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Automatically exported from code.google.com/p/fast-random-forest
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Original issue reported on code.google.com by [email protected]
on 8 Sep 2011 at 3:33
I'm using weka RandomForest from the command-line and tried to substitute
fast-random-forest.
For example:
java -cp weka.jar:FastRandomForest.jar
hr.irb.fastRandomforest.FastRandomForest ... lots of options ... -threads
<nthreads>
and I get:
Exception in thread "main" java.lang.NoClassDefFoundError:
hr/irb/fastRandomforest/FastRandomForest
java -version
java version "1.5.0_15"
Java(TM) 2 Runtime Environment, Standard Edition (build 1.5.0_15-b04)
Java HotSpot(TM) 64-Bit Server VM (build 1.5.0_15-b04, mixed mode)
In case I'm being stupid, can you add a section addressing this use-case?
Thanks,
- n
Original issue reported on code.google.com by [email protected]
on 31 May 2009 at 1:44
Hope I'm not missing something basic.
What steps will reproduce the problem?
1. //Create a test set with a different number of classes as a training set
2. cls.buildClassifier(trainData);
3. Evaluation eval = new Evaluation(trainData);
4. eval.evaluateModel(cls, testData);
What is the expected output? What do you see instead?
Expected to complete evaluation successfully uith no output.
Instead, step 4 gives exception:
Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 13
hr.irb.fastRandomForest.FastRfBagging.distributionForInstance(FastRfBagging.java:647)
hr.irb.fastRandomForest.FastRandomForest.distributionForInstance(FastRandomForest.java:644)
weka.classifiers.evaluation.Evaluation.evaluationForSingleInstance(Evaluation.java:1937)
weka.classifiers.evaluation.Evaluation.evaluateModelOnceAndRecordPrediction(Evaluation.java:1976)
weka.classifiers.evaluation.Evaluation.evaluateModel(Evaluation.java:1854)
weka.classifiers.Evaluation.evaluateModel(Evaluation.java:671)
cf.PortClassifier.main(PortClassifier.java:85)
at hr.irb.fastRandomForest.FastRfBagging.distributionForInstance(FastRfBagging.java:647)
at hr.irb.fastRandomForest.FastRandomForest.distributionForInstance(FastRandomForest.java:644)
at weka.classifiers.evaluation.Evaluation.evaluationForSingleInstance(Evaluation.java:1937)
at weka.classifiers.evaluation.Evaluation.evaluateModelOnceAndRecordPrediction(Evaluation.java:1976)
at weka.classifiers.evaluation.Evaluation.evaluateModel(Evaluation.java:1854)
at weka.classifiers.Evaluation.evaluateModel(Evaluation.java:671)
at cf.PortClassifier.main(PortClassifier.java:85)
What version of the product are you using? On what operating system?
0.99 on Ubuntu 14.04
Please provide any additional information below.
Original issue reported on code.google.com by [email protected]
on 16 Jan 2015 at 4:16
I have compared the fast random forest implementation to the new WEKA
RandomForest implementation.
Since version 3.7.1 WEKA supports parallel processing for ensemble classifiers.
I found that the speed-up of the fast-random-forest implementation is marginal,
while the quality in terms of accuracy on my test data is better using the WEKA
RandomForest implementation.
Thus, I recommend to use WEKA 3.7.1 instead of this implementation.
Original issue reported on code.google.com by [email protected]
on 20 Jan 2011 at 2:22
What steps will reproduce the problem?
1. Use SerialiationHelper to serialize a trained FastRandomForest Classifier
object
What is the expected output? What do you see instead?
While it does serialize the object, it seems to also be serializing the
instance data that was used to train the object. This makes the serialized
files potentially very large (based on training set) and makes loading
serialized objects take much longer due to having to load a training set when
it isn't needed.
What version of the product are you using? On what operating system?
0.98 frf
Java 1.6.0_31
Weka 3.6.6
Mac OSX 10.7
Please provide any additional information below.
Most likely storing instance data in some variable that also gets serialized
later.
Original issue reported on code.google.com by [email protected]
on 6 May 2012 at 8:47
What steps will reproduce the problem?
1. Create Instances with first Attribute being the class
2. build a FastRandomForest with these Instances
What version of the product are you using? On what operating system?
0.99
0.98
Solution
In FastRandomTree replace data.sortedIndices[0].length with
data.sortedIndices[attIndicesWindow[0]].length
In version 0.99 only one line has to be changed when calling buildTree() in the
run() method.
In version 0.98 several occurences have to be replaced withing the buildTree
function.
Original issue reported on code.google.com by [email protected]
on 6 May 2014 at 1:18
What steps will reproduce the problem?
Try to compile source code. Add provided weka-3.7.0.jar to build path.
Errors:
AbstractClassifier cannot be resolved to a
type Benchmark.java AbstractClassifier cannot be resolved FRFAttributeEval.java
...
Reason:
weka.classifiers.AbstractClassifier
inside weka-3.7.0.jar is missing.
Workaround:
Download and use original weka.jar instead of proivded file.
Original issue reported on code.google.com by [email protected]
on 16 Aug 2013 at 6:34
What steps will reproduce the problem?
I'm trying to do a "Hello World" of text classification. It works fine using
NaiveBayes. When I switch to FRF, I get:
Exception in thread "main" java.util.concurrent.ExecutionException:
java.lang.NullPointerException
at java.util.concurrent.FutureTask$Sync.innerGet(FutureTask.java:252)
at java.util.concurrent.FutureTask.get(FutureTask.java:111)
at hr.irb.fastRandomForest.FastRfBagging.buildClassifier(FastRfBagging.java:172)
at hr.irb.fastRandomForest.FastRandomForest.buildClassifier(FastRandomForest.java:575)
at weka.classifiers.meta.FilteredClassifier.buildClassifier(FilteredClassifier.java:442)
at weka.classifiers.evaluation.Evaluation.crossValidateModel(Evaluation.java:763)
at weka.classifiers.Evaluation.crossValidateModel(Evaluation.java:373)
at MyFRF.trainExample(MyFRF.java:87)
at MyFRF.main(MyFRF.java:48)
Caused by: java.lang.NullPointerException
at hr.irb.fastRandomForest.FastRandomTree.buildTree(FastRandomTree.java:319)
at hr.irb.fastRandomForest.FastRandomTree.run(FastRandomTree.java:195)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:334)
at java.util.concurrent.FutureTask.run(FutureTask.java:166)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:722)
It happens when I do cross-validation, or plain training.
classifier.buildClassifier(instances); // HERE
Evaluation eval = new Evaluation(instances);
eval.crossValidateModel(classifier, instances, 4, new Random(1)); // OR HERE
What version of the product are you using? On what operating system?
Latest from SVN. Latest java 1.7
Please provide any additional information below.
Relation Name: Training Instances
Num Instances: 2890
Num Attributes: 2
Name Type Nom Int Real Missing Unique Dist
1 @@class@@ Nom 100% 0% 0% 0 / 0% 0 / 0% 40
2 text Str 100% 0% 0% 0 / 0% 2544 / 88% 2712
Original issue reported on code.google.com by [email protected]
on 14 May 2013 at 5:23
If I'm not mistaken, Weka allows for both dense and sparse dataset files.
Does this work with a sparse dataset, while maintaining a sparse
representation in memory, or must it represent them in memory densely?
Original issue reported on code.google.com by [email protected]
on 10 Oct 2009 at 3:40
According to the instruction in webpage
'http://code.google.com/p/fast-random-forest/', but I still get a failure to
add FastRF jar to WEKA 3.6.6.
Original issue reported on code.google.com by [email protected]
on 29 Mar 2012 at 5:08
What steps will reproduce the problem?
1. Create an Instances object where the class attribute is not the last
attribute.
2. Try to build a classifier using FastRandomForest.
What is the expected output? What do you see instead?
NullPointerException
What version of the product are you using? On what operating system?
0.99
Please provide any additional information below.
It works fine if the Instances object is altered to make the class attribute
last. All classifiers bundled with Weka can handle the class anywhere in the
list of attributes, so this one should be changed to do that as well.
Original issue reported on code.google.com by [email protected]
on 21 Feb 2014 at 2:02
FastRandomForest cannot be compiled using the source from SVN because
build.xml has been generated by NetBeans and the needed project files for
it cannot be found (ie., there is no nbproject/ directory in the repository).
The build.xml should be either independent of the NetBeans project files or
the NetBeans project files should be added to the repository.
Original issue reported on code.google.com by [email protected]
on 18 Feb 2010 at 10:17
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