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new 955365c [SYSTEMML-2121] PCA test for codegenalg suite
955365c is described below
commit 955365c5da1a916541d734a4e9494ab61c932503
Author: Janardhan Pulivarthi <j...@protonmail.com>
AuthorDate: Sat Apr 25 22:15:06 2020 +0200
[SYSTEMML-2121] PCA test for codegenalg suite
This patch adds a test case for algorithm test with codegen
enabled against an R script.
The test matrix is as follows:
| Rewrite | Sparse | FuseAll | FuseNoRedundancy |
| ------- | ------ | -------- | ---------------- |
- Spark | 1 | 0 | 0 | 0 |
or CP | 1 | 1 | 0 | 0 |
| 0 | 0 | 0 | 0 |
| 0 | 1 | 0 | 0 |
| 0 | 0 | 1 | 0 |
| 0 | 1 | 1 | 0 |
| 0 | 0 | 0 | 1 |
| 0 | 1 | 0 | 1 |
Closes #889.
---
scripts/algorithms/PCA.dml | 14 +-
.../functions/codegenalg/partone/AlgorithmPCA.java | 213 +++++++++++++++++++++
.../scripts/functions/codegenalg/Algorithm_PCA.R | 87 +++++++++
3 files changed, 301 insertions(+), 13 deletions(-)
diff --git a/scripts/algorithms/PCA.dml b/scripts/algorithms/PCA.dml
index d165351..ea7afd7 100644
--- a/scripts/algorithms/PCA.dml
+++ b/scripts/algorithms/PCA.dml
@@ -62,19 +62,7 @@ if (model != "") {
D = ncol(A);
# perform z-scoring (centering and scaling)
- if (center == 1) {
- cm = colMeans(A);
- A = A - cm;
- }
- if (scale == 1) {
- cvars = (colSums (A^2));
- if (center == 1){
- cm = colMeans(A);
- cvars = (cvars - N*(cm^2))/(N-1);
- }
- Azscored = (A)/sqrt(cvars);
- A = Azscored;
- }
+ A = scale(A, center==1, scale==1);
# co-variance matrix
mu = colSums(A)/N;
diff --git
a/src/test/java/org/apache/sysds/test/functions/codegenalg/partone/AlgorithmPCA.java
b/src/test/java/org/apache/sysds/test/functions/codegenalg/partone/AlgorithmPCA.java
new file mode 100644
index 0000000..e0a1906
--- /dev/null
+++
b/src/test/java/org/apache/sysds/test/functions/codegenalg/partone/AlgorithmPCA.java
@@ -0,0 +1,213 @@
+/*
+ * 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.codegenalg.partone;
+
+import java.io.File;
+import java.util.HashMap;
+
+import org.junit.Test;
+import org.apache.sysds.common.Types.ExecMode;
+import org.apache.sysds.hops.OptimizerUtils;
+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;
+import org.junit.Assert;
+
+public class AlgorithmPCA extends AutomatedTestBase
+{
+ private final static String TEST_NAME1 = "Algorithm_PCA";
+ private final static String TEST_DIR = "functions/codegenalg/";
+ private final static String TEST_CLASS_DIR = TEST_DIR +
AlgorithmPCA.class.getSimpleName() + "/";
+ private final static String TEST_CONF_DEFAULT =
"SystemDS-config-codegen.xml";
+ private final static File TEST_CONF_FILE_DEFAULT = new File(SCRIPT_DIR
+ TEST_DIR, TEST_CONF_DEFAULT);
+ private final static String TEST_CONF_FUSE_ALL =
"SystemDS-config-codegen-fuse-all.xml";
+ private final static File TEST_CONF_FILE_FUSE_ALL = new File(SCRIPT_DIR
+ TEST_DIR, TEST_CONF_FUSE_ALL);
+ private final static String TEST_CONF_FUSE_NO_REDUNDANCY =
"SystemDS-config-codegen-fuse-no-redundancy.xml";
+ private final static File TEST_CONF_FILE_FUSE_NO_REDUNDANCY = new
File(SCRIPT_DIR + TEST_DIR,
+ TEST_CONF_FUSE_NO_REDUNDANCY);
+
+ private enum TestType { DEFAULT, FUSE_ALL, FUSE_NO_REDUNDANCY }
+
+ private final static double eps = 1e-5;
+
+ private final static int rows = 1468;
+ private final static int cols1 = 1007;
+ private final static int cols2 = 387;
+
+ private final static double sparsity1 = 0.7; //dense
+ private final static double sparsity2 = 0.1; //sparse
+
+ private TestType currentTestType = TestType.DEFAULT;
+
+ @Override
+ public void setUp() {
+ TestUtils.clearAssertionInformation();
+ addTestConfiguration(TEST_NAME1, new
TestConfiguration(TEST_CLASS_DIR, TEST_NAME1, new String[] { "w" }));
+ }
+
+ @Test
+ public void testPCADenseRewritesCP() {
+ runPCATest(TEST_NAME1, true, false, ExecType.CP,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCASparseRewritesCP() {
+ runPCATest(TEST_NAME1, true, true, ExecType.CP,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCADenseCP() {
+ runPCATest(TEST_NAME1, false, false, ExecType.CP,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCASparseCP() {
