bnsl.yaml

# limit for each "outer" cv fold
wallclock_limit: 120

# allowed feature groups (problem-specific)
allowed_feature_groups:
  - basic
  - basic_extended
  - lower_bounding
  #- greedy_probing
  #- A*_probing
  #- CP_probing
  #- ILP_probing

# as-autosklearn configuration

# missing values
imputer_strategy: 'median'
#imputer_strategy: 'mean'
#imputer_strategy: 'most_frequent'

# preprocessing
preprocessing_strategy: 'scale'

# if this is present with any value (even something like "no" or "False"), then
# the logarithm of the performance data will be used for training and testing
log_performance_data: True

fields_to_log:
  - "POPS-Count"
  - "VPOPS-Maximum"
  - "VPOPS-Mean"
  - "VPOPS-Standard-Deviation"
  - "PSS-Maximum"
  - "PSS-Mean"
  - "PSS-Standard-Deviation"

# autofolio configuration

# preprocessing strategies
pca: False
impute: True
scale: True

# use ASPeed to find a presolving schedule?
presolve: False

# pairwise classifiers
random_forest_classifier: True
xgboost_classifier: True

# pairwise regressors
random_forest_regressor: True