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Pieter-Jan Inghelbrecht
Pieter-Jan Inghelbrecht

Reputation: 21

Integration of Variable importance plots within the tidy modelling framework

Could somebody show me how to generate permutation-based variable implots within the tidy modelling framework? Currently, I have this:

library(tidymodels)

# variable importance
final_fit_train %>%
  pull_workflow_fit() %>%
  vip(geom = "point",
      aesthetics = list(color = cbPalette[4],
                        fill = cbPalette[4])) +
  THEME +
  ggtitle("Elastic Net")

which generates this:

enter image description here

However, I would like to have something like this

enter image description here

It's not clear to me how the rather new tidy modelling framework integrates with the current VIP package. Anybody that could help. Thanks!

https://koalaverse.github.io/vip/articles/vip.html (API of the VIP package).

Upvotes: 2

Views: 2417

Answers (1)

Julia Silge
Julia Silge

Reputation: 11613

To compute variable importance using permutation, you need just a few more pieces to put together, compared to using model-dependent variable importance.

Let's look at an example for an SVM model, which does not have model-dependent variable importance score.

library(tidymodels)
#> ── Attaching packages ──────────────────────── tidymodels 0.1.1 ──
#> ✓ broom     0.7.0      ✓ recipes   0.1.13
#> ✓ dials     0.0.8      ✓ rsample   0.0.7 
#> ✓ dplyr     1.0.0      ✓ tibble    3.0.3 
#> ✓ ggplot2   3.3.2      ✓ tidyr     1.1.0 
#> ✓ infer     0.5.3      ✓ tune      0.1.1 
#> ✓ modeldata 0.0.2      ✓ workflows 0.1.2 
#> ✓ parsnip   0.1.2      ✓ yardstick 0.0.7 
#> ✓ purrr     0.3.4
#> ── Conflicts ─────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter()  masks stats::filter()
#> x dplyr::lag()     masks stats::lag()
#> x recipes::step()  masks stats::step()

data("hpc_data")

svm_spec <- svm_poly(degree = 1, cost = 1/4) %>%
  set_engine("kernlab") %>%
  set_mode("regression")

svm_fit <- workflow() %>%
  add_model(svm_spec) %>%
  add_formula(compounds ~ .) %>%
  fit(hpc_data)

svm_fit
#> ══ Workflow [trained] ════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: svm_poly()
#> 
#> ── Preprocessor ──────────────────────────────────────────────────
#> compounds ~ .
#> 
#> ── Model ─────────────────────────────────────────────────────────
#> Support Vector Machine object of class "ksvm" 
#> 
#> SV type: eps-svr  (regression) 
#>  parameter : epsilon = 0.1  cost C = 0.25 
#> 
#> Polynomial kernel function. 
#>  Hyperparameters : degree =  1  scale =  1  offset =  1 
#> 
#> Number of Support Vectors : 2827 
#> 
#> Objective Function Value : -284.7255 
#> Training error : 0.835421

Our model is now trained, so it's ready for computing variable importance. Notice a couple of steps:

  • You pull() the fitted model object out of the workflow.
  • You have to specify the target/outcome variable, compounds.
  • In this case, we need to pass both the original training data (use training data here, not testing data) and the right underlying function for predicting (this might be tricky to figure out in some cases but for most packages will just be predict()).
library(vip)
#> 
#> Attaching package: 'vip'
#> The following object is masked from 'package:utils':
#> 
#>     vi
svm_fit %>%
  pull_workflow_fit() %>%
  vip(method = "permute", 
      target = "compounds", metric = "rsquared",
      pred_wrapper = kernlab::predict, train = hpc_data)

Created on 2020-07-17 by the reprex package (v0.3.0)

You can increase nsim here to do this more than once.

Upvotes: 3

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