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featurebyte.FeatureJobSettingAnalysis

class FeatureJobSettingAnalysis(
*,
user_id: Optional[ObjectId]=None,
name: Optional[str]=None,
created_at: Optional[datetime]=None,
updated_at: Optional[datetime]=None,
block_modification_by: List[ReferenceInfo]=None,
description: Optional[str]=None,
is_deleted: bool=False,
catalog_id: ObjectId,
event_table_id: Optional[ObjectId]=None,
event_table_candidate: Optional[EventTableCandidate]=None,
analysis_options: AnalysisOptions,
analysis_parameters: AnalysisParameters,
analysis_result: AnalysisResult,
analysis_report: str,
backtest_summaries: Optional[List[BackTestSummary]]=None
)

Description

The FeatureJobSettingAnalysis object contains the result of the analysis of the data availability and freshness of a table. The metadata held by the object includes a report and recommendation for the configuration of the feature job setting of features associated with the table. Additionally, you can perform a backtest of a manually configured feature job setting.

Parameters

  • user_id: Optional[ObjectId]

  • name: Optional[str]

  • created_at: Optional[datetime]

  • updated_at: Optional[datetime]

  • block_modification_by: List[ReferenceInfo]

  • description: Optional[str]

  • is_deleted: bool
    default: False

  • catalog_id: ObjectId

  • event_table_id: Optional[ObjectId]

  • event_table_candidate: Optional[EventTableCandidate]

  • analysis_options: AnalysisOptions

  • analysis_parameters: AnalysisParameters

  • analysis_result: AnalysisResult

  • analysis_report: str

  • backtest_summaries: Optional[List[BackTestSummary]]

Examples

>>> analysis = invoice_table.create_new_feature_job_setting_analysis(
...     analysis_date=pd.Timestamp("2023-04-10"),
...     analysis_length=3600  24  28,
... )