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a4b32b9552
* Handle missing reference values in scorer Handle missing values in reference doc during scoring where it is possible to detect an unset state for the attribute. If no reference docs contain annotation, `None` is returned instead of a score. `spacy evaluate` displays `-` for missing scores and the missing scores are saved as `None`/`null` in the metrics. Attributes without unset states: * `token.head`: relies on `token.dep` to recognize unset values * `doc.cats`: unable to handle missing annotation Additional changes: * add optional `has_annotation` check to `score_scans` to replace `doc.sents` hack * update `score_token_attr_per_feat` to handle missing and empty morph representations * fix bug in `Doc.has_annotation` for normalization of `IS_SENT_START` vs. `SENT_START` * Fix import * Update return types
251 lines
16 KiB
Markdown
251 lines
16 KiB
Markdown
---
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title: Scorer
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teaser: Compute evaluation scores
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tag: class
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source: spacy/scorer.py
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---
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The `Scorer` computes evaluation scores. It's typically created by
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[`Language.evaluate`](/api/language#evaluate). In addition, the `Scorer`
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provides a number of evaluation methods for evaluating [`Token`](/api/token) and
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[`Doc`](/api/doc) attributes.
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## Scorer.\_\_init\_\_ {#init tag="method"}
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Create a new `Scorer`.
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> #### Example
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>
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> ```python
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> from spacy.scorer import Scorer
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>
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> # Default scoring pipeline
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> scorer = Scorer()
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>
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> # Provided scoring pipeline
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> nlp = spacy.load("en_core_web_sm")
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> scorer = Scorer(nlp)
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> ```
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| Name | Description |
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| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `nlp` | The pipeline to use for scoring, where each pipeline component may provide a scoring method. If none is provided, then a default pipeline for the multi-language code `xx` is constructed containing: `senter`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat`. ~~Language~~ |
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## Scorer.score {#score tag="method"}
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Calculate the scores for a list of [`Example`](/api/example) objects using the
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scoring methods provided by the components in the pipeline.
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The returned `Dict` contains the scores provided by the individual pipeline
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components. For the scoring methods provided by the `Scorer` and use by the core
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pipeline components, the individual score names start with the `Token` or `Doc`
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attribute being scored:
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- `token_acc`, `token_p`, `token_r`, `token_f`,
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- `sents_p`, `sents_r`, `sents_f`
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- `tag_acc`, `pos_acc`, `morph_acc`, `morph_per_feat`, `lemma_acc`
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- `dep_uas`, `dep_las`, `dep_las_per_type`
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- `ents_p`, `ents_r` `ents_f`, `ents_per_type`
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- `textcat_macro_auc`, `textcat_macro_f`
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> #### Example
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>
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> ```python
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> scorer = Scorer()
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> scores = scorer.score(examples)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| **RETURNS** | A dictionary of scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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## Scorer.score_tokenization {#score_tokenization tag="staticmethod" new="3"}
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Scores the tokenization:
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- `token_acc`: number of correct tokens / number of gold tokens
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- `token_p`, `token_r`, `token_f`: precision, recall and F-score for token
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character spans
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Docs with `has_unknown_spaces` are skipped during scoring.
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> #### Example
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>
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> ```python
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> scores = Scorer.score_tokenization(examples)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| **RETURNS** | `Dict` | A dictionary containing the scores `token_acc`, `token_p`, `token_r`, `token_f`. ~~Dict[str, float]]~~ |
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## Scorer.score_token_attr {#score_token_attr tag="staticmethod" new="3"}
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Scores a single token attribute. Tokens with missing values in the reference doc
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are skipped during scoring.
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> #### Example
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>
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> ```python
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> scores = Scorer.score_token_attr(examples, "pos")
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> print(scores["pos_acc"])
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> ```
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| Name | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
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| `missing_values` | Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. ~~Set[Any]~~ |
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| **RETURNS** | A dictionary containing the score `{attr}_acc`. ~~Dict[str, float]~~ |
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## Scorer.score_token_attr_per_feat {#score_token_attr_per_feat tag="staticmethod" new="3"}
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Scores a single token attribute per feature for a token attribute in the
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Universal Dependencies
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[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
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format. Tokens with missing values in the reference doc are skipped during
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scoring.
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> #### Example
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>
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> ```python
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> scores = Scorer.score_token_attr_per_feat(examples, "morph")
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> print(scores["morph_per_feat"])
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> ```
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| Name | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
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| `missing_values` | Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. ~~Set[Any]~~ |
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| **RETURNS** | A dictionary containing the per-feature PRF scores under the key `{attr}_per_feat`. ~~Dict[str, Dict[str, float]]~~ |
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## Scorer.score_spans {#score_spans tag="staticmethod" new="3"}
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Returns PRF scores for labeled or unlabeled spans.
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> #### Example
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>
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> ```python
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> scores = Scorer.score_spans(examples, "ents")
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> print(scores["ents_f"])
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> ```
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| Name | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`. ~~Callable[[Doc, str], Iterable[Span]]~~ |
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| `has_annotation` | Defaults to `None`. If provided, `has_annotation(doc)` should return whether a `Doc` has annotation for this `attr`. Docs without annotation are skipped for scoring purposes. ~~str~~ |
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| **RETURNS** | A dictionary containing the PRF scores under the keys `{attr}_p`, `{attr}_r`, `{attr}_f` and the per-type PRF scores under `{attr}_per_type`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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## Scorer.score_deps {#score_deps tag="staticmethod" new="3"}
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Calculate the UAS, LAS, and LAS per type scores for dependency parses. Tokens
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with missing values for the `attr` (typically `dep`) are skipped during scoring.
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> #### Example
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>
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> ```python
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> def dep_getter(token, attr):
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> dep = getattr(token, attr)
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> dep = token.vocab.strings.as_string(dep).lower()
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> return dep
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>
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> scores = Scorer.score_deps(
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> examples,
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> "dep",
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> getter=dep_getter,
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> ignore_labels=("p", "punct")
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> )
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> print(scores["dep_uas"], scores["dep_las"])
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> ```
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| Name | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. ~~Callable[[Token, str], Any]~~ |
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| `head_attr` | The attribute containing the head token. ~~str~~ |
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| `head_getter` | Defaults to `getattr`. If provided, `head_getter(token, attr)` should return the head for an individual `Token`. ~~Callable[[Doc, str], Token]~~ |
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| `ignore_labels` | Labels to ignore while scoring (e.g. `"punct"`). ~~Iterable[str]~~ |
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| `missing_values` | Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. ~~Set[Any]~~ |
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| **RETURNS** | A dictionary containing the scores: `{attr}_uas`, `{attr}_las`, and `{attr}_las_per_type`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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## Scorer.score_cats {#score_cats tag="staticmethod" new="3"}
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Calculate PRF and ROC AUC scores for a doc-level attribute that is a dict
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containing scores for each label like `Doc.cats`. The returned dictionary
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contains the following scores:
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- `{attr}_micro_p`, `{attr}_micro_r` and `{attr}_micro_f`: each instance across
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each label is weighted equally
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- `{attr}_macro_p`, `{attr}_macro_r` and `{attr}_macro_f`: the average values
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across evaluations per label
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- `{attr}_f_per_type` and `{attr}_auc_per_type`: each contains a dictionary of
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scores, keyed by label
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- A final `{attr}_score` and corresponding `{attr}_score_desc` (text
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description)
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The reported `{attr}_score` depends on the classification properties:
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- **binary exclusive with positive label:** `{attr}_score` is set to the F-score
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of the positive label
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- **3+ exclusive classes**, macro-averaged F-score:
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`{attr}_score = {attr}_macro_f`
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- **multilabel**, macro-averaged AUC: `{attr}_score = {attr}_macro_auc`
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> #### Example
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>
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> ```python
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> labels = ["LABEL_A", "LABEL_B", "LABEL_C"]
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> scores = Scorer.score_cats(
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> examples,
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> "cats",
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> labels=labels
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> )
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> print(scores["cats_macro_auc"])
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> ```
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| Name | Description |
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| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. ~~Callable[[Doc, str], Dict[str, float]]~~ |
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| labels | The set of possible labels. Defaults to `[]`. ~~Iterable[str]~~ |
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| `multi_label` | Whether the attribute allows multiple labels. Defaults to `True`. ~~bool~~ |
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| `positive_label` | The positive label for a binary task with exclusive classes. Defaults to `None`. ~~Optional[str]~~ |
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| **RETURNS** | A dictionary containing the scores, with inapplicable scores as `None`. ~~Dict[str, Optional[float]]~~ |
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## Scorer.score_links {#score_links tag="staticmethod" new="3"}
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Returns PRF for predicted links on the entity level. To disentangle the
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performance of the NEL from the NER, this method only evaluates NEL links for
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entities that overlap between the gold reference and the predictions.
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> #### Example
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>
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> ```python
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> scores = Scorer.score_links(
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> examples,
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> negative_labels=["NIL", ""]
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> )
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> print(scores["nel_micro_f"])
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> ```
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| Name | Description |
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| ----------------- | ------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `negative_labels` | The string values that refer to no annotation (e.g. "NIL"). ~~Iterable[str]~~ |
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| **RETURNS** | A dictionary containing the scores. ~~Dict[str, Optional[float]]~~ |
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