medcat.components.ner.trf.deid

De-identification model.

This describes a wrapper on the regular CAT model. The idea is to simplify the use of a DeId-specific model.

It tackles two use cases 1) Creation of a deid model 2) Loading and use of a deid model

I.e for use case 1:

Instead of: cat = CAT(cdb=ner.cdb, addl_ner=ner)

You can use: deid = DeIdModel.create(ner)

And for use case 2:

Instead of: cat = CAT.load_model_pack(model_pack_path) anon_text = deid_text(cat, text)

You can use: deid = DeIdModel.load_model_pack(model_pack_path) anon_text = deid.deid_text(text)

Or if/when structured output is desired: deid = DeIdModel.load_model_pack(model_pack_path) anon_doc = deid(text) # the spacy document

The wrapper also exposes some CAT parts directly: - config - cdb

Attributes

logger

Classes

CAT

This is a collection of serialisable model parts.

CDB

The abstract serialisable base class.

ConfigTransformersNER

The transformer NER config

CoreComponentType

Generic enumeration.

NerModel

The NER model.

TransformersNER

Base class for protocol classes.

Entity

dict() -> new empty dictionary

DeIdModel

The DeID model.

Functions

replace_entities_in_text(text, entities, get_cui_name)

match_rules(rules, texts, cui2preferred_name)

Match a set of rules - pat / cui combos as post processing labels.

merge_all_preds(model_preds_by_text, rule_matches_per_text)

Conveniance method to merge predictions from rule based and deID model

merge_preds(model_preds, rule_matches[, accept_preds])

Merge predictions from rule based and deID model predictions.

Module Contents

class medcat.components.ner.trf.deid.CAT(cdb, vocab=None, config=None, model_load_path=None)

Bases: medcat.storage.serialisables.AbstractSerialisable

This is a collection of serialisable model parts.

Parameters:
__init__(cdb, vocab=None, config=None, model_load_path=None)
Parameters:
Return type:

None

cdb
vocab = None
config = None
_trainer: medcat.trainer.Trainer | None = None
_pipeline
usage_monitor
_recreate_pipe(model_load_path=None)
Parameters:

model_load_path (Optional[str])

Return type:

medcat.pipeline.pipeline.Pipeline

classmethod get_init_attrs()
Return type:

list[str]

classmethod ignore_attrs()
Return type:

list[str]

__call__(text)
Parameters:

text (str)

Return type:

Optional[medcat.tokenizing.tokens.MutableDocument]

_ensure_not_training()

Method to ensure config is not set to train.

config.components.linking.train should only be True while training and not during inference. This aalso corrects the setting if necessary.

Return type:

None

get_entities(text: str, only_cui: Literal[False] = False) → medcat.data.entities.Entities
get_entities(text: str, only_cui: Literal[True] = True) → medcat.data.entities.OnlyCUIEntities
get_entities(text: str, only_cui: bool = False) → dict | medcat.data.entities.Entities | medcat.data.entities.OnlyCUIEntities

Get the entities recognised and linked within the provided text.

This will run the text through the pipeline and annotated the recognised and linked entities.

Parameters:

  • text (str) – The text to use.

  • only_cui (bool, optional) – Whether to only output the CUIs rather than the entire context. Defaults to False.

Returns:

Union[dict, Entities, OnlyCUIEntities] – The entities found and linked within the text.

_mp_worker_func(texts_and_indices)
Parameters:

texts_and_indices (list[tuple[str, str, bool]])

Return type:

list[tuple[str, str, Union[dict, medcat.data.entities.Entities, medcat.data.entities.OnlyCUIEntities]]]

_generate_batches_by_char_length(text_iter, batch_size_chars, only_cui)
Parameters:

  • text_iter (Union[Iterator[str], Iterator[tuple[str, str]]])

  • batch_size_chars (int)

  • only_cui (bool)

Return type:

Iterator[list[tuple[str, str, bool]]]

_generate_batches(text_iter, batch_size, batch_size_chars, only_cui)
Parameters:

  • text_iter (Union[Iterator[str], Iterator[tuple[str, str]]])

  • batch_size (int)

  • batch_size_chars (int)

