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Development #140

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Sep 14, 2024
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0063936
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 12, 2024
10d8081
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 12, 2024
d3737ad
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 12, 2024
f81a964
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 12, 2024
525ea7b
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
9ae561a
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
134507d
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
1aae3e6
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
383f4f9
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
3b47b1b
Adapted seed setting to different names and ability to set seed in th…
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6a666a5
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
53f2ecc
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
24c5897
Adapted seed setting to different names and ability to set seed in th…
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f1405ff
Adapted seed setting to different names and ability to set seed in th…
denysgerasymuk799 Sep 13, 2024
437fa2f
Merge pull request #136 from DataResponsibly/feature/adapt_to_pytorch
denysgerasymuk799 Sep 13, 2024
964cebb
Improved an intergration with Pytorch Tabular
denysgerasymuk799 Sep 13, 2024
b8809df
Improved an intergration with Pytorch Tabular
denysgerasymuk799 Sep 13, 2024
f4cd643
Improved an intergration with Pytorch Tabular
denysgerasymuk799 Sep 13, 2024
ae579b0
Improved versions of dependencies to be more compatible
denysgerasymuk799 Sep 13, 2024
c61b747
Merge pull request #137 from DataResponsibly/feature/adapt_to_pytorch
denysgerasymuk799 Sep 13, 2024
15f3b6e
Merge branch 'main' into development
denysgerasymuk799 Sep 13, 2024
d869bd4
Fixed tests
denysgerasymuk799 Sep 13, 2024
8fe5c0d
Fixed tests
denysgerasymuk799 Sep 13, 2024
c03b5d5
Fixed tests
denysgerasymuk799 Sep 13, 2024
e043c38
Fixed tests for Python 3.11
denysgerasymuk799 Sep 14, 2024
f5dd82d
Fixed tests for Python 3.11
denysgerasymuk799 Sep 14, 2024
d6a6bd1
Removed duplication of requirements
denysgerasymuk799 Sep 14, 2024
107e322
Added a tutorial on how to use PyTorch Tabular models together with V…
denysgerasymuk799 Sep 14, 2024
de83d9a
Added a tutorial on how to use PyTorch Tabular models together with V…
denysgerasymuk799 Sep 14, 2024
2a9a525
Added a tutorial on how to use PyTorch Tabular models together with V…
denysgerasymuk799 Sep 14, 2024
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Adapted seed setting to different names and ability to set seed in th…
…e fit and predict methods
denysgerasymuk799 committed Sep 12, 2024
commit 10d808126db639cf0667edacafd564acd969b87e
4 changes: 3 additions & 1 deletion virny/analyzers/abstract_overall_variance_analyzer.py
Original file line number Diff line number Diff line change
@@ -157,7 +157,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b
boostrap_size=boostrap_size,
with_replacement=with_replacement,
random_state=classifier_random_state)
if 'random_state' in classifier.get_params() and has_method(classifier, 'set_params'):
if (has_method(classifier, 'get_params')
and 'random_state' in classifier.get_params()
and has_method(classifier, 'set_params')):
classifier.set_params(random_state=classifier_random_state)
classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state)

Original file line number Diff line number Diff line change
@@ -130,7 +130,9 @@ def UQ_by_boostrap(self, boostrap_size: int, with_replacement: bool, with_fit: b
boostrap_size=boostrap_size,
with_replacement=with_replacement,
random_state=classifier_random_state)
if 'random_state' in classifier.get_params() and has_method(classifier, 'set_params'):
if (has_method(classifier, 'get_params')
and 'random_state' in classifier.get_params()
and has_method(classifier, 'set_params')):
classifier.set_params(random_state=classifier_random_state)
classifier = self._fit_model(classifier, X_sample, y_sample, random_state=classifier_random_state)