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Upgraded torch to v2, fixed FutureWarning for SVM training #13

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May 22, 2024
Merged

Upgraded torch to v2, fixed FutureWarning for SVM training #13

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a176b42
Upgraded torch to v2, fixed FutureWarning for SVM training
georgeaidinis May 20, 2024
bdb085c
Merge branch 'main' of https://github.com/georgeaidinis/spare_score_fork
georgeaidinis May 20, 2024
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7 changes: 6 additions & 1 deletion setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,12 @@
packages=find_packages(),
package_data={'spare_scores':['mdl/*.pkl.gz','data/*.csv']},
include_package_data=True,
install_requires=['numpy', 'pandas', 'scikit-learn', 'torch==1.11', 'matplotlib', 'optuna'],
install_requires=['numpy',
'pandas',
'scikit-learn',
'torch<2.1',
'matplotlib',
'optuna'],
entry_points={
'console_scripts': ['spare_score=spare_scores.cli:main']
},
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4 changes: 2 additions & 2 deletions spare_scores/svm.py
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Expand Up @@ -189,11 +189,11 @@ def train_initialize(self, df, to_predict):
if self.task == 'Classification':
self.type, self.scoring, metrics = 'SVC', 'roc_auc', ['AUC', 'Accuracy', 'Sensitivity', 'Specificity', 'Precision', 'Recall', 'F1']
self.to_predict, self.classify = to_predict, list(df[to_predict].unique())
self.mdl = ([LinearSVC(max_iter=100000)] if self.kernel == 'linear' else [SVC(max_iter=100000, kernel=self.kernel)]) * len(self.folds)
self.mdl = ([LinearSVC(max_iter=100000, dual='auto')] if self.kernel == 'linear' else [SVC(max_iter=100000, kernel=self.kernel)]) * len(self.folds)
elif self.task == 'Regression':
self.type, self.scoring, metrics = 'SVR', 'neg_mean_absolute_error', ['MAE', 'RMSE', 'R2']
self.to_predict, self.classify = to_predict, None
self.mdl = [LinearSVR(max_iter=100000)] * len(self.folds)
self.mdl = [LinearSVR(max_iter=100000, dual='auto')] * len(self.folds)
self.bias_correct = {'slope':np.zeros((len(self.folds),)), 'int':np.zeros((len(self.folds),))}
self.stats = {metric: [] for metric in metrics}
logging.info(f'Training a SPARE model ({self.type}) with {len(df.index)} participants')
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