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suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(ggExtra))
suppressPackageStartupMessages(library(forcats))
range01 <- function(x){(x-min(x))/(max(x)-min(x))}
shap_summary_plot<-function(shap_values){
summary_plot <-
shap_values %>% reshape2::melt() %>% group_by(class, variable) %>%
summarise(mean = mean(abs(value))) %>%
arrange(desc(mean)) %>%
ggplot() +
# ggdark::dark_theme_classic() +
theme_classic()+
geom_col(aes(
y = variable,
x = mean,
group = class,
fill = class
), position = "stack") +
ylab("Feature")+
xlab("Mean(|Shap Value|) Average impact on model output magnitude per activity.")+
guides(fill=guide_legend(title="Activity"))
summary_plot
}
shap_summary_plot_perclass<-function(shap_values, class="G",color="#F8766D"){
shap_values <-shap_values %>% as.data.frame() %>% filter(class == {{class}} )
summary_plot <-
shap_values %>% reshape2::melt() %>% group_by(variable) %>%
summarise(mean = mean(abs(value))) %>%
ggplot() +
theme_classic()+
geom_col(aes(
x = mean,
y = fct_reorder(variable,mean)
),
fill = color
) +
ylab("Feature")+
xlab(paste0("Mean(|Shap Value|) Average impact on model output magnitude for activity ", class))+
guides(fill=guide_legend(title="Activity"))
summary_plot
}
shap_beeswarm_plot<-function(shap_values,dataset){
shap_values <- shap_values %>% reshape2::melt()
dataset<-dataset %>% mutate(class=Activity) %>% select(-Activity) %>%
reshape2::melt() %>% group_by(variable) %>%
mutate(value_scale=range01(value))
beeswarm_plot<-cbind(shap_values, feature_value=dataset$value_scale) %>% # filter(class=="GM") %>%
ggplot()+
facet_wrap(~class)+
#ggdark::dark_theme_bw()+
theme_classic()+
geom_hline(yintercept=0,
color = "red", size=0.5)+
ggforce::geom_sina(aes(x=variable,y=value,fill=feature_value),color="black", size=2.4,bins=4,alpha=0.9,shape=22)+
scale_fill_gradient(low = "yellow", high = "red", na.value = NA)+
scale_fill_gradient(low = "skyblue", high = "orange", na.value = NA)+
xlab("Feature")+ylab("SHAP value")+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1))
beeswarm_plot
}
#' Dependency plot for a particular feature. The plot considers
#' activities and FP/TP
#'
#' @param feature a particular feature to calculate
#' @param dataset a dataset with goat information
#' @param shap a shap value dataset for each feature.
#'
#' @return a dependency plot for each activity considering the selected feature
#' @export ggplot object
#'
#' @examples
#'
#' dataset <-
#' readr::read_delim("data/split/seba-caprino_loocv.tsv",
#' delim = '\t')
#' selected_variables <-
#' readr::read_delim(
#' "data/topnfeatures/seba-caprino_selected_features.tsv",
#' col_types = cols(),
#' delim = '\t'
#' )
#' dataset <-
#' dataset %>% select(selected_variables$variable,
#' Anim,
#' Activity)
#' goat_model <- readRDS("models/boost/seba-caprino_model.rds")
#' shap_values <- calculate_shap(dataset,
#' model = goat_model,
#' nsim = 30)
#' dependency_plot_full(feature = "Steps",
#' dataset = dataset,
#' shap = shap_values)
dependency_plot <- function(feature, dataset, shap) {
newdata <- dataset %>% mutate({{ feature }} := range01(!!sym(feature)))
#activities <- c("G", "GM", "W", "R")
activities<-dataset %>% pull(Activity) %>% unique()
plots <- list()
for (activity in activities) {
s <- shap[which(shap$class == activity), 1:18]
x <- newdata[which(newdata$Activity == activity), ]
data <- cbind(
shap = (s %>% as.data.frame %>% select(feature)),
feature = (x %>% select(feature)),
tp = x %>% mutate(tp = ifelse(Activity == predictions, "TP", "FP")) %>%
pull(tp)
)
names(data) <- c("shap", "feature", "tp")
p <- ggplot(data, aes(x = feature)) +
geom_point(aes(y = shap, color = tp), alpha = 0.3, size = 0.8) +
geom_smooth(aes(y = shap),
se = FALSE,
size = 0.5,
linetype = "dashed") +
geom_hline(
yintercept = 0,
color = 'red',
size = 0.5,
alpha = 0.5
) +
xlab(feature) +
labs(title = paste0("Activity ", activity)) +
ylab("SHAP Value") +
ylim(-0.1, 0.4) +
xlim(0, 1) +
theme_light() +
theme(legend.position = 'none')
p1 <-
ggMarginal(
p,
type = "histogram",
fill = 'gray',
color = 'white',
size = 10,
xparams = list(bins = 25),
yparams = list(bins = 15)
) #,margins='x')
plots[[activity]] <- p1
}
#plots
do.call(grid.arrange, c(plots, ncol = 4))
}
#' Dependency plot for a particular feature on a particular animal.
