XGBoostSageMakerEstimator.fit() returns libsvm exception when reading csv file.

I write my python code with Zeppelin 0.7.3 and Spark 2.3.0 on an EMR (emr-5.13.0) cluster to use SageMaker's XGBoost algorithm. The input data is a csv file. The first 3 lines of the file are (the first column is 0 or 1 for target class, and there is no header line):
`
0,9.6071,2,1,1,2,1,1,1,1,3,1,0,0,0,0,3,0,0,3,0,0,3,0,2,1,1,1
0,2.7296,3,1,1,1,1,1,0,0,8,1,0,0,0,0,3,0,0,3,0,0,3,0,1,1,1,1
0,10.3326,1,0,1,2,1,1,0,0,4,1,1,0,1,0,3,0,0,3,0,0,3,0,0,3,0,0`

I imported as the example does:
`
%pyspark
from pyspark import SparkContext, SparkConf
from sagemaker_pyspark import IAMRole, classpath_jars
from sagemaker_pyspark.algorithms import XGBoostSageMakerEstimator`

I initialize the estimator:
`%pyspark
xgboost_estimator = XGBoostSageMakerEstimator(
trainingInstanceType="ml.m3.xlarge",
trainingInstanceCount=1,
endpointInstanceType="ml.m3.xlarge",
endpointInitialInstanceCount=1)
xgboost_estimator.setObjective('multi:softprob')
xgboost_estimator.setNumRound(25)
xgboost_estimator.setNumClasses(2)`

I read the csv file with:
`training_data = spark.read.csv("s3://poc.sagemaker.myfile/myfile.csv", sep=",", header="false", inferSchema="true")`

training_data.show() gives:
`+---+-------+---+---+---+---+---+---+---+---+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+
|_c0| _c1|_c2|_c3|_c4|_c5|_c6|_c7|_c8|_c9|_c10|_c11|_c12|_c13|_c14|_c15|_c16|_c17|_c18|_c19|_c20|_c21|_c22|_c23|_c24|_c25|_c26|_c27|
+---+-------+---+---+---+---+---+---+---+---+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+----+
| 0| 7.1732| 1| 0| 1| 2| 2| 2| 0| 0| 5| 1| 1| 0| 1| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0|
| 0| 1.3087| 1| 0| 1| 2| 1| 1| 0| 0| 2| 1| 1| 0| 2| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0|
| 0| 3.3539| 1| 0| 1| 2| 2| 1| 0| 0| 6| 1| 1| 0| 0| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0|
| 0| 1.9767| 1| 0| 1| 1| 1| 1| 1| 1| 73| 1| 0| 0| 1| 0| 3| 0| 0| 3| 0| 1| 0| 1| 1| 0| 1| 1|
| 0| 5.7194| 1| 0| 1| 2| 1| 1| 0| 0| 3| 1| 0| 0| 0| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0|
| 0| 9.8398| 3| 1| 1| 2| 1| 1| 0| 0| 2| 1| 1| 0| 1| 0| 3| 0| 0| 3| 0| 2| 1| 1| 2| 1| 1| 1|
| 0| 2.4942| 1| 0| 1| 2| 1| 1| 0| 0| 377| 1| 1| 0| 2| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0| 3| 0| 0|
| 0| 7.9179| 4| 1| 1| 2| 1| 1| 0| 0| 4| 1| 1| 0| 2| 0| 3| 0| 0| 3| 0| 2| 0| 1| 2| 1| 1| 1|`

When I try to fit the xgboost model with:
`xgboost_model = xgboost_estimator.fit(training_data)`

The following exception returns:
`Traceback (most recent call last):
File "/tmp/zeppelin_pyspark-8068283221541252178.py", line 367, in
raise Exception(traceback.format_exc())
Exception: Traceback (most recent call last):
File "/tmp/zeppelin_pyspark-8068283221541252178.py", line 360, in
exec(code, _zcUserQueryNameSpace)
File "", line 1, in
File "/usr/local/lib/python2.7/site-packages/sagemaker_pyspark/SageMakerEstimator.py", line 253, in fit
return self._call_java("fit", dataset)
File "/usr/local/lib/python2.7/site-packages/sagemaker_pyspark/wrapper.py", line 76, in _call_java
java_value = super(SageMakerJavaWrapper, self)._call_java(name, *java_args)
File "/usr/lib/spark/python/pyspark/ml/wrapper.py", line 51, in _call_java
return _java2py(sc, m(*java_args))
File "/usr/lib/spark/python/lib/py4j-0.10.6-src.zip/py4j/java_gateway.py", line 1160, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/usr/lib/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/lib/spark/python/lib/py4j-0.10.6-src.zip/py4j/protocol.py", line 320, in get_return_value
format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o130.fit.
: java.io.IOException: Illegal schema for libsvm data, schema=StructType(StructField(_c0,IntegerType,true), StructField(_c1,DoubleType,true), StructField(_c2,IntegerType,true), StructField(_c3,IntegerType,true), StructField(_c4,IntegerType,true), StructField(_c5,IntegerType,true), StructField(_c6,IntegerType,true), StructField(_c7,IntegerType,true), StructField(_c8,IntegerType,true), StructField(_c9,IntegerType,true), StructField(_c10,IntegerType,true), StructField(_c11,IntegerType,true), StructField(_c12,IntegerType,true), StructField(_c13,IntegerType,true), StructField(_c14,IntegerType,true), StructField(_c15,IntegerType,true), StructField(_c16,IntegerType,true), StructField(_c17,IntegerType,true), StructField(_c18,IntegerType,true), StructField(_c19,IntegerType,true), StructField(_c20,IntegerType,true), StructField(_c21,IntegerType,true), StructField(_c22,IntegerType,true), StructField(_c23,IntegerType,true), StructField(_c24,IntegerType,true), StructField(_c25,IntegerType,true), StructField(_c26,IntegerType,true), StructField(_c27,IntegerType,true))
at org.apache.spark.ml.source.libsvm.LibSVMFileFormat.verifySchema(LibSVMRelation.scala:86)
at org.apache.spark.ml.source.libsvm.LibSVMFileFormat.prepareWrite(LibSVMRelation.scala:122)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:140)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:654)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:273)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:267)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:225)
at com.amazonaws.services.sagemaker.sparksdk.internal.DataUploader.writeData(DataUploader.scala:111)
at com.amazonaws.services.sagemaker.sparksdk.internal.DataUploader.uploadData(DataUploader.scala:90)
at com.amazonaws.services.sagemaker.sparksdk.SageMakerEstimator.fit(SageMakerEstimator.scala:299)
at com.amazonaws.services.sagemaker.sparksdk.SageMakerEstimator.fit(SageMakerEstimator.scala:175)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)`

Did I miss some steps so that the estimator use the libsvm libraries to process the csv input?

Reproduce the failure through XGBoostSageMakerEstimator.fit() with the Spark CSV DataFrame, then trace SageMakerEstimator.scala and DataUploader.scala around the upload path shown in the stack trace. Compare the DataFrame schema with Spark's LibSVMFileFormat.verifySchema; done means the CSV input behavior and required format are established and covered by an appropriate regression check.

Written by the indexing model from the issue text.