I am doing a classification with weka, I tried to use the filter.removeuseless, but with the same arff file, I found some differences betweeen using that in the code and in the GUI. In the code I invoked it in this way:
Normalize norm = new Normalize();
norm.setInputFormat(train);
Instances train_norm = Filter.useFilter(train, norm);
RemoveUseless ru = new RemoveUseless();
ru.setInputFormat(train_norm);
Instances train_new = Filter.useFilter(train_norm, ru);
Ranker rank = new Ranker();
InfoGainAttributeEval eval = new InfoGainAttributeEval();
eval.buildEvaluator(train_new);
The result is "strange" because the filter deleted a lot of attributes which the GUI kept as informative for the classification. (The filter in the GUI worked very well). What is the problem? Am I using it well in the code?
i solved like this:
Normalize norm = new Normalize();
norm.setInputFormat(train);
train = Filter.useFilter(train, norm);
RemoveUseless ru = new RemoveUseless();
ru.setInputFormat(train);
train = Filter.useFilter(train, ru);
Ranker rank = new Ranker();
InfoGainAttributeEval eval = new InfoGainAttributeEval();
eval.buildEvaluator(train);
Related
I'm studying Deeplearning4j (ver. 1.0.0-M1.1) for building neural networks.
I use IrisClassifier from Deeplearning4j as an example, it works fine:
//First: get the dataset using the record reader. CSVRecordReader handles loading/parsing
int numLinesToSkip = 0;
char delimiter = ',';
RecordReader recordReader = new CSVRecordReader(numLinesToSkip,delimiter);
recordReader.initialize(new FileSplit(new File(DownloaderUtility.IRISDATA.Download(),"iris.txt")));
//Second: the RecordReaderDataSetIterator handles conversion to DataSet objects, ready for use in neural network
int labelIndex = 4; //5 values in each row of the iris.txt CSV: 4 input features followed by an integer label (class) index. Labels are the 5th value (index 4) in each row
int numClasses = 3; //3 classes (types of iris flowers) in the iris data set. Classes have integer values 0, 1 or 2
int batchSize = 150; //Iris data set: 150 examples total. We are loading all of them into one DataSet (not recommended for large data sets)
DataSetIterator iterator = new RecordReaderDataSetIterator(recordReader,batchSize,labelIndex,numClasses);
DataSet allData = iterator.next();
allData.shuffle();
SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.65); //Use 65% of data for training
DataSet trainingData = testAndTrain.getTrain();
DataSet testData = testAndTrain.getTest();
//We need to normalize our data. We'll use NormalizeStandardize (which gives us mean 0, unit variance):
DataNormalization normalizer = new NormalizerStandardize();
normalizer.fit(trainingData); //Collect the statistics (mean/stdev) from the training data. This does not modify the input data
normalizer.transform(trainingData); //Apply normalization to the training data
normalizer.transform(testData); //Apply normalization to the test data. This is using statistics calculated from the *training* set
final int numInputs = 4;
int outputNum = 3;
long seed = 6;
log.info("Build model....");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.activation(Activation.TANH)
.weightInit(WeightInit.XAVIER)
.updater(new Sgd(0.1))
.l2(1e-4)
.list()
.layer(new DenseLayer.Builder().nIn(numInputs).nOut(3)
.build())
.layer(new DenseLayer.Builder().nIn(3).nOut(3)
.build())
.layer( new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.activation(Activation.SOFTMAX) //Override the global TANH activation with softmax for this layer
.nIn(3).nOut(outputNum).build())
.build();
//run the model
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
//record score once every 100 iterations
model.setListeners(new ScoreIterationListener(100));
for(int i=0; i<1000; i++ ) {
model.fit(trainingData);
}
//evaluate the model on the test set
Evaluation eval = new Evaluation(3);
INDArray output = model.output(testData.getFeatures());
eval.eval(testData.getLabels(), output);
log.info(eval.stats());
For my project, I have inputs ~30000 records (in iris example - 150).
Each record is a vector size ~7000 (in iris example - 4).
Obviously, I can't process the whole data in one DataSet - in will produce OOM for JVM.
How I can process data in multiple DataSets?
I assume it should be something like this (store DataSets in List and iterate):
...
