nannyml.stats.std.calculator module

Simple Statistics Standard Deviation Module.

class nannyml.stats.std.calculator.SummaryStatsStdCalculator(column_names: ~typing.Union[str, ~typing.List[str]], timestamp_column_name: ~typing.Optional[str] = None, chunk_size: ~typing.Optional[int] = None, chunk_number: ~typing.Optional[int] = None, chunk_period: ~typing.Optional[str] = None, chunker: ~typing.Optional[~nannyml.chunk.Chunker] = None, threshold: ~nannyml.thresholds.Threshold = StandardDeviationThreshold{'std_lower_multiplier': 3, 'std_upper_multiplier': 3, 'offset_from': <function nanmean>})[source]

Bases: AbstractCalculator

Simple Statistics Standard Deviation Calculator.

Creates a new SummaryStatsStdCalculator instance.

Parameters:

  • column_names (Union[str, List[str]]) – A string or list containing the names of features in the provided data set. Missing Values will be calculated for each entry in this list.

  • timestamp_column_name (str) – The name of the column containing the timestamp of the model prediction.

  • chunk_size (int) – Splits the data into chunks containing chunks_size observations. Only one of chunk_size, chunk_number or chunk_period should be given.

  • chunk_number (int) – Splits the data into chunk_number pieces. Only one of chunk_size, chunk_number or chunk_period should be given.

  • chunk_period (str) – Splits the data according to the given period. Only one of chunk_size, chunk_number or chunk_period should be given.

  • chunker (Chunker) – The Chunker used to split the data sets into a lists of chunks.

  • threshold (Threshold) – Defines alert thresholds strategy. Defaults to StandardDeviationThreshold()

Examples

>>> import nannyml as nml
>>> reference, analysis, _ = nml.load_synthetic_car_price_dataset()
>>> column_names = ['car_value', 'debt_to_income_ratio', 'driver_tenure']
>>> calc = nml.SummaryStatsSumCalculator(
...     column_names=column_names,
...     timestamp_column_name='timestamp',
... ).fit(reference)
>>> res = calc.calculate(analysis)
>>> for column_name in res.column_names:
...     res = res.filter(period='analysis', column_name=column_name).plot().show()