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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.bankmarketing;
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 'Bank marketing' dataset from UCI.
*
* See http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
*
* Learn when people accept or reject an offer from the bank via telephone based on income, age, education and more.
*/
public class BankMarketingClassificationMain {
public static final int NUM_CATEGORIES = 2;
public static void main(String[] args) throws Exception {
List<TelephoneCall> calls = Lists.newArrayList(new TelephoneCallParser("bank-full.csv"));
double heldOutPercentage = 0.10;
for (int run = 0; run < 20; run++) {
Collections.shuffle(calls);
int cutoff = (int) (heldOutPercentage * calls.size());
List<TelephoneCall> test = calls.subList(0, cutoff);
List<TelephoneCall> train = calls.subList(cutoff, calls.size());
OnlineLogisticRegression lr = new OnlineLogisticRegression(NUM_CATEGORIES, TelephoneCall.FEATURES, new L1())
.learningRate(1)
.alpha(1)
.lambda(0.000001)
.stepOffset(10000)
.decayExponent(0.2);
for (int pass = 0; pass < 20; pass++) {
for (TelephoneCall observation : train) {
lr.train(observation.getTarget(), observation.asVector());
}
if (pass % 5 == 0) {
Auc eval = new Auc(0.5);
for (TelephoneCall testCall : test) {
eval.add(testCall.getTarget(), lr.classifyScalar(testCall.asVector()));
}
System.out.printf("%d, %.4f, %.4f\n", pass, lr.currentLearningRate(), eval.auc());
}
}
}
}
}