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109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
from __future__ import unicode_literals
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import json
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import random
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import contextlib
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import shutil
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import pytest
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import tempfile
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from pathlib import Path
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from ...gold import GoldParse
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from ...pipeline import EntityRecognizer
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from ...lang.en import English
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try:
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unicode
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except NameError:
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unicode = str
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@pytest.fixture
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def train_data():
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return [
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["hey",[]],
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["howdy",[]],
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["hey there",[]],
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["hello",[]],
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["hi",[]],
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["i'm looking for a place to eat",[]],
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["i'm looking for a place in the north of town",[[31,36,"location"]]],
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["show me chinese restaurants",[[8,15,"cuisine"]]],
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["show me chines restaurants",[[8,14,"cuisine"]]],
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["yes",[]],
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["yep",[]],
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["yeah",[]],
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["show me a mexican place in the centre",[[31,37,"location"], [10,17,"cuisine"]]],
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["bye",[]],["goodbye",[]],
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["good bye",[]],
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["stop",[]],
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["end",[]],
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["i am looking for an indian spot",[[20,26,"cuisine"]]],
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["search for restaurants",[]],
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["anywhere in the west",[[16,20,"location"]]],
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["central indian restaurant",[[0,7,"location"],[8,14,"cuisine"]]],
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["indeed",[]],
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["that's right",[]],
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["ok",[]],
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["great",[]]
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]
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@pytest.fixture
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def additional_entity_types():
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return ['cuisine', 'location']
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@contextlib.contextmanager
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def temp_save_model(model):
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model_dir = Path(tempfile.mkdtemp())
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# store the fine tuned model
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with (model_dir / "config.json").open('w') as file_:
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data = json.dumps(model.cfg)
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if not isinstance(data, unicode):
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data = data.decode('utf8')
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file_.write(data)
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model.model.dump((model_dir / 'model').as_posix())
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yield model_dir
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shutil.rmtree(model_dir.as_posix())
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@pytest.mark.models('en')
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def test_issue910(EN, train_data, additional_entity_types):
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'''Test that adding entities and resuming training works passably OK.
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There are two issues here:
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1) We have to readd labels. This isn't very nice.
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2) There's no way to set the learning rate for the weight update, so we
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end up out-of-scale, causing it to learn too fast.
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'''
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nlp = EN
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doc = nlp(u"I am looking for a restaurant in Berlin")
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ents_before_train = [(ent.label_, ent.text) for ent in doc.ents]
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# Fine tune the ner model
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for entity_type in additional_entity_types:
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nlp.entity.add_label(entity_type)
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nlp.entity.model.learn_rate = 0.001
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for itn in range(10):
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random.shuffle(train_data)
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for raw_text, entity_offsets in train_data:
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doc = nlp.make_doc(raw_text)
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nlp.tagger(doc)
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gold = GoldParse(doc, entities=entity_offsets)
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loss = nlp.entity.update(doc, gold)
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with temp_save_model(nlp.entity) as model_dir:
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# Load the fine tuned model
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loaded_ner = EntityRecognizer.load(model_dir, nlp.vocab)
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for raw_text, entity_offsets in train_data:
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doc = nlp.make_doc(raw_text)
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nlp.tagger(doc)
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loaded_ner(doc)
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ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
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for start, end, label in entity_offsets:
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if (start, end) not in ents:
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print(ents)
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assert ents[(start, end)] == label
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