MLcomp - Viewing Run 10472

Run 10472

Creator	shabbychef
Program	SMO_weka_nominal
Dataset	blakhol_bc_test_5000
Task type	MulticlassClassification
Created	2y211d ago
Download	Login required!

Status: Done!

Total Time	39s
Max Memory Usage	433M
Domain	BinaryClassification
Learn time
Train error	0.427
Predict train time
Test error	0.435
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.
MulticlassClassification
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset7/train
=== START program2: ./run learn ../program1/data
=== START program5: ./run split ../program1/data ../program2/cv.train ../program2/cv.test
=== END program5: ./run split ../program1/data ../program2/cv.train ../program2/cv.test --- OK [0s]
===== Cross-validator: trying hyperparameter 0.01 =====
=== START _tune-hyperparameter0: ./run setHyperparameter 0.01
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter0: ./run setHyperparameter 0.01 --- OK [0s]
=== START _tune-hyperparameter0: ./run learn ../cv.train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [2s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions0
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions0 --- OK [2s]
=== START program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions0
=== END program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions0 --- OK [0s]
CV error rate 0.453333333333333 with hyperparameter 0.01

===== Cross-validator: trying hyperparameter 0.1 =====
=== START _tune-hyperparameter1: ./run setHyperparameter 0.1
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s]
=== START _tune-hyperparameter1: ./run learn ../cv.train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [3s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions1
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions1 --- OK [2s]
=== START program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions1
=== END program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions1 --- OK [0s]
CV error rate 0.453333333333333 with hyperparameter 0.1

===== Cross-validator: trying hyperparameter 1.0 =====
=== START _tune-hyperparameter2: ./run setHyperparameter 1.0
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s]
=== START _tune-hyperparameter2: ./run learn ../cv.train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [3s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions2
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions2 --- OK [2s]
=== START program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions2
=== END program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions2 --- OK [1s]
CV error rate 0.453333333333333 with hyperparameter 1.0

===== Cross-validator: trying hyperparameter 10.0 =====
=== START _tune-hyperparameter3: ./run setHyperparameter 10.0
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s]
=== START _tune-hyperparameter3: ./run learn ../cv.train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [3s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions3
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions3 --- OK [2s]
=== START program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions3
=== END program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions3 --- OK [0s]
CV error rate 0.453333333333333 with hyperparameter 10.0

===== Cross-validator: trying hyperparameter 100.0 =====
=== START _tune-hyperparameter4: ./run setHyperparameter 100.0
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s]
=== START _tune-hyperparameter4: ./run learn ../cv.train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [2s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions4
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions4 --- OK [2s]
=== START program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions4
=== END program6: ./run evaluate ../program2/cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions4 --- OK [0s]
CV error rate 0.453333333333333 with hyperparameter 100.0

Best hyperparameter value is 0.01; got CV error rate 0.453333333333333
=== END program2: ./run learn ../program1/data --- OK [28s]
=== END program1: ./run learn ../dataset7/train --- OK [28s]

===== MAIN: predict/evaluate on train data =====
=== START program8: ./run stripLabels ../dataset7/train ../program0/evalTrain.in
=== END program8: ./run stripLabels ../dataset7/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out.multiclass-output
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out.multiclass-output --- OK [3s]
=== END program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output --- OK [3s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program9: ./run evaluate ../dataset7/train ../program0/evalTrain.out
=== END program9: ./run evaluate ../dataset7/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program8: ./run stripLabels ../dataset7/test ../program0/evalTest.in
=== END program8: ./run stripLabels ../dataset7/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out.multiclass-output
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out.multiclass-output --- OK [2s]
=== END program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output --- OK [2s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s]
=== START program9: ./run evaluate ../dataset7/test ../program0/evalTest.out
=== END program9: ./run evaluate ../dataset7/test ../program0/evalTest.out --- OK [0s]


real	0m39.253s
user	0m12.873s
sys	0m3.432s

Run specification

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]) tune-hyperparameter: Sets the hyperparameter
    - (numProbes:int) 5
    - (learner:Program) SMO_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/functions/SMO.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
      
      ---------------------
      Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes by default. (In that case the coefficients in the output are based on the normalized data, not the original data --- this is important for interpreting the classifier.) Multi-class problems are solved using pairwise classification (1-vs-1 and if logistic models are built pairwise coupling according to Hastie and Tibshirani, 1998). To obtain proper probability estimates, use the option that fits logistic regression models to the outputs of the support vector machine. In the multi-class case the predicted probabilities are coupled using Hastie and Tibshirani's pairwise coupling method. Note: for improved speed normalization should be turned off when operating on SparseInstances. For more information on the SMO algorithm, see J. Platt: Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (2001). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637-649. Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. In: Advances in Neural Information Processing Systems, 1998.
      ---------------------
      
      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
    - (splitter:Program) multiclass-utils: Validates and inspects a dataset in MulticlassClassification format.
    - (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).
- (dataset:Dataset) blakhol_bc_test_5000: randomly generated data according to some model to test binary classification.
- (stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
- (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

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MLcomp version alpha Statistics: 2040 users, 637 datasets, 389 programs, 18661 runs (0 queued, 2 running), 0 workers

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