medcat.tokenizing.tokenizers

Attributes

`logger`
`TOKENIZER_PREFIX`
`_DEFAULT_TOKENIZING`
`_TOKENIZERS_REGISTRY`

Classes

`Config`	The base serialisable config.
`MutableDocument`	The mutable parts of the document.
`MutableEntity`	The mutable part of an entity.
`MutableToken`	The mutable part of a token.
`Registry`	Abstract base class for generic types.
`BaseTokenizer`	The base tokenizer protocol.
`SaveableTokenizer`	Base class for protocol classes.

Functions

`get_tokenizer_creator`(tokenizer_name)	Get the creator method for the tokenizer.
`create_tokenizer`(tokenizer_name, config)	Create the tokenizer given the init arguments.
`list_available_tokenizers`()	Get the available tokenizers.
`register_tokenizer`(name, clazz)	Register a new tokenizer.

Module Contents

class medcat.tokenizing.tokenizers.Config(/, **data)

Bases: SerialisableBaseModel

The base serialisable config.

Parameters:: data (Any)

general: General

components: Components

preprocessing: Preprocessing

cdb_maker: CDBMaker

annotation_output: AnnotationOutput

meta: ModelMeta

get_strategy()

Return type:: medcat.storage.serialisables.SerialisingStrategy

classmethod get_init_attrs()

Return type:: list[str]

classmethod ignore_attrs()

Return type:: list[str]

classmethod include_properties()

Return type:: list[str]

merge_config(other)

Merge this config with another config’s (partial) model dump.

The exepctation is that the other dict is a partial model dump. Values specified there are overwritten into the current config. Values not specified there are left intact.

The other config can have keys/values that do not exist in the config or sub-config. And they will be added where possible.

Parameters:: other (dict) – The model dump
Raises:: IncorrectConfigValues – If unable to set the attribute, trying to set incorrect value, or trying to set sub-config values in an incorrect format (non-dict).

classmethod load(path)

Parameters:: path (str)
Return type:: typing_extensions.Self

model_config: ClassVar[pydantic.config.ConfigDict]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

__class_vars__: ClassVar[set[str]]: The names of the class variables defined on the model.

__private_attributes__: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]]: Metadata about the private attributes of the model.

__signature__: ClassVar[inspect.Signature]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: ClassVar[bool] = False: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[pydantic_core.CoreSchema]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[pydantic._internal._decorators.DecoratorInfos]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[pydantic._internal._generics.PydanticGenericMetadata]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']]: The name of the post-init method for the model, if defined.

__pydantic_root_model__: ClassVar[bool] = False: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[pydantic_core.SchemaSerializer]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator]: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: dict[str, Any] | None: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__: set[str]: The names of fields explicitly set during instantiation.

__pydantic_private__: dict[str, Any] | None: Values of private attributes set on the model instance.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__init__(/, **data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)
Return type:: None

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or `None` if `config.extra` is not set to `”allow”`.
Return type:: dict[str, Any] | None

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:: A set of strings representing the fields that have been set, – i.e. that were not filled from defaults.
Return type:: set[str]

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the `Model` class with validated data.

Return type:

typing_extensions.Self

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Returns:

New model instance.

Return type:

typing_extensions.Self

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (IncEx | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

Return type:

dict[str, Any]

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

Return type:

str

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

Return type:

dict[str, Any]

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], Ellipsis]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Returns:: String representing the new class where `params` are passed to `cls` as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.
Return type:: str

model_post_init(__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters:: __context (Any)
Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Returns:

Returns `None` if the schema is already “complete” and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.

Return type:

bool | None

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

Return type:

typing_extensions.Self

classmethod model_validate_json(json_data, *, strict=None, context=None)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

Return type:

typing_extensions.Self

classmethod model_validate_strings(obj, *, strict=None, context=None)

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Return type:

typing_extensions.Self

classmethod __get_pydantic_core_schema__(source, handler, /)

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (pydantic.annotated_handlers.GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Returns:

A `pydantic-core` `CoreSchema`.

Return type:

pydantic_core.CoreSchema

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

core_schema (pydantic_core.CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.
handler (pydantic.annotated_handlers.GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Returns:

A JSON schema, as a Python object.

Return type:

pydantic.json_schema.JsonSchemaValue

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

classmethod __class_getitem__(typevar_values)

Parameters:: typevar_values (type[Any] | tuple[type[Any], Ellipsis])
Return type:: type[BaseModel] | pydantic._internal._forward_ref.PydanticRecursiveRef

__copy__()

Returns a shallow copy of the model.

Return type:: typing_extensions.Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Parameters:: memo (dict[int, Any] | None)
Return type:: typing_extensions.Self

__getattr__(item)

Parameters:: item (str)
Return type:: Any

_check_frozen(name, value)

Parameters:

name (str)
value (Any)

Return type:

None

__getstate__()

Return type:: dict[Any, Any]

__setstate__(state)

Parameters:: state (dict[Any, Any])
Return type:: None

__eq__(other)

Parameters:: other (Any)
Return type:: bool

classmethod __init_subclass__(**kwargs)

This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.

```py from pydantic import BaseModel

class MyModel(BaseModel, extra=’allow’): … ```

However, this may be deceiving, since the _actual_ calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)

Parameters:: **kwargs (typing_extensions.Unpack[pydantic.config.ConfigDict]) – Keyword arguments passed to the class definition, which set model_config

Note

You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.

__iter__()

So dict(model) works.

Return type:: TupleGenerator

__repr__()

Return type:: str

__repr_args__()

Return type:: pydantic._internal._repr.ReprArgs

__repr_name__

__repr_str__

__pretty__

__rich_repr__

__str__()

Return type:: str

property __fields__: dict[str, pydantic.fields.FieldInfo]

Return type:: dict[str, pydantic.fields.FieldInfo]

property __fields_set__: set[str]

Return type:: set[str]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Parameters:

include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)

Return type:

Dict[str, Any]

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Parameters:

include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)

Return type:

str

classmethod parse_obj(obj)

Parameters:: obj (Any)
Return type:: typing_extensions.Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Parameters:

b (str | bytes)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)

Return type:

typing_extensions.Self

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Parameters:

path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)

Return type:

typing_extensions.Self

classmethod from_orm(obj)

Parameters:: obj (Any)
Return type:: typing_extensions.Self

classmethod construct(_fields_set=None, **values)

Parameters:

_fields_set (set[str] | None)
values (Any)

Return type:

typing_extensions.Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

Return type:

typing_extensions.Self

classmethod schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE)

Parameters:

by_alias (bool)
ref_template (str)

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, **dumps_kwargs)

Parameters:

by_alias (bool)
ref_template (str)
dumps_kwargs (Any)

Return type:

str

classmethod validate(value)

Parameters:: value (Any)
Return type:: typing_extensions.Self

classmethod update_forward_refs(**localns)

Parameters:: localns (Any)
Return type:: None

_iter(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

_calculate_keys(*args, **kwargs)

Parameters:

args (Any)
kwargs (Any)

Return type:

Any

class medcat.tokenizing.tokenizers.MutableDocument

Bases: Protocol

The mutable parts of the document.

Represents parts of the document that can / should be changed by the various components.

property base: BaseDocument

The base document.

Return type:: BaseDocument

property linked_ents: list[MutableEntity]

The linked entities associated with the document.

This should be set by the linker.

Return type:: list[MutableEntity]

property ner_ents: list[MutableEntity]

All entities recognised by NER.

This should be set by the NER component.

Return type:: list[MutableEntity]

__iter__()

Return type:: Iterator[MutableToken]

__getitem__(index: int) → MutableToken
__getitem__(index: slice) → MutableEntity

__len__()

Return type:: int

get_tokens(start_index, end_index)

Get the tokens that span the specified character indices.

Parameters:

start_index (int) – The starting character index.
end_index (int) – The ending character index.

Returns:

list[MutableToken] – The list of tokens.

Return type:

list[MutableToken]

set_addon_data(path, val)

Used to add arbitrary data to the entity.

This is generally used by addons to keep track of their data.

NB! The path used needs to be registered using the register_addon_path class method.

Parameters:

path (str) – The data ID / path.
val (Any) – The value to be added.

Return type:

None

has_addon_data(path)

Checks whether the addon data for a specific path has been set.

Parameters:: path (str) – The path to check.
Returns:: bool – Whether the addon data had been set.
Return type:: bool

get_addon_data(path)

Get data added to the entity.

See add_data for details.

Parameters:: path (str) – The data ID / path.
Returns:: Any – The stored value.
Return type:: Any

get_available_addon_paths()

Gets the available addon data paths for this document.

This will only include paths that have values set.

Returns:: list[str] – List of available addon data paths.
Return type:: list[str]

classmethod register_addon_path(path, def_val=None, force=True)

Register a custom/arbitrary data path.

This can be used to store arbitrary data along with the entity for use in an addon (e.g MetaCAT).

PS: If using this, it is important to use paths namespaced to the component you’re using in order to avoid conflicts.

Parameters:

path (str) – The path to be used. Should be prefixed by component name (e.g meta_cat_id for an ID tied to the meta_cat addon)
def_val (Any) – Default value. Defaults to None.
force (bool) – Whether to forcefully add the value. Defaults to True.

Return type:

None

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.tokenizing.tokenizers.MutableEntity

Bases: Protocol

The mutable part of an entity.

This represent the changeable part of an entnity. That is, parts that should be changed by the various components.

property base: BaseEntity

The base / static entity part.

Return type:: BaseEntity

property detected_name: str

The detected name (if any) for this entity.

This should be set by the NER component.

Return type:: str

set_addon_data(path, val)

Used to add arbitrary data to the entity.

This is generally used by addons to keep track of their data.

NB! The path used needs to be registered using the register_addon_path class method.

Parameters:

path (str) – The data ID / path.
val (Any) – The value to be added.

Return type:

None

has_addon_data(path)

Checks whether the addon data for a specific path has been set.

Parameters:: path (str) – The path to check.
Returns:: bool – Whether the addon data had been set.
Return type:: bool

get_addon_data(path)

Get data added to the entity.

See add_data for details.

Parameters:: path (str) – The data ID / path.
Returns:: Any – The stored value.
Return type:: Any

get_available_addon_paths()

Gets the available addon data paths for this entity.

This will only include paths that have values set.

Returns:: list[str] – List of available addon data paths.
Return type:: list[str]

property link_candidates: list[str]

The candidates for the detected name (if any) for this entity.

This should be set by the NER component.

Return type:: list[str]

property context_similarity: float

The context similarity of the lnked entity.

This should be set by the linker component.

Return type:: float

property confidence: float

The confidence for the lnked entity.

NOTE: This seems to be unused!

Return type:: float

property cui: str

The CUI of the lnked entity.

This should be set by the linker component.

Return type:: str

property id: int

The ID of the entity within the document.

This counts all the entities recognised, not just ones that were successfully linked.

This should be set by the NER.

Return type:: int

classmethod register_addon_path(path, def_val=None, force=True)

Register a custom/arbitrary data path.

This can be used to store arbitrary data along with the entity for use in an addon (e.g MetaCAT).

PS: If using this, it is important to use paths namespaced to the component you’re using in order to avoid conflicts.

Parameters:

path (str) – The path to be used. Should be prefixed by component name (e.g meta_cat_id for an ID tied to the meta_cat addon)
def_val (Any) – Default value. Defaults to None.
force (bool) – Whether to forcefully add the value. Defaults to True.

Return type:

None

__iter__()

Return type:: Iterator[MutableToken]

__len__()

Return type:: int

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.tokenizing.tokenizers.MutableToken

Bases: Protocol

The mutable part of a token.

This protocol describes all the parts of a token that could be expected to change.

property base: BaseToken

The base portion of the token.

Return type:: BaseToken

property is_punctuation: bool

Whether the token represents punctuation.

Return type:: bool

property to_skip: bool

Whether the token should be skipped.

Return type:: bool

property lemma: str

The lemmatised version of the text.

Return type:: str

property tag: str | None

Optional tag (e.g) for normalization.

Return type:: Optional[str]

property norm: str

The normalised text.

Return type:: str

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.tokenizing.tokenizers.Registry(type, lazy_defaults=None)

Bases: Generic[P]

Abstract base class for generic types.

A generic type is typically declared by inheriting from this class parameterized with one or more type variables. For example, a generic mapping type might be defined as:

class Mapping(Generic[KT, VT]):
    def __getitem__(self, key: KT) -> VT:
        ...
    # Etc.

This class can then be used as follows:

def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT:
    try:
        return mapping[key]
    except KeyError:
        return default

Parameters:

type (Type[P])
lazy_defaults (Optional[dict[str, tuple[str, str]]])

__init__(type, lazy_defaults=None)

Parameters:

type (Type[P])
lazy_defaults (Optional[dict[str, tuple[str, str]]])

Return type:

None

_components: dict[str, Callable[Ellipsis, P]]

_type

_lazy_defaults

register(component_name, creator)

Parameters:

component_name (str)
creator (Callable[Ellipsis, P])

get_component(component_name)

Get the component that’s registered.

The component generally refers to the class, but may be another method that creates the object needed.

Parameters:: component_name (str) – The name of the component.
Raises:: MedCATRegistryException – If no component by requested name is registered.
Returns:: Callable[…, P] – The creator for the registered component.
Return type:: Callable[Ellipsis, P]

_ensure_lazy_default(component_name)

Parameters:: component_name (str)
Return type:: None

register_all_defaults()

Register all default (lazily-added) components.

Return type:: None

list_components()

List all available component names and class names.

Returns:: list[tuple[str, str]] – The list of the names and class names for each registered componetn.
Return type:: list[tuple[str, str]]

unregister_component(component_name)

Unregister a component.

Parameters:: component_name (str) – The component name.
Raises:: MedCATRegistryException – If no component by the name specified had been registered.
Returns:: Callable[…, P] – The creator of the component.
Return type:: Callable[Ellipsis, P]

unregister_all_components()

Unregister all components.

Return type:: None

__contains__(component_name)

Parameters:: component_name (str)
Return type:: bool

__getitem__(component_name)

Parameters:: component_name (str)
Return type:: Callable[Ellipsis, P]

__slots__ = ()

_is_protocol = False

classmethod __class_getitem__(params)

classmethod __init_subclass__(*args, **kwargs)

medcat.tokenizing.tokenizers.logger

medcat.tokenizing.tokenizers.TOKENIZER_PREFIX = 'tokenizer_internals_'

class medcat.tokenizing.tokenizers.BaseTokenizer

Bases: Protocol

The base tokenizer protocol.

create_entity(doc, token_start_index, token_end_index, label)

Create an entity from a document.

Parameters:

doc (MutableDocument) – The document to use.
token_start_index (int) – The token start index.
token_end_index (int) – The token end index.
label (str) – The label.

Returns:

MutableEntity – The resulting entity.

Return type:

medcat.tokenizing.tokens.MutableEntity

entity_from_tokens(tokens)

Get an entity from the list of tokens.

Parameters:: tokens (list[MutableToken]) – List of tokens.
Returns:: MutableEntity – The resulting entity.
Return type:: medcat.tokenizing.tokens.MutableEntity

__call__(text)

Parameters:: text (str)
Return type:: medcat.tokenizing.tokens.MutableDocument

classmethod create_new_tokenizer(config)

Parameters:: config (medcat.config.Config)
Return type:: typing_extensions.Self

get_doc_class()

Get the document implementation class used by the tokenizer.

This can be used (e.g) to register addon paths.

Returns:: Type[MutableDocument] – The document class.
Return type:: Type[medcat.tokenizing.tokens.MutableDocument]

get_entity_class()

Get the entity implementation class used by the tokenizer.

Returns:: Type[MutableEntity] – The entity class.
Return type:: Type[medcat.tokenizing.tokens.MutableEntity]

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.tokenizing.tokenizers.SaveableTokenizer

Bases: Protocol

Base class for protocol classes.

Protocol classes are defined as:

class Proto(Protocol):
    def meth(self) -> int:
        ...

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

class C:
    def meth(self) -> int:
        return 0

def func(x: Proto) -> int:
    return x.meth()

func(C())  # Passes static type check

See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as:

class GenProto(Protocol[T]):
    def meth(self) -> T:
        ...

save_internals_to(folder_path)

Save tokenizer internals to specified folder.

The returning folder’s basename should start with TOKENIZER_PREFIX.

Parameters:: folder_path (str) – The folder to use for the internals.
Returns:: str – The subfolder the internals were saved to.
Return type:: str

load_internals_from(folder_path)

Attempt to load internals from a folder path.

If the specified folder exists, internals will be loaded. If the folder doesn’t exist, nothing will be loaded.

The given folder’s basename should start with TOKENIZER_PREFIX.

Parameters:: folder_path (str) – The path to the folder to load internals from.
Returns:: bool – Whether the loading was successful.
Return type:: bool

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

medcat.tokenizing.tokenizers._DEFAULT_TOKENIZING: dict[str, tuple[str, str]]

medcat.tokenizing.tokenizers._TOKENIZERS_REGISTRY

medcat.tokenizing.tokenizers.get_tokenizer_creator(tokenizer_name)

Get the creator method for the tokenizer.

While this is generally just the class instance (i.e refers to the ___init__), another callable can be used internally.

Parameters:: tokenizer_name (str) – The name of the tokenizer.
Returns:: Callable[[Config], BaseTokenizer] – The creator for the tokenizer.
Return type:: Callable[[medcat.config.Config], BaseTokenizer]

medcat.tokenizing.tokenizers.create_tokenizer(tokenizer_name, config)

Create the tokenizer given the init arguments.

Parameters:

tokenizer_name (str) – The tokenizer name.
config (Config) – The config to be passed to the constructor.

Returns:

BaseTokenizer – The created tokenizer.

Return type:

BaseTokenizer

medcat.tokenizing.tokenizers.list_available_tokenizers()

Get the available tokenizers.

Returns:: list[tuple[str, str]] – The list of the name, and class name of the available tokenizer.
Return type:: list[tuple[str, str]]

medcat.tokenizing.tokenizers.register_tokenizer(name, clazz)

Register a new tokenizer.

Parameters:

name (str) – The name of the tokenizer.
clazz (Type[BaseTokenizer]) – The class of the tokenizer (i.e creator).

Return type:

None