ServerRun 39446
Creatorchuertas
Programlogreg-disnb-python
DatasetLos tres mosqueteros
Task typeMulticlassClassification
Created2y3d ago
Done! Flag_green
33s
95M
MulticlassClassification
37s
0.055
6s
0.046
3s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
d=162, n=7354 nd=1191348
datamatrix original dimension: (162, 7354)
using k=10
(162, 7354)
(1458, 7354)
datamatrix encoded dimension: (242, 7354)
[  6.14579989e-01   1.67454455e+00   6.39113763e-01   1.84521402e-02
   1.10357071e+00   5.09503604e-01   1.31887193e+00   2.33789678e+00
   1.00024247e+00   2.09885476e+00   8.46091790e-01   5.44766941e-01
   8.60480527e-01   5.94777362e-01   1.81117269e+00   1.56822651e+00
   1.94132166e-02   7.44729938e-02   9.51452305e-01   6.45421094e-01
   4.12114052e-02   5.80984039e-01   1.13377386e+00   1.14334331e+00
   1.10032593e+00   1.25104933e+00   9.69198177e-01   8.39517378e-02
   3.43492277e-01   4.56627023e-01   5.03805583e-01   7.29019016e-02
   4.56432688e-01   3.86051891e-01   3.07095289e-01   4.12114052e-02
   7.12560397e-01   3.94106666e-01   6.06294910e-02   1.69338965e+00
   9.21570128e-01   3.07095289e-01   2.27052581e-01   1.52548617e+00
   1.25768793e-01   4.06671179e-01   6.41985266e-01   5.94777362e-01
   2.27052581e-01   1.52437040e-01   3.94106666e-01   8.30343433e-01
   5.40202571e-01   7.91517000e-01   2.39955986e-01   4.20423974e-01
   2.99040514e-01   1.26844107e+00   8.46091790e-01   1.94132166e-02
   3.94106666e-01   1.52437040e-01   8.58656302e-01   1.52437040e-01
   1.18256403e+00   5.40710140e-01   1.00024247e+00   1.25104933e+00
   6.57985607e-01   7.99571774e-01   8.46091790e-01   4.66094599e-01
   5.40710140e-01   2.99040514e-01   3.07095289e-01   2.99040514e-01
   1.00758856e-01   2.14087779e-02   3.94106666e-01   2.99040514e-01
   6.09195443e-01   2.90565987e-01   9.46175248e-01   1.00024247e+00
   7.04505623e-01   1.52437040e-01   8.32338994e-01   5.76428223e-01
   1.00024247e+00   2.05312595e-01   3.29568145e-01   4.03239150e-01
   1.52437040e-01   2.52520499e-01   1.72175971e-01   2.27052581e-01
   1.00024247e+00   1.40570758e+00   2.14087779e-02   6.89238150e-01
   5.03834927e-01   2.42556768e-01   1.12002107e+00   5.94777362e-01
   8.39517378e-02   1.52437040e-01   1.86923216e-01   4.26873886e-01
   1.18256403e+00   1.69828783e-01   8.46091790e-01   5.94777362e-01
   1.20607056e-03   8.39517378e-02   1.13584420e-02   1.52437040e-01
   7.77098918e-01   1.08956186e+00   3.07095289e-01   1.00348725e+00
   7.24506289e-01   3.64253703e-01   1.16718958e-01   1.52944609e-01
   3.90903011e-03   7.40731274e-01   3.21513370e-01   9.20199762e-01
   2.52520499e-01   9.20199762e-01   2.08004386e-01   4.12455805e-01
   6.22948239e-01   1.40570758e+00   7.99571774e-01   4.56627023e-01
   3.94106666e-01   1.27217619e+00   5.67378387e-01   2.27052581e-01
   2.09885476e+00   2.09885476e+00   4.74938827e-01   3.28148698e-01
   1.05229136e-01   4.20768328e-01   1.26868772e-01   1.48490259e-01
   1.07659151e+00   3.30989609e-01   1.25494038e+00   1.01452843e+00
   3.72941855e-01   5.27638059e-01   5.61329427e-01   1.34737463e-01
   1.01153100e-01   7.26339129e-02   1.40770314e+00   7.54377453e-01
   1.47769551e+00   8.92993441e-02   5.01612119e-01   1.34076125e-01
   4.11427527e-01   4.30512142e-01   1.01816318e+00   1.15065804e+00
   3.51266507e-01   1.91452074e+00   4.92755747e-01   2.26105407e+00
   5.75760535e-02   2.68226420e-01   9.92187695e-01   3.99077679e-01
   9.28803685e-02   4.47829251e-01   1.01466055e+00   8.66418308e-01
   1.82311858e+00   4.10412609e-01   3.72065649e-01   2.74561515e-01
   5.02662073e-01   9.37699510e-01   5.27219211e-01   1.46502821e+00
   1.24046722e+00   3.07095289e-01   5.96344301e-02   5.84121805e-01
   1.25299675e+00   2.96931473e-02   4.11806243e-01   1.28741697e+00
   6.68154555e-02   6.86127140e-01   9.72843495e-01   1.47067794e+00
   2.74138755e+00   5.61404779e-01   1.05102820e+00   6.94353251e-01
   1.15924178e+00   1.12002107e+00   1.03663946e+00   9.21570128e-01
   1.13033803e+00   2.07106317e-01   1.15557077e-01   1.94132166e-02
   9.95047653e-01   1.81566513e+00   2.79724700e-02   5.15562126e-01
   9.50418282e-01   4.57030330e-01   8.09742766e-01   1.02461088e+00
   1.35113279e+00   1.39765280e+00   1.36688114e+00   1.65401785e-01
   2.14087779e-02   1.73271127e-01   6.56541563e-01   3.89023662e-01
   5.54387206e-01   3.37309068e-01   5.15562126e-01   5.47045959e-01
   2.38102418e-01   4.55326279e-02   9.86040146e-02   1.82676925e-01
   9.35965403e-02   3.90848064e-01   1.80647926e+00   1.78412527e-01
   1.09981836e+00   9.21570128e-01]
[  6.14579989e-01   1.67454455e+00   6.39113763e-01   1.84521402e-02
   1.10357071e+00   5.09503604e-01   1.31887193e+00   2.33789678e+00
   1.00024247e+00   2.09885476e+00   8.46091790e-01   5.44766941e-01
   8.60480527e-01   5.94777362e-01   1.81117269e+00   1.56822651e+00
   1.94132166e-02   7.44729938e-02   9.51452305e-01   6.45421094e-01
   4.12114052e-02   5.80984039e-01   1.13377386e+00   1.14334331e+00
   1.10032593e+00   1.25104933e+00   9.69198177e-01   8.39517378e-02
   3.43492277e-01   4.56627023e-01   5.03805583e-01   7.29019016e-02
   4.56432688e-01   3.86051891e-01   3.07095289e-01   4.12114052e-02
   7.12560397e-01   3.94106666e-01   6.06294910e-02   1.69338965e+00
   9.21570128e-01   3.07095289e-01   2.27052581e-01   1.52548617e+00
   1.25768793e-01   4.06671179e-01   6.41985266e-01   5.94777362e-01
   2.27052581e-01   1.52437040e-01   3.94106666e-01   8.30343433e-01
   5.40202571e-01   7.91517000e-01   2.39955986e-01   4.20423974e-01
   2.99040514e-01   1.26844107e+00   8.46091790e-01   1.94132166e-02
   3.94106666e-01   1.52437040e-01   8.58656302e-01   1.52437040e-01
   1.18256403e+00   5.40710140e-01   1.00024247e+00   1.25104933e+00
   6.57985607e-01   7.99571774e-01   8.46091790e-01   4.66094599e-01
   5.40710140e-01   2.99040514e-01   3.07095289e-01   2.99040514e-01
   1.00758856e-01   2.14087779e-02   3.94106666e-01   2.99040514e-01
   6.09195443e-01   2.90565987e-01   9.46175248e-01   1.00024247e+00
   7.04505623e-01   1.52437040e-01   8.32338994e-01   5.76428223e-01
   1.00024247e+00   2.05312595e-01   3.29568145e-01   4.03239150e-01
   1.52437040e-01   2.52520499e-01   1.72175971e-01   2.27052581e-01
   1.00024247e+00   1.40570758e+00   2.14087779e-02   6.89238150e-01
   5.03834927e-01   2.42556768e-01   1.12002107e+00   5.94777362e-01
   8.39517378e-02   1.52437040e-01   1.86923216e-01   4.26873886e-01
   1.18256403e+00   1.69828783e-01   8.46091790e-01   5.94777362e-01
   1.20607056e-03   8.39517378e-02   1.13584420e-02   1.52437040e-01
   7.77098918e-01   1.08956186e+00   3.07095289e-01   1.00348725e+00
   7.24506289e-01   3.64253703e-01   1.16718958e-01   1.52944609e-01
   3.90903011e-03   7.40731274e-01   3.21513370e-01   9.20199762e-01
   2.52520499e-01   9.20199762e-01   2.08004386e-01   4.12455805e-01
   6.22948239e-01   1.40570758e+00   7.99571774e-01   4.56627023e-01
   3.94106666e-01   1.27217619e+00   5.67378387e-01   2.27052581e-01
   2.09885476e+00   2.09885476e+00   4.74938827e-01   3.28148698e-01
   1.05229136e-01   4.20768328e-01   1.26868772e-01   1.48490259e-01
   1.07659151e+00   3.30989609e-01   1.25494038e+00   1.01452843e+00
   3.72941855e-01   5.27638059e-01   5.61329427e-01   1.34737463e-01
   1.01153100e-01   7.26339129e-02   1.40770314e+00   7.54377453e-01
   1.47769551e+00   8.92993441e-02   5.01612119e-01   1.34076125e-01
   4.11427527e-01   4.30512142e-01   1.01816318e+00   1.15065804e+00
   3.51266507e-01   1.91452074e+00   4.92755747e-01   2.26105407e+00
   5.75760535e-02   2.68226420e-01   9.92187695e-01   3.99077679e-01
   9.28803685e-02   4.47829251e-01   1.01466055e+00   8.66418308e-01
   1.82311858e+00   4.10412609e-01   3.72065649e-01   2.74561515e-01
   5.02662073e-01   9.37699510e-01   5.27219211e-01   1.46502821e+00
   1.24046722e+00   3.07095289e-01   5.96344301e-02   5.84121805e-01
   1.25299675e+00   2.96931473e-02   4.11806243e-01   1.28741697e+00
   6.68154555e-02   6.86127140e-01   9.72843495e-01   1.47067794e+00
   2.74138755e+00   5.61404779e-01   1.05102820e+00   6.94353251e-01
   1.15924178e+00   1.12002107e+00   1.03663946e+00   9.21570128e-01
   1.13033803e+00   2.07106317e-01   1.15557077e-01   1.94132166e-02
   9.95047653e-01   1.81566513e+00   2.79724700e-02   5.15562126e-01
   9.50418282e-01   4.57030330e-01   8.09742766e-01   1.02461088e+00
   1.35113279e+00   1.39765280e+00   1.36688114e+00   1.65401785e-01
   2.14087779e-02   1.73271127e-01   6.56541563e-01   3.89023662e-01
   5.54387206e-01   3.37309068e-01   5.15562126e-01   5.47045959e-01
   2.38102418e-01   4.55326279e-02   9.86040146e-02   1.82676925e-01
   9.35965403e-02   3.90848064e-01   1.80647926e+00   1.78412527e-01
   1.09981836e+00   9.21570128e-01]
lambs, mean: 0.00155563491861, var:9.473487691e-06
mean lambda: 0.00155563491861
(2, 242)
b is [[-1.82912461]
 [ 1.82912461]]
RUNNING THE L-BFGS-B CODE

           * * *

Machine precision = 1.084D-19
 N =          486     M =           10
 This problem is unconstrained.

At X0         0 variables are exactly at the bounds

At iterate    0    f=  6.91393D-01    |proj g|=  4.43450D-01

At iterate   10    f=  1.72024D-01    |proj g|=  4.29763D-03

At iterate   20    f=  1.64932D-01    |proj g|=  1.76040D-03

At iterate   30    f=  1.64426D-01    |proj g|=  2.79559D-04

At iterate   40    f=  1.64348D-01    |proj g|=  5.93276D-05

At iterate   50    f=  1.64333D-01    |proj g|=  6.33433D-05

At iterate   60    f=  1.64328D-01    |proj g|=  4.05254D-05

At iterate   70    f=  1.64326D-01    |proj g|=  1.49358D-05

           * * *

Tit   = total number of iterations
Tnf   = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip  = number of BFGS updates skipped
Nact  = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F     = final function value

           * * *

   N   Tit  Tnf  Tnint  Skip  Nact     Projg        F
  486   74   80      1     0     0   8.355D-06   1.643D-01
  F =  0.16432618120269168     

CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL            

 Cauchy                time 0.000E+00 seconds.
 Subspace minimization time 7.999E-03 seconds.
 Line search           time 7.696E+00 seconds.

 Total User time 7.792E+00 seconds.

=== END program1: ./run learn ../dataset2/train --- OK [37s]

===== 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
d=162, n=7354 nd=1191348
datamatrix original dimension: (162, 7354)
datamatrix encoded dimension: (242, 7354)
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [6s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]

===== 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
d=162, n=3152 nd=510624
datamatrix original dimension: (162, 3152)
datamatrix encoded dimension: (242, 3152)
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [3s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	0m47.202s
user	0m26.242s
sys	0m17.585s

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