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Add multi-output support to honest trees #86

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merged 20 commits into from
Jun 22, 2023
Merged

Add multi-output support to honest trees #86

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7005dfb
ENH attempt for multi-output
PSSF23 Jun 14, 2023
2c3606b
ENH simplify predict_proba & multi-output
PSSF23 Jun 14, 2023
f831bc4
DOC update changelog
PSSF23 Jun 14, 2023
f1ab592
Fix multi-output
adam2392 Jun 15, 2023
dd0662d
FIX simplify code & TST add multi-output test
PSSF23 Jun 15, 2023
8365c33
Merge branch 'main' into multi
adam2392 Jun 16, 2023
b259441
TST update test format & increase code coverage
PSSF23 Jun 16, 2023
16f8561
FIX optimize leaf updates with multi-output
PSSF23 Jun 16, 2023
2e00358
Update submodule commit ID
adam2392 Jun 16, 2023
fd6643a
ENH remove impute_missing_classes function
PSSF23 Jun 19, 2023
c182cad
TST optimize prior test
PSSF23 Jun 19, 2023
72306d0
TST add prior for multi-output
PSSF23 Jun 19, 2023
ee14b64
Merging main
adam2392 Jun 20, 2023
d4d2337
Merging main
adam2392 Jun 20, 2023
c14f629
FIX re-update leaf function for multi
PSSF23 Jun 20, 2023
2feb117
DOC optimize docstring & TST remove impute test
PSSF23 Jun 20, 2023
f172880
DOC correct typo
PSSF23 Jun 20, 2023
a31e1de
TST add random state
PSSF23 Jun 21, 2023
0fc8ec4
TXT optimize max sample test
PSSF23 Jun 22, 2023
0f773e1
TST optimize random seed
PSSF23 Jun 22, 2023
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1 change: 1 addition & 0 deletions sktree/tests/test_honest_forest.py
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Expand Up @@ -108,6 +108,7 @@ def test_iris_multi(criterion, max_features, honest_prior, estimator):
def test_max_samples():
max_samples_list = [8, 0.5, None]
depths = []
np.random.seed(0)
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@adam2392 adam2392 Jun 22, 2023 •
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Don't set global random seed. I just realized a lot of tests are doing this. This actually isn't thread safe which is an issue for Cythonized code.

Instead you can set a global seed = 12345 and then for each place-in for np.random., you run rng = np.random.default_rng(seed) and use rng in place of np.random

Can you do this everywhere in the file (I think just 8 places that uses np.random.seed)?

Here's a ref talking about it: https://albertcthomas.github.io/good-practices-random-number-generators/

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will do.

X = np.random.normal(0, 1, (100, 2))
X[:50] *= -1
y = [0, 1] * 50
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