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

Run 25070

Creator	internal
Program	logistic-regression-lbfgs
Dataset	wine
Task type	MulticlassClassification
Created	321d2h ago
Download	Login required!

Status: Done!

Total Time
Max Memory Usage
Domain	MulticlassClassification
Learn time	1s
Train error	0
Predict train time	1s
Test error	0.019
Predict test time	0s

Log file

Note: Maximizer.java uses unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
main() {
  Reading examples from ../dataset2/train {
    125 examples, 13 features, 3 labels
  }
  Iteration 0: objective = -1.099, numMistakes = 88/125 = 0.704
  Iteration 0: objective = -3.764, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -3.387, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -3.049, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -2.744, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -2.469, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -2.222, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -2.000, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -1.800, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -1.620, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -1.458, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -1.313, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -1.182, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -1.064, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.958, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.863, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.777, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.700, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.631, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.569, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.514, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.464, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.420, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.381, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.345, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.314, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.286, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.262, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.240, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.221, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.205, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.191, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.179, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.169, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.161, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.156, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.152, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.150, numMistakes = 6/125 = 0.048
  Iteration 0: objective = -0.150, numMistakes = 6/125 = 0.048
  Iteration 1: objective = -0.150, numMistakes = 6/125 = 0.048
  Iteration 1: objective = -0.083, numMistakes = 5/125 = 0.040
  Iteration 1: objective = -0.088, numMistakes = 5/125 = 0.040
  Iteration 2: objective = -0.083, numMistakes = 5/125 = 0.040
  Iteration 2: objective = -0.082, numMistakes = 5/125 = 0.040
  Iteration 2: objective = -0.083, numMistakes = 5/125 = 0.040
  Iteration 3: objective = -0.082, numMistakes = 5/125 = 0.040
  Iteration 3: objective = -0.078, numMistakes = 4/125 = 0.032
  Iteration 3: objective = -0.079, numMistakes = 4/125 = 0.032
  Iteration 4: objective = -0.078, numMistakes = 4/125 = 0.032
  Iteration 4: objective = -0.073, numMistakes = 4/125 = 0.032
  Iteration 4: objective = -0.073, numMistakes = 4/125 = 0.032
  Iteration 5: objective = -0.073, numMistakes = 4/125 = 0.032
  Iteration 5: objective = -0.066, numMistakes = 2/125 = 0.016
  Iteration 5: objective = -0.066, numMistakes = 2/125 = 0.016
  Iteration 6: objective = -0.066, numMistakes = 2/125 = 0.016
  Iteration 6: objective = -0.058, numMistakes = 2/125 = 0.016
  Iteration 6: objective = -0.059, numMistakes = 2/125 = 0.016
  Iteration 7: objective = -0.058, numMistakes = 2/125 = 0.016
  Iteration 7: objective = -0.051, numMistakes = 1/125 = 0.008
  Iteration 7: objective = -0.052, numMistakes = 1/125 = 0.008
  Iteration 8: objective = -0.051, numMistakes = 1/125 = 0.008
  Iteration 8: objective = -0.044, numMistakes = 0/125 = 0
  Iteration 8: objective = -0.044, numMistakes = 0/125 = 0
  Iteration 9: objective = -0.044, numMistakes = 0/125 = 0
  Iteration 9: objective = -0.037, numMistakes = 0/125 = 0
  Iteration 9: objective = -0.037, numMistakes = 0/125 = 0
