Added blb inference option to the OrthoForest
#214
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moprescu
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Jan 28, 2020
moprescu
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- Added Bootstrap of Little Bags inference to the ORF classes
- Added tests and updated notebook
- Fixed the marginal effect shape when T is a vector
- Fixed bugs and reorganized class functionality
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Also addresses issue #188 |
* Added Bootstrap of Little Bags inference to the ORF classes * Added tests and updated notebook * Fixed the marginal effect shape when T is a vector * Fixed bugs and reorganized class functionality
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| @@ -363,8 +363,7 @@ and the `ForestLearners Jupyter notebook <https://github.com/microsoft/EconML/bl | |||
| >>> est.fit(Y, T, W, W) | |||
| <econml.ortho_forest.ContinuousTreatmentOrthoForest object at 0x...> | |||
| >>> print(est.effect(W[:2])) | |||
| [[1. ] | |||
| [1.2]] | |||
| [1.00... 1.19...] | |||
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If I understand correctly, the shape changing here is good because it was wrong before.
Should I be concerned that the values themselves have changed as well?
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Yes, the shape change is intentional. The values have changed because the trees are partitioned into segments ("little bags") that share data, so the same random seed gives slightly different results now.
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Hm, so the trees are partitioned even when fit's inference option is None rather than 'blb'?
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Yes, otherwise we'd have two ways of partitioning the tress and it would be hard to keep track of. It doesn't affect the estimate much since that's done using all of the 2*n_trees leaves. The only difference is which data samples a particular tree uses, i.e. groups slice_len trees subsample from the same n_samples/2 samples but which n_samples/2 are used differs from group to group.
