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
* Handle docs with no entities
If a whole batch contains no entities it won't make it to the model, but
it's possible for individual Docs to have no entities. Before this
commit, those Docs would cause an error when attempting to concatenate
arrays because the dimensions didn't match.
It turns out the process of preparing the Ragged at the end of the span
maker forward was a little different from list2ragged, which just uses
the flatten function directly. Letting list2ragged do the conversion
avoids the dimension issue.
This did not come up before because in NEL demo projects it's typical
for data with no entities to be discarded before it reaches the NEL
component.
This includes a simple direct test that shows the issue and checks it's
resolved. It doesn't check if there are any downstream changes, so a
more complete test could be added. A full run was tested by adding an
example with no entities to the Emerson sample project.
* Add a blank instance to default training data in tests
Rather than adding a specific test, since not failing on instances with
no entities is basic functionality, it makes sense to add it to the
default set.
* Fix without modifying architecture
If the architecture is modified this would have to be a new version, but
this change isn't big enough to merit that.
* Replace EntityRuler with SpanRuler implementation
Remove `EntityRuler` and rename the `SpanRuler`-based
`future_entity_ruler` to `entity_ruler`.
Main changes:
* It is no longer possible to load patterns on init as with
`EntityRuler(patterns=)`.
* The older serialization formats (`patterns.jsonl`) are no longer
supported and the related tests are removed.
* The config settings are only stored in the config, not in the
serialized component (in particular the `phrase_matcher_attr` and
overwrite settings).
* Add migration guide to EntityRuler API docs
* docs update
* Minor edit
Co-authored-by: svlandeg <svlandeg@github.com>
* Remove imports marked as v2 leftovers
There are a few functions that were in `spacy.util` in v2, but were
moved to Thinc. In v3 these were imported in `spacy.util` so that code
could be used unchanged, but the comment over them indicates they should
always be imported from Thinc. This commit removes those imports.
It doesn't look like any DeprecationWarning was ever thrown for using
these, but it is probably fine to remove them anyway with a major
version. It is not clear that they were widely used.
* Import fix_random_seed correctly
This seems to be the only place in spaCy that was using the old import.
* Change enable/disable behavior so that arguments take precedence over config options. Extend error message on conflict. Add warning message in case of overwriting config option with arguments.
* Fix tests in test_serialize_pipeline.py to reflect changes to handling of enable/disable.
* Fix type issue.
* Move comment.
* Move comment.
* Issue UserWarning instead of printing wasabi message. Adjust test.
* Added pytest.warns(UserWarning) for expected warning to fix tests.
* Update warning message.
* Move type handling out of fetch_pipes_status().
* Add global variable for default value. Use id() to determine whether used values are default value.
* Fix default value for disable.
* Rename DEFAULT_PIPE_STATUS to _DEFAULT_EMPTY_PIPES.
* Store activations in Doc when `store_activations` is enabled
This change adds the new `activations` attribute to `Doc`. This
attribute can be used by trainable pipes to store their activations,
probabilities, and guesses for downstream users.
As an example, this change modifies the `tagger` and `senter` pipes to
add an `store_activations` option. When this option is enabled, the
probabilities and guesses are stored in `set_annotations`.
* Change type of `store_activations` to `Union[bool, List[str]]`
When the value is:
- A bool: all activations are stored when set to `True`.
- A List[str]: the activations named in the list are stored
* Formatting fixes in Tagger
* Support store_activations in spancat and morphologizer
* Make Doc.activations type visible to MyPy
* textcat/textcat_multilabel: add store_activations option
* trainable_lemmatizer/entity_linker: add store_activations option
* parser/ner: do not currently support returning activations
* Extend tagger and senter tests
So that they, like the other tests, also check that we get no
activations if no activations were requested.
* Document `Doc.activations` and `store_activations` in the relevant pipes
* Start errors/warnings at higher numbers to avoid merge conflicts
Between the master and v4 branches.
