nannyml.base module

Module containing base classes for drift calculation.

class nannyml.base.AbstractCalculator(chunk_size: int | None = None, chunk_number: int | None = None, chunk_period: str | None = None, chunker: Chunker | None = None, timestamp_column_name: str | None = None)[source]

Bases: ABC

Base class for drift calculation.

Creates a new instance of an abstract DriftCalculator.

Parameters:

  • 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.

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

calculate(data: DataFrame, *args, **kwargs) → Any[source]

Performs a calculation on the provided data.

fit(reference_data: DataFrame, *args, **kwargs) → AbstractCalculator[source]

Trains the calculator using reference data.

class nannyml.base.AbstractCalculatorResult(results_data: DataFrame, *args, **kwargs)[source]

Bases: ABC

Contains the results of a calculation and provides plotting functionality.

The result of the calculate() method of a AbstractCalculator.

It is an abstract class containing shared properties and methods across implementations. For each AbstractCalculator class there will be a corresponding AbstractCalculatorResult implementation.

Creates a new AbstractCalculatorResult instance.

Parameters:

results_data (pd.DataFrame) – The data returned by the Calculator.

DEFAULT_COLUMNS = ('key', 'chunk_index', 'start_index', 'end_index', 'start_date', 'end_date', 'period')
filter(period: str = 'analysis', metrics: str | List[str] | None = None, *args, **kwargs) → AbstractCalculatorResult[source]

Returns filtered result metric data.

abstract plot(*args, **kwargs) → Figure[source]

Plots calculation results.

to_df(multilevel: bool = True) → DataFrame[source]

Export results do pandas dataframe.

class nannyml.base.AbstractEstimator(chunk_size: int | None = None, chunk_number: int | None = None, chunk_period: str | None = None, chunker: Chunker | None = None, timestamp_column_name: str | None = None)[source]

Bases: ABC

Base class for drift calculation.

Creates a new instance of an abstract DriftCalculator.

Parameters:

  • 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.

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

estimate(data: DataFrame, *args, **kwargs) → Any[source]

Performs a calculation on the provided data.

fit(reference_data: DataFrame, *args, **kwargs) → AbstractEstimator[source]

Trains the calculator using reference data.

class nannyml.base.AbstractEstimatorResult(results_data: DataFrame)[source]

Bases: ABC

Contains the results of a drift calculation and provides additional functionality such as plotting.

The result of the calculate() method of a DriftCalculator.

It is an abstract class containing shared properties and methods across implementations. For each DriftCalculator class there will be an associated DriftResult implementation.

Creates a new DriftResult instance.

Parameters:

results_data (pd.DataFrame) – The result data of the performed calculation.

DEFAULT_COLUMNS = ['key', 'chunk_index', 'start_index', 'end_index', 'start_date', 'end_date', 'period']
filter(period: str = 'analysis', metrics: str | List[str] | None = None, *args, **kwargs) → AbstractEstimatorResult[source]

Returns result metric data.

plot(*args, **kwargs) → Figure[source]

Plot drift results.

to_df(multilevel: bool = True)[source]

Export results do pandas dataframe.