The Evolutionary Pre-Processor (version 3.0) Batch Run Report |
![]() |
Batch Details
Batch run name | cmc_3 |
Description | Contraceptive Method Choice 3 on Nyquist |
This report file | final/cmc_3/cmc_3_report.html |
data file | /home/cssip/jsherrah/EPrep3/data/cmc_3.dat |
Time of completion | Thu May 28 23:59:13 1998 |
Duration of Batch | 10 hours, 42 minutes, 42 seconds |
Random Seed | 680174088 (from clock) |
Average generations per run | 47.40 |
Average failed feature creations per run | 3553.50 |
Average fitness evalutions per run | 189304.70 |
Test Set Improvement | 21.68 % |
Data Partition
Class | Training | Validation | Test | Total |
0 | 314 | 158 | 157 | 629 |
1 | 166 | 84 | 83 | 333 |
2 | 256 | 126 | 129 | 511 |
Total | 736 | 368 | 369 | 1473 |
Summary of Results
Original Classification Errors (%)
Classifier | Training | Validation | Test |
Parallelepiped(PPD) | 67.93 | 64.67 | 66.67 |
Min. Distance to Means(MDTM) | 59.51 | 60.33 | 59.89 |
|
|
|
|
|
|
|
|
|
1 | 49.59 | 46.74 | 42.82 | 0.988 | 4 | 9 | 174 | MDTM |
2 | 53.26 | 48.64 | 46.07 | 0.995 | 6 | 9 | 622 | MDTM |
3 | 47.55 | 47.83 | 42.82 | 1.000 | 4 | 8 | 109 | MDTM |
4 | 50.00 | 46.20 | 42.28 | 0.999 | 8 | 9 | 271 | MDTM |
5 | 46.06 | 45.11 | 38.21 | 1.000 | 6 | 8 | 127 | MDTM |
6 | 49.46 | 47.55 | 40.38 | 1.000 | 4 | 6 | 40 | MDTM |
7 | 50.41 | 45.65 | 41.19 | 0.999 | 9 | 9 | 240 | MDTM |
8 | 50.41 | 47.55 | 42.28 | 1.000 | 5 | 9 | 142 | MDTM |
9 | 49.86 | 45.65 | 42.01 | 1.000 | 8 | 9 | 189 | MDTM |
10 | 53.40 | 47.01 | 50.68 | 0.386 | 7 | 9 | 353 | PPD |
Ave. | 50.00 (2.11 ) | 46.79 (1.07 ) | 42.87 (3.22 ) | 0.94 (0.18 ) | 6.10 (1.76 ) | 8.50 (0.92 ) | 226.70(156.33) | MDTM |
|
|
Total | |||
|
|
|
|||
Ground Truth |
|
112 | 11 | 34 | 157 |
|
13 | 44 | 26 | 83 | |
|
37 | 20 | 72 | 129 | |
|
162 | 75 | 132 | 369 |
Average Operator Probabilities
Operator | Average Probability |
Delete-Feature Mutation | 0.084 |
Add-Feature Mutation | 0.097 |
Hoist Mutation | 0.104 |
Truncate Mutation | 0.106 |
Swap Mutation | 0.106 |
One-Symbol Mutation | 0.099 |
All-Nodes Mutation | 0.102 |
One-Node Mutation | 0.095 |
Grow Mutation | 0.099 |
High-Level Crossover | 0.108 |
Number of Run Terminations attributed to each Criterion
Termination Criterion | Number of Terminations |
TP Criterion | 0 |
GL Criterion | 1 |
Max. Generations | 9 |
Client Abort | 0 |
Zero Validation Error | 0 |
Total | 10 |
Related Data Files
Description | Filename |
cmc_3_gen.m | Matlab plot generation function |
cmc_3_bogf_ave.{eps,gif} | Best-of-generation Fitness, averaged over 10 runs |
cmc_3_bogv_ave.{eps,gif} | Best-of-generation Validation Set Error, averaged over 10 runs |
cmc_3_avef_ave.{eps,gif} | Average fitness, averaged over 10 runs |
cmc_3_stdf_ave.{eps,gif} | Standard deviation of fitness, averaged over 10 runs |
cmc_3_nftr_ave.{eps,gif} | Average number of features per individual, averaged over 10 runs |
cmc_3_nnode_ave.{eps,gif} | Average number of nodes per individual, averaged over 10 runs |
cmc_3_nint_ave.{eps,gif} | Average number of introns per individual, averaged over 10 runs |
cmc_3_ntrl_ave.{eps,gif} | Average number of RAT trials per individual, averaged over 10 runs |
cmc_3_optimp_ave.{eps,gif} | Average improvement in fitness due to optimisation, averaged over 10 runs |
cmc_3_opprob_ave.{eps,gif} | Average probability of each genetic operator, averaged over 10 runs |
cmc_3_run_x.dat | Binary data file containing results of run x (read by Matlab functions) |
cmc_3_bor_x.prep x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-run individual for run x |
cmc_3_run_x.corr x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Feature correlation file for run x |
cmc_3_tst_bor_x.pred x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Test set predictions for best-of-run individual from run x |
cmc_3_bogf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Fitness for run x |
cmc_3_bogv_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Best-of-generation Validation Set Error for run x |
cmc_3_avef_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average fitness for run x |
cmc_3_stdf_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Standard deviation of fitness for run x |
cmc_3_nftr_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of features per individual for run x |
cmc_3_nnode_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of nodes per individual for run x |
cmc_3_nint_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of introns per individual for run x |
cmc_3_ntrl_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average number of RAT trials per individual for run x |
cmc_3_optimp_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average improvement in fitness due to optimisation for run x |
cmc_3_opprob_x.{eps,gif} x = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
Average probability of each genetic operator for run x |