From 86a3289e81dcb23f0c6208780d2b308f64838cee Mon Sep 17 00:00:00 2001 From: Claude Date: Wed, 1 Jul 2026 12:23:44 +0000 Subject: [PATCH] =?UTF-8?q?docs(readme):=20mark=20scikit-learn=E2=86=92MLJ?= =?UTF-8?q?=20conversion=20as=20planned,=20not=20shipped=20(G38)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The "Supported Patterns" table claimed `scikit-learn pipelines → MLJ.jl` as a translated pattern, but the codegen implements no sklearn→MLJ conversions: the `sk` alias is DETECTED in src/codegen/parser.rs, yet src/codegen/julia_gen.rs has zero sklearn/MLJ mappings. Marked the row ⚠ Planned to match reality. Ground-truthed the rest of the table against src/codegen/julia_gen.rs — every other row is genuinely implemented (pandas, numpy, scipy.optimize/integrate, matplotlib→Plots, R data.frame via dplyr/tidyr/stats), so only the sklearn row was an overclaim. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c205f40..80d3f15 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ Julianiser recognises and translates these common data science patterns: | `numpy` arrays | Native Julia arrays | Broadcasting, slicing, linear algebra | | `scipy.optimize` | `Optim.jl` / Julia stdlib | Minimisation, root-finding, curve fitting | | `matplotlib` / `seaborn` | `Plots.jl` / `Makie.jl` | Static and interactive plotting | -| `scikit-learn` pipelines | `MLJ.jl` | Train/predict/evaluate pattern | +| `scikit-learn` pipelines | `MLJ.jl` | ⚠ **Planned** — sklearn imports are *detected* (the `sk` alias is recognised in the parser), but no sklearn→MLJ function conversions are mapped yet in the codegen | | R `data.frame` / `tibble` | `DataFrames.jl` | dplyr-style verbs mapped to Julia | | R `apply` / `sapply` / `lapply` | Julia broadcasting / `map` | Vectorised equivalents |