Note: this page autoupdates while a run is in progress
(see end of log file)
5h0m
420M
MulticlassClassification
5m22s
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.
MulticlassClassification
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run learn ../dataset2/train --- OK [322s]
===== 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
./run:3: warning: parenthesize argument(s) for future version
Worker killed run: Current time usage 5h0m exceeds allowed 5h0m
Run specification
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) IB1_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/IB1.java' from WEKA's libraries.
The following description was taken from this classes JavaDoc information:
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Nearest-neighbour classifier. Uses normalized Euclidean distance to find the training instance closest to the given test instance, and predicts the same class as this training instance. If multiple instances have the same (smallest) distance to the test instance, the first one found is used.
For more information, see
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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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) Synthetic 75% Density, Large, Few Labels : A synthetically generated data set
Attributes:
=label(i) = argmax_j w(j)'*x(i) for randomly generated weight vectors.
=weight vectors elements are independently sampled from Normal distribution.
=density is what percentage of weight vector's elements were not set to 0
=x(i) normally distributed according to multivariate Gaussian, random parameters
(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).
When you generate a run, you can set a time limit for the run (no more than 24 hours). After that point, we will terminate the program.
Your program can use 1.5GB of memory. More information here.
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.
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