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Pipe API (#6034)

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* ensure Language passes on valid examples for initialization

* fix tagger model initialization

* check for valid get_examples across components

* assume labels were added before begin_training

* fix senter initialization

* fix morphologizer initialization

* use methods to check arguments

* test textcat init, requires thinc>=8.0.0a31

* fix tok2vec init

* fix entity linker init

* use islice

* fix simple NER

* cleanup debug model

* fix assert statements

* fix tests

* throw error when adding a label if the output layer can't be resized anymore

* fix test

* add failing test for simple_ner

* UX improvements

* morphologizer UX

* assume begin_training gets a representative set and processes the labels

* remove assumptions for output of untrained NER model

* restore test for original purpose
This commit is contained in:
Sofie Van Landeghem 2020-09-08 22:44:25 +02:00 committed by GitHub
parent 4b82882767
commit 60f22e1800
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GPG Key ID: 4AEE18F83AFDEB23
33 changed files with 507 additions and 221 deletions
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2
pyproject.toml
View File

@ -6,7 +6,7 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.0.0a30,<8.0.0a40",
"thinc>=8.0.0a31,<8.0.0a40",
"blis>=0.4.0,<0.5.0",
"pytokenizations",
"pathy"

2
requirements.txt
View File

@ -1,7 +1,7 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a30,<8.0.0a40
thinc>=8.0.0a31,<8.0.0a40
blis>=0.4.0,<0.5.0
ml_datasets>=0.1.1
murmurhash>=0.28.0,<1.1.0

4
setup.cfg
View File

@ -34,13 +34,13 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.0.0a30,<8.0.0a40
thinc>=8.0.0a31,<8.0.0a40
install_requires =
# Our libraries
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a30,<8.0.0a40
thinc>=8.0.0a31,<8.0.0a40
blis>=0.4.0,<0.5.0
wasabi>=0.8.0,<1.1.0
srsly>=2.1.0,<3.0.0

13
spacy/cli/debug_model.py
View File

@ -84,11 +84,11 @@ def debug_model(model: Model, *, print_settings: Optional[Dict[str, Any]] = None
_print_model(model, print_settings)
# STEP 1: Initializing the model and printing again
X = _get_docs()
Y = _get_output(model.ops.xp)
_set_output_dim(nO=Y.shape[-1], model=model)
# The output vector might differ from the official type of the output layer
with data_validation(False):
model.initialize(X=_get_docs(), Y=Y)
model.initialize(X=X, Y=Y)
if print_settings.get("print_after_init"):
msg.divider(f"STEP 1 - after initialization")
_print_model(model, print_settings)
@ -135,15 +135,6 @@ def _get_output(xp):
return xp.asarray([i + 10 for i, _ in enumerate(_get_docs())], dtype="float32")
def _set_output_dim(model, nO):
# the dim inference doesn't always work 100%, we need this hack like we have it in pipe.pyx
if model.has_dim("nO") is None:
model.set_dim("nO", nO)
if model.has_ref("output_layer"):
if model.get_ref("output_layer").has_dim("nO") is None:
model.get_ref("output_layer").set_dim("nO", nO)
def _print_model(model, print_settings):
layers = print_settings.get("layers", "")
parameters = print_settings.get("parameters", False)

23
spacy/errors.py
View File

@ -247,8 +247,8 @@ class Errors:
"Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}")
E065 = ("Only one of the vector table's width and shape can be specified. "
"Got width {width} and shape {shape}.")
E067 = ("Invalid BILUO tag sequence: Got a tag starting with 'I' (inside "
"an entity) without a preceding 'B' (beginning of an entity). "
E067 = ("Invalid BILUO tag sequence: Got a tag starting with {start} "
"without a preceding 'B' (beginning of an entity). "
"Tag sequence:\n{tags}")
E068 = ("Invalid BILUO tag: '{tag}'.")
E071 = ("Error creating lexeme: specified orth ID ({orth}) does not "
@ -320,10 +320,6 @@ class Errors:
"So instead of pickling the span, pickle the Doc it belongs to or "
"use Span.as_doc to convert the span to a standalone Doc object.")
E115 = ("All subtokens must have associated heads.")
E116 = ("Cannot currently add labels to pretrained text classifier. Add "
"labels before training begins. This functionality was available "
"in previous versions, but had significant bugs that led to poor "
"performance.")
E117 = ("The newly split tokens must match the text of the original token. "
"New orths: {new}. Old text: {old}.")
E118 = ("The custom extension attribute '{attr}' is not registered on the "
@ -378,8 +374,9 @@ class Errors:
"should be of equal length.")
E141 = ("Entity vectors should be of length {required} instead of the "
"provided {found}.")
E143 = ("Labels for component '{name}' not initialized. Did you forget to "
"call add_label()?")
E143 = ("Labels for component '{name}' not initialized. This can be fixed "
"by calling add_label, or by providing a representative batch of "
"examples to the component's begin_training method.")
E145 = ("Error reading `{param}` from input file.")
E146 = ("Could not access `{path}`.")
E147 = ("Unexpected error in the {method} functionality of the "
@ -483,6 +480,16 @@ class Errors:
E201 = ("Span index out of range.")
# TODO: fix numbering after merging develop into master
E921 = ("The method 'set_output' can only be called on components that have "
"a Model with a 'resize_output' attribute. Otherwise, the output "
"layer can not be dynamically changed.")
E922 = ("Component '{name}' has been initialized with an output dimension of "
"{nO} - cannot add any more labels.")
E923 = ("It looks like there is no proper sample data to initialize the "
"Model of component '{name}'. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E924 = ("The '{name}' component does not seem to be initialized properly. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E925 = ("Invalid color values for displaCy visualizer: expected dictionary "
"mapping label names to colors but got: {obj}")
E926 = ("It looks like you're trying to modify nlp.{attr} directly. This "

