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Raj-Narayanan-B
/
scania_truck
connected to https://github.com/Raj-Narayanan-B/scania_truck.git

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  1. models:
    2. 

  2.   Logistic_Regression: LogisticRegression
    3. 

  3.   SGD_Classifier: SGDClassifier
    4. 

  4.   Random Forest: RandomForestClassifier
    5. 

  5.   Ada_Boost: AdaBoostClassifier
    6. 

  6.   Grad_Boost: GradientBoostingClassifier
    7. 

  7.   Light_GBM: LGBMClassifier
    8. 

  8.   Bagging_Classifier: BaggingClassifier
    9. 

  9.   ExtraTreesClassifier: ExtraTreesClassifier
    10. 

  10.   Hist_Grad_Boost_Classifier: HistGradientBoostingClassifier
    11. 

  11.   Decision_Tree_Classifier: DecisionTreeClassifier
    12. 

  12.   XGB_Classifier: XGBClassifier
    13. 

  13.   KNN_Classifier: KNeighborsClassifier
    14. 

  14. 

  15. optuna:
    16. 

  16.   Logistic_Regression:
    17. 

  17.     penalty: trial.suggest_categorical('penalty', ['l2', None])
    18. 

  18. 

  19.   SGD_Classifier:
    20. 

  20.     loss: trial.suggest_categorical('loss', ['squared_epsilon_insensitive', 'epsilon_insensitive', 'huber', 'squared_error', 'perceptron', 'squared_hinge', 'hinge', 'log_loss', 'modified_huber'])
    21. 

  21. 

  22.   Light_GBM:
    23. 

  23.     boosting_type: trial.suggest_categorical('boosting_type', ['gbdt','dart'])
    24. 

  24.     learning_rate: trial.suggest_float('learning_rate', .00001, 1.0)
    25. 

  25.     n_estimators: trial.suggest_int('n_estimators', 100, 150)
    26. 

  26.     class_weight: trial.suggest_categorical('class_weight', ['balanced'])
    27. 

  27.     n_jobs: trial.suggest_categorical('n_jobs', [-1])
    28. 

  28. 

  29.   Random Forest:
    30. 

  30.     n_estimators: trial.suggest_int('n_estimators', 100, 150)
    31. 

  31.     criterion: trial.suggest_categorical('criterion', ['log_loss', 'entropy', 'gini'])
    32. 

  32.     max_features: trial.suggest_categorical('max_features', ['sqrt', 'log2', None])
    33. 

  33.     class_weight: trial.suggest_categorical('class_weight', ['balanced', 'balanced_subsample'])
    34. 

  34. 

  35.   Ada_Boost:
    36. 

  36.     n_estimators: trial.suggest_int('n_estimators', 100, 150)
    37. 

  37.     algorithm: trial.suggest_categorical('algorithm', ['SAMME', 'SAMME.R'])
    38. 

  38. 

  39.   Grad_Boost:
    40. 

  40.     loss: trial.suggest_categorical('loss', ['log_loss', 'exponential'])
    41. 

  41.     n_estimators: trial.suggest_int('n_estimators', 100, 150)
    42. 

  42.     criterion: trial.suggest_categorical('criterion', ['friedman_mse', 'squared_error'])
    43. 

  43.     max_features: trial.suggest_categorical('max_features', ['sqrt', 'log2', None])
    44. 

  44. 

  45.   Bagging_Classifier:
    46. 

  46.     n_estimators: trial.suggest_int('n_estimators', 50, 100)
    47. 

  47.     n_jobs: trial.suggest_categorical('n_jobs', [-1])
    48. 

  48. 

  49.   ExtraTreesClassifier:
    50. 

  50.     n_estimators: trial.suggest_int('n_estimators', 100, 500)
    51. 

  51.     criterion: trial.suggest_categorical('criterion', ['log_loss', 'entropy', 'gini'])
    52. 

  52.     max_features: trial.suggest_categorical('max_features', ['sqrt', 'log2', None])
    53. 

  53.     class_weight: trial.suggest_categorical('class_weight', ['balanced', 'balanced_subsample'])
    54. 

