Using Python Packages in R (with "reticulate")

  machine-learning, python, r, reticulate

I am trying to follow this tutorial over here : : https://hfshr.netlify.app/posts/2020-06-07-variable-inportance-with-fastshap/

This tutorial is about using a machine learning algorithm called "SHAP" that attempts to provide the user with a method to interpret the results of complicated "blackbox" style algorithms.

Following the tutorial, I was able to get everything to work – except the "force plots" at the end. I have provided the code I am using at the bottom. Could someone please help me in figuring out why these force plots are not working?

library(modeldata)
library(tidymodels)
library(tidyverse)
library(doParallel)
library(probably)
library(gt)

data("credit_data")

data("credit_data")

credit_data <- credit_data %>%
  drop_na()

set.seed(12)

# initial split
split <- initial_split(credit_data, prop = 0.75, strata = "Status")

# train/test sets
train <- training(split)
test <- testing(split)

rec <- recipe(Status ~ ., data = train) %>%
  step_bagimpute(Home, Marital, Job, Income, Assets, Debt) %>%
  step_dummy(Home, Marital, Records, Job, one_hot = T)

# Just some sensible values, not optimised by any means!
mod <- boost_tree(trees = 500,
                  mtry = 6,
                  min_n = 10,
                  tree_depth = 5) %>%
  set_engine("xgboost") %>%
  set_mode("classification")

xgboost_wflow <- workflow() %>%
  add_recipe(rec) %>%
  add_model(mod) %>%
  fit(train)

xg_res <- last_fit(xgboost_wflow,
                   split,
                   metrics = metric_set(roc_auc, pr_auc, accuracy))

preds <- xg_res %>%
  collect_predictions()

xg_res %>%
  collect_metrics()

library(vip)

# Get our model object
xg_mod <- pull_workflow_fit(xgboost_wflow)

vip(xg_mod$fit)

library(fastshap)

# Apply the preprocessing steps with prep and juice to the training data
X <- prep(rec, train) %>%
  juice() %>%
  select(-Status) %>%
  as.matrix()

# Compute shapley values
shap <- explain(xg_mod$fit, X = X, exact = TRUE)

# Create a dataframe of our training data
feat <- prep(rec, train) %>%
  juice()

autoplot(shap,
         type = "dependence",
         feature = "Amount",
         X = feat,
         smooth = TRUE,
         color_by = "Status")

predict(xgboost_wflow, train, type = "prob") %>%
  rownames_to_column("rowid") %>%
  filter(.pred_bad == min(.pred_bad) | .pred_bad == max(.pred_bad)) %>%
  gt()%>%
  fmt_number(columns = 2:3,
             decimals = 3)

library(patchwork)
p1 <- autoplot(shap, type = "contribution", row_num = 1541) +
  ggtitle("Likely bad")

p2 <- autoplot(shap, type = "contribution", row_num = 1806) +
  ggtitle("Likely good")

p1+p2

# here is the error (prior to running this code, I ran "pip install shap" in conda)

force_plot(object = shap[1541,],
           feature_values = X[1541,],
           display = "html",
           link = "logit")

Error in py_call_impl(callable, dots$args, dots$keywords) :
  TypeError: save_html() got an unexpected keyword argument 'plot_html'

Thank you

Source: Python Questions

LEAVE A COMMENT