Implement additive force plots about fastshap HOT 1 CLOSED

# Load required packages
library(fastshap)  # for fast (approximate) Shapley values
library(mlbench)   # for Friedman 1 benchmark data set
library(ranger)    # for fast random forest algorithm

# Simulate training data
set.seed(101)
trn <- as.data.frame(mlbench.friedman1(3000))
X <- subset(trn, select = -y)  # feature columns only

# Fit a random forest
set.seed(102)
rfo <- ranger(y ~ ., data =  trn)

# Prediction wrapper
pfun <- function(object, newdata) {
  predict(object, data = newdata)$predictions
}

# Compute fast (approximate) Shapley values using 10 Monte Carlo repetitions
system.time({  # estimate run time
  set.seed(5038)
  ex <- explain(rfo, X = X, pred_wrapper = pfun, nsim = 10)
})

# Construct force plot
force_plot(ex[1, ], prediction = pfun(rfo, X[1, ]), 
           baseline = mean(pfun(rfo, X)), feature_values = X[1, ])