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Add scorer option to return per-component scores #12540

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May 12, 2023
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Add scorer option to return per-component scores #12540

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af7535a
Add scorer option to return per-component scores
adrianeboyd Apr 18, 2023
855e3ea
Update help text and messages
adrianeboyd Apr 25, 2023
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84 changes: 48 additions & 36 deletions spacy/cli/evaluate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ def evaluate_cli(
gold_preproc: bool = Opt(False, "--gold-preproc", "-G", help="Use gold preprocessing"),
displacy_path: Optional[Path] = Opt(None, "--displacy-path", "-dp", help="Directory to output rendered parses as HTML", exists=True, file_okay=False),
displacy_limit: int = Opt(25, "--displacy-limit", "-dl", help="Limit of parses to render as HTML"),
per_component: bool = Opt(False, "--per-component", "-P", help="Return scores per component, only applicable when an output JSON file is specified."),
# fmt: on
):
"""
Expand All @@ -50,6 +51,7 @@ def evaluate_cli(
gold_preproc=gold_preproc,
displacy_path=displacy_path,
displacy_limit=displacy_limit,
per_component=per_component,
silent=False,
)

Expand All @@ -64,6 +66,7 @@ def evaluate(
displacy_limit: int = 25,
silent: bool = True,
spans_key: str = "sc",
per_component: bool = False,
) -> Dict[str, Any]:
msg = Printer(no_print=silent, pretty=not silent)
fix_random_seed()
Expand All @@ -78,44 +81,53 @@ def evaluate(
corpus = Corpus(data_path, gold_preproc=gold_preproc)
nlp = util.load_model(model)
dev_dataset = list(corpus(nlp))
scores = nlp.evaluate(dev_dataset)
metrics = {
"TOK": "token_acc",
"TAG": "tag_acc",
"POS": "pos_acc",
"MORPH": "morph_acc",
"LEMMA": "lemma_acc",
"UAS": "dep_uas",
"LAS": "dep_las",
"NER P": "ents_p",
"NER R": "ents_r",
"NER F": "ents_f",
"TEXTCAT": "cats_score",
"SENT P": "sents_p",
"SENT R": "sents_r",
"SENT F": "sents_f",
"SPAN P": f"spans_{spans_key}_p",
"SPAN R": f"spans_{spans_key}_r",
"SPAN F": f"spans_{spans_key}_f",
"SPEED": "speed",
}
results = {}
data = {}
for metric, key in metrics.items():
if key in scores:
if key == "cats_score":
metric = metric + " (" + scores.get("cats_score_desc", "unk") + ")"
if isinstance(scores[key], (int, float)):
if key == "speed":
results[metric] = f"{scores[key]:.0f}"
scores = nlp.evaluate(dev_dataset, per_component=per_component)
if per_component:
data = scores
if output is None:
msg.warn(
"The per-component option is enabled but there is no output JSON file provided to save the scores to."
)
else:
msg.info("Per-component scores will be saved to output JSON file.")
else:
metrics = {
"TOK": "token_acc",
"TAG": "tag_acc",
"POS": "pos_acc",
"MORPH": "morph_acc",
"LEMMA": "lemma_acc",
"UAS": "dep_uas",
"LAS": "dep_las",
"NER P": "ents_p",
"NER R": "ents_r",
"NER F": "ents_f",
"TEXTCAT": "cats_score",
"SENT P": "sents_p",
"SENT R": "sents_r",
"SENT F": "sents_f",
"SPAN P": f"spans_{spans_key}_p",
"SPAN R": f"spans_{spans_key}_r",
"SPAN F": f"spans_{spans_key}_f",
"SPEED": "speed",
}
results = {}
data = {}
for metric, key in metrics.items():
if key in scores:
if key == "cats_score":
metric = metric + " (" + scores.get("cats_score_desc", "unk") + ")"
if isinstance(scores[key], (int, float)):
if key == "speed":
results[metric] = f"{scores[key]:.0f}"
else:
results[metric] = f"{scores[key]*100:.2f}"
else:
results[metric] = f"{scores[key]*100:.2f}"
else:
results[metric] = "-"
data[re.sub(r"[\s/]", "_", key.lower())] = scores[key]
results[metric] = "-"
data[re.sub(r"[\s/]", "_", key.lower())] = scores[key]

msg.table(results, title="Results")
data = handle_scores_per_type(scores, data, spans_key=spans_key, silent=silent)
msg.table(results, title="Results")
data = handle_scores_per_type(scores, data, spans_key=spans_key, silent=silent)

if displacy_path:
factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names]
Expand Down
5 changes: 4 additions & 1 deletion spacy/language.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1372,6 +1372,7 @@ def evaluate(
scorer: Optional[Scorer] = None,
component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
scorer_cfg: Optional[Dict[str, Any]] = None,
per_component: bool = False,
) -> Dict[str, Any]:
"""Evaluate a model's pipeline components.

Expand All @@ -1383,6 +1384,8 @@ def evaluate(
arguments for specific components.
scorer_cfg (dict): An optional dictionary with extra keyword arguments
for the scorer.
per_component (bool): Whether to return the scores keyed by component
name. Defaults to False.

RETURNS (Scorer): The scorer containing the evaluation results.