DataSetIterator iterator = new RecordReaderDataSetIterator(recordReader,batchSize,labelIndex,numClasses);
List<DataSet> trainingData = new ArrayList<>();
List<DataSet> testData = new ArrayList<>();
while (iterator.hasNext()) {
DataSet allData = iterator.next();
allData.shuffle();
SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.65); //Use 65% of data for training
trainingData.add(testAndTrain.getTrain());
testData.add(testAndTrain.getTest());
}
//We need to normalize our data. We'll use NormalizeStandardize (which gives us mean 0, unit variance):
DataNormalization normalizer = new NormalizerStandardize();
for (DataSet dataSetTraining : trainingData) {
normalizer.fit(dataSetTraining); //Collect the statistics (mean/stdev) from the training data. This does not modify the input data
normalizer.transform(dataSetTraining); //Apply normalization to the training data
}
for (DataSet dataSetTest : testData) {
normalizer.transform(dataSetTest); //Apply normalization to the test data. This is using statistics calculated from the *training* set
}
...
for(int i=0; i<1000; i++ ) {
for (DataSet dataSetTraining : trainingData) {
model.fit(dataSetTraining);
}
}
But when I start evaluation, I got this error:
Exception in thread "main" java.lang.NullPointerException: Cannot read field "javaShapeInformation" because "this.jvmShapeInfo" is null
at org.nd4j.linalg.api.ndarray.BaseNDArray.dataType(BaseNDArray.java:5507)
at org.nd4j.linalg.api.ndarray.BaseNDArray.validateNumericalArray(BaseNDArray.java:5575)
at org.nd4j.linalg.api.ndarray.BaseNDArray.add(BaseNDArray.java:3087)
at com.aarcapital.aarmlclassifier.classification.FAClassifierLearning.main(FAClassifierLearning.java:117)
...
Evaluation eval = new Evaluation(26);
INDArray output = new NDArray();
for (DataSet dataSetTest : testData) {
output.add(model.output(dataSetTest.getFeatures())); // ERROR HERE
}
System.out.println("--- Output ---");
System.out.println(output);
INDArray labels = new NDArray();
for (DataSet dataSetTest : testData) {
labels.add(dataSetTest.getLabels());
}
System.out.println("--- Labels ---");
System.out.println(labels);
eval.eval(labels, output);
log.info(eval.stats());
What is correct way to iterate miltiple DataSet for learning network?
Thanx!
Firstly, always use Nd4j.create(..) for ndarrays.
Never use the implementation. That allows you to safely create ndarrays that will work whether you use cpus or gpus.
2nd: Always use the RecordReaderDataSetIterator's builder rather than the constructor. It's very long and error prone.
That is why we made the builder in the first place.
Your NullPointer actually isn't coming from where you think it is. it's due to how you're creating the ndarray. There's no data type or anything so it can't know what to expect. Nd4j.create(..) will properly setup the ndarray for you.
Beyond that you are doing things the right way. The record reader handles the batching for you.
I have following code:
StreamingLinearRegressionWithSGD regressionWithSGD =
new StreamingLinearRegressionWithSGD()
.setInitialWeights(Vectors.zeros(featuresNumber));
JavaDStream<LabeledPoint> trainingData = streamingContext.textFileStream(model.getTrainPath()).map(LabeledPoint::parse).cache();
JavaDStream<LabeledPoint> testData = streamingContext.textFileStream(model.getPredictPath()).map(LabeledPoint::parse);
regressionWithSGD.trainOn(trainingData);
regressionWithSGD.predictOnValues(testData.mapToPair(lp -> new Tuple2<>(lp.label(), lp.features()))).print();
I would like to put results to some file/db/queue and so on instead of print() is it possible?
I have figured it out
StreamingLinearRegressionWithSGD regressionWithSGD =
new StreamingLinearRegressionWithSGD()
.setInitialWeights(Vectors.zeros(featuresNumber));
JavaDStream<LabeledPoint> trainingData = streamingContext.textFileStream(model.getTrainPath()).map(LabeledPoint::parse).cache();
JavaDStream<LabeledPoint> testData = streamingContext.textFileStream(model.getPredictPath()).map(LabeledPoint::parse);
regressionWithSGD.trainOn(trainingData);
JavaDStream<Double> doubleJavaDStream=regressionWithSGD.predictOn(testData.map(labeledPoint -> labeledPoint.features()));
doubleJavaDStream.dstream().saveAsTextFiles("result","out");
So as a result we are getting result-{timestamp}.out folders.
How to pass numerical and categorical features to RandomForestRegressor in Apache Spark: MLlib in Java?
I'm able to do it with numerical or categorical, but I don't know how to implement it together.
