/*
* Copyright 2010 Ted Dunning. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are
* permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice, this list
* of conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY <COPYRIGHT HOLDER> ``AS IS'' AND ANY EXPRESS OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
* ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* The views and conclusions contained in the software and documentation are those of the
* authors and should not be interpreted as representing official policies, either expressed
* or implied, of <copyright holder>.
*/
package mia.classifier.ch16;
import com.google.common.collect.Lists;
import com.google.common.collect.Maps;
import org.apache.mahout.classifier.sgd.OnlineLogisticRegression;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import java.util.List;
import java.util.Map;
import java.util.PriorityQueue;
/**
* Returns the top n items from a classification model.
*/
public class ModelEvaluator {
private List<Item> items = Lists.newArrayList();
private OnlineLogisticRegression model;
private Map<Item, Double> itemCache = Maps.newHashMap();
private Map<Long, Double> interactionCache = Maps.newHashMap();
private FeatureEncoder encoder = new FeatureEncoder();
public List<ScoredItem> topItems(User u, int limit) {
Vector userVector = new RandomAccessSparseVector(model.numFeatures());
encoder.addUserFeatures(u, userVector);
double userScore = model.classifyScalarNoLink(userVector);
PriorityQueue<ScoredItem> r = new PriorityQueue<ScoredItem>();
for (Item item : items) {
Double itemScore = itemCache.get(item);
if (itemScore == null) {
Vector v = new RandomAccessSparseVector(model.numFeatures());
encoder.addItemFeatures(item, v);
itemScore = model.classifyScalarNoLink(v);
itemCache.put(item, itemScore);
}
long code = encoder.interactionHash(u, item);
Double interactionScore = interactionCache.get(code);
if (interactionScore == null) {
Vector v = new RandomAccessSparseVector(model.numFeatures());
encoder.addInteractions(u, item, v);
interactionScore = model.classifyScalarNoLink(v);
interactionCache.put(code, interactionScore);
}
double score = userScore + itemScore + interactionScore;
r.add(new ScoredItem(score, item));
while (r.size() > limit) {
r.poll();
}
}
return Lists.newArrayList(r);
}
public static class ScoredItem implements Comparable<ScoredItem> {
double score;
Item item;
public ScoredItem(double score, Item item) {
this.score = score;
this.item = item;
}
@Override
public int compareTo(ScoredItem other) {
int r = Double.compare(score, other.score);
if (r != 0) {
return r;
}
return item.id - other.item.id;
}
}
}