mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 18:36:36 +03:00
8cf097ca88
* Obsolete .parser, .entity etc names in favour of .pipeline
* Components no longer create models on initialization
* Models created by loading method (from_disk(), from_bytes() etc), or
.begin_training()
* Add .predict(), .set_annotations() methods in components
* Pass state through pipeline, to allow components to share information
more flexibly.
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
from __future__ import unicode_literals
|
|
import os
|
|
import random
|
|
import contextlib
|
|
import shutil
|
|
import pytest
|
|
import tempfile
|
|
from pathlib import Path
|
|
|
|
|
|
import pathlib
|
|
from ...gold import GoldParse
|
|
from ...pipeline import EntityRecognizer
|
|
from ...language import Language
|
|
|
|
try:
|
|
unicode
|
|
except NameError:
|
|
unicode = str
|
|
|
|
|
|
@pytest.fixture
|
|
def train_data():
|
|
return [
|
|
["hey",[]],
|
|
["howdy",[]],
|
|
["hey there",[]],
|
|
["hello",[]],
|
|
["hi",[]],
|
|
["i'm looking for a place to eat",[]],
|
|
["i'm looking for a place in the north of town",[[31,36,"location"]]],
|
|
["show me chinese restaurants",[[8,15,"cuisine"]]],
|
|
["show me chines restaurants",[[8,14,"cuisine"]]],
|
|
]
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def temp_save_model(model):
|
|
model_dir = Path(tempfile.mkdtemp())
|
|
model.save_to_directory(model_dir)
|
|
yield model_dir
|
|
shutil.rmtree(model_dir.as_posix())
|
|
|
|
|
|
# TODO: Fix when saving/loading is fixed.
|
|
@pytest.mark.xfail
|
|
def test_issue999(train_data):
|
|
'''Test that adding entities and resuming training works passably OK.
|
|
There are two issues here:
|
|
|
|
1) We have to readd labels. This isn't very nice.
|
|
2) There's no way to set the learning rate for the weight update, so we
|
|
end up out-of-scale, causing it to learn too fast.
|
|
'''
|
|
nlp = Language(pipeline=[])
|
|
nlp.entity = EntityRecognizer(nlp.vocab, features=Language.Defaults.entity_features)
|
|
nlp.pipeline.append(nlp.entity)
|
|
for _, offsets in train_data:
|
|
for start, end, ent_type in offsets:
|
|
nlp.entity.add_label(ent_type)
|
|
nlp.entity.model.learn_rate = 0.001
|
|
for itn in range(100):
|
|
random.shuffle(train_data)
|
|
for raw_text, entity_offsets in train_data:
|
|
doc = nlp.make_doc(raw_text)
|
|
gold = GoldParse(doc, entities=entity_offsets)
|
|
loss = nlp.entity.update(doc, gold)
|
|
|
|
with temp_save_model(nlp) as model_dir:
|
|
nlp2 = Language(path=model_dir)
|
|
|
|
for raw_text, entity_offsets in train_data:
|
|
doc = nlp2(raw_text)
|
|
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
|
|
for start, end, label in entity_offsets:
|
|
if (start, end) in ents:
|
|
assert ents[(start, end)] == label
|
|
break
|
|
else:
|
|
if entity_offsets:
|
|
raise Exception(ents)
|