apache_beam.ml.anomaly.univariate.mean module

Trackers for calculating mean in windowed fashion.

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

• SimpleSlidingMeanTracker: Calculates mean using numpy in a sliding window.

• IncLandmarkMeanTracker: Incremental mean tracker in landmark window mode.

• IncSlidingMeanTracker: Incremental mean tracker in sliding window mode.

class apache_beam.ml.anomaly.univariate.mean.MeanTracker

Bases: BaseTracker

Abstract base class for mean trackers.

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

class apache_beam.ml.anomaly.univariate.mean.SimpleSlidingMeanTracker(*args, **kwargs)

Bases: WindowedTracker, MeanTracker

Sliding window mean tracker that calculates mean using NumPy.

This tracker uses NumPy’s nanmean function to calculate the mean of the values currently in the sliding window. It’s a simple, non-incremental approach.

Parameters:

window_size – The size of the sliding window.

get()

Calculates and returns the mean of the current sliding window.

Returns:

The mean of the values in the current sliding window.

Returns NaN if the window is empty.

Return type:

float

SimpleSlidingMeanTracker__spec_type = 'SimpleSlidingMeanTracker'

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.mean.IncMeanTracker(window_mode, **kwargs)

Bases: WindowedTracker, MeanTracker

Base class for incremental mean trackers.

This class implements incremental calculation of the mean, which is more efficient for streaming data as it updates the mean with each new data point instead of recalculating from scratch.

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 mean.

Parameters:

x – The new value to be pushed.

get()

Returns the current incremental mean.

Returns:

The current incremental mean value.

Returns NaN if no valid (non-NaN) values have been pushed.

Return type:

float

class apache_beam.ml.anomaly.univariate.mean.IncLandmarkMeanTracker(*args, **kwargs)

Bases: IncMeanTracker

Landmark window mean tracker using incremental calculation.

IncLandmarkMeanTracker__spec_type = 'IncLandmarkMeanTracker'

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.mean.IncSlidingMeanTracker(*args, **kwargs)

Bases: IncMeanTracker

Sliding window mean tracker using incremental calculation.

Parameters:

window_size – The size of the sliding window.

IncSlidingMeanTracker__spec_type = 'IncSlidingMeanTracker'

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()