apache_beam.ml.anomaly.univariate.stdev module

Trackers for calculating standard deviation in windowed fashion.

This module defines different types of standard deviation trackers that operate on windows of data. It includes:

• SimpleSlidingStdevTracker: Calculates stdev using numpy in a sliding window.

• IncLandmarkStdevTracker: Incremental stdev tracker in landmark window mode.

• IncSlidingStdevTracker: Incremental stdev tracker in sliding window mode.

class apache_beam.ml.anomaly.univariate.stdev.StdevTracker

Bases: BaseTracker

Abstract base class for standard deviation trackers.

Currently, it does not add any specific functionality but provides a type hierarchy for stdev trackers.

class apache_beam.ml.anomaly.univariate.stdev.SimpleSlidingStdevTracker(*args, **kwargs)

Bases: WindowedTracker, StdevTracker

Sliding window standard deviation tracker using NumPy.

This tracker uses NumPy’s nanvar function to calculate the variance of the values currently in the sliding window and then takes the square root to get the standard deviation. It’s a simple, non-incremental approach.

get()

Calculates and returns the stdev of the current sliding window.

Returns:

The standard deviation of the values in the current sliding window.

Returns NaN if the window contains fewer than 2 elements.

Return type:

float

SimpleSlidingStdevTracker__spec_type = 'SimpleSlidingStdevTracker'

classmethod from_spec(spec: Spec, _run_init: bool = True) → Self | type[Self]

Generate a Specifiable subclass object based on a spec.

Parameters:

• spec – the specification of a Specifiable subclass object

• _run_init – whether to call __init__ or not for the initial instantiation

Returns:

the Specifiable subclass object

Return type:

Self

run_original_init() → None

Execute the original __init__ method with its saved arguments.

For instances of the Specifiable class, initialization is deferred (lazy initialization). This function forces the execution of the original __init__ method using the arguments captured during the object’s initial instantiation.

classmethod spec_type()

to_spec() → Spec

Generate a spec from a Specifiable subclass object.

Returns:

The specification of the instance.

Return type:

Spec

classmethod unspecifiable()

class apache_beam.ml.anomaly.univariate.stdev.IncStdevTracker(window_mode, **kwargs)

Bases: WindowedTracker, StdevTracker

Abstract base class for incremental standard deviation trackers.

This class implements an online algorithm for calculating standard deviation, updating the standard deviation incrementally as new data points arrive.

Parameters:

• window_mode – A WindowMode enum specifying whether the window is LANDMARK or SLIDING.

• **kwargs – Keyword arguments passed to the parent class constructor.

push(x)

Pushes a new value and updates the incremental standard deviation calculation.

Implements Welford’s online algorithm for variance, then derives stdev.

Parameters:

x – The new value to be pushed.

get()

Returns the current incremental standard deviation.

Returns:

The current incremental standard deviation value.

Returns NaN if fewer than 2 valid (non-NaN) values have been pushed.

Return type:

float

class apache_beam.ml.anomaly.univariate.stdev.IncLandmarkStdevTracker(*args, **kwargs)

Bases: IncStdevTracker

Landmark window standard deviation tracker using incremental calculation.

IncLandmarkStdevTracker__spec_type = 'IncLandmarkStdevTracker'

classmethod from_spec(spec: Spec, _run_init: bool = True) → Self | type[Self]

Generate a Specifiable subclass object based on a spec.

Parameters:

• spec – the specification of a Specifiable subclass object

• _run_init – whether to call __init__ or not for the initial instantiation

Returns:

the Specifiable subclass object

Return type:

Self

run_original_init() → None

Execute the original __init__ method with its saved arguments.

For instances of the Specifiable class, initialization is deferred (lazy initialization). This function forces the execution of the original __init__ method using the arguments captured during the object’s initial instantiation.

classmethod spec_type()

to_spec() → Spec

Generate a spec from a Specifiable subclass object.

Returns:

The specification of the instance.

Return type:

Spec

classmethod unspecifiable()

class apache_beam.ml.anomaly.univariate.stdev.IncSlidingStdevTracker(*args, **kwargs)

Bases: IncStdevTracker

Sliding window standard deviation tracker using incremental calculation.

Parameters:

window_size – The size of the sliding window.

IncSlidingStdevTracker__spec_type = 'IncSlidingStdevTracker'

classmethod from_spec(spec: Spec, _run_init: bool = True) → Self | type[Self]

Generate a Specifiable subclass object based on a spec.

Parameters:

• spec – the specification of a Specifiable subclass object

• _run_init – whether to call __init__ or not for the initial instantiation

Returns:

the Specifiable subclass object

Return type:

Self

run_original_init() → None

Execute the original __init__ method with its saved arguments.

For instances of the Specifiable class, initialization is deferred (lazy initialization). This function forces the execution of the original __init__ method using the arguments captured during the object’s initial instantiation.

classmethod spec_type()

to_spec() → Spec

Generate a spec from a Specifiable subclass object.

Returns:

The specification of the instance.

Return type:

Spec

classmethod unspecifiable()