Planes2D¶
2D Planes synthetic dataset.
This dataset is described in 1 and was adapted from 2. The features are generated using the following probabilities:
\[P(x_1 = -1) = P(x_1 = 1) = \frac{1}{2}\]
\[P(x_m = -1) = P(x_m = 0) = P(x_m = 1) = \frac{1}{3}, m=2,\ldots, 10\]
The target value is defined by the following rule:
\[\text{if}~x_1 = 1, y \leftarrow 3 + 3x_2 + 2x_3 + x_4 + \epsilon\]
\[\text{if}~x_1 = -1, y \leftarrow -3 + 3x_5 + 2x_6 + x_7 + \epsilon\]
In the expressions, \(\epsilon \sim \mathcal{N}(0, 1)\), is the noise.
Parameters¶
-
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.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'
-
Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A., 1984. Classification and regression trees. CRC press. ↩