My working code is as follows (only numerical features used for prediction)
String[] featureNumericalCols = new String[]{
"squareM",
"timeTimeToPragueCityCenter",
};
String[] featureStringCols = new String[]{ //not used
"type",
"floor",
"disposition",
};
VectorAssembler assembler = new VectorAssembler().setInputCols(featureNumericalCols).setOutputCol("features");
Dataset<Row> numericalData = assembler.transform(data);
numericalData.show();
RandomForestRegressor rf = new RandomForestRegressor().setLabelCol("price")
.setFeaturesCol("features");
// Chain indexer and forest in a Pipeline
Pipeline pipeline = new Pipeline()
.setStages(new PipelineStage[]{assembler, rf});
// Train model. This also runs the indexer.
PipelineModel model = pipeline.fit(trainingData);
// Make predictions.
Dataset<Row> predictions = model.transform(testData);
For anyone out there, this is the solution:
StringIndexer typeIndexer = new StringIndexer()
.setInputCol("type")
.setOutputCol("typeIndex");
preparedData = typeIndexer.fit(preparedData).transform(preparedData);
StringIndexer floorIndexer = new StringIndexer()
.setInputCol("floor")
.setOutputCol("floorIndex");
preparedData = floorIndexer.fit(preparedData).transform(preparedData);
StringIndexer dispositionIndexer = new StringIndexer()
.setInputCol("disposition")
.setOutputCol("dispositionIndex");
preparedData = dispositionIndexer.fit(preparedData).transform(preparedData);
String[] featureCols = new String[]{
"squareM",
"timeTimeToPragueCityCenter",
"floorIndex",
"floorIndex",
"dispositionIndex"
};
VectorAssembler assembler = new VectorAssembler().setInputCols(featureCols).setOutputCol("features");
preparedData = assembler.transform(preparedData);
// ... some more impelemtation details
RandomForestRegressor rf = new RandomForestRegressor().setLabelCol("price")
.setFeaturesCol("features");
return rf.fit(preparedData);
I am working on a project and I need some examples how to implement RandomForest in Java with weka? I did it with IBk(), it worked. If I do it with RandomForest in the same way, it does not work.
Does anyone have a simple example for me how to implement RandomForest and how to get probability for each class (i did it with IBk withclassifier.distributionForInstance(instance) Function and it returned me probabilities for each class). How can I do it for RandomForest? I will need to get probability of every tree and to combine it?
//example
ConverrterUtils.DataSource source = new ConverterUtils.DataSource ("..../edit.arff);
Instances dataset = source.getDataSet();
dataset.setClassIndex(dataset.numAttributes() - 1);
IBk classifier = new IBk(5); classifier.buildClassifier(dataset);
Instance instance = new SparseInstance(2);
instance.setValue(0, 65) //example data
instance.setValue(1, 120); //example data
double[] prediction = classifier.distributionForInstance(instance);
//now I get the probability for the first class
System.out.println("Prediction for the first class is: "+prediction[0]);
You can calculate the the infogain while buidling the Model in the RandomForest. It is much slower and requires alot of memory while buidling model. I am not so sure about the documentation. you can add options or setValues while buiilding the model.
//numFolds in number of crossvalidations usually between 1-10
//br is your bufferReader
Instances trainData = new Instances(br);
trainData.setClassIndex(trainData.numAttributes() - 1);
RandomForest rf = new RandomForest();
rf.setNumTrees(50);
//You can set the options here
String[] options = new String[2];
options[0] = "-R";
rf.setOptions(options);
rf.buildClassifier(trainData);
weka.filters.supervised.attribute.AttributeSelection as = new weka.filters.supervised.attribute.AttributeSelection();
Ranker ranker = new Ranker();
InfoGainAttributeEval infoGainAttrEval = new InfoGainAttributeEval();
as.setEvaluator(infoGainAttrEval);
as.setSearch(ranker);
as.setInputFormat(trainData);
trainData = Filter.useFilter(trainData, as);
Evaluation evaluation = new Evaluation(trainData);
evaluation.crossValidateModel(rf, trainData, numFolds, new Random(1));
// Using HashMap to store the infogain values of the attributes
int count = 0;
Map<String, Double> infogainscores = new HashMap<String, Double>();
for (int i = 0; i < trainData.numAttributes(); i++) {
String t_attr = trainData.attribute(i).name();
//System.out.println(i+trainData.attribute(i).name());
double infogain = infoGainAttrEval.evaluateAttribute(i);
if(infogain != 0){
//System.out.println(t_attr + "= "+ infogain);
infogainscores.put(t_attr, infogain);
count = count+1;
}
}
//iterating over the hashmap
Iterator it = infogainscores.entrySet().iterator();
while (it.hasNext()) {
Map.Entry pair = (Map.Entry)it.next();
System.out.println(pair.getKey()+" = "+pair.getValue());
System.out.println(pair.getKey()+" = "+pair.getValue());
it.remove(); // avoids a ConcurrentModificationException
}
I want to create a WEKA Java program that reads a group of newly created data that will be fed to a premade model from the GUI version.
