Merge pull request #5363 from FederatedAI/dev-2.0.0-rc-final-round-fix

Dev 2.0.0 rc final round fix

python/fate/ml/ensemble/algo/secureboost/hetero/guest.py

@@ -197,33 +197,33 @@ def _init_sample_scores(self, ctx: Context, label, train_data: DataFrame):
 
     def _check_label(self, label: DataFrame):
 
-        train_data_binarized_label = label.get_dummies()
-        labels = [int(label_name.split("_")[1]) for label_name in train_data_binarized_label.columns]
-        label_set = set(labels)
 
-
-        if self.objective == MULTI_CE:
-
-            if self.num_class is None or self.num_class <= 2:
-                raise ValueError(
-                    f"num_class should be set and greater than 2 for multi:ce objective, but got {self.num_class}"
-                )
-
-            if len(label_set) > self.num_class:
-                raise ValueError(
-                    f"num_class should be greater than or equal to the number of unique label in provided train data, but got {self.num_class} and {len(label_set)}"
-                )
-            if max(label_set) - 1 > self.num_class:
-                raise ValueError(
-                    f"the max label index in the provided train data should be less than or equal to num_class - 1, but got index {max(label_set)} which is > {self.num_class}"
-                )
-
-        elif self.objective == BINARY_BCE:
-            assert len(label_set) == 2, f"binary classification task should have 2 unique label, but got {label_set}"
-            assert (
-                0 in label_set and 1 in label_set
-            ), f"binary classification task should have label 0 and 1, but got {label_set}"
-            self.num_class = 2
+        if self.objective != REGRESSION:
+            train_data_binarized_label = label.get_dummies()
+            labels = [int(label_name.split("_")[1]) for label_name in train_data_binarized_label.columns]
+            label_set = set(labels)
+            if self.objective == MULTI_CE:
+
+                if self.num_class is None or self.num_class <= 2:
+                    raise ValueError(
+                        f"num_class should be set and greater than 2 for multi:ce objective, but got {self.num_class}"
+                    )
+
+                if len(label_set) > self.num_class:
+                    raise ValueError(
+                        f"num_class should be greater than or equal to the number of unique label in provided train data, but got {self.num_class} and {len(label_set)}"
+                    )
+                if max(label_set) - 1 > self.num_class:
+                    raise ValueError(
+                        f"the max label index in the provided train data should be less than or equal to num_class - 1, but got index {max(label_set)} which is > {self.num_class}"
+                    )
+
+            elif self.objective == BINARY_BCE:
+                assert len(label_set) == 2, f"binary classification task should have 2 unique label, but got {label_set}"
+                assert (
+                    0 in label_set and 1 in label_set
+                ), f"binary classification task should have label 0 and 1, but got {label_set}"
+                self.num_class = 2
         else:
             self.num_class = None