medcat.utils.filters

Classes

LinkingFilters

These describe the linking filters used alongside the model.

MedCATTrainerExportProject

dict() -> new empty dictionary

Functions

temp_changed_config(config, target, value)

Context manager to change the config temporarily (within).

project_filters(filters, project, extra_cui_filter, ...)

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

Module Contents

class medcat.utils.filters.LinkingFilters(**data)

Bases: SerialisableBaseModel

These describe the linking filters used alongside the model.

When no CUIs nor excluded CUIs are specified (the sets are empty), all CUIs are accepted. If there are CUIs specified then only those will be accepted. If there are excluded CUIs specified, they are excluded.

In some cases, there are extra filters as well as MedCATtrainer (MCT) export filters. These are expected to follow the following: extra_cui_filter ⊆ MCT filter ⊆ Model/config filter

While any other CUIs can be included in the the extra CUI filter or the MCT filter, they would not have any real effect.

cuis: set[str]
cuis_exclude: set[str]
__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.

check_filters(cui)

Checks is a CUI in the filters

Parameters:

cui (str) – The CUI in question

Returns:

bool – True if the CUI is allowed

Return type:

bool

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__')
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.utils.filters.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

medcat.utils.filters.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.utils.filters.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.