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Update sphinx-rtd-theme requirement from ~=1.0 to ~=1.3 #32

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922b2b7
Upgraded pymatgen and matminer requirements
gbrunin Jun 27, 2022
2b89bbc
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin Jun 27, 2022
1822e53
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin Nov 28, 2022
c7c6dae
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin Feb 21, 2023
c89f179
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin May 15, 2023
bdeb35b
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin Jun 5, 2023
4c50516
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin Jul 11, 2023
62c4825
backward compatibility warning
ppdebreuck Jul 11, 2023
e8d0c8e
Merge branch 'vector_pred_compat' of github.com:ppdebreuck/modnet
gbrunin Jul 11, 2023
a408f0a
Possibility to remove all NaNs features or not after featurization.
gbrunin Jul 14, 2023
b9700c7
Arg in featurize.
gbrunin Jul 14, 2023
7f41fd7
Arg in preset because there are clean_df there as well.
gbrunin Jul 14, 2023
5874ef9
Easier setting of drop_allnan.
gbrunin Jul 14, 2023
bdc8cf1
Let this for another PR.
gbrunin Jul 14, 2023
ebcb719
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin Jul 18, 2023
98e5fd2
Merge branch 'master' of https://github.com/ppdebreuck/modnet
gbrunin Aug 21, 2023
54e0119
Possibility to tune random_state in feature selection. Useful when se…
gbrunin Aug 21, 2023
622a270
Merge pull request #31 from Matgenix/random_state
gbrunin Aug 21, 2023
b39203e
Update sphinx-rtd-theme requirement from ~=1.0 to ~=1.3
dependabot[bot] Sep 1, 2023
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2 changes: 1 addition & 1 deletion docs/requirements.txt
Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
sphinx~=5.3
sphinx-rtd-theme~=1.0
sphinx-rtd-theme~=1.3
sphinxcontrib-napoleon~=0.7
8 changes: 7 additions & 1 deletion modnet/preprocessing.py
Original file line number Diff line number Diff line change
Expand Up @@ -797,6 +797,7 @@ def feature_selection(
drop_thr: float = 0.2,
n_jobs: int = None,
ignore_names: Optional[List] = [],
random_state: int = None,
):
"""Compute the mutual information between features and targets,
then apply relevance-redundancy rankings to choose the top `n`
Expand Down Expand Up @@ -859,7 +860,11 @@ def feature_selection(
else:
df = self.df_featurized.copy()
self.cross_nmi, self.feature_entropy = get_cross_nmi(
df, return_entropy=True, drop_thr=drop_thr, n_jobs=n_jobs
df,
return_entropy=True,
drop_thr=drop_thr,
n_jobs=n_jobs,
random_state=random_state,
)

if self.cross_nmi.isna().sum().sum() > 0:
Expand Down Expand Up @@ -889,6 +894,7 @@ def feature_selection(
df,
df_target,
task_type,
random_state=random_state,
)[name]

LOG.info("Computing optimal features...")
Expand Down