MLcomp version alpha Statistics: 2043 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 16156

Creator	linflyer
Program	LogitBoost_weka_nominal
Dataset	DAS_cui_hr_bi_lab
Task type	MulticlassClassification
Created	1y53d ago

Status: Done!

Total Time	11m8s
Max Memory Usage	432M
Domain	BinaryClassification
Learn time
Train error	0.157
Predict train time
Test error	0.187
Predict test time

Log file

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
MulticlassClassification
=== END _tune-hyperparameter0: ./run setHyperparameter 0.01 --- OK [0s]
=== START _tune-hyperparameter0: ./run learn ../cv.train
MulticlassClassification
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [128s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions0
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 0.161616161616162 with hyperparameter 0.01

===== Cross-validator: trying hyperparameter 0.1 =====
=== START _tune-hyperparameter1: ./run setHyperparameter 0.1
MulticlassClassification
=== END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s]
=== START _tune-hyperparameter1: ./run learn ../cv.train
MulticlassClassification
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [126s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions1
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 0.161616161616162 with hyperparameter 0.1

===== Cross-validator: trying hyperparameter 1.0 =====
=== START _tune-hyperparameter2: ./run setHyperparameter 1.0
MulticlassClassification
=== END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s]
=== START _tune-hyperparameter2: ./run learn ../cv.train
MulticlassClassification
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [129s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions2
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 [0s]
CV error rate 0.161616161616162 with hyperparameter 1.0

===== Cross-validator: trying hyperparameter 10.0 =====
=== START _tune-hyperparameter3: ./run setHyperparameter 10.0
MulticlassClassification
=== END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [1s]
=== START _tune-hyperparameter3: ./run learn ../cv.train
MulticlassClassification
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [128s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions3
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 0.161616161616162 with hyperparameter 10.0

===== Cross-validator: trying hyperparameter 100.0 =====
=== START _tune-hyperparameter4: ./run setHyperparameter 100.0
MulticlassClassification
=== END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s]
=== START _tune-hyperparameter4: ./run learn ../cv.train
MulticlassClassification
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [128s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program2/cvTestPredictions4
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 0.161616161616162 with hyperparameter 100.0

Best hyperparameter value is 0.01; got CV error rate 0.161616161616162
=== END program2: ./run learn ../program1/data --- OK [654s]
=== END program1: ./run learn ../dataset7/train --- OK [654s]

===== 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
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 [0s]
=== 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
MulticlassClassification
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out.multiclass-output --- OK [3s]
=== END program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output --- OK [3s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [3s]
=== START program9: ./run evaluate ../dataset7/test ../program0/evalTest.out
=== END program9: ./run evaluate ../dataset7/test ../program0/evalTest.out --- OK [0s]


real	11m8.919s
user	6m12.347s
sys	0m8.961s

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) LogitBoost_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/LogitBoost.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
        
        ---------------------
        Class for performing additive logistic regression. This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University. Can do efficient internal cross-validation to determine appropriate number of iterations.
        ---------------------
        
        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) DAS_cui_hr_bi_lab:
  • (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

• doTest:
  • evaluate:
    • errorRate: 0.187279151943463
    • numErrors: 53
    • numExamples: 283
    • success: true
    • time: 0
  • predict:
    • predict:
      • predict:
        • success: true
        • time: 3
      • success: true
      • time: 3
    • success: true
    • time: 3
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.157337367624811
    • numErrors: 104
    • numExamples: 661
    • success: true
    • time: 0
  • predict:
    • predict:
      • predict:
        • success: true
        • time: 3
      • success: true
      • time: 3
    • success: true
    • time: 3
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • learn:
    • bestCVErrorRate: 0.161616161616162
    • bestHyperparameter: 0.01
    • evaluate0:
      • errorRate: 0.161616161616162
      • numErrors: 32
      • numExamples: 198
      • success: true
      • time: 0
    • evaluate1:
      • errorRate: 0.161616161616162
      • numErrors: 32
      • numExamples: 198
      • success: true
      • time: 0
    • evaluate2:
      • errorRate: 0.161616161616162
      • numErrors: 32
      • numExamples: 198
      • success: true
      • time: 0
    • evaluate3:
      • errorRate: 0.161616161616162
      • numErrors: 32
      • numExamples: 198
      • success: true
      • time: 0
    • evaluate4:
      • errorRate: 0.161616161616162
      • numErrors: 32
      • numExamples: 198
      • success: true
      • time: 0
    • learn0:
      • success: true
      • time: 128
    • learn1:
      • success: true
      • time: 126
    • learn2:
      • success: true
      • time: 129
    • learn3:
      • success: true
      • time: 128
    • learn4:
      • success: true
      • time: 128
    • predict0:
      • success: true
      • time: 2
    • predict1:
      • success: true
      • time: 2
    • predict2:
      • success: true
      • time: 2
    • predict3:
      • success: true
      • time: 2
    • predict4:
      • success: true
      • time: 2
    • setHyperparameter0:
      • success: true
      • time: 0
    • setHyperparameter1:
      • success: true
      • time: 0
    • setHyperparameter2:
      • success: true
      • time: 0
    • setHyperparameter3:
      • success: true
      • time: 1
    • setHyperparameter4:
      • success: true
      • time: 0
    • split:
      • success: true
      • time: 0
    • success: true
    • time: 654
  • success: true
  • time: 654
• success: true

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