Pass objective as passible engine argument for rule_fit() #96
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EmilHvitfeldt
approved these changes
Jan 15, 2026
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| # use 4.0 or 4.1 to check with rtools40's older compiler | ||
| - {os: windows-latest, r: 'oldrel-4'} | ||
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| - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} | ||
| - {os: ubuntu-latest, r: 'release'} | ||
| - {os: ubuntu-latest, r: 'oldrel-1'} | ||
| - {os: ubuntu-latest, r: 'oldrel-2'} | ||
| - {os: ubuntu-latest, r: 'oldrel-3'} | ||
| - {os: ubuntu-latest, r: 'oldrel-4'} |
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if these are related to the GHAs failing on older versions, I would use this trick instead: https://github.com/tidymodels/parsnip/pull/1317/files#diff-9c940e8ad2b7bc4c26ec3da57b94bc00e73e2166cfed689da51a4c59bcc0a310L59-L61
| @@ -1,3 +1,20 @@ | |||
| penalties <- 10^(-5:-1) | |||
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This appears not to be used anywhere
| obs_pred <- rf_m_prob |> dplyr::filter(penalty == i) | ||
| for (i in 1:ncol(rf_prob)) { | ||
| expect_equal(obs_pred[[i]], unname(exp_pred[, i])) | ||
| expect_equal(obs_pred[[i]], unname(exp_pred[, i]), tolerance = 0.4) |
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that is quite the tolerance!
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Related to #95
There are still issues that make it continue to be difficult to make the xrf and rules fits the same.
One other issue is that tidymodels uses a one-hot encoding when creating dummy variables for xgboost while xrf uses the standard full-rank encoding produced by
model.matrix().This PR solves the objective issue and updates some tests for the new xgboost API. The probability-based tests now use a tolerance since their results are very similar. I've removed or commented out tests based on hard class predictions, as we have no way of ensuring that those will be equal (but plan to resolve this discrepancy).