Planes2D

2D Planes synthetic dataset.

This dataset is described in ¹ and was adapted from ². The features are generated using the following probabilities:

$$P(x_1=-1)=P(x_1=1)=\frac{1}{2}$$

$$P(x_m=-1)=P(x_m=0)=P(x_m=1)=\frac{1}{3},m=2,\dots ,10$$

The target value is defined by the following rule:

$$\text{if}{x}_1=1,y\leftarrow 3+3x_2+2x_3+x_4+ϵ$$

$$\text{if}{x}_1=-1,y\leftarrow -3+3x_5+2x_6+x_7+ϵ$$

In the expressions, $ϵ\sim \mathcal{N}(0,1)$, is the noise.

Parameters

• seed (int) – defaults to None

  Random seed number used for reproducibility.

Attributes

• desc

  Return the description from the docstring.

Examples

>>> from river.datasets import synth

>>> dataset = synth.Planes2D(seed=42)

>>> for x, y in dataset.take(5):
...     print(list(x.values()), y)
[-1, -1, 1, 0, -1, -1, -1, 1, -1, 1] -9.07
[1, -1, -1, -1, -1, -1, 1, 1, -1, 1] -4.25
[-1, 1, 1, 1, 1, 0, -1, 0, 1, 0] -0.95
[-1, 1, 0, 0, 0, -1, -1, 0, -1, -1] -6.10
[1, -1, 0, 0, 1, 0, -1, 1, 0, 1] 1.60

Methods

take

Iterate over the k samples.

Parameters

• k (int)

References

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1. 2DPlanes in Luís Torgo regression datasets ↩

2. Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A., 1984. Classification and regression trees. CRC press. ↩