/*
* 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();
}
}
}