FIX cast scores to CPU

Author: TomDLT

tutorials/movies_3T/05_plot_banded_ridge_model.py

@@ -360,7 +360,7 @@
 n_kernels = split_scores_mask.shape[0]
 n_voxels = Y_train.shape[1]
 split_scores = np.zeros((n_kernels, n_voxels))
-split_scores[:, mask] = split_scores_mask
+split_scores[:, mask] = backend.to_numpy(split_scores_mask)
 print("(n_kernels, n_voxels) =", split_scores.shape)
 
 ###############################################################################

tutorials/notebooks/movies_3T/05_plot_banded_ridge_model.ipynb

@@ -458,7 +458,7 @@
       },
       "outputs": [],
       "source": [
-        "from himalaya.scoring import r2_score_split\n\nY_test_pred_split = pipeline.predict(X_test, split=True)\nsplit_scores_mask = r2_score_split(Y_test[:, mask], Y_test_pred_split)\n\nprint(\"(n_kernels, n_samples_test, n_voxels_mask) =\", Y_test_pred_split.shape)\nprint(\"(n_kernels, n_voxels_mask) =\", split_scores_mask.shape)\n\n# extend to unfitted voxels\nn_kernels = split_scores_mask.shape[0]\nn_voxels = Y_train.shape[1]\nsplit_scores = np.zeros((n_kernels, n_voxels))\nsplit_scores[:, mask] = split_scores_mask\nprint(\"(n_kernels, n_voxels) =\", split_scores.shape)"
+        "from himalaya.scoring import r2_score_split\n\nY_test_pred_split = pipeline.predict(X_test, split=True)\nsplit_scores_mask = r2_score_split(Y_test[:, mask], Y_test_pred_split)\n\nprint(\"(n_kernels, n_samples_test, n_voxels_mask) =\", Y_test_pred_split.shape)\nprint(\"(n_kernels, n_voxels_mask) =\", split_scores_mask.shape)\n\n# extend to unfitted voxels\nn_kernels = split_scores_mask.shape[0]\nn_voxels = Y_train.shape[1]\nsplit_scores = np.zeros((n_kernels, n_voxels))\nsplit_scores[:, mask] = backend.to_numpy(split_scores_mask)\nprint(\"(n_kernels, n_voxels) =\", split_scores.shape)"
       ]
     },
     {

tutorials/notebooks/movies_3T/merged_for_colab.ipynb

@@ -3531,7 +3531,7 @@
     "n_kernels = split_scores_mask.shape[0]\n",
     "n_voxels = Y_train.shape[1]\n",
     "split_scores = np.zeros((n_kernels, n_voxels))\n",
-    "split_scores[:, mask] = split_scores_mask\n",
+    "split_scores[:, mask] = backend.to_numpy(split_scores_mask)\n",
     "print(\"(n_kernels, n_voxels) =\", split_scores.shape)"
    ]
   },