Remove corpus-specific tag maps from the language data for languages
without custom tokenizers. For languages with custom word segmenters
that also provide tags (Japanese and Korean), the tag maps for the
custom tokenizers are kept as the default.
The default tag maps for languages without custom tokenizers are now the
default tag map from `lang/tag_map/py`, UPOS -> UPOS.
* Reduce stored lexemes data, move feats to lookups
* Move non-derivable lexemes features (`norm / cluster / prob`) to
`spacy-lookups-data` as lookups
* Get/set `norm` in both lookups and `LexemeC`, serialize in lookups
* Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in
lookups only
* Remove serialization of lexemes data as `vocab/lexemes.bin`
* Remove `SerializedLexemeC`
* Remove `Lexeme.to_bytes/from_bytes`
* Modify normalization exception loading:
* Always create `Vocab.lookups` table `lexeme_norm` for
normalization exceptions
* Load base exceptions from `lang.norm_exceptions`, but load
language-specific exceptions from lookups
* Set `lex_attr_getter[NORM]` including new lookups table in
`BaseDefaults.create_vocab()` and when deserializing `Vocab`
* Remove all cached lexemes when deserializing vocab to override
existing normalizations with the new normalizations (as a replacement
for the previous step that replaced all lexemes data with the
deserialized data)
* Skip English normalization test
Skip English normalization test because the data is now in
`spacy-lookups-data`.
* Remove norm exceptions
Moved to spacy-lookups-data.
* Move norm exceptions test to spacy-lookups-data
* Load extra lookups from spacy-lookups-data lazily
Load extra lookups (currently for cluster and prob) lazily from the
entry point `lg_extra` as `Vocab.lookups_extra`.
* Skip creating lexeme cache on load
To improve model loading times, do not create the full lexeme cache when
loading. The lexemes will be created on demand when processing.
* Identify numeric values in Lexeme.set_attrs()
With the removal of a special case for `PROB`, also identify `float` to
avoid trying to convert it with the `StringStore`.
* Skip lexeme cache init in from_bytes
* Unskip and update lookups tests for python3.6+
* Update vocab pickle to include lookups_extra
* Update vocab serialization tests
Check strings rather than lexemes since lexemes aren't initialized
automatically, account for addition of "_SP".
* Re-skip lookups test because of python3.5
* Skip PROB/float values in Lexeme.set_attrs
* Convert is_oov from lexeme flag to lex in vectors
Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether
the lexeme has a vector.
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Limiting noun_chunks for specific langauges
* Limiting noun_chunks for specific languages
Contributor Agreement
* Addressing review comments
* Removed unused fixtures and imports
* Add fa_tokenizer in test suite
* Use fa_tokenizer in test
* Undo extraneous reformatting
Co-authored-by: adrianeboyd <adrianeboyd@gmail.com>
* Expose tokenizer rules as a property
Expose the tokenizer rules property in the same way as the other core
properties. (The cache resetting is overkill, but consistent with
`from_bytes` for now.)
Add tests and update Tokenizer API docs.
* Update Hungarian punctuation to remove empty string
Update Hungarian punctuation definitions so that `_units` does not match
an empty string.
* Use _load_special_tokenization consistently
Use `_load_special_tokenization()` and have it to handle `None` checks.
* Fix precedence of `token_match` vs. special cases
Remove `token_match` check from `_split_affixes()` so that special cases
have precedence over `token_match`. `token_match` is checked only before
infixes are split.
* Add `make_debug_doc()` to the Tokenizer
Add `make_debug_doc()` to the Tokenizer as a working implementation of
the pseudo-code in the docs.
Add a test (marked as slow) that checks that `nlp.tokenizer()` and
`nlp.tokenizer.make_debug_doc()` return the same non-whitespace tokens
for all languages that have `examples.sentences` that can be imported.
* Update tokenization usage docs
Update pseudo-code and algorithm description to correspond to
`nlp.tokenizer.make_debug_doc()` with example debugging usage.
Add more examples for customizing tokenizers while preserving the
existing defaults.
Minor edits / clarifications.
* Revert "Update Hungarian punctuation to remove empty string"
This reverts commit f0a577f7a5.
* Rework `make_debug_doc()` as `explain()`
Rework `make_debug_doc()` as `explain()`, which returns a list of
`(pattern_string, token_string)` tuples rather than a non-standard
`Doc`. Update docs and tests accordingly, leaving the visualization for
future work.
* Handle cases with bad tokenizer patterns
Detect when tokenizer patterns match empty prefixes and suffixes so that
`explain()` does not hang on bad patterns.
* Remove unused displacy image
* Add tokenizer.explain() to usage docs
* Add default to util.get_entry_point
* Tidy up entry points
* Read lookups from entry points
* Remove lookup tables and related tests
* Add lookups install option
* Remove lemmatizer tests
* Remove logic to process language data files
* Update setup.cfg
* replace unicode categories with raw list of code points
* simplifying ranges
* fixing variable length quotes
* removing redundant regular expression
* small cleanup of regexp notations
* quotes and alpha as ranges instead of alterations
* removed most regexp dependencies and features
* exponential backtracking - unit tests
* rewrote expression with pathological backtracking
* disabling double hyphen tests for now
* test additional variants of repeating punctuation
* remove regex and redundant backslashes from load_reddit script
* small typo fixes
* disable double punctuation test for russian
* clean up old comments
* format block code
* final cleanup
* naming consistency
* french strings as unicode for python 2 support
* french regular expression case insensitive
* Auto-format tests with black
* Add flake8 config
* Tidy up and remove unused imports
* Fix redefinitions of test functions
* Replace orths_and_spaces with words and spaces
* Fix compatibility with pytest 4.0
* xfail test for now
Test was previously overwritten by following test due to naming conflict, so failure wasn't reported
* Unfail passing test
* Only use fixture via arguments
Fixes pytest 4.0 compatibility
## Description
Related issues: #2379 (should be fixed by separating model tests)
* **total execution time down from > 300 seconds to under 60 seconds** 🎉
* removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure
* changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version)
* merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways)
* tidied up and rewrote existing tests wherever possible
### Todo
- [ ] move tests to `/tests` and adjust CI commands accordingly
- [x] move model test suite from internal repo to `spacy-models`
- [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~
- [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted
- [ ] update documentation on how to run tests
### Types of change
enhancement, tests
## 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.
- [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
