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test_PyodABOD.py 5.2 kB

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  1. import unittest
  2. from d3m import container, utils
  3. from d3m.metadata import base as metadata_base
  4. from detection_algorithm.PyodABOD import ABODPrimitive
  5. class ABODTest(unittest.TestCase):
  6. def test_basic(self):
  7. self.maxDiff = None
  8. main = container.DataFrame({'a': [1., 2., 3.], 'b': [2., 3., 4.], 'c': [3., 4., 5.],},
  9. columns=['a', 'b', 'c'],
  10. generate_metadata=True)
  11. print(main)
  12. self.assertEqual(utils.to_json_structure(main.metadata.to_internal_simple_structure()), [{
  13. 'selector': [],
  14. 'metadata': {
  15. # 'top_level': 'main',
  16. 'schema': metadata_base.CONTAINER_SCHEMA_VERSION,
  17. 'structural_type': 'd3m.container.pandas.DataFrame',
  18. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Table'],
  19. 'dimension': {
  20. 'name': 'rows',
  21. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularRow'],
  22. 'length': 3,
  23. },
  24. },
  25. }, {
  26. 'selector': ['__ALL_ELEMENTS__'],
  27. 'metadata': {
  28. 'dimension': {
  29. 'name': 'columns',
  30. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularColumn'],
  31. 'length': 3,
  32. },
  33. },
  34. }, {
  35. 'selector': ['__ALL_ELEMENTS__', 0],
  36. 'metadata': {'structural_type': 'numpy.float64', 'name': 'a'},
  37. }, {
  38. 'selector': ['__ALL_ELEMENTS__', 1],
  39. 'metadata': {'structural_type': 'numpy.float64', 'name': 'b'},
  40. }, {
  41. 'selector': ['__ALL_ELEMENTS__', 2],
  42. 'metadata': {'structural_type': 'numpy.float64', 'name': 'c'}
  43. }])
  44. self.assertIsInstance(main, container.DataFrame)
  45. hyperparams_class = ABODPrimitive.metadata.get_hyperparams()
  46. hyperparams = hyperparams_class.defaults()
  47. hyperparams = hyperparams.replace({'return_result': 'new',
  48. 'method': 'default',
  49. })
  50. primitive = ABODPrimitive(hyperparams=hyperparams)
  51. primitive.set_training_data(inputs=main)
  52. primitive.fit()
  53. new_main = primitive.produce(inputs=main).value
  54. new_main_score = primitive.produce_score(inputs=main).value
  55. print(new_main)
  56. print(new_main_score)
  57. self.assertEqual(utils.to_json_structure(new_main.metadata.to_internal_simple_structure()), [{
  58. 'selector': [],
  59. 'metadata': {
  60. # 'top_level': 'main',
  61. 'schema': metadata_base.CONTAINER_SCHEMA_VERSION,
  62. 'structural_type': 'd3m.container.pandas.DataFrame',
  63. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Table'],
  64. 'dimension': {
  65. 'name': 'rows',
  66. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularRow'],
  67. 'length': 3,
  68. },
  69. },
  70. }, {
  71. 'selector': ['__ALL_ELEMENTS__'],
  72. 'metadata': {
  73. 'dimension': {
  74. 'name': 'columns',
  75. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularColumn'],
  76. 'length': 1,
  77. },
  78. },
  79. }, {
  80. 'selector': ['__ALL_ELEMENTS__', 0],
  81. 'metadata': {
  82. 'name': 'Angle-base Outlier Detection Primitive0_0',
  83. 'structural_type': 'numpy.int64',
  84. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute']
  85. },
  86. }])
  87. self.assertEqual(utils.to_json_structure(new_main_score.metadata.to_internal_simple_structure()), [{
  88. 'selector': [],
  89. 'metadata': {
  90. # 'top_level': 'main',
  91. 'schema': metadata_base.CONTAINER_SCHEMA_VERSION,
  92. 'structural_type': 'd3m.container.pandas.DataFrame',
  93. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Table'],
  94. 'dimension': {
  95. 'name': 'rows',
  96. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularRow'],
  97. 'length': 3,
  98. },
  99. },
  100. }, {
  101. 'selector': ['__ALL_ELEMENTS__'],
  102. 'metadata': {
  103. 'dimension': {
  104. 'name': 'columns',
  105. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularColumn'],
  106. 'length': 1,
  107. },
  108. },
  109. }, {
  110. 'selector': ['__ALL_ELEMENTS__', 0],
  111. 'metadata': {
  112. 'name': 'Angle-base Outlier Detection Primitive0_0',
  113. 'structural_type': 'numpy.float64',
  114. 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute']
  115. },
  116. }])
  117. if __name__ == '__main__':
  118. unittest.main()

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