spaCy/spacy/vocab.pyx

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# cython: profile=True
from libc.string cimport memcpy
import numpy
2018-12-06 17:53:16 +00:00
import srsly
from thinc.api import get_array_module, get_current_ops
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
import functools
from .lexeme cimport EMPTY_LEXEME, OOV_RANK
from .lexeme cimport Lexeme
2015-01-31 05:38:58 +00:00
from .typedefs cimport attr_t
from .tokens.token cimport Token
Add Lemmatizer and simplify related components (#5848) * Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
2020-08-07 13:27:13 +00:00
from .attrs cimport LANG, ORTH
from .compat import copy_reg
from .errors import Errors
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
from .attrs import intify_attrs, NORM, IS_STOP
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
from .vectors import Vectors, Mode as VectorsMode
from .util import registry
2020-10-03 15:20:18 +00:00
from .lookups import Lookups
2017-10-27 19:07:59 +00:00
from . import util
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
2020-05-19 13:59:14 +00:00
from .lang.norm_exceptions import BASE_NORMS
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
from .lang.lex_attrs import LEX_ATTRS, is_stop, get_lang
2014-12-24 06:42:00 +00:00
def create_vocab(lang, defaults, vectors_name=None):
# If the spacy-lookups-data package is installed, we pre-populate the lookups
# with lexeme data, if available
lex_attrs = {**LEX_ATTRS, **defaults.lex_attr_getters}
# This is messy, but it's the minimal working fix to Issue #639.
lex_attrs[IS_STOP] = functools.partial(is_stop, stops=defaults.stop_words)
# Ensure that getter can be pickled
lex_attrs[LANG] = functools.partial(get_lang, lang=lang)
lex_attrs[NORM] = util.add_lookups(
lex_attrs.get(NORM, LEX_ATTRS[NORM]),
BASE_NORMS,
)
return Vocab(
lex_attr_getters=lex_attrs,
writing_system=defaults.writing_system,
get_noun_chunks=defaults.syntax_iterators.get("noun_chunks"),
vectors_name=vectors_name,
)
cdef class Vocab:
"""A look-up table that allows you to access `Lexeme` objects. The `Vocab`
instance also provides access to the `StringStore`, and owns underlying
C-data that is shared between `Doc` objects.
DOCS: https://spacy.io/api/vocab
"""
Add Lemmatizer and simplify related components (#5848) * Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
2020-08-07 13:27:13 +00:00
def __init__(self, lex_attr_getters=None, strings=tuple(), lookups=None,
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
oov_prob=-20., vectors_name=None, writing_system={},
get_noun_chunks=None, **deprecated_kwargs):
"""Create the vocabulary.
2017-10-27 17:45:19 +00:00
lex_attr_getters (dict): A dictionary mapping attribute IDs to
functions to compute them. Defaults to `None`.
strings (StringStore): StringStore that maps strings to integers, and
vice versa.
lookups (Lookups): Container for large lookup tables and dictionaries.
2020-06-15 12:56:51 +00:00
oov_prob (float): Default OOV probability.
vectors_name (str): Optional name to identify the vectors table.
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) * 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
2021-10-14 13:21:40 +00:00
get_noun_chunks (Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]):
A function that yields base noun phrases used for Doc.noun_chunks.
"""
lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {}
if lookups in (None, True, False):
lookups = Lookups()
self.cfg = {'oov_prob': oov_prob}
self.mem = Pool()
self._by_orth = PreshMap()
self.strings = StringStore()
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self.length = 0
if strings:
for string in strings:
_ = self[string]
self.lex_attr_getters = lex_attr_getters
Add Lemmatizer and simplify related components (#5848) * Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
2020-08-07 13:27:13 +00:00
self.morphology = Morphology(self.strings)
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
self.vectors = Vectors(strings=self.strings, name=vectors_name)
self.lookups = lookups
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 11:42:59 +00:00
self.writing_system = writing_system
self.get_noun_chunks = get_noun_chunks
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Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
property vectors:
def __get__(self):
return self._vectors
def __set__(self, vectors):
for s in vectors.strings:
self.strings.add(s)
self._vectors = vectors
self._vectors.strings = self.strings
@property
def lang(self):
langfunc = None
if self.lex_attr_getters:
langfunc = self.lex_attr_getters.get(LANG, None)
return langfunc("_") if langfunc else ""
def __len__(self):
"""The current number of lexemes stored.
