diff --git a/DESCRIPTION b/DESCRIPTION index 14568dcd..252ae08a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -17,6 +17,8 @@ Imports: bbotk, checkmate, data.table, + gt, + here, htmltools, miesmuschel, mlr3batchmark, diff --git a/mlr-org/_quarto.yml b/mlr-org/_quarto.yml index d3c1c3c3..52e0d88c 100644 --- a/mlr-org/_quarto.yml +++ b/mlr-org/_quarto.yml @@ -7,7 +7,7 @@ website: description: Website of the mlr organization favicon: images/favicon.ico # reader-mode: true - announcement: + announcement: icon: info-circle dismissable: true content: "The mlr3book has a [new chapter](https://mlr3book.mlr-org.com/chapters/chapter15/predsets_valid_inttune.html) on validation and internal tuning" @@ -55,6 +55,12 @@ website: - resources.qmd - book.qmd - gallery.qmd + - text: "Benchmarks" + menu: + - benchmarks_mlr3.qmd + - benchmarks_mlr3tuning.qmd + - benchmarks_mlr3fselect.qmd + - benchmarks_paradox.qmd - team.qmd - text: "More" menu: diff --git a/mlr-org/_setup.qmd b/mlr-org/_setup.qmd index b2967e5e..86fb34ae 100644 --- a/mlr-org/_setup.qmd +++ b/mlr-org/_setup.qmd @@ -5,4 +5,6 @@ library(mlr3misc) library(htmltools) library(data.table) library(reactable) + +here::i_am("mlr-org/_setup.qmd") ``` diff --git a/mlr-org/benchmark.qmd b/mlr-org/benchmark.qmd deleted file mode 100644 index a0191910..00000000 --- a/mlr-org/benchmark.qmd +++ /dev/null @@ -1,5 +0,0 @@ ---- -sidebar: false -toc: false ---- - diff --git a/mlr-org/benchmark_results/mlr3_benchmark_memory.csv b/mlr-org/benchmark_results/mlr3_benchmark_memory.csv new file mode 100644 index 00000000..1b72d75d --- /dev/null +++ b/mlr-org/benchmark_results/mlr3_benchmark_memory.csv @@ -0,0 +1,265 @@ +model_time,task,evals,renv_project,algorithm,mean_run_time,median_memory,mad_memory,mean_memory,sd_memory +1,data_1000,10,/home/mbecke16/mlr-benchmark/mlr3/default/snapshots/snapshot_2022_07_21,benchmark_sequential,0.369400000000001,201.305,0.00741299999999326,201.498,0.594657697989167 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+library(ggplot2) +library(gt) + +snapshot = mlr3misc::rowwise_table( + ~renv_project, ~mlr3, ~paradox, + "snapshot_2022_07_21", "0.13.4", "0.9.0", + "snapshot_2022_11_02", "0.14.2", "0.10.0", + "snapshot_2023_03_17", "0.15.0", "0.11.1", + "snapshot_2023_06_18", "0.16.0", "0.11.1", + "snapshot_2023_11_17", "0.17.0", "0.11.1", + "snapshot_2023_12_21", "0.17.1", "0.11.1", + "snapshot_2024_01_09", "0.17.2", "0.11.1", + "snapshot_2024_03_05", "0.18.0", "0.11.1", + "snapshot_2024_04_24", "0.19.0", "0.11.1", + "snapshot_2024_06_28", "0.20.0", "1.0.0", + "snapshot_2024_07_22", "0.20.1", "1.0.1", + "snapshot_2024_07_29", "0.20.2", "1.0.1", + "snapshot_2024_08_14", "0.20.3", "1.0.1" +) + +plot_runtime = function(data) { + ggplot(data, aes(x = mlr3, y = median_run_time)) + + geom_col(group = 1, fill = "#008080") + + geom_errorbar(aes(ymin = median_run_time - mad_run_time, ymax = median_run_time + mad_run_time), width = 0.5, position = position_dodge(0.9)) + + geom_hline(aes(yintercept = model_time * evals / 1000), linetype = "dashed") + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + labs(x = "mlr3Version", y = "Runtime [s]") + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +plot_memory = function(data) { + ggplot(data, aes(x = mlr3, y = median_memory)) + + geom_col(group = 1, fill = "#ff6347") + + geom_errorbar(aes(ymin = median_memory - mad_memory, ymax = median_memory + mad_memory), width = 0.5, position = position_dodge(0.9)) + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + labs(x = "mlr3 Version", y = "Memory [MB]") + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +create_table = function(data) { + data = data[, -c("mad_run_time", "mad_memory", "renv_project", "algorithm")] + + data_1000 = data[task == "data_1000", -"task"] + data_10000 = data[task == "data_10000", -c("task", "k", "total_model_time")] + data = merge(data_1000, data_10000, by = c("mlr3", "paradox", "model_time", "evals"), suffixes = c("", "_10000")) + + setcolorder(data, c("mlr3", "paradox", "model_time", "evals", "total_model_time", "median_run_time", "median_run_time_10000", "k", "median_memory", "median_memory_10000")) + + data %>% + gt() %>% + cols_label( + mlr3 = "mlr3 Version", + paradox = "paradox Version", + model_time = "Model Time [ms]", + evals = "Resampling Iterations", + total_model_time = "Total Model Time [s]", + median_run_time = "Median Runtime [s]", + median_run_time_10000 = "Median Runtime 10,000 [s]", + k = "K", + median_memory = "Median Memory [MB]", + median_memory_10000 = "Median Memory 10,000 [s]") %>% + fmt_number(columns = c("k", "median_run_time", "median_run_time_10000"), n_sigfig = 2) %>% + fmt_number(columns = c("median_memory", "median_memory_10000"), decimals = 0) %>% + tab_style( + style = list( + cell_fill(color = "crimson"), + cell_text(weight = "bold") + ), + locations = cells_body( + columns = "k", + rows = k > 3 + ) + ) %>% + tab_row_group( + label = "1000 Resampling Iterations", + rows = evals == 1000 + ) %>% + tab_row_group( + label = "100 Resampling Iterations", + rows = evals == 100 + ) %>% + tab_row_group( + label = "10 Resampling Iterations", + rows = evals == 10 + ) + } +``` + +# Version Histroy + +## mlr3 + +**mlr3 0.20.3** + +* refactor: Optimize runtime of fixing factor levels. +* refactor: Optimize runtime of setting row roles. +* refactor: Optimize runtime of marshalling. +* refactor: Optimize runtime of `Task$col_info` + +**mlr3 0.18.0** + +* Skip unnecessary clone of learner's state in `resample()`. + +**mlr3 0.17.1** + +* Remove `data_prototype` when resampling from `learner$state` to reduce memory consumption. +* Optimize runtime of `resample()` and `benchmark()` by reducing the number of hashing operations. +* Reduce number of threads used by data.table and BLAS to 1 when running resample() or benchmark() in parallel. + +**mlr3 0.17.0** + +* Speed up resampling by removing unnecessary calls to `packageVersion()`. +* The `design` of `benchmark()` can now include parameter settings. + +## paradox + +**paradox 1.0.1** + +* Performance improvements. + +**paradox 1.0.0** + +* Removed Param objects. ParamSet now uses a data.table internally; individual parameters are more like Domain objects now. + +# Resampling + +```{r} +#| include: false + +data_memory = fread(here::here("mlr-org/benchmark_results/mlr3_resample_memory.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_memory, mad_memory)] +data_runtime = fread(here::here("mlr-org/benchmark_results/mlr3_resample_runtime.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_run_time, mad_run_time, k)] +data = merge(data_memory, data_runtime, by = c("model_time", "task", "evals", "renv_project", "algorithm")) + +# fix +# data[renv_project == "/home/mbecke16/mlr-benchmark/mlr3/default/snapshots/snapshot_2023_01_09", renv_project := "/home/mbecke16/mlr-benchmark/mlr3/default/snapshots/snapshot_2024_01_09"] + +data[, renv_project := gsub("mlr3/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, mlr3 := factor(mlr3, levels = c("0.13.4", "0.14.2", "0.15.0", "0.16.0", "0.17.0", "0.17.1", "0.17.2", "0.18.0", "0.19.0", "0.20.0", "0.20.1", "0.20.2", "0.20.3"))] +setorderv(data, c("algorithm", "model_time", "evals"), order = c(1, 1, 1)) +data[, median_run_time := median_run_time / 1000] # ms to s +data[, mad_run_time := mad_run_time / 1000] # ms to s +data[, total_model_time := model_time * evals / 1000] #ms to s +``` + +The runtime and memory usage of the `resample()` function is compared for different mlr3 versions. +The models are trained for different amounts of time (1 ms, 10 ms, 100 ms, and 1000 ms) on the spam dataset. +The resampling iterations (`evals`) are set to 1000, 100, and 10. + +```{r} +#| eval: false +task = tsk("spam") + +learner = lrn("classif.sleep", + sleep_train = model_time / 2, + sleep_predict = model_time / 2) + +resampling = rsmp("subsampling", repeats = evals) + +resample(task, learner, resampling) +``` + +## Model Time 1000 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `resample()` with models trained for 1 s depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "resample_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `resample()` with models trained for 1 s depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "resample_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `resample()` with models trained for 1 s depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "resample_sequential" & model_time == 1000]) +``` + +## Model Time 100 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `resample()` with models trained for 100 ms depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "resample_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `resample()` with models trained for 100 ms depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "resample_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `resample()` with models trained for 100 ms depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "resample_sequential" & model_time == 100]) +``` + +## Model Time 10 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `resample()` with models trained for 10 ms depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "resample_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `resample()` with models trained for 10 ms depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "resample_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `resample()` with models trained for 10 ms depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "resample_sequential" & model_time == 10]) +``` + +## Model Time 1 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `resample()` with models trained for 1 ms depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "resample_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `resample()` with models trained for 1 ms depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "resample_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `resample()` with models trained for 1 ms depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "resample_sequential" & model_time == 1]) +``` + +# Benchmark + +```{r} +#| include: false + +data_memory = fread(here::here("mlr-org/benchmark_results/mlr3_benchmark_memory.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_memory, mad_memory)] +data_runtime = fread(here::here("mlr-org/benchmark_results/mlr3_benchmark_runtime.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_run_time, mad_run_time, k)] +data = merge(data_memory, data_runtime, by = c("model_time", "task", "evals", "renv_project", "algorithm")) + +# fix +data[renv_project == "/home/mbecke16/mlr-benchmark/mlr3/default/snapshots/snapshot_2023_01_09", renv_project := "/home/mbecke16/mlr-benchmark/mlr3/default/snapshots/snapshot_2024_01_09"] + +data[, renv_project := gsub("/home/mbecke16/mlr-benchmark/mlr3/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, mlr3 := factor(mlr3, levels = c("0.13.4", "0.14.2", "0.15.0", "0.16.0", "0.17.0", "0.17.1", "0.17.2", "0.18.0", "0.19.0", "0.20.0", "0.20.1", "0.20.2"))] +setorderv(data, c("algorithm", "model_time", "evals"), order = c(1, 1, 1)) +data[, median_run_time := median_run_time / 1000] # ms to s +data[, mad_run_time := mad_run_time / 1000] # ms to s +data[, total_model_time := model_time * evals / 1000] #ms to s +``` + +The runtime and memory usage of the `benchmark()` function is compared for different mlr3 versions. +The models are trained for different amounts of time (1 ms, 10 ms, 100 ms, and 1000 ms) on the spam dataset. +The resampling iterations (`evals`) are set to 1000, 100, and 10. + +```{r} +#| eval: false +task = tsk("spam") + +learner = lrn("classif.sleep", + sleep_train = model_time / 2, + sleep_predict = model_time / 2) + +resampling = rsmp("subsampling", repeats = instance$evals / 5) + +design = benchmark_grid(task, replicate(5, learner), resampling) + +benchmark(design) +``` + +## Model Time 1000 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `benchmark()` with models trained for 1 s depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "benchmark_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `benchmark()` with models trained for 1 s depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "benchmark_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `benchmark()` with models trained for 1 s depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "benchmark_sequential" & model_time == 1000]) +``` + +## Model Time 100 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `benchmark()` with models trained for 100 ms depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "benchmark_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `benchmark()` with models trained for 100 ms depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "benchmark_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `benchmark()` with models trained for 100 ms depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "benchmark_sequential" & model_time == 100]) +``` + +## Model Time 10 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `benchmark()` with models trained for 10 ms depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "benchmark_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `benchmark()` with models trained for 10 ms depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "benchmark_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `benchmark()` with models trained for 10 ms depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "benchmark_sequential" & model_time == 10]) +``` + +## Model Time 1 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `benchmark()` with models trained for 1 ms depending on the mlr3 version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "benchmark_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `benchmark()` with models trained for 1 ms depending on the mlr3 version. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "benchmark_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| column: body-outset +#| tbl-cap: | +#| Runtime and memory usage of `benchmark()` with models trained for 1 ms depending on the mlr3 version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "benchmark_sequential" & model_time == 1]) +``` + +# Resample Result + +The runtime and memory usage of the `rr$score()` and `rr$aggregate()` is compared for different mlr3 versions. + +## Score + +```{r} +#| eval: false +task = tsk("spam_1000") + +learner = lrn("classif.sleep", + sleep_train = model_time / 2, + sleep_predict = model_time / 2) + +resampling = rsmp("subsampling", repeats = evals) + +rr = resample(task, learner, resampling) + +rr$score(msr("classif.ce")) +``` + +```{r} +#| include: false +data_memory = fread(here::here("mlr-org/benchmark_results/mlr3_resample_result_memory.csv"))[, list(task, evals, renv_project, algorithm, median_memory, mad_memory)] +data_runtime = fread(here::here("mlr-org/benchmark_results/mlr3_resample_result_runtime.csv"))[, list(task, evals, renv_project, algorithm, median_run_time, mad_run_time)] +data = merge(data_memory, data_runtime, by = c("task", "evals", "renv_project", "algorithm")) + +data[, renv_project := gsub("/home/mbecke16/mlr-benchmark/mlr3/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, mlr3 := factor(mlr3, levels = c("0.13.4", "0.14.2", "0.15.0", "0.16.0", "0.17.0", "0.17.1", "0.17.2", "0.18.0", "0.19.0", "0.20.0", "0.20.1", "0.20.2"))] +setorderv(data, c("algorithm", "evals"), order = c(1, 1)) +data[, median_run_time := median_run_time / 1000] # ms to s +data[, mad_run_time := mad_run_time / 1000] # ms to s + +plot_runtime = function(data) { + ggplot(data, aes(x = mlr3, y = median_run_time)) + + geom_col(group = 1, fill = "#008080") + + geom_errorbar(aes(ymin = median_run_time - mad_run_time, ymax = median_run_time + mad_run_time), width = 0.5, position = position_dodge(0.9)) + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + labs(x = "mlr3Version", y = "Runtime [s]") + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +create_table = function(data) { + data = data[, -c("mad_run_time", "mad_memory", "renv_project", "algorithm")] + + data_1000 = data[task == "data_1000", -"task"] + data_10000 = data[task == "data_10000", -"task"] + data = merge(data_1000, data_10000, by = c("mlr3", "paradox", "evals"), suffixes = c("", "_10000")) + + setcolorder(data, c("mlr3", "paradox", "evals", "median_run_time", "median_run_time_10000", "median_memory", "median_memory_10000")) + + data %>% + gt() %>% + cols_label( + mlr3 = "mlr3 Version", + paradox = "paradox Version", + evals = "Resampling Iterations", + median_run_time = "Median Runtime [s]", + median_run_time_10000 = "Median Runtime 10,000 [s]", + median_memory = "Median Memory [MB]", + median_memory_10000 = "Median Memory 10,000 [MB]") %>% + fmt_number(columns = c("median_run_time", "median_run_time_10000"), n_sigfig = 2) %>% + fmt_number(columns = c("median_memory", "median_memory_10000"), decimals = 0) %>% + tab_row_group( + label = "1000 Resampling Iterations", + rows = evals == 1000 + ) %>% + tab_row_group( + label = "100 Resampling Iterations", + rows = evals == 100 + ) %>% + tab_row_group( + label = "10 Resampling Iterations", + rows = evals == 10 + ) + } +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `rr$score()` depending on the mlr3 version for different resampling iterations. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "resample_score" & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Median memory usage of `rr$score()` depending on the mlr3 version for different resampling iterations. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "resample_score" & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `rr$score()` depending on the mlr3 version for different resampling iterations. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "resample_score"]) +``` + +## Aggregate + +```{r} +#| eval: false +task = tsk("spam_1000") + +learner = lrn("classif.sleep", + sleep_train = model_time / 2, + sleep_predict = model_time / 2) + +resampling = rsmp("subsampling", repeats = evals) + +rr = resample(task, learner, resampling) + +rr$aggregate(msr("classif.ce")) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `rr$aggregate()` depending on the mlr3 version for different resampling iterations. +#| Error bars represent the median absolute devidation of the runtime. +plot_runtime(data[algorithm == "resample_aggregate" & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Median memory usage of `rr$aggregate()` depending on the mlr3 version for different resampling iterations. +#| Error bars represent the median absolute devidation of the memory usage. +plot_memory(data[algorithm == "resample_aggregate" & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `rr$aggregate()` depending on the mlr3 version for different resampling iterations. +#| The table includes runtime and memory usage for tasks of size 1000 and 10,000. +create_table(data[algorithm == "resample_aggregate"]) +``` + +# Object Size + +The size of different objects is compared for different mlr3 versions. +The size is measured in memory and after calling `serialize()` and `unserialize()`. + +```{r} +#| include: false + +data = fread(here::here("mlr-org/benchmark_results/mlr3_object_size.csv")) + +data[, renv_project := gsub("mlr3/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] + +data[, mlr3 := factor(mlr3, levels = c("0.13.4", "0.14.2", "0.15.0", "0.16.0", "0.17.0", "0.17.1", "0.17.2", "0.18.0", "0.19.0", "0.20.0", "0.20.1", "0.20.2"))] + +data = data[, -c("renv_project")] +data_1000 = data[task == "data_1000", -"task"] +data_10000 = data[task == "data_10000", -"task"] +data = merge(data_1000, data_10000, by = c("mlr3", "paradox", "evals", "object", "learner"), suffixes = c("", "_10000")) + +plot_size = function(data) { + data = melt(data, id.vars = c("mlr3", "paradox", "evals", "object", "learner"), measure.vars = c("size", "serialized_size"), value.name = "size", variable.name = "type") + + ggplot(data, aes(x = mlr3, y = size, fill= type, group = type)) + + geom_col(position = "dodge") + + labs(x = "mlr3Version", y = "Size [MB]") + + scale_fill_manual(labels = c("In Memory", "Serialized"), values = c("#008080", "#ff6347"), name = "Type") + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +plot_size_evals = function(data) { + data = melt(data, id.vars = c("mlr3", "paradox", "evals", "object", "learner"), measure.vars = c("size", "serialized_size"), value.name = "size", variable.name = "type") + + ggplot(data, aes(x = mlr3, y = size, fill= type, group = type)) + + geom_col(position = "dodge") + + labs(x = "mlr3Version", y = "Size [MB]") + + scale_fill_manual(labels = c("In Memory", "Serialization"), values = c("#008080", "#ff6347"), name = "Type") + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +create_table = function(data) { + data = data[, -c("learner", "object", "k", "k_10000")] + setcolorder(data, c("mlr3", "paradox", "evals", "size", "size_10000", "serialized_size", "serialized_size_10000")) + + data %>% + gt() %>% + cols_label( + mlr3 = "mlr3 Version", + paradox = "paradox Version", + evals = "Resampling Iterations", + size = "In Memory [MB]", + size_10000 = "In Memory 10,000 [MB]", + serialized_size = "Serialized [MB]", + serialized_size_10000 = "Serialized 10,000 [MB]") %>% + fmt_number(columns = c("size", "serialized_size", "size_10000", "serialized_size_10000"), n_sigfig = 2) %>% + tab_row_group( + label = "100 Resampling Iterations", + rows = evals == 100 + ) %>% + tab_row_group( + label = "10 Resampling Iterations", + rows = evals == 10 + ) +} +``` + +## Task + +```{r} +#| eval: false +task = tsk("spam_1000") +``` + + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `Task` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size(data[object == "task"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `Task` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "task"]) +``` + +## Learner + +```{r} +#| eval: false +learner = lrn("classif.rpart") +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size(data[object == "learner"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "learner"]) +``` + +```{r} +#| eval: false +learner$train(task) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a trained `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size(data[object == "learner trained"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a trained `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "learner trained"]) +``` + +```{r} +#| eval: false +learner$model +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a model in a `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size(data[object == "learner$model"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a model in a `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "learner$model"]) +``` + +```{r} +#| eval: false +learner$param_set +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of the `ParamSet` of a `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size(data[object == "learner$param_set"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of the `ParamSet` of a `LearnerClassifRpart` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "learner$param_set"]) +``` + +## Prediction + +```{r} +#| eval: false +pred = learner$predict(task) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `Prediction` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size_evals(data[object == "prediction"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `Prediction` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "prediction"]) +``` + +## Resampling + +```{r} +#| eval: false +resampling = rsmp("subsampling", repeats = evals) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `Resampling` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size(data[object == "resampling"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `Resampling` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "resampling"]) +``` + +```{r} +#| eval: false +resampling$instantiate(task) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a instantiated `Resampling` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size_evals(data[object == "resampling instantiated"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a instantiated `Resampling` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "resampling instantiated"]) +``` + +## Resample Result + +```{r} +#| eval: false +rr = resample(task, learner, resampling) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `ResampleResult` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size_evals(data[object == "resample result"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `ResampleResult` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "resample result"]) +``` + + +```{r} +#| eval: false +rr = resample(task, learner, resampling, store_models = TRUE) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `ResamplingResult` object with models depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size_evals(data[object == "resample result store models"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `ResamplingResult` object with models depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "resample result store models"]) +``` + +## Benchmark Result + +```{r} +#| eval: false +bmr = benchmark(task, learner, resampling) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `BenchmarkResult` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size_evals(data[object == "benchmark result"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `BenchmarkResult` object depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "benchmark result"]) +``` + +```{r} +#| eval: false +bmr = benchmark(task, learner, resampling, store_models = TRUE) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of a `BenchmarkResult` object with models depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +plot_size_evals(data[object == "benchmark result store models"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Memory usage of a `BenchmarkResult` object with models depending on the mlr3 version. +#| The size is measured in memory and after calling `serialize()` and `unserialize()`. +create_table(data[object == "benchmark result store models"]) +``` + diff --git a/mlr-org/benchmarks_mlr3fselect.qmd b/mlr-org/benchmarks_mlr3fselect.qmd new file mode 100644 index 00000000..24a60035 --- /dev/null +++ b/mlr-org/benchmarks_mlr3fselect.qmd @@ -0,0 +1,785 @@ +--- +title: "mlr3fselect" +sidebar: false +toc: true +--- + +{{< include _setup.qmd >}} + + +```{r} +#| include: false +library(data.table) +library(ggplot2) +library(gt) + +snapshot = mlr3misc::rowwise_table( + ~renv_project, ~mlr3fselect, ~bbotk, ~mlr3, ~paradox, + "snapshot_2022_12_21", "0.9.0", "0.7.2", "0.14.1", "0.11.0", + "snapshot_2023_01_26", "0.9.1", "0.7.2", "0.14.1", "0.11.0", + "snapshot_2023_02_21", "0.10.0", "0.7.2", "0.14.1", "0.11.0", + "snapshot_2023_03_02", "0.11.0", "0.7.2", "0.14.1", "0.11.0", + "snapshot_2024_04_24", "0.12.0", "0.8.0", "0.19.0", "0.11.1", + "snapshot_2024_07_05", "1.0.0", "1.0.0", "0.20.0", "1.0.1", + "snapshot_2024_07_19", "1.0.1", "1.0.1", "0.20.2", "1.0.1" +) + +plot_runtime = function(data) { + ggplot(data, aes(x = mlr3fselect, y = median_run_time)) + + geom_col(group = 1, fill = "#008080") + + geom_hline(aes(yintercept = total_model_time), linetype = "dashed") + + geom_errorbar(aes(ymin = median_run_time - mad_run_time, ymax = median_run_time + mad_run_time), width = 0.5, position = position_dodge(0.9)) + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + labs(x = "mlr3fselect Version", y = "Runtime [s]") + + theme_minimal(base_size = 9) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +plot_memory = function(data) { + ggplot(data, aes(x = mlr3fselect, y = median_memory)) + + geom_col(group = 1, fill = "#ff6347") + + geom_errorbar(aes(ymin = median_memory - mad_memory, ymax = median_memory + mad_memory), width = 0.5, position = position_dodge(0.9)) + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + labs(x = "mlr3fselect Version", y = "Memory [MB]") + + theme_minimal(base_size = 9) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +create_table = function(data) { + data = data[, -c("mad_run_time", "mad_memory", "renv_project", "algorithm", "total_model_time")] + + data_1000 = data[task == "data_1000", -"task"] + data_10000 = data[task == "data_10000", -c("task", "k")] + data = merge(data_1000, data_10000, by = c("mlr3fselect", "bbotk", "mlr3", "paradox", "model_time", "evals"), suffixes = c("", "_10000")) + + setcolorder(data, c("mlr3fselect", "bbotk", "mlr3", "paradox", "model_time", "evals", "median_run_time", "median_run_time_10000", "k", "median_memory", "median_memory_10000")) + + data %>% + gt() %>% + cols_label( + mlr3fselect = "mlr3fselect Version", + mlr3 = "mlr3 Version", + bbotk = "bbotk Version", + paradox = "paradox Version", + model_time = "Model Time [ms]", + evals = "Resampling Iterations", + median_run_time = "Median Runtime [s]", + median_run_time_10000 = "Median Runtime 10,000 [s]", + k = "K", + median_memory = "Median Memory [MB]", + median_memory_10000 = "Median Memory 10,000 [MB]") %>% + fmt_number(columns = c("k", "median_run_time", "median_run_time_10000"), n_sigfig = 2) %>% + fmt_number(columns = c("median_memory", "median_memory_10000"), decimals = 0) %>% + tab_style( + style = list( + cell_fill(color = "crimson"), + cell_text(weight = "bold") + ), + locations = cells_body( + columns = "k", + rows = k > 3 + ) + ) %>% + tab_row_group( + label = "1000 Resampling Iterations", + rows = evals == 1000 + ) %>% + tab_row_group( + label = "100 Resampling Iterations", + rows = evals == 100 + ) %>% + tab_row_group( + label = "10 Resampling Iterations", + rows = evals == 10 + ) + } +``` + +# Version History + +**mlr3fselect 1.0.1** + +* fix: Delete intermediate `BenchmarkResult` in `ObjectiveFSelectBatch` after optimization. +* refactor: Remove `x_domain` from archive. + +**mlr3fselect 0.12.0** + +* refactor: Optimize runtime of `ArchiveBatchFSelect$best()` method. + +**mlr3fselect 0.9.1** + +* refactor: Remove internal use of `mlr3pipelines`. + +**mlr3 0.18.0** + +* Skip unnecessary clone of learner's state in `resample()`. + +**mlr3 0.17.1** + +* Remove `data_prototype` when resampling from `learner$state` to reduce memory consumption. +* Optimize Median runtimeof `resample()` and `benchmark()` by reducing the number of hashing operations. + +**mlr3 0.17.0** + +* Speed up resampling by removing unnecessary calls to `packageVersion()`. +* The `design` of `benchmark()` can now include parameter settings. + +**paradox 1.0.1** + +* Performance improvements. + +# Feature Selection + +```{r} +#| include: false + +data_memory = fread(here::here("mlr-org/benchmark_results/mlr3fselect_fselect_memory.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_memory, mad_memory)] +data_runtime = fread(here::here("mlr-org/benchmark_results/mlr3fselect_fselect_runtime.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_run_time, mad_run_time, k)] +data = merge(data_memory, data_runtime, by = c("model_time", "task", "evals", "renv_project", "algorithm")) + +data[, renv_project := gsub("mlr3fselect/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, mlr3fselect := factor(mlr3fselect, levels = c("0.9.0", "0.9.1", "0.10.0", "0.11.0", "0.12.0", "1.0.0", "1.0.1"))] +setorderv(data, c("algorithm", "model_time", "evals"), order = c(1, 1, 1)) +data[, median_run_time := median_run_time / 1000] #ms to s +data[, mad_run_time := mad_run_time / 1000] #ms to s +data[, total_model_time := model_time * evals / 1000] #ms to s +fselect_median_run_time = data[, list(model_time, task, evals, renv_project, median_run_time)] +``` + +The runtime and memory usage of the `fselect()` function is compared for different mlr3fselect versions. +The function is used to perform feature selection with a random search. +The models are trained for different amounts of time (1 ms, 10 ms, 100 ms, and 1000 ms) on the spam dataset. +The resampling iterations (`evals`) are set to 1000, 100, and 10. + +```{r} +#| eval: false +learner = lrn("classif.sleep", + sleep_train = model_time / 2, + sleep_predict = model_time / 2) + +instance = fselect( + fselector = fs("random_search", batch_size = evals), + task = tsk("spam"), + learner = learner, + resampling = rsmp("holdout"), + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = evals), + store_benchmark_result = FALSE, + store_models = FALSE +) +``` + +## Model Time 1000 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect()` with models trained for 1 s depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect()` with models trained for 1 s depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect()` with models trained for 1 s depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_sequential" & model_time == 1000]) +``` + +## Model Time 100 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect()` with models trained for 100 ms depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect()` with models trained for 100 ms depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect()` with models trained for 100 ms depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_sequential" & model_time == 100]) +``` + +## Model Time 10 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect()` with models trained for 10 ms depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect()` with models trained for 10 ms depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect()` with models trained for 10 ms depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_sequential" & model_time == 10]) +``` + +## Model Time 1 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect()` with models trained for 1 ms depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect()` with models trained for 1 ms depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect()` with models trained for 1 ms depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_sequential" & model_time == 1]) +``` + +# Nested Featute Selection + +```{r} +#| include: false + +data_memory = fread(here::here("mlr-org/benchmark_results/mlr3fselect_fselect_nested_memory.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_memory, mad_memory)] +data_runtime = fread(here::here("mlr-org/benchmark_results/mlr3fselect_fselect_nested_runtime.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_run_time, mad_run_time, k)] +data = merge(data_memory, data_runtime, by = c("model_time", "task", "evals", "renv_project", "algorithm")) + +data[, renv_project := gsub("mlr3fselect/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, mlr3fselect := factor(mlr3fselect, levels = c("0.9.0", "0.9.1", "0.10.0", "0.11.0", "0.12.0", "1.0.0", "1.0.1"))] +setorderv(data, c("algorithm", "model_time", "evals"), order = c(1, 1, 1)) +data[, median_run_time := median_run_time / 1000] #ms to s +data[, mad_run_time := mad_run_time / 1000] #ms to s +data[, total_model_time := model_time * evals * 10 / 1000] #ms to s + +# calculate how much longer nested feature selection is than feature selection +data = merge(data, fselect_median_run_time, by = c("model_time", "task", "evals", "renv_project"), suffixes = c("", "_fselect")) +data[, m := median_run_time / median_run_time_fselect] + +create_table = function(data) { + data = data[, -c("mad_run_time", "mad_memory", "renv_project", "algorithm", "total_model_time")] + + data_1000 = data[task == "data_1000", -"task"] + data_10000 = data[task == "data_10000", -c("task", "k", "median_run_time_fselect", "m")] + data = merge(data_1000, data_10000, by = c("mlr3fselect", "bbotk", "mlr3", "paradox", "model_time", "evals"), suffixes = c("", "_10000")) + + setcolorder(data, c( + "mlr3fselect", + "bbotk", + "mlr3", + "paradox", + "model_time", + "evals", + "median_run_time", + "median_run_time_10000", + "k", + "median_run_time_fselect", + "m", + "median_memory", + "median_memory_10000")) + + data %>% + gt() %>% + cols_label( + mlr3fselect = "mlr3fselect Version", + mlr3 = "mlr3 Version", + bbotk = "bbotk Version", + paradox = "paradox Version", + model_time = "Model Time [ms]", + evals = "Resampling Iterations", + median_run_time = "Median Runtime [s]", + median_run_time_10000 = "Median Runtime 10,000 [s]", + k = "k", + median_run_time_fselect = "Median Runtime Feature Selection [s]", + m = "m", + median_memory = "Median Memory [MB]", + median_memory_10000 = "Median Memory 10,000 [MB]") %>% + fmt_number(columns = c("k", "median_run_time", "median_run_time_10000", "m", "median_run_time_fselect"), n_sigfig = 2) %>% + fmt_number(columns = c("median_memory", "median_memory_10000"), decimals = 0) %>% + tab_style( + style = list( + cell_fill(color = "crimson"), + cell_text(weight = "bold") + ), + locations = cells_body( + columns = "k", + rows = k > 3 + ) + ) %>% + tab_row_group( + label = "1000 Resampling Iterations", + rows = evals == 1000 + ) %>% + tab_row_group( + label = "100 Resampling Iterations", + rows = evals == 100 + ) %>% + tab_row_group( + label = "10 Resampling Iterations", + rows = evals == 10 + ) + } +``` + +The runtime and memory usage of the `fselect_nested()` function is compared for different mlr3fselect versions. +The function is used to perform nested feature selection with a random search. +The models are trained for different amounts of time (1 ms, 10 ms, 100 ms, and 1000 ms) on the spam dataset. +The resampling iterations (`evals`) are set to 1000, 100, and 10. + +```{r} +#| eval: false +learner = lrn("classif.sleep", + sleep_train = model_time / 2, + sleep_predict = model_time / 2) + +fselect_nested( + fselector = fs("random_search", batch_size = evals), + task = tsk("spam"), + learner = learner, + inner_resampling = rsmp("holdout"), + outer_resampling = rsmp("subsampling", repeats = 10), + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = evals), + store_fselect_instance = FALSE, + store_benchmark_result = FALSE, + store_models = FALSE +) +``` + +## Model Time 1000 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect_nested()` with models trained for 1 s depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_nested_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect_nested()` with models trained for 1 s depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_nested_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect_nested()` with models trained for 1 s depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_nested_sequential" & model_time == 1000]) +``` + +## Model Time 100 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect_nested()` with models trained for 100 ms depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_nested_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect_nested()` with models trained for 100 ms depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_nested_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect_nested()` with models trained for 100 ms depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_nested_sequential" & model_time == 100]) +``` + +## Model Time 10 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect_nested()` with models trained for 10 ms depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_nested_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect_nested()` with models trained for 10 ms depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_nested_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect_nested()` with