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dvc.lock 1.5 KB

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  1. prepare:
  2. cmd: python src/prepare.py data/data.xml
  3. deps:
  4. - path: data/data.xml
  5. md5: a304afb96060aad90176268345e10355
  6. - path: src/prepare.py
  7. md5: 285af85d794bb57e5d09ace7209f3519
  8. params:
  9. params.yaml:
  10. prepare.seed: 20170428
  11. prepare.split: 0.2
  12. outs:
  13. - path: data/prepared
  14. md5: 20b786b6e6f80e2b3fcf17827ad18597.dir
  15. featurize:
  16. cmd: python src/featurization.py data/prepared data/features
  17. deps:
  18. - path: data/prepared
  19. md5: 20b786b6e6f80e2b3fcf17827ad18597.dir
  20. - path: src/featurization.py
  21. md5: 02180903aa05599ceaa6ff6ee2471d00
  22. params:
  23. params.yaml:
  24. featurize.max_features: 1500
  25. featurize.ngrams: 2
  26. outs:
  27. - path: data/features
  28. md5: 58a8786b3db0d0a9cc9aa0cac7395690.dir
  29. train:
  30. cmd: python src/train.py data/features model.pkl
  31. deps:
  32. - path: data/features
  33. md5: 58a8786b3db0d0a9cc9aa0cac7395690.dir
  34. - path: src/train.py
  35. md5: ad8e71b2cca4334a7d3bb6495645068c
  36. params:
  37. params.yaml:
  38. train.n_estimators: 100
  39. train.seed: 20170428
  40. outs:
  41. - path: model.pkl
  42. md5: 2ac5be823742932401b384263a7966eb
  43. evaluate:
  44. cmd: python src/evaluate.py model.pkl data/features scores.json prc.json
  45. deps:
  46. - path: data/features
  47. md5: 58a8786b3db0d0a9cc9aa0cac7395690.dir
  48. - path: model.pkl
  49. md5: 2ac5be823742932401b384263a7966eb
  50. - path: src/evaluate.py
  51. md5: 631fd3f13434edcf4abd56a5b798e8b2
  52. outs:
  53. - path: prc.json
  54. md5: e332c017a281b420ae1e6dc847d2d8b9
  55. - path: scores.json
  56. md5: d94cf22577e274bae6c9eeefcc8e7d2c
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