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sampling.py 1.1 kB

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  1. import numpy
  2. from d3m.metadata import hyperparams
  3. class Sampling:
  4. def setup(self):
  5. self.numerical = hyperparams.Uniform(
  6. lower=0,
  7. upper=1,
  8. default=0.5,
  9. )
  10. self.enumeration = hyperparams.Enumeration(
  11. values=list(range(1000)),
  12. default=0,
  13. )
  14. def time_numerical_sampling(self):
  15. random_state = numpy.random.RandomState(0)
  16. for i in range(100000):
  17. self.numerical.sample(random_state)
  18. def time_numerical_sample_multiple(self):
  19. random_state = numpy.random.RandomState(0)
  20. for i in range(1000):
  21. self.numerical.sample_multiple(500, 500, random_state, with_replacement=False)
  22. def time_enumeration_sampling(self):
  23. random_state = numpy.random.RandomState(0)
  24. for i in range(10000):
  25. self.enumeration.sample(random_state)
  26. def time_enumeration_sample_multiple(self):
  27. random_state = numpy.random.RandomState(0)
  28. for i in range(10000):
  29. self.enumeration.sample_multiple(500, 500, random_state, with_replacement=False)

全栈的自动化机器学习系统,主要针对多变量时间序列数据的异常检测。TODS提供了详尽的用于构建基于机器学习的异常检测系统的模块,它们包括:数据处理(data processing),时间序列处理( time series processing),特征分析(feature analysis),检测算法(detection algorithms),和强化模块( reinforcement module)。这些模块所提供的功能包括常见的数据预处理、时间序列数据的平滑或变换,从时域或频域中抽取特征、多种多样的检测算