nannyml.runner module
Used as an access point to start using NannyML in its most simple form.
- class nannyml.runner.CalculatorFactory[source]
Bases:
objectA factory class that produces Metric instances based on a given magic string or a metric specification.
- registry: Dict[str, Type] = {'categorical_distribution': <class 'nannyml.distribution.categorical.calculator.CategoricalDistributionCalculator'>, 'cbpe': <class 'nannyml.performance_estimation.confidence_based.cbpe.CBPE'>, 'continuous_distribution': <class 'nannyml.distribution.continuous.calculator.ContinuousDistributionCalculator'>, 'dle': <class 'nannyml.performance_estimation.direct_loss_estimation.dle.DLE'>, 'domain_classifier': <class 'nannyml.drift.multivariate.domain_classifier.calculator.DomainClassifierCalculator'>, 'missing_values': <class 'nannyml.data_quality.missing.calculator.MissingValuesCalculator'>, 'performance': <class 'nannyml.performance_calculation.calculator.PerformanceCalculator'>, 'reconstruction_error': <class 'nannyml.drift.multivariate.data_reconstruction.calculator.DataReconstructionDriftCalculator'>, 'summary_stats_avg': <class 'nannyml.stats.avg.calculator.SummaryStatsAvgCalculator'>, 'summary_stats_median': <class 'nannyml.stats.median.calculator.SummaryStatsMedianCalculator'>, 'summary_stats_row_count': <class 'nannyml.stats.count.calculator.SummaryStatsRowCountCalculator'>, 'summary_stats_std': <class 'nannyml.stats.std.calculator.SummaryStatsStdCalculator'>, 'summary_stats_sum': <class 'nannyml.stats.sum.calculator.SummaryStatsSumCalculator'>, 'univariate_drift': <class 'nannyml.drift.univariate.calculator.UnivariateDriftCalculator'>, 'unseen_values': <class 'nannyml.data_quality.unseen.calculator.UnseenValuesCalculator'>}
- class nannyml.runner.RunContext(current_step: int, total_steps: int, current_calculator: str, current_calculator_config: Optional[Dict[str, Any]] = None, current_calculator_success: bool = True, run_success: bool = True, result: Optional[nannyml._Result] = None)[source]
Bases:
object- STEPS_PER_CALCULATOR = 3
- current_calculator: str
- current_calculator_config: Optional[Dict[str, Any]] = None
- current_calculator_success: bool = True
- current_step: int
- result: Optional[Result] = None
- run_success: bool = True
- total_steps: int
- class nannyml.runner.RunInput(reference_data: pandas.core.frame.DataFrame, analysis_data: pandas.core.frame.DataFrame, target_data: Optional[pandas.core.frame.DataFrame] = None, target_join_column: Optional[str] = None)[source]
Bases:
object- analysis_data: DataFrame
- reference_data: DataFrame
- target_data: Optional[DataFrame] = None
- target_join_column: Optional[str] = None
- class nannyml.runner.RunnerLogger(logger: Logger, console: Optional[Console] = None)[source]
Bases:
object
- nannyml.runner.get_output_writers(outputs_config: Optional[List[WriterConfig]], logger: Optional[RunnerLogger] = None) List[Tuple[Writer, Dict[str, Any]]][source]
- nannyml.runner.get_store(store_config: Optional[StoreConfig], logger: Optional[RunnerLogger] = None) Optional[Store][source]
- nannyml.runner.read_data(input_config: InputDataConfig, logger: Optional[RunnerLogger] = None) DataFrame[source]
- nannyml.runner.run(config: ~nannyml.config.Config, input: ~typing.Optional[~nannyml.runner.RunInput] = None, logger: ~logging.Logger = <Logger nannyml.runner (WARNING)>, console: ~typing.Optional[~rich.console.Console] = None, on_fit: ~typing.Optional[~typing.Callable[[~nannyml.runner.RunContext], ~typing.Any]] = None, on_calculate: ~typing.Optional[~typing.Callable[[~nannyml.runner.RunContext], ~typing.Any]] = None, on_write: ~typing.Optional[~typing.Callable[[~nannyml.runner.RunContext], ~typing.Any]] = None, on_calculator_complete: ~typing.Optional[~typing.Callable[[~nannyml.runner.RunContext], ~typing.Any]] = None, on_run_complete: ~typing.Optional[~typing.Callable[[~nannyml.runner.RunContext], ~typing.Any]] = None, on_fail: ~typing.Optional[~typing.Callable[[~nannyml.runner.RunContext, ~typing.Optional[Exception]], ~typing.Any]] = None)[source]