spaCy/spacy/ml/models/entity_linker.py

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2020-10-12 09:41:27 +00:00
from pathlib import Path
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
from typing import Optional, Callable, Iterable, List, Tuple
🏷 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
from thinc.types import Floats2d
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 17:42:27 +00:00
from thinc.api import chain, clone, list2ragged, reduce_mean, residual
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
from thinc.api import Model, Maxout, Linear, noop, tuplify, Ragged
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 17:42:27 +00:00
2020-02-28 10:57:41 +00:00
from ...util import registry
from ...kb import KnowledgeBase, Candidate, get_candidates
from ...vocab import Vocab
🏷 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
from ...tokens import Span, Doc
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
from ..extract_spans import extract_spans
from ...errors import Errors
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 17:42:27 +00:00
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
@registry.architectures("spacy.EntityLinker.v2")
🏷 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
def build_nel_encoder(
tok2vec: Model, nO: Optional[int] = None
) -> Model[List[Doc], Floats2d]:
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
with Model.define_operators({">>": chain, "&": tuplify}):
token_width = tok2vec.maybe_get_dim("nO")
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 17:42:27 +00:00
output_layer = Linear(nO=nO, nI=token_width)
model = (
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
((tok2vec >> list2ragged()) & build_span_maker())
>> extract_spans()
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 17:42:27 +00:00
>> reduce_mean()
>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 17:42:27 +00:00
>> output_layer
)
model.set_ref("output_layer", output_layer)
model.set_ref("tok2vec", tok2vec)
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
# flag to show this isn't legacy
model.attrs["include_span_maker"] = True
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
2020-02-27 17:42:27 +00:00
return model
Fix entity linker batching (#9669) * Partial fix of entity linker batching * Add import * Better name * Add `use_gold_ents` option, docs * Change to v2, create stub v1, update docs etc. * Fix error type Honestly no idea what the right type to use here is. ConfigValidationError seems wrong. Maybe a NotImplementedError? * Make mypy happy * Add hacky fix for init issue * Add legacy pipeline entity linker * Fix references to class name * Add __init__.py for legacy * Attempted fix for loss issue * Remove placeholder V1 * formatting * slightly more interesting train data * Handle batches with no usable examples This adds a test for batches that have docs but not entities, and a check in the component that detects such cases and skips the update step as thought the batch were empty. * Remove todo about data verification Check for empty data was moved further up so this should be OK now - the case in question shouldn't be possible. * Fix gradient calculation The model doesn't know which entities are not in the kb, so it generates embeddings for the context of all of them. However, the loss does know which entities aren't in the kb, and it ignores them, as there's no sensible gradient. This has the issue that the gradient will not be calculated for some of the input embeddings, which causes a dimension mismatch in backprop. That should have caused a clear error, but with numpyops it was causing nans to happen, which is another problem that should be addressed separately. This commit changes the loss to give a zero gradient for entities not in the kb. * add failing test for v1 EL legacy architecture * Add nasty but simple working check for legacy arch * Clarify why init hack works the way it does * Clarify use_gold_ents use case * Fix use gold ents related handling * Add tests for no gold ents and fix other tests * Use aligned ents function (not working) This doesn't actually work because the "aligned" ents are gold-only. But if I have a different function that returns the intersection, *then* this will work as desired. * Use proper matching ent check This changes the process when gold ents are not used so that the intersection of ents in the pred and gold is used. * Move get_matching_ents to Example * Use model attribute to check for legacy arch * Rename flag * bump spacy-legacy to lower 3.0.9 Co-authored-by: svlandeg <svlandeg@github.com>
2022-03-04 08:17:36 +00:00
def build_span_maker(n_sents: int = 0) -> Model:
model: Model = Model("span_maker", forward=span_maker_forward)
model.attrs["n_sents"] = n_sents
return model
def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callable]:
ops = model.ops
n_sents = model.attrs["n_sents"]
candidates = []
for doc in docs:
cands = []
try:
sentences = [s for s in doc.sents]
except ValueError:
# no sentence info, normal in initialization
for tok in doc:
tok.is_sent_start = tok.i == 0
sentences = [doc[:]]
for ent in doc.ents:
try:
# find the sentence in the list of sentences.
sent_index = sentences.index(ent.sent)
except AttributeError:
# Catch the exception when ent.sent is None and provide a user-friendly warning
raise RuntimeError(Errors.E030) from None
# get n previous sentences, if there are any
start_sentence = max(0, sent_index - n_sents)
# get n posterior sentences, or as many < n as there are
end_sentence = min(len(sentences) - 1, sent_index + n_sents)
# get token positions
start_token = sentences[start_sentence].start
end_token = sentences[end_sentence].end
# save positions for extraction
cands.append((start_token, end_token))
candidates.append(ops.asarray2i(cands))
candlens = ops.asarray1i([len(cands) for cands in candidates])
candidates = ops.xp.concatenate(candidates)
outputs = Ragged(candidates, candlens)
# because this is just rearranging docs, the backprop does nothing
return outputs, lambda x: []
2021-03-02 16:56:28 +00:00
@registry.misc("spacy.KBFromFile.v1")
2020-10-12 09:41:27 +00:00
def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
def kb_from_file(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
kb.from_disk(kb_path)
return kb
2020-08-23 19:15:12 +00:00
return kb_from_file
2021-03-02 16:56:28 +00:00
@registry.misc("spacy.EmptyKB.v1")
def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
def empty_kb_factory(vocab):
return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
2020-08-23 19:15:12 +00:00
return empty_kb_factory
2021-03-02 16:56:28 +00:00
@registry.misc("spacy.CandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
return get_candidates