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problemSchema.json 4.0 kB

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5 years ago
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  1. {
  2. "about":{"type":"dict", "required":true, "allow_unknown":true, "schema": {
  3. "problemID":{"type":"string", "required":true, "empty":false},
  4. "problemName":{"type":"string", "required":true, "empty":false},
  5. "problemDescription":{"type":"string", "required":false},
  6. "problemURI":{"type":"string","required":false},
  7. "taskKeywords":{"type":"list", "required":true, "schema":{"type":"string"}},
  8. "problemVersion":{"type":"string", "required":true},
  9. "problemSchemaVersion":{"type":"string", "required":true}
  10. }},
  11. "inputs":{"type":"dict","required":true,"allow_unknown":true,"schema":{
  12. "data":{"type":"list","required":true,"schema":{"type":"dict", "required": true, "allow_unknown":true, "schema": {
  13. "datasetID":{"type":"string","required":true},
  14. "targets":{"type":"list", "required":true,"schema":{"type":"dict","required":true,"allow_unknown":true,"schema":{
  15. "targetIndex":{"type":"integer","required":true},
  16. "resID":{"type":"string","required":true},
  17. "colIndex":{"type":"integer", "required":true},
  18. "colName":{"type":"string","required":true},
  19. "numClusters":{"type":"integer","required":false}
  20. }}},
  21. "forecastingHorizon":{"type":"dict", "required":false, "allow_unknown":true, "schema":{
  22. "resID":{"type":"string","required":true},
  23. "colIndex":{"type":"integer", "required":true},
  24. "colName":{"type":"string","required":true},
  25. "horizonValue":{"type":"number", "required":true}
  26. }},
  27. "privilegedData":{"type":"list", "required":false,"schema":{"type":"dict","required":true,"allow_unknown":true,"schema":{
  28. "privilegedDataIndex":{"type":"integer","required":true},
  29. "resID":{"type":"string","required":true},
  30. "colIndex":{"type":"integer", "required":true},
  31. "colName":{"type":"string","required":true}
  32. }}}
  33. }}},
  34. "dataSplits":{"type":"dict","required":false,"allow_unknown":true,"schema":{
  35. "method":{"type":"string","required":false},
  36. "testSize":{"type":"float","required":false,"min":0.0,"max":1.0},
  37. "numFolds":{"type":"integer","required":false},
  38. "stratified":{"type":"boolean","required":false},
  39. "numRepeats":{"type":"integer","required":false},
  40. "randomSeed":{"type":"integer","required":false},
  41. "splitsFile":{"type":"string", "required":false},
  42. "splitScript":{"type":"string", "required":false},
  43. "datasetViewMaps":{"type":"dict","required":false,"allow_unknown":true,"schema":{
  44. "train":{"type":"list", "required":false, "schema":{"type":"dict", "required":true, "allow_unknown":true, "schema":{
  45. "from":{"type":"string", "required":true},
  46. "to":{"type":"string", "required":true}
  47. }}},
  48. "test":{"type":"list", "required":false, "schema":{"type":"dict", "required":true, "allow_unknown":true, "schema":{
  49. "from":{"type":"string", "required":true},
  50. "to":{"type":"string", "required":true}
  51. }}},
  52. "score":{"type":"list", "required":false, "schema":{"type":"dict", "required":true, "allow_unknown":true, "schema":{
  53. "from":{"type":"string", "required":true},
  54. "to":{"type":"string", "required":true}
  55. }}}
  56. }}
  57. }},
  58. "performanceMetrics":{"type":"list","required":true,"schema":{"type":"dict","required":true,"allow_unknown":true,"schema":{
  59. "metric":{"type":"string","required":true},
  60. "K":{"type":"integer","required":false,"dependencies": {"metric":"precisionAtTopK"}},
  61. "posLabel":{"type":"string","required":false, "dependencies": {"metric":["f1","precision","recall","jaccardSimilarityScore"]}}
  62. }}}
  63. }},
  64. "expectedOutputs":{"type":"dict","required":false,"allow_unknown":true,"schema":{
  65. "predictionsFile": {"type":"string", "required":false, "default":"predictions.csv"},
  66. "scoresFile": {"type":"string", "required":false, "default":"scores.csv"}
  67. }},
  68. "dataAugmentation":{"type":"list", "required":false,"schema":{"type":"dict","allow_unknown":true,"schema":{
  69. "domain":{"type":"list","required":false, "schema":{"type":"string"}},
  70. "keywords":{"type":"list", "required":false, "schema":{"type":"string"}}
  71. }}}
  72. }

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