org.apache.predictionio.examples.java.recommendations.tutorial4.FeatureBasedAlgorithm.java Source code

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Here is the source code for org.apache.predictionio.examples.java.recommendations.tutorial4.FeatureBasedAlgorithm.java

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/*
 * 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.predictionio.examples.java.recommendations.tutorial4;

import org.apache.predictionio.controller.java.LJavaAlgorithm;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Map;
import java.util.HashMap;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.linear.ArrayRealVector;

public class FeatureBasedAlgorithm
        extends LJavaAlgorithm<FeatureBasedAlgorithmParams, PreparedData, FeatureBasedModel, Query, Float> {
    public final FeatureBasedAlgorithmParams params;
    final static Logger logger = LoggerFactory.getLogger(FeatureBasedAlgorithm.class);

    public FeatureBasedAlgorithm(FeatureBasedAlgorithmParams params) {
        this.params = params;
    }

    public FeatureBasedModel train(PreparedData data) {
        Map<Integer, RealVector> userFeatures = new HashMap<Integer, RealVector>();
        Map<Integer, Integer> userActions = new HashMap<Integer, Integer>();

        for (Integer uid : data.userInfo.keySet()) {
            userFeatures.put(uid, new ArrayRealVector(data.featureCount));
            userActions.put(uid, 0);
        }

        for (TrainingData.Rating rating : data.ratings) {
            final int uid = rating.uid;
            final int iid = rating.iid;
            final double rate = rating.rating;

            // Skip features outside the range.
            if (!(params.min <= rate && rate <= params.max))
                continue;

            final double actualRate = (rate - params.drift) * params.scale;
            final RealVector userFeature = userFeatures.get(uid);
            final RealVector itemFeature = data.itemFeatures.get(iid);
            userFeature.combineToSelf(1, actualRate, itemFeature);

            userActions.put(uid, userActions.get(uid) + 1);
        }

        // Normalize userFeatures by l-inf-norm
        for (Integer uid : userFeatures.keySet()) {
            final RealVector feature = userFeatures.get(uid);
            feature.mapDivideToSelf(feature.getLInfNorm());
        }

        // Normalize itemFeatures by weight
        Map<Integer, RealVector> itemFeatures = new HashMap<Integer, RealVector>();
        for (Integer iid : data.itemFeatures.keySet()) {
            final RealVector feature = data.itemFeatures.get(iid);
            final RealVector normalizedFeature = feature.mapDivide(feature.getL1Norm());
            itemFeatures.put(iid, normalizedFeature);
        }

        return new FeatureBasedModel(userFeatures, userActions, itemFeatures);
    }

    public Float predict(FeatureBasedModel model, Query query) {
        final int uid = query.uid;
        final int iid = query.iid;

        if (!model.userFeatures.containsKey(uid)) {
            return Float.NaN;
        }

        if (!model.itemFeatures.containsKey(iid)) {
            return Float.NaN;
        }

        final RealVector userFeature = model.userFeatures.get(uid);
        final RealVector itemFeature = model.itemFeatures.get(iid);

        return new Float(userFeature.dotProduct(itemFeature));
    }
}