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LED

LED stream generator.

This data source originates from the CART book 1. An implementation in C was donated to the UCI 2 machine learning repository by David Aha. The goal is to predict the digit displayed on a seven-segment LED display, where each attribute has a 10% chance of being inverted. It has an optimal Bayes classification rate of 74%. The particular configuration of the generator used for experiments (LED) produces 24 binary attributes, 17 of which are irrelevant.

Parameters

  • seed ('Optional[int | np.random.RandomState]') – defaults to None

    If int, seed is used to seed the random number generator; If RandomState instance, seed is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

  • noise_percentage ('float') – defaults to 0.0

    The probability that noise will happen in the generation. At each new sample generated, a random number is generated, and if it is equal or less than the noise_percentage, the led value will be switched

  • irrelevant_features ('bool') – defaults to False

    Adds 17 non-relevant attributes to the stream.

Attributes

  • desc

    Return the description from the docstring.

Examples

>>> from river.datasets import synth

>>> dataset = synth.LED(seed = 112, noise_percentage = 0.28, irrelevant_features= False)

>>> for x, y in dataset.take(5):
...     print(x, y)
{0: 0, 1: 1, 2: 1, 3: 1, 4: 0, 5: 0, 6: 0} 4
{0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 0, 6: 0} 4
{0: 1, 1: 0, 2: 1, 3: 1, 4: 0, 5: 0, 6: 1} 3
{0: 0, 1: 1, 2: 1, 3: 0, 4: 0, 5: 1, 6: 1} 0
{0: 1, 1: 1, 2: 1, 3: 1, 4: 0, 5: 1, 6: 0} 4

Methods

take

Iterate over the k samples.

Parameters

  • k (int)

Notes

An instance is generated based on the parameters passed. If has_noise is set then the total number of attributes will be 24, otherwise there will be 7 attributes.

References


  1. Leo Breiman, Jerome Friedman, R. Olshen, and Charles J. Stone. Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA,1984. 

  2. A. Asuncion and D. J. Newman. UCI Machine Learning Repository [http://www.ics.uci.edu/∼mlearn/mlrepository.html]. University of California, Irvine, School of Information and Computer Sciences,2007.