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 (int) – 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 theRandomState
instance used bynp.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 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¶
-
Leo Breiman, Jerome Friedman, R. Olshen, and Charles J. Stone. Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA,1984. ↩
-
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. ↩