MLcomp version alpha Statistics: 1208 users, 401 datasets, 328 programs, 9937 runs (0 queued, 0 running), 0 workers

Run 1806

Creator	pliang
Program	svmlight_multiclass-linear
Dataset	primary-tumor
Task type	MulticlassClassification
Created	1y122d ago
Download	Login required!

Status: Done!

Total Time	1s
Max Memory Usage	30M
Domain:	MulticlassClassification
Learn time
Train error	0.582
Predict train time
Test error	0.637
Predict test time

Log file

... (lines omitted) ...
Iter 7: .........*(NumConst=7, SV=6, CEps=5.0669, QPEps=0.0000)
Iter 8: *(NumConst=8, SV=7, CEps=0.7415, QPEps=0.0000)
Iter 9: .........*(NumConst=9, SV=8, CEps=3.4502, QPEps=0.0000)
Iter 10: *(NumConst=10, SV=9, CEps=0.4058, QPEps=0.0000)
Iter 11: *(NumConst=11, SV=9, CEps=0.4980, QPEps=0.1052)
Iter 12: .........*(NumConst=12, SV=10, CEps=2.5914, QPEps=0.0000)
Iter 13: *(NumConst=13, SV=10, CEps=0.3197, QPEps=0.1052)
Iter 14: *(NumConst=14, SV=11, CEps=0.2618, QPEps=0.0000)
Iter 15: .........*(NumConst=15, SV=11, CEps=1.3677, QPEps=0.0341)
Iter 16: *(NumConst=16, SV=13, CEps=0.1620, QPEps=0.0067)
Iter 17: .........*(NumConst=17, SV=13, CEps=1.2158, QPEps=0.0259)
Iter 18: *(NumConst=18, SV=13, CEps=0.1222, QPEps=0.0196)
Iter 19: .........*(NumConst=19, SV=14, CEps=0.7719, QPEps=0.0347)
Iter 20: *(NumConst=20, SV=13, CEps=0.1144, QPEps=0.0317)
Iter 21: .........*(NumConst=21, SV=14, CEps=0.4661, QPEps=0.0511)
Iter 22: .........*(NumConst=22, SV=15, CEps=0.3540, QPEps=0.0201)
Iter 23: .........*(NumConst=23, SV=17, CEps=0.2999, QPEps=0.0353)
Iter 24: *(NumConst=24, SV=17, CEps=0.1047, QPEps=0.0418)
Iter 25: .........*(NumConst=25, SV=18, CEps=0.1066, QPEps=0.0290)
Iter 26: .........(NumConst=25, SV=18, CEps=0.0851, QPEps=0.0290)
Final epsilon on KKT-Conditions: 0.08515
Upper bound on duality gap: 0.09253
Dual objective value: dval=99.96105
Primal objective value: pval=100.05358
Total number of constraints in final working set: 25 (of 25)
Number of iterations: 26
Number of calls to 'find_most_violated_constraint': 2490
Number of SV: 18 
Norm of weight vector: |w|=0.27909
Value of slack variable (on working set): xi=99.92949
Value of slack variable (global): xi=100.01464
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=7.53037
Runtime in cpu-seconds: 0.03
Final number of constraints in cache: 830
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [0s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2
Reading model...done.