4
spacy/gold/iob_utils.py
View File

@ -195,13 +195,15 @@ def tags_to_entities(tags):
continue
elif tag.startswith("I"):
if start is None:
raise ValueError(Errors.E067.format(tags=tags[: i + 1]))
raise ValueError(Errors.E067.format(start="I", tags=tags[: i + 1]))
continue
if tag.startswith("U"):
entities.append((tag[2:], i, i))
elif tag.startswith("B"):
start = i
elif tag.startswith("L"):
if start is None:
raise ValueError(Errors.E067.format(start="L", tags=tags[: i + 1]))
entities.append((tag[2:], start, i))
start = None
else:

33
spacy/language.py
View File

@ -656,7 +656,7 @@ class Language:
return resolved[factory_name]
def create_pipe_from_source(
self, source_name: str, source: "Language", *, name: str,
self, source_name: str, source: "Language", *, name: str
) -> Tuple[Callable[[Doc], Doc], str]:
"""Create a pipeline component by copying it from an existing model.
@ -1155,21 +1155,24 @@ class Language:
DOCS: https://nightly.spacy.io/api/language#begin_training
"""
# TODO: throw warning when get_gold_tuples is provided instead of get_examples
if get_examples is None:
get_examples = lambda: []
else: # Populate vocab
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="Language", obj=type(get_examples))
util.logger.debug(
"No 'get_examples' callback provided to 'Language.begin_training', creating dummy examples"
)
doc = Doc(self.vocab, words=["x", "y", "z"])
get_examples = lambda: [Example.from_dict(doc, {})]
# Populate vocab
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="Language", obj=type(get_examples))
raise ValueError(err)
for example in get_examples():
if not isinstance(example, Example):
err = Errors.E978.format(
name="Language.begin_training", types=type(example)
)
raise ValueError(err)
for example in get_examples():
if not isinstance(example, Example):
err = Errors.E978.format(
name="Language.begin_training", types=type(example)
)
raise ValueError(err)
for word in [t.text for t in example.reference]:
_ = self.vocab[word] # noqa: F841
for word in [t.text for t in example.reference]:
_ = self.vocab[word] # noqa: F841
if device >= 0: # TODO: do we need this here?
require_gpu(device)
if self.vocab.vectors.data.shape[1] >= 1:
@ -1187,7 +1190,7 @@ class Language:
return self._optimizer
def resume_training(
self, *, sgd: Optional[Optimizer] = None, device: int = -1,
self, *, sgd: Optional[Optimizer] = None, device: int = -1
) -> Optimizer:
"""Continue training a pretrained model.

2
spacy/ml/_iob.py
View File

@ -62,8 +62,6 @@ def forward(model: Model[Padded, Padded], Xp: Padded, is_train: bool):
def get_num_actions(n_labels: int) -> int:
# One BEGIN action per label
# One IN action per label
# One LAST action per label
# One UNIT action per label
# One OUT action
return n_labels * 2 + 1

4
spacy/ml/models/simple_ner.py
View File

@ -21,7 +21,7 @@ def BiluoTagger(
A BILUO tag sequence encodes a sequence of non-overlapping labelled spans
into tags assigned to each token. The first token of a span is given the
tag B-LABEL, the last token of the span is given the tag L-LABEL, and tokens
within the span are given the tag U-LABEL. Single-token spans are given
within the span are given the tag I-LABEL. Single-token spans are given
the tag U-LABEL. All other tokens are assigned the tag O.
The BILUO tag scheme generally results in better linear separation between
@ -86,7 +86,7 @@ def IOBTagger(
def init(model: Model[List[Doc], List[Floats2d]], X=None, Y=None) -> None:
if model.get_dim("nO") is None and Y:
if model.has_dim("nO") is None and Y:
model.set_dim("nO", Y[0].shape[1])
nO = model.get_dim("nO")
biluo = model.get_ref("biluo")

33
spacy/pipeline/entity_linker.py
View File

@ -1,3 +1,4 @@
from itertools import islice
from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List, Tuple
from pathlib import Path
import srsly
@ -128,7 +129,7 @@ class EntityLinker(Pipe):
# how many neightbour sentences to take into account
self.n_sents = cfg.get("n_sents", 0)
def require_kb(self) -> None:
def _require_kb(self) -> None:
# Raise an error if the knowledge base is not initialized.
if len(self.kb) == 0:
raise ValueError(Errors.E139.format(name=self.name))
@ -140,10 +141,11 @@ class EntityLinker(Pipe):
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
"""Initialize the pipe for training, using data examples if available.
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
@ -153,10 +155,19 @@ class EntityLinker(Pipe):
DOCS: https://nightly.spacy.io/api/entitylinker#begin_training
"""
self.require_kb()
self._ensure_examples(get_examples)
self._require_kb()
nO = self.kb.entity_vector_length
self.set_output(nO)
self.model.initialize()
doc_sample = []
vector_sample = []
for example in islice(get_examples(), 10):
doc_sample.append(example.x)
vector_sample.append(self.model.ops.alloc1f(nO))
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(vector_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(
X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
)
if sgd is None:
sgd = self.create_optimizer()
return sgd
@ -184,7 +195,7 @@ class EntityLinker(Pipe):
DOCS: https://nightly.spacy.io/api/entitylinker#update
"""
self.require_kb()
self._require_kb()
if losses is None:
losses = {}
losses.setdefault(self.name, 0.0)
@ -296,7 +307,7 @@ class EntityLinker(Pipe):
DOCS: https://nightly.spacy.io/api/entitylinker#predict
"""
self.require_kb()
self._require_kb()
entity_count = 0
final_kb_ids = []
if not docs:
@ -405,7 +416,7 @@ class EntityLinker(Pipe):
token.ent_kb_id_ = kb_id
def to_disk(
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList(),
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
) -> None:
"""Serialize the pipe to disk.
@ -422,7 +433,7 @@ class EntityLinker(Pipe):
util.to_disk(path, serialize, exclude)
def from_disk(
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList(),
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
) -> "EntityLinker":
"""Load the pipe from disk. Modifies the object in place and returns it.

