kpretterhofer commented on a change in pull request #1153: URL: https://github.com/apache/systemds/pull/1153#discussion_r558131702
########## File path: src/test/java/org/apache/sysds/test/functions/builtin/BuiltinGaussianClassifierTest.java ########## @@ -0,0 +1,142 @@ +/* + * 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.builtin; + +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; + +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; +import org.junit.Test; + +public class BuiltinGaussianClassifierTest extends AutomatedTestBase +{ + private final static String TEST_NAME = "GaussianClassifier"; + private final static String TEST_DIR = "functions/builtin/"; + private final static String TEST_CLASS_DIR = TEST_DIR + BuiltinGaussianClassifierTest.class.getSimpleName() + "/"; + + + @Override + public void setUp() { + addTestConfiguration(TEST_NAME,new TestConfiguration(TEST_CLASS_DIR, TEST_NAME,new String[]{"B"})); + } + + + @Test + public void testSmallDenseFiveClasses() { + testGaussianClassifier(80, 30, 0.9, 5); + } + + @Test + public void testSmallDenseTenClasses() { + testGaussianClassifier(80, 30, 0.9, 10); + } + + @Test + public void testBiggerDenseFiveClasses() { + testGaussianClassifier(200, 50, 0.9, 5); + } + + @Test + public void testBiggerDenseTenClasses() { + testGaussianClassifier(200, 50, 0.9, 10); + } + + @Test + public void testBiggerSparseFiveClasses() { + testGaussianClassifier(200, 50, 0.3, 5); + } + + @Test + public void testBiggerSparseTenClasses() { + testGaussianClassifier(200, 50, 0.3, 10); + } + + @Test + public void testSmallSparseFiveClasses() { + testGaussianClassifier(80, 30, 0.3, 5); + } + + @Test + public void testSmallSparseTenClasses() { + testGaussianClassifier(80, 30, 0.3, 10); + } + + public void testGaussianClassifier(int rows, int cols, double sparsity, int classes) + { + loadTestConfiguration(getTestConfiguration(TEST_NAME)); + String HOME = SCRIPT_DIR + TEST_DIR; + fullDMLScriptName = HOME + TEST_NAME + ".dml"; + ; + double varSmoothing = 1e-9; + + List<String> proArgs = new ArrayList<>(); + proArgs.add("-args"); + proArgs.add(input("X")); + proArgs.add(input("Y")); + proArgs.add(String.valueOf(varSmoothing)); + proArgs.add(output("priors")); + proArgs.add(output("means")); + proArgs.add(output("determinants")); + proArgs.add(output("invcovs")); + + programArgs = proArgs.toArray(new String[proArgs.size()]); + + rCmd = getRCmd(inputDir(), Double.toString(varSmoothing), expectedDir()); + + double[][] X = getRandomMatrix(rows, cols, 0, 100, sparsity, -1); + double[][] Y = getRandomMatrix(rows, 1, 0, 1, 1, -1); + for(int i=0; i<rows; i++){ + Y[i][0] = (int)(Y[i][0]*classes) + 1; + Y[i][0] = (Y[i][0] > classes) ? classes : Y[i][0]; + } + + writeInputMatrixWithMTD("X", X, true); + writeInputMatrixWithMTD("Y", Y, true); + + runTest(true, EXCEPTION_NOT_EXPECTED, null, -1); + + runRScript(true); + + HashMap<CellIndex, Double> priorR = readRMatrixFromExpectedDir("priors"); + HashMap<CellIndex, Double> priorSYSTEMDS= readDMLMatrixFromOutputDir("priors"); + HashMap<CellIndex, Double> meansRtemp = readRMatrixFromExpectedDir("means"); + HashMap<CellIndex, Double> meansSYSTEMDStemp = readDMLMatrixFromOutputDir("means"); + HashMap<CellIndex, Double> determinantsRtemp = readRMatrixFromExpectedDir("determinants"); + HashMap<CellIndex, Double> determinantsSYSTEMDStemp = readDMLMatrixFromOutputDir("determinants"); + HashMap<CellIndex, Double> invcovsRtemp = readRMatrixFromExpectedDir("invcovs"); + HashMap<CellIndex, Double> invcovsSYSTEMDStemp = readDMLMatrixFromOutputDir("invcovs"); + + double[][] meansR = TestUtils.convertHashMapToDoubleArray(meansRtemp); + double[][] meansSYSTEMDS = TestUtils.convertHashMapToDoubleArray(meansSYSTEMDStemp); + double[][] determinantsR = TestUtils.convertHashMapToDoubleArray(determinantsRtemp); + double[][] determinantsSYSTEMDS = TestUtils.convertHashMapToDoubleArray(determinantsSYSTEMDStemp); + double[][] invcovsR = TestUtils.convertHashMapToDoubleArray(invcovsRtemp); + double[][] invcovsSYSTEMDS = TestUtils.convertHashMapToDoubleArray(invcovsSYSTEMDStemp); + + TestUtils.compareMatrices(priorR, priorSYSTEMDS, Math.pow(10, -5.0), "priorR", "priorSYSTEMDS"); + TestUtils.compareMatricesBitAvgDistance(meansR, meansSYSTEMDS, 5L,5L, this.toString()); + TestUtils.compareMatricesBitAvgDistance(determinantsR, determinantsSYSTEMDS, (long)2E+12,(long)2E+12, this.toString()); + TestUtils.compareMatricesBitAvgDistance(invcovsR, invcovsSYSTEMDS, (long)2E+20,(long)2E+20, this.toString()); Review comment: reconstructing the input from the output is afaik not really possible - since you lose for example the number of input samples provided to the algorithm. However computing the covariance matrix again, and multiplying it with its inverse is definitely a way of proving that the inverse is indeed correct, although its R aquivalent seems to be kinda different (but as already mentioned, just because of floating point differences). ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
