add kernel hyperparameter subset in gridsearch

experimentation/Experiments.py

@@ -12,7 +12,12 @@ from .Sets import Datasets
 
 class Experiment:
     def __init__(
-        self, random_state: int, model: str, host: str, set_of_files: str
+        self,
+        random_state: int,
+        model: str,
+        host: str,
+        set_of_files: str,
+        kernel: str,
     ) -> None:
         self._random_state = random_state
         self._model_name = model
@@ -26,6 +31,7 @@ class Experiment:
         # used in gridsearch with ensembles to take best hyperparams of
         # base class or gridsearch these hyperparams as well
         self._base_params = "any"
+        self._kernel = kernel
 
     def set_base_params(self, base_params: str) -> None:
         self._base_params = base_params
@@ -73,6 +79,12 @@ class Experiment:
         """
         hyperparams = Hyperparameters(host=self._host, model=self._model_name)
         model = self._clf.get_model()
+        if self._kernel != "any":
+            # set parameters grid to only one kernel
+            if isinstance(self._clf, Models.Ensemble):
+                self._clf._base_model.select_params(self._kernel)
+            else:
+                self._clf.select_params(self._kernel)
         hyperparameters = self._clf.get_parameters()
         grid_type = "gridsearch"
         if (
@@ -111,7 +123,8 @@ class Experiment:
                 model,
                 return_train_score=True,
                 param_grid=hyperparameters,
-                n_jobs=-1,
+                n_jobs=1,
+                verbose=1,
             )
             start_time = time.time()
             grid_search.fit(X, y)

experimentation/Models.py

@@ -49,35 +49,46 @@ class ModelStree(ModelBase):
         gamma = [1e-1, 1, 1e1]
         max_features = [None, "auto"]
         split_criteria = ["impurity", "max_samples"]
+        self._linear = {
+            "random_state": [self._random_state],
+            "C": C,
+            "max_iter": max_iter,
+            "split_criteria": split_criteria,
+            "max_features": max_features,
+        }
+        self._poly = {
+            "random_state": [self._random_state],
+            "kernel": ["rbf"],
+            "C": C,
+            "gamma": gamma,
+            "max_iter": max_iter,
+            "split_criteria": split_criteria,
+            "max_features": max_features,
+        }
+        self._rbf = {
+            "random_state": [self._random_state],
+            "kernel": ["poly"],
+            "degree": [3, 5],
+            "C": C,
+            "gamma": gamma,
+            "max_iter": max_iter,
+            "split_criteria": split_criteria,
+            "max_features": max_features,
+        }
         self._param_grid = [
-            {
-                "random_state": [self._random_state],
-                "C": C,
-                "max_iter": max_iter,
-                "split_criteria": split_criteria,
-                "max_features": max_features,
-            },
-            {
-                "random_state": [self._random_state],
-                "kernel": ["rbf"],
-                "C": C,
-                "gamma": gamma,
-                "max_iter": max_iter,
-                "split_criteria": split_criteria,
-                "max_features": max_features,
-            },
-            {
-                "random_state": [self._random_state],
-                "kernel": ["poly"],
-                "degree": [3, 5],
-                "C": C,
-                "gamma": gamma,
-                "max_iter": max_iter,
-                "split_criteria": split_criteria,
-                "max_features": max_features,
-            },
+            self._linear,
+            self._poly,
+            self._rbf,
         ]
 
+    def select_params(self, kernel: str) -> None:
+        if kernel == "linear":
+            self._param_grid = [self._linear]
+        elif kernel == "poly":
+            self._param_grid = [self._poly]
+        else:
+            self._param_grid = [self._rbf]
+
 
 class ModelSVC(ModelBase):
     def __init__(self, random_state: Optional[int] = None) -> None: