[XGBoost] Add xgboostclassifier predict example in scala (#3693)

* Add xgboostclassifier predict example in scala with Iris Data Set from UCI
* Local test with spark 2.4 and spark 3.1
* Update README.md
* Add pr test scripts.

apps/run-scala-app-test.sh

@@ -190,10 +190,40 @@ echo "#App[Model-inference-example] Test 5.1: model-inference-flink: Image Class
 
 ./flink-1.7.2/bin/stop-cluster.sh
 
+echo "# Test 6.1 dllib nnframes: XGBoostClassifierTrainExample"
+#timer
+mkdir /tmp/data
+wget $FTP_URI/analytics-zoo-data/iris.data -P /tmp/data
+start=$(date "+%s")
+${SPARK_HOME}/bin/spark-submit \
+  --master local[4] \
+  --conf spark.task.cpus=2  \
+  --class com.intel.analytics.bigdl.dllib.examples.nnframes.xgboost.xgbClassifierTrainingExample \
+  ${BIGDL_ROOT}/scala/dllib/target/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \
+  /tmp/data/iris.data 2 200 /tmp/data/xgboost_model
+now=$(date "+%s")
+time9=$((now-start))
+echo "#App[Model-inference-example] Test 6.1: dllib nnframes: XGBoostClassifierTrainExample time used:$time9 seconds"
+
+echo "# Test 6.2 dllib nnframes: XGBoostClassifierPredictExample"
+#timer
+start=$(date "+%s")
+${SPARK_HOME}/bin/spark-submit \
+  --master local[4] \
+  --conf spark.task.cpus=2  \
+  --class com.intel.analytics.bigdl.dllib.examples.nnframes.xgboost.xgbClassifierPredictExample \
+  ${BIGDL_ROOT}/scala/dllib/target/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \
+  /tmp/data/iris.data  /tmp/data/xgboost_model
+now=$(date "+%s")
+time10=$((now-start))
+echo "#App[Model-inference-example] Test 6.2: dllib nnframes: XGBoostClassifierPredictExample time used:$time10 seconds"
+
 echo "#2 text-classification-training time used:$time2 seconds"
 echo "#3.1 text-classification-inference:SimpleDriver time used:$time3 seconds"
 #echo "#3.2 text-classification-inference:WebServiceDriver time used:$time4 seconds"
 echo "#4.1 recommendation-inference:SimpleScalaDriver time used:$time5 seconds"
 echo "#4.2 recommendation-inference:SimpleDriver time used:$time6 seconds"
 echo "#5.1 model-inference-flink:Text Classification time used:$time7 seconds"
 echo "#5.2 model-inference-flink:Image Classification time used:$time8 seconds"
+echo "#6.1: dllib nnframes: XGBoostClassifierTrainExample time used:$time9 seconds"
+echo "#6.2: dllib nnframes: XGBoostClassifierPredictExample time used:$time10 seconds"

scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/nnframes/xgboost/README.md

@@ -1,29 +1,31 @@
-# XGBoostClassifier Train Example
+# Prepare
 
 ## Environment
-- Spark 2.4
+- Spark 2.4 or Spark 3.1
 - BigDL 2.0 
 
 ## Data Prepare
 
 ### BigDL nightly build
 
 You can download [here](https://bigdl.readthedocs.io/en/latest/doc/release.html).
-You will get jar `bigdl-dllib-spark_2.4.6-0.14.0-build_time-jar-with-dependencies.jar`.
+For spark 2.4 you need `bigdl-dllib-spark_2.4.6-0.14.0-build_time-jar-with-dependencies.jar` or `bigdl-dllib-spark_3.1.2-0.14.0-build_time-jar-with-dependencies.jar` for spark 3.1  . 
+
 
 ### UCI iris.data
 
 You can download iris.data [here](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data).
 
+# XGBoostClassifier Train Example
 ## Run:
 
 command:
 ```
 spark-submit \
   --master local[2] \
-  --conf spark.task.cpus=2  \
+  --conf spark.task.cpus=2 \
   --class com.intel.analytics.bigdl.dllib.examples.nnframes.xgboost.xgbClassifierTrainingExample \
-  /path/to/BigDL/scala/dllib/target/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \
+  /path/to/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \
   /path/to/iris.data 2 100 /path/to/model/saved
 ```
 
@@ -46,4 +48,43 @@ parameters:
 - num_round : Int 
 - path_to_model_saved : String
 
-note: make sure num_threads is larger than spark.task.cpus.
+**note: make sure num_threads is larger than spark.task.cpus.**
+
+# XGBoostClassifier Predict Example
+## Run:
+```
+spark-submit \
+  --master local[4] \
+  --conf spark.task.cpus=2 \
+  --class com.intel.analytics.bigdl.dllib.examples.nnframes.xgboost.xgbClassifierPredictExample \
+  /path/to/bigdl-dllib-spark_2.4.6-0.14.0-SNAPSHOT-jar-with-dependencies.jar \
+  /path/to/iris.data 2 100 /path/to/model/saved
+```
+You will get output like:
+```
++------------+-----------+------------+-----------+-----------+--------------------+--------------------+----------+
+|sepal length|sepal width|petal length|petal width|      class|       rawPrediction|         probability|prediction|
++------------+-----------+------------+-----------+-----------+--------------------+--------------------+----------+
+|         5.1|        3.5|         1.4|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         4.9|        3.0|         1.4|        0.2|Iris-setosa|[2.94163084030151...|[0.98863482475280...|       0.0|
+|         4.7|        3.2|         1.3|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         4.6|        3.1|         1.5|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.0|        3.6|         1.4|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.4|        3.9|         1.7|        0.4|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         4.6|        3.4|         1.4|        0.3|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.0|        3.4|         1.5|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         4.4|        2.9|         1.4|        0.2|Iris-setosa|[2.94163084030151...|[0.97911602258682...|       0.0|
+|         4.9|        3.1|         1.5|        0.1|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.4|        3.7|         1.5|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         4.8|        3.4|         1.6|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         4.8|        3.0|         1.4|        0.1|Iris-setosa|[2.94163084030151...|[0.98863482475280...|       0.0|
+|         4.3|        3.0|         1.1|        0.1|Iris-setosa|[2.94163084030151...|[0.98863482475280...|       0.0|
+|         5.8|        4.0|         1.2|        0.2|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.7|        4.4|         1.5|        0.4|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.4|        3.9|         1.3|        0.4|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.1|        3.5|         1.4|        0.3|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.7|        3.8|         1.7|        0.3|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
+|         5.1|        3.8|         1.5|        0.3|Iris-setosa|[2.94163084030151...|[0.99256813526153...|       0.0|
++------------+-----------+------------+-----------+-----------+--------------------+--------------------+----------+
+only showing top 20 rows
+```

scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/example/nnframes/xgboost/xgbClassifierPredictExample.scala

@@ -0,0 +1,52 @@
+/*
+ * Copyright 2016 The BigDL Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.intel.analytics.bigdl.dllib.examples.nnframes.xgboost
+
+import com.intel.analytics.bigdl.dllib.NNContext
+import com.intel.analytics.bigdl.dllib.nnframes.XGBClassifierModel
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
+object xgbClassifierPredictExample {
+  def main(args: Array[String]): Unit = {
+    if (args.length < 2) {
+      println("Usage: program input_path model_path")
+      sys.exit(1)
+    }
+    val input_path = args(0)
+    val model_path = args(1)
+
+    val sc = NNContext.initNNContext()
+    val spark = SQLContext.getOrCreate(sc)
+
+    val schema = new StructType(Array(
+      StructField("sepal length", DoubleType, true),
+      StructField("sepal width", DoubleType, true),
+      StructField("petal length", DoubleType, true),
+      StructField("petal width", DoubleType, true),
+      StructField("class", StringType, true)))
+    val df = spark.read.schema(schema).csv(input_path)
+
+    val model = XGBClassifierModel.load(model_path)
+    model.setFeaturesCol(Array("sepal length", "sepal width", "petal length", "petal width"))
+
+    val results = model.transform(df)
+    results.show()
+
+    sc.stop()
+  }
+}

scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/nnframes/NNClassifier.scala

@@ -448,6 +448,11 @@ object XGBClassifierModel {
   def load(path: String, numClass: Int): XGBClassifierModel = {
     new XGBClassifierModel(XGBoostHelper.load(path, numClass))
   }
+
+  def load(path: String): XGBClassifierModel = {
+    new XGBClassifierModel(XGBoostClassificationModel.load(path))
+  }
+
 }
 
 /**