spaCy/spacy/tests/pipeline/test_senter.py
Ines Montani 43b960c01b
Refactor pipeline components, config and language data (#5759)
* Update with WIP

* Update with WIP

* Update with pipeline serialization

* Update types and pipe factories

* Add deep merge, tidy up and add tests

* Fix pipe creation from config

* Don't validate default configs on load

* Update spacy/language.py

Co-authored-by: Ines Montani <ines@ines.io>

* Adjust factory/component meta error

* Clean up factory args and remove defaults

* Add test for failing empty dict defaults

* Update pipeline handling and methods

* provide KB as registry function instead of as object

* small change in test to make functionality more clear

* update example script for EL configuration

* Fix typo

* Simplify test

* Simplify test

* splitting pipes.pyx into separate files

* moving default configs to each component file

* fix batch_size type

* removing default values from component constructors where possible (TODO: test 4725)

* skip instead of xfail

* Add test for config -> nlp with multiple instances

* pipeline.pipes -> pipeline.pipe

* Tidy up, document, remove kwargs

* small cleanup/generalization for Tok2VecListener

* use DEFAULT_UPSTREAM field

* revert to avoid circular imports

* Fix tests

* Replace deprecated arg

* Make model dirs require config

* fix pickling of keyword-only arguments in constructor

* WIP: clean up and integrate full config

* Add helper to handle function args more reliably

Now also includes keyword-only args

* Fix config composition and serialization

* Improve config debugging and add visual diff

* Remove unused defaults and fix type

* Remove pipeline and factories from meta

* Update spacy/default_config.cfg

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update spacy/default_config.cfg

* small UX edits

* avoid printing stack trace for debug CLI commands

* Add support for language-specific factories

* specify the section of the config which holds the model to debug

* WIP: add Language.from_config

* Update with language data refactor WIP

* Auto-format

* Add backwards-compat handling for Language.factories

* Update morphologizer.pyx

* Fix morphologizer

* Update and simplify lemmatizers

* Fix Japanese tests

* Port over tagger changes

* Fix Chinese and tests

* Update to latest Thinc

* WIP: xfail first Russian lemmatizer test

* Fix component-specific overrides

* fix nO for output layers in debug_model

* Fix default value

* Fix tests and don't pass objects in config

* Fix deep merging

* Fix lemma lookup data registry

Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed)

* Add types

* Add Vocab.from_config

* Fix typo

* Fix tests

* Make config copying more elegant

* Fix pipe analysis

* Fix lemmatizers and is_base_form

* WIP: move language defaults to config

* Fix morphology type

* Fix vocab

* Remove comment

* Update to latest Thinc

* Add morph rules to config

* Tidy up

* Remove set_morphology option from tagger factory

* Hack use_gpu

* Move [pipeline] to top-level block and make [nlp.pipeline] list

Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them

* Fix use_gpu and resume in CLI

* Auto-format

* Remove resume from config

* Fix formatting and error

* [pipeline] -> [components]

* Fix types

* Fix tagger test: requires set_morphology?

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 13:42:59 +02:00

68 lines
1.9 KiB
Python

import pytest
from spacy import util
from spacy.gold import Example
from spacy.lang.en import English
from spacy.language import Language
from spacy.tests.util import make_tempdir
def test_label_types():
nlp = Language()
senter = nlp.add_pipe("senter")
with pytest.raises(NotImplementedError):
senter.add_label("A")
SENT_STARTS = [0] * 14
SENT_STARTS[0] = 1
SENT_STARTS[5] = 1
SENT_STARTS[9] = 1
TRAIN_DATA = [
(
"I like green eggs. Eat blue ham. I like purple eggs.",
{"sent_starts": SENT_STARTS},
),
(
"She likes purple eggs. They hate ham. You like yellow eggs.",
{"sent_starts": SENT_STARTS},
),
]
def test_overfitting_IO():
# Simple test to try and quickly overfit the senter - ensuring the ML models work correctly
nlp = English()
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# add some cases where SENT_START == -1
train_examples[0].reference[10].is_sent_start = False
train_examples[1].reference[1].is_sent_start = False
train_examples[1].reference[11].is_sent_start = False
nlp.add_pipe("senter")
optimizer = nlp.begin_training()
for i in range(200):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["senter"] < 0.001
# test the trained model
test_text = TRAIN_DATA[0][0]
doc = nlp(test_text)
gold_sent_starts = [0] * 14
gold_sent_starts[0] = 1
gold_sent_starts[5] = 1
gold_sent_starts[9] = 1
assert [int(t.is_sent_start) for t in doc] == gold_sent_starts
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
assert [int(t.is_sent_start) for t in doc2] == gold_sent_starts