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Raise an error for textcat with <2 labels (#8584)

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* Raise an error for textcat with <2 labels

Raise an error if initializing a `textcat` component without at least
two labels.

* Add similar note to docs

* Update positive_label description in API docs
This commit is contained in:
Adriane Boyd 2021-07-06 12:35:22 +02:00
parent 6aa3fede76
commit 81e71a61f8
4 changed files with 18 additions and 4 deletions
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5
spacy/errors.py
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@ -518,6 +518,11 @@ class Errors:
E202 = ("Unsupported alignment mode '{mode}'. Supported modes: {modes}.")
# New errors added in v3.x
E867 = ("The 'textcat' component requires at least two labels because it "
"uses mutually exclusive classes where exactly one label is True "
"for each doc. For binary classification tasks, you can use two "
"labels with 'textcat' (LABEL / NOT_LABEL) or alternatively, you "
"can use the 'textcat_multilabel' component with one label.")
E870 = ("Could not serialize the DocBin because it is too large. Consider "
"splitting up your documents into several doc bins and serializing "
"each separately. spacy.Corpus.v1 will search recursively for all "

2
spacy/pipeline/textcat.py
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@ -332,6 +332,8 @@ class TextCategorizer(TrainablePipe):
else:
for label in labels:
self.add_label(label)
if len(self.labels) < 2:
raise ValueError(Errors.E867)
if positive_label is not None:
if positive_label not in self.labels:
err = Errors.E920.format(pos_label=positive_label, labels=self.labels)

6
spacy/tests/pipeline/test_textcat.py
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@ -108,6 +108,12 @@ def test_label_types(name):
textcat.add_label("answer")
with pytest.raises(ValueError):
textcat.add_label(9)
# textcat requires at least two labels
if name == "textcat":
with pytest.raises(ValueError):
nlp.initialize()
else:
nlp.initialize()
@pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])

9
website/docs/api/textcategorizer.md
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@ -10,11 +10,12 @@ api_trainable: true
---
The text categorizer predicts **categories over a whole document**. and comes in
two flavours: `textcat` and `textcat_multilabel`. When you need to predict
two flavors: `textcat` and `textcat_multilabel`. When you need to predict
exactly one true label per document, use the `textcat` which has mutually
exclusive labels. If you want to perform multi-label classification and predict
zero, one or more labels per document, use the `textcat_multilabel` component
instead.
zero, one or more true labels per document, use the `textcat_multilabel`
component instead. For a binary classification task, you can use `textcat` with
**two** labels or `textcat_multilabel` with **one** label.
Both components are documented on this page.
@ -189,7 +190,7 @@ This method was previously called `begin_training`.
| _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ |
| `positive_label` | The positive label for a binary task with exclusive classes, `None` otherwise and by default. This parameter is not available when using the `textcat_multilabel` component. ~~Optional[str]~~ |
| `positive_label` | The positive label for a binary task with exclusive classes, `None` otherwise and by default. This parameter is only used during scoring. It is not available when using the `textcat_multilabel` component. ~~Optional[str]~~ |
## TextCategorizer.predict {#predict tag="method"}