remove try to get error

Techtonique · Oct 9, 2024 · bd692f4 · bd692f4
1 parent 541f7bb
commit bd692f4
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Showing 2 changed files with 40 additions and 46 deletions.
diff --git a/mlsauce/booster/_booster_classifier.py b/mlsauce/booster/_booster_classifier.py
@@ -411,29 +411,26 @@ def fit(self, X, y, **kwargs):
             )
             X = np.column_stack((X, clustered_X))
 
-        try:
-            self.obj = boosterc.fit_booster_classifier(
-                np.asarray(X, order="C"),
-                np.asarray(y, order="C"),
-                n_estimators=self.n_estimators,
-                learning_rate=self.learning_rate,
-                n_hidden_features=self.n_hidden_features,
-                reg_lambda=self.reg_lambda,
-                alpha=self.alpha,
-                row_sample=self.row_sample,
-                col_sample=self.col_sample,
-                dropout=self.dropout,
-                tolerance=self.tolerance,
-                direct_link=self.direct_link,
-                verbose=self.verbose,
-                seed=self.seed,
-                backend=self.backend,
-                solver=self.solver,
-                activation=self.activation,
-                obj=self.base_model,
-            )
-        except ValueError:
-            pass
+        self.obj = boosterc.fit_booster_classifier(
+            np.asarray(X, order="C"),
+            np.asarray(y, order="C"),
+            n_estimators=self.n_estimators,
+            learning_rate=self.learning_rate,
+            n_hidden_features=self.n_hidden_features,
+            reg_lambda=self.reg_lambda,
+            alpha=self.alpha,
+            row_sample=self.row_sample,
+            col_sample=self.col_sample,
+            dropout=self.dropout,
+            tolerance=self.tolerance,
+            direct_link=self.direct_link,
+            verbose=self.verbose,
+            seed=self.seed,
+            backend=self.backend,
+            solver=self.solver,
+            activation=self.activation,
+            obj=self.base_model,
+        )
 
         self.n_classes_ = len(np.unique(y))  # for compatibility with sklearn
         self.n_estimators = self.obj["n_estimators"]

diff --git a/mlsauce/booster/_booster_regressor.py b/mlsauce/booster/_booster_regressor.py
@@ -281,29 +281,26 @@ def fit(self, X, y, **kwargs):
             )
             X = np.column_stack((X, clustered_X))
 
-        try:
-            self.obj = boosterc.fit_booster_regressor(
-                X=np.asarray(X, order="C"),
-                y=np.asarray(y, order="C"),
-                n_estimators=self.n_estimators,
-                learning_rate=self.learning_rate,
-                n_hidden_features=self.n_hidden_features,
-                reg_lambda=self.reg_lambda,
-                alpha=self.alpha,
-                row_sample=self.row_sample,
-                col_sample=self.col_sample,
-                dropout=self.dropout,
-                tolerance=self.tolerance,
-                direct_link=self.direct_link,
-                verbose=self.verbose,
-                seed=self.seed,
-                backend=self.backend,
-                solver=self.solver,
-                activation=self.activation,
-                obj=self.base_model,
-            )
-        except ValueError:
-            pass
+        self.obj = boosterc.fit_booster_regressor(
+            X=np.asarray(X, order="C"),
+            y=np.asarray(y, order="C"),
+            n_estimators=self.n_estimators,
+            learning_rate=self.learning_rate,
+            n_hidden_features=self.n_hidden_features,
+            reg_lambda=self.reg_lambda,
+            alpha=self.alpha,
+            row_sample=self.row_sample,
+            col_sample=self.col_sample,
+            dropout=self.dropout,
+            tolerance=self.tolerance,
+            direct_link=self.direct_link,
+            verbose=self.verbose,
+            seed=self.seed,
+            backend=self.backend,
+            solver=self.solver,
+            activation=self.activation,
+            obj=self.base_model,
+        )
 
         self.n_estimators = self.obj["n_estimators"]