medcat.utils.regression.targeting
Attributes
Exceptions
Inappropriate argument value (of correct type). |
Classes
The abstract serialisable base class. |
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dict() -> new empty dictionary |
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dict() -> new empty dictionary |
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The translation layer for translating: |
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A class describing the options for a specific placeholder. |
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The phrase changer. |
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The target phrase changer. |
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The final target. |
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The targeting option set. |
Module Contents
- class medcat.utils.regression.targeting.CDB(config)
Bases:
medcat.storage.serialisables.AbstractSerialisableThe abstract serialisable base class.
This defines some common defaults.
- Parameters:
config (medcat.config.Config)
- __init__(config)
- Parameters:
config (medcat.config.Config)
- Return type:
None
- config
- cui2info: dict[str, medcat.cdb.concepts.CUIInfo]
- name2info: dict[str, medcat.cdb.concepts.NameInfo]
- type_id2info: dict[str, medcat.cdb.concepts.TypeInfo]
- token_counts: dict[str, int]
- addl_info: dict[str, Any]
- _subnames: set[str]
- is_dirty = False
- has_changed_names = False
- classmethod get_init_attrs()
- Return type:
list[str]
- _reset_subnames()
- has_subname(name)
Whether the CDB has the specified subname.
- Parameters:
name (str) – The subname to check.
- Returns:
bool – Whether the subname is present in this CDB.
- Return type:
bool
- get_name(cui)
Returns preferred name if it exists, otherwise it will return the longest name assigned to the concept.
- Parameters:
cui (str) – Concept ID or unique identifier in this database.
- Returns:
str – The name of the concept.
- Return type:
str
- weighted_average_function(step)
Get the weighted average for steop.
- Parameters:
step (int) – The steop.
- Returns:
float – The weighted average.
- Return type:
float
- add_types(types)
Add type info to CDB.
- Parameters:
types (Iterable[tuple[str, str]]) – The raw type info.
- Return type:
None
- add_names(cui, names, name_status=ST.AUTOMATIC, full_build=False)
Adds a name to an existing concept.
- Parameters:
cui (str) – Concept ID or unique identifier in this database, all concepts that have the same CUI will be merged internally.
names (dict[str, NameDescriptor]) –
Names for this concept, or the value that if found in free text can be linked to this concept. Names is an dict like: `{name: {‘tokens’: tokens, ‘snames’: snames,
’raw_name’: raw_name}, …}`
Names should be generated by helper function ‘medcat.preprocessing.cleaners.prepare_name’
name_status (str) – One of P, N, A. Defaults to ‘A’.
full_build (bool) – If True the dictionary self.addl_info will also be populated, contains a lot of extra information about concepts, but can be very memory consuming. This is not necessary for normal functioning of MedCAT (Default value False).
- Return type:
None
- _add_concept_names(cui, names, name_status)
- Parameters:
cui (str)
names (dict[str, medcat.preprocessors.cleaners.NameDescriptor])
name_status (str)
- Return type:
None
- _add_full_build(cui, names, ontologies, description, type_ids)
- Parameters:
cui (str)
names (dict[str, medcat.preprocessors.cleaners.NameDescriptor])
ontologies (set[str])
description (str)
type_ids (set[str])
- Return type:
None
- _add_concept(cui, names, ontologies, name_status, type_ids, description, full_build=False)
Add a concept to internal Concept Database (CDB). Depending on what you are providing this will add a large number of properties for each concept.
- Parameters:
cui (str) – Concept ID or unique identifier in this database, all concepts that have the same CUI will be merged internally.
names (dict[str, NameDescriptor]) –
Names for this concept, or the value that if found in free text can be linked to this concept. Names is a dict like: `{name: {‘tokens’: tokens, ‘snames’: snames,
’raw_name’: raw_name}, …}`
Names should be generated by helper function ‘medcat.preprocessing.cleaners.prepare_name’
ontologies (set[str]) – ontologies in which the concept exists (e.g. SNOMEDCT, HPO)
name_status (str) – One of P, N, A
type_ids (set[str]) – Semantic type identifier (have a look at TUIs in UMLS or SNOMED-CT)
description (str) – Description of this concept.
full_build (bool) – If True the dictionary self.addl_info will also be populated, contains a lot of extra information about concepts, but can be very memory consuming. This is not necessary for normal functioning of MedCAT (Default Value False).
- Return type:
None
- reset_training()
Will remove all training efforts - in other words all embeddings that are learnt for concepts in the current CDB. Please note that this does not remove synonyms (names) that were potentially added during supervised/online learning.
- Return type:
None
- filter_by_cui(cuis_to_keep)
Subset the core CDB fields (dictionaries/maps).
Note that this will potenitally keep a bit more CUIs then in cuis_to_keep. It will first find all names that link to the cuis_to_keep and then find all CUIs that link to those names and keep all of them.
This also will not remove any data from cdb.addl_info - as this field can contain data of unknown structure.
- Parameters:
cuis_to_keep (Collection[str]) – CUIs that will be kept, the rest will be removed (not completely, look above).
- Raises:
Exception – If no snames and subsetting is not possible.
- Return type:
None
- remove_cui(cui)
This function takes a CUI and removes it the CDB.
It also removes the CUI from name specific per_cui_status maps as well as well as removes all the names that do not correspond to any CUIs after the removal of this one.
- Parameters:
cui (str) – The CUI to remove.
- Return type:
None
- _remove_names(cui, names)
Remove names from an existing concept - effect is this name will never again be used to link to this concept. This will only remove the name from the linker (namely name2cuis and name2cuis2status), the name will still be present everywhere else. Why? Because it is bothersome to remove it from everywhere, but could also be useful to keep the removed names in e.g. cui2names.
- Parameters:
cui (str) – Concept ID or unique identifier in this database.
names (Iterable[str]) – Names to be removed (e.g list, set, or even a dict (in which case keys will be used)).
- Return type:
None
- __eq__(other)
- Parameters:
other (Any)
- Return type:
bool
- get_cui2count_train()
- Return type:
dict[str, int]
- get_name2count_train()
- Return type:
dict[str, int]
- get_hash()
- Return type:
str
- get_basic_info()
- Return type:
medcat.data.model_card.CDBInfo
- save(save_path, serialiser=AvailableSerialisers.dill, overwrite=False)
Save CDB at path.
- Parameters:
save_path (str) – The path to save at.
serialiser (Union[ str, AvailableSerialisers], optional) – The serialiser. Defaults to AvailableSerialisers.dill.
overwrite (bool, optional) – Whether to allow overwriting existing files. Defaults to False.
- Return type:
None
- get_strategy()
- Return type:
- classmethod ignore_attrs()
- Return type:
list[str]
- classmethod include_properties()
- Return type:
list[str]
- class medcat.utils.regression.targeting.CUIInfo
Bases:
TypedDictdict() -> 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)
- cui: str
- preferred_name: str
- names: set[str]
- subnames: set[str]
- type_ids: set[str]
- description: str | None
- original_names: set[str] | None
- tags: list[str] | None
- group: str | None
- in_other_ontology: set[str] | None
- count_train: int
- context_vectors: dict[str, numpy.ndarray] | None
- average_confidence: float
- __contains__()
True if the dictionary has the specified key, else False.
- __delattr__()
Implement delattr(self, name).
- __delitem__()
Delete self[key].
- __dir__()
Default dir() implementation.
- __eq__()
Return self==value.
- __format__()
Default object formatter.
- __ge__()
Return self>=value.
- __getattribute__()
Return getattr(self, name).
- __getitem__()
x.__getitem__(y) <==> x[y]
- __gt__()
Return self>value.
- __init__()
Initialize self. See help(type(self)) for accurate signature.
- __ior__()
Return self|=value.
- __iter__()
Implement iter(self).
- __le__()
Return self<=value.
- __len__()
Return len(self).
- __lt__()
Return self<value.
- __ne__()
Return self!=value.
- __new__()
Create and return a new object. See help(type) for accurate signature.
- __or__()
Return self|value.
- __reduce__()
Helper for pickle.
- __reduce_ex__()
Helper for pickle.
- __repr__()
Return repr(self).
- __reversed__()
Return a reverse iterator over the dict keys.
- __ror__()
Return value|self.
- __setattr__()
Implement setattr(self, name, value).
- __setitem__()
Set self[key] to value.
- __sizeof__()
D.__sizeof__() -> size of D in memory, in bytes
- __str__()
Return str(self).
- __subclasshook__()
Abstract classes can override this to customize issubclass().
This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).
- clear()
D.clear() -> None. Remove all items from D.
- copy()
D.copy() -> a shallow copy of D
- get()
Return the value for key if key is in the dictionary, else default.
- items()
D.items() -> a set-like object providing a view on D’s items
- keys()
D.keys() -> a set-like object providing a view on D’s keys
- pop()
D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
If the key is not found, return the default if given; otherwise, raise a KeyError.
- popitem()
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
- setdefault()
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
- update()
D.update([E, ]**F) -> None. Update D from dict/iterable E and F. If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
- values()
D.values() -> an object providing a view on D’s values
- class medcat.utils.regression.targeting.NameInfo
Bases:
TypedDictdict() -> 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
- per_cui_status: dict[str, str]
- is_upper: bool
- count_train: int
- __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.regression.targeting.logger
- class medcat.utils.regression.targeting.TranslationLayer(cui2info, name2info, cui2children, separator, whitespace=' ')
The translation layer for translating: - CUIs to names - names to CUIs - type_ids to CUIs - CUIs to chil CUIs
The idea is to decouple these translations from the CDB instance in case something changes there.
