Tagger robustness (#6580)

* require labels in taggers

* ensure tagger works with incomplete data

spacy/pipeline/tagger.pyx

@@ -256,7 +256,7 @@ class Tagger(TrainablePipe):
         DOCS: https://nightly.spacy.io/api/tagger#get_loss
         """
         validate_examples(examples, "Tagger.get_loss")
-        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
+        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False, missing_value="")
         truths = [eg.get_aligned("TAG", as_string=True) for eg in examples]
         d_scores, loss = loss_func(scores, truths)
         if self.model.ops.xp.isnan(loss):
@@ -295,6 +295,7 @@ class Tagger(TrainablePipe):
             gold_tags = example.get_aligned("TAG", as_string=True)
             gold_array = [[1.0 if tag == gold_tag else 0.0 for tag in self.labels] for gold_tag in gold_tags]
             label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
+        self._require_labels()
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
         assert len(label_sample) > 0, Errors.E923.format(name=self.name)
         self.model.initialize(X=doc_sample, Y=label_sample)

spacy/tests/pipeline/test_tagger.py

@@ -36,6 +36,10 @@ TRAIN_DATA = [
     ("Eat blue ham", {"tags": ["V", "J", "N"]}),
 ]
 
+PARTIAL_DATA = [
+    ("I like green eggs", {"tags": ["", "V", "J", ""]}),
+]
+
 
 def test_no_label():
     nlp = Language()
@@ -87,6 +91,41 @@ def test_initialize_examples():
         nlp.initialize(get_examples=train_examples)
 
 
+def test_no_data():
+    # Test that the tagger provides a nice error when there's no tagging data / labels
+    TEXTCAT_DATA = [
+        ("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
+        ("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
+    ]
+    nlp = English()
+    nlp.add_pipe("tagger")
+    nlp.add_pipe("textcat")
+    train_examples = []
+    for t in TEXTCAT_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+    with pytest.raises(ValueError):
+        nlp.initialize(get_examples=lambda: train_examples)
+
+
+def test_incomplete_data():
+    # Test that the tagger works with incomplete information
+    nlp = English()
+    nlp.add_pipe("tagger")
+    train_examples = []
+    for t in PARTIAL_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+    for i in range(50):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+    assert losses["tagger"] < 0.00001
+
+    # test the trained model
+    test_text = "I like blue eggs"
+    doc = nlp(test_text)
+    assert doc[1].tag_ is "V"
+    assert doc[2].tag_ is "J"
+
 def test_overfitting_IO():
     # Simple test to try and quickly overfit the tagger - ensuring the ML models work correctly
     nlp = English()