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

daal4py/sklearn/ensemble/tests/test_decision_forest.py

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

tests/test_examples.py

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

tests/unittest_data/decision_forest_regression_batch_20230101.csv

@@ -0,0 +1,127 @@
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