Reading test examples... (71 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 71.8310
Zero/one-error on test set: 71.83% (20 correct, 51 incorrect, 71 total)
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [0s]
CV error rate 0.71830985915493 with hyperparameter 1.0

===== Cross-validator: trying hyperparameter 10.0 =====
=== START _tune-hyperparameter3: ./run setHyperparameter 10.0
=== END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s]
=== START _tune-hyperparameter3: ./run learn ../cv.train
Using hyperparameter c = 10.0
Reading training examples... (166 examples) done
Training set properties: 22 features, 21 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=44.0560, QPEps=0.0000)
Iter 3: .........*(NumConst=3, SV=3, CEps=129.3584, QPEps=0.0000)
Iter 4: *(NumConst=4, SV=3, CEps=29.5768, QPEps=0.0001)
Iter 5: .........*(NumConst=5, SV=4, CEps=99.5495, QPEps=41.3072)
Iter 6: .........*(NumConst=6, SV=4, CEps=78.8455, QPEps=0.0000)
Iter 7: *(NumConst=7, SV=5, CEps=14.9859, QPEps=6.6675)
Iter 8: *(NumConst=8, SV=6, CEps=8.3849, QPEps=3.9148)
Iter 9: .........*(NumConst=9, SV=7, CEps=49.4499, QPEps=23.2017)
Iter 10: *(NumConst=10, SV=8, CEps=24.7299, QPEps=5.6561)
Iter 11: *(NumConst=11, SV=9, CEps=6.4356, QPEps=3.0739)
Iter 12: *(NumConst=12, SV=7, CEps=5.2268, QPEps=0.0004)
Iter 13: .........*(NumConst=13, SV=9, CEps=35.6214, QPEps=2.0682)
Iter 14: .........*(NumConst=14, SV=8, CEps=27.6036, QPEps=0.0000)
Iter 15: *(NumConst=15, SV=10, CEps=3.9942, QPEps=0.0000)
Iter 16: *(NumConst=16, SV=11, CEps=4.2572, QPEps=1.9445)
Iter 17: .........*(NumConst=17, SV=11, CEps=14.0104, QPEps=1.2613)
Iter 18: *(NumConst=18, SV=10, CEps=2.5546, QPEps=0.4281)
Iter 19: *(NumConst=19, SV=10, CEps=1.7611, QPEps=0.6611)
Iter 20: .........*(NumConst=20, SV=11, CEps=11.9807, QPEps=1.1621)
Iter 21: .........*(NumConst=21, SV=12, CEps=8.9836, QPEps=0.6810)
Iter 22: *(NumConst=22, SV=12, CEps=1.1070, QPEps=0.4041)
Iter 23: .........*(NumConst=23, SV=12, CEps=6.7897, QPEps=0.3879)
Iter 24: *(NumConst=24, SV=12, CEps=0.7252, QPEps=0.1120)
Iter 25: .........*(NumConst=25, SV=13, CEps=3.6359, QPEps=0.2978)
Iter 26: *(NumConst=26, SV=13, CEps=0.6334, QPEps=0.1104)
Iter 27: *(NumConst=27, SV=14, CEps=0.6185, QPEps=0.2214)
Iter 28: *(NumConst=28, SV=15, CEps=0.4750, QPEps=0.0597)
Iter 29: .........*(NumConst=29, SV=16, CEps=2.6171, QPEps=0.1785)
Iter 30: *(NumConst=30, SV=15, CEps=0.3186, QPEps=0.0824)
Iter 31: *(NumConst=31, SV=16, CEps=0.2870, QPEps=0.0745)
Iter 32: .........*(NumConst=32, SV=17, CEps=0.8121, QPEps=0.3173)
Iter 33: *(NumConst=33, SV=21, CEps=0.5079, QPEps=0.2292)
Iter 34: *(NumConst=34, SV=20, CEps=0.5774, QPEps=0.2033)
Iter 35: *(NumConst=35, SV=19, CEps=0.3822, QPEps=0.1368)
Iter 36: *(NumConst=36, SV=17, CEps=0.3243, QPEps=0.1332)
Iter 37: *(NumConst=37, SV=18, CEps=0.3634, QPEps=0.1081)
Iter 38: *(NumConst=38, SV=17, CEps=0.2218, QPEps=0.0720)
Iter 39: *(NumConst=39, SV=18, CEps=0.1849, QPEps=0.0896)
Iter 40: *(NumConst=40, SV=18, CEps=0.2252, QPEps=0.0696)
Iter 41: .........*(NumConst=41, SV=20, CEps=0.4607, QPEps=0.1979)
Iter 42: *(NumConst=42, SV=20, CEps=0.3019, QPEps=0.0998)
Iter 43: *(NumConst=43, SV=19, CEps=0.2404, QPEps=0.1188)
Iter 44: *(NumConst=44, SV=22, CEps=0.2790, QPEps=0.0889)
Iter 45: *(NumConst=45, SV=20, CEps=0.2290, QPEps=0.0839)
Iter 46: *(NumConst=46, SV=17, CEps=0.1706, QPEps=0.0648)
Iter 47: *(NumConst=47, SV=19, CEps=0.1656, QPEps=0.0701)
Iter 48: *(NumConst=48, SV=19, CEps=0.1191, QPEps=0.0581)
Iter 49: *(NumConst=49, SV=18, CEps=0.1264, QPEps=0.0506)
Iter 50: *(NumConst=50, SV=18, CEps=0.1194, QPEps=0.0504)
Iter 51: *(NumConst=51, SV=21, CEps=0.1043, QPEps=0.0266)
Iter 52: .........*(NumConst=52, SV=19, CEps=0.4244, QPEps=0.1942)
Iter 53: *(NumConst=53, SV=21, CEps=0.2408, QPEps=0.1033)
Iter 54: *(NumConst=53, SV=22, CEps=0.2819, QPEps=0.1196)
Iter 55: .........*(NumConst=53, SV=22, CEps=0.2876, QPEps=0.1168)
Iter 56: *(NumConst=54, SV=20, CEps=0.1969, QPEps=0.0937)
Iter 57: *(NumConst=55, SV=20, CEps=0.2785, QPEps=0.0643)
Iter 58: *(NumConst=56, SV=20, CEps=0.1473, QPEps=0.0519)
Iter 59: *(NumConst=57, SV=22, CEps=0.1409, QPEps=0.0558)
Iter 60: *(NumConst=58, SV=21, CEps=0.1232, QPEps=0.0599)
Iter 61: *(NumConst=59, SV=20, CEps=0.1087, QPEps=0.0465)
Iter 62: .........*(NumConst=60, SV=21, CEps=0.2259, QPEps=0.0988)
Iter 63: *(NumConst=61, SV=21, CEps=0.1539, QPEps=0.0592)
Iter 64: .........*(NumConst=62, SV=21, CEps=0.1665, QPEps=0.0769)
Iter 65: *(NumConst=63, SV=23, CEps=0.1463, QPEps=0.0726)
Iter 66: *(NumConst=64, SV=21, CEps=0.1053, QPEps=0.0504)
Iter 67: *(NumConst=64, SV=23, CEps=0.1363, QPEps=0.0526)
Iter 68: *(NumConst=65, SV=22, CEps=0.1176, QPEps=0.0442)
Iter 69: *(NumConst=66, SV=22, CEps=0.1091, QPEps=0.0507)
Iter 70: .........*(NumConst=67, SV=24, CEps=0.1341, QPEps=0.0643)
Iter 71: .........*(NumConst=68, SV=24, CEps=0.1204, QPEps=0.0554)
Iter 72: .........(NumConst=68, SV=24, CEps=0.0909, QPEps=0.0554)
Final epsilon on KKT-Conditions: 0.09092
Upper bound on duality gap: 0.92994
Dual objective value: dval=998.38950
Primal objective value: pval=999.31944
Total number of constraints in final working set: 68 (of 71)
Number of iterations: 72
Number of calls to 'find_most_violated_constraint': 3652
Number of SV: 24 
Norm of weight vector: |w|=1.79472
Value of slack variable (on working set): xi=99.69902
Value of slack variable (global): xi=99.77089
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=7.53141
Runtime in cpu-seconds: 0.06
Final number of constraints in cache: 830
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3
Reading model...done.
