Note: this page autoupdates while a run is in progress
(see end of log file)
Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
=== START program2: ./run learn ../program1/data
=== END program2: ./run learn ../program1/data --- OK [20s]
=== END program1: ./run learn ../dataset3/train --- OK [21s]
===== MAIN: predict/evaluate on train data =====
=== START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in
=== END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output
Worker killed run: Current time usage 5h0m exceeds allowed 5h0m
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) binary-to-multi: Allows multiclass classification to be run on binary classification datasets (trivial reduction).
(multiclassLearner:Program[MulticlassClassification]) LWL_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/lazy/LWL.java' from WEKA's libraries.
The following description was taken from this classes JavaDoc information:
Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler.
Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression).
For more info, see
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003.
C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..
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
Go to the page for the run and look at the log file for signs of the responsible error.
You can also download the run and run it locally on your machine (a README file should
be included in the download which provides more information).
We said that a run was simply a program/dataset pair, but that's not the full story.
A run actually includes other helper programs such as the evaluation program and
various programs for reductions (e.g., one-versus-all, hyperparameter tuning).
More formally, a run is a given by a run specification,
which can be found on the page for any run.
A run specification is a tree where each internal node represents a program
and its children represents the arguments to be passed into its constructor.
For example, the one-versus-all program takes your binary classification program
as a constructor argument and behaves like a multiclass classification program.