  • only_cui (bool)

Return type:

Iterator[list[tuple[str, str, bool]]]

_generate_simple_batches(text_iter, batch_size, only_cui)
Parameters:

  • text_iter (Union[Iterator[str], Iterator[tuple[str, str]]])

  • batch_size (int)

  • only_cui (bool)

Return type:

Iterator[list[tuple[str, str, bool]]]

_mp_one_batch_per_process(executor, batch_iter, external_processes)
Parameters:

  • executor (concurrent.futures.ProcessPoolExecutor)

  • batch_iter (Iterator[list[tuple[str, str, bool]]])

  • external_processes (int)

Return type:

Iterator[tuple[str, Union[dict, medcat.data.entities.Entities, medcat.data.entities.OnlyCUIEntities]]]

get_entities_multi_texts(texts, only_cui=False, n_process=1, batch_size=-1, batch_size_chars=1000000)

Get entities from multiple texts (potentially in parallel).

If n_process > 1, n_process - 1 new processes will be created and data will be processed on those as well as the main process in parallel.

Parameters:

  • texts (Union[Iterable[str], Iterable[tuple[str, str]]]) – The input text. Either an iterable of raw text or one with in the format of (text_index, text).

  • only_cui (bool) – Whether to only return CUIs rather than other information like start/end and annotated value. Defaults to False.

  • n_process (int) – Number of processes to use. Defaults to 1.

  • batch_size (int) – The number of texts to batch at a time. A batch of the specified size will be given to each worker process. Defaults to -1 and in this case the character count will be used instead.

  • batch_size_chars (int) – The maximum number of characters to process in a batch. Each process will be given batch of texts with a total number of characters not exceeding this value. Defaults to 1,000,000 characters. Set to -1 to disable.

Yields:

Iterator[tuple[str, Union[dict, Entities, OnlyCUIEntities]]] – The results in the format of (text_index, entities).

Return type:

Iterator[tuple[str, Union[dict, medcat.data.entities.Entities, medcat.data.entities.OnlyCUIEntities]]]

_get_entity(ent, doc_tokens, cui)
Parameters:
Return type:

medcat.data.entities.Entity

get_addon_output(ent)

Get the addon output for the entity.

This includes a key-value pair for each addon that provides some. Sometimes same-type addons may combine their output under the same key.

Parameters:

ent (MutableEntity) – The entity in quesiton.

Raises:

ValueError – If unable to merge multiple addon output.

Returns:

dict[str, dict] – All the addon output.

Return type:

dict[str, dict]

_doc_to_out_entity(ent, doc_tokens, only_cui)
Parameters:
Return type:

tuple[int, Union[medcat.data.entities.Entity, str]]

_doc_to_out(doc, only_cui, out_with_text=False)
Parameters:
Return type:

Union[medcat.data.entities.Entities, medcat.data.entities.OnlyCUIEntities]

property trainer

The trainer object.

save_model_pack(target_folder, pack_name=DEFAULT_PACK_NAME, serialiser_type='dill', make_archive=True, only_archive=False, add_hash_to_pack_name=True, change_description=None)

Save model pack.

The resulting model pack name will have the hash of the model pack in its name if (and only if) the default model pack name is used.

Parameters:

  • target_folder (str) – The folder to save the pack in.

  • pack_name (str, optional) – The model pack name. Defaults to DEFAULT_PACK_NAME.

  • serialiser_type (Union[str, AvailableSerialisers], optional) – The serialiser type. Defaults to ‘dill’.

  • make_archive (bool) – Whether to make the arhive /.zip file. Defaults to True.

  • only_archive (bool) – Whether to clear the non-compressed folder. Defaults to False.

  • add_hash_to_pack_name (bool) – Whether to add the hash to the pack name. This is only relevant if pack_name is specified. Defaults to True.

  • change_description (Optional[str]) – If provided, this the description will be added to the model description. Defaults to None.

Returns:

str – The final model pack path.

Return type:

str

_get_hash()
Return type:

str

_versioning(change_description)
Parameters:

change_description (Optional[str])

Return type:

str

classmethod attempt_unpack(zip_path)

Attempt unpack the zip to a folder and get the model pack path.

If the folder already exists, no unpacking is done.

Parameters:

zip_path (str) – The ZIP path

Returns:

str – The model pack path

Return type:

str

classmethod load_model_pack(model_pack_path)

Load the model pack from file.

Parameters:

model_pack_path (str) – The model pack path.

Raises:

ValueError – If the saved data does not represent a model pack.

Returns:

CAT – The loaded model pack.

Return type:

CAT

classmethod load_cdb(model_pack_path)

Loads the concept database from the provided model pack path

Parameters:

model_pack_path (str) – path to model pack, zip or dir.

Returns:

CDB – The loaded concept database

Return type:

medcat.cdb.CDB

get_model_card(as_dict: Literal[True]) → medcat.data.model_card.ModelCard
get_model_card(as_dict: Literal[False]) → str

Get the model card either a (nested) dict or a json string.

Parameters:

as_dict (bool) – Whether to return as dict. Defaults to False.

Returns:

Union[str, ModelCard] – The model card.

__eq__(other)
Parameters:

other (Any)

Return type:

bool

add_addon(addon)
Parameters:

addon (medcat.components.addons.addons.AddonComponent)

Return type:

None

get_strategy()
Return type:

SerialisingStrategy

classmethod include_properties()
Return type:

list[str]

class medcat.components.ner.trf.deid.CDB(config)

Bases: medcat.storage.serialisables.AbstractSerialisable

The abstract serialisable base class.

This defines some common defaults.

Parameters:

config (medcat.config.Config)

__init__(config)
Parameters:

config (medcat.config.Config)

Return type:

None

config
cui2info: dict[str, medcat.cdb.concepts.CUIInfo]
name2info: dict[str, medcat.cdb.concepts.NameInfo]
type_id2info: dict[str, medcat.cdb.concepts.TypeInfo]
token_counts: dict[str, int]
addl_info: dict[str, Any]
_subnames: set[str]
is_dirty = False
has_changed_names = False
classmethod get_init_attrs()
Return type:

list[str]

_reset_subnames()
has_subname(name)

Whether the CDB has the specified subname.

Parameters:

name (str) – The subname to check.

Returns:

bool – Whether the subname is present in this CDB.

Return type:

bool

get_name(cui)

Returns preferred name if it exists, otherwise it will return the longest name assigned to the concept.

Parameters:

cui (str) – Concept ID or unique identifier in this database.

Returns:

str – The name of the concept.

Return type:

str

weighted_average_function(step)

Get the weighted average for steop.

Parameters:

step (int) – The steop.

Returns:

float – The weighted average.

Return type:

float

add_types(types)

Add type info to CDB.

Parameters:

types (Iterable[tuple[str, str]]) – The raw type info.

Return type:

None

add_names(cui, names, name_status=ST.AUTOMATIC, full_build=False)

Adds a name to an existing concept.

Parameters:

  • cui (str) – Concept ID or unique identifier in this database, all concepts that have the same CUI will be merged internally.

  • names (dict[str, NameDescriptor]) –

    Names for this concept, or the value that if found in free text can be linked to this concept. Names is an dict like: `{name: {‘tokens’: tokens, ‘snames’: snames,

    ’raw_name’: raw_name}, …}`

    Names should be generated by helper function ‘medcat.preprocessing.cleaners.prepare_name’

  • name_status (str) – One of P, N, A. Defaults to ‘A’.

  • full_build (bool) – If True the dictionary self.addl_info will also be populated, contains a lot of extra information about concepts, but can be very memory consuming. This is not necessary for normal functioning of MedCAT (Default value False).

Return type:

None

_add_concept_names(cui, names, name_status)
Parameters:
Return type:

None

_add_full_build(cui, names, ontologies, description, type_ids)
Parameters:
Return type:

None

_add_concept(cui, names, ontologies, name_status, type_ids, description, full_build=False)

Add a concept to internal Concept Database (CDB). Depending on what you are providing this will add a large number of properties for each concept.

Parameters:

  • cui (str) – Concept ID or unique identifier in this database, all concepts that have the same CUI will be merged internally.

