Java Spark MLlib: "ERROR OWLQN: Failure! Reset History: breeze.optimize.NaNHistory:" error occurred for logistic regression in the ml library

Question

I just tried using the Apache Spark ml library for logistic regression, but whenever I tried it, an error message appeared, for example

"ERROR OWLQN: Failed! Reset History: breeze.optimize.NaNHistory:"

An example dataset for logistic regression is as follows:

+-----+---------+---------+---------+--------+-------------+
|state|dayOfWeek|hourOfDay|minOfHour|secOfMin|     features|
+-----+---------+---------+---------+--------+-------------+
|  1.0|      7.0|      0.0|      0.0|     0.0|(4,[0],[7.0])|

And for logistic regression there is a code:

//Data Set
StructType schema = new StructType(
new StructField[]{
    new StructField("state", DataTypes.DoubleType, false, Metadata.empty()),
    new StructField("dayOfWeek", DataTypes.DoubleType, false, Metadata.empty()),
    new StructField("hourOfDay", DataTypes.DoubleType, false, Metadata.empty()),
    new StructField("minOfHour", DataTypes.DoubleType, false, Metadata.empty()),
    new StructField("secOfMin", DataTypes.DoubleType, false, Metadata.empty())
});
List<Row> dataFromRDD = bucketsForMLs.map(p -> {
    return RowFactory.create(p.label(), p.features().apply(0), p.features().apply(1), p.features().apply(2), p.features().apply(3));
}).collect();

Dataset<Row> stateDF = sparkSession.createDataFrame(dataFromRDD, schema);
String[] featureCols = new String[]{"dayOfWeek", "hourOfDay", "minOfHour", "secOfMin"};
VectorAssembler vectorAssembler = new VectorAssembler().setInputCols(featureCols).setOutputCol("features");
Dataset<Row> stateDFWithFeatures = vectorAssembler.transform(stateDF);

StringIndexer labelIndexer = new StringIndexer().setInputCol("state").setOutputCol("label");
Dataset<Row> stateDFWithLabelAndFeatures = labelIndexer.fit(stateDFWithFeatures).transform(stateDFWithFeatures);

MLRExecutionForDF mlrExe = new MLRExecutionForDF(javaSparkContext);
mlrExe.execute(stateDFWithLabelAndFeatures);

// Logistic Regression part
LogisticRegressionModel lrModel = new LogisticRegression().setMaxIter(maxItr).setRegParam(regParam).setElasticNetParam(elasticNetParam)  
// This part would occur error
.fit(stateDFWithLabelAndFeatures);