I'm using the model builder addon for OpenNLP to create a better NER model.
According to this post, I have used the code posted by markg :
public class ModelBuilderAddonUse {
private static List<String> getSentencesFromSomewhere() throws Exception
{
List<String> list = new ArrayList<String>();
BufferedReader reader = new BufferedReader(new FileReader("D:\\Work\\workspaces\\default\\UpdateModel\\documentrequirements.docx"));
String line;
while ((line = reader.readLine()) != null)
{
list.add(line);
}
reader.close();
return list;
}
public static void main(String[] args) throws Exception {
/**
* establish a file to put sentences in
*/
File sentences = new File("D:\\Work\\workspaces\\default\\UpdateModel\\sentences.text");
/**
* establish a file to put your NER hits in (the ones you want to keep based
* on prob)
*/
File knownEntities = new File("D:\\Work\\workspaces\\default\\UpdateModel\\knownentities.txt");
/**
* establish a BLACKLIST file to put your bad NER hits in (also can be based
* on prob)
*/
File blacklistedentities = new File("D:\\Work\\workspaces\\default\\UpdateModel\\blentities.txt");
/**
* establish a file to write your annotated sentences to
*/
File annotatedSentences = new File("D:\\Work\\workspaces\\default\\UpdateModel\\annotatedSentences.txt");
/**
* establish a file to write your model to
*/
File theModel = new File("D:\\Work\\workspaces\\default\\UpdateModel\\nl-ner-person.bin");
//------------create a bunch of file writers to write your results and sentences to a file
FileWriter sentenceWriter = new FileWriter(sentences, true);
FileWriter blacklistWriter = new FileWriter(blacklistedentities, true);
FileWriter knownEntityWriter = new FileWriter(knownEntities, true);
//set some thresholds to decide where to write hits, you don't have to use these at all...
double keeperThresh = .95;
double blacklistThresh = .7;
/**
* Load your model as normal
*/
TokenNameFinderModel personModel = new TokenNameFinderModel(new File("D:\\Work\\workspaces\\default\\UpdateModel\\nl-ner-person.bin"));
NameFinderME personFinder = new NameFinderME(personModel);
/**
* do your normal NER on the sentences you have
*/
for (String s : getSentencesFromSomewhere()) {
sentenceWriter.write(s.trim() + "\n");
sentenceWriter.flush();
String[] tokens = s.split(" ");//better to use a tokenizer really
Span[] find = personFinder.find(tokens);
double[] probs = personFinder.probs();
String[] names = Span.spansToStrings(find, tokens);
for (int i = 0; i < names.length; i++) {
//YOU PROBABLY HAVE BETTER HEURISTICS THAN THIS TO MAKE SURE YOU GET GOOD HITS OUT OF THE DEFAULT MODEL
if (probs[i] > keeperThresh) {
knownEntityWriter.write(names[i].trim() + "\n");
}
if (probs[i] < blacklistThresh) {
blacklistWriter.write(names[i].trim() + "\n");
}
}
personFinder.clearAdaptiveData();
blacklistWriter.flush();
knownEntityWriter.flush();
}
//flush and close all the writers
knownEntityWriter.flush();
knownEntityWriter.close();
sentenceWriter.flush();
sentenceWriter.close();
blacklistWriter.flush();
blacklistWriter.close();
/**
* THIS IS WHERE THE ADDON IS GOING TO USE THE FILES (AS IS) TO CREATE A NEW MODEL. YOU SHOULD NOT HAVE TO RUN THE FIRST PART AGAIN AFTER THIS RUNS, JUST NOW PLAY WITH THE
* KNOWN ENTITIES AND BLACKLIST FILES AND RUN THE METHOD BELOW AGAIN UNTIL YOU GET SOME DECENT RESULTS (A DECENT MODEL OUT OF IT).
*/
DefaultModelBuilderUtil.generateModel(sentences, knownEntities, blacklistedentities, theModel, annotatedSentences, "person", 3);
}
}
It also runs, but my output quits at :
annotated sentences: 1862
knowns: 58
Building Model using 1862 annotations
reading training data...
But in the example in the post it should go futher like this :
Indexing events using cutoff of 5
Computing event counts... done. 561755 events
Indexing... done.
Sorting and merging events... done. Reduced 561755 events to 127362.
Done indexing.
Incorporating indexed data for training...
done.
Number of Event Tokens: 127362
Number of Outcomes: 3
Number of Predicates: 106490
...done.
Can anyone help me to fix this problem, so I can generate a model?
I have searched realy a lot but cant find any good documutation about it.
Would really appreciat it, thanks.
Correct the path to your training data file like this:
File sentences = new File("D:/Work/workspaces/default/UpdateModel/sentences.text");
instead of
File sentences = new File("D:\\Work\\workspaces\\default\\UpdateModel\\sentences.text");
Update
This is how is used, by adding the files to the project folder. Try it like this -
File sentences = new File("src/training/resources/CreateModel/sentences.txt");
Check my respository for reference on Github
This should help.
Related
I'm using Java with JDBC to run MySql code. I want to execute a DDL script, but JDBC can only execute a single statement at a time, which makes it unsuitable to execute a whole .sql file out of the box.
What I'm trying to do is use Antlr4 to parse the .sql file so I can break up each individual statement and then iteratively execute them with JDBC.
I've gotten this far:
InputStream resourceAsStream = Main.class.getClassLoader()
.getResourceAsStream("an-arbitrary-ddl.sql");
CharStream codePointCharStream = CharStreams.fromStream(resourceAsStream);
MySqlLexer tokenSource = new MySqlLexer(new CaseChangingCharStream(codePointCharStream, true));
TokenStream tokenStream = new CommonTokenStream(tokenSource);
MySqlParser mySqlParser = new MySqlParser(tokenStream);
// Where do I go from here?
I'm sure I'm just not searching for the correct terms because I'm new to Antlr and manually parsing code. I can't find any reference from here as to what I need to do to get individual sql statements out of the MySqlParser. What do I need to do next?
A parser is not the right tool for this kind of problem. A statement splitter is pretty easy to write manually and much faster if you do it yourself. I implemented such a splitter in C++ in MySQL Workbench. Shouldn't be difficult to port this to Java. The code is very fast (1 Mio LOC SQL code in under 1 sec on an average machine). A parser would need much longer.
I'm sure this can be improved, however, as the most simple way I could create this was creating a listener and provide the constructor with a Consumer<String> object. The listener looks at individual statements and recursively constructs them. There is probably a more optimal solution, however, I no longer have time to try to optimize this if there is.
/**
* #author Paul Nelson Baker
* #see GitHub
* #see LinkedIn
* #since 2018-09
*/
public class SqlStatementListener extends MySqlParserBaseListener {
private final Consumer<String> sqlStatementConsumer;
public SqlStatementListener(Consumer<String> sqlStatementConsumer) {
this.sqlStatementConsumer = sqlStatementConsumer;
}
#Override
public void enterSqlStatement(MySqlParser.SqlStatementContext ctx) {
if (ctx.getChildCount() > 0) {
StringBuilder stringBuilder = new StringBuilder();
recreateStatementString(ctx.getChild(0), stringBuilder);
stringBuilder.setCharAt(stringBuilder.length() - 1, ';');
String recreatedSqlStatement = stringBuilder.toString();
sqlStatementConsumer.accept(recreatedSqlStatement);
}
super.enterSqlStatement(ctx);
}
private void recreateStatementString(ParseTree currentNode, StringBuilder stringBuilder) {
if (currentNode instanceof TerminalNode) {
stringBuilder.append(currentNode.getText());
stringBuilder.append(' ');
}
for (int i = 0; i < currentNode.getChildCount(); i++) {
recreateStatementString(currentNode.getChild(i), stringBuilder);
}
}
}
Next you need to traverse the statements, the string consumer from earlier allows you to lazily redirect the output wherever you need. This can be as simple as just printing to stdout, however, it can just as easily be used to append to a list.
public List<String> mySqlStatementsFrom(String sourceCode) {
List<String> statements = new ArrayList<>();
mySqlStatementsToConsumer(sourceCode, statements::add);
return statements;
}
public void mySqlStatementsToConsumer(String sourceCode, Consumer<String> mySqlStatementConsumer) {
CharStream codePointCharStream = CharStreams.fromString(sourceCode);
MySqlLexer tokenSource = new MySqlLexer(new CaseChangingCharStream(codePointCharStream, true));
TokenStream tokenStream = new CommonTokenStream(tokenSource);
MySqlParser mySqlParser = new MySqlParser(tokenStream);
SqlStatementListener statementListener = new SqlStatementListener(mySqlStatementConsumer);
ParseTreeWalker.DEFAULT.walk(statementListener, mySqlParser.sqlStatements());
}
I am using Weka to do a text classification. I have created a NaiveBayes model using the Weka GUI, and I have saved that model and then was trying to use this model to classify instances of a training set. This is my code :
Classifier clsClassifier = (Classifier) weka.core.SerializationHelper.read("Source/test/80percentModel.model");
StringToWordVector filter = new StringToWordVector();
BufferedReader reader = new BufferedReader(
new FileReader("Source/test/clt.train.arff"));
Instances trainingData = new Instances(reader);
reader.close();
trainingData.setClassIndex(trainingData.numAttributes() - 1);
filter.setInputFormat(trainingData);
BufferedReader reader2 = new BufferedReader(
new FileReader("Source/test/clt.test.arff"));
Instances testingData = new Instances(reader2);
reader2.close();
testingData.setClassIndex(testingData.numAttributes() - 1);
testingData = Filter.useFilter(testingData, filter);
System.out.println(testingData.numInstances());
for (int j = 0; j < testingData.numInstances(); j++) {
double res = clsClassifier.classifyInstance(testingData.get(j));
System.out.println(testingData.classAttribute().value((int)res));
}
I am getting the following error :
java.lang.IllegalArgumentException: Src and Dest differ in # of attributes: 1 != 1781
at weka.core.RelationalLocator.copyRelationalValues(RelationalLocator.java:87)
at weka.filters.Filter.copyValues(Filter.java:405)
at weka.filters.Filter.push(Filter.java:326)
at weka.filters.unsupervised.attribute.StringToWordVector.input(StringToWordVector.java:655)
at weka.classifiers.meta.FilteredClassifier.filterInstance(FilteredClassifier.java:672)
at weka.classifiers.meta.FilteredClassifier.distributionForInstance(FilteredClassifier.java:699)
at weka.classifiers.AbstractClassifier.classifyInstance(AbstractClassifier.java:173)
at test.WekaClassification.main(WekaClassification.java:66)
I dont quite get what I am doing wrong here. Why is there a mismatch in the number of attributes ? and is this the correct way to apply a trained model in a testData set ?
There are some possibilities, but your Error Shows that the number of attributes are not equal in your trained and dataset. They must be completely in a same format, type and value. The test file also should have the values for the label attribute. Check to see if in Weka GUI you get the same Error or not? StringToWordVector may not Filter in an affordable manner. check its output by watching the contents.
I am currently working on a word search program for my Java class, and I thought I would do some research on other similar programs before I get started. (See how they work before I write my own version.) I have stumbled across this program:
“Java Word Search Solver” by tmck-code via Code Review, Feb 2015.
It looks very well written, but I cannot figure out how to input my file-name for the puzzle and word list method.
Example:
/**
* A method that loads a dictionary text file into a tree structure
* #param filename The dictionary file to load
* #return The Red-Black tree containing the dictionary
*/
private static ArrayList<String> loadDict(String filename) {
ArrayList<String> dict = new ArrayList<String>();
try {
BufferedReader in = new BufferedReader(
new FileReader(filename));
String word;
while( (word = in.readLine()) != null ) {
dict.add(word);
}
} catch( IOException e ) {
System.err.println("A file error occurred: " + filename );
System.exit(1);
}
return dict;
}
Where in this selection of code do I put my file name (wordlist.txt)?
You do not put this anywhere in the code. I read a little through to code in your link. The path of your file gets passed to programm as you call it.
If you call it via a console (e.g. cmd.exe) it should look somehow like this:
C:\Users\yourName> java WordSearch.java "/path/to/your/file.txt"
You do not put anything of the information in the code itself. The programm in your link just uses the console arguments
I wrote a WEKA java code to train 4 classifiers. I saved the classifiers models and want to use them to predict new unseen instances (think about it as someone who wants to test whether a tweet is positive or negative).
I used StringToWordsVector filter on the training data. And to avoid the "Src and Dest differ in # of attributes" error I used the following code to train the filter using the trained data before applying the filter on the new instance to try and predict whether a new instance is positive or negative. And I just can't get it right.
Classifier cls = (Classifier) weka.core.SerializationHelper.read("models/myModel.model"); //reading one of the trained classifiers
BufferedReader datafile = readDataFile("Tweets/tone1.ARFF"); //read training data
Instances data = new Instances(datafile);
data.setClassIndex(data.numAttributes() - 1);
Filter filter = new StringToWordVector(50);//keep 50 words
filter.setInputFormat(data);
Instances filteredData = Filter.useFilter(data, filter);
// rebuild classifier
cls.buildClassifier(filteredData);
String testInstance= "Text that I want to use as an unseen instance and predict whether it's positive or negative";
System.out.println(">create test instance");
FastVector attributes = new FastVector(2);
attributes.addElement(new Attribute("text", (FastVector) null));
// Add class attribute.
FastVector classValues = new FastVector(2);
classValues.addElement("Negative");
classValues.addElement("Positive");
attributes.addElement(new Attribute("Tone", classValues));
// Create dataset with initial capacity of 100, and set index of class.
Instances tests = new Instances("test istance", attributes, 100);
tests.setClassIndex(tests.numAttributes() - 1);
Instance test = new Instance(2);
// Set value for message attribute
Attribute messageAtt = tests.attribute("text");
test.setValue(messageAtt, messageAtt.addStringValue(testInstance));
test.setDataset(tests);
Filter filter2 = new StringToWordVector(50);
filter2.setInputFormat(tests);
Instances filteredTests = Filter.useFilter(tests, filter2);
System.out.println(">train Test filter using training data");
Standardize sfilter = new Standardize(); //Match the number of attributes between src and dest.
sfilter.setInputFormat(filteredData); // initializing the filter with training set
filteredTests = Filter.useFilter(filteredData, sfilter); // create new test set
ArffSaver saver = new ArffSaver(); //save test data to ARFF file
saver.setInstances(filteredTests);
File unseenFile = new File ("Tweets/unseen.ARFF");
saver.setFile(unseenFile);
saver.writeBatch();
When I try to Standardize the Input data using the filtered training data I get a new ARFF file (unseen.ARFF) but with 2000 (same number of training data) instances where most of the values are negative. I don't understand why or how to remove those instances.
System.out.println(">Evaluation"); //without the following 2 lines I get ArrayIndexOutOfBoundException.
filteredData.setClassIndex(filteredData.numAttributes() - 1);
filteredTests.setClassIndex(filteredTests.numAttributes() - 1);
Evaluation eval = new Evaluation(filteredData);
eval.evaluateModel(cls, filteredTests);
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
Printing the evaluation results I want to see for example a percentage of how positive or negative this instance is but instead I get the following. I also want to see 1 instance instead of 2000. Any help on how to do this will be great.
> Results
======
Correlation coefficient 0.0285
Mean absolute error 0.8765
Root mean squared error 1.2185
Relative absolute error 409.4123 %
Root relative squared error 121.8754 %
Total Number of Instances 2000
Thanks
use eval.predictions(). It is an java.util.ArrayList<Prediction>. Then you can use Prediction.weight() method to get how much positive or negative your test variable is....
cls.distributionForInstance(newInst) returns the probability distribution for an instance. Check the docs
I have reached a good solution and here I share my code with you. This trains a classifier using WEKA Java code then use it to predict new unseen instances. Some parts - like paths - are hardcoded but you can easily modify the method to take parameters.
/**
* This method performs classification of unseen instance.
* It starts by training a model using a selection of classifiers then classifiy new unlabled instances.
*/
public static void predict() throws Exception {
//start by providing the paths for your training and testing ARFF files make sure both files have the same structure and the exact classes in the header
//initialise classifier
Classifier classifier = null;
System.out.println("read training arff");
Instances train = new Instances(new BufferedReader(new FileReader("Train.arff")));
train.setClassIndex(0);//in my case the class was the first attribute thus zero otherwise it's the number of attributes -1
System.out.println("read testing arff");
Instances unlabeled = new Instances(new BufferedReader(new FileReader("Test.arff")));
unlabeled.setClassIndex(0);
// training using a collection of classifiers (NaiveBayes, SMO (AKA SVM), KNN and Decision trees.)
String[] algorithms = {"nb","smo","knn","j48"};
for(int w=0; w<algorithms.length;w++){
if(algorithms[w].equals("nb"))
classifier = new NaiveBayes();
if(algorithms[w].equals("smo"))
classifier = new SMO();
if(algorithms[w].equals("knn"))
classifier = new IBk();
if(algorithms[w].equals("j48"))
classifier = new J48();
System.out.println("==========================================================================");
System.out.println("training using " + algorithms[w] + " classifier");
Evaluation eval = new Evaluation(train);
//perform 10 fold cross validation
eval.crossValidateModel(classifier, train, 10, new Random(1));
String output = eval.toSummaryString();
System.out.println(output);
String classDetails = eval.toClassDetailsString();
System.out.println(classDetails);
classifier.buildClassifier(train);
}
Instances labeled = new Instances(unlabeled);
// label instances (use the trained classifier to classify new unseen instances)
for (int i = 0; i < unlabeled.numInstances(); i++) {
double clsLabel = classifier.classifyInstance(unlabeled.instance(i));
labeled.instance(i).setClassValue(clsLabel);
System.out.println(clsLabel + " -> " + unlabeled.classAttribute().value((int) clsLabel));
}
//save the model for future use
ObjectOutputStream out = new ObjectOutputStream(new FileOutputStream("myModel.dat"));
out.writeObject(classifier);
out.close();
System.out.println("===== Saved model =====");
}
I try to Save an Excel file. The Excel file is a template with makros (*.xltm). I can open the file and edit the content, but if i try to save the destination Excel file is corrupt.
I try to save the file with:
int id = _workbook.getIDsOfNames(new String[] {"Save"})[0];
_workbook.invoke(id);
or/and
_xlsClientSite.save(_file, true);
You might try specifying a file format in your Save call.
If you're lucky, you can find the file format code you need in the Excel help. If you can't find what you need there, you'll have to get your hands dirty using the OLEVIEW.EXE program. There's likely a copy of it sitting on your hard drive somewhere, but if not, it's easy enough to find a copy with a quick Google search.
To use OLEVIEW.EXE:
Run it
Crack open the 'Type Libraries' entry
Find the version of Excel that you're using
Open that item
Search the enormous pile of text that's displayed for the string 'XlFileFormat'
Examine the XLFileFormat enum for a code that seems promising
If you are using Office2007 ("Excel12") like I am, you might try one of these values:
xlOpenXMLWorkbookMacroEnabled = 52
xlOpenXMLTemplateMacroEnabled = 53
Here's a method that I use to save Excel files using OLE:
/**
* Save the given workbook in the specified format.
*
* #param controlSiteAuto the OLE control site to use
* #param filepath the file to save to
* #param formatCode XlFileFormat code representing the file format to save as
* #param replaceExistingWithoutPrompt true to replace an existing file quietly, false to ask the user first
*/
public void saveWorkbook(OleAutomation controlSiteAuto, String filepath, Integer formatCode, boolean replaceExistingWithoutPrompt) {
Variant[] args = null;
Variant result = null;
try {
// suppress "replace existing?" prompt, if necessary
if (replaceExistingWithoutPrompt) {
setPropertyOnObject(controlSiteAuto, "Application", "DisplayAlerts", "False");
}
// if the given formatCode is null, for some reason, use a reasonable default
if (formatCode == null) {
formatCode = 51; // xlWorkbookDefault=51
}
// save the workbook
int[] id = controlSiteAuto.getIDsOfNames(new String[] {"SaveAs", "FileName", "FileFormat"});
args = new Variant[2];
args[0] = new Variant(filepath);
args[1] = new Variant(formatCode);
result = controlSiteAuto.invoke(id[0], args);
if (result == null || !result.getBoolean()) {
throw new RuntimeException("Unable to save active workbook");
}
// enable alerts again, if necessary
if (replaceExistingWithoutPrompt) {
setPropertyOnObject(controlSiteAuto, "Application", "DisplayAlerts", "True");
}
} finally {
cleanup(args);
cleanup(result);
}
}
protected void cleanup(Variant[] variants) {
if (variants != null) {
for (int i = 0; i < variants.length; i++) {
if (variants[i] != null) {
variants[i].dispose();
}
}
}
}