medcat.cat

Attributes

`DEFAULT_PACK_NAME`
`COMPONENTS_FOLDER`
`TOKENIZER_PREFIX`
`ModelCard`
`AVOID_LEGACY_CONVERSION_ENVIRON`
`logger`

Classes

`CDB`	The abstract serialisable base class.
`Vocab`	Vocabulary used to store word embeddings for context similarity
`Config`	The base serialisable config.
`Trainer`
`AvailableSerialisers`	Describes the available serialisers.
`AbstractSerialisable`	The abstract serialisable base class.
`Hasher`	A consistent hasher.
`Pipeline`	The pipeline for the NLP process.
`MutableDocument`	The mutable parts of the document.
`MutableEntity`	The mutable part of an entity.
`SaveableTokenizer`	Base class for protocol classes.
`Entity`	dict() -> new empty dictionary
`Entities`	dict() -> new empty dictionary
`OnlyCUIEntities`	dict() -> new empty dictionary
`AbstractCoreComponent`	Base class for protocol classes.
`HashableComponet`	Base class for protocol classes.
`AddonComponent`	Base/abstract addon component class.
`UsageMonitor`
`CAT`	This is a collection of serialisable model parts.

Functions

`get_important_config_parameters`(config)
`serialise`(serialiser_type, obj, target_folder[, overwrite])	Serialise an object based on the specified serialiser type.
`deserialise`(folder_path[, ignore_folders_prefix, ...])	Deserialise contents of a folder.
`ensure_folder_if_parent`(folder_name)	Ensure the folder exists if its parent folder exists.
`is_legacy_model_pack`(model_pack_path)	Check if the model pack is a legacy model pack.

Module Contents

medcat.cat.DEFAULT_PACK_NAME = 'medcat2_model_pack'

medcat.cat.COMPONENTS_FOLDER = 'saved_components'

class medcat.cat.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:

cui (str)
names (dict[str, medcat.preprocessors.cleaners.NameDescriptor])
name_status (str)

Return type:

None

_add_full_build(cui, names, ontologies, description, type_ids)

Parameters:

cui (str)
names (dict[str, medcat.preprocessors.cleaners.NameDescriptor])
ontologies (set[str])
description (str)
type_ids (set[str])

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.cat.Vocab

Bases: medcat.storage.serialisables.AbstractSerialisable

Vocabulary used to store word embeddings for context similarity calculation. Also used by the spell checker - but not for fixing the spelling only for checking is something correct.

Properties:

vocab (dict[str, WordDescriptor]):

Map from word to attributes, e.g. {‘house’:: {‘vector’: <np.array>, ‘count’: <int>, …}, …}

index2word (dict[int, str]):

From word to an index - used for negative sampling

vec_index2word (dict):

Same as index2word but only words that have vectors

__init__()

Return type:: None

vocab: dict[str, WordDescriptor]

index2word: dict[int, str]

vec_index2word: dict[int, str]

cum_probs: numpy.ndarray

inc_or_add(word, cnt=1, vec=None)

Add a word or increase its count.

Parameters:

word (str) – Word to be added
cnt (int) – By how much should the count be increased, or to what should it be set if a new word. (Default value = 1)
vec (Optional[np.ndarray]) – Word vector (Default value = None)

Return type:

None

remove_all_vectors()

Remove all stored vector representations.

Return type:: None

remove_words_below_cnt(cnt)

Remove all words with frequency below cnt.

Parameters:: cnt (int) – Word count limit.
Return type:: None

_rebuild_index()

inc_wc(word, cnt=1)

Incraese word count by cnt.

Parameters:

word (str) – For which word to increase the count
cnt (int) – By how muhc to increase the count (Default value = 1)

Return type:

None

add_vec(word, vec)

Add vector to a word.

Parameters:

word (str) – To which word to add the vector.
vec (np.ndarray) – The vector to add.

Return type:

None

reset_counts(cnt=1)

Reset the count for all word to cnt.

Parameters:: cnt (int) – New count for all words in the vocab. (Default value = 1)
Return type:: None

update_counts(tokens)

Given a list of tokens update counts for words in the vocab.

Parameters:: tokens (list[str]) – Usually a large block of text split into tokens/words.
Return type:: None

add_word(word, cnt=1, vec=None, replace=True)

Add a word to the vocabulary

Parameters:

word (str) – The word to be added, it should be lemmatized and lowercased
cnt (int) – Count of this word in your dataset (Default value = 1)
vec (Optional[np.ndarray]) – The vector representation of the word (Default value = None)
replace (bool) – Will replace old vector representation (Default value = True)

Return type:

None

add_words(path, replace=True)

Adds words to the vocab from a file, the file is required to have the following format (vec being optional):

<word> <cnt>[ <vec_space_separated>]

e.g. one line: the word house with 3 dimensional vectors: house 34444 0.3232 0.123213 1.231231

Parameters:

path (str) – path to the file with words and vectors
replace (bool) – existing words in the vocabulary will be replaced. Defaults to True.

Return type:

None

init_cumsums()

Initialise cumulative sums.

This is in place of the unigram table. But similarly to it, this approach allows generating a list of indices that match the probabilistic distribution expected as per the word counts of each word.

Return type:: None

get_negative_samples(n=6, ignore_punct_and_num=False)

Get N negative samples.

Parameters:

n (int) – How many words to return (Default value = 6)
ignore_punct_and_num (bool) – Whether to ignore punctuation and numbers. Defaults to False.

Raises:

Exception – If no unigram table is present.

Returns:

list[int] – Indices for words in this vocabulary.

Return type:

list[int]

get_vectors(indices)

Parameters:: indices (list[int])
Return type:: list[numpy.ndarray]

__getitem__(word)

Parameters:: word (str)
Return type:: int

vec(word)

Parameters:: word (str)
Return type:: Optional[numpy.ndarray]

count(word)

Parameters:: word (str)
Return type:: int

item(word)

Parameters:: word (str)
Return type:: WordDescriptor

__contains__(word)

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

__eq__(other)

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

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

Save Vocab 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:: Vocab

get_strategy()

Return type:: SerialisingStrategy

classmethod get_init_attrs()

Return type:: list[str]

classmethod ignore_attrs()

Return type:: list[str]

classmethod include_properties()

Return type:: list[str]

class medcat.cat.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

medcat.cat.get_important_config_parameters(config)

Parameters:: config (Config)
Return type:: dict[str, Any]

class medcat.cat.Trainer(cdb, caller, pipeline)

Parameters:

cdb (medcat.cdb.CDB)
caller (Callable[[str], medcat.tokenizing.tokens.MutableDocument])
pipeline (medcat.pipeline.pipeline.Pipeline)

__init__(cdb, caller, pipeline)

Parameters:

cdb (medcat.cdb.CDB)
caller (Callable[[str], medcat.tokenizing.tokens.MutableDocument])
pipeline (medcat.pipeline.pipeline.Pipeline)

cdb

config

caller

_pipeline

train_unsupervised(data_iterator, nepochs=1, fine_tune=True, progress_print=1000)

Runs training on the data, note that the maximum length of a line or document is 1M characters. Anything longer will be trimmed.

Parameters:

data_iterator (Iterable) – Simple iterator over sentences/documents, e.g. a open file or an array or anything that we can use in a for loop.
nepochs (int) – Number of epochs for which to run the training.
fine_tune (bool) – If False old training will be removed.
progress_print (int) – Print progress after N lines.
checkpoint (Optional[medcat.utils.checkpoint.CheckpointUT]) – The MedCAT checkpoint object
is_resumed (bool) – If True resume the previous training; If False, start a fresh new training.

Return type:

None

_train_unsupervised(data_iterator, nepochs=1, fine_tune=True, progress_print=1000)

Parameters:

data_iterator (Iterable)
nepochs (int)
fine_tune (bool)
progress_print (int)

Return type:

None

_reset_cui_counts(train_set, reset_val=100)

Parameters:

train_set (medcat.data.mctexport.MedCATTrainerExport)
reset_val (int)

train_supervised_raw(data, reset_cui_count=False, nepochs=1, print_stats=0, use_filters=False, terminate_last=False, use_overlaps=False, use_cui_doc_limit=False, test_size=0, devalue_others=False, use_groups=False, never_terminate=False, train_from_false_positives=False, extra_cui_filter=None, disable_progress=False, train_addons=False)

Train supervised based on the raw data provided.

The raw data is expected in the following format: {‘projects’:

[ # list of projects

{ # project 1
‘name’: ‘<some name>’, # list of documents ‘documents’: [{‘name’: ‘<some name>’, # document 1

‘text’: ‘<text of the document>’, # list of annotations ‘annotations’: [# annotation 1

{‘start’: -1, ‘end’: 1, ‘cui’: ‘cui’, ‘value’: ‘<text value>’}, …],

}, …]

}, …

]

}

Please take care that this is more a simulated online training then upervised.

When filtering, the filters within the CAT model are used first, then the ones from MedCATtrainer (MCT) export filters, and finally the extra_cui_filter (if set). That is to say, the expectation is: extra_cui_filter ⊆ MCT filter ⊆ Model/config filter.

Parameters:

data (dict[str, list[dict[str, dict]]]) – The raw data, e.g from MedCATtrainer on export.
reset_cui_count (bool) – Used for training with weight_decay (annealing). Each concept has a count that is there from the beginning of the CDB, that count is used for annealing. Resetting the count will significantly increase the training impact. This will reset the count only for concepts that exist in the the training data.
nepochs (int) – Number of epochs for which to run the training.
print_stats (int) – If > 0 it will print stats every print_stats epochs.
use_filters (bool) – Each project in medcattrainer can have filters, do we want to respect those filters when calculating metrics.
terminate_last (bool) – If true, concept termination will be done after all training.
use_overlaps (bool) – Allow overlapping entities, nearly always False as it is very difficult to annotate overlapping entities.
use_cui_doc_limit (bool) – If True the metrics for a CUI will be only calculated if that CUI appears in a document, in other words if the document was annotated for that CUI. Useful in very specific situations when during the annotation process the set of CUIs changed.
test_size (float) – If > 0 the data set will be split into train test based on this ration. Should be between 0 and 1. Usually 0.1 is fine.
devalue_others (bool) – Check add_name for more details.
use_groups (bool) – If True concepts that have groups will be combined and stats will be reported on groups.
never_terminate (bool) – If True no termination will be applied
train_from_false_positives (bool) – If True it will use false positive examples detected by medcat and train from them as negative examples.
extra_cui_filter (Optional[set]) – This filter will be intersected with all other filters, or if all others are not set then only this one will be used.
checkpoint (Optional[Optional[medcat.utils.checkpoint.Checkpoint]) – The MedCAT Checkpoint object
disable_progress (bool) – Whether to disable the progress output (tqdm). Defaults to False.
train_addons (bool) – Whether to also train the addons (e.g MetaCATs). Defaults to False.

Returns:

tuple – Consisting of the following parts fp (dict):

False positives for each CUI.

fn (dict):: False negatives for each CUI.
tp (dict):: True positives for each CUI.
p (dict):: Precision for each CUI.
r (dict):: Recall for each CUI.
f1 (dict):: F1 for each CUI.
cui_counts (dict):: Number of occurrence for each CUI.
examples (dict):: FP/FN examples of sentences for each CUI.

Return type:

tuple

_train_meta_cat(addon, data)

Parameters:

addon (medcat.components.addons.addons.AddonComponent)
data (medcat.data.mctexport.MedCATTrainerExport)

Return type:

None

_train_addons(data)

Parameters:: data (medcat.data.mctexport.MedCATTrainerExport)

_perform_epoch(current_project, current_document, train_set, disable_progress, extra_cui_filter, use_filters, train_from_false_positives, devalue_others, terminate_last, never_terminate)

Parameters:

current_project (int)
current_document (int)
train_set (medcat.data.mctexport.MedCATTrainerExport)
disable_progress (bool)
extra_cui_filter (Optional[set[str]])
use_filters (bool)
train_from_false_positives (bool)
devalue_others (bool)
terminate_last (bool)
never_terminate (bool)

Return type:

None

_train_supervised_for_project(project, current_document, train_from_false_positives, devalue_others)

Parameters:

project (medcat.data.mctexport.MedCATTrainerExportProject)
current_document (int)
train_from_false_positives (bool)
devalue_others (bool)

_train_supervised_for_project2(docs, current_document, train_from_false_positives, devalue_others)

Parameters:

docs (list[medcat.data.mctexport.MedCATTrainerExportDocument])
current_document (int)
train_from_false_positives (bool)
devalue_others (bool)

unlink_concept_name(cui, name, preprocessed_name=False)

Unlink a concept name from the CUI (or all CUIs if full_unlink), removes the link from the Concept Database (CDB). As a consequence medcat will never again link the name to this CUI - meaning the name will not be detected as a concept in the future.

Parameters:

cui (str) – The CUI from which the name will be removed.
name (str) – The span of text to be removed from the linking dictionary.
preprocessed_name (bool) – Whether the name being used is preprocessed.

Return type:

None

Examples

>>> # To never again link C0020538 to HTN
>>> cat.unlink_concept_name('C0020538', 'htn', False)

add_and_train_concept(cui, name, mut_doc=None, mut_entity=None, ontologies=set(), name_status='A', type_ids=set(), description='', full_build=True, negative=False, devalue_others=False, do_add_concept=True)

Add a name to an existing concept, or add a new concept, or do not do anything if the name or concept already exists. Perform training if spacy_entity and spacy_doc are set.

Parameters:

cui (str) – CUI of the concept.
name (str) – Name to be linked to the concept (in the case of MedCATtrainer this is simply the selected value in text, no preprocessing or anything needed).
mut_doc (Optional[MutableDocument]) – Spacy representation of the document that was manually annotated.
(mut_entity (mut_entity) –

Optional[Union[list[MutableToken],
MutableEntity]]):

Given the spacy document, this is the annotated span of text - list of annotated tokens that are marked with this CUI.
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).
negative (bool) – Is this a negative or positive example.
devalue_others (bool) – If set, cuis to which this name is assigned and are not cui will receive negative training given that negative=False.
do_add_concept (bool) – Whether to add concept to CDB.
mut_entity (Optional[Union[list[medcat.tokenizing.tokens.MutableToken], medcat.tokenizing.tokens.MutableEntity]])

Return type:

None

property _pn_configs: tuple[medcat.config.config.General, medcat.config.config.Preprocessing, medcat.config.config.CDBMaker]

Return type:: tuple[medcat.config.config.General, medcat.config.config.Preprocessing, medcat.config.config.CDBMaker]

medcat.cat.serialise(serialiser_type, obj, target_folder, overwrite=False)

Serialise an object based on the specified serialiser type.

Parameters:

serialiser_type (Union[str, AvailableSerialisers]) – The serialiser type.
obj (Serialisable) – The object to serialise.
target_folder (str) – The folder to serialise into.
overwrite (bool) – Whether to allow overwriting. Defaults to False.

Return type:

None

class medcat.cat.AvailableSerialisers

Bases: enum.Enum

Describes the available serialisers.

dill

json

write_to(file_path)

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

classmethod from_file(file_path)

Parameters:: file_path (str)
Return type:: AvailableSerialisers

__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.

medcat.cat.deserialise(folder_path, ignore_folders_prefix=set(), ignore_folders_suffix=set(), **init_kwargs)

Deserialise contents of a folder.

Extra init keyword arguments can be provided if needed. These are generally: - cnf: The config relevant to the components - tokenizer (BaseTokenizer): The base tokenizer for the model - cdb (CDB): The CDB for the model - vocab (Vocab): The Vocab for the model - model_load_path (Optional[str]): The model load path,

but not the component load path

This method finds the serialiser to be used based on the files on disk.

Parameters:

folder_path (str) – The folder to serialise.
ignore_folders_prefix (set[str]) – The prefixes of folders to ignore.
ignore_folders_suffix (set[str]) – The suffixes of folders to ignore.

Returns:

Serialisable – The deserialised object.

Return type:

medcat.storage.serialisables.Serialisable

class medcat.cat.AbstractSerialisable

The abstract serialisable base class.

This defines some common defaults.

get_strategy()

Return type:: SerialisingStrategy

classmethod get_init_attrs()

Return type:: list[str]

classmethod ignore_attrs()

Return type:: list[str]

classmethod include_properties()

Return type:: list[str]

__eq__(other)

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

medcat.cat.ensure_folder_if_parent(folder_name)

Ensure the folder exists if its parent folder exists.

Create a folder if the parent folder exists. If the parent folder does not exist, raise an error.

Parameters:: folder_name (str) – The target folder.
Raises:: ValueError – If the parent folder does not exist.
Return type:: None

class medcat.cat.Hasher(dumper=dumps)

A consistent hasher.

This class is able to hash the same object(s) to the same value every time. This is in contrast to the normal hashing in python that does not guarantee identical results over multiple runs.

Parameters:: dumper (Callable[[Any, bool], bytes], optional) – The dumper to be used. Defaults to the dumps method.

__init__(dumper=dumps)

Parameters:: dumper (Callable[[Any, bool], bytes])

m

_dumper

update(obj, length=False)

Update the hasher with the object in question.

If length = True is passed, only the length of the byte array corresponding to the data is considered Otherwise the entire byte array is used.

Parameters:

obj (Any) – The object to be added / hashed.
length (bool, optional) – Whether to only dump the length of the file array. Defaults to False.

Return type:

None

update_bytes(b)

Update the hasher with a byte array.

Parameters:: b (bytes) – The byte array to update with.
Return type:: None

hexdigest()

Get the hex for the current hash state.

Returns:: str – The hex representation of the hashed objects.
Return type:: str

class medcat.cat.Pipeline(cdb, vocab, model_load_path, old_pipe=None)

The pipeline for the NLP process.

This class is responsible to initial creation of the NLP document, as well as running through of all the components and addons.

Parameters:

cdb (medcat.cdb.CDB)
vocab (Optional[medcat.vocab.Vocab])
model_load_path (Optional[str])
old_pipe (Optional[Pipeline])

__init__(cdb, vocab, model_load_path, old_pipe=None)

Parameters:

cdb (medcat.cdb.CDB)
vocab (Optional[medcat.vocab.Vocab])
model_load_path (Optional[str])
old_pipe (Optional[Pipeline])

cdb

vocab: medcat.vocab.Vocab

config

_tokenizer

_components: list[medcat.components.types.CoreComponent] = []

_addons: list[medcat.components.addons.addons.AddonComponent] = []

property tokenizer: medcat.tokenizing.tokenizers.BaseTokenizer

The raw tokenizer (with no components).

Return type:: medcat.tokenizing.tokenizers.BaseTokenizer

property tokenizer_with_tag: medcat.tokenizing.tokenizers.BaseTokenizer

The tokenizer with the tagging component.

Return type:: medcat.tokenizing.tokenizers.BaseTokenizer

_init_tokenizer()

Return type:: medcat.tokenizing.tokenizers.BaseTokenizer

_init_component(comp_type, model_load_path)

Parameters:

comp_type (medcat.components.types.CoreComponentType)
model_load_path (Optional[str])

Return type:

medcat.components.types.CoreComponent

_get_loaded_components_paths(model_load_path)

Parameters:: model_load_path (Optional[str])
Return type:: tuple[dict[str, str], dict[tuple[str, str], str]]

_load_saved_core_component(cct_name, comp_folder_path)

Parameters:

cct_name (str)
comp_folder_path (str)

Return type:

medcat.components.types.CoreComponent

_init_components(model_load_path, old_pipe)

Parameters:

model_load_path (Optional[str])
old_pipe (Optional[Pipeline])

Return type:

None

_get_loaded_addon_path(cnf, loaded_addon_component_paths)

Parameters:

cnf (medcat.config.config.ComponentConfig)
loaded_addon_component_paths (dict[tuple[str, str], str])

Return type:

Optional[str]

_load_addon(cnf, load_from)

Parameters:

cnf (medcat.config.config.ComponentConfig)
load_from (str)

Return type:

medcat.components.addons.addons.AddonComponent

_init_addon(cnf, loaded_addon_component_paths, old_pipe)

Parameters:

cnf (medcat.config.config.ComponentConfig)
loaded_addon_component_paths (dict[tuple[str, str], str])
old_pipe (Optional[Pipeline])

Return type:

medcat.components.addons.addons.AddonComponent

get_doc(text)

Get the document for this text.

This essentially runs the tokenizer over the text.

Parameters:: text (str) – The input text.
Returns:: MutableDocument – The resulting document.
Return type:: medcat.tokenizing.tokens.MutableDocument

entity_from_tokens(tokens)

Get the entity from the list of tokens.

This effectively turns a list of (consecutive) documents into an entity.

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

get_component(ctype)

Get the core component by the component type.

Parameters:: ctype (CoreComponentType) – The core component type.
Raises:: ValueError – If no component by that type is found.
Returns:: CoreComponent – The corresponding core component.
Return type:: medcat.components.types.CoreComponent

add_addon(addon)

Parameters:: addon (medcat.components.addons.addons.AddonComponent)
Return type:: None

save_components(serialiser_type, components_folder)

Parameters:

serialiser_type (Union[medcat.storage.serialisers.AvailableSerialisers, str])
components_folder (str)

Return type:

None

iter_all_components()

Return type:: Iterable[medcat.components.types.BaseComponent]

iter_addons()

Return type:: Iterable[medcat.components.addons.addons.AddonComponent]

class medcat.cat.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.cat.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.cat.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.cat.TOKENIZER_PREFIX = 'tokenizer_internals_'

class medcat.cat.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

class medcat.cat.Entities

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)

entities: dict[int, Entity]

tokens: list[str]

text: str | None

__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

class medcat.cat.OnlyCUIEntities

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)

entities: dict[int, str]

tokens: list[str]

text: str | None

__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.cat.ModelCard

class medcat.cat.AbstractCoreComponent

Bases: CoreComponent

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:
        ...

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

get_type()

Return type:: CoreComponentType

property name: str

The name of the component.

Return type:: str

__call__(doc)

Parameters:: doc (medcat.tokenizing.tokens.MutableDocument)
Return type:: medcat.tokenizing.tokens.MutableDocument

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:

typing_extensions.Self

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.cat.HashableComponet

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:
        ...

get_hash()

Return type:: str

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.cat.AddonComponent

Bases: medcat.components.types.BaseComponent, Protocol

Base/abstract addon component class.

NAME_PREFIX: str = 'addon_'

NAME_SPLITTER: str = '.'

config: medcat.config.config.ComponentConfig

property addon_type: str

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

classmethod get_folder_name_for_addon_and_name(addon_type, name)

Parameters:

addon_type (str)
name (str)

Return type:

str

get_folder_name()

Return type:: str

property full_name: str

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

Return type:: str

property include_in_output: bool

Return type:: bool

get_output_key_val(ent)

Parameters:: ent (medcat.components.types.MutableEntity)
Return type:: tuple[str, dict[str, Any]]

property name: str

The name of the component.

Return type:: str

__call__(doc)

Parameters:: doc (medcat.tokenizing.tokens.MutableDocument)
Return type:: medcat.tokenizing.tokens.MutableDocument

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:

typing_extensions.Self

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

medcat.cat.is_legacy_model_pack(model_pack_path)

Check if the model pack is a legacy model pack.

Parameters:: model_pack_path (str) – The path to the model pack (unzipped).
Returns:: bool – True if the model pack is a legacy model pack, False otherwise.
Return type:: bool

medcat.cat.AVOID_LEGACY_CONVERSION_ENVIRON = 'MEDCAT_AVOID_LECACY_CONVERSION'

class medcat.cat.UsageMonitor(model_hash, config)

Parameters:

model_hash (Union[Callable[[], str], str])
config (medcat.config.config.UsageMonitor)

__init__(model_hash, config)

Parameters:

model_hash (Union[Callable[[], str], str])
config (medcat.config.config.UsageMonitor)

Return type:

None

config

log_buffer: list[str] = []

_model_hash

property model_hash: str

Return type:: str

property log_file

_get_auto_logs_location()

_setup_auto_logs()

property should_monitor: bool

Return type:: bool

_should_log()

Return type:: bool

log_inference(input_text_len, nr_of_ents_found)

Parameters:

input_text_len (int)
nr_of_ents_found (int)

Return type:

None

_flush_logs()

Return type:: None

__del__()

medcat.cat.logger

class medcat.cat.CAT(cdb, vocab=None, config=None, model_load_path=None)

Bases: medcat.storage.serialisables.AbstractSerialisable

This is a collection of serialisable model parts.

Parameters:

cdb (medcat.cdb.CDB)
vocab (Union[medcat.vocab.Vocab, None])
config (Optional[medcat.config.config.Config])
model_load_path (Optional[str])

__init__(cdb, vocab=None, config=None, model_load_path=None)

Parameters:

cdb (medcat.cdb.CDB)
vocab (Union[medcat.vocab.Vocab, None])
config (Optional[medcat.config.config.Config])
model_load_path (Optional[str])

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:

ent (medcat.tokenizing.tokens.MutableEntity)
doc_tokens (list[str])
cui (str)

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:

ent (medcat.tokenizing.tokens.MutableEntity)
doc_tokens (list[str])
only_cui (bool)

Return type:

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

_doc_to_out(doc, only_cui, out_with_text=False)

Parameters:

doc (medcat.tokenizing.tokens.MutableDocument)
only_cui (bool)
out_with_text (bool)

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]