chore: import upstream snapshot with attribution

tests/test_span_classification.py

@@ -0,0 +1,57 @@
+from cleanlab.experimental.span_classification import (
+    find_label_issues,
+    display_issues,
+    get_label_quality_scores,
+)
+import numpy as np
+import pytest
+import warnings
+
+warnings.filterwarnings("ignore")
+words = [["I", "love", "Cleanlab", "Inc"], ["A", "new", "park"]]
+
+pred_probs = [
+    np.array([0.3, 0.2, 0.9, 0.1]),
+    np.array([0.1, 0.1, 0.9]),
+]
+
+labels = [[0, 0, 1, 1], [0, 0, 1]]
+class_names = ["O", "Span"]
+
+
+@pytest.mark.parametrize(
+    "test_labels",
+    [labels, [np.array(l) for l in labels]],
+    ids=["list labels", "np.array labels"],
+)
+@pytest.mark.filterwarnings("ignore::DeprecationWarning")
+def test_find_label_issues(test_labels):
+    issues = find_label_issues(test_labels, pred_probs)
+    assert isinstance(issues, list)
+    assert len(issues) == 1
+    assert issues[0] == (0, 3)
+
+
+issues = find_label_issues(labels, pred_probs)
+
+
+def test_display_issues():
+    display_issues(issues, words)
+    display_issues(issues, tokens=words, labels=labels)
+    display_issues(issues, words, pred_probs=pred_probs)
+    display_issues(issues, words, pred_probs=pred_probs, labels=labels)
+    display_issues(issues, words, pred_probs=pred_probs, labels=labels, class_names=class_names)
+
+
+@pytest.fixture(name="label_quality_scores")
+def fixture_label_quality_scores():
+    sentence_scores, token_info = get_label_quality_scores(labels, pred_probs)
+    return sentence_scores, token_info
+
+
+def test_get_label_quality_scores(label_quality_scores):
+    sentence_scores, token_info = label_quality_scores
+    assert len(sentence_scores) == 2
+    assert np.allclose(sentence_scores, [0.1, 0.9])
+    assert len(token_info) == 2
+    assert np.allclose(token_info[0], [0.7, 0.8, 0.9, 0.1])