| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| */ |
| |
| package org.apache.sysds.test.applications; |
| |
| import java.util.ArrayList; |
| import java.util.Arrays; |
| import java.util.Collection; |
| import java.util.HashMap; |
| import java.util.List; |
| |
| import org.junit.Test; |
| import org.junit.runner.RunWith; |
| import org.junit.runners.Parameterized; |
| import org.junit.runners.Parameterized.Parameters; |
| import org.apache.sysds.runtime.matrix.data.MatrixValue.CellIndex; |
| import org.apache.sysds.test.AutomatedTestBase; |
| import org.apache.sysds.test.TestUtils; |
| |
| @RunWith(value = Parameterized.class) |
| @net.jcip.annotations.NotThreadSafe |
| public class LinearRegressionTest extends AutomatedTestBase { |
| |
| protected final static String TEST_DIR = "applications/linear_regression/"; |
| protected final static String TEST_NAME = "LinearRegression"; |
| protected String TEST_CLASS_DIR = TEST_DIR + LinearRegressionTest.class.getSimpleName() + "/"; |
| |
| protected int numRecords, numFeatures; |
| protected double sparsity; |
| |
| public LinearRegressionTest(int rows, int cols, double sp) { |
| numRecords = rows; |
| numFeatures = cols; |
| sparsity = sp; |
| } |
| |
| @Parameters |
| public static Collection<Object[]> data() { |
| Object[][] data = new Object[][] { |
| //sparse tests (sparsity=0.01) |
| {100, 50, 0.01}, {1000, 500, 0.01}, {10000, 750, 0.01}, {100000, 1000, 0.01}, |
| //dense tests (sparsity=0.7) |
| {100, 50, 0.7}, {1000, 500, 0.7}, {10000, 750, 0.7} }; |
| return Arrays.asList(data); |
| } |
| |
| @Override |
| public void setUp() |
| { |
| addTestConfiguration(TEST_CLASS_DIR, TEST_NAME); |
| } |
| |
| @Test |
| public void testLinearRegression() { |
| System.out.println("------------ BEGIN " + TEST_NAME + " TEST WITH {" + numRecords + ", " + numFeatures |
| + ", " + sparsity + "} ------------"); |
| |
| int rows = numRecords; |
| int cols = numFeatures; |
| |
| getAndLoadTestConfiguration(TEST_NAME); |
| |
| List<String> proArgs = new ArrayList<>(); |
| |
| proArgs.add("-stats"); |
| proArgs.add("-args"); |
| proArgs.add(input("v")); |
| proArgs.add(input("y")); |
| proArgs.add(Double.toString(Math.pow(10, -8))); |
| proArgs.add(output("w")); |
| programArgs = proArgs.toArray(new String[proArgs.size()]); |
| |
| fullDMLScriptName = getScript(); |
| |
| rCmd = getRCmd(inputDir(), Double.toString(Math.pow(10, -8)), expectedDir()); |
| |
| double[][] v = getRandomMatrix(rows, cols, 0, 1, sparsity, -1); |
| double[][] y = getRandomMatrix(rows, 1, 1, 10, 1, -1); |
| writeInputMatrixWithMTD("v", v, true); |
| writeInputMatrixWithMTD("y", y, true); |
| |
| /* |
| * Expected number of jobs: |
| * Rand - 1 job |
| * Computation before while loop - 4 jobs |
| * While loop iteration - 10 jobs |
| * Final output write - 1 job |
| */ |
| int expectedNumberOfJobs = 16; |
| runTest(true, EXCEPTION_NOT_EXPECTED, null, expectedNumberOfJobs); |
| |
| runRScript(true); |
| |
| HashMap<CellIndex, Double> wR = readRMatrixFromFS("w"); |
| HashMap<CellIndex, Double> wSYSTEMDS= readDMLMatrixFromHDFS("w"); |
| TestUtils.compareMatrices(wR, wSYSTEMDS, Math.pow(10, -10), "wR", "wSYSTEMDS"); |
| } |
| } |