/*
* 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.fm;
import java.util.ArrayList;
import java.util.Random;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.serde2.objectinspector.ListObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.DoubleObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
import org.junit.Assert;
import org.junit.Test;
public class ArrayModelTest {
private static final boolean DEBUG_PRINT = false;
private static void println(String msg) {
if (DEBUG_PRINT)
System.out.println(msg);
}
@Test
public void testClassification() throws HiveException {
final int ROW = 10, COL = 40;
FactorizationMachineUDTF udtf = new FactorizationMachineUDTF();
ListObjectInspector xOI = ObjectInspectorFactory.getStandardListObjectInspector(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
DoubleObjectInspector yOI = PrimitiveObjectInspectorFactory.javaDoubleObjectInspector;
ObjectInspector paramOI = ObjectInspectorUtils.getConstantObjectInspector(
PrimitiveObjectInspectorFactory.javaStringObjectInspector,
"-adareg -factors 20 -classification -seed 31 -iters 10 -int_feature -p " + COL);
udtf.initialize(new ObjectInspector[] {xOI, yOI, paramOI});
FactorizationMachineModel model = udtf.initModel(udtf._params);
Assert.assertTrue("Actual class: " + model.getClass().getName(),
model instanceof FMArrayModel);
float accuracy = 0.f;
final Random rnd = new Random(201L);
for (int numberOfIteration = 0; numberOfIteration < 10000; numberOfIteration++) {
ArrayList<IntFeature[]> fArrayList = new ArrayList<IntFeature[]>();
ArrayList<Double> ans = new ArrayList<Double>();
for (int i = 0; i < ROW; i++) {
ArrayList<IntFeature> feature = new ArrayList<IntFeature>();
for (int j = 1; j <= COL; j++) {
if (i < (0.5f * ROW)) {
if (j == 1) {
feature.add(new IntFeature(j, 1.d));
} else if (j < 0.5 * COL) {
if (rnd.nextFloat() < 0.2f) {
feature.add(new IntFeature(j, rnd.nextDouble()));
}
}
} else {
if (j > 0.5f * COL) {
if (rnd.nextFloat() < 0.2f) {
feature.add(new IntFeature(j, rnd.nextDouble()));
}
}
}
}
IntFeature[] x = new IntFeature[feature.size()];
feature.toArray(x);
fArrayList.add(x);
final double y;
if (i < ROW * 0.5f) {
y = -1.0d;
} else {
y = 1.0d;
}
ans.add(y);
udtf.process(new Object[] {toStringArray(x), y});
}
int bingo = 0;
int total = fArrayList.size();
for (int i = 0; i < total; i++) {
double tmpAns = ans.get(i);
if (tmpAns < 0) {
tmpAns = 0;
} else {
tmpAns = 1;
}
double p = model.predict(fArrayList.get(i));
int predicted = p > 0.5 ? 1 : 0;
if (predicted == tmpAns) {
bingo++;
}
}
accuracy = bingo / (float) total;
println("Accuracy = " + accuracy);
}
udtf.runTrainingIteration(10);
Assert.assertTrue("Expected accuracy greather than 0.95f: " + accuracy, accuracy > 0.95f);
}
@Test
public void testRegression() throws HiveException {
final int ROW = 1000, COL = 80;
FactorizationMachineUDTF udtf = new FactorizationMachineUDTF();
ListObjectInspector xOI = ObjectInspectorFactory.getStandardListObjectInspector(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
DoubleObjectInspector yOI = PrimitiveObjectInspectorFactory.javaDoubleObjectInspector;
ObjectInspector paramOI = ObjectInspectorUtils.getConstantObjectInspector(
PrimitiveObjectInspectorFactory.javaStringObjectInspector,
"-factors 20 -seed 31 -eta 0.001 -lambda0 0.1 -sigma 0.1 -int_feature -p " + COL);
udtf.initialize(new ObjectInspector[] {xOI, yOI, paramOI});
FactorizationMachineModel model = udtf.initModel(udtf._params);
Assert.assertTrue("Actual class: " + model.getClass().getName(),
model instanceof FMArrayModel);
double diff = 0.d;
final Random rnd = new Random(201L);
for (int numberOfIteration = 0; numberOfIteration < 100; numberOfIteration++) {
ArrayList<IntFeature[]> fArrayList = new ArrayList<IntFeature[]>();
ArrayList<Double> ans = new ArrayList<Double>();
for (int i = 0; i < ROW; i++) {
ArrayList<IntFeature> feature = new ArrayList<IntFeature>();
for (int j = 1; j <= COL; j++) {
if (i < (0.5f * ROW)) {
if (j == 1) {
feature.add(new IntFeature(j, 1.d));
} else if (j < 0.5 * COL) {
if (rnd.nextFloat() < 0.2f) {
feature.add(new IntFeature(j, rnd.nextDouble()));
}
}
} else {
if (j > (0.5f * COL)) {
if (rnd.nextFloat() < 0.2f) {
feature.add(new IntFeature(j, rnd.nextDouble()));
}
}
}
}
IntFeature[] x = new IntFeature[feature.size()];
feature.toArray(x);
fArrayList.add(x);
final double y;
if (i < ROW * 0.5f) {
y = 0.1d;
} else {
y = 0.4d;
}
ans.add(y);
udtf.process(new Object[] {toStringArray(x), y});
}
diff = 0.d;
for (int i = 0; i < fArrayList.size(); i++) {
double predicted = model.predict(fArrayList.get(i));
double actual = ans.get(i);
double tmpDiff = predicted - actual;
diff += tmpDiff * tmpDiff;
}
println("diff = " + diff);
}
Assert.assertTrue("diff = " + diff, diff < 5.d);
}
private static String[] toStringArray(IntFeature[] x) {
final int size = x.length;
final String[] ret = new String[size];
for (int i = 0; i < size; i++) {
ret[i] = x[i].toString();
}
return ret;
}
}