MLcomp version alpha Statistics: 2040 users, 645 datasets, 389 programs, 18768 runs (0 queued, 2 running), 0 workers

Run 26613

Creator	internal
Program	logistic-regression-lbfgs
Dataset	HumanActivityRecognition_WearableAccelerometers
Task type	MulticlassClassification
Created	318d20h ago

Status: Done!

Total Time
Max Memory Usage
Domain	MulticlassClassification
Learn time	2m58s
Train error	0.071
Predict train time	2s
Test error	0.076
Predict test time	2s

Log file

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  Iteration 31: objective = -0.458, numMistakes = 2180/18815 = 0.116
  Iteration 31: objective = -0.461, numMistakes = 2188/18815 = 0.116
  Iteration 32: objective = -0.458, numMistakes = 2180/18815 = 0.116
  Iteration 32: objective = -0.426, numMistakes = 2182/18815 = 0.116
  Iteration 32: objective = -0.428, numMistakes = 2179/18815 = 0.116
  Iteration 33: objective = -0.426, numMistakes = 2182/18815 = 0.116
  Iteration 33: objective = -0.402, numMistakes = 1939/18815 = 0.103
  Iteration 33: objective = -0.403, numMistakes = 1953/18815 = 0.104
  Iteration 34: objective = -0.402, numMistakes = 1939/18815 = 0.103
  Iteration 34: objective = -0.377, numMistakes = 1889/18815 = 0.100
  Iteration 34: objective = -0.379, numMistakes = 1885/18815 = 0.100
  Iteration 35: objective = -0.377, numMistakes = 1889/18815 = 0.100
  Iteration 35: objective = -0.367, numMistakes = 1849/18815 = 0.098
  Iteration 35: objective = -0.368, numMistakes = 1857/18815 = 0.099
  Iteration 36: objective = -0.367, numMistakes = 1849/18815 = 0.098
  Iteration 36: objective = -0.355, numMistakes = 1790/18815 = 0.095
  Iteration 36: objective = -0.355, numMistakes = 1789/18815 = 0.095
  Iteration 37: objective = -0.355, numMistakes = 1790/18815 = 0.095
  Iteration 37: objective = -0.356, numMistakes = 1793/18815 = 0.095
  Iteration 37: objective = -0.354, numMistakes = 1787/18815 = 0.095
  Iteration 37: objective = -0.352, numMistakes = 1787/18815 = 0.095
  Iteration 37: objective = -0.351, numMistakes = 1785/18815 = 0.095
  Iteration 37: objective = -0.351, numMistakes = 1784/18815 = 0.095
  Iteration 37: objective = -0.350, numMistakes = 1782/18815 = 0.095
  Iteration 37: objective = -0.350, numMistakes = 1781/18815 = 0.095
  Iteration 37: objective = -0.350, numMistakes = 1776/18815 = 0.094
  Iteration 37: objective = -0.350, numMistakes = 1781/18815 = 0.095
  Iteration 38: objective = -0.350, numMistakes = 1776/18815 = 0.094
  Iteration 38: objective = -0.346, numMistakes = 1729/18815 = 0.092
  Iteration 38: objective = -0.346, numMistakes = 1737/18815 = 0.092
  Iteration 39: objective = -0.346, numMistakes = 1729/18815 = 0.092
  Iteration 39: objective = -0.342, numMistakes = 1706/18815 = 0.091
  Iteration 39: objective = -0.342, numMistakes = 1705/18815 = 0.091
  Iteration 40: objective = -0.342, numMistakes = 1706/18815 = 0.091
  Iteration 40: objective = -0.336, numMistakes = 1647/18815 = 0.088
  Iteration 40: objective = -0.336, numMistakes = 1656/18815 = 0.088
  Iteration 41: objective = -0.336, numMistakes = 1647/18815 = 0.088
  Iteration 41: objective = -0.330, numMistakes = 1578/18815 = 0.084
  Iteration 41: objective = -0.330, numMistakes = 1578/18815 = 0.084
  Iteration 41: objective = -0.329, numMistakes = 1581/18815 = 0.084
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  Iteration 42: objective = -0.327, numMistakes = 1587/18815 = 0.084
  Iteration 42: objective = -0.326, numMistakes = 1578/18815 = 0.084
  Iteration 42: objective = -0.325, numMistakes = 1570/18815 = 0.083
  Iteration 42: objective = -0.325, numMistakes = 1559/18815 = 0.083
  Iteration 42: objective = -0.325, numMistakes = 1553/18815 = 0.083
  Iteration 42: objective = -0.325, numMistakes = 1556/18815 = 0.083
  Iteration 43: objective = -0.325, numMistakes = 1553/18815 = 0.083
  Iteration 43: objective = -0.323, numMistakes = 1554/18815 = 0.083
  Iteration 43: objective = -0.323, numMistakes = 1541/18815 = 0.082
  Iteration 43: objective = -0.323, numMistakes = 1542/18815 = 0.082
  Iteration 43: objective = -0.323, numMistakes = 1545/18815 = 0.082
  Iteration 43: objective = -0.323, numMistakes = 1544/18815 = 0.082
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  Iteration 44: objective = -0.320, numMistakes = 1539/18815 = 0.082
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  Iteration 45: objective = -0.320, numMistakes = 1539/18815 = 0.082
  Iteration 45: objective = -0.318, numMistakes = 1544/18815 = 0.082
  Iteration 45: objective = -0.318, numMistakes = 1539/18815 = 0.082
  Iteration 46: objective = -0.318, numMistakes = 1544/18815 = 0.082
  Iteration 46: objective = -0.316, numMistakes = 1527/18815 = 0.081
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  Iteration 47: objective = -0.313, numMistakes = 1517/18815 = 0.081
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  Iteration 48: objective = -0.313, numMistakes = 1517/18815 = 0.081
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  Iteration 49: objective = -0.312, numMistakes = 1526/18815 = 0.081
  Iteration 49: objective = -0.310, numMistakes = 1492/18815 = 0.079
  Iteration 49: objective = -0.310, numMistakes = 1489/18815 = 0.079
  Iteration 49: objective = -0.310, numMistakes = 1486/18815 = 0.079
  Iteration 49: objective = -0.310, numMistakes = 1496/18815 = 0.080
  Iteration 50: objective = -0.310, numMistakes = 1486/18815 = 0.079
  Iteration 50: objective = -0.310, numMistakes = 1515/18815 = 0.081
  Iteration 50: objective = -0.309, numMistakes = 1505/18815 = 0.080
  Iteration 50: objective = -0.309, numMistakes = 1508/18815 = 0.080
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  Iteration 51: objective = -0.309, numMistakes = 1501/18815 = 0.080
  Iteration 51: objective = -0.308, numMistakes = 1467/18815 = 0.078
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  Iteration 51: objective = -0.308, numMistakes = 1474/18815 = 0.078
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  Iteration 52: objective = -0.306, numMistakes = 1496/18815 = 0.080
  Iteration 52: objective = -0.306, numMistakes = 1490/18815 = 0.079
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  Iteration 52: objective = -0.306, numMistakes = 1490/18815 = 0.079
  Iteration 53: objective = -0.306, numMistakes = 1488/18815 = 0.079
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  Iteration 54: objective = -0.304, numMistakes = 1500/18815 = 0.080
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  Iteration 56: objective = -0.302, numMistakes = 1527/18815 = 0.081
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  Iteration 56: objective = -0.302, numMistakes = 1514/18815 = 0.080
  Iteration 56: objective = -0.302, numMistakes = 1515/18815 = 0.081
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  Iteration 58: objective = -0.299, numMistakes = 1540/18815 = 0.082
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  Iteration 61: objective = -0.296, numMistakes = 1473/18815 = 0.078
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  Iteration 63: objective = -0.294, numMistakes = 1425/18815 = 0.076
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  Iteration 86: objective = -0.280, numMistakes = 1414/18815 = 0.075
  Iteration 86: objective = -0.280, numMistakes = 1408/18815 = 0.075
  Iteration 86: objective = -0.279, numMistakes = 1408/18815 = 0.075
  Iteration 86: objective = -0.279, numMistakes = 1405/18815 = 0.075
  Iteration 86: objective = -0.279, numMistakes = 1405/18815 = 0.075
  Iteration 86: objective = -0.279, numMistakes = 1401/18815 = 0.074
  Iteration 87: objective = -0.279, numMistakes = 1405/18815 = 0.075
  Iteration 87: objective = -0.279, numMistakes = 1370/18815 = 0.073
  Iteration 87: objective = -0.279, numMistakes = 1373/18815 = 0.073
  Iteration 87: objective = -0.279, numMistakes = 1377/18815 = 0.073
  Iteration 87: objective = -0.279, numMistakes = 1381/18815 = 0.073
  Iteration 88: objective = -0.279, numMistakes = 1377/18815 = 0.073
  Iteration 88: objective = -0.279, numMistakes = 1396/18815 = 0.074
  Iteration 88: objective = -0.279, numMistakes = 1390/18815 = 0.074
  Iteration 88: objective = -0.279, numMistakes = 1390/18815 = 0.074
  Iteration 88: objective = -0.279, numMistakes = 1389/18815 = 0.074
  Iteration 89: objective = -0.279, numMistakes = 1390/18815 = 0.074
  Iteration 89: objective = -0.278, numMistakes = 1359/18815 = 0.072
  Iteration 89: objective = -0.278, numMistakes = 1360/18815 = 0.072
  Iteration 89: objective = -0.278, numMistakes = 1369/18815 = 0.073
  Iteration 89: objective = -0.278, numMistakes = 1372/18815 = 0.073
  Iteration 90: objective = -0.278, numMistakes = 1369/18815 = 0.073
  Iteration 90: objective = -0.278, numMistakes = 1386/18815 = 0.074
  Iteration 90: objective = -0.278, numMistakes = 1382/18815 = 0.073
  Iteration 90: objective = -0.278, numMistakes = 1380/18815 = 0.073
  Iteration 90: objective = -0.278, numMistakes = 1381/18815 = 0.073
  Iteration 91: objective = -0.278, numMistakes = 1380/18815 = 0.073
  Iteration 91: objective = -0.277, numMistakes = 1344/18815 = 0.071
  Iteration 91: objective = -0.277, numMistakes = 1350/18815 = 0.072
  Iteration 91: objective = -0.277, numMistakes = 1351/18815 = 0.072
  Iteration 91: objective = -0.277, numMistakes = 1354/18815 = 0.072
  Iteration 91: objective = -0.277, numMistakes = 1356/18815 = 0.072
  Iteration 91: objective = -0.277, numMistakes = 1359/18815 = 0.072
  Iteration 92: objective = -0.277, numMistakes = 1356/18815 = 0.072
  Iteration 92: objective = -0.277, numMistakes = 1358/18815 = 0.072
  Iteration 92: objective = -0.277, numMistakes = 1360/18815 = 0.072
  Iteration 92: objective = -0.277, numMistakes = 1360/18815 = 0.072
  Iteration 92: objective = -0.277, numMistakes = 1361/18815 = 0.072
  Iteration 93: objective = -0.277, numMistakes = 1360/18815 = 0.072
  Iteration 93: objective = -0.277, numMistakes = 1328/18815 = 0.071
  Iteration 93: objective = -0.277, numMistakes = 1336/18815 = 0.071
  Iteration 93: objective = -0.277, numMistakes = 1342/18815 = 0.071
  Iteration 93: objective = -0.277, numMistakes = 1342/18815 = 0.071
  Iteration 93: objective = -0.277, numMistakes = 1348/18815 = 0.072
  Iteration 94: objective = -0.277, numMistakes = 1342/18815 = 0.071
  Iteration 94: objective = -0.276, numMistakes = 1347/18815 = 0.072
  Iteration 94: objective = -0.276, numMistakes = 1345/18815 = 0.071
  Iteration 94: objective = -0.276, numMistakes = 1346/18815 = 0.072
  Iteration 94: objective = -0.276, numMistakes = 1341/18815 = 0.071
  Iteration 95: objective = -0.276, numMistakes = 1346/18815 = 0.072
  Iteration 95: objective = -0.276, numMistakes = 1328/18815 = 0.071
  Iteration 95: objective = -0.276, numMistakes = 1333/18815 = 0.071
  Iteration 95: objective = -0.276, numMistakes = 1334/18815 = 0.071
  Iteration 95: objective = -0.276, numMistakes = 1334/18815 = 0.071
  Iteration 96: objective = -0.276, numMistakes = 1334/18815 = 0.071
  Iteration 96: objective = -0.276, numMistakes = 1346/18815 = 0.072
  Iteration 96: objective = -0.276, numMistakes = 1346/18815 = 0.072
  Iteration 96: objective = -0.276, numMistakes = 1344/18815 = 0.071
  Iteration 96: objective = -0.276, numMistakes = 1343/18815 = 0.071
  Iteration 97: objective = -0.276, numMistakes = 1344/18815 = 0.071
  Iteration 97: objective = -0.276, numMistakes = 1330/18815 = 0.071
  Iteration 97: objective = -0.276, numMistakes = 1330/18815 = 0.071
  Iteration 97: objective = -0.276, numMistakes = 1332/18815 = 0.071
  Iteration 97: objective = -0.276, numMistakes = 1330/18815 = 0.071
  Iteration 97: objective = -0.276, numMistakes = 1333/18815 = 0.071
  Iteration 97: objective = -0.276, numMistakes = 1333/18815 = 0.071
  Iteration 98: objective = -0.276, numMistakes = 1333/18815 = 0.071
  Iteration 98: objective = -0.276, numMistakes = 1335/18815 = 0.071
  Iteration 98: objective = -0.276, numMistakes = 1333/18815 = 0.071
  Iteration 98: objective = -0.276, numMistakes = 1332/18815 = 0.071
  Iteration 98: objective = -0.276, numMistakes = 1332/18815 = 0.071
  Iteration 99: objective = -0.276, numMistakes = 1332/18815 = 0.071
  Iteration 99: objective = -0.275, numMistakes = 1342/18815 = 0.071
  Iteration 99: objective = -0.275, numMistakes = 1344/18815 = 0.071
  Iteration 99: objective = -0.275, numMistakes = 1343/18815 = 0.071
  Iteration 99: objective = -0.275, numMistakes = 1342/18815 = 0.071
  Iteration 99: objective = -0.275, numMistakes = 1337/18815 = 0.071
  Iteration 99: objective = -0.275, numMistakes = 1334/18815 = 0.071
  Writing parameters to params
} [2m57s]
=== END program1: ./run learn ../dataset2/train --- OK [178s]