models trained for 10 ms depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_nested_sequential" & model_time == 10]) +``` + +## Model Time 1 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `fselect_nested()` with models trained for 1 ms depending on the mlr3fselect version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "fselect_nested_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `fselect_nested()` with models trained for 1 ms depending on the mlr3fselect version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "fselect_nested_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `fselect_nested()` with models trained for 1 ms depending on the mlr3fselect version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "fselect_nested_sequential" & model_time == 1]) +``` + +# Object Size + + +```{r} +#| include: false + +data = fread(here::here("mlr-org/benchmark_results/mlr3fselect_object_size.csv")) + +data[, renv_project := gsub("mlr3fselect/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] + +data[, mlr3fselect := factor(mlr3fselect, levels = c("0.9.0", "0.9.1", "0.10.0", "0.11.0", "0.12.0", "1.0.0", "1.0.1"))] +#setorderv(data, c("algorithm", "evals"), order = c(1, 1)) + +data = data[, -c("renv_project")] +data_1000 = data[task == "data_1000", -"task"] +data_10000 = data[task == "data_10000", -"task"] +data = merge(data_1000, data_10000, by = c("mlr3fselect", "mlr3", "paradox", "evals", "object", "learner"), suffixes = c("", "_10000")) + +plot_size = function(data) { + data = melt(data, id.vars = c("mlr3fselect", "mlr3", "paradox", "evals", "object", "learner"), measure.vars = c("size", "serialized_size"), value.name = "size", variable.name = "type") + + ggplot(data, aes(x = mlr3fselect, y = size, fill= type, group = type)) + + geom_col(position = "dodge") + + labs(x = "mlr3fselect Version", y = "Size [MB]") + + scale_fill_manual(labels = c("In Memory", "Serialized"), values = c("#008080", "#ff6347"), name = "Type") + + theme_minimal(base_size = 9) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +plot_size_evals = function(data) { + data = melt(data, id.vars = c("mlr3fselect", "mlr3", "paradox", "evals", "object", "learner"), measure.vars = c("size", "serialized_size"), value.name = "size", variable.name = "type") + + ggplot(data, aes(x = mlr3fselect, y = size, fill= type, group = type)) + + geom_col(position = "dodge") + + labs(x = "mlr3fselect Version", y = "Size [MB]") + + scale_fill_manual(labels = c("In Memory", "Serialization"), values = c("#008080", "#ff6347"), name = "Type") + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + theme_minimal(base_size = 9) + + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="bottom") +} +``` + +## Automated Feature Selection + +Feature selection without storing any additional information. + +```{r} +#| eval: false +afs = auto_fselector( + fselector = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_models = FALSE, + store_benchmark_result = FALSE, + store_tuning_instance = FALSE +) +``` + +```{r} +#| echo: false +plot_size(data[object == "afs"]) +``` + +```{r} +#| eval: false +afs$train(task) +``` + +Since we do not store any information about the optimization, the size of the objects should not increase the number of evaluations. + +```{r} +#| echo: false +plot_size_evals(data[object == "afs$train"]) +``` + +```{r} +#| eval: false +afs$model +``` + +The model slot only saves the final model and should therefore not increase with the number of evaluations. + +```{r} +#| echo: false +plot_size_evals(data[object == "afs$model"]) +``` + +## Automated Feature Selection with Instance + +```{r} +#| eval: false +afs = auto_fselector( + fselector = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_fselect_instance = TRUE, + store_benchmark_result = FALSE, + store_models = FALSE +) +``` + +```{r} +#| eval: false +afs$train(task) +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_instance$train"]) +``` + +```{r} +#| eval: false +afs$fselect_instance +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_instance$fselect_instance"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$objective +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_instance$objective"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$archive +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_instance$archive"]) +``` + +## Automated Feature Selection with Benchmark Result + +```{r} +#| eval: false +afs = auto_fselector( + fselector = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_fselect_instance = TRUE, + store_benchmark_result = TRUE, + store_models = FALSE +) +``` + +```{r} +#| eval: false +afs$train(task) +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_bmr$train"]) +``` + +```{r} +#| eval: false +afs$fselect_instance +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_bmr$fselect_instance"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$objective +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_bmr$objective"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$archive +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_bmr$archive"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$archive$benchmark_result +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_bmr$benchmark_result"]) +``` + + +## Automated Feature Selection with Models + +```{r} +#| eval: false +afs = auto_fselector( + fselector = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_fselect_instance = TRUE, + store_benchmark_result = TRUE, + store_models = TRUE +) +``` + +```{r} +#| eval: false +afs$train(task) +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_models$train"]) +``` + +```{r} +#| eval: false +afs$fselect_instance +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_models$fselect_instance"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$objective +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_models$objective"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$archive +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_models$archive"]) +``` + +```{r} +#| eval: false +afs$fselect_instance$archive$benchmark_result +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "afs_models$benchmark_result"]) +``` diff --git a/mlr-org/benchmarks_mlr3tuning.qmd b/mlr-org/benchmarks_mlr3tuning.qmd new file mode 100644 index 00000000..5a0f512a --- /dev/null +++ b/mlr-org/benchmarks_mlr3tuning.qmd @@ -0,0 +1,745 @@ +--- +title: "mlr3tuning" +sidebar: false +toc: true +--- + +{{< include _setup.qmd >}} + + +```{r} +#| include: false +library(data.table) +library(ggplot2) +library(gt) + +snapshot = mlr3misc::rowwise_table( + ~renv_project, ~mlr3tuning, ~bbotk, ~mlr3, ~paradox, + "snapshot_2022_12_22", "0.17.2", "0.7.2", "0.14.1", "0.11.0", + "snapshot_2023_03_08", "0.18.0", "0.7.2", "0.14.1", "0.11.0", + "snapshot_2023_06_26", "0.19.0", "0.7.2", "0.16.1", "0.11.1", + "snapshot_2023_11_20", "0.19.1", "0.7.3", "0.17.0", "0.11.1", + "snapshot_2023_11_28", "0.19.2", "0.7.3", "0.17.0", "0.11.1", + "snapshot_2024_04_24", "0.20.0", "0.8.0", "0.19.0", "0.11.1", + "snapshot_2024_07_05", "1.0.0", "1.0.0", "0.20.0", "1.0.1" +) + +plot_runtime = function(data) { + ggplot(data, aes(x = mlr3tuning, y = median_run_time)) + + geom_col(group = 1, fill = "#008080") + + geom_hline(aes(yintercept = total_model_time), linetype = "dashed") + + geom_errorbar(aes(ymin = median_run_time - mad_run_time, ymax = median_run_time + mad_run_time), width = 0.5, position = position_dodge(0.9)) + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + labs(x = "mlr3tuning Version", y = "Runtime [s]") + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +plot_memory = function(data) { + ggplot(data, aes(x = mlr3tuning, y = median_memory)) + + geom_col(group = 1, fill = "#ff6347") + + geom_errorbar(aes(ymin = median_memory - mad_memory, ymax = median_memory + mad_memory), width = 0.5, position = position_dodge(0.9)) + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + labs(x = "mlr3tuning Version", y = "Memory [MB]") + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +create_table = function(data) { + data = data[, -c("mad_run_time", "mad_memory", "renv_project", "algorithm", "total_model_time")] + + data_1000 = data[task == "data_1000", -"task"] + data_10000 = data[task == "data_10000", -c("task", "k")] + data = merge(data_1000, data_10000, by = c("mlr3tuning", "bbotk", "mlr3", "paradox", "model_time", "evals"), suffixes = c("", "_10000")) + + setcolorder(data, c("mlr3tuning", "bbotk", "mlr3", "paradox", "model_time", "evals", "median_run_time", "median_run_time_10000", "k", "median_memory", "median_memory_10000")) + + data %>% + gt() %>% + cols_label( + mlr3tuning = "mlr3tuning Version", + mlr3 = "mlr3 Version", + paradox = "paradox Version", + model_time = "Model Time [ms]", + evals = "Resampling Iterations", + median_run_time = "Median Runtime [s]", + median_run_time_10000 = "Median Runtime 10,000 [s]", + k = "K", + median_memory = "Median Memory [MB]", + median_memory_10000 = "Median Memory 10,000 [MB]") %>% + fmt_number(columns = c("k", "median_run_time", "median_run_time_10000"), n_sigfig = 2) %>% + fmt_number(columns = c("median_memory", "median_memory_10000"), decimals = 0) %>% + tab_style( + style = list( + cell_fill(color = "crimson"), + cell_text(weight = "bold") + ), + locations = cells_body( + columns = "k", + rows = k > 3 + ) + ) %>% + tab_row_group( + label = "1000 Resampling Iterations", + rows = evals == 1000 + ) %>% + tab_row_group( + label = "100 Resampling Iterations", + rows = evals == 100 + ) %>% + tab_row_group( + label = "10 Resampling Iterations", + rows = evals == 10 + ) + } +``` + +# Version History + +**mlr3 0.18.0** + +* Skip unnecessary clone of learner's state in `resample()`. + +**mlr3 0.17.1** + +* Remove `data_prototype` when resampling from `learner$state` to reduce memory consumption. +* Optimize Median runtimeof `resample()` and `benchmark()` by reducing the number of hashing operations. + +**mlr3 0.17.0** + +* Speed up resampling by removing unnecessary calls to `packageVersion()`. +* The `design` of `benchmark()` can now include parameter settings. + +**paradox 1.0.1** + +* Performance improvements. + +# Hyperparameter Optimization + +```{r} +#| include: false +data_memory = fread(here::here("mlr-org/benchmark_results/mlr3tuning_tune_memory.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_memory, mad_memory)] +data_runtime = fread(here::here("mlr-org/benchmark_results/mlr3tuning_tune_runtime.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_run_time, mad_run_time, k)] +data = merge(data_memory, data_runtime, by = c("model_time", "task", "evals", "renv_project", "algorithm")) + +data[, renv_project := gsub("mlr3tuning/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, mlr3tuning := factor(mlr3tuning, levels = c("0.17.2", "0.18.0", "0.19.0", "0.19.1", "0.19.2", "0.20.0", "1.0.0"))] +setorderv(data, c("algorithm", "model_time", "evals"), order = c(1, 1, 1)) +data[, median_run_time := median_run_time / 1000] #ms to s +data[, mad_run_time := mad_run_time / 1000] #ms to s +data[, total_model_time := model_time * evals / 1000] #ms to s +tune_median_run_time = data[, list(model_time, task, evals, renv_project, median_run_time)] +``` + +The runtime and memory usage of the `tune()` function is compared for different mlr3tuning versions. +The function is used to perform hyperparameter optimization with a random search. +The models are trained for different amounts of time (1 ms, 10 ms, 100 ms, and 1000 ms) on the spam dataset. +The resampling iterations (`evals`) are set to 1000, 100, and 10. + +```{r} +#| eval: false +learner = lrn("classif.sleep", + sleep_train = model_time / 2, + sleep_predict = model_time / 2) + +instance = tune( + tuner = fs("random_search", batch_size = evals), + task = tsk("spam"), + learner = learner, + resampling = rsmp("holdout"), + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = evals), + store_benchmark_result = FALSE, + store_models = FALSE +) +``` + +## Model Time 1000 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune()` with models trained for 1 s depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune()` with models trained for 1 s depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune()` with models trained for 1 s depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_sequential" & model_time == 1000]) +``` + +## Model Time 100 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune()` with models trained for 100 ms depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune()` with models trained for 100 ms depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune()` with models trained for 100 ms depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_sequential" & model_time == 100]) +``` + +## Model Time 10 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune()` with models trained for 10 ms depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune()` with models trained for 10 ms depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune()` with models trained for 10 ms depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_sequential" & model_time == 10]) +``` + +## Model Time 1 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune()` with models trained for 1 ms depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune()` with models trained for 1 ms depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune()` with models trained for 1 ms depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_sequential" & model_time == 1]) +``` + + +# Nested Hyperparameter Optimization + +```{r} +#| include: false + +data_memory = fread(here::here("mlr-org/benchmark_results/mlr3tuning_tune_nested_memory.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_memory, mad_memory)] +data_runtime = fread(here::here("mlr-org/benchmark_results/mlr3tuning_tune_nested_runtime.csv"))[, list(model_time, task, evals, renv_project, algorithm, median_run_time, mad_run_time, k)] +data = merge(data_memory, data_runtime, by = c("model_time", "task", "evals", "renv_project", "algorithm")) + +data[, renv_project := gsub("mlr3tuning/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, mlr3tuning := factor(mlr3tuning, levels = c("0.17.2", "0.18.0", "0.19.0", "0.19.1", "0.19.2", "0.20.0", "1.0.0"))] +setorderv(data, c("algorithm", "model_time", "evals"), order = c(1, 1, 1)) +data[, median_run_time := median_run_time / 1000] #ms to s +data[, mad_run_time := mad_run_time / 1000] #ms to s +data[, total_model_time := model_time * evals * 10 / 1000] #ms to s + +# calculate how much longer nested feature selection is than feature selection +data = merge(data, tune_median_run_time, by = c("model_time", "task", "evals", "renv_project"), suffixes = c("", "_tune")) +data[, m := median_run_time / median_run_time_tune] + +create_table = function(data) { + data = data[, -c("mad_run_time", "mad_memory", "renv_project", "algorithm", "total_model_time")] + + data_1000 = data[task == "data_1000", -"task"] + data_10000 = data[task == "data_10000", -c("task", "k", "median_run_time_tune", "m")] + data = merge(data_1000, data_10000, by = c("mlr3tuning", "bbotk", "mlr3", "paradox", "model_time", "evals"), suffixes = c("", "_10000")) + + setcolorder(data, c( + "mlr3tuning", + "bbotk", + "mlr3", + "paradox", + "model_time", + "evals", + "median_run_time", + "median_run_time_10000", + "k", + "median_run_time_tune", + "m", + "median_memory", + "median_memory_10000")) + + data %>% + gt() %>% + cols_label( + mlr3tuning = "mlr3tuning Version", + mlr3 = "mlr3 Version", + paradox = "paradox Version", + model_time = "Model Time [ms]", + evals = "Resampling Iterations", + median_run_time = "Median Runtime [s]", + median_run_time_10000 = "Median Runtime 10,000 [s]", + k = "K", + median_run_time_tune = "Median Runtime Tune [s]", + m = "m", + median_memory = "Median Memory [MB]", + median_memory_10000 = "Median Memory 10,000 [MB]") %>% + fmt_number(columns = c("k", "median_run_time", "median_run_time_10000", "m", "median_run_time_tune"), n_sigfig = 2) %>% + fmt_number(columns = c("median_memory", "median_memory_10000"), decimals = 0) %>% + tab_style( + style = list( + cell_fill(color = "crimson"), + cell_text(weight = "bold") + ), + locations = cells_body( + columns = "k", + rows = k > 3 + ) + ) %>% + tab_row_group( + label = "1000 Resampling Iterations", + rows = evals == 1000 + ) %>% + tab_row_group( + label = "100 Resampling Iterations", + rows = evals == 100 + ) %>% + tab_row_group( + label = "10 Resampling Iterations", + rows = evals == 10 + ) + } +``` + + +## Model Time 1000 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune_nested()` with models trained for 1 s depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_nested_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune_nested()` with models trained for 1 s depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_nested_sequential" & model_time == 1000 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune_nested()` with models trained for 1 s depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_nested_sequential" & model_time == 1000]) +``` + +## Model Time 100 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune_nested()` with models trained for 100 ms depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_nested_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune_nested()` with models trained for 100 ms depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_nested_sequential" & model_time == 100 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune_nested()` with models trained for 100 ms depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_nested_sequential" & model_time == 100]) +``` + +## Model Time 10 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune_nested()` with models trained for 10 ms depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_nested_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune_nested()` with models trained for 10 ms depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_nested_sequential" & model_time == 10 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune_nested()` with models trained for 10 ms depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_nested_sequential" & model_time == 10]) +``` + +## Model Time 1 ms + +```{r} +#| echo: false +#| fig-cap: | +#| Median runtime of `tune_nested()` with models trained for 1 ms depending on the mlr3tuning version. +#| The dashed line indicates the total training time of the models. +#| Error bars represent the median absolute deviation of the runtime. +plot_runtime(data[algorithm == "tune_nested_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Memory usage of `tune_nested()` with models trained for 1 ms depending on the mlr3tuning version. +#| Error bars represent the median absolute deviation of the memory usage. +plot_memory(data[algorithm == "tune_nested_sequential" & model_time == 1 & task == "data_1000"]) +``` + +```{r} +#| echo: false +#| tbl-cap: | +#| Runtime and memory usage of `tune_nested()` with models trained for 1 ms depending on the mlr3tuning version. +#| The K factor shows how much longer the runtime is than the model training. +#| A red background indicates that the runtime is 3 times larger than the total training time of the models. +create_table(data[algorithm == "tune_nested_sequential" & model_time == 1]) +``` + +# Object Size + +```{r} +#| include: false + +data = fread(here::here("mlr-org/benchmark_results/mlr3tuning_object_size.csv")) + +data[, renv_project := gsub("mlr3tuning/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] + +data[, mlr3tuning := factor(mlr3tuning, levels = c("0.17.2", "0.18.0", "0.19.0", "0.19.1", "0.19.2", "0.20.0", "1.0.0"))] +#setorderv(data, c("algorithm", "evals"), order = c(1, 1)) + +data = data[, -c("renv_project")] +data_1000 = data[task == "data_1000", -"task"] +data_10000 = data[task == "data_10000", -"task"] +data = merge(data_1000, data_10000, by = c("mlr3tuning", "mlr3", "paradox", "evals", "object", "learner"), suffixes = c("", "_10000")) + +plot_size = function(data) { + data = melt(data, id.vars = c("mlr3tuning", "mlr3", "paradox", "evals", "object", "learner"), measure.vars = c("size", "serialized_size"), value.name = "size", variable.name = "type") + + ggplot(data, aes(x = mlr3tuning, y = size, fill= type, group = type)) + + geom_col(position = "dodge") + + labs(x = "mlr3tuning Version", y = "Size [MB]") + + scale_fill_manual(labels = c("In Memory", "Serialized"), values = c("#008080", "#ff6347"), name = "Type") + + theme_minimal(base_size = 9) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +plot_size_evals = function(data) { + data = melt(data, id.vars = c("mlr3tuning", "mlr3", "paradox", "evals", "object", "learner"), measure.vars = c("size", "serialized_size"), value.name = "size", variable.name = "type") + + ggplot(data, aes(x = mlr3tuning, y = size, fill= type, group = type)) + + geom_col(position = "dodge") + + labs(x = "mlr3tuning Version", y = "Size [MB]") + + scale_fill_manual(labels = c("In Memory", "Serialization"), values = c("#008080", "#ff6347"), name = "Type") + + facet_wrap(~evals, scales = "free_y", labeller = labeller(evals = function(value) sprintf("%s Resampling Iterations", value))) + + theme_minimal(base_size = 9) + + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="bottom") +} +``` + +## Automated Tuning + +Tuning without storing any additional information. + +```{r} +#| eval: false +at = auto_tuner( + tuner = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_models = FALSE, + store_benchmark_result = FALSE, + store_tuning_instance = FALSE +) +``` + +```{r} +#| echo: false +plot_size(data[object == "at"]) +``` + +```{r} +#| eval: false +at$train(task) +``` + +Since we do not store any information about the optimization, the size of the objects should not increase the number of evaluations. + +```{r} +#| echo: false +plot_size_evals(data[object == "at$train"]) +``` + +```{r} +#| eval: false +at$model +``` + +The model slot only saves the final model and should therefore not increase with the number of evaluations. + +```{r} +#| echo: false +plot_size_evals(data[object == "at$model"]) +``` + +## Automated Tuning with Instance + +```{r} +#| eval: false +at = auto_tuner( + tuner = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_tuning_instance = TRUE, + store_benchmark_result = FALSE, + store_models = FALSE +) +``` + +```{r} +#| eval: false +at$train(task) +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_instance$train"]) +``` + +```{r} +#| eval: false +at$tuning_instance +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_instance$tuning_instance"]) +``` + +```{r} +#| eval: false +at$tuning_instance$objective +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_instance$objective"]) +``` + +```{r} +#| eval: false +at$tuning_instance$archive +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_instance$archive"]) +``` + +## Automated Tuning with Benchmark Result + +```{r} +#| eval: false +at = auto_tuner( + tuner = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_tuning_instance = TRUE, + store_benchmark_result = TRUE, + store_models = FALSE +) +``` + +```{r} +#| eval: false +at$train(task) +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_bmr$train"]) +``` + +```{r} +#| eval: false +at$tuning_instance +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_bmr$tuning_instance"]) +``` + +```{r} +#| eval: false +at$tuning_instance$objective +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_bmr$objective"]) +``` + +```{r} +#| eval: false +at$tuning_instance$archive +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_bmr$archive"]) +``` + +```{r} +#| eval: false +at$tuning_instance$archive$benchmark_result +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_bmr$benchmark_result"]) +``` + + +## Automated Tuning with Models + +```{r} +#| eval: false +at = auto_tuner( + tuner = fs("random_search", batch_size = instance$evals), + learner = learner, + resampling = resampling, + measure = msr("classif.ce"), + terminator = trm("evals", n_evals = instance$evals), + store_tuning_instance = TRUE, + store_benchmark_result = TRUE, + store_models = TRUE +) +``` + +```{r} +#| eval: false +at$train(task) +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_models$train"]) +``` + +```{r} +#| eval: false +at$tuning_instance +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_models$tuning_instance"]) +``` + +```{r} +#| eval: false +at$tuning_instance$objective +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_models$objective"]) +``` + +```{r} +#| eval: false +at$tuning_instance$archive +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_models$archive"]) +``` + +```{r} +#| eval: false +at$tuning_instance$archive$benchmark_result +``` + +```{r} +#| echo: false +plot_size_evals(data[object == "at_models$benchmark_result"]) +``` diff --git a/mlr-org/benchmarks_paradox.qmd b/mlr-org/benchmarks_paradox.qmd new file mode 100644 index 00000000..208b0ced --- /dev/null +++ b/mlr-org/benchmarks_paradox.qmd @@ -0,0 +1,442 @@ +--- +title: "paradox" +sidebar: false +toc: true +--- + +{{< include _setup.qmd >}} + +```{r} +#| include: false +library(data.table) +library(ggplot2) +library(gt) +``` + +```{r} +#| include: false +data = fread(here::here("mlr-org/benchmark_results/paradox_param_set_runtime.csv")) + +snapshot = data.table( + renv_project = c("snapshot_2023_03_17", "snapshot_2024_06_11", "snapshot_2024_07_09", "snapshot_2024_07_30"), + paradox = c("0.11.1", "1.0.0", "1.0.1", "1.0.0.c") +) + +data[, renv_project := gsub("paradox/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] +data[, paradox := factor(paradox)] +setorderv(data, c("algorithm", "size", "type")) + +plot_runtime = function(data) { + ggplot(data, aes(x = paradox, y = median_run_time)) + + geom_col(group = 1, fill = "#008080") + + geom_errorbar(aes(ymin = pmax(0, median_run_time - mad_run_time), ymax = median_run_time + mad_run_time), width = 0.5, position = position_dodge(0.9)) + + facet_wrap(~size, scales = "free_y", labeller = labeller(size = function(value) sprintf("%s Parameters", value))) + + labs(x = "paradox