Test predicting for out-of-domain time points #273
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EmilHvitfeldt
approved these changes
Dec 4, 2023
| fit(Surv(time, status) ~ ., data = lung) | ||
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| expect_no_error( | ||
| preds <- predict(mod, obs_without_NA, type = "survival", eval_time = eval_time_obs_max_and_ood) |
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This prediction differs in output format than then other ones with this one being
str(preds)
tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
$ .pred: list<tibble[,2]> [1:1]
..$ : tibble [2 × 2] (S3: tbl_df/tbl/data.frame)
.. ..$ .eval_time : num [1:2] 1022 2000
.. ..$ .pred_survival: num [1:2] NA NA
..@ ptype: tibble [0 × 2] (S3: tbl_df/tbl/data.frame)
.. ..$ .eval_time : num(0)
.. ..$ .pred_survival: num(0) opossed to partykit which produces
str(preds)
tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
$ .pred:List of 1
..$ : tibble [2 × 2] (S3: tbl_df/tbl/data.frame)
.. ..$ .eval_time : num [1:2] 1022 2000
.. ..$ .pred_survival: num [1:2] 0.0499 0.0499| fit(Surv(time, status) ~ ., data = lung_orsf) | ||
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| expect_no_error( | ||
| preds <- predict(mod, obs_without_NA, type = "survival", eval_time = eval_time_obs_max_and_ood) |
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closes #10
I've taken "out of domain" to mean eval time points not within the observed range of time points. This PR tests that this does cause censored to error and leaves the prediction value to the individual engines.