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/*
* 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.functions.ternary;
import java.util.HashMap;
import org.junit.Test;
import org.apache.sysds.api.DMLScript;
import org.apache.sysds.common.Types.ExecMode;
import org.apache.sysds.lops.LopProperties.ExecType;
import org.apache.sysds.runtime.matrix.data.MatrixValue.CellIndex;
import org.apache.sysds.test.AutomatedTestBase;
import org.apache.sysds.test.TestConfiguration;
import org.apache.sysds.test.TestUtils;
/**
*
*/
public class CovarianceWeightsTest extends AutomatedTestBase
{
private final static String TEST_NAME = "CovarianceWeights";
private final static String TEST_DIR = "functions/ternary/";
private final static String TEST_CLASS_DIR = TEST_DIR + CovarianceWeightsTest.class.getSimpleName() + "/";
private final static double eps = 1e-10;
private final static int rows = 1871;
private final static int maxVal = 7;
private final static double sparsity1 = 0.65;
private final static double sparsity2 = 0.05;
@Override
public void setUp()
{
TestUtils.clearAssertionInformation();
addTestConfiguration(TEST_NAME, new TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[] { "R" }) );
}
@Test
public void testCovarianceWeightsDenseCP()
{
runCovarianceTest(false, ExecType.CP);
}
@Test
public void testCovarianceWeightsSparseCP()
{
runCovarianceTest(true, ExecType.CP);
}
@Test
public void testCovarianceWeightsDenseSP()
{
runCovarianceTest(false, ExecType.SPARK);
}
@Test
public void testCovarianceWeightsSparseSP()
{
runCovarianceTest(true, ExecType.SPARK);
}
/**
*
* @param sparseM1
* @param sparseM2
* @param instType
*/
private void runCovarianceTest( boolean sparse, ExecType et)
{
//rtplatform for MR
ExecMode platformOld = rtplatform;
switch( et ){
case SPARK: rtplatform = ExecMode.SPARK; break;
default: rtplatform = ExecMode.HYBRID; break;
}
boolean sparkConfigOld = DMLScript.USE_LOCAL_SPARK_CONFIG;
if( rtplatform == ExecMode.SPARK )
DMLScript.USE_LOCAL_SPARK_CONFIG = true;
try
{
TestConfiguration config = getTestConfiguration(TEST_NAME);
loadTestConfiguration(config);
String HOME = SCRIPT_DIR + TEST_DIR;
fullDMLScriptName = HOME + TEST_NAME + ".dml";
programArgs = new String[]{"-args", input("A"),
input("B"), input("C"), output("R")};
fullRScriptName = HOME + TEST_NAME + ".R";
rCmd = "Rscript" + " " + fullRScriptName + " " + inputDir() + " " + expectedDir();
//generate actual dataset (always dense because values <=0 invalid)
double sparsitya = sparse ? sparsity2 : sparsity1;
double[][] A = getRandomMatrix(rows, 1, 1, maxVal, sparsitya, 7);
writeInputMatrixWithMTD("A", A, true);
double[][] B = getRandomMatrix(rows, 1, 1, 1, 1.0, 34);
writeInputMatrixWithMTD("B", B, true);
double[][] C = getRandomMatrix(rows, 1, 1, 1, 1.0, 8623);
writeInputMatrixWithMTD("C", C, true);
runTest(true, false, null, -1);
runRScript(true);
//compare matrices
HashMap<CellIndex, Double> dmlfile = readDMLMatrixFromOutputDir("R");
HashMap<CellIndex, Double> rfile = readRMatrixFromExpectedDir("R");
TestUtils.compareMatrices(dmlfile, rfile, eps, "Stat-DML", "Stat-R");
}
finally
{
rtplatform = platformOld;
DMLScript.USE_LOCAL_SPARK_CONFIG = sparkConfigOld;
}
}
}