  Iteration 10: objective = -0.037, numMistakes = 0/125 = 0
  Iteration 10: objective = -0.030, numMistakes = 0/125 = 0
  Iteration 10: objective = -0.031, numMistakes = 0/125 = 0
  Iteration 11: objective = -0.030, numMistakes = 0/125 = 0
  Iteration 11: objective = -0.025, numMistakes = 0/125 = 0
  Iteration 11: objective = -0.025, numMistakes = 0/125 = 0
  Iteration 12: objective = -0.025, numMistakes = 0/125 = 0
  Iteration 12: objective = -0.019, numMistakes = 0/125 = 0
  Iteration 12: objective = -0.020, numMistakes = 0/125 = 0
  Iteration 13: objective = -0.019, numMistakes = 0/125 = 0
  Iteration 13: objective = -0.014, numMistakes = 0/125 = 0
  Iteration 13: objective = -0.015, numMistakes = 0/125 = 0
  Iteration 14: objective = -0.014, numMistakes = 0/125 = 0
  Iteration 14: objective = -0.010, numMistakes = 0/125 = 0
  Iteration 14: objective = -0.010, numMistakes = 0/125 = 0
  Iteration 15: objective = -0.010, numMistakes = 0/125 = 0
  Iteration 15: objective = -0.007, numMistakes = 0/125 = 0
  Iteration 15: objective = -0.007, numMistakes = 0/125 = 0
  Iteration 16: objective = -0.007, numMistakes = 0/125 = 0
  Iteration 16: objective = -0.003, numMistakes = 0/125 = 0
  Iteration 16: objective = -0.003, numMistakes = 0/125 = 0
  Iteration 17: objective = -0.003, numMistakes = 0/125 = 0
  Iteration 17: objective = -0.002, numMistakes = 0/125 = 0
  Iteration 17: objective = -0.002, numMistakes = 0/125 = 0
  Iteration 18: objective = -0.002, numMistakes = 0/125 = 0
  Iteration 18: objective = -9.43e-04, numMistakes = 0/125 = 0
  Iteration 18: objective = -0.001, numMistakes = 0/125 = 0
  Iteration 19: objective = -9.43e-04, numMistakes = 0/125 = 0
  Iteration 19: objective = -3.26e-04, numMistakes = 0/125 = 0
  Iteration 19: objective = -3.59e-04, numMistakes = 0/125 = 0
  Iteration 20: objective = -3.26e-04, numMistakes = 0/125 = 0
  Iteration 20: objective = -1.80e-04, numMistakes = 0/125 = 0
  Iteration 20: objective = -1.78e-04, numMistakes = 0/125 = 0
  Iteration 20: objective = -1.78e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.78e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -0.011, numMistakes = 1/125 = 0.008
  Iteration 21: objective = -0.007, numMistakes = 1/125 = 0.008
  Iteration 21: objective = -0.004, numMistakes = 0/125 = 0
  Iteration 21: objective = -0.002, numMistakes = 0/125 = 0
  Iteration 21: objective = -0.001, numMistakes = 0/125 = 0
  Iteration 21: objective = -8.08e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -5.35e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -3.76e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -2.80e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -2.20e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.82e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.56e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.39e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.28e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.21e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.16e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.14e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.13e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.13e-04, numMistakes = 0/125 = 0
  Iteration 21: objective = -1.14e-04, numMistakes = 0/125 = 0
  Iteration 22: objective = -1.13e-04, numMistakes = 0/125 = 0
  Iteration 22: objective = -7.48e-05, numMistakes = 0/125 = 0
  Iteration 22: objective = -7.79e-05, numMistakes = 0/125 = 0
  Iteration 23: objective = -7.48e-05, numMistakes = 0/125 = 0
  Iteration 23: objective = -5.39e-05, numMistakes = 0/125 = 0
  Iteration 23: objective = -5.56e-05, numMistakes = 0/125 = 0
  Iteration 24: objective = -5.39e-05, numMistakes = 0/125 = 0
  Iteration 24: objective = -2.28e-05, numMistakes = 0/125 = 0
  Iteration 24: objective = -2.45e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -2.28e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -5.20e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -4.10e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -3.37e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -2.88e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -2.54e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -2.30e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -2.13e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -2.01e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -1.93e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -1.88e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -1.85e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -1.83e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -1.83e-05, numMistakes = 0/125 = 0
  Iteration 25: objective = -1.83e-05, numMistakes = 0/125 = 0
  Iteration 26: objective = -1.83e-05, numMistakes = 0/125 = 0
  Iteration 26: objective = -1.13e-05, numMistakes = 0/125 = 0
  Iteration 26: objective = -1.18e-05, numMistakes = 0/125 = 0
  Iteration 27: objective = -1.13e-05, numMistakes = 0/125 = 0
  Iteration 27: objective = -7.95e-06, numMistakes = 0/125 = 0
  Iteration 27: objective = -8.23e-06, numMistakes = 0/125 = 0
  Iteration 28: objective = -7.95e-06, numMistakes = 0/125 = 0
  Iteration 28: objective = -3.88e-06, numMistakes = 0/125 = 0
  Iteration 28: objective = -4.15e-06, numMistakes = 0/125 = 0
  Iteration 29: objective = -3.88e-06, numMistakes = 0/125 = 0
  Iteration 29: objective = -1.96e-06, numMistakes = 0/125 = 0
  Iteration 29: objective = -2.08e-06, numMistakes = 0/125 = 0
  Iteration 30: objective = -1.96e-06, numMistakes = 0/125 = 0
  Iteration 30: objective = -8.17e-07, numMistakes = 0/125 = 0
  Iteration 30: objective = -8.90e-07, numMistakes = 0/125 = 0
  Iteration 31: objective = -8.17e-07, numMistakes = 0/125 = 0
  Iteration 31: objective = -3.42e-07, numMistakes = 0/125 = 0
  Iteration 31: objective = -3.72e-07, numMistakes = 0/125 = 0
  Iteration 32: objective = -3.42e-07, numMistakes = 0/125 = 0
  Iteration 32: objective = -1.34e-07, numMistakes = 0/125 = 0
  Iteration 32: objective = -1.45e-07, numMistakes = 0/125 = 0
  Iteration 33: objective = -1.34e-07, numMistakes = 0/125 = 0
  Iteration 33: objective = -5.45e-08, numMistakes = 0/125 = 0
  Iteration 33: objective = -5.50e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -5.45e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -6.82e-06, numMistakes = 0/125 = 0
  Iteration 34: objective = -2.86e-06, numMistakes = 0/125 = 0
  Iteration 34: objective = -1.34e-06, numMistakes = 0/125 = 0
  Iteration 34: objective = -6.86e-07, numMistakes = 0/125 = 0
  Iteration 34: objective = -3.84e-07, numMistakes = 0/125 = 0
  Iteration 34: objective = -2.32e-07, numMistakes = 0/125 = 0
  Iteration 34: objective = -1.50e-07, numMistakes = 0/125 = 0
  Iteration 34: objective = -1.04e-07, numMistakes = 0/125 = 0
  Iteration 34: objective = -7.65e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -5.95e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -4.87e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -4.17e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -3.72e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -3.43e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -3.25e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -3.16e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -3.12e-08, numMistakes = 0/125 = 0
  Iteration 34: objective = -3.12e-08, numMistakes = 0/125 = 0
  Iteration 35: objective = -3.12e-08, numMistakes = 0/125 = 0
  Writing parameters to params
}
=== END program1: ./run learn ../dataset2/train --- OK [1s]

===== 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 [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
main() {
  Reading parameters from params
  Reading examples from ../program0/evalTrain.in {
    125 examples, 13 features, 3 labels
  }
  Predicting
}
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]

===== 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 [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
main() {
  Reading parameters from params
  Reading examples from ../program0/evalTest.in {
    53 examples, 13 features, 3 labels
  }
  Predicting
}
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	0m9.442s
user	0m3.508s
sys	0m1.104s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) logistic-regression-lbfgs: L2 regularized multiclass logistic regression optimized using LBFGS. Features are normalized to have mean 0 and variance 1 over examples on which the feature is active. Default: no regularization.
  • (dataset:Dataset) wine: 178 examples, 13 features
  • (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).

Results

• doTest:
  • evaluate:
    • errorRate: 0.0188679245283019
    • numErrors: 1
    • numExamples: 53
    • success: true
    • time: 1
  • predict:
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.0
    • numErrors: 0
    • numExamples: 125
    • success: true
    • time: 0
  • predict:
    • success: true
    • time: 1
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • success: true
  • time: 1
• success: true

Comments: