mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
# coding: utf8
|
|
from __future__ import unicode_literals, division, print_function
|
|
|
|
import plac
|
|
import json
|
|
from collections import defaultdict
|
|
import cytoolz
|
|
from pathlib import Path
|
|
import dill
|
|
import tqdm
|
|
from thinc.neural._classes.model import Model
|
|
from thinc.neural.optimizers import linear_decay
|
|
from timeit import default_timer as timer
|
|
import random
|
|
import numpy.random
|
|
|
|
from ..tokens.doc import Doc
|
|
from ..scorer import Scorer
|
|
from ..gold import GoldParse, merge_sents
|
|
from ..gold import GoldCorpus, minibatch
|
|
from ..util import prints
|
|
from .. import util
|
|
from .. import about
|
|
from .. import displacy
|
|
from ..compat import json_dumps
|
|
|
|
random.seed(0)
|
|
numpy.random.seed(0)
|
|
|
|
|
|
@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(cmd, 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.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)
|
|
prints(displacy_path, title="Generated %s parses as HTML" % displacy_limit)
|
|
|
|
|
|
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")
|