Use WEKA in your Java code

Table of Contents

Instances

ARFF File

Pre 3.5.5 and 3.4.x

3.5.5 and newer

Database

Option handling

Filter

Filtering on-the-fly

Batch filtering

Calling conventions

Classification

Building a Classifier

Batch

Incremental

Evaluating

Cross-validation

Train/test set

Statistics

ROC curves/AUC

Classifying instances

Clustering

Building a Clusterer

Batch

Incremental

Evaluating

Clustering instances

Classes to clusters evaluation

Attribute selection

Meta-Classifier

Filter

Low-level

Note on randomization

See also

Examples

Links

The most common components you might want to use are

Instances - your data

Filter - for preprocessing the data

Classifier/Clusterer - built on the processed data

Evaluating - how good is the classifier/clusterer?

Attribute selection - removing irrelevant attributes from your data

The following sections explain how to use them in your own code. A link to an  example class can be found at the end of this page, under the  Links section. The classifiers and filters always list their options in the Javadoc API ( book,  stable,  developer version) specification.

You might also want to check out the  Weka Examples collection, containing examples for the different versions of Weka. Another, more comprehensive, source of information is the chapter  Using the API of the Weka manual for the stable-3.6 and developer version ( snapshots and releases later than 09/08/2009).

Instances

ARFF File

Pre 3.5.5 and 3.4.x

Reading from an  ARFF file is straightforward:

import weka.core.Instances;
import java.io.BufferedReader;
import java.io.FileReader;
...
BufferedReader reader = new BufferedReader(
new FileReader("/some/where/data.arff"));
Instances data = new Instances(reader);
reader.close();
// setting class attribute
data.setClassIndex(data.numAttributes() - 1);

The class index indicates the target attribute used for classification. By default, in an ARFF file, it is the last attribute, which explains why it's set to numAttributes-1.

You  must set it if your instances are used as a parameter of a weka function (e.g.,:  weka.classifiers.Classifier.buildClassifier(data))

3.5.5 and newer

The  DataSource class is not limited to ARFF files. It can also read CSV files and other formats (basically all file formats that Weka can import via its converters).

import weka.core.converters.ConverterUtils.DataSource;
...
DataSource source = new DataSource("/some/where/data.arff");
Instances data = source.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (data.classIndex() == -1)
data.setClassIndex(data.numAttributes() - 1);

Database

Reading from  Databases is slightly more complicated, but still very easy. First, you'll have to modify your  DatabaseUtils.props file to reflect your database connection. Suppose you want to connect to a  MySQL server that is running on the local machine on the default port  3306. The MySQL JDBC driver is called  Connector/J. (The driver class is  org.gjt.mm.mysql.Driver.) The database where your target data resides is called  some_database. Since you're only reading, you can use the default user  nobody without a password. Your props file must contain the following lines:

jdbcDriver=org.gjt.mm.mysql.Driver
 jdbcURL=jdbc:mysql://localhost:3306/some_database
Secondly, your Java code needs to look like this to load the data from the database:
import weka.core.Instances;
import weka.experiment.InstanceQuery;
...
InstanceQuery query = new InstanceQuery();
query.setUsername("nobody");
query.setPassword("");
query.setQuery("select * from whatsoever");
// You can declare that your data set is sparse
// query.setSparseData(true);
Instances data = query.retrieveInstances();

Notes:

Don't forget to add the JDBC driver to your CLASSPATH.

For MS Access, you must use the JDBC-ODBC-bridge that is part of a JDK. The Windows databases article explains how to do this.

InstanceQuery automatically converts VARCHAR database columns to NOMINAL attributes, and long TEXT database columns to STRING attributes. So if you use InstanceQuery to do text mining against text that appears in a VARCHAR column, Weka will regard such text as nominal values. Thus it will fail to tokenize and mine that text. Use the NominalToString or StringToNominal filter (package weka.filters.unsupervised.attribute) to convert the attributes into the correct type.

Option handling

Weka schemes that implement the  weka.core.OptionHandler interface, such as classifiers, clusterers, and filters, offer the following methods for setting and retrieving options:

void setOptions(String[] options)
String[] getOptions()
There are several ways of setting the options:
Manually creating a String array:
String[] options = new String[2];
options[0] = "-R";
options[1] = "1";
Using a single command-line string and using the splitOptions method of the weka.core.Utils class to turn it into an array:
String[] options = weka.core.Utils.splitOptions("-R 1");
Using the

OptionsToCode.java

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class to automatically turn a command line into code. Especially handy if the command line contains nested classes that have their own options, such as kernels for SMO:

java OptionsToCode weka.classifiers.functions.SMO

will generate output like this:

// create new instance of scheme
weka.classifiers.functions.SMO scheme = new weka.classifiers.functions.SMO();
// set options
scheme.setOptions(weka.core.Utils.splitOptions("-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
Also, the

OptionTree.java

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tool allows you to view a nested options string, e.g., used at the command line, as a tree. This can help you spot nesting errors.

Filter

A filter has two different properties:

supervised or unsupervised

either takes the class attribute into account or not

attribute- or instance-based

e.g., removing a certain attribute or removing instances that meet a certain condition

Most filters implement the  OptionHandler interface, which means you can set the options via a String array, rather than setting them each manually via  set-methods.

For example, if you want to remove the  first attribute of a dataset, you need this filter

weka.filters.unsupervised.attribute.Remove

with this option

-R 1

If you have an  Instances object, called  data, you can create and apply the filter like this:

import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;
...
String[] options = new String[2];
options[0] = "-R"; // "range"
options[1] = "1"; // first attribute
Remove remove = new Remove(); // new instance of filter
remove.setOptions(options); // set options
remove.setInputFormat(data); // inform filter about dataset **AFTER** setting options
Instances newData = Filter.useFilter(data, remove); // apply filter

Filtering on-the-fly

The  FilteredClassifier meta-classifier is an easy way of filtering data on the fly. It removes the necessity of filtering the data before the classifier can be trained. Also, the data need not be passed through the trained filter again at prediction time. The following is an example of using this meta-classifier with the  Remove filter and  J48 for getting rid of a numeric ID attribute in the data:

import weka.classifiers.meta.FilteredClassifier;
import weka.classifiers.trees.J48;
import weka.filters.unsupervised.attribute.Remove;
...
Instances train = ... // from somewhere
Instances test = ... // from somewhere
// filter
Remove rm = new Remove();
rm.setAttributeIndices("1"); // remove 1st attribute
// classifier
J48 j48 = new J48();
j48.setUnpruned(true); // using an unpruned J48
// meta-classifier
FilteredClassifier fc = new FilteredClassifier();
fc.setFilter(rm);
fc.setClassifier(j48);
// train and make predictions
fc.buildClassifier(train);
for (int i = 0; i < test.numInstances(); i++) {
double pred = fc.classifyInstance(test.instance(i));
System.out.print("ID: " + test.instance(i).value(0));
System.out.print(", actual: " + test.classAttribute().value((int) test.instance(i).classValue()));
System.out.println(", predicted: " + test.classAttribute().value((int) pred));
}

Other handy meta-schemes in Weka:

weka.clusterers.FilteredClusterer (since 3.5.4)

weka.associations.FilteredAssociator (since 3.5.6)

Batch filtering

On the command line, you can enable a second input/output pair (via  -r and  -s) with the  -b option, in order to process the second file with the same filter setup as the first one. Necessary, if you're using attribute selection or standardization - otherwise you end up with incompatible datasets. This is done fairly easy, since one initializes the filter only once with the  setInputFormat(Instances) method, namely with the training set, and then applies the filter subsequently to the training set  and the test set. The following example shows how to apply the  Standardize filter to a train and a test set.

Instances train = ... // from somewhere
Instances test = ... // from somewhere
Standardize filter = new Standardize();
filter.setInputFormat(train); // initializing the filter once with training set
Instances newTrain = Filter.useFilter(train, filter); // configures the Filter based on train instances and returns filtered instances
Instances newTest = Filter.useFilter(test, filter); // create new test set

Calling conventions

The  setInputFormat(Instances) method  always has to be the last call before the filter is applied, e.g., with  Filter.useFilter(Instances,Filter).  Why? First, it is the convention for using filters and, secondly, lots of filters generate the header of the output format in the  setInputFormat(Instances) method with the currently set options (setting otpions  after this call doesn't have any effect any more).

Classification

The necessary classes can be found in this package:

weka.classifiers

Building a Classifier

Batch

A Weka classifier is rather simple to train on a given dataset. E.g., we can train an unpruned C4.5 tree algorithm on a given dataset  data. The training is done via the  buildClassifier(Instances) method.

import weka.classifiers.trees.J48;

...
String[] options = new String[1];
options[0] = "-U"; // unpruned tree
J48 tree = new J48(); // new instance of tree
tree.setOptions(options); // set the options
tree.buildClassifier(data); // build classifier

Incremental

Classifiers implementing the  weka.classifiers.UpdateableClassifier interface can be trained incrementally. This conserves memory, since the data doesn't have to be loaded into memory all at once. See the Javadoc of this interface to see what classifiers are implementing it.

The actual process of training an incremental classifier is fairly simple:

Call buildClassifier(Instances) with the structure of the dataset (may or may not contain any actual data rows).

Subsequently call the updateClassifier(Instance) method to feed the classifier new weka.core.Instance objects, one by one.

Here is an example using data from a  weka.core.converters.ArffLoader to train  weka.classifiers.bayes.NaiveBayesUpdateable:

// load data
ArffLoader loader = new ArffLoader();
loader.setFile(new File("/some/where/data.arff"));
Instances structure = loader.getStructure();
structure.setClassIndex(structure.numAttributes() - 1);
 
// train NaiveBayes
NaiveBayesUpdateable nb = new NaiveBayesUpdateable();
nb.buildClassifier(structure);
Instance current;
while ((current = loader.getNextInstance(structure)) != null)
nb.updateClassifier(current);

A working example is

IncrementalClassifier.java

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.

Evaluating

Cross-validation

If you only have a training set and no test you might want to evaluate the classifier by using 10 times 10-fold cross-validation. This can be easily done via the  Evaluation class. Here we  seed the random selection of our folds for the CV with  1. Check out the  Evaluation class for more information about the statistics it produces.

import weka.classifiers.Evaluation;
import java.util.Random;
...
Evaluation eval = new Evaluation(newData);
eval.crossValidateModel(tree, newData, 10, new Random(1));

Note: The classifier (in our example  tree) should not be trained when handed over to the  crossValidateModel method.  Why? If the classifier does not abide to the Weka convention that a classifier must be re-initialized every time the  buildClassifiermethod is called (in other words: subsequent calls to the  buildClassifier method always return the same results), you will get inconsistent and worthless results. The  crossValidateModel takes care of training and evaluating the classifier. (It creates a copy of the original classifier that you hand over to the  crossValidateModel for each run of the cross-validation.)

