mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 01:46:28 +03:00
274 lines
8.9 KiB
Python
274 lines
8.9 KiB
Python
import pytest
|
|
from thinc.config import Config, ConfigValidationError
|
|
import spacy
|
|
from spacy.lang.en import English
|
|
from spacy.language import Language
|
|
from spacy.util import registry, deep_merge_configs, load_model_from_config
|
|
from spacy.ml.models import build_Tok2Vec_model, build_tb_parser_model
|
|
|
|
from ..util import make_tempdir
|
|
|
|
|
|
nlp_config_string = """
|
|
[training]
|
|
batch_size = 666
|
|
|
|
[nlp]
|
|
lang = "en"
|
|
pipeline = ["tok2vec", "tagger"]
|
|
|
|
[components]
|
|
|
|
[components.tok2vec]
|
|
factory = "tok2vec"
|
|
|
|
[components.tok2vec.model]
|
|
@architectures = "spacy.HashEmbedCNN.v1"
|
|
pretrained_vectors = null
|
|
width = 342
|
|
depth = 4
|
|
window_size = 1
|
|
embed_size = 2000
|
|
maxout_pieces = 3
|
|
subword_features = true
|
|
dropout = null
|
|
|
|
[components.tagger]
|
|
factory = "tagger"
|
|
|
|
[components.tagger.model]
|
|
@architectures = "spacy.Tagger.v1"
|
|
|
|
[components.tagger.model.tok2vec]
|
|
@architectures = "spacy.Tok2VecTensors.v1"
|
|
width = ${components.tok2vec.model:width}
|
|
"""
|
|
|
|
|
|
parser_config_string = """
|
|
[model]
|
|
@architectures = "spacy.TransitionBasedParser.v1"
|
|
nr_feature_tokens = 99
|
|
hidden_width = 66
|
|
maxout_pieces = 2
|
|
|
|
[model.tok2vec]
|
|
@architectures = "spacy.HashEmbedCNN.v1"
|
|
pretrained_vectors = null
|
|
width = 333
|
|
depth = 4
|
|
embed_size = 5555
|
|
window_size = 1
|
|
maxout_pieces = 7
|
|
subword_features = false
|
|
dropout = null
|
|
"""
|
|
|
|
|
|
@registry.architectures.register("my_test_parser")
|
|
def my_parser():
|
|
tok2vec = build_Tok2Vec_model(
|
|
width=321,
|
|
embed_size=5432,
|
|
pretrained_vectors=None,
|
|
window_size=3,
|
|
maxout_pieces=4,
|
|
subword_features=True,
|
|
char_embed=True,
|
|
nM=64,
|
|
nC=8,
|
|
conv_depth=2,
|
|
bilstm_depth=0,
|
|
dropout=None,
|
|
)
|
|
parser = build_tb_parser_model(
|
|
tok2vec=tok2vec, nr_feature_tokens=7, hidden_width=65, maxout_pieces=5
|
|
)
|
|
return parser
|
|
|
|
|
|
def test_create_nlp_from_config():
|
|
config = Config().from_str(nlp_config_string)
|
|
with pytest.raises(ConfigValidationError):
|
|
nlp, _ = load_model_from_config(config, auto_fill=False)
|
|
nlp, resolved = load_model_from_config(config, auto_fill=True)
|
|
assert nlp.config["training"]["batch_size"] == 666
|
|
assert len(nlp.config["training"]) > 1
|
|
assert nlp.pipe_names == ["tok2vec", "tagger"]
|
|
assert len(nlp.config["components"]) == 2
|
|
assert len(nlp.config["nlp"]["pipeline"]) == 2
|
|
nlp.remove_pipe("tagger")
|
|
assert len(nlp.config["components"]) == 1
|
|
assert len(nlp.config["nlp"]["pipeline"]) == 1
|
|
with pytest.raises(ValueError):
|
|
bad_cfg = {"yolo": {}}
|
|
load_model_from_config(Config(bad_cfg), auto_fill=True)
|
|
with pytest.raises(ValueError):
|
|
bad_cfg = {"pipeline": {"foo": "bar"}}
|
|
load_model_from_config(Config(bad_cfg), auto_fill=True)
|
|
|
|
|
|
def test_create_nlp_from_config_multiple_instances():
|
|
"""Test that the nlp object is created correctly for a config with multiple
|
|
instances of the same component."""