Reading test examples... (71 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 59.1549
Zero/one-error on test set: 59.15% (29 correct, 42 incorrect, 71 total)
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [0s]
CV error rate 0.591549295774648 with hyperparameter 10.0

===== Cross-validator: trying hyperparameter 100.0 =====
=== START _tune-hyperparameter4: ./run setHyperparameter 100.0
=== END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s]
=== START _tune-hyperparameter4: ./run learn ../cv.train
Using hyperparameter c = 100.0
Reading training examples... (166 examples) done
Training set properties: 22 features, 21 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=44.0560, QPEps=0.0000)
Iter 3: .........*(NumConst=3, SV=3, CEps=151.7225, QPEps=0.0000)
Iter 4: *(NumConst=4, SV=4, CEps=44.4191, QPEps=0.0000)
Iter 5: *(NumConst=5, SV=5, CEps=55.8156, QPEps=0.0000)
Iter 6: *(NumConst=6, SV=5, CEps=24.1801, QPEps=0.0001)
Iter 7: .........*(NumConst=7, SV=7, CEps=126.8471, QPEps=17.1396)
Iter 8: *(NumConst=8, SV=7, CEps=38.6300, QPEps=0.0000)
Iter 9: *(NumConst=9, SV=7, CEps=47.0036, QPEps=13.2809)
Iter 10: *(NumConst=10, SV=7, CEps=14.8719, QPEps=6.7441)
Iter 11: *(NumConst=11, SV=7, CEps=29.0089, QPEps=0.0001)
Iter 12: *(NumConst=12, SV=7, CEps=15.2835, QPEps=0.0001)
Iter 13: .........*(NumConst=13, SV=8, CEps=125.2805, QPEps=0.0001)
Iter 14: *(NumConst=14, SV=8, CEps=19.6210, QPEps=0.0001)
Iter 15: *(NumConst=15, SV=8, CEps=15.6583, QPEps=0.0000)
Iter 16: .........*(NumConst=16, SV=10, CEps=154.0125, QPEps=12.6998)
Iter 17: .........*(NumConst=17, SV=11, CEps=161.5156, QPEps=18.0627)
Iter 18: *(NumConst=18, SV=8, CEps=28.9561, QPEps=0.0000)
Iter 19: *(NumConst=19, SV=9, CEps=16.2250, QPEps=0.0001)
Iter 20: .........*(NumConst=20, SV=9, CEps=160.2975, QPEps=0.0001)
Iter 21: *(NumConst=21, SV=10, CEps=23.0202, QPEps=0.0000)
Iter 22: .........*(NumConst=22, SV=11, CEps=102.5036, QPEps=24.6774)
Iter 23: *(NumConst=23, SV=10, CEps=14.1241, QPEps=5.9513)
Iter 24: *(NumConst=24, SV=9, CEps=28.4755, QPEps=0.0000)
Iter 25: *(NumConst=25, SV=11, CEps=22.7212, QPEps=0.9615)
Iter 26: *(NumConst=26, SV=11, CEps=16.4126, QPEps=5.5396)
Iter 27: *(NumConst=27, SV=10, CEps=10.9014, QPEps=0.0000)
Iter 28: .........*(NumConst=28, SV=11, CEps=53.1059, QPEps=2.0823)
Iter 29: *(NumConst=29, SV=13, CEps=16.7446, QPEps=6.3909)
Iter 30: *(NumConst=30, SV=13, CEps=12.9006, QPEps=5.4782)
Iter 31: *(NumConst=31, SV=12, CEps=11.1347, QPEps=4.3261)
Iter 32: *(NumConst=32, SV=11, CEps=13.8611, QPEps=4.3591)
Iter 33: *(NumConst=33, SV=11, CEps=8.9304, QPEps=0.8032)
Iter 34: *(NumConst=34, SV=11, CEps=10.1111, QPEps=0.0779)
Iter 35: *(NumConst=35, SV=10, CEps=8.8101, QPEps=4.1427)
Iter 36: *(NumConst=36, SV=11, CEps=6.4797, QPEps=1.2211)
Iter 37: .........*(NumConst=37, SV=13, CEps=37.6279, QPEps=3.0051)
