use of org.apache.sysml.api.mlcontext.Script in project incubator-systemml by apache.
the class MLContextTest method testInputTupleSeqWithMetadataDML.
@SuppressWarnings({ "rawtypes", "unchecked" })
@Test
public void testInputTupleSeqWithMetadataDML() {
System.out.println("MLContextTest - Tuple sequence with metadata DML");
List<String> list1 = new ArrayList<String>();
list1.add("1,2");
list1.add("3,4");
JavaRDD<String> javaRDD1 = sc.parallelize(list1);
RDD<String> rdd1 = JavaRDD.toRDD(javaRDD1);
List<String> list2 = new ArrayList<String>();
list2.add("5,6");
list2.add("7,8");
JavaRDD<String> javaRDD2 = sc.parallelize(list2);
RDD<String> rdd2 = JavaRDD.toRDD(javaRDD2);
MatrixMetadata mm1 = new MatrixMetadata(2, 2);
MatrixMetadata mm2 = new MatrixMetadata(2, 2);
Tuple3 tuple1 = new Tuple3("m1", rdd1, mm1);
Tuple3 tuple2 = new Tuple3("m2", rdd2, mm2);
List tupleList = new ArrayList();
tupleList.add(tuple1);
tupleList.add(tuple2);
Seq seq = JavaConversions.asScalaBuffer(tupleList).toSeq();
Script script = dml("print('sums: ' + sum(m1) + ' ' + sum(m2));").in(seq);
setExpectedStdOut("sums: 10.0 26.0");
ml.execute(script);
}
use of org.apache.sysml.api.mlcontext.Script in project incubator-systemml by apache.
the class MLContextTest method testFunctionNoReturnValuePYDML.
@Test
public void testFunctionNoReturnValuePYDML() {
System.out.println("MLContextTest - function with no return value PYDML");
String s = "def hello():\n\tprint('no return value')\nhello()";
Script script = pydml(s);
setExpectedStdOut("no return value");
ml.execute(script);
}
use of org.apache.sysml.api.mlcontext.Script in project incubator-systemml by apache.
the class MLContextTest method testDataFrameSumPYDMLVectorWithIDColumn.
@Test
public void testDataFrameSumPYDMLVectorWithIDColumn() {
System.out.println("MLContextTest - DataFrame sum PYDML, vector with ID column");
List<Tuple2<Double, Vector>> list = new ArrayList<Tuple2<Double, Vector>>();
list.add(new Tuple2<Double, Vector>(1.0, Vectors.dense(1.0, 2.0, 3.0)));
list.add(new Tuple2<Double, Vector>(2.0, Vectors.dense(4.0, 5.0, 6.0)));
list.add(new Tuple2<Double, Vector>(3.0, Vectors.dense(7.0, 8.0, 9.0)));
JavaRDD<Tuple2<Double, Vector>> javaRddTuple = sc.parallelize(list);
JavaRDD<Row> javaRddRow = javaRddTuple.map(new DoubleVectorRow());
List<StructField> fields = new ArrayList<StructField>();
fields.add(DataTypes.createStructField(RDDConverterUtils.DF_ID_COLUMN, DataTypes.DoubleType, true));
fields.add(DataTypes.createStructField("C1", new VectorUDT(), true));
StructType schema = DataTypes.createStructType(fields);
Dataset<Row> dataFrame = spark.createDataFrame(javaRddRow, schema);
MatrixMetadata mm = new MatrixMetadata(MatrixFormat.DF_VECTOR_WITH_INDEX);
Script script = pydml("print('sum: ' + sum(M))").in("M", dataFrame, mm);
setExpectedStdOut("sum: 45.0");
ml.execute(script);
}
use of org.apache.sysml.api.mlcontext.Script in project incubator-systemml by apache.
the class MLContextTest method testOutputListStringCSVDenseDML.
@Test
public void testOutputListStringCSVDenseDML() {
System.out.println("MLContextTest - output List String CSV Dense DML");
String s = "M = matrix('1 2 3 4', rows=2, cols=2); print(toString(M));";
Script script = dml(s).out("M");
MLResults results = ml.execute(script);
MatrixObject mo = results.getMatrixObject("M");
List<String> lines = MLContextConversionUtil.matrixObjectToListStringCSV(mo);
Assert.assertEquals("1.0,2.0", lines.get(0));
Assert.assertEquals("3.0,4.0", lines.get(1));
}
use of org.apache.sysml.api.mlcontext.Script in project incubator-systemml by apache.
the class MLContextTest method testFunctionNoReturnValueForceFunctionCallPYDML.
@Test
public void testFunctionNoReturnValueForceFunctionCallPYDML() {
System.out.println("MLContextTest - function with no return value, force function call PYDML");
String s = "def hello():\n\tif (1==1):\n\t\tprint('')\n\tprint('no return value, force function call')\nhello()";
Script script = pydml(s);
setExpectedStdOut("no return value, force function call");
ml.execute(script);
}
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