./run:3: warning: parenthesize argument(s) for future version
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 [311s]
===== 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
MulticlassClassification
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [323s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [6s]
===== 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 [3s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [109s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s]
real 12m43.620s
user 5m17.996s
sys 0m9.169s
Run specification
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) HyperPipes_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/misc/HyperPipes.java' from WEKA's libraries.
The following description was taken from this classes JavaDoc information:
---------------------
Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that "most contains the instance".
Does not handle numeric class, or missing values in test cases. Extremely simple algorithm, but has the advantage of being extremely fast, and works quite well when you have "smegloads" of attributes.
---------------------
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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