mirror of
https://github.com/explosion/spaCy.git
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121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, division, print_function
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import plac
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import json
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from collections import defaultdict
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import cytoolz
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from pathlib import Path
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import dill
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import tqdm
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from thinc.neural._classes.model import Model
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from thinc.neural.optimizers import linear_decay
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from timeit import default_timer as timer
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import random
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import numpy.random
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from ..tokens.doc import Doc
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from ..scorer import Scorer
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from ..gold import GoldParse, merge_sents
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from ..gold import GoldCorpus, minibatch
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from ..util import prints
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from .. import util
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from .. import about
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from .. import displacy
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from ..compat import json_dumps
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random.seed(0)
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numpy.random.seed(0)
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@plac.annotations(
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model=("Model name or path", "positional", None, str),
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data_path=("Location of JSON-formatted evaluation data", "positional", None, str),
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gold_preproc=("Use gold preprocessing", "flag", "G", bool),
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gpu_id=("Use GPU", "option", "g", int),
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displacy_path=("Directory to output rendered parses as HTML", "option", "dp", str),
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displacy_limit=("Limit of parses to render as HTML", "option", "dl", int)
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)
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def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False,
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displacy_path=None, displacy_limit=25):
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"""
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Evaluate a model. To render a sample of parses in a HTML file, set an output
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directory as the displacy_path argument.
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"""
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if gpu_id >= 0:
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util.use_gpu(gpu_id)
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util.set_env_log(False)
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data_path = util.ensure_path(data_path)
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displacy_path = util.ensure_path(displacy_path)
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if not data_path.exists():
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prints(data_path, title="Evaluation data not found", exits=1)
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if displacy_path and not displacy_path.exists():
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prints(displacy_path, title="Visualization output directory not found", exits=1)
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corpus = GoldCorpus(data_path, data_path)
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nlp = util.load_model(model)
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dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
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begin = timer()
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scorer = nlp.evaluate(dev_docs, verbose=False)
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end = timer()
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nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
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print_results(scorer, time=end - begin, words=nwords,
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wps=nwords / (end - begin))
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if displacy_path:
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docs, golds = zip(*dev_docs)
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render_deps = 'parser' in nlp.meta.get('pipeline', [])
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render_ents = 'ner' in nlp.meta.get('pipeline', [])
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render_parses(docs, displacy_path, model_name=model, limit=displacy_limit,
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deps=render_deps, ents=render_ents)
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prints(displacy_path, title="Generated %s parses as HTML" % displacy_limit)
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def render_parses(docs, output_path, model_name='', limit=250, deps=True, ents=True):
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docs[0].user_data['title'] = model_name
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if ents:
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with (output_path / 'entities.html').open('w') as file_:
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html = displacy.render(docs[:limit], style='ent', page=True)
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file_.write(html)
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if deps:
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with (output_path / 'parses.html').open('w') as file_:
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html = displacy.render(docs[:limit], style='dep', page=True, options={'compact': True})
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file_.write(html)
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def print_progress(itn, losses, dev_scores, wps=0.0):
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scores = {}
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for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
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'ents_p', 'ents_r', 'ents_f', 'wps']:
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scores[col] = 0.0
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scores['dep_loss'] = losses.get('parser', 0.0)
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scores['ner_loss'] = losses.get('ner', 0.0)
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scores['tag_loss'] = losses.get('tagger', 0.0)
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scores.update(dev_scores)
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scores['wps'] = wps
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tpl = '\t'.join((
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'{:d}',
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'{dep_loss:.3f}',
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'{ner_loss:.3f}',
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'{uas:.3f}',
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'{ents_p:.3f}',
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'{ents_r:.3f}',
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'{ents_f:.3f}',
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'{tags_acc:.3f}',
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'{token_acc:.3f}',
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'{wps:.1f}'))
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print(tpl.format(itn, **scores))
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def print_results(scorer, time, words, wps):
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results = {
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'Time': '%.2f s' % time,
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'Words': words,
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'Words/s': '%.0f' % wps,
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'TOK': '%.2f' % scorer.token_acc,
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'POS': '%.2f' % scorer.tags_acc,
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'UAS': '%.2f' % scorer.uas,
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'LAS': '%.2f' % scorer.las,
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'NER P': '%.2f' % scorer.ents_p,
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'NER R': '%.2f' % scorer.ents_r,
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'NER F': '%.2f' % scorer.ents_f}
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util.print_table(results, title="Results")
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