The multilevel support vector machine framework KaSVM.
Our framework is build on the multilevel support vector machine approach of https://github.com/esadr/mlsvm and introduces several new coarsening strategies.
While regular SVM perform the entire training in one - time consuming - optimization step, multilevel SVMs first build a hierarchy of problems decreasing in size that resemble the original problem and then train an SVM model for each hierarchy level benefiting from the solved models of previous levels.
Our framework utilizes the near-linear time label propagation algorithm for the construction of the problem hierarchy and we are experimenting with other coarsening strategies.
Faster Support Vector Machines [ALENEX19] - Sebastian Schlag, Matthias Schmitt, Christian Schulz
To compile install the following dependencies through your package manager:
- Scons (http://www.scons.org/)
- OpenMPI (http://www.open-mpi.de/)
Once you installed these packages you need to compile some additional dependencies.
cd extern/thundersvm
git submodule init eigen && git submodule update
mkdir build && cd build
cmake -DUSE_CUDA=OFF -DUSE_EIGEN=ON ..
make -j 4The following (Ubuntu/Debian) packages are needed for BayesOpt
libboost-dev cmake g++
then
cd extern/bayesopt
mkdir build && cd build
cmake ..
make -j 4you can test if the compile step worked with
./bin/bo_braninCompile KaSVM with with
scons program=prepare variant=optimized_output -j 4
scons program=kasvm variant=optimized_output -j 4Before using the framework the data needs to be preprocessed.
The prepare utility can read CSV and LibSVM files.
It normalized the data and seperates the classes.
./optimized_output/prepare examples/twonorm.csvoutput of prepare
read csv time 0.0533679 rows: 7400 cols: 20 normalization time 0.00080204 splitting time 0.00121903 nodes - min 3703 maj 3697 finished writing features to examples/twonorm_min_data in 0.0366721 finished writing features to examples/twonorm_maj_data in 0.035835
After the data has been preprocessed run the kasvm program.
As path argument use the path of the original data file without the extension.
Please note that to reproduce our experiments usually no special configuration is needed as the default values are the values we report in Section 5 of our paper.
./optimized_output/kasvm --helpUsage: ./optimized_output/kasvm [-b] [--help] FILE [--seed=<int>] [-e <int>] [-k <int>] [-s <double>] [--validation=TYPE] [--validation_percent=<double>] [--validation_seperate] [-n <int>] [--stop_rule=VARIANT] [--fix_num_vert_stop=<int>] [--matching=TYPE] [--cluster_upperbound=<int>] [--label_propagation_iterations=<int>] [--diameter_upperbound=<double>] [--beta=<double>] [--refinement=TYPE] [-C <double>] [-g <double>] [--num_skip_ms=<int>] [--no_inherit_ud] [--export_graph] [--output_filename=<string>] [--export_model=<string>] [--timeout=<int>] [-c <int>]
--help Print help.
FILE Path to graph file to partition.
--seed=<int> Seed to use for the PRNG.
-e, --num_experiments=<int> Number of experiments i.e. full kfold runs (default 1)
-k, --kfold_iterations=<int> Number of kfold iterations (Default: 5)
-s, --sample=<double> Percentage of data that is use. Usefull if very slow on large datasets (Default: 1.0 aka use all data)
--validation=TYPE Type of validation. One of {kfold, kfold_import, once, train_test_split} (Default: kfold)
--validation_percent=<double> Percentage of data that is use for validation (Default: 0.1)
--validation_seperate Should the validation data be also used for training (Default: 'no' for kasvm 'yes' for single_level - this flag invertse the choice)
-n, --num_nn=<int> Number of nearest neighbors to consider when building the graphs. (Default: 10)
-b, --bidirectional Make the nearest neighbor graph bidirectional
--stop_rule=VARIANT Stop rule to use. One of {simple-fix}. Default: simple-fix
--fix_num_vert_stop=<int> Number of vertices to fix stop coarsening at.
--matching=TYPE Type of matchings to use during coarsening. One of {random, gpa, randomgpa, lp_clustering, simple_clustering, low_diameter}.
--cluster_upperbound=<int> Set a size-constraint on the size of a cluster. Default: none
--label_propagation_iterations=<int> Set the number of label propgation iterations. Default: 10.
--diameter_upperbound=<double> Set a size-constraint on the size of a low diameter cluster. Default: 20
--beta=<double> value of the beta parameter when using low diameter clustering. (Default: 0.4)
--refinement=TYPE Type of refinement. One of {ud, bayes, fix} (Default: ud)
-C <double> value of the C parameter when using fix refinement. (use logarithmic scale)
-g <double> value of the gamma parameter when using fix refinement. (use logarithmic scale)
--num_skip_ms=<int> Size of the problem on which no model selection is skipped and only the best parameters of the previous level are used (Default: 10000)
--no_inherit_ud Don't inherit the first UD sweep and do only the second UD sweep in the refinement.
--export_graph Export the graph at every level (this exits after one multilevel cycle).
--output_filename=<string> Specify the name of the output file (that contains the partition).
--export_model=<string> Specify the path of the output model (it contains the trained SVM model for later usage) ( a number and ".model" will be appended to the path).
--timeout=<int> Timeout in seconds after the timeout (for a single kfold) run is readched the program is aborted (Default: 0)
-c, --n_cores=<int> How many cores are used (Default: 0 aka. every core)
./optimized_output/kasvm examples/twonormThis will run the LPSVM variant on the twonorm dataset which is included in the
repo.
In our paper we report BEST_AC_TEST and ~BEST_GM_TEST as the “regular” result and
INIT_AC_TEST and INIT_GM_TEST as the “fast” result.
Example output:
file: examples/twonorm num_experiments: 1 kfold_iterations: 5 sample_percent: 1 validation_type: 0 validation_percent: 0.1 validation_seperate: 0 bidirectional: 0 stop rule: 0 fix_num_vert_stop: 500 matching type: 3 cluster_upperbound: 2147483647 upper_bound_partition: 2147483647 label_iterations: 10 node_ordering: 1 diameter_upperbound: 20 beta: 0.4 refinement_type: 0 num_skip_ms: 10000 inherit_ud: 1 timeout: 0 cores: 0 seed: 0 \/\/\/\/\/\/\/\/\/ EXPERIMENT 0 \/\/\/\/\/\/\/ io time: 0.134555 full graph - min: 3703 maj: 3697 features: 20 ------------- K-FOLD ITERATION 0 ------------- fold time: 0.111028 graph - min: 2963 maj: 2958 val - min: 296 maj: 295 test - min: 740 maj: 739 calc new weights took 0.000131845 no of coarser vertices 90 and no of edges 5530 calc new weights took 0.000130177 no of coarser vertices 86 and no of edges 5198 coarsening time: 0.0519309 coarse nodes - min: 90 maj: 86 1st sweep with initial logC=0 logGamma=0 log C= 5.00 log gamma= 0.00 time=0.01 ACC=0.501 Gmean=0.000 SVs=112 (59,53) log C= -5.00 log gamma= -2.50 time=0.00 ACC=0.883 Gmean=0.876 SVs=135 (68,67) log C= 10.00 log gamma= 7.50 time=0.01 ACC=0.501 Gmean=0.000 SVs=176 (90,86) log C= -2.50 log gamma= 5.00 time=0.00 ACC=0.501 Gmean=0.000 SVs=176 (90,86) log C= 0.00 log gamma= -7.50 time=0.00 ACC=0.970 Gmean=0.969 SVs=46 (22,24) log C= 15.00 log gamma= 2.50 time=0.01 ACC=0.501 Gmean=0.000 SVs=176 (90,86) log C= 12.50 log gamma= -5.00 time=0.00 ACC=0.959 Gmean=0.959 SVs=15 (7,8) log C= 7.50 log gamma=-10.00 time=0.00 ACC=0.949 Gmean=0.949 SVs=12 (4,8) log C= 2.50 log gamma= 10.00 time=0.01 ACC=0.501 Gmean=0.000 SVs=176 (90,86) 2nd sweep with logC=12.5 logGamma=-5 log C: 12.5 log g: -5 AC:0.959 SN:0.973 SP:0.946 PPV:0.947 NPV:0.972 F1:0.960 GM:0.959 SV_min:7 SV_maj:8 TP:288 TN:279 FP:16 FN:8 log C= 10.00 log gamma= -5.00 time=0.00 ACC=0.959 Gmean=0.959 SVs=15 (7,8) log C= 12.50 log gamma= 2.50 time=0.01 ACC=0.501 Gmean=0.000 SVs=176 (90,86) log C= 14.99 log gamma= -7.50 time=0.00 ACC=0.946 Gmean=0.945 SVs=13 (4,9) log C= 15.00 log gamma= 0.00 time=0.00 ACC=0.501 Gmean=0.000 SVs=112 (59,53) BEST (15,-7.5) log C: 14.994 log g: -7.5 AC:0.946 SN:0.980 SP:0.912 PPV:0.918 NPV:0.978 F1:0.948 GM:0.945 SV_min:4 SV_maj:9 TP:290 TN:269 FP:26 FN:6 init train time: 0.103 inital validation on testing: log C: 14.994 log g: -7.5 AC:0.969 SN:0.977 SP:0.961 PPV:0.961 NPV:0.977 F1:0.969 GM:0.969 SV_min:4 SV_maj:9 TP:723 TN:710 FP:29 FN:17 init test time: 0.0026 UD refinement at level 1 minority uncoarsed uncoarsened nodes 2963 SV 4 resulting new_data 23 majority uncoarsed uncoarsened nodes 2958 SV 9 resulting new_data 117 current level nodes min 23 maj 117 2nd sweep with logC=15 logGamma=-7.5 log C= 12.49 log gamma= -7.50 time=0.00 ACC=0.948 Gmean=0.947 SVs=34 (14,20) log C= 14.99 log gamma= 0.00 time=0.00 ACC=0.501 Gmean=0.058 SVs=140 (23,117) log C= 15.02 log gamma=-10.00 time=0.00 ACC=0.956 Gmean=0.956 SVs=23 (11,12) log C= 14.99 log gamma= -2.50 time=0.00 ACC=0.508 Gmean=0.130 SVs=138 (23,115) log C= 15.02 log gamma= -5.00 time=0.00 ACC=0.932 Gmean=0.931 SVs=50 (15,35) log C= 14.99 log gamma= -7.50 time=0.00 ACC=0.948 Gmean=0.947 SVs=34 (14,20) BEST (15,-10) log C: 15.019 log g: -10 AC:0.956 SN:0.936 SP:0.976 PPV:0.975 NPV:0.938 F1:0.955 GM:0.956 SV_min:11 SV_maj:12 TP:277 TN:288 FP:7 FN:19 refinement at level 0 took 0.0337 refinement time 0.0338 best validation on testing data: test time 0.00313 log C: 15.019 log g: -10 AC:0.938 SN:0.896 SP:0.980 PPV:0.978 NPV:0.904 F1:0.935 GM:0.937 SV_min:11 SV_maj:12 TP:663 TN:724 FP:15 FN:77 iteration time: 0.3027 Exporting model to ./svm0.model ------------- K-FOLD ITERATION 1 ------------- fold time: 0.1114 graph - min: 2963 maj: 2958 val - min: 296 maj: 295 test - min: 740 maj: 739 calc new weights took 0.0001 no of coarser vertices 86 and no of edges 4774 calc new weights took 0.0001 no of coarser vertices 95 and no of edges 6086 coarsening time: 0.0519 coarse nodes - min: 86 maj: 95 1st sweep with initial logC=0 logGamma=0 log C= 5.00 log gamma= 0.00 time=0.00 ACC=0.503 Gmean=0.058 SVs=120 (60,60) log C= -5.00 log gamma= -2.50 time=0.00 ACC=0.981 Gmean=0.981 SVs=142 (71,71) log C= 10.00 log gamma= 7.50 time=0.01 ACC=0.499 Gmean=0.000 SVs=181 (86,95) log C= -2.50 log gamma= 5.00 time=0.00 ACC=0.499 Gmean=0.000 SVs=181 (86,95) log C= 0.00 log gamma= -7.50 time=0.00 ACC=0.978 Gmean=0.978 SVs=46 (22,24) log C= 15.00 log gamma= 2.50 time=0.01 ACC=0.499 Gmean=0.000 SVs=181 (86,95) log C= 12.50 log gamma= -5.00 time=0.00 ACC=0.975 Gmean=0.975 SVs=17 (5,12) log C= 7.50 log gamma=-10.00 time=0.00 ACC=0.963 Gmean=0.962 SVs=10 (2,8) log C= 2.50 log gamma= 10.00 time=0.01 ACC=0.499 Gmean=0.000 SVs=181 (86,95) 2nd sweep with logC=7.5 logGamma=-10 log C: 7.5 log g: -10 AC:0.963 SN:0.990 SP:0.936 PPV:0.939 NPV:0.989 F1:0.964 GM:0.962 SV_min:2 SV_maj:8 TP:293 TN:276 FP:19 FN:3 log C= 5.00 log gamma=-10.00 time=0.00 ACC=0.976 Gmean=0.976 SVs=15 (7,8) log C= 7.50 log gamma= -2.50 time=0.00 ACC=0.905 Gmean=0.902 SVs=48 (22,26) log C= 12.50 log gamma= -9.29 time=0.00 ACC=0.963 Gmean=0.962 SVs=10 (2,8) log C= 15.00 log gamma= -5.00 time=0.00 ACC=0.975 Gmean=0.975 SVs=17 (5,12) BEST (12.5,-9.29) log C: 12.5 log g: -9.2908 AC:0.963 SN:0.990 SP:0.936 PPV:0.939 NPV:0.989 F1:0.964 GM:0.962 SV_min:2 SV_maj:8 TP:293 TN:276 FP:19 FN:3 init train time: 0.0955 inital validation on testing: log C: 12.5 log g: -9.2908 AC:0.957 SN:0.986 SP:0.927 PPV:0.931 NPV:0.986 F1:0.958 GM:0.956 SV_min:2 SV_maj:8 TP:730 TN:685 FP:54 FN:10 init test time: 0.00253 UD refinement at level 1 minority uncoarsed uncoarsened nodes 2963 SV 2 resulting new_data 13 majority uncoarsed uncoarsened nodes 2958 SV 8 resulting new_data 85 current level nodes min 13 maj 85 2nd sweep with logC=12.5 logGamma=-9.29 log C= 10.00 log gamma= -9.29 time=0.00 ACC=0.949 Gmean=0.949 SVs=21 (9,12) log C= 12.50 log gamma= -1.79 time=0.00 ACC=0.501 Gmean=0.058 SVs=98 (13,85) log C= 14.99 log gamma=-13.87 time=0.00 ACC=0.948 Gmean=0.948 SVs=18 (9,9) log C= 14.98 log gamma= -4.29 time=0.00 ACC=0.848 Gmean=0.837 SVs=56 (13,43) log C= 15.00 log gamma= -6.79 time=0.00 ACC=0.942 Gmean=0.942 SVs=32 (11,21) log C= 12.50 log gamma= -9.29 time=0.00 ACC=0.949 Gmean=0.949 SVs=21 (9,12) BEST (15,-13.9) log C: 14.986 log g: -13.875 AC:0.948 SN:0.939 SP:0.956 PPV:0.955 NPV:0.940 F1:0.947 GM:0.948 SV_min:9 SV_maj:9 TP:278 TN:282 FP:13 FN:18 refinement at level 0 took 0.0211 refinement time 0.0212 best validation on testing data: test time 0.0026 log C: 12.5 log g: -9.2908 AC:0.957 SN:0.986 SP:0.927 PPV:0.931 NPV:0.986 F1:0.958 GM:0.956 SV_min:2 SV_maj:8 TP:730 TN:685 FP:54 FN:10 iteration time: 0.2828 Exporting model to ./svm1.model ------------- K-FOLD ITERATION 2 ------------- fold time: 0.1130 graph - min: 2963 maj: 2958 val - min: 296 maj: 295 test - min: 740 maj: 739 calc new weights took 0.0002 no of coarser vertices 87 and no of edges 5602 calc new weights took 0.0001 no of coarser vertices 87 and no of edges 5510 coarsening time: 0.0524 coarse nodes - min: 87 maj: 87 1st sweep with initial logC=0 logGamma=0 log C= 5.00 log gamma= 0.00 time=0.00 ACC=0.499 Gmean=0.000 SVs=109 (53,56) log C= -5.00 log gamma= -2.50 time=0.00 ACC=0.909 Gmean=0.905 SVs=133 (67,66) log C= 10.00 log gamma= 7.50 time=0.00 ACC=0.501 Gmean=0.000 SVs=174 (87,87) log C= -2.50 log gamma= 5.00 time=0.00 ACC=0.501 Gmean=0.000 SVs=174 (87,87) log C= 0.00 log gamma= -7.50 time=0.00 ACC=0.978 Gmean=0.978 SVs=44 (21,23) log C= 15.00 log gamma= 2.50 time=0.00 ACC=0.501 Gmean=0.000 SVs=174 (87,87) log C= 12.50 log gamma= -5.00 time=0.00 ACC=0.971 Gmean=0.971 SVs=13 (7,6) log C= 7.50 log gamma=-10.00 time=0.00 ACC=0.968 Gmean=0.968 SVs=9 (4,5) log C= 2.50 log gamma= 10.00 time=0.00 ACC=0.499 Gmean=0.000 SVs=174 (87,87) 2nd sweep with logC=7.5 logGamma=-10 log C: 7.5 log g: -10 AC:0.968 SN:0.980 SP:0.956 PPV:0.957 NPV:0.979 F1:0.968 GM:0.968 SV_min:4 SV_maj:5 TP:290 TN:282 FP:13 FN:6 log C= 5.00 log gamma=-10.00 time=0.00 ACC=0.971 Gmean=0.971 SVs=14 (7,7) log C= 7.50 log gamma= -2.50 time=0.00 ACC=0.975 Gmean=0.975 SVs=42 (19,23) log C= 12.50 log gamma= -9.15 time=0.00 ACC=0.968 Gmean=0.968 SVs=9 (4,5) log C= 15.00 log gamma= -5.00 time=0.00 ACC=0.971 Gmean=0.971 SVs=13 (7,6) BEST (12.5,-9.15) log C: 12.5 log g: -9.153 AC:0.968 SN:0.980 SP:0.956 PPV:0.957 NPV:0.979 F1:0.968 GM:0.968 SV_min:4 SV_maj:5 TP:290 TN:282 FP:13 FN:6 init train time: 0.0815 inital validation on testing: log C: 12.5 log g: -9.153 AC:0.966 SN:0.965 SP:0.968 PPV:0.967 NPV:0.965 F1:0.966 GM:0.966 SV_min:4 SV_maj:5 TP:714 TN:715 FP:24 FN:26 init test time: 0.00231 UD refinement at level 1 minority uncoarsed uncoarsened nodes 2963 SV 4 