spaCy/website/docs/api/scorer.md

title	teaser	tag	source
Scorer	Compute evaluation scores	class	spacy/scorer.py

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__

Create a new Scorer.

Example

from spacy.scorer import Scorer

# Default scoring pipeline
scorer = Scorer()

# Provided scoring pipeline
nlp = spacy.load("en_core_web_sm")
scorer = Scorer(nlp)

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 is constructed using the default_lang and default_pipeline settings. Optional[Language]
default_lang	The language to use for a default pipeline if nlp is not provided. Defaults to xx. str
default_pipeline	The pipeline components to use for a default pipeline if nlp is not provided. Defaults to ("senter", "tagger", "morphologizer", "parser", "ner", "textcat"). Iterable[string]
keyword-only
\*\*kwargs	Any additional settings to pass on to the individual scoring methods. Any

Scorer.score

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
• cats_score (depends on config, description provided in cats_score_desc), cats_micro_p, cats_micro_r, cats_micro_f, cats_macro_p, cats_macro_r, cats_macro_f, cats_macro_auc, cats_f_per_type, cats_auc_per_type

Example

scorer = Scorer()
scores = scorer.score(examples)

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

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.

Example

scores = Scorer.score_tokenization(examples)

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

Scorer.score_token_attr

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

Example

scores = Scorer.score_token_attr(examples, "pos")
print(scores["pos_acc"])

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

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.

Example

scores = Scorer.score_token_attr_per_feat(examples, "morph")
print(scores["morph_per_feat"])

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

Returns PRF scores for labeled or unlabeled spans.

Example

scores = Scorer.score_spans(examples, "ents")
print(scores["ents_f"])

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. CallableDoc, 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. str
labeled	Defaults to True. If set to False, two spans will be considered equal if their start and end match, irrespective of their label. bool
allow_overlap	Defaults to False. Whether or not to allow overlapping spans. If set to False, the alignment will automatically resolve conflicts. 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

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

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

Example

labels = ["LABEL_A", "LABEL_B", "LABEL_C"]
scores = Scorer.score_cats(
    examples,
    "cats",
    labels=labels
)
print(scores["cats_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. CallableDoc, 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

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.

Example

scores = Scorer.score_links(
    examples,
    negative_labels=["NIL", ""]
)
print(scores["nel_micro_f"])

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]]

get_ner_prf

Compute micro-PRF and per-entity PRF scores.

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