axis.
`y_n_breaks`	(optional) Number of breaks to show on the y-axis of the plot. `y_n_breaks` is used to determine the `y_breaks` argument in case it is unset.
`y_breaks`	(optional) Break points on the y-axis of the plot.
`shap_feature`	(optional) Feature(s) whose SHAP values are used for creating the SHAP dependence plot.
`interaction_feature`	(optional) Feature(s) whose values are used to colour points of the `shap_feature`.
`width`	(optional) Width of the plot. A default value is derived from the number of facets.
`height`	(optional) Height of the plot. A default value is derived from the number of features and the number of facets.
`units`	(optional) Plot size unit. Either `cm` (default), `mm` or `⁠in⁠`.
`export_collection`	(optional) Exports the collection if TRUE.
`...`	Arguments passed on to `extract_performance`, `as_familiar_collection`, `ggplot2::ggsave` `data` A `dataObject` object, `data.table` or `data.frame` that constitutes the data that are assessed. `is_pre_processed` Flag that indicates whether the data was already pre-processed externally, e.g. normalised and clustered. Only used if the `data` argument is a `data.table` or `data.frame`. `cl` Cluster created using the `parallel` package. This cluster is then used to speed up computation through parallellisation. `evaluation_times` One or more time points that are used for in analysis of survival problems when data has to be assessed at a set time, e.g. calibration. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. Only used for `survival` outcomes. `ensemble_method` Method for ensembling predictions from models for the same sample. Available methods are: `median` (default): Use the median of the predicted values as the ensemble value for a sample. `mean`: Use the mean of the predicted values as the ensemble value for a sample. `metric` One or more metrics for assessing model performance. See the vignette on performance metrics for the available metrics. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. `verbose` Flag to indicate whether feedback should be provided on the computation and extraction of various data elements. `message_indent` Number of indentation steps for messages shown during computation and extraction of various data elements. `detail_level` (optional) Sets the level at which results are computed and aggregated. `ensemble`: Results are computed at the ensemble level, i.e. over all models in the ensemble. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the model performance of the ensemble model for each bootstrap. `hybrid` (default): Results are computed at the level of models in an ensemble. This means that, for example, bias-corrected estimates of model performance are directly computed using the models in the ensemble. If there are at least 20 trained models in the ensemble, performance is computed for each model, in contrast to `ensemble` where performance is computed for the ensemble of models. If there are less than 20 trained models in the ensemble, bootstraps are created so that at least 20 point estimates can be made. `model`: Results are computed at the model level. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the performance of the model for each bootstrap. Note that each level of detail has a different interpretation for bootstrap confidence intervals. For `ensemble` and `model` these are the confidence intervals for the ensemble and an individual model, respectively. That is, the confidence interval describes the range where an estimate produced by a respective ensemble or model trained on a repeat of the experiment may be found with the probability of the confidence level. For `hybrid`, it represents the range where any single model trained on a repeat of the experiment may be found with the probability of the confidence level. By definition, confidence intervals obtained using `hybrid` are at least as wide as those for `ensemble`. `hybrid` offers the correct interpretation if the goal of the analysis is to assess the result of a single, unspecified, model. `hybrid` is generally computationally less expensive then `ensemble`, which in turn is somewhat less expensive than `model`. A non-default `detail_level` parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. `list("auc_data"="ensemble", "model_performance"="hybrid")`. This parameter can be set for the following data elements: `auc_data`, `decision_curve_analyis`, `model_performance`, `permutation_vimp`, `ice_data`, `prediction_data` and `confusion_matrix`. If results are computed from 10 samples or fewer, `ensemble` is automatically used. This prevents issues where evaluation steps do not have a required minimum number of samples for `hybrid` or `model`. `estimation_type` (optional) Sets the type of estimation that should be possible. This has the following options: `point`: Point estimates. `bias_correction` or `bc`: Bias-corrected estimates. A bias-corrected estimate is computed from (at least) 20 point estimates, and `familiar` may bootstrap the data to create them. `bootstrap_confidence_interval` or `bci` (default): Bias-corrected estimates with bootstrap confidence intervals (Efron and Hastie, 2016). The number of point estimates required depends on the `confidence_level` parameter, and `familiar` may bootstrap the data to create them. As with `detail_level`, a non-default `estimation_type` parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. `list("auc_data"="bci", "model_performance"="point")`. This parameter can be set for the following data elements: `auc_data`, `decision_curve_analyis`, `model_performance`, `permutation_vimp`, `ice_data`, and `prediction_data`. `aggregate_results` (optional) Flag that signifies whether results should be aggregated during evaluation. If `estimation_type` is `bias_correction` or `bc`, aggregation leads to a single bias-corrected estimate. If `estimation_type` is `bootstrap_confidence_interval` or `bci`, aggregation leads to a single bias-corrected estimate with lower and upper boundaries of the confidence interval. This has no effect if `estimation_type` is `point`. The default value is equal to `TRUE` except when assessing metrics to assess model performance, as the default violin plot requires underlying data. As with `detail_level` and `estimation_type`, a non-default `aggregate_results` parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. `list("auc_data"=TRUE, , "model_performance"=FALSE)`. This parameter exists for the same elements as `estimation_type`. `confidence_level` (optional) Numeric value for the level at which confidence intervals are determined. In the case bootstraps are used to determine the confidence intervals bootstrap estimation, `familiar` uses the rule of thumb `n = 20 / ci.level` to determine the number of required bootstraps. The default value is `0.95`. `bootstrap_ci_method` (optional) Method used to determine bootstrap confidence intervals (Efron and Hastie, 2016). The following methods are implemented: `percentile` (default): Confidence intervals obtained using the percentile method. `bc`: Bias-corrected confidence intervals. Note that the standard method is not implemented because this method is often not suitable due to non-normal distributions. The bias-corrected and accelerated (BCa) method is not implemented yet. `familiar_data_names` Names of the dataset(s). Only used if the `object` parameter is one or more `familiarData` objects. `collection_name` Name of the collection. `device` Device to use. Can either be a device function (e.g. png), or one of "eps", "ps", "tex" (pictex), "pdf", "jpeg", "tiff", "png", "bmp", "svg" or "wmf" (windows only). If `NULL` (default), the device is guessed based on the `filename` extension. `scale` Multiplicative scaling factor. `dpi` Plot resolution. Also accepts a string input: "retina" (320), "print" (300), or "screen" (72). Only applies when converting pixel units, as is typical for raster output types. `limitsize` When `TRUE` (the default), `ggsave()` will not save images larger than 50x50 inches, to prevent the common error of specifying dimensions in pixels. `bg` Background colour. If `NULL`, uses the `plot.background` fill value from the plot theme. `create.dir` Whether to create new directories if a non-existing directory is specified in the `filename` or `path` (`TRUE`) or return an error (`FALSE`, default). If `FALSE` and run in an interactive session, a prompt will appear asking to create a new directory when necessary.

Details

This function creates SHAP dependence plots, which show how the marginal contributions of a feature to the predicted value depend on its value.

Available splitting variables are: vimp_method, learner, data_set, evaluation_time (survival outcome only) and positive_class (categorical outcomes). The default is to facet by evaluation_time or positive_class, and split by vimp_method, learner and data_set. color_by is not used.

Labelling methods such as set_vimp_method_names or set_learner_names can be applied to the familiarCollection object to update labels, and order the output in the figure.