RETURNS (int): The current number of lexemes stored.
"""
return self.length
def add_flag(self, flag_getter, int flag_id=-1):
"""Set a new boolean flag to words in the vocabulary.
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The flag_getter function will be called over the words currently in the
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vocab, and then applied to new words as they occur. You'll then be able
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to access the flag value on each token using token.check_flag(flag_id).
See also: `Lexeme.set_flag`, `Lexeme.check_flag`, `Token.set_flag`,
`Token.check_flag`.
flag_getter (callable): A function `f(str) -> bool`, to get the
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flag value.
flag_id (int): An integer between 1 and 63 (inclusive), specifying
the bit at which the flag will be stored. If -1, the lowest
available bit will be chosen.
RETURNS (int): The integer ID by which the flag value can be checked.
DOCS: https://spacy.io/api/vocab#add_flag
"""
if flag_id == -1:
for bit in range(1, 64):
if bit not in self.lex_attr_getters:
flag_id = bit
break
else:
raise ValueError(Errors.E062)
elif flag_id >= 64 or flag_id < 1:
raise ValueError(Errors.E063.format(value=flag_id))
for lex in self:
lex.set_flag(flag_id, flag_getter(lex.orth_))
self.lex_attr_getters[flag_id] = flag_getter
return flag_id
cdef const LexemeC* get(self, Pool mem, str string) except NULL:
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"""Get a pointer to a `LexemeC` from the lexicon, creating a new
`Lexeme` if necessary using memory acquired from the given pool. If the
pool is the lexicon's own memory, the lexeme is saved in the lexicon.
"""
if string == "":
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return &EMPTY_LEXEME
cdef LexemeC* lex
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cdef hash_t key = self.strings[string]
lex = <LexemeC*>self._by_orth.get(key)
cdef size_t addr
if lex != NULL:
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assert lex.orth in self.strings
if lex.orth != key:
raise KeyError(Errors.E064.format(string=lex.orth,
orth=key, orth_id=string))
return lex
else:
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return self._new_lexeme(mem, string)
cdef const LexemeC* get_by_orth(self, Pool mem, attr_t orth) except NULL:
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"""Get a pointer to a `LexemeC` from the lexicon, creating a new
`Lexeme` if necessary using memory acquired from the given pool. If the
pool is the lexicon's own memory, the lexeme is saved in the lexicon.
"""
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if orth == 0:
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return &EMPTY_LEXEME
cdef LexemeC* lex
lex = <LexemeC*>self._by_orth.get(orth)
if lex != NULL:
return lex
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else:
return self._new_lexeme(mem, self.strings[orth])
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cdef const LexemeC* _new_lexeme(self, Pool mem, str string) except NULL:
# I think this heuristic is bad, and the Vocab should always
# own the lexemes. It avoids weird bugs this way, as it's how the thing
# was originally supposed to work. The best solution to the growing
# memory use is to periodically reset the vocab, which is an action
# that should be up to the user to do (so we don't need to keep track
# of the doc ownership).
# TODO: Change the C API so that the mem isn't passed in here.
mem = self.mem
#if len(string) < 3 or self.length < 10000:
# mem = self.mem
cdef bint is_oov = mem is not self.mem
lex = <LexemeC*>mem.alloc(1, sizeof(LexemeC))
lex.orth = self.strings.add(string)
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lex.length = len(string)
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if self.vectors is not None:
lex.id = self.vectors.key2row.get(lex.orth, OOV_RANK)
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else:
lex.id = OOV_RANK
if self.lex_attr_getters is not None:
for attr, func in self.lex_attr_getters.items():
value = func(string)
if isinstance(value, str):
value = self.strings.add(value)
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
2020-05-19 13:59:14 +00:00
if value is not None:
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Lexeme.set_struct_attr(lex, attr, value)
if not is_oov:
self._add_lex_to_vocab(lex.orth, lex)
if lex == NULL:
raise ValueError(Errors.E085.format(string=string))
return lex
cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex) except -1:
self._by_orth.set(lex.orth, <void*>lex)
self.length += 1
def __contains__(self, key):
"""Check whether the string or int key has an entry in the vocabulary.
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string (str): The ID string.
RETURNS (bool) Whether the string has an entry in the vocabulary.
