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 – defaults to 0

Determines the classification function to use. Possible choices are 0, 1, 2, 3.

• noise – defaults to 0.0

Determines the amount of observations for which the target sign will be flipped.

• seed (int) – defaults to None

Random seed number used for reproducibility.

Attributes¶

• desc

Return the description from the docstring.

Examples¶

>>> from river 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)