35
spacy/pipeline/morphologizer.pyx
View File

@ -2,6 +2,7 @@
from typing import Optional
import srsly
from thinc.api import SequenceCategoricalCrossentropy, Model, Config
from itertools import islice
from ..tokens.doc cimport Doc
from ..vocab cimport Vocab
@ -112,6 +113,7 @@ class Morphologizer(Tagger):
raise ValueError(Errors.E187)
if label in self.labels:
return 0
self._allow_extra_label()
# normalize label
norm_label = self.vocab.morphology.normalize_features(label)
# extract separate POS and morph tags
@ -128,10 +130,11 @@ class Morphologizer(Tagger):
return 1
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data examples if available.
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
@ -141,9 +144,8 @@ class Morphologizer(Tagger):
DOCS: https://nightly.spacy.io/api/morphologizer#begin_training
"""
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="Morphologizer", obj=type(get_examples))
raise ValueError(err)
self._ensure_examples(get_examples)
# First, fetch all labels from the data
for example in get_examples():
for i, token in enumerate(example.reference):
pos = token.pos_
@ -157,8 +159,25 @@ class Morphologizer(Tagger):
if norm_label not in self.cfg["labels_morph"]:
self.cfg["labels_morph"][norm_label] = morph
self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
self.set_output(len(self.labels))
self.model.initialize()
if len(self.labels) <= 1:
raise ValueError(Errors.E143.format(name=self.name))
doc_sample = []
label_sample = []
for example in islice(get_examples(), 10):
gold_array = []
for i, token in enumerate(example.reference):
pos = token.pos_
morph = token.morph_
morph_dict = Morphology.feats_to_dict(morph)
if pos:
morph_dict[self.POS_FEAT] = pos
norm_label = self.vocab.strings[self.vocab.morphology.add(morph_dict)]
gold_array.append([1.0 if label == norm_label else 0.0 for label in self.labels])
doc_sample.append(example.x)
label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd

4
spacy/pipeline/multitask.pyx
View File

@ -90,7 +90,7 @@ class MultitaskObjective(Tagger):
label = self.make_label(token)
if label is not None and label not in self.labels:
self.labels[label] = len(self.labels)
self.model.initialize()
self.model.initialize() # TODO: fix initialization by defining X and Y
if sgd is None:
sgd = self.create_optimizer()
return sgd
@ -178,7 +178,7 @@ class ClozeMultitask(Pipe):
pass
def begin_training(self, get_examples, pipeline=None, sgd=None):
self.model.initialize()
self.model.initialize() # TODO: fix initialization by defining X and Y
X = self.model.ops.alloc((5, self.model.get_ref("tok2vec").get_dim("nO")))
self.model.output_layer.begin_training(X)
if sgd is None:

45
spacy/pipeline/pipe.pyx
View File

@ -160,6 +160,20 @@ cdef class Pipe:
"""
raise NotImplementedError(Errors.E931.format(method="add_label", name=self.name))
def _require_labels(self) -> None:
"""Raise an error if the component's model has no labels defined."""
if not self.labels or list(self.labels) == [""]:
raise ValueError(Errors.E143.format(name=self.name))
def _allow_extra_label(self) -> None:
"""Raise an error if the component can not add any more labels."""
if self.model.has_dim("nO") and self.model.get_dim("nO") == len(self.labels):
if not self.is_resizable():
raise ValueError(Errors.E922.format(name=self.name, nO=self.model.get_dim("nO")))
def create_optimizer(self):
"""Create an optimizer for the pipeline component.
@ -171,9 +185,12 @@ cdef class Pipe:
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data examples if available.
This method needs to be implemented by each Pipe component,
ensuring the internal model (if available) is initialized properly
using the provided sample of Example objects.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
@ -183,16 +200,24 @@ cdef class Pipe:
DOCS: https://nightly.spacy.io/api/pipe#begin_training
"""
self.model.initialize()
if sgd is None:
sgd = self.create_optimizer()
return sgd
raise NotImplementedError(Errors.E931.format(method="add_label", name=self.name))
def _ensure_examples(self, get_examples):
if get_examples is None or not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name=self.name, obj=type(get_examples))
raise ValueError(err)
if not get_examples():
err = Errors.E930.format(name=self.name, obj=get_examples())
raise ValueError(err)
def is_resizable(self):
return hasattr(self, "model") and "resize_output" in self.model.attrs
def set_output(self, nO):
if self.model.has_dim("nO") is not False:
self.model.set_dim("nO", nO)
if self.model.has_ref("output_layer"):
self.model.get_ref("output_layer").set_dim("nO", nO)
if self.is_resizable():
self.model.attrs["resize_output"](self.model, nO)
else:
raise NotImplementedError(Errors.E921)
def use_params(self, params):
"""Modify the pipe's model, to use the given parameter values. At the