Train/test set

In case you have a dedicated test set, you can train the classifier and then evaluate it on this test set. In the following example, a J48 is instantiated, trained and then evaluated. Some statistics are printed to  stdout:

import weka.core.Instances;
import weka.classifiers.Evaluation;
import weka.classifiers.trees.J48;
...
Instances train = ... // from somewhere
Instances test = ... // from somewhere
// train classifier
Classifier cls = new J48();
cls.buildClassifier(train);
// evaluate classifier and print some statistics
Evaluation eval = new Evaluation(train);
eval.evaluateModel(cls, test);
System.out.println(eval.toSummaryString("\nResults\n======\n", false));

Statistics

Some methods for retrieving the results from the evaluation:

nominal class
correct() - number of correctly classified instances (see also incorrect())
pctCorrect() - percentage of correctly classified instances (see also pctIncorrect())
kappa() - Kappa statistics
numeric class
correlationCoefficient() - correlation coefficient
general
meanAbsoluteError() - the mean absolute error
rootMeanSquaredError() - the root mean squared error
unclassified() - number of unclassified instances
pctUnclassified() - percentage of unclassified instances

If you want to have the exact same behavior as from the command line, use this call:

import weka.classifiers.trees.J48;
import weka.classifiers.Evaluation;
...
String[] options = new String[2];
options[0] = "-t";
options[1] = "/some/where/somefile.arff";
System.out.println(Evaluation.evaluateModel(new J48(), options));

ROC curves/AUC

Since Weka 3.5.1, you can also generate ROC curves/AUC with the predictions Weka recorded during testing. You can access these predictions via the  predictions() method of the  Evaluation class. See the  Generating ROC curve article for a full example of how to generate ROC curves.

Classifying instances

In case you have an unlabeled dataset that you want to classify with your newly trained classifier, you can use the following code snippet. It loads the file  /some/where/unlabeled.arff, uses the previously built classifier  tree to label the instances, and saves the labeled data as  /some/where/labeled.arff.

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.FileReader;
import java.io.FileWriter;
import weka.core.Instances;
...
// load unlabeled data
Instances unlabeled = new Instances(
new BufferedReader(
new FileReader("/some/where/unlabeled.arff")));
 
// set class attribute
unlabeled.setClassIndex(unlabeled.numAttributes() - 1);
 
// create copy
Instances labeled = new Instances(unlabeled);
 
// label instances
for (int i = 0; i < unlabeled.numInstances(); i++) {
double clsLabel = tree.classifyInstance(unlabeled.instance(i));
labeled.instance(i).setClassValue(clsLabel);
}
// save labeled data
BufferedWriter writer = new BufferedWriter(
new FileWriter("/some/where/labeled.arff"));
writer.write(labeled.toString());
writer.newLine();
writer.flush();
writer.close();

Note on nominal classes:

If you're interested in the distribution over all the classes, use the method distributionForInstance(Instance). This method returns a double array with the probability for each class.

The returned double value from classifyInstance (or the index in the array returned by distributionForInstance) is just the index for the string values in the attribute. That is, if you want the string representation for the class label returned above clsLabel, then you can print it like this:

System.out.println(clsLabel + " -> " + unlabeled.classAttribute().value((int) clsLabel));

Clustering

Clustering is similar to classification. The necessary classes can be found in this package:

weka.clusterers

Building a Clusterer

Batch

A clusterer is built in much the same way as a classifier, but the  buildClusterer(Instances) method instead of  buildClassifier(Instances). The following code snippet shows how to build an  EM clusterer with a maximum of  100 iterations.

import weka.clusterers.EM;

...
String[] options = new String[2];
options[0] = "-I"; // max. iterations
options[1] = "100";
EM clusterer = new EM(); // new instance of clusterer
clusterer.setOptions(options); // set the options
clusterer.buildClusterer(data); // build the clusterer

Incremental

Clusterers implementing the  weka.clusterers.UpdateableClusterer interface can be trained incrementally (available since version 3.5.4). This conserves memory, since the data doesn't have to be loaded into memory all at once. See the Javadoc for this interface to see which clusterers implement it.

The actual process of training an incremental clusterer is fairly simple:

Call buildClusterer(Instances) with the structure of the dataset (may or may not contain any actual data rows).

Subsequently call the updateClusterer(Instance) method to feed the clusterer new weka.core.Instance objects, one by one.

Call updateFinished() after all Instance objects have been processed, for the clusterer to perform additional computations.

