|
21 | 21 | from sklearn.utils.testing import assert_array_equal |
22 | 22 | from sklearn.utils.testing import assert_array_almost_equal |
23 | 23 | from sklearn.utils.testing import assert_warns |
24 | | -from sklearn.utils.testing import ignore_warnings |
25 | 24 |
|
26 | 25 | from sklearn.metrics import auc |
27 | 26 | from sklearn.metrics import average_precision_score |
@@ -308,7 +307,7 @@ def test_roc_curve_toydata(): |
308 | 307 | tpr, fpr, _ = roc_curve(y_true, y_score) |
309 | 308 | assert_raises(ValueError, roc_auc_score, y_true, y_score) |
310 | 309 | assert_array_almost_equal(tpr, [0., 0.5, 1.]) |
311 | | - assert_array_almost_equal(fpr, [np.nan, np.nan, np.nan]) |
| 310 | + assert_array_almost_equal(fpr, [np.nan, np.nan, np.nan]) |
312 | 311 |
|
313 | 312 | y_true = [1, 1] |
314 | 313 | y_score = [0.25, 0.75] |
@@ -743,7 +742,7 @@ def check_lrap_without_tie_and_increasing_score(lrap_score): |
743 | 742 |
|
744 | 743 | # Check for growing number of consecutive relevant label |
745 | 744 | for n_relevant in range(1, n_labels): |
746 | | - # Check for a bunch of position |
| 745 | + # Check for a bunch of position |
747 | 746 | for pos in range(n_labels - n_relevant): |
748 | 747 | y_true = np.zeros((1, n_labels)) |
749 | 748 | y_true[0, pos:pos + n_relevant] = 1 |
|
0 commit comments