Version", y = "Runtime [ms]") + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +create_table = function(data) { + data = data[, -c("mad_run_time", "renv_project", "mean_run_time", "sd_run_time", "algorithm")] + + data_numeric = data[type == "numeric", -"type"] + data_categorical = data[type == "categorical", -"type"] + data_mixed = data[type == "mixed", -"type"] + + data = merge(data_numeric, data_categorical, by = c("paradox", "size"), suffixes = c("", "_categorical")) + data = merge(data, data_mixed, by = c("paradox", "size"), suffixes = c("", "_mixed")) + + + setcolorder(data, c("paradox", "size", "median_run_time", "median_run_time_categorical", "median_run_time_mixed")) + + data %>% + gt() %>% + cols_label( + paradox = "paradox Version", + size = "n Parameters", + median_run_time = "Median Runtime Numeric [ms]", + median_run_time_categorical = "Median Runtime Categorical [ms]", + median_run_time_mixed = "Median Runtime Mixed [ms]") %>% + fmt_number(columns = c("median_run_time", "median_run_time_categorical", "median_run_time_mixed"), n_sigfig = 2) %>% + tab_row_group( + label = "500 Parameters", + rows = size == 500 + ) %>% + tab_row_group( + label = "50 Parameters", + rows = size == 50 + ) %>% + tab_row_group( + label = "5 Parameters", + rows = size == 5 + ) + } +``` + +# Parameter Set + + +The runtime of different `ParamSet` functions is compared on different paradox versions. +The number of parameters is 5, 50 and 500. + +## Initialization + +```{r} +#| eval: false +ps( + param_1 = p_dbl(lower = 0, upper = 1), + ... +) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `ps()` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_initialize" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_initialize"]) +``` + +## Ids + +### Default + +```{r} +#| eval: false +param_set$ids() +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$ids()` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_ids" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_ids"]) +``` + +### Class + +```{r} +#| eval: false +param_set$ids(class = "ParamDbl") +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$ids(class)` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_ids_class" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_ids_class"]) +``` + +### Tags + +```{r} +#| eval: false +param_set$ids(tags = "a") +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$ids(type)` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_ids_tags" & type == "numeric"]) +``` + + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_ids_tags"]) +``` + +### Tags and Class + +```{r} +#| eval: false +param_set$ids(class = "ParamDbl", tags = "a") +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$ids(class, tags)` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_ids_class_tags" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_ids_class_tags"]) +``` + +## Get Values + +### Default + +```{r} +#| eval: false +param_set$get_values() +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$get_values()` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_get_values" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_get_values"]) +``` + +### Class + +```{r} +#| eval: false +param_set$get_values(class = "ParamDbl") +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$get_values(class)` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_get_values_class" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_get_values_class"]) +``` + +### Tags + +```{r} +#| eval: false +param_set$get_values(tags = "a") +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$get_values(tags)` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_get_values_tags" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_get_values_tags"]) +``` + +### Tags and Class + +```{r} +#| eval: false +param_set$get_values(class = "ParamDbl", tags = "a") +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$get_values(class, tags)` depending on the numbers of parameters and the paradox version. +plot_runtime(data[algorithm == "param_set_get_values_class_tags" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_get_values_class_tags"]) +``` + +## Set Values + +```{r} +#| eval: false +param_set$set_values(.values = values) +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `$set_values()` depending on the numbers of values and the paradox version. +plot_runtime(data[algorithm == "param_set_set_values" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_set_values"]) +``` + +## Lower + +```{r} +#| eval: false +param_set$lower +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `lower` depending on the numbers of values and the paradox version. +plot_runtime(data[algorithm == "param_set_lower" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_lower"]) +``` + +## Upper + +```{r} +#| eval: false +param_set$upper +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `lower` depending on the numbers of values and the paradox version. +plot_runtime(data[algorithm == "param_set_upper" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_upper"]) +``` + +## Class + +```{r} +#| eval: false +param_set$class +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `lower` depending on the numbers of values and the paradox version. +plot_runtime(data[algorithm == "param_set_class" & type == "numeric"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_class"]) +``` + +## Levels + +```{r} +#| eval: false +param_set$levels +``` + +```{r} +#| echo: false +#| fig-cap: | +#| Runtime of `lower` depending on the numbers of values and the paradox version. +plot_runtime(data[algorithm == "param_set_levels" & type == "categorical"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[algorithm == "param_set_levels"]) +``` + +# Object Size + +```{r} +#| include: false + +data = fread(here::here("mlr-org/benchmark_results/paradox_object_size.csv")) + +data[, renv_project := gsub("paradox/default/snapshots/", "", renv_project)] +data = data[snapshot, on = "renv_project"] + +data[, paradox := factor(paradox, levels = c("0.11.1", "1.0.0", "1.0.1", "1.0.0.c"))] + +data = data[, -c("renv_project")] + +plot_size = function(data) { + data = melt(data, id.vars = c("paradox", "object", "set_size"), measure.vars = c("size", "serialized_size"), value.name = "size", variable.name = "type") + + ggplot(data, aes(x = paradox, y = size, fill= type, group = type)) + + geom_col(position = "dodge") + + labs(x = "paradox Version", y = "Size [MB]") + + scale_fill_manual(labels = c("In Memory", "Serialized"), values = c("#008080", "#ff6347"), name = "Type") + + facet_wrap(~set_size, scales = "free_y", labeller = labeller(size = function(value) sprintf("%s Parameters", value))) + + theme_minimal(base_size = 7) + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +} + +create_table = function(data) { + data = data[, -c("object", "k")] + setcolorder(data, c("paradox", "set_size", "size", "serialized_size")) + + data %>% + gt() %>% + cols_label( + paradox = "paradox Version", + set_size = "n Parameters", + size = "In Memory [MB]", + serialized_size = "Serialized [MB]") %>% + fmt_number(columns = c("size", "serialized_size"), n_sigfig = 2) %>% + tab_row_group( + label = "500 Parameters", + rows = set_size == 500 + ) %>% + tab_row_group( + label = "50 Parameters", + rows = set_size == 50 + ) %>% + tab_row_group( + label = "5 Parameters", + rows = set_size == 5 + ) +} +``` + +## Parameter Set + +```{r} +#| eval: false +ps( + param_1 = p_dbl(lower = 0, upper = 1) +) +``` + +```{r} +#| echo: false +plot_size(data[object == "param_set"]) +``` + +```{r} +#| echo: false +#| column: body-outset +create_table(data[object == "param_set"]) +``` diff --git a/mlr-org/pipeops.qmd b/mlr-org/pipeops.qmd index 3b101c60..51cbe2d0 100644 --- a/mlr-org/pipeops.qmd +++ b/mlr-org/pipeops.qmd @@ -27,14 +27,7 @@ content = as.data.table(mlr_pipeops, objects = TRUE)[, .(key, label, packages, t content = content[key %nin% c( "crankcompose", "distrcompose", - "breslowcompose", - "fda.cor", - "fda.extract", - "fda.flatten", - "fda.fpca", - "fda.interpol", - "fda.scalerange", - "fda.smooth")] # deprecated, no help page + "breslowcompose")] # deprecated, no help page content[, packages := map(packages, function(value) setdiff(value, "mlr3pipelines"))] content = content[, .(key, label, packages, tags)] diff --git a/mlr-org/renv/activate.R b/mlr-org/renv/activate.R index 0eb51088..33b742aa 100644 --- a/mlr-org/renv/activate.R +++ b/mlr-org/renv/activate.R @@ -6,9 +6,7 @@ local({ attr(version, "sha") <- NULL # the project directory - project <- Sys.getenv("RENV_PROJECT") - if (!nzchar(project)) - project <- getwd() + project <- getwd() # use start-up diagnostics if enabled diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALSE") @@ -33,14 +31,6 @@ local({ if (!is.null(override)) return(override) - # if we're being run in a context where R_LIBS is already set, - # don't load -- presumably we're being run as a sub-process and - # the parent process has already set up library paths for us - rcmd <- Sys.getenv("R_CMD", unset = NA) - rlibs <- Sys.getenv("R_LIBS", unset = NA) - if (!is.na(rlibs) && !is.na(rcmd)) - return(FALSE) - # next, check environment variables # TODO: prefer using the configuration one in the future envvars <- c( @@ -60,22 +50,9 @@ local({ }) - # bail if we're not enabled - if (!enabled) { - - # if we're not enabled, we might still need to manually load - # the user profile here - profile <- Sys.getenv("R_PROFILE_USER", unset = "~/.Rprofile") - if (file.exists(profile)) { - cfg <- Sys.getenv("RENV_CONFIG_USER_PROFILE", unset = "TRUE") - if (tolower(cfg) %in% c("true", "t", "1")) - sys.source(profile, envir = globalenv()) - } - + if (!enabled) return(FALSE) - } - # avoid recursion if (identical(getOption("renv.autoloader.running"), TRUE)) { warning("ignoring recursive attempt to run renv autoloader") @@ -97,84 +74,84 @@ local({ if ("renv" %in% loadedNamespaces()) unloadNamespace("renv") - # load bootstrap tools + # load bootstrap tools ansify <- function(text) { if (renv_ansify_enabled()) renv_ansify_enhanced(text) else renv_ansify_default(text) } - + renv_ansify_enabled <- function() { - + override <- Sys.getenv("RENV_ANSIFY_ENABLED", unset = NA) if (!is.na(override)) return(as.logical(override)) - + pane <- Sys.getenv("RSTUDIO_CHILD_PROCESS_PANE", unset = NA) if (identical(pane, "build")) return(FALSE) - + testthat <- Sys.getenv("TESTTHAT", unset = "false") if (tolower(testthat) %in% "true") return(FALSE) - + iderun <- Sys.getenv("R_CLI_HAS_HYPERLINK_IDE_RUN", unset = "false") if (tolower(iderun) %in% "false") return(FALSE) - + TRUE - + } - + renv_ansify_default <- function(text) { text } - + renv_ansify_enhanced <- function(text) { - + # R help links pattern <- "`\\?(renv::(?:[^`])+)`" replacement <- "`\033]8;;ide:help:\\1\a?\\1\033]8;;\a`" text <- gsub(pattern, replacement, text, perl = TRUE) - + # runnable code pattern <- "`(renv::(?:[^`])+)`" replacement <- "`\033]8;;ide:run:\\1\a\\1\033]8;;\a`" text <- gsub(pattern, replacement, text, perl = TRUE) - + # return ansified text text - + } - + renv_ansify_init <- function() { - + envir <- renv_envir_self() if (renv_ansify_enabled()) assign("ansify", renv_ansify_enhanced, envir = envir) else assign("ansify", renv_ansify_default, envir = envir) - + } - + `%||%` <- function(x, y) { if (is.null(x)) y else x } - + catf <- function(fmt, ..., appendLF = TRUE) { - + quiet <- getOption("renv.bootstrap.quiet", default = FALSE) if (quiet) return(invisible()) - + msg <- sprintf(fmt, ...) cat(msg, file = stdout(), sep = if (appendLF) "\n" else "") - + invisible(msg) - + } - + header <- function(label, ..., prefix = "#", @@ -185,40 +162,40 @@ local({ n <- max(n - nchar(label) - nchar(prefix) - 2L, 8L) if (n <= 0) return(paste(prefix, label)) - + tail <- paste(rep.int(suffix, n), collapse = "") paste0(prefix, " ", label, " ", tail) - + } - + heredoc <- function(text, leave = 0) { - + # remove leading, trailing whitespace trimmed <- gsub("^\\s*\\n|\\n\\s*$", "", text) - + # split into lines lines <- strsplit(trimmed, "\n", fixed = TRUE)[[1L]] - + # compute common indent indent <- regexpr("[^[:space:]]", lines) common <- min(setdiff(indent, -1L)) - leave text <- paste(substring(lines, common), collapse = "\n") - + # substitute in ANSI links for executable renv code ansify(text) - + } - + startswith <- function(string, prefix) { substring(string, 1, nchar(prefix)) == prefix } - + bootstrap <- function(version, library) { - + friendly <- renv_bootstrap_version_friendly(version) section <- header(sprintf("Bootstrapping renv %s", friendly)) catf(section) - + # attempt to download renv catf("- Downloading renv ... ", appendLF = FALSE) withCallingHandlers( @@ -230,7 +207,7 @@ local({ ) catf("OK") on.exit(unlink(tarball), add = TRUE) - + # now attempt to install catf("- Installing renv ... ", appendLF = FALSE) withCallingHandlers( @@ -241,177 +218,177 @@ local({ } ) catf("OK") - + # add empty line to break up bootstrapping from normal output catf("") - + return(invisible()) } - + renv_bootstrap_tests_running <- function() { getOption("renv.tests.running", default = FALSE) } - + renv_bootstrap_repos <- function() { - + # get CRAN repository cran <- getOption("renv.repos.cran", "https://cloud.r-project.org") - + # check for repos override repos <- Sys.getenv("RENV_CONFIG_REPOS_OVERRIDE", unset = NA) if (!is.na(repos)) { - + # check for RSPM; if set, use a fallback repository for renv rspm <- Sys.getenv("RSPM", unset = NA) if (identical(rspm, repos)) repos <- c(RSPM = rspm, CRAN = cran) - + return(repos) - + } - + # check for lockfile repositories repos <- tryCatch(renv_bootstrap_repos_lockfile(), error = identity) if (!inherits(repos, "error") && length(repos)) return(repos) - + # retrieve current repos repos <- getOption("repos") - + # ensure @CRAN@ entries are resolved repos[repos == "@CRAN@"] <- cran - + # add in renv.bootstrap.repos if set default <- c(FALLBACK = "https://cloud.r-project.org") extra <- getOption("renv.bootstrap.repos", default = default) repos <- c(repos, extra) - + # remove duplicates that might've snuck in dupes <- duplicated(repos) | duplicated(names(repos)) repos[!dupes] - + } - + renv_bootstrap_repos_lockfile <- function() { - + lockpath <- Sys.getenv("RENV_PATHS_LOCKFILE", unset = "renv.lock") if (!file.exists(lockpath)) return(NULL) - + lockfile <- tryCatch(renv_json_read(lockpath), error = identity) if (inherits(lockfile, "error")) { warning(lockfile) return(NULL) } - + repos <- lockfile$R$Repositories if (length(repos) == 0) return(NULL) - + keys <- vapply(repos, `[[`, "Name", FUN.VALUE = character(1)) vals <- vapply(repos, `[[`, "URL", FUN.VALUE = character(1)) names(vals) <- keys - + return(vals) - + } - + renv_bootstrap_download <- function(version) { - + sha <- attr(version, "sha", exact = TRUE) - + methods <- if (!is.null(sha)) { - + # attempting to bootstrap a development version of renv c( function() renv_bootstrap_download_tarball(sha), function() renv_bootstrap_download_github(sha) ) - + } else { - + # attempting to bootstrap a release version of renv c( function() renv_bootstrap_download_tarball(version), function() renv_bootstrap_download_cran_latest(version), function() renv_bootstrap_download_cran_archive(version) ) - + } - + for (method in methods) { path <- tryCatch(method(), error = identity) if (is.character(path) && file.exists(path)) return(path) } - + stop("All download methods failed") - + } - + renv_bootstrap_download_impl <- function(url, destfile) { - + mode <- "wb" - + # https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17715 fixup <- Sys.info()[["sysname"]] == "Windows" && substring(url, 1L, 5L) == "file:" - + if (fixup) mode <- "w+b" - + args <- list( url = url, destfile = destfile, mode = mode, quiet = TRUE ) - + if ("headers" %in% names(formals(utils::download.file))) { headers <- renv_bootstrap_download_custom_headers(url) if (length(headers) && is.character(headers)) args$headers <- headers } - + do.call(utils::download.file, args) - + } - + renv_bootstrap_download_custom_headers <- function(url) { - + headers <- getOption("renv.download.headers") if (is.null(headers)) return(character()) - + if (!is.function(headers)) stopf("'renv.download.headers' is not a function") - + headers <- headers(url) if (length(headers) == 0L) return(character()) - + if (is.list(headers)) headers <- unlist(headers, recursive = FALSE, use.names = TRUE) - + ok <- is.character(headers) && is.character(names(headers)) && all(nzchar(names(headers))) - + if (!ok) stop("invocation of 'renv.download.headers' did not return a named character vector") - + headers - + } - + renv_bootstrap_download_cran_latest <- function(version) { - + spec <- renv_bootstrap_download_cran_latest_find(version) type <- spec$type repos <- spec$repos - + baseurl <- utils::contrib.url(repos = repos, type = type) ext <- if (identical(type, "source")) ".tar.gz" @@ -421,39 +398,39 @@ local({ ".tgz" name <- sprintf("renv_%s%s", version, ext) url <- paste(baseurl, name, sep = "/") - + destfile <- file.path(tempdir(), name) status <- tryCatch( renv_bootstrap_download_impl(url, destfile), condition = identity ) - + if (inherits(status, "condition")) return(FALSE) - + # report success and return destfile - + } - + renv_bootstrap_download_cran_latest_find <- function(version) { - + # check whether binaries are supported on this system binary <- getOption("renv.bootstrap.binary", default = TRUE) && !identical(.Platform$pkgType, "source") && !identical(getOption("pkgType"), "source") && Sys.info()[["sysname"]] %in% c("Darwin", "Windows") - + types <- c(if (binary) "binary", "source") - + # iterate over types + repositories for (type in types) { for (repos in renv_bootstrap_repos()) { - + # build arguments for utils::available.packages() call args <- list(type = type, repos = repos) - + # add custom headers if available -- note that # utils::available.packages() will pass this to download.file() if ("headers" %in% names(formals(utils::download.file))) { @@ -461,7 +438,7 @@ local({ if (length(headers) && is.character(headers)) args$headers <- headers } - + # retrieve package database db <- tryCatch( as.data.frame( @@ -470,83 +447,83 @@ local({ ), error = identity ) - + if (inherits(db, "error")) next - + # check for compatible entry entry <- db[db$Package %in% "renv" & db$Version %in% version, ] if (nrow(entry) == 0) next - + # found it; return spec to caller spec <- list(entry = entry, type = type, repos = repos) return(spec) - + } } - + # if we got here, we failed to find renv fmt <- "renv %s is not available from your declared package repositories" stop(sprintf(fmt, version)) - + } - + renv_bootstrap_download_cran_archive <- function(version) { - + name <- sprintf("renv_%s.tar.gz", version) repos <- renv_bootstrap_repos() urls <- file.path(repos, "src/contrib/Archive/renv", name) destfile <- file.path(tempdir(), name) - + for (url in urls) { - + status <- tryCatch( renv_bootstrap_download_impl(url, destfile), condition = identity ) - + if (identical(status, 0L)) return(destfile) - + } - + return(FALSE) - + } - + renv_bootstrap_download_tarball <- function(version) { - + # if the user has provided the path to a tarball via # an environment variable, then use it tarball <- Sys.getenv("RENV_BOOTSTRAP_TARBALL", unset = NA) if (is.na(tarball)) return() - + # allow directories if (dir.exists(tarball)) { name <- sprintf("renv_%s.tar.gz", version) tarball <- file.path(tarball, name) } - + # bail if it doesn't exist if (!file.exists(tarball)) { - + # let the user know we weren't able to honour their request fmt <- "- RENV_BOOTSTRAP_TARBALL is set (%s) but does not exist." msg <- sprintf(fmt, tarball) warning(msg) - + # bail return() - + } - + catf("- Using local tarball '%s'.", tarball) tarball - + } - + renv_bootstrap_github_token <- function() { for (envvar in c("GITHUB_TOKEN", "GITHUB_PAT", "GH_TOKEN")) { envval <- Sys.getenv(envvar, unset = NA) @@ -554,13 +531,13 @@ local({ return(envval) } } - + renv_bootstrap_download_github <- function(version) { - + enabled <- Sys.getenv("RENV_BOOTSTRAP_FROM_GITHUB", unset = "TRUE") if (!identical(enabled, "TRUE")) return(FALSE) - + # prepare download options token <- renv_bootstrap_github_token() if (nzchar(Sys.which("curl")) && nzchar(token)) { @@ -576,25 +553,25 @@ local({ options(download.file.method = "wget", download.file.extra = extra) on.exit(do.call(base::options, saved), add = TRUE) } - + url <- file.path("https://api.github.com/repos/rstudio/renv/tarball", version) name <- sprintf("renv_%s.tar.gz", version) destfile <- file.path(tempdir(), name) - + status <- tryCatch( renv_bootstrap_download_impl(url, destfile), condition = identity ) - + if (!identical(status, 0L)) return(FALSE) - + renv_bootstrap_download_augment(destfile) - + return(destfile) - + } - + # Add Sha to DESCRIPTION. This is stop gap until #890, after which we # can use renv::install() to fully capture metadata. renv_bootstrap_download_augment <- function(destfile) { @@ -602,13 +579,13 @@ local({ if (is.null(sha)) { return() } - + # Untar tempdir <- tempfile("renv-github-") on.exit(unlink(tempdir, recursive = TRUE), add = TRUE) untar(destfile, exdir = tempdir) pkgdir <- dir(tempdir, full.names = TRUE)[[1]] - + # Modify description desc_path <- file.path(pkgdir, "DESCRIPTION") desc_lines <- readLines(desc_path) @@ -622,173 +599,170 @@ local({ paste("RemoteSha: ", sha) ) writeLines(c(desc_lines[desc_lines != ""], remotes_fields), con = desc_path) - + # Re-tar local({ old <- setwd(tempdir) on.exit(setwd(old), add = TRUE) - + tar(destfile, compression = "gzip") }) invisible() } - + # Extract the commit hash from a git archive. Git archives include the SHA1 # hash as the comment field of the tarball pax extended header # (see https://www.kernel.org/pub/software/scm/git/docs/git-archive.html) # For GitHub archives this should be the first header after the default one # (512 byte) header. renv_bootstrap_git_extract_sha1_tar <- function(bundle) { - + # open the bundle for reading # We use gzcon for everything because (from ?gzcon) # > Reading from a connection which does not supply a 'gzip' magic # > header is equivalent to reading from the original connection conn <- gzcon(file(bundle, open = "rb", raw = TRUE)) on.exit(close(conn)) - + # The default pax header is 512 bytes long and the first pax extended header # with the comment should be 51 bytes long # `52 comment=` (11 chars) + 40 byte SHA1 hash len <- 0x200 + 0x33 res <- rawToChar(readBin(conn, "raw", n = len)[0x201:len]) - + if (grepl("^52 comment=", res)) { sub("52 comment=", "", res) } else { NULL } } - + renv_bootstrap_install <- function(version, tarball, library) { - + # attempt to install it into project library dir.create(library, showWarnings = FALSE, recursive = TRUE) output <- renv_bootstrap_install_impl(library, tarball) - + # check for successful install status <- attr(output, "status") if (is.null(status) || identical(status, 0L)) return(status) - + # an error occurred; report it header <- "installation of renv failed" lines <- paste(rep.int("=", nchar(header)), collapse = "") text <- paste(c(header, lines, output), collapse = "\n") stop(text) - + } - + renv_bootstrap_install_impl <- function(library, tarball) { - + # invoke using system2 so we can capture and report output bin <- R.home("bin") exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R" R <- file.path(bin, exe) - + args <- c( "--vanilla", "CMD", "INSTALL", "--no-multiarch", "-l", shQuote(path.expand(library)), shQuote(path.expand(tarball)) ) - + system2(R, args, stdout = TRUE, stderr = TRUE) - + } - + renv_bootstrap_platform_prefix <- function() { - + # construct version prefix version <- paste(R.version$major, R.version$minor, sep = ".") prefix <- paste("R", numeric_version(version)[1, 1:2], sep = "-") - + # include SVN revision for development versions of R # (to avoid sharing platform-specific artefacts with released versions of R) devel <- identical(R.version[["status"]], "Under development (unstable)") || identical(R.version[["nickname"]], "Unsuffered Consequences") - + if (devel) prefix <- paste(prefix, R.version[["svn rev"]], sep = "-r") - + # build list of path components components <- c(prefix, R.version$platform) - + # include prefix if provided by user prefix <- renv_bootstrap_platform_prefix_impl() if (!is.na(prefix) && nzchar(prefix)) components <- c(prefix, components) - + # build prefix paste(components, collapse = "/") - + } - + renv_bootstrap_platform_prefix_impl <- function() { - + # if an explicit prefix has been supplied, use it prefix <- Sys.getenv("RENV_PATHS_PREFIX", unset = NA) if (!is.na(prefix)) return(prefix) - + # if the user has requested an automatic prefix, generate it auto <- Sys.getenv("RENV_PATHS_PREFIX_AUTO", unset = NA) - if (is.na(auto) && getRversion() >= "4.4.0") - auto <- "TRUE" - if (auto %in% c("TRUE", "True", "true", "1")) return(renv_bootstrap_platform_prefix_auto()) - + # empty string on failure "" - + } - + renv_bootstrap_platform_prefix_auto <- function() { - + prefix <- tryCatch(renv_bootstrap_platform_os(), error = identity) if (inherits(prefix, "error") || prefix %in% "unknown") { - + msg <- paste( "failed to infer current operating system", "please file a bug report at https://github.com/rstudio/renv/issues", sep = "; " ) - + warning(msg) - + } - + prefix - + } - + renv_bootstrap_platform_os <- function() { - + sysinfo <- Sys.info() sysname <- sysinfo[["sysname"]] - + # handle Windows + macOS up front if (sysname == "Windows") return("windows") else if (sysname == "Darwin") return("macos") - + # check for os-release files for (file in c("/etc/os-release", "/usr/lib/os-release")) if (file.exists(file)) return(renv_bootstrap_platform_os_via_os_release(file, sysinfo)) - + # check for redhat-release files if (file.exists("/etc/redhat-release")) return(renv_bootstrap_platform_os_via_redhat_release()) - + "unknown" - + } - + renv_bootstrap_platform_os_via_os_release <- function(file, sysinfo) { - + # read /etc/os-release release <- utils::read.table( file = file, @@ -798,13 +772,13 @@ local({ comment.char = "#", stringsAsFactors = FALSE ) - + vars <- as.list(release$Value) names(vars) <- release$Key - + # get os name os <- tolower(sysinfo[["sysname"]]) - + # read id id <- "unknown" for (field