- Parameters:
- __init__(cui2info, name2info, cui2children, separator, whitespace=' ')
- Parameters:
cui2info (dict[str, medcat.cdb.concepts.CUIInfo])
name2info (dict[str, medcat.cdb.concepts.NameInfo])
cui2children (dict[str, set[str]])
separator (str)
whitespace (str)
- Return type:
None
- cui2info
- name2info
- separator
- whitespace = ' '
- type_id2cuis: dict[str, set[str]]
- cui2children
- get_names_of(cui, only_prefnames)
Get the preprocessed names of a CUI.
This method preporcesses the names by replacing the separator (generally ~) with the appropriate whitespace (` `).
If the concept is not in the underlying CDB, an empty list is returned.
- Parameters:
cui (str) – The concept in question.
only_prefnames (bool) – Whether to only return a preferred name.
- Returns:
list[str] – The list of names.
- Return type:
list[str]
- get_preferred_name(cui)
Get the preferred name of a concept.
If no preferred name is found, the random ‘first’ name is selected.
- Parameters:
cui (str) – The concept ID.
- Returns:
str – The preferred name.
- Return type:
str
- get_first_name(cui)
Get the preprocessed (potentially) arbitrarily first name of the given concept.
If the concept does not exist, the CUI itself is returned.
PS: The “first” name may not be consistent across runs since it relies on set order.
- Parameters:
cui (str) – The concept ID.
- Returns:
str – The first name.
- Return type:
str
- get_direct_children(cui)
Get the direct children of a concept.
This means only the children, but not grandchildren.
If the underlying CDB doesn’t list children for this CUI, an empty list is returned.
- Parameters:
cui (str) – The concept in question.
- Returns:
list[str] – The (potentially empty) list of direct children.
- Return type:
list[str]
- get_direct_parents(cui)
Get the direct parent(s) of a concept.
- PS: This method can be quite a CPU heavy one since it relies
on running through all the parent-children relationships since the child->parent(s) relationship isn’t normally kept track of.
- Parameters:
cui (str) – _description_
- Returns:
list[str] – _description_
- Return type:
list[str]
- get_children_of(found_cuis, cui, depth=1)
Get the children of the specifeid CUI in the listed CUIs (if they exist).
- Parameters:
found_cuis (Iterable[str]) – The list of CUIs to look in
cui (str) – The target parent CUI
depth (int) – The depth to carry out the search for
- Returns:
list[str] – The list of children found
- Return type:
list[str]
- classmethod from_CDB(cdb)
Construct a TranslationLayer object from a context database (CDB).
This translation layer will refer to the same dicts that the CDB refers to. While there is no obvious reason these should be modified, it’s something to keep in mind.
- Parameters:
cdb (CDB) – The CDB
- Returns:
TranslationLayer – The subsequent TranslationLayer
- Return type:
- class medcat.utils.regression.targeting.TargetPlaceholder(/, **data)
Bases:
pydantic.BaseModelA class describing the options for a specific placeholder.
- Parameters:
data (Any)
- placeholder: str
- target_cuis: list[str]
- onlyprefnames: bool = False
- model_config: ClassVar[pydantic.config.ConfigDict]
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]]
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.
This replaces Model.__fields__ from Pydantic V1.
- model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- __class_vars__: ClassVar[set[str]]
The names of the class variables defined on the model.
- __private_attributes__: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]]
Metadata about the private attributes of the model.
- __signature__: ClassVar[inspect.Signature]
The synthesized __init__ [Signature][inspect.Signature] of the model.
- __pydantic_complete__: ClassVar[bool] = False
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[pydantic_core.CoreSchema]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool]
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[pydantic._internal._decorators.DecoratorInfos]
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[pydantic._internal._generics.PydanticGenericMetadata]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
The name of the post-init method for the model, if defined.
- __pydantic_root_model__: ClassVar[bool] = False
Whether the model is a [RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[pydantic_core.SchemaSerializer]
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_validator__: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator]
The pydantic-core SchemaValidator used to validate instances of the model.
- __pydantic_extra__: dict[str, Any] | None
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_fields_set__: set[str]
The names of fields explicitly set during instantiation.
- __pydantic_private__: dict[str, Any] | None
Values of private attributes set on the model instance.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')
- __init__(/, **data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `”allow”`.
- Return type:
dict[str, Any] | None
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
A set of strings representing the fields that have been set, – i.e. that were not filled from defaults.
- Return type:
set[str]
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.
- Returns:
A new instance of the `Model` class with validated data.
- Return type:
typing_extensions.Self
- model_copy(*, update=None, deep=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- Parameters:
update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.
- Returns:
New model instance.
- Return type:
typing_extensions.Self
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (IncEx | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A dictionary representation of the model.
- Return type:
dict[str, Any]
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A JSON string representation of the model.
- Return type:
str
- classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- Return type:
dict[str, Any]
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (tuple[type[Any], Ellipsis]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
- Returns:
String representing the new class where `params` are passed to `cls` as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- Return type:
str
- model_post_init(__context)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- Parameters:
__context (Any)
- Return type:
None
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.
- Returns:
Returns `None` if the schema is already “complete” and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- Return type:
bool | None
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)
Validate a pydantic model instance.
- Parameters:
obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
- Raises:
ValidationError – If the object could not be validated.
- Returns:
The validated model instance.
- Return type:
typing_extensions.Self
- classmethod model_validate_json(json_data, *, strict=None, context=None)
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- Return type:
typing_extensions.Self
- classmethod model_validate_strings(obj, *, strict=None, context=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Return type:
typing_extensions.Self
- classmethod __get_pydantic_core_schema__(source, handler, /)
Hook into generating the model’s CoreSchema.
- Parameters:
source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (pydantic.annotated_handlers.GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Returns:
A `pydantic-core` `CoreSchema`.
- Return type:
pydantic_core.CoreSchema
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (pydantic_core.CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.
handler (pydantic.annotated_handlers.GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Returns:
A JSON schema, as a Python object.
- Return type:
pydantic.json_schema.JsonSchemaValue
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- Parameters:
**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
- Return type:
None
- classmethod __class_getitem__(typevar_values)
- Parameters:
typevar_values (type[Any] | tuple[type[Any], Ellipsis])
- Return type:
type[BaseModel] | pydantic._internal._forward_ref.PydanticRecursiveRef
- __copy__()
Returns a shallow copy of the model.
- Return type:
typing_extensions.Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- Parameters:
memo (dict[int, Any] | None)
- Return type:
typing_extensions.Self
- __getattr__(item)
- Parameters:
item (str)
- Return type:
Any
- _check_frozen(name, value)
- Parameters:
name (str)
value (Any)
- Return type:
None
- __getstate__()
- Return type:
dict[Any, Any]
- __setstate__(state)
- Parameters:
state (dict[Any, Any])
- Return type:
None
- __eq__(other)
- Parameters:
other (Any)
- Return type:
bool
- classmethod __init_subclass__(**kwargs)
This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.
```py from pydantic import BaseModel
class MyModel(BaseModel, extra=’allow’): … ```
However, this may be deceiving, since the _actual_ calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)
- Parameters:
**kwargs (typing_extensions.Unpack[pydantic.config.ConfigDict]) – Keyword arguments passed to the class definition, which set model_config
Note
You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.
- __iter__()
So dict(model) works.
- Return type:
TupleGenerator
- __repr__()
- Return type:
str
- __repr_args__()
- Return type:
pydantic._internal._repr.ReprArgs
- __repr_name__
- __repr_str__
- __pretty__
- __rich_repr__
- __str__()
- Return type:
str
- property __fields__: dict[str, pydantic.fields.FieldInfo]
- Return type:
dict[str, pydantic.fields.FieldInfo]
- property __fields_set__: set[str]
- Return type:
set[str]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- Return type:
Dict[str, Any]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)
- Return type:
str
- classmethod parse_obj(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
b (str | bytes)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod from_orm(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod construct(_fields_set=None, **values)
- Parameters:
_fields_set (set[str] | None)
values (Any)
- Return type:
typing_extensions.Self
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- Return type:
typing_extensions.Self
- classmethod schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE)
- Parameters:
by_alias (bool)
ref_template (str)
- Return type:
Dict[str, Any]
- classmethod schema_json(*, by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, **dumps_kwargs)
- Parameters:
by_alias (bool)
ref_template (str)
dumps_kwargs (Any)
- Return type:
str
- classmethod validate(value)
- Parameters:
value (Any)
- Return type:
typing_extensions.Self
- classmethod update_forward_refs(**localns)
- Parameters:
localns (Any)
- Return type:
None
- _iter(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _copy_and_set_values(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- classmethod _get_value(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _calculate_keys(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- class medcat.utils.regression.targeting.PhraseChanger(/, **data)
Bases:
pydantic.BaseModelThe phrase changer.
This is class used as a preprocessor for phrases with multiple placeholders. It allows swapping in the rest of the placeholders while leaving in the one that’s being tested for.
- Parameters:
data (Any)
- preprocess_placeholders: list[tuple[str, str]]
- __call__(phrase)
- Parameters:
phrase (str)
- Return type:
str
- classmethod empty()
Gets the empty phrase changer.
That is a phrase changer that makes no changes to the phrase.
- Returns:
PhraseChanger – The empty phrase changer.
- Return type:
- model_config: ClassVar[pydantic.config.ConfigDict]
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]]
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.
This replaces Model.__fields__ from Pydantic V1.
- model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- __class_vars__: ClassVar[set[str]]
The names of the class variables defined on the model.
- __private_attributes__: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]]
Metadata about the private attributes of the model.
- __signature__: ClassVar[inspect.Signature]
The synthesized __init__ [Signature][inspect.Signature] of the model.
- __pydantic_complete__: ClassVar[bool] = False
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[pydantic_core.CoreSchema]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool]
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[pydantic._internal._decorators.DecoratorInfos]
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[pydantic._internal._generics.PydanticGenericMetadata]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
The name of the post-init method for the model, if defined.
