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puneethp
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dvc_session

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  1. split:
    2. 

  2.   cmd: python src/train_test_split.py -i data/iris.csv -o data/split/
    3. 

  3.   deps:
    4. 

  4.   - path: data/iris.csv
    5. 

  5.     md5: 7fafe97ac39bd89f1718ff44e5ff667e
    6. 

  6.   - path: src/train_test_split.py
    7. 

  7.     md5: 2a33b1aedfbdf36e9ab7d1e46bd3778d
    8. 

  8.   outs:
    9. 

  9.   - path: data/split
    10. 

  10.     md5: cc7cd1229f7bd51dfa622f78551d30e5.dir
    11. 

  11. featurize:
    12. 

  12.   cmd: python src/feature_engineering.py -i data/split/ -o data/features/ -o data/models/pca/
    13. 

  13.   deps:
    14. 

  14.   - path: data/split
    15. 

  15.     md5: cc7cd1229f7bd51dfa622f78551d30e5.dir
    16. 

  16.   - path: src/feature_engineering.py
    17. 

  17.     md5: 6995fabca56fe2c37053f45b80a01c76
    18. 

  18.   params:
    19. 

  19.     params.yaml:
    20. 

  20.       pca:
    21. 

  21.         n_components: 2
    22. 

  22.   outs:
    23. 

  23.   - path: data/features
    24. 

  24.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    25. 

  25.   - path: data/models/pca/metrics.csv
    26. 

  26.     md5: 53a33493ee1d14c41c652c457b91bb2b
    27. 

  27.   - path: data/models/pca/model.gz
    28. 

  28.     md5: 9d97c5887c3e0e333e1a33ddbb5a64fa
    29. 

  29. train_logistic:
    30. 

  30.   cmd: python src/logistic_regression.py -i data/features/ -o data/models/logistic/
    31. 

  31.   deps:
    32. 

  32.   - path: data/features
    33. 

  33.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    34. 

  34.   - path: src/logistic_regression.py
    35. 

  35.     md5: 85eebbfdd91d46555296af2fad5731a3
    36. 

  36.   params:
    37. 

  37.     params.yaml:
    38. 

  38.       logistic:
    39. 

  39.         penalty: l2
    40. 

  40.         n_jobs: 4
    41. 

  41.   outs:
    42. 

  42.   - path: data/models/logistic/metrics.csv
    43. 

  43.     md5: 2e6949aea4cc5f97f05d860584f57ee2
    44. 

  44.   - path: data/models/logistic/model.gz
    45. 

  45.     md5: 5d142e71dfd770442499fc483cba9a17
    46. 

  46. train_svc:
    47. 

  47.   cmd: python src/linear_svc.py -i data/features/ -o data/models/svc/
    48. 

  48.   deps:
    49. 

  49.   - path: data/features
    50. 

  50.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    51. 

  51.   - path: src/linear_svc.py
    52. 

  52.     md5: bef3d75e9fdedcb26e94182d5338fddf
    53. 

  53.   params:
    54. 

  54.     params.yaml:
    55. 

  55.       svc:
    56. 

  56.         penalty: l2
    57. 

  57.   outs:
    58. 

  58.   - path: data/models/svc/metrics.csv
    59. 

  59.     md5: 95b63047921e8b77b1ef437b7dd323a4
    60. 

  60.   - path: data/models/svc/model.gz
    61. 

  61.     md5: ee8ead1ca79bf0f58f18ccd1faf26743
    62. 

  62. train_forrest:
    63. 

  63.   cmd: python src/random_forrest.py -i data/features/ -o data/models/r_forrest/
    64. 

  64.   deps:
    65. 

  65.   - path: data/features
    66. 

  66.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    67. 

  67.   - path: src/random_forrest.py
    68. 

  68.     md5: e918c4e9be1c0ca28db9c215f8c53d0b
    69. 

  69.   params:
    70. 

  70.     params.yaml:
    71. 

  71.       forrest:
    72. 

  72.         n_estimators: 10
    73. 

  73.         max_samples: 30
    74. 

  74.         n_jobs: 4
    75. 

  75.   outs:
    76. 

  76.   - path: data/models/r_forrest/metrics.csv
    77. 

  77.     md5: 64d531fb350ae55f0c2e07b8bbbd243a
    78. 

  78.   - path: data/models/r_forrest/model.gz
    79. 

  79.     md5: e355e50b9ea1733b54621c4871c2fa7e
    80. 

  80. train_ensemble:
    81. 

  81.   cmd: python src/ensemble.py -i data/features/ -m data/models/ -o data/models/ensemble/
    82. 

  82.   deps:
    83. 

  83.   - path: data/features
    84. 

  84.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    85. 

  85.   - path: data/models/logistic/model.gz
    86. 

  86.     md5: 5d142e71dfd770442499fc483cba9a17
    87. 

  87.   - path: data/models/r_forrest/model.gz
    88. 

  88.     md5: e355e50b9ea1733b54621c4871c2fa7e
    89. 

  89.   - path: data/models/svc/model.gz
    90. 

  90.     md5: ee8ead1ca79bf0f58f18ccd1faf26743
    91. 

  91.   - path: src/ensemble.py
    92. 

  92.     md5: 87cf1ab5aa611e388d9170859240aca8
    93. 

  93.   params:
    94. 

  94.     params.yaml:
    95. 

  95.       ensemble:
    96. 

  96.         voting: hard
    97. 

  97.   outs:
    98. 

  98.   - path: data/models/ensemble/metrics.csv
    99. 

  99.     md5: 82dda51857f4190ca741aaba1cfda8b7
    100. 

  100.   - path: data/models/ensemble/model.gz
    101. 

  101.     md5: b4f83aa1921acda7160b548ec7886823
    102.
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