spaCy/website/docs/api/scorer.md

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2019-05-24 12:06:04 +00:00
---
title: Scorer
teaser: Compute evaluation scores
tag: class
source: spacy/scorer.py
---
The `Scorer` computes and stores evaluation scores. It's typically created by
[`Language.evaluate`](/api/language#evaluate).
## Scorer.\_\_init\_\_ {#init tag="method"}
Create a new `Scorer`.
> #### Example
>
> ```python
> 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 {#score tag="method"}
Update the evaluation scores from a single [`Doc`](/api/doc) /
[`GoldParse`](/api/goldparse) pair.
> #### Example
>
> ```python
> 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
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 20:31:31 +00:00
| 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` <Tag variant="new">2.1.5</Tag> | dict | Scores per entity label. Keyed by label, mapped to a dict of `p`, `r` and `f` scores. |
| `textcat_score` <Tag variant="new">2.2</Tag> | 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` <Tag variant="new">2.2</Tag> | dict | Scores per textcat label, keyed by label. |
| `scores` | dict | All scores, keyed by type. |