Scorer

class

Compute evaluation scores

The Scorer computes evaluation scores. It’s typically created by Language.evaluate. In addition, the Scorer provides a number of evaluation methods for evaluating Token and Doc attributes.

Scorer.init method

Create a new Scorer.

Name	Description
`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`

Scorer.score method

Calculate the scores for a list of Example objects using the scoring methods provided by the components in the pipeline.

The returned Dict contains the scores provided by the individual pipeline components. For the scoring methods provided by the Scorer and use by the core pipeline components, the individual score names start with the Token or Doc attribute being scored:

token_acc, token_p, token_r, token_f,
sents_p, sents_r, sents_f
tag_acc, pos_acc, morph_acc, morph_per_feat, lemma_acc
dep_uas, dep_las, dep_las_per_type
ents_p, ents_r ents_f, ents_per_type
textcat_macro_auc, textcat_macro_f

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
RETURNS	A dictionary of scores. `Dict[str, Union[float, Dict[str, float]]]`

Scorer.score_tokenization staticmethodv3.0

Scores the tokenization:

token_acc: number of correct tokens / number of gold tokens
token_p, token_r, token_f: precision, recall and F-score for token character spans

Docs with has_unknown_spaces are skipped during scoring.

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
RETURNS	`Dict`

Scorer.score_token_attr staticmethodv3.0

Scores a single token attribute. Tokens with missing values in the reference doc are skipped during scoring.

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
`attr`	The attribute to score. `str`
keyword-only
`getter`	Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. `Callable[[Token, str], Any]`
`missing_values`	Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. `Set[Any]`
RETURNS	A dictionary containing the score `{attr}_acc`. `Dict[str, float]`

Scorer.score_token_attr_per_feat staticmethodv3.0

Scores a single token attribute per feature for a token attribute in the Universal Dependencies FEATS format. Tokens with missing values in the reference doc are skipped during scoring.

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
`attr`	The attribute to score. `str`
keyword-only
`getter`	Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. `Callable[[Token, str], Any]`
`missing_values`	Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. `Set[Any]`
RETURNS	A dictionary containing the per-feature PRF scores under the key `{attr}_per_feat`. `Dict[str, Dict[str, float]]`

Scorer.score_spans staticmethodv3.0

Returns PRF scores for labeled or unlabeled spans.

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
`attr`	The attribute to score. `str`
keyword-only
`getter`	Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`. `Callable[[Doc, str], Iterable[Span]]`
`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]]`
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]]]`

Scorer.score_deps staticmethodv3.0

Calculate the UAS, LAS, and LAS per type scores for dependency parses. Tokens with missing values for the attr (typically dep) are skipped during scoring.

Example

def dep_getter(token, attr):
    dep = getattr(token, attr)
    dep = token.vocab.strings.as_string(dep).lower()
    return dep

scores = Scorer.score_deps(
    examples,
    "dep",
    getter=dep_getter,
    ignore_labels=("p", "punct")
)
print(scores["dep_uas"], scores["dep_las"])

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
`attr`	The attribute to score. `str`
keyword-only
`getter`	Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. `Callable[[Token, str], Any]`
`head_attr`	The attribute containing the head token. `str`
`head_getter`	Defaults to `getattr`. If provided, `head_getter(token, attr)` should return the head for an individual `Token`. `Callable[[Doc, str], Token]`
`ignore_labels`	Labels to ignore while scoring (e.g. `"punct"`). `Iterable[str]`
`missing_values`	Attribute values to treat as missing annotation in the reference annotation. Defaults to `{0, None, ""}`. `Set[Any]`
RETURNS	A dictionary containing the scores: `{attr}_uas`, `{attr}_las`, and `{attr}_las_per_type`. `Dict[str, Union[float, Dict[str, float]]]`

Scorer.score_cats staticmethodv3.0

Calculate PRF and ROC AUC scores for a doc-level attribute that is a dict containing scores for each label like Doc.cats. The returned dictionary contains the following scores:

{attr}_micro_p, {attr}_micro_r and {attr}_micro_f: each instance across each label is weighted equally
{attr}_macro_p, {attr}_macro_r and {attr}_macro_f: the average values across evaluations per label
{attr}_f_per_type and {attr}_auc_per_type: each contains a dictionary of scores, keyed by label
A final {attr}_score and corresponding {attr}_score_desc (text description)

The reported {attr}_score depends on the classification properties:

binary exclusive with positive label: {attr}_score is set to the F-score of the positive label
3+ exclusive classes, macro-averaged F-score: {attr}_score = {attr}_macro_f
multilabel, macro-averaged AUC: {attr}_score = {attr}_macro_auc

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
`attr`	The attribute to score. `str`
keyword-only
`getter`	Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. `Callable[[Doc, str], Dict[str, float]]`
labels	The set of possible labels. Defaults to `[]`. `Iterable[str]`
`multi_label`	Whether the attribute allows multiple labels. Defaults to `True`. `bool`
`positive_label`	The positive label for a binary task with exclusive classes. Defaults to `None`. `Optional[str]`
RETURNS	A dictionary containing the scores, with inapplicable scores as `None`. `Dict[str, Optional[float]]`

Scorer.score_links staticmethodv3.0

Returns PRF for predicted links on the entity level. To disentangle the performance of the NEL from the NER, this method only evaluates NEL links for entities that overlap between the gold reference and the predictions.

Name	Description
`examples`	The `Example` objects holding both the predictions and the correct gold-standard annotations. `Iterable[Example]`
keyword-only
`negative_labels`	The string values that refer to no annotation (e.g. “NIL”). `Iterable[str]`
RETURNS	A dictionary containing the scores. `Dict[str, Optional[float]]`

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