edu.stanford.nlp.parser.nndep.Util Maven / Gradle / Ivy
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/*
* @Author: Danqi Chen
* @Email: [email protected]
* @Created: 2014-08-25
* @Last Modified: 2014-10-05
*/
package edu.stanford.nlp.parser.nndep;
import edu.stanford.nlp.io.IOUtils;
import edu.stanford.nlp.io.RuntimeIOException;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.process.CoreLabelTokenFactory;
import edu.stanford.nlp.stats.Counter;
import edu.stanford.nlp.stats.Counters;
import edu.stanford.nlp.stats.IntCounter;
import edu.stanford.nlp.util.CoreMap;
import java.util.*;
import java.io.*;
/**
*
* Some utility functions
*
* @author Danqi Chen
* @author Jon Gauthier
*/
class Util {
private Util() {} // static methods
private static Random random;
/**
* Normalize word embeddings by setting mean = rMean, std = rStd
*/
public static double[][] scaling(double[][] A, double rMean, double rStd) {
int count = 0;
double mean = 0.0;
double std = 0.0;
for (double[] aA : A)
for (int j = 0; j < aA.length; ++j) {
count += 1;
mean += aA[j];
std += aA[j] * aA[j];
}
mean = mean / count;
std = Math.sqrt(std / count - mean * mean);
System.err.printf("Scaling word embeddings:");
System.err.printf("(mean = %.2f, std = %.2f) -> (mean = %.2f, std = %.2f)", mean, std, rMean, rStd);
double[][] rA = new double[A.length][A[0].length];
for (int i = 0; i < rA.length; ++ i)
for (int j = 0; j < rA[i].length; ++ j)
rA[i][j] = (A[i][j] - mean) * rStd / std + rMean;
return rA;
}
/**
* Normalize word embeddings by setting mean = 0, std = 1
*/
public static double[][] scaling(double[][] A) {
return scaling(A, 0.0, 1.0);
}
// return strings sorted by frequency, and filter out those with freq. less than cutOff.
/**
* Build a dictionary of words collected from a corpus.
*
* Filters out words with a frequency below the given {@code cutOff}.
*
* @return Words sorted by decreasing frequency, filtered to remove
* any words with a frequency below {@code cutOff}
*/
public static List generateDict(List str, int cutOff)
{
Counter freq = new IntCounter<>();
for (String aStr : str)
freq.incrementCount(aStr);
List keys = Counters.toSortedList(freq, false);
List dict = new ArrayList<>();
for (String word : keys) {
if (freq.getCount(word) >= cutOff)
dict.add(word);
}
return dict;
}
public static List generateDict(List str)
{
return generateDict(str, 1);
}
/**
* @return Shared random generator used in this package
*/
static Random getRandom() {
if (random != null)
return random;
else
return getRandom(System.currentTimeMillis());
}
/**
* Set up shared random generator to use the given seed.
*
* @return Shared random generator object
*/
static Random getRandom(long seed) {
random = new Random(seed);
System.err.printf("Random generator initialized with seed %d%n", seed);
return random;
}
public static List getRandomSubList(List input, int subsetSize)
{
int inputSize = input.size();
if (subsetSize > inputSize)
subsetSize = inputSize;
Random random = getRandom();
for (int i = 0; i < subsetSize; i++)
{
int indexToSwap = i + random.nextInt(inputSize - i);
T temp = input.get(i);
input.set(i, input.get(indexToSwap));
input.set(indexToSwap, temp);
}
return input.subList(0, subsetSize);
}
// TODO replace with GrammaticalStructure#readCoNLLGrammaticalStructureCollection
public static void loadConllFile(String inFile, List sents, List trees, boolean unlabeled, boolean cPOS)
{
CoreLabelTokenFactory tf = new CoreLabelTokenFactory(false);
BufferedReader reader = null;
try {
reader = IOUtils.readerFromString(inFile);
CoreMap sentence = new CoreLabel();
List sentenceTokens = new ArrayList<>();
DependencyTree tree = new DependencyTree();
for (String line : IOUtils.getLineIterable(reader, false)) {
String[] splits = line.split("\t");
if (splits.length < 10) {
trees.add(tree);
sentence.set(CoreAnnotations.TokensAnnotation.class, sentenceTokens);
sents.add(sentence);
tree = new DependencyTree();
sentence = new CoreLabel();
sentenceTokens = new ArrayList<>();
} else {
String word = splits[1],
pos = cPOS ? splits[3] : splits[4],
depType = splits[7];
int head = Integer.parseInt(splits[6]);
CoreLabel token = tf.makeToken(word, 0, 0);
token.setTag(pos);
token.set(CoreAnnotations.CoNLLDepParentIndexAnnotation.class, head);
token.set(CoreAnnotations.CoNLLDepTypeAnnotation.class, depType);
sentenceTokens.add(token);
if (!unlabeled)
tree.add(head, depType);
else
tree.add(head, Config.UNKNOWN);
}
}
} catch (IOException e) {
throw new RuntimeIOException(e);
} finally {
IOUtils.closeIgnoringExceptions(reader);
}
}
public static void loadConllFile(String inFile, List sents, List trees)
{
loadConllFile(inFile, sents, trees, false, false);
}
public static void writeConllFile(String outFile, List sentences, List trees)
{
try
{
PrintWriter output = IOUtils.getPrintWriter(outFile);
for (int i = 0; i < sentences.size(); i++)
{
CoreMap sentence = sentences.get(i);
DependencyTree tree = trees.get(i);
List tokens = sentence.get(CoreAnnotations.TokensAnnotation.class);
for (int j = 1, size = tokens.size(); j <= size; ++j)
{
CoreLabel token = tokens.get(j - 1);
output.printf("%d\t%s\t_\t%s\t%s\t_\t%d\t%s\t_\t_%n",
j, token.word(), token.tag(), token.tag(),
tree.getHead(j), tree.getLabel(j));
}
output.println();
}
output.close();
}
catch (Exception e) {
throw new RuntimeIOException(e);
}
}
public static void printTreeStats(String str, List trees)
{
System.err.println(Config.SEPARATOR + " " + str);
int nTrees = trees.size();
int nonTree = 0;
int multiRoot = 0;
int nonProjective = 0;
for (DependencyTree tree : trees) {
if (!tree.isTree())
++nonTree;
else
{
if (!tree.isProjective())
++nonProjective;
if (!tree.isSingleRoot())
++multiRoot;
}
}
System.err.printf("#Trees: %d%n", nTrees);
System.err.printf("%d tree(s) are illegal (%.2f%%).%n", nonTree, nonTree * 100.0 / nTrees);
System.err.printf("%d tree(s) are legal but have multiple roots (%.2f%%).%n", multiRoot, multiRoot * 100.0 / nTrees);
System.err.printf("%d tree(s) are legal but not projective (%.2f%%).%n", nonProjective, nonProjective * 100.0 / nTrees);
}
public static void printTreeStats(List trees)
{
printTreeStats("", trees);
}
}