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standardValues.json 2.3 kB

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5 years ago
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  1. {
  2. "unit": [
  3. "seconds",
  4. "minutes",
  5. "days",
  6. "weeks",
  7. "months",
  8. "years",
  9. "unspecified"
  10. ],
  11. "qualValueType": [
  12. "boolean",
  13. "integer",
  14. "real",
  15. "string",
  16. "dict"
  17. ],
  18. "resType": [
  19. "image",
  20. "video",
  21. "audio",
  22. "speech",
  23. "text",
  24. "graph",
  25. "edgeList",
  26. "table",
  27. "timeseries",
  28. "raw"
  29. ],
  30. "colType": [
  31. "boolean",
  32. "integer",
  33. "real",
  34. "string",
  35. "categorical",
  36. "dateTime",
  37. "realVector",
  38. "json",
  39. "geojson",
  40. "unknown"
  41. ],
  42. "role": [
  43. "index",
  44. "multiIndex",
  45. "key",
  46. "attribute",
  47. "suggestedTarget",
  48. "timeIndicator",
  49. "locationIndicator",
  50. "boundaryIndicator",
  51. "interval",
  52. "instanceWeight",
  53. "boundingPolygon",
  54. "suggestedPrivilegedData",
  55. "suggestedGroupingKey",
  56. "edgeSource",
  57. "directedEdgeSource",
  58. "undirectedEdgeSource",
  59. "multiEdgeSource",
  60. "simpleEdgeSource",
  61. "edgeTarget",
  62. "directedEdgeTarget",
  63. "undirectedEdgeTarget",
  64. "multiEdgeTarget",
  65. "simpleEdgeTarget"
  66. ],
  67. "resObject": [
  68. "item"
  69. ],
  70. "resComponent": [
  71. "nodes",
  72. "edges"
  73. ],
  74. "taskKeywords": [
  75. "classification",
  76. "regression",
  77. "clustering",
  78. "linkPrediction",
  79. "vertexNomination",
  80. "vertexClassification",
  81. "communityDetection",
  82. "graphMatching",
  83. "forecasting",
  84. "collaborativeFiltering",
  85. "objectDetection",
  86. "semiSupervised",
  87. "binary",
  88. "multiClass",
  89. "multiLabel",
  90. "univariate",
  91. "multivariate",
  92. "overlapping",
  93. "nonOverlapping",
  94. "tabular",
  95. "relational",
  96. "nested",
  97. "image",
  98. "audio",
  99. "video",
  100. "speech",
  101. "text",
  102. "graph",
  103. "multiGraph",
  104. "timeSeries",
  105. "grouped",
  106. "geospatial",
  107. "remoteSensing",
  108. "lupi",
  109. "missingMetadata"
  110. ],
  111. "method": [
  112. "holdOut",
  113. "kFold"
  114. ],
  115. "metric": [
  116. "accuracy",
  117. "precision",
  118. "recall",
  119. "f1",
  120. "f1Micro",
  121. "f1Macro",
  122. "rocAuc",
  123. "rocAucMacro",
  124. "rocAucMicro",
  125. "meanSquaredError",
  126. "rootMeanSquaredError",
  127. "meanAbsoluteError",
  128. "rSquared",
  129. "normalizedMutualInformation",
  130. "jaccardSimilarityScore",
  131. "precisionAtTopK",
  132. "objectDetectionAP",
  133. "hammingLoss",
  134. "hitsAtK",
  135. "meanReciprocalRank"
  136. ]
  137. }

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