Check textcat values for validity (#11763)

* Check textcat values for validity

* Fix error numbers

* Clean up vals reference

* Check category value validity through training

The _validate_categories is called in update, which for multilabel is
inherited from the single label component.

* Formatting

spacy/errors.py

@@ -544,6 +544,8 @@ class Errors(metaclass=ErrorsWithCodes):
             "during training, make sure to include it in 'annotating components'")
 
     # New errors added in v3.x
+    E851 = ("The 'textcat' component labels should only have values of 0 or 1, "
+            "but found value of '{val}'.")
     E852 = ("The tar file pulled from the remote attempted an unsafe path "
             "traversal.")
     E853 = ("Unsupported component factory name '{name}'. The character '.' is "

spacy/pipeline/textcat.py

@@ -293,7 +293,7 @@ class TextCategorizer(TrainablePipe):
         bp_scores(gradient)
         if sgd is not None:
             self.finish_update(sgd)
-        losses[self.name] += (gradient**2).sum()
+        losses[self.name] += (gradient ** 2).sum()
         return losses
 
     def _examples_to_truth(
@@ -327,7 +327,7 @@ class TextCategorizer(TrainablePipe):
         not_missing = self.model.ops.asarray(not_missing)  # type: ignore
         d_scores = scores - truths
         d_scores *= not_missing
-        mean_square_error = (d_scores**2).mean()
+        mean_square_error = (d_scores ** 2).mean()
         return float(mean_square_error), d_scores
 
     def add_label(self, label: str) -> int:
@@ -401,5 +401,9 @@ class TextCategorizer(TrainablePipe):
     def _validate_categories(self, examples: Iterable[Example]):
         """Check whether the provided examples all have single-label cats annotations."""
         for ex in examples:
-            if list(ex.reference.cats.values()).count(1.0) > 1:
+            vals = list(ex.reference.cats.values())
+            if vals.count(1.0) > 1:
                 raise ValueError(Errors.E895.format(value=ex.reference.cats))
+            for val in vals:
+                if not (val == 1.0 or val == 0.0):
+                    raise ValueError(Errors.E851.format(val=val))

spacy/pipeline/textcat_multilabel.py

@@ -192,6 +192,8 @@ class MultiLabel_TextCategorizer(TextCategorizer):
             for label in labels:
                 self.add_label(label)
         subbatch = list(islice(get_examples(), 10))
+        self._validate_categories(subbatch)
+
         doc_sample = [eg.reference for eg in subbatch]
         label_sample, _ = self._examples_to_truth(subbatch)
         self._require_labels()
@@ -202,4 +204,8 @@ class MultiLabel_TextCategorizer(TextCategorizer):
     def _validate_categories(self, examples: Iterable[Example]):
         """This component allows any type of single- or multi-label annotations.
         This method overwrites the more strict one from 'textcat'."""
-        pass
+        # check that annotation values are valid
+        for ex in examples:
+            for val in ex.reference.cats.values():
+                if not (val == 1.0 or val == 0.0):
+                    raise ValueError(Errors.E851.format(val=val))

spacy/tests/pipeline/test_textcat.py

@@ -360,6 +360,30 @@ def test_label_types(name):
         nlp.initialize()
 
 
+@pytest.mark.parametrize(
+    "name,get_examples",
+    [
+        ("textcat", make_get_examples_single_label),
+        ("textcat_multilabel", make_get_examples_multi_label),
+    ],
+)
+def test_invalid_label_value(name, get_examples):
+    nlp = Language()
+    textcat = nlp.add_pipe(name)
+    example_getter = get_examples(nlp)
+
+    def invalid_examples():
+        # make one example with an invalid score
+        examples = example_getter()
+        ref = examples[0].reference
+        key = list(ref.cats.keys())[0]
+        ref.cats[key] = 2.0
+        return examples
+
+    with pytest.raises(ValueError):
+        nlp.initialize(get_examples=invalid_examples)
+
+
 @pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])
 def test_no_label(name):
     nlp = Language()