spaCy/spacy/cli/evaluate.py
2020-06-28 15:34:28 +02:00

182 lines
5.9 KiB
Python

from typing import Optional, List, Dict
from timeit import default_timer as timer
from wasabi import Printer
from pathlib import Path
import re
import srsly
from ..gold import Corpus
from ..tokens import Doc
from ._app import app, Arg, Opt
from ..scorer import Scorer
from .. import util
from .. import displacy
@app.command("evaluate")
def evaluate_cli(
# fmt: off
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help="Location of JSON-formatted evaluation data", exists=True),
output: Optional[Path] = Opt(None, "--output", "-o", help="Output JSON file for metrics", dir_okay=False),
gpu_id: int = Opt(-1, "--gpu-id", "-g", help="Use GPU"),
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"),
# fmt: on
):
"""
Evaluate a model. To render a sample of parses in a HTML file, set an
output directory as the displacy_path argument.
"""
evaluate(
model,
data_path,
output=output,
gpu_id=gpu_id,
gold_preproc=gold_preproc,
displacy_path=displacy_path,
displacy_limit=displacy_limit,
silent=False,
)
def evaluate(
model: str,
data_path: Path,
output: Optional[Path],
gpu_id: int = -1,
gold_preproc: bool = False,
displacy_path: Optional[Path] = None,
displacy_limit: int = 25,
silent: bool = True,
) -> Scorer:
msg = Printer(no_print=silent, pretty=not silent)
util.fix_random_seed()
if gpu_id >= 0:
util.use_gpu(gpu_id)
util.set_env_log(False)
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, data_path)
nlp = util.load_model(model)
dev_dataset = list(corpus.dev_dataset(nlp, gold_preproc=gold_preproc))
begin = timer()
scorer = nlp.evaluate(dev_dataset, verbose=False)
end = timer()
nwords = sum(len(ex.predicted) for ex in dev_dataset)
results = {
"Time": f"{end - begin:.2f} s",
"Words": nwords,
"Words/s": f"{nwords / (end - begin):.0f}",
"TOK": f"{scorer.token_acc:.2f}",
"TAG": f"{scorer.tags_acc:.2f}",
"POS": f"{scorer.pos_acc:.2f}",
"MORPH": f"{scorer.morphs_acc:.2f}",
"UAS": f"{scorer.uas:.2f}",
"LAS": f"{scorer.las:.2f}",
"NER P": f"{scorer.ents_p:.2f}",
"NER R": f"{scorer.ents_r:.2f}",
"NER F": f"{scorer.ents_f:.2f}",
"Textcat AUC": f"{scorer.textcat_auc:.2f}",
"Textcat F": f"{scorer.textcat_f:.2f}",
"Sent P": f"{scorer.sent_p:.2f}",
"Sent R": f"{scorer.sent_r:.2f}",
"Sent F": f"{scorer.sent_f:.2f}",
}
data = {re.sub(r"[\s/]", "_", k.lower()): v for k, v in results.items()}
msg.table(results, title="Results")
if scorer.ents_per_type:
data["ents_per_type"] = scorer.ents_per_type
print_ents_per_type(msg, scorer.ents_per_type)
if scorer.textcats_f_per_cat:
data["textcats_f_per_cat"] = scorer.textcats_f_per_cat
print_textcats_f_per_cat(msg, scorer.textcats_f_per_cat)
if scorer.textcats_auc_per_cat:
data["textcats_auc_per_cat"] = scorer.textcats_auc_per_cat
print_textcats_auc_per_cat(msg, scorer.textcats_auc_per_cat)
if displacy_path:
docs = [ex.predicted for ex in dev_dataset]
render_deps = "parser" in nlp.meta.get("pipeline", [])
render_ents = "ner" in nlp.meta.get("pipeline", [])
render_parses(
docs,
displacy_path,
model_name=model,
limit=displacy_limit,
deps=render_deps,
ents=render_ents,
)
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 render_parses(
docs: List[Doc],
output_path: Path,
model_name: str = "",
limit: int = 250,
deps: bool = True,
ents: 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)
def print_ents_per_type(msg: Printer, scores: Dict[str, Dict[str, float]]) -> None:
data = [
(k, f"{v['p']:.2f}", f"{v['r']:.2f}", f"{v['f']:.2f}")
for k, v in scores.items()
]
msg.table(
data,
header=("", "P", "R", "F"),
aligns=("l", "r", "r", "r"),
title="NER (per type)",
)
def print_textcats_f_per_cat(msg: Printer, scores: Dict[str, Dict[str, float]]) -> None:
data = [
(k, f"{v['p']:.2f}", f"{v['r']:.2f}", f"{v['f']:.2f}")
for k, v in scores.items()
]
msg.table(
data,
header=("", "P", "R", "F"),
aligns=("l", "r", "r", "r"),
title="Textcat F (per type)",
)
def print_textcats_auc_per_cat(
msg: Printer, scores: Dict[str, Dict[str, float]]
) -> None:
msg.table(
[(k, f"{v['roc_auc_score']:.2f}") for k, v in scores.items()],
header=("", "ROC AUC"),
aligns=("l", "r"),
title="Textcat ROC AUC (per label)",
)