#' The plot considers activities and FP/TP
#'
#' @param feature a particular feature to calculate
#' @param dataset a dataset with goat information
#' @param shap a shap value dataset for each feature.
#' @param anim the id of the animal
#' @return a dependency plot for each activity considering the selected feature
#' @export ggplot object
#'
#' @examples
#'
#' dataset <-
#' readr::read_delim("data/split/seba-caprino_loocv.tsv",
#' delim = '\t')
#' selected_variables <-
#' readr::read_delim(
#' "data/topnfeatures/seba-caprino_selected_features.tsv",
#' col_types = cols(),
#' delim = '\t'
#' )
#' dataset <-
#' dataset %>% select(selected_variables$variable,
#' Anim,
#' Activity)
#' goat_model <- readRDS("models/boost/seba-caprino_model.rds")
#' shap_values <- calculate_shap(dataset,
#' model = goat_model,
#' nsim = 30)
#' dependency_plot_anim(feature = "Steps",
#' dataset = dataset,
#' shap = shap_values,
#' anim = 'a13')
dependency_plot_anim<- function(feature,dataset,shap,anim){
newdata <- dataset %>% mutate({{feature}} := range01(!!sym(feature)))
plots<-list()
activities<-newdata %>% filter(Anim == anim) %>% pull(Activity) %>% unique()
for (activity in activities) {
s <- shap[which(shap$class == activity &
shap$Anim == anim
), 1:18]
x <- newdata[which(newdata$Activity == activity &
newdata$Anim == anim
),]
data <- cbind(shap=(s %>% as.data.frame %>% select(feature)),
feature = (x %>% select(feature)),
tp = x %>% mutate(tp=ifelse(Activity == predictions,"TP","FP")) %>% pull(tp) )
names(data)<-c("shap","feature","tp")
p <- ggplot(data, aes(x = feature)) +
geom_point(aes(y = shap, color = tp), alpha = 0.3, size = 1.8) +
geom_smooth(aes(y = shap),
se = FALSE,
size = 0.5,
linetype = "dashed") +
geom_hline(
yintercept = 0,
color = 'red',
size = 0.5,
alpha = 0.5
) +
xlab(feature) +
labs(title = paste0("Activity ", activity)) +
ylab("SHAP Value") +
ylim(-0.1, 0.4) +
xlim(0, 1) +
theme_light() +
theme(legend.position = 'none')
p1 <-
ggMarginal(
p,
type = "histogram",
fill = 'gray',
color = 'white',
size = 15,
xparams = list(bins = 25),
yparams = list(bins = 15)
) #,margins='x')
plots[[activity]] <- p1
}
do.call(grid.arrange, c(plots, ncol = length(activities)))
}
#' contribution plot for SHAP values
#'
#' @param shap shap values for a particular class, animal, etc.
#' @param num_row the row number of the observation to show
#'
#' @return ggplot object
#' @export
#'
#' @examples
#'
#' shap_values_G <- calculate_shap_class(
#' dataset = dataset,
#' new_data = newdata,
#' model= model,
#' nsim = 100,
#' function_class = p_function_G,
#' class_name ="G")
#' p1 <- contribution_plot(shap_values_G,num_row = 1) +
#' labs(title="Anim a13: class G (FN)", subtitle = "SHAP analysis for class G")
#'
contribution_plot <-function(s, num_row = 1){
s<-s[num_row,]
s <- data.frame(
Variable = names(s[,1:15]),
Importance = apply(s[,1:15], MARGIN = 2, FUN = function(x) sum(x))
)
ggplot(s, aes(Variable, Importance, Importance,fill=Importance) )+
geom_col() +
coord_flip() +
xlab("") +
ylab("Shapley value")+
theme_classic()+
theme(legend.position = 'none')
}
contribution_plot_w_feature <-function(s, f, num_row = 1){
d <- data.frame(
variable = names(s[num_row,1:15]),
importance = apply(s[num_row,1:15], MARGIN = 2, FUN = function(x) sum(x)),
value = apply(f[num_row,1:15], MARGIN = 2, FUN = function(x) sum(x))
)
ggplot(d, aes(variable, importance, value,fill=value) )+
geom_col() +
geom_text(aes(label=round(value,digits = 2),hjust = 1.0),size=2)+
coord_flip() +
xlab("") +
ylab("Shapley value")+
scale_fill_gradient(low = 'lightgray', high = 'skyblue')+
theme_classic()+
theme(legend.position = 'none')
}
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