[dask] Fix missing value for scikit-learn interface. (#5435)

tests/python/test_with_dask.py

@@ -97,6 +97,58 @@ def test_from_dask_array():
             assert np.all(single_node_predt == from_arr.compute())
 
 
+def test_dask_missing_value_reg():
+    with LocalCluster(n_workers=5) as cluster:
+        with Client(cluster) as client:
+            X_0 = np.ones((20 // 2, kCols))
+            X_1 = np.zeros((20 // 2, kCols))
+            X = np.concatenate([X_0, X_1], axis=0)
+            np.random.shuffle(X)
+            X = da.from_array(X)
+            X = X.rechunk(20, 1)
+            y = da.random.randint(0, 3, size=20)
+            y.rechunk(20)
+            regressor = xgb.dask.DaskXGBRegressor(verbosity=1, n_estimators=2,
+                                                  missing=0.0)
+            regressor.client = client
+            regressor.set_params(tree_method='hist')
+            regressor.fit(X, y, eval_set=[(X, y)])
+            dd_predt = regressor.predict(X).compute()
+
+            np_X = X.compute()
+            np_predt = regressor.get_booster().predict(
+                xgb.DMatrix(np_X, missing=0.0))
+            np.testing.assert_allclose(np_predt, dd_predt)
+
+
+def test_dask_missing_value_cls():
+    # Multi-class doesn't handle empty DMatrix well.  So we use lesser workers.
+    with LocalCluster(n_workers=2) as cluster:
+        with Client(cluster) as client:
+            X_0 = np.ones((kRows // 2, kCols))
+            X_1 = np.zeros((kRows // 2, kCols))
+            X = np.concatenate([X_0, X_1], axis=0)
+            np.random.shuffle(X)
+            X = da.from_array(X)
+            X = X.rechunk(20, None)
+            y = da.random.randint(0, 3, size=kRows)
+            y = y.rechunk(20, 1)
+            cls = xgb.dask.DaskXGBClassifier(verbosity=1, n_estimators=2,
+                                             tree_method='hist',
+                                             missing=0.0)
+            cls.client = client
+            cls.fit(X, y, eval_set=[(X, y)])
+            dd_predt = cls.predict(X).compute()
+
+            np_X = X.compute()
+            np_predt = cls.get_booster().predict(
+                xgb.DMatrix(np_X, missing=0.0))
+            np.testing.assert_allclose(np_predt, dd_predt)
+
+            cls = xgb.dask.DaskXGBClassifier()
+            assert hasattr(cls, 'missing')
+
+
 def test_dask_regressor():
     with LocalCluster(n_workers=5) as cluster:
         with Client(cluster) as client: