Java tutorial
/* * 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; import com.google.common.base.Preconditions; import com.google.common.io.Closeables; import java.io.DataInput; import java.io.DataInputStream; import java.io.DataOutput; import java.io.DataOutputStream; import java.io.File; import java.io.FileInputStream; import java.io.IOException; import java.io.InputStream; import java.io.OutputStream; import java.util.ArrayList; import java.util.HashMap; import java.util.Iterator; import java.util.List; import java.util.Map; import org.apache.hadoop.io.Writable; /** * Encapsulates everything we need to know about a model and how it reads and vectorizes its input. * This encapsulation allows us to coherently save and restore a model from a file. This also * allows us to keep command line arguments that affect learning in a coherent way. */ public class LogisticModelParameters implements Writable { private String targetVariable; private Map<String, String> typeMap; private int numFeatures; private boolean useBias; private int maxTargetCategories; private List<String> targetCategories; private double lambda; private double learningRate; private CsvRecordFactory csv; private OnlineLogisticRegression lr; /** * Returns a CsvRecordFactory compatible with this logistic model. The reason that this is tied * in here is so that we have access to the list of target categories when it comes time to save * the model. If the input isn't CSV, then calling setTargetCategories before calling saveTo will * suffice. * * @return The CsvRecordFactory. */ public CsvRecordFactory getCsvRecordFactory() { if (csv == null) { csv = new CsvRecordFactory(getTargetVariable(), getTypeMap()).maxTargetValue(getMaxTargetCategories()) .includeBiasTerm(useBias()); if (targetCategories != null) { csv.defineTargetCategories(targetCategories); } } return csv; } /** * Creates a logistic regression trainer using the parameters collected here. * * @return The newly allocated OnlineLogisticRegression object */ public OnlineLogisticRegression createRegression() { if (lr == null) { lr = new OnlineLogisticRegression(getMaxTargetCategories(), getNumFeatures(), new L1()) .lambda(getLambda()).learningRate(getLearningRate()).alpha(1 - 1.0e-3); } return lr; } /** * Saves a model to an output stream. */ public void saveTo(OutputStream out) throws IOException { Closeables.close(lr, false); targetCategories = getCsvRecordFactory().getTargetCategories(); write(new DataOutputStream(out)); } /** * Reads a model from a stream. */ public static LogisticModelParameters loadFrom(InputStream in) throws IOException { LogisticModelParameters result = new LogisticModelParameters(); result.readFields(new DataInputStream(in)); return result; } /** * Reads a model from a file. * @throws IOException If there is an error opening or closing the file. */ public static LogisticModelParameters loadFrom(File in) throws IOException { try (InputStream input = new FileInputStream(in)) { return loadFrom(input); } } @Override public void write(DataOutput out) throws IOException { out.writeUTF(targetVariable); out.writeInt(typeMap.size()); for (Map.Entry<String, String> entry : typeMap.entrySet()) { out.writeUTF(entry.getKey()); out.writeUTF(entry.getValue()); } out.writeInt(numFeatures); out.writeBoolean(useBias); out.writeInt(maxTargetCategories); if (targetCategories == null) { out.writeInt(0); } else { out.writeInt(targetCategories.size()); for (String category : targetCategories) { out.writeUTF(category); } } out.writeDouble(lambda); out.writeDouble(learningRate); // skip csv lr.write(out); } @Override public void readFields(DataInput in) throws IOException { targetVariable = in.readUTF(); int typeMapSize = in.readInt(); typeMap = new HashMap<>(typeMapSize); for (int i = 0; i < typeMapSize; i++) { String key = in.readUTF(); String value = in.readUTF(); typeMap.put(key, value); } numFeatures = in.readInt(); useBias = in.readBoolean(); maxTargetCategories = in.readInt(); int targetCategoriesSize = in.readInt(); targetCategories = new ArrayList<>(targetCategoriesSize); for (int i = 0; i < targetCategoriesSize; i++) { targetCategories.add(in.readUTF()); } lambda = in.readDouble(); learningRate = in.readDouble(); csv = null; lr = new OnlineLogisticRegression(); lr.readFields(in); } /** * Sets the types of the predictors. This will later be used when reading CSV data. If you don't * use the CSV data and convert to vectors on your own, you don't need to call this. * * @param predictorList The list of variable names. * @param typeList The list of types in the format preferred by CsvRecordFactory. */ public void setTypeMap(Iterable<String> predictorList, List<String> typeList) { Preconditions.checkArgument(!typeList.isEmpty(), "Must have at least one type specifier"); typeMap = new HashMap<>(); Iterator<String> iTypes = typeList.iterator(); String lastType = null; for (Object x : predictorList) { // type list can be short .. we just repeat last spec if (iTypes.hasNext()) { lastType = iTypes.next(); } typeMap.put(x.toString(), lastType); } } /** * Sets the target variable. If you don't use the CSV record factory, then this is irrelevant. * * @param targetVariable The name of the target variable. */ public void setTargetVariable(String targetVariable) { this.targetVariable = targetVariable; } /** * Sets the number of target categories to be considered. * * @param maxTargetCategories The number of target categories. */ public void setMaxTargetCategories(int maxTargetCategories) { this.maxTargetCategories = maxTargetCategories; } public void setNumFeatures(int numFeatures) { this.numFeatures = numFeatures; } public void setTargetCategories(List<String> targetCategories) { this.targetCategories = targetCategories; maxTargetCategories = targetCategories.size(); } public List<String> getTargetCategories() { return this.targetCategories; } public void setUseBias(boolean useBias) { this.useBias = useBias; } public boolean useBias() { return useBias; } public String getTargetVariable() { return targetVariable; } public Map<String, String> getTypeMap() { return typeMap; } public void setTypeMap(Map<String, String> map) { this.typeMap = map; } public int getNumFeatures() { return numFeatures; } public int getMaxTargetCategories() { return maxTargetCategories; } public double getLambda() { return lambda; } public void setLambda(double lambda) { this.lambda = lambda; } public double getLearningRate() { return learningRate; } public void setLearningRate(double learningRate) { this.learningRate = learningRate; } }