fix api

Signed-off-by: xadupre <xadupre@microsoft.com>
xadupre · Oct 3, 2024 · 782b057 · 782b057
1 parent add4294
commit 782b057
Showing 1 changed file with 18 additions and 7 deletions.
diff --git a/tests/xgboost/test_xgboost_converters.py b/tests/xgboost/test_xgboost_converters.py
@@ -360,8 +360,13 @@ def test_xgboost_classifier_i5450_softmax(self):
         iris = load_iris()
         X, y = iris.data, iris.target
         X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
-        clr = XGBClassifier(objective="multi:softmax", max_depth=1, n_estimators=2)
-        clr.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=40)
+        clr = XGBClassifier(
+            objective="multi:softmax",
+            max_depth=1,
+            n_estimators=2,
+            early_stopping_rounds=40,
+        )
+        clr.fit(X_train, y_train, eval_set=[(X_test, y_test)])
         initial_type = [("float_input", FloatTensorType([None, 4]))]
         onx = convert_xgboost(
             clr, initial_types=initial_type, target_opset=TARGET_OPSET
@@ -386,8 +391,13 @@ def test_xgboost_classifier_i5450(self):
         iris = load_iris()
         X, y = iris.data, iris.target
         X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
-        clr = XGBClassifier(objective="multi:softprob", max_depth=1, n_estimators=2)
-        clr.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=40)
+        clr = XGBClassifier(
+            objective="multi:softprob",
+            max_depth=1,
+            n_estimators=2,
+            early_stopping_rounds=40,
+        )
+        clr.fit(X_train, y_train, eval_set=[(X_test, y_test)])
         initial_type = [("float_input", FloatTensorType([None, 4]))]
         onx = convert_xgboost(
             clr, initial_types=initial_type, target_opset=TARGET_OPSET
@@ -725,9 +735,10 @@ def test_xgb_classifier_13(self):
             colsample_bytree=0.75,
             random_state=42,
             verbosity=0,
+            early_stopping_rounds=40,
         )
 
-        clr.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=40)
+        clr.fit(X_train, y_train, eval_set=[(X_test, y_test)])
 
         initial_type = [("float_input", FloatTensorType([None, 797]))]
         onx = convert_xgboost(
@@ -750,20 +761,20 @@ def test_xgb_classifier_13_2(self):
             X.values.astype(np.float32), y.values.astype(np.float32), random_state=2022
         )
 
+        eval_metric = ["logloss", "auc", "error"]
         model_param = {
             "objective": "binary:logistic",
             "n_estimators": 1000,
             "early_stopping_rounds": 113,
             "random_state": 42,
             "max_depth": 3,
+            "eval_metric": eval_metric,
         }
-        eval_metric = ["logloss", "auc", "error"]
         model = XGBClassifier(**model_param)
         model.fit(
             X=x_train,
             y=y_train,
             eval_set=[(x_test, y_test)],
-            eval_metric=eval_metric,
             verbose=False,
         )