aind_behavior_curriculum.curriculum.Stage

class aind_behavior_curriculum.curriculum.Stage(*, name: str, task: ~aind_behavior_curriculum.curriculum.TTask, graph: ~aind_behavior_curriculum.curriculum.PolicyGraph = <factory>, start_policies: ~typing.List[~aind_behavior_curriculum.curriculum.Policy] = <factory>, metrics_provider: ~aind_behavior_curriculum.curriculum.MetricsProvider | None = None)[source]

Bases: AindBehaviorModel, Generic[TTask]

Instance of a Task. Task Parameters may change according to rules defined in BehaviorGraph. Stage manages a BehaviorGraph instance with a read/write API.

__init__(**data: Any) → None[source]

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.

Methods

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

add_policy(policy)

Adds a floating policy to the Stage adjacency graph.

add_policy_transition(start_policy, ...)

Add policy transition between two policies: Policy_A -> Policy_B.

construct([_fields_set])

copy(*[, include, exclude, update, deep])

Returns a copy of the model.

dict(*[, include, exclude, by_alias, ...])

from_orm(obj)

get_task_parameters()

See current task parameters of Task.

json(*[, include, exclude, by_alias, ...])

model_construct([_fields_set])

Creates a new instance of the Model class with validated data.

model_copy(*[, update, deep])

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

model_dump(*[, mode, include, exclude, ...])

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

model_dump_json(*[, indent, include, ...])

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

model_json_schema([by_alias, ref_template, ...])

Generates a JSON schema for a model class.

model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

model_post_init(_Stage__context)

Runs after model_construct to ensure that the initial policies update the PolicyGraph

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, ...])

Validate a pydantic model instance.

model_validate_json(json_data, *[, strict, ...])

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

model_validate_strings(obj, *[, strict, context])

Validate the given object with string data against the Pydantic model.

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

remove_policy(policy)

Removes policy and all associated incoming/outgoing transition rules from the stage graph.

remove_policy_transition(start_policy, ...)

Removes transition with options to remove start/end policies associated with the transition.

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

see_policies()

See policies of policy graph.

see_policy_transitions(policy)

TTask See transitions of stage in policy graph.

set_policy_transition_priority(policy, ...)

Change the order of policy transitions listed under a policy.

set_start_policies(start_policies[, ...])

Sets stage's start policies to start policies provided.

set_task_parameters(task_params)

Set task with new set of task parameters.

update_forward_refs(**localns)

validate(value)

validate_stage()

Validates that the stage can be (de)serialized.

Attributes

model_computed_fields

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra

Get extra fields set during validation.

model_fields

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

name

task

graph

start_policies

metrics_provider

add_policy(policy: Policy) → None[source]

Adds a floating policy to the Stage adjacency graph.

add_policy_transition(start_policy: Policy, dest_policy: Policy, rule: PolicyTransition) → None[source]

Add policy transition between two policies: Policy_A -> Policy_B.

If Policy_A has been added to stage before, this method starts a transition

from the existing Policy_A.

If Policy_B has been added to stage before, this method creates a transition

into the existing Policy_B.

NOTE: The order in which this method is called sets the order of transition priority.

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self[source]

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

  • include – Optional set or mapping specifying which fields to include in the copied model.

  • exclude – Optional set or mapping specifying which fields to exclude in the copied model.

  • update – Optional dictionary of field-value pairs to override field values in the copied model.

  • 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.

get_task_parameters() → TaskParameters[source]

See current task parameters of Task.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'str_strip_whitespace': True, 'strict': True, 'validate_assignment': True, 'validate_default': True}[source]

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self[source]

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 – 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 – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self[source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

  • 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.

  • deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) → dict[str, Any][source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

  • 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.

  • include – A set of fields to include in the output.

  • exclude – A set of fields to exclude from the output.

  • context – Additional context to pass to the serializer.

  • by_alias – Whether to use the field’s alias in the dictionary key if defined.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that are set to their default value.

  • exclude_none – Whether to exclude fields that have a value of None.

  • round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) → str[source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

  • indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

  • include – Field(s) to include in the JSON output.

  • exclude – Field(s) to exclude from the JSON output.

  • context – Additional context to pass to the serializer.

  • by_alias – Whether to serialize using field aliases.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that are set to their default value.

  • exclude_none – Whether to exclude fields that have a value of None.

  • round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str][source]

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.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any][source]

Generates a JSON schema for a model class.

Parameters:

  • by_alias – Whether to use attribute aliases or not.

  • ref_template – The reference template.

  • schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

  • mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str[source]

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 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.

model_post_init(_Stage__context)[source]

Runs after model_construct to ensure that the initial policies update the PolicyGraph

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None[source]

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 – Whether to force the rebuilding of the model schema, defaults to False.

  • raise_errors – Whether to raise errors, defaults to True.

  • _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

  • _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.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | None = None) → Self[source]

Validate a pydantic model instance.

Parameters:

  • obj – The object to validate.

  • strict – Whether to enforce types strictly.

  • from_attributes – Whether to extract data from object attributes.

  • context – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: Any | None = None) → Self[source]

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

  • json_data – The JSON data to validate.

  • strict – Whether to enforce types strictly.

  • 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.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None) → Self[source]

Validate the given object with string data against the Pydantic model.

Parameters:

  • obj – The object containing string data to validate.

  • strict – Whether to enforce types strictly.

  • context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

remove_policy(policy: Policy) → None[source]

Removes policy and all associated incoming/outgoing transition rules from the stage graph. NOTE: Removed nodes and transitions have the side effect of changing transition priority.

remove_policy_transition(start_policy: Policy, dest_policy: Policy, rule: PolicyTransition, remove_start_policy: bool = False, remove_dest_policy: bool = False) → None[source]

Removes transition with options to remove start/end policies associated with the transition. NOTE: Removed nodes and transitions has the side effect of changing transition priority.

see_policies() → List[Policy][source]

See policies of policy graph.

see_policy_transitions(policy: Policy) → List[Tuple[PolicyTransition, Policy]][source]

TTask See transitions of stage in policy graph.

set_policy_transition_priority(policy: Policy, policy_transitions: List[Tuple[PolicyTransition, Policy]]) → None[source]

Change the order of policy transitions listed under a policy. To use, call see_policy_transitions() and order the transitions in the desired priority from left -> right.

set_start_policies(start_policies: Policy | Iterable[Policy], append_non_existing: bool = True) → None[source]

Sets stage’s start policies to start policies provided. Input overwrites existing start policies.

set_task_parameters(task_params: TaskParameters) → None[source]

Set task with new set of task parameters. Task re-validates TaskParameters on assignment.

validate_stage() → Self[source]

Validates that the stage can be (de)serialized.