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Merged
StatguyUser merged 1 commit into from
Mar 18, 2023
Merged

updated files #24

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3 changes: 2 additions & 1 deletion build/lib/TextFeatureSelection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1065,7 +1065,8 @@ def _getData(self):
data_dict[i]=temp_dict

if self.use_class_weight:
class_weights_dict[i] = compute_class_weight(class_weight="balanced",classes=np.unique(label_list_train),y=label_list_train)
temp_val = compute_class_weight(class_weight="balanced",classes=np.unique(label_list_train),y=label_list_train)
class_weights_dict[i] = dict(zip(np.unique(label_list_train),temp_val))

return data_dict,class_weights_dict

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Binary file removed dist/TextFeatureSelection-0.2.3.tar.gz
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Binary file renamed ...xtFeatureSelection-0.2.3-py3-none-any.whl → ...xtFeatureSelection-0.2.4-py3-none-any.whl
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Binary file added dist/TextFeatureSelection-0.2.4.tar.gz
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -12,7 +12,7 @@

setup(
name='TextFeatureSelection',
version='0.2.3',
version='0.2.4',
description='Python library for feature selection for text features. It has filter method, genetic algorithm and TextFeatureSelectionEnsemble for improving text classification models. Helps improve your machine learning models',
long_description=long_description,
long_description_content_type='text/markdown', # This is important!
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2 changes: 1 addition & 1 deletion src/TextFeatureSelection.egg-info/PKG-INFO
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@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: TextFeatureSelection
Version: 0.2.3
Version: 0.2.4
Summary: Python library for feature selection for text features. It has filter method, genetic algorithm and TextFeatureSelectionEnsemble for improving text classification models. Helps improve your machine learning models
Home-page: https://github.com/StatguyUser/TextFeatureSelection
Author: StatguyUser
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3 changes: 2 additions & 1 deletion src/TextFeatureSelection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1065,7 +1065,8 @@ def _getData(self):
data_dict[i]=temp_dict

if self.use_class_weight:
class_weights_dict[i] = compute_class_weight(class_weight="balanced",classes=np.unique(label_list_train),y=label_list_train)
temp_val = compute_class_weight(class_weight="balanced",classes=np.unique(label_list_train),y=label_list_train)
class_weights_dict[i] = dict(zip(np.unique(label_list_train),temp_val))

return data_dict,class_weights_dict

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