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* Support nowrap setting in util.prints * Tidy up and fix whitespace * Simplify script and use read_jsonl helper * Add JSON schemas (see #2928) * Deprecate Doc.print_tree Will be replaced with Doc.to_json, which will produce a unified format * Add Doc.to_json() method (see #2928) Converts Doc objects to JSON using the same unified format as the training data. Method also supports serializing selected custom attributes in the doc._. space. * Remove outdated test * Add write_json and write_jsonl helpers * WIP: Update spacy train * Tidy up spacy train * WIP: Use wasabi for formatting * Add GoldParse helpers for JSON format * WIP: add debug-data command * Fix typo * Add missing import * Update wasabi pin * Add missing import * 💫 Refactor CLI (#2943) To be merged into #2932. ## Description - [x] refactor CLI To use [`wasabi`](https://github.com/ines/wasabi) - [x] use [`black`](https://github.com/ambv/black) for auto-formatting - [x] add `flake8` config - [x] move all messy UD-related scripts to `cli.ud` - [x] make converters function that take the opened file and return the converted data (instead of having them handle the IO) ### Types of change enhancement ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Update wasabi pin * Delete old test * Update errors * Fix typo * Tidy up and format remaining code * Fix formatting * Improve formatting of messages * Auto-format remaining code * Add tok2vec stuff to spacy.train * Fix typo * Update wasabi pin * Fix path checks for when train() is called as function * Reformat and tidy up pretrain script * Update argument annotations * Raise error if model language doesn't match lang * Document new train command
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from spacy.cli.schemas import validate_json, get_schema
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import pytest
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@pytest.fixture(scope="session")
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def training_schema():
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return get_schema("training")
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def test_json_schema_get():
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schema = get_schema("training")
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assert schema
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with pytest.raises(ValueError):
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schema = get_schema("xxx")
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@pytest.mark.parametrize(
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"data",
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[
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{"text": "Hello world"},
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{"text": "Hello", "ents": [{"start": 0, "end": 5, "label": "TEST"}]},
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],
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)
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def test_json_schema_training_valid(data, training_schema):
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errors = validate_json([data], training_schema)
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assert not errors
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@pytest.mark.parametrize(
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"data,n_errors",
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[
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({"spans": []}, 1),
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({"text": "Hello", "ents": [{"start": "0", "end": "5", "label": "TEST"}]}, 2),
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({"text": "Hello", "ents": [{"start": 0, "end": 5}]}, 1),
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({"text": "Hello", "ents": [{"start": 0, "end": 5, "label": "test"}]}, 1),
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({"text": "spaCy", "tokens": [{"pos": "PROPN"}]}, 2),
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],
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)
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def test_json_schema_training_invalid(data, n_errors, training_schema):
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errors = validate_json([data], training_schema)
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assert len(errors) == n_errors
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