23
spacy/pipeline/senter.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, profile=True, binding=True
from itertools import islice
import srsly
from thinc.api import Model, SequenceCategoricalCrossentropy, Config
@ -124,10 +126,11 @@ class SentenceRecognizer(Tagger):
return float(loss), d_scores
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data examples if available.
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
@ -137,8 +140,18 @@ class SentenceRecognizer(Tagger):
DOCS: https://nightly.spacy.io/api/sentencerecognizer#begin_training
"""
self.set_output(len(self.labels))
self.model.initialize()
self._ensure_examples(get_examples)
doc_sample = []
label_sample = []
assert self.labels, Errors.E924.format(name=self.name)
for example in islice(get_examples(), 10):
doc_sample.append(example.x)
gold_tags = example.get_aligned("SENT_START")
gold_array = [[1.0 if tag == gold_tag else 0.0 for tag in self.labels] for gold_tag in gold_tags]
label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd

30
spacy/pipeline/simple_ner.py
View File

@ -3,6 +3,7 @@ from thinc.types import Floats2d
from thinc.api import SequenceCategoricalCrossentropy, set_dropout_rate, Model
from thinc.api import Optimizer, Config
from thinc.util import to_numpy
from itertools import islice
from ..errors import Errors
from ..gold import Example, spans_from_biluo_tags, iob_to_biluo, biluo_to_iob
@ -168,18 +169,29 @@ class SimpleNER(Pipe):
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
):
self._ensure_examples(get_examples)
all_labels = set()
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="SimpleNER", obj=type(get_examples))
raise ValueError(err)
for example in get_examples():
all_labels.update(_get_labels(example))
for label in sorted(all_labels):
self.add_label(label)
labels = self.labels
n_actions = self.model.attrs["get_num_actions"](len(labels))
self.model.set_dim("nO", n_actions)
self.model.initialize()
if label != "":
self.add_label(label)
doc_sample = []
label_sample = []
self._require_labels()
for example in islice(get_examples(), 10):
doc_sample.append(example.x)
gold_tags = example.get_aligned_ner()
if not self.is_biluo:
gold_tags = biluo_to_iob(gold_tags)
gold_array = [
[1.0 if tag == gold_tag else 0.0 for tag in self.get_tag_names()]
for gold_tag in gold_tags
]
label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if pipeline is not None:
self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
self.loss_func = SequenceCategoricalCrossentropy(
@ -206,6 +218,6 @@ def _has_ner(example: Example) -> bool:
def _get_labels(example: Example) -> Set[str]:
labels = set()
for ner_tag in example.get_aligned("ENT_TYPE", as_string=True):
if ner_tag != "O" and ner_tag != "-":
if ner_tag != "O" and ner_tag != "-" and ner_tag != "":
labels.add(ner_tag)
return labels

43
spacy/pipeline/tagger.pyx
View File

@ -5,6 +5,7 @@ import srsly
from thinc.api import Model, set_dropout_rate, SequenceCategoricalCrossentropy, Config
from thinc.types import Floats2d
import warnings
from itertools import islice
from ..tokens.doc cimport Doc
from ..morphology cimport Morphology
@ -258,10 +259,11 @@ class Tagger(Pipe):
return float(loss), d_scores
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data examples if available.
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects..
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
@ -271,32 +273,24 @@ class Tagger(Pipe):
DOCS: https://nightly.spacy.io/api/tagger#begin_training
"""
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="Tagger", obj=type(get_examples))
raise ValueError(err)
tags = set()
self._ensure_examples(get_examples)
doc_sample = []
label_sample = []
tags = set()
for example in get_examples():
for token in example.y:
tags.add(token.tag_)
if len(doc_sample) < 10:
doc_sample.append(example.x)
if not doc_sample:
doc_sample.append(Doc(self.vocab, words=["hello"]))
if token.tag_:
tags.add(token.tag_)
for tag in sorted(tags):
self.add_label(tag)
if len(self.labels) == 0:
err = Errors.E1006.format(name="Tagger")
raise ValueError(err)
self.set_output(len(self.labels))
if doc_sample:
label_sample = [
self.model.ops.alloc2f(len(doc), len(self.labels))
for doc in doc_sample
]
self.model.initialize(X=doc_sample, Y=label_sample)
else:
self.model.initialize()
for example in islice(get_examples(), 10):
doc_sample.append(example.x)
gold_tags = example.get_aligned("TAG", as_string=True)
gold_array = [[1.0 if tag == gold_tag else 0.0 for tag in self.labels] for gold_tag in gold_tags]
label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd
@ -313,6 +307,7 @@ class Tagger(Pipe):
raise ValueError(Errors.E187)
if label in self.labels:
return 0
self._allow_extra_label()
self.cfg["labels"].append(label)
self.vocab.strings.add(label)
return 1

44
spacy/pipeline/textcat.py
View File

@ -1,3 +1,4 @@
from itertools import islice
from typing import Iterable, Tuple, Optional, Dict, List, Callable, Iterator, Any
from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
from thinc.types import Floats2d
@ -128,11 +129,6 @@ class TextCategorizer(Pipe):
"""
return tuple(self.cfg.setdefault("labels", []))
def require_labels(self) -> None:
"""Raise an error if the component's model has no labels defined."""
if not self.labels:
raise ValueError(Errors.E143.format(name=self.name))
@labels.setter
def labels(self, value: Iterable[str]) -> None:
self.cfg["labels"] = tuple(value)
@ -311,17 +307,7 @@ class TextCategorizer(Pipe):
raise ValueError(Errors.E187)
if label in self.labels:
return 0
if self.model.has_dim("nO"):
# This functionality was available previously, but was broken.
# The problem is that we resize the last layer, but the last layer
# is actually just an ensemble. We're not resizing the child layers
# - a huge problem.
raise ValueError(Errors.E116)
# smaller = self.model._layers[-1]
# larger = Linear(len(self.labels)+1, smaller.nI)
# copy_array(larger.W[:smaller.nO], smaller.W)
# copy_array(larger.b[:smaller.nO], smaller.b)
# self.model._layers[-1] = larger
self._allow_extra_label()
self.labels = tuple(list(self.labels) + [label])
return 1
@ -332,10 +318,11 @@ class TextCategorizer(Pipe):
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
"""Initialize the pipe for training, using data examples if available.
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
@ -345,22 +332,19 @@ class TextCategorizer(Pipe):
DOCS: https://nightly.spacy.io/api/textcategorizer#begin_training
"""
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="TextCategorizer", obj=type(get_examples))
raise ValueError(err)
self._ensure_examples(get_examples)
subbatch = [] # Select a subbatch of examples to initialize the model
for example in get_examples():
for example in islice(get_examples(), 10):
if len(subbatch) < 2:
subbatch.append(example)
for cat in example.y.cats:
self.add_label(cat)
self.require_labels()
docs = [eg.reference for eg in subbatch]
if not docs: # need at least one doc
docs = [Doc(self.vocab, words=["hello"])]
truths, _ = self._examples_to_truth(subbatch)
self.set_output(len(self.labels))
self.model.initialize(X=docs, Y=truths)
doc_sample = [eg.reference for eg in subbatch]
label_sample, _ = self._examples_to_truth(subbatch)
self._require_labels()
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd

16
spacy/pipeline/tok2vec.py
View File

@ -1,5 +1,6 @@
from typing import Iterator, Sequence, Iterable, Optional, Dict, Callable, List, Tuple
from thinc.api import Model, set_dropout_rate, Optimizer, Config
from itertools import islice
from .pipe import Pipe
from ..gold import Example, validate_examples
@ -209,10 +210,11 @@ class Tok2Vec(Pipe):
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
):
"""Initialize the pipe for training, using data examples if available.
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
@ -222,8 +224,12 @@ class Tok2Vec(Pipe):
DOCS: https://nightly.spacy.io/api/tok2vec#begin_training
"""
docs = [Doc(self.vocab, words=["hello"])]
self.model.initialize(X=docs)
self._ensure_examples(get_examples)
doc_sample = []
for example in islice(get_examples(), 10):
doc_sample.append(example.x)
assert doc_sample, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample)
def add_label(self, label):
raise NotImplementedError

20
spacy/pipeline/transition_parser.pyx
View File

@ -244,7 +244,7 @@ cdef class Parser(Pipe):
int nr_class, int batch_size) nogil:
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
with gil:
assert self.moves.n_moves > 0
assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
is_valid = <int*>calloc(self.moves.n_moves, sizeof(int))
cdef int i, guess
cdef Transition action
@ -378,7 +378,7 @@ cdef class Parser(Pipe):
cdef int i
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
assert self.moves.n_moves > 0
assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
@ -406,9 +406,7 @@ cdef class Parser(Pipe):
self.model.attrs["resize_output"](self.model, nO)
def begin_training(self, get_examples, pipeline=None, sgd=None, **kwargs):
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="DependencyParser/EntityRecognizer", obj=type(get_examples))
raise ValueError(err)
self._ensure_examples(get_examples)
self.cfg.update(kwargs)
lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
@ -430,9 +428,6 @@ cdef class Parser(Pipe):
if sgd is None:
sgd = self.create_optimizer()
doc_sample = []
for example in islice(get_examples(), 10):
doc_sample.append(example.predicted)
if pipeline is not None:
for name, component in pipeline:
if component is self:
@ -441,10 +436,11 @@ cdef class Parser(Pipe):
doc_sample = list(component.pipe(doc_sample, batch_size=8))
else:
doc_sample = [component(doc) for doc in doc_sample]
if doc_sample:
self.model.initialize(doc_sample)
else:
self.model.initialize()
if not doc_sample:
for example in islice(get_examples(), 10):
doc_sample.append(example.predicted)
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(doc_sample)
if pipeline is not None:
self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
return sgd

