src/test/java/org/apache/sysds/test/component/compress/colgroup/JolEstimateOLETest.java

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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
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 *
 *   http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.sysds.test.component.compress.colgroup;

import java.util.ArrayList;
import java.util.Collection;

import org.apache.sysds.runtime.compress.colgroup.ColGroup.CompressionType;
import org.apache.sysds.runtime.matrix.data.MatrixBlock;
import org.apache.sysds.runtime.util.DataConverter;
import org.apache.sysds.test.TestUtils;
import org.apache.sysds.test.component.compress.CompressibleInputGenerator;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
import org.junit.runners.Parameterized.Parameters;

@RunWith(value = Parameterized.class)
public class JolEstimateOLETest extends JolEstimateTest {

	@Parameters
	public static Collection<Object[]> data() {
		ArrayList<Object[]> tests = new ArrayList<>();

		MatrixBlock mb;
		// base tests
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 1 } });
		tests.add(new Object[] { mb, new int[] { 1, 2, 2, 1 }, 0 });
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0 } });
		tests.add(new Object[] { mb, new int[] { 0, 1, 0, 0 }, 0 });
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 0 } });
		tests.add(new Object[] { mb, new int[] { 0, 1, 0, 0 }, 0 });

		// The size of the compression increase at repeated values.
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 5, 0 } });
		tests.add(new Object[] { mb, new int[] { 1, 2, 2, 1 }, 0 });
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 5, 5, 0 } });
		tests.add(new Object[] { mb, new int[] { 1, 2, 3, 1 }, 0 });
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 5, 5, 5, 0 } });
		tests.add(new Object[] { mb, new int[] { 1, 2, 4, 1 }, 0 });
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 5, 5, 5, 5, 5, 5 } });
		tests.add(new Object[] { mb, new int[] { 1, 2, 7, 1 }, 0 });

		// all values grow by 1 if new value is introduced
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 5, 7, 0 } });
		tests.add(new Object[] { mb, new int[] { 2, 3, 4, 2 }, 0 });
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 5, 2, 1, 0 } });
		tests.add(new Object[] { mb, new int[] { 3, 4, 6, 3 }, 0 });
		mb = DataConverter.convertToMatrixBlock(new double[][] { { 0, 0, 0, 0, 5, 2, 1, 3, 6, 7 } });
		tests.add(new Object[] { mb, new int[] { 6, 7, 12, 6 }, 0 });

		// Dense random... Horrible compression
		mb = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(1, 100, 0, 100, 1.0, 7));
		tests.add(new Object[] { mb, new int[] { 100, 100 + 1, 200, 100 }, 0 });
		mb = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(1, 1000, 0, 100, 1.0, 7));
		tests.add(new Object[] { mb, new int[] { 1000, 1000 + 1, 2000, 1000 }, 0 });
		mb = DataConverter.convertToMatrixBlock(TestUtils.generateTestMatrix(1, 10000, 0, 100, 1.0, 7));
		tests.add(new Object[] { mb, new int[] { 10000, 10000 + 1, 20000, 10000 }, 0 });

		// Random rounded numbers dense
		mb = DataConverter.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 1523, 0, 99, 1.0, 7)));
		tests.add(new Object[] { mb, new int[] { 99, 100, 1616, 99 }, 0 });
		mb = DataConverter.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 4000, 0, 255, 1.0, 7)));
		tests.add(new Object[] { mb, new int[] { 255, 256, 4250, 255 }, 0 });

		// Sparse rounded numbers
		mb = DataConverter.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 1523, 0, 99, 0.1, 7)));
		tests.add(new Object[] { mb, new int[] { 76, 77, 225, 76 }, 0 });
		mb = DataConverter
				.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 1621, 0, 99, 0.1, 142)));
		tests.add(new Object[] { mb, new int[] { 81, 82, 238, 81 }, 0 });
		mb = DataConverter
				.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 2321, 0, 99, 0.1, 512)));
		tests.add(new Object[] { mb, new int[] { 92, 93, 332, 92 }, 0 });
		mb = DataConverter.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 4000, 0, 255, 0.1, 7)));
		tests.add(new Object[] { mb, new int[] { 195, 196, 573, 195 }, 0 });

		mb = DataConverter.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 1523, 0, 99, 0.5, 7)));
		tests.add(new Object[] { mb, new int[] { 98, 99, 826, 99 }, 0 });
		mb = DataConverter
				.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 1621, 0, 99, 0.5, 142)));
		tests.add(new Object[] { mb, new int[] { 99, 100, 913, 99 }, 0 });
		mb = DataConverter
				.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 2321, 0, 99, 0.5, 512)));
		tests.add(new Object[] { mb, new int[] { 99, 100, 1292, 99 }, 0 });
		mb = DataConverter.convertToMatrixBlock(TestUtils.round(TestUtils.generateTestMatrix(1, 4000, 0, 255, 0.5, 7)));
		tests.add(new Object[] { mb, new int[] { 255, 256, 2208, 255 }, 0 });

		// Paper
		mb = DataConverter.convertToMatrixBlock(
				new double[][] { { 7, 3, 7, 7, 3, 7, 3, 3, 7, 3 }, { 6, 4, 6, 5, 4, 5, 4, 4, 6, 4 } });
		tests.add(new Object[] { mb, new int[] { 6, 4, 13, 3 }, 0 });

		// Dream Inputs
		int[] cols = new int[] { 2, 6, 111 };
		int[] rows = new int[] { 10, 121, 513 };
		int[] unique = new int[] { 3, 5 };
		for (int y : cols) {
			for (int x : rows) {
				for (int u : unique) {
					mb = CompressibleInputGenerator.getInput(x, y, CompressionType.OLE, u, 1.0, 5);
					tests.add(new Object[] { mb, new int[] { u * y, u + 1, x + u, u }, 0 });
				}
			}
		}

		// Sparse test.
		mb = CompressibleInputGenerator.getInput(571, 1, CompressionType.OLE, 40, 0.6, 5);
		tests.add(new Object[] { mb, new int[] { 40 * 1, 40 + 1, ((571 + 40) / 10) * 6, 40 }, 0 });

		return tests;
	}

	public JolEstimateOLETest(MatrixBlock mb, int[] sizes, int tolerance) {
		super(mb, sizes, tolerance);
	}

	@Override
	public CompressionType getCT() {
		return ole;
	}
}