enable xgb_model in scklearn XGBClassifier and test. (#4092)

* Enable xgb_model parameter in XGClassifier scikit-learn API

#3049

* add test_XGBClassifier_resume():

test for xgb_model parameter in XGBClassifier API.

* Update test_with_sklearn.py

* Fix lint

python-package/xgboost/sklearn.py

@@ -709,7 +709,7 @@ def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
                               evals=evals,
                               early_stopping_rounds=early_stopping_rounds,
                               evals_result=evals_result, obj=obj, feval=feval,
-                              verbose_eval=verbose, xgb_model=None,
+                              verbose_eval=verbose, xgb_model=xgb_model,
                               callbacks=callbacks)
 
         self.objective = xgb_options["objective"]

tests/python/test_with_sklearn.py

@@ -609,3 +609,42 @@ def test_RFECV():
                             scale_pos_weight=0.5, silent=True)
     rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='neg_log_loss')
     rfecv.fit(X, y)
+
+
+def test_XGBClassifier_resume():
+    from sklearn.datasets import load_breast_cancer
+    from sklearn.metrics import log_loss
+
+    with TemporaryDirectory() as tempdir:
+        model1_path = os.path.join(tempdir, 'test_XGBClassifier.model')
+        model1_booster_path = os.path.join(tempdir, 'test_XGBClassifier.booster')
+
+        X, Y = load_breast_cancer(return_X_y=True)
+
+        model1 = xgb.XGBClassifier(learning_rate=0.3, seed=0, n_estimators=8)
+        model1.fit(X, Y)
+
+        pred1 = model1.predict(X)
+        log_loss1 = log_loss(pred1, Y)
+
+        # file name of stored xgb model
+        model1.save_model(model1_path)
+        model2 = xgb.XGBClassifier(learning_rate=0.3, seed=0, n_estimators=8)
+        model2.fit(X, Y, xgb_model=model1_path)
+
+        pred2 = model2.predict(X)
+        log_loss2 = log_loss(pred2, Y)
+
+        assert np.any(pred1 != pred2)
+        assert log_loss1 > log_loss2
+
+        # file name of 'Booster' instance Xgb model
+        model1.get_booster().save_model(model1_booster_path)
+        model2 = xgb.XGBClassifier(learning_rate=0.3, seed=0, n_estimators=8)
+        model2.fit(X, Y, xgb_model=model1_booster_path)
+
+        pred2 = model2.predict(X)
+        log_loss2 = log_loss(pred2, Y)
+
+        assert np.any(pred1 != pred2)
+        assert log_loss1 > log_loss2