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spaCy/website/docs/api/scorer.md
adrianeboyd b5d999e510 Add textcat to train CLI (#4226) 
* Add doc.cats to spacy.gold at the paragraph level

Support `doc.cats` as `"cats": [{"label": string, "value": number}]` in
the spacy JSON training format at the paragraph level.

* `spacy.gold.docs_to_json()` writes `docs.cats`

* `GoldCorpus` reads in cats in each `GoldParse`

* Update instances of gold_tuples to handle cats

Update iteration over gold_tuples / gold_parses to handle addition of
cats at the paragraph level.

* Add textcat to train CLI

* Add textcat options to train CLI
* Add textcat labels in `TextCategorizer.begin_training()`
* Add textcat evaluation to `Scorer`:
  * For binary exclusive classes with provided label: F1 for label
  * For 2+ exclusive classes: F1 macro average
  * For multilabel (not exclusive): ROC AUC macro average (currently
relying on sklearn)
* Provide user info on textcat evaluation settings, potential
incompatibilities
* Provide pipeline to Scorer in `Language.evaluate` for textcat config
* Customize train CLI output to include only metrics relevant to current
pipeline
* Add textcat evaluation to evaluate CLI

* Fix handling of unset arguments and config params

Fix handling of unset arguments and model confiug parameters in Scorer
initialization.

* Temporarily add sklearn requirement

* Remove sklearn version number

* Improve Scorer handling of models without textcats

* Fixing Scorer handling of models without textcats

* Update Scorer output for python 2.7

* Modify inf in Scorer for python 2.7

* Auto-format

Also make small adjustments to make auto-formatting with black easier and produce nicer results

* Move error message to Errors

* Update documentation

* Add cats to annotation JSON format [ci skip]

* Fix tpl flag and docs [ci skip]

* Switch to internal roc_auc_score

Switch to internal `roc_auc_score()` adapted from scikit-learn.

* Add AUCROCScore tests and improve errors/warnings

* Add tests for AUCROCScore and roc_auc_score
* Add missing error for only positive/negative values
* Remove unnecessary warnings and errors

* Make reduced roc_auc_score functions private

Because most of the checks and warnings have been stripped for the
internal functions and access is only intended through `ROCAUCScore`,
make the functions for roc_auc_score adapted from scikit-learn private.

* Check that data corresponds with multilabel flag

Check that the training instances correspond with the multilabel flag,
adding the multilabel flag if required.

* Add textcat score to early stopping check

* Add more checks to debug-data for textcat

* Add example training data for textcat

* Add more checks to textcat train CLI

* Check configuration when extending base model
* Fix typos

* Update textcat example data

* Provide licensing details and licenses for data
* Remove two labels with no positive instances from jigsaw-toxic-comment
data.


Co-authored-by: Ines Montani <ines@ines.io>
2019-09-15 22:31:31 +02:00

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title teaser tag source
Scorer Compute evaluation scores class spacy/scorer.py

The Scorer computes and stores evaluation scores. It's typically created by Language.evaluate.

Scorer.__init__

Create a new Scorer.

Example

from spacy.scorer import Scorer

scorer = Scorer()
Name Type Description
eval_punct bool Evaluate the dependency attachments to and from punctuation.
RETURNS Scorer The newly created object.

Scorer.score

Update the evaluation scores from a single Doc / GoldParse pair.

Example

scorer = Scorer()
scorer.score(doc, gold)
Name Type Description
doc Doc The predicted annotations.
gold GoldParse The correct annotations.
verbose bool Print debugging information.
punct_labels tuple Dependency labels for punctuation. Used to evaluate dependency attachments to punctuation if eval_punct is True.

Properties

Name Type Description
token_acc float Tokenization accuracy.
tags_acc float Part-of-speech tag accuracy (fine grained tags, i.e. Token.tag).
uas float Unlabelled dependency score.
las float Labelled dependency score.
ents_p float Named entity accuracy (precision).
ents_r float Named entity accuracy (recall).
ents_f float Named entity accuracy (F-score).
ents_per_type 2.1.5 dict Scores per entity label. Keyed by label, mapped to a dict of p, r and f scores.
textcat_score 2.2 float F-score on positive label for binary exclusive, macro-averaged F-score for 3+ exclusive, macro-averaged AUC ROC score for multilabel (-1 if undefined).
textcats_per_cat 2.2 dict Scores per textcat label, keyed by label.
scores dict All scores, keyed by type.