DOCS: https://spacy.io/api/vocab#contains
"""
cdef hash_t int_key
if isinstance(key, bytes):
int_key = self.strings[key.decode("utf8")]
elif isinstance(key, str):
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int_key = self.strings[key]
else:
int_key = key
lex = self._by_orth.get(int_key)
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return lex is not NULL
def __iter__(self):
"""Iterate over the lexemes in the vocabulary.
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YIELDS (Lexeme): An entry in the vocabulary.
DOCS: https://spacy.io/api/vocab#iter
"""
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cdef attr_t key
cdef size_t addr
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for key, addr in self._by_orth.items():
lex = Lexeme(self, key)
yield lex
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def __getitem__(self, id_or_string):
"""Retrieve a lexeme, given an int ID or a unicode string. If a
previously unseen unicode string is given, a new lexeme is created and
stored.
id_or_string (int or str): The integer ID of a word, or its unicode
string. If `int >= Lexicon.size`, `IndexError` is raised. If
`id_or_string` is neither an int nor a unicode string, `ValueError`
is raised.
RETURNS (Lexeme): The lexeme indicated by the given ID.
EXAMPLE:
>>> apple = nlp.vocab.strings["apple"]
>>> assert nlp.vocab[apple] == nlp.vocab[u"apple"]
DOCS: https://spacy.io/api/vocab#getitem
"""
cdef attr_t orth
if isinstance(id_or_string, str):
orth = self.strings.add(id_or_string)
else:
orth = id_or_string
return Lexeme(self, orth)
cdef const TokenC* make_fused_token(self, substrings) except NULL:
cdef int i
tokens = <TokenC*>self.mem.alloc(len(substrings) + 1, sizeof(TokenC))
for i, props in enumerate(substrings):
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props = intify_attrs(props, strings_map=self.strings,
_do_deprecated=True)
token = &tokens[i]
# Set the special tokens up to have arbitrary attributes
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lex = <LexemeC*>self.get_by_orth(self.mem, props[ORTH])
token.lex = lex
for attr_id, value in props.items():
Token.set_struct_attr(token, attr_id, value)
# NORM is the only one that overlaps between the two
# (which is maybe not great?)
if attr_id != NORM:
Lexeme.set_struct_attr(lex, attr_id, value)
return tokens
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@property
def vectors_length(self):
return self.vectors.shape[1]
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def reset_vectors(self, *, width=None, shape=None):
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"""Drop the current vector table. Because all vectors must be the same
width, you have to call this to change the size of the vectors.
"""
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if width is not None and shape is not None:
raise ValueError(Errors.E065.format(width=width, shape=shape))
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elif shape is not None:
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
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self.vectors = Vectors(strings=self.strings, shape=shape)
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else:
width = width if width is not None else self.vectors.shape[1]
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
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self.vectors = Vectors(strings=self.strings, shape=(self.vectors.shape[0], width))
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def deduplicate_vectors(self):
if self.vectors.mode != VectorsMode.default:
raise ValueError(Errors.E858.format(
mode=self.vectors.mode,
alternative=""
))
ops = get_current_ops()
xp = get_array_module(self.vectors.data)
filled = xp.asarray(
sorted(list({row for row in self.vectors.key2row.values()}))
)
# deduplicate data and remap keys
data = numpy.unique(ops.to_numpy(self.vectors.data[filled]), axis=0)
data = ops.asarray(data)
if data.shape == self.vectors.data.shape:
# nothing to deduplicate
return
row_by_bytes = {row.tobytes(): i for i, row in enumerate(data)}
key2row = {
key: row_by_bytes[self.vectors.data[row].tobytes()]
for key, row in self.vectors.key2row.items()
}
# replace vectors with deduplicated version
self.vectors = Vectors(strings=self.strings, data=data, name=self.vectors.name)
for key, row in key2row.items():
self.vectors.add(key, row=row)
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def prune_vectors(self, nr_row, batch_size=1024):
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"""Reduce the current vector table to `nr_row` unique entries. Words
mapped to the discarded vectors will be remapped to the closest vector
among those remaining.
For example, suppose the original table had vectors for the words:
['sat', 'cat', 'feline', 'reclined']. If we prune the vector table to
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two rows, we would discard the vectors for 'feline' and 'reclined'.
