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LEDDrift

LED stream generator with concept drift.

This class is an extension of the LED generator whose purpose is to add concept drift to the stream.

Parameters

  • seed

    Type → int | None

    Default → None

    Random seed for reproducibility.

  • noise_percentage

    Type → float

    Default → 0.0

    The probability that noise will happen in the generation. At each new sample generated, a random number is generated, and if it is equal or less than the noise_percentage, the led value will be switched

  • irrelevant_features

    Type → bool

    Default → False

    Adds 17 non-relevant attributes to the stream.

  • n_drift_features

    Type → int

    Default → 0

    The number of attributes that have drift.

Attributes

  • desc

    Return the description from the docstring.

Examples

from river.datasets import synth

dataset = synth.LEDDrift(seed = 112, noise_percentage = 0.28,
                         irrelevant_features= True, n_drift_features=4)

for x, y in dataset.take(5):
    print(list(x.values()), y)

[1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1] 7
[1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0] 6
[0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1] 1
[1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1] 6
[1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0] 7

Methods

take

Iterate over the k samples.

Parameters

  • k — 'int'

Notes

An instance is generated based on the parameters passed. If has_noise is set then the total number of attributes will be 24, otherwise there will be 7 attributes.