Cleanup

Signed-off-by: Adam Li <[email protected]>
neurodata · Sep 28, 2023 · 2f06e76 · 2f06e76
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commit 2f06e76
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Showing 2 changed files with 13 additions and 6 deletions.
diff --git a/benchmarks_nonasv/notebooks/compare_coleman_and_permutation_forest.ipynb b/benchmarks_nonasv/notebooks/compare_coleman_and_permutation_forest.ipynb
@@ -1223,6 +1223,18 @@
       "(125, 600, 1) (125, 600, 1)\n",
       "X1:  0.004975124378109453\n",
       "(125, 600, 1) (125, 600, 1)\n",
+      "X500:  0.004975124378109453\n",
+      "(125, 600, 1) (125, 600, 1)\n",
+      "X2:  0.004975124378109453\n",
+      "(125, 600, 1) (125, 600, 1)\n",
+      "X1:  0.004975124378109453\n",
+      "(125, 600, 1) (125, 600, 1)\n",
+      "X500:  1.0\n",
+      "(125, 600, 1) (125, 600, 1)\n",
+      "X2:  0.004975124378109453\n",
+      "(125, 600, 1) (125, 600, 1)\n",
+      "X1:  1.0\n",
+      "(125, 600, 1) (125, 600, 1)\n",
       "X500:  0.004975124378109453\n"
      ]
     }

diff --git a/sktree/stats/forestht.py b/sktree/stats/forestht.py
@@ -785,14 +785,9 @@ def _statistic(
                 )
 
         # determine if there are any nans in the final posterior array
-        # Average all posteriors (n_samples_test, n_outputs)
-        # posterior_forest = np.nanmean(posterior_arr, axis=0)
-
-        # # Find the row indices with NaN values in any column
-        # nonnan_indices = np.where(~np.isnan(posterior_forest).any(axis=1))[0]
+        # Find the row indices with NaN values in any column
         nonnan_indices = np.all(~np.isnan(posterior_arr), axis=(0, 2))
         samples = nonnan_indices
-        print(nonnan_indices)
 
         # Ignore all NaN values (samples not tested)
         y_true_final = y[(nonnan_indices), :]