define threshold for scoring textcat in TextCat config (#6055)

* define threshold for scoring textcat in TextCat config

* fix unit test and documentation

spacy/pipeline/textcat.py

@@ -56,7 +56,7 @@ subword_features = true
 @Language.factory(
     "textcat",
     assigns=["doc.cats"],
-    default_config={"labels": [], "model": DEFAULT_TEXTCAT_MODEL},
+    default_config={"labels": [], "threshold": 0.5, "model": DEFAULT_TEXTCAT_MODEL},
     scores=[
         "cats_score",
         "cats_score_desc",
@@ -75,6 +75,7 @@ def make_textcat(
     name: str,
     model: Model[List[Doc], List[Floats2d]],
     labels: Iterable[str],
+    threshold: float,
 ) -> "TextCategorizer":
     """Create a TextCategorizer compoment. The text categorizer predicts categories
     over a whole document. It can learn one or more labels, and the labels can
@@ -86,8 +87,9 @@ def make_textcat(
         scores for each category.
     labels (list): A list of categories to learn. If empty, the model infers the
         categories from the data.
+    threshold (float): Cutoff to consider a prediction "positive".
     """
-    return TextCategorizer(nlp.vocab, model, name, labels=labels)
+    return TextCategorizer(nlp.vocab, model, name, labels=labels, threshold=threshold)
 
 
 class TextCategorizer(Pipe):
@@ -103,6 +105,7 @@ class TextCategorizer(Pipe):
         name: str = "textcat",
         *,
         labels: Iterable[str],
+        threshold: float,
     ) -> None:
         """Initialize a text categorizer.
 
@@ -111,6 +114,7 @@ class TextCategorizer(Pipe):
         name (str): The component instance name, used to add entries to the
             losses during training.
         labels (Iterable[str]): The labels to use.
+        threshold (float): Cutoff to consider a prediction "positive".
 
         DOCS: https://nightly.spacy.io/api/textcategorizer#init
         """
@@ -118,7 +122,7 @@ class TextCategorizer(Pipe):
         self.model = model
         self.name = name
         self._rehearsal_model = None
-        cfg = {"labels": labels}
+        cfg = {"labels": labels, "threshold": threshold}
         self.cfg = dict(cfg)
 
     @property
@@ -371,5 +375,6 @@ class TextCategorizer(Pipe):
             labels=self.labels,
             multi_label=self.model.attrs["multi_label"],
             positive_label=positive_label,
+            threshold=self.cfg["threshold"],
             **kwargs,
         )

spacy/tests/serialize/test_serialize_pipeline.py

@@ -136,7 +136,7 @@ def test_serialize_textcat_empty(en_vocab):
     # See issue #1105
     cfg = {"model": DEFAULT_TEXTCAT_MODEL}
     model = registry.make_from_config(cfg, validate=True)["model"]
-    textcat = TextCategorizer(en_vocab, model, labels=["ENTITY", "ACTION", "MODIFIER"])
+    textcat = TextCategorizer(en_vocab, model, labels=["ENTITY", "ACTION", "MODIFIER"], threshold=0.5)
     textcat.to_bytes(exclude=["vocab"])
 
 

website/docs/api/textcategorizer.md

@@ -30,15 +30,17 @@ architectures and their arguments and hyperparameters.
 > from spacy.pipeline.textcat import DEFAULT_TEXTCAT_MODEL
 > config = {
 >    "labels": [],
+>    "threshold": 0.5,
 >    "model": DEFAULT_TEXTCAT_MODEL,
 > }
 > nlp.add_pipe("textcat", config=config)
 > ```
 
-| Setting  | Description                                                                                                                                                      |
+| Setting     | Description                                                                                                                                                      |
-| -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `labels` | A list of categories to learn. If empty, the model infers the categories from the data. Defaults to `[]`. ~~Iterable[str]~~                                      |
+| `labels`    | A list of categories to learn. If empty, the model infers the categories from the data. Defaults to `[]`. ~~Iterable[str]~~                                      |
-| `model`  | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
+| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~                                                                   |
+| `model`     | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
 
 ```python
 %%GITHUB_SPACY/spacy/pipeline/textcat.py
@@ -58,7 +60,7 @@ architectures and their arguments and hyperparameters.
 >
 > # Construction from class
 > from spacy.pipeline import TextCategorizer
-> textcat = TextCategorizer(nlp.vocab, model)
+> textcat = TextCategorizer(nlp.vocab, model, labels=[], threshold=0.5)
 > ```
 
 Create a new pipeline instance. In your application, you would normally use a
@@ -72,6 +74,7 @@ shortcut for this and instantiate the component using its string name and
 | `name`         | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                        |
 | _keyword-only_ |                                                                                                                            |
 | `labels`       | The labels to use. ~~Iterable[str]~~                                                                                       |
+| `threshold`    | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~                             |
 
 ## TextCategorizer.\_\_call\_\_ {#call tag="method"}