resulting new_data 29 majority uncoarsed uncoarsened nodes 2958 SV 5 resulting new_data 31 current level nodes min 29 maj 31 2nd sweep with logC=12.5 logGamma=-9.15 log C= 10.00 log gamma= -9.15 time=0.00 ACC=0.934 Gmean=0.934 SVs=19 (11,8) log C= 12.50 log gamma= -1.65 time=0.00 ACC=0.567 Gmean=0.368 SVs=60 (29,31) log C= 15.02 log gamma=-15.20 time=0.00 ACC=0.936 Gmean=0.936 SVs=15 (9,6) log C= 15.00 log gamma= -4.15 time=0.00 ACC=0.942 Gmean=0.942 SVs=52 (26,26) log C= 15.00 log gamma= -6.65 time=0.00 ACC=0.931 Gmean=0.930 SVs=24 (12,12) log C= 12.50 log gamma= -9.15 time=0.00 ACC=0.934 Gmean=0.934 SVs=19 (11,8) BEST (15,-15.2) log C: 15.019 log g: -15.2 AC:0.936 SN:0.929 SP:0.942 PPV:0.942 NPV:0.930 F1:0.935 GM:0.936 SV_min:9 SV_maj:6 TP:275 TN:278 FP:17 FN:21 refinement at level 0 took 0.0137 refinement time 0.0138 best validation on testing data: test time 0.00233 log C: 12.5 log g: -9.153 AC:0.966 SN:0.965 SP:0.968 PPV:0.967 NPV:0.965 F1:0.966 GM:0.966 SV_min:4 SV_maj:5 TP:714 TN:715 FP:24 FN:26 iteration time: 0.2631 Exporting model to ./svm2.model ------------- K-FOLD ITERATION 3 ------------- fold time: 0.1130 graph - min: 2963 maj: 2958 val - min: 296 maj: 295 test - min: 740 maj: 739 calc new weights took 0.0001 no of coarser vertices 79 and no of edges 4394 calc new weights took 0.0001 no of coarser vertices 83 and no of edges 4904 coarsening time: 0.0518 coarse nodes - min: 79 maj: 83 1st sweep with initial logC=0 logGamma=0 log C= 5.00 log gamma= 0.00 time=0.00 ACC=0.514 Gmean=0.174 SVs=100 (49,51) log C= -5.00 log gamma= -2.50 time=0.00 ACC=0.964 Gmean=0.964 SVs=130 (65,65) log C= 10.00 log gamma= 7.50 time=0.00 ACC=0.501 Gmean=0.058 SVs=162 (79,83) log C= -2.50 log gamma= 5.00 time=0.00 ACC=0.499 Gmean=0.000 SVs=162 (79,83) log C= 0.00 log gamma= -7.50 time=0.00 ACC=0.973 Gmean=0.973 SVs=44 (22,22) log C= 15.00 log gamma= 2.50 time=0.00 ACC=0.501 Gmean=0.058 SVs=161 (79,82) log C= 12.50 log gamma= -5.00 time=0.00 ACC=0.966 Gmean=0.966 SVs=20 (10,10) log C= 7.50 log gamma=-10.00 time=0.00 ACC=0.971 Gmean=0.971 SVs=13 (8,5) log C= 2.50 log gamma= 10.00 time=0.00 ACC=0.501 Gmean=0.058 SVs=162 (79,83) 2nd sweep with logC=7.5 logGamma=-10 log C: 7.5 log g: -10 AC:0.971 SN:0.976 SP:0.966 PPV:0.967 NPV:0.976 F1:0.971 GM:0.971 SV_min:8 SV_maj:5 TP:289 TN:285 FP:10 FN:7 log C= 5.00 log gamma=-10.00 time=0.00 ACC=0.970 Gmean=0.970 SVs=17 (9,8) log C= 7.50 log gamma= -2.50 time=0.00 ACC=0.949 Gmean=0.949 SVs=40 (20,20) log C= 12.50 log gamma=-11.54 time=0.00 ACC=0.971 Gmean=0.971 SVs=13 (8,5) log C= 15.00 log gamma= -5.00 time=0.00 ACC=0.966 Gmean=0.966 SVs=20 (10,10) BEST (12.5,-11.5) log C: 12.5 log g: -11.537 AC:0.971 SN:0.976 SP:0.966 PPV:0.967 NPV:0.976 F1:0.971 GM:0.971 SV_min:8 SV_maj:5 TP:289 TN:285 FP:10 FN:7 init train time: 0.0807 inital validation on testing: log C: 12.5 log g: -11.537 AC:0.974 SN:0.969 SP:0.978 PPV:0.978 NPV:0.969 F1:0.974 GM:0.974 SV_min:8 SV_maj:5 TP:717 TN:723 FP:16 FN:23 init test time: 0.00264 UD refinement at level 1 minority uncoarsed uncoarsened nodes 2963 SV 8 resulting new_data 54 majority uncoarsed uncoarsened nodes 2958 SV 5 resulting new_data 58 current level nodes min 54 maj 58 2nd sweep with logC=12.5 logGamma=-11.5 log C= 10.00 log gamma= -9.14 time=0.00 ACC=0.937 Gmean=0.937 SVs=23 (6,17) log C= 12.50 log gamma= -4.04 time=0.00 ACC=0.944 Gmean=0.944 SVs=79 (35,44) log C= 15.00 log gamma= -9.35 time=0.00 ACC=0.937 Gmean=0.937 SVs=22 (6,16) log C= 14.98 log gamma= -6.54 time=0.00 ACC=0.936 Gmean=0.936 SVs=32 (13,19) log C= 15.00 log gamma= -9.04 time=0.00 ACC=0.939 Gmean=0.939 SVs=23 (6,17) log C= 12.50 log gamma=-11.54 time=0.00 ACC=0.941 Gmean=0.941 SVs=20 (6,14) BEST (12.5,-11.5) log C: 12.5 log g: -11.537 AC:0.941 SN:0.959 SP:0.922 PPV:0.925 NPV:0.958 F1:0.942 GM:0.941 SV_min:6 SV_maj:14 TP:284 TN:272 FP:23 FN:12 refinement at level 0 took 0.0207 refinement time 0.0208 best validation on testing data: test time 0.00261 log C: 12.5 log g: -11.537 AC:0.974 SN:0.969 SP:0.978 PPV:0.978 NPV:0.969 F1:0.974 GM:0.974 SV_min:8 SV_maj:5 TP:717 TN:723 FP:16 FN:23 iteration time: 0.2691 Exporting model to ./svm3.model ------------- K-FOLD ITERATION 4 ------------- fold time: 0.1152 graph - min: 2963 maj: 2958 val - min: 296 maj: 295 test - min: 740 maj: 739 calc new weights took 0.0001 no of coarser vertices 88 and no of edges 5118 calc new weights took 0.0001 no of coarser vertices 85 and no of edges 5520 coarsening time: 0.0520 coarse nodes - min: 88 maj: 85 1st sweep with initial logC=0 logGamma=0 log C= 5.00 log gamma= 0.00 time=0.00 ACC=0.501 Gmean=0.000 SVs=115 (59,56) log C= -5.00 log gamma= -2.50 time=0.00 ACC=0.662 Gmean=0.567 SVs=135 (68,67) log C= 10.00 log gamma= 7.50 time=0.00 ACC=0.501 Gmean=0.000 SVs=173 (88,85) log C= -2.50 log gamma= 5.00 time=0.00 ACC=0.501 Gmean=0.000 SVs=173 (88,85) log C= 0.00 log gamma= -7.50 time=0.00 ACC=0.985 Gmean=0.985 SVs=46 (22,24) log C= 15.00 log gamma= 2.50 time=0.01 ACC=0.501 Gmean=0.000 SVs=173 (88,85) log C= 12.50 log gamma= -5.00 time=0.00 ACC=0.980 Gmean=0.980 SVs=20 (10,10) log C= 7.50 log gamma=-10.00 time=0.00 ACC=0.983 Gmean=0.983 SVs=10 (7,3) log C= 2.50 log gamma= 10.00 time=0.00 ACC=0.501 