 | Run 27710 |
| Creator | sidaw |
| Program | logreg-dis-python |
| Dataset | 20news-hardware |
| Task type | MulticlassClassification |
| Created | 286d20h ago |
| Download | Login required! |
Done! 
| 11m3s | |
| 997M | |
| BinaryClassification | |
| 6m25s | |
| 0 | |
| 1m49s | |
| 0.136 | |
| 2m51s |
Log file
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
=== START program2: ./run learn ../program1/data
datamatrix original dimension: (35213, 959)
way too many dimensions, so just using 2 in each dimension
using k=2
(35213, 959)
datamatrix encoded dimension: (27800, 959)
(2, 27800)
b is [[-0.17439781]
[ 0.17439781]]
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 1.084D-19
N = 55602 M = 10
This problem is unconstrained.
At X0 0 variables are exactly at the bounds
At iterate 0 f= 6.93354D-01 |proj g|= 5.48946D-02
At iterate 10 f= 1.55925D-02 |proj g|= 1.16888D-03
At iterate 20 f= 1.06093D-02 |proj g|= 4.59496D-04
At iterate 30 f= 1.02803D-02 |proj g|= 3.36601D-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
55602 32 35 1 0 0 7.661D-06 1.027D-02
F = 1.02719607284918728E-002
CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL
Cauchy time 0.000E+00 seconds.
Subspace minimization time 9.121E-01 seconds.
Line search time 5.045E+01 seconds.
Total User time 5.312E+01 seconds.
=== END program2: ./run learn ../program1/data --- OK [385s]
=== END program1: ./run learn ../dataset3/train --- OK [385s]
===== MAIN: predict/evaluate on train data =====
=== START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in
=== END program4: ./run stripLabels ../dataset3/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
datamatrix original dimension: (35213, 959)
datamatrix encoded dimension: (27800, 959)
=== END program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output --- OK [108s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [109s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [0s]
===== MAIN: predict/evaluate on test data =====
=== START program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in
=== END program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output
datamatrix original dimension: (35093, 411)
datamatrix encoded dimension: (27680, 411)
extra dimensions exist in the training set that is not in the test set
=== END program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output --- OK [171s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [171s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]
real 11m6.158s
user 8m27.440s
sys 2m27.381s
Run specification 
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