Expand Down Expand Up @@ -1415,7 +1418,7 @@ def evaluate(
for eg, doc in zip(examples, docs):
eg.predicted = doc
end_time = timer()
results = scorer.score(examples)
results = scorer.score(examples, per_component=per_component)
n_words = sum(len(eg.predicted) for eg in examples)
results["speed"] = n_words / (end_time - start_time)
return results
Expand Down
16 changes: 13 additions & 3 deletions spacy/scorer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,20 +121,30 @@ def __init__(
nlp.add_pipe(pipe)
self.nlp = nlp

def score(self, examples: Iterable[Example]) -> Dict[str, Any]:
def score(
self, examples: Iterable[Example], *, per_component: bool = False
) -> Dict[str, Any]:
"""Evaluate a list of Examples.

examples (Iterable[Example]): The predicted annotations + correct annotations.
per_component (bool): Whether to return the scores keyed by component
name. Defaults to False.
RETURNS (Dict): A dictionary of scores.

DOCS: https://spacy.io/api/scorer#score
"""
scores = {}
if hasattr(self.nlp.tokenizer, "score"):
scores.update(self.nlp.tokenizer.score(examples, **self.cfg)) # type: ignore
if per_component:
scores["tokenizer"] = self.nlp.tokenizer.score(examples, **self.cfg)
else:
scores.update(self.nlp.tokenizer.score(examples, **self.cfg)) # type: ignore
for name, component in self.nlp.pipeline:
if hasattr(component, "score"):
scores.update(component.score(examples, **self.cfg))
if per_component:
scores[name] = component.score(examples, **self.cfg)
else:
scores.update(component.score(examples, **self.cfg))
return scores

@staticmethod
Expand Down
15 changes: 15 additions & 0 deletions spacy/tests/test_scorer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,6 +115,14 @@ def test_tokenization(sented_doc):
assert scores["token_r"] == approx(0.33333333)
assert scores["token_f"] == 0.4

# per-component scoring
scorer = Scorer()
scores = scorer.score([example], per_component=True)
assert scores["tokenizer"]["token_acc"] == 0.5
assert scores["tokenizer"]["token_p"] == 0.5
assert scores["tokenizer"]["token_r"] == approx(0.33333333)
assert scores["tokenizer"]["token_f"] == 0.4


def test_sents(sented_doc):
scorer = Scorer()
Expand Down Expand Up @@ -278,6 +286,13 @@ def test_tag_score(tagged_doc):
assert results["morph_per_feat"]["Poss"]["f"] == 0.0
assert results["morph_per_feat"]["Number"]["f"] == approx(0.72727272)

# per-component scoring
scorer = Scorer()
results = scorer.score([example], per_component=True)
assert results["tagger"]["tag_acc"] == 0.9
assert results["morphologizer"]["pos_acc"] == 0.9
assert results["morphologizer"]["morph_acc"] == approx(0.8)


def test_partial_annotation(en_tokenizer):
pred_doc = en_tokenizer("a b c d e")
Expand Down
25 changes: 13 additions & 12 deletions website/docs/api/cli.mdx
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Expand Up @@ -1163,18 +1163,19 @@ skew. To render a sample of dependency parses in a HTML file using the
$ python -m spacy benchmark accuracy [model] [data_path] [--output] [--code] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit]
```

| Name | Description |
| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model` | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~ |
| `data_path` | Location of evaluation data in spaCy's [binary format](/api/data-formats#training). ~~Path (positional)~~ |
| `--output`, `-o` | Output JSON file for metrics. If not set, no metrics will be exported. ~~Optional[Path] \(option)~~ |
| `--code`, `-c` <Tag variant="new">3</Tag> | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--gold-preproc`, `-G` | Use gold preprocessing. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
| `--displacy-path`, `-dp` | Directory to output rendered parses as HTML. If not set, no visualizations will be generated. ~~Optional[Path] \(option)~~ |
| `--displacy-limit`, `-dl` | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Training results and optional metrics and visualizations. |
| Name | Description |
| ---------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model` | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~ |
| `data_path` | Location of evaluation data in spaCy's [binary format](/api/data-formats#training). ~~Path (positional)~~ |
| `--output`, `-o` | Output JSON file for metrics. If not set, no metrics will be exported. ~~Optional[Path] \(option)~~ |
| `--code`, `-c` <Tag variant="new">3</Tag> | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
| `--gold-preproc`, `-G` | Use gold preprocessing. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
| `--displacy-path`, `-dp` | Directory to output rendered parses as HTML. If not set, no visualizations will be generated. ~~Optional[Path] \(option)~~ |
| `--displacy-limit`, `-dl` | Number of parses to generate per file. Defaults to `25`. Keep in mind that a significantly higher number might cause the `.html` files to render slowly. ~~int (option)~~ |
| `--per-component`, `-P` <Tag variant="new">3.6</Tag> | Whether to return the scores keyed by component name. Defaults to `False`. ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Training results and optional metrics and visualizations. |

### speed {id="benchmark-speed", version="3.5", tag="command"}

Expand Down
19 changes: 10 additions & 9 deletions website/docs/api/language.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -382,15 +382,16 @@ objects instead of tuples of `Doc` and `GoldParse` objects.
> print(scores)
> ```

| Name | Description |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `batch_size` | The batch size to use. ~~Optional[int]~~ |
| `scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. ~~Optional[Scorer]~~ |
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
| `scorer_cfg` | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. ~~Optional[Dict[str, Any]]~~ |
| **RETURNS** | A dictionary of evaluation scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
| Name | Description |
| -------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `batch_size` | The batch size to use. ~~Optional[int]~~ |
| `scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. ~~Optional[Scorer]~~ |
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
| `scorer_cfg` | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. ~~Optional[Dict[str, Any]]~~ |
| `per_component` <Tag variant="new">3.6</Tag> | Whether to return the scores keyed by component name. Defaults to `False`. ~~bool~~ |
| **RETURNS** | A dictionary of evaluation scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |

## Language.use_params {id="use_params",tag="contextmanager, method"}

Expand Down