spaCy/spacy/cli/evaluate.py
Adriane Boyd 3637148c4d
Add scorer option to return per-component scores (#12540)
* Add scorer option to return per-component scores

Add `per_component` option to `Language.evaluate` and `Scorer.score` to
return scores keyed by `tokenizer` (hard-coded) or by component name.

Add option to `evaluate` CLI to score by component. Per-component scores
can only be saved to JSON.

* Update help text and messages
2023-05-12 15:36:54 +02:00

250 lines
9.1 KiB
Python

from typing import Optional, List, Dict, Any, Union
from wasabi import Printer
from pathlib import Path
import re
import srsly
from thinc.api import fix_random_seed
from ..training import Corpus
from ..tokens import Doc
from ._util import app, Arg, Opt, setup_gpu, import_code, benchmark_cli
from ..scorer import Scorer
from .. import util
from .. import displacy
@benchmark_cli.command(
"accuracy",
)
@app.command("evaluate")
def evaluate_cli(
# fmt: off
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
output: Optional[Path] = Opt(None, "--output", "-o", help="Output JSON file for metrics", dir_okay=False),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
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
):
"""
Evaluate a trained pipeline. Expects a loadable spaCy pipeline and evaluation
data in the binary .spacy format. The --gold-preproc option sets up the
evaluation examples with gold-standard sentences and tokens for the
predictions. Gold preprocessing helps the annotations align to the
tokenization, and may result in sequences of more consistent length. However,
it may reduce runtime accuracy due to train/test skew. To render a sample of
dependency parses in a HTML file, set as output directory as the
displacy_path argument.
DOCS: https://spacy.io/api/cli#benchmark-accuracy
"""
import_code(code_path)
evaluate(
model,
data_path,
output=output,
use_gpu=use_gpu,
gold_preproc=gold_preproc,
displacy_path=displacy_path,
displacy_limit=displacy_limit,
per_component=per_component,
silent=False,
)
def evaluate(
model: str,
data_path: Path,
output: Optional[Path] = None,
use_gpu: int = -1,
gold_preproc: bool = False,
displacy_path: Optional[Path] = None,
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()
setup_gpu(use_gpu, silent=silent)
data_path = util.ensure_path(data_path)
output_path = util.ensure_path(output)
displacy_path = util.ensure_path(displacy_path)
if not data_path.exists():
msg.fail("Evaluation data not found", data_path, exits=1)
if displacy_path and not displacy_path.exists():
msg.fail("Visualization output directory not found", displacy_path, exits=1)
corpus = Corpus(data_path, gold_preproc=gold_preproc)
nlp = util.load_model(model)
dev_dataset = list(corpus(nlp))
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] = "-"
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)
if displacy_path:
factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names]
docs = list(nlp.pipe(ex.reference.text for ex in dev_dataset[:displacy_limit]))
render_deps = "parser" in factory_names
render_ents = "ner" in factory_names
render_spans = "spancat" in factory_names
render_parses(
docs,
displacy_path,
model_name=model,
limit=displacy_limit,
deps=render_deps,
ents=render_ents,
spans=render_spans,
)
msg.good(f"Generated {displacy_limit} parses as HTML", displacy_path)
if output_path is not None:
srsly.write_json(output_path, data)
msg.good(f"Saved results to {output_path}")
return data
def handle_scores_per_type(
scores: Dict[str, Any],
data: Dict[str, Any] = {},
*,
spans_key: str = "sc",
silent: bool = False,
) -> Dict[str, Any]:
msg = Printer(no_print=silent, pretty=not silent)
if "morph_per_feat" in scores:
if scores["morph_per_feat"]:
print_prf_per_type(msg, scores["morph_per_feat"], "MORPH", "feat")
data["morph_per_feat"] = scores["morph_per_feat"]
if "dep_las_per_type" in scores:
if scores["dep_las_per_type"]:
print_prf_per_type(msg, scores["dep_las_per_type"], "LAS", "type")
data["dep_las_per_type"] = scores["dep_las_per_type"]
if "ents_per_type" in scores:
if scores["ents_per_type"]:
print_prf_per_type(msg, scores["ents_per_type"], "NER", "type")
data["ents_per_type"] = scores["ents_per_type"]
if f"spans_{spans_key}_per_type" in scores:
if scores[f"spans_{spans_key}_per_type"]:
print_prf_per_type(
msg, scores[f"spans_{spans_key}_per_type"], "SPANS", "type"
)
data[f"spans_{spans_key}_per_type"] = scores[f"spans_{spans_key}_per_type"]
if "cats_f_per_type" in scores:
if scores["cats_f_per_type"]:
print_prf_per_type(msg, scores["cats_f_per_type"], "Textcat F", "label")
data["cats_f_per_type"] = scores["cats_f_per_type"]
if "cats_auc_per_type" in scores:
if scores["cats_auc_per_type"]:
print_textcats_auc_per_cat(msg, scores["cats_auc_per_type"])
data["cats_auc_per_type"] = scores["cats_auc_per_type"]
return scores
def render_parses(
docs: List[Doc],
output_path: Path,
model_name: str = "",
limit: int = 250,
deps: bool = True,
ents: bool = True,
spans: bool = True,
):
docs[0].user_data["title"] = model_name
if ents:
html = displacy.render(docs[:limit], style="ent", page=True)
with (output_path / "entities.html").open("w", encoding="utf8") as file_:
file_.write(html)
if deps:
html = displacy.render(
docs[:limit], style="dep", page=True, options={"compact": True}
)
with (output_path / "parses.html").open("w", encoding="utf8") as file_:
file_.write(html)
if spans:
html = displacy.render(docs[:limit], style="span", page=True)
with (output_path / "spans.html").open("w", encoding="utf8") as file_:
file_.write(html)
def print_prf_per_type(
msg: Printer, scores: Dict[str, Dict[str, float]], name: str, type: str
) -> None:
data = []
for key, value in scores.items():
row = [key]
for k in ("p", "r", "f"):
v = value[k]
row.append(f"{v * 100:.2f}" if isinstance(v, (int, float)) else v)
data.append(row)
msg.table(
data,
header=("", "P", "R", "F"),
aligns=("l", "r", "r", "r"),
title=f"{name} (per {type})",
)
def print_textcats_auc_per_cat(
msg: Printer, scores: Dict[str, Dict[str, float]]
) -> None:
msg.table(
[
(k, f"{v:.2f}" if isinstance(v, (float, int)) else v)
for k, v in scores.items()
],
header=("", "ROC AUC"),
aligns=("l", "r"),
title="Textcat ROC AUC (per label)",
)