Gmean=0.000 SVs=173 (88,85) 2nd sweep with logC=7.5 logGamma=-10 log C: 7.5 log g: -10 AC:0.983 SN:0.980 SP:0.986 PPV:0.986 NPV:0.980 F1:0.983 GM:0.983 SV_min:7 SV_maj:3 TP:290 TN:291 FP:4 FN:6 log C= 5.00 log gamma=-10.00 time=0.00 ACC=0.980 Gmean=0.980 SVs=16 (10,6) log C= 7.50 log gamma= -2.50 time=0.00 ACC=0.621 Gmean=0.491 SVs=44 (25,19) log C= 12.50 log gamma= -9.74 time=0.02 ACC=0.983 Gmean=0.983 SVs=10 (7,3) log C= 15.00 log gamma= -5.00 time=0.01 ACC=0.980 Gmean=0.980 SVs=20 (10,10) BEST (12.5,-9.74) log C: 12.5 log g: -9.7435 AC:0.983 SN:0.980 SP:0.986 PPV:0.986 NPV:0.980 F1:0.983 GM:0.983 SV_min:7 SV_maj:3 TP:290 TN:291 FP:4 FN:6 init train time: 0.104 inital validation on testing: log C: 12.5 log g: -9.7435 AC:0.983 SN:0.982 SP:0.984 PPV:0.984 NPV:0.982 F1:0.983 GM:0.983 SV_min:7 SV_maj:3 TP:727 TN:727 FP:12 FN:13 init test time: 0.0028 UD refinement at level 1 minority uncoarsed uncoarsened nodes 2963 SV 7 resulting new_data 58 majority uncoarsed uncoarsened nodes 2958 SV 3 resulting new_data 67 current level nodes min 58 maj 67 2nd sweep with logC=12.5 logGamma=-9.74 log C= 10.00 log gamma= -9.74 time=0.00 ACC=0.956 Gmean=0.956 SVs=21 (10,11) log C= 12.50 log gamma= -2.24 time=0.00 ACC=0.819 Gmean=0.801 SVs=125 (58,67) log C= 15.00 log gamma=-11.82 time=0.00 ACC=0.956 Gmean=0.956 SVs=20 (10,10) log C= 14.99 log gamma= -4.74 time=0.00 ACC=0.961 Gmean=0.961 SVs=53 (29,24) log C= 15.00 log gamma= -7.24 time=0.00 ACC=0.964 Gmean=0.964 SVs=30 (15,15) log C= 12.50 log gamma= -9.74 time=0.00 ACC=0.956 Gmean=0.956 SVs=21 (10,11) BEST (15,-11.8) log C: 14.997 log g: -11.82 AC:0.956 SN:0.932 SP:0.980 PPV:0.979 NPV:0.935 F1:0.955 GM:0.956 SV_min:10 SV_maj:10 TP:276 TN:289 FP:6 FN:20 refinement at level 0 took 0.0273 refinement time 0.0274 best validation on testing data: test time 0.00249 log C: 12.5 log g: -9.7435 AC:0.983 SN:0.982 SP:0.984 PPV:0.984 NPV:0.982 F1:0.983 GM:0.983 SV_min:7 SV_maj:3 TP:727 TN:727 FP:12 FN:13 iteration time: 0.3008 Exporting model to ./svm4.model -------------- K-FOLD DONE -------------- KFOLD_TIME 0.1127 COARSE_TIME 0.0520 COARSE_MIN 86.0000 COARSE_MAJ 87.2000 HIERARCHY_MIN_SIZE 1.0000 HIERARCHY_MAJ_SIZE 1.0000 INIT_TRAIN_TIME 0.0928 INIT_AC 0.9662 INIT_GM 0.9659 INIT_AC_TEST 0.9697 INIT_GM_TEST 0.9696 LEVEL0_AC 0.9472 LEVEL0_GM 0.9471 REFINEMENT_TIME 0.0234 [BEST_INDEX] fold 0: 1 fold 1: 0 fold 2: 0 fold 3: 0 fold 4: 0 BEST_AC 0.9682 BEST_SN 0.9723 BEST_SP 0.9641 BEST_GM 0.9680 BEST_F1 0.9683 TEST_TIME 0.0026 BEST_AC_TEST 0.9635 BEST_SN_TEST 0.9597 BEST_SP_TEST 0.9673 BEST_GM_TEST 0.9632 BEST_F1_TEST 0.9632 TIME 0.2837