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datasetDoc.json 1.5 kB

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  1. {
  2. "about": {
  3. "datasetID": "graph_dataset_1",
  4. "datasetName": "Test graph dataset in GML format",
  5. "description": "Based on LL1_net_nomination_seed_dataset",
  6. "datasetSchemaVersion": "4.0.0",
  7. "redacted": false,
  8. "datasetVersion": "4.0.0",
  9. "digest": "fcca6f975627035daa978a24e93aa74861aa93bb885eb8d782d24e5f46376f14"
  10. },
  11. "dataResources": [
  12. {
  13. "resID": "G1",
  14. "resPath": "graphs/G1.gml",
  15. "resType": "graph",
  16. "resFormat": {
  17. "text/vnd.gml": [
  18. "gml"
  19. ]
  20. },
  21. "isCollection": false
  22. },
  23. {
  24. "resID": "learningData",
  25. "resPath": "tables/learningData.csv",
  26. "resType": "table",
  27. "resFormat": {
  28. "text/csv": [
  29. "csv"
  30. ]
  31. },
  32. "isCollection": false,
  33. "columnsCount": 3,
  34. "columns": [
  35. {
  36. "colIndex": 0,
  37. "colName": "d3mIndex",
  38. "colType": "integer",
  39. "role": [
  40. "index"
  41. ]
  42. },
  43. {
  44. "colIndex": 1,
  45. "colName": "G1.nodeID",
  46. "colType": "integer",
  47. "role": [
  48. "attribute"
  49. ],
  50. "refersTo": {
  51. "resID": "G1",
  52. "resObject": {
  53. "nodeAttribute": "nodeID"
  54. }
  55. }
  56. },
  57. {
  58. "colIndex": 2,
  59. "colName": "classLabel",
  60. "colType": "categorical",
  61. "role": [
  62. "suggestedTarget"
  63. ]
  64. }
  65. ]
  66. }
  67. ]
  68. }

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

Contributors (1)