Shapley value bar plots for several explanation objects
Make plots to visualize and compare the estimated Shapley values for a list of explain() objects applied to the same data and model. For group-wise Shapley values, the features values plotted are the mean feature values for all features in each group.
plot_SV_several_approaches( explanation_list, index_explicands = NULL, index_explicands_sort = FALSE, only_these_features = NULL, plot_phi0 = FALSE, digits = 4, add_zero_line = FALSE, axis_labels_n_dodge = NULL, axis_labels_rotate_angle = NULL, horizontal_bars = TRUE, facet_scales = "free", facet_ncol = 2, geom_col_width = 0.85, brewer_palette = NULL, include_group_feature_means = FALSE )
explanation_list: A list of explain() objects applied to the same data and model. If the entries in the list are named, then the function use these names. Otherwise, they default to the approach names (with integer suffix for duplicates) for the explanation objects in explanation_list.
index_explicands: Integer vector. Which of the explicands (test observations) to plot. E.g. if you have explained 10 observations using explain(), you can generate a plot for the first 5 observations/explicands and the 10th by setting index_x_explain = c(1:5, 10). The argument index_explicands_sort must be FALSE to plot the explicand in the order specified in index_x_explain.
index_explicands_sort: Boolean. If FALSE (default), then shapr plots the explicands in the order specified in index_explicands. If TRUE, then shapr sort the indices in increasing order based on their id.
only_these_features: String vector. Containing the names of the features which are to be included in the bar plots.
plot_phi0: Boolean. If we are to include the in the bar plots or not.
digits: Integer. Number of significant digits to use in the feature description. Applicable for plot_type "bar" and "waterfall"
add_zero_line: Boolean. If we are to add a black line for a feature contribution of 0.
axis_labels_n_dodge: Integer. The number of rows that should be used to render the labels. This is useful for displaying labels that would otherwise overlap.
axis_labels_rotate_angle: Numeric. The angle of the axis label, where 0 means horizontal, 45 means tilted, and 90 means vertical. Compared to setting the angle inggplot2::theme() / ggplot2::element_text(), this also uses some heuristics to automatically pick the hjust and vjust that you probably want.
horizontal_bars: Boolean. Flip Cartesian coordinates so that horizontal becomes vertical, and vertical, horizontal. This is primarily useful for converting geoms and statistics which display y conditional on x, to x conditional on y. See ggplot2::coord_flip().
facet_scales: Should scales be free ("free", the default), fixed ("fixed"), or free in one dimension ("free_x", "free_y")? The user has to change the latter manually depending on the value of horizontal_bars.
facet_ncol: Integer. The number of columns in the facet grid. Default is facet_ncol = 2.
geom_col_width: Numeric. Bar width. By default, set to 85% of the ggplot2::resolution() of the data.
brewer_palette: String. Name of one of the color palettes from RColorBrewer::RColorBrewer(). If NULL, then the function uses the default ggplot2::ggplot() color scheme. The following palettes are available for use with these scales:
include_group_feature_means: Logical. Whether to include the average feature value in a group on the y-axis or not. If FALSE (default), then no value is shown for the groups. If TRUE, then shapr includes the mean of the features in each group.
A ggplot2::ggplot() object.
## Not run: if (requireNamespace("xgboost", quietly = TRUE) && requireNamespace("ggplot2", quietly = TRUE)) { # Get the data data("airquality") data <- data.table::as.data.table(airquality) data <- data[complete.cases(data), ] # Define the features and the response x_var <- c("Solar.R", "Wind", "Temp", "Month") y_var <- "Ozone" # Split data into test and training data set ind_x_explain <- 1:12 x_train <- data[-ind_x_explain, ..x_var] y_train <- data[-ind_x_explain, get(y_var)] x_explain <- data[ind_x_explain, ..x_var] # Fitting a basic xgboost model to the training data model <- xgboost::xgboost( data = as.matrix(x_train), label = y_train, nround = 20, verbose = FALSE ) # Specifying the phi_0, i.e. the expected prediction without any features phi0 <- mean(y_train) # Independence approach explanation_independence <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "independence", phi0 = phi0, n_MC_samples = 1e2 ) # Empirical approach explanation_empirical <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "empirical", phi0 = phi0, n_MC_samples = 1e2 ) # Gaussian 1e1 approach explanation_gaussian_1e1 <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "gaussian", phi0 = phi0, n_MC_samples = 1e1 ) # Gaussian 1e2 approach explanation_gaussian_1e2 <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = "gaussian", phi0 = phi0, n_MC_samples = 1e2 ) # Combined approach explanation_combined <- explain( model = model, x_explain = x_explain, x_train = x_train, approach = c("gaussian", "ctree", "empirical"), phi0 = phi0, n_MC_samples = 1e2 ) # Create a list of explanations with names explanation_list <- list( "Ind." = explanation_independence, "Emp." = explanation_empirical, "Gaus. 1e1" = explanation_gaussian_1e1, "Gaus. 1e2" = explanation_gaussian_1e2, "Combined" = explanation_combined ) # The function uses the provided names. plot_SV_several_approaches(explanation_list) # We can change the number of columns in the grid of plots and add other visual alterations plot_SV_several_approaches(explanation_list, facet_ncol = 3, facet_scales = "free_y", add_zero_line = TRUE, digits = 2, brewer_palette = "Paired", geom_col_width = 0.6 ) + ggplot2::theme_minimal() + ggplot2::theme(legend.position = "bottom", plot.title = ggplot2::element_text(size = 0)) # We can specify which explicands to plot to get less chaotic plots and make the bars vertical plot_SV_several_approaches(explanation_list, index_explicands = c(1:2, 5, 10), horizontal_bars = FALSE, axis_labels_rotate_angle = 45 ) # We can change the order of the features by specifying the # order using the `only_these_features` parameter. plot_SV_several_approaches(explanation_list, index_explicands = c(1:2, 5, 10), only_these_features = c("Temp", "Solar.R", "Month", "Wind") ) # We can also remove certain features if we are not interested in them # or want to focus on, e.g., two features. The function will give a # message to if the user specifies non-valid feature names. plot_SV_several_approaches(explanation_list, index_explicands = c(1:2, 5, 10), only_these_features = c("Temp", "Solar.R"), plot_phi0 = TRUE ) } ## End(Not run)
Lars Henry Berge Olsen
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