  • names (dict[str, NameDescriptor]) –

    Names for this concept, or the value that if found in free text can be linked to this concept. Names is a dict like: `{name: {‘tokens’: tokens, ‘snames’: snames,

    ’raw_name’: raw_name}, …}`

    Names should be generated by helper function ‘medcat.preprocessing.cleaners.prepare_name’

  • ontologies (set[str]) – ontologies in which the concept exists (e.g. SNOMEDCT, HPO)

  • name_status (str) – One of P, N, A

  • type_ids (set[str]) – Semantic type identifier (have a look at TUIs in UMLS or SNOMED-CT)

  • description (str) – Description of this concept.

  • full_build (bool) – If True the dictionary self.addl_info will also be populated, contains a lot of extra information about concepts, but can be very memory consuming. This is not necessary for normal functioning of MedCAT (Default Value False).

Return type:

None

reset_training()

Will remove all training efforts - in other words all embeddings that are learnt for concepts in the current CDB. Please note that this does not remove synonyms (names) that were potentially added during supervised/online learning.

Return type:

None

filter_by_cui(cuis_to_keep)

Subset the core CDB fields (dictionaries/maps).

Note that this will potenitally keep a bit more CUIs then in cuis_to_keep. It will first find all names that link to the cuis_to_keep and then find all CUIs that link to those names and keep all of them.

This also will not remove any data from cdb.addl_info - as this field can contain data of unknown structure.

Parameters:

cuis_to_keep (Collection[str]) – CUIs that will be kept, the rest will be removed (not completely, look above).

Raises:

Exception – If no snames and subsetting is not possible.

Return type:

None

remove_cui(cui)

This function takes a CUI and removes it the CDB.

It also removes the CUI from name specific per_cui_status maps as well as well as removes all the names that do not correspond to any CUIs after the removal of this one.

Parameters:

cui (str) – The CUI to remove.

Return type:

None

_remove_names(cui, names)

Remove names from an existing concept - effect is this name will never again be used to link to this concept. This will only remove the name from the linker (namely name2cuis and name2cuis2status), the name will still be present everywhere else. Why? Because it is bothersome to remove it from everywhere, but could also be useful to keep the removed names in e.g. cui2names.

Parameters:

  • cui (str) – Concept ID or unique identifier in this database.

  • names (Iterable[str]) – Names to be removed (e.g list, set, or even a dict (in which case keys will be used)).

Return type:

None

__eq__(other)
Parameters:

other (Any)

Return type:

bool

get_cui2count_train()
Return type:

dict[str, int]

get_name2count_train()
Return type:

dict[str, int]

get_hash()
Return type:

str

get_basic_info()
Return type:

medcat.data.model_card.CDBInfo

save(save_path, serialiser=AvailableSerialisers.dill, overwrite=False)

Save CDB at path.

Parameters:

  • save_path (str) – The path to save at.

  • serialiser (Union[ str, AvailableSerialisers], optional) – The serialiser. Defaults to AvailableSerialisers.dill.

  • overwrite (bool, optional) – Whether to allow overwriting existing files. Defaults to False.

Return type:

None

classmethod load(path)
Parameters:

path (str)

Return type:

CDB

get_strategy()
Return type:

SerialisingStrategy

classmethod ignore_attrs()
Return type:

list[str]

classmethod include_properties()
Return type:

list[str]

class medcat.components.ner.trf.deid.ConfigTransformersNER(/, **data)

Bases: medcat.config.config.SerialisableBaseModel

The transformer NER config

Parameters:

data (Any)

general: General
class Config
extra = 'allow'
validate_assignment = True
get_hash(hasher=None)
Parameters:

hasher (Optional[medcat.utils.hasher.Hasher])

Return type:

str

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.components.ner.trf.deid.CoreComponentType

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

tagging
token_normalizing
ner
linking
__new__(value)
_generate_next_value_(start, count, last_values)

Generate the next value when not given.

name: the name of the member start: the initial start value or None count: the number of existing members last_value: the last value assigned or None

classmethod _missing_(value)
__repr__()
__str__()
__dir__()

Returns all members and all public methods

__format__(format_spec)

Returns format using actual value type unless __str__ has been overridden.

