我正在尝试在 Java 中运行 Mallet 并收到以下错误。
Couldn't open cc.mallet.util.MalletLogger resources/logging.properties file.
Perhaps the 'resources' directories weren't copied into the 'class' directory.
Continuing.
我正在尝试从 Mallet 的网站 ( http://mallet.cs.umass.edu/topics-devel.php ) 运行示例。下面是我的代码。任何帮助表示赞赏。
package scriptAnalyzer;
import cc.mallet.util.*;
import cc.mallet.types.*;
import cc.mallet.pipe.*;
import cc.mallet.pipe.iterator.*;
import cc.mallet.topics.*;
import java.util.*;
import java.util.regex.*;
import java.io.*;
public class Mallet {
public static void main(String[] args) throws Exception {
String filePath = "C:/mallet/ap.txt";
// Begin by importing documents from text to feature sequences
ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
// Pipes: lowercase, tokenize, remove stopwords, map to features
pipeList.add( new CharSequenceLowercase() );
pipeList.add( new CharSequence2TokenSequence(Pattern.compile("\\p{L}[\\p{L}\\p{P}]+\\p{L}")) );
pipeList.add( new TokenSequenceRemoveStopwords(new File("stoplists/en.txt"), "UTF-8", false, false, false) );
pipeList.add( new TokenSequence2FeatureSequence() );
InstanceList instances = new InstanceList (new SerialPipes(pipeList));
Reader fileReader = new InputStreamReader(new FileInputStream(new File(filePath)), "UTF-8");
instances.addThruPipe(new CsvIterator (fileReader, Pattern.compile("^(\\S*)[\\s,]*(\\S*)[\\s,]*(.*)$"),
3, 2, 1)); // data, label, name fields
// Create a model with 100 topics, alpha_t = 0.01, beta_w = 0.01
// Note that the first parameter is passed as the sum over topics, while
// the second is the parameter for a single dimension of the Dirichlet prior.
int numTopics = 5;
ParallelTopicModel model = new ParallelTopicModel(numTopics, 1.0, 0.01);
model.addInstances(instances);
// Use two parallel samplers, which each look at one half the corpus and combine
// statistics after every iteration.
model.setNumThreads(2);
// Run the model for 50 iterations and stop (this is for testing only,
// for real applications, use 1000 to 2000 iterations)
model.setNumIterations(50);
model.estimate();
// Show the words and topics in the first instance
// The data alphabet maps word IDs to strings
Alphabet dataAlphabet = instances.getDataAlphabet();
FeatureSequence tokens = (FeatureSequence) model.getData().get(0).instance.getData();
LabelSequence topics = model.getData().get(0).topicSequence;
Formatter out = new Formatter(new StringBuilder(), Locale.US);
for (int position = 0; position < tokens.getLength(); position++) {
out.format("%s-%d ", dataAlphabet.lookupObject(tokens.getIndexAtPosition(position)), topics.getIndexAtPosition(position));
}
System.out.println(out);
// Estimate the topic distribution of the first instance,
// given the current Gibbs state.
double[] topicDistribution = model.getTopicProbabilities(0);
// Get an array of sorted sets of word ID/count pairs
ArrayList<TreeSet<IDSorter>> topicSortedWords = model.getSortedWords();
// Show top 5 words in topics with proportions for the first document
for (int topic = 0; topic < numTopics; topic++) {
Iterator<IDSorter> iterator = topicSortedWords.get(topic).iterator();
out = new Formatter(new StringBuilder(), Locale.US);
out.format("%d\t%.3f\t", topic, topicDistribution[topic]);
int rank = 0;
while (iterator.hasNext() && rank < 5) {
IDSorter idCountPair = iterator.next();
out.format("%s (%.0f) ", dataAlphabet.lookupObject(idCountPair.getID()), idCountPair.getWeight());
rank++;
}
System.out.println(out);
}
// Create a new instance with high probability of topic 0
StringBuilder topicZeroText = new StringBuilder();
Iterator<IDSorter> iterator = topicSortedWords.get(0).iterator();
int rank = 0;
while (iterator.hasNext() && rank < 5) {
IDSorter idCountPair = iterator.next();
topicZeroText.append(dataAlphabet.lookupObject(idCountPair.getID()) + " ");
rank++;
}
// Create a new instance named "test instance" with empty target and source fields.
InstanceList testing = new InstanceList(instances.getPipe());
testing.addThruPipe(new Instance(topicZeroText.toString(), null, "test instance", null));
TopicInferencer inferencer = model.getInferencer();
double[] testProbabilities = inferencer.getSampledDistribution(testing.get(0), 10, 1, 5);
System.out.println("0\t" + testProbabilities[0]);
}
}