Java SparkSession.createDataFrame方法代码示例

import org.apache.spark.sql.SparkSession; //导入方法依赖的package包/类
private static Dataset<Row> generateData_week_timepoints_by_10_minutes(SparkSession spark) {
    StructField[] structFields = new StructField[1];
    org.apache.spark.sql.types.DataType dataType = DataTypes.IntegerType;
    String column = "timepoint";
    StructField structField = new StructField(column, dataType, true, Metadata.empty());
    structFields[0] = structField;

    StructType structType = new StructType(structFields);

    List<Row> rows = new ArrayList<>();

    int weekTotalMinutes = 7 * 24 * 60;
    int timepointIntervalMinutes = 10;
    for (int i = 0; i < weekTotalMinutes / timepointIntervalMinutes; i++) {
        Object[] objects = new Object[structFields.length];
        objects[0] = i;
        Row row = RowFactory.create(objects);
        rows.add(row);
    }

    Dataset<Row> df = spark.createDataFrame(rows, structType);
    return df;
}
 

import org.apache.spark.sql.SparkSession; //导入方法依赖的package包/类
private static Dataset<Row> generateData_numbers_1k(SparkSession spark) {
    StructField[] structFields = new StructField[1];
    org.apache.spark.sql.types.DataType dataType = DataTypes.IntegerType;
    String column = "number";
    StructField structField = new StructField(column, dataType, true, Metadata.empty());
    structFields[0] = structField;

    StructType structType = new StructType(structFields);

    List<Row> rows = new ArrayList<>();

    for (int i = 0; i <= 1000; i++) {
        Object[] objects = new Object[structFields.length];
        objects[0] = i;
        Row row = RowFactory.create(objects);
        rows.add(row);
    }

    Dataset<Row> df = spark.createDataFrame(rows, structType);
    return df;
}
 

import org.apache.spark.sql.SparkSession; //导入方法依赖的package包/类
public static void main(String[] args) {
   SparkSession spark = SparkSession
     .builder().master("local").config("spark.sql.warehouse.dir", "file:///C:/Users/sumit.kumar/Downloads/bin/warehouse")
     .appName("JavaEstimatorTransformerParamExample")
     .getOrCreate();
   Logger rootLogger = LogManager.getRootLogger();
rootLogger.setLevel(Level.WARN);
   // $example on$
   // Prepare training data.
   List<Row> dataTraining = Arrays.asList(
       RowFactory.create(1.0, Vectors.dense(0.0, 1.1, 0.1)),
       RowFactory.create(0.0, Vectors.dense(2.0, 1.0, -1.0)),
       RowFactory.create(0.0, Vectors.dense(2.0, 1.3, 1.0)),
       RowFactory.create(1.0, Vectors.dense(0.0, 1.2, -0.5))
   );
   StructType schema = new StructType(new StructField[]{
       new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
       new StructField("features", new VectorUDT(), false, Metadata.empty())
   });
   Dataset<Row> training = spark.createDataFrame(dataTraining, schema);

   // Create a LogisticRegression instance. This instance is an Estimator.
   LogisticRegression lr = new LogisticRegression();
   // Print out the parameters, documentation, and any default values.
   System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");

   // We may set parameters using setter methods.
   lr.setMaxIter(10).setRegParam(0.01);

   // Learn a LogisticRegression model. This uses the parameters stored in lr.
   LogisticRegressionModel model1 = lr.fit(training);
   // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
   // we can view the parameters it used during fit().
   // This prints the parameter (name: value) pairs, where names are unique IDs for this
   // LogisticRegression instance.
   System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());

   // We may alternatively specify parameters using a ParamMap.
   ParamMap paramMap = new ParamMap()
     .put(lr.maxIter().w(20))  // Specify 1 Param.
     .put(lr.maxIter(), 30)  // This overwrites the original maxIter.
     .put(lr.regParam().w(0.1), lr.threshold().w(0.55));  // Specify multiple Params.

   // One can also combine ParamMaps.
   ParamMap paramMap2 = new ParamMap()
     .put(lr.probabilityCol().w("myProbability"));  // Change output column name
   ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);

   // Now learn a new model using the paramMapCombined parameters.
   // paramMapCombined overrides all parameters set earlier via lr.set* methods.
   LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
   System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());

   // Prepare test documents.
   List<Row> dataTest = Arrays.asList(
       RowFactory.create(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
       RowFactory.create(0.0, Vectors.dense(3.0, 2.0, -0.1)),
       RowFactory.create(1.0, Vectors.dense(0.0, 2.2, -1.5))
   );
   Dataset<Row> test = spark.createDataFrame(dataTest, schema);

   // Make predictions on test documents using the Transformer.transform() method.
   // LogisticRegression.transform will only use the 'features' column.
   // Note that model2.transform() outputs a 'myProbability' column instead of the usual
   // 'probability' column since we renamed the lr.probabilityCol parameter previously.
   Dataset<Row> results = model2.transform(test);
   Dataset<Row> rows = results.select("features", "label", "myProbability", "prediction");
   for (Row r: rows.collectAsList()) {
     System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
       + ", prediction=" + r.get(3));
   }
   // $example off$

   spark.stop();
 }