Iter 38: *(NumConst=38, SV=13, CEps=5.9200, QPEps=1.5474)
Iter 39: *(NumConst=39, SV=13, CEps=5.4374, QPEps=0.8016)
Iter 40: .........*(NumConst=40, SV=14, CEps=30.7549, QPEps=0.4645)
Iter 41: *(NumConst=41, SV=15, CEps=6.3223, QPEps=2.0682)
Iter 42: *(NumConst=42, SV=15, CEps=4.3761, QPEps=1.1172)
Iter 43: .........*(NumConst=43, SV=16, CEps=10.4217, QPEps=1.0030)
Iter 44: *(NumConst=44, SV=17, CEps=3.9072, QPEps=0.6469)
Iter 45: *(NumConst=45, SV=16, CEps=5.3288, QPEps=0.6473)
Iter 46: *(NumConst=46, SV=18, CEps=3.7730, QPEps=1.8855)
Iter 47: *(NumConst=47, SV=17, CEps=4.3623, QPEps=1.8837)
Iter 48: *(NumConst=48, SV=16, CEps=3.1214, QPEps=0.9895)
Iter 49: *(NumConst=49, SV=16, CEps=2.3175, QPEps=0.5675)
Iter 50: *(NumConst=50, SV=16, CEps=2.0221, QPEps=0.6212)
Iter 51: *(NumConst=51, SV=17, CEps=1.2800, QPEps=0.6372)
Iter 52: *(NumConst=52, SV=16, CEps=1.9958, QPEps=0.3828)
Iter 53: *(NumConst=53, SV=16, CEps=1.5227, QPEps=0.4638)
Iter 54: *(NumConst=54, SV=16, CEps=1.3073, QPEps=0.5889)
Iter 55: *(NumConst=55, SV=18, CEps=1.2339, QPEps=0.5592)
Iter 56: *(NumConst=56, SV=17, CEps=1.1040, QPEps=0.1924)
Iter 57: .........*(NumConst=56, SV=19, CEps=6.5975, QPEps=2.3590)
Iter 58: *(NumConst=57, SV=21, CEps=3.5087, QPEps=1.7329)
Iter 59: *(NumConst=57, SV=18, CEps=2.8339, QPEps=1.0437)
Iter 60: *(NumConst=57, SV=17, CEps=1.9117, QPEps=0.7359)
Iter 61: *(NumConst=57, SV=18, CEps=1.7861, QPEps=0.7750)
Iter 62: *(NumConst=58, SV=22, CEps=1.4505, QPEps=0.5927)
Iter 63: *(NumConst=58, SV=20, CEps=1.7692, QPEps=0.5621)
Iter 64: *(NumConst=58, SV=22, CEps=2.0904, QPEps=0.5884)
Iter 65: *(NumConst=59, SV=19, CEps=1.5744, QPEps=0.6270)
Iter 66: *(NumConst=60, SV=18, CEps=1.3687, QPEps=0.6378)
Iter 67: *(NumConst=58, SV=18, CEps=1.0033, QPEps=0.3771)
Iter 68: *(NumConst=58, SV=17, CEps=0.9677, QPEps=0.4079)
Iter 69: *(NumConst=58, SV=18, CEps=1.0418, QPEps=0.4819)
Iter 70: *(NumConst=59, SV=17, CEps=0.7948, QPEps=0.3714)
Iter 71: *(NumConst=60, SV=16, CEps=0.7660, QPEps=0.3353)
Iter 72: *(NumConst=60, SV=15, CEps=0.9653, QPEps=0.3650)
Iter 73: .........*(NumConst=59, SV=21, CEps=3.2117, QPEps=0.9210)
Iter 74: *(NumConst=60, SV=24, CEps=2.6237, QPEps=0.3079)
Iter 75: *(NumConst=61, SV=19, CEps=1.6012, QPEps=0.7175)
Iter 76: *(NumConst=61, SV=25, CEps=0.8340, QPEps=0.4143)
Iter 77: *(NumConst=62, SV=15, CEps=1.0863, QPEps=0.3017)
Iter 78: *(NumConst=63, SV=19, CEps=1.0658, QPEps=0.4137)
Iter 79: *(NumConst=63, SV=16, CEps=0.6399, QPEps=0.1359)
Iter 80: *(NumConst=63, SV=18, CEps=1.2949, QPEps=0.2886)
Iter 81: *(NumConst=63, SV=17, CEps=0.9156, QPEps=0.2843)
Iter 82: *(NumConst=64, SV=17, CEps=0.7611, QPEps=0.2947)
Iter 83: *(NumConst=65, SV=19, CEps=0.7352, QPEps=0.2410)
Iter 84: *(NumConst=65, SV=18, CEps=0.8443, QPEps=0.2821)
Iter 85: *(NumConst=66, SV=19, CEps=0.6800, QPEps=0.2926)
Iter 86: *(NumConst=67, SV=24, CEps=0.6092, QPEps=0.2989)
Iter 87: *(NumConst=68, SV=21, CEps=0.5726, QPEps=0.1425)
Iter 88: *(NumConst=68, SV=19, CEps=0.5682, QPEps=0.2030)
Iter 89: *(NumConst=69, SV=20, CEps=0.5350, QPEps=0.2638)
Iter 90: *(NumConst=70, SV=24, CEps=0.4653, QPEps=0.2321)
Iter 91: *(NumConst=70, SV=22, CEps=0.6772, QPEps=0.2324)
Iter 92: *(NumConst=71, SV=22, CEps=0.4295, QPEps=0.1981)
Iter 93: *(NumConst=72, SV=22, CEps=0.3652, QPEps=0.1341)
Iter 94: *(NumConst=73, SV=22, CEps=0.6190, QPEps=0.1580)
Iter 95: *(NumConst=74, SV=21, CEps=0.3736, QPEps=0.1481)
Iter 96: *(NumConst=73, SV=21, CEps=0.3706, QPEps=0.1283)
Iter 97: *(NumConst=74, SV=21, CEps=0.4051, QPEps=0.1419)
Iter 98: *(NumConst=74, SV=24, CEps=0.3690, QPEps=0.1654)
Iter 99: *(NumConst=74, SV=23, CEps=0.3264, QPEps=0.1498)
Iter 100: *(NumConst=75, SV=23, CEps=0.3425, QPEps=0.1464)
Iter 101: *(NumConst=76, SV=22, CEps=0.3886, QPEps=0.1452)
Iter 102: .........*(NumConst=77, SV=23, CEps=1.6389, QPEps=0.3126)
Iter 103: *(NumConst=78, SV=28, CEps=0.6685, QPEps=0.2608)
Iter 104: *(NumConst=79, SV=28, CEps=1.0264, QPEps=0.2930)
Iter 105: *(NumConst=80, SV=24, CEps=0.5390, QPEps=0.2284)
Iter 106: *(NumConst=81, SV=25, CEps=0.4778, QPEps=0.2111)
Iter 107: *(NumConst=80, SV=28, CEps=0.4000, QPEps=0.1812)
Iter 108: *(NumConst=80, SV=25, CEps=0.4601, QPEps=0.1987)
Iter 109: *(NumConst=81, SV=24, CEps=0.4098, QPEps=0.1929)
Iter 110: *(NumConst=82, SV=23, CEps=0.3069, QPEps=0.1243)
Iter 111: *(NumConst=83, SV=27, CEps=0.3017, QPEps=0.1458)
Iter 112: *(NumConst=83, SV=25, CEps=0.4106, QPEps=0.1173)
Iter 113: *(NumConst=84, SV=26, CEps=0.3296, QPEps=0.1344)
Iter 114: *(NumConst=83, SV=23, CEps=0.3004, QPEps=0.1471)
Iter 115: *(NumConst=83, SV=24, CEps=0.2931, QPEps=0.1321)
Iter 116: *(NumConst=84, SV=24, CEps=0.3765, QPEps=0.1400)
Iter 117: *(NumConst=84, SV=23, CEps=0.2900, QPEps=0.1155)
Iter 118: *(NumConst=85, SV=27, CEps=0.2174, QPEps=0.1027)
Iter 119: *(NumConst=86, SV=24, CEps=0.3165, QPEps=0.0888)
Iter 120: *(NumConst=86, SV=25, CEps=0.1883, QPEps=0.0904)
Iter 121: *(NumConst=87, SV=25, CEps=0.2309, QPEps=0.0886)
Iter 122: *(NumConst=87, SV=22, CEps=0.2435, QPEps=0.0919)
Iter 123: .........*(NumConst=85, SV=23, CEps=0.5078, QPEps=0.2041)
Iter 124: *(NumConst=82, SV=25, CEps=0.3362, QPEps=0.1517)
Iter 125: *(NumConst=82, SV=28, CEps=0.3806, QPEps=0.1295)
Iter 126: *(NumConst=78, SV=28, CEps=0.4434, QPEps=0.1479)
Iter 127: *(NumConst=79, SV=26, CEps=0.2862, QPEps=0.1421)
Iter 128: *(NumConst=77, SV=23, CEps=0.2379, QPEps=0.1111)
Iter 129: *(NumConst=77, SV=24, CEps=0.2041, QPEps=0.0705)
Iter 130: *(NumConst=75, SV=26, CEps=0.1955, QPEps=0.0954)
Iter 131: *(NumConst=76, SV=24, CEps=0.3235, QPEps=0.0945)
Iter 132: *(NumConst=77, SV=24, CEps=0.2232, QPEps=0.0928)
