BaseTuner inplace of SampleClassMixin

__init__.py

@@ -1,11 +1,12 @@
 from .tune_classifier import *
 from .tune_regressor import *
+from .baseline import *
 from .sample import *
 from typing import Iterable
 
 
 __all__: Iterable[str] = [
-    "baseline.mixin",
+    "base",
     "tests.test_tuners",
     "tests.utils",
     "ensemble_classifier",

baseline/__init__.py

@@ -1,7 +1,8 @@
-from .mixin import *
+from .base import *
 from typing import Iterable
 
 
 __all__: Iterable[str] = [
-    "SampleClassMixin"
+    "SampleClassMixin",
+    "BaseTuner"
 ]

baseline/mixin.py → baseline/base.py

@@ -4,7 +4,7 @@
 from dataclasses import dataclass
 from typing import Optional, Dict, Any, Tuple, Iterable, Callable
 
-@dataclass
+
 class SampleClassMixin:
 
     def _is_space_type(self, space: Iterable, type: Callable) -> bool:
@@ -19,10 +19,7 @@ def is_valid_float_space(self, space: Iterable) -> bool:
     def is_valid_categorical_space(self, space: Iterable) -> bool:
         return (not self.is_valid_float_space(space)) and (not self.is_valid_float_space(space))
 
-    def _sample_params(self, trial: Optional[Trial]=None) -> Dict[str, Any]: 
-        if trial is None: raise ValueError("Method should be called in an optuna trial study")
-
-    def model(self, trial: Optional[Trial]) -> Any:
+    def _in_trial(self, trial: Optional[Trial]=None) -> Dict[str, Any]: 
         if trial is None: raise ValueError("Method should be called in an optuna trial study")
 
     def _evaluate_params(self, model_class: Callable, params: Dict[str, Any]):
@@ -78,5 +75,14 @@ def _evaluate_sampled_model(
         return model
 
 
+
+@dataclass
+class BaseTuner(SampleClassMixin):
+
+    model: Any = None
+
+    def sample_params(self, trial: Trial) -> Dict[str, Any]:
+        super()._in_trial(trial)
+
     def sample_model(self, trial: Trial) -> Any:
-        pass
+        super()._in_trial(trial)

sample.py

@@ -1,7 +1,8 @@
-from baseline import SampleClassMixin
-from optuna.trial import Trial
-from tune_regressor import regressor_tuning_entities
-from tune_classifier import classifier_tuning_entities
+import inspect
+from baseline import BaseTuner
+from optuna.trial import Trial, FrozenTrial
+from tune_regressor import regressor_tuning_entities, regressor_tuner_model_class_dict
+from tune_classifier import classifier_tuning_entities, classifier_tuner_model_class_dict
 from typing import Iterable, Dict, Optional
 
 
@@ -22,7 +23,22 @@ def sample_models_with_params(
     else:
         search_space:  Dict[str, object] = classifier_tuning_entities
 
-    tuner_obj: SampleClassMixin = trial.suggest_categorical("model_tuner", list(search_space.values()))
+    tuner_obj: BaseTuner = trial.suggest_categorical("model_tuner", list(search_space.values()))
     model = tuner_obj.sample_model(trial)
 
     return model
+
+
+def make_sampled_model(best_trial: FrozenTrial, **kwargs):
+    model_tuner = best_trial.params["model_tuner"]
+    tuner_model_class_dict = {**regressor_tuner_model_class_dict, **classifier_tuner_model_class_dict}
+    model_class = tuner_model_class_dict[model_tuner.__class__.__name__]
+
+    model_params_names = list(inspect.signature(model_class.__dict__["__init__"]).parameters.keys())
+    best_params_dict = {
+        k.replace(f"{model_tuner.__class__.__name__}_", "") : v 
+        for k, v in best_trial.params.items() 
+        if k.replace(f"{model_tuner.__class__.__name__}_", "") in model_params_names
+        }
+
+    return model_class(**best_params_dict)