R/plot_ceteris_paribus_2d.R In ingredients: Effects and Importances of Model Ingredients

Documented in plot.ceteris_paribus_2d_explainer

```#' Plot Ceteris Paribus 2D Explanations
#'
#' This function plots What-If Plots for a single prediction / observation.
#'
#' @param x a ceteris paribus explainer produced with the \code{ceteris_paribus_2d()} function
#' @param ... currently will be ignored
#' @param facet_ncol number of columns for the \code{\link[ggplot2]{facet_wrap}}
#' @param add_raster if \code{TRUE} then \code{geom_raster} will be added to present levels with diverging colors
#' @param add_contour if \code{TRUE} then \code{geom_contour} will be added to present contours
#' @param bins number of contours to be added
#' @param add_observation if \code{TRUE} then \code{geom_point} will be added to present observation that is explained
#' @param pch character, symbol used to plot observations
#' @param size numeric, size of individual datapoints
#'
#' @references Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. \url{https://ema.drwhy.ai/}
#'
#' @return a \code{ggplot2} object
#'
#' @examples
#' library("DALEX")
#' library("ingredients")
#' library("ranger")
#'
#' \donttest{
#' apartments_rf_model <- ranger(m2.price ~., data = apartments)
#'
#' explainer_rf <- explain(apartments_rf_model,
#'                         data = apartments_test[,-1],
#'                         y = apartments_test[,1],
#'                         verbose = FALSE)
#'
#' new_apartment <- apartments_test[1,]
#' new_apartment
#'
#' wi_rf_2d <- ceteris_paribus_2d(explainer_rf, observation = new_apartment)
#'
#' plot(wi_rf_2d)
#'
#' # HR data
#' model <- ranger(status ~ gender + age + hours + evaluation + salary, data = HR,
#'                 probability = TRUE)
#'
#' pred1 <- function(m, x)   predict(m, x)\$predictions[,1]
#'
#' explainer_rf_fired <- explain(model,
#'                               data = HR[,1:5],
#'                               y = as.numeric(HR\$status == "fired"),
#'                               predict_function = pred1,
#'                               label = "fired")
#' new_emp <- HR[1,]
#' new_emp
#'
#' wi_rf_2d <- ceteris_paribus_2d(explainer_rf_fired, observation = new_emp)
#'
#' plot(wi_rf_2d)
#' }
#'
#' @export
plot.ceteris_paribus_2d_explainer <- function(x, ..., facet_ncol = NULL, add_raster = TRUE,
pch = "+", size = 6) {
all_responses <- x
class(all_responses) <- "data.frame"

midpoint <- mean(all_responses\$y_hat, na.rm = TRUE)
new_x1 <- y_hat <- new_x2 <- NULL

pred <- attr(x, "prediction")\$observation
all_pairs <- unique(all_responses[,c("vname1","vname2")])
observations <- lapply(1:nrow(all_pairs), function(i) {
pair <- all_pairs[i,]
data.frame(vname1 = pair\$vname1,
vname2 = pair\$vname2,
new_x1 = pred[,as.character(pair\$vname1)],
new_x2 = pred[,as.character(pair\$vname2)],
y_hat = midpoint)
})
observation <- do.call(rbind, observations)

pl <- ggplot(all_responses, aes(new_x1, new_x2, fill = y_hat, z = y_hat)) +
facet_wrap(vname1 ~ vname2, scales = "free", ncol = facet_ncol) +
xlab("") + ylab("") +
scale_fill_gradient2(name = 'Prediction', midpoint = midpoint,
low = "#2cd9dd", high = "#ff4940", mid = "#f0f0f4")
# or use scale_fill_gradient with midpoint

pl <- pl + geom_raster(data = all_responses)
}

pl <- pl + geom_contour(data = all_responses, color = "white", alpha = 0.5, bins = bins)
}

pl <- pl + geom_point(data = observation, fill = "black", pch = pch, size = size)
}
pl + theme_drwhy_blank()
}

theme_drwhy_blank <- function() {
theme_bw(base_line_size = 0) %+replace%
theme(axis.ticks = element_blank(), legend.background = element_blank(),
legend.key = element_blank(), panel.background = element_blank(),
panel.border = element_blank(), strip.background = element_blank(),
plot.background = element_blank(), complete = TRUE,
legend.direction = "horizontal", legend.position = "top",
plot.title = element_text(color = "#371ea3", size = 16, hjust = 0),
plot.subtitle = element_text(color = "#371ea3", size = 14, hjust = 0),
axis.line.x = element_line(color = "white"),
axis.ticks.x = element_line(color = "white"),
axis.title = element_text(color = "#371ea3"),
axis.text = element_text(color = "#371ea3", size = 10),
strip.text = element_text(color = "#371ea3", size = 12, hjust = 0, margin = margin(0, 0, 1, 0)))
}
```

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