[GitHub] [systemds] kpretterhofer commented on a change in pull request #1153: Gaussian Classifier

[GitBox <gi...@apache.org>]

kpretterhofer commented on a change in pull request #1153:
URL: https://github.com/apache/systemds/pull/1153#discussion_r558095262



##########
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:
       thanks for the feedback. I will commit the suggested changes as soon as possible.
   Just one follow up question to this specific suggestion:
   Do you mean I should check wether covariance * invCovariance is indeed the identity matrix?
   If so, since the covariance matrix is not returned (but just the inverse), should I compute, 
   the respective covariance matrices again in the test dml file, and then return the product, to check
   wether it is indeed the identity? 
   If this is not what you meant, it would be great if you could clarify. 
   Thanks :)




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