Mv

Mv artificial dataset.

Artificial dataset composed of both nominal and numeric features, whose features present co-dependencies. Originally described in ¹.

The features are generated using the following expressions:

• $x_1$: uniformly distributed over [-5, 5].

• $x_2$: uniformly distributed over [-15, -10].

• $x_3$:

  • if $x_1>0$, $x_3\leftarrow$ 'green'

  • else $x_3\leftarrow$ 'red' with probability $0.4$ and $x_3\leftarrow$ 'brown' with probability $0.6$.

• $x_4$:

  • if $x_3=$ 'green', $x_4\leftarrow x_1+2x_2$

  • else $x_4=\frac{x_1}{2}$ with probability $0.3$ and $x_4=\frac{x_2}{2}$ with probability $0.7$.

• $x_5$: uniformly distributed over [-1, 1].

• $x_6\leftarrow x_4\times ϵ$, where $ϵ$ is uniformly distributed over [0, 5].

• $x_7$: 'yes' with probability $0.3$, and 'no' with probability $0.7$.

• $x_8$: 'normal' if $x_5<0.5$ else 'large'.

• $x_9$: uniformly distributed over [100, 500].

• $x_{10}$: uniformly distributed integer over the interval [1000, 1200].

The target value is generated using the following rules:

• if $x_2>2$, $y\leftarrow 35-0.5x_4$

• else if $-2\le x_4\le 2$, $y\leftarrow 10-2x_1$

• else if $x_7=$ 'yes', $y\leftarrow 3-\frac{x_1}{x_4}$

• else if $x_8=$ 'normal', $y\leftarrow x_6+x_1$

• else $y\leftarrow \frac{x_1}{2}$.

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.Mv(seed=42)

>>> for x, y in dataset.take(5):
...     print(list(x.values()), y)
[1.39, -14.87, 'green', -28.35, -0.44, -31.64, 'no', 'normal', 370.67, 1178.43] -30.25
[-4.13, -12.89, 'red', -2.06, 0.01, -0.27, 'yes', 'normal', 359.95, 1108.98] 1.00
[-2.79, -12.05, 'brown', -1.39, 0.61, -4.87, 'no', 'large', 162.19, 1191.44] 15.59
[-1.63, -14.53, 'red', -7.26, 0.20, -29.33, 'no', 'normal', 314.49, 1194.62] -30.96
[-1.21, -12.23, 'brown', -6.11, 0.72, -17.66, 'no', 'large', 118.32, 1045.57] -0.60

Methods

take

Iterate over the k samples.

Parameters

• k (int)

References

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1. Mv in Luís Torgo regression datasets ↩