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ahuber21ahuber21
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chore: update tests after feature selection change (#1213)
* chore: update unit test reference values * test(df): allow up to 50% disagreement on log loss * Add updated df_regr numbers for 2023.1.1 * update tests to support both 2023.0.1 and later versions
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‎daal4py/sklearn/ensemble/tests/test_decision_forest.py‎

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@@ -33,7 +33,12 @@
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ACCURACY_RATIO = 0.95 if daal_check_version((2021, 'P', 400)) else 0.85
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MSE_RATIO = 1.07
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LOG_LOSS_RATIO = 1.4 if daal_check_version((2021, 'P', 400)) else 1.55
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if daal_check_version((2023, 'P', 101)):
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LOG_LOSS_RATIO = 1.5
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elif daal_check_version((2021, 'P', 400)):
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LOG_LOSS_RATIO = 1.4
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else:
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LOG_LOSS_RATIO = 1.55
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ROC_AUC_RATIO = 0.96
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RNG = np.random.RandomState(0)
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IRIS = load_iris()

‎tests/test_examples.py‎

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@@ -185,6 +185,12 @@ def test_svm_batch(self):
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'decision_forest_regression_batch.csv', lambda r: r[1].prediction, (2023, 'P', 1)),
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('decision_forest_regression_hist_batch',
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'decision_forest_regression_batch.csv', lambda r: r[1].prediction, (2023, 'P', 1)),
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('decision_forest_regression_default_dense_batch',
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'decision_forest_regression_batch_20230101.csv',
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lambda r: r[1].prediction, (2023, 'P', 101)),
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('decision_forest_regression_hist_batch',
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'decision_forest_regression_batch_20230101.csv',
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lambda r: r[1].prediction, (2023, 'P', 101)),
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('decision_tree_classification_batch', 'decision_tree_classification_batch.csv',
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lambda r: r[1].prediction),
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('decision_tree_regression_batch', 'decision_tree_regression_batch.csv',

‎tests/unittest_data/decision_forest_regression_batch_20230101.csv‎

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