pdstools.infinity.resources.prediction_studio.base¶

Attributes¶

DEPLOYMENT_MODE

Exceptions¶

ModelValidationError

Exception for errors during model validation.

Classes¶

`Metrics`	dict() -> new empty dictionary
`ModelAttributes`	dict() -> new empty dictionary
`Model`	Helper class that provides a standard way to create an ABC using
`Prediction`	Helper class that provides a standard way to create an ABC using
`Notification`	Helper class that provides a standard way to create an ABC using
`UploadedModel`	Helper class that provides a standard way to create an ABC using
`LocalModel`	Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
`Repository`	Helper class that provides a standard way to create an ABC using
`DataMartExport`	Helper class that provides a standard way to create an ABC using
`PredictionStudioBase`	Helper class that provides a standard way to create an ABC using
`AsyncPredictionStudioBase`	Helper class that provides a standard way to create an ABC using
`ChampionChallenger`	Helper class that provides a standard way to create an ABC using

Module Contents¶

DEPLOYMENT_MODE¶

class Metrics¶

Bases: TypedDict

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

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

lift: float¶

performance: float¶

performanceMeasure: Literal['AUC']¶

class ModelAttributes¶

Bases: TypedDict

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

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

modelId: str¶

status: str¶

label: str¶

type: str¶

subject: str¶

objective: str¶

outcome_type: str¶

model_type: str¶

modeling_technique: str¶

source: str¶

targetLabels: List[Dict[Literal['label'], str]]¶

alternativeLabels: List[Dict[Literal['label'], str]]¶

metrics: Dict[str, Any]¶

class Model(client: pdstools.infinity.client.SyncAPIClient, *, modelId: str, label: str, modelType: str, status: str, componentName: str | None = None, source: str | None = None, lastUpdateTime: str | None = None, modelingTechnique: str | None = None, updatedBy: str | None = None)¶

Bases: pdstools.infinity.internal._resource.SyncAPIResource, abc.ABC

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

Parameters:

client (pdstools.infinity.client.SyncAPIClient)
modelId (str)
label (str)
modelType (str)
status (str)
componentName (Union[str, None])
source (Union[str, None])
lastUpdateTime (Union[str, None])
modelingTechnique (Union[str, None])
updatedBy (Union[str, None])

model_id¶

label¶

model_type¶

modeling_technique = None¶

source = None¶

status¶

updated_by = None¶

abstract describe() → ModelAttributes¶

Return type:: ModelAttributes

class Prediction(client: pdstools.infinity.client.SyncAPIClient, *, predictionId: str, label: str, status: str, lastUpdateTime: str, objective: str | None = None, subject: str | None = None)¶

Bases: pdstools.infinity.internal._resource.SyncAPIResource, abc.ABC

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

Parameters:

client (pdstools.infinity.client.SyncAPIClient)
predictionId (str)
label (str)
status (str)
lastUpdateTime (str)
objective (Optional[str])
subject (Optional[str])

prediction_id¶

label¶

objective = None¶

subject = None¶

status¶

last_update_time¶

abstract get_metric(*, metric: Literal['Performance', 'Total_responses', 'Lift', 'Success_rate'], timeframe: Literal['7d', '4w', '3m', '6m']) → polars.DataFrame¶

Parameters:

metric (Literal['Performance', 'Total_responses', 'Lift', 'Success_rate'])
timeframe (Literal['7d', '4w', '3m', '6m'])

Return type:

polars.DataFrame

abstract describe()¶

class Notification(client: pdstools.infinity.client.SyncAPIClient, *, description: str, modelType: str, notificationID: str, notificationType: str, notificationMnemonic: str, context: str, impact: str, triggerTime: str, modelID: str | None = None, predictionID: str | None = None)¶

Bases: pdstools.infinity.internal._resource.SyncAPIResource, abc.ABC

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

Parameters:

client (pdstools.infinity.client.SyncAPIClient)
description (str)
modelType (str)
notificationID (str)
notificationType (str)
notificationMnemonic (str)
context (str)
impact (str)
triggerTime (str)
modelID (Union[str, None])
predictionID (Union[str, None])

notification_type¶

notification_id¶

notification_mnemonic¶

description¶

model_type¶

impact¶

trigger_time¶

class UploadedModel¶

Bases: abc.ABC

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

exception ModelValidationError¶

Bases: Exception

Exception for errors during model validation.

class LocalModel(/, **data: Any)¶

Bases: pydantic.BaseModel

Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

A base class for creating Pydantic models.

Attributes:

__class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __pydantic_custom_init__: Whether the model has a custom __init__ function. __pydantic_decorators__: Metadata containing the decorators defined on the model.

This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: 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__: Parent namespace of the model, used for automatic rebuilding of models. __pydantic_post_init__: The name of the post-init method for the model, if defined. __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel]. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]: is set to ‘allow’.

__pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance.