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.