... (lines omitted) ...
260 out of 3952
270 out of 3952
280 out of 3952
290 out of 3952
300 out of 3952
310 out of 3952
320 out of 3952
330 out of 3952
340 out of 3952
350 out of 3952
360 out of 3952
370 out of 3952
380 out of 3952
390 out of 3952
400 out of 3952
410 out of 3952
420 out of 3952
430 out of 3952
440 out of 3952
450 out of 3952
460 out of 3952
470 out of 3952
480 out of 3952
490 out of 3952
500 out of 3952
510 out of 3952
520 out of 3952
530 out of 3952
540 out of 3952
550 out of 3952
560 out of 3952
570 out of 3952
580 out of 3952
590 out of 3952
600 out of 3952
610 out of 3952
620 out of 3952
630 out of 3952
640 out of 3952
650 out of 3952
660 out of 3952
670 out of 3952
680 out of 3952
690 out of 3952
700 out of 3952
710 out of 3952
720 out of 3952
730 out of 3952
740 out of 3952
750 out of 3952
760 out of 3952
770 out of 3952
780 out of 3952
790 out of 3952
800 out of 3952
810 out of 3952
820 out of 3952
830 out of 3952
840 out of 3952
850 out of 3952
860 out of 3952
870 out of 3952
880 out of 3952
890 out of 3952
900 out of 3952
910 out of 3952
920 out of 3952
930 out of 3952
940 out of 3952
950 out of 3952
960 out of 3952
970 out of 3952
980 out of 3952
990 out of 3952
1000 out of 3952
1010 out of 3952
1020 out of 3952
1030 out of 3952
1040 out of 3952
1050 out of 3952
1060 out of 3952
1070 out of 3952
1080 out of 3952
1090 out of 3952
1100 out of 3952
1110 out of 3952
1120 out of 3952
1130 out of 3952
1140 out of 3952
1150 out of 3952
1160 out of 3952
1170 out of 3952
1180 out of 3952
1190 out of 3952
1200 out of 3952
1210 out of 3952
1220 out of 3952
1230 out of 3952
1240 out of 3952
1250 out of 3952
1260 out of 3952
1270 out of 3952
1280 out of 3952
1290 out of 3952
1300 out of 3952
1310 out of 3952
1320 out of 3952
1330 out of 3952
1340 out of 3952
1350 out of 3952
1360 out of 3952
1370 out of 3952
1380 out of 3952
1390 out of 3952
1400 out of 3952
1410 out of 3952
1420 out of 3952
1430 out of 3952
1440 out of 3952
1450 out of 3952
1460 out of 3952
1470 out of 3952
1480 out of 3952
1490 out of 3952
1500 out of 3952
1510 out of 3952
1520 out of 3952
1530 out of 3952
1540 out of 3952
1550 out of 3952
1560 out of 3952
1570 out of 3952
1580 out of 3952
1590 out of 3952
1600 out of 3952
1610 out of 3952
1620 out of 3952
1630 out of 3952
1640 out of 3952
1650 out of 3952
1660 out of 3952
1670 out of 3952
1680 out of 3952
1690 out of 3952
1700 out of 3952
1710 out of 3952
1720 out of 3952
1730 out of 3952
1740 out of 3952
1750 out of 3952
1760 out of 3952
1770 out of 3952
1780 out of 3952
1790 out of 3952
1800 out of 3952
1810 out of 3952
1820 out of 3952
1830 out of 3952
1840 out of 3952
1850 out of 3952
1860 out of 3952
1870 out of 3952
1880 out of 3952
1890 out of 3952
1900 out of 3952
1910 out of 3952
1920 out of 3952
1930 out of 3952
1940 out of 3952
1950 out of 3952
1960 out of 3952
1970 out of 3952
1980 out of 3952
1990 out of 3952
2000 out of 3952
2010 out of 3952
2020 out of 3952
2030 out of 3952
2040 out of 3952
2050 out of 3952
2060 out of 3952
2070 out of 3952
2080 out of 3952
2090 out of 3952
2100 out of 3952
2110 out of 3952
2120 out of 3952
2130 out of 3952
2140 out of 3952
2150 out of 3952
2160 out of 3952
2170 out of 3952
2180 out of 3952
2190 out of 3952
2200 out of 3952
2210 out of 3952
2220 out of 3952
2230 out of 3952
2240 out of 3952
2250 out of 3952
2260 out of 3952
2270 out of 3952
2280 out of 3952
2290 out of 3952
2300 out of 3952
2310 out of 3952
2320 out of 3952
2330 out of 3952
2340 out of 3952
2350 out of 3952
2360 out of 3952
2370 out of 3952
2380 out of 3952
2390 out of 3952
2400 out of 3952
2410 out of 3952
2420 out of 3952
2430 out of 3952
2440 out of 3952
2450 out of 3952
2460 out of 3952
2470 out of 3952
2480 out of 3952
2490 out of 3952
2500 out of 3952
2510 out of 3952
2520 out of 3952
2530 out of 3952
2540 out of 3952
2550 out of 3952
2560 out of 3952
2570 out of 3952
2580 out of 3952
2590 out of 3952
2600 out of 3952
2610 out of 3952
2620 out of 3952
2630 out of 3952
2640 out of 3952
2650 out of 3952
2660 out of 3952
2670 out of 3952
2680 out of 3952
2690 out of 3952
2700 out of 3952
2710 out of 3952
2720 out of 3952
2730 out of 3952
2740 out of 3952
2750 out of 3952
2760 out of 3952
2770 out of 3952
2780 out of 3952
2790 out of 3952
2800 out of 3952
2810 out of 3952
2820 out of 3952
2830 out of 3952
2840 out of 3952
2850 out of 3952
2860 out of 3952
2870 out of 3952
2880 out of 3952
2890 out of 3952
2900 out of 3952
2910 out of 3952
