NoDrift¶

Dummy class used to turn off concept drift detection capabilities of adaptive models.

It always signals that no concept drift was detected.

Attributes¶

drift_detected

Whether or not a drift is detected following the last update.

Examples¶

from river import drift
from river import evaluate
from river import forest
from river import metrics
from river.datasets import synth

dataset = synth.ConceptDriftStream(
    seed=8,
    position=500,
    width=40,
).take(700)

We can turn off the warning detection capabilities of Adaptive Random Forest (ARF) or other similar models. Thus, the base models will reset immediately after identifying a drift, bypassing the background model building phase:

adaptive_model = forest.ARFClassifier(
    leaf_prediction="mc",
    warning_detector=drift.NoDrift(),
    seed=8
)

We can also turn off the concept drift handling capabilities completely:

stationary_model = forest.ARFClassifier(
    leaf_prediction="mc",
    warning_detector=drift.NoDrift(),
    drift_detector=drift.NoDrift(),
    seed=8
)

Let's put that to test:

for x, y in dataset:
    adaptive_model.learn_one(x, y)
    stationary_model.learn_one(x, y)

The adaptive model:

adaptive_model.n_drifts_detected()

adaptive_model.n_warnings_detected()

The stationary one:

stationary_model.n_drifts_detected()

stationary_model.n_warnings_detected()

Methods¶

update

Update the detector with a single data point.

Parameters

x — int | float