+ runPCATest(TEST_NAME1, false, true, ExecType.CP,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCADenseRewritesSP() {
+ runPCATest(TEST_NAME1, true, false, ExecType.SPARK,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCASparseRewritesSP() {
+ runPCATest(TEST_NAME1, true, true, ExecType.SPARK,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCADenseSP() {
+ runPCATest(TEST_NAME1, false, false, ExecType.SPARK,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCASparseSP() {
+ runPCATest(TEST_NAME1, false, true, ExecType.SPARK,
TestType.DEFAULT);
+ }
+
+ @Test
+ public void testPCADenseRewritesCPFuseAll() {
+ runPCATest(TEST_NAME1, true, false, ExecType.CP,
TestType.FUSE_ALL);
+ }
+
+ @Test
+ public void testPCASparseRewritesCPFuseAll() {
+ runPCATest(TEST_NAME1, true, true, ExecType.CP,
TestType.FUSE_ALL);
+ }
+
+ @Test
+ public void testPCADenseRewritesSPFuseAll() {
+ runPCATest(TEST_NAME1, true, false, ExecType.SPARK,
TestType.FUSE_ALL);
+ }
+
+ @Test
+ public void testPCASparseRewritesSPFuseAll() {
+ runPCATest(TEST_NAME1, true, true, ExecType.SPARK,
TestType.FUSE_ALL);
+ }
+
+ @Test
+ public void testPCADenseRewritesCPFuseNoRedundancy() {
+ runPCATest(TEST_NAME1, true, false, ExecType.CP,
TestType.FUSE_NO_REDUNDANCY);
+ }
+
+ @Test
+ public void testPCASparseRewritesCPFuseNoRedundancy() {
+ runPCATest(TEST_NAME1, true, true, ExecType.CP,
TestType.FUSE_NO_REDUNDANCY);
+ }
+
+ @Test
+ public void testPCADenseRewritesSPFuseNoRedundancy() {
+ runPCATest(TEST_NAME1, true, false, ExecType.SPARK,
TestType.FUSE_NO_REDUNDANCY);
+ }
+
+ @Test
+ public void testPCASparseRewritesSPFuseNoRedundancy() {
+ runPCATest(TEST_NAME1, true, true, ExecType.SPARK,
TestType.FUSE_NO_REDUNDANCY);
+ }
+
+ private void runPCATest(String testname, boolean rewrites, boolean
sparse, ExecType instType, TestType testType)
+ {
+ boolean oldFlag = OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION;
+ ExecMode platformOld = setExecMode(instType);
+
+ try {
+ String TEST_NAME = testname;
+ TestConfiguration config =
getTestConfiguration(TEST_NAME);
+ loadTestConfiguration(config);
+
+ fullDMLScriptName = "scripts/algorithms/PCA.dml";
+ // pass OFMT=text flag, since readDMLMatrixFromHDFS()
uses " " separator, not a "," separator.
+ programArgs = new String[]{ "-explain", "-stats",
"-nvargs", "OFMT=TEXT","INPUT="+input("A"),
+ "OUTPUT="+output("")};
+
+ rCmd = getRCmd(inputDir(), expectedDir());
+
+ OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION =
rewrites;
+
+ //generate actual datasets
+ int cols = (instType==ExecType.SPARK) ? cols2 : cols1;
+ double[][] A = getRandomMatrix(rows, cols, 0, 1,
sparse?sparsity2:sparsity1, 714);
+ writeInputMatrixWithMTD("A", A, true);
+
+ runTest(true, false, null, -1);
+ runRScript(true);
+
+ //compare matrices
+ HashMap<CellIndex, Double> dmleval =
readDMLMatrixFromHDFS("dominant.eigen.values");
+ HashMap<CellIndex, Double> reval =
readRMatrixFromFS("dominant.eigen.values");
+ HashMap<CellIndex, Double> dmlevec =
readDMLMatrixFromHDFS("dominant.eigen.vectors");
+ HashMap<CellIndex, Double> revec =
readDMLMatrixFromHDFS("dominant.eigen.vectors");
+ HashMap<CellIndex, Double> dmlstd =
readDMLMatrixFromHDFS("dominant.eigen.standard.deviations");
+ HashMap<CellIndex, Double> rstd =
readRMatrixFromFS("dominant.eigen.standard.deviations");
+ TestUtils.compareMatrices(dmleval, reval, eps,
"Stat-DML", "Stat-R");
+ TestUtils.compareMatrices(dmlevec, revec, eps,
"Stat-DML", "Stat-R");
+ TestUtils.compareMatrices(dmlstd, rstd, eps,
"Stat-DML", "Stat-R");
+
Assert.assertTrue(heavyHittersContainsSubString("spoof") ||
heavyHittersContainsSubString("sp_spoof"));
+ }
+ finally {
+ resetExecMode(platformOld);
+ OptimizerUtils.ALLOW_ALGEBRAIC_SIMPLIFICATION = oldFlag;
+ OptimizerUtils.ALLOW_AUTO_VECTORIZATION = true;
+ OptimizerUtils.ALLOW_OPERATOR_FUSION = true;
+ }
+ }
+
+ /**
+ * Override default configuration with custom test configuration to
ensure
+ * scratch space and local temporary directory locations are also
updated.
