allow that downsampling negatives does nto produce negatives at all

src/pie_modules/document/processing/text_pair.py

@@ -1,4 +1,5 @@
 import copy
+import logging
 import random
 from collections import defaultdict
 from collections.abc import Iterator
@@ -18,6 +19,8 @@
 )
 from pie_modules.utils.span import are_nested
 
+logger = logging.getLogger(__name__)
+
 S = TypeVar("S", bound=Span)
 S2 = TypeVar("S2", bound=Span)
 
@@ -237,11 +240,12 @@ def add_negative_coref_relations(
 
         max_num_negative = int(len(positive_rels) * downsampling_factor)
         if max_num_negative == 0:
-            raise ValueError(
+            logger.warning(
                 f"downsampling with factor={downsampling_factor} and number of "
                 f"positive relations={len(positive_rels)} does not produce any negatives"
             )
-        random.shuffle(negative_rels)
+        else:
+            random.shuffle(negative_rels)
         negative_rels = negative_rels[:max_num_negative]
     for rel in negative_rels:
         new_rels2new_docs[rel].binary_coref_relations.append(rel)

tests/document/processing/test_text_pair.py

@@ -281,10 +281,7 @@ def test_construct_negative_documents(positive_and_negative_documents):
     ]
 
 
-def test_construct_negative_documents_with_downsampling(positive_documents):
-    # set fixed seed because the negatives will get shuffled
-    random.seed(42)
-    docs = list(add_negative_coref_relations(positive_documents, downsampling_factor=1.0))
+def _get_all_all_rels_and_scores(docs):
     all_texts = [(doc.text, doc.text_pair) for doc in docs]
     all_scores = [[coref_rel.score for coref_rel in doc.binary_coref_relations] for doc in docs]
     all_rels_resolved = [doc.binary_coref_relations.resolve() for doc in docs]
@@ -293,6 +290,14 @@ def test_construct_negative_documents_with_downsampling(positive_documents):
         (texts, list(zip(scores, rels_resolved)))
         for texts, scores, rels_resolved in zip(all_texts, all_scores, all_rels_resolved)
     ]
+    return all_rels_and_scores
+
+
+def test_construct_negative_documents_with_downsampling(positive_documents, caplog):
+    # set fixed seed because the negatives will get shuffled
+    random.seed(42)
+    docs = list(add_negative_coref_relations(positive_documents, downsampling_factor=1.0))
+    all_rels_and_scores = _get_all_all_rels_and_scores(docs)
 
     # check number relations
     all_rels_flat = [rel for doc in docs for rel in doc.binary_coref_relations]
@@ -334,6 +339,39 @@ def test_construct_negative_documents_with_downsampling(positive_documents):
         ),
     ]
 
+    # sampling target is too low
+    caplog.clear()
+    docs = list(add_negative_coref_relations(positive_documents, downsampling_factor=0.0))
+    assert caplog.messages == [
+        "downsampling with factor=0.0 and number of positive relations=4 does not produce any negatives"
+    ]
+    # check number relations
+    all_rels_flat = [rel for doc in docs for rel in doc.binary_coref_relations]
+    # positives: 2 x number of positives (we add instances with swapped texts)
+    assert len([rel.score for rel in all_rels_flat if rel.score > 0.0]) == 4
+    # negatives
+    assert len([rel.score for rel in all_rels_flat if rel.score == 0.0]) == 0
+    # check actual content
+    all_rels_and_scores = _get_all_all_rels_and_scores(docs)
+    assert all_rels_and_scores == [
+        (
+            ("And she founded C.", "Entity A works at B."),
+            [(1.0, ("coref", (("PERSON", "she"), ("PERSON", "Entity A"))))],
+        ),
+        (
+            ("Bob loves his cat.", "She sleeps a lot."),
+            [(1.0, ("coref", (("ANIMAL", "his cat"), ("ANIMAL", "She"))))],
+        ),
+        (
+            ("Entity A works at B.", "And she founded C."),
+            [(1.0, ("coref", (("PERSON", "Entity A"), ("PERSON", "she"))))],
+        ),
+        (
+            ("She sleeps a lot.", "Bob loves his cat."),
+            [(1.0, ("coref", (("ANIMAL", "She"), ("ANIMAL", "his cat"))))],
+        ),
+    ]
+
     # no positives
     doc2 = TextPairDocumentWithLabeledSpansAndBinaryCorefRelations(
         id="0", text="Bob loves his cat.", text_pair="She sleeps a lot."
@@ -349,14 +387,6 @@ def test_construct_negative_documents_with_downsampling(positive_documents):
         "max_num_negative"
     )
 
-    # sampling target is too low
-    with pytest.raises(ValueError) as e:
-        list(add_negative_coref_relations(positive_documents, downsampling_factor=0.0))
-    assert (
-        str(e.value)
-        == "downsampling with factor=0.0 and number of positive relations=4 does not produce any negatives"
-    )
-
 
 def test_construct_text_document_from_text_pair_coref_document(positive_and_negative_documents):
     glue_text = "<s><s>"