org.apache.mahout.classifier.sgd.pserver.AdultIncomeClassificationMain.java Source code

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Here is the source code for org.apache.mahout.classifier.sgd.pserver.AdultIncomeClassificationMain.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.mahout.classifier.sgd.pserver;

import com.google.common.collect.Lists;
import org.apache.mahout.classifier.evaluation.Auc;
import org.apache.mahout.classifier.sgd.L1;
import org.apache.mahout.classifier.sgd.OnlineLogisticRegression;

import java.util.Collections;
import java.util.List;

/**
 * Uses the SGD classifier on the 'Adult' dataset from UCI.
 *
 * See http://archive.ics.uci.edu/ml/datasets/Adult
 *
 * Learn the income of people based on their information.
 */
public class AdultIncomeClassificationMain {

    public static final int NUM_CATEGORIES = 2;

    public static void main(String[] args) throws Exception {
        List<AdultData> adults = Lists.newArrayList(new AdultDataParser("adult.data.txt"));

        double heldOutPercentage = 0.10;

        for (int run = 0; run < 20; run++) {
            Collections.shuffle(adults);
            int cutoff = (int) (heldOutPercentage * adults.size());
            List<AdultData> test = adults.subList(0, cutoff);
            List<AdultData> train = adults.subList(cutoff, adults.size());

            OnlineLogisticRegression lr = new OnlineLogisticRegression(NUM_CATEGORIES, AdultData.FEATURES, new L1())
                    .learningRate(1).alpha(1).lambda(0.000001).stepOffset(10000).decayExponent(0.2);
            for (int pass = 0; pass < 20; pass++) {
                for (AdultData adultData : train) {
                    lr.train(adultData.getTarget(), adultData.asVector());
                }
                if (pass % 5 == 0) {
                    Auc eval = new Auc(0.5);
                    for (AdultData testCall : test) {
                        eval.add(testCall.getTarget(), lr.classifyScalar(testCall.asVector()));
                    }
                    System.out.printf("%d, %.4f, %.4f\n", pass, lr.currentLearningRate(), eval.auc());
                }
            }
        }
    }
}