+ */
+ @Override
+ protected File getConfigTemplateFile() {
+ // Instrumentation in this test's output log to show custom
configuration file used for template.
+ String message = "This test case overrides default
configuration with ";
+ if(currentTestType == AlgorithmPCA.TestType.FUSE_ALL){
+ System.out.println(message +
TEST_CONF_FILE_FUSE_ALL.getPath());
+ return TEST_CONF_FILE_FUSE_ALL;
+ } else if(currentTestType == TestType.FUSE_NO_REDUNDANCY){
+ System.out.println(message +
TEST_CONF_FILE_FUSE_NO_REDUNDANCY.getPath());
+ return TEST_CONF_FILE_FUSE_NO_REDUNDANCY;
+ } else {
+ System.out.println(message +
TEST_CONF_FILE_DEFAULT.getPath());
+ return TEST_CONF_FILE_DEFAULT;
+ }
+ }
+}
diff --git a/src/test/scripts/functions/codegenalg/Algorithm_PCA.R
b/src/test/scripts/functions/codegenalg/Algorithm_PCA.R
new file mode 100644
index 0000000..338e6a1
--- /dev/null
+++ b/src/test/scripts/functions/codegenalg/Algorithm_PCA.R
@@ -0,0 +1,87 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+#
+# This script performs Principal Component Analysis (PCA) on the given input
data.
+#
+
+args <- commandArgs(TRUE)
+library("Matrix")
+
+A = readMM(paste(args[1], "A.mtx", sep=""));
+K = ncol(A);
+projectData = 0;
+model = "";
+center = 0;
+scale = 0;
+
+
+if (model != "") {
+ # reuse existing model to project data
+} else if (model == "") {
+
+ N = nrow(A);
+ D = ncol(A);
+
+ # 1. perform z-scoring (centering and scaling)
+ if (center == 1) {
+ cm = matrix(1, nrow(A), 1) %*% colMeans(A);
+ A = A - cm
+ }
+ if (scale == 1) {
+ cvars = (colSums(A^2));
+ if (center == 1){
+ #cm = colMeans(A);
+ cvars = (cvars - N*(colMeans(A)^2))/(N-1);
+ }
+ Azscored = A / sqrt(cvars);
+ A = Azscored;
+ }
+
+ # 2. compute co-variance matrix
+ mu = colSums(A)/N;
+ C = (t(A) %*% A)/(N-1) - (N/(N-1))*(mu) %*% t(mu);
+
+ # 3. compute eigen vectors and values
+ R <- eigen(C);
+ evalues = R$values;
+ evectors = R$vectors;
+
+ # 4. make an index of values sorted according to magnitude of evalues
+ decreasing_Idx = order(as.vector(evalues), decreasing=TRUE);
+ diagmat = table(seq(1,D), decreasing_Idx);
+ # 5. sorts eigen values by decreasing order
+ evalues = diagmat %*% evalues;
+ # 6. sorts eigen vectors column-wise in the order of decreasing eigen values
+ evectors = evectors %*% diagmat;
+
+ # 7. select K dominant eigen vectors
+ nvec = ncol(evectors); # Here `nvec=K`
+ eval_dominant = evalues[1:K, 1];
+ evec_dominant = evectors[1:K,];
+
+ # 8. compute the std. deviation of dominant evalues
+ eval_stdev_dominant = sqrt(eval_dominant);
+
+ writeMM(as(eval_stdev_dominant, "CsparseMatrix"),
paste(args[2],"dominant.eigen.standard.deviations", sep=""));
+ writeMM(as(eval_dominant, "CsparseMatrix"), paste(args[2],
"dominant.eigen.values", sep=""));
+ writeMM(as(evec_dominant, "CsparseMatrix"),
paste(args[2],"dominant.eigen.vectors", sep=""));
+}
\ No newline at end of file
