Examples with MLResults org.apache.sysml.api.mlcontext.MLResults used on opensource projects

Example 16 with MLResults

use of org.apache.sysml.api.mlcontext.MLResults in project incubator-systemml by apache.

the class MLContextTest method testOutputDataFramePYDMLVectorWithIDColumn.

@Test
public void testOutputDataFramePYDMLVectorWithIDColumn() {
    System.out.println("MLContextTest - output DataFrame PYDML, vector with ID column");
    String s = "M = full('1 2 3 4', rows=2, cols=2)";
    Script script = pydml(s).out("M");
    MLResults results = ml.execute(script);
    Dataset<Row> dataFrame = results.getDataFrameVectorWithIDColumn("M");
    List<Row> list = dataFrame.collectAsList();
    Row row1 = list.get(0);
    Assert.assertEquals(1.0, row1.getDouble(0), 0.0);
    Assert.assertArrayEquals(new double[] { 1.0, 2.0 }, ((Vector) row1.get(1)).toArray(), 0.0);
    Row row2 = list.get(1);
    Assert.assertEquals(2.0, row2.getDouble(0), 0.0);
    Assert.assertArrayEquals(new double[] { 3.0, 4.0 }, ((Vector) row2.get(1)).toArray(), 0.0);
}

Example 17 with MLResults

use of org.apache.sysml.api.mlcontext.MLResults in project incubator-systemml by apache.

the class MLContextTest method testOutputJavaRDDStringCSVSparseDML.

/**
 * Reading from dense and sparse matrices is handled differently, so we have
 * tests for both dense and sparse matrices.
 */
@Test
public void testOutputJavaRDDStringCSVSparseDML() {
    System.out.println("MLContextTest - output Java RDD String CSV Sparse DML");
    String s = "M = matrix(0, rows=10, cols=10); M[1,1]=1; M[1,2]=2; M[2,1]=3; M[2,2]=4; print(toString(M));";
    Script script = dml(s).out("M");
    MLResults results = ml.execute(script);
    JavaRDD<String> javaRDDStringCSV = results.getJavaRDDStringCSV("M");
    List<String> lines = javaRDDStringCSV.collect();
    Assert.assertEquals("1.0,2.0", lines.get(0));
    Assert.assertEquals("3.0,4.0", lines.get(1));
}

Example 18 with MLResults

use of org.apache.sysml.api.mlcontext.MLResults in project incubator-systemml by apache.

the class MLContextTest method testOutputDataFramePYDML.

@Test
public void testOutputDataFramePYDML() {
    System.out.println("MLContextTest - output DataFrame PYDML");
    String s = "M = full('1 2 3 4', rows=2, cols=2)";
    Script script = pydml(s).out("M");
    MLResults results = ml.execute(script);
    Dataset<Row> dataFrame = results.getDataFrame("M");
    List<Row> list = dataFrame.collectAsList();
    Row row1 = list.get(0);
    Assert.assertEquals(1.0, row1.getDouble(0), 0.0);
    Assert.assertEquals(1.0, row1.getDouble(1), 0.0);
    Assert.assertEquals(2.0, row1.getDouble(2), 0.0);
    Row row2 = list.get(1);
    Assert.assertEquals(2.0, row2.getDouble(0), 0.0);
    Assert.assertEquals(3.0, row2.getDouble(1), 0.0);
    Assert.assertEquals(4.0, row2.getDouble(2), 0.0);
}

Example 19 with MLResults

use of org.apache.sysml.api.mlcontext.MLResults in project incubator-systemml by apache.

the class MLContextTest method testOutputDataFrameOfVectorsDML.

@Test
public void testOutputDataFrameOfVectorsDML() {
    System.out.println("MLContextTest - output DataFrame of vectors DML");
    String s = "m=matrix('1 2 3 4',rows=2,cols=2);";
    Script script = dml(s).out("m");
    MLResults results = ml.execute(script);
    Dataset<Row> df = results.getDataFrame("m", true);
    Dataset<Row> sortedDF = df.sort(RDDConverterUtils.DF_ID_COLUMN);
    // verify column types
    StructType schema = sortedDF.schema();
    StructField[] fields = schema.fields();
    StructField idColumn = fields[0];
    StructField vectorColumn = fields[1];
    Assert.assertTrue(idColumn.dataType() instanceof DoubleType);
    Assert.assertTrue(vectorColumn.dataType() instanceof VectorUDT);
    List<Row> list = sortedDF.collectAsList();
    Row row1 = list.get(0);
    Assert.assertEquals(1.0, row1.getDouble(0), 0.0);
    Vector v1 = (DenseVector) row1.get(1);
    double[] arr1 = v1.toArray();
    Assert.assertArrayEquals(new double[] { 1.0, 2.0 }, arr1, 0.0);
    Row row2 = list.get(1);
    Assert.assertEquals(2.0, row2.getDouble(0), 0.0);
    Vector v2 = (DenseVector) row2.get(1);
    double[] arr2 = v2.toArray();
    Assert.assertArrayEquals(new double[] { 3.0, 4.0 }, arr2, 0.0);
}

Example 20 with MLResults

use of org.apache.sysml.api.mlcontext.MLResults in project incubator-systemml by apache.

the class MLContextTest method testOutputDataFrameDMLDoublesWithIDColumn.

@Test
public void testOutputDataFrameDMLDoublesWithIDColumn() {
    System.out.println("MLContextTest - output DataFrame DML, doubles with ID column");
    String s = "M = matrix('1 2 3 4', rows=2, cols=2);";
    Script script = dml(s).out("M");
    MLResults results = ml.execute(script);
    Dataset<Row> dataFrame = results.getDataFrameDoubleWithIDColumn("M");
    List<Row> list = dataFrame.collectAsList();
    Row row1 = list.get(0);
    Assert.assertEquals(1.0, row1.getDouble(0), 0.0);
    Assert.assertEquals(1.0, row1.getDouble(1), 0.0);
    Assert.assertEquals(2.0, row1.getDouble(2), 0.0);
    Row row2 = list.get(1);
    Assert.assertEquals(2.0, row2.getDouble(0), 0.0);
    Assert.assertEquals(3.0, row2.getDouble(1), 0.0);
    Assert.assertEquals(4.0, row2.getDouble(2), 0.0);
}