Enhance inplace prediction. (#6653)

* Accept array interface for csr and array.
* Accept an optional proxy dmatrix for metainfo.

This constructs an explicit `_ProxyDMatrix` type in Python.

* Remove unused doc.
* Add strict output.

tests/python/test_predict.py

@@ -80,20 +80,28 @@ def test_predict_leaf():
 
 class TestInplacePredict:
     '''Tests for running inplace prediction'''
+    @classmethod
+    def setup_class(cls):
+        cls.rows = 100
+        cls.cols = 10
+
+        cls.rng = np.random.RandomState(1994)
+
+        cls.X = cls.rng.randn(cls.rows, cls.cols)
+        cls.y = cls.rng.randn(cls.rows)
+
+        dtrain = xgb.DMatrix(cls.X, cls.y)
+
+        cls.booster = xgb.train({'tree_method': 'hist'},
+                                dtrain, num_boost_round=10)
+
+        cls.test = xgb.DMatrix(cls.X[:10, ...])
+
     def test_predict(self):
-        rows = 1000
-        cols = 10
+        booster = self.booster
+        X = self.X
+        test = self.test
 
-        np.random.seed(1994)
-
-        X = np.random.randn(rows, cols)
-        y = np.random.randn(rows)
-        dtrain = xgb.DMatrix(X, y)
-
-        booster = xgb.train({'tree_method': 'hist'},
-                            dtrain, num_boost_round=10)
-
-        test = xgb.DMatrix(X[:10, ...])
         predt_from_array = booster.inplace_predict(X[:10, ...])
         predt_from_dmatrix = booster.predict(test)
 
@@ -111,7 +119,7 @@ class TestInplacePredict:
             return np.all(copied_predt == inplace_predt)
 
         for i in range(10):
-            run_threaded_predict(X, rows, predict_dense)
+            run_threaded_predict(X, self.rows, predict_dense)
 
         def predict_csr(x):
             inplace_predt = booster.inplace_predict(sparse.csr_matrix(x))
@@ -120,4 +128,14 @@ class TestInplacePredict:
             return np.all(copied_predt == inplace_predt)
 
         for i in range(10):
-            run_threaded_predict(X, rows, predict_csr)
+            run_threaded_predict(X, self.rows, predict_csr)
+
+    def test_base_margin(self):
+        booster = self.booster
+
+        base_margin = self.rng.randn(self.rows)
+        from_inplace = booster.inplace_predict(data=self.X, base_margin=base_margin)
+
+        dtrain = xgb.DMatrix(self.X, self.y, base_margin=base_margin)
+        from_dmatrix = booster.predict(dtrain)
+        np.testing.assert_allclose(from_dmatrix, from_inplace)