medcat.utils.cdb_state ====================== .. py:module:: medcat.utils.cdb_state Attributes ---------- .. autoapisummary:: medcat.utils.cdb_state.logger medcat.utils.cdb_state.CDBState Classes ------- .. autoapisummary:: medcat.utils.cdb_state.NameInfo medcat.utils.cdb_state.CUIInfo medcat.utils.cdb_state.ModelMeta Functions --------- .. autoapisummary:: medcat.utils.cdb_state._get_attr medcat.utils.cdb_state._set_attr medcat.utils.cdb_state.copy_cdb_state medcat.utils.cdb_state.save_cdb_state medcat.utils.cdb_state.apply_cdb_state medcat.utils.cdb_state._clear_state medcat.utils.cdb_state._reapply_state medcat.utils.cdb_state.load_and_apply_cdb_state medcat.utils.cdb_state.captured_state_cdb medcat.utils.cdb_state.in_memory_state_capture medcat.utils.cdb_state.on_disk_memory_capture Module Contents --------------- .. py:class:: NameInfo Bases: :py:obj:`TypedDict` dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2) .. py:attribute:: name :type: str .. py:attribute:: per_cui_status :type: dict[str, str] .. py:attribute:: is_upper :type: bool .. py:attribute:: count_train :type: int .. py:method:: __contains__() True if the dictionary has the specified key, else False. .. py:method:: __delattr__() Implement delattr(self, name). .. py:method:: __delitem__() Delete self[key]. .. py:method:: __dir__() Default dir() implementation. .. py:method:: __eq__() Return self==value. .. py:method:: __format__() Default object formatter. .. py:method:: __ge__() Return self>=value. .. py:method:: __getattribute__() Return getattr(self, name). .. py:method:: __getitem__() x.__getitem__(y) <==> x[y] .. py:method:: __gt__() Return self>value. .. py:method:: __init__() Initialize self. See help(type(self)) for accurate signature. .. py:method:: __ior__() Return self|=value. .. py:method:: __iter__() Implement iter(self). .. py:method:: __le__() Return self<=value. .. py:method:: __len__() Return len(self). .. py:method:: __lt__() Return self size of D in memory, in bytes .. py:method:: __str__() Return str(self). .. py:method:: __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). .. py:method:: clear() D.clear() -> None. Remove all items from D. .. py:method:: copy() D.copy() -> a shallow copy of D .. py:method:: get() Return the value for key if key is in the dictionary, else default. .. py:method:: items() D.items() -> a set-like object providing a view on D's items .. py:method:: keys() D.keys() -> a set-like object providing a view on D's keys .. py:method:: 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. .. py:method:: 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. .. py:method:: 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. .. py:method:: 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] .. py:method:: values() D.values() -> an object providing a view on D's values .. py:class:: CUIInfo Bases: :py:obj:`TypedDict` dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2) .. py:attribute:: cui :type: str .. py:attribute:: preferred_name :type: str .. py:attribute:: names :type: set[str] .. py:attribute:: subnames :type: set[str] .. py:attribute:: type_ids :type: set[str] .. py:attribute:: description :type: Optional[str] .. py:attribute:: original_names :type: Optional[set[str]] .. py:attribute:: tags :type: Optional[list[str]] .. py:attribute:: group :type: Optional[str] .. py:attribute:: in_other_ontology :type: Optional[set[str]] .. py:attribute:: count_train :type: int .. py:attribute:: context_vectors :type: Optional[dict[str, numpy.ndarray]] .. py:attribute:: average_confidence :type: float .. py:method:: __contains__() True if the dictionary has the specified key, else False. .. py:method:: __delattr__() Implement delattr(self, name). .. py:method:: __delitem__() Delete self[key]. .. py:method:: __dir__() Default dir() implementation. .. py:method:: __eq__() Return self==value. .. py:method:: __format__() Default object formatter. .. py:method:: __ge__() Return self>=value. .. py:method:: __getattribute__() Return getattr(self, name). .. py:method:: __getitem__() x.__getitem__(y) <==> x[y] .. py:method:: __gt__() Return self>value. .. py:method:: __init__() Initialize self. See help(type(self)) for accurate signature. .. py:method:: __ior__() Return self|=value. .. py:method:: __iter__() Implement iter(self). .. py:method:: __le__() Return self<=value. .. py:method:: __len__() Return len(self). .. py:method:: __lt__() Return self size of D in memory, in bytes .. py:method:: __str__() Return str(self). .. py:method:: __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). .. py:method:: clear() D.clear() -> None. Remove all items from D. .. py:method:: copy() D.copy() -> a shallow copy of D .. py:method:: get() Return the value for key if key is in the dictionary, else default. .. py:method:: items() D.items() -> a set-like object providing a view on D's items .. py:method:: keys() D.keys() -> a set-like object providing a view on D's keys .. py:method:: 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. .. py:method:: 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. .. py:method:: 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. .. py:method:: 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] .. py:method:: values() D.values() -> an object providing a view on D's values .. py:class:: ModelMeta(/, **data) Bases: :py:obj:`SerialisableBaseModel` The base serialisable config. .. py:attribute:: description :type: str :value: 'N/A' .. py:attribute:: ontology :type: list[str] :value: [] .. py:attribute:: location :type: str :value: 