Iter 133: *(NumConst=76, SV=28, CEps=0.1871, QPEps=0.0660)
Iter 134: *(NumConst=77, SV=27, CEps=0.2523, QPEps=0.0787)
Iter 135: *(NumConst=78, SV=27, CEps=0.1738, QPEps=0.0862)
Iter 136: *(NumConst=77, SV=27, CEps=0.1783, QPEps=0.0778)
Iter 137: *(NumConst=76, SV=28, CEps=0.1501, QPEps=0.0647)
Iter 138: *(NumConst=77, SV=27, CEps=0.1463, QPEps=0.0688)
Iter 139: *(NumConst=78, SV=25, CEps=0.1938, QPEps=0.0607)
Iter 140: *(NumConst=77, SV=26, CEps=0.1760, QPEps=0.0615)
Iter 141: *(NumConst=78, SV=25, CEps=0.1159, QPEps=0.0518)
Iter 142: *(NumConst=78, SV=24, CEps=0.1373, QPEps=0.0571)
Iter 143: *(NumConst=79, SV=27, CEps=0.1052, QPEps=0.0463)
Iter 144: *(NumConst=80, SV=27, CEps=0.1613, QPEps=0.0521)
Iter 145: .........*(NumConst=81, SV=28, CEps=0.2934, QPEps=0.1338)
Iter 146: *(NumConst=81, SV=30, CEps=0.2128, QPEps=0.1008)
Iter 147: *(NumConst=82, SV=27, CEps=0.2200, QPEps=0.0956)
Iter 148: *(NumConst=82, SV=28, CEps=0.1679, QPEps=0.0711)
Iter 149: *(NumConst=83, SV=28, CEps=0.1693, QPEps=0.0775)
Iter 150: *(NumConst=83, SV=26, CEps=0.1107, QPEps=0.0518)
Iter 151: *(NumConst=84, SV=28, CEps=0.1624, QPEps=0.0536)
Iter 152: *(NumConst=84, SV=29, CEps=0.1305, QPEps=0.0509)
Iter 153: *(NumConst=83, SV=29, CEps=0.1443, QPEps=0.0514)
Iter 154: .........*(NumConst=81, SV=29, CEps=0.1404, QPEps=0.0566)
Iter 155: *(NumConst=82, SV=30, CEps=0.1377, QPEps=0.0659)
Iter 156: *(NumConst=82, SV=29, CEps=0.1145, QPEps=0.0428)
Iter 157: *(NumConst=80, SV=30, CEps=0.1200, QPEps=0.0449)
Iter 158: *(NumConst=80, SV=29, CEps=0.1014, QPEps=0.0385)
Iter 159: *(NumConst=81, SV=29, CEps=0.1023, QPEps=0.0473)
Iter 160: *(NumConst=81, SV=28, CEps=0.1034, QPEps=0.0446)
Iter 161: .........*(NumConst=82, SV=28, CEps=0.1189, QPEps=0.0566)
Iter 162: *(NumConst=81, SV=27, CEps=0.1156, QPEps=0.0533)
Iter 163: .........*(NumConst=80, SV=28, CEps=0.1101, QPEps=0.0529)
Iter 164: *(NumConst=81, SV=28, CEps=0.1050, QPEps=0.0421)
Iter 165: .........*(NumConst=81, SV=31, CEps=0.1089, QPEps=0.0467)
Iter 166: *(NumConst=82, SV=30, CEps=0.1126, QPEps=0.0449)
Iter 167: .........*(NumConst=82, SV=31, CEps=0.1071, QPEps=0.0436)
Iter 168: .........(NumConst=82, SV=31, CEps=0.0789, QPEps=0.0436)
Final epsilon on KKT-Conditions: 0.07886
Upper bound on duality gap: 7.82949
Dual objective value: dval=9859.45438
Primal objective value: pval=9867.28387
Total number of constraints in final working set: 82 (of 167)
Number of iterations: 168
Number of calls to 'find_most_violated_constraint': 3818
Number of SV: 31 
Norm of weight vector: |w|=16.76578
Value of slack variable (on working set): xi=97.21289
Value of slack variable (global): xi=97.26738
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=2.59754
Runtime in cpu-seconds: 0.17
Final number of constraints in cache: 830
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4
Reading model...done.