These words would then be remapped to the closest remaining vector
-- so "feline" would have the same vector as "cat", and "reclined"
would have the same vector as "sat".
The similarities are judged by cosine. The original vectors may
be large, so the cosines are calculated in minibatches, to reduce
memory usage.
nr_row (int): The number of rows to keep in the vector table.
batch_size (int): Batch of vectors for calculating the similarities.
Larger batch sizes might be faster, while temporarily requiring
more memory.
RETURNS (dict): A dictionary keyed by removed words mapped to
`(string, score)` tuples, where `string` is the entry the removed
word was mapped to, and `score` the similarity score between the
two words.
DOCS: https://spacy.io/api/vocab#prune_vectors
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"""
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
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if self.vectors.mode != VectorsMode.default:
raise ValueError(Errors.E858.format(
mode=self.vectors.mode,
alternative=""
))
ops = get_current_ops()
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xp = get_array_module(self.vectors.data)
# Make sure all vectors are in the vocab
for orth in self.vectors:
self[orth]
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# Make prob negative so it sorts by rank ascending
# (key2row contains the rank)
priority = [(-lex.prob, self.vectors.key2row[lex.orth], lex.orth)
for lex in self if lex.orth in self.vectors.key2row]
priority.sort()
indices = xp.asarray([i for (prob, i, key) in priority], dtype="uint64")
keys = xp.asarray([key for (prob, i, key) in priority], dtype="uint64")
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keep = xp.ascontiguousarray(self.vectors.data[indices[:nr_row]])
toss = xp.ascontiguousarray(self.vectors.data[indices[nr_row:]])
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
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self.vectors = Vectors(strings=self.strings, data=keep, keys=keys[:nr_row], name=self.vectors.name)
💫 Port master changes over to develop (#2979) * Create aryaprabhudesai.md (#2681) * Update _install.jade (#2688) Typo fix: "models" -> "model" * Add FAC to spacy.explain (resolves #2706) * Remove docstrings for deprecated arguments (see #2703) * When calling getoption() in conftest.py, pass a default option (#2709) * When calling getoption() in conftest.py, pass a default option This is necessary to allow testing an installed spacy by running: pytest --pyargs spacy * Add contributor agreement * update bengali token rules for hyphen and digits (#2731) * Less norm computations in token similarity (#2730) * Less norm computations in token similarity * Contributor agreement * Remove ')' for clarity (#2737) Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know. * added contributor agreement for mbkupfer (#2738) * Basic support for Telugu language (#2751) * Lex _attrs for polish language (#2750) * Signed spaCy contributor agreement * Added polish version of english lex_attrs * Introduces a bulk merge function, in order to solve issue #653 (#2696) * Fix comment * Introduce bulk merge to increase performance on many span merges * Sign contributor agreement * Implement pull request suggestions * Describe converters more explicitly (see #2643) * Add multi-threading note to Language.pipe (resolves #2582) [ci skip] * Fix formatting * Fix dependency scheme docs (closes #2705) [ci skip] * Don't set stop word in example (closes #2657) [ci skip] * Add words to portuguese language _num_words (#2759) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Update Indonesian model (#2752) * adding e-KTP in tokenizer exceptions list * add exception token * removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception * add tokenizer exceptions list * combining base_norms with norm_exceptions * adding norm_exception * fix double key in lemmatizer * remove unused import on punctuation.py * reformat stop_words to reduce number of lines, improve readibility * updating tokenizer exception * implement is_currency for lang/id * adding orth_first_upper in tokenizer_exceptions * update the norm_exception list * remove bunch of abbreviations * adding contributors file * Fixed spaCy+Keras example (#2763) * bug fixes in keras example * created contributor agreement * Adding French hyphenated first name (#2786) * Fix typo (closes #2784) * Fix typo (#2795) [ci skip] Fixed typo on line 6 "regcognizer --> recognizer" * Adding basic support for Sinhala language. (#2788) * adding Sinhala language package, stop words, examples and lex_attrs. * Adding contributor agreement * Updating contributor agreement * Also include lowercase norm exceptions * Fix error (#2802) * Fix error ValueError: cannot resize an array that references or is referenced by another array in this way. Use the resize function * added spaCy Contributor Agreement * Add charlax's contributor agreement (#2805) * agreement of contributor, may I introduce a tiny pl languge contribution (#2799) * Contributors agreement * Contributors agreement * Contributors agreement * Add jupyter=True to