add test for running evaluate on an nlp pipeline with two distinct textcat components

spacy/tests/test_language.py

@@ -58,6 +58,29 @@ def nlp():
     return nlp
 
 
+@pytest.fixture
+def nlp_multi():
+    nlp = Language(Vocab())
+    textcat_multilabel = nlp.add_pipe("textcat_multilabel")
+    for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
+        textcat_multilabel.add_label(label)
+    nlp.initialize()
+    return nlp
+
+
+@pytest.fixture
+def nlp_both():
+    nlp = Language(Vocab())
+    textcat = nlp.add_pipe("textcat")
+    for label in ("POSITIVE", "NEGATIVE"):
+        textcat.add_label(label)
+    textcat_multilabel = nlp.add_pipe("textcat_multilabel")
+    for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
+        textcat_multilabel.add_label(label)
+    nlp.initialize()
+    return nlp
+
+
 def test_language_update(nlp):
     text = "hello world"
     annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
@@ -91,6 +114,9 @@ def test_language_evaluate(nlp):
     example = Example.from_dict(doc, annots)
     scores = nlp.evaluate([example])
     assert scores["speed"] > 0
+    assert scores["cats_f_per_type"].get("POSITIVE") is not None
+    assert scores["cats_f_per_type"].get("NEGATIVE") is not None
+    assert scores["cats_f_per_type"].get("BUG") is None
 
     # test with generator
     scores = nlp.evaluate(eg for eg in [example])
@@ -126,6 +152,35 @@ def test_evaluate_no_pipe(nlp):
     nlp.evaluate([Example.from_dict(doc, annots)])
 
 
+def test_evaluate_textcat(nlp_multi):
+    """Test that evaluate works with a multilabel textcat pipe."""
+    text = "hello world"
+    annots = {"doc_annotation": {"cats": {"FEATURE": 1.0, "QUESTION": 1.0}}}
+    doc = Doc(nlp_multi.vocab, words=text.split(" "))
+    example = Example.from_dict(doc, annots)
+    scores = nlp_multi.evaluate([example])
+    assert scores["cats_f_per_type"].get("FEATURE") is not None
+    assert scores["cats_f_per_type"].get("QUESTION") is not None
+    assert scores["cats_f_per_type"].get("REQUEST") is not None
+    assert scores["cats_f_per_type"].get("BUG") is not None
+    assert scores["cats_f_per_type"].get("POSITIVE") is None
+    assert scores["cats_f_per_type"].get("NEGATIVE") is None
+
+
+def test_evaluate_both(nlp_both):
+    """Test that evaluate works with two textcat pipes."""
+    text = "hello world"
+    annots = {"doc_annotation": {"cats": {"FEATURE": 1.0, "QUESTION": 1.0, "POSITIVE": 1.0, "NEGATIVE": 0.0}}}
+    doc = Doc(nlp_both.vocab, words=text.split(" "))
+    example = Example.from_dict(doc, annots)
+    scores = nlp_both.evaluate([example])
+    assert scores["cats_f_per_type"].get("FEATURE") is not None
+    assert scores["cats_f_per_type"].get("QUESTION") is not None
+    assert scores["cats_f_per_type"].get("BUG") is not None
+    assert scores["cats_f_per_type"].get("POSITIVE") is not None
+    assert scores["cats_f_per_type"].get("NEGATIVE") is not None
+
+
 def vector_modification_pipe(doc):
     doc.vector += 1
     return doc