- __pydantic_root_model__: ClassVar[bool] = False
Whether the model is a [RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[pydantic_core.SchemaSerializer]
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_validator__: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator]
The pydantic-core SchemaValidator used to validate instances of the model.
- __pydantic_extra__: dict[str, Any] | None
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_fields_set__: set[str]
The names of fields explicitly set during instantiation.
- __pydantic_private__: dict[str, Any] | None
Values of private attributes set on the model instance.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')
- __init__(/, **data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `”allow”`.
- Return type:
dict[str, Any] | None
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
A set of strings representing the fields that have been set, – i.e. that were not filled from defaults.
- Return type:
set[str]
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.
- Returns:
A new instance of the `Model` class with validated data.
- Return type:
typing_extensions.Self
- model_copy(*, update=None, deep=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- Parameters:
update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.
- Returns:
New model instance.
- Return type:
typing_extensions.Self
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (IncEx | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A dictionary representation of the model.
- Return type:
dict[str, Any]
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A JSON string representation of the model.
- Return type:
str
- classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- Return type:
dict[str, Any]
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (tuple[type[Any], Ellipsis]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
- Returns:
String representing the new class where `params` are passed to `cls` as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- Return type:
str
- model_post_init(__context)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- Parameters:
__context (Any)
- Return type:
None
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.
- Returns:
Returns `None` if the schema is already “complete” and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- Return type:
bool | None
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)
Validate a pydantic model instance.
- Parameters:
obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
- Raises:
ValidationError – If the object could not be validated.
- Returns:
The validated model instance.
- Return type:
typing_extensions.Self
- classmethod model_validate_json(json_data, *, strict=None, context=None)
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- Return type:
typing_extensions.Self
- classmethod model_validate_strings(obj, *, strict=None, context=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Return type:
typing_extensions.Self
- classmethod __get_pydantic_core_schema__(source, handler, /)
Hook into generating the model’s CoreSchema.
- Parameters:
source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (pydantic.annotated_handlers.GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Returns:
A `pydantic-core` `CoreSchema`.
- Return type:
pydantic_core.CoreSchema
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (pydantic_core.CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.
handler (pydantic.annotated_handlers.GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Returns:
A JSON schema, as a Python object.
- Return type:
pydantic.json_schema.JsonSchemaValue
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- Parameters:
**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
- Return type:
None
- classmethod __class_getitem__(typevar_values)
- Parameters:
typevar_values (type[Any] | tuple[type[Any], Ellipsis])
- Return type:
type[BaseModel] | pydantic._internal._forward_ref.PydanticRecursiveRef
- __copy__()
Returns a shallow copy of the model.
- Return type:
typing_extensions.Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- Parameters:
memo (dict[int, Any] | None)
- Return type:
typing_extensions.Self
- __getattr__(item)
- Parameters:
item (str)
- Return type:
Any
- _check_frozen(name, value)
- Parameters:
name (str)
value (Any)
- Return type:
None
- __getstate__()
- Return type:
dict[Any, Any]
- __setstate__(state)
- Parameters:
state (dict[Any, Any])
- Return type:
None
- __eq__(other)
- Parameters:
other (Any)
- Return type:
bool
- classmethod __init_subclass__(**kwargs)
This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.
```py from pydantic import BaseModel
class MyModel(BaseModel, extra=’allow’): … ```
However, this may be deceiving, since the _actual_ calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)
- Parameters:
**kwargs (typing_extensions.Unpack[pydantic.config.ConfigDict]) – Keyword arguments passed to the class definition, which set model_config
Note
You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.
- __iter__()
So dict(model) works.
- Return type:
TupleGenerator
- __repr__()
- Return type:
str
- __repr_args__()
- Return type:
pydantic._internal._repr.ReprArgs
- __repr_name__
- __repr_str__
- __pretty__
- __rich_repr__
- __str__()
- Return type:
str
- property __fields__: dict[str, pydantic.fields.FieldInfo]
- Return type:
dict[str, pydantic.fields.FieldInfo]
- property __fields_set__: set[str]
- Return type:
set[str]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- Return type:
Dict[str, Any]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)
- Return type:
str
- classmethod parse_obj(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
b (str | bytes)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod from_orm(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod construct(_fields_set=None, **values)
- Parameters:
_fields_set (set[str] | None)
values (Any)
- Return type:
typing_extensions.Self
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- Return type:
typing_extensions.Self
- classmethod schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE)
- Parameters:
by_alias (bool)
ref_template (str)
- Return type:
Dict[str, Any]
- classmethod schema_json(*, by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, **dumps_kwargs)
- Parameters:
by_alias (bool)
ref_template (str)
dumps_kwargs (Any)
- Return type:
str
- classmethod validate(value)
- Parameters:
value (Any)
- Return type:
typing_extensions.Self
- classmethod update_forward_refs(**localns)
- Parameters:
localns (Any)
- Return type:
None
- _iter(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _copy_and_set_values(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- classmethod _get_value(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _calculate_keys(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- class medcat.utils.regression.targeting.TargetedPhraseChanger(/, **data)
Bases:
pydantic.BaseModelThe target phrase changer.
It includes the phrase changer (for preprocessing) along with the relevant concept and the placeholder it will replace.
- Parameters:
data (Any)
- changer: PhraseChanger
- placeholder: str
- cui: str
- onlyprefnames: bool
- model_config: ClassVar[pydantic.config.ConfigDict]
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]]
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.
This replaces Model.__fields__ from Pydantic V1.
- model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- __class_vars__: ClassVar[set[str]]
The names of the class variables defined on the model.
- __private_attributes__: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]]
Metadata about the private attributes of the model.
- __signature__: ClassVar[inspect.Signature]
The synthesized __init__ [Signature][inspect.Signature] of the model.
- __pydantic_complete__: ClassVar[bool] = False
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[pydantic_core.CoreSchema]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool]
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[pydantic._internal._decorators.DecoratorInfos]
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[pydantic._internal._generics.PydanticGenericMetadata]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
The name of the post-init method for the model, if defined.
- __pydantic_root_model__: ClassVar[bool] = False
Whether the model is a [RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[pydantic_core.SchemaSerializer]
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_validator__: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator]
The pydantic-core SchemaValidator used to validate instances of the model.
- __pydantic_extra__: dict[str, Any] | None
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_fields_set__: set[str]
The names of fields explicitly set during instantiation.
- __pydantic_private__: dict[str, Any] | None
Values of private attributes set on the model instance.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')
- __init__(/, **data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `”allow”`.
- Return type:
dict[str, Any] | None
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
A set of strings representing the fields that have been set, – i.e. that were not filled from defaults.
- Return type:
set[str]
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.
- Returns:
A new instance of the `Model` class with validated data.
- Return type:
typing_extensions.Self
- model_copy(*, update=None, deep=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- Parameters:
update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.
- Returns:
New model instance.
- Return type:
typing_extensions.Self
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (IncEx | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A dictionary representation of the model.
- Return type:
dict[str, Any]
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A JSON string representation of the model.
- Return type:
str
- classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- Return type:
dict[str, Any]
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (tuple[type[Any], Ellipsis]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
- Returns:
String representing the new class where `params` are passed to `cls` as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- Return type:
str
- model_post_init(__context)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- Parameters:
__context (Any)
- Return type:
None
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.
- Returns:
Returns `None` if the schema is already “complete” and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- Return type:
bool | None
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)
Validate a pydantic model instance.
- Parameters:
obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
- Raises:
ValidationError – If the object could not be validated.
- Returns:
The validated model instance.
- Return type:
typing_extensions.Self
- classmethod model_validate_json(json_data, *, strict=None, context=None)
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- Return type:
typing_extensions.Self
- classmethod model_validate_strings(obj, *, strict=None, context=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Return type:
typing_extensions.Self
- classmethod __get_pydantic_core_schema__(source, handler, /)
Hook into generating the model’s CoreSchema.
- Parameters:
source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (pydantic.annotated_handlers.GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Returns:
A `pydantic-core` `CoreSchema`.
- Return type:
pydantic_core.CoreSchema
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (pydantic_core.CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.
handler (pydantic.annotated_handlers.GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Returns:
A JSON schema, as a Python object.
- Return type:
pydantic.json_schema.JsonSchemaValue
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- Parameters:
**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
- Return type:
None
- classmethod __class_getitem__(typevar_values)
- Parameters:
typevar_values (type[Any] | tuple[type[Any], Ellipsis])
- Return type:
type[BaseModel] | pydantic._internal._forward_ref.PydanticRecursiveRef
- __copy__()
Returns a shallow copy of the model.
- Return type:
typing_extensions.Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- Parameters:
memo (dict[int, Any] | None)
- Return type:
typing_extensions.Self
- __getattr__(item)
- Parameters:
item (str)
- Return type:
Any
- _check_frozen(name, value)
- Parameters:
name (str)
value (Any)
- Return type:
None
- __getstate__()
- Return type:
dict[Any, Any]
- __setstate__(state)
- Parameters:
state (dict[Any, Any])
- Return type:
None
- __eq__(other)
- Parameters:
other (Any)
- Return type:
bool
- classmethod __init_subclass__(**kwargs)
This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.
```py from pydantic import BaseModel
class MyModel(BaseModel, extra=’allow’): … ```
However, this may be deceiving, since the _actual_ calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)
- Parameters:
**kwargs (typing_extensions.Unpack[pydantic.config.ConfigDict]) – Keyword arguments passed to the class definition, which set model_config
Note
You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.
- __iter__()
So dict(model) works.
