plexus.dashboard.api.models.evaluation module

Evaluation Model - Python representation of the GraphQL Evaluation type.

This model represents individual Evaluations in the system, tracking: - Accuracy and performance metrics - Processing status and progress - Error states and details - Relationships to accounts, scorecards, and scores

All mutations (create/update) are performed in background threads for non-blocking operation.

class plexus.dashboard.api.models.evaluation.Evaluation(id: str, type: str, accountId: str, status: str, createdAt: datetime.datetime, updatedAt: datetime.datetime, client: plexus.dashboard.api.client._BaseAPIClient | None = None, parameters: Dict | None = None, metrics: Dict | None = None, inferences: int | None = None, accuracy: float | None = None, cost: float | None = None, startedAt: datetime.datetime | None = None, elapsedSeconds: int | None = None, estimatedRemainingSeconds: int | None = None, totalItems: int | None = None, processedItems: int | None = None, errorMessage: str | None = None, errorDetails: Dict | None = None, scorecardId: str | None = None, scoreId: str | None = None, confusionMatrix: Dict | None = None, scoreGoal: str | None = None, datasetClassDistribution: Dict | None = None, isDatasetClassDistributionBalanced: bool | None = None, predictedClassDistribution: Dict | None = None, isPredictedClassDistributionBalanced: bool | None = None, taskId: str | None = None)

Bases: BaseModel

__init__(id: str, type: str, accountId: str, status: str, createdAt: datetime, updatedAt: datetime, client: _BaseAPIClient | None = None, parameters: Dict | None = None, metrics: Dict | None = None, inferences: int | None = None, accuracy: float | None = None, cost: float | None = None, startedAt: datetime | None = None, elapsedSeconds: int | None = None, estimatedRemainingSeconds: int | None = None, totalItems: int | None = None, processedItems: int | None = None, errorMessage: str | None = None, errorDetails: Dict | None = None, scorecardId: str | None = None, scoreId: str | None = None, confusionMatrix: Dict | None = None, scoreGoal: str | None = None, datasetClassDistribution: Dict | None = None, isDatasetClassDistributionBalanced: bool | None = None, predictedClassDistribution: Dict | None = None, isPredictedClassDistributionBalanced: bool | None = None, taskId: str | None = None)

accountId: str

accuracy: float | None = None

confusionMatrix: Dict | None = None

cost: float | None = None

classmethod create(client: _BaseAPIClient, type: str, accountId: str, *, status: str = 'PENDING', scorecardId: str | None = None, scoreId: str | None = None, taskId: str | None = None, **kwargs) → Evaluation

Create a new Evaluation.

createdAt: datetime

datasetClassDistribution: Dict | None = None

elapsedSeconds: int | None = None

errorDetails: Dict | None = None

errorMessage: str | None = None

estimatedRemainingSeconds: int | None = None

classmethod fields() → str

Fields to request in queries and mutations

classmethod from_dict(data: Dict[str, Any], client: _BaseAPIClient) → Evaluation

Create an instance from a dictionary of data

classmethod get_by_id(id: str, client: _BaseAPIClient, include_score_results: bool = False) → Evaluation

inferences: int | None = None

isDatasetClassDistributionBalanced: bool | None = None

isPredictedClassDistributionBalanced: bool | None = None

metrics: Dict | None = None

parameters: Dict | None = None

predictedClassDistribution: Dict | None = None

processedItems: int | None = None

scoreGoal: str | None = None

scoreId: str | None = None

scorecardId: str | None = None

startedAt: datetime | None = None

status: str

taskId: str | None = None

totalItems: int | None = None

type: str

update(**kwargs) → None

Update Evaluation fields in a background thread.

This is a non-blocking operation - the mutation is performed in a background thread.

Args:

**kwargs: Fields to update

updatedAt: datetime