nannyml.stats.count.calculator module

Simple Statistics Average Calculator

class nannyml.stats.count.calculator.SummaryStatsRowCountCalculator(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

SummaryStatsRowCountCalculator implementation

Creates a new SummaryStatsRowCountCalculator instance.

Parameters:

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

Examples

>>> import nannyml as nml
>>> reference, analysis, _ = nml.load_synthetic_car_price_dataset()
>>> calc = nml.SummaryStatsRowCountCalculator(
...     timestamp_column_name='timestamp',
... ).fit(reference)
>>> res = calc.calculate(analysis)
>>> res.plot().show()