Add training infos getters at Python level

python/egobox/tests/test_gpmix.py

@@ -22,11 +22,11 @@ def griewank(x):
 
 class TestGpMix(unittest.TestCase):
     def setUp(self):
-        xt = np.array([[0.0, 1.0, 2.0, 3.0, 4.0]]).T
-        yt = np.array([[0.0, 1.0, 1.5, 0.9, 1.0]]).T
+        self.xt = np.array([[0.0, 1.0, 2.0, 3.0, 4.0]]).T
+        self.yt = np.array([[0.0, 1.0, 1.5, 0.9, 1.0]]).T
 
         gpmix = egx.GpMix()  # or egx.Gpx.builder()
-        self.gpx = gpmix.fit(xt, yt)
+        self.gpx = gpmix.fit(self.xt, self.yt)
 
     def test_gpx_kriging(self):
         gpx = self.gpx
@@ -76,6 +76,12 @@ def test_gpx_save_load(self):
             0.0, gpx2.predict_var(np.array([[1.1]])).item(), delta=1e-3
         )
 
+    def test_training_params(self):
+        self.assertEquals(self.gpx.dims(), (1, 1))
+        (xdata, ydata) = self.gpx.training_data()
+        np.testing.assert_array_equal(xdata, self.xt)
+        np.testing.assert_array_equal(ydata, self.yt)
+
     def test_kpls_griewank(self):
         lb = -600
         ub = 600
@@ -106,6 +112,7 @@ def test_kpls_griewank(self):
         for builder in builders:
             gpx = builder.fit(x_train, y_train)
             y_pred = gpx.predict(x_test)
+            self.assertEqual(100, gpx.dims()[0])
             error = np.linalg.norm(y_pred - y_test) / np.linalg.norm(y_test)
             print("   RMS error: " + str(error))
 

src/gp_mix.rs

@@ -10,6 +10,7 @@
 //! See the [tutorial notebook](https://github.com/relf/egobox/doc/Gpx_Tutorial.ipynb) for usage.
 //!
 use crate::types::*;
+use egobox_gp::metrics::CrossValScore;
 use egobox_moe::{Clustered, MixtureGpSurrogate, ThetaTuning};
 #[allow(unused_imports)] // Avoid linting problem
 use egobox_moe::{GpMixture, GpSurrogate, GpSurrogateExt};
@@ -356,6 +357,31 @@ impl Gpx {
             .into_pyarray_bound(py)
     }
 
+    /// Get the input and output dimensions of the surrogate
+    ///
+    /// Returns
+    ///     the couple (nx, ny)
+    ///
+    fn dims(&self) -> (usize, usize) {
+        self.0.dims()
+    }
+
+    /// Get the nt training data points used to fit the surrogate
+    ///
+    /// Returns
+    ///     the couple (ndarray[nt, nx], ndarray[nt, ny])
+    ///
+    fn training_data<'py>(
+        &self,
+        py: Python<'py>,
+    ) -> (Bound<'py, PyArray2<f64>>, Bound<'py, PyArray2<f64>>) {
+        let (xdata, ydata) = self.0.training_data();
+        (
+            xdata.to_owned().into_pyarray_bound(py),
+            ydata.to_owned().into_pyarray_bound(py),
+        )
+    }
+
     /// Get optimized thetas hyperparameters (ie once GP experts are fitted)
     ///
     /// Returns