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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| title: "Tips to speed up computation" | ||
| output: rmarkdown::html_vignette | ||
| vignette: > | ||
| %\VignetteIndexEntry{Tips to speed up computation} | ||
| %\VignetteEngine{knitr::rmarkdown} | ||
| %\VignetteEncoding{UTF-8} | ||
| --- | ||
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| ```{r, include = FALSE} | ||
| knitr::opts_chunk$set( | ||
| collapse = TRUE, | ||
| comment = "#>" | ||
| ) | ||
| ``` | ||
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| ```{r setup} | ||
| library(aorsf) | ||
| ``` | ||
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| ## Go faster | ||
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| Analyses can slow to a crawl when models need hours to run. In this article you will find a few tricks to prevent this bottleneck when using `orsf()`. We'll use the `flchain` data from `survival` to demonstrate. | ||
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| ```{r} | ||
| data("flchain", package = 'survival') | ||
| flc <- flchain | ||
| # do this to avoid orsf() throwing an error about time to event = 0 | ||
| flc <- flc[flc$futime > 0, ] | ||
| # modify names | ||
| names(flc)[names(flc) == 'futime'] <- 'time' | ||
| names(flc)[names(flc) == 'death'] <- 'status' | ||
| ``` | ||
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| Our `flc` data has `r nrow(flc)` rows and `r ncol(flc)` columns: | ||
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| ```{r} | ||
| head(flc) | ||
| ``` | ||
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| ## Use `orsf_control_fast()` | ||
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| This is the default `control` value for `orsf()` and its run-time compared to other approaches can be striking. For example: | ||
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| ```{r} | ||
| time_fast <- system.time( | ||
| expr = orsf(flc, time+status~., na_action = 'na_impute_meanmode', | ||
| control = orsf_control_fast(), n_tree = 10) | ||
| ) | ||
| time_net <- system.time( | ||
| expr = orsf(flc, time+status~., na_action = 'na_impute_meanmode', | ||
| control = orsf_control_net(), n_tree = 10) | ||
| ) | ||
| # control_fast() is much faster | ||
| time_net['elapsed'] / time_fast['elapsed'] | ||
| ``` | ||
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| ## Use `n_thread` | ||
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| The `n_thread` argument uses multi-threading to run `aorsf` functions in parallel when possible. If you know how many threads you want, e.g. you want exactly 5, just say `n_thread = 5`. If you aren't sure how many threads you have available but want to use as many as you can, say `n_thread = 0` and `aorsf` will figure out the number for you. | ||
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| ```{r} | ||
| time_1_thread <- system.time( | ||
| expr = orsf(flc, time+status~., na_action = 'na_impute_meanmode', | ||
| n_thread = 1, n_tree = 500) | ||
| ) | ||
| time_5_thread <- system.time( | ||
| expr = orsf(flc, time+status~., na_action = 'na_impute_meanmode', | ||
| n_thread = 5, n_tree = 500) | ||
| ) | ||
| time_auto_thread <- system.time( | ||
| expr = orsf(flc, time+status~., na_action = 'na_impute_meanmode', | ||
| n_thread = 0, n_tree = 500) | ||
| ) | ||
| # 5 threads and auto thread are both about 3 times faster than one thread | ||
| time_1_thread['elapsed'] / time_5_thread['elapsed'] | ||
| time_1_thread['elapsed'] / time_auto_thread['elapsed'] | ||
| ``` | ||
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| Because R is a single threaded language, multi-threading cannot be applied when `orsf()` needs to call R functions from C++, which occurs when a customized R function is used to find linear combination of variables or compute prediction accuracy. | ||
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| ## Do less | ||
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| There are some defaults in `orsf()` that can be adjusted to make it run faster: | ||
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| - set `n_retry` to 0 instead of 3 (the default) | ||
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| - set `oobag_pred_type` to 'none' instead of 'surv' (the default) | ||
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| - set 'importance' to 'none' instead of 'anova' (the default) | ||
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| - increase `split_min_events`, `split_min_obs`, `leaf_min_events`, or `leaf_min_obs` to make trees stop growing sooner | ||
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| - increase `split_min_stat` to make trees stop growing sooner | ||
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| Applying these tips: | ||
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| ```{r} | ||
| time_lightweight <- system.time( | ||
| expr = orsf(flc, time+status~., na_action = 'na_impute_meanmode', | ||
| n_thread = 0, n_tree = 500, n_retry = 0, | ||
| oobag_pred_type = 'none', importance = 'none', | ||
| split_min_events = 20, leaf_min_events = 10, | ||
| split_min_stat = 10) | ||
| ) | ||
| # about two times faster than auto thread with defaults | ||
| time_auto_thread['elapsed'] / time_lightweight['elapsed'] | ||
| ``` | ||
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| While these default values do make `orsf()` run slower, they also usually make its predictions more accurate or make the fit easier to interpret. | ||
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| ## Show progress | ||
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| Setting `verbose_progress = TRUE` doesn't make anything run faster, but it can help make it *feel* like things are running less slow. | ||
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| ```{r} | ||
| verbose_fit <- orsf(flc, time+status~., | ||
| na_action = 'na_impute_meanmode', | ||
| n_thread = 0, | ||
| n_tree = 500, | ||
| verbose_progress = TRUE) | ||
| ``` | ||
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