MLcomp version alpha Statistics: 1202 users, 398 datasets, 328 programs, 9930 runs (0 queued, 0 running), 0 workers

Run 2966

Creator	internal
Program	GaussianProcesses_weka_numeric
Dataset	autos
Task type	Regression
Created	1y120d ago
Download	Login required!

Status: Failed!

Note: this page autoupdates while a run is in progress

Reason	(see end of log file)
Total Time	1s
Max Memory Usage	246M
Domain:	Regression
Learn time
Train MSE	0.267
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
Regression
=== END program1: ./run learn ../dataset2/train --- OK [1s]

===== 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
Regression
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
./run:3: warning: parenthesize argument(s) for future version
java.lang.IllegalArgumentException: Src and Dest differ in # of attributes!!
	at weka.core.RelationalLocator.copyRelationalValues(RelationalLocator.java:88)
	at weka.filters.Filter.copyValues(Filter.java:359)
	at weka.filters.Filter.push(Filter.java:276)
	at weka.filters.unsupervised.attribute.ReplaceMissingValues.convertInstance(ReplaceMissingValues.java:280)
	at weka.filters.unsupervised.attribute.ReplaceMissingValues.input(ReplaceMissingValues.java:140)
	at weka.classifiers.functions.GaussianProcesses.classifyInstance(GaussianProcesses.java:401)
	at MainScript.main(MainScript.java:55)
Regression
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
Failed: True file has 61 examples but prediction file has 0 examples
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- FAILED [0s]

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) GaussianProcesses_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/GaussianProcesses.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Implements Gaussian Processes for regression without hyperparameter-tuning. For more information see David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
    ---------------------
    
    NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
    
    
  • (dataset:Dataset) autos: 205 examples, 77 features
  • (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

• doTest:
  • evaluate:
    • message: True file has 61 examples but prediction file has 0 examples
    • success: false
    • time: 0
  • predict:
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • meanSquaredError: 0.267148195836763
    • numExamples: 144
    • success: true
    • time: 0
    • totalMeanSquaredError: 38.4693402004939
  • predict:
    • success: true
    • time: 1
  • strip:
    • success: true
    • time: 0
• exitCode: 1
• learn:
  • success: true
  • time: 1
• success: false

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