sweep.yaml

method: bayes
metric:
  goal: minimize
  name: Step
parameters:
  max_depth:
    distribution: int_uniform
    max: 40
    min: 5
  max_features:
    distribution: categorical
    values:
    - auto
    - sqrt
  min_samples_leaf:
    distribution: int_uniform
    max: 10
    min: 1
  min_samples_split:
    distribution: int_uniform
    max: 20
    min: 3
  n_estimators:
    distribution: int_uniform
    max: 3500
    min: 550

#  learning_rate: 
#    distribution: uniform
#    max: 0.8
#    min: 0.05

#  n_neighbors: 
#    distribution: int_uniform
#    max: 15
#    min: 5

#  model_name:
#    distribution: categorical
#    values:
#    - ElasticNet 
#    - RandomForestRegressor
#    - GradientBoostingRegressor
#    - AdaBoostRegressor
#    - DecisionTreeRegressor
#    - KNeighborsRegressor
#    - Ridge

program: .\src\sweep_train.py
command:
  - C:\Users\Teja\Documents\_INFOTECH\jobs\stepstone_demo\env_end2endml\Scripts\python.exe
  - ${program}