[jvm-packages] Allow for bypassing spark missing value check (#4805)

* Allow for bypassing spark missing value check

* Update documentation for dealing with missing values in spark xgboost

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/MissingValueHandlingSuite.scala

@@ -150,4 +150,32 @@ class MissingValueHandlingSuite extends FunSuite with PerTest {
       new XGBoostClassifier(paramMap).fit(inputDF)
     }
   }
+
+  test("specify a non-zero missing value but set allow_non_zero_missing_value " +
+    "does not stop application") {
+    val spark = ss
+    import spark.implicits._
+    ss.sparkContext.setLogLevel("INFO")
+    // spark uses 1.5 * (nnz + 1.0) < size as the condition to decide whether using sparse or dense
+    // vector,
+    val testDF = Seq(
+      (7.0f, 0.0f, -1.0f, 1.0f, 1.0),
+      (1.0f, 0.0f, 1.0f, 1.0f, 1.0),
+      (0.0f, 1.0f, 0.0f, 1.0f, 0.0),
+      (1.0f, 0.0f, 1.0f, 1.0f, 1.0),
+      (1.0f, -1.0f, 0.0f, 1.0f, 0.0),
+      (0.0f, 0.0f, 0.0f, 1.0f, 1.0),
+      (-1.0f, 0.0f, 0.0f, 1.0f, 1.0)
+    ).toDF("col1", "col2", "col3", "col4", "label")
+    val vectorAssembler = new VectorAssembler()
+      .setInputCols(Array("col1", "col2", "col3", "col4"))
+      .setOutputCol("features")
+    val inputDF = vectorAssembler.transform(testDF).select("features", "label")
+    inputDF.show()
+    val paramMap = List("eta" -> "1", "max_depth" -> "2",
+      "objective" -> "binary:logistic", "missing" -> -1.0f,
+      "num_workers" -> 1, "allow_non_zero_for_missing_value" -> "true").toMap
+    val model = new XGBoostClassifier(paramMap).fit(inputDF)
+    model.transform(inputDF).collect()
+  }
 }