from __future__ import unicode_literals import json 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 ...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"]]], ] @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()) 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 = English(entity=False) nlp.entity = EntityRecognizer(nlp.vocab, features=English.Defaults.entity_features) for _, offsets in train_data: for start, end, ent_type in offsets: nlp.entity.add_label(ent_type) 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 = English(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) not in ents: print(ents) assert ents[(start, end)] == label