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* Licensed to the Apache Software Foundation (ASF) under one
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* 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
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package org.apache.sysds.test.functions.builtin;
import org.junit.Test;
import org.apache.sysds.api.DMLScript;
import org.apache.sysds.common.Types;
import org.apache.sysds.hops.OptimizerUtils;
import org.apache.sysds.lops.LopProperties;
import org.apache.sysds.runtime.matrix.data.MatrixValue;
import org.apache.sysds.test.AutomatedTestBase;
import org.apache.sysds.test.TestConfiguration;
import org.apache.sysds.test.TestUtils;
import java.util.HashMap;
public class BuiltinMulticlassSVMTest extends AutomatedTestBase {
private final static String TEST_NAME = "multisvm";
private final static String TEST_DIR = "functions/builtin/";
private static final String TEST_CLASS_DIR = TEST_DIR + BuiltinMulticlassSVMTest.class.getSimpleName() + "/";
private final static double eps = 0.001;
private final static int rows = 1000;
private final static int colsX = 200;
private final static double spSparse = 0.01;
private final static double spDense = 0.7;
private final static int max_iter = 10;
@Override
public void setUp() {
TestUtils.clearAssertionInformation();
addTestConfiguration(TEST_NAME, new TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[] {"C"}));
}
@Test
public void testMSVMDense() {
runMSVMTest(false, false, eps, 1.0, max_iter, LopProperties.ExecType.CP);
}
@Test
public void testMSVMSparse() {
runMSVMTest(true, false, eps, 1.0, max_iter, LopProperties.ExecType.CP);
}
@Test
public void testMSVMInterceptSpark() {
runMSVMTest(true, true, eps, 1.0, max_iter, LopProperties.ExecType.SPARK);
}
@Test
public void testMSVMSparseLambda2() {
runMSVMTest(true, true, eps, 2.0, max_iter, LopProperties.ExecType.CP);
}
@Test
public void testMSVMSparseLambda100CP() {
runMSVMTest(true, true, 1, 100, max_iter, LopProperties.ExecType.CP);
}
@Test
public void testMSVMSparseLambda100Spark() {
runMSVMTest(true, true, 1, 100, max_iter, LopProperties.ExecType.SPARK);
}
@Test
public void testMSVMIteration() {
runMSVMTest(true, true, 1, 2.0, 100, LopProperties.ExecType.CP);
}
@Test
public void testMSVMDenseIntercept() {
runMSVMTest(false, true, eps, 1.0, max_iter, LopProperties.ExecType.CP);
}
private void runMSVMTest(boolean sparse, boolean intercept, double eps, double lambda, int run,
LopProperties.ExecType instType) {
Types.ExecMode platformOld = setExecMode(instType);
boolean oldFlag = OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION;
boolean sparkConfigOld = DMLScript.USE_LOCAL_SPARK_CONFIG;
try {
loadTestConfiguration(getTestConfiguration(TEST_NAME));
double sparsity = sparse ? spSparse : spDense;
String HOME = SCRIPT_DIR + TEST_DIR;
fullDMLScriptName = HOME + TEST_NAME + ".dml";
programArgs = new String[] {"-nvargs", "X=" + input("X"), "Y=" + input("Y"), "model=" + output("model"),
"inc=" + String.valueOf(intercept).toUpperCase(), "eps=" + eps, "lam=" + lambda, "max=" + run};
fullRScriptName = HOME + TEST_NAME + ".R";
rCmd = getRCmd(inputDir(),
Boolean.toString(intercept),
Double.toString(eps),
Double.toString(lambda),
Integer.toString(run),
expectedDir());
double[][] X = getRandomMatrix(rows, colsX, 0, 1, sparsity, 2);
double[][] Y = getRandomMatrix(rows, 1, 0, 10, 1, 3);
Y = TestUtils.round(Y);
writeInputMatrixWithMTD("X", X, true);
writeInputMatrixWithMTD("Y", Y, true);
runTest(true, false, null, -1);
runRScript(true);
HashMap<MatrixValue.CellIndex, Double> dmlfile = readDMLMatrixFromOutputDir("model");
HashMap<MatrixValue.CellIndex, Double> rfile = readRMatrixFromExpectedDir("model");
TestUtils.compareMatrices(dmlfile, rfile, eps, "Stat-DML", "Stat-R");
}
finally {
rtplatform = platformOld;
DMLScript.USE_LOCAL_SPARK_CONFIG = sparkConfigOld;
OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION = oldFlag;
OptimizerUtils.ALLOW_AUTO_VECTORIZATION = true;
OptimizerUtils.ALLOW_OPERATOR_FUSION = true;
}
}
}