Update tests due to differences in Py 310 and newer Py versions

niklases · Oct 30, 2024 · 562118f · 562118f
1 parent 4d8a0b8
commit 562118f
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Showing 2 changed files with 10 additions and 5 deletions.
diff --git a/pypef/utils/performance.py b/pypef/utils/performance.py
@@ -18,7 +18,7 @@
 from sklearnex import patch_sklearn
 patch_sklearn(verbose=False)
 from sklearn.metrics import (
-    mean_squared_error, r2_score,
+    root_mean_squared_error, r2_score,
     precision_score, accuracy_score, recall_score, 
     balanced_accuracy_score, f1_score, matthews_corrcoef,
     average_precision_score, roc_curve, auc
@@ -54,7 +54,7 @@ def get_performances(
     y_true = list(y_true)
     y_pred = list(y_pred)
     r_squared = r2_score(y_true, y_pred)
-    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
+    rmse = root_mean_squared_error(y_true, y_pred)
     stddev = np.std(y_true, ddof=1)
     nrmse = rmse / stddev
     with warnings.catch_warnings():  # catching RunTime warning when there's no variance in an array,

diff --git a/tests/test_api_functions.py b/tests/test_api_functions.py
@@ -45,8 +45,8 @@ def test_gremlin():
 
 
 def test_dataset_b_results():
-    train_seqs, train_vars, train_ys = get_sequences_from_file(ls_b)
-    test_seqs, test_vars, test_ys = get_sequences_from_file(ts_b)
+    train_seqs, _train_vars, train_ys = get_sequences_from_file(ls_b)
+    test_seqs, _test_vars, test_ys = get_sequences_from_file(ts_b)
     aaindex = "WOLR810101.txt"
     x_fft_train, _ = AAIndexEncoding(full_aaidx_txt_path(aaindex), train_seqs).collect_encoded_sequences()
     x_fft_test, _ = AAIndexEncoding(full_aaidx_txt_path(aaindex), test_seqs).collect_encoded_sequences()
@@ -58,4 +58,9 @@ def test_dataset_b_results():
         regressor='pls_loocv'
     )  
     # Dataset B PLS_LOOCV results: R², RMSE, NRMSE, Pearson's r, Spearman's rho 
-    np.testing.assert_almost_equal(performances[:5], [0.72, 14.48, 0.52, 0.86, 0.89], decimal=2)
+    # RMSE, in Python 3.10 14.669 and from Python 3.11 on 14.17:
+    np.testing.assert_almost_equal(performances[1], 14.48, decimal=0)
+    # R²
+    np.testing.assert_almost_equal(performances[0], 0.72, decimal=2)
+    #  NRMSE, Pearson's r, Spearman's rho
+    np.testing.assert_almost_equal(performances[2:5], [0.52, 0.86, 0.89], decimal=2)