__hash__()
__reduce_ex__(proto)
name()

The name of the Enum member.

value()

The value of the Enum member.

class medcat.components.ner.trf.deid.NerModel(cat)

The NER model.

This wraps a CAT instance and simplifies its use as a NER model.

It provides methods for creating one from a TransformersNER as well as loading from a model pack (along with some validation).

It also exposes some useful parts of the CAT it wraps such as the config and the concept database.

Parameters:

cat (medcat.cat.CAT)

__init__(cat)
Parameters:

cat (medcat.cat.CAT)

Return type:

None

cat
train(json_path, *args, **kwargs)

Train the underlying transformers NER model.

All the extra arguments are passed to the TransformersNER train method.

Parameters:

  • json_path (Union[str, list, None]) – The JSON file path to read the training data from.

  • *args – Additional arguments for TransformersNER.train .

  • **kwargs – Additional keyword arguments for TransformersNER.train .

Returns:

Tuple[Any, Any, Any] – df, examples, dataset

Return type:

tuple[Any, Any, Any]

eval(json_path, *args, **kwargs)

Evaluate the underlying transformers NER model. All the extra arguments are passed to the TransformersNER eval method. :param json_path: The JSON file path to read the training data from. :type json_path: Union[str, list, None] :param *args: Additional arguments for TransformersNER.eval . :param **kwargs: Additional keyword arguments for TransformersNER.eval .

Returns:

Tuple[Any, Any, Any] – df, examples, dataset

Parameters:

json_path (Union[str, list, None])

Return type:

tuple[Any, Any, Any]

__call__(text, *args, **kwargs)

Get the annotated document for text.

Undefined arguments and keyword arguments get passed on to the equivalent CAT method.

Parameters:

  • text (Optional[str]) – The input text.

  • *args – Additional arguments for cat.__call__ .

  • **kwargs – Additional keyword arguments for cat.__call__ .

Returns:

Optional[Doc] – The annotated document.

Return type:

Optional[medcat.tokenizing.tokens.MutableDocument]

get_entities(text, *args, **kwargs)

Gets the entities recognized within a given text.

The output format is identical to CAT.get_entities.

Undefined arguments and keyword arguments get passed on to CAT.get_entities.

Parameters:

  • text (str) – The input text.

  • *args – Additional arguments for cat.get_entities .

  • **kwargs – Additional keyword arguments for cat.get_entities .

Returns:

dict – The output entities.

Return type:

Union[dict, medcat.data.entities.Entities, medcat.data.entities.OnlyCUIEntities]

add_new_concepts(cui2preferred_name, with_random_init=False)

Add new concepts to the model and the concept database.

Invoking this requires subsequent retraining on the model.

Parameters:

  • cui2preferred_name (dict[str, str]) –

    Dictionary where each key is the literal ID of the concept to be added and each value is

    its preferred name.

  • with_random_init (bool) – Whether to use the random init strategy for the new concepts. Defaults to False.

Return type:

None

property trf_ner: medcat.components.ner.trf.transformers_ner.TransformersNER
Return type:

medcat.components.ner.trf.transformers_ner.TransformersNER

property config: medcat.config.Config
Return type:

medcat.config.Config

property cdb: medcat.cdb.CDB
Return type:

medcat.cdb.CDB

classmethod load_model_pack(model_pack_path, config=None)

Load NER model from model pack.

The method first wraps the loaded CAT instance.

Parameters:

  • config (Optional[dict]) – Config for DeId model pack (primarily for stride of overlap window)

  • model_pack_path (str) – The model pack path.

Returns:

NerModel – The resulting DeI model.

Return type:

NerModel

medcat.components.ner.trf.deid.replace_entities_in_text(text, entities, get_cui_name, redact=False)
Parameters:

  • text (str)

  • entities (Dict)

  • get_cui_name (Callable[[str], str])

  • redact (bool)

Return type:

str

class medcat.components.ner.trf.deid.TransformersNER(cdb, base_tokenizer, component, config=None, training_arguments=None)

Bases: medcat.components.types.AbstractCoreComponent

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:
        ...
Parameters:
name = 'transformers_ner'

The name of the component.

_def_serialiser
__init__(cdb, base_tokenizer, component, config=None, training_arguments=None)
Parameters:
Return type:

None

_component
classmethod create_new(cdb, base_tokenizer, config=None, training_arguments=None)
Parameters:
Return type:

TransformersNER

classmethod create_new_component(cnf, tokenizer, cdb, vocab, model_load_path)

Create a new component or load one off disk if load path presented.

This may raise an exception if the wrong type of config is provided.

Parameters:

  • cnf (ComponentConfig) – The config relevant to this components.

  • tokenizer (BaseTokenizer) – The base tokenizer.

  • cdb (CDB) – The CDB.

  • vocab (Vocab) – The Vocab.

  • model_load_path (Optional[str]) – Model load path (if present).

Returns:

Self – The new components.

Return type:

TransformersNER

classmethod load_existing(cdb, base_tokenizer, load_path, training_arguments=None, config=None)
Parameters:
Return type:

TransformersNER

get_type()
property should_save: bool
Return type:

bool

save(folder, overwrite=False)
Parameters:

  • folder (str)

  • overwrite (bool)

Return type:

None

__call__(doc)
Parameters:

doc (medcat.tokenizing.tokens.MutableDocument)

Return type:

medcat.tokenizing.tokens.MutableDocument

get_folder_name()
Return type:

str

serialise_to(folder_path)
Parameters:

folder_path (str)

Return type:

None

classmethod deserialise_from(folder_path, **init_kwargs)
Parameters:

folder_path (str)

Return type:

TransformersNER

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]

NAME_PREFIX = 'core_'
property full_name: str

Name with the component type (e.g ner, linking, meta).

Return type:

str

is_core()

Whether the component is a core component or not.

Returns:

bool – Whether this is a core component.

Return type:

bool

__slots__ = ()
_is_protocol = True
_is_runtime_protocol = False
classmethod __init_subclass__(*args, **kwargs)
classmethod __class_getitem__(params)
class medcat.components.ner.trf.deid.Entity

Bases: TypedDict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:

d = {} for k, v in iterable:

d[k] = v

dict(**kwargs) -> new dictionary initialized with the name=value pairs

in the keyword argument list. For example: dict(one=1, two=2)

pretty_name: str
cui: str
type_ids: list[str]
source_value: str
detected_name: str
acc: float
context_similarity: float
start: int
end: int
id: int
meta_anns: dict[str, MetaAnnotation]
context_left: list[str]
context_center: list[str]
context_right: list[str]
__contains__()

True if the dictionary has the specified key, else False.

__delattr__()

Implement delattr(self, name).

__delitem__()

Delete self[key].

__dir__()

Default dir() implementation.

__eq__()

Return self==value.

__format__()

Default object formatter.

__ge__()

Return self>=value.

__getattribute__()

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__()

Return self>value.

__init__()

Initialize self. See help(type(self)) for accurate signature.

__ior__()

Return self|=value.

__iter__()

Implement iter(self).

__le__()

Return self<=value.

__len__()

Return len(self).

__lt__()

Return self<value.

__ne__()

Return self!=value.

__new__()

Create and return a new object. See help(type) for accurate signature.

__or__()

Return self|value.

__reduce__()

Helper for pickle.

__reduce_ex__()

Helper for pickle.

__repr__()

Return repr(self).

__reversed__()

Return a reverse iterator over the dict keys.

__ror__()

Return value|self.

__setattr__()

Implement setattr(self, name, value).

__setitem__()

Set self[key] to value.

__sizeof__()

D.__sizeof__() -> size of D in memory, in bytes

__str__()

Return str(self).

__subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

clear()

D.clear() -> None. Remove all items from D.

copy()

D.copy() -> a shallow copy of D

get()

Return the value for key if key is in the dictionary, else default.

items()

D.items() -> a set-like object providing a view on D’s items

keys()

D.keys() -> a set-like object providing a view on D’s keys

pop()

D.pop(k[,d]) -> v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault()

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update()

D.update([E, ]**F) -> None. Update D from dict/iterable E and F. If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()

D.values() -> an object providing a view on D’s values

medcat.components.ner.trf.deid.logger
class medcat.components.ner.trf.deid.DeIdModel(cat)

Bases: medcat.components.ner.trf.model.NerModel

The DeID model.

