Add base margin to sklearn interface. (#5151)

tests/python/test_basic_models.py

@@ -132,6 +132,21 @@ class TestModels(unittest.TestCase):
         bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay)
         assert isinstance(bst, xgb.core.Booster)
 
+    def test_boost_from_prediction(self):
+        # Re-construct dtrain here to avoid modification
+        margined = xgb.DMatrix(dpath + 'agaricus.txt.train')
+        bst = xgb.train({'tree_method': 'hist'}, margined, 1)
+        predt_0 = bst.predict(margined, output_margin=True)
+        margined.set_base_margin(predt_0)
+        bst = xgb.train({'tree_method': 'hist'}, margined, 1)
+        predt_1 = bst.predict(margined)
+
+        assert np.any(np.abs(predt_1 - predt_0) > 1e-6)
+
+        bst = xgb.train({'tree_method': 'hist'}, dtrain, 2)
+        predt_2 = bst.predict(dtrain)
+        assert np.all(np.abs(predt_2 - predt_1) < 1e-6)
+
     def test_custom_objective(self):
         param = {'max_depth': 2, 'eta': 1, 'verbosity': 0}
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]

tests/python/test_with_sklearn.py

@@ -695,3 +695,23 @@ def test_XGBClassifier_resume():
 
         assert np.any(pred1 != pred2)
         assert log_loss1 > log_loss2
+
+
+def test_boost_from_prediction():
+    from sklearn.datasets import load_breast_cancer
+    X, y = load_breast_cancer(return_X_y=True)
+    model_0 = xgb.XGBClassifier(
+        learning_rate=0.3, random_state=0, n_estimators=4)
+    model_0.fit(X=X, y=y)
+    margin = model_0.predict(X, output_margin=True)
+
+    model_1 = xgb.XGBClassifier(
+        learning_rate=0.3, random_state=0, n_estimators=4)
+    model_1.fit(X=X, y=y, base_margin=margin)
+    predictions_1 = model_1.predict(X, base_margin=margin)
+
+    cls_2 = xgb.XGBClassifier(
+        learning_rate=0.3, random_state=0, n_estimators=8)
+    cls_2.fit(X=X, y=y)
+    predictions_2 = cls_2.predict(X, base_margin=margin)
+    assert np.all(predictions_1 == predictions_2)