mirror of
https://github.com/explosion/spaCy.git
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da7ad97519
* Update `TextCatBOW` to use the fixed `SparseLinear` layer A while ago, we fixed the `SparseLinear` layer to use all available parameters: https://github.com/explosion/thinc/pull/754 This change updates `TextCatBOW` to `v3` which uses the new `SparseLinear_v2` layer. This results in a sizeable improvement on a text categorization task that was tested. While at it, this `spacy.TextCatBOW.v3` also adds the `length_exponent` option to make it possible to change the hidden size. Ideally, we'd just have an option called `length`. But the way that `TextCatBOW` uses hashes results in a non-uniform distribution of parameters when the length is not a power of two. * Replace TexCatBOW `length_exponent` parameter by `length` We now round up the length to the next power of two if it isn't a power of two. * Remove some tests for TextCatBOW.v2 * Fix missing import
430 lines
14 KiB
Python
430 lines
14 KiB
Python
import os
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import sys
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from pathlib import Path
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import pytest
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import srsly
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from typer.testing import CliRunner
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from spacy.cli._util import app, get_git_version
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from spacy.tokens import Doc, DocBin, Span
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from .util import make_tempdir, normalize_whitespace
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def has_git():
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try:
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get_git_version()
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return True
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except RuntimeError:
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return False
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def test_convert_auto():
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with make_tempdir() as d_in, make_tempdir() as d_out:
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for f in ["data1.iob", "data2.iob", "data3.iob"]:
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Path(d_in / f).touch()
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# ensure that "automatic" suffix detection works
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result = CliRunner().invoke(app, ["convert", str(d_in), str(d_out)])
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assert "Generated output file" in result.stdout
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out_files = os.listdir(d_out)
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assert len(out_files) == 3
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assert "data1.spacy" in out_files
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assert "data2.spacy" in out_files
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assert "data3.spacy" in out_files
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def test_convert_auto_conflict():
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with make_tempdir() as d_in, make_tempdir() as d_out:
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for f in ["data1.iob", "data2.iob", "data3.json"]:
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Path(d_in / f).touch()
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# ensure that "automatic" suffix detection warns when there are different file types
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result = CliRunner().invoke(app, ["convert", str(d_in), str(d_out)])
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assert "All input files must be same type" in result.stdout
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out_files = os.listdir(d_out)
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assert len(out_files) == 0
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def test_benchmark_accuracy_alias():
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# Verify that the `evaluate` alias works correctly.
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result_benchmark = CliRunner().invoke(app, ["benchmark", "accuracy", "--help"])
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result_evaluate = CliRunner().invoke(app, ["evaluate", "--help"])
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assert normalize_whitespace(result_benchmark.stdout) == normalize_whitespace(
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result_evaluate.stdout.replace("spacy evaluate", "spacy benchmark accuracy")
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)
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def test_debug_data_trainable_lemmatizer_cli(en_vocab):
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train_docs = [
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Doc(en_vocab, words=["I", "like", "cats"], lemmas=["I", "like", "cat"]),
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Doc(
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en_vocab,
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words=["Dogs", "are", "great", "too"],
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lemmas=["dog", "be", "great", "too"],
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),
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]
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dev_docs = [
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Doc(en_vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]),
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Doc(en_vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]),
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]
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with make_tempdir() as d_in:
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train_bin = DocBin(docs=train_docs)
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train_bin.to_disk(d_in / "train.spacy")
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dev_bin = DocBin(docs=dev_docs)
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dev_bin.to_disk(d_in / "dev.spacy")
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# `debug data` requires an input pipeline config
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CliRunner().invoke(
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app,
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[
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"init",
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"config",
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f"{d_in}/config.cfg",
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"--lang",
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"en",
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"--pipeline",
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"trainable_lemmatizer",
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],
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)
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result_debug_data = CliRunner().invoke(
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app,
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[
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"debug",
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"data",
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f"{d_in}/config.cfg",
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"--paths.train",
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f"{d_in}/train.spacy",
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"--paths.dev",
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f"{d_in}/dev.spacy",
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],
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)
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# Instead of checking specific wording of the output, which may change,
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# we'll check that this section of the debug output is present.
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assert "= Trainable Lemmatizer =" in result_debug_data.stdout
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# project tests
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CFG_FILE = "myconfig.cfg"
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SAMPLE_PROJECT = {
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"title": "Sample project",
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"description": "This is a project for testing",
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"assets": [
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{
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"dest": "assets/spacy-readme.md",
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"url": "https://github.com/explosion/spaCy/raw/dec81508d28b47f09a06203c472b37f00db6c869/README.md",
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"checksum": "411b2c89ccf34288fae8ed126bf652f7",
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},
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{
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"dest": "assets/citation.cff",
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"url": "https://github.com/explosion/spaCy/raw/master/CITATION.cff",
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"checksum": "c996bfd80202d480eb2e592369714e5e",
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"extra": True,
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},
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],
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"commands": [
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{
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"name": "ok",
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"help": "print ok",
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"script": ["python -c \"print('okokok')\""],
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},
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{
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"name": "create",
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"help": "make a file",
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"script": [f"python -m spacy init config {CFG_FILE}"],
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"outputs": [f"{CFG_FILE}"],
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},
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],
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}
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SAMPLE_PROJECT_TEXT = srsly.yaml_dumps(SAMPLE_PROJECT)
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@pytest.fixture
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def project_dir():
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with make_tempdir() as pdir:
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(pdir / "project.yml").write_text(SAMPLE_PROJECT_TEXT)
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yield pdir
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def test_project_document(project_dir):
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readme_path = project_dir / "README.md"
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assert not readme_path.exists(), "README already exists"
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result = CliRunner().invoke(
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app, ["project", "document", str(project_dir), "-o", str(readme_path)]
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)
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assert result.exit_code == 0
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assert readme_path.is_file()
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text = readme_path.read_text("utf-8")
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assert SAMPLE_PROJECT["description"] in text
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def test_project_assets(project_dir):
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asset_dir = project_dir / "assets"
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assert not asset_dir.exists(), "Assets dir is already present"
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result = CliRunner().invoke(app, ["project", "assets", str(project_dir)])
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assert result.exit_code == 0
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assert (asset_dir / "spacy-readme.md").is_file(), "Assets not downloaded"
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# check that extras work
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result = CliRunner().invoke(app, ["project", "assets", "--extra", str(project_dir)])
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assert result.exit_code == 0
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assert (asset_dir / "citation.cff").is_file(), "Extras not downloaded"
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def test_project_run(project_dir):
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# make sure dry run works
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test_file = project_dir / CFG_FILE
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result = CliRunner().invoke(
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app, ["project", "run", "--dry", "create", str(project_dir)]
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)
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assert result.exit_code == 0
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assert not test_file.is_file()
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result = CliRunner().invoke(app, ["project", "run", "create", str(project_dir)])
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assert result.exit_code == 0
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assert test_file.is_file()
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result = CliRunner().invoke(app, ["project", "run", "ok", str(project_dir)])
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assert result.exit_code == 0
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assert "okokok" in result.stdout
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@pytest.mark.skipif(not has_git(), reason="git not installed")
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@pytest.mark.parametrize(
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"options",
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[
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"",
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# "--sparse",
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"--branch v3",
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"--repo https://github.com/explosion/projects --branch v3",
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],
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)
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def test_project_clone(options):
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with make_tempdir() as workspace:
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out = workspace / "project"
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target = "benchmarks/ner_conll03"
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if not options:
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options = []
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else:
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options = options.split()
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result = CliRunner().invoke(
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app, ["project", "clone", target, *options, str(out)]
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)
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assert result.exit_code == 0
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assert (out / "README.md").is_file()
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def test_project_push_pull(project_dir):
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proj = dict(SAMPLE_PROJECT)
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remote = "xyz"
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with make_tempdir() as remote_dir:
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proj["remotes"] = {remote: str(remote_dir)}
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proj_text = srsly.yaml_dumps(proj)
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(project_dir / "project.yml").write_text(proj_text)
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test_file = project_dir / CFG_FILE
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result = CliRunner().invoke(app, ["project", "run", "create", str(project_dir)])
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assert result.exit_code == 0
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assert test_file.is_file()
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result = CliRunner().invoke(app, ["project", "push", remote, str(project_dir)])
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assert result.exit_code == 0
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test_file.unlink()
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assert not test_file.exists()
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result = CliRunner().invoke(app, ["project", "pull", remote, str(project_dir)])
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assert result.exit_code == 0
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assert test_file.is_file()
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def test_find_function_valid():
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# example of architecture in main code base
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function = "spacy.TextCatBOW.v3"
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result = CliRunner().invoke(app, ["find-function", function, "-r", "architectures"])
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assert f"Found registered function '{function}'" in result.stdout
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assert "textcat.py" in result.stdout
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result = CliRunner().invoke(app, ["find-function", function])
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assert f"Found registered function '{function}'" in result.stdout
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assert "textcat.py" in result.stdout
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# example of architecture in spacy-legacy
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function = "spacy.TextCatBOW.v1"
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result = CliRunner().invoke(app, ["find-function", function])
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assert f"Found registered function '{function}'" in result.stdout
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assert "spacy_legacy" in result.stdout
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assert "textcat.py" in result.stdout
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def test_find_function_invalid():
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# invalid registry
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function = "spacy.TextCatBOW.v3"
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registry = "foobar"
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result = CliRunner().invoke(
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app, ["find-function", function, "--registry", registry]
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)
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assert f"Unknown function registry: '{registry}'" in result.stdout
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# invalid function
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function = "spacy.TextCatBOW.v666"
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result = CliRunner().invoke(app, ["find-function", function])
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assert f"Couldn't find registered function: '{function}'" in result.stdout
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example_words_1 = ["I", "like", "cats"]
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example_words_2 = ["I", "like", "dogs"]
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example_lemmas_1 = ["I", "like", "cat"]
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example_lemmas_2 = ["I", "like", "dog"]
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example_tags = ["PRP", "VBP", "NNS"]
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example_morphs = [
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"Case=Nom|Number=Sing|Person=1|PronType=Prs",
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"Tense=Pres|VerbForm=Fin",
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"Number=Plur",
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]
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example_deps = ["nsubj", "ROOT", "dobj"]
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example_pos = ["PRON", "VERB", "NOUN"]
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example_ents = ["O", "O", "I-ANIMAL"]
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example_spans = [(2, 3, "ANIMAL")]
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TRAIN_EXAMPLE_1 = dict(
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words=example_words_1,
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lemmas=example_lemmas_1,
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tags=example_tags,
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morphs=example_morphs,
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deps=example_deps,
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heads=[1, 1, 1],
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pos=example_pos,
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ents=example_ents,
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spans=example_spans,
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cats={"CAT": 1.0, "DOG": 0.0},
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)
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TRAIN_EXAMPLE_2 = dict(
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words=example_words_2,
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lemmas=example_lemmas_2,
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tags=example_tags,
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morphs=example_morphs,
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deps=example_deps,
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heads=[1, 1, 1],
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pos=example_pos,
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ents=example_ents,
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spans=example_spans,
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cats={"CAT": 0.0, "DOG": 1.0},
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)
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@pytest.mark.slow
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@pytest.mark.parametrize(
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"component,examples",
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[
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("tagger", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("trainable_lemmatizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("parser", [TRAIN_EXAMPLE_1] * 30),
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("ner", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("spancat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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("textcat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
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],
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)
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def test_init_config_trainable(component, examples, en_vocab):
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if component == "textcat":
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train_docs = []
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for example in examples:
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doc = Doc(en_vocab, words=example["words"])
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doc.cats = example["cats"]
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train_docs.append(doc)
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elif component == "spancat":
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train_docs = []
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for example in examples:
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doc = Doc(en_vocab, words=example["words"])
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doc.spans["sc"] = [
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Span(doc, start, end, label) for start, end, label in example["spans"]
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]
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train_docs.append(doc)
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else:
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train_docs = []
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for example in examples:
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# cats, spans are not valid kwargs for instantiating a Doc
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example = {k: v for k, v in example.items() if k not in ("cats", "spans")}
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doc = Doc(en_vocab, **example)
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train_docs.append(doc)
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with make_tempdir() as d_in:
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train_bin = DocBin(docs=train_docs)
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train_bin.to_disk(d_in / "train.spacy")
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dev_bin = DocBin(docs=train_docs)
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dev_bin.to_disk(d_in / "dev.spacy")
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init_config_result = CliRunner().invoke(
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app,
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[
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"init",
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"config",
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f"{d_in}/config.cfg",
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"--lang",
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"en",
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"--pipeline",
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component,
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],
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)
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assert init_config_result.exit_code == 0
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train_result = CliRunner().invoke(
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app,
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[
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"train",
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f"{d_in}/config.cfg",
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"--paths.train",
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f"{d_in}/train.spacy",
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"--paths.dev",
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f"{d_in}/dev.spacy",
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"--output",
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f"{d_in}/model",
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],
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)
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assert train_result.exit_code == 0
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assert Path(d_in / "model" / "model-last").exists()
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@pytest.mark.slow
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@pytest.mark.parametrize(
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"component,examples",
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[("tagger,parser,morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2] * 15)],
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)
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def test_init_config_trainable_multiple(component, examples, en_vocab):
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train_docs = []
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for example in examples:
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example = {k: v for k, v in example.items() if k not in ("cats", "spans")}
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doc = Doc(en_vocab, **example)
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train_docs.append(doc)
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with make_tempdir() as d_in:
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train_bin = DocBin(docs=train_docs)
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train_bin.to_disk(d_in / "train.spacy")
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dev_bin = DocBin(docs=train_docs)
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dev_bin.to_disk(d_in / "dev.spacy")
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init_config_result = CliRunner().invoke(
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app,
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[
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"init",
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"config",
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f"{d_in}/config.cfg",
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"--lang",
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"en",
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"--pipeline",
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component,
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],
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)
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assert init_config_result.exit_code == 0
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train_result = CliRunner().invoke(
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app,
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[
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"train",
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f"{d_in}/config.cfg",
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"--paths.train",
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f"{d_in}/train.spacy",
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"--paths.dev",
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f"{d_in}/dev.spacy",
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"--output",
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f"{d_in}/model",
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],
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)
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assert train_result.exit_code == 0
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assert Path(d_in / "model" / "model-last").exists()
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