spaCy/spacy/tests/regression/test_issue910.py
2017-06-04 22:39:29 +02:00

109 lines
3.4 KiB
Python

from __future__ import unicode_literals
import json
import random
import contextlib
import shutil
import pytest
import tempfile
from pathlib import Path
from ...gold import GoldParse
from ...pipeline import EntityRecognizer
from ...lang.en import English
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"]]],
["yes",[]],
["yep",[]],
["yeah",[]],
["show me a mexican place in the centre",[[31,37,"location"], [10,17,"cuisine"]]],
["bye",[]],["goodbye",[]],
["good bye",[]],
["stop",[]],
["end",[]],
["i am looking for an indian spot",[[20,26,"cuisine"]]],
["search for restaurants",[]],
["anywhere in the west",[[16,20,"location"]]],
["central indian restaurant",[[0,7,"location"],[8,14,"cuisine"]]],
["indeed",[]],
["that's right",[]],
["ok",[]],
["great",[]]
]
@pytest.fixture
def additional_entity_types():
return ['cuisine', 'location']
@contextlib.contextmanager
def temp_save_model(model):
model_dir = Path(tempfile.mkdtemp())
# store the fine tuned model
with (model_dir / "config.json").open('w') as file_:
data = json.dumps(model.cfg)
if not isinstance(data, unicode):
data = data.decode('utf8')
file_.write(data)
model.model.dump((model_dir / 'model').as_posix())
yield model_dir
shutil.rmtree(model_dir.as_posix())
@pytest.mark.models('en')
def test_issue910(EN, train_data, additional_entity_types):
'''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 = EN
doc = nlp(u"I am looking for a restaurant in Berlin")
ents_before_train = [(ent.label_, ent.text) for ent in doc.ents]
# Fine tune the ner model
for entity_type in additional_entity_types:
nlp.entity.add_label(entity_type)
nlp.entity.model.learn_rate = 0.001
for itn in range(10):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
nlp.tagger(doc)
gold = GoldParse(doc, entities=entity_offsets)
loss = nlp.entity.update(doc, gold)
with temp_save_model(nlp.entity) as model_dir:
# Load the fine tuned model
loaded_ner = EntityRecognizer.load(model_dir, nlp.vocab)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
nlp.tagger(doc)
loaded_ner(doc)
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
for start, end, label in entity_offsets:
if (start, end) not in ents:
print(ents)
assert ents[(start, end)] == label