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TimeStampValidation.py 3.3 kB

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5 years ago
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  1. import os
  2. import typing
  3. import numpy
  4. from d3m import container, utils as d3m_utils
  5. from d3m.metadata import base as metadata_base
  6. from d3m.metadata import hyperparams
  7. from d3m.primitive_interfaces import base, transformer
  8. __all__ = ('TimeStampValidationPrimitive',)
  9. Inputs = container.DataFrame
  10. Outputs = container.DataFrame
  11. class Hyperparams(hyperparams.Hyperparams):
  12. pass
  13. class TimeStampValidationPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hyperparams]):
  14. """
  15. A primitive to check time series is sorted by time stamp , if not then return sorted time series
  16. """
  17. __author__ = "DATA Lab at Texas A&M University",
  18. metadata = metadata_base.PrimitiveMetadata(
  19. {
  20. 'id': '5f791b09-e16f-42e1-bc53-39de308f5861',
  21. 'version': '0.1.0',
  22. 'name': 'Time Stamp Validation',
  23. 'python_path': 'd3m.primitives.tods.data_processing.timestamp_validation',
  24. 'keywords': ['Time Stamp', 'Sort Order'],
  25. 'source': {
  26. 'name': 'DATA Lab at Texas A&M University',
  27. 'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/data_processing/TimeStampValidation.py'],
  28. 'contact': 'mailto:khlai037@tamu.edu'
  29. },
  30. 'installation': [
  31. {'type': metadata_base.PrimitiveInstallationType.PIP,
  32. 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
  33. git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
  34. ),
  35. }
  36. ],
  37. 'algorithm_types': [
  38. metadata_base.PrimitiveAlgorithmType.DATA_PROFILING ,
  39. ],
  40. 'primitive_family': metadata_base.PrimitiveFamily.DATA_VALIDATION,
  41. }
  42. )
  43. def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
  44. """
  45. Args:
  46. inputs: Container DataFrame
  47. timeout: Default
  48. iterations: Default
  49. Returns:
  50. Container DataFrame sorted by Time Stamp
  51. """
  52. self.logger.info('Time Stamp order validation called')
  53. outputs = inputs
  54. try:
  55. if (self._is_time_stamp_sorted(inputs, 'timestamp')):
  56. outputs = inputs
  57. else:
  58. outputs = inputs.sort_values(by=["timestamp"])
  59. self._update_metadata(outputs)
  60. outputs.reset_index(drop=True, inplace=True)
  61. self.logger.info('Type of data : %s',type(outputs))
  62. except Exception as e :
  63. self.logger.error('Time Stamp order validation error %s :',e)
  64. print(self.logger.info(base.CallResult(outputs).value))
  65. return base.CallResult(outputs)
  66. def _is_time_stamp_sorted(self,input:Inputs,column:str = 'timestamp') -> bool :
  67. """
  68. Args:
  69. input: Container Dataframe
  70. column: Column Name
  71. Returns:
  72. Boolean : True if timestamp column is sorted False if not
  73. """
  74. return all(input[column][i] <= input[column][i+1] for i in range(len(input[column])-1))
  75. def _update_metadata(self, outputs):
  76. outputs.metadata = outputs.metadata.generate(outputs)

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