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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. ~~Optional[Callable[[Doc], bool]]~~ |
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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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