Author: gsingers
Date: Thu Nov 3 18:20:59 2011
New Revision: 1197253
URL: http://svn.apache.org/viewvc?rev=1197253&view=rev
Log:
added some comments
Modified:
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/preparation/PreparePreferenceMatrixJob.java
Modified:
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/preparation/PreparePreferenceMatrixJob.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/preparation/PreparePreferenceMatrixJob.java?rev=1197253&r1=1197252&r2=1197253&view=diff
==============================================================================
---
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/preparation/PreparePreferenceMatrixJob.java
(original)
+++
mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/preparation/PreparePreferenceMatrixJob.java
Thu Nov 3 18:20:59 2011
@@ -56,13 +56,13 @@ public class PreparePreferenceMatrixJob
addInputOption();
addOutputOption();
addOption("maxPrefsPerUser", "mppu", "max number of preferences to
consider per user, " +
- "users with more preferences will be sampled down");
+ "users with more preferences will be sampled down");
addOption("minPrefsPerUser", "mp", "ignore users with less preferences
than this "
- + "(default: " + DEFAULT_MIN_PREFS_PER_USER + ')',
String.valueOf(DEFAULT_MIN_PREFS_PER_USER));
+ + "(default: " + DEFAULT_MIN_PREFS_PER_USER + ')',
String.valueOf(DEFAULT_MIN_PREFS_PER_USER));
addOption("booleanData", "b", "Treat input as without pref values",
Boolean.FALSE.toString());
addOption("ratingShift", "rs", "shift ratings by this value", "0.0");
- Map<String,String> parsedArgs = parseArguments(args);
+ Map<String, String> parsedArgs = parseArguments(args);
if (parsedArgs == null) {
return -1;
}
@@ -70,27 +70,27 @@ public class PreparePreferenceMatrixJob
int minPrefsPerUser =
Integer.parseInt(parsedArgs.get("--minPrefsPerUser"));
boolean booleanData = Boolean.valueOf(parsedArgs.get("--booleanData"));
float ratingShift = Float.parseFloat(parsedArgs.get("--ratingShift"));
-
+ //convert items to an internal index
Job itemIDIndex = prepareJob(getInputPath(), getOutputPath(ITEMID_INDEX),
TextInputFormat.class,
- ItemIDIndexMapper.class, VarIntWritable.class, VarLongWritable.class,
ItemIDIndexReducer.class,
- VarIntWritable.class, VarLongWritable.class,
SequenceFileOutputFormat.class);
+ ItemIDIndexMapper.class, VarIntWritable.class,
VarLongWritable.class, ItemIDIndexReducer.class,
+ VarIntWritable.class, VarLongWritable.class,
SequenceFileOutputFormat.class);
itemIDIndex.setCombinerClass(ItemIDIndexReducer.class);
itemIDIndex.waitForCompletion(true);
-
+ //convert user preferences into a vector per user
Job toUserVectors = prepareJob(getInputPath(),
getOutputPath(USER_VECTORS), TextInputFormat.class,
- ToItemPrefsMapper.class, VarLongWritable.class, booleanData ?
VarLongWritable.class : EntityPrefWritable.class,
- ToUserVectorsReducer.class, VarLongWritable.class,
VectorWritable.class, SequenceFileOutputFormat.class);
+ ToItemPrefsMapper.class, VarLongWritable.class, booleanData ?
VarLongWritable.class : EntityPrefWritable.class,
+ ToUserVectorsReducer.class, VarLongWritable.class,
VectorWritable.class, SequenceFileOutputFormat.class);
toUserVectors.getConfiguration().setBoolean(RecommenderJob.BOOLEAN_DATA,
booleanData);
toUserVectors.getConfiguration().setInt(ToUserVectorsReducer.MIN_PREFERENCES_PER_USER,
minPrefsPerUser);
toUserVectors.getConfiguration().set(ToEntityPrefsMapper.RATING_SHIFT,
String.valueOf(ratingShift));
toUserVectors.waitForCompletion(true);
-
+ //we need the number of users later
int numberOfUsers = (int)
toUserVectors.getCounters().findCounter(ToUserVectorsReducer.Counters.USERS).getValue();
TasteHadoopUtils.writeInt(numberOfUsers, getOutputPath(NUM_USERS),
getConf());
-
+ //build the rating matrix
Job toItemVectors = prepareJob(getOutputPath(USER_VECTORS),
getOutputPath(RATING_MATRIX),
- ToItemVectorsMapper.class, IntWritable.class, VectorWritable.class,
ToItemVectorsReducer.class,
- IntWritable.class, VectorWritable.class);
+ ToItemVectorsMapper.class, IntWritable.class,
VectorWritable.class, ToItemVectorsReducer.class,
+ IntWritable.class, VectorWritable.class);
toItemVectors.setCombinerClass(ToItemVectorsReducer.class);
/* configure sampling regarding the uservectors */