* Add `store_activations` to docstrings.
* Replace store_activations setter by set_store_activations method
Setters that take a different type than what the getter returns are still
problematic for MyPy. Replace the setter by a method, so that type inference
works everywhere.
* Use dict comprehension suggested by @svlandeg
* Revert "Use dict comprehension suggested by @svlandeg"
This reverts commit 6e7b958f70.
* EntityLinker: add type annotations to _add_activations
* _store_activations: make kwarg-only, remove doc_scores_lens arg
* set_annotations: add type annotations
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* TextCat.predict: return dict
* Make the `TrainablePipe.store_activations` property a bool
This means that we can also bring back `store_activations` setter.
* Remove `TrainablePipe.activations`
We do not need to enumerate the activations anymore since `store_activations` is
`bool`.
* Add type annotations for activations in predict/set_annotations
* Rename `TrainablePipe.store_activations` to `save_activations`
* Error E1400 is not used anymore
This error was used when activations were still `Union[bool, List[str]]`.
* Change wording in API docs after store -> save change
* docs: tag (save_)activations as new in spaCy 4.0
* Fix copied line in morphologizer activations test
* Don't train in any test_save_activations test
* Rename activations
- "probs" -> "probabilities"
- "guesses" -> "label_ids", except in the edit tree lemmatizer, where
"guesses" -> "tree_ids".
* Remove unused W400 warning.
This warning was used when we still allowed the user to specify
which activations to save.
* Formatting fixes
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Replace "kb_ids" by a constant
* spancat: replace a cast by an assertion
* Fix EOF spacing
* Fix comments in test_save_activations tests
* Do not set RNG seed in activation saving tests
* Revert "spancat: replace a cast by an assertion"
This reverts commit 0bd5730d16.
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* 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
* adding unit test for spacy.load with disable/exclude string arg
* allow pure strings in from_config
* update docs
* upstream type adjustements
* docs update
* make docstring more consistent
* Update spacy/language.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* two more cleanups
* fix type in internal method
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Enable flag on spacy.load: foundation for include, enable arguments.
* Enable flag on spacy.load: fixed tests.
* Enable flag on spacy.load: switched from pretrained model to empty model with added pipes for tests.
* Enable flag on spacy.load: switched to more consistent error on misspecification of component activity. Test refactoring. Added to default config.
* Enable flag on spacy.load: added support for fields not in pipeline.
* Enable flag on spacy.load: removed serialization fields from supported fields.
* Enable flag on spacy.load: removed 'enable' from config again.
* Enable flag on spacy.load: relaxed checks in _resolve_component_activation_status() to allow non-standard pipes.
* Enable flag on spacy.load: fixed relaxed checks for _resolve_component_activation_status() to allow non-standard pipes. Extended tests.
* Enable flag on spacy.load: comments w.r.t. resolution workarounds.
* Enable flag on spacy.load: remove include fields. Update website docs.
* Enable flag on spacy.load: updates w.r.t. changes in master.
* Implement Doc.from_json(): update docstrings.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): remove newline.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): change error message for E1038.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Enable flag on spacy.load: wrapped docstring for _resolve_component_status() at 80 chars.
* Enable flag on spacy.load: changed exmples for enable flag.
* Remove newline.
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix docstring for Language._resolve_component_status().
* Rename E1038 to E1042.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* 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>
* 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>
* Pipe name override in config: added check with warning, added removal of name override from config, extended tests.
* Pipoe name override in config: added pytest UserWarning.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* add v1 and v2 tests for tok2vec architectures
* textcat architectures are not "layers"
* test older textcat architectures
* test older parser architecture
* 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
* Fix get_matching_ents
Not sure what happened here - the code prior to this commit simply does
not work. It's already covered by entity linker tests, which were
succeeding in the NEL PR, but couldn't possibly succeed on master.
* Fix test
Test was indented inside another test and so doesn't seem to have been
running properly.
* 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>
