Support NaN values in UpliftTree and UpliftRandomForest#860
Support NaN values in UpliftTree and UpliftRandomForest#860aman-coder03 wants to merge 6 commits intouber:masterfrom
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The Read the Docs build failure appears to be due to Cython extensions requiring a compiler in the RTD environment. |
jeongyoonlee
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jeongyoonlee
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Thanks for your contribution, @aman-coder03. I left a few comments. Can you address them? Also, please add test code to tests/test_uplift_trees.py accordingly. Thanks!
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| for value in lsUnique: | ||
| len_X_l = group_counts_by_divide(columnValues, value, is_split_by_gt, treatment_idx, y, left_count_arr) |
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We need this code block. Is there any reason you removed it?
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| early_stopping_flag = False |
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We need this code block for early stopping.
causalml/inference/tree/uplift.pyx
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NaN handling is needed here in the percentile calculation.
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@jeongyoonlee i will look into this! |
aman-coder03
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hey @jeongyoonlee i have implemented the changes, please have a look! |
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@aman-coder03, I see some code blocks necessary (e.g., code for the CTS evaluation criteria) are removed without any comments. Also, some tests are failing. |
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Thanks for pointing that out. I will restore the original CTS and evaluation blocks and limit changes strictly to NaN routing logic to preserve original behavior @jeongyoonlee |
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@jeongyoonlee can you please have a look at the code changes now |
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This PR adds native support for missing values (NaNs) in
UpliftTreeandUpliftRandomForest.Each candidate split evaluates both possible NaN routing directions
(left/right) and learns the optimal routing per node, similar to
scikit-learn’s decision tree behavior.
The learned NaN routing is stored in each tree node and applied
consistently during training, pruning, filling, and prediction.
This resolves #802.