spaCy/spacy/cli/evaluate.py
2018-02-13 12:42:23 +01:00

110 lines
3.9 KiB
Python

# coding: utf8
from __future__ import unicode_literals, division, print_function
import plac
from timeit import default_timer as timer
from ..gold import GoldCorpus
from ..util import prints
from .. import util
from .. import displacy
@plac.annotations(
model=("model name or path", "positional", None, str),
data_path=("location of JSON-formatted evaluation data", "positional",
None, str),
gold_preproc=("use gold preprocessing", "flag", "G", bool),
gpu_id=("use GPU", "option", "g", int),
displacy_path=("directory to output rendered parses as HTML", "option",
"dp", str),
displacy_limit=("limit of parses to render as HTML", "option", "dl", int))
def evaluate(model, data_path, gpu_id=-1, gold_preproc=False, displacy_path=None,
displacy_limit=25):
"""
Evaluate a model. To render a sample of parses in a HTML file, set an
output directory as the displacy_path argument.
"""
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)
displacy_path = util.ensure_path(displacy_path)
if not data_path.exists():
prints(data_path, title="Evaluation data not found", exits=1)
if displacy_path and not displacy_path.exists():
prints(displacy_path, title="Visualization output directory not found",
exits=1)
corpus = GoldCorpus(data_path, data_path)
nlp = util.load_model(model)
dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
begin = timer()
scorer = nlp.evaluate(dev_docs, verbose=False)
end = timer()
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
print_results(scorer, time=end - begin, words=nwords,
wps=nwords / (end - begin))
if displacy_path:
docs, golds = zip(*dev_docs)
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 = "Generated %s parses as HTML" % displacy_limit
prints(displacy_path, title=msg)
def render_parses(docs, output_path, model_name='', limit=250, deps=True,
ents=True):
docs[0].user_data['title'] = model_name
if ents:
with (output_path / 'entities.html').open('w') as file_:
html = displacy.render(docs[:limit], style='ent', page=True)
file_.write(html)
if deps:
with (output_path / 'parses.html').open('w') as file_:
html = displacy.render(docs[:limit], style='dep', page=True,
options={'compact': True})
file_.write(html)
def print_progress(itn, losses, dev_scores, wps=0.0):
scores = {}
for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
'ents_p', 'ents_r', 'ents_f', 'wps']:
scores[col] = 0.0
scores['dep_loss'] = losses.get('parser', 0.0)
scores['ner_loss'] = losses.get('ner', 0.0)
scores['tag_loss'] = losses.get('tagger', 0.0)
scores.update(dev_scores)
scores['wps'] = wps
tpl = '\t'.join((
'{:d}',
'{dep_loss:.3f}',
'{ner_loss:.3f}',
'{uas:.3f}',
'{ents_p:.3f}',
'{ents_r:.3f}',
'{ents_f:.3f}',
'{tags_acc:.3f}',
'{token_acc:.3f}',
'{wps:.1f}'))
print(tpl.format(itn, **scores))
def print_results(scorer, time, words, wps):
results = {
'Time': '%.2f s' % time,
'Words': words,
'Words/s': '%.0f' % wps,
'TOK': '%.2f' % scorer.token_acc,
'POS': '%.2f' % scorer.tags_acc,
'UAS': '%.2f' % scorer.uas,
'LAS': '%.2f' % scorer.las,
'NER P': '%.2f' % scorer.ents_p,
'NER R': '%.2f' % scorer.ents_r,
'NER F': '%.2f' % scorer.ents_f}
util.print_table(results, title="Results")