'N/A' .. py:attribute:: hash :type: str :value: '' .. py:attribute:: history :type: list[str] .. py:attribute:: last_saved :type: datetime.datetime .. py:attribute:: unsup_trained :type: list[TrainingDescriptor] :value: [] .. py:attribute:: sup_trained :type: list[TrainingDescriptor] :value: [] .. py:attribute:: medcat_version :type: str :value: '' .. py:attribute:: saved_environ :type: medcat.utils.envsnapshot.Environment .. py:method:: mark_saved_now() .. py:method:: add_unsup_training(start_time, num_docs, num_epochs = 1, project_name = 'N/A') Add unsupervised training information based on data. NOTE: This will mark down the time taken for training by comparing the start time to the current time. :param start_time: The time at which the training was started. :type start_time: datetime :param num_docs: The number of documents trained. :type num_docs: int :param num_epochs: The number of epochs. Defaults to 1. :type num_epochs: int, optional :param project_name: The project name. Defaults to 'N/A'. :type project_name: str, optional .. py:method:: add_sup_training(start_time, num_docs, project_name) Add supervised training information based on data. NOTE: This will mark down the time taken for training by comparing the start time to the current time. NOTE: This will be called for every project being trained separately. So if there's a MCT export being trained with multiple projects, multiple different training instances will be recorded. :param start_time: The time at which the training was started. :type start_time: datetime :param num_docs: The number of documents that were trained. :type num_docs: int :param project_name: The project name. :type project_name: str .. py:method:: prepare_and_report_training(data_iterator, num_epochs, supervised = False, project_name = 'N/A') Context manager for preparing training. This is used so that we can get the number of items in the data during training. :param data_iterator: The data to be trained. :type data_iterator: C :param num_epochs: The number of epochs to be used. :type num_epochs: int :param supervised: Whether training is supervised. Defaults to False. :type supervised: bool, optional :param project_name: The project name. Defaults to 'N/A'. :type project_name: str, optional :Yields: *Iterator[C]* -- The same data that was input. .. py:method:: get_strategy() .. py:method:: get_init_attrs() :classmethod: .. py:method:: ignore_attrs() :classmethod: .. py:method:: include_properties() :classmethod: .. py:method:: merge_config(other) Merge this config with another config's (partial) model dump. The exepctation is that the `other` dict is a partial model dump. Values specified there are overwritten into the current config. Values not specified there are left intact. The `other` config can have keys/values that do not exist in the config or sub-config. And they will be added where possible. :param other: The model dump :type other: dict :raises IncorrectConfigValues: If unable to set the attribute, trying to set incorrect value, or trying to set sub-config values in an incorrect format (non-dict). .. py:method:: load(path) :classmethod: .. py:attribute:: model_config :type: ClassVar[pydantic.config.ConfigDict] Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. .. py:attribute:: model_fields :type: 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. .. py:attribute:: model_computed_fields :type: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects. .. py:attribute:: __class_vars__ :type: ClassVar[set[str]] The names of the class variables defined on the model. .. py:attribute:: __private_attributes__ :type: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]] Metadata about the private attributes of the model. .. py:attribute:: __signature__ :type: ClassVar[inspect.Signature] The synthesized `__init__` [`Signature`][inspect.Signature] of the model. .. py:attribute:: __pydantic_complete__ :type: ClassVar[bool] :value: False Whether model building is completed, or if there are still undefined fields. .. py:attribute:: __pydantic_core_schema__ :type: ClassVar[pydantic_core.CoreSchema] The core schema of the model. .. py:attribute:: __pydantic_custom_init__ :type: ClassVar[bool] Whether the model has a custom `__init__` method. .. py:attribute:: __pydantic_decorators__ :type: ClassVar[pydantic._internal._decorators.DecoratorInfos] Metadata containing the decorators defined on the model. This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. .. py:attribute:: __pydantic_generic_metadata__ :type: 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. .. py:attribute:: __pydantic_parent_namespace__ :type: ClassVar[Dict[str, Any] | None] :value: None Parent namespace of the model, used for automatic rebuilding of models. .. py:attribute:: __pydantic_post_init__ :type: ClassVar[None | Literal['model_post_init']] The name of the post-init method for the model, if defined. .. py:attribute:: __pydantic_root_model__ :type: ClassVar[bool] :value: False Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. .. py:attribute:: __pydantic_serializer__ :type: ClassVar[pydantic_core.SchemaSerializer] The `pydantic-core` `SchemaSerializer` used to dump instances of the model. .. py:attribute:: __pydantic_validator__ :type: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator] The `pydantic-core` `SchemaValidator` used to validate instances of the model. .. py:attribute:: __pydantic_extra__ :type: dict[str, Any] | None A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`. .. py:attribute:: __pydantic_fields_set__ :type: set[str] The names of fields explicitly set during instantiation. .. py:attribute:: __pydantic_private__ :type: dict[str, Any] | None Values of private attributes set on the model instance. .. py:attribute:: __slots__ :value: ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__') .. py:method:: __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. .. py:property:: model_extra :type: 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"`.** .. py:property:: model_fields_set :type: 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. .. py:method:: model_construct(_fields_set = None, **values) :classmethod: 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. :param _fields_set: 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. :param values: Trusted or pre-validated data dictionary. :Returns: **A new instance of the `Model` class with validated data.** .. py:method:: model_copy(*, update = None, deep = False) Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy Returns a copy of the model. :param update: Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. :param deep: Set to `True` to make a deep copy of the model. :Returns: **New model instance.** .. py:method:: 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. :param mode: 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. :param include: A set of fields to include in the output. :param exclude: A set of fields to exclude from the output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to use the field's alias in the dictionary key if defined. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A dictionary representation of the model.** .. py:method:: 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. :param indent: Indentation to use in the JSON output. If None is passed, the output will be compact. :param include: Field(s) to include in the JSON output. :param exclude: Field(s) to exclude from the JSON output. :param context: Additional context to pass to the serializer. :param by_alias: Whether to serialize using field aliases. :param exclude_unset: Whether to exclude fields that have not been explicitly set. :param exclude_defaults: Whether to exclude fields that are set to their default value. :param exclude_none: Whether to exclude fields that have a value of `None`. :param round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. :param warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. :param serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. :Returns: **A JSON string representation of the model.** .. py:method:: model_json_schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, schema_generator = GenerateJsonSchema, mode = 'validation') :classmethod: Generates a JSON schema for a model class. :param by_alias: Whether to use attribute aliases or not. :param ref_template: The reference template. :param schema_generator: To override the logic used to generate the JSON schema, as a subclass of `GenerateJsonSchema` with your desired modifications :param mode: The mode in which to generate the schema. :Returns: **The JSON schema for the given model class.** .. py:method:: model_parametrized_name(params) :classmethod: Compute the class name for parametrizations of generic classes. This method can be overridden to achieve a custom naming scheme for generic BaseModels. :param params: 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. .. py:method:: 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. .. py:method:: model_rebuild(*, force = False, raise_errors = True, _parent_namespace_depth = 2, _types_namespace = None) :classmethod: 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. :param force: Whether to force the rebuilding of the model schema, defaults to `False`. :param raise_errors: Whether to raise errors, defaults to `True`. :param _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. :param _types_namespace: 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`.** .. py:method:: model_validate(obj, *, strict = None, from_attributes = None, context = None) :classmethod: Validate a pydantic model instance. :param obj: The object to validate. :param strict: Whether to enforce types strictly. :param from_attributes: Whether to extract data from object attributes. :param context: Additional context to pass to the validator. :raises ValidationError: If the object could not be validated. :Returns: **The validated model instance.** .. py:method:: model_validate_json(json_data, *, strict = None, context = None) :classmethod: Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing Validate the given JSON data against the Pydantic model. :param json_data: The JSON data to validate. :param strict: Whether to enforce types strictly. :param context: 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. .. py:method:: model_validate_strings(obj, *, strict = None, context = None) :classmethod: Validate the given object with string data against the Pydantic model. :param obj: The object containing string data to validate. :param strict: Whether to enforce types strictly. :param context: Extra variables to pass to the validator. :Returns: **The validated Pydantic model.** .. py:method:: __get_pydantic_core_schema__(source, handler, /) :classmethod: Hook into generating the model's CoreSchema. :param source: The class we are generating a schema for. This will generally be the same as the `cls` argument if this is a classmethod. :param handler: A callable that calls into Pydantic's internal CoreSchema generation logic. :Returns: **A `pydantic-core` `CoreSchema`.