tests for make_span_group with negative labels

spacy/tests/pipeline/test_spancat.py

@@ -126,10 +126,12 @@ def test_make_spangroup_multilabel(max_positive, nr_results):
     indices = ngram_suggester([doc])[0].dataXd
     assert_array_equal(OPS.to_numpy(indices), numpy.asarray([[0, 1], [1, 2], [0, 2]]))
     labels = ["Thing", "City", "Person", "GreatCity"]
+    for label in labels:
+        spancat.add_label(label)
     scores = numpy.asarray(
         [[0.2, 0.4, 0.3, 0.1], [0.1, 0.6, 0.2, 0.4], [0.8, 0.7, 0.3, 0.9]], dtype="f"
     )
-    spangroup = spancat._make_span_group_multilabel(doc, indices, scores, labels)
+    spangroup = spancat._make_span_group_multilabel(doc, indices, scores)
     assert len(spangroup) == nr_results
 
     # first span is always the second token "London"
@@ -159,7 +161,6 @@ def test_make_spangroup_multilabel(max_positive, nr_results):
     assert spangroup[-1].label_ == "GreatCity"
     assert_almost_equal(0.9, spangroup.attrs["scores"][-1], 5)
 
-
 @pytest.mark.parametrize(
     "threshold,allow_overlap,nr_results",
     [(0.05, True, 3), (0.05, False, 1), (0.5, True, 2), (0.5, False, 1)],
@@ -180,11 +181,13 @@ def test_make_spangroup_singlelabel(threshold, allow_overlap, nr_results):
     indices = ngram_suggester([doc])[0].dataXd
     assert_array_equal(OPS.to_numpy(indices), numpy.asarray([[0, 1], [1, 2], [0, 2]]))
     labels = ["Thing", "City", "Person", "GreatCity"]
+    for label in labels:
+        spancat.add_label(label)
     scores = numpy.asarray(
         [[0.2, 0.4, 0.3, 0.1], [0.1, 0.6, 0.2, 0.4], [0.8, 0.7, 0.3, 0.9]], dtype="f"
     )
     spangroup = spancat._make_span_group_singlelabel(
-        doc, indices, scores, labels, allow_overlap
+        doc, indices, scores, allow_overlap
     )
     assert len(spangroup) == nr_results
     if threshold > 0.4:
@@ -209,6 +212,66 @@ def test_make_spangroup_singlelabel(threshold, allow_overlap, nr_results):
             assert spangroup[0].text == "Greater London"
 
 
+def test_make_spangroup_negative_label():
+    fix_random_seed(0)
+    nlp_single = Language()
+    nlp_multi = Language()
+    spancat_single = nlp_single.add_pipe(
+        "spancat",
+        config={
+            "spans_key": SPAN_KEY,
+            "threshold": 0.1,
+            "max_positive": 1,
+        },
+    )
+    spancat_multi = nlp_multi.add_pipe(
+        "spancat",
+        config={
+            "spans_key": SPAN_KEY,
+            "threshold": 0.1,
+            "max_positive": 2,
+        },
+    )
+    spancat_single.add_negative_label = True
+    spancat_multi.add_negative_label = True
+    doc = nlp_single.make_doc("Greater London")
+    labels = ["Thing", "City", "Person", "GreatCity"]
+    for label in labels:
+        spancat_multi.add_label(label)
+        spancat_single.add_label(label)
+    ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2])
+    indices = ngram_suggester([doc])[0].dataXd
+    assert_array_equal(OPS.to_numpy(indices), numpy.asarray([[0, 1], [1, 2], [0, 2]]))
+    scores = numpy.asarray(
+        [[0.2, 0.4, 0.3, 0.1, 0.1], [0.1, 0.6, 0.2, 0.4, 0.9], [0.8, 0.7, 0.3, 0.9, 0.1]], dtype="f"
+    )
+    spangroup_multi = spancat_multi._make_span_group_multilabel(
+        doc, indices, scores
+    )
+    spangroup_single = spancat_single._make_span_group_singlelabel(
+        doc, indices, scores
+    )
+    assert len(spangroup_single) == 2
+    assert spangroup_single[0].text == "Greater"
+    assert spangroup_single[0].label_ == "City"
+    assert spangroup_single[1].text == "Greater London"
+    assert spangroup_single[1].label_ == "GreatCity"
+
+    assert len(spangroup_multi) == 6
+    assert spangroup_multi[0].text == "Greater"
+    assert spangroup_multi[0].label_ == "City"
+    assert spangroup_multi[1].text == "Greater"
+    assert spangroup_multi[1].label_ == "Person"
+    assert spangroup_multi[2].text == "London"
+    assert spangroup_multi[2].label_ == "City"
+    assert spangroup_multi[3].text == "London"
+    assert spangroup_multi[3].label_ == "GreatCity"
+    assert spangroup_multi[4].text == "Greater London"
+    assert spangroup_multi[4].label_ == "Thing"
+    assert spangroup_multi[5].text == "Greater London"
+    assert spangroup_multi[5].label_ == "GreatCity"
+
+
 def test_ngram_suggester(en_tokenizer):
     # test different n-gram lengths
     for size in [1, 2, 3]: