feat: New base class for python-based learners #484
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be-marc
requested changes
Sep 22, 2025
R/LearnerPythonClassif.R
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| initialize = function(id, | ||
| feature_types = c("logical","integer","numeric","factor","ordered"), | ||
| predict_types = c("response", "prob"), | ||
| param_set = paradox::ParamSet$new(), |
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| param_set = paradox::ParamSet$new(), | |
| param_set = ps() |
R/LearnerPythonClassif.R
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| # Python requirements | ||
| py_packages, | ||
| python_version, | ||
| method = c("auto","virtualenv","conda"), |
R/LearnerPythonClassif.R
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| method = c("auto","virtualenv","conda"), | ||
| envname = NULL) { | ||
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| method = match.arg(method) |
R/LearnerPythonClassif.R
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| method = match.arg(method) | ||
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| base_ps = ps( |
R/LearnerPythonClassif.R
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| structure( | ||
| list( | ||
| fitted = fit$model, # PyObject | ||
| meta = meta, # any R metadata (e.g., classes) |
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Add the elements of meta directly to the returned list.
R/LearnerPythonClassif.R
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| .predict = function(task) { | ||
| self$ensure_deps() | ||
| if (is.null(self$model)) stop("Model not trained yet.") |
R/LearnerPythonClassif.R
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| class_labels = task$class_names | ||
| ) | ||
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| if ("prob" %in% self$predict_types && !is.null(res$prob)) { |
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This is also handled in mlr3. Take a look at the other learners.
R/LearnerPythonClassif.R
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| reticulate::py_require(model$py_modules, python_version = model$py_version) | ||
| pickle <- reticulate::import("pickle") | ||
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| raw <- as.raw(pickle$dumps(model$fitted)) |
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Maybe this will crash with models larger than 4 GB. Can you test this?
R/LearnerPythonClassif.R
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| # capture any additional classes (e.g. "classif.tabpfn_model"), keep first one | ||
| learner_class <- setdiff(class(model), "pybytes_model") | ||
| learner_class <- if (length(learner_class)) learner_class[1L] else NULL |
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This should be an assert. learner_class should never be NULL
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When you are finished with this, you can add a helper function for converting config space objects from python to paradox in R. |
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This pr implements a novel base class for Python based learners.
Open ToDo's: