* enable fuzzy matching
* add fuzzy param to EntityMatcher
* include rapidfuzz_capi
not yet used
* fix type
* add FUZZY predicate
* add fuzzy attribute list
* fix type properly
* tidying
* remove unnecessary dependency
* handle fuzzy sets
* simplify fuzzy sets
* case fix
* switch to FUZZYn predicates
use Levenshtein distance.
remove fuzzy param.
remove rapidfuzz_capi.
* revert changes added for fuzzy param
* switch to polyleven
(Python package)
* enable fuzzy matching
* add fuzzy param to EntityMatcher
* include rapidfuzz_capi
not yet used
* fix type
* add FUZZY predicate
* add fuzzy attribute list
* fix type properly
* tidying
* remove unnecessary dependency
* handle fuzzy sets
* simplify fuzzy sets
* case fix
* switch to FUZZYn predicates
use Levenshtein distance.
remove fuzzy param.
remove rapidfuzz_capi.
* revert changes added for fuzzy param
* switch to polyleven
(Python package)
* fuzzy match only on oov tokens
* remove polyleven
* exclude whitespace tokens
* don't allow more edits than characters
* fix min distance
* reinstate FUZZY operator
with length-based distance function
* handle sets inside regex operator
* remove is_oov check
* attempt build fix
no mypy failure locally
* re-attempt build fix
* don't overwrite fuzzy param value
* move fuzzy_match
to its own Python module to allow patching
* move fuzzy_match back inside Matcher
simplify logic and add tests
* Format tests
* Parametrize fuzzyn tests
* Parametrize and merge fuzzy+set tests
* Format
* Move fuzzy_match to a standalone method
* Change regex kwarg type to bool
* Add types for fuzzy_match
- Refactor variable names
- Add test for symmetrical behavior
* Parametrize fuzzyn+set tests
* Minor refactoring for fuzz/fuzzy
* Make fuzzy_match a Matcher kwarg
* Update type for _default_fuzzy_match
* don't overwrite function param
* Rename to fuzzy_compare
* Update fuzzy_compare default argument declarations
* allow fuzzy_compare override from EntityRuler
* define new Matcher keyword arg
* fix type definition
* Implement fuzzy_compare config option for EntityRuler and SpanRuler
* Rename _default_fuzzy_compare to fuzzy_compare, remove from reexported objects
* Use simpler fuzzy_compare algorithm
* Update types
* Increase minimum to 2 in fuzzy_compare to allow one transposition
* Fix predicate keys and matching for SetPredicate with FUZZY and REGEX
* Add FUZZY6..9
* Add initial docs
* Increase default fuzzy to rounded 30% of pattern length
* Update docs for fuzzy_compare in components
* Update EntityRuler and SpanRuler API docs
* Rename EntityRuler and SpanRuler setting to matcher_fuzzy_compare
To having naming similar to `phrase_matcher_attr`, rename
`fuzzy_compare` setting for `EntityRuler` and `SpanRuler` to
`matcher_fuzzy_compare. Organize next to `phrase_matcher_attr` in docs.
* Fix schema aliases
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix typo
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add FUZZY6-9 operators and update tests
* Parameterize test over greedy
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix type for fuzzy_compare to remove Optional
* Rename to spacy.levenshtein_compare.v1, move to spacy.matcher.levenshtein
* Update docs following levenshtein_compare renaming
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* add test for running evaluate on an nlp pipeline with two distinct textcat components
* cleanup
* merge dicts instead of overwrite
* don't add more labels to the given set
* Revert "merge dicts instead of overwrite"
This reverts commit 89bee0ed77.
* Switch tests to separate scorer keys rather than merged dicts
* Revert unrelated edits
* Switch textcat scorers to v2
* formatting
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Strings in replacement nodes where not added to the `StringStore`
when `EditTreeLemmatizer` was initialized from a set of labels. The
corresponding test did not capture this because it added the strings
through the examples that were passed to the initialization.
This change fixes both this bug in the initialization as the 'shadowing'
of the bug in the test.
* Check textcat values for validity
* Fix error numbers
* Clean up vals reference
* Check category value validity through training
The _validate_categories is called in update, which for multilabel is
inherited from the single label component.
* Formatting
* Update textcat scorer threshold behavior
For `textcat` (with exclusive classes) the scorer should always use a
threshold of 0.0 because there should be one predicted label per doc and
the numeric score for that particular label should not matter.
* Rename to test_textcat_multilabel_threshold
* Remove all uses of threshold for multi_label=False
* Update Scorer.score_cats API docs
* Add tests for score_cats with thresholds
* Update textcat API docs
* Fix types
* Convert threshold back to float
* Fix threshold type in docstring
* Improve formatting in Scorer API docs
* replicate bug with tok2vec in annotating components
* add overfitting test with a frozen tok2vec
* remove broadcast from predict and check doc.tensor instead
* remove broadcast
* proper error
* slight rephrase of documentation
* Enable Cython<->Python bindings for `Pipe` and `TrainablePipe` methods
* `pipes_with_nvtx_range`: Skip hooking methods whose signature cannot be ascertained
When loading pipelines from a config file, the arguments passed to individual pipeline components is validated by `pydantic` during init. For this, the validation model attempts to parse the function signature of the component's c'tor/entry point so that it can check if all mandatory parameters are present in the config file.
When using the `models_and_pipes_with_nvtx_range` as a `after_pipeline_creation` callback, the methods of all pipeline components get replaced by a NVTX range wrapper **before** the above-mentioned validation takes place. This can be problematic for components that are implemented as Cython extension types - if the extension type is not compiled with Python bindings for its methods, they will have no signatures at runtime. This resulted in `pydantic` matching the *wrapper's* parameters with the those in the config and raising errors.
To avoid this, we now skip applying the wrapper to any (Cython) methods that do not have signatures.
* 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
* account for NER labels with a hyphen in the name
* cleanup
* fix docstring
* add return type to helper method
* shorter method and few more occurrences
* user helper method across repo
* fix circular import
* partial revert to avoid circular import
* detect cycle during projectivize
* not complete test to detect cycle in projectivize
* boolean to int type to propagate error
* use unordered_set instead of set
* moved error message to errors
* removed cycle from test case
* use find instead of count
* cycle check: only perform one lookup
* Return bool again from _has_head_as_ancestor
Communicate presence of cycles through an output argument.
* Switch to returning std::pair to encode presence of a cycle
The has_cycle pointer is too easy to misuse. Ideally, we would have a
sum type like Rust's `Result` here, but C++ is not there yet.
* _is_non_proj_arc: clarify what we are returning
* _has_head_as_ancestor: remove count
We are now explicitly checking for cycles, so the algorithm must always
terminate. Either we encounter the head, we find a root, or a cycle.
* _is_nonproj_arc: simplify condition
* Another refactor using C++ exceptions
* Remove unused error code
* Print graph with cycle on exception
* Include .hh files in source package
* Add FIXME comment
* cycle detection test
* find cycle when starting from problematic vertex
Co-authored-by: Daniël de Kok <me@danieldk.eu>
* Add SpanRuler component
Add a `SpanRuler` component similar to `EntityRuler` that saves a list
of matched spans to `Doc.spans[spans_key]`. The matches from the token
and phrase matchers are deduplicated and sorted before assignment but
are not otherwise filtered.
* Update spacy/pipeline/span_ruler.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix cast
* Add self.key property
* Use number of patterns as length
* Remove patterns kwarg from init
* Update spacy/tests/pipeline/test_span_ruler.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add options for spans filter and setting to ents
* Add `spans_filter` option as a registered function'
* Make `spans_key` optional and if `None`, set to `doc.ents` instead of
`doc.spans[spans_key]`.
* Update and generalize tests
* Add test for setting doc.ents, fix key property type
* Fix typing
* Allow independent doc.spans and doc.ents
* If `spans_key` is set, set `doc.spans` with `spans_filter`.
* If `annotate_ents` is set, set `doc.ents` with `ents_fitler`.
* Use `util.filter_spans` by default as `ents_filter`.
* Use a custom warning if the filter does not work for `doc.ents`.
* Enable use of SpanC.id in Span
* Support id in SpanRuler as Span.id
* Update types
* `id` can only be provided as string (already by `PatternType`
definition)
* Update all uses of Span.id/ent_id in Doc
* Rename Span id kwarg to span_id
* Update types and docs
* Add ents filter to mimic EntityRuler overwrite_ents
* Refactor `ents_filter` to take `entities, spans` args for more
filtering options
* Give registered filters more descriptive names
* Allow registered `filter_spans` filter
(`spacy.first_longest_spans_filter.v1`) to take any number of
`Iterable[Span]` objects as args so it can be used for spans filter
or ents filter
* Implement future entity ruler as span ruler
Implement a compatible `entity_ruler` as `future_entity_ruler` using
`SpanRuler` as the underlying component:
* Add `sort_key` and `sort_reverse` to allow the sorting behavior to be
customized. (Necessary for the same sorting/filtering as in
`EntityRuler`.)
* Implement `overwrite_overlapping_ents_filter` and
`preserve_existing_ents_filter` to support
`EntityRuler.overwrite_ents` settings.
* Add `remove_by_id` to support `EntityRuler.remove` functionality.
* Refactor `entity_ruler` tests to parametrize all tests to test both
`entity_ruler` and `future_entity_ruler`
* Implement `SpanRuler.token_patterns` and `SpanRuler.phrase_patterns`
properties.
Additional changes:
* Move all config settings to top-level attributes to avoid duplicating
settings in the config vs. `span_ruler/cfg`. (Also avoids a lot of
casting.)
* Format
* Fix filter make method name
* Refactor to use same error for removing by label or ID
* Also provide existing spans to spans filter
* Support ids property
* Remove token_patterns and phrase_patterns
* Update docstrings
* Add span ruler docs
* Fix types
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Move sorting into filters
* Check for all tokens in seen tokens in entity ruler filters
* Remove registered sort key
* Set Token.ent_id in a backwards-compatible way in Doc.set_ents
* Remove sort options from API docs
* Update docstrings
* Rename entity ruler filters
* Fix and parameterize scoring
* Add id to Span API docs
* Fix typo in API docs
* Include explicit labeled=True for scorer
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix TODO about typing
Fix was simple: just request an array2f.
* Add type ignore
Maxout has a more restrictive type than the residual layer expects (only
Floats2d vs any Floats).
* Various cleanup
This moves a lot of lines around but doesn't change any functionality.
Details:
1. use `continue` to reduce indentation
2. move sentence doc building inside conditional since it's otherwise
unused
3. reduces some temporary assignments
* Parser: use C saxpy/sgemm provided by the Ops implementation
This is a backport of https://github.com/explosion/spaCy/pull/10747
from the parser refactor branch. It eliminates the explicit calls
to BLIS, instead using the saxpy/sgemm provided by the Ops
implementation.
This allows us to use Accelerate in the parser on M1 Macs (with
an updated thinc-apple-ops).
Performance of the de_core_news_lg pipe:
BLIS 0.7.0, no thinc-apple-ops: 6385 WPS
BLIS 0.7.0, thinc-apple-ops: 36455 WPS
BLIS 0.9.0, no thinc-apple-ops: 19188 WPS
BLIS 0.9.0, thinc-apple-ops: 36682 WPS
This PR, thinc-apple-ops: 38726 WPS
Performance of the de_core_news_lg pipe (only tok2vec -> parser):
BLIS 0.7.0, no thinc-apple-ops: 13907 WPS
BLIS 0.7.0, thinc-apple-ops: 73172 WPS
BLIS 0.9.0, no thinc-apple-ops: 41576 WPS
BLIS 0.9.0, thinc-apple-ops: 72569 WPS
This PR, thinc-apple-ops: 87061 WPS
* Require thinc >=8.1.0,<8.2.0
* Lower thinc lowerbound to 8.1.0.dev0
* Use best CPU ops for CBLAS when the parser model is on the GPU
* Fix another unguarded cblas() call
* Fix: use ops as a shorthand for self.model.ops
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Make changes to typing
* Correction
* Format with black
* Corrections based on review
* Bumped Thinc dependency version
* Bumped blis requirement
* Correction for older Python versions
* Update spacy/ml/models/textcat.py
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
* Corrections based on review feedback
* Readd deleted docstring line
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
* Add failing test
* Partial fix for issue
This kind of works. The issue with token length mismatches is gone. The
problem is that when you get empty lists of encodings to compare, it
fails because the sizes are not the same, even though they're both zero:
(0, 3) vs (0,). Not sure why that happens...
* Short circuit on empties
* Remove spurious check
The check here isn't needed now the the short circuit is fixed.
* Update spacy/tests/pipeline/test_entity_linker.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Use "eg", not "example"
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Alignment: use a simplified ragged type for performance
This introduces the AlignmentArray type, which is a simplified version
of Ragged that performs better on the simple(r) indexing performed for
alignment.
* AlignmentArray: raise an error when using unsupported index
* AlignmentArray: move error messages to Errors
* AlignmentArray: remove simlified ... with simplifications
* AlignmentArray: fix typo that broke a[n:n] indexing
* Add edit tree lemmatizer
Co-authored-by: Daniël de Kok <me@danieldk.eu>
* Hide edit tree lemmatizer labels
* Use relative imports
* Switch to single quotes in error message
* Type annotation fixes
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Reformat edit_tree_lemmatizer with black
* EditTreeLemmatizer.predict: take Iterable
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Validate edit trees during deserialization
This change also changes the serialized representation. Rather than
mirroring the deep C structure, we use a simple flat union of the match
and substitution node types.
