MLcomp version alpha Statistics: 2044 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 27397

Creator	favre
Program	RotationForest_weka_nominal
Dataset	cemst-auditor-performance-prediction2-asr3
Task type	MulticlassClassification
Created	296d5h ago
Download	Login required!

Status: Failed!

Note: this page autoupdates while a run is in progress

Reason	On ./run predict <in> <out>, your program (../program1) did not write to <out>.
Total Time	2h43m
Max Memory Usage	419M
Domain	MulticlassClassification
Learn time	2h42m
Train error
Predict train time	1m33s
Test error
Predict test time

Log file

Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
MulticlassClassification
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
	at weka.core.Instance.copy(Instance.java:256)
	at weka.core.Instances.add(Instances.java:291)
	at weka.classifiers.trees.j48.C45Split.resetDistribution(C45Split.java:441)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:274)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
	at weka.classifiers.trees.j48.C45PruneableClassifierTree.getEstimatedErrorsForBranch(C45PruneableClassifierTree.java:278)
MulticlassClassification
=== END program1: ./run learn ../dataset2/train --- OK [9739s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
java.io.FileNotFoundException: model_file (No such file or directory)
	at java.io.FileInputStream.open(Native Method)
	at java.io.FileInputStream.<init>(FileInputStream.java:137)
	at java.io.FileInputStream.<init>(FileInputStream.java:96)
	at weka.core.SerializationHelper.read(SerializationHelper.java:232)
	at MainScript.main(MainScript.java:47)
MulticlassClassification
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [93s]
Failed: On ./run predict <in> <out>, your program (../program1) did not write to <out>.

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) RotationForest_weka_nominal: This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/meta/RotationForest.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Class for construction a Rotation Forest. Can do classification and regression depending on the base learner. For more information, see Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630. URL http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211.
    ---------------------
    
    NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
    
    NOTE: WEKA's Classifiers read a data in the .arff format. For Multiclass datasets, the SVMlight format converted to .arff multiclass format so they can be read by WEKA programs
  • (dataset:Dataset) cemst-auditor-performance-prediction2-asr3: Complementary Evaluation Measures for Speech Transcription, train-test split for task auditor-performance-prediction with asr3, reduced feature set
  • (stripper:Program[Strip]) multiclass-utils: Validates and inspects a dataset in MulticlassClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTrain:
  • predict:
    • success: true
    • time: 93
  • strip:
    • success: true
    • time: 1
• exitCode: 1
• learn:
  • success: true
  • time: 9739
• message: On ./run predict , your program (../program1) did not write to .
• success: false

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