phenotypic.tune.score.CompositeScorer#

class phenotypic.tune.score.CompositeScorer(*, scorers: list[~typing.Annotated[~typing.Any, ~pydantic.functional_validators.BeforeValidator(func=~phenotypic.sdk_.typing_._deserialize_operation_value, json_schema_input_type=PydanticUndefined), ~pydantic.functional_validators.AfterValidator(func=<phenotypic.sdk_.typing_._RequireValue object at 0x7f9589cdf6d0>), ~pydantic.functional_serializers.PlainSerializer(func=~phenotypic.sdk_.typing_._serialize_operation_value, return_type=PydanticUndefined, when_used=always)]] = [], weights: dict[str, float] | None = None, multi_objective: bool = False, blend: ~typing.Literal['tchebycheff', 'weighted_mean'] = 'tchebycheff', rho: float = 0.05)[source]#

Bases: Scorer

Blend several Scorer children into one objective (scalar or Pareto).

Parameters:

scorers (list[Annotated[Any, BeforeValidator(func=~phenotypic.sdk_.typing_._deserialize_operation_value, json_schema_input_type=PydanticUndefined), AfterValidator(func=<phenotypic.sdk_.typing_._RequireValue object at 0x7f9589cdf6d0>), PlainSerializer(func=~phenotypic.sdk_.typing_._serialize_operation_value, return_type=PydanticUndefined, when_used=always)]]) – The child objectives to compose. Each is a ScorerField so any Scorer subclass — including a nested CompositeScorer — round-trips through the polymorphic registry. Child i owns the "s{i}." term namespace.
weights (dict[str, float] | None) – Optional per-child weights for the single-objective scalar blend, keyed by child handle ("s0", "s1", …). The meaning is blend-dependent: under blend="tchebycheff" they are per-axis Tchebycheff weights (they steer the max term, uniform 1.0 when None); under blend="weighted_mean" they are arithmetic weights for the compensatory mean. Ignored when multi_objective is True.
multi_objective (bool) – When True, finalize() returns a dict[str, float] of per-child objectives (the plan §0a sidecar) instead of a scalar, so the composite can drive a Pareto study.
blend (Literal['tchebycheff', 'weighted_mean']) – The single-objective combiner. "tchebycheff" (default) is the conjunctive augmented-Tchebycheff cost over the pinned study-global active set — worst-axis-dominant, so a single weak axis cannot be masked by a strong one. "weighted_mean" is the compensatory arithmetic-mean opt-out. The geometric-mean-of-cost blend is no longer offered (it inverts the conjunctive property — a single perfect axis would annihilate the product).
rho (float) – The augmented-Tchebycheff augmentation coefficient (advanced-only; default 0.05). It scales the weighted-L1 term that breaks ties between weakly-dominated points; →0 recovers the plain Tchebycheff (admits weakly-dominated winners), large values drift toward the weighted sum (spec §6.4). Ignored under blend="weighted_mean" / multi_objective.

Raises:

pydantic.ValidationError – If the nested scorer graph contains a cycle — a composite reachable from itself (direct self-nesting or a deeper back-edge).

Examples

Compose two children and read the prefixed, merged per-image terms (the QCScorer here scores a perfect 96-well count match):

>>> import pandas as pd
>>> from phenotypic.analysis import ExpectedVsDetectedCount
>>> from phenotypic.tune.score import CompositeScorer, QCScorer
>>> layout = pd.DataFrame(
...     {"MetadataImage_ImageName": ["p"] * 96, "Object_Label": list(range(96))}
... )
>>> qc = QCScorer(
...     check=ExpectedVsDetectedCount(
...         metadata=layout, groupby=["MetadataImage_ImageName"]
...     )
... )
>>> comp = CompositeScorer(scorers=[qc, qc])
>>> terms = comp.score_image(None, layout)
>>> sorted(terms)
['s0.Count', 's1.Count']
>>> round(comp.finalize(terms), 3)  # augmented Tchebycheff of two perfect (cost-0) children
0.001

Flip to multi-objective and finalize returns the per-child sidecar:

>>> comp_mo = CompositeScorer(scorers=[qc, qc], multi_objective=True)
>>> {k: round(v, 3) for k, v in comp_mo.finalize(terms).items()}
{'s0': 0.0, 's1': 0.0}

