medcat.trainer

Attributes

logger

Classes

MutableDocument	The mutable parts of the document.
MutableEntity	The mutable part of an entity.
MutableToken	The mutable part of a token.
CDB	The abstract serialisable base class.
General	The general part of the config
Preprocessing	The preprocessing part of the config
CDBMaker	The Context Database (CDB) making part of the config
MedCATTrainerExport	dict() -> new empty dictionary
MedCATTrainerExportProject	dict() -> new empty dictionary
MedCATTrainerExportDocument	dict() -> new empty dictionary
NameDescriptor
CoreComponentType	Generic enumeration.
TrainableComponent	Base class for protocol classes.
AddonComponent	Base/abstract addon component class.
Pipeline	The pipeline for the NLP process.
Trainer

Functions

temp_changed_config(config, target, value)	Context manager to change the config temporarily (within).
make_mc_train_test(data, cdb[, test_size])	Make train set.
get_false_positives(doc, spacy_doc)	Get the false positives within a trainer export.
project_filters(filters, project, extra_cui_filter, ...)	Context manager with per project filters based on a trainer export.
count_all_annotations(export)	Count the number of annotations in a trainer export.
iter_anns(export)	Iterate over all the annotations in a trainer export.
prepare_name(raw_name, nlp, names, configs)	Generates different forms of a name. Will edit the provided names

Module Contents

class medcat.trainer.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.trainer.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.trainer.MutableToken

Bases: Protocol

The mutable part of a token.

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

property base: BaseToken

The base portion of the token.

Return type:

BaseToken

property is_punctuation: bool

Whether the token represents punctuation.

Return type:

bool

property to_skip: bool

Whether the token should be skipped.

Return type:

bool

property lemma: str

The lemmatised version of the text.

Return type:

str

property tag: str | None

Optional tag (e.g) for normalization.

Return type:

Optional[str]

property norm: str

The normalised text.

Return type:

str

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.trainer.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.trainer.General(/, **data)

Bases: SerialisableBaseModel

The general part of the config

Parameters:

data (Any)

nlp: NLPConfig

usage_monitor: UsageMonitor

Checkpointing config

log_level: int = 20

Logging config for everything | ‘tagger’ can be disabled, but will cause a drop in performance

log_format: str = '%(levelname)s:%(name)s: %(message)s'

log_path: str = './medcat.log'

separator: str = '~'

Separator that will be used to merge tokens of a name. Once a CDB is built this should always stay the same.

spell_check: bool = True

Should we check spelling - note that this makes things much slower, use only if necessary. The only thing necessary for the spell checker to work is vocab.dat and cdb.dat built with concepts in the respective language.

diacritics: bool = False

Should we process diacritics - for languages other than English, symbols such as ‘é, ë, ö’ can be relevant. Note that this makes spell_check slower.

spell_check_deep: bool = False

If True the spell checker will try harder to find mistakes, this can slow down things drastically.

spell_check_len_limit: int = 7

Spelling will not be checked for words with length less than this

show_nested_entities: bool = False

If set to True functions like get_entities and get_json will return nested_entities and overlaps

full_unlink: bool = False

When unlinking a name from a concept should we do full_unlink (means unlink a name from all concepts, not just the one in question)

workers: int

Number of workers used by a parallelizable pipeline component

make_pretty_labels: str | None = None

Should the labels of entities (shown in displacy) be pretty or just ‘concept’. Slows down the annotation pipeline should not be used when annotating millions of documents. If None it will be the string “concept”, if short it will be CUI, if long it will be CUI | Name | Confidence

map_cui_to_group: bool = False

If the cdb.addl_info[‘cui2group’] is provided and this option enabled, each CUI will be mapped to the group

simple_hash: bool = False

Whether to use a simple hash.

NOTE: While using a simple hash is faster at save time, it is less reliable due to not taking into account all the details of the changes.

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.trainer.Preprocessing(/, **data)

Bases: SerialisableBaseModel

The preprocessing part of the config

Parameters:

data (Any)

words_to_skip: set

This words will be completely ignored from concepts and from the text (must be a Set)

keep_punct: set

All punct will be skipped by default, here you can set what will be kept

do_not_normalize: set[str]

e.g. running -> run Values are detailed part-of-speech tags. See: - https://spacy.io/usage/linguistic-features#pos-tagging - Label scheme section per model at https://spacy.io/models/en

Type:

Should specific word types be normalized

skip_stopwords: bool = False

Should stopwords be skipped/ignored when processing input

min_len_normalize: int = 5

Nothing below this length will ever be normalized (input tokens or concept names), normalized means lemmatized in this case

stopwords: set | None = None

If None the default set of stowords from spacy will be used. This must be a Set.

NB! For these changes to take effect, the pipe would need to be recreated.

max_document_length: int = 1000000

Documents longer than this will be trimmed.

NB! For these changes to take effect, the pipe would need to be recreated.

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.trainer.CDBMaker(/, **data)

Bases: SerialisableBaseModel

The Context Database (CDB) making part of the config

Parameters:

data (Any)

name_versions: list = ['LOWER', 'CLEAN']

Name versions to be generated.

multi_separator: str = '|'

If multiple names or type_ids for a concept present in one row of a CSV, they are separated by the specified character.

remove_parenthesis: int = 5

Should preferred names with parenthesis be cleaned 0 means no, else it means if longer than or equal e.g. Head (Body part) -> Head

min_letters_required: int = 2

Minimum number of letters required in a name to be accepted for a concept

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.trainer.temp_changed_config(config, target, value)

Context manager to change the config temporarily (within).

Parameters:

• config (BaseModel) – The config in question.

• target (str) – The attribute name to change.

• value (Any) – The temporary value to use.

Raises:

IllegalConfigPathException – If no previous value is available.

medcat.trainer.make_mc_train_test(data, cdb, test_size=0.2)

Make train set.

This is a disaster.

Parameters:

• data (MedCATTrainerExport) – The data.

• cdb (CDB) – The concept database.

• test_size (float) – The test size. Defaults to 0.2.

Returns:

tuple – The train set, the test set, the test annotations, and the total annotations

Return type:

tuple

medcat.trainer.get_false_positives(doc, spacy_doc)

Get the false positives within a trainer export.

Parameters:

• doc (MedCATTrainerExportDocument) – The trainer export.

• spacy_doc (MutableDocument) – The annotated document.

Returns:

list[MutableEntity] – The list of false positive entities.

Return type:

list[medcat.tokenizing.tokens.MutableEntity]

medcat.trainer.project_filters(filters, project, extra_cui_filter, use_project_filters)

Context manager with per project filters based on a trainer export.

Parameters:

• filters (LinkingFilters) – The current config.

• project (MedCATTrainerExportProject) – The trainer export.

• extra_cui_filter (Optional[set[str]]) – Extra cui filters.

• use_project_filters (bool) – Whether to use project filters.

class medcat.trainer.MedCATTrainerExport

Bases: typing_extensions.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)

projects: list[MedCATTrainerExportProject]

__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.trainer.MedCATTrainerExportProject

Bases: typing_extensions.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)

name: str

id: Any

cuis: str

tuis: str | None

documents: list[MedCATTrainerExportDocument]

__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.trainer.MedCATTrainerExportDocument

Bases: typing_extensions.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)

name: str

id: Any

last_modified: str

text: str

annotations: list[MedCATTrainerExportAnnotation]

__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.trainer.count_all_annotations(export)

Count the number of annotations in a trainer export.

Parameters:

export (MedCATTrainerExport) – The trainer export.

Returns:

int – The total number of annotations.

Return type:

int

medcat.trainer.iter_anns(export)

Iterate over all the annotations in a trainer export.

Parameters:

export (MedCATTrainerExport) – The trainer export.

Yields:

Iterator[tuple[MedCATTrainerExportProjectInfo,

MedCATTrainerExportDocument, MedCATTrainerExportAnnotation]]:

The project info, the document, and the annotation.

Return type:

Iterator[tuple[MedCATTrainerExportProjectInfo, MedCATTrainerExportDocument, MedCATTrainerExportAnnotation]]

medcat.trainer.prepare_name(raw_name, nlp, names, configs)

Generates different forms of a name. Will edit the provided names dictionary and add information generated from the name.

Parameters:

• nlp (BaseTokenizer) – The tokenizer.

• names (dict[str, NameDescriptor]) – Dictionary of existing names for this concept in this row of a CSV. The new generated name versions and other required information will be added here.

• configs (tuple[LGeneral, LPreprocessing, LCDBMaker]) – Applicable configs for medcat.

• raw_name (str)

Returns:

names (dict) – The updated dictionary of prepared names.

Return type:

dict[str, NameDescriptor]

class medcat.trainer.NameDescriptor

tokens: list[str]

snames: set[str]

raw_name: str

is_upper: bool

class medcat.trainer.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.trainer.TrainableComponent

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:
        ...

train(cui, entity, doc, negative=False, names=[])

Train the component.

This should only apply to the linker.

Parameters:

• cui (str) – The CUI to train.

• entity (BaseEntity) – The entity we’re at.

• doc (BaseDocument) – The document within which we’re working.

• negative (bool) – Whether or not the example is negative. Defaults to False.

• names (list[str]/dict) – Optionally used to update the status of a name-cui pair in the CDB.

Return type:

None

__slots__ = ()

_is_protocol = True

_is_runtime_protocol = False

classmethod __init_subclass__(*args, **kwargs)

classmethod __class_getitem__(params)

class medcat.trainer.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)

class medcat.trainer.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]

medcat.trainer.logger

class medcat.trainer.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]