** .. py:method:: __get_pydantic_json_schema__(core_schema, handler, /) :classmethod: Hook into generating the model's JSON schema. :param core_schema: 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. :param handler: 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.** .. py:method:: __pydantic_init_subclass__(**kwargs) :classmethod: 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. :param \*\*kwargs: Any keyword arguments passed to the class definition that aren't used internally by pydantic. .. py:method:: __class_getitem__(typevar_values) :classmethod: .. py:method:: __copy__() Returns a shallow copy of the model. .. py:method:: __deepcopy__(memo = None) Returns a deep copy of the model. .. py:method:: __getattr__(item) .. py:method:: _check_frozen(name, value) .. py:method:: __getstate__() .. py:method:: __setstate__(state) .. py:method:: __eq__(other) .. py:method:: __init_subclass__(**kwargs) :classmethod: 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.) :param \*\*kwargs: 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. .. py:method:: __iter__() So `dict(model)` works. .. py:method:: __repr__() .. py:method:: __repr_args__() .. py:attribute:: __repr_name__ .. py:attribute:: __repr_str__ .. py:attribute:: __pretty__ .. py:attribute:: __rich_repr__ .. py:method:: __str__() .. py:property:: __fields__ :type: dict[str, pydantic.fields.FieldInfo] .. py:property:: __fields_set__ :type: set[str] .. py:method:: dict(*, include = None, exclude = None, by_alias = False, exclude_unset = False, exclude_defaults = False, exclude_none = False) .. py:method:: 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) .. py:method:: parse_obj(obj) :classmethod: .. py:method:: parse_raw(b, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: parse_file(path, *, content_type = None, encoding = 'utf8', proto = None, allow_pickle = False) :classmethod: .. py:method:: from_orm(obj) :classmethod: .. py:method:: construct(_fields_set = None, **values) :classmethod: .. py:method:: 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) ``` :param include: Optional set or mapping specifying which fields to include in the copied model. :param exclude: Optional set or mapping specifying which fields to exclude in the copied model. :param update: Optional dictionary of field-value pairs to override field values in the copied model. :param deep: 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.** .. py:method:: schema(by_alias = True, ref_template = DEFAULT_REF_TEMPLATE) :classmethod: .. py:method:: schema_json(*, by_alias = True, ref_template = DEFAULT_REF_TEMPLATE, **dumps_kwargs) :classmethod: .. py:method:: validate(value) :classmethod: .. py:method:: update_forward_refs(**localns) :classmethod: .. py:method:: _iter(*args, **kwargs) .. py:method:: _copy_and_set_values(*args, **kwargs) .. py:method:: _get_value(*args, **kwargs) :classmethod: .. py:method:: _calculate_keys(*args, **kwargs) .. py:data:: logger .. py:data:: CDBState CDB State. This is a dictionary of the parts of the CDB that change during (supervised) training. It can be used to store and restore the state of a CDB after modifying it. Currently, the following fields are saved: - name2info - cui2info - token_counts - _subnames - config.meta .. py:function:: _get_attr(cdb, path) .. py:function:: _set_attr(cdb, path, val) .. py:function:: copy_cdb_state(cdb) Creates a (deep) copy of the CDB state. Grabs the fields that correspond to the state, creates deep copies, and returns the copies. :param cdb: The CDB from which to grab the state. :Returns: **CDBState** -- The copied state. .. py:function:: save_cdb_state(cdb, file_path) Saves CDB state in a file. Currently uses `dill.dump` to save the relevant fields/values. :param cdb: The CDB from which to grab the state. :param file_path: The file to dump the state. :type file_path: str .. py:function:: apply_cdb_state(cdb, state) Apply the specified state to the specified CDB. This overwrites the current state of the CDB with one provided. :param cdb: The CDB to apply the state to. :param state: The state to use. :type state: CDBState .. py:function:: _clear_state(cdb) .. py:function:: _reapply_state(cdb, state) .. py:function:: load_and_apply_cdb_state(cdb, file_path) Delete current CDB state and apply CDB state from file. This first deletes the current state of the CDB. This is to save memory. The idea is that saving the staet on disk will save on RAM usage. But it wouldn't really work too well if upon load, two instances were still in memory. :param cdb: The CDB to apply the state to. :param file_path: The file where the state has been saved to. :type file_path: str .. py:function:: captured_state_cdb(cdb, save_state_to_disk = False) A context manager that captures and re-applies the initial CDB state. The context manager captures/copies the initial state of the CDB when entering. It then allows the user to modify the state (i.e training). Upon exit re-applies the initial CDB state. If RAM is an issue, it is recommended to use `save_state_to_disk`. Otherwise the copy of the original state will be held in memory. If saved on disk, a temporary file is used and removed afterwards. :param cdb: The CDB to use. :param save_state_to_disk: Whether to save state on disk or hold in memory. Defaults to False. :type save_state_to_disk: bool :Yields: None .. py:function:: in_memory_state_capture(cdb) Capture the CDB state in memory. :param cdb: The CDB to use. :Yields: None .. py:function:: on_disk_memory_capture(cdb) Capture the CDB state in a temporary file. :param cdb: The CDB to use :Yields: None