SEA¶
SEA synthetic dataset.
Implementation of the data stream with abrupt drift described in 1. Each observation is composed of 3 features. Only the first two features are relevant. The target is binary, and is positive if the sum of the features exceeds a certain threshold. There are 4 thresholds to choose from. Concept drift can be introduced by switching the threshold anytime during the stream.
-
Variant 0:
True
if \(att1 + att2 > 8\) -
Variant 1:
True
if \(att1 + att2 > 9\) -
Variant 2:
True
if \(att1 + att2 > 7\) -
Variant 3:
True
if \(att1 + att2 > 9.5\)
Parameters¶
-
variant
Default →
0
Determines the classification function to use. Possible choices are 0, 1, 2, 3.
-
noise
Default →
0.0
Determines the amount of observations for which the target sign will be flipped.
-
seed
Type → int | None
Default →
None
Random seed number used for reproducibility.
Attributes¶
-
desc
Return the description from the docstring.
Examples¶
from river.datasets import synth
dataset = synth.SEA(variant=0, seed=42)
for x, y in dataset.take(5):
print(x, y)
{0: 6.39426, 1: 0.25010, 2: 2.75029} False
{0: 2.23210, 1: 7.36471, 2: 6.76699} True
{0: 8.92179, 1: 0.86938, 2: 4.21921} True
{0: 0.29797, 1: 2.18637, 2: 5.05355} False
{0: 0.26535, 1: 1.98837, 2: 6.49884} False
Methods¶
take
Iterate over the k samples.
Parameters
- k — 'int'