===== 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 {
    18815 examples, 22 features, 5 labels
  } [1.4s, cum. 1.5s]
  Predicting
} [1.8s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [2s]

===== 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 {
    8063 examples, 22 features, 5 labels
  }
  Predicting
} [1.0s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	3m12.658s
user	3m1.731s
sys	0m2.032s

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) HumanActivityRecognition_WearableAccelerometers: This is part of a dataset we'll publish in October, 25th of 2012. You can check our website to get it for free. The data come from triaxial wearable accelerometers mounted at the subjects' waist, left thigh, right ankle, and right upper arm. The 4 accelerometers were used at the same time, to collect data from 4 subjects doing 5 activities. Each activity is a class in the dataset:
    
    class
    1=sitting
    2=sitting down
    3=standing
    4=standing up
    5=walking
    
    The dataset has the label (class) and 22 columns as follow:
    
    class
    1=subject (4 subjects)
    2=gender
    3=age
    4=how_tall_in_meters
    5=weight
    6=-body_mass_index
    7=waist_x
    8=waist_y
    9=waist_z
    10=left_thigh_x
    11=left_thigh_y
    12=left_thigh_z
    13=right_ankle_x
    14=right_ankle_y
    15=right_ankle_z
    16=right_upper_arm_x
    17=right_upper_arm_y
    18=right_upper_arm_z
    19=module_vector_waist
    20=module_vector_left_thigh
    21=module_vector_right_ankle
    22=module_vector_right_upper_arm
    
    The dataset can't be published before October 25th, but you can use this one to work on the data and wait for the complete dataset with 165,633 samples. If you have any doubt, please ask us: wugulino@inf.puc-rio.br
    Have a good work!
    
    Ugulino (http://groupware.les.inf.puc-rio.br/ugulino)
  • (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: 0.0756542229939229
    • numErrors: 610
    • numExamples: 8063
    • success: true
    • time: 0
  • predict:
    • success: true
    • time: 2
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.0710603242094074
    • numErrors: 1337
    • numExamples: 18815
    • success: true
    • time: 2
  • predict:
    • success: true
    • time: 2
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • success: true
  • time: 178
• success: true

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