medcat2.components.addons.meta_cat.meta_cat

Attributes

logger

_META_ANNS_PATH

_SHARE_TOKENS_PATH

Exceptions

MisconfiguredMetaCATException

Inappropriate argument value (of correct type).

Classes

Hasher

A consistent hasher.

BaseTokenizer

The base tokenizer protocol.

ConfigMetaCAT

The MetaCAT part of the config

AddonComponent

Base/abstract addon component class.

TokenizerWrapperBase

Helper class that provides a standard way to create an ABC using

AbstractSerialisable

The abstract serialisable base class.

SerialisingStrategy

Describes the strategy for serialising.

MutableDocument

The mutable parts of the document.

MutableEntity

The mutable part of an entity.

CDB

The abstract serialisable base class.

Vocab

Vocabulary used to store word embeddings for context similarity

MedCATTrainerExportDocument

dict() -> new empty dictionary

MetaCATAddon

Base/abstract addon component class.

MetaCAT

The MetaCAT class used for training 'Meta-Annotation' models,

Functions

predict(model, data, config)

Predict on data used in the meta_cat.pipe

train_model(model, data, config[, save_dir_path])

Trains a LSTM model and BERT with autocheckpoints

set_all_seeds(seed)

eval_model(model, data, config, tokenizer)

Evaluate a trained model on the provided data

prepare_from_json(data, cntx_left, cntx_right, tokenizer)

Convert the data from a json format into a CSV-like format for

encode_category_values(data[, ...])

Converts the category values in the data outputted by

prepare_for_oversampled_data(data, tokenizer)

Convert the data from a json format into a CSV-like format for

serialise(serialiser_type, obj, target_folder[, overwrite])

Serialise an object based on the specified serialiser type.

deserialise(folder_path[, ignore_folders_prefix, ...])

Deserialise contents of a folder.

Module Contents

class medcat2.components.addons.meta_cat.meta_cat.Hasher(dumper=dumps)

A consistent hasher.

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

Parameters:

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

__init__(dumper=dumps)
Parameters:

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

m
_dumper
update(obj, length=False)

Update the hasher with the object in question.

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

Parameters:

  • obj (Any) – The object to be added / hashed.

  • length (bool, optional) – Whether to only dump the length of the file array. Defaults to False.

Return type:

None

update_bytes(b)

Update the hasher with a byte array.

Parameters:

b (bytes) – The byte array to update with.

Return type:

None

hexdigest()

Get the hex for the current hash state.

Returns:

str – The hex representation of the hashed objects.

Return type:

str

class medcat2.components.addons.meta_cat.meta_cat.BaseTokenizer

Bases: Protocol

The base tokenizer protocol.

create_entity(doc, token_start_index, token_end_index, label)

Create an entity from a document.

Parameters:

  • doc (MutableDocument) – The document to use.

  • token_start_index (int) – The token start index.

  • token_end_index (int) – The token end index.

  • label (str) – The label.

Returns:

MutableEntity – The resulting entity.

Return type:

medcat2.tokenizing.tokens.MutableEntity

entity_from_tokens(tokens)

Get an entity from the list of tokens.

Parameters:

tokens (list[MutableToken]) – List of tokens.

Returns:

MutableEntity – The resulting entity.

Return type:

medcat2.tokenizing.tokens.MutableEntity

__call__(text)
Parameters:

text (str)

Return type:

medcat2.tokenizing.tokens.MutableDocument

classmethod get_init_args(config)
Parameters:

config (medcat2.config.Config)

Return type:

list[Any]

classmethod get_init_kwargs(config)
Parameters:

config (medcat2.config.Config)

Return type:

dict[str, Any]

get_doc_class()

Get the document implementation class used by the tokenizer.

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

Returns:

Type[MutableDocument] – The document class.

Return type:

Type[medcat2.tokenizing.tokens.MutableDocument]

get_entity_class()

Get the entity implementation class used by the tokenizer.

Returns:

Type[MutableEntity] – The entity class.

Return type:

Type[medcat2.tokenizing.tokens.MutableEntity]

__slots__ = ()
_is_protocol = True
_is_runtime_protocol = False
classmethod __init_subclass__(*args, **kwargs)
classmethod __class_getitem__(params)
class medcat2.components.addons.meta_cat.meta_cat.ConfigMetaCAT(/, **data)

Bases: medcat2.config.config.ComponentConfig

The MetaCAT part of the config

Parameters:

data (Any)

general: General
model: Model
train: Train
class Config
extra = 'allow'
validate_assignment = True
comp_name: str = 'default'

The name of the component.

If a custom implementation is required, it needs to be registered using `medcat2.components.types.register_core_component(

<core component type>, <component name>, <implementing class>)

By default, only the ‘default’ component is registered.

init_args: list

These are the positional arguments required to construct the component.

For default components, these will be automatically filled. However, if a custom component is used, these would need to be set manually.

init_kwargs: dict

These are the keyword arguments required to construct the component.

For default components, these will be automatically filled. However, if a custom component is used, these would need to be set manually.

classmethod ignore_attrs()
get_strategy()
Return type:

medcat2.storage.serialisables.SerialisingStrategy

classmethod get_init_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).

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

medcat2.components.addons.meta_cat.meta_cat.predict(model, data, config)

Predict on data used in the meta_cat.pipe

Parameters:

  • model (nn.Module) – The model.

  • data (list[tuple[list[int], int, Optional[int]]]) – Data in the format: [[<input_ids>, <cpos>], …]

  • config (ConfigMetaCAT) – Configuration for this meta_cat instance.

Returns:

  • predictions (list[int]) – For each row of input data a prediction

  • confidence (list[float]) – For each prediction a confidence value

Return type:

tuple

medcat2.components.addons.meta_cat.meta_cat.train_model(model, data, config, save_dir_path=None)

Trains a LSTM model and BERT with autocheckpoints

Parameters:

  • model (nn.Module) – The model

  • data (list) – The data.

  • config (ConfigMetaCAT) – MetaCAT config.

  • save_dir_path (Optional[str]) – The save dir path if required. Defaults to None.

Returns:

dict – The classification report for the winner.

Raises:

Exception – If auto-save is enabled but no save dir path is provided.

Return type:

dict

medcat2.components.addons.meta_cat.meta_cat.set_all_seeds(seed)
Parameters:

seed (int)

Return type:

None

medcat2.components.addons.meta_cat.meta_cat.eval_model(model, data, config, tokenizer)

Evaluate a trained model on the provided data

Parameters:
Returns:

dict – Results (precision, recall, f1, examples, confusion matrix)

Return type:

dict

medcat2.components.addons.meta_cat.meta_cat.prepare_from_json(data, cntx_left, cntx_right, tokenizer, cui_filter=None, replace_center=None, prerequisites={}, lowercase=True)

Convert the data from a json format into a CSV-like format for training. This function is not very efficient (the one working with documents as part of the meta_cat.pipe method is much better). If your dataset is > 1M documents think about rewriting this function - but would be strange to have more than 1M manually annotated documents.

Parameters:

  • data (dict) – Loaded output of MedCATtrainer. If we have a my_export.json from MedCATtrainer, than data = json.load(<my_export>).

  • cntx_left (int) – Size of context to get from the left of the concept

  • cntx_right (int) – Size of context to get from the right of the concept

  • tokenizer (TokenizerWrapperBase) – Something to split text into tokens for the LSTM/BERT/whatever meta models.

  • replace_center (Optional[str]) – If not None the center word (concept) will be replaced with whatever this is.

  • prerequisites (dict) –

    A map of prerequisites, for example our data has two meta-annotations (experiencer, negation). Assume I want to create a dataset for negation but only in those cases where experiencer=patient, my prerequisites would be:

    {‘Experiencer’: ‘Patient’} - Take care that the CASE has to

    match whatever is in the data. Defaults to {}.

  • lowercase (bool) – Should the text be lowercased before tokenization. Defaults to True.

  • cui_filter (Optional[set]) – CUI filter if set. Defaults to None.

Returns:

out_data (dict) –

Example: {‘category_name’: [(‘<category_value>’, ‘<[tokens]>’,

‘<center_token>’), …], …}

Return type:

dict

medcat2.components.addons.meta_cat.meta_cat.encode_category_values(data, existing_category_value2id=None, category_undersample=None)

Converts the category values in the data outputted by prepare_from_json into integer values.

Parameters:

  • data (dict) – Output of prepare_from_json.

  • existing_category_value2id (Optional[dict]) – Map from category_value to id (old/existing).

  • category_undersample – Name of class that should be used to undersample the data (for 2 phase learning)

Returns:

  • dict – New data with integers inplace of strings for category values.

  • dict – New undersampled data (for 2 phase learning) with integers inplace of strings for category values

  • dict – Map from category value to ID for all categories in the data.

Return type:

tuple

medcat2.components.addons.meta_cat.meta_cat.prepare_for_oversampled_data(data, tokenizer)

Convert the data from a json format into a CSV-like format for training. This function is not very efficient (the one working with documents as part of the meta_cat.pipe method is much better). If your dataset is > 1M documents think about rewriting this function - but would be strange to have more than 1M manually annotated documents.

Parameters:

  • data (list) –

    Oversampled data expected in the following format: [[[‘text’,’of’,’the’,’document’], [index of medical entity],

    ”label” ],

    [‘text’,’of’,’the’,’document’], [index of medical entity],

    ”label” ]]

  • tokenizer (TokenizerWrapperBase) – Something to split text into tokens for the LSTM/BERT/whatever meta models.

Returns:

data_sampled (list) – The processed data in the format that can be merged with the output from prepare_from_json. [[<[tokens]>, [index of medical entity], “label” ], <[tokens]>, [index of medical entity], “label” ]]

Return type:

list

class medcat2.components.addons.meta_cat.meta_cat.AddonComponent

Bases: medcat2.components.types.BaseComponent, Protocol

Base/abstract addon component class.

NAME_PREFIX: str = 'addon_'
NAME_SPLITTER: str = '.'
config: medcat2.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

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 (medcat2.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 (medcat2.tokenizing.tokens.MutableDocument)

Return type:

medcat2.tokenizing.tokens.MutableDocument

classmethod get_init_args(tokenizer, cdb, vocab, model_load_path)

Get the init arguments for the component.

Parameters:

  • tokenizer (BaseTokenizer) – The tokenizer.

  • cdb (CDB) – The CDB.

  • vocab (Vocab) – The Vocab.

  • model_load_path (Optional[str]) – The model load path (or None).

Returns:

list[Any] – The list of init arguments.

Return type:

list[Any]

classmethod get_init_kwargs(tokenizer, cdb, vocab, model_load_path)

Get init keyword arguments for the component.

Parameters:

  • tokenizer (BaseTokenizer) – The tokenizer.

  • cdb (CDB) – The CDB.

  • vocab (Vocab) – The Vocab.

  • model_load_path (Optional[str]) – The model load path (or None).

Returns:

dict[str, Any] – The keywrod arguments.

Return type:

dict[str, Any]

__slots__ = ()
_is_protocol = True
_is_runtime_protocol = False
classmethod __init_subclass__(*args, **kwargs)
classmethod __class_getitem__(params)
class medcat2.components.addons.meta_cat.meta_cat.TokenizerWrapperBase(hf_tokenizer=None)

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

Parameters:

hf_tokenizer (Optional[tokenizers.Tokenizer])

name: str
__init__(hf_tokenizer=None)
Parameters:

hf_tokenizer (Optional[tokenizers.Tokenizer])

Return type:

None

hf_tokenizers = None
__call__(text: str) → dict
__call__(text: list[str]) → list[dict]
abstract save(dir_path)
Parameters:

dir_path (str)

Return type:

None

classmethod load(dir_path, model_variant='', **kwargs)
Abstractmethod:

Parameters:

  • dir_path (str)

  • model_variant (Optional[str])

Return type:

tokenizers.Tokenizer

abstract get_size()
Return type:

int

abstract token_to_id(token)
Parameters:

token (str)

Return type:

Union[int, list[int]]

abstract get_pad_id()
Return type:

Union[Optional[int], list[int]]

ensure_tokenizer()
Return type:

tokenizers.Tokenizer

__slots__ = ()
medcat2.components.addons.meta_cat.meta_cat.serialise(serialiser_type, obj, target_folder, overwrite=False)

Serialise an object based on the specified serialiser type.

Parameters:

  • serialiser_type (Union[str, AvailableSerialisers]) – The serialiser type.

  • obj (Serialisable) – The object to serialise.

  • target_folder (str) – The folder to serialise into.

  • overwrite (bool) – Whether to allow overwriting. Defaults to False.

Return type:

None

medcat2.components.addons.meta_cat.meta_cat.deserialise(folder_path, ignore_folders_prefix=set(), ignore_folders_suffix=set(), **init_kwargs)

Deserialise contents of a folder.

Extra init keyword arguments can be provided if needed.

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

Parameters:

  • folder_path (str) – The folder to serialise.

  • ignore_folders_prefix (set[str]) – The prefixes of folders to ignore.

  • ignore_folders_suffix (set[str]) – The suffixes of folders to ignore.

Returns:

Serialisable – The deserialised object.

Return type:

medcat2.storage.serialisables.Serialisable

class medcat2.components.addons.meta_cat.meta_cat.AbstractSerialisable

The abstract serialisable base class.

This defines some common defaults.

get_strategy()
Return type:

SerialisingStrategy

classmethod get_init_attrs()
Return type:

list[str]

classmethod ignore_attrs()
Return type:

list[str]

classmethod include_properties()
Return type:

list[str]

__eq__(other)
Parameters:

other (Any)

Return type:

bool

class medcat2.components.addons.meta_cat.meta_cat.SerialisingStrategy

Bases: enum.Enum

Describes the strategy for serialising.

SERIALISABLE_ONLY

Only serialise attributes that are of Serialisable type

SERIALISABLES_AND_DICT

Serialise attributes that are Serialisable as well as the rest of .__dict__

DICT_ONLY

Only include the object’s .__dict__

MANUAL

Use manual serialisation defined by the object itself.

NOTE: In this case, most of the logic defined within here will

likely be ignored.

_is_suitable_in_dict(attr_name, attr, obj)
Parameters:
Return type:

bool

_is_suitable_part(attr_name, part, obj)
Parameters:
Return type:

bool

_iter_obj_items(obj)
Parameters:

obj (Serialisable)

Return type:

Iterable[tuple[str, Any]]

_iter_obj_values(obj)
Parameters:

obj (Serialisable)

Return type:

Iterable[Any]

get_dict(obj)

Gets the appropriate parts of the __dict__ of the object.

I.e this filters out parts that shouldn’t be included.

Parameters:

obj (Serialisable) – The serialisable object.

Returns:

dict[str, Any] – The filtered attributes map.

Return type:

dict[str, Any]

get_parts(obj)

Gets the matching serialisable parts of the object.

This includes only serialisable parts, and only if specified by the strategy.

Returns:

list[tuple[Serialisable, str]] – The serialisable parts with names.

Parameters:

obj (Serialisable)

Return type:

list[tuple[Serialisable, str]]

__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 medcat2.components.addons.meta_cat.meta_cat.MutableDocument

Bases: Protocol

The mutable parts of the document.

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

property base: BaseDocument

The base document.

Return type:

BaseDocument

property final_ents: list[MutableEntity]

The linked entities associated with the document.

This should be set by the linker.

Return type:

list[MutableEntity]

property all_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
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

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

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 medcat2.components.addons.meta_cat.meta_cat.MutableEntity

Bases: Protocol

The mutable part of an entity.

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

property base: BaseEntity

The base / static entity part.

Return type:

BaseEntity

property detected_name: str

The detected name (if any) for this entity.

This should be set by the NER component.

Return type:

str

set_addon_data(path, val)

Used to add arbitrary data to the entity.

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

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

Parameters:

  • path (str) – The data ID / path.

  • val (Any) – The value to be added.

Return type:

None

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

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 medcat2.components.addons.meta_cat.meta_cat.CDB(config)

Bases: medcat2.storage.serialisables.AbstractSerialisable

The abstract serialisable base class.

This defines some common defaults.

Parameters:

config (medcat2.config.Config)

__init__(config)
Parameters:

config (medcat2.config.Config)

Return type:

None

config
cui2info: dict[str, medcat2.cdb.concepts.CUIInfo]
name2info: dict[str, medcat2.cdb.concepts.NameInfo]
type_id2info: dict[str, medcat2.cdb.concepts.TypeInfo]
token_counts: dict[str, int]
addl_info: dict[str, Any]
_subnames: set[str]
is_dirty = False
has_changed_names = False
classmethod get_init_attrs()
Return type:

list[str]

_reset_subnames()
has_subname(name)

Whether the CDB has the specified subname.

Parameters:

name (str) – The subname to check.

Returns:

bool – Whether the subname is present in this CDB.

Return type:

bool

get_name(cui)

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

Parameters:

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

Returns:

str – The name of the concept.

Return type:

str

weighted_average_function(step)

Get the weighted average for steop.

Parameters:

step (int) – The steop.

Returns:

float – The weighted average.

Return type:

float

add_types(types)

Add type info to CDB.

Parameters:

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

Return type:

None

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

Adds a name to an existing concept.

Parameters:

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

  • names (dict[str, NameDescriptor]) –

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

    ’raw_name’: raw_name}, …}`

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

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

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

Return type:

None

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

None

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

None

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

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

Parameters:

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

  • names (dict[str, NameDescriptor]) –

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

    ’raw_name’: raw_name}, …}`

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

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

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

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

  • description (str) – Description of this concept.

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

Return type:

None

reset_training()

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

Return type:

None

filter_by_cui(cuis_to_keep)

Subset the core CDB fields (dictionaries/maps).

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

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

Parameters:

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

Raises:

Exception – If no snames and subsetting is not possible.

Return type:

None

remove_cui(cui)

This function takes a CUI and removes it the CDB.

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

Parameters:

cui (str) – The CUI to remove.

Return type:

None

_remove_names(cui, names)

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

Parameters:

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

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

Return type:

None

__eq__(other)
Parameters:

other (Any)

Return type:

bool

get_cui2count_train()
Return type:

dict[str, int]

get_name2count_train()
Return type:

dict[str, int]

get_hash()
Return type:

str

get_basic_info()
Return type:

medcat2.data.model_card.CDBInfo

get_strategy()
Return type:

SerialisingStrategy

classmethod ignore_attrs()
Return type:

list[str]

classmethod include_properties()
Return type:

list[str]

class medcat2.components.addons.meta_cat.meta_cat.Vocab

Bases: medcat2.storage.serialisables.AbstractSerialisable

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

Properties:
vocab (dict[str, WordDescriptor]):
Map from word to attributes, e.g. {‘house’:

{‘vector’: <np.array>, ‘count’: <int>, …}, …}

index2word (dict[int, str]):

From word to an index - used for negative sampling

vec_index2word (dict):

Same as index2word but only words that have vectors

__init__()
Return type:

None

vocab: dict[str, WordDescriptor]
index2word: dict[int, str]
vec_index2word: dict[int, str]
cum_probs: numpy.ndarray
inc_or_add(word, cnt=1, vec=None)

Add a word or increase its count.

Parameters:

  • word (str) – Word to be added

  • cnt (int) – By how much should the count be increased, or to what should it be set if a new word. (Default value = 1)

  • vec (Optional[np.ndarray]) – Word vector (Default value = None)

Return type:

None

remove_all_vectors()

Remove all stored vector representations.

Return type:

None

remove_words_below_cnt(cnt)

Remove all words with frequency below cnt.

Parameters:

cnt (int) – Word count limit.

Return type:

None

_rebuild_index()
inc_wc(word, cnt=1)

Incraese word count by cnt.

Parameters:

  • word (str) – For which word to increase the count

  • cnt (int) – By how muhc to increase the count (Default value = 1)

Return type:

None

add_vec(word, vec)

Add vector to a word.

Parameters:

  • word (str) – To which word to add the vector.

  • vec (np.ndarray) – The vector to add.

Return type:

None

reset_counts(cnt=1)

Reset the count for all word to cnt.

Parameters:

cnt (int) – New count for all words in the vocab. (Default value = 1)

Return type:

None

update_counts(tokens)

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

Parameters:

tokens (list[str]) – Usually a large block of text split into tokens/words.

Return type:

None

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

Add a word to the vocabulary

Parameters:

  • word (str) – The word to be added, it should be lemmatized and lowercased

  • cnt (int) – Count of this word in your dataset (Default value = 1)

  • vec (Optional[np.ndarray]) – The vector representation of the word (Default value = None)

  • replace (bool) – Will replace old vector representation (Default value = True)

Return type:

None

add_words(path, replace=True)

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

<word> <cnt>[ <vec_space_separated>]

e.g. one line: the word house with 3 dimensional vectors

house 34444 0.3232 0.123213 1.231231

Parameters:

  • path (str) – path to the file with words and vectors

  • replace (bool) – existing words in the vocabulary will be replaced. Defaults to True.

Return type:

None

init_cumsums()

Initialise cumulative sums.

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

Return type:

None

get_negative_samples(n=6, ignore_punct_and_num=False)

Get N negative samples.

Parameters:

  • n (int) – How many words to return (Default value = 6)

  • ignore_punct_and_num (bool) – Whether to ignore punctuation and numbers. Defaults to False.

Raises:

Exception – If no unigram table is present.

Returns:

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

Return type:

list[int]

get_vectors(indices)
Parameters:

indices (list[int])

Return type:

list[numpy.ndarray]

__getitem__(word)
Parameters:

word (str)

Return type:

int

vec(word)
Parameters:

word (str)

Return type:

Optional[numpy.ndarray]

count(word)
Parameters:

word (str)

Return type:

int

item(word)
Parameters:

word (str)

Return type:

WordDescriptor

__contains__(word)
Parameters:

word (str)

Return type:

bool

__eq__(other)
Parameters:

other (Any)

Return type:

bool

get_strategy()
Return type:

SerialisingStrategy

classmethod get_init_attrs()
Return type:

list[str]

classmethod ignore_attrs()
Return type:

list[str]

classmethod include_properties()
Return type:

list[str]

medcat2.components.addons.meta_cat.meta_cat.logger
medcat2.components.addons.meta_cat.meta_cat._META_ANNS_PATH = 'meta_cat_meta_anns'
medcat2.components.addons.meta_cat.meta_cat._SHARE_TOKENS_PATH = 'meta_cat_share_tokens'
class medcat2.components.addons.meta_cat.meta_cat.MedCATTrainerExportDocument

Bases: TypedDict

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

(key, value) pairs

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

d = {} for k, v in iterable:

d[k] = v

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

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

name: str
confidence: float
value: str
__contains__()

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

__delattr__()

Implement delattr(self, name).

__delitem__()

Delete self[key].

__dir__()

Default dir() implementation.

__eq__()

Return self==value.

__format__()

Default object formatter.

__ge__()

Return self>=value.

__getattribute__()

Return getattr(self, name).

__getitem__()

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

__gt__()

Return self>value.

__init__()

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

__ior__()

Return self|=value.

__iter__()

Implement iter(self).

__le__()

Return self<=value.

__len__()

Return len(self).

__lt__()

Return self<value.

__ne__()

Return self!=value.

__new__()

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

__or__()

Return self|value.

__reduce__()

Helper for pickle.

__reduce_ex__()

Helper for pickle.

__repr__()

Return repr(self).

__reversed__()

Return a reverse iterator over the dict keys.

__ror__()

Return value|self.

__setattr__()

Implement setattr(self, name, value).

__setitem__()

Set self[key] to value.

__sizeof__()

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

__str__()

Return str(self).

__subclasshook__()

Abstract classes can override this to customize issubclass().

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

clear()

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

copy()

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

get()

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

items()

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

keys()

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

pop()

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

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

popitem()

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

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

setdefault()

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

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

update()

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

values()

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

class medcat2.components.addons.meta_cat.meta_cat.MetaCATAddon(config, base_tokenizer, meta_cat)

Bases: medcat2.components.addons.addons.AddonComponent

Base/abstract addon component class.

Parameters:
addon_type = 'meta_cat'
output_key = 'meta_anns'
config: medcat2.config.config_meta_cat.ConfigMetaCAT
__init__(config, base_tokenizer, meta_cat)
Parameters:
Return type:

None

base_tokenizer
_mc
_name
property mc: MetaCAT
Return type:

MetaCAT

classmethod create_new(config, base_tokenizer, tokenizer)

Factory method to create a new MetaCATAddon instance.

Parameters:
Return type:

MetaCATAddon

classmethod load_existing(cnf, base_tokenizer, load_path)

Factory method to load an existing MetaCATAddon from disk.

Parameters:
Return type:

MetaCATAddon

property name: str

The name of the component.

Return type:

str

_init_tokenizer(cnf, pack_save_path)
Parameters:
Return type:

Optional[medcat2.components.addons.meta_cat.meta_cat_tokenizers.TokenizerWrapperBase]

__call__(doc)
Parameters:

doc (medcat2.tokenizing.tokens.MutableDocument)

Return type:

medcat2.tokenizing.tokens.MutableDocument

classmethod get_init_args(tokenizer, cdb, vocab, model_load_path)

Get the init arguments for the component.

Parameters:

  • tokenizer (BaseTokenizer) – The tokenizer.

  • cdb (CDB) – The CDB.

  • vocab (Vocab) – The Vocab.

  • model_load_path (Optional[str]) – The model load path (or None).

Returns:

list[Any] – The list of init arguments.

Return type:

list[Any]

classmethod get_init_kwargs(tokenizer, cdb, vocab, model_load_path)

Get init keyword arguments for the component.

Parameters:

  • tokenizer (BaseTokenizer) – The tokenizer.

  • cdb (CDB) – The CDB.

  • vocab (Vocab) – The Vocab.

  • model_load_path (Optional[str]) – The model load path (or None).

Returns:

dict[str, Any] – The keywrod arguments.

Return type:

dict[str, Any]

load(folder_path)
Parameters:

folder_path (str)

Return type:

MetaCAT

_load_tokenizer(tokenizer_folder)
Parameters:

tokenizer_folder (str)

Return type:

Optional[medcat2.components.addons.meta_cat.meta_cat_tokenizers.TokenizerWrapperBase]

classmethod _get_meta_cat_and_tokenizer_paths(folder_path)
Parameters:

folder_path (str)

Return type:

tuple[str, str]

save(folder_path)
Parameters:

folder_path (str)

Return type:

None

_init_data_paths()
property include_in_output: bool
Return type:

bool

get_output_key_val(ent)
Parameters:

ent (medcat2.tokenizing.tokens.MutableEntity)

Return type:

tuple[str, dict[str, Any]]

serialise_to(folder_path)
Parameters:

folder_path (str)

Return type:

None

classmethod deserialise_from(folder_path, **init_kwargs)
Parameters:

folder_path (str)

Return type:

MetaCATAddon

get_strategy()
Return type:

medcat2.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]

get_hash()
Return type:

str

NAME_PREFIX: str = 'addon_'
NAME_SPLITTER: str = '.'
is_core()

Whether the component is a core component or not.

Returns:

bool – Whether this is a core component.

Return type:

bool

get_folder_name()
Return type:

str

property full_name: str

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

Return type:

str

__slots__ = ()
_is_protocol = True
_is_runtime_protocol = False
classmethod __init_subclass__(*args, **kwargs)
classmethod __class_getitem__(params)
class medcat2.components.addons.meta_cat.meta_cat.MetaCAT(tokenizer=None, embeddings=None, config=None, _model_state_dict=None)

Bases: medcat2.storage.serialisables.AbstractSerialisable

The MetaCAT class used for training ‘Meta-Annotation’ models, i.e. annotations of clinical concept annotations. These are also known as properties or attributes of recognise entities sin similar tools such as MetaMap and cTakes.

This is a flexible model agnostic class that can learns any meta-annotation task, i.e. any multi-class classification task for recognised terms.

Parameters:

  • tokenizer (TokenizerWrapperBase) –

    The Huggingface tokenizer instance. This can be a pre-trained tokenzier instance from a BERT-style model, or trained from scratch for the Bi-LSTM (w. attention) model that is currentl

    used in most deployments.

  • embeddings (Tensor, numpy.ndarray) – embedding mapping (sub)word input id n-dim (sub)word embedding.

  • config (ConfigMetaCAT) – the configuration for MetaCAT. Param descriptions available in ConfigMetaCAT docs.

  • _model_state_dict (Optional[dict[str, Any]])

name = 'meta_cat'
_component_lock
classmethod get_init_attrs()
Return type:

list[str]

classmethod ignore_attrs()
Return type:

list[str]

classmethod include_properties()
Return type:

list[str]

property _model_state_dict
__init__(tokenizer=None, embeddings=None, config=None, _model_state_dict=None)
Parameters:
Return type:

None

config = None
tokenizer = None
embeddings
model
get_model(embeddings)

Get the model

Parameters:

embeddings (Optional[Tensor]) – The embedding densor

Raises:

ValueError – If the meta model is not LSTM or BERT

Returns:

nn.Module – The module

Return type:

torch.nn.Module

get_hash()

A partial hash trying to catch differences between models.

Returns:

str – The hex hash.

Return type:

str

train_from_json(json_path, save_dir_path=None, data_oversampled=None, overwrite=False)

Train or continue training a model give a json_path containing a MedCATtrainer export. It will continue training if an existing model is loaded or start new training if the model is blank/new.

Parameters:

  • json_path (Union[str, list]) – Path/Paths to a MedCATtrainer export containing the meta_annotations we want to train for.

  • save_dir_path (Optional[str]) – In case we have aut_save_model (meaning during the training the best model will be saved) we need to set a save path. Defaults to None.

  • data_oversampled (Optional[list]) – In case of oversampling being performed, the data will be passed in the parameter allowing the model to be trained on original + synthetic data.

  • overwrite (bool) – Whether to allow overwriting the file if/when appropriate.

Returns:

dict – The resulting report.

Return type:

dict

train_raw(data_loaded, save_dir_path=None, data_oversampled=None, overwrite=False)

Train or continue training a model given raw data. It will continue training if an existing model is loaded or start new training if the model is blank/new.

The raw data is expected in the following format: {

‘projects’: [ # list of projects
{

‘name’: ‘<project_name>’, ‘documents’: [ # list of documents

{

‘name’: ‘<document_name>’, ‘text’: ‘<text_of_document>’, ‘annotations’: [ # list of annotations

{

# start index of the annotation ‘start’: -1, ‘end’: 1, # end index of the annotation ‘cui’: ‘cui’, ‘value’: ‘<annotation_value>’

],

]

]

}

Parameters:

  • data_loaded (dict) – The raw data we want to train for.

  • save_dir_path (Optional[str]) – In case we have aut_save_model (meaning during the training the best model will be saved) we need to set a save path. Defaults to None.

  • data_oversampled (Optional[list]) –

    In case of oversampling being performed, the data will be passed in the parameter allowing the model to be trained on original + synthetic data. The format of which is expected: [[[‘text’,’of’,’the’,’document’], [index of medical entity],

    ”label” ],

    [‘text’,’of’,’the’,’document’], [index of medical entity],

    ”label” ]]

  • overwrite (bool) – Whether to allow overwriting the file if/when appropriate.

Returns:

dict – The resulting report.

Raises:

  • Exception – If no save path is specified, or category name not in data.

  • AssertionError – If no tokeniser is set

  • FileNotFoundError – If phase_number is set to 2 and model.dat file is not found

  • KeyError – If phase_number is set to 2 and model.dat file contains mismatched architecture

Return type:

dict

eval(json_path)

Evaluate from json.

Parameters:

json_path (str) – The json file ath

Returns:

dict – The resulting model dict

Raises:

  • AssertionError – If self.tokenizer

  • Exception – If the category name does not exist

Return type:

dict

get_ents(doc)
Parameters:

doc (medcat2.tokenizing.tokens.MutableDocument)

Return type:

Iterable[medcat2.tokenizing.tokens.MutableEntity]

prepare_document(doc, input_ids, offset_mapping, lowercase)

Prepares document.

Parameters:

  • doc (Doc) – The document

  • input_ids (list) – Input ids

  • offset_mapping (list) – Offset mappings

  • lowercase (bool) – Whether to use lower case replace center

Returns:

tuple[dict, list] – Entity id to index mapping and Samples

Return type:

tuple[dict, list]

static batch_generator(stream, batch_size_chars)

Generator for batch of documents.

Parameters:

  • stream (Iterable[MutableDocument]) – The document stream

  • batch_size_chars (int) – Number of characters per batch

Yields:

list[MutableDocument] – The batch of documents.

Return type:

Iterable[list[medcat2.tokenizing.tokens.MutableDocument]]

_set_meta_anns(doc, id2category_value)
Parameters:
Return type:

medcat2.tokenizing.tokens.MutableDocument

__call__(doc)

Process one document, used in the spacy pipeline for sequential document processing.

Parameters:

doc (Doc) – A spacy document

Returns:

Doc – The same spacy document.

Return type:

medcat2.tokenizing.tokens.MutableDocument

get_model_card(as_dict=False)

A minimal model card.

Parameters:

as_dict (bool) – Return the model card as a dictionary instead of a str. Defaults to False.

Returns:

Union[str, dict] – An indented JSON object. OR A JSON object in dict form.

Return type:

Union[str, dict]

__repr__()

Prints the model_card for this MetaCAT instance.

Returns:

  • the ‘Model Card’ for this MetaCAT instance. This includes NER+L

  • config and any MetaCATs

get_strategy()
Return type:

SerialisingStrategy

__eq__(other)
Parameters:

other (Any)

Return type:

bool

exception medcat2.components.addons.meta_cat.meta_cat.MisconfiguredMetaCATException

Bases: ValueError

Inappropriate argument value (of correct type).

____(*args)
class __cause__

exception cause

class __context__

exception context

__delattr__()

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__()

Return self==value.

__format__()

Default object formatter.

__ge__()

Return self>=value.

__getattribute__()

Return getattr(self, name).

__gt__()

Return self>value.

__hash__()

Return hash(self).

__init__()

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

__le__()

Return self<=value.

__lt__()

Return self<value.

__ne__()

Return self!=value.

__new__()

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

__reduce__()
__reduce_ex__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__()

Implement setattr(self, name, value).

__setstate__()
__sizeof__()

Size of object 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).

class __suppress_context__
class __traceback__
class args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.