mirror of https://github.com/explosion/spaCy.git
136 lines
13 KiB
Markdown
136 lines
13 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).
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In addition, the `Scorer` provides a number of evaluation methods for evaluating
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`Token` and `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 | Type | Description |
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| ----------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `nlp` | Language | 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`. |
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| **RETURNS** | `Scorer` | The newly created object. |
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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: `token_acc`, `token_p/r/f`, `sents_p/r/f`, `tag_acc`,
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`pos_acc`, `morph_acc`, `morph_per_feat`, `lemma_acc`, `dep_uas`, `dep_las`,
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`dep_las_per_type`, `ents_p/r/f`, `ents_per_type`, `textcat_macro_auc`,
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`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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> scorer.score(examples)
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> ```
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| Name | Type | Description |
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| ----------- | ------------------- | --------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
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| **RETURNS** | `Dict` | A dictionary of scores. |
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## Scorer.score_tokenization {#score_tokenization tag="staticmethod"}
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Scores the tokenization:
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- `token_acc`: # correct tokens / # gold tokens
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- `token_p/r/f`: PRF for token character spans
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| Name | Type | Description |
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| ----------- | ------------------- | --------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
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| **RETURNS** | `Dict` | A dictionary containing the scores `token_acc/p/r/f`. |
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## Scorer.score_token_attr {#score_token_attr tag="staticmethod"}
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Scores a single token attribute.
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| Name | Type | Description |
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| ----------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
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| `attr` | `str` | The attribute to score. |
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| `getter` | `callable` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
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| **RETURNS** | `Dict` | A dictionary containing the score `attr_acc`. |
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## Scorer.score_token_attr_per_feat {#score_token_attr_per_feat tag="staticmethod"}
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Scores a single token attribute per feature for a token attribute in UFEATS
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format.
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| Name | Type | Description |
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| ----------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
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| `attr` | `str` | The attribute to score. |
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| `getter` | `callable` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
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| **RETURNS** | `Dict` | A dictionary containing the per-feature PRF scores unders the key `attr_per_feat`. |
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## Scorer.score_spans {#score_spans tag="staticmethod"}
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Returns PRF scores for labeled or unlabeled spans.
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| Name | Type | Description |
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| ----------- | ------------------- | --------------------------------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
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| `attr` | `str` | The attribute to score. |
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| `getter` | `callable` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`. |
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| **RETURNS** | `Dict` | A dictionary containing the PRF scores under the keys `attr_p/r/f` and the per-type PRF scores under `attr_per_type`. |
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## Scorer.score_deps {#score_deps tag="staticmethod"}
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Calculate the UAS, LAS, and LAS per type scores for dependency parses.
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| Name | Type | Description |
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| --------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
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| `attr` | `str` | The attribute containing the dependency label. |
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| `getter` | `callable` | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
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| `head_attr` | `str` | The attribute containing the head token. |
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| `head_getter` | `callable` | Defaults to `getattr`. If provided, `head_getter(token, attr)` should return the head for an individual `Token`. |
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| `ignore_labels` | `Tuple` | Labels to ignore while scoring (e.g., `punct`). |
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| **RETURNS** | `Dict` | A dictionary containing the scores: `attr_uas`, `attr_las`, and `attr_las_per_type`. |
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## Scorer.score_cats {#score_cats tag="staticmethod"}
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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 reported overall score
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depends on the scorer settings.
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| Name | Type | Description |
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| ---------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
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| `attr` | `str` | The attribute to score. |
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| `getter` | `callable` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. |
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| labels | `Iterable[str]` | The set of possible labels. Defaults to `[]`. |
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| `multi_label` | `bool` | Whether the attribute allows multiple labels. Defaults to `True`. |
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| `positive_label` | `str` | The positive label for a binary task with exclusive classes. Defaults to `None`. |
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| **RETURNS** | `Dict` | A dictionary containing the scores, with inapplicable scores as `None`: 1) for all: `attr_score` (one of `attr_f` / `attr_macro_f` / `attr_macro_auc`), `attr_score_desc` (text description of the overall score), `attr_f_per_type`, `attr_auc_per_type`; 2) for binary exclusive with positive label: `attr_p/r/f`; 3) for 3+ exclusive classes, macro-averaged fscore: `attr_macro_f`; 4) for multilabel, macro-averaged AUC: `attr_macro_auc` |
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