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SEA

SEA#

class frouros.datasets.synthetic.SEA(seed: int | None = None)#

SEA generator [street2001streaming].

Parameters:

seed (Optional[int]) – seed value, defaults to None

References:

[street2001streaming]

Street, W. Nick, and YongSeog Kim. “A streaming ensemble algorithm (SEA) for large-scale classification.” Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. 2001.

Example:

>>> from frouros.datasets.synthetic import SEA
>>> sea = SEA(seed=31)
>>> dataset = sea.generate_dataset(block=1, noise=0.1, num_samples=5)
>>> for X, y in dataset:
...     print(X, y)
[2.86053822 9.58105567 7.70312932] 0
[2.08165462 1.36917049 9.08373802] 0
[8.36483632 1.12172604 8.3489916 ] 0
[2.44680795 1.36231348 7.22094455] 1
[1.28477715 2.20364007 5.19211202] 1
generate_dataset(block: int, noise: float = 0.1, num_samples: int = 12500) → Iterator[Tuple[ndarray, int]]#

Generate dataset.

Parameters:

  • block (int) – block to generate samples from, must be 1, 2, 3 or 4

  • noise (float) – ratio of samples with a noisy class, defaults to 0.1

  • num_samples (int) – number of samples to generate, defaults to 12500

Returns:

generator with the samples

Return type:

Iterator[Tuple[np.ndarray, int]]

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