This wraps a CAT instance and simplifies its use as a de-identification model.

It provides methods for creating one from a TransformersNER as well as loading from a model pack (along with some validation).

It also exposes some useful parts of the CAT it wraps such as the config and the concept database.

Parameters:

cat (medcat.cat.CAT)

__init__(cat)
Parameters:

cat (medcat.cat.CAT)

Return type:

None

cat
train(json_path, *args, **kwargs)

Train the underlying transformers NER model.

All the extra arguments are passed to the TransformersNER train method.

Parameters:

  • json_path (Union[str, list, None]) – The JSON file path to read the training data from.

  • *args – Additional arguments for TransformersNER.train .

  • **kwargs – Additional keyword arguments for TransformersNER.train .

Returns:

Tuple[Any, Any, Any] – df, examples, dataset

Return type:

tuple[Any, Any, Any]

deid_text(text, redact=False)

Deidentify text and potentially redact information.

De-identified text. If redaction is enabled, identifiable entities will be replaced with starts (e.g *****). Otherwise, the replacement will be the CUI or in other words, the type of information that was hidden (e.g [PATIENT]).

Parameters:

  • text (str) – The text to deidentify.

  • redact (bool) – Whether to redact the information.

Returns:

str – The deidentified text.

Return type:

str

classmethod load_model_pack(model_pack_path, config=None)

Load DeId model from model pack.

The method first loads the CAT instance.

It then makes sure that the model pack corresponds to a valid DeId model.

Parameters:

  • config (Optional[dict]) – Config for DeId model pack (primarily for stride of overlap window)

  • model_pack_path (str) – The model pack path.

Raises:

ValueError – If the model pack does not correspond to a DeId model.

Returns:

DeIdModel – The resulting DeI model.

Return type:

DeIdModel

classmethod _is_deid_model(cat)
Parameters:

cat (medcat.cat.CAT)

Return type:

bool

classmethod _get_reason_not_deid(cat)
Parameters:

cat (medcat.cat.CAT)

Return type:

str

classmethod create(cdb, cnf)
Parameters:
eval(json_path, *args, **kwargs)

Evaluate the underlying transformers NER model. All the extra arguments are passed to the TransformersNER eval method. :param json_path: The JSON file path to read the training data from. :type json_path: Union[str, list, None] :param *args: Additional arguments for TransformersNER.eval . :param **kwargs: Additional keyword arguments for TransformersNER.eval .

Returns:

Tuple[Any, Any, Any] – df, examples, dataset

Parameters:

json_path (Union[str, list, None])

Return type:

tuple[Any, Any, Any]

__call__(text, *args, **kwargs)

Get the annotated document for text.

Undefined arguments and keyword arguments get passed on to the equivalent CAT method.

Parameters:

  • text (Optional[str]) – The input text.

  • *args – Additional arguments for cat.__call__ .

  • **kwargs – Additional keyword arguments for cat.__call__ .

Returns:

Optional[Doc] – The annotated document.

Return type:

Optional[medcat.tokenizing.tokens.MutableDocument]

get_entities(text, *args, **kwargs)

Gets the entities recognized within a given text.

The output format is identical to CAT.get_entities.

Undefined arguments and keyword arguments get passed on to CAT.get_entities.

Parameters:

  • text (str) – The input text.

  • *args – Additional arguments for cat.get_entities .

  • **kwargs – Additional keyword arguments for cat.get_entities .

Returns:

dict – The output entities.

Return type:

Union[dict, medcat.data.entities.Entities, medcat.data.entities.OnlyCUIEntities]

add_new_concepts(cui2preferred_name, with_random_init=False)

Add new concepts to the model and the concept database.

Invoking this requires subsequent retraining on the model.

Parameters:

  • cui2preferred_name (dict[str, str]) –

    Dictionary where each key is the literal ID of the concept to be added and each value is

    its preferred name.

  • with_random_init (bool) – Whether to use the random init strategy for the new concepts. Defaults to False.

Return type:

None

property trf_ner: medcat.components.ner.trf.transformers_ner.TransformersNER
Return type:

medcat.components.ner.trf.transformers_ner.TransformersNER

property config: medcat.config.Config
Return type:

medcat.config.Config

property cdb: medcat.cdb.CDB
Return type:

medcat.cdb.CDB

medcat.components.ner.trf.deid.match_rules(rules, texts, cui2preferred_name)

Match a set of rules - pat / cui combos as post processing labels. Uses a cat DeID model for pretty name mapping. :param rules: List of tuples of pattern and cui :type rules: list[tuple[str, str]] :param texts: List of texts to match rules on :type texts: list[str] :param cui2preferred_name: Dictionary of CUI to

preferred name, likely to be cat.cdb.cui2preferred_name.

Parameters:

  • rules (list[tuple[str, str]])

  • texts (list[str])

  • cui2preferred_name (dict[str, str])

Return type:

list[list[medcat.data.entities.Entity]]

Examples

>>> cat = CAT.load_model_pack(model_pack_path)
...
>>> rules = [
    ('(123) 456-7890', '134'),
    ('1234567890', '134'),
    ('123.456.7890', '134'),
    ('1234567890', '134'),
    ('1234567890', '134'),
]
>>> texts = [
    'My phone number is (123) 456-7890',
    'My phone number is 1234567890',
    'My phone number is 123.456.7890',
    'My phone number is 1234567890',
]
>>> matches = match_rules(rules, texts, cat.cdb.cui2preferred_name)
Returns:

List[List[Dict]] – List of lists of predictions from match_rules

Parameters:

  • rules (list[tuple[str, str]])

  • texts (list[str])

  • cui2preferred_name (dict[str, str])

Return type:

list[list[medcat.data.entities.Entity]]

medcat.components.ner.trf.deid.merge_all_preds(model_preds_by_text, rule_matches_per_text, accept_preds=True)

Conveniance method to merge predictions from rule based and deID model predictions.

Parameters:

  • model_preds_by_text (list[list[Entity]]) – List of predictions from cat.get_entities(), then [list(m[‘entities’].values()) for m in model_preds]

  • rule_matches_per_text (list[list[Entity]]) – List of predictions from output of running match_rules

  • accept_preds (bool) – Uses the predicted label from the model, model_preds_by_text, over the rule matches if they overlap. Defaults to using model preds over rules.

Returns:

list[list[Entity]] – List of lists of predictions from merge_all_preds

Return type:

list[list[medcat.data.entities.Entity]]

medcat.components.ner.trf.deid.merge_preds(model_preds, rule_matches, accept_preds=True)

Merge predictions from rule based and deID model predictions. :param model_preds: predictions from cat.get_entities() :type model_preds: list[Entity] :param rule_matches: predictions from output of running

match_rules on the same text

Parameters:

  • accept_preds (bool) – uses the predicted label from the model, model_preds, over the rule matches if they overlap. Defaults to using model preds over rules.

  • model_preds (list[medcat.data.entities.Entity])

  • rule_matches (list[Entity])

Return type:

list[medcat.data.entities.Entity]

Examples

>>> # a list of predictions from `cat.get_entities()`
>>> model_preds = [
    [
        {'cui': '134', 'start': 10, 'end': 20, 'acc': 1.0,
         'pretty_name': 'Phone Number'},
        {'cui': '134', 'start': 25, 'end': 35, 'acc': 1.0,
         'pretty_name': 'Phone Number'}
    ]
]
>>> # a list of predictions from `match_rules`
>>> rule_matches = [
    [
        {'cui': '134', 'start': 10, 'end': 20, 'acc': 1.0,
         'pretty_name': 'Phone Number'},
        {'cui': '134', 'start': 25, 'end': 35, 'acc': 1.0,
         'pretty_name': 'Phone Number'}
    ]
]
>>> merged_preds = merge_preds(model_preds, rule_matches)
Returns:

list[Entity] – List of predictions from merge_preds

Parameters:
Return type:

list[medcat.data.entities.Entity]