in c("ID", "ID_LIKE")) { @@ -813,7 +787,7 @@ local({ break } } - + # read version version <- "unknown" for (field in c("UBUNTU_CODENAME", "VERSION_CODENAME", "VERSION_ID", "BUILD_ID")) { @@ -822,17 +796,17 @@ local({ break } } - + # join together paste(c(os, id, version), collapse = "-") - + } - + renv_bootstrap_platform_os_via_redhat_release <- function() { - + # read /etc/redhat-release contents <- readLines("/etc/redhat-release", warn = FALSE) - + # infer id id <- if (grepl("centos", contents, ignore.case = TRUE)) "centos" @@ -840,73 +814,73 @@ local({ "redhat" else "unknown" - + # try to find a version component (very hacky) version <- "unknown" - + parts <- strsplit(contents, "[[:space:]]")[[1L]] for (part in parts) { - + nv <- tryCatch(numeric_version(part), error = identity) if (inherits(nv, "error")) next - + version <- nv[1, 1] break - + } - + paste(c("linux", id, version), collapse = "-") - + } - + renv_bootstrap_library_root_name <- function(project) { - + # use project name as-is if requested asis <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT_ASIS", unset = "FALSE") if (asis) return(basename(project)) - + # otherwise, disambiguate based on project's path id <- substring(renv_bootstrap_hash_text(project), 1L, 8L) paste(basename(project), id, sep = "-") - + } - + renv_bootstrap_library_root <- function(project) { - + prefix <- renv_bootstrap_profile_prefix() - + path <- Sys.getenv("RENV_PATHS_LIBRARY", unset = NA) if (!is.na(path)) return(paste(c(path, prefix), collapse = "/")) - + path <- renv_bootstrap_library_root_impl(project) if (!is.null(path)) { name <- renv_bootstrap_library_root_name(project) return(paste(c(path, prefix, name), collapse = "/")) } - + renv_bootstrap_paths_renv("library", project = project) - + } - + renv_bootstrap_library_root_impl <- function(project) { - + root <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT", unset = NA) if (!is.na(root)) return(root) - + type <- renv_bootstrap_project_type(project) if (identical(type, "package")) { userdir <- renv_bootstrap_user_dir() return(file.path(userdir, "library")) } - + } - + renv_bootstrap_validate_version <- function(version, description = NULL) { - + # resolve description file # # avoid passing lib.loc to `packageDescription()` below, since R will @@ -914,121 +888,122 @@ local({ # this function should only be called after 'renv' is loaded # https://github.com/rstudio/renv/issues/1625 description <- description %||% packageDescription("renv") - + # check whether requested version 'version' matches loaded version of renv sha <- attr(version, "sha", exact = TRUE) valid <- if (!is.null(sha)) renv_bootstrap_validate_version_dev(sha, description) else renv_bootstrap_validate_version_release(version, description) - + if (valid) return(TRUE) - + # the loaded version of renv doesn't match the requested version; # give the user instructions on how to proceed - dev <- identical(description[["RemoteType"]], "github") - remote <- if (dev) + remote <- if (!is.null(description[["RemoteSha"]])) { paste("rstudio/renv", description[["RemoteSha"]], sep = "@") - else + } else { paste("renv", description[["Version"]], sep = "@") - + } + # display both loaded version + sha if available friendly <- renv_bootstrap_version_friendly( version = description[["Version"]], - sha = if (dev) description[["RemoteSha"]] + sha = description[["RemoteSha"]] + ) + + fmt <- paste( + "renv %1$s was loaded from project library, but this project is configured to use renv %2$s.", + "- Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile.", + "- Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library.", + sep = "\n" ) - - fmt <- heredoc(" - renv %1$s was loaded from project library, but this project is configured to use renv %2$s. - - Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile. - - Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library. - ") catf(fmt, friendly, renv_bootstrap_version_friendly(version), remote) - + FALSE - + } - + renv_bootstrap_validate_version_dev <- function(version, description) { expected <- description[["RemoteSha"]] is.character(expected) && startswith(expected, version) } - + renv_bootstrap_validate_version_release <- function(version, description) { expected <- description[["Version"]] is.character(expected) && identical(expected, version) } - + renv_bootstrap_hash_text <- function(text) { - + hashfile <- tempfile("renv-hash-") on.exit(unlink(hashfile), add = TRUE) - + writeLines(text, con = hashfile) tools::md5sum(hashfile) - + } - + renv_bootstrap_load <- function(project, libpath, version) { - + # try to load renv from the project library if (!requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) return(FALSE) - + # warn if the version of renv loaded does not match renv_bootstrap_validate_version(version) - + # execute renv load hooks, if any hooks <- getHook("renv::autoload") for (hook in hooks) if (is.function(hook)) tryCatch(hook(), error = warnify) - + # load the project renv::load(project) - + TRUE - + } - + renv_bootstrap_profile_load <- function(project) { - + # if RENV_PROFILE is already set, just use that profile <- Sys.getenv("RENV_PROFILE", unset = NA) if (!is.na(profile) && nzchar(profile)) return(profile) - + # check for a profile file (nothing to do if it doesn't exist) path <- renv_bootstrap_paths_renv("profile", profile = FALSE, project = project) if (!file.exists(path)) return(NULL) - + # read the profile, and set it if it exists contents <- readLines(path, warn = FALSE) if (length(contents) == 0L) return(NULL) - + # set RENV_PROFILE profile <- contents[[1L]] if (!profile %in% c("", "default")) Sys.setenv(RENV_PROFILE = profile) - + profile - + } - + renv_bootstrap_profile_prefix <- function() { profile <- renv_bootstrap_profile_get() if (!is.null(profile)) return(file.path("profiles", profile, "renv")) } - + renv_bootstrap_profile_get <- function() { profile <- Sys.getenv("RENV_PROFILE", unset = "") renv_bootstrap_profile_normalize(profile) } - + renv_bootstrap_profile_set <- function(profile) { profile <- renv_bootstrap_profile_normalize(profile) if (is.null(profile)) @@ -1036,25 +1011,25 @@ local({ else Sys.setenv(RENV_PROFILE = profile) } - + renv_bootstrap_profile_normalize <- function(profile) { - + if (is.null(profile) || profile %in% c("", "default")) return(NULL) - + profile - + } - + renv_bootstrap_path_absolute <- function(path) { - + substr(path, 1L, 1L) %in% c("~", "/", "\\") || ( substr(path, 1L, 1L) %in% c(letters, LETTERS) && substr(path, 2L, 3L) %in% c(":/", ":\\") ) - + } - + renv_bootstrap_paths_renv <- function(..., profile = TRUE, project = NULL) { renv <- Sys.getenv("RENV_PATHS_RENV", unset = "renv") root <- if (renv_bootstrap_path_absolute(renv)) NULL else project @@ -1062,50 +1037,50 @@ local({ components <- c(root, renv, prefix, ...) paste(components, collapse = "/") } - + renv_bootstrap_project_type <- function(path) { - + descpath <- file.path(path, "DESCRIPTION") if (!file.exists(descpath)) return("unknown") - + desc <- tryCatch( read.dcf(descpath, all = TRUE), error = identity ) - + if (inherits(desc, "error")) return("unknown") - + type <- desc$Type if (!is.null(type)) return(tolower(type)) - + package <- desc$Package if (!is.null(package)) return("package") - + "unknown" - + } - + renv_bootstrap_user_dir <- function() { dir <- renv_bootstrap_user_dir_impl() path.expand(chartr("\\", "/", dir)) } - + renv_bootstrap_user_dir_impl <- function() { - + # use local override if set override <- getOption("renv.userdir.override") if (!is.null(override)) return(override) - + # use R_user_dir if available tools <- asNamespace("tools") if (is.function(tools$R_user_dir)) return(tools$R_user_dir("renv", "cache")) - + # try using our own backfill for older versions of R envvars <- c("R_USER_CACHE_DIR", "XDG_CACHE_HOME") for (envvar in envvars) { @@ -1113,7 +1088,7 @@ local({ if (!is.na(root)) return(file.path(root, "R/renv")) } - + # use platform-specific default fallbacks if (Sys.info()[["sysname"]] == "Windows") file.path(Sys.getenv("LOCALAPPDATA"), "R/cache/R/renv") @@ -1121,109 +1096,109 @@ local({ "~/Library/Caches/org.R-project.R/R/renv" else "~/.cache/R/renv" - + } - + renv_bootstrap_version_friendly <- function(version, shafmt = NULL, sha = NULL) { sha <- sha %||% attr(version, "sha", exact = TRUE) parts <- c(version, sprintf(shafmt %||% " [sha: %s]", substring(sha, 1L, 7L))) paste(parts, collapse = "") } - + renv_bootstrap_exec <- function(project, libpath, version) { if (!renv_bootstrap_load(project, libpath, version)) renv_bootstrap_run(version, libpath) } - + renv_bootstrap_run <- function(version, libpath) { - + # perform bootstrap bootstrap(version, libpath) - + # exit early if we're just testing bootstrap if (!is.na(Sys.getenv("RENV_BOOTSTRAP_INSTALL_ONLY", unset = NA))) return(TRUE) - + # try again to load if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { return(renv::load(project = getwd())) } - + # failed to download or load renv; warn the user msg <- c( "Failed to find an renv installation: the project will not be loaded.", "Use `renv::activate()` to re-initialize the project." ) - + warning(paste(msg, collapse = "\n"), call. = FALSE) - + } - + renv_json_read <- function(file = NULL, text = NULL) { - + jlerr <- NULL - + # if jsonlite is loaded, use that instead if ("jsonlite" %in% loadedNamespaces()) { - - json <- tryCatch(renv_json_read_jsonlite(file, text), error = identity) + + json <- catch(renv_json_read_jsonlite(file, text)) if (!inherits(json, "error")) return(json) - + jlerr <- json - + } - + # otherwise, fall back to the default JSON reader - json <- tryCatch(renv_json_read_default(file, text), error = identity) + json <- catch(renv_json_read_default(file, text)) if (!inherits(json, "error")) return(json) - + # report an error if (!is.null(jlerr)) stop(jlerr) else stop(json) - + } - + renv_json_read_jsonlite <- function(file = NULL, text = NULL) { - text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + text <- paste(text %||% read(file), collapse = "\n") jsonlite::fromJSON(txt = text, simplifyVector = FALSE) } - + renv_json_read_default <- function(file = NULL, text = NULL) { - + # find strings in the JSON - text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + text <- paste(text %||% read(file), collapse = "\n") pattern <- '["](?:(?:\\\\.)|(?:[^"\\\\]))*?["]' locs <- gregexpr(pattern, text, perl = TRUE)[[1]] - + # if any are found, replace them with placeholders replaced <- text strings <- character() replacements <- character() - + if (!identical(c(locs), -1L)) { - + # get the string values starts <- locs ends <- locs + attr(locs, "match.length") - 1L strings <- substring(text, starts, ends) - + # only keep those requiring escaping strings <- grep("[[\\]{}:]", strings, perl = TRUE, value = TRUE) - + # compute replacements replacements <- sprintf('"\032%i\032"', seq_along(strings)) - + # replace the strings mapply(function(string, replacement) { replaced <<- sub(string, replacement, replaced, fixed = TRUE) }, strings, replacements) - + } - + # transform the JSON into something the R parser understands transformed <- replaced transformed <- gsub("{}", "`names<-`(list(), character())", transformed, fixed = TRUE) @@ -1231,38 +1206,38 @@ local({ transformed <- gsub("[]}]", ")", transformed, perl = TRUE) transformed <- gsub(":", "=", transformed, fixed = TRUE) text <- paste(transformed, collapse = "\n") - + # parse it json <- parse(text = text, keep.source = FALSE, srcfile = NULL)[[1L]] - + # construct map between source strings, replaced strings map <- as.character(parse(text = strings)) names(map) <- as.character(parse(text = replacements)) - + # convert to list map <- as.list(map) - + # remap strings in object - remapped <- renv_json_read_remap(json, map) - + remapped <- renv_json_remap(json, map) + # evaluate eval(remapped, envir = baseenv()) - + } - - renv_json_read_remap <- function(json, map) { - + + renv_json_remap <- function(json, map) { + # fix names if (!is.null(names(json))) { lhs <- match(names(json), names(map), nomatch = 0L) rhs <- match(names(map), names(json), nomatch = 0L) names(json)[rhs] <- map[lhs] } - + # fix values if (is.character(json)) return(map[[json]] %||% json) - + # handle true, false, null if (is.name(json)) { text <- as.character(json) @@ -1273,16 +1248,16 @@ local({ else if (text == "null") return(NULL) } - + # recurse if (is.recursive(json)) { for (i in seq_along(json)) { - json[i] <- list(renv_json_read_remap(json[[i]], map)) + json[i] <- list(renv_json_remap(json[[i]], map)) } } - + json - + } # load the renv profile, if any diff --git a/mlr-org/torch_pipeops.qmd b/mlr-org/torch_pipeops.qmd index f8b788c0..b852462e 100644 --- a/mlr-org/torch_pipeops.qmd +++ b/mlr-org/torch_pipeops.qmd @@ -21,7 +21,6 @@ library("mlr3torch") #| column: page content = as.data.table(mlr_pipeops) -content = content[key %nin% c("crankcompose", "distrcompose")] # deprecated, no help page content[, packages := map(packages, function(value) setdiff(value, "mlr3pipelines"))] content = content[, .(key, label, packages, tags)] content = content[mlr3misc::map_lgl(packages, function(ps) "torch" %in% ps), ]