Reading test examples... (71 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 59.1549
Zero/one-error on test set: 59.15% (29 correct, 42 incorrect, 71 total)
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [0s]
CV error rate 0.591549295774648 with hyperparameter 100.0

Best hyperparameter value is 10.0; got CV error rate 0.591549295774648
=== END program1: ./run learn ../dataset6/train --- OK [1s]

===== MAIN: predict/evaluate on train data =====
=== START program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in
=== END program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out
Reading model...done.
Reading test examples... (237 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 57.8059
Zero/one-error on test set: 57.81% (100 correct, 137 incorrect, 237 total)
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [0s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out
=== END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in
=== END program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out
Reading model...done.
Reading test examples... (102 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 48.0392
Zero/one-error on test set: 48.04% (53 correct, 49 incorrect, 102 total)
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [0s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out
=== END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [0s]


real	0m1.681s
user	0m0.924s
sys	0m0.172s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) tune-hyperparameter: Sets the hyperparameter
    • (numProbes:int) 5
    • (learner:Program) svmlight_multiclass-linear: SVMlight for multiclass classification (http://svmlight.joachims.org/svm_multiclass.html)
    • (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) primary-tumor: 339 examples, 22 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: 0.637254901960784
    • numErrors: 65
    • numExamples: 102
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.582278481012658
    • numErrors: 138
    • numExamples: 237
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • bestCVErrorRate: 0.591549295774648
  • bestHyperparameter: 10.0
  • evaluate0:
    • errorRate: 0.971830985915493
    • numErrors: 69
    • numExamples: 71
    • success: true
    • time: 0
  • evaluate1:
    • errorRate: 0.774647887323944
    • numErrors: 55
    • numExamples: 71
    • success: true
    • time: 0
  • evaluate2:
    • errorRate: 0.71830985915493
    • numErrors: 51
    • numExamples: 71
    • success: true
    • time: 0
  • evaluate3:
    • errorRate: 0.591549295774648
    • numErrors: 42
    • numExamples: 71
    • success: true
    • time: 0
  • evaluate4:
    • errorRate: 0.591549295774648
    • numErrors: 42
    • numExamples: 71
    • success: true
    • time: 0
  • learn0:
    • success: true
    • time: 0
  • learn1:
    • success: true
    • time: 0
  • learn2:
    • success: true
    • time: 0
  • learn3:
    • success: true
    • time: 0
  • learn4:
    • success: true
    • time: 1
  • predict0:
    • success: true
    • time: 0
  • predict1:
    • success: true
    • time: 0
  • predict2:
    • success: true
    • time: 0
  • predict3:
    • success: true
    • time: 0
  • predict4:
    • success: true
    • time: 0
  • setHyperparameter0:
    • success: true
    • time: 0
  • setHyperparameter1:
    • success: true
    • time: 0
  • setHyperparameter2:
    • success: true
    • time: 0
  • setHyperparameter3:
    • success: true
    • time: 0
  • setHyperparameter4:
    • success: true
    • time: 0
  • split:
    • success: true
    • time: 0
  • success: true
  • time: 1
• success: true

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