at.storm.bolt.SVMBolt.java Source code

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Here is the source code for at.storm.bolt.SVMBolt.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 at.storm.bolt;

import java.io.File;
import java.util.Map;

import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;

import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.tuple.Tuple;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.google.gson.JsonObject;

import at.storm.bolt.values.data.FeatureGenerationBoltData;
import at.storm.bolt.values.data.SVMBoltData;
import at.storm.bolt.values.statistic.tuple.TupleStatistic;
import at.storm.commons.Configuration;
import at.storm.commons.Dataset;
import at.storm.commons.svm.SVM;
import at.storm.commons.util.io.SerializationUtils;

public class SVMBolt extends BaseRichBolt {
    public static final String ID = "support-vector-maschine-bolt";
    public static final String CONF_LOGGING = ID + ".logging";
    private static final long serialVersionUID = -6790858930924043126L;
    private static final Logger LOG = LoggerFactory.getLogger(SVMBolt.class);

    private boolean m_logging = false;
    private Dataset m_dataset;
    private svm_model m_model;
    private OutputCollector collector;

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(SVMBoltData.getSchema());
    }

    @SuppressWarnings("rawtypes")
    public void prepare(Map config, TopologyContext context, OutputCollector collector) {
        // Optional set logging
        if (config.get(CONF_LOGGING) != null) {
            m_logging = (Boolean) config.get(CONF_LOGGING);
        } else {
            m_logging = false;
        }

        this.collector = collector;

        LOG.info("Loading SVM model...");
        m_dataset = Configuration.getDataSetTwitch();
        m_model = SerializationUtils
                .deserialize(m_dataset.getDatasetPath() + File.separator + SVM.SVM_MODEL_FILE_SER);

        if (m_model == null) {
            LOG.error("Could not load SVM model! File: " + m_dataset.getDatasetPath() + File.separator
                    + SVM.SVM_MODEL_FILE_SER);
            throw new RuntimeException();
        }
    }

    public void execute(Tuple tuple) {
        FeatureGenerationBoltData featureGenerationValue = FeatureGenerationBoltData.getFromTuple(tuple);
        JsonObject jsonObject = featureGenerationValue.getJsonObject();
        TupleStatistic tupleStatistic = featureGenerationValue.getTupleStatistic();
        Map<Integer, Double> featureVector = featureGenerationValue.getFeatureVector();

        // Create feature nodes
        svm_node[] testNodes = new svm_node[featureVector.size()];
        int i = 0;
        for (Map.Entry<Integer, Double> feature : featureVector.entrySet()) {
            svm_node node = new svm_node();
            node.index = feature.getKey();
            node.value = feature.getValue();
            testNodes[i] = node;
            i++;
        }

        double predictedClass = svm.svm_predict(m_model, testNodes);

        JsonObject score = new JsonObject();
        score.addProperty("score", convertRating(predictedClass));

        jsonObject.add("sentiment", score);

        if (m_logging) {
            LOG.info("SVM all done log: " + jsonObject.toString());
        }

        collector.emit(tuple, new SVMBoltData(jsonObject, tupleStatistic));
        collector.ack(tuple);
    }

    private static int convertRating(double predicted) {
        if (predicted == 0.0) {
            return -1;
        } else if (predicted == 1.0) {
            return 0;
        } else if (predicted == 2.0) {
            return 1;
        } else {
            throw new IllegalArgumentException("SVM prediction couldnt be converted to a rating between -1 and 1");
        }
    }

}