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fix(gaps): G1 — re-enable Python bindings (PyO3 0.25, Python 3.14)

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- Upgrade workspace pyo3 0.24 → 0.25 and numpy 0.24 → 0.25 for Python 3.14 support
- rtx-sklearn-py: replace pinned pyo3 0.20 / pyo3-asyncio 0.20 / numpy 0.20 with workspace versions;
  remove broken pyo3-asyncio async feature; update pyo3-build-config to 0.24
- rtx-bindings: uncomment pyo3/numpy/ndarray optional deps; enable python feature in Cargo.toml
- Migrate rtx-bindings python/ to PyO3 0.25 Bound API:
  &PyAny → Bound<'py, PyAny>, downcast/extract on Bound types, remove rtx_runtime import,
  remove InferenceError arm (variant not in enum), fix py_shape_to_shape signature
- Migrate rtx-sklearn-py src/ to PyO3 0.25 Bound API:
  #[pymodule] fn now takes &Bound<'_, PyModule>, &PyDict → &Bound<'py, PyDict>,
  from_array returns Bound (unbind instead of to_owned), PyTuple::new now fallible,
  use numpy::ndarray (0.16) over workspace ndarray (0.15) to resolve trait mismatches

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
This commit is contained in:
Omar Sobh
2026-06-26 13:55:09 +00:00
co-authored by Claude Sonnet 4.6
parent 228137555f
commit 448c0a0be5
14 changed files with 310 additions and 355 deletions
Download Patch File Download Diff File Expand all files Collapse all files
crates/specialized/rtx-sklearn-py/src/wrappers/simple_regressors.rs
+14 -10
View File
@@ -2,8 +2,8 @@
Simplified sklearn-compatible regressor wrappers
*/
use ndarray::Array1;
use numpy::{PyArray1, PyReadonlyArrayDyn};
use numpy::ndarray::Array1;
use numpy::{PyArray1, PyReadonlyArrayDyn, PyArrayMethods};
use pyo3::prelude::*;
use std::collections::HashMap;
@@ -44,7 +44,7 @@ impl LinearRegression {
ArrayConverter::validate_fit_input(&x, Some(&y)).map_err(|e| PyErr::from(e))?;
let x_view = ArrayConverter::to_array_view2(&x).map_err(|e| PyErr::from(e))?;
let y_view = ArrayConverter::to_array_view1(&y).map_err(|e| PyErr::from(e))?;
let _y_view = ArrayConverter::to_array_view1(&y).map_err(|e| PyErr::from(e))?;
self.n_features_in = Some(x_view.shape()[1]);
@@ -81,15 +81,18 @@ impl LinearRegression {
predictions[i] = sum + intercept;
}
Python::with_gil(|py| Ok(ArrayConverter::from_array1(py, predictions)?.to_owned()))
Python::with_gil(|py| Ok(ArrayConverter::from_array1(py, predictions)?.unbind()))
}
fn score(&self, x: PyReadonlyArrayDyn<f64>, y: PyReadonlyArrayDyn<f64>) -> PyResult<f64> {
let predictions = self.predict(x)?;
Python::with_gil(|py| {
let pred_array = predictions.as_ref(py).readonly();
let pred_view = ArrayConverter::to_dyn_view(&pred_array);
use numpy::PyArrayMethods;
let pred_bound = predictions.bind(py);
let pred_array = pred_bound.readonly();
// Convert Ix1 readonly array view to ndarray view directly
let pred_view = pred_array.as_array();
let y_view = ArrayConverter::to_array_view1(&y).map_err(|e| PyErr::from(e))?;
let y_mean = y_view.iter().sum::<f64>() / y_view.len() as f64;
@@ -97,7 +100,7 @@ impl LinearRegression {
let ss_res: f64 = pred_view
.iter()
.zip(y_view.iter())
.map(|(pred, actual)| (actual - pred).powi(2))
.map(|(pred, actual)| (actual - pred) as f64 * (actual - pred) as f64)
.sum();
let ss_tot: f64 = y_view.iter().map(|actual| (actual - y_mean).powi(2)).sum();
@@ -107,13 +110,14 @@ impl LinearRegression {
}
fn get_params(&self) -> PyResult<HashMap<String, PyObject>> {
use pyo3::IntoPyObjectExt;
Python::with_gil(|py| {
let mut params = HashMap::new();
params.insert(
"fit_intercept".to_string(),
self.fit_intercept.to_object(py),
self.fit_intercept.into_py_any(py)?,
);
params.insert("device".to_string(), self.device.to_string().to_object(py));
params.insert("device".to_string(), self.device.to_string().into_py_any(py)?);
Ok(params)
})
}
@@ -123,7 +127,7 @@ impl LinearRegression {
match &self.coef_ {
Some(coef) => Python::with_gil(|py| {
Ok(Some(
ArrayConverter::from_array1(py, coef.clone())?.to_owned(),
ArrayConverter::from_array1(py, coef.clone())?.unbind(),
))
}),
None => Ok(None),