- Return type:
TupleGenerator
- __repr__()
- Return type:
str
- __repr_args__()
- Return type:
pydantic._internal._repr.ReprArgs
- __repr_name__
- __repr_str__
- __pretty__
- __rich_repr__
- __str__()
- Return type:
str
- property __fields__: dict[str, pydantic.fields.FieldInfo]
- Return type:
dict[str, pydantic.fields.FieldInfo]
- property __fields_set__: set[str]
- Return type:
set[str]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- Return type:
Dict[str, Any]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)
- Return type:
str
- classmethod parse_obj(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
b (str | bytes)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod from_orm(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod construct(_fields_set=None, **values)
- Parameters:
_fields_set (set[str] | None)
values (Any)
- Return type:
typing_extensions.Self
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- Return type:
typing_extensions.Self
- classmethod schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE)
- Parameters:
by_alias (bool)
ref_template (str)
- Return type:
Dict[str, Any]
- classmethod schema_json(*, by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, **dumps_kwargs)
- Parameters:
by_alias (bool)
ref_template (str)
dumps_kwargs (Any)
- Return type:
str
- classmethod validate(value)
- Parameters:
value (Any)
- Return type:
typing_extensions.Self
- classmethod update_forward_refs(**localns)
- Parameters:
localns (Any)
- Return type:
None
- _iter(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _copy_and_set_values(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- classmethod _get_value(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _calculate_keys(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- class medcat.utils.regression.targeting.FinalTarget(/, **data)
Bases:
pydantic.BaseModelThe final target.
This involves the final phrase (which (potentially) has other placeholder replaced in it), the placeholder to be replaced, and the CUI and specific name being used.
- Parameters:
data (Any)
- placeholder: str
- cui: str
- name: str
- final_phrase: str
- model_config: ClassVar[pydantic.config.ConfigDict]
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]]
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.
This replaces Model.__fields__ from Pydantic V1.
- model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- __class_vars__: ClassVar[set[str]]
The names of the class variables defined on the model.
- __private_attributes__: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]]
Metadata about the private attributes of the model.
- __signature__: ClassVar[inspect.Signature]
The synthesized __init__ [Signature][inspect.Signature] of the model.
- __pydantic_complete__: ClassVar[bool] = False
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__: ClassVar[pydantic_core.CoreSchema]
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool]
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[pydantic._internal._decorators.DecoratorInfos]
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[pydantic._internal._generics.PydanticGenericMetadata]
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
The name of the post-init method for the model, if defined.
- __pydantic_root_model__: ClassVar[bool] = False
Whether the model is a [RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__: ClassVar[pydantic_core.SchemaSerializer]
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_validator__: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator]
The pydantic-core SchemaValidator used to validate instances of the model.
- __pydantic_extra__: dict[str, Any] | None
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_fields_set__: set[str]
The names of fields explicitly set during instantiation.
- __pydantic_private__: dict[str, Any] | None
Values of private attributes set on the model instance.
- __slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')
- __init__(/, **data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `”allow”`.
- Return type:
dict[str, Any] | None
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
A set of strings representing the fields that have been set, – i.e. that were not filled from defaults.
- Return type:
set[str]
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.
- Returns:
A new instance of the `Model` class with validated data.
- Return type:
typing_extensions.Self
- model_copy(*, update=None, deep=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
Returns a copy of the model.
- Parameters:
update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.
- Returns:
New model instance.
- Return type:
typing_extensions.Self
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (IncEx | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A dictionary representation of the model.
- Return type:
dict[str, Any]
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)
Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A JSON string representation of the model.
- Return type:
str
- classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- Return type:
dict[str, Any]
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (tuple[type[Any], Ellipsis]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
- Returns:
String representing the new class where `params` are passed to `cls` as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- Return type:
str
- model_post_init(__context)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- Parameters:
__context (Any)
- Return type:
None
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.
- Returns:
Returns `None` if the schema is already “complete” and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- Return type:
bool | None
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)
Validate a pydantic model instance.
- Parameters:
obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
- Raises:
ValidationError – If the object could not be validated.
- Returns:
The validated model instance.
- Return type:
typing_extensions.Self
- classmethod model_validate_json(json_data, *, strict=None, context=None)
Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- Return type:
typing_extensions.Self
- classmethod model_validate_strings(obj, *, strict=None, context=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Return type:
typing_extensions.Self
- classmethod __get_pydantic_core_schema__(source, handler, /)
Hook into generating the model’s CoreSchema.
- Parameters:
source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (pydantic.annotated_handlers.GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.
- Returns:
A `pydantic-core` `CoreSchema`.
- Return type:
pydantic_core.CoreSchema
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (pydantic_core.CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.
handler (pydantic.annotated_handlers.GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Returns:
A JSON schema, as a Python object.
- Return type:
pydantic.json_schema.JsonSchemaValue
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- Parameters:
**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
- Return type:
None
- classmethod __class_getitem__(typevar_values)
- Parameters:
typevar_values (type[Any] | tuple[type[Any], Ellipsis])
- Return type:
type[BaseModel] | pydantic._internal._forward_ref.PydanticRecursiveRef
- __copy__()
Returns a shallow copy of the model.
- Return type:
typing_extensions.Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- Parameters:
memo (dict[int, Any] | None)
- Return type:
typing_extensions.Self
- __getattr__(item)
- Parameters:
item (str)
- Return type:
Any
- _check_frozen(name, value)
- Parameters:
name (str)
value (Any)
- Return type:
None
- __getstate__()
- Return type:
dict[Any, Any]
- __setstate__(state)
- Parameters:
state (dict[Any, Any])
- Return type:
None
- __eq__(other)
- Parameters:
other (Any)
- Return type:
bool
- classmethod __init_subclass__(**kwargs)
This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.
```py from pydantic import BaseModel
class MyModel(BaseModel, extra=’allow’): … ```
However, this may be deceiving, since the _actual_ calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)
- Parameters:
**kwargs (typing_extensions.Unpack[pydantic.config.ConfigDict]) – Keyword arguments passed to the class definition, which set model_config
Note
You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.
- __iter__()
So dict(model) works.
- Return type:
TupleGenerator
- __repr__()
- Return type:
str
- __repr_args__()
- Return type:
pydantic._internal._repr.ReprArgs
- __repr_name__
- __repr_str__
- __pretty__
- __rich_repr__
- __str__()
- Return type:
str
- property __fields__: dict[str, pydantic.fields.FieldInfo]
- Return type:
dict[str, pydantic.fields.FieldInfo]
- property __fields_set__: set[str]
- Return type:
set[str]
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- Return type:
Dict[str, Any]
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Parameters:
include (IncEx | None)
exclude (IncEx | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)
- Return type:
str
- classmethod parse_obj(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
b (str | bytes)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- Return type:
typing_extensions.Self
- classmethod from_orm(obj)
- Parameters:
obj (Any)
- Return type:
typing_extensions.Self
- classmethod construct(_fields_set=None, **values)
- Parameters:
_fields_set (set[str] | None)
values (Any)
- Return type:
typing_extensions.Self
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- Return type:
typing_extensions.Self
- classmethod schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE)
- Parameters:
by_alias (bool)
ref_template (str)
- Return type:
Dict[str, Any]
- classmethod schema_json(*, by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, **dumps_kwargs)
- Parameters:
by_alias (bool)
ref_template (str)
dumps_kwargs (Any)
- Return type:
str
- classmethod validate(value)
- Parameters:
value (Any)
- Return type:
typing_extensions.Self
- classmethod update_forward_refs(**localns)
- Parameters:
localns (Any)
- Return type:
None
- _iter(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _copy_and_set_values(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- classmethod _get_value(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- _calculate_keys(*args, **kwargs)
- Parameters:
args (Any)
kwargs (Any)
- Return type:
Any
- class medcat.utils.regression.targeting.OptionSet(/, **data)
Bases:
pydantic.BaseModelThe targeting option set.
This describes all the target placeholders and concepts needed.
- Parameters:
data (Any)
- options: list[TargetPlaceholder]
- allow_any_combinations: bool = False
- classmethod from_dict(section)
Construct a OptionSet instance from a dict.
The assumed structure is: {
- ‘placeholders’: [
{ ‘placeholder’: <e.g {DIAGNOSIS}’>, ‘cuis’: <the CUI>, ‘prefname-only’: ‘true’ }, <potentially more>],
‘any-combination’: <True or False>
}
The prefname-only key is optional.
- Parameters:
section (dict[str, Any]) – The dict to parse
- Raises:
ProblematicOptionSetException – If incorrect number of CUIs when not allowing any combination
ProblematicOptionSetException – If placeholders not a list
ProblematicOptionSetException – If multiple placehodlers with same place holder
- Returns:
OptionSet – The resulting OptionSet
- Return type:
- to_dict()
Convert the OptionSet to a dict.
- Returns:
dict – The dict representation
- Return type:
dict
- _get_all_combinations(cur_opts, other_opts, translation)
- Parameters:
cur_opts (TargetPlaceholder)
other_opts (list[TargetPlaceholder])
translation (TranslationLayer)
- Return type:
Iterator[tuple[PhraseChanger, str]]
- estimate_num_of_subcases()
Get the number of distinct subcases.
This includes ones that can be calculated without the knowledge of the underlying CDB. I.e it doesn’t care for the number of names involved per CUI but only takes into account what is described in the option set itself.
If any combination is allowed, then the answer is the combination of the number of target concepts per option. If any combination is not allowed, then the answer is simply the number of target concepts for an option (they should all have the same number).
- Returns:
int – Te number of subcases.
- Return type:
int
- get_preprocessors_and_targets(translation)
Get the targeted phrase changers.
- Parameters:
translation (TranslationLayer) – The translaton layer.
- Yields:
Iterator[TargetedPhraseChanger] – Thetarget phrase changers.
- Return type:
Iterator[TargetedPhraseChanger]
- 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
- exception medcat.utils.regression.targeting.ProblematicOptionSetException(*args)
Bases:
ValueErrorInappropriate argument value (of correct type).
- Parameters:
args (object)
- __init__(*args)
Initialize self. See help(type(self)) for accurate signature.
- Parameters:
args (object)
- Return type:
None
- 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).
- __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.