* Move edit_trees to _edit_tree_internals
* Fix invalid edit tree format error message
* edit_tree_lemmatizer: remove outdated TODO comment
* Rename factory name to trainable_lemmatizer
* Ignore type instead of casting truths to List[Union[Ints1d, Floats2d, List[int], List[str]]] for thinc v8.0.14
* Switch to Tagger.v2
* Add documentation for EditTreeLemmatizer
* docs: Fix 3.2 -> 3.3 somewhere
* trainable_lemmatizer documentation fixes
* docs: EditTreeLemmatizer is in edit_tree_lemmatizer.py
Co-authored-by: Daniël de Kok <me@danieldk.eu>
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Tagger: use unnormalized probabilities for inference
Using unnormalized softmax avoids use of the relatively expensive exp function,
which can significantly speed up non-transformer models (e.g. I got a speedup
of 27% on a German tagging + parsing pipeline).
* Add spacy.Tagger.v2 with configurable normalization
Normalization of probabilities is disabled by default to improve
performance.
* Update documentation, models, and tests to spacy.Tagger.v2
* Move Tagger.v1 to spacy-legacy
* docs/architectures: run prettier
* Unnormalized softmax is now a Softmax_v2 option
* Require thinc 8.0.14 and spacy-legacy 3.0.9
* Add save_candidates attribute
* Change spancat api
* Add unit test
* reimplement method to produce a list of doc
* Add method to docs
* Add new version tag
* Add intended use to docstring
* prettier formatting
* Partial fix of entity linker batching
* Add import
* Better name
* Add `use_gold_ents` option, docs
* Change to v2, create stub v1, update docs etc.
* Fix error type
Honestly no idea what the right type to use here is.
ConfigValidationError seems wrong. Maybe a NotImplementedError?
* Make mypy happy
* Add hacky fix for init issue
* Add legacy pipeline entity linker
* Fix references to class name
* Add __init__.py for legacy
* Attempted fix for loss issue
* Remove placeholder V1
* formatting
* slightly more interesting train data
* Handle batches with no usable examples
This adds a test for batches that have docs but not entities, and a
check in the component that detects such cases and skips the update step
as thought the batch were empty.
* Remove todo about data verification
Check for empty data was moved further up so this should be OK now - the
case in question shouldn't be possible.
* Fix gradient calculation
The model doesn't know which entities are not in the kb, so it generates
embeddings for the context of all of them.
However, the loss does know which entities aren't in the kb, and it
ignores them, as there's no sensible gradient.
This has the issue that the gradient will not be calculated for some of
the input embeddings, which causes a dimension mismatch in backprop.
That should have caused a clear error, but with numpyops it was causing
nans to happen, which is another problem that should be addressed
separately.
This commit changes the loss to give a zero gradient for entities not in
the kb.
* add failing test for v1 EL legacy architecture
* Add nasty but simple working check for legacy arch
* Clarify why init hack works the way it does
* Clarify use_gold_ents use case
* Fix use gold ents related handling
* Add tests for no gold ents and fix other tests
* Use aligned ents function (not working)
This doesn't actually work because the "aligned" ents are gold-only. But
if I have a different function that returns the intersection, *then*
this will work as desired.
* Use proper matching ent check
This changes the process when gold ents are not used so that the
intersection of ents in the pred and gold is used.
* Move get_matching_ents to Example
* Use model attribute to check for legacy arch
* Rename flag
* bump spacy-legacy to lower 3.0.9
Co-authored-by: svlandeg <svlandeg@github.com>
* fixing argument order for rehearse
* rehearse test for ner and tagger
* rehearse bugfix
* added test for parser
* test for multilabel textcat
* rehearse fix
* remove debug line
* Update spacy/tests/training/test_rehearse.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update spacy/tests/training/test_rehearse.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Kádár Ákos <akos@onyx.uvt.nl>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Make core projectivization methods cdef nogil
While profiling the parser, I noticed that relatively a lot of time is
spent in projectivization. This change rewrites the functions in the
core loops as cdef nogil for efficiency.
In C++-land, we use vector in place of Python lists and absent heads
are represented as -1 in place of None.
* _heads_to_c: add assertion
Validation should be performed by the caller, but this assertion ensures that
we are not reading/writing out of bounds with incorrect input.
* Auto-format code with black
* add black requirement to dev dependencies and pin to 22.x
* ignore black dependency for comparison with setup.cfg
Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com>
Co-authored-by: svlandeg <svlandeg@github.com>
Instead of the running the actual suggester, which may require
annotation from annotating components that is not necessarily present in
the reference docs, use the built-in 1-gram suggester.