Here is the program:
import java.util.ArrayList;
import weka.classifiers.Classifier;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instances;
import weka.core.Utils;
public class UseModelWithData {
public static void main(String[] args) throws Exception {
// load model
String rootPath = "G:/";
Classifier classifier = (Classifier) weka.core.SerializationHelper.read(rootPath+"j48.model");
// create instances
Attribute attr1 = new Attribute("age");
Attribute attr2 = new Attribute("menopause");
Attribute attr3 = new Attribute("tumor-size");
Attribute attr4 = new Attribute("inv-nodes");
Attribute attr5 = new Attribute("node-caps");
Attribute attr6 = new Attribute("deg-malig");
Attribute attr7 = new Attribute("breast");
Attribute attr8 = new Attribute("breast-quad");
Attribute attr9 = new Attribute("irradiat");
Attribute attr10 = new Attribute("Class");
ArrayList<Attribute> attributes = new ArrayList<Attribute>();
attributes.add(attr1);
attributes.add(attr2);
attributes.add(attr3);
attributes.add(attr4);
attributes.add(attr5);
attributes.add(attr6);
attributes.add(attr7);
attributes.add(attr8);
attributes.add(attr9);
attributes.add(attr10);
// predict instance class values
Instances testing = new Instances("Test dataset", attributes, 0);
// add data
double[] values = new double[testing.numAttributes()];
values[0] = testing.attribute(0).addStringValue("60-69");
values[1] = testing.attribute(1).addStringValue("ge40");
values[2] = testing.attribute(2).addStringValue("10-14");
values[3] = testing.attribute(3).addStringValue("15-17");
values[4] = testing.attribute(4).addStringValue("yes");
values[5] = testing.attribute(5).addStringValue("2");
values[6] = testing.attribute(6).addStringValue("right");
values[7] = testing.attribute(7).addStringValue("right_up");
values[8] = testing.attribute(0).addStringValue("yes");
values[9] = Utils.missingValue();
// add data to instance
testing.add(new DenseInstance(1.0, values));
// instance row to predict
int index = 10;
// perform prediction
double myValue = classifier.classifyInstance(testing.instance(10));
// get the name of class value
String prediction = testing.classAttribute().value((int) myValue);
System.out.println("The predicted value of the instance ["
+ Integer.toString(index) + "]: " + prediction);
}
}
My references include:
Using a premade WEKA model in Java
the WEKA Manual provided in the 3.7.10 version - 17.3 Creating datasets in memory
Creating a single instance for classification in WEKA
So far the part where I create a new Instance inside the script causes the following error:
Exception in thread "main" java.lang.IndexOutOfBoundsException: Index: 10, Size: 1
in the line
double myValue = classifier.classifyInstance(testing.instance(10));
I just want to use a latest row of instance values to a premade WEKA model. How do I solve this?
Resources
Program file
Arff file
j48.model
You have the error because you are trying to access the 11th instance and have only created one.
If you always want to access the last instance you might try the following:
double myValue = classifier.classifyInstance(testing.lastInstance());
Additionally, I don't believe that you are creating the instances you hope for. After looking at your provided ".arff" file, which I believe you are trying to mimic, I think you should proceed making instances as follows:
FastVector atts;
FastVector attAge;
Instances testing;
double[] vals;
// 1. set up attributes
atts = new FastVector();
//age
attAge = new FastVector();
attAge.addElement("10-19");
attAge.addElement("20-29");
attAge.addElement("30-39");
attAge.addElement("40-49");
attAge.addElement("50-59");
attAge.addElement("60-69");
attAge.addElement("70-79");
attAge.addElement("80-89");
attAge.addElement("90-99");
atts.addElement(new Attribute("age", attAge));
// 2. create Instances object
testing = new Instances("breast-cancer", atts, 0);
// 3. fill with data
vals = new double[testing.numAttributes()];
vals[0] = attAge.indexOf("10-19");
testing.add(new DenseInstance(1.0, vals));
// 4. output data
System.out.println(testing);
Of course I did not create the whole dataset, but the technique would be the same.