Note: this page autoupdates while a run is in progress
On ./run predict <in> <out>, your program (../program1) did not write to <out>.
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 ../dataset2/train
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
=== END program1: ./run learn ../dataset2/train --- OK [295s]
===== 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 [7s]
=== 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)
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [276s]
Failed: On ./run predict <in> <out>, your program (../program1) did not write to <out>.
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) FilteredClassifier_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/FilteredClassifier.java' from WEKA's libraries.
The following description was taken from this classes JavaDoc information:
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.
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.