Here is an example using data from a  weka.core.converters.ArffLoader to train  weka.clusterers.Cobweb:

// load data
ArffLoader loader = new ArffLoader();
loader.setFile(new File("/some/where/data.arff"));
Instances structure = loader.getStructure();
 
// train Cobweb
Cobweb cw = new Cobweb();
cw.buildClusterer(structure);
Instance current;
while ((current = loader.getNextInstance(structure)) != null)
cw.updateClusterer(current);
cw.updateFinished();

A working example is

IncrementalClusterer.java

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.

Evaluating

For evaluating a clusterer, you can use the  ClusterEvaluation class. In this example, the number of clusters found is written to output:

import weka.clusterers.ClusterEvaluation;

import weka.clusterers.Clusterer;

...
ClusterEvaluation eval = new ClusterEvaluation();
Clusterer clusterer = new EM(); // new clusterer instance, default options
clusterer.buildClusterer(data); // build clusterer
eval.setClusterer(clusterer); // the cluster to evaluate
eval.evaluateClusterer(newData); // data to evaluate the clusterer on
System.out.println("# of clusters: " + eval.getNumClusters()); // output # of clusters

Or, in the case of  density based clusters, you can cross-validate the clusterer (Note: with  MakeDensityBasedClusterer you can turn any clusterer into a density-based one):

import weka.clusterers.ClusterEvaluation;
import weka.clusterers.DensityBasedClusterer;
import weka.core.Instances;
import java.util.Random;
...
Instances data = ... // from somewhere
DensityBasedClusterer clusterer = new ... // the clusterer to evaluate
double logLikelyhood =
ClusterEvaluation.crossValidateModel( // cross-validate
clusterer, data, 10, // with 10 folds
new Random(1)); // and random number generator with seed 1

Or, if you want the same behavior/print-out from command line, use this call:

import weka.clusterers.EM;
import weka.clusterers.ClusterEvaluation;
...
String[] options = new String[2];
options[0] = "-t";
options[1] = "/some/where/somefile.arff";
System.out.println(ClusterEvaluation.evaluateClusterer(new EM(), options));

Clustering instances

The only difference with regard to classification is the method name. Instead of  classifyInstance(Instance), it is now  clusterInstance(Instance). The method for obtaining the distribution is still the same, i.e., distributionForInstance(Instance).

Classes to clusters evaluation

If your data contains a class attribute and you want to check how well the generated clusters fit the classes, you can perform a so-called  classes to clusters evaluation. The Weka Explorer offers this functionality, and it's quite easy to implement. These are the necessary steps (complete source code:

ClassesToClusters.java

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):
load the data and set the class attribute
Instances data = new Instances(new BufferedReader(new FileReader("/some/where/file.arff")));
data.setClassIndex(data.numAttributes() - 1);
generate the class-less data to train the clusterer with
weka.filters.unsupervised.attribute.Remove filter = new weka.filters.unsupervised.attribute.Remove();
filter.setAttributeIndices("" + (data.classIndex() + 1));
filter.setInputFormat(data);
Instances dataClusterer = Filter.useFilter(data, filter);
train the clusterer, e.g., EM
EM clusterer = new EM();
// set further options for EM, if necessary...
clusterer.buildClusterer(dataClusterer);
evaluate the clusterer with the data still containing the class attribute
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(clusterer);
eval.evaluateClusterer(data);
print the results of the evaluation to stdout
System.out.println(eval.clusterResultsToString());

Attribute selection

There is no real need to use the attribute selection classes directly in your own code, since there are already a meta-classifier and a filter available for applying attribute selection, but the low-level approach is still listed for the sake of completeness. The following examples all use  CfsSubsetEval and  GreedyStepwise (backwards). The code listed below is taken from the

