* 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>
* 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
* 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.
This change changes the type of left/right-arc collections from
vector[ArcC] to unordered_map[int, vector[Arc]], so that the arcs are
keyed by the head. This allows us to find all the left/right arcs for a
particular head in constant time in StateC::{L,R}.
Benchmarks with long docs (N is the number of text repetitions):
Before (using #10019):
N Time (s)
400 3.2
800 5.0
1600 9.5
3200 23.2
6400 66.8
12800 220.0
After (this commit):
N Time (s)
400 3.1
800 4.3
1600 6.7
3200 12.0
6400 22.0
12800 42.0
Related to #9858 and #10019.
* Speed up the StateC::L feature function
This function gets the n-th most-recent left-arc with a particular head.
Before this change, StateC::L would construct a vector of all left-arcs
with the given head and then pick the n-th most recent from that vector.
Since the number of left-arcs strongly correlates with the doc length
and the feature is constructed for every transition, this can make
transition-parsing quadratic.
With this change StateC::L:
- Searches left-arcs backwards.
- Stops early when the n-th matching transition is found.
- Does not construct a vector (reducing memory pressure).
This change doesn't avoid the linear search when the transition that is
queried does not occur in the left-arcs. Regardless, performance is
improved quite a bit with very long docs:
Before:
N Time
400 3.3
800 5.4
1600 11.6
3200 30.7
After:
N Time
400 3.2
800 5.0
1600 9.5
3200 23.2
We can probably do better with more tailored data structures, but I
first wanted to make a low-impact PR.
Found while investigating #9858.
* StateC::L: simplify loop
* Span/SpanGroup: wrap SpanC in shared_ptr
When a Span that was retrieved from a SpanGroup was modified, these
changes were not reflected in the SpanGroup because the underlying
SpanC struct was copied.
This change applies the solution proposed by @nrodnova, to wrap SpanC in
a shared_ptr. This makes a SpanGroup and Spans derived from it share the
same SpanC. So, changes made through a Span are visible in the SpanGroup
as well.
Fixes#9556
* Test that a SpanGroup is modified through its Spans
* SpanGroup.push_back: remove nogil
Modifying std::vector is not thread-safe.
* C++ >= 11 does not allow const T in vector<T>
* Add Span.span_c as a shorthand for Span.c.get
Since this method is cdef'ed, it is only visible from Cython, so we
avoid using raw pointers in Python
Replace existing uses of span.c.get() to use this new method.
* Fix formatting
* Style fix: pointer types
* SpanGroup.to_bytes: reduce number of shared_ptr::get calls
* Mark SpanGroup modification test with issue
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Replace all basestring references with unicode
`basestring` was a compatability type introduced by Cython to make
dealing with utf-8 strings in Python2 easier. In Python3 it is
equivalent to the unicode (or str) type.
I replaced all references to basestring with unicode, since that was
used elsewhere, but we could also just replace them with str, which
shoudl also be equivalent.
All tests pass locally.
* Replace all references to unicode type with str
Since we only support python3 this is simpler.
* Remove all references to unicode type
This removes all references to the unicode type across the codebase and
replaces them with `str`, which makes it more drastic than the prior
commits. In order to make this work importing `unicode_literals` had to
be removed, and one explicit unicode literal also had to be removed (it
is unclear why this is necessary in Cython with language level 3, but
without doing it there were errors about implicit conversion).
When `unicode` is used as a type in comments it was also edited to be
`str`.
Additionally `coding: utf8` headers were removed from a few files.
* Support a cfg field in transition system
* Make NER 'has gold' check use right alignment for span
* Pass 'negative_samples_key' property into NER transition system
* Add field for negative samples to NER transition system
* Check neg_key in NER has_gold
* Support negative examples in NER oracle
* Test for negative examples in NER
* Fix name of config variable in NER
* Remove vestiges of old-style partial annotation
* Remove obsolete tests
* Add comment noting lack of support for negative samples in parser
* Additions to "neg examples" PR (#8201)
* add custom error and test for deprecated format
* add test for unlearning an entity
* add break also for Begin's cost
* add negative_samples_key property on Parser
* rename
* extend docs & fix some older docs issues
* add subclass constructors, clean up tests, fix docs
* add flaky test with ValueError if gold parse was not found
* remove ValueError if n_gold == 0
* fix docstring
* Hack in environment variables to try out training
* Remove hack
* Remove NER hack, and support 'negative O' samples
* Fix O oracle
* Fix transition parser
* Remove 'not O' from oracle
* Fix NER oracle
* check for spans in both gold.ents and gold.spans and raise if so, to prevent memory access violation
* use set instead of list in consistency check
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Preserve existing ENT_KB_ID annotation in NER
Preserve `ent_kb_id` annotation on existing entity spans, which is not
preserved by the transition system.
* Simplify kb_id assignment
* Simplify further
* Add test for #7035
* Update test for issue 7056
* Fix test
* Fix transitions method used in testing
* Fix state eol detection when rebuffer
* Clean up redundant fix
* clean up of ner tests
* beam_parser tests
* implement get_beam_parses and scored_parses for the dep parser
* we don't have to add the parse if there are no arcs
* small fixes and formatting
* bring test_issue4313 up-to-date, currently fails
* formatting
* add get_beam_parses method back
* add scored_ents function
* delete tag map
* Get basic beam tests working
* Get basic beam tests working
* Compile _beam_utils
* Remove prints
* Test beam density
* Beam parser seems to train
* Draft beam NER
* Upd beam
* Add hypothesis as dev dependency
* Implement missing is-gold-parse method
* Implement early update
* Fix state hashing
* Fix test
* Fix test
* Default to non-beam in parser constructor
* Improve oracle for beam
* Start refactoring beam
* Update test
* Refactor beam
* Update nn
* Refactor beam and weight by cost
* Update ner beam settings
* Update test
* Add __init__.pxd
* Upd test
* Fix test
* Upd test
* Fix test
* Remove ring buffer history from StateC
* WIP change arc-eager transitions
* Add state tests
* Support ternary sent start values
* Fix arc eager
* Fix NER
* Pass oracle cut size for beam
* Fix ner test
* Fix beam
* Improve StateC.clone
* Improve StateClass.borrow
* Work directly with StateC, not StateClass
* Remove print statements
* Fix state copy
* Improve state class
* Refactor parser oracles
* Fix arc eager oracle
* Fix arc eager oracle
* Use a vector to implement the stack
* Refactor state data structure
* Fix alignment of sent start
* Add get_aligned_sent_starts method
* Add test for ae oracle when bad sentence starts
* Fix sentence segment handling
* Avoid Reduce that inserts illegal sentence
* Update preset SBD test
* Fix test
* Remove prints
* Fix sent starts in Example
* Improve python API of StateClass
* Tweak comments and debug output of arc eager
* Upd test
* Fix state test
* Fix state test
* Refactor Docs.is_ flags
* Add derived `Doc.has_annotation` method
* `Doc.has_annotation(attr)` returns `True` for partial annotation
* `Doc.has_annotation(attr, require_complete=True)` returns `True` for
complete annotation
* Add deprecation warnings to `is_tagged`, `is_parsed`, `is_sentenced`
and `is_nered`
* Add `Doc._get_array_attrs()`, which returns a full list of `Doc` attrs
for use with `Doc.to_array`, `Doc.to_bytes` and `Doc.from_docs`. The
list is the `DocBin` attributes list plus `SPACY` and `LENGTH`.
Notes on `Doc.has_annotation`:
* `HEAD` is converted to `DEP` because heads don't have an unset state
* Accept `IS_SENT_START` as a synonym of `SENT_START`
Additional changes:
* Add `NORM`, `ENT_ID` and `SENT_START` to default attributes for
`DocBin`
* In `Doc.from_array()` the presence of `DEP` causes `HEAD` to override
`SENT_START`
* In `Doc.from_array()` using `attrs` other than
`Doc._get_array_attrs()` (i.e., a user's custom list rather than our
default internal list) with both `HEAD` and `SENT_START` shows a warning
that `HEAD` will override `SENT_START`
* `set_children_from_heads` does not require dependency labels to set
sentence boundaries and sets `sent_start` for all non-sentence starts to
`-1`
* Fix call to set_children_form_heads
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Clean up spacy.tokens
* Update `set_children_from_heads`:
* Don't check `dep` when setting lr_* or sentence starts
* Set all non-sentence starts to `False`
* Use `set_children_from_heads` in `Token.head` setter
* Reduce similar/duplicate code (admittedly adds a bit of overhead)
* Update sentence starts consistently
* Remove unused `Doc.set_parse`
* Minor changes:
* Declare cython variables (to avoid cython warnings)
* Clean up imports
* Modify set_children_from_heads to set token range
Modify `set_children_from_heads` so that it adjust tokens within a
specified range rather then the whole document.
Modify the `Token.head` setter to adjust only the tokens affected by the
new head assignment.
Follow-ups to the parser efficiency fix.
* Avoid introducing new counter for number of pushes
* Base cut on number of transitions, keeping it more even
* Reintroduce the randomization we had in v2.
The parser training makes use of a trick for long documents, where we
use the oracle to cut up the document into sections, so that we can have
batch items in the middle of a document. For instance, if we have one
document of 600 words, we might make 6 states, starting at words 0, 100,
200, 300, 400 and 500.
The problem is for v3, I screwed this up and didn't stop parsing! So
instead of a batch of [100, 100, 100, 100, 100, 100], we'd have a batch
of [600, 500, 400, 300, 200, 100]. Oops.
The implementation here could probably be improved, it's annoying to
have this extra variable in the state. But this'll do.
This makes the v3 parser training 5-10 times faster, depending on document
lengths. This problem wasn't in v2.
* moving syntax folder to _parser_internals
* moving nn_parser and transition_system
* move nn_parser and transition_system out of internals folder
* moving nn_parser code into transition_system file
* rename transition_system to transition_parser
* moving parser_model and _state to ml
* move _state back to internals
* The Parser now inherits from Pipe!
* small code fixes
* removing unnecessary imports
* remove link_vectors_to_models
* transition_system to internals folder
* little bit more cleanup
* newlines
