* Clean up old Matcher call style related stuff
In v2 Matcher.add was called with (key, on_match, *patterns). In v3 this
was changed to (key, patterns, *, on_match=None), but there were various
points where the old call syntax was documented or handled specially.
This removes all those.
The Matcher itself didn't need any code changes, as it just gives a
generic type error. However the PhraseMatcher required some changes
because it would automatically "fix" the old call style.
Surprisingly, the tokenizer was still using the old call style in one
place.
After these changes tests failed in two places:
1. one test for the "new" call style, including the "old" call style. I
removed this test.
2. deserializing the PhraseMatcher fails because the input docs are a
set.
I am not sure why 2 is happening - I guess it's a quirk of the
serialization format? - so for now I just convert the set to a list when
deserializing. The check that the input Docs are a List in the
PhraseMatcher is a new check, but makes it parallel with the other
Matchers, which seemed like the right thing to do.
* Add notes related to input docs / deserialization type
* Remove Typing import
* Remove old note about call style change
* Apply suggestions from code review
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Use separate method for setting internal doc representations
In addition to the title change, this changes the internal dict to be a
defaultdict, instead of a dict with frequent use of setdefault.
* Add _add_from_arrays for unpickling
* Cleanup around adding from arrays
This moves adding to internal structures into the private batch method,
and removes the single-add method.
This has one behavioral change for `add`, in that if something is wrong
with the list of input Docs (such as one of the items not being a Doc),
valid items before the invalid one will not be added. Also the callback
will not be updated if anything is invalid. This change should not be
significant.
This also adds a test to check failure when given a non-Doc.
* Update spacy/matcher/phrasematcher.pyx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Switch to mecab-ko as default Korean tokenizer
Switch to the (confusingly-named) mecab-ko python module for default Korean
tokenization.
Maintain the previous `natto-py` tokenizer as
`spacy.KoreanNattoTokenizer.v1`.
* Temporarily run tests with mecab-ko tokenizer
* Fix types
* Fix duplicate test names
* Update requirements test
* Revert "Temporarily run tests with mecab-ko tokenizer"
This reverts commit d2083e7044.
* Add mecab_args setting, fix pickle for KoreanNattoTokenizer
* Fix length check
* Update docs
* Formatting
* Update natto-py error message
Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com>
Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com>
* Add token and span custom attributes to to_json()
* Change logic for to_json
* Add functionality to from_json
* Small adjustments
* Move token/span attributes to new dict key
* Fix test
* Fix the same test but much better
* Add backwards compatibility tests and adjust logic
* Add test to check if attributes not set in underscore are not saved in the json
* Add tests for json compatibility
* Adjust test names
* Fix tests and clean up code
* Fix assert json tests
* small adjustment
* adjust naming and code readability
* Adjust naming, added more tests and changed logic
* Fix typo
* Adjust errors, naming, and small test optimization
* Fix byte tests
* Fix bytes tests
* Change naming and json structure
* update schema
* Update spacy/schemas.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/tokens/doc.pyx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/tokens/doc.pyx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/schemas.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update schema for underscore attributes
* Adjust underscore schema
* adjust schema tests
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Map `Span.id` to `Token.ent_id` in all cases when setting `Doc.ents`
* Reset `Token.ent_id` and `Token.ent_kb_id` when setting `Doc.ents`
* Make `Span.ent_id` an alias of `Span.id` rather than a read-only view
of the root token's `ent_id` annotation
* cleanup Tokenizer fields
* remove unused object from vocab
* remove IS_OOV_DEPRECATED
* add back in as FLAG13
* FLAG 18 instead
* import fix
* fix clumpsy fingers
* revert symbol changes in favor of #11352
* bint instead of bool
* adding spans to doc_annotation in Example.to_dict
* to_dict compatible with from_dict: tuples instead of spans
* use strings for label and kb_id
* Simplify test
* Update data formats docs
Co-authored-by: Stefanie Wolf <stefanie.wolf@vitecsoftware.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Added examples for Slovene
* Update spacy/lang/sl/examples.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Corrected a typo in one of the sentences
* Updated support for Slovenian
* Some minor changes to corrections
* Added forint currency
* Corrected HYPHENS_PERMITTED regex and some formatting
* Minor changes
* Un-xfail tokenizer test
* Format
Co-authored-by: Luka Dragar <D20124481@mytudublin.ie>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* add additional REL_OP
* change to condition and new rel_op symbols
* add operators to docs
* add the anchor while we're in here
* add tests
Co-authored-by: Peter Baumgartner <5107405+pmbaumgartner@users.noreply.github.com>
After the precomputable affine table of shape [nB, nF, nO, nP] is
computed, padding with shape [1, nF, nO, nP] is assigned to the first
row of the precomputed affine table. However, when we are indexing the
precomputed table, we get a row of shape [nF, nO, nP]. CuPy versions
before 10.0 cannot paper over this shape difference.
This change fixes compatibility with CuPy < 10.0 by squeezing the first
dimension of the padding before assignment.
* precompute_hiddens/Parser: do not look up CPU ops
`get_ops("cpu")` is quite expensive. To avoid this, we want to cache the
result as in #11068. However, for 3.x we do not want to change the ABI.
So we avoid the expensive lookup by using NumpyOps. This should have a
minimal impact, since `get_ops("cpu")` was only used when the model ops
were `CupyOps`. If the ops are `AppleOps`, we are still passing through
the correct BLAS implementation.
* _NUMPY_OPS -> NUMPY_OPS
* `strings`: More roubust type checking of keys/IDs, coerce `int`-like types to `hash_t`
* Preserve existing public API behaviour
* Fix return type
* Replace `bool` with `bint`, rename to `_try_coerce_to_hash`, replace `id` with `hash`
* Avoid unnecessary re-encoding and re-calculation of strings and hashs respectively
* Rename variables named `hash`
Add comment on early return
* `TrainablePipe`: Add NVTX range decorator
* Annotate `TrainablePipe` subclasses with NVTX ranges
* Export function signature to allow introspection of args in tests
* Revert "Annotate `TrainablePipe` subclasses with NVTX ranges"
This reverts commit d8684f7372.
* Revert "Export function signature to allow introspection of args in tests"
This reverts commit f4405ca3ad.
* Revert "`TrainablePipe`: Add NVTX range decorator"
This reverts commit 26536eb6b8.
* Add `spacy.pipes_with_nvtx_range` pipeline callback
* Show warnings for all missing user-defined pipe functions that need to be annotated
Fix imports, typos
* Rename `DEFAULT_ANNOTATABLE_PIPE_METHODS` to `DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS`
Reorder import
* Walk model nodes directly whilst applying NVTX ranges
Ignore pipe method wrapper when applying range
* Min_max_operators
1. Modified API and Usage for spaCy website to include min_max operator
2. Modified matcher.pyx to include min_max function {n,m} and its variants
3. Modified schemas.py to include min_max validation error
4. Added test cases to test_matcher_api.py, test_matcher_logic.py and test_pattern_validation.py
* attempt to fix mypy/pydantic compat issue
* formatting
* Update spacy/tests/matcher/test_pattern_validation.py
Co-authored-by: Source-Shen <82353723+Source-Shen@users.noreply.github.com>
Co-authored-by: svlandeg <svlandeg@github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* vectors: avoid expensive comparisons between numpy ints and Python ints
* vectors: avoid failure on lists of ints
* Convert another numpy int to Python
Distinguish between vectors that are 0 vs. missing vectors when warning
about missing vectors.
Update `Doc.has_vector` to match `Span.has_vector` and
`Token.has_vector` for cases where the vocab has vectors but none of the
tokens in the container have vectors.
* Handle Russian, Ukrainian and Bulgarian
* Corrections
* Correction
* Correction to comment
* Changes based on review
* Correction
* Reverted irrelevant change in punctuation.py
* Remove unnecessary group
* Reverted accidental change
