/* * 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 hivemall.mf; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils; import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory; import org.junit.Assert; import org.junit.Test; public class MatrixFactorizationAdaGradUDTFTest { private static final boolean DEBUG_PRINT = false; private static void print(String msg) { if (DEBUG_PRINT) System.out.print(msg); } private static void println(String msg) { if (DEBUG_PRINT) System.out.println(msg); } private static void println() { if (DEBUG_PRINT) System.out.println(); } @Test public void test() throws HiveException { println("--------------------------\n test()"); OnlineMatrixFactorizationUDTF mf = new MatrixFactorizationAdaGradUDTF(); ObjectInspector intOI = PrimitiveObjectInspectorFactory.javaIntObjectInspector; ObjectInspector floatOI = PrimitiveObjectInspectorFactory.javaFloatObjectInspector; ObjectInspector param = ObjectInspectorUtils.getConstantObjectInspector( PrimitiveObjectInspectorFactory.javaStringObjectInspector, new String("-factor 3")); ObjectInspector[] argOIs = new ObjectInspector[] {intOI, intOI, floatOI, param}; mf.initialize(argOIs); float[][] rating = { {5, 3, 0, 1}, {4, 0, 0, 1}, {1, 1, 0, 5}, {1, 0, 0, 4}, {0, 1, 5, 4}}; Object[] args = new Object[3]; final int num_iters = 100; for (int iter = 0; iter < num_iters; iter++) { for (int row = 0; row < rating.length; row++) { for (int col = 0, size = rating[row].length; col < size; col++) { //print(row + "," + col + ","); args[0] = row; args[1] = col; args[2] = (float) rating[row][col]; //println((float) rating[row][col]); mf.process(args); } } } for (int row = 0; row < rating.length; row++) { for (int col = 0, size = rating[row].length; col < size; col++) { double predicted = mf.predict(row, col); print(rating[row][col] + "[" + predicted + "]\t"); Assert.assertEquals(rating[row][col], predicted, 0.2d); } println(); } } }