fixed optuna examples

examples/optimization/conditional_parameters.py

@@ -18,6 +18,7 @@
 train_data, test_data = train_test_split(data, test_size=0.2, stratify=data["TARGET"], random_state=42)
 
 
+# replacing default _sample function in OptunaTuner class with this function
 def sample(optimization_search_space, trial, suggested_params):
  trial_values = copy.copy(suggested_params)
  trial_values["feature_fraction"] = trial.suggest_uniform("feature_fraction", low=0.5, high=1.0)

examples/optimization/custom_search_space.py

@@ -8,9 +8,8 @@
 from sklearn.model_selection import train_test_split
 
 from lightautoml.automl.presets.tabular_presets import TabularAutoML
-from lightautoml.ml_algo.tuning.base import Distribution
-from lightautoml.ml_algo.tuning.base import SearchSpace
 from lightautoml.tasks import Task
+from lightautoml.ml_algo.tuning.base import Uniform
 
 
 # load and prepare data
@@ -22,8 +21,8 @@
  task=Task("binary"),
  lgb_params={
  "optimization_search_space": {
- "feature_fraction": SearchSpace(Distribution.UNIFORM, low=0.5, high=1.0),
- "min_sum_hessian_in_leaf": SearchSpace(Distribution.LOGUNIFORM, low=1e-3, high=10.0),
+ "feature_fraction": Uniform(low=0.5, high=1.0),
+ "min_sum_hessian_in_leaf": Uniform(low=1e-3, high=10.0, log=True),
  }
  },
 )

examples/optimization/sequential_parameter_search.py

@@ -12,13 +12,12 @@
 from lightautoml.automl.presets.tabular_presets import TabularAutoML
 from lightautoml.tasks import Task
 
-
 # load and prepare data
 data = pd.read_csv("./data/sampled_app_train.csv")
 train_data, test_data = train_test_split(data, test_size=0.2, stratify=data["TARGET"], random_state=42)
 
 
-def sample(optimization_search_space, trial, suggested_params):
+def sample(trial, suggested_params):
  trial_values = copy.copy(suggested_params)
 
  for feature_fraction in range(10):

lightautoml/ml_algo/base.py

@@ -8,6 +8,7 @@
 from typing import Any
 from typing import Dict
 from typing import List
+from typing import Callable
 from typing import Optional
 from typing import Sequence
 from typing import Tuple
@@ -48,7 +49,13 @@ class MLAlgo(ABC):
  """
 
  _default_params: Dict = {}
- optimization_search_space: Dict = {}
+
+ # Dict is a default search space representation that is used for simple cases
+ # Callable is used for complex cases like conditional search space as described in
+ # LightAutoML/examples/optimization/conditional_parameters.py
+ # Called in _get_objective function in OptunaTuner class
+ optimization_search_space: Union[Dict, Callable] = {}
+
  # TODO: add checks here
  _fit_checks: Tuple = ()
  _transform_checks: Tuple = ()

lightautoml/ml_algo/tuning/optuna.py

@@ -273,11 +273,18 @@ def objective(trial: optuna.trial.Trial) -> float:
  estimated_n_trials=estimated_n_trials,
  )
 
- _ml_algo.params = self._sample(
- trial=trial,
- optimization_search_space=optimization_search_space,
- suggested_params=_ml_algo.init_params_on_input(train_valid_iterator),
- )
+ if callable(optimization_search_space):
+ _ml_algo.params = optimization_search_space(
+ trial=trial,
+ optimization_search_space=None,
+ suggested_params=_ml_algo.init_params_on_input(train_valid_iterator),
+ )
+ else:
+ _ml_algo.params = self._sample(
+ trial=trial,
+ optimization_search_space=optimization_search_space,
+ suggested_params=_ml_algo.init_params_on_input(train_valid_iterator),
+ )
 
  output_dataset = _ml_algo.fit_predict(train_valid_iterator=train_valid_iterator)