| /* |
| * 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.apache.commons.logging.Log; |
| import org.apache.commons.logging.LogFactory; |
| import org.apache.sysds.runtime.matrix.data.MatrixValue.CellIndex; |
| import org.apache.sysds.runtime.meta.MatrixCharacteristics; |
| import org.apache.sysds.test.AutomatedTestBase; |
| import org.apache.sysds.test.TestUtils; |
| import org.junit.Test; |
| import org.junit.runner.RunWith; |
| import org.junit.runners.Parameterized; |
| import org.junit.runners.Parameterized.Parameters; |
| |
| @RunWith(value = Parameterized.class) |
| @net.jcip.annotations.NotThreadSafe |
| public class MDABivariateStatsTest extends AutomatedTestBase |
| { |
| private static final Log LOG = LogFactory.getLog(MDABivariateStatsTest.class.getName()); |
| protected final static String TEST_DIR = "applications/mdabivar/"; |
| protected final static String TEST_NAME = "MDABivariateStats"; |
| protected String TEST_CLASS_DIR = TEST_DIR + MDABivariateStatsTest.class.getSimpleName() + "/"; |
| |
| protected int n, m, label_index, label_measurement_level; |
| |
| public MDABivariateStatsTest(int n, int m, int li, int lml) { |
| this.n = n; |
| this.m = m; |
| this.label_index = li; |
| this.label_measurement_level = lml; |
| } |
| |
| @Parameters |
| public static Collection<Object[]> data() { |
| Object[][] data = new Object[][] { |
| { 1000, 100, 1, 1 }, { 1000, 100, 100, 0}, |
| { 10000, 100, 1, 1 }, { 10000, 100, 100, 0}}; |
| return Arrays.asList(data); |
| } |
| |
| @Override |
| public void setUp() { |
| addTestConfiguration(TEST_CLASS_DIR, TEST_NAME); |
| } |
| |
| @Test |
| public void testMDABivariateStats() { |
| LOG.debug(" BEGIN " + TEST_NAME + " TEST WITH {" + n + ", " + m |
| + ", " + label_index + ", " + label_measurement_level + "}"); |
| |
| getAndLoadTestConfiguration(TEST_NAME); |
| |
| List<String> proArgs = new ArrayList<>(); |
| proArgs.add("-stats"); |
| proArgs.add("-args"); |
| proArgs.add(input("X")); |
| proArgs.add(Integer.toString(label_index)); |
| proArgs.add(input("feature_indices")); |
| proArgs.add(Integer.toString(label_measurement_level)); |
| proArgs.add(input("feature_measurement_levels")); |
| proArgs.add(output("stats")); |
| proArgs.add(output("tests")); |
| proArgs.add(output("covariances")); |
| proArgs.add(output("standard_deviations")); |
| proArgs.add(output("contingency_tables_counts")); |
| proArgs.add(output("contingency_tables_label_values")); |
| proArgs.add(output("contingency_tables_feature_values")); |
| proArgs.add(output("feature_values")); |
| proArgs.add(output("feature_counts")); |
| proArgs.add(output("feature_means")); |
| proArgs.add(output("feature_standard_deviations")); |
| programArgs = proArgs.toArray(new String[proArgs.size()]); |
| |
| fullDMLScriptName = getScript(); |
| |
| rCmd = getRCmd(inputDir(), Integer.toString(label_index), Integer.toString(label_measurement_level), expectedDir()); |
| |
| double[][] X = getRandomMatrix(n, m, 0, 1, 1, System.currentTimeMillis()); |
| for(int i=0; i<X.length; i++) |
| for(int j=m/2; j<X[i].length; j++){ |
| //generating a 5-valued categorical random variable |
| if(X[i][j] < 0.2) X[i][j] = 1; |
| else if(X[i][j] < 0.4) X[i][j] = 2; |
| else if(X[i][j] < 0.6) X[i][j] = 3; |
| else if(X[i][j] < 0.8) X[i][j] = 4; |
| else X[i][j] = 5; |
| } |
| |
| double[][] feature_indices = new double[m-1][1]; |
| double[][] feature_measurement_levels = new double[m-1][1]; |
| int pos = 0; |
| for(int i=1; i<=m; i++) |
| if(i != label_index){ |
| feature_indices[pos][0] = i; |
| feature_measurement_levels[pos][0] = (i > m/2) ? 0 : 1; |
| pos++; |
| } |
| |
| MatrixCharacteristics mcX = new MatrixCharacteristics(n, m, -1, -1); |
| writeInputMatrixWithMTD("X", X, true, mcX); |
| |
| MatrixCharacteristics mc_features = new MatrixCharacteristics(m-1, 1, -1, -1); |
| writeInputMatrixWithMTD("feature_indices", feature_indices, true, mc_features); |
| writeInputMatrixWithMTD("feature_measurement_levels", feature_measurement_levels, true, mc_features); |
| |
| int expectedNumberOfJobs = -1; |
| runTest(true, EXCEPTION_NOT_EXPECTED, null, expectedNumberOfJobs); |
| |
| runRScript(true); |
| |
| HashMap<CellIndex, Double> statsSYSTEMDS = readDMLMatrixFromHDFS("stats"); |
| HashMap<CellIndex, Double> statsR = readRMatrixFromFS("stats"); |
| |
| TestUtils.compareMatrices(statsSYSTEMDS, statsR, 0.000001, "statsSYSTEMDS", "statsR"); |
| } |
| } |