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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 * Remove outdated test * Add write_json and write_jsonl helpers * WIP: Update spacy train * Tidy up spacy train * WIP: Use wasabi for formatting * Add GoldParse helpers for JSON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
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from spacy.matcher import PhraseMatcher
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from spacy.tokens import Doc
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from ..util import get_doc
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def test_matcher_phrase_matcher(en_vocab):
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doc = Doc(en_vocab, words=["Google", "Now"])
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matcher = PhraseMatcher(en_vocab)
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matcher.add("COMPANY", None, doc)
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doc = Doc(en_vocab, words=["I", "like", "Google", "Now", "best"])
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assert len(matcher(doc)) == 1
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def test_phrase_matcher_length(en_vocab):
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matcher = PhraseMatcher(en_vocab)
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assert len(matcher) == 0
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matcher.add("TEST", None, Doc(en_vocab, words=["test"]))
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assert len(matcher) == 1
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matcher.add("TEST2", None, Doc(en_vocab, words=["test2"]))
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assert len(matcher) == 2
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def test_phrase_matcher_contains(en_vocab):
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matcher = PhraseMatcher(en_vocab)
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matcher.add("TEST", None, Doc(en_vocab, words=["test"]))
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assert "TEST" in matcher
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assert "TEST2" not in matcher
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def test_phrase_matcher_string_attrs(en_vocab):
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words1 = ["I", "like", "cats"]
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pos1 = ["PRON", "VERB", "NOUN"]
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words2 = ["Yes", ",", "you", "hate", "dogs", "very", "much"]
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pos2 = ["INTJ", "PUNCT", "PRON", "VERB", "NOUN", "ADV", "ADV"]
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pattern = get_doc(en_vocab, words=words1, pos=pos1)
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matcher = PhraseMatcher(en_vocab, attr="POS")
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matcher.add("TEST", None, pattern)
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doc = get_doc(en_vocab, words=words2, pos=pos2)
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matches = matcher(doc)
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assert len(matches) == 1
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match_id, start, end = matches[0]
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assert match_id == en_vocab.strings["TEST"]
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assert start == 2
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assert end == 5
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def test_phrase_matcher_string_attrs_negative(en_vocab):
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"""Test that token with the control codes as ORTH are *not* matched."""
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words1 = ["I", "like", "cats"]
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pos1 = ["PRON", "VERB", "NOUN"]
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words2 = ["matcher:POS-PRON", "matcher:POS-VERB", "matcher:POS-NOUN"]
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pos2 = ["X", "X", "X"]
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pattern = get_doc(en_vocab, words=words1, pos=pos1)
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matcher = PhraseMatcher(en_vocab, attr="POS")
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matcher.add("TEST", None, pattern)
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doc = get_doc(en_vocab, words=words2, pos=pos2)
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matches = matcher(doc)
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assert len(matches) == 0
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def test_phrase_matcher_bool_attrs(en_vocab):
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words1 = ["Hello", "world", "!"]
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words2 = ["No", "problem", ",", "he", "said", "."]
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pattern = Doc(en_vocab, words=words1)
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matcher = PhraseMatcher(en_vocab, attr="IS_PUNCT")
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matcher.add("TEST", None, pattern)
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doc = Doc(en_vocab, words=words2)
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matches = matcher(doc)
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assert len(matches) == 2
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match_id1, start1, end1 = matches[0]
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match_id2, start2, end2 = matches[1]
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assert match_id1 == en_vocab.strings["TEST"]
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assert match_id2 == en_vocab.strings["TEST"]
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assert start1 == 0
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assert end1 == 3
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assert start2 == 3
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assert end2 == 6
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