2920 out of 3952
2930 out of 3952
2940 out of 3952
2950 out of 3952
2960 out of 3952
2970 out of 3952
2980 out of 3952
2990 out of 3952
3000 out of 3952
3010 out of 3952
3020 out of 3952
3030 out of 3952
3040 out of 3952
3050 out of 3952
3060 out of 3952
3070 out of 3952
3080 out of 3952
3090 out of 3952
3100 out of 3952
3110 out of 3952
3120 out of 3952
3130 out of 3952
3140 out of 3952
3150 out of 3952
3160 out of 3952
3170 out of 3952
3180 out of 3952
3190 out of 3952
3200 out of 3952
3210 out of 3952
3220 out of 3952
3230 out of 3952
3240 out of 3952
3250 out of 3952
3260 out of 3952
3270 out of 3952
3280 out of 3952
3290 out of 3952
3300 out of 3952
3310 out of 3952
3320 out of 3952
3330 out of 3952
3340 out of 3952
3350 out of 3952
3360 out of 3952
3370 out of 3952
3380 out of 3952
3390 out of 3952
3400 out of 3952
3410 out of 3952
3420 out of 3952
3430 out of 3952
3440 out of 3952
3450 out of 3952
3460 out of 3952
3470 out of 3952
3480 out of 3952
3490 out of 3952
3500 out of 3952
3510 out of 3952
3520 out of 3952
3530 out of 3952
3540 out of 3952
3550 out of 3952
3560 out of 3952
3570 out of 3952
3580 out of 3952
3590 out of 3952
3600 out of 3952
3610 out of 3952
3620 out of 3952
3630 out of 3952
3640 out of 3952
3650 out of 3952
3660 out of 3952
3670 out of 3952
3680 out of 3952
3690 out of 3952
3700 out of 3952
3710 out of 3952
3720 out of 3952
3730 out of 3952
3740 out of 3952
3750 out of 3952
3760 out of 3952
3770 out of 3952
3780 out of 3952
3790 out of 3952
3800 out of 3952
3810 out of 3952
3820 out of 3952
3830 out of 3952
3840 out of 3952
3850 out of 3952
3860 out of 3952
3870 out of 3952
3880 out of 3952
3890 out of 3952
3900 out of 3952
3910 out of 3952
3920 out of 3952
3930 out of 3952
3940 out of 3952
3950 out of 3952
=== END program1: ./run learn ../dataset2/train --- OK [8947s]
===== 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 [2s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
[1] "../program0/evalTrain.in"
[1] "../program0/evalTrain.out"
[1] "Computing SVD..."
[1] "Computing KNN and merging..."
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1096s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [6s]
===== 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
[1] "../program0/evalTest.in"
[1] "../program0/evalTest.out"
[1] "Computing SVD..."
[1] "Computing KNN and merging..."
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [226s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]
real 171m18.480s
user 170m20.367s
sys 0m28.738s
Run specification
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) xterm-knnsvd_shrunk_unbias_rev-r: KNN with 21 neighbors and cosine distance, and SVD with EM, 10 iterations, and K=10. Combined using fixed weights of 1/3 and 2/3 resp.
(dataset:Dataset) movielens1m: 1M MovieLens movie ratings dataset from http://www.grouplens.org/.
The original dataset contains 1,000,209 anonymous ratings (1-5 stars) of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.
This sub-dataset includes the ratings of 5,000 randomly selected users. A single rating from each user was withheld to form the test set.
See included README.txt for more information.
(stripper:Program[Strip]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.
(evaluator:Program[Evaluate]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.
When you generate a run, you can set a time limit for the run (no more than 24 hours). After that point, we will terminate the program.
Your program can use 1.5GB of memory. More information here.
Go to the page for the run and look at the log file for signs of the responsible error.
You can also download the run and run it locally on your machine (a README file should
be included in the download which provides more information).
We said that a run was simply a program/dataset pair, but that's not the full story.
A run actually includes other helper programs such as the evaluation program and
various programs for reductions (e.g., one-versus-all, hyperparameter tuning).
More formally, a run is a given by a run specification,
which can be found on the page for any run.
A run specification is a tree where each internal node represents a program
and its children represents the arguments to be passed into its constructor.
For example, the one-versus-all program takes your binary classification program
as a constructor argument and behaves like a multiclass classification program.
Must be logged in to post comments.