| /* |
| * 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.jmlc; |
| |
| import java.io.IOException; |
| import java.util.ArrayList; |
| |
| import org.junit.Assert; |
| import org.junit.Test; |
| import org.apache.sysds.api.jmlc.Connection; |
| import org.apache.sysds.api.jmlc.PreparedScript; |
| import org.apache.sysds.api.jmlc.ResultVariables; |
| import org.apache.sysds.runtime.controlprogram.parfor.stat.Timing; |
| import org.apache.sysds.runtime.io.IOUtilFunctions; |
| import org.apache.sysds.test.AutomatedTestBase; |
| import org.apache.sysds.test.TestConfiguration; |
| |
| public class ReuseModelVariablesTest extends AutomatedTestBase |
| { |
| private final static String TEST_NAME1 = "reuse-glm-predict"; |
| private final static String TEST_NAME2 = "reuse-msvm-predict"; |
| private final static String TEST_DIR = "functions/jmlc/"; |
| private final static String MODEL_FILE = "sentiment_model.mtx"; |
| private final static String TEST_CLASS_DIR = TEST_DIR + ReuseModelVariablesTest.class.getSimpleName() + "/"; |
| |
| private final static int rows = 107; |
| private final static int cols = 46; //fixed |
| |
| private final static int nRuns = 10; |
| |
| private final static double sparsity1 = 0.7; |
| private final static double sparsity2 = 0.1; |
| |
| |
| @Override |
| public void setUp() { |
| addTestConfiguration(TEST_NAME1, new TestConfiguration(TEST_CLASS_DIR, TEST_NAME1, new String[] { "predicted_y" }) ); |
| addTestConfiguration(TEST_NAME2, new TestConfiguration(TEST_CLASS_DIR, TEST_NAME2, new String[] { "predicted_y" }) ); |
| } |
| |
| @Test |
| public void testJMLCScoreGLMDense() throws IOException { |
| runJMLCReuseTest(TEST_NAME1, false, false); |
| } |
| |
| @Test |
| public void testJMLCScoreGLMSparse() throws IOException { |
| runJMLCReuseTest(TEST_NAME1, true, false); |
| } |
| |
| @Test |
| public void testJMLCScoreGLMDenseReuse() throws IOException { |
| runJMLCReuseTest(TEST_NAME1, false, true); |
| } |
| |
| @Test |
| public void testJMLCScoreGLMSparseReuse() throws IOException { |
| runJMLCReuseTest(TEST_NAME1, true, true); |
| } |
| |
| @Test |
| public void testJMLCScoreMSVMDense() throws IOException { |
| runJMLCReuseTest(TEST_NAME2, false, false); |
| } |
| |
| @Test |
| public void testJMLCScoreMSVMSparse() throws IOException { |
| runJMLCReuseTest(TEST_NAME2, true, false); |
| } |
| |
| @Test |
| public void testJMLCScoreMSVMDenseReuse() throws IOException { |
| runJMLCReuseTest(TEST_NAME2, false, true); |
| } |
| |
| @Test |
| public void testJMLCScoreMSVMSparseReuse() throws IOException { |
| runJMLCReuseTest(TEST_NAME2, true, true); |
| } |
| |
| private void runJMLCReuseTest( String testname, boolean sparse, boolean modelReuse ) |
| throws IOException |
| { |
| String TEST_NAME = testname; |
| |
| TestConfiguration config = getTestConfiguration(TEST_NAME); |
| loadTestConfiguration(config); |
| |
| //generate inputs |
| ArrayList<double[][]> Xset = generateInputs(nRuns, rows, cols, sparse?sparsity2:sparsity1); |
| |
| //run DML via JMLC |
| ArrayList<double[][]> Yset = execDMLScriptviaJMLC( TEST_NAME, Xset, modelReuse ); |
| |
| //check non-empty y |
| Assert.assertEquals(Xset.size(), Yset.size()); |
| } |
| |
| private static ArrayList<double[][]> execDMLScriptviaJMLC( String testname, ArrayList<double[][]> X, boolean modelReuse) |
| throws IOException |
| { |
| Timing time = new Timing(true); |
| |
| ArrayList<double[][]> ret = new ArrayList<>(); |
| |
| //establish connection to SystemDS |
| Connection conn = new Connection(); |
| |
| try |
| { |
| //read and precompile script |
| String script = conn.readScript(SCRIPT_DIR + TEST_DIR + testname + ".dml"); |
| PreparedScript pstmt = conn.prepareScript(script, new String[]{"X","W"}, new String[]{"predicted_y"}); |
| |
| //read model |
| String modelData = conn.readScript(SCRIPT_DIR + TEST_DIR + MODEL_FILE ); |
| double[][] W = conn.convertToDoubleMatrix(modelData, rows, cols); |
| |
| if( modelReuse ) |
| pstmt.setMatrix("W", W, true); |
| |
| //execute script multiple times |
| for( int i=0; i<nRuns; i++ ) |
| { |
| //bind input parameters |
| if( !modelReuse ) |
| pstmt.setMatrix("W", W); |
| pstmt.setMatrix("X", X.get(i)); |
| |
| //execute script |
| ResultVariables rs = pstmt.executeScript(); |
| |
| //get output parameter |
| double[][] Y = rs.getMatrix("predicted_y"); |
| ret.add(Y); //keep result for comparison |
| } |
| } |
| catch(Exception ex) |
| { |
| ex.printStackTrace(); |
| throw new IOException(ex); |
| } |
| finally { |
| IOUtilFunctions.closeSilently(conn); |
| } |
| |
| System.out.println("JMLC scoring w/ "+nRuns+" runs in "+time.stop()+"ms."); |
| |
| return ret; |
| } |
| |
| private ArrayList<double[][]> generateInputs( int num, int rows, int cols, double sparsity ) |
| { |
| ArrayList<double[][]> ret = new ArrayList<>(); |
| |
| for( int i=0; i<num; i++ ) |
| { |
| double[][] X = getRandomMatrix(rows, cols, -1, 1, sparsity, System.nanoTime()); |
| ret.add(X); |
| } |
| |
| return ret; |
| } |
| |
| } |