AttributeSelectionTest.java

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Meta-Classifier
The following meta-classifier performs a preprocessing step of attribute selection before the data gets presented to the base classifier (in the example here, this is  J48).
Instances data = ... // from somewhere
AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
J48 base = new J48();
classifier.setClassifier(base);
classifier.setEvaluator(eval);
classifier.setSearch(search);
// 10-fold cross-validation
Evaluation evaluation = new Evaluation(data);
evaluation.crossValidateModel(classifier, data, 10, new Random(1));
System.out.println(evaluation.toSummaryString());

Filter

The filter approach is straightforward: after setting up the filter, one just filters the data through the filter and obtains the reduced dataset.
Instances data = ... // from somewhere
AttributeSelection filter = new AttributeSelection(); // package weka.filters.supervised.attribute!
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
filter.setEvaluator(eval);
filter.setSearch(search);
filter.setInputFormat(data);
// generate new data
Instances newData = Filter.useFilter(data, filter);
System.out.println(newData);

Low-level

If neither the meta-classifier nor filter approach is suitable for your purposes, you can use the attribute selection classes themselves.

Instances data = ... // from somewhere
AttributeSelection attsel = new AttributeSelection(); // package weka.attributeSelection!
CfsSubsetEval eval = new CfsSubsetEval();
GreedyStepwise search = new GreedyStepwise();
search.setSearchBackwards(true);
attsel.setEvaluator(eval);
attsel.setSearch(search);
attsel.SelectAttributes(data);
// obtain the attribute indices that were selected
int[] indices = attsel.selectedAttributes();
System.out.println(Utils.arrayToString(indices));

Note on randomization

Most machine learning schemes, like classifiers and clusterers, are susceptible to the ordering of the data. Using a different seed for randomizing the data will most likely produce a different result. For example, the Explorer, or a classifier/clusterer run from the command line, uses only a seeded  java.util.Random number generator, whereas the  weka.core.Instances.getgetRandomNumberGenerator(int) (which the

WekaDemo.java

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uses) also takes the data into account for seeding. Unless one runs 10-fold cross-validation 10 times and averages the results, one will most likely get different results.

See also
Weka Examples - pointer to collection of example classes
Databases - for more information about using databases in Weka (includes ODBC, e.g., for MS Access)
weka/experiment/DatabaseUtils.props - the database setup file
Generating cross-validation folds (Java approach) - in case you want to run 10-fold cross-validation manually
Generating classifier evaluation output manually - if you want to generate some of the evaluation statistics output manually
Creating Instances on-the-fly - explains how to generate a weka.core.Instances object from scratch
Save Instances to an ARFF File - shows how to output a dataset
Using the Experiment API

Examples

The following are a few sample classes for using various parts of the Weka API:

WekaDemo.java

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(book, stable-3.6, developer)

little demo class that loads data from a file, runs it through a filter and trains/evaluates a classifier

ClusteringDemo.java

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(book, stable-3.6, developer)

a basic example for using the clusterer API

ClassesToClusters.java

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(book, stable-3.6, developer)

performs a classes to clusters evaluation like in the Explorer

AttributeSelectionTest.java

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(book, stable-3.6, developer)

example code for using the attribute selection API

M5PExample.java

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(book, stable-3.6, developer)

example using M5P to obtain data from database, train model, serialize it to a file, and use this serialized model to make predictions again.

OptionsToCode.java

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(book, stable-3.6, developer)

turns a Weka command line for a scheme with options into Java code, correctly escaping quotes and backslashes.

OptionTree.java

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(book, stable-3.6, developer)

displays nested Weka options as tree.

IncrementalClassifier.java

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(book, stable-3.6, developer)

Example class for how to train an incremental classifier (in this case, weka.classifiers.bayes.NaiveBayesUpdateable).

IncrementalClusterer.java

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(stable-3.6, developer)

Example class for how to train an incremental clusterer (in this case, weka.clusterers.Cobweb).

Links

Weka API

Book version

Stable 3.6 version

Developer version