org.apache.mahout.cf.taste.hadoop.item.RecommenderJob Maven / Gradle / Ivy
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* 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
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* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
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* See the License for the specific language governing permissions and
* limitations under the License.
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package org.apache.mahout.cf.taste.hadoop.item;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.OutputFormat;
import org.apache.hadoop.mapreduce.lib.input.MultipleInputs;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.cf.taste.hadoop.EntityEntityWritable;
import org.apache.mahout.cf.taste.hadoop.RecommendedItemsWritable;
import org.apache.mahout.cf.taste.hadoop.preparation.PreparePreferenceMatrixJob;
import org.apache.mahout.cf.taste.hadoop.similarity.item.ItemSimilarityJob;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.iterator.sequencefile.PathType;
import org.apache.mahout.math.VarIntWritable;
import org.apache.mahout.math.VarLongWritable;
import org.apache.mahout.math.hadoop.similarity.cooccurrence.RowSimilarityJob;
import org.apache.mahout.math.hadoop.similarity.cooccurrence.measures.VectorSimilarityMeasures;
import java.util.List;
import java.util.Map;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
/**
* Runs a completely distributed recommender job as a series of mapreduces.
*
* Preferences in the input file should look like {@code userID, itemID[, preferencevalue]}
*
*
* Preference value is optional to accommodate applications that have no notion of a preference value (that is, the user
* simply expresses a preference for an item, but no degree of preference).
*
*
*
* The preference value is assumed to be parseable as a {@code double}. The user IDs and item IDs are
* parsed as {@code long}s.
*
*
* Command line arguments specific to this class are:
*
*
* - --input(path): Directory containing one or more text files with the preference data
* - --output(path): output path where recommender output should go
* - --similarityClassname (classname): Name of vector similarity class to instantiate or a predefined similarity
* from {@link org.apache.mahout.math.hadoop.similarity.cooccurrence.measures.VectorSimilarityMeasure}
* - --usersFile (path): only compute recommendations for user IDs contained in this file (optional)
* - --itemsFile (path): only include item IDs from this file in the recommendations (optional)
* - --filterFile (path): file containing comma-separated userID,itemID pairs. Used to exclude the item from the
* recommendations for that user (optional)
* - --numRecommendations (integer): Number of recommendations to compute per user (10)
* - --booleanData (boolean): Treat input data as having no pref values (false)
* - --maxPrefsPerUser (integer): Maximum number of preferences considered per user in final
* recommendation phase (10)
* - --maxSimilaritiesPerItem (integer): Maximum number of similarities considered per item (100)
* - --minPrefsPerUser (integer): ignore users with less preferences than this in the similarity computation (1)
* - --maxPrefsPerUserInItemSimilarity (integer): max number of preferences to consider per user in
* the item similarity computation phase,
* users with more preferences will be sampled down (1000)
* - --threshold (double): discard item pairs with a similarity value below this
*
*
* General command line options are documented in {@link AbstractJob}.
*
* Note that because of how Hadoop parses arguments, all "-D" arguments must appear before all other
* arguments.
*/
public final class RecommenderJob extends AbstractJob {
public static final String BOOLEAN_DATA = "booleanData";
public static final String DEFAULT_PREPARE_PATH = "preparePreferenceMatrix";
private static final int DEFAULT_MAX_SIMILARITIES_PER_ITEM = 100;
private static final int DEFAULT_MAX_PREFS = 500;
private static final int DEFAULT_MIN_PREFS_PER_USER = 1;
@Override
public int run(String[] args) throws Exception {
addInputOption();
addOutputOption();
addOption("numRecommendations", "n", "Number of recommendations per user",
String.valueOf(AggregateAndRecommendReducer.DEFAULT_NUM_RECOMMENDATIONS));
addOption("usersFile", null, "File of users to recommend for", null);
addOption("itemsFile", null, "File of items to recommend for", null);
addOption("filterFile", "f", "File containing comma-separated userID,itemID pairs. Used to exclude the item from "
+ "the recommendations for that user (optional)", null);
addOption("userItemFile", "uif", "File containing comma-separated userID,itemID pairs (optional). "
+ "Used to include only these items into recommendations. "
+ "Cannot be used together with usersFile or itemsFile", null);
addOption("booleanData", "b", "Treat input as without pref values", Boolean.FALSE.toString());
addOption("maxPrefsPerUser", "mxp",
"Maximum number of preferences considered per user in final recommendation phase",
String.valueOf(UserVectorSplitterMapper.DEFAULT_MAX_PREFS_PER_USER_CONSIDERED));
addOption("minPrefsPerUser", "mp", "ignore users with less preferences than this in the similarity computation "
+ "(default: " + DEFAULT_MIN_PREFS_PER_USER + ')', String.valueOf(DEFAULT_MIN_PREFS_PER_USER));
addOption("maxSimilaritiesPerItem", "m", "Maximum number of similarities considered per item ",
String.valueOf(DEFAULT_MAX_SIMILARITIES_PER_ITEM));
addOption("maxPrefsInItemSimilarity", "mpiis", "max number of preferences to consider per user or item in the "
+ "item similarity computation phase, users or items with more preferences will be sampled down (default: "
+ DEFAULT_MAX_PREFS + ')', String.valueOf(DEFAULT_MAX_PREFS));
addOption("similarityClassname", "s", "Name of distributed similarity measures class to instantiate, "
+ "alternatively use one of the predefined similarities (" + VectorSimilarityMeasures.list() + ')', true);
addOption("threshold", "tr", "discard item pairs with a similarity value below this", false);
addOption("outputPathForSimilarityMatrix", "opfsm", "write the item similarity matrix to this path (optional)",
false);
addOption("randomSeed", null, "use this seed for sampling", false);
addFlag("sequencefileOutput", null, "write the output into a SequenceFile instead of a text file");
Map> parsedArgs = parseArguments(args);
if (parsedArgs == null) {
return -1;
}
Path outputPath = getOutputPath();
int numRecommendations = Integer.parseInt(getOption("numRecommendations"));
String usersFile = getOption("usersFile");
String itemsFile = getOption("itemsFile");
String filterFile = getOption("filterFile");
String userItemFile = getOption("userItemFile");
boolean booleanData = Boolean.valueOf(getOption("booleanData"));
int maxPrefsPerUser = Integer.parseInt(getOption("maxPrefsPerUser"));
int minPrefsPerUser = Integer.parseInt(getOption("minPrefsPerUser"));
int maxPrefsInItemSimilarity = Integer.parseInt(getOption("maxPrefsInItemSimilarity"));
int maxSimilaritiesPerItem = Integer.parseInt(getOption("maxSimilaritiesPerItem"));
String similarityClassname = getOption("similarityClassname");
double threshold = hasOption("threshold")
? Double.parseDouble(getOption("threshold")) : RowSimilarityJob.NO_THRESHOLD;
long randomSeed = hasOption("randomSeed")
? Long.parseLong(getOption("randomSeed")) : RowSimilarityJob.NO_FIXED_RANDOM_SEED;
Path prepPath = getTempPath(DEFAULT_PREPARE_PATH);
Path similarityMatrixPath = getTempPath("similarityMatrix");
Path explicitFilterPath = getTempPath("explicitFilterPath");
Path partialMultiplyPath = getTempPath("partialMultiply");
AtomicInteger currentPhase = new AtomicInteger();
int numberOfUsers = -1;
if (shouldRunNextPhase(parsedArgs, currentPhase)) {
ToolRunner.run(getConf(), new PreparePreferenceMatrixJob(), new String[]{
"--input", getInputPath().toString(),
"--output", prepPath.toString(),
"--minPrefsPerUser", String.valueOf(minPrefsPerUser),
"--booleanData", String.valueOf(booleanData),
"--tempDir", getTempPath().toString(),
});
numberOfUsers = HadoopUtil.readInt(new Path(prepPath, PreparePreferenceMatrixJob.NUM_USERS), getConf());
}
if (shouldRunNextPhase(parsedArgs, currentPhase)) {
/* special behavior if phase 1 is skipped */
if (numberOfUsers == -1) {
numberOfUsers = (int) HadoopUtil.countRecords(new Path(prepPath, PreparePreferenceMatrixJob.USER_VECTORS),
PathType.LIST, null, getConf());
}
//calculate the co-occurrence matrix
ToolRunner.run(getConf(), new RowSimilarityJob(), new String[]{
"--input", new Path(prepPath, PreparePreferenceMatrixJob.RATING_MATRIX).toString(),
"--output", similarityMatrixPath.toString(),
"--numberOfColumns", String.valueOf(numberOfUsers),
"--similarityClassname", similarityClassname,
"--maxObservationsPerRow", String.valueOf(maxPrefsInItemSimilarity),
"--maxObservationsPerColumn", String.valueOf(maxPrefsInItemSimilarity),
"--maxSimilaritiesPerRow", String.valueOf(maxSimilaritiesPerItem),
"--excludeSelfSimilarity", String.valueOf(Boolean.TRUE),
"--threshold", String.valueOf(threshold),
"--randomSeed", String.valueOf(randomSeed),
"--tempDir", getTempPath().toString(),
});
// write out the similarity matrix if the user specified that behavior
if (hasOption("outputPathForSimilarityMatrix")) {
Path outputPathForSimilarityMatrix = new Path(getOption("outputPathForSimilarityMatrix"));
Job outputSimilarityMatrix = prepareJob(similarityMatrixPath, outputPathForSimilarityMatrix,
SequenceFileInputFormat.class, ItemSimilarityJob.MostSimilarItemPairsMapper.class,
EntityEntityWritable.class, DoubleWritable.class, ItemSimilarityJob.MostSimilarItemPairsReducer.class,
EntityEntityWritable.class, DoubleWritable.class, TextOutputFormat.class);
Configuration mostSimilarItemsConf = outputSimilarityMatrix.getConfiguration();
mostSimilarItemsConf.set(ItemSimilarityJob.ITEM_ID_INDEX_PATH_STR,
new Path(prepPath, PreparePreferenceMatrixJob.ITEMID_INDEX).toString());
mostSimilarItemsConf.setInt(ItemSimilarityJob.MAX_SIMILARITIES_PER_ITEM, maxSimilaritiesPerItem);
outputSimilarityMatrix.waitForCompletion(true);
}
}
//start the multiplication of the co-occurrence matrix by the user vectors
if (shouldRunNextPhase(parsedArgs, currentPhase)) {
Job partialMultiply = new Job(getConf(), "partialMultiply");
Configuration partialMultiplyConf = partialMultiply.getConfiguration();
MultipleInputs.addInputPath(partialMultiply, similarityMatrixPath, SequenceFileInputFormat.class,
SimilarityMatrixRowWrapperMapper.class);
MultipleInputs.addInputPath(partialMultiply, new Path(prepPath, PreparePreferenceMatrixJob.USER_VECTORS),
SequenceFileInputFormat.class, UserVectorSplitterMapper.class);
partialMultiply.setJarByClass(ToVectorAndPrefReducer.class);
partialMultiply.setMapOutputKeyClass(VarIntWritable.class);
partialMultiply.setMapOutputValueClass(VectorOrPrefWritable.class);
partialMultiply.setReducerClass(ToVectorAndPrefReducer.class);
partialMultiply.setOutputFormatClass(SequenceFileOutputFormat.class);
partialMultiply.setOutputKeyClass(VarIntWritable.class);
partialMultiply.setOutputValueClass(VectorAndPrefsWritable.class);
partialMultiplyConf.setBoolean("mapred.compress.map.output", true);
partialMultiplyConf.set("mapred.output.dir", partialMultiplyPath.toString());
if (usersFile != null) {
partialMultiplyConf.set(UserVectorSplitterMapper.USERS_FILE, usersFile);
}
if (userItemFile != null) {
partialMultiplyConf.set(IDReader.USER_ITEM_FILE, userItemFile);
}
partialMultiplyConf.setInt(UserVectorSplitterMapper.MAX_PREFS_PER_USER_CONSIDERED, maxPrefsPerUser);
boolean succeeded = partialMultiply.waitForCompletion(true);
if (!succeeded) {
return -1;
}
}
if (shouldRunNextPhase(parsedArgs, currentPhase)) {
//filter out any users we don't care about
/* convert the user/item pairs to filter if a filterfile has been specified */
if (filterFile != null) {
Job itemFiltering = prepareJob(new Path(filterFile), explicitFilterPath, TextInputFormat.class,
ItemFilterMapper.class, VarLongWritable.class, VarLongWritable.class,
ItemFilterAsVectorAndPrefsReducer.class, VarIntWritable.class, VectorAndPrefsWritable.class,
SequenceFileOutputFormat.class);
boolean succeeded = itemFiltering.waitForCompletion(true);
if (!succeeded) {
return -1;
}
}
String aggregateAndRecommendInput = partialMultiplyPath.toString();
if (filterFile != null) {
aggregateAndRecommendInput += "," + explicitFilterPath;
}
Class outputFormat = parsedArgs.containsKey("--sequencefileOutput")
? SequenceFileOutputFormat.class : TextOutputFormat.class;
//extract out the recommendations
Job aggregateAndRecommend = prepareJob(
new Path(aggregateAndRecommendInput), outputPath, SequenceFileInputFormat.class,
PartialMultiplyMapper.class, VarLongWritable.class, PrefAndSimilarityColumnWritable.class,
AggregateAndRecommendReducer.class, VarLongWritable.class, RecommendedItemsWritable.class,
outputFormat);
Configuration aggregateAndRecommendConf = aggregateAndRecommend.getConfiguration();
if (itemsFile != null) {
aggregateAndRecommendConf.set(AggregateAndRecommendReducer.ITEMS_FILE, itemsFile);
}
if (userItemFile != null) {
aggregateAndRecommendConf.set(IDReader.USER_ITEM_FILE, userItemFile);
}
if (filterFile != null) {
setS3SafeCombinedInputPath(aggregateAndRecommend, getTempPath(), partialMultiplyPath, explicitFilterPath);
}
setIOSort(aggregateAndRecommend);
aggregateAndRecommendConf.set(AggregateAndRecommendReducer.ITEMID_INDEX_PATH,
new Path(prepPath, PreparePreferenceMatrixJob.ITEMID_INDEX).toString());
aggregateAndRecommendConf.setInt(AggregateAndRecommendReducer.NUM_RECOMMENDATIONS, numRecommendations);
aggregateAndRecommendConf.setBoolean(BOOLEAN_DATA, booleanData);
boolean succeeded = aggregateAndRecommend.waitForCompletion(true);
if (!succeeded) {
return -1;
}
}
return 0;
}
private static void setIOSort(JobContext job) {
Configuration conf = job.getConfiguration();
conf.setInt("io.sort.factor", 100);
String javaOpts = conf.get("mapred.map.child.java.opts"); // new arg name
if (javaOpts == null) {
javaOpts = conf.get("mapred.child.java.opts"); // old arg name
}
int assumedHeapSize = 512;
if (javaOpts != null) {
Matcher m = Pattern.compile("-Xmx([0-9]+)([mMgG])").matcher(javaOpts);
if (m.find()) {
assumedHeapSize = Integer.parseInt(m.group(1));
String megabyteOrGigabyte = m.group(2);
if ("g".equalsIgnoreCase(megabyteOrGigabyte)) {
assumedHeapSize *= 1024;
}
}
}
// Cap this at 1024MB now; see https://issues.apache.org/jira/browse/MAPREDUCE-2308
conf.setInt("io.sort.mb", Math.min(assumedHeapSize / 2, 1024));
// For some reason the Merger doesn't report status for a long time; increase
// timeout when running these jobs
conf.setInt("mapred.task.timeout", 60 * 60 * 1000);
}
public static void main(String[] args) throws Exception {
ToolRunner.run(new Configuration(), new RecommenderJob(), args);
}
}