|
|
config = Config().from_str(nlp_config_string)
|
|
config["components"] = {
|
|
"t2v": config["components"]["tok2vec"],
|
|
"tagger1": config["components"]["tagger"],
|
|
"tagger2": config["components"]["tagger"],
|
|
}
|
|
config["nlp"]["pipeline"] = list(config["components"].keys())
|
|
nlp, _ = load_model_from_config(config, auto_fill=True)
|
|
assert nlp.pipe_names == ["t2v", "tagger1", "tagger2"]
|
|
assert nlp.get_pipe_meta("t2v").factory == "tok2vec"
|
|
assert nlp.get_pipe_meta("tagger1").factory == "tagger"
|
|
assert nlp.get_pipe_meta("tagger2").factory == "tagger"
|
|
pipeline_config = nlp.config["components"]
|
|
assert len(pipeline_config) == 3
|
|
assert list(pipeline_config.keys()) == ["t2v", "tagger1", "tagger2"]
|
|
assert nlp.config["nlp"]["pipeline"] == ["t2v", "tagger1", "tagger2"]
|
|
|
|
|
|
def test_serialize_nlp():
|
|
""" Create a custom nlp pipeline from config and ensure it serializes it correctly """
|
|
nlp_config = Config().from_str(nlp_config_string)
|
|
nlp, _ = load_model_from_config(nlp_config, auto_fill=True)
|
|
nlp.begin_training()
|
|
assert "tok2vec" in nlp.pipe_names
|
|
assert "tagger" in nlp.pipe_names
|
|
assert "parser" not in nlp.pipe_names
|
|
assert nlp.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
|
|
|
|
with make_tempdir() as d:
|
|
nlp.to_disk(d)
|
|
nlp2 = spacy.load(d)
|
|
assert "tok2vec" in nlp2.pipe_names
|
|
assert "tagger" in nlp2.pipe_names
|
|
assert "parser" not in nlp2.pipe_names
|
|
assert nlp2.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
|
|
|
|
|
|
def test_serialize_custom_nlp():
|
|
""" Create a custom nlp pipeline and ensure it serializes it correctly"""
|
|
nlp = English()
|
|
parser_cfg = dict()
|
|
parser_cfg["model"] = {"@architectures": "my_test_parser"}
|
|
nlp.add_pipe("parser", config=parser_cfg)
|
|
nlp.begin_training()
|
|
|
|
with make_tempdir() as d:
|
|
nlp.to_disk(d)
|
|
nlp2 = spacy.load(d)
|
|
model = nlp2.get_pipe("parser").model
|
|
model.get_ref("tok2vec")
|
|
upper = model.get_ref("upper")
|
|
# check that we have the correct settings, not the default ones
|
|
assert upper.get_dim("nI") == 65
|
|
|
|
|
|
def test_serialize_parser():
|
|
""" Create a non-default parser config to check nlp serializes it correctly """
|
|
nlp = English()
|
|
model_config = Config().from_str(parser_config_string)
|
|
parser = nlp.add_pipe("parser", config=model_config)
|
|
parser.add_label("nsubj")
|
|
nlp.begin_training()
|
|
|
|
with make_tempdir() as d:
|
|
nlp.to_disk(d)
|
|
nlp2 = spacy.load(d)
|
|
model = nlp2.get_pipe("parser").model
|
|
model.get_ref("tok2vec")
|
|
upper = model.get_ref("upper")
|
|
# check that we have the correct settings, not the default ones
|
|
assert upper.get_dim("nI") == 66
|
|
|
|
|
|
def test_deep_merge_configs():
|
|
config = {"a": "hello", "b": {"c": "d"}}
|
|
defaults = {"a": "world", "b": {"c": "e", "f": "g"}}
|
|
merged = deep_merge_configs(config, defaults)
|
|
assert len(merged) == 2
|
|
assert merged["a"] == "hello"
|
|
assert merged["b"] == {"c": "d", "f": "g"}
|
|
config = {"a": "hello", "b": {"@test": "x", "foo": 1}}
|
|
defaults = {"a": "world", "b": {"@test": "x", "foo": 100, "bar": 2}, "c": 100}
|
|
merged = deep_merge_configs(config, defaults)
|
|
assert len(merged) == 3
|
|
assert merged["a"] == "hello"
|
|
assert merged["b"] == {"@test": "x", "foo": 1, "bar": 2}
|
|
assert merged["c"] == 100
|
|
config = {"a": "hello", "b": {"@test": "x", "foo": 1}, "c": 100}
|
|
defaults = {"a": "world", "b": {"@test": "y", "foo": 100, "bar": 2}}
|
|
merged = deep_merge_configs(config, defaults)
|
|
assert len(merged) == 3
|
|
assert merged["a"] == "hello"
|
|
assert merged["b"] == {"@test": "x", "foo": 1}
|
|
assert merged["c"] == 100
|
|
# Test that leaving out the factory just adds to existing
|
|
config = {"a": "hello", "b": {"foo": 1}, "c": 100}
|
|
defaults = {"a": "world", "b": {"@test": "y", "foo": 100, "bar": 2}}
|
|
merged = deep_merge_configs(config, defaults)
|
|
assert len(merged) == 3
|
|
assert merged["a"] == "hello"
|
|
assert merged["b"] == {"@test": "y", "foo": 1, "bar": 2}
|
|
assert merged["c"] == 100
|
|
|
|
|
|
def test_config_nlp_roundtrip():
|
|
"""Test that a config prduced by the nlp object passes training config
|
|
validation."""
|
|
nlp = English()
|
|
nlp.add_pipe("entity_ruler")
|
|
nlp.add_pipe("ner")
|
|
new_nlp, new_config = load_model_from_config(nlp.config, auto_fill=False)
|
|
assert new_nlp.config == nlp.config
|
|
assert new_nlp.pipe_names == nlp.pipe_names
|
|
assert new_nlp._pipe_configs == nlp._pipe_configs
|
|
assert new_nlp._pipe_meta == nlp._pipe_meta
|
|
assert new_nlp._factory_meta == nlp._factory_meta
|
|
|
|
|
|
def test_serialize_config_language_specific():
|
|
"""Test that config serialization works as expected with language-specific
|
|
factories."""
|
|
name = "test_serialize_config_language_specific"
|
|
|
|
@English.factory(name, default_config={"foo": 20})
|
|
def custom_factory(nlp: Language, name: str, foo: int):
|
|
return lambda doc: doc
|
|
|
|
nlp = Language()
|
|
assert not nlp.has_factory(name)
|
|
nlp = English()
|
|
assert nlp.has_factory(name)
|
|
nlp.add_pipe(name, config={"foo": 100}, name="bar")
|
|
pipe_config = nlp.config["components"]["bar"]
|
|
assert pipe_config["foo"] == 100
|
|
assert pipe_config["factory"] == name
|
|
|
|
with make_tempdir() as d:
|
|
nlp.to_disk(d)
|
|
nlp2 = spacy.load(d)
|
|
assert nlp2.has_factory(name)
|
|
assert nlp2.pipe_names == ["bar"]
|
|
assert nlp2.get_pipe_meta("bar").factory == name
|
|
pipe_config = nlp2.config["components"]["bar"]
|
|
assert pipe_config["foo"] == 100
|
|
assert pipe_config["factory"] == name
|
|
|
|
config = Config().from_str(nlp2.config.to_str())
|
|
config["nlp"]["lang"] = "de"
|
|
with pytest.raises(ValueError):
|
|
# German doesn't have a factory, only English does
|
|
load_model_from_config(config)
|
|
|
|
|
|
def test_serialize_config_missing_pipes():
|
|
config = Config().from_str(nlp_config_string)
|
|
config["components"].pop("tok2vec")
|
|
assert "tok2vec" in config["nlp"]["pipeline"]
|
|
assert "tok2vec" not in config["components"]
|
|
with pytest.raises(ValueError):
|
|
load_model_from_config(config, auto_fill=True)
|