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…t. still need to incorporate the step logging
| library(data.table) | ||
| library(tidytable) | ||
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| cc18_collection = ocl(99) |
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| cc18_collection = ocl(99) | |
| options(mlr3oml.cache = TRUE) | |
| cc18_collection = ocl(99) |
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You can also add this to your .Rprofile
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| library(here) | ||
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| # define the tasks |
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tsk("oml", task_id = 1067)
similarly hard-code for every task
this will ignore the OpenML resampling
| # define the learners | ||
| mlp = lrn("classif.mlp", | ||
| activation = nn_relu, | ||
| neurons = to_tune( |
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| neurons = to_tune( | |
| neurons = to_tune(ps(n_layers = p_int(lower = 1, upper = 10), latent = p_int(10, 500), .extra_trafo = function(x, param_set) { | |
| list(neurons = rep(x$latent, x$n_layers)) | |
| })) |
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I think this won't work because the libraries don't allow parameter transformations. When I try to run the experiment I get this error:
Error: Inner tuning and parameter transformations are currently not supported.
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My solution for now:
n_layers_values <- 1:10
latent_dim_values <- seq(10, 500, by = 10)
neurons_search_space <- mapply(
neurons,
expand.grid(n_layers = n_layers_values, latent_dim = latent_dim_values)$n_layers,
expand.grid(n_layers = n_layers_values, latent_dim = latent_dim_values)$latent_dim,
SIMPLIFY = FALSE
)
mlp = lrn("classif.mlp",
activation = nn_relu,
# neurons = to_tune(ps(
# n_layers = p_int(lower = 1, upper = 10), latent = p_int(10, 500),
# .extra_trafo = function(x, param_set) {
# list(neurons = rep(x$latent, x$n_layers))
# })
# ),
neurons = to_tune(neurons_search_space)
| c(10, 10), c(10, 20), c(20, 10), c(20, 20) | ||
| ) | ||
| ), | ||
| batch_size = to_tune(16, 32, 64), |
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go bigger
| batch_size = to_tune(16, 32, 64), | |
| batch_size = to_tune(16, 32, 64, 128, 256), |
| tuner = tnr("grid_search"), | ||
| resampling = rsmp("cv"), | ||
| measure = msr("classif.acc"), | ||
| term_evals = 10 |
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likely need more than 10
| term_evals = 10 | |
| term_evals = 100 |
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Run on GPU server (but without all cores)
| # define an AutoTuner that wraps the classif.mlp | ||
| at = auto_tuner( | ||
| learner = mlp, | ||
| tuner = tnr("grid_search"), |
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use MBO: more efficient
| ), | ||
| batch_size = to_tune(16, 32, 64), | ||
| p = to_tune(0.1, 0.9), | ||
| epochs = to_tune(upper = 1000L, internal = TRUE), |
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Consider reducing the max number of epochs
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| bmrdt = as.data.table(bmr) | ||
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| fwrite(bmrdt, here("R", "rf_Use_case", "results", "bmrdt.csv")) |
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typo
| fwrite(bmrdt, here("R", "rf_Use_case", "results", "bmrdt.csv")) | |
| fwrite(bmrdt, here("R", "rf_use_case", "results", "bmrdt.csv")) |
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Run this with 10 evaluations on the GPU server (with not all of the cores) and report how long it takes.
Only parallelize the learners (one thread per learner) using future.
Run this experiment using the github installation (main branch) of mlr3torch. This will properly handle the interop threads
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