  54. 

  55.   Hist_Grad_Boost_Classifier:
    56. 

  56.     max_iter: trial.suggest_int('max_iter', 100, 800)
    57. 

  57. 

  58.   Decision_Tree_Classifier:
    59. 

  59.     criterion: trial.suggest_categorical('criterion', ['log_loss', 'entropy', 'gini'])
    60. 

  60.     splitter: trial.suggest_categorical('splitter', ['best', 'random'])
    61. 

  61.     max_features: trial.suggest_categorical('max_features', ['sqrt', 'log2', None])
    62. 

  62. 

  63.   XGB_Classifier:
    64. 

  64.     n_estimators: trial.suggest_int('n_estimators', 100, 500)
    65. 

  65.     learning_rate: trial.suggest_float('learning_rate', .00001, 1.0)
    66. 

  66.     booster: trial.suggest_categorical('booster', ['gbtree', 'gblinear', 'dart'])
    67. 

  67.     tree_method: trial.suggest_categorical('tree_method', ['exact', 'approx', 'hist'])
    68. 

  68. 

  69.   KNN_Classifier:
    70. 

  70.     n_neighbors: trial.suggest_int('n_neighbors', 3, 11, step=2)
    71. 

  71.     weights: trial.suggest_categorical('weights', ['uniform', 'distance'])
    72. 

  72.     algorithm: trial.suggest_categorical('algorithm', ['auto', 'ball_tree', 'kd_tree', 'brute'])
    73. 

  73. 

  74.   # MLP_Classifier:
    75. 

  75.   #   hidden_layer_sizes: trial.suggest_categorical('hidden_layer_sizes', [(500,), (500, 300, 200, 150,), (700, 500, 300, 100, ), (1500, 800, 400, 200, )])
    76. 

  76.   #   activation: trial.suggest_categorical('activation', ['identity', 'logistic', 'tanh' , 'relu'])
    77. 

  77.   #   learning_rate: trial.suggest_categorical('learning_rate', ['constant', 'invscaling', 'adaptive'])
    78. 

  78.   #   max_iter: trial.suggest_int('max_iter', 100, 800)
    79. 

  79. 

  80.   Stacked_Classifier:
    81. 

  81.     stack_method: trial.suggest_categorical('stack_method', ['auto', 'predict'])
    82. 

  82.     passthrough: trial.suggest_categorical('passthrough', [True, False])
    83. 

  83. # hyperopt:
    84. 

  84. #   Logistic_Regression:
    85. 

  85. #     penalty: hp.choice('penalty', ['l2', None])
    86. 

  86. 

  87. #   SGD_Classifier:
    88. 

  88. #     loss: hp.choice('loss', ['squared_epsilon_insensitive', 'epsilon_insensitive', 'huber', 'squared_error', 'perceptron', 'squared_hinge', 'hinge', 'log_loss', 'modified_huber'])
    89. 

  89. 

  90. #   Random Forest:
    91. 

  91. #     n_estimators: scope.int(hp.quniform('n_estimators', 100, 150, 1))
    92. 

  92. #     criterion: hp.choice('criterion', ['log_loss', 'entropy', 'gini'])
    93. 

  93. #     max_features: hp.choice('max_features', ['sqrt', 'log2', None])
    94. 

  94. #     class_weight: hp.choice('class_weight', ['balanced', 'balanced_subsample'])
    95. 

  95. 

  96. #   Ada_Boost:
    97. 

  97. #     n_estimators: scope.int(hp.quniform('n_estimators', 100, 150, 1))
    98. 

  98. #     algorithm: hp.choice('algorithm', ['SAMME', 'SAMME.R'])
    99. 

  99. 

  100. #   Grad_Boost:
    101. 

  101. #     loss: hp.choice('loss', ['log_loss', 'exponential'])
    102. 

  102. #     n_estimators: scope.int(hp.quniform('n_estimators', 100, 150, 1))
    103. 

  103. #     criterion: hp.choice('criterion', ['friedman_mse', 'squared_error'])
    104. 

  104. #     max_features: hp.choice('max_features', ['sqrt', 'log2', None])
    105. 

  105. 

  106. #   Bagging_Classifier:
    107. 

  107. #     n_estimators: scope.int(hp.quniform('n_estimators', 50, 100, 1))
    108. 

  108. 

  109. #   ExtraTreesClassifier:
    110. 

  110. #     n_estimators: scope.int(hp.quniform('n_estimators', 100, 1000, 1))
    111. 

  111. #     criterion: hp.choice('criterion', ['log_loss', 'entropy', 'gini'])
    112. 

  112. #     max_features: hp.choice('max_features', ['sqrt', 'log2', None])
    113. 

  113. #     class_weight: hp.choice('class_weight', ['balanced', 'balanced_subsample'])
    114. 

  114. 

  115. #   Hist_Grad_Boost_Classifier:
    116. 

  116. #     max_iter: scope.int(hp.quniform('max_iter', 100, 800, 1))
    117. 

  117. 

  118. #   Decision_Tree_Classifier:
    119. 

  119. #     criterion: hp.choice('criterion', ['log_loss', 'entropy', 'gini'])
    120. 

  120. #     splitter: hp.choice('splitter', ['best', 'random'])
    121. 

  121. #     max_features: hp.choice('max_features', ['sqrt', 'log2', None])
    122. 

  122. 

  123. #   XGB_Classifier:
    124. 

  124. #     n_estimators: scope.int(hp.quniform('n_estimators', 100, 200, 1))
    125. 

  125. #     learning_rate: hp.uniform('learning_rate', .00001, 1.0)
    126. 

  126. #     booster: hp.choice('booster', ['gbtree', 'gblinear', 'dart'])
    127. 

  127. #     tree_method: hp.choice('tree_method', ['exact', 'approx', 'hist'])
    128. 

  128. 

  129. #   KNN_Classifier:
    130. 

  130. #     n_neighbors: scope.int(hp.quniform('n_neighbors', 3, 11, 2))
    131. 

  131. #     weights: hp.choice('weights', ['uniform', 'distance'])
    132. 

  132. #     algorithm: hp.choice('algorithm', ['auto', 'ball_tree', 'kd_tree', 'brute'])
    133. 

  133. 

  134. # MLP_Classifier:
    135. 

  135. #   hidden_layer_sizes: hp.choice('hidden_layer_sizes', [(500,), (500, 300, 200, 150,), (700, 500, 300, 100, ), (1500, 800, 400, 200, )])
    136. 

  136. #   activation: hp.choice('activation', ['identity', 'logistic', 'tanh' , 'relu'])
    137. 

  137. #   learning_rate: hp.choice('learning_rate', ['constant', 'invscaling', 'adaptive'])
    138. 

  138. #   max_iter: scope.int(hp.quniform('max_iter', 100, 800, 1))
    139. 

  139. 

  140. # Stacked_Classifier:
    141. 

  141. #   stack_method: hp.choice('stack_method', ['auto', 'predict'])
    142. 

  142. #   passthrough: hp.choice('passthrough', [True, False])
    143. 

  143. 

  144. # Challenger_Stacked_Classifier: mlflow-artifacts:/aacd27cc7a9d429ab2439e37a4e8cdfb/d3eb9ead8a5c49f9b8299efea62d58a2/artifacts/candidate_Stacked_Classifier/model.pkl
    145. 

  145. # Challenger_Voting_Classifier: mlflow-artifacts:/cd6eb5acbadd414fb5d32e02d983d99b/b2aea20ef6094e1a94b1567aea90753f/artifacts/candidate_Voting_Classifier/model.pkl
    146. 

  146. # Final_Estimator: mlflow-artifacts:/f2a1d0cb47994e43887a7bf8ff11fbc0/4f79ae8649044b24a593ec1c748e012b/artifacts/challenger_LGBMClassifier/model.pkl
    147. 

  147. # Champion_Estimator: mlflow-artifacts:/cd6eb5acbadd414fb5d32e02d983d99b/b2aea20ef6094e1a94b1567aea90753f/artifacts/candidate_Voting_Classifier/model.pkl
    148.
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