Methods

`__init__`	Create a new model by parsing and validating input data from keyword arguments.
`availability`	Whether the composite can contribute any signal.
`construct`
`copy`	Returns a copy of the model.
`dict`
`finalize`	Blend the per-child scalars — scalar (default) or dict (Pareto).
`from_orm`
`json`
`model_construct`	Creates a new instance of the Model class with validated data.
`model_copy`	!!! abstract "Usage Documentation"
`model_dump`	!!! abstract "Usage Documentation"
`model_dump_json`	!!! abstract "Usage Documentation"
`model_json_schema`	Generates a JSON schema for a model class.
`model_parametrized_name`	Compute the class name for parametrizations of generic classes.
`model_post_init`	This function is meant to behave like a BaseModel method to initialise private attributes.
`model_rebuild`	Try to rebuild the pydantic-core schema for the model.
`model_validate`	Validate a pydantic model instance.
`model_validate_json`	!!! abstract "Usage Documentation"
`model_validate_strings`	Validate the given object with string data against the Pydantic model.
`objective_names`	The ordered objective-axis names of a multi-objective composite.
`parse_file`
`parse_obj`
`parse_raw`
`schema`
`schema_json`
`score_image`	Merge every child's per-image terms under a per-child prefix.
`set_active_set`	Pin the study-global active set (child handles available study-wide).
`update_forward_refs`
`validate`

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.
`scorers`
`weights`
`multi_objective`
`blend`
`rho`

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

scorers: list[ScorerField]#

weights: dict[str, float] | None#

multi_objective: bool#

blend: CompositeBlend#

rho: float#

set_active_set(handles: tuple[str, ...]) → None[source]#

Pin the study-global active set (child handles available study-wide).

Called once by the engine after meta-validation, before the trial loop, so every trial’s Tchebycheff max and normalizer use the same fixed roster (§6.3 plumbing SF3). Idempotent.

Parameters:: handles (tuple[str, ...]) – The available child handles (a subset of objective_names()), in objective order.
Return type:: None

objective_names() → list[str][source]#

The ordered objective-axis names of a multi-objective composite.

Returns the per-child handles (["s0", "s1", …]) in scorers order — exactly the keys finalize() emits when multi_objective is True, and therefore the Trial.objectives / objectives_json keys and the pareto/ axis labels. Stable across a study so the per-objective directions, the per-objective best pipelines, and the front parquet all align.

Returns:: The child handles in order; [] for an empty composite.
Return type:: list[str]

availability() → bool[source]#

Whether the composite can contribute any signal.

Pinned rule: a composite is available iff at least one child is available. A child that abstains contributes no terms, but as long as one child can score, the composite produces a usable (partial) objective; an empty composite, or one all of whose children abstain, is unavailable and the engine degrades to its fallback.

Returns:: True if any child reports Scorer.availability(); False for an empty composite or one whose children all abstain.
Return type:: bool

score_image(image: Any, measurements: pandas.DataFrame) → dict[str, float][source]#

Merge every child’s per-image terms under a per-child prefix.

Each child i is scored on (image, measurements) and its terms are re-keyed "s{i}.<term>" so two children emitting the same term name never collide and finalize() can re-group them.

Parameters:

image (Any) – The processed image, passed to every child unchanged.
measurements (pandas.DataFrame) – The candidate pipeline’s measurement frame, passed to every child unchanged.

Returns:

The union of all children’s terms, each prefixed with its child handle.

Return type:

dict[str, float]

finalize(terms: Mapping[str, float]) → float | dict[str, float][source]#

Blend the per-child scalars — scalar (default) or dict (Pareto).

Re-groups the prefixed terms back to their originating child, calls each child’s own Scorer.finalize() over its un-prefixed sub-terms (projecting a child’s own multi-objective dict to its mean), then:

multi_objective=True → returns {handle: child_scalar} over every axis in objective_names() order — the plan §0a sidecar the Evaluator stashes on EvaluationResult.objectives. A child that abstains (no terms this run) is floored to 1.0 (the worst cost; the study minimizes) rather than dropped, so the dict keys + order stay invariant and exactly match the multi-objective study’s fixed directions (now minimize). A dropped axis would otherwise make the NSGA-II value vector the wrong length and crash Optuna’s tell mid-run; the 1.0 floor also mirrors the journal Pareto _vector fill so the journal and Optuna backends agree on an abstaining axis.
blend="weighted_mean" → the compensatory weighted arithmetic mean of the per-child cost scalars (missing weights default to 1.0).
blend="tchebycheff" (default) → the conjunctive augmented Tchebycheff cost (_tchebycheff()) over the pinned study-global active set, so a single weak (high-cost) axis dominates the max and cannot be masked by a strong one. The roster is restricted to the pinned active set (the children available study-wide, §6.3): a study-wide abstainer is simply not an objective (dropped from both the max and the normalizer), while per-image abstention is already a fewer-samples matter handled upstream by the robust aggregate — so a present-but-absent-this-call child is NOT flooded into the max.

Parameters:: terms (Mapping[str, float]) – The robust-aggregated, child-prefixed terms (the output of score_image() after the Evaluator’s per-term aggregation).
Returns:: The scalar objective for the single-objective path (worst cost 1.0 for no scored children/terms), or the per-child dict (one entry per axis, abstainers floored) for the multi-objective path.
Return type:: float | dict[str, float]

__copy__() → Self#

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo: dict[int, Any] | None = None) → Self#

Returns a deep copy of the model.

Parameters:: memo (dict[int, Any] | None)
Return type:: Self

classmethod __get_pydantic_json_schema__(core_schema: CoreSchema, handler: GetJsonSchemaHandler, /) → JsonSchemaValue#

Hook into generating the model’s JSON schema.

Parameters:

core_schema (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 (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:

JsonSchemaValue

__init__(**data: Any) → None#

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

__iter__() → Generator[tuple[str, Any], None, None]#

So dict(model) works.

Return type:: Generator[tuple[str, Any], None, None]

__pretty__(fmt: Callable[[Any], Any], **kwargs: Any) → Generator[Any]#

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Parameters:

fmt (Callable[[Any], Any])
kwargs (Any)

Return type:

Generator[Any]

classmethod __pydantic_init_subclass__(**kwargs: Any) → None#

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after basic class initialization is complete. In particular, attributes like model_fields will be present when this is called, but forward annotations are not guaranteed to be resolved yet, meaning that creating an instance of the class may fail.

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

Note

You may want to override [__pydantic_on_complete__()][pydantic.main.BaseModel.__pydantic_on_complete__] instead, which is called once the class and its fields are fully initialized and ready for validation.

classmethod __pydantic_on_complete__() → None#

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before [__pydantic_init_subclass__()][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using [model_rebuild()][pydantic.main.BaseModel.model_rebuild].

Return type:: None

__repr_name__() → str#

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_recursion__(object: Any) → str#

Returns the string representation of a recursive object.

Parameters:: object (Any)
Return type:: str

__rich_repr__() → RichReprResult#

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: RichReprResult

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

Parameters:

_fields_set (set[str] | None)
values (Any)

Return type:

Self

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 (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (AbstractSetIntStr | 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:

Self

Parameters:

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)
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)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)

Return type:

Dict[str, Any]

classmethod from_orm(obj: Any) → Self#

Parameters:: obj (Any)
Return type:: Self

Parameters:

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)
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)
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

model_computed_fields = {}#

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

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:

Self

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

!!! abstract “Usage Documentation”: [model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note: The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

update (Mapping[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:

Self

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

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 (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) – A set of fields to include in the output.
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) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
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]

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#json-mode)

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.
ensure_ascii (bool) – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.
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) – Field(s) to include in the JSON output.
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) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

Return type:

str

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”.

model_fields = {'blend': FieldInfo(annotation=Literal['tchebycheff', 'weighted_mean'], required=False, default='tchebycheff'), 'multi_objective': FieldInfo(annotation=bool, required=False, default=False), 'rho': FieldInfo(annotation=float, required=False, default=0.05), 'scorers': FieldInfo(annotation=list[Annotated[Any, BeforeValidator, AfterValidator, PlainSerializer]], required=False, default=[]), 'weights': FieldInfo(annotation=Union[dict[str, float], NoneType], required=False, default=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.

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', *, union_format: ~typing.Literal['any_of', 'primitive_type_array'] = 'any_of') → dict[str, Any]#

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.
union_format (Literal['any_of', 'primitive_type_array']) –
The format to use when combining schemas from unions together. Can be one of:
- ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – 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: tuple[type[Any], ...]) → str#

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], ...]) – 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: Any, /) → None#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

Return type:

None

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

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

Return type:

Self

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#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.
extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

Return type:

Self

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.
extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Return type:

Self

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

Parameters:

path (str | Path)
content_type (str | None)
encoding (str)
proto (DeprecatedParseProtocol | None)
allow_pickle (bool)

Return type:

Self

classmethod parse_obj(obj: Any) → Self#

Parameters:: obj (Any)
Return type:: Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

Parameters:

b (str | bytes)
content_type (str | None)
encoding (str)
proto (DeprecatedParseProtocol | None)
allow_pickle (bool)

Return type:

Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]#

Parameters:

by_alias (bool)
ref_template (str)

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str#

Parameters:

by_alias (bool)
ref_template (str)
dumps_kwargs (Any)

Return type:

str

classmethod update_forward_refs(**localns: Any) → None#

Parameters:: localns (Any)
Return type:: None

classmethod validate(value: Any) → Self#

Parameters:: value (Any)
Return type:: Self

phenotypic.tune.score.CompositeScorer#

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