PeriodicTrigger

Generate pseudo drift detection signals.

There are two approaches¹:

• fixed where the drift signal is generated every t_0 samples. - random corresponds to a pseudo-random drift detection strategy.

Parameters

• trigger_method (str) – defaults to fixed

  The trigger method to use.
  * fixed
  * random

• t_0 (int) – defaults to 300

  Reference point to define triggers.

• w (int) – defaults to 0

  Auxiliary parameter whose purpose is twofold:
  - if trigger_method="fixed", the periodic drift signals will only start after an initial warm-up period randomly defined between [0, w]. Useful to avoid that all ensemble members are reset at the same time when periodic triggers are used as the adaptation strategy.
  - if trigger_method="random", w defines the probability bounds of triggering a drift. The chance of triggering a drift is \(0.5\) after observing t_0 instances and becomes \(1\) after monitoring t_0 + w / 2 instances. A sigmoid function is used to produce values between [0, 1] that are used as the reset probabilities.

• dynamic_cloning (bool) – defaults to False

  Whether to change the seed and w values each time clone() is called.

• seed (int) – defaults to None

  Random seed for reproducibility.

Attributes

• drift_detected

  Concept drift alarm. True if concept drift is detected.

Examples

>>> import random
>>> from river import drift

>>> rng = random.Random(42)

The observed values will not affect the periodic triggers.

>>> data = [rng.gauss(0, 1) for _ in range(1000)]

Let's start with the fixed drift signals:

>>> ptrigger = PeriodicTrigger(t_0=500, seed=42)
>>> for i, v in enumerate(data):
...     _ = ptrigger.update(v)
...     if ptrigger.drift_detected:
...         print(f"Drift detected at instance {i}.")
Drift detected at instance 499.
Drift detected at instance 999.

Now, the random drift signals:

>>> rtrigger = PeriodicTrigger(
...     trigger_method="random",
...     t_0=500,
...     w=100,
...     dynamic_cloning=True,
...     seed=42
... )
>>> for i, v in enumerate(data):
...     _ = rtrigger.update(v)
...     if rtrigger.drift_detected:
...         print(f"Drift detected at instance {i}.")
Drift detected at instance 368.
Drift detected at instance 817.

Remember to set a w > 0 value if random triggers are used:

>>> try:
...     PeriodicTrigger(trigger_method="random")
... except ValueError as ve:
...     print(ve)
The 'w' value must be greater than zero when 'trigger_method' is 'random'.

Since we set dynamic_cloning to True, a clone of the periodic trigger will have its internal paramenters changed:

>>> rtrigger = rtrigger.clone()
>>> for i, v in enumerate(data):
...     _ = rtrigger.update(v)
...     if rtrigger.drift_detected:
...         print(f"Drift detected at instance {i}.")
Drift detected at instance 429.
Drift detected at instance 728.

Methods

update

Update the change detector with a single data point.

Parameters

• x (numbers.Number)

Returns

DriftDetector: self

Notes

When used in ensembles, a naive implementation of periodic drift signals would make all ensemble members reset at the same time. To avoid that, the dynamic_cloning parameter can be set to True. In this case, every time the clone method of this detector is called in an ensemble a new seed is defined. If dynamic_cloning=True and trigger_method="fixed", a new w between [0, t_0] will also be created for the new cloned instance.

References

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1. Heitor Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer, and Albert Bifet. On Ensemble Techniques for Data Stream Regression. IJCNN'20. International Joint Conference on Neural Networks. 2020. ↩