20
spacy/tests/doc/test_add_entities.py
View File

@ -1,5 +1,6 @@
from spacy.gold import Example
from spacy.pipeline import EntityRecognizer
from spacy.tokens import Span
from spacy.tokens import Span, Doc
from spacy import registry
import pytest
@ -7,6 +8,12 @@ from ..util import get_doc
from spacy.pipeline.ner import DEFAULT_NER_MODEL
def _ner_example(ner):
doc = Doc(ner.vocab, words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"])
gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
return Example.from_dict(doc, gold)
def test_doc_add_entities_set_ents_iob(en_vocab):
text = ["This", "is", "a", "lion"]
doc = get_doc(en_vocab, text)
@ -18,10 +25,8 @@ def test_doc_add_entities_set_ents_iob(en_vocab):
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
ner = EntityRecognizer(en_vocab, model, **config)
ner.begin_training(lambda: [])
ner.begin_training(lambda: [_ner_example(ner)])
ner(doc)
assert len(list(doc.ents)) == 0
assert [w.ent_iob_ for w in doc] == (["O"] * len(doc))
doc.ents = [(doc.vocab.strings["ANIMAL"], 3, 4)]
assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"]
@ -31,6 +36,7 @@ def test_doc_add_entities_set_ents_iob(en_vocab):
def test_ents_reset(en_vocab):
"""Ensure that resetting doc.ents does not change anything"""
text = ["This", "is", "a", "lion"]
doc = get_doc(en_vocab, text)
config = {
@ -41,11 +47,11 @@ def test_ents_reset(en_vocab):
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
ner = EntityRecognizer(en_vocab, model, **config)
ner.begin_training(lambda: [])
ner.begin_training(lambda: [_ner_example(ner)])
ner(doc)
assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
orig_iobs = [t.ent_iob_ for t in doc]
doc.ents = list(doc.ents)
assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
assert [t.ent_iob_ for t in doc] == orig_iobs
def test_add_overlapping_entities(en_vocab):

21
spacy/tests/parser/test_add_label.py
View File

@ -35,7 +35,7 @@ def test_init_parser(parser):
def _train_parser(parser):
fix_random_seed(1)
parser.add_label("left")
parser.begin_training(lambda: [], **parser.cfg)
parser.begin_training(lambda: [_parser_example(parser)], **parser.cfg)
sgd = Adam(0.001)
for i in range(5):
@ -47,16 +47,25 @@ def _train_parser(parser):
return parser
def _parser_example(parser):
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
return Example.from_dict(doc, gold)
def _ner_example(ner):
doc = Doc(ner.vocab, words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"])
gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
return Example.from_dict(doc, gold)
def test_add_label(parser):
parser = _train_parser(parser)
parser.add_label("right")
sgd = Adam(0.001)
for i in range(100):
losses = {}
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
example = Example.from_dict(doc, gold)
parser.update([example], sgd=sgd, losses=losses)
parser.update([_parser_example(parser)], sgd=sgd, losses=losses)
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
doc = parser(doc)
assert doc[0].dep_ == "right"
@ -75,7 +84,7 @@ def test_add_label_deserializes_correctly():
ner1.add_label("C")
ner1.add_label("B")
ner1.add_label("A")
ner1.begin_training(lambda: [])
ner1.begin_training(lambda: [_ner_example(ner1)])
ner2 = EntityRecognizer(Vocab(), model, **config)
# the second model needs to be resized before we can call from_bytes

2
spacy/tests/parser/test_parse.py
View File

@ -85,7 +85,7 @@ def test_parser_merge_pp(en_tokenizer):
pos = ["DET", "NOUN", "ADP", "DET", "NOUN", "VERB"]
tokens = en_tokenizer(text)
doc = get_doc(
tokens.vocab, words=[t.text for t in tokens], deps=deps, heads=heads, pos=pos,
tokens.vocab, words=[t.text for t in tokens], deps=deps, heads=heads, pos=pos
)
with doc.retokenize() as retokenizer:
for np in doc.noun_chunks:

8
spacy/tests/parser/test_preset_sbd.py
View File

@ -14,6 +14,12 @@ def vocab():
return Vocab(lex_attr_getters={NORM: lambda s: s})
def _parser_example(parser):
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
return Example.from_dict(doc, gold)
@pytest.fixture
def parser(vocab):
config = {
@ -28,7 +34,7 @@ def parser(vocab):
parser.cfg["hidden_width"] = 32
# parser.add_label('right')
parser.add_label("left")
parser.begin_training(lambda: [], **parser.cfg)
parser.begin_training(lambda: [_parser_example(parser)], **parser.cfg)
sgd = Adam(0.001)
for i in range(10):

20
spacy/tests/pipeline/test_entity_linker.py
View File

@ -281,11 +281,12 @@ def test_append_invalid_alias(nlp):
def test_preserving_links_asdoc(nlp):
"""Test that Span.as_doc preserves the existing entity links"""
vector_length = 1
@registry.misc.register("myLocationsKB.v1")
def dummy_kb() -> Callable[["Vocab"], KnowledgeBase]:
def create_kb(vocab):
mykb = KnowledgeBase(vocab, entity_vector_length=1)
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=8, entity_vector=[1])
@ -305,10 +306,9 @@ def test_preserving_links_asdoc(nlp):
ruler = nlp.add_pipe("entity_ruler")
ruler.add_patterns(patterns)
el_config = {"kb_loader": {"@misc": "myLocationsKB.v1"}, "incl_prior": False}
el_pipe = nlp.add_pipe("entity_linker", config=el_config, last=True)
el_pipe.begin_training(lambda: [])
el_pipe.incl_context = False
el_pipe.incl_prior = True
entity_linker = nlp.add_pipe("entity_linker", config=el_config, last=True)
nlp.begin_training()
assert entity_linker.model.get_dim("nO") == vector_length
# test whether the entity links are preserved by the `as_doc()` function
text = "She lives in Boston. He lives in Denver."
@ -373,6 +373,7 @@ def test_overfitting_IO():
# Simple test to try and quickly overfit the NEL component - ensuring the ML models work correctly
nlp = English()
nlp.add_pipe("sentencizer")
vector_length = 3
# Add a custom component to recognize "Russ Cochran" as an entity for the example training data
patterns = [
@ -393,7 +394,7 @@ def test_overfitting_IO():
# create artificial KB - assign same prior weight to the two russ cochran's
# Q2146908 (Russ Cochran): American golfer
# Q7381115 (Russ Cochran): publisher
mykb = KnowledgeBase(vocab, entity_vector_length=3)
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
mykb.add_alias(
@ -406,14 +407,17 @@ def test_overfitting_IO():
return create_kb
# Create the Entity Linker component and add it to the pipeline
nlp.add_pipe(
entity_linker = nlp.add_pipe(
"entity_linker",
config={"kb_loader": {"@misc": "myOverfittingKB.v1"}},
last=True,
)
# train the NEL pipe
optimizer = nlp.begin_training()
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
assert entity_linker.model.get_dim("nO") == vector_length
assert entity_linker.model.get_dim("nO") == entity_linker.kb.entity_vector_length
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)

72
spacy/tests/pipeline/test_morphologizer.py
View File

@ -25,27 +25,61 @@ TRAIN_DATA = [
},
),
# test combinations of morph+POS
("Eat blue ham", {"morphs": ["Feat=V", "", ""], "pos": ["", "ADJ", ""]},),
("Eat blue ham", {"morphs": ["Feat=V", "", ""], "pos": ["", "ADJ", ""]}),
]
def test_no_label():
nlp = Language()
nlp.add_pipe("morphologizer")
with pytest.raises(ValueError):
nlp.begin_training()
def test_implicit_label():
nlp = Language()
nlp.add_pipe("morphologizer")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.begin_training(get_examples=lambda: train_examples)
def test_no_resize():
nlp = Language()
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "VERB")
nlp.begin_training()
# this throws an error because the morphologizer can't be resized after initialization
with pytest.raises(ValueError):
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "ADJ")
def test_begin_training_examples():
nlp = Language()
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
def test_overfitting_IO():
# Simple test to try and quickly overfit the morphologizer - ensuring the ML models work correctly
nlp = English()
morphologizer = nlp.add_pipe("morphologizer")
nlp.add_pipe("morphologizer")
train_examples = []
for inst in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(inst[0]), inst[1]))
for morph, pos in zip(inst[1]["morphs"], inst[1]["pos"]):
if morph and pos:
morphologizer.add_label(
morph + Morphology.FEATURE_SEP + "POS" + Morphology.FIELD_SEP + pos
)
elif pos:
morphologizer.add_label("POS" + Morphology.FIELD_SEP + pos)
elif morph:
morphologizer.add_label(morph)
optimizer = nlp.begin_training()
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
for i in range(50):
losses = {}
@ -55,18 +89,8 @@ def test_overfitting_IO():
# test the trained model
test_text = "I like blue ham"
doc = nlp(test_text)
gold_morphs = [
"Feat=N",
"Feat=V",
"",
"",
]
gold_pos_tags = [
"NOUN",
"VERB",
"ADJ",
"",
]
gold_morphs = ["Feat=N", "Feat=V", "", ""]
gold_pos_tags = ["NOUN", "VERB", "ADJ", ""]
assert [t.morph_ for t in doc] == gold_morphs
assert [t.pos_ for t in doc] == gold_pos_tags

14
spacy/tests/pipeline/test_senter.py
View File

@ -30,6 +30,20 @@ TRAIN_DATA = [
),
]
def test_begin_training_examples():
nlp = Language()
senter = nlp.add_pipe("senter")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
def test_overfitting_IO():
# Simple test to try and quickly overfit the senter - ensuring the ML models work correctly

69
spacy/tests/pipeline/test_simple_ner.py
View File

@ -1,3 +1,4 @@
import pytest
from spacy.lang.en import English
from spacy.gold import Example
from spacy import util
@ -5,11 +6,73 @@ from ..util import make_tempdir
TRAIN_DATA = [
("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
("Who is Shaka S Khan?", {"entities": [(7, 19, "PERSON")]}),
("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
]
def test_no_label():
nlp = English()
nlp.add_pipe("simple_ner")
with pytest.raises(ValueError):
nlp.begin_training()
def test_implicit_label():
nlp = English()
ner = nlp.add_pipe("simple_ner")
train_examples = []
ner.add_label("ORG")
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.begin_training(get_examples=lambda: train_examples)
@pytest.mark.skip(reason="Should be fixed")
def test_untrained():
# This shouldn't crash, but it does when the simple_ner produces an invalid sequence like ['L-PERSON', 'L-ORG']
nlp = English()
ner = nlp.add_pipe("simple_ner")
ner.add_label("PERSON")
ner.add_label("LOC")
ner.add_label("ORG")
nlp.begin_training()
nlp("Example sentence")
def test_resize():
nlp = English()
ner = nlp.add_pipe("simple_ner")
ner.add_label("PERSON")
ner.add_label("LOC")
nlp.begin_training()
assert len(ner.labels) == 2
ner.add_label("ORG")
nlp.begin_training()
assert len(ner.labels) == 3
def test_begin_training_examples():
nlp = English()
ner = nlp.add_pipe("simple_ner")
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for ent in annotations.get("entities"):
ner.add_label(ent[2])
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: train_examples[0])
with pytest.raises(ValueError):
nlp.begin_training(get_examples=lambda: [])
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
def test_overfitting_IO():
# Simple test to try and quickly overfit the SimpleNER component - ensuring the ML models work correctly
nlp = English()
@ -17,9 +80,7 @@ def test_overfitting_IO():
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for ent in annotations.get("entities"):
ner.add_label(ent[2])
optimizer = nlp.begin_training()
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
for i in range(50):
losses = {}

55
spacy/tests/pipeline/test_tagger.py
View File

@ -34,6 +34,56 @@ TRAIN_DATA = [
]
def test_no_label():
nlp = Language()
nlp.add_pipe("tagger")
with pytest.raises(ValueError):
nlp.begin_training()
def test_no_resize():
nlp = Language()
tagger = nlp.add_pipe("tagger")
tagger.add_label("N")
tagger.add_label("V")
assert tagger.labels == ("N", "V")
nlp.begin_training()
assert tagger.model.get_dim("nO") == 2
# this throws an error because the tagger can't be resized after initialization
with pytest.raises(ValueError):
tagger.add_label("J")
def test_implicit_label():
nlp = Language()
nlp.add_pipe("tagger")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.begin_training(get_examples=lambda: train_examples)
def test_begin_training_examples():
nlp = Language()
tagger = nlp.add_pipe("tagger")
train_examples = []
for tag in TAGS:
tagger.add_label(tag)
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: train_examples[0])
with pytest.raises(ValueError):
nlp.begin_training(get_examples=lambda: [])
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
def test_overfitting_IO():
# Simple test to try and quickly overfit the tagger - ensuring the ML models work correctly
nlp = English()
@ -41,9 +91,8 @@ def test_overfitting_IO():
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
for tag in TAGS:
tagger.add_label(tag)
optimizer = nlp.begin_training()
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
assert tagger.model.get_dim("nO") == len(TAGS)
for i in range(50):
losses = {}

50
spacy/tests/pipeline/test_textcat.py
View File

@ -80,6 +80,51 @@ def test_label_types():
textcat.add_label(9)
def test_no_label():
nlp = Language()
nlp.add_pipe("textcat")
with pytest.raises(ValueError):
nlp.begin_training()
def test_implicit_label():
nlp = Language()
textcat = nlp.add_pipe("textcat")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.begin_training(get_examples=lambda: train_examples)
def test_no_resize():
nlp = Language()
textcat = nlp.add_pipe("textcat")
textcat.add_label("POSITIVE")
textcat.add_label("NEGATIVE")
nlp.begin_training()
assert textcat.model.get_dim("nO") == 2
# this throws an error because the textcat can't be resized after initialization
with pytest.raises(ValueError):
textcat.add_label("NEUTRAL")
def test_begin_training_examples():
nlp = Language()
textcat = nlp.add_pipe("textcat")
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for label, value in annotations.get("cats").items():
textcat.add_label(label)
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
def test_overfitting_IO():
# Simple test to try and quickly overfit the textcat component - ensuring the ML models work correctly
fix_random_seed(0)
@ -89,9 +134,8 @@ def test_overfitting_IO():
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for label, value in annotations.get("cats").items():
textcat.add_label(label)
optimizer = nlp.begin_training()
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
assert textcat.model.get_dim("nO") == 2
for i in range(50):
losses = {}

2
spacy/tests/regression/test_issue2501-3000.py
View File

@ -20,7 +20,7 @@ def test_issue2564():
nlp = Language()
tagger = nlp.add_pipe("tagger")
tagger.add_label("A")
tagger.begin_training(lambda: [])
nlp.begin_training()
doc = nlp("hello world")
assert doc.is_tagged
docs = nlp.pipe(["hello", "world"])

10
spacy/tests/regression/test_issue3501-4000.py
View File

@ -251,6 +251,12 @@ def test_issue3803():
assert [t.like_num for t in doc] == [True, True, True, True, True, True]
def _parser_example(parser):
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
return Example.from_dict(doc, gold)
def test_issue3830_no_subtok():
"""Test that the parser doesn't have subtok label if not learn_tokens"""
config = {
@ -264,7 +270,7 @@ def test_issue3830_no_subtok():
parser = DependencyParser(Vocab(), model, **config)
parser.add_label("nsubj")
assert "subtok" not in parser.labels
parser.begin_training(lambda: [])
parser.begin_training(lambda: [_parser_example(parser)])
assert "subtok" not in parser.labels
@ -281,7 +287,7 @@ def test_issue3830_with_subtok():
parser = DependencyParser(Vocab(), model, **config)
parser.add_label("nsubj")
assert "subtok" not in parser.labels
parser.begin_training(lambda: [])
parser.begin_training(lambda: [_parser_example(parser)])
assert "subtok" in parser.labels

4
spacy/tests/regression/test_issue5230.py
View File

@ -64,7 +64,7 @@ def tagger():
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
tagger.add_label("A")
tagger.begin_training(lambda: [], pipeline=nlp.pipeline)
nlp.begin_training()
return tagger
@ -85,7 +85,7 @@ def entity_linker():
# need to add model for two reasons:
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
entity_linker.begin_training(lambda: [], pipeline=nlp.pipeline)
nlp.begin_training()
return entity_linker

1
spacy/tests/test_tok2vec.py
View File

@ -89,6 +89,7 @@ def test_init_tok2vec():
tok2vec = nlp.add_pipe("tok2vec")
assert tok2vec.listeners == []
nlp.begin_training()
assert tok2vec.model.get_dim("nO")
cfg_string = """