MLcomp version alpha Statistics: 1208 users, 401 datasets, 328 programs, 9937 runs (0 queued, 0 running), 0 workers

Run 7699

Creator	internal
Program	PaceRegression_weka_numeric
Dataset	regression-sample
Task type	Regression
Created	1y123d 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	6s
Max Memory Usage	416M
Domain:	Regression
Learn time
Train MSE
Predict train time
Test MSE
Predict test time

Log file

./run:3: warning: parenthesize argument(s) for future version
Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
Regression
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
./run:3: warning: parenthesize argument(s) for future version
weka.core.WekaException: weka.classifiers.functions.PaceRegression: Not enough training instances with class labels (required: 24, provided: 4)!
	at weka.core.Capabilities.test(Capabilities.java:1126)
	at weka.core.Capabilities.test(Capabilities.java:1011)
	at weka.core.Capabilities.testWithFail(Capabilities.java:1290)
	at weka.classifiers.functions.PaceRegression.buildClassifier(PaceRegression.java:271)
	at MainScript.main(MainScript.java:27)
Regression
=== END program1: ./run learn ../dataset2/train --- OK [0s]

===== 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 [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
./run:3: warning: parenthesize argument(s) for future version
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)
Regression
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
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) PaceRegression_weka_numeric: This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/functions/PaceRegression.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.
    ---------------------
    
    NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
    
    
  • (dataset:Dataset) regression-sample: Sample dataset for sanity checking.
  • (stripper:Program[Strip]) regression-utils: Validates and inspects a dataset in Regression format.
  • (evaluator:Program[Evaluate]) regression-evaluator: Evaluates predictions of Regression datasets (continuous outputs).

Results

• doTrain:
  • predict:
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• exitCode: 1
• learn:
  • success: true
  • time: 0
• message: On ./run predict , your program (../program1) did not write to .
• success: false

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