displacy.render in documentation (#2806) * Revert "Also include lowercase norm exceptions" This reverts commit 70f4e8adf37cfcfab60be2b97d6deae949b30e9e. * Remove deprecated encoding argument to msgpack * Set up dependency tree pattern matching skeleton (#2732) * Fix bug when too many entity types. Fixes #2800 * Fix Python 2 test failure * Require older msgpack-numpy * Restore encoding arg on msgpack-numpy * Try to fix version pin for msgpack-numpy * Update Portuguese Language (#2790) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols * Extended punctuation and norm_exceptions in the Portuguese language * Correct error in spacy universe docs concerning spacy-lookup (#2814) * Update Keras Example for (Parikh et al, 2016) implementation (#2803) * bug fixes in keras example * created contributor agreement * baseline for Parikh model * initial version of parikh 2016 implemented * tested asymmetric models * fixed grevious error in normalization * use standard SNLI test file * begin to rework parikh example * initial version of running example * start to document the new version * start to document the new version * Update Decompositional Attention.ipynb * fixed calls to similarity * updated the README * import sys package duh * simplified indexing on mapping word to IDs * stupid python indent error * added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround * Fix typo (closes #2815) [ci skip] * Update regex version dependency * Set version to 2.0.13.dev3 * Skip seemingly problematic test * Remove problematic test * Try previous version of regex * Revert "Remove problematic test" This reverts commit bdebbef45552d698d390aa430b527ee27830f11b. * Unskip test * Try older version of regex * 💫 Update training examples and use minibatching (#2830) <!--- Provide a general summary of your changes in the title. --> ## Description Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results. ### Types of change enhancements ## 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. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Visual C++ link updated (#2842) (closes #2841) [ci skip] * New landing page * Add contribution agreement * Correcting lang/ru/examples.py (#2845) * Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement * Correct some grammatical inaccuracies in lang\ru\examples.py * Move contributor agreement to separate file * Set version to 2.0.13.dev4 * Add Persian(Farsi) language support (#2797) * Also include lowercase norm exceptions * Remove in favour of https://github.com/explosion/spaCy/graphs/contributors * Rule-based French Lemmatizer (#2818) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class. ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> - Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version. - Add several files containing exhaustive list of words for each part of speech - Add some lemma rules - Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX - Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned - Modify the lemmatize function to check in lookup table as a last resort - Init files are updated so the model can support all the functionalities mentioned above - Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py ## 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. - [X] My changes don't require a change to the documentation, or if they do, I've added all required information. * Set version to 2.0.13 * Fix formatting and consistency * Update docs for new version [ci skip] * Increment version [ci skip] * Add info on wheels [ci skip] * Adding "This is a sentence" example to Sinhala (#2846) * Add wheels badge * Update badge [ci skip] * Update README.rst [ci skip] * Update murmurhash pin * Increment version to 2.0.14.dev0 * Update GPU docs for v2.0.14 * Add wheel to setup_requires * Import prefer_gpu and require_gpu functions from Thinc * Add tests for prefer_gpu() and require_gpu() * Update requirements and setup.py * Workaround bug in thinc require_gpu * Set version to v2.0.14 * Update push-tag script * Unhack prefer_gpu * Require thinc 6.10.6 * Update prefer_gpu and require_gpu docs [ci skip] * Fix specifiers for GPU * Set version to 2.0.14.dev1 * Set version to 2.0.14 * Update Thinc version pin * Increment version * Fix msgpack-numpy version pin * Increment version * Update version to 2.0.16 * Update version [ci skip] * Redundant ')' in the Stop words' example (#2856) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [ ] I have submitted the spaCy Contributor Agreement. - [ ] 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. * Documentation improvement regarding joblib and SO (#2867) Some documentation improvements ## Description 1. Fixed the dead URL to joblib 2. Fixed Stack Overflow brand name (with space) ### Types of change Documentation ## 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. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * raise error when setting overlapping entities as doc.ents (#2880) * Fix out-of-bounds access in NER training The helper method state.B(1) gets the index of the first token of the buffer, or -1 if no such token exists. Normally this is safe because we pass this to functions like state.safe_get(), which returns an empty token. Here we used it directly as an array index, which is not okay! This error may have been the cause of out-of-bounds access errors during training. Similar errors may still be around, so much be hunted down. Hunting this one down took a long time...I printed out values across training runs and diffed, looking for points of divergence between runs, when no randomness should be allowed. * Change PyThaiNLP Url (#2876) * Fix missing comma * Add example showing a fix-up rule for space entities * Set version to 2.0.17.dev0 * Update regex version * Revert "Update regex version" This reverts commit 62358dd867d15bc6a475942dff34effba69dd70a. * Try setting older regex version, to align with conda * Set version to 2.0.17 * Add spacy-js to universe [ci-skip] * Add spacy-raspberry to universe (closes #2889) * Add script to validate universe json [ci skip] * Removed space in docs + added contributor indo (#2909) * - removed unneeded space in documentation * - added contributor info * Allow input text of length up to max_length, inclusive (#2922) * Include universe spec for spacy-wordnet component (#2919) * feat: include universe spec for spacy-wordnet component * chore: include spaCy contributor agreement * Minor formatting changes [ci skip] * Fix image [ci skip] Twitter URL doesn't work on live site * Check if the word is in one of the regular lists specific to each POS (#2886) * 💫 Create random IDs for SVGs to prevent ID clashes (#2927) Resolves #2924. ## Description Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.) ### Types of change bug fix ## 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. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix typo [ci skip] * fixes symbolic link on py3 and windows (#2949) * fixes symbolic link on py3 and windows during setup of spacy using command python -m spacy link en_core_web_sm en closes #2948 * Update spacy/compat.py Co-Authored-By: cicorias <cicorias@users.noreply.github.com> * Fix formatting * Update universe [ci skip] * Catalan Language Support (#2940) * Catalan language Support * Ddding Catalan to documentation * Sort languages alphabetically [ci skip] * Update tests for pytest 4.x (#2965) <!--- Provide a general summary of your changes in the title. --> ## Description - [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize)) - [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here) ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## 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. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix regex pin to harmonize with conda (#2964) * Update README.rst * Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977) Fixes #2976 * Fix typo * Fix typo * Remove duplicate file * Require thinc 7.0.0.dev2 Fixes bug in gpu_ops that would use cupy instead of numpy on CPU * Add missing import * Fix error IDs * Fix tests
2018-11-29 15:30:29 +00:00
syn_keys, syn_rows, scores = self.vectors.most_similar(toss, batch_size=batch_size)
syn_keys = ops.to_numpy(syn_keys)
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remap = {}
for i, key in enumerate(ops.to_numpy(keys[nr_row:])):
self.vectors.add(key, row=syn_rows[i][0])
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word = self.strings[key]
synonym = self.strings[syn_keys[i][0]]
score = scores[i][0]
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remap[word] = (synonym, score)
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return remap
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Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
def get_vector(self, orth):
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"""Retrieve a vector for a word in the vocabulary. Words can be looked
up by string or int ID. If the current vectors do not contain an entry
for the word, a 0-vector with the same number of dimensions as the
current vectors is returned.
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
orth (int / unicode): The hash value of a word, or its unicode string.
RETURNS (numpy.ndarray or cupy.ndarray): A word vector. Size
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and shape determined by the `vocab.vectors` instance. Usually, a
numpy ndarray of shape (300,) and dtype float32.
DOCS: https://spacy.io/api/vocab#get_vector
2017-05-28 09:45:32 +00:00
"""
if isinstance(orth, str):
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orth = self.strings.add(orth)
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
if self.has_vector(orth):
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return self.vectors[orth]
xp = get_array_module(self.vectors.data)
vectors = xp.zeros((self.vectors_length,), dtype="f")
return vectors
2017-05-28 09:45:32 +00:00
2017-05-30 21:34:50 +00:00
def set_vector(self, orth, vector):
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"""Set a vector for a word in the vocabulary. Words can be referenced
by string or int ID.
orth (int / str): The word.
vector (numpy.ndarray or cupy.nadarry[ndim=1, dtype='float32']): The vector to set.
DOCS: https://spacy.io/api/vocab#set_vector
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"""
if isinstance(orth, str):
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orth = self.strings.add(orth)
if self.vectors.is_full and orth not in self.vectors:
new_rows = max(100, int(self.vectors.shape[0]*1.3))
if self.vectors.shape[1] == 0:
width = vector.size
else:
width = self.vectors.shape[1]
self.vectors.resize((new_rows, width))
lex = self[orth] # Add word to vocab if necessary
row = self.vectors.add(orth, vector=vector)
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
if row >= 0:
lex.rank = row
2017-05-30 21:34:50 +00:00
2017-05-28 09:45:32 +00:00
def has_vector(self, orth):
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"""Check whether a word has a vector. Returns False if no vectors have
been loaded. Words can be looked up by string or int ID.
orth (int / str): The word.
RETURNS (bool): Whether the word has a vector.
DOCS: https://spacy.io/api/vocab#has_vector
"""
if isinstance(orth, str):
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orth = self.strings.add(orth)
return orth in self.vectors
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property lookups:
def __get__(self):
return self._lookups
def __set__(self, lookups):
self._lookups = lookups
if lookups.has_table("lexeme_norm"):
self.lex_attr_getters[NORM] = util.add_lookups(
self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]),
self.lookups.get_table("lexeme_norm"),
)
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def to_disk(self, path, *, exclude=tuple()):
"""Save the current state to a directory.
path (str or Path): A path to a directory, which will be created if
it doesn't exist.
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) * 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
2021-10-14 13:21:40 +00:00
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/vocab#to_disk
"""
path = util.ensure_path(path)
if not path.exists():
path.mkdir()
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
2020-05-19 13:59:14 +00:00
setters = ["strings", "vectors"]
if "strings" not in exclude:
self.strings.to_disk(path / "strings.json")
if "vectors" not in exclude:
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
self.vectors.to_disk(path, exclude=["strings"])
if "lookups" not in exclude:
self.lookups.to_disk(path)
2020-07-29 13:14:07 +00:00
def from_disk(self, path, *, exclude=tuple()):
"""Loads state from a directory. Modifies the object in place and
returns it.
path (str or Path): A path to a directory.
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) * 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
2021-10-14 13:21:40 +00:00
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Vocab): The modified `Vocab` object.
DOCS: https://spacy.io/api/vocab#to_disk
"""
path = util.ensure_path(path)
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
2020-05-19 13:59:14 +00:00
getters = ["strings", "vectors"]
if "strings" not in exclude:
self.strings.from_disk(path / "strings.json") # TODO: add exclude?
if "vectors" not in exclude:
if self.vectors is not None:
self.vectors.from_disk(path, exclude=["strings"])
if "lookups" not in exclude:
self.lookups.from_disk(path)
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
2020-05-19 13:59:14 +00:00
if "lexeme_norm" in self.lookups:
self.lex_attr_getters[NORM] = util.add_lookups(
self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), self.lookups.get_table("lexeme_norm")
)
self.length = 0
self._by_orth = PreshMap()
2017-05-28 21:34:12 +00:00
return self
2016-11-01 11:25:36 +00:00
2020-07-29 13:14:07 +00:00
def to_bytes(self, *, exclude=tuple()):
"""Serialize the current state to a binary string.
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) * 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
2021-10-14 13:21:40 +00:00
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized form of the `Vocab` object.
DOCS: https://spacy.io/api/vocab#to_bytes
"""
2017-08-18 18:46:56 +00:00
def deserialize_vectors():
if self.vectors is None:
return None
else:
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
2021-10-27 12:08:31 +00:00
return self.vectors.to_bytes(exclude=["strings"])
getters = {
"strings": lambda: self.strings.to_bytes(),
"vectors": deserialize_vectors,
2020-05-21 16:39:06 +00:00
"lookups": lambda: self.lookups.to_bytes(),
}
2017-05-29 08:14:20 +00:00
return util.to_bytes(getters, exclude)
2020-07-29 13:14:07 +00:00
def from_bytes(self, bytes_data, *, exclude=tuple()):
"""Load state from a binary string.
bytes_data (bytes): The data to load from.
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) * 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
2021-10-14 13:21:40 +00:00
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Vocab): The `Vocab` object.
DOCS: https://spacy.io/api/vocab#from_bytes
"""
2017-08-18 18:46:56 +00:00
def serialize_vectors(b):
if self.vectors is None:
return None
else:
Add support for floret vectors (#8909) * Add support for fasttext-bloom hash-only vectors Overview: * Extend `Vectors` to have two modes: `default` and `ngram` * `default` is the default mode and equivalent to the current `Vectors` * `ngram` supports the hash-only ngram tables from `fasttext-bloom` * Extend `spacy.StaticVectors.v2` to handle both modes with no changes for `default` vectors * Extend `spacy init vectors` to support ngram tables The `ngram` mode **only** supports vector tables produced by this fork of fastText, which adds an option to represent all vectors using only the ngram buckets table and which uses the exact same ngram generation algorithm and hash function (`MurmurHash3_x64_128`). `fasttext-bloom` produces an additional `.hashvec` table, which can be loaded by `spacy init vectors --fasttext-bloom-vectors`. https://github.com/adrianeboyd/fastText/tree/feature/bloom Implementation details: * `Vectors` now includes the `StringStore` as `Vectors.strings` so that the API can stay consistent for both `default` (which can look up from `str` or `int`) and `ngram` (which requires `str` to calculate the ngrams). * In ngram mode `Vectors` uses a default `Vectors` object as a cache since the ngram vectors lookups are relatively expensive. * The default cache size is the same size as the provided ngram vector table. * Once the cache is full, no more entries are added. The user is responsible for managing the cache in cases where the initial documents are not representative of the texts. * The cache can be resized by setting `Vectors.ngram_cache_size` or cleared with `vectors._ngram_cache.clear()`. * The API ends up a bit split between methods for `default` and for `ngram`, so functions that only make sense for `default` or `ngram` include warnings with custom messages suggesting alternatives where possible. * `Vocab.vectors` becomes a property so that the string stores can be synced when assigning vectors to a vocab. * `Vectors` serializes its own config settings as `vectors.cfg`. * The `Vectors` serialization methods have added support for `exclude` so that the `Vocab` can exclude the `Vectors` strings while serializing. Removed: * The `minn` and `maxn` options and related code from `Vocab.get_vector`, which does not work in a meaningful way for default vector tables. * The unused `GlobalRegistry` in `Vectors`. * Refactor to use reduce_mean Refactor to use reduce_mean and remove the ngram vectors cache. * Rename to floret * Rename to floret in error messages * Use --vectors-mode in CLI, vector init * Fix vectors mode in init * Remove unused var * Minor API and docstrings adjustments * Rename `--vectors-mode` to `--mode` in `init vectors` CLI * Rename `Vectors.get_floret_vectors` to `Vectors.get_batch` and support both modes. * Minor updates to Vectors docstrings. * Update API docs for Vectors and init vectors CLI * Update types for StaticVectors
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return self.vectors.from_bytes(b, exclude=["strings"])
setters = {
"strings": lambda b: self.strings.from_bytes(b),
"vectors": lambda b: serialize_vectors(b),
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"lookups": lambda b: self.lookups.from_bytes(b),
}
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util.from_bytes(bytes_data, setters, exclude)
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
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if "lexeme_norm" in self.lookups:
self.lex_attr_getters[NORM] = util.add_lookups(
self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), self.lookups.get_table("lexeme_norm")
)
self.length = 0
self._by_orth = PreshMap()
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return self
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def _reset_cache(self, keys, strings):
# I'm not sure this made sense. Disable it for now.
raise NotImplementedError
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def pickle_vocab(vocab):
sstore = vocab.strings
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vectors = vocab.vectors
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morph = vocab.morphology
_unused_object = vocab._unused_object
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lex_attr_getters = srsly.pickle_dumps(vocab.lex_attr_getters)
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
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lookups = vocab.lookups
get_noun_chunks = vocab.get_noun_chunks
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return (unpickle_vocab,
(sstore, vectors, morph, _unused_object, lex_attr_getters, lookups, get_noun_chunks))
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def unpickle_vocab(sstore, vectors, morphology, _unused_object,
lex_attr_getters, lookups, get_noun_chunks):
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cdef Vocab vocab = Vocab()
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vocab.vectors = vectors
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vocab.strings = sstore
vocab.morphology = morphology
vocab._unused_object = _unused_object
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vocab.lex_attr_getters = srsly.pickle_loads(lex_attr_getters)
Reduce stored lexemes data, move feats to lookups (#5238) * 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>
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vocab.lookups = lookups
vocab.get_noun_chunks = get_noun_chunks
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return vocab
copy_reg.pickle(Vocab, pickle_vocab, unpickle_vocab)